Integrating Higher-Order Dynamics and Roadway-Compliance into Constrained ILQR-based Trajectory Planning for Autonomous Vehicles

This paper addresses the advancements in on-road trajectory planning for Autonomous Passenger Vehicles (APV). Trajectory planning aims to produce a globally optimal route for APVs, considering various factors such as vehicle dynamics, constraints, and detected obstacles. Traditional techniques involve a combination of sampling methods followed by optimization algorithms, where the former ensures global awareness and the latter refines for local optima. Notably, the Constrained Iterative Linear Quadratic Regulator (CILQR) optimization algorithm has recently emerged, adapted for APV systems, emphasizing improved safety and comfort. However, existing implementations utilizing the vehicle bicycle kinematic model may not guarantee controllable trajectories. We augment this model by incorporating higher-order terms, including the first and second-order derivatives of curvature and longitudinal jerk. This inclusion facilitates a richer representation in our cost and constraint design. We also address roadway compliance, emphasizing adherence to lane boundaries and directions, which past work often overlooked. Lastly, we adopt a relaxed logarithmic barrier function to address the CILQR's dependency on feasible initial trajectories. The proposed methodology is then validated through simulation and real-world experiment driving scenes in real time.Comment: 6 pages, 3 figure

WMFormer++: Nested Transformer for Visible Watermark Removal via Implict Joint Learning

Watermarking serves as a widely adopted approach to safeguard media copyright. In parallel, the research focus has extended to watermark removal techniques, offering an adversarial means to enhance watermark robustness and foster advancements in the watermarking field. Existing watermark removal methods mainly rely on UNet with task-specific decoder branches--one for watermark localization and the other for background image restoration. However, watermark localization and background restoration are not isolated tasks; precise watermark localization inherently implies regions necessitating restoration, and the background restoration process contributes to more accurate watermark localization. To holistically integrate information from both branches, we introduce an implicit joint learning paradigm. This empowers the network to autonomously navigate the flow of information between implicit branches through a gate mechanism. Furthermore, we employ cross-channel attention to facilitate local detail restoration and holistic structural comprehension, while harnessing nested structures to integrate multi-scale information. Extensive experiments are conducted on various challenging benchmarks to validate the effectiveness of our proposed method. The results demonstrate our approach's remarkable superiority, surpassing existing state-of-the-art methods by a large margin

Evaluation of the effectiveness and mechanism of action of the Chang-Kang-Fang formula combined with bifid triple viable capsules on diarrhea-predominant irritable bowel syndrome

IntroductionThe Chang-Kang-Fang (CKF) formula, a traditional Chinese herbal formula, can decrease serotonin (5-HT) levels and treat irritable bowel syndrome (IBS). Probiotics have a better synergistic effect on diarrhea-predominant IBS (IBS-D) when combined with 5-HT3 receptor antagonists. The present study aimed to elucidate the efficacy and the mechanisms of action of the CKF formula combined with bifid triple viable capsules (PFK) against IBS-D.MethodsThe rat models of IBS-D were induced by gavage with senna decoction plus restraint stress. The CKF formula, PFK and their combination were administered to the rats. Their effects were evaluated based on general condition of the rats and the AWR score. The levels of 5-HT and fos protein in the colon and hippocampus were measured by immunohistochemistry. The levels of SP and VIP, as well as ZO-1 and occludin in the colon, were determined by enzyme-linked immunosorbent assay and immunohistochemistry. The intestinal microbiota in faeces was analyzed by 16S rRNA high-throughput sequencing.ResultsThe results showed that the oral CKF formula combined with PFK (CKF + PFK) could significantly relieve the symptoms of IBS-D, including elevating the weight rate and decreasing the AWR score. Compared with the MC group, administration of CKF + PFK significantly reduced the expression of fos in the colon and hippocampus and that of 5-HT, SP and VIP in the colon and increased the levels of 5-HT in the hippocampus and ZO-1 and occludin in the colon. The above indexes exhibited statistical significance in the CKF + PFK group relative to those in the other groups. Moreover, treatment with CKF + PFK improved the diversity of intestinal microbiota and the abundance of Firmicutes, Lachnospiraceae and Ruminococcaceae but decreased those of Bacteroidetes and Prevotellaceae.ConclusionsThe CKF formula combined with PFK may have a synergistic effect on IBS-D by slowing gastrointestinal motility, lowering visceral hypersensitivity, enhancing the intestinal barrier function and modulating the composition of intestinal microbiota

Realistic texture synthesis for point-based fruitage phenotype.

Although current 3D scanner technology can acquire textural images from a point model, visible seams in the image, inconvenient data acquisition and occupancy of a large space during use are points of concern for outdoor fruit models. In this paper, an SPSDW (simplification and perception based subdivision followed by down-sampling weighted average) method is proposed to balance memory usage and texture synthesis quality using a crop fruit, such as apples, as a research subject for a point-based fruit model. First, the quadtree method is improved to make splitting more efficient, and a reasonable texton descriptor is defined to promote query efficiency. Then, the color perception feature is extracted from the image for all pixels. Next, an advanced sub-division scheme and down-sampling strategy are designed to optimize memory space. Finally, a weighted oversampling method is proposed for high-quality texture mixing. This experiment demonstrates that the SPSDW method preserves the mixed texture more realistically and smoothly and preserves color memory up to 94%, 84.7% and 85.7% better than the two-dimesional processing, truncating scalar quantitative and color vision model methods, respectively

Analysis of lncRNA-Associated ceRNA Network Reveals Potential lncRNA Biomarkers in Human Colon Adenocarcinoma

Background/Aims: Long non-coding RNAs (lncRNAs) acting as competing endogenous RNAs (ceRNAs) play significant roles in the development of tumors, but the functions of specific lncRNAs and lncRNA-related ceRNA networks have not been fully elucidated for colon adenocarcinoma (COAD). In this study, we aimed to clarify the lncRNA-microRNA (miRNA)-mRNA ceRNA network and potential lncRNA biomarkers in COAD. Methods: We extracted data from The Cancer Genome Atlas (TCGA) and identified COAD-specific mRNAs, miRNAs, and lncRNAs. The biological processes in Gene Ontology (GO) and the Kyoto Encyclopedia of Genes and Genomes (KEGG) were analyzed for COAD-specific mRNAs. We then constructed a ceRNA network of COAD-specific mRNAs, miRNAs and lncRNAs and analyzed the correlation between expression patterns and clinical features of the lncRNAs involved. After identifying potential mRNA targets of 4 lncRNAs related to overall survival (OS), we conducted stepwise analysis of these targets through GO and KEGG. Using tissue samples from our own patients, we also verified certain analytical results using quantitative real-time PCR (qRT-PCR). Results: Data from 521 samples (480 tumor tissue and 41 adjacent non-tumor tissue samples) were extracted from TCGA. A total of 258 specific lncRNAs, 206 specific miRNAs, and 1467 specific mRNAs were identified (absolute log2 [fold change] > 2, false discovery rate < 0.01). Analysis of KEGG revealed that specific mRNAs were enriched in cancer-related pathways. The ceRNA network was constructed with 64 lncRNAs, 18 miRNAs, and 42 mRNAs. Among these lncRNAs involved in the network, 3 lncRNAs (LINC00355, HULC, and IGF2-AS) were confirmed to be associated with certain clinical features and 4 lncRNAs (HOTAIR, LINC00355, KCNQ1OT1, and TSSC1-IT1) were found to be negatively linked to OS (log-rank p < 0.05). KEGG showed that the potential mRNA targets of these 4 lncRNAs may be concentrated in the MAPK pathway. Certain results were validated by qRT-PCR. Conclusion: This study providing novel insights into the lncRNA-miRNA-mRNA ceRNA network and reveals potential lncRNA biomarkers in COAD

Digital relief generation from 3D models

It is difficult to extend image-based relief generation to high-relief generation, as the images contain insufficient height information. To generate reliefs from three-dimensional (3D) models, it is necessary to extract the height fields from the model, but this can only generate bas-reliefs. To overcome this problem, an efficient method is proposed to generate bas-reliefs and high-reliefs directly from 3D meshes. To produce relief features that are visually appropriate, the 3D meshes are first scaled. 3D unsharp masking is used to enhance the visual features in the 3D mesh, and average smoothing and Laplacian smoothing are implemented to achieve better smoothing results. A nonlinear variable scaling scheme is then employed to generate the final bas-reliefs and high-reliefs. Using the proposed method, relief models can be generated from arbitrary viewing positions with different gestures and combinations of multiple 3D models. The generated relief models can be printed by 3D printers. The proposed method provides a means of generating both high-reliefs and bas-reliefs in an efficient and effective way under the appropriate scaling factors

Preclinical Evaluation of Radioiodinated Hoechst 33258 for Early Prediction of Tumor Response to Treatment of Vascular-Disrupting Agents

This study aimed to explore the use of 131I-Hoechst 33258 (131I-H33258) for early prediction of tumor response to vascular-disrupting agents (VDAs) with combretastatin-A4 phosphate (CA4P) as a representative. Necrosis avidity of 131I-H33258 was evaluated in mouse models with muscle necrosis and blocking was used to confirm the tracer specificity. Therapy response was evaluated by 131I-H33258 SPECT/CT imaging 24 h after CA4P therapy in W256 tumor-bearing rats. Radiotracer uptake in tumors was validated ex vivo using γ-counting, autoradiography, and histopathological staining. Results showed that 131I-H33258 had predominant necrosis avidity and could specifically bind to necrotic tissue. SPECT/CT imaging demonstrated that an obvious “hot spot” could be observed in the CA4P-treated tumor. Ex vivo γ-counting revealed 131I-H33258 uptake in tumors was increased 2.8-fold in rats treated with CA4P relative to rats treated with vehicle. Autoradiography and corresponding H&E staining suggested that 131I-H33258 was mainly localized in necrotic tumor area and the higher overall uptake in the treated tumors was attributed to the increased necrosis. These results suggest that 131I-H33258 can be used to image induction of cell necrosis 24 h after CA4P therapy, which support further molecular design of probes based on scaffold H33258 for monitoring of tumor response to VDAs treatment

A self-adaptive segmentation method for a point cloud

The segmentation of a point cloud is one of the key technologies for three-dimensional reconstruction, and the segmentation from three-dimensional views can facilitate reverse engineering. In this paper, we propose a self-adaptive segmentation algorithm, which can address challenges related to the region-growing algorithm, such as inconsistent or excessive segmentation. Our algorithm consists of two main steps: automatic selection of seed points according to extracted features and segmentation of the points using an improved region-growing algorithm. The benefits of our approach are the ability to select seed points without user intervention and the reduction of the influence of noise. We demonstrate the robustness and effectiveness of our algorithm on different point cloud models and the results show that the segmentation accuracy rate achieves 96%

Laminar flame characteristics of natural gas and dissociated methanol mixtures diluted by nitrogen

The effect of dissociated methanol (H2:CO=2:1 by volume) on laminar burning velocity of natural gas (methane as the main component) was studied by using a constant volume bomb (CVB). Nitrogen, as diluent gas, was added into the natural gas (CH4) - dissociated methanol (DM) mixtures to investigate the dilution effect. Experiments were conducted at initial temperature of 343 K and initial pressure of 0.3 MPa with equivalence ratios from 0.8 to 1.4. Laminar burning velocities were calculated through Schlieren photographs, correlation of in-cylinder pressure data and Chemkin-Pro. Results show an increase in laminar burning velocity with initial temperature and proportion of dissociated methanol but a decrease with initial pressure and proportion of nitrogen. The laminar burning velocities were 25.1 cm/s, 38.7 cm/s and 83.2 cm/s respectively at stoichiometric ratio when the proportions of the dissociated methanol were 0%, 40% and 80%. Adding more dissociated methanol tends to shift the peak burning velocity towards the richer side while adding nitrogen has the opposite effect. More dissociated methanol will lead to earlier cellularity