Investigation of Key Parameters for Hydraulic Optimization of an Inlet Duct Based on a Whole Waterjet Propulsion Pump System

The hydraulic performance of an inlet duct directly affects the overall performance of a waterjet propulsion system. Key parameters for the hydraulic optimization of the inlet duct are explored using the computational fluid dynamics (CFD) technology to improve the hydraulic performance of the waterjet propulsion system. In the CFD simulation and experiment, an inlet duct with different flow and geometric parameters is simulated. By comparing grid sensitivity and different turbulence models, a suitable grid size and a turbulence model are determined. The comparison between the numerical simulation and the experiment shows that the numerical results are reliable. The results of the calculation and analysis of different speed cases show that the ship speed affects the efficiency of the waterjet propulsion system. In particular, the system efficiency increases first and then decreases with an increase in the ship speed. Under the conditions of constant ship speed and rotational speed, the influence of the length and dip angle of the inlet duct on the waterjet propulsion system is investigated using a single factor method. The results show that the dip angle has an obvious effect on the hydraulic performance of the inlet duct, and an extremely small angle of inclination will lead to poor flow patterns in the inlet passage. When the length is approximately six times the inlet duct outlet diameter, and the dip angle is 30°–35°, the hydraulic performance of the waterjet propulsion pump system is satisfactory

TAE: A Semi-supervised Controllable Behavior-aware Trajectory Generator and Predictor

Trajectory generation and prediction are two interwoven tasks that play important roles in planner evaluation and decision making for intelligent vehicles. Most existing methods focus on one of the two and are optimized to directly output the final generated/predicted trajectories, which only contain limited information for critical scenario augmentation and safe planning. In this work, we propose a novel behavior-aware Trajectory Autoencoder (TAE) that explicitly models drivers' behavior such as aggressiveness and intention in the latent space, using semi-supervised adversarial autoencoder and domain knowledge in transportation. Our model addresses trajectory generation and prediction in a unified architecture and benefits both tasks: the model can generate diverse, controllable and realistic trajectories to enhance planner optimization in safety-critical and long-tailed scenarios, and it can provide prediction of critical behavior in addition to the final trajectories for decision making. Experimental results demonstrate that our method achieves promising performance on both trajectory generation and prediction.Comment: an updated version, change figures and references. 8 pages, robotics conference, about trajectory augmentation and prediction for intelligent vehicle system

Safety-driven Interactive Planning for Neural Network-based Lane Changing

Neural network-based driving planners have shown great promises in improving task performance of autonomous driving. However, it is critical and yet very challenging to ensure the safety of systems with neural network based components, especially in dense and highly interactive traffic environments. In this work, we propose a safety-driven interactive planning framework for neural network-based lane changing. To prevent over conservative planning, we identify the driving behavior of surrounding vehicles and assess their aggressiveness, and then adapt the planned trajectory for the ego vehicle accordingly in an interactive manner. The ego vehicle can proceed to change lanes if a safe evasion trajectory exists even in the predicted worst case; otherwise, it can stay around the current lateral position or return back to the original lane. We quantitatively demonstrate the effectiveness of our planner design and its advantage over baseline methods through extensive simulations with diverse and comprehensive experimental settings, as well as in real-world scenarios collected by an autonomous vehicle company

Safety-Assured Speculative Planning with Adaptive Prediction

Recently significant progress has been made in vehicle prediction and planning algorithms for autonomous driving. However, it remains quite challenging for an autonomous vehicle to plan its trajectory in complex scenarios when it is difficult to accurately predict its surrounding vehicles' behaviors and trajectories. In this work, to maximize performance while ensuring safety, we propose a novel speculative planning framework based on a prediction-planning interface that quantifies both the behavior-level and trajectory-level uncertainties of surrounding vehicles. Our framework leverages recent prediction algorithms that can provide one or more possible behaviors and trajectories of the surrounding vehicles with probability estimation. It adapts those predictions based on the latest system states and traffic environment, and conducts planning to maximize the expected reward of the ego vehicle by considering the probabilistic predictions of all scenarios and ensure system safety by ruling out actions that may be unsafe in worst case. We demonstrate the effectiveness of our approach in improving system performance and ensuring system safety over other baseline methods, via extensive simulations in SUMO on a challenging multi-lane highway lane-changing case study

Crises and opportunities in terms of energy and AI technologies during the COVID-19 pandemic [Editorial]

Editorial

Evoke: Evoking Critical Thinking Abilities in LLMs via Reviewer-Author Prompt Editing

Large language models (LLMs) have made impressive progress in natural language processing. These models rely on proper human instructions (or prompts) to generate suitable responses. However, the potential of LLMs are not fully harnessed by commonly-used prompting methods: many human-in-the-loop algorithms employ ad-hoc procedures for prompt selection; while auto prompt generation approaches are essentially searching all possible prompts randomly and inefficiently. We propose Evoke, an automatic prompt refinement framework. In Evoke, there are two instances of a same LLM: one as a reviewer (LLM-Reviewer), it scores the current prompt; the other as an author (LLM-Author), it edits the prompt by considering the edit history and the reviewer's feedback. Such an author-reviewer feedback loop ensures that the prompt is refined in each iteration. We further aggregate a data selection approach to Evoke, where only the hard samples are exposed to the LLM. The hard samples are more important because the LLM can develop deeper understanding of the tasks out of them, while the model may already know how to solve the easier cases. Experimental results show that Evoke significantly outperforms existing methods. For instance, in the challenging task of logical fallacy detection, Evoke scores above 80, while all other baseline methods struggle to reach 20

MOHO: Learning Single-view Hand-held Object Reconstruction with Multi-view Occlusion-Aware Supervision

Previous works concerning single-view hand-held object reconstruction typically utilize supervision from 3D ground truth models, which are hard to collect in real world. In contrast, abundant videos depicting hand-object interactions can be accessed easily with low cost, although they only give partial object observations with complex occlusion. In this paper, we present MOHO to reconstruct hand-held object from a single image with multi-view supervision from hand-object videos, tackling two predominant challenges including object's self-occlusion and hand-induced occlusion. MOHO inputs semantic features indicating visible object parts and geometric embeddings provided by hand articulations as partial-to-full cues to resist object's self-occlusion, so as to recover full shape of the object. Meanwhile, a novel 2D-3D hand-occlusion-aware training scheme following the synthetic-to-real paradigm is proposed to release hand-induced occlusion. In the synthetic pre-training stage, 2D-3D hand-object correlations are constructed by supervising MOHO with rendered images to complete the hand-concealed regions of the object in both 2D and 3D space. Subsequently, MOHO is finetuned in real world by the mask-weighted volume rendering supervision adopting hand-object correlations obtained during pre-training. Extensive experiments on HO3D and DexYCB datasets demonstrate that 2D-supervised MOHO gains superior results against 3D-supervised methods by a large margin. Codes and key assets will be released soon

Analysis of high-speed angular ball bearing lubrication based on bi-directional fluid-solid coupling

The lubrication of angular contact ball bearings under high-speed motion conditions is particularly important to the working performance of rolling bearings. Combining the contact characteristics of fluid domain and solid domain, a lubrication calculation model for angular contact ball bearings is established based on the RNG k-ε method. The pressure and velocity characteristics of the bearing basin under the conditions of rotational speed, number of balls and lubricant parameters are analyzed, and the lubrication conditions and dynamics of the angular contact ball bearings under different working conditions are obtained. The results show that the lubricant film pressure will rise with increasing speed and viscosity of the lubricant. The number of balls affects the pressure and velocity distribution of the flow field inside the bearing but has a small effect on the values of the characteristic parameters of the bearing flow field. The established CFD model provides a new approach to study the effect of fluid flow on bearing performance in angular contact ball bearings

The Max b-HLH-LZ Can Transduce into Cells and Inhibit c-Myc Transcriptional Activities

The inhibition of the functions of c-Myc (endogenous and oncogenic) was recently shown to provide a spectacular therapeutic index in cancer mouse models, with complete tumor regression and minimal side-effects in normal tissues. This was achieved by the systemic and conditional expression of omomyc, the cDNA of a designed mutant of the b-HLH-LZ of c-Myc named Omomyc. The overall mode of action of Omomyc consists in the sequestration of Max and the concomitant competition of the Omomyc/Max complex with the endogenous c-Myc/Max heterodimer. This leads to the inhibition of the transactivation of Myc target genes involved in proliferation and metabolism. While this body of work has provided extraordinary insights to guide the future development of new cancer therapies that target c-Myc, Omomyc itself is not a therapeutic agent. In this context, we sought to exploit the use of a b-HLH-LZ to inhibit c-Myc in a cancer cell line in a more direct fashion. We demonstrate that the b-HLH-LZ domain of Max (Max*) behaves as a bona fide protein transduction domain (PTD) that can efficiently transduce across cellular membrane via through endocytosis and translocate to the nucleus. In addition, we show that the treatment of HeLa cells with Max* leads to a reduction of metabolism and proliferation rate. Accordingly, we observe a decrease of the population of HeLa cells in S phase, an accumulation in G1/G0 and the induction of apoptosis. In agreement with these phenotypic changes, we show by q-RT-PCR that the treatment of HeLa cells with Max* leads to the activation of the transcription c-Myc repressed genes as well as the repression of the expression of c-Myc activated genes. In addition to the novel discovery that the Max b-HLH-LZ is a PTD, our findings open up new avenues and strategies for the direct inhibition of c-Myc with b-HLH-LZ analogs

Evolutionary origin of genomic structural variations in domestic yaks

Yak has been subject to natural selection, human domestication and interspecific introgression during its evolution. However, genetic variants favored by each of these processes have not been distinguished previously. We constructed a graph-genome for 47 genomes of 7 cross-fertile bovine species. This allowed detection of 57,432 high-resolution structural variants (SVs) within and across the species, which were genotyped in 386 individuals. We distinguished the evolutionary origins of diverse SVs in domestic yaks by phylogenetic analyses. We further identified 334 genes overlapping with SVs in domestic yaks that bore potential signals of selection from wild yaks, plus an additional 686 genes introgressed from cattle. Nearly 90% of the domestic yaks were introgressed by cattle. Introgression of an SV spanning the KIT gene triggered the breeding of white domestic yaks. We validated a significant association of the selected stratified SVs with gene expression, which contributes to phenotypic variations. Our results highlight that SVs of different origins contribute to the phenotypic diversity of domestic yaks