A study on parking supply optimization in central business districts considering the two way interaction between car traveling and parking

This paper proposes a bilevel programming model to optimize the parking supply in a central business district (CBD) considering the interaction between car traveling and car parking. The upper model (UM) determines the number and locations of parking lots in a CBD with the objective of minimizing the average impedance of all car trips. The lower model (LM) is a modal split and assignment combination model for calculating the traffic flow under various parking supply schemes. In addition to the bilevel model, a gravity model (GM) is proposed to calculate the car trips that are induced by the added parking lots. The interaction between car traveling and parking can be simulated by the feedbacks between the UM and LM. A case study is performed with real data from Dalian City. The results show that there is a negative correlation between parking supply increments and the average traveling impedance when the number of parking spaces is lower than the optimal value; however, the average traveling impedance will start to increase with the increase in parking supply when the number of parking spaces is higher than the optimal value

Stock market prediction using weighted inter-transaction class association rule mining and evolutionary algorithm

Evolutionary computation and data mining are two fascinating fields that have attracted many researchers. This paper proposes a new rule mining method, named genetic network programming (GNP), to solve the prediction problem using the evolutionary algorithm. Compared with the conventional association rule methods that do not consider the weight factor, the proposed algorithm provides many advantages in financial prediction, since it can discover relationships among the attributes of different transactions. Experimental results on data from the New York Exchange Market show that the new method outperforms other conventional models in terms of both accuracy and profitability, and the proposed method can establish more important and accurate rules than the conventional methods. The results confirmed the effectiveness of the proposed data mining method in financial prediction

Yeast Extract Promotes Cell Growth and Induces Production of Polyvinyl Alcohol-Degrading Enzymes

Polyvinyl alcohol-degrading enzymes (PVAases) have a great potential in bio-desizing processes for its low environmental impact and low energy consumption. In this study, the effect of yeast extract on PVAases production was investigated. A strategy of four-point yeast extract addition was developed and applied to maximize cell growth and PVAases production. As a result, the maximum dry cell weight achieved was 1.48 g/L and the corresponding PVAases activity was 2.99 U/mL, which are 46.5% and 176.8% higher than the control, respectively. Applying this strategy in a 7 L fermentor increased PVAases activity to 3.41 U/mL. Three amino acids (glycine, serine, and tyrosine) in yeast extract play a central role in the production of PVAases. These results suggest that the new strategy of four-point yeast extract addition could benefit PVAases production

Remote Sensing Scene Classification Based on Convolutional Neural Networks Pre-Trained Using Attention-Guided Sparse Filters

Open access articleSemantic-level land-use scene classification is a challenging problem, in which deep learning methods, e.g., convolutional neural networks (CNNs), have shown remarkable capacity. However, a lack of sufficient labeled images has proved a hindrance to increasing the land-use scene classification accuracy of CNNs. Aiming at this problem, this paper proposes a CNN pre-training method under the guidance of a human visual attention mechanism. Specifically, a computational visual attention model is used to automatically extract salient regions in unlabeled images. Then, sparse filters are adopted to learn features from these salient regions, with the learnt parameters used to initialize the convolutional layers of the CNN. Finally, the CNN is further fine-tuned on labeled images. Experiments are performed on the UCMerced and AID datasets, which show that when combined with a demonstrative CNN, our method can achieve 2.24% higher accuracy than a plain CNN and can obtain an overall accuracy of 92.43% when combined with AlexNet. The results indicate that the proposed method can effectively improve CNN performance using easy-to-access unlabeled images and thus will enhance the performance of land-use scene classification especially when a large-scale labeled dataset is unavailable

SpOctA: A 3D Sparse Convolution Accelerator with Octree-Encoding-Based Map Search and Inherent Sparsity-Aware Processing

Point-cloud-based 3D perception has attracted great attention in various applications including robotics, autonomous driving and AR/VR. In particular, the 3D sparse convolution (SpConv) network has emerged as one of the most popular backbones due to its excellent performance. However, it poses severe challenges to real-time perception on general-purpose platforms, such as lengthy map search latency, high computation cost, and enormous memory footprint. In this paper, we propose SpOctA, a SpConv accelerator that enables high-speed and energy-efficient point cloud processing. SpOctA parallelizes the map search by utilizing algorithm-architecture co-optimization based on octree encoding, thereby achieving 8.8-21.2x search speedup. It also attenuates the heavy computational workload by exploiting inherent sparsity of each voxel, which eliminates computation redundancy and saves 44.4-79.1% processing latency. To optimize on-chip memory management, a SpConv-oriented non-uniform caching strategy is introduced to reduce external memory access energy by 57.6% on average. Implemented on a 40nm technology and extensively evaluated on representative benchmarks, SpOctA rivals the state-of-the-art SpConv accelerators by 1.1-6.9x speedup with 1.5-3.1x energy efficiency improvement.Comment: Accepted to ICCAD 202

TODE-Trans: Transparent Object Depth Estimation with Transformer

Transparent objects are widely used in industrial automation and daily life. However, robust visual recognition and perception of transparent objects have always been a major challenge. Currently, most commercial-grade depth cameras are still not good at sensing the surfaces of transparent objects due to the refraction and reflection of light. In this work, we present a transformer-based transparent object depth estimation approach from a single RGB-D input. We observe that the global characteristics of the transformer make it easier to extract contextual information to perform depth estimation of transparent areas. In addition, to better enhance the fine-grained features, a feature fusion module (FFM) is designed to assist coherent prediction. Our empirical evidence demonstrates that our model delivers significant improvements in recent popular datasets, e.g., 25% gain on RMSE and 21% gain on REL compared to previous state-of-the-art convolutional-based counterparts in ClearGrasp dataset. Extensive results show that our transformer-based model enables better aggregation of the object's RGB and inaccurate depth information to obtain a better depth representation. Our code and the pre-trained model will be available at https://github.com/yuchendoudou/TODE.Comment: Submitted to ICRA202

The order of expression is a key factor in the production of active transglutaminase in Escherichia coli by co-expression with its pro-peptide

<p>Abstract</p> <p>Background</p> <p><it>Streptomyces </it>transglutaminase (TGase) is naturally synthesized as zymogen (pro-TGase), which is then processed to produce active enzyme by the removal of its N-terminal pro-peptide. This pro-peptide is found to be essential for overexpression of soluble TGase in <it>E. coli</it>. However, expression of pro-TGase by <it>E. coli </it>requires protease-mediated activation <it>in vitro</it>. In this study, we developed a novel co- expression method for the direct production of active TGase in <it>E. coli</it>.</p> <p>Results</p> <p>A TGase from <it>S. hygroscopicus </it>was expressed in <it>E. coli </it>only after fusing with the pelB signal peptide, but fusion with the signal peptide induced insoluble enzyme. Therefore, alternative protocol was designed by co-expressing the TGase and its pro-peptide as independent polypeptides under a single T7 promoter using vector pET-22b(+). Although the pro-peptide was co-expressed, the TGase fused without the signal peptide was undetectable in both soluble and insoluble fractions of the recombinant cells. Similarly, when both genes were expressed in the order of the TGase and the pro-peptide, the solubility of TGase fused with the signal peptide was not improved by the co-expression with its pro-peptide. Interestingly, active TGase was only produced by the cells in which the pro-peptide and the TGase were fused with the signal peptide and sequentially expressed. The purified recombinant and native TGase shared the similar catalytic properties.</p> <p>Conclusions</p> <p>Our results indicated that the pro-peptide can assist correct folding of the TGase inter-molecularly in <it>E. coli</it>, and expression of pro-peptide prior to that of TGase was essential for the production of active TGase. The co-expression strategy based on optimizing the order of gene expression could be useful for the expression of other functional proteins that are synthesized as a precursor.</p

Epigallocatechin-3-gallate enhances key enzymatic activities of hepatic thioredoxin and glutathione systems in selenium-optimal mice but activates hepatic Nrf2 responses in selenium-deficient mice

AbstractSelenium participates in the antioxidant defense mainly through a class of selenoproteins, including thioredoxin reductase. Epigallocatechin-3-gallate (EGCG) is the most abundant and biologically active catechin in green tea. Depending upon the dose and biological systems, EGCG may function either as an antioxidant or as an inducer of antioxidant defense via its pro-oxidant action or other unidentified mechanisms. By manipulating the selenium status, the present study investigated the interactions of EGCG with antioxidant defense systems including the thioredoxin system comprising of thioredoxin and thioredoxin reductase, the glutathione system comprising of glutathione and glutathione reductase coupled with glutaredoxin, and the Nrf2 system. In selenium-optimal mice, EGCG increased hepatic activities of thioredoxin reductase, glutathione reductase and glutaredoxin. These effects of EGCG appeared to be not due to overt pro-oxidant action because melatonin, a powerful antioxidant, did not influence the increase. However, in selenium-deficient mice, with low basal levels of thioredoxin reductase 1, the same dose of EGCG did not elevate the above-mentioned enzymes; intriguingly EGCG in turn activated hepatic Nrf2 response, leading to increased heme oxygenase 1 and NAD(P)H:quinone oxidoreductase 1 protein levels and thioredoxin activity. Overall, the present work reveals that EGCG is a robust inducer of the Nrf2 system only in selenium-deficient conditions. Under normal physiological conditions, in selenium-optimal mice, thioredoxin and glutathione systems serve as the first line defense systems against the stress induced by high doses of EGCG, sparing the activation of the Nrf2 system