YOLO-BEV: Generating Bird's-Eye View in the Same Way as 2D Object Detection

Vehicle perception systems strive to achieve comprehensive and rapid visual interpretation of their surroundings for improved safety and navigation. We introduce YOLO-BEV, an efficient framework that harnesses a unique surrounding cameras setup to generate a 2D bird's-eye view of the vehicular environment. By strategically positioning eight cameras, each at a 45-degree interval, our system captures and integrates imagery into a coherent 3x3 grid format, leaving the center blank, providing an enriched spatial representation that facilitates efficient processing. In our approach, we employ YOLO's detection mechanism, favoring its inherent advantages of swift response and compact model structure. Instead of leveraging the conventional YOLO detection head, we augment it with a custom-designed detection head, translating the panoramically captured data into a unified bird's-eye view map of ego car. Preliminary results validate the feasibility of YOLO-BEV in real-time vehicular perception tasks. With its streamlined architecture and potential for rapid deployment due to minimized parameters, YOLO-BEV poses as a promising tool that may reshape future perspectives in autonomous driving systems

Design and Fabrication of a MEMS Flow Sensor and Its Application in Precise Liquid Dispensing

A high speed MEMS flow sensor to enhance the reliability and accuracy of a liquid dispensing system is proposed. Benefitting from the sensor information feedback, the system can self-adjust the open time of the solenoid valve to accurately dispense desired volumes of reagent without any pre-calibration. First, an integrated high-speed liquid flow sensor based on the measurement of the pressure difference across a flow channel is presented. Dimensions of the micro-flow channel and two pressure sensors have been appropriately designed to meet the static and dynamic requirements of the liquid dispensing system. Experiments results show that the full scale (FS) flow measurement ranges up to 80 μL/s, with a nonlinearity better than 0.51% FS. Secondly, a novel closed-loop control strategy is proposed to calculate the valve open time in each dispensing cycle, which makes the system immune to liquid viscosity, pressure fluctuation, and other sources of error. Finally, dispensing results show that the system can achieve better dispensing performance, and the coefficient of variance (CV) for liquid dispensing is below 3% at 1 μL and below 4% at 100 nL

Critical success criteria for B2B E-commerce systems in Chinese medical supply chain.

The paper presents an exploratory investigation to determine and prioritise the critical success criteria, which can measure and guide the successful application and performance improvement of business to business e-commerce system (BBECS) in a medical supply chain's selling and buying functions, in the context of global business expansion. The research reveals that the buying and the selling functions have different prioritisations on the majority of the determined critical success measuring criteria. These criteria are categorised into three Critical Success Measuring Criteria Groups, for the selling and the buying functions, respectively, guiding medical supply chain members in harnessing the full advantage of a BBECS. For the selling function, the top critical success measuring criteria are as follows: integrating information searching/transmission and application processes, ensuring the reliability and timeliness of technical support, ensuring recognition and acceptance of e-commerce processes, displaying the organisation's business focus and product/service provisions online, securing a large scale/amount of business transactions, adjusting production outputs and inventory levels and having more registered users than competitors do. The top critical success measuring criteria for the buying function are as follows: securing the establishment of business relationships between businesses, displaying the measures ensuring mutual trust and cooperation online, ensuring employees' recognition of the benefit of e-commerce in increasing revenue, ensuring the contribution to the development and realisation of corporate strategy, achieving cost reduction for the organisation, making the purchase of famous brand products available/doable, securing a large scale/amount of business transactions, and ensuring the attainability of products/services at a lower price

Ticagrelor vs Clopidogrel in CYP2C19 loss-of-function carriers with Stroke or TIA

BACKGROUNDComparisons between ticagrelor- aspirin and clopidogrel-aspirin in CYP2C19 loss-of-function carriers have not been well studied for secondary stroke prevention.METHODSWe conducted a randomized, double-blind, placebo-controlled trial of 6,412 patients with a minor ischemic stroke or TIA who carried CYP2C19 LOF alleles determined by point-of-care testing. Patients were randomly assigned within 24 hours after symptom onset, in a 1:1 ratio to receive ticagrelor (180 mg loading dose on day 1 followed by 90 mg twice daily for days 2 through 90) or clopidogrel (300 mg loading dose on day 1 followed by 75 mg per day for days 2 through 90), plus aspirin (75-300 mg loading dose followed by 75 mg daily for 21 days). The primary efficacy outcome was stroke and the primary safety outcome was severe or moderate bleeding, both within 90 days. RESULTSStroke occurred within 90 days in 191 (6.0%) versus 243 (7.6%), respectively (hazard ratio, 0.77; 95% confidence interval, 0.64 to 0.94; P=0.008). Moderate or severe bleeding occurred in 9 patients (0.3%) in the ticagrelor-aspirin group and in 11 patients (0.3%) in the clopidogrel-aspirin group; any bleeding event occurred in 170 patients (5.3%) vs 80 (2.5%), respectively. CONCLUSIONSAmong Chinese patients with minor ischemic stroke or TIA within 24 hours after symptoms onset who were carriers of CYP2C19 loss-of-function alleles, ticagrelor- aspirin was modestly better than clopidogrel-aspirin for reducing the risk of stroke but was associated with more total bleeding events at 90 days. (CHANCE-2 ClinicalTrials.gov number, NCT04078737.

Using Product Network Analysis to Optimize Product-to-Shelf Assignment Problems

A good product-to-shelf assignment strategy not only helps customers easily find desired product items but also increases retailer profit. Recent research has attempted to solve product-to-shelf problems using product association analysis, a powerful data mining tool that can detect significant co-purchase rules underlying a large amount of purchase transaction data. While some studies have developed efficient approaches for this task, they largely overlook important factors related to optimizing product-to-shelf assignment, including product characteristics, physical proximity, and category constraints. This paper proposes a three-stage product-to-shelf assignment method to address this shortcoming. The first stage constructs a product relationship network that represents the purchase association among product items. The second stage derives the centrality value of each product item through network analysis. Based on the centrality of each product, an item is classified as an attraction item, an opportunity item, or a trivial item. The third stage considers purchase association, physical relationship, and category constraint when evaluating the location preference of each product. Based on the location preference values, a product assignment algorithm is then developed to optimize locations for opportunity items. A series of analyses and comparisons on the performance of different network types are conducted. It is found that the two network types provide variant managerial meanings for store managers. In addition, the implementation and experimental results show the proposed method is feasible and helpful

Compressed FWI inversion with non-monotone line search LBFGS

Full waveform inversion (FWI) is a high quality seismic imaging method. It is a nonlinear inversion problem which usually needs the monotone line search method to be solved. However, the speed of convergence for such a simple search technique is relatively slow. In this paper, we combine the non-monotone line search technique with the LBFGS method and apply them to the frequency-domain FWI. We test this new method on a two-dimensional Marmousi model. The results show that the method is robust. Comparing with the monotone line search method, the new method could improve the convergence rate of FWI. We also test the new method with a two-dimensional conventional streamer data set and the results show some improvements compared with the conventional FWI method.</p

Seismic data denoising using double sparsity dictionary and alternating direction method of multipliers

Recently, the dictionary learning plays a more and more important role in seismic data denoising. Compared with the fixed-basis transform (e.g., Fourier transform, wavelet transform, curvelet transform, contourlet transform and shearlet transform), the denoising of dictionary learning is better because of adaptive sparse representation of seismic data. However, dictionary learning often produces artifacts due to no prior-constraint structural information. In this paper, we propose a new denoising approach, which has double sparsity and combines the advantage of fixed-basis transform and dictionary learning. The whole work-flow of the new denoising approach is as follows. Firstly, we can obtain sparse coefficients of seismic data via shearlet transform. Secondly, sparse coefficients are divided into some suitable size blocks which are regarded as training sets. Thirdly, the alternating direction method of multipliers (ADMM) is used in sparse coding to update dictionary coefficients. Then, the data-driven tight frame (DDTF) is used in dictionary updating to update dictionary atoms. Again, the ADMM is used to resolve the convex optimization problem, and we reshape output blocks to obtain new sparse coefficients. Finally, the hard-thresholding and inverse shearlet transform are applied to new sparse coefficients to achieve denoising. The synthetic data and field data experiments show that the new denoising approach obtain better result than fixed-basis transform and dictionary learning. In conclusion, the new denoising approach can attenuate artifacts and improve the quality of seismic data denoising.</p

Characterization and corrosion property of nano-rod-like HA on fluoride coating supported on Mg-Zn-Ca alloy

The poor corrosion resistance of biodegradable magnesium alloys is the dominant factor that limits their clinical application. In this study, to deal with this challenge, fluoride coating was prepared on Mg–Zn–Ca alloy as the inner coating and then hydroxyapatite (HA) coating as the outer coating was deposited on fluoride coating by pulse reverse current electrodeposition (PRC-HA/MgF2). As a comparative study, the microstructure and corrosion properties of the composite coating with the outer coating fabricated by traditional constant current electrodeposition (TED-HA/MgF2) were also investigated. Scanning electron microscopy (SEM) images of the coatings show that the morphology of PRC-HA/MgF2 coating is dense and uniform, and presents nano-rod-like structure. Compared with that of TED-HA/MgF2, the corrosion current density of Mg alloy coated with PRC-HA/MgF2 coatings decreases from 5.72 × 10−5 A/cm2 to 4.32 × 10−7 A/cm2, and the corrosion resistance increases by almost two orders of magnitude. In immersion tests, samples coated with PRC-HA/MgF2 coating always show the lowest hydrogen evolution amount, and could induce deposition of the hexagonal structure-apatite on the surface rapidly. The results show that the corrosion resistance and the bioactivity of the coatings have been improved by adopting double-pulse current mode in the process of preparing HA on fluoride coating, and the PRC-HA/MgF2 coating is worth of further investigation