BEV-LaneDet: a Simple and Effective 3D Lane Detection Baseline

3D lane detection which plays a crucial role in vehicle routing, has recently been a rapidly developing topic in autonomous driving. Previous works struggle with practicality due to their complicated spatial transformations and inflexible representations of 3D lanes. Faced with the issues, our work proposes an efficient and robust monocular 3D lane detection called BEV-LaneDet with three main contributions. First, we introduce the Virtual Camera that unifies the in/extrinsic parameters of cameras mounted on different vehicles to guarantee the consistency of the spatial relationship among cameras. It can effectively promote the learning procedure due to the unified visual space. We secondly propose a simple but efficient 3D lane representation called Key-Points Representation. This module is more suitable to represent the complicated and diverse 3D lane structures. At last, we present a light-weight and chip-friendly spatial transformation module named Spatial Transformation Pyramid to transform multiscale front-view features into BEV features. Experimental results demonstrate that our work outperforms the state-of-the-art approaches in terms of F-Score, being 10.6% higher on the OpenLane dataset and 5.9% higher on the Apollo 3D synthetic dataset, with a speed of 185 FPS. The source code will released at https://github.com/gigo-team/bev_lane_det.Comment: Accepted by CVPR202

Effect of Fans’ Placement on the Indoor Thermal Environment of Typical Tunnel-Ventilated Multi-Floor Pig Buildings Using Numerical Simulation

An increasing number of large pig farms are being built in multi-floor pig buildings (MFPBs) in China. Currently, the ventilation system of MFPB varies greatly and lacks common standards. This work aims to compare the ventilation performance of three popular MFPB types with different placement of fans using the Computational Fluid Dynamics (CFD) technique. After being validated with field-measured data, the CFD models were extended to simulate the air velocity, air temperature, humidity, and effective temperature of the three MFPBs. The simulation results showed that the ventilation rate of the building with outflowing openings in the endwall and fans installed on the top of the shaft was approximately 25% less than the two buildings with fans installed on each floor. The ventilation rate of each floor increased from the first to the top floor for both buildings with a shaft, while no significant difference was observed in the building without a shaft. Increasing the shaft’s width could mitigate the variation in the ventilation rate of each floor. The effective temperature distribution at the animal level was consistent with the air velocity distribution. Therefore, in terms of the indoor environmental condition, the fans were recommended to be installed separately on each floor

Optimal channel selection of remanufacturing firms with considering asymmetric information in platform economy

With the rapid development of e-commerce platforms, and considering that online return rate is relatively high and third-party stores on e-commerce platforms need to adopt third-party logistics, thus remanufacturing firms face the challenge of deciding whether to enter e-commerce platforms. To address this challenge, our paper considers a remanufacturing firm, an e-commerce platform, and a third-party logistics provider. Moreover, according to whether the remanufacturing firm enters the platform and whether the information is symmetrical, we develop three theoretical models: Model NP (the firm doesn’t enter platform), Model YP (the firm enters platform with symmetric information) and Model YA (the firm enters platform with asymmetric information). Some main insights are obtained. We find that whether remanufacturing firms should enter the platform depends not only on the annual service fee charged by the platform but also on the carbon tax price set by the government. Interestingly, improved consumers’ satisfaction with online remanufactured products is not necessarily conducive to enhancing the willingness of remanufacturing firms to enter e-commerce platforms. Finally, we find that when the production quantity constraint of the remanufactured products is not binding, if the actual production cost of remanufactured products is high and consumers’ satisfaction with offline remanufactured products is relatively low, information disclosure will benefit remanufacturing firms, however, when the production quantity constraint of the remanufactured products is binding, information disclosure has no impact on the remanufacturing firms’ profits and operational decisions

Quantification and Driving Factors of Cultivated Land Fragmentation in Rapidly Urbanizing Area: A Case Study in Guangdong Province

Cultivated land resources are crucial for food security and economic and social development. However, with the acceleration of urbanization and shifts in land use, cultivated land fragmentation (CLF) has emerged as a significant factor constraining the sustainable development of agriculture in China. As the most urbanized region, optimizing cultivated land resources and coordinating urban and rural development has become an urgent issue for rural sustainable development in Guangdong Province. This study analyzes the spatiotemporal characteristics of CLF in Guangdong Province from 2000 to 2020 using landscape pattern indices, CRITIC empowerment, and a multiscale geographically weighted regression (MGWR) model. The cultivated land fragmentation index (CLFI) for Guangdong Province reveals a fluctuating trend from 2000 to 2012, increasing from 0.453 in 2012 to 0.641 in 2020. The CLFI is notably high in the Pearl River Delta region, as well as in Meizhou and Maoming. The results show the dynamic changes of the driving factors of CLF at the county scale in 2000, 2010, and 2020. Slope and grain output consistently emerge as key driving factors of CLF. Furthermore, agricultural benefits played a significant role in 2000 and 2020, whereas the coefficient for social economic development was more pronounced in 2010. By identifying the heterogeneity of the driving factors, this study suggests that strategies to address CLF should comprehensively consider aspects such as the optimization of cultivated land resources, farmers’ interests, industrial restructuring, and the multifunctional development of farmland. The study findings can assist government policy-making for rural sustainable development, addressing CLF and food insecurity, and alleviating the regional development imbalance and urban–rural income gap, with the ultimate aim of achieving common prosperity

Predicting Ventilation Rate in a Naturally Ventilated Dairy Barn in Wind-Forced Conditions Using Machine Learning Techniques

Precise ventilation rate estimation of a naturally ventilated livestock building can benefit the control of the indoor environment. Machine learning has become a useful technique in many research fields and might be applied to ventilation rate prediction. This paper developed a machine-learning model for ventilation rate prediction from batch computational fluid dynamics (CFD) simulation results. By comparing deep neural networks (DNN), support vector regression (SVR), and random forest (RF), the best machine learning algorithm was selected. By comparing the modeling scheme of direct single-output (ventilation rate) and indirect multiple-output (predict averaged air velocities normal to the openings, then calculate the ventilation rate), the performances of the machine learning models widely applied in ventilation rate prediction were evaluated. In addition, this paper further evaluated the impact of adding indoor air velocity measurement in ventilation rate prediction. The results showed that the modeling performance of the DNN algorithm (Mean Absolute Percentage Error (MAPE) = 20.1%) was better than those of the SVR (MAPE = 23.2%) and RF algorithm (MAPE = 21.0%). The scheme of multiple-output performed better (MAPE < 8%) than the single-output scheme (MAPE = 20.1%), where MAPE was the mean absolute percentage error. Additionally, the comparison of modeling schemes with different inputs showed that the predictive accuracy could be improved by adding indoor velocities to the inputs. The MAPE decreased from 7.7% in the scheme without indoor velocity to 4.4% in the scheme with one indoor velocity, and 3.1% in the scheme with two indoor velocities. The location of the additional air velocity affected the accuracy of the predictive model, with the ones at the bottom layer performing better in the prediction than those at the top layer. This study enables a real-time and accurate prediction of the ventilation rate of a barn and provides a recommendation for optimal indoor sensor placement

Room-temperature synthesis of nonstoichiometric copper sulfide (Cu2−xS) for sodium ion storage

Nonstoichiometric transition metal chalcogenides, characterized by intrinsic vacancy defects and high conductivity, have garnered significant interest for their diverse applications in catalysis, sensing, biomedicine, and energy conversion. Nevertheless, conventional synthesis strategies often necessitate harsh conditions or intricate procedures. It remains challenging to develop a rapid, facile, energy-efficient, and environmental-friendly strategy for the preparation of nonstoichiometric chalcogenides. Herein, we propose a surprisingly efficient yet simple method for the preparation of nonstoichiometric face-centered cubic (fcc) Cu2−xS (0 \u3c x \u3c 1) nanoparticles, which are p-type semiconducting and non-toxic, by simply mixing aqueous solutions of Cu2+ with excess S2−/HS− at room temperature. The Cu2−xS is resulted from the redox reaction between the Cu2+ and excess S2−/HS− with S22− as the side product, as has been demonstrated by the color change and the UV-Vis characterization of the supernatant. Moreover, the cyclic utilization of the excess S2−/HS− for repeatedly synthesizing Cu2−xS is demonstrated. In contrast, the mixing of similar amounts of Cu2+ and S2−/HS− produces hexagonal CuS through the well-known precipitation reaction. The cubic Cu2−xS exhibits outstanding rate capability and cycling stability as an anode material for sodium ion batteries, maintaining high specific capacities of 288 and 237 mA h g−1 at rates of 2 and 5 A g−1 respectively after 3000 cycles. Density functional theory (DFT) calculations unveil the exceptional Na+ storage properties of the as-prepared cubic Cu2−xS, attributing them to its elevated structural stability. Moreover, the substantiation of a reduced Na+-diffusion barrier energy provides theoretical reinforcement to these observations. The inorganic synthesis chemistry reported in this work paves a new pathway for the preparation of nonstoichiometric transition metal sulfides. In addition, the exceptional sodium-ion storage properties and the related understanding offer novel insights for optimizing the ion storage performances of transition metal chalcogenides