CylinderTag: An Accurate and Flexible Marker for Cylinder-Shape Objects Pose Estimation Based on Projective Invariants

High-precision pose estimation based on visual markers has been a thriving research topic in the field of computer vision. However, the suitability of traditional flat markers on curved objects is limited due to the diverse shapes of curved surfaces, which hinders the development of high-precision pose estimation for curved objects. Therefore, this paper proposes a novel visual marker called CylinderTag, which is designed for developable curved surfaces such as cylindrical surfaces. CylinderTag is a cyclic marker that can be firmly attached to objects with a cylindrical shape. Leveraging the manifold assumption, the cross-ratio in projective invariance is utilized for encoding in the direction of zero curvature on the surface. Additionally, to facilitate the usage of CylinderTag, we propose a heuristic search-based marker generator and a high-performance recognizer as well. Moreover, an all-encompassing evaluation of CylinderTag properties is conducted by means of extensive experimentation, covering detection rate, detection speed, dictionary size, localization jitter, and pose estimation accuracy. CylinderTag showcases superior detection performance from varying view angles in comparison to traditional visual markers, accompanied by higher localization accuracy. Furthermore, CylinderTag boasts real-time detection capability and an extensive marker dictionary, offering enhanced versatility and practicality in a wide range of applications. Experimental results demonstrate that the CylinderTag is a highly promising visual marker for use on cylindrical-like surfaces, thus offering important guidance for future research on high-precision visual localization of cylinder-shaped objects. The code is available at: https://github.com/wsakobe/CylinderTag.Comment: 15 pages, 22 figures. This work has been submitted to the IEEE for possible publication. Copyright may be transferred without notice, after which this version may no longer be accessibl

Towards Predicting Equilibrium Distributions for Molecular Systems with Deep Learning

Advances in deep learning have greatly improved structure prediction of molecules. However, many macroscopic observations that are important for real-world applications are not functions of a single molecular structure, but rather determined from the equilibrium distribution of structures. Traditional methods for obtaining these distributions, such as molecular dynamics simulation, are computationally expensive and often intractable. In this paper, we introduce a novel deep learning framework, called Distributional Graphormer (DiG), in an attempt to predict the equilibrium distribution of molecular systems. Inspired by the annealing process in thermodynamics, DiG employs deep neural networks to transform a simple distribution towards the equilibrium distribution, conditioned on a descriptor of a molecular system, such as a chemical graph or a protein sequence. This framework enables efficient generation of diverse conformations and provides estimations of state densities. We demonstrate the performance of DiG on several molecular tasks, including protein conformation sampling, ligand structure sampling, catalyst-adsorbate sampling, and property-guided structure generation. DiG presents a significant advancement in methodology for statistically understanding molecular systems, opening up new research opportunities in molecular science.Comment: 80 pages, 11 figure

Traditional Chinese medicine residues promote the growth and quality of Salvia miltiorrhiza Bunge by improving soil health under continuous monoculture

Continuous monoculture of crops has resulted in reduced yields and quality, as well as soil deterioration. Although traditional Chinese medicine residues (TCMRs) are known to promote plant growth and soil health, few studies have investigated their effectiveness in continuous monoculture soils. Here, we studied the impact of chemical fertilizers (CF) and four TCMRs with antibacterial activities on the growth of S. miltiorrhiza (a widely used medicinal plant in China), accumulation of active ingredients in plants, and soil health under continuous monoculture conditions. Compared with no fertilizer (CK) and CF, fermented Sophora flavescens radix residue (SFRf) and fermented and unfermented Moutan cortex residue (MCRf and MCRu, respectively) resulted in a reduction of the disease index of root rot, while CF did not. The CF and four TCMR treatments increased the accumulation of nitrogen (N) (42.8-124.6% and 17.0-101.7%), phosphorous (P) (19.8-74.7% and 8.3-27.4%), and potassium (K) (104.1-212.0% and 9.3-51.8%) in shoots and roots compared to CK. The differences in nutrient accumulation between the CF and TCMR treatments were statistically insignificant, excepted for the N accumulation in the roots. All fertilization treatments increased plant biomass compared to CK, with increases of 25.57-89.86% and 2.62-35.28% in shoots and roots, respectively. The SFRf treatment exhibited the most significant enhancement in both shoot and root biomass. CF significantly reduced the accumulation of seven active ingredients in roots by 23.90-78.95% compared to CK, whereas each TCMR increased accumulation of certain active ingredients. The TCMR treatments effectively improved the health of deteriorated soil by enhancing soil physicochemical properties, restoring the balance of the microbial community, recruiting beneficial bacteria, and reducing the relative abundance of the pathogen Fusarium. The SFRf treatment exhibited superior performance in improving soil health than other treatments. Overall, the TCMRs outperformed CF in restoring soil health and promoting the yield and quality of S. miltiorrhiza. These findings offer guidance for improving the health of continuous cropping soil and recycling TCMRs

Air pollution perception in ten countries during the COVID-19 pandemic

NTNU Norwegian University of Science and TechnologypublishedVersionPaid Open AccessUNIT agreemen

The Rejuvenating Effect in Hot Asphalt Recycling by Mortar Transfer Ratio and Image Analysis

Using a rejuvenator to improve the performance of asphalt pavement is an effective and economic way of hot asphalt recycling. This research analyzes the rejuvenating effect on aged asphalt by means of a Mortar Transfer Ratio (MTR) test, which concerns the ratio of asphalt mortar that moves from recycled aggregates (RAP aggregates) to fresh added aggregates when aged asphalt is treated with a regenerating agent and comes into contact with fresh aggregates. The proposed MTR test analyzes the regeneration in terms of the softening degree on aged asphalt when the rejuvenator is applied. The covered area ratio is studied with an image analyzing tool to understand the possibility of mortar transferring from RAP aggregates to fresh aggregates. Additionally, a micro-crack closure test is conducted and observed through a microscope. The repairing ability and diffusion characteristics of micro-cracks can therefore be analyzed. The test results demonstrate that the proposed mortar transfer ratio is a feasible way to evaluate rejuvenator diffusion during hot recycling. The mortar transfer ratio and uncovered area ratio on fresh aggregates are compatible, and can be used to quantify the contribution of the rejuvenator. Within a certain temperature range, the diffusing effect of the rejuvenator is better when the diffusing temperature is higher. The diffusion time of the rejuvenator is optimum when diffusion occurs for 4–8 h. When the rejuvenator is properly applied, the rough and cracking surface can be repaired, resulting in better covered aggregates. The micro-closure analysis visually indicates that rejuvenators can be used to repair the RAP aggregates during hot recycling

Modeling Nutrition Quality and Storage of Forage Using Climate Data and Normalized-Difference Vegetation Index in Alpine Grasslands

Quantifying forage nutritional quality and pool at various spatial and temporal scales are major challenges in quantifying global nitrogen and phosphorus cycles, and the carrying capacity of grasslands. In this study, we modeled forage nutrition quality and storage using climate data under fencing conditions, and using climate data and a growing-season maximum normalized-difference vegetation index under grazing conditions based on four different methods (i.e., multiple linear regression, random-forest models, support-vector machines and recursive-regression trees) in the alpine grasslands of Tibet. Our results implied that random-forest models can have greater potential ability in modeling forage nutrition quality and storage than the other three methods. The relative biases between simulated nutritional quality using random-forest models and the observed nutritional quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were lower than 2.00% and 6.00%, respectively. The RMSE between simulated nutrition quality using random-forest models and the observed nutrition quality, and between simulated nutrition storage using random-forest models and the observed nutrition storage, were no more than 0.99% and 4.50 g m−2, respectively. Therefore, random-forest models based on climate data and/or the normalized-difference vegetation index can be used to model forage nutrition quality and storage in the alpine grasslands of Tibet

Aging Mechanism and Rejuvenating Possibility of SBS Copolymers in Asphalt Binders

The styrene–butadiene–styrene (SBS)-modified asphalt pavement has been in growing demand in the road construction field owing to its workable mechanical property and temperature durability. This paper prepared a penetrative rejuvenator (PR) with waste cooking oil (WCO) and emulsified asphalt, then applied PR on SBS copolymers to investigate its aging and rejuvenating effects in an asphalt binder. After a thin film oven test (TFOT) and ultraviolet (UV) aging of SBS copolymers, Fourier transform infrared (FTIR) spectra were used to analyse the aged copolymers’ chemical structure. Moreover, both aged and rejuvenated SBS copolymers were added into a fresh asphalt binder to get two kinds of modified asphalt binders, namely, MAAC (modified by aged copolymer) and MARC (modified by rejuvenated copolymer). Aiming to analyse the monomer effect of SBS copolymers in the asphalt binder, the rheological characteristic with dynamic shear rheometer (DSR), chemical structure with FTIR and physical properties with penetration, soft point and ductility tests were investigated using MAAC and MAAC samples. The results showed that rejuvenated SBS copolymer could improve MAAC’s viscoelasticity, but from FTIR spectral analysis, PR resulted in no chemical changes to SBS copolymers. A tough coat which made MAAC of higher stiffness was observed on the copolymer surface after thermal treatment. UV caused evidently negative effects on SBS copolymer because of accelerating oxidation by ozone, which brought about high possibility of cracks during servicing periods of asphalt pavement. In addition, MAAC was inferior in both rheological and physical properties, which reflected the significance and necessity in consideration of alleviating SBS copolymer aging in field

Energy Consumption and Environment Performance Analysis of Induction-Healed Asphalt Pavement by Life Cycle Assessment (LCA)

In this paper, the sustainability of induced healing asphalt pavement is demonstrated by comparing the impact of asphalt pavement maintained by induced healing asphalt pavement technology and traditional maintenance methods (such as milling and overlaying). The functional unit selected is a 1-km lane with an analysis period of 20 years. The stages to be considered are material manufacturing, paving, maintenance, milling and demolition. Two case studies were analyzed to assess the impact of different technologies on the energy consumption and environmental performance of each maintenance alternative. By comparing the energy consumption and environmental emissions of the whole life cycle of pavement under the two technical conditions, the results show that the total energy consumption of traditional asphalt pavement is about 2.5 times that of induction-healed asphalt pavement, and the total greenhouse gas (GHG) emissions of the former are twice as much as that of the latter