BSDF Importance Baking: A Lightweight Neural Solution to Importance Sampling General Parametric BSDFs

Parametric Bidirectional Scattering Distribution Functions (BSDFs) are pervasively used because of their flexibility to represent a large variety of material appearances by simply tuning the parameters. While efficient evaluation of parametric BSDFs has been well-studied, high-quality importance sampling techniques for parametric BSDFs are still scarce. Existing sampling strategies either heavily rely on approximations, resulting in high variance, or solely perform sampling on a portion of the whole BSDF slice. Moreover, many of the sampling approaches are specifically paired with certain types of BSDFs. In this paper, we seek an efficient and general way for importance sampling parametric BSDFs. We notice that the nature of importance sampling is the mapping between a uniform distribution and the target distribution. Specifically, when BSDF parameters are given, the mapping that performs importance sampling on a BSDF slice can be simply recorded as a 2D image that we name as importance map. Following this observation, we accurately precompute the importance maps using a mathematical tool named optimal transport. Then we propose a lightweight neural network to efficiently compress the precomputed importance maps. In this way, we have brought parametric BSDF important sampling to the precomputation stage, avoiding heavy runtime computation. Since this process is similar to light baking where a set of images are precomputed, we name our method importance baking. Together with a BSDF evaluation network and a PDF (probability density function) query network, our method enables full multiple importance sampling (MIS) without any revision to the rendering pipeline. Our method essentially performs perfect importance sampling. Compared with previous methods, we demonstrate reduced noise levels on rendering results with a rich set of appearances

Research on gas monitoring fusion of fixed place and mobile situation in coal mine underground

Existing gas monitoring of fixed place and mobile situation in coal mine underground is realized by safety monitoring system and intelligent gas inspection system separately. Monitoring data in the two systems has no relevance. Application status of the two systems was analyzed as well as existing problems. A viewpoint was proposed that data of the two systems could be fused, thus gas monitoring in fixed places and mobile gas detection in operation places could be supplementary to each other, so as to realize underground gas monitoring with no dead space. Main problems in data fusion of the two systems were researched, which were unifying monitoring data property, position information, data storage mode and data expression mode, and unifying monitoring data, time information and space information. Key technologies of the data fusion were also researched including main data management platform, accurate positioning of underground and a map of GIS

Realization of A Knowledge-based Intelligent System for Power Dispatching Plan Management

With the expanding of power grid scale in Chinese metropolis, the task intensity of power dispatchers increases rapidly in regulation of the power system operation structure and states to deal with everyday scheduled maintenance. In this paper, we propose a knowledge-based intelligent system developed to deal with daily management of the power dispatching plans. The system will analyse all the operation state changing tasks arranged for the next day and group the plans according to their association. It will automatically check the security of each power dispatching plan and generate the corresponding dispatching-order tickets. The proposed system builds up power grid ontology knowledge and first-order logic rules and integrates techniques of knowledge reasoning, natural language understanding and network topology analysis. Application shows that it can effectively realize the day-ahead power dispatching plan management (PDPM) instead of the human dispatchers

Visual Inspection Method for Metal Rolls Based on Multi-Scale Spatial Location Feature

Metal rolls in a non-ferrous-metal manufacturing workshop manifest the characteristics of symmetry, multiple scales and mutual covering, which poses great challenges for metal roll detection. To solve this problem, firstly, an efficient attention mechanism algorithm named ECLAM (efficient capture location attendant model) is proposed for capturing spatial position features efficiently, to obtain complete location information for metal rolls in a complex environment. ECLAM can improve the ability to extract the spatial features of backbone networks and reduce the influence of the non-critical background. In addition, in order to give feature maps a larger receptive field and improve the weight of location information in multi-scale feature maps, a nonlinear feature fusion module named LFFM (location feature fusion module) is used to fuse two adjacent feature images. Finally, a multi-scale object detection network named L-MSNet (location-based multi-scale object detection network) based on the combination of ECLAM and LFFM is proposed and used to accurately detect multi-scale metal rolls. In the experiments, multi-scale metal roll images are collected from an actual non-ferrous-metal manufacturing workshop. On this basis, a pixel-level image dataset is constructed. Comparative experiments show that, compared with other object detection methods, L-MSNet can detect multi-scale metal rolls more accurately. The average accuracy is improved by 2% to 5%, and the average accuracy of small and medium-sized objects is also significantly improved by 3% to 6%

Visual Inspection Method for Metal Rolls Based on Multi-Scale Spatial Location Feature

Metal rolls in a non-ferrous-metal manufacturing workshop manifest the characteristics of symmetry, multiple scales and mutual covering, which poses great challenges for metal roll detection. To solve this problem, firstly, an efficient attention mechanism algorithm named ECLAM (efficient capture location attendant model) is proposed for capturing spatial position features efficiently, to obtain complete location information for metal rolls in a complex environment. ECLAM can improve the ability to extract the spatial features of backbone networks and reduce the influence of the non-critical background. In addition, in order to give feature maps a larger receptive field and improve the weight of location information in multi-scale feature maps, a nonlinear feature fusion module named LFFM (location feature fusion module) is used to fuse two adjacent feature images. Finally, a multi-scale object detection network named L-MSNet (location-based multi-scale object detection network) based on the combination of ECLAM and LFFM is proposed and used to accurately detect multi-scale metal rolls. In the experiments, multi-scale metal roll images are collected from an actual non-ferrous-metal manufacturing workshop. On this basis, a pixel-level image dataset is constructed. Comparative experiments show that, compared with other object detection methods, L-MSNet can detect multi-scale metal rolls more accurately. The average accuracy is improved by 2% to 5%, and the average accuracy of small and medium-sized objects is also significantly improved by 3% to 6%

Efficacy of quadruple therapy with clarithromycin based on faecal molecular antimicrobial susceptibility tests as first-line treatment for Helicobacter pylori infection: a protocol of a single-centre, single-blind, randomised clinical trial in China

Introduction Helicobacter pylori is the most well-known risk factor for gastric cancer. Antibiotic resistance is the main reason for the failure of H. pylori eradication, and understanding the antibiotic resistance before treatment may be the main determinant of successful eradication of H. pylori. This study aims to evaluate the efficacy and safety of quadruple therapy based on faecal molecular antimicrobial susceptibility tests for the first-line eradication of H. pylori infection.Methods and analysis This is a single-centre, single-blind, randomised controlled trial, enrolling 855 patients with H. pylori infection. Patients are randomised to three groups for a 14-day treatment: group A: amoxicillin- and clarithromycin-based bismuth-containing quadruple therapy (BQT) (rabeprazole 10 mg, amoxicillin 1 g, clarithromycin 500 mg and colloidal bismuth 200 mg two times per day); group B: clarithromycin medication history-based BQT (rabeprazole 10 mg, amoxicillin 1 g, furazolidone 100 mg (with clarithromycin medication history)/clarithromycin 500 mg (without clarithromycin medication history) and colloidal bismuth 200 mg two times per day); group C: antimicrobial susceptibility test-based BQT (rabeprazole 10 mg, amoxicillin 1 g, clarithromycin 500 mg (clarithromycin-sensitive)/furazolidone 100 mg (clarithromycin resistant) and colloidal bismuth 200 mg two times per day). The primary end point is the eradication rate. The secondary end points are the incidence of adverse events and compliance.Ethics and dissemination This study was approved by the Ethics Committee of Second Affiliated Hospital, School of Medicine, Zhejiang University (Number 20230103). The results will be published in the appropriate peer-reviewed journal.Trial registration number NCT05718609

In Vivo Metabolic Response upon Exposure to Gold Nanorod Core/Silver Shell Nanostructures: Modulation of Inflammation and Upregulation of Dopamine

With the increasing applications of silver nanoparticles (Ag NPs), the concerns of widespread human exposure as well as subsequent health risks have been continuously growing. The acute and chronic toxicities of Ag NPs in cellular tests and animal tests have been widely investigated. Accumulating evidence shows that Ag NPs can induce inflammation, yet the overall mechanism is incomplete. Herein, using gold nanorod core/silver shell nanostructures (Au@Ag NRs) as a model system, we studied the influence on mice liver and lungs from the viewpoint of metabolism. In agreement with previous studies, Au@Ag NRs’ intravenous exposure caused inflammatory reaction, accompanying with metabolic alterations, including energy metabolism, membrane/choline metabolism, redox metabolism, and purine metabolism, the disturbances of which contribute to inflammation. At the same time, dopamine metabolism in liver was also changed. This is the first time to observe the production of dopamine in non-neural tissue after treatment with Ag NPs. As the upregulation of dopamine resists inflammation, it indicates the activation of antioxidant defense systems against oxidative stress induced by Au@Ag NRs. In the end, our findings deepened the understanding of molecular mechanisms of Ag NPs-induced inflammation and provide assistance in the rational design of their biomedical applications

Analysis and evaluation for core module based on product family

Aiming at the evolution problem of core module in product family design, the module evolution law and process evaluation were analyzed. A method for law analysis and evaluation in the module evolution was proposed. The relationship between customer demand and module design was also studied. The core module standardization and universal indicators based on existing products were analyzed. An analytical model for core module evolution was established in the process of needs analysis, and the model was trained by the history data. The changing law of design and craft was obtained by demand evidence reasoning in core module. Based on the evolution law analysis, the design parameter and reuse degree were also analyzed for core parts. Comprehensive evaluation value for core module evolution was gained. Finally, the effectiveness and feasibility of the method was tested by core module design of small wheel loaders

The Effect of Combining Millet and Corn Straw as Source Forage for Beef Cattle Diets on Ruminal Degradability and Fungal Community

Three ruminal cannulated Simmental crossbreed bulls (approximately 3 years of age and with 380 ± 20 kg live weight at initiation of the experiment) were used in a 3 × 3 Latin square experiment in order to determine the effects of the treatments on ruminal pH and degradability of nutrients, as well as the rumen fungal community. The experimental periods were 21 d, with 18 d of adjustment to the respective dietary treatments and 3 d of sample collection. Treatments consisted of a basal diet containing a 47.11% composition of two sources of forage as follows: (1) 100% millet straw (MILLSTR), (2) 50:50 millet straw and corn straw (COMB), and (3) 100% corn straw (CORNSTR). Dry matter (DM), crude protein (CP), neutral detergent fiber (NDF), and acid detergent fiber (ADF) were tested for ruminal degradability using the nylon bag method, which was incubated for 6, 12, 24, 36, 48, and 72 h, and rumen fungal community in rumen fluid was determined by high-throughput gene sequencing technology. Ruminal pH was not affected by treatments. At 72 h, compared to MILLSTR, DM degradability of CORNSTR was 4.8% greater (p < 0.05), but when corn was combined with millet straw, the difference in DM degradability was 9.4%. During the first 24 h, degradability of CP was lower for CORNSTR, intermediate for MILLSTR, and higher for COMB. However, at 72 h, MILLSTR and COMB had a similar CP degradability value, staying greater than the CP degradability value of the CORNSTR treatment. Compared to MILLSTR, the rumen degradability of NDF was greater for CORNSTR and intermediate for the COMB. There was a greater degradability for ADF in CORNSTR, intermediate for COMB, and lower for MILLSTR. In all treatments, Ascomycota and Basidiomycota were dominant flora. Abundance of Basidiomycota in the group COMB was higher (p < 0.05) than that in the group CORNSTR at 12 h. Relative to the fungal genus level, the Thelebolus, Cladosporium, and Meyerozyma were the dominant fungus, and the abundance of Meyerozyma in COMB and CORNSTR were greater (p < 0.05) than MILLSTR at 12, 24, and 36 h of incubation. In conclusion, it is suggested to feed beef cattle with different proportions of millet straw and corn straw combinations