Explicit solutions for a class of nonlinear backward stochastic differential equations and their nodal sets

In this paper, we investigate a class of nonlinear backward stochastic differential equations (BSDEs) arising from financial economics, and give specific information about the nodal sets of the related solutions. As applications, we are able to obtain the explicit solutions to an interesting class of nonlinear BSDEs including the k-ignorance BSDE arising from the modeling of ambiguity of asset pricing

Vision Language Models in Autonomous Driving and Intelligent Transportation Systems

The applications of Vision-Language Models (VLMs) in the fields of Autonomous Driving (AD) and Intelligent Transportation Systems (ITS) have attracted widespread attention due to their outstanding performance and the ability to leverage Large Language Models (LLMs). By integrating language data, the vehicles, and transportation systems are able to deeply understand real-world environments, improving driving safety and efficiency. In this work, we present a comprehensive survey of the advances in language models in this domain, encompassing current models and datasets. Additionally, we explore the potential applications and emerging research directions. Finally, we thoroughly discuss the challenges and research gap. The paper aims to provide researchers with the current work and future trends of VLMs in AD and ITS

Dielectric properties of hydrogen-incorporated chemical vapor deposited diamond thin films

Diamond thin films with a broad range of microstructures from a ultrananocrystalline diamond (UNCD) form developed at Argonne National Laboratory to a microcrystalline diamond (MCD) form have been grown with different hydrogen percentages in the Ar/CH4 gas mixture used in the microwave plasma enhanced chemical vapor deposition (CVD) process. The dielectric properties of the CVD diamond thin films have been studied using impedance and dc measurements on metal-diamond-metal test structures. Close correlations have been observed between the hydrogen content in the bulk of the diamond films, measured by elastic recoil detection (ERD), and their electrical conductivity and capacitance-frequency (C-f) behaviors. Addition of hydrogen gas in the Ar/CH4 gas mixture used to grow the diamond films appears to have two main effects depending on the film microstructure, namely, (a) in the UNCD films, hydrogen incorporates into the atomically abrupt grain boundaries satisfying sp2 carbon dangling bonds, resulting in increased resistivity, and (b) in MCD, atomic hydrogen produced in the plasma etches preferentially the graphitic phase codepositing with the diamond phase, resulting in the statistical survival and growth of large diamond grains and dominance of the diamond phase, and thus having significant impact on the dielectric properties of these films

Nitrogen-doped mesoporous activated carbon from Lentinus edodes residue: an optimized adsorbent for pharmaceuticals in aqueous solutions

In this study, phosphoric acid activation was employed to synthesize nitrogen-doped mesoporous activated carbon (designated as MR1) from Lentinus edodes (shiitake mushroom) residue, while aiming to efficiently remove acetaminophen (APAP), carbamazepine (CBZ), and metronidazole (MNZ) from aqueous solutions. We characterized the physicochemical properties of the produced adsorbents using scanning electron microscopy (SEM), nitrogen adsorption isotherms, and X-ray photoelectron spectroscopy (XPS). MR1, MR2, and MR3 were prepared using phosphoric acid impregnation ratios of 1, 2, and 3 mL/g, respectively. Notably, MR1 exhibited a significant mesoporous structure with a volume of 0.825 cm3/g and a quaternary nitrogen content of 2.6%. This endowed MR1 with a high adsorption capacity for APAP, CBZ, and MNZ, positioning it as a promising candidate for water purification applications. The adsorption behavior of the contaminants followed the Freundlich isotherm model, suggesting a multilayer adsorption process. Notably, MR1 showed excellent durability and recyclability, maintaining 95% of its initial adsorption efficiency after five regeneration cycles and indicating its potential for sustainable use in water treatment processes

Automatic Truss Design with Reinforcement Learning

Truss layout design, namely finding a lightweight truss layout satisfying all the physical constraints, is a fundamental problem in the building industry. Generating the optimal layout is a challenging combinatorial optimization problem, which can be extremely expensive to solve by exhaustive search. Directly applying end-to-end reinforcement learning (RL) methods to truss layout design is infeasible either, since only a tiny portion of the entire layout space is valid under the physical constraints, leading to particularly sparse rewards for RL training. In this paper, we develop AutoTruss, a two-stage framework to efficiently generate both lightweight and valid truss layouts. AutoTruss first adopts Monte Carlo tree search to discover a diverse collection of valid layouts. Then RL is applied to iteratively refine the valid solutions. We conduct experiments and ablation studies in popular truss layout design test cases in both 2D and 3D settings. AutoTruss outperforms the best-reported layouts by 25.1% in the most challenging 3D test cases, resulting in the first effective deep-RL-based approach in the truss layout design literature.Comment: IJCAI2023. The codes are available at https://github.com/StigLidu/AutoTrus

Influence of long-term fertilization on soil aggregates stability and organic carbon occurrence characteristics in karst yellow soil of Southwest China

Current research has long focused on soil organic carbon and soil aggregates stability. However, the effects of different long-term fertilization on the composition of yellow soil aggregates and the characteristics of the occurrence of organic carbon in the karst region of Southwest China are still unclear. Based on a 25-year long-term located experiment on yellow soil, soil samples from the 0–20 cm soil layer were collected and treated with different fertilizers (CK: unfertilized control; NPK: chemical fertilizer; 1/4 M + 3/4 NP: 25% chemical fertilizer replaced by 25% organic fertilizer; 1/2 M + 1/2 NP: 50% chemical fertilizer replaced by organic fertilizer; and M: organic fertilizer). In water-stable aggregates, soil aggregates stability, total organic carbon (TOC), easily oxidized organic carbon (EOC), carbon preservation capacity (CPC), and carbon pool management index (CPMI) were analyzed. The findings demonstrated that the order of the average weight diameter (MWD), geometric mean diameter (GWD), and macro-aggregate content (R0.25) of stable water aggregates was M > CK > 1/2M +1/2NP > 1/4M +3/4NP> NPK. The MWD, GWD, and R0.25 of NPK treatment significantly decreased by 32.6%, 43.2%, and 7.0 percentage points, respectively, compared to CK treatment. The order of TOC and EOC content in aggregates of different particle sizes was M > 1/2M +1/2NP > 1/4M +3/4NP> CK > NPK, and it increased as the rate of organic fertilizer increased. In macro-aggregates and bulk soil, the CPC of TOC (TOPC) and EOC (EOPC), as well as CPMI, were arranged as M > 1/2M +1/2NP > 1/4M +3/4NP> CK > NPK, but the opposite was true for micro-aggregates. In bulk soil treated with organic fertilizer, the TOPC, EOPC, and CPMI significantly increased by 27.4%–53.8%, 29.7%–78.1%, 29.7–82.2 percentage points, respectively, compared to NPK treatment. Redundancy analysis and stepwise regression analysis show that TOC was the main physical and chemical factor affecting the aggregates stability, and the TOPC in micro-aggregates has the most direct impact. In conclusion, the primary cause of the decrease in SOC caused by the long-term application of chemical fertilizer was the loss of organic carbon in macro-aggregates. An essential method to increase soil nutrient supply and improve yellow soil productivity was to apply an organic fertilizer to increase aggregates stability, storage and activity of SOC in macro-aggregates

Palaeosedimentary Environment and Formation Mechanism of High-Quality Xujiahe Source Rocks, Sichuan Basin, South China

AbstractTriassic Xujiahe source rocks, the main gas source of shallow tight gas, are the most typical continental coal-bearing source rocks in the Sichuan Basin, South China. However, the organic matter enrichment section cannot be identified easily, leading to limited progress in the exploration of coal-bearing tight gas. This paper reveals the main controlling factors of the organic matter enrichment, reconstructs the evolution process of the Xujiahe palaeosedimentary environment, proposes a dynamic enrichment mechanism of the organic matter, and determines the organic matter enrichment section of the high-quality coal-bearing source rocks by geochemical characteristics of the source rocks, major elements, and trace elements. The results show that the Xujiahe sedimentary environment can be divided into a fluctuating stage of transitional sedimentation, stable stage of transitional sedimentation, fluctuating stage of continental sedimentation, and stable stage of continental sedimentation. The Xujiahe source rocks were featured with high-quality coal-bearing source rocks with high total organic carbon and maturity and good parent material in the stable stage of transitional sedimentation and fluctuating stage of continental sedimentation, in which the water was connected with the Palaeo-Tethys Ocean with abundant terrestrial organisms. The water was shallow in the fluctuating stage of transitional sedimentation with a low sedimentation rate, leading to poor organic matter enrichment. The Palaeo-Tethys Ocean withdrew westward from the Yangtze plate in the late period of the fluctuating stage of continental sedimentation, leading to the absence of algae and dinosteranes and a decrease in biological productivity in the stable stage of continental sedimentation. Therefore, high terrestrial inputs and biological productivity and high sedimentation rate were conducive to the organic matter preservation in the coal-bearing source rocks

Evaluation of the clinical value of 10 estimating glomerular filtration rate equations and construction of a prediction model for kidney damage in adults from central China

ObjectivesThis study aimed to evaluate 10 estimating glomerular filtration rate (eGFR) equations in central China population and construct a diagnostic prediction model for assessing the kidney damage severity.MethodsThe concordance of 10 eGFR equations was investigated in healthy individuals from central China, and their clinical effectiveness in diagnosing kidney injury was evaluated. Subsequently, relevant clinical indicators were selected to develop a clinical prediction model for kidney damage.ResultsThe overall concordance between CKD-EPIASR-Scr and CKD-EPI2021-Scr was the highest (weightedκ = 0.964) in healthy population. The CG formula, CKD-EPIASR-Scr and CKD-EPI2021-Scr performed better than others in terms of concordance with referenced GFR (rGFR), but had poor ability to distinguish between rGFR < 90 or < 60 mL/min·1.73 m2. This finding was basically consistent across subgroups. Finally, two logistic regression prediction models were constructed based on rGFR < 90 or 60 mL/min·1.73 m2. The area under the curve of receiver operating characteristic values of two prediction models were 0.811 vs 0.846 in training set and 0.812 vs 0.800 in testing set.ConclusionThe concordance of CKD-EPIASR-Scr and CKD-EPI2021-Scr was the highest in the central China population. The Cockcroft-Gault formula, CKD-EPIASR-Scr, and CKD-EPI2021-Scr more accurately reflected true kidney function, while performed poorly in the staging diagnosis of CKD. The diagnostic prediction models showed the good clinical application performance in identifying mild or moderate kidney injury. These findings lay a solid foundation for future research on renal function assessment and predictive equations