Coordination Strategy of Dual-Channel Supply Chain for Fresh Product Under the Fresh-Keeping Efforts

In the context of high loss in the storage and transportation of fresh agricultural products, in order to help company make reasonable fresh-keeping decisions and reduce losses, we established a leading supplier of fresh agricultural products in two level dual channel supply chain model based on consumer utility function, and using Stackelberg game method to solve the optimal pricing and optimal fresh-keeping decision of fresh agricultural supplier and retailer under centralized decision-making and decentralized decision-making model. Research shows: (1) Under centralized decision-making model, the highest profit does not affect the cooperation and achieve complete coordination regardless of the bargaining power of the retailer; (2) High cost factor of fresh-keeping efforts makes supplier and retailer more inclined to lower prices to attract consumers. (3)The “revenue sharing + fresh-keeping cost sharing” coordination strategy provided by the supplier can increase the respective profits of both parties and achieve complete coordination of the dual-channel supply chain of fresh agricultural products

Prompt, Plan, Perform: LLM-based Humanoid Control via Quantized Imitation Learning

In recent years, reinforcement learning and imitation learning have shown great potential for controlling humanoid robots' motion. However, these methods typically create simulation environments and rewards for specific tasks, resulting in the requirements of multiple policies and limited capabilities for tackling complex and unknown tasks. To overcome these issues, we present a novel approach that combines adversarial imitation learning with large language models (LLMs). This innovative method enables the agent to learn reusable skills with a single policy and solve zero-shot tasks under the guidance of LLMs. In particular, we utilize the LLM as a strategic planner for applying previously learned skills to novel tasks through the comprehension of task-specific prompts. This empowers the robot to perform the specified actions in a sequence. To improve our model, we incorporate codebook-based vector quantization, allowing the agent to generate suitable actions in response to unseen textual commands from LLMs. Furthermore, we design general reward functions that consider the distinct motion features of humanoid robots, ensuring the agent imitates the motion data while maintaining goal orientation without additional guiding direction approaches or policies. To the best of our knowledge, this is the first framework that controls humanoid robots using a single learning policy network and LLM as a planner. Extensive experiments demonstrate that our method exhibits efficient and adaptive ability in complicated motion tasks

Numerical study on the evolution law and correction method of turbine characteristics of the gas turbine under alternative fuel conditions

Reducing carbon emissions is an urgent need in the field of marine power. Gas turbines are of great importance in the marine industry. The use of clean or industrial-associated fuels can increase the fuel adaptability of designed, manufactured, or in-service gas turbines to realize the goal of expanding fuel sources, reducing fuel waste, lowering energy demand, and remitting environmental pressure. By changing from fossil fuel to alternative energy, the change in the physical properties of the combustion products will lead to changes in the working medium of the turbines, which result in a profound effect on the performance. In this study, based on the actual law of working medium property change, the evolution mechanism of turbine characteristics is lucubrated in depth, focusing on the key parameters of the influence of working medium properties on turbine characteristics under alternative fuel conditions, and a correction method is proposed to predict the evolution law of the turbine characteristics as working medium varies

Rice Yield Estimation Using Parcel-Level Relative Spectral Variables From UAV-Based Hyperspectral Imagery

Time-series Vegetation Indices (VIs) are usually used for estimating grain yield. However, multi-temporal VIs may be affected by different background, illumination, and atmospheric conditions, so the absolute differences among time-series VIs may include the effects induced from external conditions in addition to vegetation changes, which will pose a negative effect on the accuracy of crop yield estimation. Therefore, in this study, the parcel-based relative vegetation index (ΔVI) and the parcel-based relative yield are proposed and further used to estimate rice yield. Hyperspectral images at key growth stages, including tillering stage, jointing stage, booting stage, heading stage, filling stage, and ripening stage, as well as rice yield, were obtained with Rikola hyperspectral imager mounted on Unmanned Aerial Vehicle (UAV) in 2017 growing season. Three types of parcel-level relative vegetation indices, including Relative Normalized Difference Vegetation Index (RNDVI), Relative Ratio Vegetation Index (RRVI), and Relative Difference Vegetation Index (RDVI) are created by using all possible two-band combinations of discrete channels from 500 to 900 nm. The optimal VI type and its band combinations at different growth stages are identified for rice yield estimation. Furthermore, the optimal combinations of different growth stages for yield estimation are determined by F-test and validated using leave-one-out cross validation (LOOCV) method. The comparison results show that, for the single-growth-stage model, RNDVI[880,712] at booting stage has the best correlation with rice yield with a R2-value of 0.75. For the multiple-growth-stage model, RNDVI[808,744] at jointing stage, RNDVI[880,712] at booting stage and RNDVI[808,744] at filling stage gain a higher R2-value of 0.83 with the mean absolute percentage error of estimated rice yield of 3%. The study demonstrates that the proposed method with parcel-level relative vegetation indices and relative yield can achieve higher yield estimation accuracy because it can make full use of the advantage that remote sensing can monitor relative changes accurately. The new method will further enrich the technology system for crop yield estimation based on remotely sensed data

Epigenome-wide association data implicates DNA methylation-mediated genetic risk in psoriasis

Abstract Background Psoriasis is a chronic inflammatory skin disease characterized by epidermal hyperproliferation and altered keratinocyte differentiation and inflammation and is caused by the interplay of genetic and environmental factors. Previous studies have revealed that DNA methylation (DNAm) and genetic makers are closely associated with psoriasis, and strong evidences have shown that DNAm can be controlled by genetic factors, which attracted us to evaluate the relationship among DNAm, genetic makers, and disease status. Methods We utilized the genome-wide methylation data of psoriatic skin (PP, N = 114) and unaffected control skin (NN, N = 62) tissue samples in our previous study, and we performed whole-genome genotyping with peripheral blood of the same samples to evaluate the underlying genetic effect on skin DNA methylation. Causal inference test (CIT) was used to assess whether DNAm regulate genetic variation and gain a better understanding of the epigenetic basis of psoriasis susceptibility. Results We identified 129 SNP-CpG pairs achieving the significant association threshold, which constituted 28 unique methylation quantitative trait loci (MethQTL) and 34 unique CpGs. There are 18 SNPs were associated with psoriasis at a Bonferoni-corrected P < 0.05, and these 18 SNPs formed 93 SNP-CpG pairs with 17 unique CpG sites. We found that 11 of 93 SNP-CpG pairs, composed of 5 unique SNPs and 3 CpG sites, presented a methylation-mediated relationship between SNPs and psoriasis. The 3 CpG sites were located on the body of C1orf106, the TSS1500 promoter region of DMBX1 and the body of SIK3. Conclusions This study revealed that DNAm of some genes can be controlled by genetic factors and also mediate risk variation for psoriasis in Chinese Han population and provided novel molecular insights into the pathogenesis of psoriasis