A Multi-Agent Optimal Bidding Strategy in Multi-Operator VPPs Based on SGHSA

As an individual plant participating in the power market, the virtual power plant (VPP) is regarded as the ultimate configuration of the energy Internet, and effective dispatching is a challenge. This paper proposes a multi-agent optimal bidding strategy based on a self-adaptive global optimal harmony search algorithm (SGHSA) to solve the problem of multi-operator participation in virtual power station scheduling. The method takes multiple agents to simulate the bidding process in the VPPs and distributes the profits for the operators based on the market mechanism to optimize the distributed energy resources (DERs). Case studies are provided and show that the proposed method realizes the optimal distribution of power generation and demand level, which improves the comprehensive advantage of the VPP in electricity market transactions

Enhancing the fairness of AI prediction models by Quasi-Pareto improvement among heterogeneous thyroid nodule population

Abstract Artificial Intelligence (AI) models for medical diagnosis often face challenges of generalizability and fairness. We highlighted the algorithmic unfairness in a large thyroid ultrasound dataset with significant diagnostic performance disparities across subgroups linked causally to sample size imbalances. To address this, we introduced the Quasi-Pareto Improvement (QPI) approach and a deep learning implementation (QP-Net) combining multi-task learning and domain adaptation to improve model performance among disadvantaged subgroups without compromising overall population performance. On the thyroid ultrasound dataset, our method significantly mitigated the area under curve (AUC) disparity for three less-prevalent subgroups by 0.213, 0.112, and 0.173 while maintaining the AUC for dominant subgroups; we also further confirmed the generalizability of our approach on two public datasets: the ISIC2019 skin disease dataset and the CheXpert chest radiograph dataset. Here we show the QPI approach to be widely applicable in promoting AI for equitable healthcare outcomes

Profiling of Volatile Compounds and Associated Gene Expression and Enzyme Activity during Fruit Development in Two Cucumber Cultivars

<div><p>Changes in volatile content, as well as associated gene expression and enzyme activity in developing cucumber fruits were investigated in two <i>Cucumis sativus</i> L. lines (No. 26 and No. 14) that differ significantly in fruit flavor. Total volatile, six-carbon (C6) aldehyde, linolenic and linoleic acid content were higher during the early stages, whereas the nine-carbon (C9) aldehyde content was higher during the latter stages in both lines. Expression of <i>C</i>. <i>sativus</i> hydroperoxide lyase (<i>CsHPL</i>) mirrored 13-hydroperoxide lyase (13-HPL) enzyme activity in variety No. 26, whereas <i>CsHPL</i> expression was correlated with 9-hydroperoxide lyase (9-HPL) enzyme activity in cultivar No. 14. 13-HPL activity decreased significantly, while LOX (lipoxygenase) and 9-HPL activity increased along with fruit ripening in both lines, which accounted for the higher C6 and C9 aldehyde content at 0-6 day post anthesis (dpa) and 9-12 dpa, respectively. Volatile compounds from fruits at five developmental stages were analyzed by principal component analysis (PCA), and heatmaps of volatile content, gene expression and enzyme activity were constructed.</p></div

Changes in <i>LOX</i> expression during fruit development.

<p>Changes in <i>LOX</i> expression during fruit development.</p

A Simulation and Validation of CLM during Freeze-Thaw on the Tibetan Plateau

The applicability of a new soil hydraulic property of frozen soil scheme applied in Community Land Model 4.5 (CLM4.5), in conjunction with an impedance factor for the presence of soil ice, was validated through two offline numerical simulations conducted at Madoi (GS) and Zoige (ZS) on the Tibetan Plateau (TP). Sensitivity analysis was conducted via replacing the new soil hydraulic property scheme in CLM4.5 by the old one, using default CLM4.5 runs as reference. Results indicated that the new parameterization scheme ameliorated the surface dry biases at ZS but enlarged the wet biases which existed at GS site due to ignoring the gravel effect. The wetter surface condition in CLM4.5 also leads to a warmer surface soil temperature because of the greater heat capacity of liquid water. In addition, the combined impact of new soil hydraulic property schemes and the ice impedance function on the simulated soil moisture lead to the more reasonable simulation of the starting dates of freeze-thaw cycle, especially at the thawing stage. The improvements also lead to the more reasonable turbulent fluxes simulations. Meanwhile, the decreased snow cover fraction in CLM4.5 resulted in a lower albedo, which tended to increase net surface radiation compared to previous versions. Further optimizing is needed to take the gravel into account in the numerical description of thermal-hydrological interactions

Changes in ADH activity.

<p>Changes in ADH activity.</p

Changes in 9-HPL activity during fruit development.

<p>Changes in 9-HPL activity during fruit development.</p

Aroma values, odor thresholds and volatile content of cucumber fruits.

<p>Aroma values, odor thresholds and volatile content of cucumber fruits.</p

PCA of the different developmental stages based on the first 2 PCA results from volatile compounds.

<p>PCA of the different developmental stages based on the first 2 PCA results from volatile compounds.</p

Principal Component analysis (PCA) score plot of volatile compounds during different fruit developmental stages.

<p>Round red dots represent volatile compounds from different stages in No.14, and empty rhombuses represent volatile compounds from different stages in No.26. Five independent time course studies, each from triplicate samples, were performed for each time point and used in the analysis, for a total of 10 data points.</p