Micro-economic Analysis of the Physical Constrained Markets: Game Theory Application to Competitive Electricity Markets

Competition has been introduced in the electricity markets with the goal of reducing prices and improving efficiency. The basic idea which stays behind this choice is that, in competitive markets, a greater quantity of the good is exchanged at a lower and a lower price, leading to higher market efficiency. Electricity markets are pretty different from other commodities mainly due to the physical constraints related to the network structure that may impact the market performance. The network structure of the system on which the economic transactions need to be undertaken poses strict physical and operational constraints. Strategic interactions among producers that game the market with the objective of maximizing their producer surplus must be taken into account when modeling competitive electricity markets. The physical constraints, specific of the electricity markets, provide additional opportunity of gaming to the market players. Game theory provides a tool to model such a context. This paper discussed the application of game theory to physical constrained electricity markets with the goal of providing tools for assessing the market performance and pinpointing the critical network constraints that may impact the market efficiency. The basic models of game theory specifically designed to represent the electricity markets will be presented. IEEE30 bus test system of the constrained electricity market will be discussed to show the network impacts on the market performances in presence of strategic bidding behavior of the producers.Comment: Accepted for publication in the European Journal of Physics B. Presented at the Int. Conf. NEXT-SigmaPhi, 13-18 August 2005, Cret

Damage Detection Based on Cross-Term Extraction from Bilinear Time-Frequency Distributions

Abundant damage information is implicated in the bilinear time-frequency distribution of structural dynamic signals, which could provide effective support for structural damage identification. Signal time-frequency analysis methods are reviewed, and the characters of linear time-frequency distribution and bilinear time-frequency distribution typically represented by the Wigner-Ville distribution are compared. The existence of the cross-term and its application in structural damage detection are demonstrated. A method of extracting the dominant term is proposed, which combines the short-time Fourier spectrum and Wigner-Ville distribution; then two-dimensional time-frequency transformation matrix is constructed and the complete cross-term is extracted finally. The distribution character of which could be applied to the structural damage identification. Through theoretical analysis, model experiment and numerical simulation of the girder structure, the change rate of cross-term amplitude is validated to identify the damage location and degree. The effectiveness of the cross-term of bilinear time-frequency distribution for damage detection is confirmed and the analytical method of damage identification used in structural engineering is available

Pyroptosis: a new insight into intestinal inflammation and cancer

Pyroptosis is an innate immune response triggered by the activation of inflammasomes by various influencing factors, characterized by cell destruction. It impacts the immune system and cancer immunotherapy. In recent years, the roles of pyroptosis and inflammasomes in intestinal inflammation and cancer have been continuously confirmed. This article reviews the latest progress in pyroptosis mechanisms, new discoveries of inflammasomes, mutual regulation between inflammasomes, and their applications in intestinal diseases. Additionally, potential synergistic treatment mechanisms of intestinal diseases with pyroptosis are summarized, and challenges and future directions are discussed, providing new ideas for pyroptosis therapy

Variability of particulate organic carbon in inland waters observed from MODIS Aqua imagery

LETTER • THE FOLLOWING ARTICLE IS OPEN ACCESS Variability of particulate organic carbon in inland waters observed from MODIS Aqua imagery Hongtao Duan1, Lian Feng2, Ronghua Ma1, Yuchao Zhang1 and Steven Arthur Loiselle3 Published 19 August 2014 • © 2014 IOP Publishing Ltd Environmental Research Letters, Volume 9, Number 8 Article PDF Figures References Citations PDF 2919 Total downloads Cited by 6 articles Article has an altmetric score of 1 Turn on MathJax Share this article Article information Abstract Surface concentrations of particulate organic carbon (POC) in shallow inland lakes were estimated using MODIS Aqua data. A power regression model of the direct empirical relationship between POC and the atmospherically Rayleigh-corrected MODIS product (Rrc,645-Rrc,1240)/(Rrc,859-Rrc,1240) was developed (R2 = 0.72, RMSE = 35.86 μgL−1, p < 0.0001, N = 47) and validated (RMSE = 44.46 μgL−1, N = 16) with field data from 56 lakes in the Middle and Lower reaches of the Yangtze River, China. This algorithm was applied to an 11 year series of MODIS data to determine the spatial and temporal distribution of POC in a wide range of lakes with different trophic and optical properties. The results indicate that there is a general increase in minimum POC concentrations in lakes from middle to lower reaches of the Yangtze River. The temporal dynamics of springtime POC in smaller lakes were found to be influenced by local meteorological conditions, in particular precipitation and wind speed, while larger lakes were found to be more sensitive to air temperature

Time-series MODIS Image-based Retrieval and Distribution Analysis of Total Suspended Matter Concentrations in Lake Taihu (China)

Although there has been considerable effort to use remotely sensed images to provide synoptic maps of total suspended matter (TSM), there are limited studies on universal TSM retrieval models. In this paper, we have developed a TSM retrieval model for Lake Taihu using TSM concentrations measured in situ and a time series of quasi-synchronous MODIS 250 m images from 2005. After simple geometric and atmospheric correction, we found a significant relationship (R = 0.8736, N = 166) between in situ measured TSM concentrations and MODIS band normalization difference of band 3 and band 1. From this, we retrieved TSM concentrations in eight regions of Lake Taihu in 2007 and analyzed the characteristic distribution and variation of TSM. Synoptic maps of model-estimated TSM of 2007 showed clear geographical and seasonal variations. TSM in Central Lake and Southern Lakeshore were consistently higher than in other regions, while TSM in East Taihu was generally the lowest among the regions throughout the year. Furthermore, a wide range of TSM concentrations appeared from winter to summer. TSM in winter could be several times that in summer

IL6/adiponectin/HMGB1 feedback loop mediates adipocyte and macrophage crosstalk and M2 polarization after myocardial infarction

BackgroundDifferences in border zone contribute to different outcomes post-infarction, such as left ventricular aneurysm (LVA) and myocardial infarction (MI). LVA usually forms within 24 h of the onset of MI and may cause heart rupture; however, LVA surgery is best performed 3 months after MI. Few studies have investigated the LVA model, the differences in border zones between LVA and MI, and the mechanism in the border zone.MethodsThe LVA, MI, and SHAM mouse models were used. Echocardiography, Masson’s trichrome staining, and immunofluorescence staining were performed, and RNA sequencing of the border zone was conducted. The adipocyte-conditioned medium-treated hypoxic macrophage cell line and LVA and MI mouse models were employed to determine the effects of the hub gene, adiponectin (ADPN), on macrophages. Quantitative polymerase chain reaction (qPCR), Western blot analysis, transmission electron microscopy, and chromatin immunoprecipitation (ChIP) assays were conducted to elucidate the mechanism in the border zone. Human subepicardial adipose tissue and blood samples were collected to validate the effects of ADPN.ResultsA novel, simple, consistent, and low-cost LVA mouse model was constructed. LVA caused a greater reduction in contractile functions than MI owing to reduced wall thickness and edema in the border zone. ADPN impeded cardiac edema and promoted lymphangiogenesis by increasing macrophage infiltration post-infarction. Adipocyte-derived ADPN promoted M2 polarization and sustained mitochondrial quality via the ADPN/AdipoR2/HMGB1 axis. Mechanistically, ADPN impeded macrophage HMGB1 inflammation and decreased interleukin-6 (IL6) and HMGB1 secretion. The secretion of IL6 and HMGB1 increased ADPN expression via STAT3 and the co-transcription factor, YAP, in adipocytes. Based on ChIP and Dual-Glo luciferase experiments, STAT3 promoted ADPN transcription by binding to its promoter in adipocytes. In vivo, ADPN promoted lymphangiogenesis and decreased myocardial injury after MI. These phenotypes were rescued by macrophage depletion or HMGB1 knockdown in macrophages. Supplying adipocytes overexpressing STAT3 decreased collagen disposition, increased lymphangiogenesis, and impaired myocardial injury. However, these effects were rescued after HMGB1 knockdown in macrophages. Overall, the IL6/ADPN/HMGB1 axis was validated using human subepicardial tissue and blood samples. This axis could serve as an independent factor in overweight MI patients who need coronary artery bypass grafting (CABG) treatment.ConclusionThe IL6/ADPN/HMGB1 loop between adipocytes and macrophages in the border zone contributes to different clinical outcomes post-infarction. Thus, targeting the IL6/ADPN/HMGB1 loop may be a novel therapeutic approach for cardiac lymphatic regulation and reduction of cell senescence post-infarction

YOLOC-tiny: a generalized lightweight real-time detection model for multiripeness fruits of large non-green-ripe citrus in unstructured environments

This study addresses the challenges of low detection precision and limited generalization across various ripeness levels and varieties for large non-green-ripe citrus fruits in complex scenarios. We present a high-precision and lightweight model, YOLOC-tiny, built upon YOLOv7, which utilizes EfficientNet-B0 as the feature extraction backbone network. To augment sensing capabilities and improve detection accuracy, we embed a spatial and channel composite attention mechanism, the convolutional block attention module (CBAM), into the head’s efficient aggregation network. Additionally, we introduce an adaptive and complete intersection over union regression loss function, designed by integrating the phenotypic features of large non-green-ripe citrus, to mitigate the impact of data noise and efficiently calculate detection loss. Finally, a layer-based adaptive magnitude pruning strategy is employed to further eliminate redundant connections and parameters in the model. Targeting three types of citrus widely planted in Sichuan Province—navel orange, Ehime Jelly orange, and Harumi tangerine—YOLOC-tiny achieves an impressive mean average precision (mAP) of 83.0%, surpassing most other state-of-the-art (SOTA) detectors in the same class. Compared with YOLOv7 and YOLOv8x, its mAP improved by 1.7% and 1.9%, respectively, with a parameter count of only 4.2M. In picking robot deployment applications, YOLOC-tiny attains an accuracy of 92.8% at a rate of 59 frames per second. This study provides a theoretical foundation and technical reference for upgrading and optimizing low-computing-power ground-based robots, such as those used for fruit picking and orchard inspection

ASFL-YOLOX: an adaptive spatial feature fusion and lightweight detection method for insect pests of the Papilionidae family

IntroductionInsect pests from the family Papilionidae (IPPs) are a seasonal threat to citrus orchards, causing damage to young leaves, affecting canopy formation and fruiting. Existing pest detection models used by orchard plant protection equipment lack a balance between inference speed and accuracy.MethodsTo address this issue, we propose an adaptive spatial feature fusion and lightweight detection model for IPPs, called ASFL-YOLOX. Our model includes several optimizations, such as the use of the Tanh-Softplus activation function, integration of the efficient channel attention mechanism, adoption of the adaptive spatial feature fusion module, and implementation of the soft Dlou non-maximum suppression algorithm. We also propose a structured pruning curation technique to eliminate unnecessary connections and network parameters.ResultsExperimental results demonstrate that ASFL-YOLOX outperforms previous models in terms of inference speed and accuracy. Our model shows an increase in inference speed by 29 FPS compared to YOLOv7-x, a higher mAP of approximately 10% than YOLOv7-tiny, and a faster inference frame rate on embedded platforms compared to SSD300 and Faster R-CNN. We compressed the model parameters of ASFL-YOLOX by 88.97%, reducing the number of floating point operations per second from 141.90G to 30.87G while achieving an mAP higher than 95%.DiscussionOur model can accurately and quickly detect fruit tree pest stress in unstructured orchards and is suitable for transplantation to embedded systems. This can provide technical support for pest identification and localization systems for orchard plant protection equipment