Symmetrical Short-Circuit Parameters Comparison of DFIG–WT

Renewable energy with new resources is depleting the fossil fuel-based energy resources. Renewable energy sources (such as wind energy) based power generators are important energy conversion machines and have widely industrial and commercial applications due to their superior performance, and the fact that they endure faults well and are environmentally friendly. The study of the transient behavior of such generators under fault condition has drawn much attention. This study presents Doubly-Fed Induction Generator (DFIG) perturbation during a symmetrical (three-phase) short circuit (SSC) at different points. Simulation results reveal that after a fault occurs, there is decay of SC parameters (transient time, maximum current, steady-state and voltage dip) at the point of common coupling (PCC) and the grid-side converter (GSC) of DFIG. Simulation results depict a more sensitive and robust point during a SSC of DFIG. Current findings present the main difference between the PCC and the GSC during SSC faults. These comparisons provide a more precise understanding of fault diagnosis reliability with reduced complexity, stability, and optimization of the system. This study verified by the simulation results helps us understand and improve the performance of sensor sensibility (measurements), develop control schemes, protection strategy and select a more accurate and proficient system among other wind energy conversion systems (WECS)

SCEI: A Smart-Contract Driven Edge Intelligence Framework for IoT Systems

Federated learning (FL) utilizes edge computing devices to collaboratively train a shared model while each device can fully control its local data access. Generally, FL techniques focus on learning model on independent and identically distributed (iid) dataset and cannot achieve satisfiable performance on non-iid datasets (e.g. learning a multi-class classifier but each client only has a single class dataset). Some personalized approaches have been proposed to mitigate non-iid issues. However, such approaches cannot handle underlying data distribution shift, namely data distribution skew, which is quite common in real scenarios (e.g. recommendation systems learn user behaviors which change over time). In this work, we provide a solution to the challenge by leveraging smart-contract with federated learning to build optimized, personalized deep learning models. Specifically, our approach utilizes smart contract to reach consensus among distributed trainers on the optimal weights of personalized models. We conduct experiments across multiple models (CNN and MLP) and multiple datasets (MNIST and CIFAR-10). The experimental results demonstrate that our personalized learning models can achieve better accuracy and faster convergence compared to classic federated and personalized learning. Compared with the model given by baseline FedAvg algorithm, the average accuracy of our personalized learning models is improved by 2% to 20%, and the convergence rate is about 2$\times$ faster. Moreover, we also illustrate that our approach is secure against recent attack on distributed learning.Comment: 12 pages, 9 figure

Ferroptosis in inflammatory arthritis: A promising future

Ferroptosis is a kind of regulatory cell death (RCD) caused by iron accumulation and lipid peroxidation, which is characterized by mitochondrial morphological changes and has a complex regulatory network. Ferroptosis has been gradually emphasized in the pathogenesis of inflammatory arthritis. In this review, we summarized the relevant research on ferroptosis in various inflammatory arthritis including rheumatoid arthritis (RA), osteoarthritis, gout arthritis, and ankylosing spondylitis, and focused on the relationship between RA and ferroptosis. In patients with RA and animal models of RA, there was evidence of iron overload and lipid peroxidation, as well as mitochondrial dysfunction that may be associated with ferroptosis. Ferroptosis inducers have shown good application prospects in tumor therapy, and some anti-rheumatic drugs such as methotrexate and sulfasalazine have been shown to have ferroptosis modulating effects. These phenomena suggest that the role of ferroptosis in the pathogenesis of inflammatory arthritis will be worth further study. The development of therapeutic strategies targeting ferroptosis for patients with inflammatory arthritis may be a promising future

One-Step Process for Environment-Friendly Preparation of Agar Oligosaccharides From Gracilaria lemaneiformis by the Action of Flammeovirga sp. OC4

Oligosaccharides extracted from agar Gracilaria lemaneiformis (G. lemaneiformis) have stronger physiological activities and a higher value than agar itself, but the pollution caused by the extraction process greatly restricts the sustainable use of agar. In this study, four bacterial strains with a high ability to degrade G. lemaneiformis were isolated from seawater by in situ enrichment in the deep sea. Among them, Flammeovirga sp. OC4, identified by morphological observation and its 16S rRNA sequencing (98.07% similarity to type strain JL-4 of Flammeovirga aprica), was selected. The optimum temperature and pH of crude enzyme produced by Flammeovirga sp. OC4 were 50°C and 8, respectively. More than 60% of the maximum enzyme activity remained after storage at pH 5.0–10.0 for 60 min. Both Mn2+ and Ba2+ could enhance the enzyme activity. A “one-step process” for preparation of oligosaccharides from G. lemaneiformis was established using Flammeovirga sp. OC4. After optimization of the Plackett–Burman (PB) design and response surface methodology (RSM), the yield of oligosaccharides was increased by 36.1% from 2.71 to 3.09 g L−1 in a 250-mL fermenter with optimized parameters: 30 g L−1G. lemaneiformis powder, 4.84 g L−1 (NH4)2SO4, 44.8-mL working medium volume at 36.7°C, and a shaking speed of 200 × g for 42 h. The extracted oligosaccharides were identified by thin layer chromatography (TLC) and ion chromatography, which consisted of neoagarobiose, agarotriose, neoagarotetraose, agaropentaose, and neoagarohexaose. These results provided an alternative approach for environment-friendly and sustainable utilization of algae

Should Government Play a Strict or Lenient Role? An Evolutionary Game Analysis of Implementing the Forest Ecological Bank Policy

As one of the specific practices of natural resource index trading, the forest ecological bank policy (FEB) is essentially a market-based tool. With the deepening of ecological governance, the FEB policy has also become the main method chosen to solve the economic development problems in ecologically rich “low-lying” areas. However, in the process of implementing the FEB policy, the differences in the demands of various stakeholders were found to have led to a complex game phenomenon, resulting in deviations in policy implementation. This study constructs a multiplayer evolutionary game model between local governments and enterprises of different scales and analyzes the evolutionary stabilization strategy (ESS) in the implementation of the FEB policy. The results show that, under different conditions, there are three stabilization strategies in the evolutionary game system, these correspond to F1 (0, 0, 0), F4 (0, 1, 1), and F5 (1, 0, 0), respectively, the implications are that the strict government role with an active regulatory strategy leads to companies of different sizes refusing to participate (i.e., F5) and the lax government role with a negative regulatory strategy leads to companies of different sizes refusing to participate (i.e., F1) or choosing to participate (i.e., F4). Among them, the strict government role stimulates the companies to participate in the FEB policy through the high intensity of government regulation. In addition, as the policy continues to be implemented, the influence of the strict regulation on the “participation” behavior of the companies decreases. Conversely, the lax government role allows the companies to give full play to their autonomy and obtain higher ecological and environmental benefits

An Overview of Plant Phenolic Compounds and Their Importance in Human Nutrition and Management of Type 2 Diabetes

In this paper, the biosynthesis process of phenolic compounds in plants is summarized, which includes the shikimate, pentose phosphate and phenylpropanoid pathways. Plant phenolic compounds can act as antioxidants, structural polymers (lignin), attractants (flavonoids and carotenoids), UV screens (flavonoids), signal compounds (salicylic acid and flavonoids) and defense response chemicals (tannins and phytoalexins). From a human physiological standpoint, phenolic compounds are vital in defense responses, such as anti-aging, anti-inflammatory, antioxidant and anti-proliferative activities. Therefore, it is beneficial to eat such plant foods that have a high antioxidant compound content, which will cut down the incidence of certain chronic diseases, for instance diabetes, cancers and cardiovascular diseases, through the management of oxidative stress. Furthermore, berries and other fruits with low-amylase and high-glucosidase inhibitory activities could be regarded as candidate food items in the control of the early stages of hyperglycemia associated with type 2 diabetes