Energy consumption analysis of power grid distribution transformers based on an improved genetic algorithm.

With the promotion of energy transformation, the utilization ratio of electrical power is progressively rising. Since electrical power is challenging to store, real-time production and consumption become imperative, imposing significant demands on the dependability and operational efficiency of electrical power apparatus. Suppose the load distribution among multiple transformers within a transformer network exhibits inequality. In such instances, it will amplify the total energy consumption during the voltage conversion process, and local, long-term high-load transformer networks become more susceptible to failures. In this article, we scrutinize the matter of transformer energy utilization in the context of electricity transmission within grid systems. We propose a methodology grounded on genetic algorithms to optimize transformer energy usage by dynamically redistributing loads among diverse transformers based on their operational status monitoring. In our experimentation, we employed three distinct approaches to enhance energy efficiency. The experimental findings evince that this approach facilitates swifter attainment of the optimal power level and diminishes the overall energy consumption during transformer operation. Moreover, it exhibits a heightened responsiveness to fluctuations in power demand from the electrical grid. Experimental results manifest that this technique can truncate monitoring time by 27% and curtail the overall energy consumption of the distribution transformer network by 11.81%. Lastly, we deliberate upon the potential applications of genetic algorithms in the realm of power equipment management and energy optimization issues.

Smart grid energy scheduling based on improved dynamic programming algorithm and LSTM.

The optimal scheduling of energy in a smart grid is crucial to the energy consumption of the entire grid. In fact, for larger grids, intelligent scheduling may result in substantial energy savings. Herein, we introduce an enhanced dynamic programming algorithm (DPA) that utilizes two state variables to derive the optimal power supply schedule. The algorithm accounts for the dynamic states of both batteries and supercapacitors in the power supply system to augment the performance of the dynamic programming model. Additionally, this study incorporates a long short-term memory (LSTM) deep learning model, which integrates various environmental factors such as temperature, humidity, wind, and precipitation to predict grid power consumption. This serves as a mid-point pre-processing step for smart grid energy consumption scheduling. Our simulation experiments confirm that the proposed method significantly reduces energy consumption, surpassing similar grid energy consumption scheduling algorithms. This is critical for the establishment of smart grids and the reduction of energy consumption and emissions.

Construction of Intelligent Substation-Optimized Networking Communication Network Based on Source-Network-Load Interaction Environment.

Smart grids, which possess flexibility, cleanliness, safety, economy, and friendliness, have drawn a lot of attention from all over the world in an era of rapid social and economic development, power technology change, and energy and environmental constraints. However, there are still issues with the design, installation, and operation of smart substations, such as insufficient LAN integration, difficulty quantifying network performance, and inability to keep track of communication. The basic meaning and key technologies of substation communication standards are used as the research object in this paper, and the basic attributes of substation data flow, such as source, type, and function, are qualitatively analyzed. The mathematical model that is most closely fitted is 2.8% more effective. The research object is the topology of the process bus. Through the comparison of various solutions, including star topology, ring topology, bus topology, and mesh topology, the advantages and disadvantages of each topology networking scheme are revealed along with the particular functional requirements of the substation process layer. Further discussion is given to the crucial topology-related technologies of network congestion and flow control.

The Association between 5-Hydroxytryptamine Receptor 1B rs13212041 Polymorphism and Trait Anxiety in Chinese Han College Subjects.

Trait anxiety is a vulnerable personality factor for anxiety and depression. High levels of trait anxiety confer an elevated risk for the development of anxiety and other psychiatric disorders. There is evidence that 5-hydroxytryptamine receptor 1B (5-HT1B) gene polymorphisms play an important role in emotional disorders. Genotyping for four single-nucleotide polymorphisms (SNP) (rs11568817, rs130058, rs6297, and rs13212041) was conducted for 388 high trait anxious (HTA) individuals and 463 low traitanxious (LTA) individuals in Chinese Han college subjects. The results showed that the frequencies of the C-allele and TC + CC genotype of rs13212041 in the LTA individuals were higher than that in the HTA individuals (p = 0.025 and p = 0.014, respectively). Both the C-allele and TC + CC genotype were associated with trait anxiety decreasing (OR = 0.771 and OR = 0.71, respectively). Furthermore, different gene model analysis also showed that the C allele was a protective factor for trait anxiety in Chinese Han college subjects. These findings suggest that 5-HT1B rs13212014 may play a role in trait anxiety among China Han college subjects. The rs13212014 polymorphism may be involved in decreasing the risk of trait anxiety. These results also provide a novel insight into the molecular mechanism underlying trait anxiety.

Progranulin promotes functional recovery and neurogenesis in the subventricular zone of adult mice after cerebral ischemia.

Progranulin (PGRN), a secreted glycosylated protein, has been reported to attenuate ischemia-induced cerebral injury through anti-inflammation, attenuation of blood-brain barrier disruption and neuroprotection. However, the effect of PGRN on neurogenesis in the subventricular zone (SVZ) after cerebral ischemia remains unclear. In this study, adult C57BL/6 mice were subjected to permanent middle cerebral artery occlusion (pMCAO), and different doses of recombinant mouse PGRN (r-PGRN, 0.3 ng, 1 ng, 5 ng) were intracerebroventricularly administered 30 min after pMCAO. Results showed that 1 ng r-PGRN markedly reduced infarct volume and rescued functional deficits 24 h after pMCAO. Meanwhile, 1 ng r-PGRN increased SVZ cell proliferation, as shown by a high number of bromodeoxyuridine-positive (BrdU+) cells and Ki-67+ cells in the ischemic ipsilateral SVZ 7 d after pMCAO. Additionally, PGRN increased the percentage of BrdU+/Doublecortin (DCX)+ cells in the ipsilateral SVZ 14 d after pMCAO and increased the percentage of new neurons (BrdU+/NeuN+ cells) in the peri-infarct striatum 28 d after pMCAO, suggesting that PGRN promotes neuronal differentiation. PGRN also upregulated phosphorylation of ERK1/2 and Akt in the ipsilateral SVZ 3 d after pMCAO. Our data indicate that PGRN treatment promotes acute functional recovery; most importantly, it also stimulates neurogenesis in the SVZ, which could be beneficial for long-term recovery after cerebral ischemia. The increase in neurogenesis could be associated with activation of the MAPK/ERK and PI3K/Akt pathways. These results suggest a potential new strategy utilizing PGRN in ischemic stroke therapy.

Tea polyphenols protect learning and memory in sleep-deprived mice by promoting AMPA receptor internalization.

Chronic sleep loss caused lots of health problems, also including cognition impairment. Tea is one of the most popular drinks when people stay up late. Nevertheless, the effects of tea on sleep deprivation-induced cognition impairment are still unclear. In the present study, we found 24-h sleep deprivation (S-DEP) increased membrane α-amino-3-hydroxy-5-methyl-4-isoxa-zolep-propionate (AMPA) receptor level through a tumor necrosis factor α (TNFα)-dependent pathway in hippocampi. Blocking elevated TNFα level can protect S-DEP mice from impaired learning ability according to behavioral test. Tea polyphenols, major active compounds in green tea, suppressed TNFα production through downregulating TNFα converting enzyme (TACE) level. Meanwhile, tea polyphenols treatment could ameliorate recognition impairment and anxiety-like behaviors in S-DEP mice. The aforementioned results demonstrate cognition protective effects of tea polyphenols in S-DEP mice model, which provide a theoretical basis for the treatments of S-DEP-induced cognition impairment by targeting the TACE/TNFα/AMPA pathway.

In vitro effects of recombinant zebrafish IFN on spring viremia of carp virus and infectious hematopoietic necrosis virus.

In order to evaluate the effects of fish recombinant interferon (rIFN) on fish pathogenic rhabdoviruses, the zebrafish (Danio rerio) IFN (DreIFN) allele B gene was cloned and expressed in Escherichia coli. In addition, the effects of recombinant DreIFN (rDreIFN) on spring viremia of carp virus (SVCV), infectious hematopoietic necrosis virus (IHNV), and vesicular stomatitis virus (VSV) were surveyed in fish and chicken cells. The mature peptide of DreIFN allele B gene encodes 163 amino acids. Residues 3 and 98 are a pair of cysteines that likely form an intrachain disulfide bridge. rDreIFN protein was detected as a band at 21.6 kDa by SDS-PAGE. The purified rDreIFN has anti-SVCV and anti-IHNV activity of 3 x 10(4) U/mg-10(7) U/mg. The results indicate that rDreIFN has higher activity against SVCV and IHNV on epithelioma papulosum cyprinid (EPC) than on grass carp (C. idellus) ovary (CO) cell lines and no activity against VSV on chick embryo fibroblasts (CEF).