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With the rapid development of renewable energy sources such as wind and solar, the net load characteristics of power systems have undergone fundamental changes. This paper defines quantitative analysis indicators for net load characteristics and examines how these characteristics evolve as the proportion of wind and solar energy increases. By identifying inflection points in the system’s adjustment capabilities, we categorize power systems into low, medium, and high renewable energy penetration. We then establish adjustment models that incorporate traditional coal power, hydropower, natural gas generation, adjustable loads, system interconnections, pumped-storage hydroelectricity, and new energy storage technologies. A genetic algorithm is employed to optimize and balance the net load curves under varying renewable energy proportions, analyzing the mechanism behind net load balance. A case study, based on real operational data from 2023 for a provincial power grid in western China, which is rich in renewable resources, conducts a quantitative analysis of the system’s adjustment capability inflection point and net load balancing strategies. The results demonstrate that the proposed method effectively captures the evolution of the system’s net load and reveals the mechanisms of net load balancing under different renewable energy penetration levels.

Background: Under fasting conditions, the pathway converting gluconeogenesis precursors into muscle glycogen becomes crucial due to reduced glycogen reserves. However, there is limited research on skeletal muscle gluconeogenesis and the impact of fasting on gluconeogenic gene expression. Methods: Sheep fetal skeletal muscle cells cultured in vitro were used to study the effects of varying lactic acid concentrations (0 to 30 mM) and 2.5 mM glucose on the expression of gluconeogenesis-related genes after 6 h of fasting. The effects on mRNA and protein expression of key genes involved in skeletal muscle gluconeogenesis were measured by quantitative real time polymerase chain reaction (qRT-PCR), immunofluorescence, and western blotting at 48 h. Results: Fasting increased the expression of key gluconeogenic genes, fructose-1,6-bisphosphatase 2 (FBP2), glucose-6-phosphatase 3 (G6PC3), pyruvate kinase M (PKM), monocarboxylate transporter1 (MCTS1), glucose transporter type 4 (GLUT4), pyruvate carboxylase (PC), and lactate dehydrogenase A (LDHA). The mRNA levels of FBP2, G6PC3, and MCTS1 significantly decreased with glucose addition. Additionally, 10 mM lactic acid significantly promoted the expression of FBP2, PC, MCTS1, LDHA, GLUT4, and PKM while inhibiting phosphoenolpyruvate carboxykinase (PEPCK) expression. At the protein level, 10 mM lactic acid significantly increased FBP2 and PKM protein expression. Conclusions: This study shows that fasting regulates key gluconeogenic gene expression in sheep skeletal muscle cells and highlights the role of lactic acid in inducing these gene expressions.

This paper introduces a new way to plan and manage the use of wind and solar power, along with traditional thermal power (TP) and batteries, to get the most environmental and economic benefits. It uses a special kind of artificial intelligence, called conditional generative adversarial networks (CGAN), to predict how much power wind and solar sources will produce. Subsequently, it takes into account the dynamic line–rated power (DLRP) in order to determine the dynamic transmission capacity of lines associated with wind and solar power generation. The primary objectives are to reduce the operating costs of TP plants, maximize the utilization of wind and solar energy, minimize power deviations in electricity transmission, and enhance revenue from electricity transmission. To solve this complex problem, the paper uses a smart method to simplify the model, making it possible to find solutions with CPLEX. Tests on a small network with six nodes show that this approach not only saves money but also makes better use of clean energy sources.

To overcome the slow development of mechanical strengths of sulphoaluminate cement (CSA) in winter construction, this paper investigates the effects of different amounts of admixing Li2CO3 on the setting time, compressive strength, and cooling curve of CSA paste in negative temperatures (−5 °C, −10 °C, and −20 °C). The influence of admixing Li2CO3 on the hydration process and microstructure of CSA paste in a negative temperature environment is also studied using X-ray diffraction (XRD)-Rietveld quantitative analysis, Thermogravimetry (TG) analysis, and Scanning electron microscopy (SEM) observation. The results show that adding Li2CO3 into CSA can significantly improve the mechanical properties at an early age, shorten the curing time, and reduce the freezing point under negative temperatures. The XRD-Rietveld and TG analysis indicates that adding Li2CO3 can increase the consumption of C4A3S̅ and promote the generation of AFt at negative temperatures. It is recommended that the optimum dosage of Li2CO3 should be 0.16 wt% when used as an admixture for CSA systems in negative temperatures.

In response to the complexity and high volatility of original load data affecting the accuracy of load forecasting, a combined method for short-term load forecasting considering the characteristics of components of seasonal and trend decomposition using local regression (STL) is proposed. The original load data are decomposed into a trend component, seasonal component, and residual component using STL. Then, considering the characteristics of each component, a long short-term memory (LSTM) neural network, a convolutional neural network (CNN), and Gaussian process regression (GPR) are used to predict the trend component, seasonal component, and residual component, respectively. The final outcome of the load forecasting is obtained by summing the forecasted results of each individual component. A specific case study is conducted to compare the proposed combined method with LSTM, CNN, GPR, STL-LSTM, STL-CNN, and STL-GPR prediction methods. Through comparison, the proposed combined method exhibits lower errors and higher accuracy, demonstrating the effectiveness of this method.

The proposed approach involves a method of joint optimization configuration for wind–solar–thermal-storage (WSTS) power energy bases utilizing a dynamic inertia weight chaotic particle swarm optimization (DIWCPSO) algorithm. The power generated from the combination of wind and solar energy is analyzed quantitatively by using the average complementarity index (ACI) to determine the optimal ratio of wind and solar installations. We constructed a multi-objective optimization configuration model for the WSTS power generation systems, considering the equivalent annual income and the optimal energy consumption level as objective functions of the system. We solved the model using the chaotic particle swarm optimization algorithm with linearly decreasing dynamic inertia weight. To validate the effectiveness of the proposed approach, we conducted a simulation using the 2030 power energy base planning data of a particular region in Inner Mongolia. The results demonstrate that the proposed method significantly improves the annual income, enhances the consumption of wind–solar energy, and boosts the power transmission capacity of the system.

Short-term power load forecasting involves the stable operation and optimal scheduling of the power system. Accurate load forecasting can improve the safety and economy of the power grid. Therefore, how to predict power load quickly and accurately has become one of the urgent problems to be solved. Based on the optimization parameter selection and data preprocessing of the improved Long Short-Term Memory Network, the study first integrated particle swarm optimization algorithm to achieve parameter optimization. Then, combined with convolutional neural network, the power load data were processed to optimize the data and reduce noise, thereby enhancing model performance. Finally, simulation experiments were conducted. The PSO-CNN-LSTM model was tested on the GEFC dataset and demonstrated stability of up to 90%. This was 22% higher than the competing CNN-LSTM model and at least 30% higher than the LSTM model. The PSO-CNN-LSTM model was trained with a step size of 1.9×10^4, the relative mean square error was 0.2345×10^-4. However, when the CNN-LSTM and LSTM models were trained for more than 2.0×10^4 steps, they still did not achieve the target effect. In addition, the fitting error of the PSOCNN-LSTM model in the GEFC dataset was less than 1.0×10^-7. In power load forecasting, the PSOCNN- LSTM model's predicted results had an average absolute error of less than 1.0% when compared to actual data. This was an improvement of at least 0.8% compared to the average absolute error of the CNNLSTM prediction model. These experiments confirmed that the prediction model that combined two methods had further improved the speed and accuracy of power load prediction compared to traditional prediction models, providing more guarantees for safe and stable operation of the power system.

Background & Aims: Non-alcoholic fatty liver disease (NAFLD) has a prevalence of ∼25% worldwide, with significant public health consequences yet few effective treatments. Human genetics can help elucidate novel biology and identify targets for new therapeutics. Genetic variants in mitochondrial amidoxime-reducing component 1 (MTARC1) have been associated with NAFLD and liver-related mortality; however, its pathophysiological role and the cell type(s) mediating these effects remain unclear. We aimed to investigate how MTARC1 exerts its effects on NAFLD by integrating human genetics with in vitro and in vivo studies of mARC1 knockdown. Methods: Analyses including multi-trait colocalisation and Mendelian randomisation were used to assess the genetic associations of MTARC1. In addition, we established an in vitro long-term primary human hepatocyte model with metabolic readouts and used the Gubra Amylin NASH (GAN)-diet non-alcoholic steatohepatitis mouse model treated with hepatocyte-specific N-acetylgalactosamine (GalNAc)–siRNA to understand the in vivo impacts of MTARC1. Results: We showed that genetic variants within the MTARC1 locus are associated with liver enzymes, liver fat, plasma lipids, and body composition, and these associations are attributable to the same causal variant (p.A165T, rs2642438 G>A), suggesting a shared mechanism. We demonstrated that increased MTARC1 mRNA had an adverse effect on these traits using Mendelian randomisation, implying therapeutic inhibition of mARC1 could be beneficial. In vitro mARC1 knockdown decreased lipid accumulation and increased triglyceride secretion, and in vivo GalNAc–siRNA-mediated knockdown of mARC1 lowered hepatic but increased plasma triglycerides. We found alterations in pathways regulating lipid metabolism and decreased secretion of 3-hydroxybutyrate upon mARC1 knockdown in vitro and in vivo. Conclusions: Collectively, our findings from human genetics, and in vitro and in vivo hepatocyte-specific mARC1 knockdown support the potential efficacy of hepatocyte-specific targeting of mARC1 for treatment of NAFLD. Impact and implications: We report that genetically predicted increases in MTARC1 mRNA associate with poor liver health. Furthermore, knockdown of mARC1 reduces hepatic steatosis in primary human hepatocytes and a murine NASH model. Together, these findings further underscore the therapeutic potential of targeting hepatocyte MTARC1 for NAFLD.

This paper provides an optimization strategy for flexible operation at the system level to guide the real-time flexible ramping of the power grid. Firstly, the concept of regional power grid flexibility is clarified, and the ramping factor is proposed as a flexibility metric. On this basis, taking the output priority of each node as the objective function and considering constraints such as line flow, power balance, and system flexibility, a mathematical model for regional power grid transmitting flexible power externally is proposed. Compared with the traditional models, this model focuses more on flexibility rather than economy. A case analysis based on the improved IEEE 30-Bus System verifies the feasibility and effectiveness of the proposed strategy and its advantages over the traditional models.

DNA methylation is an important epigenetic regulatory form that regulates gene expression and tissue development. This study compared the effects of high fiber, low protein (HFLP) and low fiber, high protein (LFHP) diets on the DNA methylation profile of twin lambs’ muscles, their effect on glycolysis/gluconeogenesis and related pathways by transcriptome and deep whole-genome bisulfite sequencing (WGBS). Results identified 1,945 differentially methylated regions (DMRs) and 1,471 differentially methylated genes (DMGs). Also, 487 differentially expressed transcripts belonging to 368 differentially expressed genes (DEGs) were discovered between the twin lambs under different diets. Eleven overlapped genes were detected between the DEGs and the DMGs. FKBP5 and FOXO1 were detected to be significantly different. The FOXO1 regulated cAMP and the glycolysis/gluconeogenesis pathways. The glycolysis/gluconeogenesis, and the FOXO pathways were significantly enriched. The expressions of HOMER1 and FOXO1 in the HFLP group were significantly higher than those in the LFHP group. There is a significant correlation between the upregulated gene expression and hypomethylation of HOMER1 and FOXO1 gene in HFLP group. The results showed that FOXO1 induces PDK4 expression in muscle while regulating FKBP5 activity, which stimulates glucose production by activating specific gluconeogenesis target genes. The FOXO1 was able to regulate the glucose metabolism, the cAMP and the occurrence of glycolysis/gluconeogenesis pathways. This study showed that feed type can affect the methylation levels of the glycolysis related gluconeogenesis genes and interaction pathways, providing new ideas for a better understanding of the regulation of muscle energy metabolism and feed development.

Aiming at the problem of loss imbalance in operation of three-level active neutral point clamped (ANPC) converter switching devices, this article provides an improved balancing strategy. Take phase as an example. The reasons for the loss of the three-level ANPC converter′s unbalanced loss were analyzed, and the space vector pulse width modulation (SVPWM) strategy was used to reduce switch action to achieve loss balance by reasonably selecting the same transition state. Compared with the traditional SVPWM strategy, this method reduced the unbalance of the bridge arm switch from 23.12% to 6.37%, and the unbalance of the clamp switch from 11.19% to 4.71%. It can be used to increase the capacity and service life of converters in places such as motor frequency conversion speed regulation and high-voltage power transmission.

Abstract Background Exacerbation‐prone asthma is a feature of severe disease. However, the basis for its persistency remains unclear. Objectives To determine the clinical and transcriptomic features of frequent exacerbators (FEs) and persistent FEs (PFEs) in the U‐BIOPRED cohort. Methods We compared features of FE (≥2 exacerbations in past year) to infrequent exacerbators (IE,

Asthma affects more than 300 million people globally and is both under diagnosed and under treated. The most recent and largest genome-wide association study investigating moderate to severe asthma to date was carried out in 2019 and identified 25 independent signals. However, as new and in-depth downstream databases become available, the translational analysis of these signals into target genes and pathways is timely. In this study, unique (U-BIOPRED) and publicly available datasets (HaploReg, Open Target Genetics and GTEx) were investigated for the 25 GWAS signals to identify 37 candidate causal genes. Additional traits associated with these signals were identified through PheWAS using the UK Biobank resource, with asthma and eosinophilic traits amongst the strongest associated. Gene expression omnibus dataset examination identified 13 candidate genes with altered expression profiles in the airways and blood of asthmatic subjects, including MUC5AC and STAT6. Gene expression analysis through publicly available datasets highlighted lung tissue cell specific expression, with both MUC5AC and SLC22A4 genes showing enriched expression in ciliated cells. Gene enrichment pathway and interaction analysis highlighted the dominance of the HLA-DQA1/A2/B1/B2 gene cluster across many immunological diseases including asthma, type I diabetes, and rheumatoid arthritis. Interaction and prediction analyses found IL33 and IL18R1 to be key co-localization partners for other genes, predicted that CD274 forms co-expression relationships with 13 other genes, including the HLA-DQA1/A2/B1/B2 gene cluster and that MUC5AC and IL37 are co-expressed. Drug interaction analysis revealed that 11 of the candidate genes have an interaction with available therapeutics. This study provides significant insight into these GWAS signals in the context of cell expression, function, and disease relationship with the view of informing future research and drug development efforts for moderate-severe asthma.

Ketamine is a short-acting analgesic that also has anti-depressant effects. Here the authors show in rat models of chronic pain that low-dose ketamine can induce an anti-aversive state that persists after the initial short term analgesia has ended.

Summary: Acute pain evokes protective neural and behavioral responses. Chronic pain, however, disrupts normal nociceptive processing. The prefrontal cortex (PFC) is known to exert top-down regulation of sensory inputs; unfortunately, how individual PFC neurons respond to an acute pain signal is not well characterized. We found that neurons in the prelimbic region of the PFC increased firing rates of the neurons after noxious stimulations in free-moving rats. Chronic pain, however, suppressed both basal spontaneous and pain-evoked firing rates. Furthermore, we identified a linear correlation between basal and evoked firing rates of PFC neurons, whereby a decrease in basal firing leads to a nearly 2-fold reduction in pain-evoked response in chronic pain states. In contrast, enhancing basal PFC activity with low-frequency optogenetic stimulation scaled up prefrontal outputs to inhibit pain. These results demonstrate a cortical gain control system for nociceptive regulation and establish scaling up prefrontal outputs as an effective neuromodulation strategy to inhibit pain. : Dale et al. find that acute pain increases activity levels in the prefrontal cortex. Chronic pain reduces both basal spontaneous and pain-evoked activity in this region, whereas neurostimulation to restore basal activities can in turn enhance nociception-evoked prefrontal activities to inhibit pain. Keywords: chronic pain, neuromodulation, prefrontal cortex, PFC, cortical gain control

Abstract It has long been speculated that cues on the human face exist that allow observers to make reliable judgments of others’ personality traits. However, direct evidence of association between facial shapes and personality is missing from the current literature. This study assessed the personality attributes of 834 Han Chinese volunteers (405 males and 429 females), utilising the five-factor personality model (‘Big Five’), and collected their neutral 3D facial images. Dense anatomical correspondence was established across the 3D facial images in order to allow high-dimensional quantitative analyses of the facial phenotypes. In this paper, we developed a Partial Least Squares (PLS) -based method. We used composite partial least squares component (CPSLC) to test association between the self-tested personality scores and the dense 3D facial image data, then used principal component analysis (PCA) for further validation. Among the five personality factors, agreeableness and conscientiousness in males and extraversion in females were significantly associated with specific facial patterns. The personality-related facial patterns were extracted and their effects were extrapolated on simulated 3D facial models.

Summary: Uncovering spatial representations from large-scale ensemble spike activity in specific brain circuits provides valuable feedback in closed-loop experiments. We develop a graphics processing unit (GPU)-powered population-decoding system for ultrafast reconstruction of spatial positions from rodents’ unsorted spatiotemporal spiking patterns, during run behavior or sleep. In comparison with an optimized quad-core central processing unit (CPU) implementation, our approach achieves an ∼20- to 50-fold increase in speed in eight tested rat hippocampal, cortical, and thalamic ensemble recordings, with real-time decoding speed (approximately fraction of a millisecond per spike) and scalability up to thousands of channels. By accommodating parallel shuffling in real time (computation time

Charge transport of topological semimetals is the focus of intensive investigations because of their nontrivial band topology. Heat transport of these materials, on the other hand, is largely unexplored and remains elusive. Here, we report on an observation of unprecedented, giant magnetic quantum oscillations of thermal conductivity in the prototypical Weyl semimetal TaAs. The oscillations are antiphase with the quantum oscillating electronic density of states of a Weyl pocket, and their amplitudes amount to 2 orders of magnitude of the estimation based on the Wiedemann-Franz law. Our analyses show that all the conventional heat-transport mechanisms through diffusions of propagating electrons, phonons, and electron-hole bipolar excitations are far inadequate to account for these phenomena. Taking further experimental facts that the parallel field configuration favors much-higher magnetothermal conductivity, we propose that the newly proposed chiral zero sound provides a reasonable explanation to these exotic phenomena. More work focusing on other topological semimetals along the same line is badly called for.

Human facial morphology is a combination of many complex traits. Little is known about the genetic basis of common facial morphological variation. Existing association studies have largely used simple landmark-distances as surrogates for the complex morphological phenotypes of the face. However, this can result in decreased statistical power and unclear inference of shape changes. In this study, we applied a new image registration approach that automatically identified the salient landmarks and aligned the sample faces using high density pixel points. Based on this high density registration, three different phenotype data schemes were used to test the association between the common facial morphological variation and 10 candidate SNPs, and their performances were compared. The first scheme used traditional landmark-distances; the second relied on the geometric analysis of 15 landmarks and the third used geometric analysis of a dense registration of ∼30,000 3D points. We found that the two geometric approaches were highly consistent in their detection of morphological changes. The geometric method using dense registration further demonstrated superiority in the fine inference of shape changes and 3D face modeling. Several candidate SNPs showed potential associations with different facial features. In particular, one SNP, a known risk factor of non-syndromic cleft lips/palates, rs642961 in the IRF6 gene, was validated to strongly predict normal lip shape variation in female Han Chinese. This study further demonstrated that dense face registration may substantially improve the detection and characterization of genetic association in common facial variation.