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Addressing the challenge of coordinated dispatch between wind/solar and thermal power in new energy grids, this research proposes a thermal power unit output prediction method based on a Transformer-BiLSTM hybrid deep learning model. First, a simulated annealing algorithm optimizes the output configuration of solar thermal power plants to mitigate fluctuations in wind and solar combined generation. An ant colony-greedy algorithm is then integrated to determine the optimal dispatch data for thermal power units, constructing a high-quality training dataset under physical constraints. In the model design, a bidirectional long short-term memory network captures short-term temporal features, while the Transformer’s multi-head self-attention mechanism models long-term dependencies. The model innovatively incorporates the learnable positional encoding to enhance temporal awareness. Experimental results demonstrate accurate predictions, with the power constraint mechanism effectively correcting over-limit forecasts. This ensures 98.7% of predictions during low-load periods comply with unit technical specifications. Compared to existing methods, this model avoids data limitations and manual feature engineering bottlenecks through the end-to-end wind–solar–thermal mapping, providing a high-precision solution for dispatch decisions in renewable-dominated grids.

During citrus fruit development, exogenous gibberellin (GA) and 6-benzylaminopurine (6-BA, a synthetic cytokinin (CTK)) are both known to promote citrus peel thickness; however, the differences in their regulatory mechanisms on cell wall metabolism in citrus peels remain unclear. In this study, we found that GA treatment significantly increased cell wall polysaccharides in citrus peels, such as pectin and cellulose, whereas 6-BA treatment led to a notable accumulation of lignin. RNA-sequencing data revealed that several fruit ripening-related cell wall degradation genes, such as PME3 , PL18 , and EXPA1 / 8 , exhibited decreased expression levels in both GA and 6-BA treatments. Additionally, a set of cell wall polysaccharide synthesis genes was upregulated in response to GA treatment but was largely downregulated in 6-BA-treated peels. Conversely, a group of lignin biosynthesis genes was upregulated in 6-BA-treated peels. GA treatment inhibited DELLA proteins (encoded by RGA and GAI ) in the GA signaling pathway, whereas 6-BA treatment increased the expression of B-ARRs ( ARR1 and ARR2 ) in the CTK signaling pathway. Furthermore, GA treatment elevated endogenous CTK levels, while 6-BA treatment also enhanced endogenous GA content, suggesting a reciprocal interaction between these two hormonal pathways.

Power grid monitoring alarm event refers to the abnormal state notification triggered by monitoring signals during the operation of various devices, networks or business systems in the power system. These alarm events are not only huge in number, but also diverse and complex in correlation. It is difficult to effectively deal with complex and changeable alarm scenarios when evaluating the risk of alarm signals, resulting in low stability and low accuracy of intelligent identification of alarm events. Therefore, this paper proposes an intelligent identification method of power grid monitoring alarm events that integrates knowledge base and large language model. By dividing multi-attribute labels, transforming monitoring alarm data into binary network and calculating feature set, the construction process of knowledge base is designed. Using the natural language processing ability of large-scale language model combined with structured knowledge base data, the structured information retrieved by combining knowledge base is deeply understood and reasoned. On this basis, the fusion process of knowledge base and large language model is designed, and finally the intelligent identification of power grid monitoring alarm events is realized. The experimental results show that the defect risk level of this method is low, and the effect is good when evaluating the defect risk of alarm signals, and the stability keeps rising, and the alarm accuracy is high. In practical application, it can realize the abnormal and accurate alarm of power grid monitoring alarm event information.Expert experience and knowledge are stored in a structured form.The large language model processes complex textual information and identifies potential issues.The combination of the knowledge base and large language model maximizes their respective advantages.

Peelability, a crucial commercial trait for fresh-eating citrus, has received limited research attention regarding its underlying mechanisms. This study investigated three late-maturing citrus cultivars, namely ‘Qingjian’ (QJ), ‘Mingrijian’ (MRJ), and ‘Chunjian’ (CJ), analyzing their peelability development using texture analysis and exploring the physiological and biochemical factors influencing peeling difficulty. The results showed that peelability improved with fruit maturation, reaching its peak at full ripeness, with the following order of peeling difficulty: QJ (hardest) > MRJ (intermediate) > CJ (easiest). At full maturity, QJ (the most difficult to peel) exhibited more regularly shaped peel cells with fewer intercellular spaces, lower intracellular organic matter accumulation, and higher levels of cell wall polysaccharides, calcium (Ca), and abscisic acid (ABA). These characteristics may be linked to the lower relative expression of soluble sugar (TS)-related genes (CCR4A, SPP1) and the titratable acid (TA)-related gene (CsCit1), as well as the higher relative expression of ABA biosynthesis genes (NCED1, NCED2). Correlation analyses demonstrated that citrus peel firmness and adhesion strength are significantly associated with multiple growth and developmental characteristics, including fruit morphometric parameters, peel cellular architecture, intracellular organic compound content, cell wall polysaccharide levels and related degradative enzyme activities, calcium concentrations, and endogenous phytohormone profiles. These findings provide valuable insights for studying peelability mechanisms and improving fruit quality in citrus breeding.

Key pigments and genes associated with peel color variation between Shiranui Mandarin and its Citrine Mutant were investigated. Combining liquid chromatography with tandem mass spectrometry (LC-MS/MS) analysis detected carotenoids in the peel of Shiranui mandarin and its mutant, Citrine Shiranui. Transcriptomic analysis revealed genes enriched in the carotenoid biosynthesis and chlorophyll degradation pathways. Weighted Gene Coexpression Network Analysis (WGCNA) predicted potential regulatory transcription factors. Dual-Luciferase Reporter Assays (LUC) demonstrated the regulatory relationships between the predicted transcription factors and key functional genes involved in carotenoid biosynthesis and chlorophyll degradation. During fruit ripening, the accumulation of β-cryptoxanthin was significantly lower in Citrine Shiranui. In fully ripened fruit, β-cryptoxanthin accounted for 43.62% of the peel pigments in Shiranui, compared to only 8.21% in Citrine Shiranui. Additionally, while chlorophyll degradation occurred during ripening in both varieties, Citrine Shiranui retained higher chlorophyll level. Notably, the expression patterns of CitBCH1 and CitNCY1 were associated with reduced β-cryptoxanthin synthesis and delayed chlorophyll degradation in Citrine Shiranui. LUC further demonstrated that CitBCH1 and CitNCY1 were positively regulated by an ethylene-responsive transcription factor (CitERF5) and an ABA-responsive element-binding factor (CitABF2), respectively. In summary, the citrine/yellow peel color of the Citrine Shiranui mutant primarily resulted from reduced β-cryptoxanthin accumulation due to down-regulation of CitBCH1, while the retention of chlorophyll, linked to lower CitNCY1 expression, might also contribute modestly to the phenotype.

The power flow variation of the distribution network has super fluctuation, which produces equivalent random disturbance, which affects the characterization of peak frequency of the current signal, resulting in low accuracy of power flow calculation results. Therefore, a random power flow calculation method of distribution network based on improved Newton Raphson algorithm is proposed. The current signal is reconstructed based on the wavelet threshold de-noising method. The peak frequency of current signal is used to classify the random power flow data of distribution network and eliminate the non power flow signal. Newton Raphson algorithm is improved by using Jacobian matrix, and the power flow model estimated by Newton Raphson algorithm is established to realize the random power flow calculation of distribution network. The test results show that the average relative error and standard deviation of power flow calculation results for different connected power nodes are low, which can provide more accurate data for power flow control.

Due to the multiplicity of fixed-value defect states of relay protection equipment, it is difficult to guarantee the effect of identification directly through a single indicator. Therefore, this paper proposes research on closed-loop identification technology for the full life cycle of fixed-value defect states of relay protection equipment. The generalized transformation ratio of the relay protection circuit is analyzed based on the CT basic transformation ratio, cable voltage drop correction coefficient, filter attenuation correction coefficient, and ADC quantization error correction coefficient. The relationship between the transformation ratio and the fixed value state of the relay protection equipment enables the identification of defective states. In the test results, the design technology can effectively identify fixed-value defects at different stages and to varying degrees.

The optimization of electric vehicles (EVs) charging piles configurations is explored across varying demand levels, land prices, and unit time cost, with the aim of minimizing users’ waiting times and construction operation and maintenance costs. The analysis indicates that a significant reduction in user waiting times can be achieved through the increase of charging piles, with only a marginal rise in construction operation and maintenance cost, proving to be economically beneficial, especially in scenarios of high user time cost. After determining the minimum number of charging piles according to the charging demand, adding 1 to 4 charging piles based on it is the most direct and convenient method to determine the configuration of charging piles.

With the rapid development of material science and manufacturing capabilities, hydraulic technology is increasingly high-pressure, lightweight and miniaturizing. Micro plunger pump is widely used in the field of deep-sea hydraulic equipment and advanced intelligent hydraulic equipment, owing to its high-power density, high output pressure and many other advantages. Its broad application aims to examine the inlet and outlet distribution valve spring parameters change on the micro high-pressure plunger pump volumetric efficiency, by changing the inlet and outlet distribution valve. This paper is based on the simulation of AMESim engineering software to derive multiple sets of data. It compared and analysed the specific effect of different spring stiffness and opening pressure on the volumetric efficiency of the micro high-pressure plunger pump which was then verified through experiment. Results of this study have certain reference significance for the design of the spring of the inlet and outlet distribution valve of the micro high-pressure plunger pump, which facilitates the optimization and improvement of the dynamic performance of the micro high-pressure plunger pump.

Objectives In this study, we describe the patterns of antibiotic prescription for neonates based on World Health Organization’s (WHO) Essential Medicines List Access, Watch, and Reserve (AWaRe), and the Management of Antibiotic Classification (MAC) Guidelines in China. Methods One-day point-prevalence surveys (PPS) on antimicrobial prescriptions were conducted on behalf of hospitalized neonates in China from September 1 and November 30, annually from 2017 to 2019. Results Data was collected for a total of 2674 neonatal patients from 15 hospitals in 9 provinces across China of which 1520 were newborns who received at least one antibiotic agent. A total of 1943 antibiotic prescriptions were included in the analysis. The most commonly prescribed antibiotic was meropenem (11.8%). The most common reason for prescribing antibiotic to neonates was pneumonia (44.2%). There were 419 (21.6%), 1343 (69.1%) and 6 (0.3%) antibiotic prescriptions in the Access, Watch and Reserve groups, respectively. According to MAC Guidelines in China, there were 1090 (56.1%) antibiotic agents in the Restricted and 414 (21.3%) in the Special group. Conclusion Broad-spectrum antibiotics included in the Watch and Special groups were likely to be overused in Chinese neonates.