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Dissolved gas analysis (DGA) is an effective online fault diagnosis technique for large oil-immersed transformers. However, due to the limited number of DGA data, most deep learning models will be overfitted and the classification accuracy cannot be guaranteed. Therefore, this paper has introduced the idea of deep neural networks into the multi-grained cascade forest (gcForest), which is a tree-based deep learning model, and proposed an improved gcForest that can be accelerated by GPU. Firstly, in order to extract features more effectively and reduce memory consumption, the multi-grained scanning of gcForest is replaced by convolutional neural networks. Secondly, the cascade forest (CasForest) is replaced by cascade eXtreme gradient boosting (CasXGBoost) to improve the classification ability. Finally, 235 DGA samples are used to train and evaluate the proposed model. The average fault diagnosis accuracy of the improved gcForest is 88.08 %, while the average recall, precision, and Fl-score are 0.89, 0.90, 0.89, respectively. Moreover, the proposed method still has high fault diagnosis accuracy for datasets of different sizes.

Abstract Background Depression and chronic constipation often co-occur, but the reciprocal influence between the two remains unclear. The purpose of this study is to explore the potential association between depression and chronic constipation. Methods This study initially utilized data from National Health and Nutrition Examination Survey (NHANES) 2005–2010 to explore the correlation between depression scores and chronic constipation, assessing the non-linear relationship between the two. Subsequently, we conducted a two-sample Mendelian randomization (MR) analysis to evaluate the causal relationship between depression and major depression with chronic constipation. The Inverse Variance Weighting (IVW) method served as the primary reference, supplemented by sensitivity tests. Finally, a reverse MR analysis was performed to assess the presence of any reverse causation. The STROBE-MR checklist for the reporting of MR studies was used in this study. Results In the NHANES analysis, survey-weighted logistic regression revealed a significantly positive correlation between depression scores and chronic constipation (OR = 1.04, 95% CI = 1.02–1.07, p = 0.002), even after adjusting for the included covariates. The nonlinear analysis using Restricted Cubic Splines (RCS) enhanced the robustness of the association (P-non-liner = 0.01). The MR analysis also confirmed the causal relationship between depression (OR = 11.43, 95% CI = 1.85–70.67, p = 0.008) and major depression (OR = 1.12, 95% CI = 1.03–1.22, p = 0.007) with chronic constipation, passing rigorous sensitivity tests. No evidence of reverse causation was observed in the reverse MR analysis (P > 0.05). Conclusions Depression is positively correlated with the risk of chronic constipation. Therefore, enhancing attention to chronic constipation in patients with depression may be effective in clinical practice.

Abstract Various dielectric response processes in the oil‐paper insulation are sensitively affected by the insulating states (ageing degrees and moisture contents). However, the existing research is still incomplete in revealing the microscopic mechanisms of various dielectric response processes in oil‐paper insulation with different insulating states. Given this issue, the genetic algorithm is first adopted to extract the Dissado–Hill (D–H) model parameters by simulating the frequency domain spectroscopy (FDS) of oil‐paper insulation. Then, the change laws of the extracted D–H model parameters are adopted to reveal the microscopic mechanisms of various dielectric response processes. Microscopic mechanisms of four dielectric response processes are studied, which are quasi‐dc relaxation, loss peak relaxation, optical frequency relaxation, and DC conductance. Meanwhile, due to the dielectric response processes being dominated by various polar particles (methanol, ethanol, furfural, and water molecule), the contents of various ageing by‐products dissolved in the insulating oil are measured to support the above analysis. In this respect, a dielectric theoretical reference for the FDS technique to research the insulating states of oil‐paper insulation is provided.

2-Heptanol is an important secondary alcohol, applied in food, pharmaceutical and plastic industries. However, current industrial synthetic methods require energy intensive hydrogenation of 2-heptanone using homogeneous noble catalysts in alkaline medium. In this work, we presented a systematic study on enhanced transfer hydrogenation of 2-heptanone to 2-heptanol using non-noble Co/ZnO catalysts without alkaline promoters. The strong synergism of Co and ZnO contributes to formation of amorphous metallic Co species and electronically reconfigured CoO species, leading to a 20-fold activity enhancement at 180 °C and a TOF of 89.4 h−1 over Co0.09/ZnO-500 catalysts. Structure-dependency studies further revealed that, the lattice strain between Co and ZnO induced the generation of multi-phased Co/CoO/Co:ZnO interfaces, facilitating tandem H2 generation, spillover and activation of C=O bond (of 2-heptanone molecule), respectively. The insights of molecular catalysis on interface of amorphous Co/ZnO for enhanced C=O bond activation, provide fundamental understanding for transfer hydrogenation of various other ketones into valuable alcohols.

As the penetration of power electronic interfaced power sources increases rapidly, grid-forming controlled voltage source converters (VSCs) are deemed as an effective alternative to synchronous generators. As a typical grid-forming control scheme, droop control is commonly utilized due to its simplicity of construction and stability of operation. VSCs driven by droop controls, such as multi-loop droop control (MLDC) and single-loop droop control (SLDC) are capable of actively establishing voltage and frequency of grid-forming converters. However, the transient performance of SLDC at different faults is less explored to date. In this work, an in-depth comparative assessment of large-signal stability against the established MLDC is first presented by means of phase portrait. It is revealed that, both control strategies have similar transient behaviors with their basic scheme. However, with the introducing of the low-pass filters which can emulate the inertia, SLDC generally exhibits a larger power angle overshoot during the fault and therefore has poorer transient stability as compared that of MLDC. To address this issue, this work improves the damping of SLDC by incorporating differential compensation link into it. Furthermore, a parameter design guideline that balances damping and inertia, hence the transient stability and frequency stability, is proposed. The analysis is supported by real-time hardware-in-the-loop experimental results.

Framing theory is a widely accepted theoretical framework in the field of news communication studies, frequently employed to analyze the content of news reports. This paper innovatively introduces framing theory into the text summarization task and proposes a news text summarization method based on framing theory to address the global context of rapidly increasing speed and scale of information dissemination. Traditional text summarization methods often overlook the implicit deep-level semantic content and situational frames in news texts, and the method proposed in this paper aims to fill this gap. Our deep learning-based news frame identification module can automatically identify frame elements in the text and predict the dominant frame of the text. The frame-aware summarization generation model (FrameSum) can incorporate the identified frame feature into the text representation and attention mechanism, ensuring that the generated summary focuses on the core content of the news report while maintaining high information coverage, readability, and objectivity. Through empirical studies on the standard CNN/Daily Mail dataset, we found that this method performs significantly better in improving summary quality and maintaining the accuracy of news facts.

Abstract The ageing kinetic model is regarded as an effective tool to describe the degradation of transformer paper insulation. However, the temperature of the transformer winding is unevenly distributed, and the existing ageing kinetic models only depend on experimental data of accelerated thermal ageing at a constant temperature. Given this issue, this work proposes an improved second‐order kinetic model accounting for the non‐uniform thermal field. First, oil‐paper insulation samples with different ageing stages are prepared under the non‐uniform thermal field. Then, the sample's experimental data of moisture dispersion and distribution of degree of polymerisation are collected. On this basis, the improved second‐order kinetic model is derived to simulate insulating paper's ageing process subjected to an axial temperature gradient. Further, the relationship of the parameters in the improved second‐order kinetic model with the temperature and moisture content at different winding heights is obtained. Finally, the proposed model is validated, and the results show that the proposed model is more scientific than the traditional models to describe the degradation of paper insulation. In that respect, this work illustrates an improved second‐order kinetic model to provide a reference for the ageing assessment/life prediction of transformer paper insulation under the non‐uniform thermal field.

Protection of cultural relics and sites is of great significance. In this study, the new gray-green thin-layer biological crust on the rammed soil surface at the Shanhaiguan Great Wall in China was found. The emergence of this material has substantially improved the resistance of the rammed Earth Great Wall to rain erosion. 16S rRNA gene sequencing on the surface crusts of rammed Earth was performed. Results show the biological crusts were mainly algae-based composite crusts containing fungi. At the genus level, microalgae and Sphingomycetes were predominant. Under scanning electron microscopy (SEM), algae filaments dominated by filamentous algae overlapped and intertwined with each other. Furthermore, polysaccharide organic matter secreted by algae formed a covering film. The two formed a complex spatial network structure to envelop soil particles, which enhances erosion resistance. The conformable biological crusting is expected to be used as a new civil engineering material for the protection of rammed Earth sites in the future.

Oil–paper insulation is the critical insulation element in the modern power system. Under a harsh operating environment, oil–paper insulation will deteriorate gradually, resulting in electrical accidents. Thus, it is important to evaluate and monitor the insulation state of oil–paper insulation. Firstly, this paper introduces the geometric structure and physical components of oil–paper insulation and shows the main reasons and forms of oil–paper insulation’s degradation. Then, this paper reviews the existing condition assessment techniques for oil–paper insulation, such as the dissolved gas ratio analysis, aging kinetic model, cellulose–water adsorption isotherm, oil–paper moisture balance curve, and dielectric response technique. Additionally, the advantages and limitations of the above condition assessment techniques are discussed. In particular, this paper highlights the dielectric response technique and introduces its evaluation principle in detail: (1) collecting the dielectric response data, (2) extracting the feature parameters from the collected dielectric response data, and (3) establishing the condition assessment models based on the extracted feature parameters and the machine learning techniques. Finally, two full potential studies are proposed, which research hotspots’ oil–paper insulation and the electrical–chemical joint evaluation technique. In summary, this paper concludes the principles, advantages and limitation of the existing condition assessment techniques for oil–paper insulation, and we put forward two potential research avenues.

Abstract The polarization information contained in the frequency‐domain spectroscopy (FDS) could be utilised for characterising the ageing degree of transformer oil‐immersed insulation. However, the polarization information will be obscured by its conductance effect, it is thus hard to analyse the polarization behaviour by using the traditional FDS technique. Given this issue, an alternative approach is proposed to analyse the polarization information using FDS decoupled by logarithmic‐derivative spectroscopy (LDS). Findings proved that the polarization loss information generated by polarization behaviour can be separated from the total dispersion loss. The proposed feature parameters are later used to analyse the polarization loss information under ageing impact. Also, the polarization loss surface is constructed by using these feature parameters to characterise the ageing degrees of oil‐immersed samples. Finally, the potential possibility of using the proposed method to analyse the polarization loss and ageing degree are respectively discussed.

As the main insulation material of the power electronic transformer (PET), polyimide (PI) is easy to accumulate space charge under the continuous high-frequency pulse voltage, which will threaten the operation of PET and cause electric field distortion. At present, the microscopic mechanism of charge transport under high-frequency voltage is unclear. Numerical simulation is an effective method to explore the transport characteristics of space charge. The simulation of charge transport under DC voltage is mainly based on the bipolar charge transport (BCT) model. However, the charge transport behaviors under high-frequency pulse voltage cannot be simulated by the traditional models accurately. Given this, based on the BCT model, the High-Frequency Voltage Charge Transport (HFCT) model is proposed, considering nonlinear charge mobility of the polymer and the effect of the frequency. Finally, the space charge and electric field distribution under square pulse wave voltages with different rising times are simulated based on the HVCT model, concluding that the reduction of rising time will promote the accumulation of space charge. In view of this, the HVCT model is expected to optimize the insulation design of PET and provide a reference for the dynamic simulation of space charge.

The rapid growth of photovoltaic installed capacity exacerbates the power management challenges faced by photovoltaic power stations, emphasizing the importance of accurate and stable photovoltaic generation forecasting. As a result, researchers conducted research and developed several photovoltaic power prediction models. However, many prediction models focus exclusively on the algorithm structure in order to improve model accuracy, oblivious to how the dataset is constructed and divided for the prediction model. This paper proposes a comprehensive model to address this gap. To be more precise, the differential evolution algorithm is constantly looking for optimal values between different populations and determining the best way to construct datasets for prediction tasks. Multi-task learning enables the transfer of knowledge between related tasks via parameter sharing layers, referring to the accuracy and stability of prediction models. Overall, the proposed model achieves high prediction accuracy and stability. The prediction error of the proposed model is less than 450W in RMSE, NRMSE is less than 2.5%, and R-Square is greater than 99% in multiple prediction tasks. Additionally, when compared to other single-task prediction models with an R-Square greater than 96%, the proposed model further reduces the root mean squared error by an average of 28% and the standard deviation of root mean squared error by 54%.

The polarization/depolarization curve (PDC), which contains abundant insulation information, is widely used to evaluate the insulation condition of the cross-linked polyethylene (XLPE) cables. However, the insulation condition assessing the result of the XLPE cables is usually inaccurate using the PDC technology because the parameters of the current Debye model are determined in a uniform aging hypothesis, instead of a real non-uniform situation. To solve this problem, the modified Debye model considering the real non-uniform situation is proposed, and the effectiveness of the modified model is proved by the comparison of the PDC test results with non-uniform and uniform aging materials. It is found that the parameters of the extended Debye model tend to overestimate the cable insulation performance, while the characteristic parameters obtained by the modified Debye model better reflect the real situation of cable non-uniform aging. Result analysis shows that the proposed method has better accuracy in the insulation condition evaluation of cables.

We developed a rapid method of total phosphorus (TP) determination in fresh water by optimizing the volumes and concentrations of the oxidant (potassium persulfate), reductant (ascorbic acid), and colorant (molybdate + antimony potassium tartrate). Instead of a UV spectrophotometer, as used in the national standard method (GB 11893-89), we used the iMark Microplate Absorbance Reader to measure the absorbance. The principle of TP determination was identical to that of the national standard method as the water sample was digested under a neutral condition and phosphorus was oxidized to orthophosphate and colored to measure absorbance. In the developed method, we adjusted the volumes of the oxidant and reductant to 80 µL and 0.1 g/mL, respectively, and the concentration of the colorant to 31.00 + 0.32 mol/mL. The water samples collected from Lake Taihu in Maoming, Guangdong, China, were used in the experiment. The feasibility and applicability of the developed method were validated by determining TP in freshwater samples. The results showed that the developed method has excellent accuracy and precision compared with the national standard method. In addition, the national standard method was simplified in terms of the digestion and determination of TP, whereby the volume of chemical reagents and the determination time were reduced. The efficiency of TP determination was improved such that the developed method can be used for monitoring water quality to prevent environmental problems. [ABSTRACT FROM AUTHOR]

In this paper, we introduce the notion of Rota-Baxter Lie 2-algebras, which is a categorification of Rota-Baxter Lie algebras. We prove that the category of Rota-Baxter Lie 2-algebras and the category of 2-term Rota-Baxter L∞-algebras are equivalent. We introduce the notion of a crossed module of Rota-Baxter Lie algebras, and show that there is a one-to-one correspondence between strict 2-term Rota-Baxter L∞-algebras and crossed modules of Rota-Baxter Lie algebras. At last, as applications of the crossed modules of Rota-Baxter Lie algebras, we give constructions of crossed modules of pre-Lie algebras and crossed modules of Lie algebras from them. [ABSTRACT FROM AUTHOR]