Power system load forecasting using mobility optimization and multi-task learning in COVID-19.

Affected by the new coronavirus (COVID-19) pandemic, global energy production and consumption have changed a lot. It is unknown whether conventional short-term load forecasting methods based on single-task, single-region, and conventional indicators can accurately capture the load pattern during the COVID-19 and should be carefully studied. In this paper, we make the following contributions: 1) A mobility-optimized load forecasting method based on multi-task learning and long short-term memory network is innovatively proposed to alleviate the impact of the COVID-19 on short-term load forecasting. The incorporation of mobility data and data sharing layers potentially reduces the difficulty of capturing the load patterns and improves the generalization of the load forecasting models. 2) The real public data collected from multiple agencies and companies in the United States and European countries are used to conduct horizontal and vertical tests. These tests prove the failure of the conventional models and methods in the COVID-19 and demonstrate the high accuracy (error mostly less than 1%) and robustness of the proposed model. 3) The Shapley additive explanations technology based on game theory is innovatively introduced to improve the objectivity of the models. It visualizes that mobility indicators are of great help to the accurate load forecasting. Besides, the non-synchronous relationships between the indicators' correlations and contributions to the load have been proved.

Extracellular vesicles-encapsulated let-7i shed from bone mesenchymal stem cells suppress lung cancer via KDM3A/DCLK1/FXYD3 axis.

Accumulating evidence has suggested that extracellular vesicles (EVs) play a crucial role in lung cancer treatment. Thus, we aimed to investigate the modulatory role of bone marrow mesenchymal stem cell (BMSC)-EV-derived let-7i and their molecular mechanism in lung cancer progression. Microarray-based analysis was applied to predict lung cancer-related miRNAs and their downstream genes. RT-qPCR and Western blot analyses were conducted to determine Let-7i, lysine demethylase 3A (KDM3A), doublecortin-like kinase 1 (DCLK1) and FXYD domain-containing ion transport regulator 3 (FXYD3) expressions, after which dual-luciferase reporter gene assay and ChIP assay were used to identify the relationship among them. After loss- and gain-of-function assays, the effects of let-7i, KDM3A, DCLK1 and FXYD3 on the biological characteristics of lung cancer cells were assessed. Finally, tumour growth in nude mice was assessed by xenograft tumours in nude mice. Bioinformatics analysis screened out the let-7i and its downstream gene, that is KDM3A. The findings showed the presence of a high expression of KDM3A and DCLK1 and reduced expression of let-7i and FXYD3 in lung cancer. KDM3A elevated DCLK1 by removing the methylation of H3K9me2. Moreover, DCLK1 suppressed the FXYD3 expression. BMSC-EV-derived let-7i resulted in the down-regulation of KDM3A expression and reversed its promoting role in lung cancer development. Consistently, in vivo experiments in nude mice also confirmed that tumour growth was suppressed by the BMSC-EV-derived let-7i. In conclusion, our findings demonstrated that the BMSC-EV-derived let-7i possesses an inhibitory role in lung cancer progression through the KDM3A/DCLK1/FXYD3 axis, suggesting a new molecular target for lung cancer treatment.

Rates, predictors, and mortality of sepsis-associated acute kidney injury: a systematic review and meta-analysis.

BACKGROUND: Due to the high incidence and mortality of sepsis-associated acute kidney injury, a significant number of studies have explored the causes of sepsis-associated acute kidney injury (AKI). However, the opinions on relevant predictive risk factors remain inconclusive. This study aimed to provide a systematic review and meta-analysis to determine the predisposing factors for sepsis-associated AKI. METHOD: A systematic literature search was performed in the Medline, Embase, Cochrane Library, PubMed, and Web of Science, databases, with an end-date of 25th May 2019. Valid data were retrieved in compliance with specific inclusion and exclusion criteria. RESULT: Forty-seven observational studies were included for analysis, achieving a cumulative patient number of 55,911. The highest incidence of AKI was caused by septic shock. Thirty-one potential risk factors were included in the meta-analysis. Analysis showed that 20 factors were statistically significant. The odds ratio (OR) and 95% confidence interval (CI), as well as the prevalence of the most frequently-seen predisposing factors for sepsis-associated AKI, were as follows: septic shock [2.88 (2.36-3.52), 60.47%], hypertension [1.43 (1.20-1.70), 38.39%], diabetes mellitus [1.59 (1.47-1.71), 27.57%], abdominal infection [1.44 (1.32-1.58), 30.87%], the administration of vasopressors [2.95 (1.67-5.22), 64.61%], the administration of vasoactive drugs [3.85 (1.89-7.87), 63.22%], mechanical ventilation [1.64 (1.24-2.16), 68.00%], positive results from blood culture [1.60 (1.35-1.89), 41.19%], and a history of smoking [1.60 (1.09-2.36), 43.09%]. Other risk factors included cardiovascular diseases, coronary artery diseases, liver diseases, unknown infections, the administration of diuretics and ACEI/ARB, the infection caused by gram-negative bacteria, and organ transplantation. CONCLUSION: Risk factors of S-AKI arise from a wide range of sources, making it difficult to predict and prevent this condition. Comorbidities, and certain drugs, are the main risk factors for S-AKI. Our review can provide guidance on the application of interventions to reduce the risks associated with sepsis-associated acute kidney injury and can also be used to tailor patient-specific treatment plans and management strategies in clinical practice.

Silencing of TRPM8 inhibits aggressive tumor phenotypes and enhances gemcitabine sensitivity in pancreatic cancer.

The transient receptor potential TRPM8 ion channel is required for cellular proliferation in pancreatic epithelia and adenocarcinoma. To elucidate the mechanism that mediates the function of TRPM8, we examined its role in the proliferation and invasion of pancreatic cancer (PC) cells. TRPM8 expression increased in both the PC tissues and cell lines; a high TRPM8 expression was correlated with poorer prognosis in patients with PC. In PC cell lines, PACN-1 and BxPC-3, Ca2+ influxes could be evoked by TRPM8; the sensitivity of PC cells to gemcitabine was increased, while the proliferation and invasion of PC cells were suppressed after RNA interference-mediated silencing of TRPM8. The mechanism of TRPM8 in gemcitabine-based chemotherapy was then investigated. The expression and activity of multidrug resistance-associated proteins, P-gp, MRP-2, LRP, was significantly reduced in response to TRPM8 silence. Moreover, TRPM8 knockdown significantly increased hENT1 protein levels and the ratio of Bax/Bcl-2 while decreased the protein levels of RRM1. Thus, TRPM8 is required for PC cell proliferation and invasion and was closely related to the gemcitabine sensitivity of PC. The modulation of TRPM8 expression may help improve treatment response of PC by combining with traditional chemotherapy.

Quantum-level investigation of air decomposed pollutants gas sensor (Pd-modified g-C3N4) influenced by micro-water content.

In the electrical industry, there are many hazardous gases that pollute the environment and even jeopardize human health, so timely detection and effective control of these hazardous gases is of great significance. In this work, the gas-sensitive properties of Pd-modified g-C3N4 interface for each hazardous gas molecule were investigated from a microscopic viewpoint, taking the hazardous gases (CO, NOx) that may be generated in the power industry as the detection target. Then, the performance of Pd-modifiedg-C3N4 was evaluated for practical applications as a gas sensor material. Novelly, an unconventional means was designed to briefly predict the effect of humidity on the adsorption properties of this sensor material. The final results found that Pd-modified g-C3N4 is most suitable as a potential gas-sensitizing material for NO2 gas sensors, followed by CO. Interestingly, Pd-modified g-C3N4 is less suitable as a potential gas-sensitizing material for NO gas sensors, but has the potential to be used as a NO cleaner (adsorbent). Unconventional simulation explorations of humidity effects show that in practical applications Pd-modified g-C3N4 remains a promising material for gas sensing in specific humidity environments. This work reveals the origin of the excellent properties of Pd-modified g-C3N4 as a gas sensor material and provides new ideas for the detection and treatment of these three hazardous gases.

Highly Sensitive Gas Sensor for Detection of Air Decomposition Pollutant (CO, NOx): Popular Metal Oxide (ZnO, TiO2)-Doped MoS2 Surface.

When partial discharges occur in air-insulated equipment, the air decomposes to produce a variety of contamination products, resulting in a reduction in the insulation performance of the insulated equipment. By monitoring the concentration of typical decomposition products (CO, NO, and NO2) within the insulated equipment, potential insulation faults can be diagnosed. MoS2 has shown promising applications as a gas-sensitive semiconductor material, and doping metal oxides can improve the gas-sensitive properties of the material. Therefore, in this work, MoS2 has been doped using the popular metal oxides (ZnO, TiO2) of the day, and its gas-sensitive properties to the typical decomposition products of air have been analyzed and compared using density functional theory (DFT) calculations. The stability of the doped system was investigated using molecular dynamics methods. The related adsorption mechanism was analyzed by adsorption configuration, energy band structure, density of states (DOS) analysis, total electron density (TED) analysis, and differential charge density (DCD) analysis. Finally, the practical application of related sensing performance is evaluated. The results show that the doping of metal oxide nanoparticles greatly improves the conductivity, gas sensitivity, and adsorption selectivity of MoS2 monolayer to air decomposition products. The sensing response of ZnO-MoS2 for CO at room temperature (25 °C) reaches 161.86 with a good recovery time (0.046 s). TiO2-MoS2 sensing response to NO2 reaches 3.5 × 106 at 25 °C with a good recovery time (0.108 s). This study theoretically solves the industrial challenge of recycling sensing materials and provides theoretical value for the application of resistive chemical sensors in air-insulated equipment.

A 15.65-solar-mass black hole in an eclipsing binary in the nearby spiral galaxy M 33.

Stellar-mass black holes are found in X-ray-emitting binary systems, where their mass can be determined from the dynamics of their companion stars. Models of stellar evolution have difficulty producing black holes in close binaries with masses more than ten times that of the Sun (>10; ref. 4), which is consistent with the fact that the most massive stellar black holes known so far all have masses within one standard deviation of 10. Here we report a mass of (15.65 +/- 1.45) for the black hole in the recently discovered system M 33 X-7, which is located in the nearby galaxy Messier 33 (M 33) and is the only known black hole that is in an eclipsing binary. To produce such a massive black hole, the progenitor star must have retained much of its outer envelope until after helium fusion in the core was completed. On the other hand, in order for the black hole to be in its present 3.45-day orbit about its (70.0 +/- 6.9) companion, there must have been a 'common envelope' phase of evolution in which a significant amount of mass was lost from the system. We find that the common envelope phase could not have occurred in M 33 X-7 unless the amount of mass lost from the progenitor during its evolution was an order of magnitude less than what is usually assumed in evolutionary models of massive stars.

Colonization and phytoremediation potential for Miscanthus sacchariflorus in copper tailings.

A pot experiment was conducted to explore the effects of copper (Cu) tailings with various proportions in the substrate on seed germination and morphological traits of the plant. Concurrently, to identify the adaptive and tolerance strategies of the plant to Cu tailings, the uptake and accumulation of the plant to heavy metals, variations in soil enzymatic activities, and metal speciation in the blank, rhizospheric, and non-rhizospheric soils were estimated. Cu tailings at 25% proportion in the substrate exerted no significant negative effects on seed germination and seedling growth. However, Cu tailings at higher proportions (≧50%) inhibited seed germination and disturbed the plant physiological metabolism and growth. More biomass allocated to the plant roots could contribute to more heavy metals being immobilized, arresting their translocation from roots to shoots. This was accepted as a crucial defense mechanism for the plant against heavy metal co-contamination. The plant can improve the biological properties of Cu tailings in terms of enhanced invertase and phosphatase activities. And in turn, this can effectively alleviate heavy metal phytotoxicity. Simultaneously, it markedly decreased exchangeable Cu and Zn content in the rhizosphere in 25% Cu tailings treatments. In 50% Cu tailings treatment, no differences were observed in Zn speciation between rhizosphere and non-rhizosphere soils. In 75% Cu tailings, compared to the non-rhizosphere, an obvious reduction in exchangeable Cu in rhizosphere occurred, while an opposite tendency was demonstrated in carbonate-bound Zn. The plant could successfully colonize in Cu tailings, and represent a phytoremediation potential in Cu tailings.

Concentration Prediction of Polymer Insulation Aging Indicator-Alcohols in Oil Based on Genetic Algorithm-Optimized Support Vector Machines.

The predictive model of aging indicator based on intelligent algorithms has become an auxiliary method for the aging condition of transformer polymer insulation. However, most of the current research on the concentration prediction of aging products focuses on dissolved gases in oil, and the concentration prediction of alcohols in oil is ignored. As new types of aging indicators, alcohols (methanol, ethanol) are becoming prevalent in the aging evaluation of transformer polymer insulation. To address this, this study proposes a prediction model for the concentration of alcohols based on a genetic-algorithm-optimized support vector machine (GA-SVM). Firstly, accelerated thermal aging experiments on oil-paper insulation are conducted, and the concentration of alcohols is measured. Then, the data of the past 4 days of aging are used as the input feature of SVM, and the GA algorithm is utilized to optimize the kernel function parameter and penalty factor of SVM. Moreover, the concentrations of methanol and ethanol are predicted, after which the prediction accuracy of other algorithms and GA-SVM are compared. Finally, an industrial software program for predicting the concentration of methanol and ethanol is established. The results show that the mean square errors (MSE) of methanol and ethanol concentration predictions of the model proposed in this paper are 0.008 and 0.003, respectively. The prediction model proposed in this paper can track changes in methanol and ethanol concentrations well, providing a theoretical basis for the field of alcohol concentration prediction in transformer oil.

Adverse childhood experiences and risk of cancer during adulthood: A systematic review and meta-analysis.

BACKGROUND: Research findings on the relationship between adverse childhood experiences (ACEs) and the risk of cancer were inconsistent. OBJECTIVE: The purpose of this study was to perform a quantitative synthesis of the preceding research findings. PARTICIPANTS AND SETTING: System review and meta-analysis. METHODS: Electronic database of PubMed, Embase, Web of Science and Cochrane Library were systematically searched to identify relevant observational studies published not later than September 9, 2020. Specifically, original articles that reported the risk of cancer in adult populations that experienced ACEs before the age of 18 were selected. All pooled odds ratios (ORs) and 95 % confidence intervals (CIs) were calculated using a random-effects model. Publication bias was examined using funnel plots, and sensitivity analysis was used to explore the stability of the pooled results. RESULTS: A total of 18 studies involving 406,210 participants were included in this review. Individuals with 2 or 3 kinds of ACEs (OR = 1.35, 95%CI:1.12,1.62) or at least 4 ACEs (OR = 2.17, 95%CI: 1.76,2.68) were at increased risk of cancer when compared with individuals with no ACEs. Of the different types of ACEs examined, physical abuse (OR = 1.23, 95%CI:1.05,1.43), sexual abuse (OR = 1.26, 95%CI:1.02,1.56), exposure to intimate partner violence (OR = 1.26, 95%CI:1.12,1.41) and financial difficulties in the family (OR = 1.16, 95%CI:1.00,1.33) were associated with the risk of any cancer. CONCLUSIONS: These findings suggest that multiple ACEs may be a risk factor for cancer development. Therefore, prevention of ACEs and interventions for supporting those affected by ACEs are necessary.

Analysis of prevalence and influencing factors of stroke in elderly hypertensive patients: Based on the screening plan for the high-risk population of stroke in Jiading District, Shanghai.

BACKGROUND: The purpose of this study is to investigate and analyze the prevalence and influencing factors of stroke in hypertensive patients aged 60 and above in Jiading District, Shanghai. METHODS: The population-based study included 18,724 screened people with hypertension (age ≥ 60 years, 48.7% women). From 2016 to 2019, data on demographics, potential influencing factors and health status were collected through face-to-face interviews, physical examinations, and laboratory tests. Logistic multivariate logistic regression model was used to analyze the influencing factors associated with stroke. RESULTS: Among the object of study from 2016 to 2019, 2,025 patients were screened for stroke, with the overall prevalence rate of 10.82% (10.41%-11.23%). Multivariate adjusted model analysis showed that dyslipidemia (OR:1.31,95%CI:1.19-1.45), lack of exercise (OR:1.91,95%CI:1.32-2.76), atrial fibrillation [OR:1.49,95%CI:1.35-1.65), family history of stroke (OR:2.18,95%CI:1.6-2.88) were the significant independent influencing factors of stroke in hypertensive patients over 60 years old. When these four factors were combined, compared with participants without any of these factors, the multi-adjusted odds ratios (95% confidence interval) of risk of stroke for persons concurrently having one, two and three or more of these factors were 1.89 (1.67-2.13), 2.15 (1.86-2.47) and 6.84 (4.90-9.55), respectively (linear trend P < 0.001); after multivariate adjustment, the family history of stroke had additive interaction with lack of exercise [RERI = 1.08(0.22-1.94), AP = 0.19(0.04-0.35), S = 1.31(1.02-1.69)], dyslipidemia [RERI = 0.87(0.41-1.33), AP = 0.23(0.08-0.38), S = 1.46(1.04-2.05)]. CONCLUSION: The prevalence of stroke was high in hypertensive patients aged 60 and above in Jiading District, Shanghai. Dyslipidemia, lack of exercise, atrial fibrillation and family history of stroke were significantly associated with stroke in hypertensive population. Stroke risk can be increased especially when multiple factors coexisting, and family history of stroke combined with a lack of exercise or dyslipidemia.

LncRNA VPS9D1-AS1 Promotes Malignant Progression of Lung Adenocarcinoma by Targeting miRNA-30a-5p/KIF11 Axis.

Objective: This research probed into the molecular mechanisms of long non-coding RNA (lncRNA) VPS9D1 Antisense RNA 1 (VPS9D1-AS1) in lung adenocarcinoma (LUAD). Methods: lncRNA expression level was evaluated bioinformatically, and its downstream miRNA/mRNA regulatory axis was predicted by bioinformatics methods as well. qRT-PCR was used to measure VPS9D1-AS1, miRNA-30a-5p, and kinesin family member 11 (KIF11) expression. Western blot was performed to measure KIF11 protein expression. Proliferation, migration, and invasion of LUAD cells were all observed by cell biological function experiments. Dual-luciferase assay detected binding between miRNA-30a-5p and VPS9D1-AS1 or KIF11, respectively. RIP experiment detected interaction between VPS9D1-AS1 and miRNA-30a-5p. Results: VPS9D1-AS1 and KIF11 were increased in LUAD, whereas miRNA-30a-5p was decreased. VPS9D1-AS1 promoted the malignant progression of LUAD cells and could sponge miRNA-30a-5p. MiRNA-30a-5p could restore the impact of VPS9D1-AS1 on LUAD cells. KIF11 was a target downstream of miRNA-30a-5p. VPS9D1-AS1 could upregulate KIF11 expression through competitively sponging miRNA-30a-5p, and KIF11 could restore the impact of miRNA-30a-5p on LUAD cells. Conclusion: VPS9D1-AS1 could foster malignant progression of LUAD via regulating miRNA-30a-5p/KIF11 axis, suggesting that VPS9D1-AS1 is key to regulating the malignant progression of LUAD.

Prediction of Moisture and Aging Conditions of Oil-Immersed Cellulose Insulation Based on Fingerprints Database of Dielectric Modulus.

Frequency-domain spectroscopy (FDS) is demonstrated to be affected by electrode polarization and conductance behavior in the low-frequency ranges, which causes the unreliable prediction results of transformer cellulose insulation. In order to solve this issue, a fingerprint database based on the dielectric modulus is reported to predict the degree of polymerization (DP) and moisture content of cellulose insulation. In the current work, the relevant fingerprints that characterize the insulation conditions are obtained by studying the dielectric modulus curves of cellulose insulation with various insulation conditions, as well as the DC conductivity of transformer oil. Then, the dielectric modulus fingerprint database is established in the lab, and the accuracy of the reported fingerprint database is later verified. As a potential tool, the dielectric modulus fingerprint database is tested by several samples, and the results demonstrate that the accuracy of this method is more than 80%. In that respect, an interesting discovery of this paper is that the dielectric modulus fingerprint database may be a helpful tool for conditions prediction of the transformer cellulose insulation system.

Moisture Prediction of Transformer Oil-Immersed Polymer Insulation by Applying a Support Vector Machine Combined with a Genetic Algorithm.

The support vector machine (SVM) combined with the genetic algorithm (GA) has been utilized for the fault diagnosis of transformers since its high accuracy. In addition to the fault diagnosis, the condition assessment of transformer oil-immersed insulation conveys the crucial engineering significance as well. However, the approaches for getting GA-SVM used to the moisture prediction of oil-immersed insulation have been rarely reported. In view of this issue, this paper pioneers the application of GA-SVM and frequency domain spectroscopy (FDS) to realize the moisture prediction of transformer oil-immersed insulation. In the present work, a method of constructing a GA-SVM multi-classifier for moisture diagnosis based on the fitting analysis model is firstly reported. Then, the feasibility and reliability of the reported method are proved by employing the laboratory and field test experiments. The experimental results indicate that the reported prediction model might be serviced as a potential tool for the moisture prediction of transformer oil-immersed polymer insulation.

Clinical Effect of Preservation or Nonpreservation of Left Colic Artery in Total Mesorectal Excision under Laparoscopy: A Meta-analysis.

BACKGROUND AND AIMS: To investigate the clinical effect of preservation or nonpreservation of the left colic artery (LCA) in total mesorectal excision (TME) under laparoscopy. METHODS: The words, like "rectal cancer," "left colonic artery," and "laparoscopy," were used as the retrieval terms, and the keyword retrieval method was adopted. The retrieval period was set as from January 1, 2013, to June 1, 2018. We searched databases including PubMed, Web of Science, and China National Knowledge Infrastructure (CNKI) to collect randomized and controlled trials which compared the effect of preservation or nonpreservation of the LCA in TME under laparoscopy. Two researchers independently carried out literature screening, data extraction, and literature quality evaluation; Review Manager 5.3 was used for the meta-analysis. RESULTS: Seven studies including 1467 cases were identified for the meta-analysis. As showed by the meta-analysis, compared with the LCA nonpreservation group, the LCA preservation group had significantly reduced incidence of anastomotic leakage (OR = 0.44, CI = [0.30, 0.65], P < 0.0001) and postoperative urinary and sexual dysfunction (OR = 0.26, CI = [0.09, 0.78], P = 0.02) and significantly shorter time for intestinal function recovery (WMD = -0.26, CI = [-0.41, -0.11], P = 0.0008). There were no significant differences between the two groups in the duration of surgery, blood loss, number of dissected lymph nodes, or postoperative hospital stay. CONCLUSIONS: From the results, the LCA preservation group seems to achieve comparable success with acceptable safety outcomes. Therefore, this surgical method can be recommended in the clinical practice.

Correction for Polarization Current Curve of Polymer Insulation Materials in Transformers Considering the Temperature and Moisture Effects.

Depending on the study of the master curve technique, a temperature correction model for the polarization current of transformer polymer (cellulose) insulation, considering the effects of both moisture content (mc%) and temperature is proposed. In the current work, the shift factors of polarization current curves of samples with various moisture contents are extracted at different temperatures. Then, the variation law among the shift factor, test temperature, and moisture content are studied so as to establish the corresponding functional relationship. The findings reveal that the modified model derived from the above functional relationship could be employed to perform the temperature correction of oil-immersed polymer samples with various insulation states. Therefore, the proposed temperature correction model in this paper will promote the state assessment of the field transformer polymer insulation.

Influence of Oil-Pressboard Mass Ratio on the Equilibrium Characteristics of Furfural under Oil Replacement Conditions.

The distribution behavior of furfural in insulation systems is influenced by the oil-pressboard mass ratio. In addition, the equilibrium distribution of furfural between oil and pressboard will be disturbed after oil replacement. Therefore, it is of great significance to study the distribution ratio of furfural in oil with various oil-pressboard mass ratios after oil replacement. In this research, an accelerated thermal aging experiment and oil replacement experiment were conducted in the lab. Furthermore, the equilibrium characteristics of furfural dissolved in oil with various oil-pressboard mass ratios were studied. Multiple regression analysis was used to analyze the relationship between the oil-pressboard mass ratios and the distribution ratio of furfural in oil. The equilibrium distribution model of furfural was thus obtained. Afterwards, the modified furfural distribution model under oil replacement conditions was established. A novel scheme is provided for analyzing the equilibrium characteristics of furfural under various oil-pressboard mass ratios after oil replacement. The work of this paper is expected to improve the accuracy of furfural analysis.

Analysis on the Temperature Field and the Ampacity of XLPE Submarine HV Cable Based on Electro-Thermal-Flow Multiphysics Coupling Simulation.

The operating temperature and the ampacity are important parameters to reflect the operating state of cross-linked polyethylene (XLPE) submarine high voltage (HV) cables, and it is of great significance to study the electrothermal coupling law of submarine cable under the seawater flow field. In this study, according to the actual laying conditions of the submarine cable, a multi-physical coupling model of submarine cable is established based on the electromagnetic field, heat transfer field, and fluid field by using the COMSOL finite element simulation software. This model can help to analyze how the temperature and ampacity of the submarine cable are affected by different laying methods, seawater velocity, seawater temperature, laying depth, and soil thermal conductivity. The experimental results show that the pipe laying method can lead to the highest cable conductor temperature, even exceeding the maximum heat-resistant operating temperature of the insulation, and the corresponding ampacity is minimum, so heat dissipation is required. Besides, the conductor temperature and the submarine cable ampacity have a linear relationship with the seawater temperature, and small seawater velocity can significantly improve the submarine cable ampacity. Temperature correction coefficients and ampacity correction coefficients for steady-state seawater are proposed. Furthermore, the laying depth and soil thermal conductivity have great impact on the temperature field and the ampacity of submarine cable, so measures (e.g., artificial backfilling) in areas with low thermal conductivity are needed to improve the submarine cable ampacity.

Occupational exposure to respirable dust from the coal-fired power generation process: sources, concentration, and health risk assessment.

To investigate the contamination levels of respirable dust released in the work environment and the induced workers' health risk at a coal-fired power plant, we collected 405 dust samples from different dusty workstations by personal sampling during the coal-fired power generation process. Then, an inhalation risk assessment model from the USEPA was combined with the Monte Carlo simulation method to quantitatively evaluate the health risk caused by dust inhalation. Of 10 workstations researched, the dust concentration in the most workstations exceeded the prescribed occupational exposure limit. Workers engaged in ash removal suffered the highest health risk at 4.08 × 10-6 ± 2.85 × 10-6 (95% CI), closely followed by those involved in other job categories. The results can contribute to the formulation of targeted dust prevention measures and implementation of risk management for the coal-fired power sector.

Optimization of Ethanol Detection by Automatic Headspace Method for Cellulose Insulation Aging of Oil-immersed Transformers.

The method using ethanol to evaluate the cellulose insulation aging condition of oil-immersed transformers has been proposed. At present, the dominating method for detecting ethanol in insulating oil is to use headspace-gas-chromatography-mass-spectrometry (HS-GC-MS). However, the problem of quantitative inaccuracy will be sometimes encountered in the actual detection process due to improper instrument parameter setting and improper manual operation. In this study, as an aging marker, ethanol in transformer insulating oil was separated by using VF-624 ms capillary column. The effects of gas-chromatography-mass-spectrometry (GC-MS) optimization conditions, headspace equilibrium temperature, headspace equilibrium time and standard solution preparation method on the determination of ethanol content in oil were discussed, and optimized measures were proposed. The experimental results showed that the measurement can be more accurate under the headspace temperature of 80 °C and the headspace time of 40 min, and relative standard deviation percentage (RSD%) could reach to 4.62% under this condition. It was also pointed out that, for the preparation of standard solution, the method which controlled the sampling volume of anhydrous ethanol by microliter syringe could make the peak area of ethanol chromatogram have a better linear relationship with the standard curve. Under the similar linear range, the goodness of fitting curve without diluting process could be as high as 0.9993, while the method of preparing the stock solution and diluting stepwise to obtain the fitting curve only had a goodness of 0.9910. The method was validated by standard addition recovery test, and the recovery values obtained were between 90.3% and 95.8%. The optimized method is of great significance for the measurement of ethanol dissolved in insulating oil.