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This paper presents an approach to determine the DGA warning values for a transformer experimental platform by using the normal data from on-line DGA. In order to collect normal data and PD-defect data, an experimental platform of a 10 kV transformer with a capacity of 100 kVA is established for long-term operation. Five sets of DGA data are obtained, 1 set from the normal data, 2 sets from the PD-defect in developing-discharge stage (D1-1) and 2 sets from PD-defect in severe-discharge stage (D1-2). All sets of data are analyzed and found better suitable for the lognormal distribution, in which there are 2 parameters, µ and σ. It is found that the position parameters µ for 5 sets of data are nearly linear with the PD values and the scale parameters σ for 5 sets of data are nearly equal for certain discharges. Therefore, PD-defect data distribution can be approximately deduced from the normal data. In general, there is a common domain between the distributions of either PD-defect data and normal data or D1-1 and D1-2. A ROC curve, widely used in medical diagnosis, is employed to determine the warning values in common domain data. The optimal cut-point in common domain between the normal data and the deductive data of D1-1 is chosen as the Typical Value, and the cut-point between the deductive data of D1-1 and that of D1-2 is chosen as the Alarm Value.

The increased global demand for lithium is rapidly depleting the lithium ore reserves. Therefore, attention has turned to the recovery of lithium from aqueous solutions, such as lithium-containing brine. Compared with other methods of lithium recovery, adsorption is energy efficient and simple to implement, increasing demand for selective lithium adsorbents. In this study, a selective lithium-ion adsorbent, H4Ti5– xLa xO12, was synthesized via the sol–gel method, followed by heat treatment and acid washing. The effects of the temperature and degree of lanthanum doping ( x) on the crystalline phase, morphology, lithium-ion adsorption capacity, and lithium-ion selectivity of the ion sieve were investigated, and the optimal synthetic conditions were determined. We found that doping with La3+ cations ( x = 0.01) increased the lithium-ion adsorption capacity (23.96 mg g−1 at 25 °C at pH = 12; 8.2% higher than before doping), rate, and selectivity. In addition, the ion sieve could be used over multiple adsorption–desorption cycles with only a minor reduction in the lithium-ion adsorption capacity (22.88 mg g−1). Overall, these results suggest that doping with La3+ cations stabilized the H4Ti5– xLa xO12 crystal structure, alleviated particle agglomeration, expanded the lithium-ion channels, and decreased the resistance to lithium-ion migration, thus improving adsorption performance. The findings suggest that the proposed ion sieve has practical applications in the selective recovery of lithium from aqueous solutions containing a mixture of metal ions.

The purpose of this study was to investigate the factors affecting telomere length (TL) in coke oven workers by analyzing the interaction between miRNAs polymorphisms and coke oven emissions (COEs) exposure.A total of 544 coke oven workers and 238 healthy controls were recruited. Peripheral blood was collected from the subjects, genomic DNA was extracted, leukocyte TL was detected by real-time quantitative polymerase chain reaction, and fifteen polymorphisms of eight miRNAs were genotyped by flight mass spectrometry.Statistical analysis showed that the peripheral blood DNA TL in the exposure group was shorter than that in the control group (P 0.001). Generalized linear model found that COEs-exposure [β (95%CI) = -0.427 (-0.556, -0.299), P 0.001], genotype CC+CT for miR-612 rs1144925 [β (95%CI) = -0.367 (-0.630, -0.104), P = 0.006], and the interaction of miR-181B1 rs12039395 TT genotype and COEs-exposure [β (95% CI) = 0.564 (0.108, 1.020), P = 0.015] were associated with the shortened TL.COEs-exposure and miR-612 rs1144925 TT could promote telomere shortening in coke oven workers. The interaction of miR-181B1 rs12039395 TT genotype and COEs-exposure could protect telomere. This provides clues for further mechanistic studies between miRNA and telomere damage.

Adolescence is a key period of growth in life, and the psychological development of teenagers in this period is in a stage of rapid maturity without maturity, with distinct characteristics of self-contradiction. People's in-depth understanding of the concept of mental health has prompted people to have a new understanding of the function of physical exercise, trying to make an in-depth study of the function of physical exercise. Teenagers are considered to be the main force for the future development of our society, so in the process of educating teenagers, we should not only pay attention to the exercise of physical quality but also pay attention to the education of psychological quality. With the rapid development of modern society and science and technology, human life is becoming more and more prosperous, and people have to face various conflicts and troubles. The faster pace of life makes people face more pressure. In this paper, the interaction between adolescent physical exercise and mental health factors is discussed, and the promotion effect of physical exercise on college students' mental health is analyzed, so as to provide theoretical basis for further improving adolescent mental health.

BACKGROUND COVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR). OBJECTIVE The main objective of this study was to explore the value of the discharged CFR (DCFR) to make more accurate forecasts of epidemic trends of COVID-19 in Italy. METHODS We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of deaths to discharged cases（including deaths and recovered cases） to calculate the total DCFR (tDCFR), monthly DCFR (mDCFR), and stage DCFR (sDCFR). Furthermore, we analyzed the trend in the mDCFR between January and December 2020 using joinpoint regression analysis, used ArcGIS version 10.7 to visualize the spatial distribution of the epidemic CFR, and assigned different colors to each province based on the CFR or tDCFR. RESULTS We calculated the numbers and obtained the new indices of the tDCFR and mDCFR for calculating the fatality rate. The results showed that the tDCFR and mDCFR fluctuated greatly from January to May. They first showed a rapid increase followed by a rapid decline after reaching the peak. The map showed that the provinces with a high tDCFR were Emilia-Romagna, Puglia, and Lombardia. The change trend of the mDCFR over time was divided into the following 2 stages: the first stage (from January to May) and the second stage (from June to December). With regard to worldwide COVID-19 statistics, among 6 selected countries, the United States had the highest tDCFR (4.26%), while the tDCFR of the remaining countries was between 0.98% and 2.72%. CONCLUSIONS We provide a new perspective for assessing the fatality of COVID-19 in Italy, which can use ever-changing data to calculate a more accurate CFR and scientifically predict the development trend of the epidemic.

Background COVID-19, which emerged in December 2019, has spread rapidly around the world and has become a serious public health event endangering human life. With regard to COVID-19, there are still many unknowns, such as the exact case fatality rate (CFR). Objective The main objective of this study was to explore the value of the discharged CFR (DCFR) to make more accurate forecasts of epidemic trends of COVID-19 in Italy. Methods We retrieved the epidemiological data of COVID-19 in Italy published by the John Hopkins Coronavirus Resource Center. We then used the proportion of deaths to discharged cases（including deaths and recovered cases） to calculate the total DCFR (tDCFR), monthly DCFR (mDCFR), and stage DCFR (sDCFR). Furthermore, we analyzed the trend in the mDCFR between January and December 2020 using joinpoint regression analysis, used ArcGIS version 10.7 to visualize the spatial distribution of the epidemic CFR, and assigned different colors to each province based on the CFR or tDCFR. Results We calculated the numbers and obtained the new indices of the tDCFR and mDCFR for calculating the fatality rate. The results showed that the tDCFR and mDCFR fluctuated greatly from January to May. They first showed a rapid increase followed by a rapid decline after reaching the peak. The map showed that the provinces with a high tDCFR were Emilia-Romagna, Puglia, and Lombardia. The change trend of the mDCFR over time was divided into the following 2 stages: the first stage (from January to May) and the second stage (from June to December). With regard to worldwide COVID-19 statistics, among 6 selected countries, the United States had the highest tDCFR (4.26%), while the tDCFR of the remaining countries was between 0.98% and 2.72%. Conclusions We provide a new perspective for assessing the fatality of COVID-19 in Italy, which can use ever-changing data to calculate a more accurate CFR and scientifically predict the development trend of the epidemic.

Background To identify hub genes from the competing endogenous RNA (ceRNA) network of lung adenocarcinoma (LUAD) and to explore their potential functions on prognosis of patients from a single-cell perspective. Methods We performed RNA-sequencing of LUAD to construct ceRNA regulatory network, integrating with public databases to identify the vital pathways related to patients’ prognosis and to reveal the expression level of hub genes under different conditions, the functional enrichment of co-expressed genes and their potential immune-related mechanisms. Results ZC3H12D-hsa-miR-4443-ENST00000630242 axis was found to be related with LUAD. Lower ZC3H12D expression was significantly associated with shorter overall survival (OS) of patients (HR = 2.007, P P P Conclusion ZC3H12D-hsa-miR-4443-ENST00000630242 ceRNA network was identified in LUAD. ZC3H12D could affect prognosis of patients by regulating mRNA, miRNA, lncRNA, immune cells and immune molecules. Therefore, it may serve as a vital predictive marker and could be regarded as a potential therapeutic target for LUAD in the future.

The interpolation based on prediction-error filter (PEF) is one of the most effective approaches recently proposed for seismic data reconstruction. However, the number of effective regression equations for estimating the filter coefficients will be much less when missing many seismic traces, which makes the estimated filter coefficients inaccurate or even impossible to be estimated. To improve the accuracy of filter coefficients, in this paper, we design a multiscale and multidirectional PEF, in which the number of effective regression equations can be increased much more, and use it to seismic data reconstruction. First, we estimate the adaptive different directional PEFs using the known data in different scales. The known data can be regularly sampled with randomly or regularly missing, or even both of them. Then, we interpolate missing seismic traces using estimated PEF and the sparse known traces. The regularization in least-squares inversion controls the adaptivity of multiscale multidirectional PEF. The use of more effective regression equations in inversion makes the filter coefficients more accurate. In addition, the multiscale filter can conveniently deal with the case of the simultaneous existence of randomly and regularly missing, while the conventional methods have to be treated separately for randomly and regularly missing. The applicability and effectiveness of the proposed method are examined by synthetic and field data examples.

Background Accumulating evidence indicates that long non-coding RNAs (lncRNAs) are involving in the tumorigenesis and metastasis of lung cancer. The aim of the study is to systematically characterize the lncRNA-associated competing endogenous RNA (ceRNA) network and identify key lncRNAs in the development of stage I lung adenocarcinoma (LUAD). Methods Totally, 1,955 DEmRNAs, 165 DEmiRNAs and 1,107 DElncRNAs were obtained in 10 paired normal and LUAD tissues. And a total of 8,912 paired lncRNA-miRNA-mRNA network was constructed. Using the Cancer Genome Atlas (TCGA) dataset, the module of ME turquoise was revealed to be most relevant to the progression of LUAD though Weighted Gene Co-expression Network Analysis (WGCNA). Results Of the lncRNAs identified, LINC00639, RP4-676L2.1 and FENDRR were in ceRNA network established by our RNA-sequencing dataset. Using univariate Cox regression analysis, FENDRR was a risk factor of progression free survival (PFS) of stage I LUAD patients (HRs = 1.69, 95%CI 1.07–2.68, P FENDRR in paired normal and LUAD tissues was detected significant by real-time quantitative (qRT-PCR) (P Conclusions This study, for the first time, deciphered the regulatory role of FENDRR/miR-6815-5p axis in the progression of early-stage LUAD, which is needed to be established in vitro and in vivo.

Drought research under climate change is of great scientific significance. For Land Use and Land Cover Change (LUCC), temperature and rainfall in climate change, which factor has a greater impact on runoff change in alpine mountainous areas? Can the increase of rainfall in the alpine mountainous area completely eliminate the drought driven by temperature rise? This study takes the upper reaches of Heihe River basin (URHRB) as an example, the URHRB's Soil and Water Assessment Tool (SWAT) model is constructed. Based on 58 scenarios and The Budyko Framework, here we show that a)climate change has a greater contribution to runoff than LUCC, effect of increased rainfall greater than temperature rising on runoff in alpine mountainous area; b)the drought of 57.14% of UHRRB’s sub-basins have eased, 42.86% of the sub-basins is more serious, the increase in rainfall can't completely eliminate the drought driven by temperature rise. This study coupling SWAT simulation with Budyko Framework and other methods solves the problem of lack of data in alpine mountainous areas, and more accurately quantifies the impact of climate change, LUCC on runoff changes, realizing theoretical and method innovation. The results of this study provide a scientific paradigm for solving scientific problems in similar regions in China and other countries, and have important promotion value.

Background: The symptom of chronic obstructive pulmonary disease (COPD) and giant pulmonary bulla (GPB) also called giant emphysematous bulla (GEB) is too similar to distinguish, and the treatment of the two diseases are completely different because of the different pathology. We investigated the plasma lipid profiles from patients of COPD and GPB to find targeted lipid changes responsible for differentiation between two diseases.Methods: Plasma was collected from 9 patients with COPD, and 10 patients with COPD and GPB. Extracted lipids were analyzed using high performance liquid chromatography-triple quadrupole mass spectrometry (HPLC-QqQ-MS) to characterize lipid profiles. Principal components analysis (PCA), orthogonal projection to latent structures-discriminant analysis (OPLS-DA) and variable importance in projection (VIP) scores were used to estimate the importance of each lipid variable.Results: The HPLC-QqQ-MS method identified 582 kinds of lipids at negative mode, and 577 kinds of lipids at positive mode. Lipid profiles were significantly different between COPD and GPB. Principal discriminant phospholipids between COPD and GPB were LPC(16:1)+AcO, LPC(16:0)+AcO, LPC(18:2)+AcO, LPC(18:1)+AcO, PC(16:0/22:5)+AcO, PC(18:2/18:2)+AcO, LPE(18:0)-H, LPE(16:0)-H, LPE(18:2)-H, PE(18:1/18:2)-H and FFA(24:0)(GPB group were significantly decreased compared to COPD group, VIP>1 and P).Conclusions: Our study provides insights into the alteration of the plasma lipid profile of GPB patients, commonly resulting from COPD, that may lead to improved GPB treatment and differentiation of this disease from COPD. Furthermore, LPC (16:0) +AcO was found to have a high potential to be a possible biomarker to distinguish the two diseases.

Zircon U–Pb dating, geochemistry, and Sr–Nd–Hf isotope data are reported for monzonite from the Qingshanpu pluton on the eastern margin of Longshoushan. The zircon LA-ICP-MS U–Pb age data showed that the emplacement age of the monzonite is 442.6 ± 4.8 Ma. The rocks are characterized by high SiO2 contents (73.15%–73.85%), high K2O contents (3.67%–3.99%), and low P2O5 contents (0.042%–0.049%). The aluminum saturation index was 1.03–1.05, and the molecular number of standard corundum was 0.58%–0.77%. Rocks are enriched in Rb, Sr, U, K, and Pb and depleted in Nb, Ti, Zr, P, and Eu, thereby indicating that they are weakly peraluminous I-type granites. The monzonite in the Qingshanpu pluton had lower ISr values, which ranged from 0.70850 to 0.70904, and the εNd(t) values ranged from -8.54 to -6.49. The εHf(t) values ranged from -9.75 to 0.42, and the two-stage model age TC DM ranged from 1406 to 2043 Ma. This study concluded that the North Qilian subducted beneath the Alxa Plate at the end of the Early Paleozoic, causing elastic collision and eventually leading to closure. During elastic collision, several parts of the orogenic belt collapsed and were dismantled. The remelted ancient crust mixed with a small amount of mantle material during its ascent and finally formed the Qingshanbao pluton.

Low frequency oscillation in power system has become the main factor limited the development of power grid in China. Taking the generator power oscillation accident caused by PSS abnormal operation in a gas power plant of China as an example, the accident process is introduced in detail, and the causes of the accident are preliminarily discussed through the analysis of related data. The generator model is established with RTDS platform, then the influences of the rotational inertia, and speed acquisition methods of the unit and setting of the swing reactance on generator PSS(Power System Stabilizer) suppressing power oscillation effect are simulated and analyzed. According to the research results, the accident causes are further analyzed, and relevant suggestions are given for how to avoid such accidents.

The Non-negative Inverse Eigenvalue Problem (NIEP) is a sub-problem extracted from inverse eigenvalue problem with a long history from 1930s determining the sufficient and necessary conditions in order that, σ={ λ 1,…, λ n }to be the spectrum of an entry wise non-negative n x n matrix. There had been many excellent researchers and scholars who contributed to discover many practical theories. The following of this paper would be then separated into two parts to further analyze the NIEP. In the first section of the paper, some important preexisting conclusions would be demonstrated and the groups’ understanding of these indispensable theories would be expressed. In the second section, three special and wildly used matrix, including tridiagongal matrix, circulant matrix, and symmetric matrix would be considered. The solution of the NIEP of these matrices done by the group would be expressed.

Non-Supported Liquid Membrane Extraction (NSLME) is a new development extraction technology based on Supported Liquid Membrane Extraction (SLME). Sample extraction assembly is composed of three phases: an acceptor phase: phosphate–sodium dihydrogen phosphate buffer solution at the bottom; an organic phase: chloroform applied as the non-supported liquid membrane in the middle layer; and a donor phase: aqueous solution samples containing alkaloids in the upper layer. The whole system is maintained stable by density difference among the three layers that avoided the mutual interferences. The alkaloid in the donor phase can spread to the underlying acidic acceptor phase, where it is able to form water soluble salt in the acid environment, and thus cannot return to the middle organic phase. Therefore, the transmission of alkaloid is a one-way path, and the extraction of alkaloids can be achieved and enriched. In this study, the extraction of alkaloid was carried out by using matrine aqueous solution as the donor phase, and the extraction quantity and efficiency were investigated by GC/MS. This study evaluated the relationship between extracted quantity and extraction time. The effects of extraction temperature, membrane thickness, pH value of acceptor phase on extraction quantity and efficiency were also studied, and the optimal extraction condition was found. The extracted quantity achieved the largest amount at 45 °C when pure phosphoric acid was applied as the acceptor phase; the organic solvent volume was 0.2 mL. The extraction of alkaloid from Radix Sophorae Tonkinensis was performed under the optimized condition. The extraction rate of matrine was up to 54% after a four-hour extraction. A huge advantage of NSLME technology is that the extracted alkaloid enjoyed high purity compared with that extracted by the traditional Liquid–Liquid Extraction (LLE).

Rationale Most gastric cancer patients are diagnosed at mid- to late-stage and lose the chance of radical surgery, medical treatment is especially important to prolong the survival of patients. Apatinib mesylate, which is a small molecule vascular endothelial growth factor receptor 2 tyrosine kinase inhibitor, could be used as antiangiogenesis therapy for gastric cancer. Patient concerns A 67-year-old man sought medical care for upper abdominal discomfort. Diagnosis The patient was diagnosed as mixed medullary differentiated gastric adenocarcinoma, and immunohistochemistry suggested HER-2 (2+). Interventions The patient received chemotherapy consisting of oxaliplatin combined with S-1 as first-line treatment, and targeted therapy with apatinib mesylate as second-line treatment. Outcomes After 4 months of first-line chemotherapy, the patient received apatinib treatment immediately at a dose of 500 mg/d orally and died of cardiac arrest with 8.5 months of overall survival. During this period of targeted therapy with apatinib mesylate, this male patient suffered mammary gland development besides other common adverse reactions. Lessons This case report is the first to report the case of male mammary gland development after oral apatinib.

Forest aboveground biomass (AGB) was mapped throughout China using large footprint LiDAR waveform data from the Geoscience Laser Altimeter System (GLAS) onboard NASA’s Ice, Cloud, and land Elevation Satellite (ICESat), Moderate Resolution Imaging Spectro-radiometer (MODIS) imagery and forest inventory data. The entire land of China was divided into seven zones according to the geographic characteristics of the forests. The forest AGB prediction models were separately developed for different forest types in each of the seven forest zones at GLAS footprint level from GLAS waveform parameters and biomass derived from height and diameter at breast height (DBH) field observation. Some waveform parameters used in the prediction models were able to reduce the effects of slope on biomass estimation. The models of GLAS-based biomass estimates were developed by using GLAS footprints with slopes less than 20° and slopes ≥ 20°, respectively. Then, all GLAS footprint biomass and MODIS data were used to establish Random Forest regression models for extrapolating footprint AGB to a nationwide scale. The total amount of estimated AGB in Chinese forests around 2006 was about 12,622 Mt vs. 12,617 Mt derived from the seventh national forest resource inventory data. Nearly half of all provinces showed a relative error (%) of less than 20%, and 80% of total provinces had relative errors less than 50%.

The estimation of forest biomass from synthetic aperture radar (SAR) data is limited by the lack of forest structure information. Interferometric synthetic aperture radar (InSAR) provides a means for the extraction of forest structure. The crucial issue in InSAR application is to parameterize forest structure and to link the parameter with InSAR observations. Model-based analysis enables exploring the theoretical linkages between InSAR observations and forest structure free from temporal decorrelation effects. In this paper, a semicoherent model (SCSR) was first developed and verified. A series of simulations at L-band was then made for both homogeneous and heterogeneous forests generated from a forest growth model. The forest structure was parameterized by four height indices. Aside from the height of scattering phase center (HSPC), the depth of scattering phase center (DSPC) was also proposed to characterize the scattering phase center of InSAR. The results showed that the behavior of homogeneous forest on InSAR data was quite different from that of heterogeneous forest. Special care was needed when the retrieval algorithms of forest biomass developed on a homogeneous forest were applied to a heterogeneous forest. Crown size-weighted height (CWH) and Lorey's height were correlated with the HSPC at all polarizations and with the DSPC at copolarization in both cases of homogeneous and heterogeneous forests. These findings indicated that CWH could be an alternative biomass indicator of the Lorey's height for biomass estimation, which can be derived from the combination of InSAR data and the elevation of the forest canopy top from lidar or high-resolution stereo images.

Mapping of forest biomass over large area and in higher accuracy becomes more and more important for researches on global carbon cycle and climate change. The feasibility and problems of forest biomass estimations based on lookup table (LUT) methods using ALOS PALSAR data are investigated in this study. Using of the forest structures from a forest growth model as inputs to a three dimensional radar backscattering model, a lookup table is built. Two types of searching methods (Nearest Distance (ND) and Distance Threshold (DT)) are used to find solutions from lookup table. When a simulated dataset is used to test the lookup table, the RMSE of biomass estimation are 39.133 Mg/ha (R2= 0.748) from ND and 26.699 Mg/ha (R2 = 0.886) from DT using dual-polarization data for forest with medium rough soil surface. All results show that DT is superior to ND. Comparisons of biomass from forest inventory data with that inversed from look up table using DT method over eight forest farms shows RMSE of 18.564 Mg/ha and 15.392 Mg/ha from PALSAR data with and without correction of the scattering mechanism, respectively. For the entire Lushuihe forest Bureau, the errors of the biomass estimation are - 13.8 Mg/ha (- 8.6%) and - 5.5 Mg/ha (- 3.5%) using PALSAR data with and without correction of scattering mechanisms due to terrain, respectively. The results shows that the radar image corrected data could be directly used for biomass estimation using the lookup table method.

The use of lidar and radar instruments to measure forest structure attributes such as height and biomass at global scales is being considered for a future Earth Observation satellite mission, DESDynI (Deformation, Ecosystem Structure, and Dynamics of Ice). Large footprint lidar makes a direct measurement of the heights of scatterers in the illuminated footprint and can yield accurate information about the vertical profile of the canopy within lidar footprint samples. Synthetic Aperture Radar (SAR) is known to sense the canopy volume, especially at longer wavelengths and provides image data. Methods for biomass mapping by a combination of lidar sampling and radar mapping need to be developed. In this study, several issues in this respect were investigated using aircraft borne lidar and SAR data in Howland, Maine, USA. The stepwise regression selected the height indices rh50 and rh75 of the Laser Vegetation Imaging Sensor (LVIS) data for predicting field measured biomass with a R(exp 2) of 0.71 and RMSE of 31.33 Mg/ha. The above-ground biomass map generated from this regression model was considered to represent the true biomass of the area and used as a reference map since no better biomass map exists for the area. Random samples were taken from the biomass map and the correlation between the sampled biomass and co-located SAR signature was studied. The best models were used to extend the biomass from lidar samples into all forested areas in the study area, which mimics a procedure that could be used for the future DESDYnI Mission. It was found that depending on the data types used (quad-pol or dual-pol) the SAR data can predict the lidar biomass samples with R2 of 0.63-0.71, RMSE of 32.0-28.2 Mg/ha up to biomass levels of 200-250 Mg/ha. The mean biomass of the study area calculated from the biomass maps generated by lidar- SAR synergy 63 was within 10% of the reference biomass map derived from LVIS data. The results from this study are preliminary, but do show the potential of the combined use of lidar samples and radar imagery for forest biomass mapping. Various issues regarding lidar/radar data synergies for biomass mapping are discussed in the paper.

It is well known that impulse voltages strongly affect the reliability of gas-insulated switchgear (GIS) under actual operational conditions. The alternating-current (ac) withstand voltage test and its corresponding partial discharge (PD) measurement are inadequate for the detection of all the hidden flaws in GIS. Such inadequacy can be compensated by additional impulse withstand voltage test and its PD detection measurement. However, the bulkiness and inefficiency of aperiodic impulse voltage generator make it unsuitable for on-site application. Therefore, in practical tests, oscillating impulse voltage (OIV) is generally adopted instead of aperiodic impulse voltage. In this paper, we measure the PD inception voltage (PDIV) and the breakdown characteristics of a needle-plate gap in SF6 gas under four types of impulse voltages by electrical and optical methods. The 50% probability PDIV is analyzed as a function of gas pressure. The physical mechanisms of the PD under the four impulse voltages are discussed. The PD characteristics under ac voltage are investigated for comparison. Experimental results reveal that the PD development process under the two aperiodic impulse voltages was mainly dominated by the precursor mechanism which consists of streamer and leader stages, while the PD propagation under the two OIVs was affected by the displacement current produced by oscillation. The difference of the PD characteristics under the aperiodic impulse voltage and OIV, and ac voltage are also presented.

The objective of the study is to modify supported liquid membranes (SLMs) by designing a self-made double-supported liquid membrane (DSLMs). SLMs is a traditional extraction system that adopts organic solvent immersed membrane to separate the donor and acceptor phase. However, the system cannot maintain long extraction time due to the fast evaporation of the organic solvent where the easy volatile organic solvent is selected as organic phase. DSLMs system manages to solve the problem by enclosing the organic solvent between two membranes. This system has been successfully applied to the extraction of nicotine from local tobacco. The optical conditions for extraction nicotine were found and the amount of extracted nicotine were analyzed by GC/MS. Results showed that the extracting time could be held for 24 h and the amount of extracted nicotine got to 43%.

The forest ecosystem in the Northeastern Asia (NEA) has been undergoing dramatic changes because of forest fires and massive logging. MODIS (Moderate Resolution Imaging Spectroradiometer) vegetation indexes product MOD13A1 from 2000 to 2006 were re-composited for forest mapping circa year 2000 in the region. The study region was divided into four sub-regions with distinct natural climate regimes, and a TM/ETM+ scene was selected as a test site for each of the sub-regions. The process of mapping forest from MODIS data consists of two steps that follow the logic sequence of class definition. First, a 2-D Feature Space Grid Split (FSGS) algorithm was developed to identify forested areas by use of its dark object attributes. The producer and user accuracies of forest/non-forest mapping reached over 90% at test sites, and 72.22% and 88.26% comparing with the national LCLU map in the areas within China. The forested areas were then stratified into four forest types by a decision tree classifier from temporal MODIS data for each of the sub-regions. The forest classification was validated for pure forests using the results from TM/ETM+ classification. The comparison showed high producer and user accuracies: 86.78% and 91.14% for evergreen needle forest, 90.6% and 92.4% for deciduous needle forest, and 82.99% and 97.19% for deciduous broadleaf forest, although confusion existed between mixed forest and deciduous broadleaf forest. The forest map was also compared with MODIS land cover and Global Land Cover 2000 (GLC2000) products.

A 3-D coherent radar backscatter model for forest canopies was developed and used to improve the understanding of synthetic aperture radar (SAR) interferometric data. The model was based on a realistic 3-D spatial structure of a forest stand, in which every scatterer has its deterministic location. A backscattering signal from a scatterer was mapped into a pixel according to its range or signal time delay. The range or the time delay also determines the phase of the scattered field. All scattering matrices within a pixel were coherently added to yield the total backscattering field of the pixel. The coherent radar backscatter model takes into account not only the scattering contribution from the scatterers in the forest canopy but also the direct backscattering of the ground surface. Forest stands with three different spatial structures were simulated using L-system and field measurements. The number and sizes of trees in these forest stands were identical, but the 2-D arrangements of the trees were different. The interferometric SAR (InSAR) signals of these scenes were simulated using the 3-D coherent SAR model, and the heights of scattering phase centers were estimated from the simulated InSAR data. The results reported in this paper show that the spatial structures of vegetation play an important role in the location of the scattering phase center. The height of scattering phase center depends on canopy height, attenuation of canopy, and the gaps within the canopy. This paper shows that the spatial structure needs to be considered when the InSAR data are used for the estimation of forest structural parameters.

Some alkaloids and alkali metal salts were analyzed by fast atom bombardment mass spectrometry (FABMS) with glycerol as matrix, the intensities of their protonated molecules varying with time were investigated. It was found that the curve was decreased exponentially and both alkaloids and salt sample can be fitted by equation I = a + b e − kt . a , b were constants with a characteristic distribution depending on the ionizability of the sample in the matrix, t was time which was expressed by scan number in this experiment. It was observed that parameter a would be zero when the sample was salt of alkali; when sample was alkaloid, the ratio of initial intensities of protonated molecules was only related to the concentration of the sample, which can be used to analyze sample quantitatively.

The degradation of 100 mg/L phenol solution under ultraviolet (UV) in the presence of TiO2 and in the absence of it was analyzed with gas chromatography/mass spectrometry (GC–MS) and high performance liquid chromatography (HPLC). After 12 h of degradation, the products under the two conditions were both derivatized by MSTFA and TMIS, and then analyzed by GC–MS. The results show the main intermediates are the same. They are hydroquinone, resorcinol, catechol, 1,2,3-benzenetriol, (E)-2-butenedioic acid, 2-hydroxy-propaldehyde, glycerol, 3-hydroxy-propyl acid, and hydroxy-acetic acid. The chromatogram given by HPLC indicates that the presence of TiO2 is not in favor of the degradation when the concentration of phenol is 100 mg/L; its catalysis becomes evident when the concentration gets lower. Based on the intermediates, it can be proved that besides OH, H is also an important active free radical in the degradation.