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Cutting performance and tool wear of two ceramic tools, SiAlON and TiC-whisker-reinforced Si3N4, in the side milling processes of Inconel 718 are evaluated in comparison, including cutting force, temperature, surface morphology, tool wear and corresponding mechanism. Results show that these two ceramic tools has advantages and disadvantages respectively, due to the properties of ceramic matrixes and the evolutions of build-up edges. SiAlON ceramic tool has better resistance to wear, but causes poor surface quality. TiC-whisker-reinforced Si3N4 ceramic tool generates better surface quality, but bears severe wear. Brittle damage, as the main mode of wear, occurs to both ceramic tools in different formations. SiAlON ceramic tool is featured by crater-like damage on blades while TiC-whisker-reinforced Si3N4 ceramic tool is featured by whole-layer damage on flank faces.

Tool wear monitoring and prediction are of great importance for machining precision and surface integrity. In order to ensure good quality of the machining components, tool wear should be monitored and predicted in time. However, the traditional methods greatly depend on feature selection and extraction, and accuracy and generalization are limited. In this paper, a novel model based on deep learning was proposed to monitor and predict tap tool wear. Firstly, 1D CNN was designed to extract features from the vibration data, and then the residual block with the dilated convolution was specially developed to accept the features from 1D CNN. After that, the fully connected neural networks were designed to predicate tool wear. Moreover, to verify the superiority and generalization of the proposed method, the taping experiment from a real engine cylinder head production line was conducted. During the tapping process, the vibration signal and tool wear were collected. And then the training experiment was conducted with the collected data. The prediction results with the proposed model were compared with those of the state-of-the-art algorithms. The compared results showed that the proposed model was more robust and accurate for tool wear prediction.

Additive manufacturing (AM), an advanced manufacturing technology, has nowadays become increasingly popular in aerospace and biomedical fields since it provides the feasibility of producing complex-shaped Ti6Al4V components with faster speed and lower material waste. In order to achieve desirable shape dimensions and surface characteristics, finish machining operations are generally required. However, premature tool failure usually occurs with common carbide tools during the machining process while the performance of solid ceramic tool for AM Ti6Al4V alloy still remains unclear. To this end, the present work aims at studying the cutting responses of additive manufactured Ti6Al4V alloy with solid ceramic tool under dry high-speed milling processes. Various topics including cutting forces and cutting temperature fields as function of machining parameters were analysed. Furthermore, attention was also paid on the chip morphology, machined surface quality and tool wear mechanisms. The results indicate that the feed rate has greater impact on the magnitudes of cutting forces and temperature fields compared to the cutting speed. The continuous and free broken chips were generated during the milling process and the typically serrated morphology of chips were observed. The solid ceramic tool produces favorable machined surfaces under high-speed machining process apart from the tool edge marks. The mechanisms responsible for tool wear were determined to be micro chipping and chip adhesion owing to the mechanical and thermal loading.

Carbon monoxide (CO) is regarded as a useful tracer of biomass burning and anthropogenic pollution, so CO measurements can provide valuable information about the intensity of various anthropogenic activities. However, the emission estimates of CO based on inventories are associated with high uncertainties, especially in China. As CO is co-emitted with CO2 in the combustion of carbonaceous fuels, the relationship between CO and CO2 is often used to estimate regional CO emissions. Hefei is located in the area of eastern central China, which is one of the most industrialized regions in China, with severe regional air pollution. The enhancement slopes of ∆CO to ∆CO2 were calculated and compared from ground-based remote sensing observations, surface in-situ measurements, satellite and emission inventory data at the Hefei site during the period from September 2015 to August 2017. Both inventory based ratios of ΔCO to ΔCO2 are significantly larger than the ratios based on the observation data, including Fourier Transform Spectrometer (FTS) data, in-situ data, and satellite data. Further the CO emissions in the central China were estimated from the enhancement slopes of ∆CO/∆CO2 combined with the CO2 emission inventory. The CO emission estimated from the ground-based FTS observations and the Peking University (PKU) inventory based CO2 emission is about 10.96 ± 0.88 and 11.95 ± 0.71 Tg CO yr−1 during the 2015–2016 and the 2016–2017 period, respectively. The CO emission estimated from the ground-based FTS observations and the Emission Database for Global Atmospheric Research (EDGAR) inventory based CO2 emission is about 11.27 ± 0.91 and 12.35 ± 0.74 Tg CO yr−1, respectively. So the CO emissions estimated from the ground-based FTS data and the different inventory based CO2 emission show a good agreement. However, CO emissions derived from FTS data are substantially lower than those calculated directly from the inventories, i.e. there is a large difference between CO emissions derived from FTS and CO emissions directly derived from the two inventories. The phenomenon suggests that the emission inventories greatly overestimate the actual CO emission in the study area. This study estimates the regional CO emissions from ground-based remote sensing observations and investigates how much the difference is between the emissions from inventories and ground-based measurements.

A series of novel thiazolidine-4-one urea analogues were designed, synthesized and biologically evaluated. The structure-activity relationship (SAR) at several positions of the scaffolds was investigated and its binding mode was analyzed by molecular modeling studies. Compound 17b proved to be the most potent one, and IC50 values against A549 and HT-29 cancer cell lines were 0.65 μM and 0.11 μM, respectively. The results of kinase profile demonstrated that compound 17b is a multikinase inhibitor that potently inhibits FLT3 (IC50 = 8.6 nM) and VEGFR2 (IC50 = 18.7 nM). The results of real-time live-cell imaging indicated that compound 17b showed excellent cytotoxicity and anti-proliferative activity against HT-29 cancer cells in a time- and dose-dependent manner, which was significantly potent than that of Cabozantinib. In addition, in vitro antitumor activity was associated with inducing cancer cell apoptosis and suppression of cancer cell migration.

In this study, a novel series of 4,6,7-trisubstituted quinoline analogues bearing thiazolidinones were designed and synthesized based on our previous study. Among them, the most potent compound 15i, 4-((4-(4-(3-(2-(2,6-difluorophenyl)-4-oxothiazolidin-3-yl)ureido)-2-fluorophenoxy)-6-methoxyquinolin-7-yl)oxy)-N,N-diethylpiperidine-1-carboxamide was identified as a multi-kinase inhibitor. The results of MTT assay revealed in vitro antitumor activities against HT-29 cells of compound 15i with an IC50 value of 0.19 μM which was 14.5-fold more potent than that of Regorafenib. In the cellular context, significant antiproliferation, cytotoxicity and induction of apoptosis on HT-29 cells in a dose- and time-dependent manner were confirmed by IncuCyte live-cell imaging assays. Moreover, compound 15i strongly induced apoptosis by arresting cell cycle into the G2/M phase. No antiproliferation and cytotoxicity against human normal colorectal mucosa epithelial cell FHC was observed at 10.0 μg/mL or lower concentrations which indicated that the toxicity to normal cells of compound 15i was much lower than that of Regorafenib. Based on the above findings, further structural modification will be conducted for the development of more potent kinase inhibitors as anticancer agents.

Nickel-based superalloys have become one of the most promising and potential materials in gas turbine engine components contributing to aerospace industry due to their outstanding mechanical properties (high ductility, high temperate strength, etc.). However, the poor machinability (lower thermal conductivity, high work hardening, etc.) and low machining efficiency of nickel-based superalloys have further hampered it to be widely applied in the potential aerospace applications. In this work, we systematically investigated the effects of various cutting speeds (i.e. 100–400 m/min) on tool wear and failure mechanisms of the SiAlON ceramic tools in the dry cutting of nickel-based superalloys by performing high-speed turning experiments. The tool life and wear evolution with the cutting distance at various cutting speeds were studied; the response relationship of tool flank wear, cutting speed, and cutting distance was visually presented. The optimal cutting speed range could be 200 m/min∼300 m/min in our cutting conditions considering the tool life and the response relationship. Further, tool wear and failure mechanisms are demonstrated to be the adhesion, catastrophic fracture, and microcracks at flank wear land; and the micro-chipping is observed at rake face at a low cutting speed of 100 m/min. Additionally, at a higher cutting speed of 400 m/min, the wear and failure mechanisms are severe adhesion, cutting edge micro-chipping, microcracks, but none of catastrophic fracture failure. This disparity can be attributed to the different alternating dominant effects in strain hardening and thermal softening mechanisms behind the machining process.

This study presents the characterization of XCH4 (averaged-column dry air mole fraction) variability and trends using the near-infrared (NIR) solar spectra recorded by the ground-based high-resolution Fourier transform spectrometer (FTS) over Hefei, China. An annual increase of 10.55 ± 2.87 ppb yr−1 (0.56 ± 0.15% yr−1) in CH4 concentrations was deduced from the three years’ worth of FTS measurements. Seasonal cycles and three typical diurnal variation modes were captured. The maximum CH4 concentration occurred in late summer or early autumn, while the minimum CH4 concentration occurred in winter. CH4 concentrations in August were 30–55 ppb higher than those in January. In most cases, the diurnal variability exhibited an increasing trend in summer or early autumn, while it exhibited a decreasing trend or remained relatively constant in other seasons. We compared the FTS data with the GOSAT data and the GEOS-Chem model data. The average difference between the GOSAT and FTS data (GOSAT minus FTS) was 1.60 ppb (0.09%) ±13.0 ppb (0.70%), and between the smoothed GEOS-Chem model data and FTS data (GEOS-Chem minus FTS) was −8.09 ppb (0.45%) ±17.83 ppb (0.95%). The differences between GOSAT and FTS were season-independent; however, obvious season-dependent differences were observed between GEOS-Chem and FTS. The GOSAT and GEOS-Chem models could reproduce the seasonal cycle observed by FTS with correlation coefficients (r) of 0.77 and 0.86, respectively.

Aurora kinases are known to be overexpressed in various solid tumors and implicated in oncogenesis and tumor progression. A series of nicotinamide derivatives were synthesized and their biological activities were evaluated, including kinase inhibitory activity against Aur A and Aur B and in vitro antitumor activity against SW620, HT-29, NCI-H1975 and Hela cancer cell lines. In addition, the study of antiproliferation, cytotoxicity and apoptosis was performed meanwhile. As the most potent inhibitor of Aur A, 4-((3-bromo-4-fluorophenyl)amino)-6-chloro-N-(4-((6,7-dimethoxyquinolin-4-yl)oxy)-3-fluorophenyl)nicotinamide (10l) showed excellent antitumor activity against SW620 and NCI-H1975 with IC50 values were 0.61 and 1.06 μM, while the IC50 values of reference compound were 3.37 and 6.67 μM, respectively. Furthermore, binding mode studies indicated that compound 10l forms better interaction with Aur A.

Ulcerative colitis (UC), a serious threat to public health, is one of the main forms of inflammatory bowel disease, whereas the molecular mechanisms underlying ulcerative colitis induced by inflammation still remain elusive. NPLR6 gene is previously shown to regulate intestinal homeostasis and regulate the colonic microbial ecology. Here, we report that microRNA-650 (miR-650) plays an important role in the pathogenesis of UC as an upstream regulator of NPLR6 gene. MiR-650 is proved overexpressed in the inflamed mucosa of patients with ulcerative colitis and the DSS induced colitis model mice by qRT-PCR. Over-expression of miR-650 leads to increased apoptosis of Caco-2 and IEC-6 cells, and the DSS-induced mice aggravation, while knock-down of miR-650 shows opposite effects. Through constructing luciferase reporter genes containing 3′-untranslated regions of NLRP6, we further demonstrate that miR-650 inhibits NLRP6 through binding to its 3′-untranslated regions. Overexpression of NLRP6 in Caco-2 and IEC-6 cells suppress the increase apoptosis induced by miR-650 overexpression. Overall, the findings of this study indicate the role of miR-650 in ulcerative colitis, which provides a new target for therapeutic treatment.

Tool wear is a key factor in the cutting process, which directly affects the machining precision and part quality. Accurate tool wear prediction can make proper tool change at an early stage to reduce downtime and enhance product quality. However, traditional methods can not meet the high requirements of the intelligent manufacturing. Therefore, a novel method based on deep learning is proposed to improve the prediction accuracy of tool wear. The multi-scale feature fusion was implemented by the developed parallel convolutional neural networks. The channel attention mechanism combined with the residual connection was developed to consider the weight of the different feature map to enhance the performance of the model. The different tool wear prediction experiments were implemented to verify the superiority of the developed method, and the prediction results of tool wear are more robust and accurate than current methods. Finally, a tool wear monitoring system was developed and applied to the tapping process of the engine cylinder to ensure the quality of the engine cylinder and the stability of the machining process.

Monitoring and diagnostics are vitally important in smart manufacturing systems since early detection can reduce downtime, protect environment, improve work efficiency, and save cost. The current work for monitoring and diagnostics mainly process the system condition data from multisensor by some mainstream machine learning and deep learning methods. However, these methods’ performance is limited by the following weaknesses: (1) The multi-sensor information are not well used, and its feature fusion is not considerd. (2) Current advanced methods, such as convolution neural network (CNN), long short-term memory neural network (LSTM), are still facing some problems due to their inherent structures. CNN does not consider the sequential and temporal dependency; LSTM does not consider spatial correlation. Thus, a novel integrated model based on deep learning and multi-sensor feature fusion is proposed. The developed parallel convolutional neural network (PCNN) in the integrated model can achieve multisensory feature fusion to overcome the first weakness. The integrated CNN, deep residual networks (DRN), LSTM, can solve the second point. Specifically, the signals collected from multiple sensors are turned into multi-channel images, and the PCNN is designed to extract and fused the features of the converted images. Then, DRN and LSTM are developed to accept the extracted high-dimensional features by the designed CNN and generate the prediction results by fully connected neural networks. Two experiments, including cutting tool monitoring and bearing fault diagnosis, are conducted to validate the superiority and robustness of the proposed method. Compared with the state-of-the-art algorithms, the results show that the proposed model is more robust and accurate.

In this investigation, a novel series of quinoline analogues bearing thiazolidinones were designed and synthesized based on our previous study. Among them, the most potent compound 11k, 4-((4-(4-(3-(2-(2,6-difluorophenyl)-4-oxothiazolidin-3-yl)ureido)phenoxy)-6-methoxyquinolin-7-yl)oxy)-N-isopropylpiperidine-1-carboxamide, possessed submicromolar c-Met and Ron inhibitory activities. In addition, enzymatic assays against a mini-panel of kinases (c-Kit, B-Raf, c-Src, IGF1R, PDGFRα and AXL) were performed, the results showed that compound 11k exhibited moderate inhibitory activity against PDGFRα, c-Src and AXL. MTT assay revealed in vitro antitumor activities against HT-29 cells of compound 11k with an IC50 value of 0.31 μM which was 9.3- and 34.2-fold more potent than that of Regorafenib (IC50 = 2.87 μM) and Cabozantinib (IC50 = 10.6 μM). Preliminary antitumor mechanisms were also investigated by cellular assays. Considerable cytotoxicity, antiproliferation and induction of apoptosis of compound 11k in a dose- and time-dependent manner were confirmed by IncuCyte live-cell imaging assays. Treatment with compound 11k caused slight G2-or M-phase arrest in HT-29 cells. Further cell selectivity of compound 11k showed that it was not active against human normal colorectal mucosa epithelial cell FHC at 10.0 μg/mL. The above results support further structural modification of compound 11k to improve its inhibitory activity, which will lead to more potent anticancer agents.

Summary The adaptor protein CARD9 links detection of fungi by surface receptors to the activation of the NF-κB pathway. Mice deficient in CARD9 exhibit dysbiosis and are more susceptible to colitis. Here we examined the impact of Card9 deficiency in the development of colitis-associated colon cancer (CAC). Treatment of Card9−/− mice with AOM-DSS resulted in increased tumor loads as compared to WT mice and in the accumulation of myeloid-derived suppressor cells (MDSCs) in tumor tissue. The impaired fungicidal functions of Card9−/− macrophages led to increased fungal loads and variation in the overall composition of the intestinal mycobiota, with a notable increase in C. tropicalis. Bone marrow cells incubated with C. tropicalis exhibited MDSC features and suppressive functions. Fluconazole treatment suppressed CAC in Card9−/− mice and was associated with decreased MDSC accumulation. The frequency of MDSCs in tumor tissues of colon cancer patients correlated positively with fungal burden, pointing to the relevance of this regulatory axis in human disease.

A generic model of grid-connected doubly-fed induction generator (DFIG)-type wind turbines represented by differential-algebraic equations (DAE) is proposed for short-term transient-stability analysis. From the view of analytical research, vital simplifications are made to the complex physical model of DFIGs, whereas characteristic dynamics are still preserved. When the connection between the wind turbine and grid is weak, transient voltage collapse caused by singularity-induced instability (SII) is observed and analyzed adopting the generic model. In addition, it is shown that collapse might also occur when state variables of the dynamical system get out of control when the connection between the wind turbine and grid is stiff, where fast drop of dc-bus voltage occurs. Critical indices for evaluating transient-stability performances of the DFIG are proposed and parameter sensitivity analysis is carried out. Physical meanings of SII in generators/units with power–electronic interface in vector-oriented control frame are generalized. Novel interaction between the grid and these nonconventional generators/units is highlighted.

A precise knowledge of ozone seasonal evolution and photochemical production regime in polluted troposphere in China has important policy implications for ozone pollution controls especially in megacities where ozone pollution is common throughout the year. In this study, we used tropospheric ozone, CO and HCHO columns derived from high resolution Fourier transform infrared spectrometry (FTS) in Hefei, China, tropospheric NO2 columns deduced from overpass Ozone Monitoring Instrument (OMI), surface meteorological data, and a back trajectory cluster analysis technique to investigate ozone seasonal evolution and photochemical production regime in eastern China from 2014–2017. A pronounced seasonal cycle for tropospheric ozone is captured by FTS, where high levels of tropospheric ozone occurs in spring and summer, and low levels of tropospheric ozone occurs in autumn and winter. Day-to-day variations in spring and summer are in most cases larger than those in autumn and winter. At the same time, it shows that the tropospheric ozone roughly increases over time at the first half year and reaches the maximum in June, and then it decreases over time at the second half year. Tropospheric ozone columns in June are, on average, 0.5×1018 molecules*cm−2 (47.6 %) higher than those in December which has a mean value of 1.05×1018 molecules*cm−2. The OMI time series shows similar behaviour. The measured features can basically be reproduced by GEOS-Chem and WRF-Chem data but with slight shifts in the timing of the seasonal maximum. Back trajectories analysis shows that: air pollutions in megacities in central-southern China, northwest China, and the key pollution area, i.e., Yangtze River Delta area in eastern China, dominates the contributions to the observed tropospheric ozone levels, while the contributions from the other two key pollution areas, i.e., Beijing-Tianjin-Hebei in north China and Pearl River Delta in south China, are very small; Air masses generated from polluted areas have more transportations to the observed area in spring and summer than in autumn and winter, and hence have more contributions to the observed tropospheric ozone levels. Correlations between tropospheric ozone and meteorological data disclosed that spring and summer is more favorable to photochemical ozone production than in autumn and winter. Finally, the HCHO/NO2 ratio is used as a proxy to investigate the chemical sensitivity of ozone production (PO3). The results show that the PO3 is mainly NOx limited in summer, while it is mainly VOC or mix VOC-NOx limited in winter. Statistics show that NOx limited, mix VOC-NOx limited, and VOC limited PO3 accounts for 60.1 %, 28.7 %, and 11 %, respectively.

Enteral nutrition (EN) can improve clinical outcomes as an important treatment in critically ill patients. However, when patients suffer from gastrointestinal function disorders, intestinal intolerance occurs and EN administration may be delayed and even fails to perform. Pectin, a structural heteropolysaccharide, could protect gastrointestinal function from disorders in many gastrointestianl diseases. The present study aimed to determine whether pectin-supplemented EN was safe and improved clinical outcomes in intensive care unit (ICU) patients.Patients enrolled in ICU from August 2014 to January 2015 were randomized to EN group and pectin-supplemented EN group (PEC/EN group). Both group received isonitrogenous, isocaloric EN support within 36 hours after ICU admission, and last for 6 days. The primary endpoints were 30-day mortality and gastrointestinal intolerance.There were 125 patients included in this study (63 in EN group, and 62 in PEC/EN group). The results showed that the 30-day mortality was 4.8% in EN group and 1.61% in PEC/EN group (p=0.317). PEC/EN group had a smaller gastrointestinal intolerance rate than EN group (41.3% vs 27.4%, p=0.04). Furthermore, there were shorter times to reach full EN (13.0±5.12 vs 9.99±1.91, p=0.05), length of ICU stay (17.9±9.72 vs 13.8±8.59, p0.001), and length of hospital stay (32.9±19.0 vs 23.4±13.2, p0.001) in EN group than those in PEC/EN group.These results revealed that pectin- supplemented EN was safe, and could improve clinical outcomes in ICU patients.

This paper introduces a hybrid model that incorporates a convolutional neural network (CNN) with a stacked bi-directional and uni-directional LSTM (SBULSTM) network, named CNN-SBULSTM, to address sequence data in the task of tool remaining useful life (RUL) prediction. In the CNN-SBULSTM network, CNN is firstly utilized for local feature extraction and dimension reduction. Then SBULSTM network is designed to denoise and encode the temporal information. Finally, multiple fully connected layers are built on the top of the CNN-SBULSTM network to add non-linearity to the output, and one regression layer is utilized to generate the target RUL. The cyber-physical system (CPS) is used to collect the internal controller signals and the external sensor signals during milling process. The proposed hybrid model and several other published methods are applied to the datasets acquired from milling experiments. The comparison and analysis results indicate that the integrated framework is applicable to track the tool wear evolution and predict its RUL with the average prediction accuracy reaching up to 90%.

Wind energy integration research generally relies on complex sensors located at remote sites. The procedure for generating high-level synthetic information from databases containing large amounts of low-level data must therefore account for possible sensor failures and imperfect input data. Data-mining methods are widely used for recognizing the relationship between wind farm power output and wind speed, which is important for wind power prediction. Incorrect and unnatural data has great influence on the results. To address this problem, the paper presents an empirical methodology that can efficiently preprocess and filter the raw wind data using a two-stage ensemble of diverse models. First, abnormal features are extracted from raw wind data and the dataset is labeled according to the wind farm operation state records and the characters of typical abnormal data. Next, a two-stage classification model is built by Random Forest (RF) and Gradient Boosting Decision Tree (GBDT). In the first stage, a RF classifier is trained with the labeled dataset as input. In the second stage, a GBDT classifier is trained with the labeled dataset and the RF classification result as input. Finally, the testing set is predicted respectively by the two trained models and the average of forecast values of the RF model and the GBDT model are considered as the final result. The methodology was tested successfully on the data collected from a large wind farm in northeast China.

In order to coordinate automatic voltage control (AVC) systems of a large interconnected system, a multi-level multi-area hybrid automatic voltage control (MLMA-HAVC) system was constructed. This system began its trial operation in the Northeast China Grid in January 2010, and for the first time in China and abroad it realized automatic close-loop control of multi-area and multi-level interconnected power grid and multi-objective self-approaching optimization in aspects of security, high quality and economic operation. This system has three breakthroughs in theory and engineering application: 1) Established the MLMA-HAVC theory to solve multi-objective optimization of large-scale system; 2) proposed reactive power/voltage coordination control method to inhibit or further eliminate regional oscillations; 3) presented advanced state estimation algorithm to guarantee acquisition of high reliability data. This paper summarizes the basic principle of MLMA-HAVC, and reports engineering realization of MLMA-HAVC system in the Northeast China Grid.

The present study applied fast-track surgery (FTS) concepts and exclusive enteral nutrition (EEN) to a rat model of heterotopic intestinal transplantation (HIT). A total of 96 pairs of Sprague-Dawley rats were randomly distributed into three groups, as follows: i) The conventional group (group 1); ii) the FTS group (group 2); and iii) the FTS with EEN group (EEN group). FTS alterations to the HIT protocol were as follows: i) The use of sevoflurane as an anesthetic; ii) alterations to the order of the procedure and iii) a modified suturing technique. In addition, the EEN group rats underwent an early EEN gavage. The operation time, success rate, recovery state and morphological characteristics of the grafts were compared among the groups. The average operative time was significantly decreased in the group 2 and EEN group rats (137.44±16.03 and 139.67±15.25 min, respectively), as compared with the group 1 rats (169.36±13.72 min; P14 days was significantly increased in the group 2 (87.5%) and EEN group (90.6%) rats, as compared with the group 1 rats (68.7%; P

Background Temporary bowel ligation (TL) has been proposed to prevent contamination as a damage control procedure in multiple bowel perforations. However, bacteria translocation and intestinal ischemia may develop in a prolonged duration. We here hypothesized that intraintestinal drainage combined with temporary ligation (D-TL) would decrease intestinal injury and improve survivals in a gunshot multiple bowel perforation swine model in the setting of a damage control surgery. Materials and methods The abdomen was shot one time with an experimental modified gun whereas pigs were hemorrhaged to a mean arterial pressure of 40 mm Hg and maintained in shock for 40 min. Cold lactated Ringer solution was gradually infused to induce hypothermia. Animals were randomized to primary anastomosis, TL and intraintestinal D-TL groups ( n = 8). Animals were resuscitated for 12 h with the shed blood and lactated Ringer solution. Delayed anastomosis was performed in TL and D-TL animals after resuscitation. Surviving animals were humanely killed 24 h after operation. Systemic hemodynamic parameters were recorded and blood samples were obtained for biochemical assays. Intra-abdominal pressure, portal vein and peripheral vein bacterial cultures, small intestine hematoxylin-eosin staining, and transmission electron microscopy examination were performed at 0, 2, 6, 12, and 24 h after the surgery. Results All animals suffered extreme physiologic conditions as follows: hypothermia, severe acidosis, hypotension, and depressed cardiac output. Compared with the primary anastomosis and TL group, D-TL animals required less resuscitation fluid, suffered a lower intra-abdominal hypertension and bacterial translocation, normalized lactate levels faster, had lower serum creatine kinase, aspartate aminotransferase levels and tissue TNF-α level, and nuclear factor-kB activations and thus had greater early survival. Conclusions Compared with primary intestinal anastomosis and TL, rapid bowel ligation combined with intraintestinal drainage as a damage control adjunct improved survivals in a multiple bowel perforation swine model in the setting of damage control surgery.

A fuzzy decoupling technique is proposed for model validation of large-scale power systems. To solve the problem of incredibility lying in power system dynamic simulations, a wide area pattern (WAP) is defined which reflects the parameter effects of certain component to the space-time trajectories, especially with consideration of the characteristics of huge amount of measurements and model parameters in large-scale power systems. Based on the clustering analysis of WAPs of certain component throughout the grid, a fuzzy mapping relationship can be obtained between the model parameters and the space-time trajectories, and then measurement-based parameter calibration can be performed according to the strong mapping relationships. A calibration priority indicator is also given. At last, the rationality of the method is discussed in the IEEE 39 bus system.

With the vibrating-reed technique, the internal friction Q −1 and resonant frequency f associated with the pinned flux of (Bi 0.9 Pb 0.1 ) 2 Sr 2 Ca 2 Cu 3 O 10+ y and YBa 2 Cu 3 O 7− x ceramics were measured as a function of the angle θ between the transverse magnetic field orientation and the vibrating direction (perpendicular to the face of the sample) at fixed temperature and magnetic field. A relation between Q −1 (or f ) and θ, i.e. Q −1 (or f ) α ( H a cos θ ) 1.5 was found, and this indicates that only the magnetic field component parallel to the vibrating direction H a cos θ gives rise to the enhancement of internal friction and resonant frequency in ceramic samples. The anisotropic deformation of the flux-line lattice is used to explain the observed results.

This paper describes a new feature detection algorithm, which bases on color and curvature. Since color provides valuable information in object recognition and description, a color invariant space is adopted instead of the gray space. With this method, the distinctive features extracted from images are invariant to image scale and rotation. To reduce the computational complexity and improve the quality of detection, the cascade filtering approach is employed. Details of the new feature descriptor are described, along with test results.

Model validation is a basic and important work to ensure the dynamic simulation accuracy which influences the decision of power system design, planning, operation and control. Because large scale power systems have a great many components, the model validation work is very complex. A measurement-based model validation software(PSMV) is introduced in the paper. Current simulation accuracy can be evaluated by trajectory comparison between measurements and simulations all around the grid. Effects of models and parameters to the dynamic trajectories during a disturbance can be investigated by modification of model types and parameter values. Dominant parameters that can be reliable estimated for a certain trajectory are selected by trajectory sensitivity ranking. Parameter estimation of large scale power systems can also be performed efficiently using the software. The software has been used in Northeast China Power Grid Company.