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Through school-enterprise cooperation, jointly formulate industry-university cooperation and collaborative education programs, build innovative talent training models and curriculum systems, strengthen internship training, highlight practical capabilities, and cultivate the combination of knowledge and skills, technology and management, ability and quality combining applied talents. Establish cross-disciplinary and professional innovation courses, explore the establishment of a new mechanism for cross-faculty, cross-disciplinary, and cross-professional training of innovative and entrepreneurial talents, and promote the transformation of talent training from a single type of discipline to a multi-disciplinary integration.

Highlights • Lipids change overtime in normal fascial healing in the early post-surgery period. • Specific lipid species are correlated with the changes of inflammation cells and fibroblasts. • Lipid species in the present study are considered as predictive markers for the formation of incisional hernia., Background Failure of fascial healing in the abdominal wall can result in incisional hernia, which is one of the most common complications after laparotomy. Understanding the molecular healing process of abdominal fascia may provide lipid markers of incisional hernia or therapeutic targets that allow prevention or treatment of incisional hernias. Purpose This study aims to investigate temporal and in situ changes of lipids during the normal healing process of abdominal fascia in the first postoperative week. Methods Open hemicolectomy was performed in a total of 35 Wistar rats. The midline fascia was closed identically for all rats using a single continuous suturing technique. These animals were sacrificed with equal numbers (n = 5) at each of 7-time points (6, 12, 24, 48, 72, 120, and 168 h. The local and temporal changes of lipids were examined with mass spectrometry imaging and correlated to histologically scored changes during healing using hematoxylin and eosin staining. Results Two phosphatidylcholine lipid species (PC O-38:5 and PC 38:4) and one phosphatidylethanolamine lipid (PE O-16:1_20:4) were found to significantly correlate with temporal changes of inflammation. A phosphatidylcholine (PC 32:0) and a monosialodihexosylganglioside (GM3 34:1;2) were found to correlate with fibroblast cell growth. Conclusion Glycerophospholipids and gangliosides are strongly involved in the normal healing process of abdominal fascia and their locally fluctuating concentrations are considered as potential lipid markers and therapeutic targets of fascial healing.

The impact of environmental regulation on enterprise innovation is closely related to the competitiveness of the enterprise and sustainable development of the regional economy, but existing research does not provide a consistent view. This paper summarizes the impacts of environmental regulation on enterprise innovation from the perspectives of technological innovation, product innovation, system innovation and ecological innovation. We find that the impacts of environmental regulation on enterprise innovation behaviour are complex, and that the impacts can be reflected together by the four aspects above and even by their interaction. Moreover, the impacts are not limited to the creation of new technologies, products, and systems but also include their adoption and application. In particular, whether the Porter hypothesis is true and which versions of the Porter hypothesis environmental regulation causes in enterprise innovation depend on enterprise characteristics, means of environmental regulation, and enterprises' strategic behaviours in an enterprise ecosystem. Finally, we propose five potential research directions: quantifying the degree of enterprise innovation caused by environmental regulation, the impacts of environmental regulation on sustainable economic development from an enterprise ecosystem perspective, the impacts of enterprise innovation on environmental regulation, the role of enterprise initiative in the relationship between environmental regulation and enterprise innovation, and social security issues and the integration of eliminated enterprises resulting from environmental regulation.

An electronic nose (EN) is a bionic system that relies on an array of gas sensors for effective odor recognition. Since the gas-sensor drift would depress the EN performance, we proposed an adaptive Domain based subspace learning method considering both Maximizing label feature Dependency and Minimizing feature Redundancy (DMDMR) to address the EN based drift issue. Considering the inconsistent data distribution caused by drift, the proposed method learns a time-varying common subspace with similar distribution for both regular and recent drifted EN responses. In order to preserve useful classification and topological structure information simultaneously, the Hilbert-Schmidt independence criterion ( HSIC ) has been adopted to measure the dependence between features and labels while the feature redundancy is reduced according to the PCA criterion. An EN drift dataset acquired from a 16-gas-sensor EN system over 36 months was adopted in the experiments. To verify the effectiveness of the proposed method, we used the variations of both first and second order statistics to measure the movement of data distribution under drift condition. The results show that the data distribution in the subspace learned by DMDMR has stronger stability than the one in original space. Furthermore, the positive effects of DMDMR have been exhibited versus other state-of-the-art methods in recognition. The performance of DMDMR paradigm has demonstrated obvious superiority to other paradigms. These results prove that the proposed subspace learning approach is suitable for EN based drift counteraction and can be successfully implemented.

Background The association between health literacy and depressive symptoms has been examined among Chinese middle school students, however there is no evidence are available from college students. Aims This study aimed to examined the association between health literacy and depressive symptoms among Chinese college students. Methods This cross-sectional study recruited 2771 college students in Southwest University. Depressive symptoms was assessed using Zung self-rating depression scale and a score of ≥ 50 represented having depressive symptoms. We used Chinese adolescent interactive health literacy questionnaire to assess health literacy. Multivariate logistic regressions analysis was applied to assess the association between health literacy and depressive symptoms. Results The prevalence of depressive symptoms was 34.9% (967/2771). Multivariate logistic regressions analysis showed an inverse association between health literacy and depressive symptoms after adjusting for potential confounders. The multivariate adjusted ORs (95%CIs) for depressive symptoms across quartiles of health literacy level were 1.000 (reference), 0.48 (0.39, 0.60), 0.25 (0.19, 0.32), and 0.16 (0.12, 0.21) (P for trend

Point-of-Interest recommendation is an efficient way to explore interesting unknown locations in social media mining of social networks. In order to solve the problem of sparse data and inaccuracy of single user model, we propose a User-City-Sequence Probabilistic Generation Model (UCSPGM) integrating a collective individual self-adaptive Markov model and the topic model. The collective individual self-adaptive Markov model consists of three parts such as the collective Markov model, the individual self-adaptive Markov model and the self-adaptive rank method. The former determines the topic sequence for all users in system and mines the behavioral patterns of users in a large environment. The later mines behavioral patterns for each user in a small environment. The last determines a self-adaptive-rank for each user in niche. We conduct a large amount of experiments to verify the effectiveness and efficiency of our method.

We study the influence of the fiber Bragg grating (FBG)'s bandwidth on the performance of the long distance point sensing system based on random fiber laser. The results show that the optical signal-to-noise ratio (OSNR) decreases gradually when the bandwidth of the FBG increases by simulation, under the same pump power, which is demonstrated by the experiment. This work could provide a reference for designing a long-distance optical fiber sensing system.

Muscle strength can be a predictor of depressive symptoms among the elderly. We conducted a prospective study aiming to examine the association between change of handgrip strength and the incidence risk of depressive symptoms among Chinese female college students. Handgrip strength was used as a representative indicator of skeletal muscle strength, and a handheld digital smedley dynamometer was applied to measure handgrip strength. We also used the 20-item Zung self-rating depression scale to evaluate depressive status, and a score of ≥50 indicated moderate-to-severe depressive symptoms. During a 1-year follow-up period, the incidence of depressive symptoms is 10.7%. Multivariate logistic regressions analysis revealed that the multivariable-adjusted ORs (95% CI) of depressive symptoms for the categories of handgrip strength change was 1.00 (reference) for group 1, 0.57 (0.28, 1.19) for group 2, 0.41 (0.19, 0.89) for group 3 and 0.33 (0.11, 0.99) for group 4 (p = 0.018). This study indicated that change of handgrip strength level over one-year period is negatively associated with risk of depressive symptoms among Chinese female college students.

Skipping breakfast has been suggested to increase the risk of depressive symptoms, but there is no information regarding young adults. We aimed to investigate the relationship between the frequency of breakfast consumption and the risk of depressive symptoms among Chinese college students. We investigated a cross-sectional (n = 1060) and one-year prospective (n = 757) relationship between the frequency of breakfast consumption and the risk of depressive symptoms. The frequency of breakfast consumption was categorized into &ldquo, &le, 1 time/week&rdquo, &ldquo, 2&ndash, 5 times/week&rdquo, or &ldquo, &ge, 6 times/week&rdquo, Depressive symptoms were assessed using the 20-item Zung self-rating depression scale (SDS) with an SDS score of &ge, 50 to indicate moderate to severe depressive symptoms. In the cross-sectional analysis, the adjusted odds ratios (ORs) and 95% confidence intervals (CIs) of depressive symptoms related with the breakfast consumption categories were 1.00 (reference) for &ge, 6 times/week, 1.761 (95% CI: 1.131, 2.742) for 2&ndash, 5 times/week, and 3.780 (95% CI: 1.719, 8.311) for &le, 1 time/week (p for trend: &lt, 0.001) after adjusting for these potential confounders. Similarly, in the one-year prospective analysis, we found that 10.2% of participants was classified as having moderate to severe depressive symptoms. Multivariate logistic regressions analysis revealed a significant negative relationship between the frequency of breakfast consumption and the risk of depressive symptoms. The ORs (95% CI) for depressive symptoms with decreasing breakfast consumption frequency were 1.00 (reference) for &ge, 6 times/week, 2.045 (1.198, 3.491) for 2&ndash, 5 times/week, and 2.722 (0.941, 7.872) for &le, 1 time/week (p for trend: 0.005). This one-year prospective cohort study showed that skipping breakfast is related to increased risk of depressive symptoms among Chinese college students. Future research using interventional or experimental studies is required to explore the causal relationship between the effects of breakfast consumption and depressive symptoms.

Background Tianshu capsule (TSC), a formula of traditional Chinese medicine, has been widely used in clinical practice for prophylactic treatment of headaches in China. However, former clinical trials of TSC were small, and lack of a standard set of diagnostic criteria to enroll patients. The study was conducted to re-evaluate the efficacy and safety of TSC post-marketing in an extending number of migraineurs who have diagnosed migraine with the International Classification of Headache Disorders, 3rd edition (beta version, ICHD-3β). Methods The study was a double-blind, randomized, placebo-controlled clinical trial that conducted at 20 clinical centers in China. At enrollment, patients between 18 and 65 years of age diagnosed with migraine were assigned to receive either TSC (4.08 g, three times daily) or a matched placebo according to a randomization protocol. The primary endpoint was a relative reduction of 50% or more in the frequency of headache attacks. The secondary outcomes included a reduction in the incidence of headache, the visual analogue scale of headache attacks, days of acute analgesic usage, and percentage of patients with a decrease of 50% or more in headache severity. Accompanying symptoms were also assessed. Results One thousand migraine patients were initially enrolled in the study, and 919 of them completed the trial. Following the 12-week treatment, significant improvement was observed in the TSC group concerning both primary and secondary outcomes. After therapy discontinuation, the gap between the TSC group and the placebo group in efficacy outcomes continued to increase. There were no severe adverse effects. Conclusions TSC is an effective, well-tolerated medicine for prophylactic treatment of migraine, and still have prophylactic effect after medicine discontinuation. Trial registration ClinicalTrials.gov Identifier: NCT02035111; Data of registration: 2014-01-10.

China is one of a few countries in the world where karst is intensively developed and karst water is heavily utilized as water supply sources. Shanxi is such a Province with the largest karst distribution in places in Northern China, where 19 large karst springs and their catchments are identified to provide important sources of the water supply and ecosystem functioning in Shanxi. Over the years, many problems associated with utilization of karst springs in Shanxi cropped out, including the decrease in spring flow, decline of groundwater level, groundwater contamination and pollution, etc., which severely restrict the sustainable utilization of karst water resources in Shanxi. Through the retrieval and analysis of some 200 local and international publications, this paper critically reviews the research results of karst springs in the region from the perspective of spring flow trend, precipitation recharge and time-lag, evaluation of karst water resources, water chemistry and environmental isotopes with purposing assession, and further evaluates the integrity of the aquifer system including vulnerability, impacts of coal mining and engineering activities on karst groundwater, delineation of spring catchment sub-systems, protection and management measures. It is concluded that human activities and climate change are the primary and secondary factors negatively affecting karst springs, respectively. The impacts of human activities on karst springs are mainly facilitated by intensive development of karst water, mining drainage, engineering construction and other activities. While karst water in parts of Shanxi spring catchments is polluted to various degrees, hence it is recommended to mainstream the protection of karst spring water in the areas of strategic importance. This paper will contribute towards the establishment of sustainable development and utilization of karst water in Shanxi and even in Northern China.

Illegal logging has become a global issue because of its adverse effects on biodiversity and climate change. In order to reduce illegal logging, many countries around the world have introduced regulations for the international trade of forest products. This paper examines the effects of these efforts on international trade of forest products. The analysis is conducted using the Heckscher–Ohlin–Vanek model, where the number of regulations against illegal logging is used to describe the level of efforts, and is included as an explanatory variable for national net export of forest products. The results show that efforts against illegal logging have had significant and positive impacts on the net export of forest products.

Determination of genetic diversity and population structure plays an important role in supporting genetic improvement programs and future conservation plans. In this study, 352 individuals representing nine Chinese indigenous goat populations distributed in China were genotyped at 15 microsatellite loci. The mean number of alleles (MNA) per breed ranged from 3.867 (Matou goat, MT) to 5.400 (Ujumqin white goat, UW), the expected heterozygosity (He) varied from 0.482 (Hainan black goat, HNB) to 0.659 (UW). Allelic diversity and heterozygosity measures in the studied populations were much lower than Chinese Cashmere and meat type goats. Global F statistics revealed 9.8% of total variance explained among breeds while 90.2% of variance was due to diversities within breeds. Three major population clusters were observed broadly conforming to geographical locations of different goat populations. Three-dimensional scatterplot derived from three largest principal components supported the observed phylogeny based on genetic distance estimations. Goats from Northern China and Island region were distinct while strong admixture was observed among goat populations from Central China. The study revealed market orientation and geographical distances among populations might have contributed to the genetic structure and population sub-division among Chinese indigenous goats. Our study provided a new insight into understanding the genetic diversity and structure of Chinese indigenous goat breeds, and will be helpful to determine the strategies for breeding and conservation programs.

Background: It is well established that greater internet use is related to an increased risk of musculoskeletal pain among adolescents. The relationship between internet addiction, a unique condition involving severe internet overuse, and musculoskeletal pain has, however, not been reported. The aim of this study was to examine the association between internet addiction and the risk of musculoskeletal pain in different body parts among Chinese college freshmen. Methods: A cross-sectional study was conducted among 4211 Chinese college freshmen. Internet addiction status was evaluated using the 20-item Young's Internet Addiction Test. Internet addiction was defined as internet addiction score ≥ 50. Musculoskeletal pain was assessed using a self-report questionnaire. Results: Among all subjects, 29.2% reported neck pain, 33.9% shoulder pain, 3.8% elbow pain, 7.9% wrist/hand pain, and 27.9% low back and waist pain. The prevalence of internet addiction was 17.4%. After adjusting for potential confounders, internet addiction was significantly associated with risk of neck, shoulder, elbow, wrist/hand, low back and waist pain. The odds ratios and 95% confidence intervals (CI) for neck pain with severe internet addiction status were 1.00 (reference), 1.451 (1.221, 1.725), and 1.994 (1.608, 2.473) (P for trends

Production scheduling under time-of-use electricity tariffs has become an efficient way for energy-intensive industries to decrease energy costs. However, when production tasks are over-concentrated in one scheduling cycle, the effectiveness of time-of-use electricity tariffs is no longer significant. This makes the introduction of self-generation power plant appealing for energy-intensive industries. This paper addresses an integrated scheduling problem from an iron-steel plant equipped with self-generation equipment under time-of-use electricity tariffs. In this problem, time-of-use electricity tariffs, the self-generation cost, and the on-grid electrovalence influence the total electricity cost simultaneously. A multi-objective mathematical model with energy-awareness is developed to optimize the production schedules and electricity cost jointly. An improved SPEA2 based on the relationship propagation chain is tailored for the problem, including scheduling solution encoding, crossover and mutation. A real-life case study from a Chinese iron-steel plant equipped with self-generation equipment demonstrates that the proposed methods can provide a high-quality scheduling scheme and the total electricity cost can be significantly reduced.

An electronic nose (E-nose) system is regularly composed of a gas sensor array and certain pattern-recognition algorithms. With the use of E-nose, the gas sensors inevitably undergo physical changes, which causes gas-sensor drift to invalid algorithm models of E-noses. In this study, we intend to explore a suitable approach for online E-nose drift calibration. Considering drift calibration samples cannot be obtained directly during continuous odor detection, we have adopted Active Learning (AL) paradigm to select calibration samples from previous tested samples and provide their categories by querying. Further, we deal with the class imbalance problem of drift calibration set caused by traditional AL instance-selection strategy. We propose a new strategy named Dual-Rule Sampling (DRS) to simultaneously measure sample uncertainty and minority-class similarity. The high uncertain instances being close to minority-class are selected for drift calibration when class imbalance occurs. We have used two datasets to evaluate the performance of DRS. The experimental results show that DRS reaches the highest recognition score among all the tested methodologies by emphasizing the minority-class recognition improvement. We can conclude that DRS successfully implements online E-nose drift calibration in continuous odor detection.

As a kind of intelligent instrument, an electronic tongue (E-tongue) realizes liquid analysis with an electrode-sensor array and certain machine learning methods. The large amplitude pulse voltammetry (LAPV) is a regular E-tongue type that prefers to collect a large amount of response data at a high sampling frequency within a short time. Therefore, a fast and effective feature extraction method is necessary for machine learning methods. Considering the fact that massive common-mode components (high correlated signals) in the sensor-array responses would depress the recognition performance of the machine learning models, we have proposed an alternative feature extraction method named feature specificity enhancement (FSE) for feature specificity enhancement and feature dimension reduction. The proposed FSE method highlights the specificity signals by eliminating the common mode signals on paired sensor responses. Meanwhile, the radial basis function is utilized to project the original features into a nonlinear space. Furthermore, we selected the kernel extreme learning machine (KELM) as the recognition part owing to its fast speed and excellent flexibility. Two datasets from LAPV E-tongues have been adopted for the evaluation of the machine-learning models. One is collected by a designed E-tongue for beverage identification and the other one is a public benchmark. For performance comparison, we introduced several machine-learning models consisting of different combinations of feature extraction and recognition methods. The experimental results show that the proposed FSE coupled with KELM demonstrates obvious superiority to other models in accuracy, time consumption and memory cost. Additionally, low parameter sensitivity of the proposed model has been demonstrated as well.

Hepatocellular carcinoma (HCC) is the second cause of cancer-related death worldwide, and the incidence rate of liver cancer has continuously increased, with approximately 750,000 new diagnosed cases each year. Especially in China, both the incidence and mortality rate of HCC have been ranked second among all cancers. Importantly, HCC mortality rate is similar to its incidence rate, indicating that most patients with liver cancer die from HCC. In clinical practice, liver cancers are usually diagnosed by detecting alpha-fetoprotein (AFP) and abdominal ultrasound. However, abnormal AFP is usually detected at late stages of liver cancer, in which most patients are refractory to surgery, radiotherapy and chemotherapy. Moreover, AFP is not detectable in some liver cancer patients. In this study, we aimed to establish an alternative diagnostic method for liver cancer patients by analyzing hidden patterns and relationships among multiple specific markers of liver cancers. By building a predictive classification of liver cancer and the relationship between different markers, a support vector machine (SVM) classifier was developed. Our SVM classifier integrated 22 specific markers. Our results revealed that the input of these 22 markers into the classifier could accurately determine the exitence of HCC in a patient. Our established SVM classifier may achieve the early prediction of liver cancer, thereby improving the accuracy of diagnosis and treatment of live cancer patients.

Treatment of missing data has become increasingly significant in scientific research and engineering applications. The classic imputation strategy based on the K nearest neighbours (KNN) has been widely used to solve the plague problem. However, former studies do not give much attention to feature relevance, which has a significant impact on the selection of nearest neighbours. As a result, biased results may appear in similarity measurements. In this paper, we propose a novel method to impute missing data, named feature weighted grey KNN (FWGKNN) imputation algorithm. This approach employs mutual information (MI) to measure feature relevance. We present an experimental evaluation for five UCI datasets in three missingness mechanisms with various missing rates. Experimental results show that feature relevance has a non-ignorable influence on missing data estimation based on grey theory, and our method is considered superior to the other four estimation strategies. Moreover, the classification bias can be significantly reduced by using our approach in classification tasks.

Environmental non-governmental organizations (ENGOs) play an increasingly important role in the process of urban environmental governance, especially in some developing countries such as China. However, existing studies pay little attention to such an issue in China. In this paper, we consider 113 cities in China from the pollution information transparency index (PITI) list released by ENGOs as the treatment group and some other cities as the control group, and use the difference-in-differences (DID) model and propensity score matching DID (PSM-DID) model to explore the role of ENGOs in China's urban environmental governance. The results show that ENGOs play a significantly positive and robust role in China's urban environmental governance. Furthermore, using regression analysis for eastern, central, and western China, we find that the influence of ENGOs exists in eastern and central China rather than in western China. In addition, the results of the Placebo test indicate that the effect of ENGOs shows an upward trend since 2008. We suggest that ENGOs' role should be strengthened in China, and governments at various levels should take into account environmental information released by ENGOs and consider appropriate measures to improve local environment quality using the obtained information.

The separator plays very important role in the lithium ion batteries preventing electric contact of electrodes but permeable to ionic flow. This article reviews the recent progress in LIB separator research and related industrial technology. Two issues, how to build a thermal-resistant separator and how to improve ionic conductivity of the separator, are focused. This review includes four parts: the factors affecting separators performance, gel polymer electrolyte, separators produced by stretching method and its modification, new methods and new materials in LIB separator fabrication. Finally, the challenges and prospects related to LIB separators are summarized.

Electrical load tracking is a critical strategy for energy intensive industries to provide demand response (DR) in today’s electricity markets. It can take advantage of time-of-use (TOU) tariffs in steelmaking-refining-continuous casting (SRCC) scheduling. In this paper, we develop a continuous-time mixed integer nonlinear programming (MINLP) model subject to energy-awareness and TOU tariffs to manage the electrical load tracking scheduling of SRCC. Due to the complex cases among load intervals of electrical load tracking, time-slots of TOU tariffs and processing cycles of jobs result from different time granularities of the electrical load tracking and TOU tariffs, we formulate the objective functions with derived general formulations that can apply to all cases. An improved strength Pareto evolutionary algorithm 2, AHSPEA2, is developed to solve this proposed model, whose search ability and population diversity are enhanced greatly by two strategies, the arithmetic crossover operator and the improved hybrid self-adaptive mutation operators. The computational results demonstrate that AHSPEA2 is far superior and prove its effectiveness in providing high-quality scheduling plans which follow the pre-contracted load curve carefully to decrease deviations and reduce electricity costs simultaneously.

Although investment and R&D activities can exert significant effects on energy-related industrial CO2 emissions (EICE), related factors have not been fairly uncovered in the existing index decomposition studies. This paper extends the previous logarithmic mean Divisia index (LMDI) decomposition model by introducing three novel factors (R&D intensity, investment intensity, and R&D efficiency). The extended model not only considers the conventional drivers of EICE, but also reflects the microeconomic effects of investment and R&D behaviors on EICE. Furthermore, taking Shanghai as an example, which is the economic center and leading CO2 emitter in China, we use the extended model to decompose and explain EICE changes. Also, we incorporate renewable energy sources into the proposed model to carry out an alternative decomposition analysis at Shanghai׳s entire industrial level. The results show that among conventional (macroeconomic) factors, expanding output scale is mainly responsible for the increase in EICE, and industrial structure adjustment is the most significant factor in mitigating EICE. Regardless of renewable energy sources, the emission-reduction effect of energy intensity focused on by the Chinese government is less than the expected due to the rebound effect, but the introduction of renewable energy sources intensifies its mitigating effect, partly resulting from the transmission from the abating effect of industrial structure adjustment. The effect of energy structure is the weakest. Although all the three novel factors exert significant effects on EICE, they are more sensitive to policy interventions than conventional factors. R&D intensity presents an obvious mitigating effect, while investment intensity and R&D efficiency display an overall promotion effect with some volatility. The introduction of renewable energy sources intensifies the promotion effect of R&D efficiency as a result of the “green paradox” effect. Finally, we propose that CO2 mitigation efforts should be made by considering both macroeconomic and microeconomic factors in order to achieve a desirable emission–reduction effect.

This study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by conventional petrophysical methods in wells without core data. 4950 samples of 15 wells with core data have been used to train the neural network, and robust ELM algorithm provides fast and accurate prediction results. The network model is then applied to estimate porosity and permeability for the remaining wells. Based on the predicted results, reservoir parameter distribution character has been analyzed. Potential zone has been proposed for further research in the survey.

This study focuses on reservoir parameter estimation using extreme learning machine in heterogeneous sandstone reservoir. The specific aim of work is to obtain accurate porosity and permeability which has proven to be difficult by conventional petrophysical methods in wells without core data. 4950 samples from 8 wells with core data have been used to train and validate the neural network, and robust ELM algorithm provides fast and accurate prediction results, which is also testified by comparison with BP (back propagation) network and SVM (support vector machine) approaches. The network model is then applied to estimate porosity and permeability for the remaining wells. The predicted attributes match well with the oil test conclusions. Based on the estimations, reservoir porosity and permeability have been mapped and analyzed. Two favorable zones have been suggested for further research in the survey.

Multidrug resistance and various adverse side effects have long been major problems in cancer chemotherapy. Recently, chemotherapy has gradually transitioned from mono-substance therapy to multidrug therapy. As a result, the drug cocktail strategy has gained more recognition and wider use. It is believed that properly-formulated drug combinations have greater therapeutic efficacy than single drugs. Tea is a popular beverage consumed by cancer patients and the general public for its perceived health benefits. The major bioactive molecules in green tea are catechins, a class of flavanols. The combination of green tea extract or green tea catechins and anticancer compounds has been paid more attention in cancer treatment. Previous studies demonstrated that the combination of chemotherapeutic drugs and green tea extract or tea polyphenols could synergistically enhance treatment efficacy and reduce the adverse side effects of anticancer drugs in cancer patients. In this review, we summarize the experimental evidence regarding the effects of green tea-derived polyphenols in conjunction with chemotherapeutic drugs on anti-tumor activity, toxicology, and pharmacokinetics. We believe that the combination of multidrug cancer treatment with green tea catechins may improve treatment efficacy and diminish negative side effects.

Members of a plane-frame structure may be subjected to a combination of lateral loads, end moments, and axial forces. Analysis of such a structure may be classified as elementary, first-order, or second-order, depending on the load conditions, magnitude of displacements, and method of solution. A numerical procedure is presented for the solution of geometrically nonlinear problems considering second-order effects. After a brief literature review, the paper presents the assumptions pertinent to the research; the relationship between the engineering strain and Green strain tensor considering small strain and moderate to large rotations; finite-element consideration in the derivation of the incremental stiffness matrices using increments of total potential energy and the concepts of a variational method; a procedure for the solution of the nonlinear problems and its algorithm; numerical examples and comparison of the numerical results with published literature; and conclusions and results. The algorithm that is presented is useful in identifying the differences between the first- and second-order analysis of structural systems.

Bnip3L, also known as NIX, is a homolog of the E1B 19K/Bcl-2 binding and pro-apoptotic protein Bnip3 which can bind to Bcl-2 to elaborate that effect. In tumor cells, Bnip3L played a role in tumor growth inhibition, but some studies argued hypoxia-induced autophagy via Bnip3L was a survival mechanism that promoted tumor progression. In heart muscle, it related to decreased myocardial function. However, its function in intracerebral hemorrhage (ICH) is still not clear. In this frame, we found the Bnip3L expression increased in the perihematomal region in adult rats after performed ICH. Double immunofluorenscence staining manifested that Bnip3L co-located with neurons, not astrocytes or oligodendrocytes. Furthermore, we detected that neuronal apoptosis marker active caspase-3 had colocalizations with Bnip3L. In addition, colocalizations and co-immunoprecipitation between Bnip3L and Bcl-2, consistent with previous study, were also found. All our findings suggested that Bnip3L might be involved in the pathophysiology of ICH.

LiFePO 4 battery is considered to be an important component for making future transportation more energy-efficient and less dependent on petroleum through hybrid, plug-in hybrid, electric, or fuel cell vehicles. This paper focuses on the impacts of ambient temperate on the cycle life of LiFePO 4 battery. The experimental procedures for batteries are developed, and a test plan on the cycle of charge and discharge with the current of 1C is made. Lower or higher test temperature is divided with a step length of 10°C. At each temperature, the discharge capacity and impedance are measured every 20 cycles. After 100 cycles testing, the discharge capacity and impedance curves are plotted. With the help of the curves, this paper analyzes the influence of temperature on discharge capacity and impedance. Finally, some suggestions about how temperature influences battery cycle life are presented.

In this paper, discussions were made on using DFT and IDFT to calculate the transient acceleration response of a single degree system. Two key points were discussed: the sampling frequencies and the time span. Simulation were made the transient response of a single degree system's transient response, it was found that the sampling frequencies have the greatest influence on the calculation error. The calculation precision's relation with the sampling frequencies were discussed, it shows that to keep calculation error in 10%, 99% energy in the low frequency should be calculated correctly.

Because there are so many subsystems in traditional experimental statistical energy analysis method, the workload of measuring the parameters is very big. This paper presents a hybrid experimental statistical energy analysis method, in which some of the parameters are determined by analytical method, thus it reduces the number of the unknown measured parameters. This article described the test steps of this method and also given numerical simulation examples. Results showed that the statistical energy equation established by this method can accurately forecast structural vibration of complex system. Finally, the method to choose positions of excitation points was also discussed.

The parametric finite element model of the anti-hook performance of a double armor submarine cable was given according to the hook test of the submarine cables. The finite element simulation was performed. The stress-strain and compressive deformation were discussed. Orthogonal experimental design method was used in the optimization of the structural parameters of submarine cables. Through studies it was found that the thickness of the rubber has the greatest influence on the anti-hook performance of submarine cables. At the meantime, the observed best design and the possibly best design was given with respect to the performance-price ratio.

To measure the workload and state of network operation, a predictable algorithm based on the grey model, neural network and compensator error is present in this paper. The new algorithm has a prominent effect in reflecting the variable trend of data. The simulation results show that the integrated model can improve the prediction precision obviously compared to the other algorithms.

The network traffic is the important parameter that measures the burden of network movement and network appearance .It also plays an important role in network layout, traffic management. In traffic management, traffic model is used to evaluate the mechanism of joint control and predict network performance. The Grey Model has good effect in reflecting the variable trend of data. With the development of this theory, it is widely used in applications .Many improved and new generation methods have been proposed. In this paper, we use farther advanced GM(1,1) model to predict network traffic on the basis of others.

During recent years we have seen several large-scale crises. The 9/11 terror attacks, tsunamis, storms, floods and bombings have all caused a great deal of damage. A common factor in these crises has been the need for information and one important source of information is usually web sites. In this work we investigate and design an overload control system for web sites that are vital in crises. The overload control system uses content adaption to dynamically control web site performance.

The High Temperature Gas-cooled Reactor coupled with gas turbine (HTGR-GT) is supposed to be one of the candidates for the future nuclear power plants in both electricity and hydrogen production. The HTGR-GT cycle is theoretically based on the closed Brayton cycle with recuperator, inter-cooler and pre-cooler. In this paper, the exergy analysis on a typical HTGR coupled with Gas Turbine was presented. Besides the core outlet temperature of the cycle, other operating parameters have been calculated to see their effect of the exergy efficiency of the whole cycle. The results show that, the compressor pressure ratio (Y) of the cycle has a great effect on the exergy losses of both heat-exchange and work components. And only the exergy loss of the recuperator decreases with the increase of Y, especially sharply when Y is small, while the exergy losses of other components increases. So there are optimized Y under different working conditions. The effect of other operating parameters, like pressure drop, recuperation efficiency and isentropic efficiencies of compressors and turbine, has been evaluated. The effect of two formers is similar, both obvious at the range of near the optimized Y, and Y goes down slightly when these two operating conditions aremore » better. Compressors and turbine, as the work components of the cycle, their isentropic efficiency is quite important to the exergy efficiency of the cycle. The bigger Y, the more effect. Whatever, the isentropic efficiency of compressors and turbine is already quite high. (authors)« less

Meat quality is an important trait in the pig industry. To identify genomic regions and haplotype blocks responsible for meat quality traits in pigs, a genome-wide association study was conducted for five traits including intramuscular fat content, pH at 45 min and 24 h, drip loss within 24 h and water-holding capacity in 231 Yorkshire barrows using illumina porcine 60k SNP chips. The results showed that a total of 344 single nucleotide polymorphisms (SNP) were significantly associated with five meat quality traits (P-4). Moreover, 323 SNPs were within the reported QTL regions, of which 21 were novel. Also, 158 SNPs fell into the proximal region of meat quality related genes. In addition, 25 haplotype blocks based on 116 SNPs were revealed with SNP combination patterns for five traits. Our study added new SNP information for identification of meat quality traits in pigs and will help elucidate the mechanisms of meat quality in pigs.