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To identify the abnormal characteristics of the oplegnathus punctatus is great importance to the detection of iridovirus disease in the breeding environment. In this paper, an advanced neural network model to identify the characteristics of the oplegnathus punctatus and predict its different periods of suffering from iridovirus disease is proposed based on the establishment of a data set. First of all, a standard format data set of oplegnathus punctatus and an abnormal format date set are established in order to verify the effectiveness of the method in this paper. And then, the feature extraction fusion method is used for preprocessing in terms of the abnormal format data set, which combines the edge features extracted by the improved multi-template Sobel operator and the color features extracted by the HSV model. Finally, an improved VGG-GoogleNet network recognition model comes into being through the fusion and improvement of the VGG and GoogleNet neural network structure. The experiments results show that the prediction accuracy rate for oplegnathus punctatus suffering from iridovirus disease in the the abnormal format data set and the standard format data set are improved, which reach 98.55% and 69.18%.

End-to-end training with global optimization have popularized graph neural networks (GNNs) for node classification, yet inadvertently introduced vulnerabilities to adversarial edge-perturbing attacks. Adversaries can exploit the inherent opened interfaces of GNNs' input and output, perturbing critical edges and thus manipulating the classification results. Current defenses, due to their persistent utilization of global-optimization-based end-to-end training schemes, inherently encapsulate the vulnerabilities of GNNs. This is specifically evidenced in their inability to defend against targeted secondary attacks. In this paper, we propose the Graph Agent Network (GAgN) to address the aforementioned vulnerabilities of GNNs. GAgN is a graph-structured agent network in which each node is designed as an 1-hop-view agent. Through the decentralized interactions between agents, they can learn to infer global perceptions to perform tasks including inferring embeddings, degrees and neighbor relationships for given nodes. This empowers nodes to filtering adversarial edges while carrying out classification tasks. Furthermore, agents' limited view prevents malicious messages from propagating globally in GAgN, thereby resisting global-optimization-based secondary attacks. We prove that single-hidden-layer multilayer perceptrons (MLPs) are theoretically sufficient to achieve these functionalities. Experimental results show that GAgN effectively implements all its intended capabilities and, compared to state-of-the-art defenses, achieves optimal classification accuracy on the perturbed datasets.

FinTech lending (e.g., micro-lending) has played a significant role in facilitating financial inclusion. It has reduced processing times and costs, enhanced the user experience, and made it possible for people to obtain loans who may not have qualified for credit from traditional lenders. However, there are concerns about the potentially biased algorithmic decision-making during loan screening. Machine learning algorithms used to evaluate credit quality can be influenced by representation bias in the training data, as we only have access to the default outcome labels of approved loan applications, for which the borrowers' socioeconomic characteristics are better than those of rejected ones. In this case, the model trained on the labeled data performs well on the historically approved population, but does not generalize well to borrowers of low socioeconomic background. In this paper, we investigate the problem of representation bias in loan screening for a real-world FinTech lending platform. We propose a new Transformer-based sequential loan screening model with self-supervised contrastive learning and domain adaptation to tackle this challenging issue. We use contrastive learning to train our feature extractor on unapproved (unlabeled) loan applications and use domain adaptation to generalize the performance of our label predictor. We demonstrate the effectiveness of our model through extensive experimentation in the real-world micro-lending setting. Our results show that our model significantly promotes the inclusiveness of funding decisions, while also improving loan screening accuracy and profit by 7.10% and 8.95%, respectively. We also show that incorporating the test data into contrastive learning and domain adaptation and labeling a small ratio of test data can further boost model performance.

The prevalent communication efficient federated learning (FL) frameworks usually take advantages of model gradient compression or model distillation. However, the unbalanced local data distributions (either in quantity or quality) of participating clients, contributing non-equivalently to the global model training, still pose a big challenge to these works. In this paper, we propose FedCliP, a novel communication efficient FL framework that allows faster model training, by adaptively learning which clients should remain active for further model training and pruning those who should be inactive with less potential contributions. We also introduce an alternative optimization method with a newly defined contribution score measure to facilitate active and inactive client determination. We empirically evaluate the communication efficiency of FL frameworks with extensive experiments on three benchmark datasets under both IID and non-IID settings. Numerical results demonstrate the outperformance of the porposed FedCliP framework over state-of-the-art FL frameworks, i.e., FedCliP can save 70% of communication overhead with only 0.2% accuracy loss on MNIST datasets, and save 50% and 15% of communication overheads with less than 1% accuracy loss on FMNIST and CIFAR-10 datasets, respectively., Comment: 9 pages, 3 figures, 3 tables

This study assesses the spatiotemporal development of land use systems and climate variability in Southwestern Ghana over the past five decades using integrated remote sensing techniques and existing literature. We demonstrated the relationship between Normalized Difference Vegetative Index, Normalized Difference Water Index, Normalized Difference Built-up Index, surface temperature and precipitation using geoinformatics and Pearson’s correlation coefficient (r). We found change in land use systems in Southwestern Ghana to be immensely driven by economic and socio-political factors. Interestingly, some biophysical factors have somewhat contributed to this change. Findings revealed a drastic decline in forested areas (−334.8 km2 yr−1) and waterbodies (−4.79 km2 yr−1), along with a dramatic increase in built-up (+137.93 km2 yr−1) and farmlands/shrubs (+131.97 km2 yr−1). Change in prevailing microclimatic conditions can be associated with land cover change, considering the impact of major drivers observed over the given period. Results showed a very weak positive correlation between vegetation and temperature (r = 0.214). Similarly, built-up correlated positively with vegetation (r = 0.165), water-index (r = 0.818; strong correlation or evidence of association) and temperature (r = 0.266). In contrast, other used variables correlated negatively with precipitation. The study serves a seminal guide to land use developers and institutors for effective and sustainable use of natural resources.

Magnaporthe oryzae is a potent fungus that adversely affects rice yield. Combinatorial techniques of prevention, toxic chemicals, and fungicide are used to remedy rice blast infection. We reported the role of Hrip1 in cell death elicitation and expression of systematic acquired resistance that could potentially stifle M. oryzae infection. In this study, transcriptome and metabolomic techniques were used to investigate the mechanism by which Hrip1 reprogramed the transcriptome of rice seedlings to confer immunity against M. oryzae. Our results showed that Hrip1 induces cell wall thickening and phytoalexin elicitation to confer immunity against M. oryzae infection. Hrip1 activates key lignin biosynthetic genes and myeloblastosis transcription factors that act as molecular switches for lignin production. Lignin content was increased by 68.46% and more after 48 h onwards in Hrip1-treated seedlings compared to the control treatment. Further analysis of cell wall morphology using the transmission electron microscopy technique revealed over 100% cell wall robustness. Hrip1 also induced the expression of 24 diterpene synthases. These include class I and II terpene synthases, cytochrome P450 subfamilies (OsCYP76M and OsCYP71Z), and momilactones synthases. The relationship between the expression of these genes and metabolic elicitation was analyzed using ultra-performance liquid chromatography–tandem mass spectrometry. Enhanced amounts of momilactones A and B, oryzalactone, and phytocassane A and G were detected in the Hrip1-treated leaves. We also identified seven benzoxazinoid genes (BX1-BX7) that could improve rice immunity. Our findings show that Hrip1 confers dual immunity by leveraging lignin and phytoalexins for physical and chemical resistance. This study provides novel insights into the mechanisms underlying Hrip1-treated plant immunity.

As the micro-video apps become popular, the numbers of micro-videos and users increase rapidly, which highlights the importance of micro-video recommendation. Although the micro-video recommendation can be naturally treated as the sequential recommendation, the previous sequential recommendation models do not fully consider the characteristics of micro-video apps, and in their inductive biases, the role of positions is not in accord with the reality in the micro-video scenario. Therefore, in the paper, we present a model named PDMRec (Position Decoupled Micro-video Recommendation). PDMRec applies separate self-attention modules to model micro-video information and the positional information and then aggregate them together, avoid the noisy correlations between micro-video semantics and positional information being encoded into the sequence embeddings. Moreover, PDMRec proposes contrastive learning strategies which closely match with the characteristics of the micro-video scenario, thus reducing the interference from micro-video positions in sequences. We conduct the extensive experiments on two real-world datasets. The experimental results shows that PDMRec outperforms existing multiple state-of-the-art models and achieves significant performance improvements., Comment: accepted by ECML PKDD2022

We develop a structural econometric model to capture the decision dynamics of human evaluators on an online micro-lending platform, and estimate the model parameters using a real-world dataset. We find two types of biases in gender, preference-based bias and belief-based bias, are present in human evaluators' decisions. Both types of biases are in favor of female applicants. Through counterfactual simulations, we quantify the effect of gender bias on loan granting outcomes and the welfare of the company and the borrowers. Our results imply that both the existence of the preference-based bias and that of the belief-based bias reduce the company's profits. When the preference-based bias is removed, the company earns more profits. When the belief-based bias is removed, the company's profits also increase. Both increases result from raising the approval probability for borrowers, especially male borrowers, who eventually pay back loans. For borrowers, the elimination of either bias decreases the gender gap of the true positive rates in the credit risk evaluation. We also train machine learning algorithms on both the real-world data and the data from the counterfactual simulations. We compare the decisions made by those algorithms to see how evaluators' biases are inherited by the algorithms and reflected in machine-based decisions. We find that machine learning algorithms can mitigate both the preference-based bias and the belief-based bias., Comment: accepted by KDD 2021, MLCM workshop

The out-of-plane compression behaviour of 6061-T6 aluminum alloy super-stub honeycomb cellular structures without and with friction stir welding (FSW) facesheets are presented in this paper. A total of twelve axially compressed experiments on large-scale specimens, six with square hollow section (SHS) cores and six with hexagonal hollow section (HHS) cores, were conducted, with failure modes, ultimate resistances and axial load-end shortening curves analysed. The accuracy of finite element (FE) models was validated in accordance with test results. The numerical data obtained from extensive parametric analyses combined with test data were subsequently used to evaluate the applicability of existing design rules in Chinese, European and American aluminium alloy specifications. The results showed that the three specifications generally yielded very conservative predictions for the out-of-plane compression resistances of SHS and HHS super-stub honeycomb cores without and with FSW facesheets by about 30–37%. Design recommendations on the cross-section effective thickness are finally proposed and shown to provide much more accurate and consistent predictions than current design methods. The research results are beneficial to the application and development of large-scale super-stub honeycomb structures in structural engineering, such as the helicopter landing platforms, the base of fluid and gas tanks and ship decks.

Integrating culture into Chinese classes has drawn an increasing attention to the field of language education. More and more educators of Chinese have emphasized the significance, urgency, and necessity of combining culture into the process of learning Chinese with a primary focus on how to integrate culture into Chinese classes, textbooks, and language practices.This study aims to distinguish the differences among Chinese natives (L1), Chinese heritage learners (CHL), and the U.S. non-heritage learners of Chinese (L2) from their self-introductions in Chinese.The distinctions of cultural awareness behind the language among these three groups were determined, such as their attitudes toward hometown, the way to explain Chinese names, the usage of idiomatic phrases, hobbies, and so forth. The research findingsaim to draw on pedagogical implications for integrating culture into teaching Chinese courses, particularly teaching self-introduction.

To investigate the correlation between TCM syndrome element, syndrome distribution and cardiac color ultrasound, 90 patients are divided into five groups according to the syndrome type: syndrome of qi deficiency of heart and lung, deficiency of both qi and yin, syndrome of qi deficiency and blood stagnation, syndrome of water overflowing due to yang deficiency, and syndrome of yang deficiency of heart and kidney, to extract and analyze the distribution of CHF syndrome element. 18 healthy volunteers are enrolled as the normal control group to study the correlation between NYHA cardiac function grading and cardiac color ultrasound. The results show that the distribution of CHF syndrome element is as follows: root deficiency syndrome element in which disease location is dominated by heart (94.4%) and disease natures is dominated by qi deficiency (82.2%), and the superficial sthenia syndrome element dominated by the blood stagnation (70.0%). Conclusion: The CHF pathogenesis is characterized by deficient root and excessive superficiality, disease location is dominated by heart, and the symptom element of disease nature is rooted by qi deficiency and is marked by blood stagnation. Cardiac color ultrasound is an important examination method for CHF, its parameters have significant differences among the syndrome type groups, and it can be used as an important auxiliary means for CHF TCM syndrome differentiation. Different CHF syndrome types are significantly different in the levels, which can be used as important reference index for CHF TCM syndrome differentiation.

In a precise positioning system, weak signal errors caused by the influence of a human body on signal transmission in complex environments are a main cause of the reduced reliability of communication and positioning accuracy. Therefore, eliminating the influence of interference from human crawling waves on signal transmissions in complex environments is an important task in improving positioning systems. To conclude, an experimental environment is designed in this paper and a method using the Ultra-Wideband (UWB) Local Positioning System II (UWB LPS), called Bayesian Compressed Sensing-Crawling Waves (BCS-CW), is proposed to eliminate the impact of crawling waves using Bayesian compressive sensing. First, analyse the transmission law for crawling waves on the human body. Second, Bayesian compressive sensing is used to recover the UWB crawling wave signal. Then, the algorithm is combined with the maximum likelihood estimation and iterative approximation algorithms to determine the label position. Finally, through experimental verification, the positioning accuracy of this method is shown to be greatly improved compared to that of other algorithms.

Goal: To investigate the clinical benefits of partial body weight support for the function of Cardiopulmonary and Cardiac autonomic nerve in the early stage of Heart failure rehabilitation. Materials and Methods: We selected 90 patients with heart failure, divided into observation group (n = 45) and control group (n = 45). Both patients had the conventional drug therapy, while the observation group had the partial body weight support additionally within the 3 months treatment period. Serological examination includes brain natriuretic peptide (BNP) and aldosterone. Echocardiography detects left ventricular morphology, cardiac ejection function (EF) and cardiac autonomic nerve function. Minnesota quality of life scale (MHL) evaluates the life quality of the patients. Results: Before any treatment, there is no significant difference of serum brain natriuretic peptide (BNP), aldosterone, cardiac autonomic nerve function and the Minnesota quality of life scale (MHL) (P > 0.05). After treatment, outcome measures declined, including serum brain natriuretic peptide (BNP) and aldosterone (P < 0.01), LVESD and ESV (P < 0.01), LVEDD and EDV (P > 0.05). Outcome measures raised, including SV, CI, EF%, ΔD%. Among them, EF% had significant difference with P value < 0.05, and ΔD% with P value < 0.01. Both LF and HF raised, but LF/HF declined. The Minnesota quality of life scale (MHL) is significantly lower than before. Conclusion: Partial body weight support obviously reduces serum brain natriuretic peptide (BNP) and aldosterone, as well as improves the function of cardiopulmonary and cardiac autonomic nerve of the patients with heart failure, which at last improves the life quality.

Many practical recommender systems provide item recommendation for different users only via mining user-item interactions but totally ignoring the rich attribute information of items that users interact with. In this paper, we propose an attribute-augmented graph neural network model named Murzim. Murzim takes as input the graphs constructed from the user-item interaction sequences and corresponding item attribute sequences. By combining the GNNs with node aggregation and an attention network, Murzim can capture user preference patterns, generate embeddings for user-item interaction sequences, and then generate recommendations through next-item prediction. We conduct extensive experiments on multiple datasets. Experimental results show that Murzim outperforms several state-of-the-art methods in terms of recall and MRR, which illustrates that Murzim can make use of item attribute information to produce better recommendations. At present, Murzim has been deployed in MX Player, one of India's largest streaming platforms, and is recommending videos for tens of thousands of users.

Additional file 4: Table S4. Significantly enriched KEGG pathways of DEGs in B. cinerea.

Additional file 3: Table S3. Significantly enriched KEGG pathways of DEGs in C. sativus.

Interactions between users and videos are the major data source of performing video recommendation. Despite lots of existing recommendation methods, user behaviors on videos, which imply the complex relations between users and videos, are still far from being fully explored. In the paper, we present a model named Sagittarius. Sagittarius adopts a graph convolutional neural network to capture the influence between users and videos. In particular, Sagittarius differentiates between different user behaviors by weighting and fuses the semantics of user behaviors into the embeddings of users and videos. Moreover, Sagittarius combines multiple optimization objectives to learn user and video embeddings and then achieves the video recommendation by the learned user and video embeddings. The experimental results on multiple datasets show that Sagittarius outperforms several state-of-the-art models in terms of recall, unique recall and NDCG.

Additional file 1.

In view of the problem that it is difficult to evaluate the overload fault risk of distribution transformer, this paper proposes a data-driven thermal evaluation method of distribution transformer, points out that the difference equation of heat transfer is difficult to be applied to the thermal evaluation of the distribution transformer. Considering the three-phase load coefficient of distribution transformer, the risk evaluation model of overload fault of distribution transformer circulation neural network is established. The purpose of thermal evaluation of distribution transformer is achieved by predicting the highest oil temperature. The results show that the method is accurate, and the validity and practicability of the method are verified.

There is a risk of feeder over-current tripping in loop closing operation of distribution network. Without depending on the specific network parameters, a method for determining the closed loop in the distribution network is proposed so as to analyze the calculation method of voltage and power distribution in the distribution network, and a neural network model for determining the closed-loop distribution network is established. By calculating the power and current of the distribution network when the loop is closed, the purpose of determining whether the closed-to-be loop is closed or not is achieved. The specific example shows that the method takes into account the change of power flow in distribution network when the loop is closed, and the calculation of closed-loop current is more accurate and practical.

A series of acylhydrazone derivatives (4a–4s) were synthesized via microwave irradiation with short reaction time and good yields. The crystal structure of compound 4j was further determined by single-crystal X-ray diffraction. All of the derivatives were evaluated in vitro for influenza neuraminidase inhibitory activities. Among them, compounds 4l and 4n showed the most potent activity with IC50 values of 59.86 and 49.97 μM, respectively. Molecular docking was performed to study the interactions between the protein and compound 4n. The docking result revealed the potential dual-site binding model of the title compounds (“430 cavity-Linker-SA cavity” model) which provided a new idea for the design of novel influenza neuraminidase inhibitors.

Objective: This Meta-analysis was aimed to systematically studying the effects of muscle strength training on the walking ability like balance ability, mobility ability of Parkinson's disease patients and then provide useful theoretical reference to further researches on exercise treatments on Parkinson’s disease by retrieving and collecting articles about muscle strength training. Methods This literature review was finally determined on searching PubMed, Elsevier, Web of science, China Journal Full-text Database (CNKI), WanFang Data and other Chinese and foreign databases and combined with manual search. The limit of the search time was from the date when the literature was recorded to 2019. A comprehensive collection of randomly controlled trials of muscle strength training on the walking ability of Parkinson's patients was done. Researchers used the Cochrance risk assessment tool to evaluate the methodological quality of the selected articles, and the ReMan 5.3.5 software to statistically process the obtained data. Results A total of 13 RCTs and 627 samples were included in this study. Meta-analysis of BBS balance scale show that MD=4.67 (95%CI, I2=97%, P=0.52) between muscle strength training group and non-exercise intervention group; MD=-2.67 between muscle strength training group and exercise intervention control group (95%CI, I2=7%, P

In order to find novel herbicidal active compounds, a series of N-(2,2-dimethyl-7-alkoxy-2,3-dihydro-benzofuran-5-yl)-2-(4-arylxoyphenoxy)propionamides was designed, prepared and evaluated for their herbicidal activity. Bioassay results showed that most of the title compounds had excellent and selective herbicidal activity against the monocotyledonous grasses. In particular, compounds 1g and 1m showed 100% inhibition against the growth of three monocotyledonous grasses under both postemergence and preemergence treatments at the dose of 2250 g/hm2 (1 hm2=104 m2), and could be used as lead compounds for further development of novel potential herbicidal agents.

As a key support for smart grids, the smart substation has formed a mature technical solutions after long-term development. In recent years, the internet of things technology has been applied in the whole aspects of power system transmission and distribution. Based on the extensive data collection and fusion, it has improved the state awareness, intelligent decision-making, and asset management capabilities of the power system. With the application of the internet of things technology, the development direction and technical architecture changes of smart substations are important topics that need to be considered at present. Based on the analysis of the current substation development route, this paper studies the applicability of electric power internet of things technology in smart substations, and proposes a smart substation technology solution that combines traditional technology routes and internet of things technologies, wishing to provide some reference for the research of smart substations. Chinese Library Classification Number: TM715

There is a noticeable discrepancy in the ability to control reduced explosion overpressure between flat bursting panels and curved bursting panels with the same static activation overpressure. Flat bursting plates were observed to leak at approximately 80% of the static activation overpressure lower than curved bursting plates. A new experimental technique is proposed in our paper. Three different vent areas of flat and curved bursting panels were tested, there was significant difference in structural stiffness between flat bursting panels and curved bursting panels, which is the reason the discrepancy in the ability to control reduced explosion overpressure. The structural stiffness of the flat bursting panels is poorer than that of the other, and a greater deformation of the flat bursting panels occurs under the same load. The membrane stress caused by the explosion overpressure therefore produces a larger value in the flat bursting panels which causes it to open prematurely. Moreover, the smaller the vent area that is, the more significant discrepancy in controlling the reduced explosion overpressure between both bursting panels is. This experimental and theoretical result in our paper provides some useful experience for the method of explosion venting.

By changing soil properties, plants can modify their growth environment. Although the soil microbiota is known to play a key role in the resulting plant-soil feedbacks, the proximal mechanisms underlying this phenomenon remain unknown. We found that benzoxazinoids, a class of defensive secondary metabolites that are released by roots of cereals such as wheat and maize, alter root-associated fungal and bacterial communities, decrease plant growth, increase jasmonate signaling and plant defenses, and suppress herbivore performance in the next plant generation. Complementation experiments demonstrate that the benzoxazinoid breakdown product 6-methoxy-benzoxazolin-2-one (MBOA), which accumulates in the soil during the conditioning phase, is both sufficient and necessary to trigger the observed phenotypic changes. Sterilization, fungal and bacterial profiling and complementation experiments reveal that MBOA acts indirectly by altering root-associated microbiota. Our results reveal a mechanism by which plants determine the composition of rhizosphere microbiota, plant performance and plant-herbivore interactions of the next generation., Plants can modify soil microbiota through root exudation, but how this process influences plant health in turn is often unclear. Here, Hu et al. show that maize benzoxazinoids released into the soil modify root-associated microbiota and thereby increase leaf defenses of the next plant generation.

In a precision positioning system, one important source of positioning error is the clock synchronization problem, which is caused by multiple base stations. Therefore, eliminating the clock synchronization problem in a no-line-of-sight (NLOS) environment plays an important role in reducing errors in a positioning system. To address this problem, this study designs a practical experimental environment and proposes the concepts of Time Difference of Arrival (TDOA) and non-line-of-sight-Time Difference of Arrival (N-TDOA). First, the improved TDOA algorithm is used to determine the tag’s position; second, the tag’s trajectory is drawn at different times; then, a map of the monitored area is loaded, and the tag trajectory is displayed in the actual experimental environment. The experimental results show that the N-TDOA algorithm synchronizes the base stations at the algorithmic layer; thus, deploying network cables or wires to achieve clock synchronization is unnecessary: wireless deployment can be used. In an NLOS environment, the N-TDOA method significantly improves the positioning accuracy compared with that of other algorithms, which facilitates further trajectory tracking research. Overall, the proposed approach improves both the accuracy and stability of trajectory tracking.

Multilayers of Ti doped diamond-like carbon (Ti-DLC) coatings were deposited on aluminum alloys by filtered cathodic vacuum arc (FCVA) technology using C2H2 as a reactive gas. The effect of different Ti transition layer thicknesses on the structure, mechanical and adhesion properties of the coatings, was investigated by scanning electron microscopy (SEM), Raman spectroscopy, X-ray photoelectron spectroscopy (XPS), nanoindentation and a scratch tester. The results showed that the Ti transition layer could improve interfacial transition between the coating and the substrate, which was beneficial in obtaining excellent adhesion of the coatings. The Ti transition layer thickness had no significant influence on the composition and structure of the coatings, whereas it affected the distortion of the sp2-C bond angle and length. Nanoindentation and scratch test results indicated that the mechanical and adhesion properties of the Ti-DLC coatings depended on the Ti transition layer thickness. The Ti transition layer proved favorable in decreasing the residual compressive stress of the coating. As the Ti transition layer thickness increased, the hardness value of the coating gradually decreased. However, its elastic modulus and adhesion exhibited an initial decrease followed by an increasing fluctuation. Among them, the Ti-DLC coating with a Ti transition layer thickness of 1.1 &mu, m exhibited superior mechanical properties.

Background Right heart failure is a major problem of young adults with congenital heart disease (ACHD) that has been corrected by cardiac surgery. In left ventricular dysfunction (LVD), β-blocker therapy improved cytokine concentrations and clinical status. Thus, the effect of bisoprolol on clinical status and plasma cytokine concentrations in ACHD patients with right heart failure after surgical correction for Tetralogy of Fallot was investigated. Methods and Results A prospective, randomized, double-blind, placebo controlled study for a duration of 6 months was carried out. A total of 34 patients (30.9±9.5 years; New York Heart Association I or II) with brain natriuretic peptide plasma concentrations >100 pg/ml and peak oxygen uptake

A highly atom-economic procedure for the preparation of reduced graphene oxide/Mn3O4 (rGO/Mn3O4) composites is reported. Pristine graphene oxide/manganese sulfate (GO/MnSO4) suspension produced by modified Hummers method is utilized with high efficiency, which has been in situ converted into GO/Mn3O4 hybrid composite by air oxidation, then into rGO/Mn3O4 composite by means of dielectric barrier discharge (DBD) plasma-assisted deoxygenation. The Mn3O4 content of the rGO/Mn3O4 composites can be readily tailored. It is observed that Mn3O4 nanoparticles of 15-24 nm are well-dispersed on graphene sheets with Mn3O4 loading as high as 90%. The specific capacitance of the as-prepared rGO/Mn3O4 hybrids with 90% Mn3O4 reaches 193 F g(-1) when employed as the electrode material in neutral Na2SO4 electrolyte solutions (76 F g(-1) for pristine graphene and 95 F g(-1) for pure Mn3O4), which indicates the positive synergetic effects from both graphene and attached Mn3O4. The method developed in this study should offer a new technique for the large scale and highly atom-economic production of graphene/MnOx composites for many applications.

The performance of double row V-type inertial gas-liquid separator has been studied by numerical computation and experimental studied. The aerodynamics performance and separation efficiency of double row V-type inertial gas-liquid separator with different blade spacing are acquired by numerical simulation. The experimental research on aerodynamics performance of separator with specific blade spacing has been done on the special wind tunnel test bench. The experimental results agree well with the numerical simulation results. The researches can help to make prediction for performances in engineering and provide theoretical footing for optimizing the design.

With the growing pervasiveness of the Internet, online search for commercial goods and services is constantly increasing, as more and more people search and purchase goods from the Internet. Most of the current algorithms for product search are based on adaptations of theoretical models devised for “classic” information retrieval. However, the decision mechanism that underlies the process of buying a product is different than the process of judging a document as relevant or not. So, applying theories of relevance for the task of product search may not be the best approach. We propose a theory model for product search based on expected utility theory from economics. Specifically, we propose a ranking technique in which we rank highest the products that generate the highest consumer surplus after the purchase. In a sense, we rank highest the products that are the “best value for money” for a specific user. Our approach naturally builds on decades of research in the field of economics and presents a solid theoretical foundation in which further research can build on. We instantiate our research by building a search engine for hotels, and show how we can build algorithms that naturally take into account consumer demographics, heterogeneity of consumer preferences, and also account for the varying price of the hotel rooms. Our extensive user studies demonstrate an overwhelming preference for the rankings generated by our techniques, compared to a large number of existing strong baselines.

By the calculation of G-L Index and MIIT Index between China and Japan from 2001 to 2011, this thesis concludes that industry trade in service is primarily featured by intra-industry trade. On this basis, using the analysis by gradually quoting and illustrating the variable data figures out that market opening, different demand and economics of scale are the critical factors for influencing intra-industry trade in Sino-Japan service industry. Relevant policy conclusions are also suggested at the end of this thesis.

Inflammation contributes to the pathogenesis of atherosclerosis. Proinflammatory cytokines, including interleukin-1 (IL-1), may be involved in the local inflammation occurring in the vessel wall. Vascular smooth muscle cells express the unprocessed IL-1beta precursor molecule. Invading leukocytes, such as monocytes or polymorphonuclear granulocytes (PMN) may activate the IL-1beta precursor during atherogenesis. Thus, we investigated the capacity of PMN to process IL-1beta and IL-18 precursors. Processing was analyzed using Western blot and bioassay for IL-1-activity was performed. As few as 80 to 400 PMN/mL detectably processed preIL-1beta. PMN also cleaved the caspase-1 substrate preIL-18. The preIL-1beta and preIL-18 cleavage products were located at the same apparent molecular weight as those resulting from cleavage by monocyte-derived caspase-1. PMN expressed caspase-1 mRNA and immunoreactive protein. The N-terminus of the preIL-1beta cleavage product expressed the sequence expected for caspase-1 cleavage. The cleavage product was active in the bioassay for IL-1 activity, and the caspase-1 inhibitor YVAD blocked processing. We have shown previously that SMC can block processing of preIL-1 by caspase-1. In contrast, SMC do not block processing of PARP by caspase-3. Here, we show that SMC also inhibited the PMN-mediated processing of recombinant and native preIL-1beta or preIL-18 depending on the cell number, whereas EC or fibroblasts did not block processing. Our results indicate that PMN can activate preIL-1beta in a caspase-1-like fashion. During inflammatory processes, PMN may activate preIL-1beta released from SMC, thereby altering IL-1-mediated cardiovascular functions, including contractility, apoptosis, and cytokine production.

A highly atom-economic procedure for the preparation of reduced graphene oxide/Mn3O4 (rGO/Mn3O4) composites is reported. Pristine graphene oxide/manganese sulfate (GO/MnSO4) suspension produced by modified Hummers method is utilized with high efficiency, which has been in situ converted into GO/Mn3O4 hybrid composite by air oxidation, then into rGO/Mn3O4 composite by means of dielectric barrier discharge (DBD) plasma-assisted deoxygenation. The Mn3O4 content of the rGO/Mn3O4 composites can be readily tailored. It is observed that Mn3O4 nanoparticles of 15–24 nm are well-dispersed on graphene sheets with Mn3O4 loading as high as 90%. The specific capacitance of the as-prepared rGO/Mn3O4 hybrids with 90% Mn3O4 reaches 193 F g−1 when employed as the electrode material in neutral Na2SO4 electrolyte solutions (76 F g−1 for pristine graphene and 95 F g−1 for pure Mn3O4), which indicates the positive synergetic effects from both graphene and attached Mn3O4. The method developed in this study should offer a new technique for the large scale and highly atom-economic production of graphene/MnOx composites for many applications.

In the hierarchical control paradigm of a smart grid cyber-physical system, decentralized local agents (LAs)can potentially be compromised by opportunistic attackers to manipulate electricity prices for illicit financial gains. In this paper, to address such opportunistic attacks, we propose a Dirichlet-based detection scheme (DDOA), where a Dirichlet based probabilistic model is built to assess the reputation levels of LAs. Initial reputation levels of the LAs are first trained using the proposed model, based on their historical operating observations. An adaptive detection algorithm with reputation incentive mechanism is then employed to detect opportunistic attackers. We demonstrate the utility of our proposed scheme using data collected from the IEEE 39-bus power system with the PowerWorld simulator. Refereed/Peer-reviewed