Search

International audience; Active biodegradable packaging films were developed with polyvinyl alcohol (PVA), chitosan (CS) and D-Limonene (DL). The effect of various DL content levels (0%, 2.5%, 5%, 7.5%, and 10% w/w) on the structural, mechanical, biodegradable and antimicrobial properties of PVA/CS films was systematically studied. Good compatibility between DL and PVA/CS, and good dispersion of DL in the PVA/CS matrix were demonstrated by Fourier transform infrared (FTIR) spectroscopy and scanning electron microscopy (SEM). PVA/CS films greatly improved their antibacterial, mechanical and barrier properties, which are associated with the considerable biodegradability, after they were incorporated with DL. However, the overloading with DL may weaken hydrogen bonds between polymer chains, resulting in the negative effects on the physical performances of the film. Particularly, the PVA/CS/DL-5% film showed the highest water contact angle and transmittance value, and also showed effective preservations of packaged mango fruits during 10 day of storage at 20 +/- 2 degrees C, based on the characterization by fruits weight loss, decay rate, firmness, titratable acidity, soluble solids, and ascorbic acid. Consequently, DL/PVA/CS composite films may be a promising eco-friendly packaging material for food preservation. (C) 2018 Published by Elsevier B.V.

With the progress of intelligent and digital healthcare, wearable sensors are attracting considerable attention due to their portable and real-time monitoring capabilities. Among them, wearable gas sensors, which can detect both gas markers from the human body and hazardous gas from the environment, are particularly gaining tremendous interest. To ensure the gas sensors can be worn and carried easily, most of them were fabricated on flexible substrates. However, some traditional fabrication techniques of gas sensors such as lithography and chemical vapor deposited, are incompatible with most flexible substrates due to the flexible substrates cannot endure the harsh fabricated conditions, for instance, high temperature. Therefore, fabrication techniques for wearable gas sensors are extremely limited, thus a summary of which is necessary. Here, recent advances in the fabrication techniques of wearable gas sensors are presented. Fabrication techniques included coating techniques, printing techniques, spinning techniques, and transferring techniques are discussed in detail, respectively.

Quasi-two-dimensional (2D) β-Ga2O3 is a rediscovered metal-oxide semiconductor with an ultra-wide bandgap of 4.6–4.9 eV. It has been reported to be a promising material for next-generation power and radio frequency electronics. Field effect transistors (FETs) that can switch at high voltage are key components in power and radio frequency devices, and reliable Ohmic contacts are essential for high FET performance. However, obtaining low contact resistance on β-Ga2O3 FETs is difficult since reactions between β-Ga2O3 and metal contacts are not fully understood. Herein, we experimentally demonstrate the importance of reactions at the metal/β-Ga2O3 interface and the corresponding effects of these reactions on FET performance. When Ti is employed as the metal contact, annealing of β-Ga2O3 FETs in argon can effectively transform Schottky contacts into Ohmic contacts and permit a large drain current density of ~ 3.1 mA/μm. The contact resistance (Rcontact) between the Ti electrodes and β-Ga2O3 decreased from ~ 430 to ~ 0.387 Ω·mm after annealing. X-ray photoelectron spectroscopy (XPS) confirmed the formation of oxygen vacancies at the Ti/β-Ga2O3 interface after annealing, which is believed to cause the improved FET performance. The results of this study pave the way for greater application of β-Ga2O3 in electronics.

International audience; To establish a “green” biorefinery extraction of apple pomace pectin, a sequential pretreatment with three natural deep eutectic solvents (NADES, choline chloride (CC): glycerol (G); CC: lactic acid (LA); potassium carbonate (K): G) was used prior to hot water extraction. A synergistic effect of CC:G and CC:LA pretreatments was observed and led to the highest recovery of pectin. The sequential NADES/water extraction process also provided a mean to tailor pectin main structure. It was explained as resulting from ion exchange and individual NADES components effects. The 13C solid state NMR T1ρH and THH parameters indicated a reorganization of cellulose in the residues following extraction of pectin, notably after alkaline K:G pretreatment/water extraction. Hence, sequential NADES pretreatments/water extraction represents a “green” alternative to mild mineral acid to extract pectin and to tailor its main structures, while the residual pomace can be further sources of valuable compounds and polymers.

In order to build a high-quality team of teachers in vocational higher education institutions, and investigate the current situation of teachers of common required courses in vocational higher education institutions of Hainan Province, an empirical research is conducted. A questionnaire survey was carried out in aspects of satisfaction of teaching and scientific research, professional quality, work saturation and teacher’s job satisfaction. The research results shows that the professional quality of teachers of common required courses cannot significantly influence the satisfaction of teaching and scientific research; Job saturation can positively influence teaching and research satisfaction; teaching and research satisfaction can positively influence teachers’ job satisfaction.

With the development of information technology, the demand for information security is increasing. For more convenient and safer needs, the encryption technology based on biometrics has developed rapidly. Among them, iris technology has become an important research object of information security research due to the stability of iris characteristics and its difficulty in forgery. In this paper, the iris feature encryption technology based on the iris is studied by using the method of deep learning as the feature classification method and the iris feature as the research object. The simulation experiment is carried out by using the common iris database. The results show that the method can greatly improve the consistency of iris encryption and improve the security of encryption and decryption process.

Background/Aims: Treatments targeting cancer stem cells (CSCs) are most effective cancer therapy, whereas determination of CSCs is challenging. We have recently reported that Lgr5-positive cells are cancer stem cells (CSCs) in human skin squamous cell carcinoma (SCC). Ginsenoside Rh2 (GRh2) has been shown to significantly inhibit growth of some types of cancers, whereas its effects on the SCC have not been examined. Methods: Here, we transduced human SCC cells with lentivirus carrying GFP reporter under Lgr5 promoter. The transduced SCC cells were treated with different doses of GRh2, and then analyzed cell viability by CCK-8 assay and MTT assay. The effects of GRh2 on Lgr5-positive CSCs were determined by fow cytometry and by tumor sphere formation. Autophagy-associated protein and β-catenin were measured by Western blot. Expression of short hairpin small interfering RNA (shRNA) for Atg7 and β-catenin were used to inhibit autophagy and β-catenin signaling pathway, respectively, as loss-of-function experiments. Results: We found that GRh2 dose-dependently reduced SCC viability, possibly through reduced the number of Lgr5-positive CSCs. GRh2 increased autophagy and reduced β-catenin signaling in SCC cells. Inhibition of autophagy abolished the effects of GRh2 on β-catenin and cell viability, while increasing β-catenin abolished the effects of GRh2 on autophagy and cell viability. Conclusion: Taken together, our data suggest that GRh2 inhibited SCC growth, possibly through reduced the number of Lgr5-positive CSCs. This may be conducted through an interaction between autophagy and β-catenin signaling.

The present study was conducted to investigate the effects of dietary acidolysis-oxidized konjac glucomannan (A-OKGM) (0%, 0.4%, 0.8%, and 1.6%) supplementation on the immunity and expression of immune-related genes in Schizothorax prenanti. After feeding for eight weeks, the serum and guts were used for measurement of biochemical parameters, and immune-related gene expression in the gut were also analyzed by real-time quantitative polymerase chain reaction (RT-qPCR). C-reactive protein and IgM levels were significantly higher in the A-OKGM fed groups than in the control group, regardless of the dosage. The 0.4% and 1.6% A-OKGM groups showed significant up-regulation of tumor necrosis factor α (TNFα) in the anterior gut. The 0.8% and 1.6% A-OKGM groups also showed significantly enhanced TNFα expression in the mid- and distal guts. Interleukin-1β (IL-1β) expression in the anterior gut of fish fed with 0.4% and 1.6% A-OKGM diets was significantly enhanced. The 0.8% and 1.6% A-OKGM diets resulted in significantly increased the expression of IL-1β in the distal gut. Similarly, the interleukin-6 (IL-6) messenger RNA (mRNA) levels in the 0.4% and 1.6% diet groups were significantly higher in the anterior gut. The 0.8% and 1.6% A-OKGM diet groups showed significant induction of IL-6 gene expression in the distal gut. A-OKGM modified from KGM can act as an immunostimulant to enhance the immunity of S. prenanti.

Credit scoring process, the most important part in credit risk management, aims at estimating the probability that an applicant will perform bad credit behaviors (e.g., loan default). Managing and developing effective and reliable risk assessment procedures in order to mitigate potential loss caused by new applicants heavily relies on the performance of scoring process. Traditionally this process is manually developed, which is time-consuming. In this paper, we propose an automated credit risk management process based on machine learning to ease the scoring process in order to reduce the human effort. This process is data driven: it leverages machine learning to automatically analyze vast amounts of historical data and build predictive model. We evaluate our process with a real-world proprietary dataset and achieved good performance, which shows the feasibility of using machine learning to facilitate the credit risk management process.

Software requirement analysis is an essential step in software development process, which defines what is to be built in a project. Requirements are mostly written in text and will later evolve to fine-grained and actionable artifacts with details about system configurations, technology stacks, etc. Tracing the evolution of requirements enables stakeholders to determine the origin of each requirement and understand how well the software's design reflects to its requirements. Reckoning requirements traceability is not a trivial task, we focus on applying machine learning approach to classify traceability between various associated requirements. In particular, we investigate a 2-learner, ontology-based approach, where we train two classifiers to separately exploit two types of features, lexical features and features derived from a hand-built ontology. In comparison to a supervised baseline system that uses only lexical features, our approach yields a relative error reduction of 25.9%. Most interestingly, results do not deteriorate when the hand-built ontology is replaced with its automatically constructed counterpart.

UML (Unified Modeling Language) is designed for all stages of software development, but it lacks precise semantic information. MDE (Model-driven engineering) takes the model as the primary software product and its main research direction is modeling and model transformation. We propose to explore the entity abstraction scenario where no existing classes fit as the representative entity for other classes through the data, information and knowledge recreation of existing classes and relationships with the introduction of knowledge graphs. In this paper, we proposed a knowledge graph to enhance model design between models.

Cancer stem cells (CSCs) in most human tumors are commonly identified and enriched using similar strategies for identifying normal stem cells, including flow cytometry assays for side population, high aldehyde dehydrogenase (ALDH) activity, and CD133 positivity. Thus, development of a method for isolating a specific cancer using cancer-specific characteristic appears to be potentially important. Here, we reported extremely high Lgr5 levels in the specimen from skin squamous cell carcinoma (SCC) in patients. Using SCC cell line A431, we detected high Lgr5 and CD133 levels in ALDH-high or side population from these cancer cells. To figure out whether Lgr5 is a marker of CSCs in SCC, we transfected A431 cells with a Lgr5-creERT-2A-DTR/Cag-Loxp-GFP-STOP-Loxp-RFP plasmid and purified transfected cells (tA431) based on GFP by flow cytometry. 4-Hydroxytamoxifen (4-OHT) was given to label Lgr5-positive cells with RFP, for comparison to GFP-positive Lgr5-negative cells. Lgr5-positive cells grew significantly faster than Lgr5-negative cells, and the fold increase in growth of Lgr5-positive vs Lgr5-negative cells is significantly higher than SP vs non-SP, or ALDH-high vs ALDH-low, or CD133-positive vs CD133-negative cells. Moreover, in Lgr5-negative population, Lgr5-positive re-appeared in culture with time, suggesting that Lgr5-positive cells can be regenerated from Lgr5-negative cells. Furthermore, the growth of tA431 cells significantly decreased upon a single dose of diphtheria toxin (DT)/4-OHT to eliminate Lgr5-positive cell lineage, while multiple doses of DT/4-OHT nearly completely inhibited tA431 cell growth. Taken together, our data provide compelling data to demonstrate that Lgr5-positive cells are CSCs in skin SCC.

In recent years, with the rapid development of the Internet, our life has undergone tremendous changes, and the Internet has become an indispensable part of us. With the popularity of the Internet, more and more people use the Internet to transmit multimedia information such as images [1]. Studies have shown that about 75% of human information from the outside world comes from the visual system, that is, from images. Image is not unfamiliar to us. However, in recent years, the security of image information in the transmission process has become increasingly prominent. Such image information as cases, design drawings and so on need to be encrypted when they are transmitted on the Internet. Scrambling technology is an image encryption technology. There are many commonly used image scrambling algorithms. This paper first introduces the image scrambling algorithm based on Arnold transform. Then, in order to improve the image scrambling performance, the application of Arnold transform matrix in image scrambling is analyzed. On this basis, a double scrambling image encryption algorithm based on two-dimensional Arnold transform is proposed. This algorithm uses two-dimensional Arnold transform at the same time. Gray scale and position scrambling [3]. The simulation results show that the improved algorithm can achieve better scrambling effect and encryption effect, which is conducive to the study of image encryption.

With the advent of the Web era, data has exploded, with tens of thousands of textual data being generated every day. Traditional text sentiment analysis methods are mainly based on lexicon and machine learning-based methods. These methods show certain limitations as the data size increases. Here, we propose a new knowledge map design method based on convolutional neural network. For the academic literature data crawled from HowNet and Baidu Academic Website with the theme of computer, the corresponding ontology database, the fusion application of multiple data sources, and the mapping of different ontology libraries through data fusion are constructed for data sources in different fields. The global ontology library then uses the entity alignment and entity link methods for knowledge acquisition and fusion. Finally, the convolutional neural network is used for training and testing. The experimental results show that the subject search task can not only obtain the book through the convolution network, the effective academic literature in the question bank can also be used to obtain the relevance of the keyword in the search results, which verifies the effectiveness of the method.

The instrument of Fetal Heart Rate (FHR) monitoring based on the technique of ultrasonic Doppler is harmful to the baby. In order to overcome the limitations a new method is proposed in this paper which is a non-invasive FHR monitoring method based on low-power Bluetooth technology. and is combined with the most widely used smartphone and low-power Bluetooth technology in the paper. In the first step the sensor SCP1000-D01 picked up mixed signal from maternal’s abdomen. In the next course of action the FHR signal is separated from the mixed signal by pre-amplification processing circuit, signal processing circuit, A/D conversion and optical coupling circuit. Results show that the signal collected by the sensor is processed by Blind source separation algorithm based on two order statistics to extract real-time clear FHR signal. The extracted signal is transmitted to the smartphone through the low-power Bluetooth technology and reflected in the phone.

A health monitoring system is designed for smart management for personal health in this paper. Self-description and self-reasoning, the characteristics of ontology, make the link between machine and knowledge, knowledge point and the relevant knowledge point more closer. This system has certain application value on managing and analyzing basic health data. Database in this system is realized by storing user’s health data and disease information. Health monitoring system is user-centric which can record user’s health condition data and make recommendations for user according to the result of health data analyzing. User’s health status is expressed by a specific domain, like overweight and underweight. The experiment result shows that the health monitoring system can make a steady management for personal health on a long track record of health status and strict logic reasoning.

Traditional examination by the teacher to choose, group, organization, after the examination, and then by the examination papers to correct, determine the results. Therefore, many factors, such as the standardization degree, the difficulty degree and the rationality of the evaluation, are more easily influenced by the subjective factors. And traditional examination group, organization, examination paper evaluation, performance analysis, test paper retained heavy workload big, such as task. In addition, the traditional test feedback ability is weak, embodied in an exam paper choose optional the gender is big, and the analysis of the results stay on average, pass the surface level. Some of the existing online examination system to achieve a paperless examination, But in the volume group, and result analysis, the basic is the same as the traditional way. This paper mainly discusses the key technology of the development of the intelligent test system, focusing on generating test paper algorithm design and achievement data mining algorithm for the analysis of the design, so as to improve the system test paper quality and performance analysis for teaching and learning in the future to provide detailed decision analysis.

C4.5 algorithm is one of the ten classic algorithms of data mining, it is a series of algorithms used in machine learning and data mining. This paper mainly studies the application of C4.5 algorithm to the analysis of performance data, to dig out the hidden relationship between various factors and test results, to provide a fair and objective analysis of the quality of the teaching assessment and provide decision support for the teaching improvement in the future. This paper focuses on the C4.5 algorithm, including the division rules, the calculation of the information gain rate and algorithm workflow, and finally through an example to explain the specific application of the C4.5 algorithm.

Fibrous sheath CABYR binding protein (FSCB) is regulated by protein kinase A (PKA)-mediated tyrosine phosphorylation in the spermatozoa capacitation. Recently, we showed that FSCB phosphorylation activated spermatozoa motility. Nevertheless, the underlying mechanisms have not been completely elucidated. Here, we showed that FSCB phosphorylation inhibited SUMOylation of two crucial proteins ROPN1/ROPN1L that are associated with PKA/A kinase activity and spermatozoa motility. Suppression of SUMOylation of ROPN1/ROPN1L mimicked the effects of FSCB phosphorylation on spermatozoa motility. Immunoprecipitation assay showed that phosphorylated FSCB had a significantly higher affinity to ROPN1/ROPN1L than non-phosphorylated FSCB. Together, our data suggest that FSCB phosphorylation may regulate mouse spermatozoa capacitation through suppressing SUMOylation of ROPN1/ROPN1L, which sheds new light on creating a therapeutic strategy targeting FSCB phosphorylation in the study of infertility.

Software system development is guided by the evolution of requirements. In this paper, we address the task of requirements traceability, which is concerned with providing bi-directional traceability between various requirements, enabling users to find the origin of each requirement and track every change made to it. We propose a knowledge-rich approach to the task, where we extend a supervised baseline system with (1) additional training instances derived from human-provided annotator rationales; and (2) additional features derived from a hand-built ontology. Experiments demonstrate that our approach yields a relative error reduction of 11.1‐19.7%.

In recent years, more organizations began to address usability problems to improve their competitive position and foster customer loyalty. Although there are some classification schemes to address usability problems, there is no cause-effect framework to enable in-process usability feedback and improvement. Orthogonal defect classification (ODC) had been widely used in software development process, but with insufficient attention to usability problems. In this paper, we present a classification and analysis framework to integrate usability practices with ODC framework. With our framework, the cause-effect relationship can be identified. By addressing the usability problems during the whole development cycle, human computer interface (HCI) and software process can be improved together. A case study was conducted to demonstrate the viability and effectiveness of our framework.

In recent years, mobile healthcare has received increasing attention. As the wrist-pulse diagnosis in traditional Chinese medicine(TCM) only needs the wrist pulse information of a patient, without any other physiological data and invasive checking, it is a promising technique for mobile healthcare in terms of cost and convenience. But the pulse-based diagnosis requires the sophisticated and long-term training of the physicians. So it is urgent to develop a digitalized method to objectify and standardize the pulse-based diagnosis process. In this paper we design a wrist-pulse sensing and analyzing prototype system which involves a general pulse-sensing device and a cirrhosis diagnosis scheme based on the captured pulse information. The experimental results show that the accuracy of the proposed system reaches to 87.09% in cirrhosis identification.

Tongue diagnosis is widely used in the Traditional Chinese Medicine (TCM) and tongue image classification based on pattern recognition plays an important role in the development of the modernization of TCM. However, due to labeled tongue samples are rare and costly or time consuming to obtain, most of the existing methods such as SVM utilize labeled training samples merely. Therefore the classifiers usually have poor performance. In contrast, Universum SVM is a promising method which incorporates a priori knowledge into the learning process with labeled data and irrelevant data (also called universum data). In tongue image classification, the number of irrelevant instances could be very large since there are many irrelevant categories for a certain tongue's type. But not all the irrelevant instances joined in training can improve the classifier's performance. So an algorithm of selecting the universum samples is also introduced in this paper. Experimental results show that the Universum SVM classifier is improved and the algorithm of selecting universum samples is effective.

At present, most e-commerce recommendation systems act only as just a single tool and provide a single recommendation model. However, due to the complexity of e-commerce system itself, different occasions and customers of different identities require different types of recommendation services. In the paper, we study the architecture of complex e-commerce which can collect multiple types of data, use a variety of recommendation algorithms order to meet the needs of different types of recommendation.

The core of large-scale PKI (Public Key Infrastructure) is digital certificate, while in the service of certificate, the existing factors of password disclosure and expiration will lead to the certificate invalidation. Presently, PKI Administration always adopts the method of Certificate Revocation List ('CRL') to summarize the invalidation certificate. However, the biggest bottleneck of CRL application is the high peak value requirement rate for CRL storage, which will require the hardware unit with high performance but expensive price. The paper attempted to refine the CRL issuing mechanism in network structure based on the distributive concept of P2P network and then put forward a new issuing mechanism, distributive CRL issuing. Meanwhile, we took the theoretical analysis for it and the result has showed its advantages.

By the popularization of the Internet application, the performance requirement of Web servers is becoming higher and higher. Using several hosts as the cluster to provide services together is the common scheme for the moment, with high performance-price ratio, high reliability and scalability, while the key performance of the cluster system is the balancing algorithm. Based on the LVS project, this paper uses the multiple-linear regression to figure out the loading capacity of each host in the cluster and then balance the loading distribution to increase the loading capacity of the whole Web cluster.