基于深度学习的秀丽隐杆线虫显微图像分割方法

利用计算机实现自动、准确的秀丽隐杆线虫(C.elegans)的各项形态学参数分析,至关重要的是从显微图像上分割出线虫体态,但由于显微镜下的图像噪声较多,线虫边缘像素与周围环境相似,而且线虫的体态具有鞭毛和其他附着物需要分离,多方面因素导致设计一个鲁棒性的C.elegans分割算法仍然面临着挑战。针对这些问题,提出了一种基于深度学习的线虫分割方法,通过训练掩模区域卷积神经网络(Mask R-CNN)学习线虫形态特征实现自动分割。首先,通过改进多级特征池化将高级语义特征与低级边缘特征融合,结合大幅度软最大损失(LMSL)损失算法改进损失计算;然后,改进非极大值抑制;最后,引入全连接融合分支等方法对分割结果进行进一步优化。实验结果表明,相比原始的Mask R-CNN,该方法平均精确率(AP)提升了4.3个百分点,平均交并比(mIOU)提升了4个百分点。表明所提出的深度学习分割方法能够有效提高分割准确率,在显微图像中更加精确地分割出线虫体。     

Some picture fuzzy Bonferroni mean operators with their application to multicriteria decision making

In this paper, we extend the Bonferroni mean (BM) operator with the picture fuzzy numbers (PFNs) to propose novel picture fuzzy aggregation operators and demonstrate their application to multicriteria decision making (MCDM). On the basis of the algebraic operational rules of PFNs and BM, we introduce some aggregation operators: the picture fuzzy Bonferroni mean, the picture fuzzy normalized weighted Bonferroni mean, and the picture fuzzy ordered weighted Bonferroni mean. Then, a new picture fuzzy MCDM method is proposed with the help of the proposed operators. Lastly, a practical application of proposed model is given to verify the developed model and related results of the proposed model is compared with the results of the existing models to indicate its applicability.     

红外热成像在乳腺疾病检测的应用研究

乳腺疾病已严重危害女性身心健康,其中乳腺癌更位居全球范围内女性癌症发病率和死亡率首位,因此乳腺癌的早期发现意义重大。传统结构影像学早期检测疾病具有一定局限性,而红外热成像作为功能成像技术可为乳腺癌的早期筛查提供有效线索。因此本文主要就红外热成像在乳腺疾病的早期检测及预后评估的应用价值进行综述。     

Semantically enriched Massive Open Online Courses (MOOCs) platform

Massive Open Online Courses (MOOCs) are becoming an essential source of information for both students and teachers. Noticeably, MOOCs have to adapt to the fast development of new technologies; they also have to satisfy the current generation of online students. The current MOOCs’ Management Systems, such as Coursera, Udacity, edX, etc., use content management platforms where content are organized in a hierarchical structure. We envision a new generation of MOOCs that support interpretability with formal semantics by using the SemanticWeb and the online social networks. Semantic technologies support more flexible information management than that offered by the current MOOCs’ platforms. Annotated information about courses, video lectures, assignments, students, teachers, etc., can be composed from heterogeneous sources, including contributions from the communities in the forum space. These annotations, combined with legacy data, build foundations for more efficient information discovery in MOOCs’ platforms. In this article we review various Collaborative Semantic Filtering technologies for building Semantic MOOCs’ management system, then, we present a prototype of a semantic middle-sized platform implemented at Western Kentucky University that answers these aforementioned requirements.     

Economic growth,CO2 emissions,and energy consumption in the five ASEAN countries

This paper investigates the relationship between economic growth, carbon dioxide (CO₂) emissions, and energy consumption with an aim to test the validity of the Environmental Kuznets Curve (EKC) hypothesis in five ASEAN (Association of South East Asian Nations) countries (Indonesia, Malaysia, Philippines, Singapore, and Thailand) by applying the panel smooth transition regression (PSTR) model as a new econometric technique. The PSTR model is more flexible and appropriate for describing cross-country heterogeneity and time instability. Our empirical results strongly rejected the null hypothesis of linearity, and the test for no remaining nonlinearity indicated a model with one transition function and two threshold parameters. The first regime (levels of GDP per capita below 4686 USD) showed that environmental degradation increases with economic growth while the trend was reversed in the second regime (GDP per capita above 4686 USD). The results also showed that energy consumption with either the first or the second regime lead to increase CO₂. The overall results support the validity of the EKC hypothesis in the ASEAN countries.     

Optimized PI+ load–frequency controller using BWNN approach for an interconnected reheat power system with RFB and hydrogen electrolyser units

This paper investigates a renewable energy resource’s application to the Load–Frequency Control of interconnected power system. The Proportional-Integral (PI) controllers are replaced with Proportional-Integral Plus (PI+) controllers in a two area interconnected thermal power system without/with the fast acting energy storage devices and are designed based on Control Performance Standards (CPS) using conventional/Beta Wavelet Neural Network (BWNN) approaches. The energy storing devices Hydrogen generative Aqua Electroliser (HAE) with Fuel cell and Redox Flow Battery (RFB) are incorporated to the two area interconnected thermal power system to efficiently damp out the electromechanical oscillations in the power system because of their inherent efficient storage capacity in addition to the kinetic energy of the generator rotor, which can share the sudden changes in power requirements. The system was simulated and the frequency deviations in area 1 and area 2 and tie-line power deviations for 5% step- load disturbance in area 1 are obtained. The comparison of frequency deviations and tie-line power deviations of the two area interconnected thermal power system with HAE and RFB designed with BWNN controller reveals that the PI+ controller designed using BWNN approach is found to be superior than that of output response obtained using PI+ controller. Moreover the BWNN based PI+ controller exhibits a better transient and steady state response for the interconnected power system with Hydrogen generative Aqua Electroliser (AE) unit than that of the system with Redox Flow Battery (RFB) unit.     

Integrated circuit and system design for renewable energy inverters

This study proposes that a novel integrated circuit (IC) and system design for renewable energy inverters can harvest renewable energy to power direct current (DC) and alternating current (AC) loads. In addition, an intelligent synthesis and management tool is developed to design the proposed system and to judge the system’s operational maintenance decisions. Finally, a renewable energy inverter’s information is posted to an online system. Users can obtain the proposed system’s information at any time and place. The accurate and superior performance of the proposed IC and system is confirmed by computer simulations and experimental results.     

Mechanical properties of a self-healing fibre reinforced epoxy composites

Inspired by biological systems in which damage triggers an autonomic healing response, a polymer composite material that can heal itself when cracked has been developed. In this work, compression and tensile properties of a self-healed fibre reinforced epoxy composites were investigated. Microencapsulated epoxy and mercaptan healing agents were incorporated into a glass fibre reinforced epoxy matrix to produce a polymer composite capable of self-healing. The self-repair microcapsules in the epoxy resin would break as a result of microcrack expansion in the matrix, and letting out the strong repair agent to recover the mechanical strength with a relative healing efficiency of up to 140% which is a ratio of healed property value to initial property value or healing efficiency up to 119% if using the healed strength with the damaged strength.     

Influence of pores arrangement on stability of photonic structures during sintering

Discrete Element Method (DEM) has been used for numerical investigation of sintering-induced structural deformations occurring in inverse opal photonic structures. The influence of the initial arrangement of template particles on the stability of highly porous inverse opal α-Al₂O₃ structures has been analyzed. The material transport, densification, as well as formation of defects and cracks have been compared for various case studies. Three different stages of defects formation have been distinguished starting with local defects ending with intrapore cracks. The results show that the packing of the template particles defined during the template self-assembly process play a crucial role in the later structural deformation upon thermal exposure. The simulation results are in very good agreement with experimental data obtained from SEM images and previous studies by ptychographic X-ray tomography.     

Hydroxyapatite/bioactive glass functionally graded materials (FGM) for bone tissue engineering

In this work, HA/bioactive glass Functionally Graded Materials (FGMs) are obtained for the first time by means of Spark Plasma Sintering (SPS). Two series of highly dense 5 layered products, namely FGMS1 and FGMS2, are prepared under optimized SPS conditions, i.e. 1000 °C/2 min/16 MPa and 800 °C/2 min/50 MPa, respectively, using a die with varying cross section.Results arising from XRD, SEM, mechanical and biological characterization in SBF, evidence that lower temperature and higher-pressure levels used for FGMS2 samples provide better materials in terms of microstructure, compactness, hardness, elastic modulus and in vitro bioactivity. Indeed, a fully sintered and crack-free microstructure with no crystallisation at the top layer (100% bioactive glass) is correspondingly produced.The obtainment of such FGMs is quite promising, since it permits to vary the relative volume fractions of the two constituents and, consequently, tailor the biological response for specific clinical applications.