滨海地区钢筋混凝土结构物的耐久性,主要受到环境的氯盐及混凝土品质两个因素所影响。本研究团队与台大土木和营建院协助公路总局,进行多年混凝土现地曝晒试验及大气中氯盐量之调查,目前现地曝晒试验混凝土之曝晒期达9年,氯盐量调查方面已充份掌握全省之氯盐量分布。因现地曝晒试验需要更长之曝晒期,本计划将进行喷雾盐雾试验,并配合曝晒试验中飞来盐与现地暴露试验混凝土内之氯离子分布量,以补足现地曝晒试验之混凝土,尚未达到稳定表面氯离子含量及稳定时间之资料。混凝土品质方面,以贮盐浸渍试验求得之氯离子扩散系数,探讨一般、飞灰及炉石混凝土,随龄期而减小之扩散系数衰减值。最後将环境氯盐及混凝土品质,两个影响混凝土结构物耐久性的主要因素,应用于Fick’s Law的扩散方程式,评估临海地区的钢筋混凝土耐久性及预测其服务年限。并将曝晒期约12年之混凝土试体钻心取样,分析其氯离子含量分布曲线。以考虑环境氯盐影响因子及混凝土品质之氯离子分布曲线与现地暴露混凝土内之氯离子分布量加以比较,探讨本计划预测值之准确性。

PROJECT SUMMARY/ABSTRACT The inter-disciplinary Training Program in Cancer Research at Fox Chase Cancer Center, currently in its 40th year, focuses on preparing a cadre of outstanding cancer scientists through rigorous research and impactful, targeted mentoring. Continued funds are requested to support 10 postdoctoral trainees. The highly collaborative participating faculty consist of 50 Cancer Center members of the Fox Chase Cancer Center and Temple Health. Collectively, these faculty possess interests that span the cancer spectrum, including cell growth control, signal transduction, inflammation, epigenetics, cancer prevention, bio-behavioral cancer research, and developmental therapeutics. The postdoctoral pool is extremely strong; there is an average of 3-4 applicants per open slot, and the majority of applicants received graduate training at outstanding institutions, and published in high-impact journals. The program offers diverse training in areas relevant to cancer research, with a combination of didactic and laboratory-based training, including seminar series and journal clubs, scientific writing courses, career development seminars, and individualized development plans. Input from the postdoctoral pool is extensive and highly valued in shaping this program; trainee satisfaction is best exemplified by our long-standing placement in the Top 20 Best Places in the United States for Postdoctoral Training?, as published in The Scientist magazine (through 2012). Trainee slots are highly competitive, all slots have been continuously filled for the last 3 funding cycles, and >75% of trainees who have completed training report that they are still doing research directly relevant to cancer. Appointments are made for one year, with an option to renew for a second year of support. A chief objective of our Research Training Plan is to equip our trainees with the skills needed to transition to impactful careers in cancer biology and medicine, recognizing that fundamental contributions to the cancer effort can be achieved in a wide array of career paths. In summary, this proposal continues a long history of excellence in training in cancer biology at Fox Chase.

the Internet,and society's dependence on it,have grown,the structure and dynamics of the network,and how it relates to the political economy in which it is embedded,are gathering increasing attention by researchers,operators and policy makers.This project offers a new platform and supporting tools for collecting,analyzing,querying,and interpreting measurements of the Internet ecosystem.It directly supports the NSF goal of providing robust,secure cyberinfrastructure to accelerate research,education,and new capabilities in data-intensive science and engineering.Broader impacts of this project include increased public awareness of Internet structure,dynamics,performance,and evolution,which informs discussions of critical issues in current and future large-scale networking.This Platform for Applied Network Data Analysis(PANDA)integrates existing research infrastructure measurement and analysis components previously developed by the Center for Applied Internet Data Analysis,and enables new scientific directions,experiments,and data products for a wide range of research activities.The design of PANDA emphasizes efficient indexing and processing of terabyte archives,advanced visualization tools to show geographic and economic aspects of Internet structure,and detailed interpretation of displayed results.The project actively engages collaborators from four targeted disciplines:networking,security,economics,and public policy.Activities include workshops to establish and stimulate multi-disciplinary collaborations,development of online video tutorials targeting non-networking experts and classroom-focused materials,an annotated bibliography and discussion forum,and a strategic advisory board.This award is supported jointly by both the Data and Networking programs within the Office of Advanced Cyberinfrastructure.

nnotation：The scientific significance of the project is the fundamental bases for analysis of prefracture critical local area and its applications from the standpoint of the new gradient plasticity theory,related to the Taylor scale of the material dislocation structure.The relevance of the project consists in a system analysis,a probability-statistical description and prediction of the increasing contribution of local damage,causing a decrease in critical values of the fracture resistance parameters of materials and the carrying capacity of structural components. The theoretical part of the project is Conventional Mechanism-based Strain Gradient(CMSG)plasticity theory.For the stress and strain rates constitutional equations of CMSG,two variants of the crack tip stress fields will be proposed.On this basis,the fracture resistance parameters will be derived in the form of amplitude stress intensity factors.For the first type of stress fields the amplitude coefficient will be obtained by formulating the Rice J-integral for gradient plasticity conditions.For the second type of stress fields CMSG amplitude coefficients will be derived by decomposing the solution into quantitative and qualitative components.The equation for the amplitude coefficients will be presented in a normalized form with respect to the main parameter of the Taylor dislocation model in form of the intrinsic material length in strain gradient plasticity associated with the Burgers vector and the flow stress.Thereby new quantitative indicators of fracture resistance for the CMSG theory will be introduced,establishing the relationship between processes occurring at the micro and macro levels in relation to the parameter of the material structure.These indicators will be the basis of the computational,experimental and generalizing parts of the project.In the theoretical part of the project special attention will be given to the background of the areas of dominance of the CMSG plasticity theory in terms of the characteristic size of the material structure,the singularity exponent of the stress fields and the strain gradient at the crack tip. The objects for the experimental,numerical and generalizing parts of the project will be five types of structural materials with various properties and structures,namely,three types of steels and two types of titanium and aluminum alloys.The experimental part of the project consists of two parts.The first part is related to the structure of state models in the critical zone,which predetermines the composition and obtaining procedure for the constants of the constitutional equations of the gradient plasticity theory(CMSG),including the basic mechanical characteristics and parameters of the material structure for each material studied.The second part of the experimental studies will be related to the determination of the crack growth resistance characteristics using two types of test specimens.For the total number of tested 61 bending and compact specimens,a wide variation of the specimen’s dimensions and the initial crack lengths will be implemented to provide in-plane and out-of-plane constraint effects.Interpretation of experimental results on fracture toughness characteristics will be presented in terms of the classical elastic stress intensity factor(SIF),previously introduced plastic SIF and proposed in the theoretical part of the project a new nonlinear amplitude coefficient of gradient plasticity theory(CMSG).The experimentally established trends and differences in fracture toughness in the interpretation by different models for each material will be used in the final part of the project for predicting the probability of fracture and quantifying the effects of strain gradients at the crack tip. The scientific content of the computational part of the project relates to the algorithm development,its implementation and the results of the stress fields kinetics near the crack tip according to the selected gradient plasticity theory(CMSG).The constitutional equations of the CMSG plasticity theory will be embedded in the ANSYS FEM complex and a new system of resolving equations will be developed.To analyze the parameters of the stress-strain state(SSS)at the crack tip based on the gradient plasticity theory(CMSG),it will be necessary to develop new principles for the FEM meshes topology of studying objects,which will correspond to the nanoscale Taylor’s level of the dislocation structure of the material.It is planned to conduct a parametric numerical studies with a variation in the level of applied nominal stresses,the characteristic size of the material structure and material properties in the range from pure elasticity to almost ideal plasticity.The whole complex of numerical calculations is aimed to compare the most popular elastic-plastic model of Hutchinson-Rice-Rosengren(the HRR-theory)with the proposed version of the gradient plasticity theory(CMSG).On this basis,approximation equations of governing parameters in the local damage zone will be found as a function of the characteristic size of the dominance area of the CMSG theory.In order to interpret the experimental data on the fracture toughness of five types of materials(three types of steels and two types of titanium and aluminum alloys),obtained on the bending and compact specimens for each of 61 combinations of fracture load and crack length,a complex of numerical calculations will be performed to determine the values of elastic and plastic SIFs,as well as a new amplitude coefficient introduced in the theoretical part of the project for the gradient plasticity theory(CMSG). In the generalizing part of the project,a probabilistic analysis of the gradient plasticity theory(CMSG)by the static fracture resistance parameters will be presented.For this purpose,an iterative algorithm for the fracture probability estimation using generalized parameters and Weibull distribution functions will be developed and implemented.The elastic and plastic SIFs found in the experimental and numerical parts,as well as the new amplitude coefficient of the gradient plasticity theory(CMSG)introduced in the theoretical part will be sequentially used as generalized parameters for each of the 61 tested specimens for five types of structural materials.New formulations of the equivalent generalized parameter and global fracture probability will be presented.An equivalent generalized fracture probability parameter normalized with respect to the range of variation will be introduced,taking into account the in-plane and out-of-plane constraint effects.As a result,probabilistic-statistical assessment of the tendency to fracture within the gradient plasticity theory(CMSG)and classical elastic-plastic models will be presented and an analysis of the preferred use of the studying materials will be given.Recommendations on the application of the developed methodology will be formulated on the basis of theoretical,experimental and numerical studies to assess the carrying capacity of structural components during their operation by the individual technical state. Expected results：The main outcome of the project will be generalizing complex methodology for probability-statistical assessment of the tendency to fracture in terms of the gradient plasticity theory(CMSG)with respect to the classical elastic-plastic models;an analysis of the preferred use of the studying materials will be given on this basis.Recommendations on the application of the developed methodology will be formulated on the basis of theoretical,experimental and numerical studies to assess the carrying capacity of structural components during their operation by the individual technical state. The scientific significance of the project is the fundamental foundations for analysis of prefracture critical local area and its applications from the standpoint of the new gradient plasticity theory(CMSG),related to the Taylor scale of the material dislocation structure.The relevance of the project consists in a system analysis,a probabilistic-statistical description and prediction of the increasing contribution of local damage,causing a decrease in critical values of the fracture resistance parameters of materials and the carrying capacity of structural components. Expected results will be categorized as theoretical,experimental,and applied.The scientific priority will be the introduction and background of a new fracture resistance characteristic of materials and structural components for gradient plasticity in the form of amplitude stress intensity factors.The novelty of the experimental results will consist in the established trends in behavior of the fracture resistance characteristics and the analysis and interpretation of experimental data on the basis of traditional and proposed in the project approaches.The developed numerical complex for analyzing stress-strain state based on the constitutional equations of the gradient plasticity theory(CMSG)in the framework of industrial FEM codes ANSYS and ABAQUS will have the independent importance.The developed complex can be replicated to the testing and research laboratories of universities and enterprises.The applied component of the project is focused on the direct practical use of research results,during the formation of which the classes of construction materials(steel,titanium and aluminum alloys)most demanded in high-tech engineering were selected.

Проект направлен на создание новых синтетических методов получения соединений на основе 5-замещенных 4-тригалогенацетилфуран-2,3-дионов и выявленные среди них наиболее перспективных кандидатов для дальнейших фармакологических исследований. Авторами на основе химических превращений фуран-2,3-дионов(нуклеофильные реакции,термолиз)будут разработаны синтетические методы получения разнообразных структур,которые будут исследоваться на наличие фармакологической активности.На основе вновь полученных данных и уже имеющихся в литературе будут отобраны перспективные кандидаты для дальнейших углубленных биологических испытаний c целью разработки новых лекарственных препаратов отечественного производства.

We have implemented an AI-enabled system for automatically analysing the elderly’s behaviour by their daily life usage data of the smart appliances, e.g., television or refrigerator. To the best of our knowledge, this is a first study on using smart appliances’ data to analyse the elderly’s behaviour. The results are encouraging to demonstrate the feasibility to use this framework to monitor the elderly’s symptom status when they are living alone. Motivated by the success of HSS1.0, we re-defined the task, and made modifications of the original audio recordings, to make a new database, i.e., HSS1.5. We are now working towards to release both HSS1.0 and HSS1.5 in the near future workshops and/or challenges. Then, we can make a fair comparison of the participants’ contributions, and summarise the state-of-the-art in current heart sound classification studies. In addition, we have a collaborative work with other AI experts on the multi-label learning algorithms. With an international collaboration between Japan, China, Germany, and UK, we have made a series pilot studies/reviews on COVID-19. We are now working on the development of an open source deep learning toolkit, i.e., deepSELF, which has a combination of the state-of-the-art deep learning, transfer learning, and end-to-end learning frameworks. By using this powerful toolkit, we have successfully beat the official baseline in the ComParE 2020 Challenge on the Mask Task. We also contribute two other collaborative papers on music and education AI based applications. We have built an open access heart sound database, i.e., heart sounds Shenzhen corpus (HSS1.0), which is the largest open access heart sound database collected from single medical centre. In addition, we made a benchmark work on using both the classic machine learning (ML) models and the state-of-the-art deep learning (DL) models. A pilot study based on HSS1.0 by using wavelet analysis combining with deep recurrent neural networks (RNNs) were presented. We also investigated the capacity of ML and/or DL for new tasks in other interdisciplinary fields including robotic control, materials science, security surveillance, ecology, and smart grid. A perspective study on using affective computing technologies to facilitate the mental health monitoring and therapy was published. We proposed a novel transfer learning models pre-trained by audio data for heart sound classification task, which was demonstrated to be superior to the representations extracted by models pre-trained by the widely used image data. We gave a brief opinion work on computer audition (CA) for healthcare applications. Moreover, we summarised and conducted a comprehensive review study on snore sound classification using CA methods. Based on the aforementioned studies, we found that：First, a standard open access database is the prerequisite for a sustainable research of artificial intelligence (AI) based healthcare applications. Second, we can collaborate more with researchers from other fields to commonly contribute to innovations. The current achievements of this research have already been much more than original plan. In future work, we will investigate more potential valuable fields. First, we will continuously investigate the ML and DL methods for analysis of some types of intensive longitudinal biomedical signals, e.g., spontaneous physical activity. We would like to understand some fundamental knowledge about the features and models for the classification and/or regression tasks. Second, we will make an intensive study on leveraging CA based methods for analysing the speech of COVID-19 patients. We are already collaborating with the colleagues from a background of medicine in Wuhan, China, and the AI researchers in Germany and UK. We would like to present a benchmark work of the database and a series of studies on using the state-of-the-art signal processing and ML methods for analysis of the database. We are also planning to organise several challenges and/or workshops on the topic of AI for fighting against the COVID-19. Third, we will develop another open source toolkit for data augmentation, which is essential to improve the generalisation of DL models, specifically, for data scarcity scenarios. Fourth, we will investigate the ML strategies, like semi-supervised learning, active learning, and cooperative learning, for their capacity in reducing the human annotation works. Fifth, we will start some specific AI based healthcare applications for elderly people who are living alone. Reason：We have implemented an AI-enabled system for automatically analysing the elderly’s behaviour by their daily life usage data of the smart appliances, e.g., television or refrigerator. To the best of our knowledge, this is a first study on using smart appliances’ data to analyse the elderly’s behaviour. The results are encouraging to demonstrate the feasibility to use this framework to monitor the elderly’s symptom status when they are living alone. Motivated by the success of HSS1.0, we re-defined the task, and made modifications of the original audio recordings, to make a new database, i.e., HSS1.5. We are now working towards to release both HSS1.0 and HSS1.5 in the near future workshops and/or challenges. Then, we can make a fair comparison of the participants’ contributions, and summarise the state-of-the-art in current heart sound classification studies. In addition, we have a collaborative work with other AI experts on the multi-label learning algorithms. With an international collaboration between Japan, China, Germany, and UK, we have made a series pilot studies/reviews on COVID-19. We are now working on the development of an open source deep learning toolkit, i.e., deepSELF, which has a combination of the state-of-the-art deep learning, transfer learning, and end-to-end learning frameworks. By using this powerful toolkit, we have successfully beat the official baseline in the ComParE 2020 Challenge on the Mask Task. We also contribute two other collaborative papers on music and education AI based applications.；Outline of Research at the Start：This research aims to leverage the power of AI for analyzing and monitoring the daily behavior of the patients suffering from psychiatric diseases via the biomedical intensive longitudinal data. We will investigate the state-of-the-art techniques of machine learning, deep learning, and signal processing for their capacity on screening the patients from the healthy control. In addition, we will explore the feasibility to use the paradigm of AI to implement an automatic monitoring and evaluation system for subject’s health status by IoT sensor data.；

The notion of almost Gorenstein local ring given by V. Barucci and R. Froeberg for one-dimensional analytically unramified Noetherian local rings is generalized for arbitrary Noetherian local rings and a basic theory is developed. The higher dimensional definition is safely introduced also for Noetherian local/graded rings. The theory of almost Gorenstein Rees algebras associated to ideals is well developed and there are obtained satisfactory results in the case where the base rings are regular local rings or the case where the ideals are generated by subsystems of parameters in Cohen-Macaulay local rings.