S190 interpretation techniques development and application to New York State water resources

There are no author-identified significant results in this report

S190 interpretation techniques development and application to New York State water resources

There are no author-identified significant results in this report

S190 interpretation techniques development and application to New York State water resources

The author has identified the following significant results. The program has demonstrated that Skylab imagery can be utilized to regularly monitor eutrophication indices of lakes, such as chlorophyll concentration and photic zone depth. The relationship between the blue to green reflectance ratio and chlorophyll concentration was shown, along with changes in lake properties caused by chlorophyll, lignin, and humic acid using reflectance ratios and changes. A data processing technique was developed for detecting atmospheric fluctuations occurring over a large lake

Exploiting Cognitive Structure for Adaptive Learning

Adaptive learning, also known as adaptive teaching, relies on learning path recommendation, which sequentially recommends personalized learning items (e.g., lectures, exercises) to satisfy the unique needs of each learner. Although it is well known that modeling the cognitive structure including knowledge level of learners and knowledge structure (e.g., the prerequisite relations) of learning items is important for learning path recommendation, existing methods for adaptive learning often separately focus on either knowledge levels of learners or knowledge structure of learning items. To fully exploit the multifaceted cognitive structure for learning path recommendation, we propose a Cognitive Structure Enhanced framework for Adaptive Learning, named CSEAL. By viewing path recommendation as a Markov Decision Process and applying an actor-critic algorithm, CSEAL can sequentially identify the right learning items to different learners. Specifically, we first utilize a recurrent neural network to trace the evolving knowledge levels of learners at each learning step. Then, we design a navigation algorithm on the knowledge structure to ensure the logicality of learning paths, which reduces the search space in the decision process. Finally, the actor-critic algorithm is used to determine what to learn next and whose parameters are dynamically updated along the learning path. Extensive experiments on real-world data demonstrate the effectiveness and robustness of CSEAL.Comment: Accepted by KDD 2019 Research Track. In Proceedings of the 25th ACM SIGKDD International Conference on Knowledge Discovery & Data Mining (KDD'19

Radiative transport analysis of electromagnetic propagation in isotropic plasma turbulence

The problem of electromagnetic wave propagation in a turbulent plasma is formulated in terms of the radiative transport equation. A singular eigenfunction solution is obtained for the case of isotropic plasma turbulence, and detailed numerical calculations are presented. The intensity distribution is studied as a function of the turbulent spectrum and relative strength of scattering attenuation to total attenuation. For a highly forward peaked scattering law characteristic of many physical situations it is found that the reflected backscatter intensity is relatively insensitive to the angle of incidence, except as grazing incidence is approached. The importance of multiple scatter is studied as a function of the properties of the medium

Characterization of MOS Sensors for R-32 and R-454B Leaks

Owing to concerns about climate change, many jurisdictions are phasing out high global warming potential refrigerants in HVAC&R systems. Their near-term replacements are class A2L (mildly-flammable) refrigerants. Area monitoring detectors will be required for most future residential, commercial, and industrial HVAC systems that use these refrigerants. UL Standard 60335-2-40 requires these detectors to have a set-point of 25% of the lower flammability limit (LFL) and to detect the set-point within 10 s when exposed to a gas mixture at the LFL. Inexpensive detectors that meet these requirements do not exist, which has delayed the adoption of A2L refrigerants. A technology with good potential is based on metal-oxide semiconductors (MOS). MOS detectors are tested here, considering their response to leaks of R-32 and R-454B. They are characterized here for their sensitivity, response time, false alarms from contaminants, and poisoning. The sensors have good sensitivity with a steady-state output that is linear with respect to the logarithm of concentration. The sensors fail narrowly to meet the 10 s response time requirement for both R-32 and R-454B. The sensors do not alarm when exposed to the contaminants in the standard. However, several of the contaminants do poison the sensors, at least temporarily

Dynamic Key-Value Memory Networks for Knowledge Tracing

Knowledge Tracing (KT) is a task of tracing evolving knowledge state of students with respect to one or more concepts as they engage in a sequence of learning activities. One important purpose of KT is to personalize the practice sequence to help students learn knowledge concepts efficiently. However, existing methods such as Bayesian Knowledge Tracing and Deep Knowledge Tracing either model knowledge state for each predefined concept separately or fail to pinpoint exactly which concepts a student is good at or unfamiliar with. To solve these problems, this work introduces a new model called Dynamic Key-Value Memory Networks (DKVMN) that can exploit the relationships between underlying concepts and directly output a student's mastery level of each concept. Unlike standard memory-augmented neural networks that facilitate a single memory matrix or two static memory matrices, our model has one static matrix called key, which stores the knowledge concepts and the other dynamic matrix called value, which stores and updates the mastery levels of corresponding concepts. Experiments show that our model consistently outperforms the state-of-the-art model in a range of KT datasets. Moreover, the DKVMN model can automatically discover underlying concepts of exercises typically performed by human annotations and depict the changing knowledge state of a student.Comment: To appear in 26th International Conference on World Wide Web (WWW), 201

Foreign Direct Investments in Business Services: Transforming the Visegrád Four Region into a Knowledge-based Economy?

Foreign direct investments (FDIs) in the service sector are widely attributed an important role in bringing more skill-intensive activities into the Visegrad Four (V4). This region—comprising Poland, the Czech Republic, Hungary and Slovakia—relied heavily on FDIs in manufacturing, which was often found to generate activities with limited skill content. This contribution deconstructs the chaotic concept of “business services” by analysing the actual nature of service sector activities outsourced and offshored to the V4. Using the knowledge-based economy (KBE) as a benchmark, the paper assesses the potential of service sector outsourcing in contributing to regional competitiveness by increasing the innovative capacity. It also discusses the role of state policies towards service sector FDI (SFDI). The analysis combines data obtained from case studies undertaken in service sector outsourcing projects in V4 countries. Moreover, it draws on interviews with senior employees of investment promotion agencies and publicly available data and statistics on activities within the service sector in the region. It argues that the recent inward investments in business services in the V4 mainly utilize existing local human capital resources, and their contribution to the development of the KBE is limited to employment creation and demand for skilled labour