Existence of solutions for a class of hemivariational inequality problems

AbstractIn this paper, we are concerned with the existence of solutions for a class of Hartman–Stampacchia type hemivariational inequalities by using the Clarke generalized directional derivative and the Galerkin approximation method. Two existence results of solutions for the generalized pseudomonotone mapping hemivariational inequality and elliptic hemivariational inequality are obtained

Thermoelectric Power Generation for Heat Recovery in Automotive Industries

Researches on integrating thermoelectric power generator into various vehicle platform have witness a surge in solid demand of improving thermal efficiency and CO2 emission reduction from automotive industries. Many prototypes were built and tested in different segments of the car. Position at exhaust gas recirculation valve and downstream of catalyst are preferred by car manufacturers as easiness of installation. Up to 4% improvement of fuel economy has been claimed under an ideal road driving cycle. Much focuses on the lightweighting of the thermoelectric power generator whilst producing stable electric power output under an intermittent working load. However, major barriers to real application still exist due to low conversion efficiency of thermoelectric material and poor heat exchanger system design. Although heat source can be high up to 800 K, actual temperature at thermoelectric legs are much less than that. Thermal losses are inevitable through the heat exchanger route and effectiveness of heat transfer become the key to system development in future. Thermoelectric material working in higher temperature could be a breakthrough and game changer for waste heat recovery in automotive industries

High-Resolution Structure of the N-Terminal Endonuclease Domain of the Lassa Virus L Polymerase in Complex with Magnesium Ions

Lassa virus (LASV) causes deadly hemorrhagic fever disease for which there are no vaccines and limited treatments. LASV-encoded L polymerase is required for viral RNA replication and transcription. The functional domains of L–a large protein of 2218 amino acid residues–are largely undefined, except for the centrally located RNA-dependent RNA polymerase (RdRP) motif. Recent structural and functional analyses of the N-terminal region of the L protein from lymphocytic choriomeningitis virus (LCMV), which is in the same Arenaviridae family as LASV, have identified an endonuclease domain that presumably cleaves the cap structures of host mRNAs in order to initiate viral transcription. Here we present a high-resolution crystal structure of the N-terminal 173-aa region of the LASV L protein (LASV L173) in complex with magnesium ions at 1.72 Å. The structure is highly homologous to other known viral endonucleases of arena- (LCMV NL1), orthomyxo- (influenza virus PA), and bunyaviruses (La Crosse virus NL1). Although the catalytic residues (D89, E102 and K122) are highly conserved among the known viral endonucleases, LASV L endonuclease structure shows some notable differences. Our data collected from in vitro endonuclease assays and a reporter-based LASV minigenome transcriptional assay in mammalian cells confirm structural prediction of LASV L173 as an active endonuclease. The high-resolution structure of the LASV L endonuclease domain in complex with magnesium ions should aid the development of antivirals against lethal Lassa hemorrhagic fever

Effect of resistive load on the performance of an organic Rankine cycle with a scroll expander

An experimental investigation is performed for an organic Rankine cycle system with different electrical resistive loads. The test rig is set up with a small scroll expander-generator unit, a boiler and a magnetically coupled pump. R134a is used as the working fluid in the system. The experimental results reveal that the resistive load coupled to the scroll expander-generator unit affects the expander performance and power output characteristics. It is found that an optimum pressure ratio exists for the maximum power output. The optimal pressure ratio of the expander decreases markedly as the resistive load gets higher. The optimum pressure ratio of the scroll expander is 3.6 at a rotation speed of 3450 r/min for a resistive load of 18.6 Ω. The maximum electrical power output is 564.5 W and corresponding isentropic and volumetric efficiencies are 78% and 83% respectively

Temperature Matrix-Based Data Placement Optimization in Edge Computing Environment

The scale of data shows an explosive growth trend, with wide use of cloud storage. However, there are challenges such as network latency and energy consumption. The emergence of edge computing brings data close to the edge of the network, making it a good supplement to cloud computing. The spatiotemporal characteristics of data have been largely ignored in studies of data placement and storage optimization. To this end, a temperature matrix-based data placement method using an improved Hungarian algorithm (TEMPLIH) is proposed in this work. A temperature matrix is used to reflect the influence of data characteristics on its placement. A data replica matrix selection algorithm based on temperature matrix (RSA-TM) is proposed to meet latency requirements. Then, an improved Hungarian algorithm based on replica matrix (IHA-RM) is proposed, which satisfies the balance among the multiple goals of latency, cost, and load balancing. Compared with other data placement strategies, experiments show that the proposed method can effectively reduce the cost of data placement while meeting user access latency requirements and maintaining a reasonable load balance between edge servers. Further improvement is discussed and the idea of regional value is proposed

Novel Modelling Strategies for High-frequency Stock Trading Data

Full electronic automation in stock exchanges has recently become popular, generating high-frequency intraday data and motivating the development of near real-time price forecasting methods. Machine learning algorithms are widely applied to mid-price stock predictions. Processing raw data as inputs for prediction models (e.g., data thinning and feature engineering) can primarily affect the performance of the prediction methods. However, researchers rarely discuss this topic. This motivated us to propose three novel modelling strategies for processing raw data. We illustrate how our novel modelling strategies improve forecasting performance by analyzing high-frequency data of the Dow Jones 30 component stocks. In these experiments, our strategies often lead to statistically significant improvement in predictions. The three strategies improve the F1 scores of the SVM models by 0.056, 0.087, and 0.016, respectively.Comment: 28 pages, 5 tables, 5 figure

Parameter analysis of thermoelectric generator/dc-dc converter system with maximum power point tracking

The power generated from TEG is relatively unstable owing to temperature variations at its hot and cold side terminals. The dc-dc converters can provide more stable power output thereby improving the overall efficiency of TEG system. However, to facilitate better performance improvement, maximum power point tracking (MPPT) algorithm can be applied to extract maximum power from TEG system. Therefore, parameter analysis of a TEG/dc-dc converter system in different modes is being carried out. A TEG-dc-dc boost converter model is analysed in both MPPT and direct pulse width modulation (PWM) modes subjected to a variable load. To further study the capability of dc-dc converters to stabilise the TEG power output, increasing ramp and random hot side temperature is applied to the MPPT and direct PWM based modes so that the effect on output parameters i.e. voltage and power, can be analysed. It is noted that even for the random temperature input to the TEG, the output voltage resulting from the converter is almost constant. Therefore dc-dc converters are able to stabilise the power generated from TEG. It is also observed that dc-dc converter with MPPT based model is able to effectively extract the maximum power without having to adjust any component from the MPPT algorithm as it is the case with direct PWM based model. From the study, it has been established that proper selection of converter components is necessary to reduce converter losses as well interferences on the load connected to TEG-dc-dc converter system

Neural Cognitive Diagnosis for Intelligent Education Systems

Cognitive diagnosis is a fundamental issue in intelligent education, which aims to discover the proficiency level of students on specific knowledge concepts. Existing approaches usually mine linear interactions of student exercising process by manual-designed function (e.g., logistic function), which is not sufficient for capturing complex relations between students and exercises. In this paper, we propose a general Neural Cognitive Diagnosis (NeuralCD) framework, which incorporates neural networks to learn the complex exercising interactions, for getting both accurate and interpretable diagnosis results. Specifically, we project students and exercises to factor vectors and leverage multi neural layers for modeling their interactions, where the monotonicity assumption is applied to ensure the interpretability of both factors. Furthermore, we propose two implementations of NeuralCD by specializing the required concepts of each exercise, i.e., the NeuralCDM with traditional Q-matrix and the improved NeuralCDM+ exploring the rich text content. Extensive experimental results on real-world datasets show the effectiveness of NeuralCD framework with both accuracy and interpretability

A Potential New Target for REST-mediated Chronic Pain

https://openworks.mdanderson.org/sumexp21/1214/thumbnail.jp