Modelling of airflow and aerosol particle movement in buildings.

SIGLEAvailable from British Library Document Supply Centre-DSC:DX198075 / BLDSC - British Library Document Supply CentreGBUnited Kingdo

Extended Quark Potential Model from Random Phase Approximation

The quark potential model is extended to include the sea quark excitation using the random phase approximation (RPA). The effective quark interaction preserves the important Quantum Chromodynamics (QCD) properties -- chiral symmetry and confinement simultaneously. A primary qualitive analysis shows that the $\pi$ meson as a well-known typical Goldstone boson and the other mesons made up of valence $q\bar{q}$ quark pair such as the $\rho$ meson can also be described in this extended quark potential model

An Iterative Optimization and Learning-based IoT System for Energy Management of Connected Buildings

Buildings account for nearly 40% of primary energy and 36% of greenhouse emissions, which is one of the main factors driving climate change. Reducing energy consumption in buildings toward zero-energy buildings is a vital pillar to ensure that future climate and energy targets are reached. However, due to the high uncertainty of building loads and customer comfort demands, and extremely nonlinear building thermal characteristics, developing an effective zero-energy building energy management (BEM) technology is facing great challenges. This paper proposes a novel learning-based and iterative IoT system to address these challenges to achieve the zero-energy objective in BEM of connected buildings. Firstly, all buildings in the IoT-based BEM system share their operation data with an aggregator. Secondly, the aggregator uses these historical data to train a deep reinforcement learning model based on the Deep Deterministic Policy Gradient method. The learning model generates pre-cooling or pre-heating control actions to achieve zero-energy BEM for building heating ventilation and air conditioning (HVAC) systems. Thirdly, for solving the coupling problem between HVAC systems and building internal heat gain loads, an iterative optimization algorithm is developed to integrate physics-based and learning-based models to minimize the deviation between the on-site solar photovoltaic generated energy and the actual building energy consumption by properly scheduling building loads, electric vehicle charging cycles and the energy-storage system. Lastly, the optimal load operation scheduling is generated by considering customers’ comfort requirements. All connected buildings then operate their loads based on the load operation schedule issued by the aggregator. The proposed learning-based and iterative IoT system is validated via simulation with real-world building data from the Pecan Street project

Atomic-layered Au clusters on α-MoC as catalysts for the low-temperature water-gas shift reaction

The water-gas shift (WGS) reaction (where carbon monoxide plus water yields dihydrogen and carbon dioxide) is an essential process for hydrogen generation and carbon monoxide removal in various energy-related chemical operations. This equilibrium-limited reaction is favored at a low working temperature. Potential application in fuel cells also requires a WGS catalyst to be highly active, stable, and energy-efficient and to match the working temperature of on-site hydrogen generation and consumption units. We synthesized layered gold (Au) clusters on a molybdenum carbide (α-MoC) substrate to create an interfacial catalyst system for the ultralow-temperature WGS reaction. Water was activated over α-MoC at 303 kelvin, whereas carbon monoxide adsorbed on adjacent Au sites was apt to react with surface hydroxyl groups formed from water splitting, leading to a high WGS activity at low temperatures

Computer simulation for study of contact force in slider-disk interface

Includes bibliographical references (pages 93-97)The estimate of contact force and pressure at slider-disk interface is critical to the study of disk wear. Wear mechanism shows that the volume of wear is always proportional to the contact force or pressure. However, no exact experimental method can be used to measure the total contact force and real contact area. A computer simulation method is developed in this thesis based on the dynamic model, which takes account of asperity deformation and the statistical nature of asperity interaction. Elastic contact theory is adopted in the program. Using this simulation program, parameters which affect contact force and pressure are discussed; hence, a suggestion of using a "micro-slider" and small initial vertical velocity in start/stop operation is proposed in slider design.M.S. (Master of Science

Chaotic encryption algorithm with scrambling diffusion based on the Josephus cycle

Digital images are characterized by high redundancy and strong interpixel correlation. Breaking the correlation between data and improving sensitivity are crucial to protecting image information. To effectively achieve this goal, a chaotic encryption algorithm based on Josephus cycle scrambling diffusion is proposed in this paper. First, the adaptive key is generated by the Hash function to generate the initial value of the chaotic system, which is highly related to the plaintext image. The generation of the adaptive key can effectively resist plaintext attacks. Second, the pseudorandom sequence generated by the two-difference chaotic mapping is applied as the step sequence and direction sequence of Josephus traversal and optimizes Josephus traversal via variable steps and directions; the ranks of plain-text images are scrambled by the Josephus cycle to break the strong correlation between pixels. Finally, the initial cipher-text is divided into blocks to complete the Josephus cycle scrambling diffusion of image blocks, intrablock pixel bits and bit planes. The double permutations at the pixel level and bit level break the high correlation between pixels. Compared with the previous studies, our algorithm’s average entropy of encrypted images is 7.9994, which has slightly improved. The correlation coefficient of the cryptographic image fluctuates up and down by approximately 0. In addition, the algorithm has the advantages of a large key space, high key sensitivity, anti-robust attack, and feasible encryption efficiency

Optimal Confidence Intervals for the Relative Risk and Odds Ratio

The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the two parameters through approximate or exact confidence intervals. However, these intervals may be liberal or conservative. A natural question is whether the intervals can be further improved in maintaining the correct confidence coefficient of an approximate interval or shortening an exact but conservative interval. In this article, when two independent binomials are observed we offer an effort to improve any of the existing intervals by applying the -function method. In particular, if the given interval is approximate, then the improved interval is exact; if the given interval is exact, then the improved interval is a subset of the given interval. This method is also applied multiple times to the improved intervals until the final resultant interval cannot be shortened any further. To demonstrate the effectiveness of the method, we use three real datasets to illustrate in detail how several good intervals in practice are improved. Two exact intervals are then recommended for estimating each of the two parameters in different scenarios

Optimal Confidence Intervals for the Relative Risk and Odds Ratio

The relative risk and odds ratio are widely used in many fields, including biomedical research, to compare two treatments. Extensive research has been done to infer the two parameters through approximate or exact confidence intervals. However, these intervals may be liberal or conservative. A natural question is whether the intervals can be further improved in maintaining the correct confidence coefficient of an approximate interval or shortening an exact but conservative interval. In this article, when two independent binomials are observed we offer an effort to improve any of the existing intervals by applying the -function method. In particular, if the given interval is approximate, then the improved interval is exact; if the given interval is exact, then the improved interval is a subset of the given interval. This method is also applied multiple times to the improved intervals until the final resultant interval cannot be shortened any further. To demonstrate the effectiveness of the method, we use three real datasets to illustrate in detail how several good intervals in practice are improved. Two exact intervals are then recommended for estimating each of the two parameters in different scenarios