The LBFGS Quasi-Newtonian Method for Molecular Modeling Prion AGAAAAGA Amyloid Fibrils

Experimental X-ray crystallography, NMR (Nuclear Magnetic Resonance) spectroscopy, dual polarization interferometry, etc are indeed very powerful tools to determine the 3-Dimensional structure of a protein (including the membrane protein); theoretical mathematical and physical computational approaches can also allow us to obtain a description of the protein 3D structure at a submicroscopic level for some unstable, noncrystalline and insoluble proteins. X-ray crystallography finds the X-ray final structure of a protein, which usually need refinements using theoretical protocols in order to produce a better structure. This means theoretical methods are also important in determinations of protein structures. Optimization is always needed in the computer-aided drug design, structure-based drug design, molecular dynamics, and quantum and molecular mechanics. This paper introduces some optimization algorithms used in these research fields and presents a new theoretical computational method - an improved LBFGS Quasi-Newtonian mathematical optimization method - to produce 3D structures of Prion AGAAAAGA amyloid fibrils (which are unstable, noncrystalline and insoluble), from the potential energy minimization point of view. Because the NMR or X-ray structure of the hydrophobic region AGAAAAGA of prion proteins has not yet been determined, the model constructed by this paper can be used as a reference for experimental studies on this region, and may be useful in furthering the goals of medicinal chemistry in this field

How to Price Your House: Exploring Price Determinants of Online Accommodation Rental

Tourism and hospitality have emerged as one of the pioneering sectors of sharing economy. However, homeowners who lack knowledge background are confused with pricing. Traditional hotel pricing and rental pricing methods may be not suitable for online accommodation rental. Therefore, CRISP-DM, the data analysis framework, is used to solve this problem. The price prediction model is established via the house data from the Airbnb.com. Finally, 33 determinants closely related to the price are found, and the most important 10 determinants are sorted. The study also finds several interesting rules: (1) the basic situation of housing is an important determinant, (2) online rental houses with more convenient transaction conditions have higher price, (3) providing more facilities and services can increase the price, (4) some determinants in traditional hotel pricing are not efficient in sharing houses. These findings can help the homeowners to understand customers and improve their own house and pricing

Assessment and Life-Cycle Analysis of Recycled Materials for Sustainable Highway

Recycled materials replacing part of virgin materials in highway applications has shown great benefits to the society and environment. Beneficial use of recycled materials can save landfill places, sparse natural resources, and energy consumed in milling and hauling virgin materials. Low price of recycled materials is favorable to cost-saving in pavement projects. Considering the availability of recycled materials in the State of Maryland (MD), four abundant recycled materials, recycled concrete aggregate (RCA), recycled asphalt pavement (RAP), foundry sand (FS), and dredged materials (DM), were studied. A survey was conducted to collect the information of current usage of the four recycled materials in States’ Department of Transportation (DOTs). Based on literature review, mechanical and environmental properties, recommendations, and suggested test standards were investigated separately for the four recycled materials in different applications. Constrains in using these materials were further studied in order to provide recommendations for the development of related MD specifications. To measure social and environmental benefits from using recycled materials, life-cycle assessment was carried out with life-cycle analysis (LCA) program, PaLATE, and green highway rating system, BEST-in-Highway. The survey results indicated the wide use of RAP and RCA in hot mix asphalt (HMA) and graded aggregate base (GAB) respectively, while FS and DM are less used in field. Environmental concerns are less, but the possibly low quality and some adverse mechanical characteristics may hinder the widely use of these recycled materials. Technical documents and current specifications provided by State DOTs are good references to the usage of these materials in MD. Literature review showed consistent results with the survey. Studies from experimental research or site tests showed satisfactory performance of these materials in highway applications, when the substitution rate, gradation, temperature, moisture, or usage of additives, etc. meet some requirements. The results from LCA revealed significant cost savings in using recycled materials. Energy and water consumption, gas emission, and hazardous waste generation generally showed reductions to some degree. Use of new recycled technologies will contribute to more sustainable highways

Urban Heat Projections and Adaptations in a Changing Climate, Washington D.C. as a Case Study

Carbon emission from human activities has changed the Earth’s overall climate and intensified extreme weather and climate events. Climate risks are regionally uneven due to different vulnerability levels of populations, infrastructures, and natural resources. Assessing local-scale risk is important in supporting climate preparation, adaptation planning, and policy development for cities to overcome climate change. This dissertation developed the Asynchronous Regional Regression Model (ARRM) that statistically downscales data of Coupled Model Intercomparison Project Phase Five (CIMP5) into locations of observing stations, employed the Weather Research and Forecasting (WRF) model that dynamically downscales data of Community Earth System Model version one (CESM1) into fine-grid results, and proposed a framework to assess adaptation strategies for vulnerable infrastructure systems incorporating the probabilistic risk approach. Based on those models and methods, this dissertation projected the trend and level of the urban heat island (UHI) effect and heat waves in the rest of the 21st century for Washington D.C. and its surrounding areas, evaluated mitigation options for heat waves, and assessed adaptation strategies for electrical power systems in such area. Projections based on the higher greenhouse gas (GHG) concentration scenario, Representative Concentration Pathway (RCP) 8.5, indicate a growing trend of heat waves at Washington D.C. in the rest of the century. The amplitude of heat waves may grow by 5.7°C, and frequency and duration may increase by more than twofold by the end of the century. The UHI effect may increase in summer and decrease in winter. The lower scenario, RCP 2.6, leads to slight decay of heat waves after a half-century of increase, and a minor change in the UHI effect. Five heat wave mitigation strategies based on cool roofs, green roofs, and reflective pavements were evaluated in three future time periods. Results indicated that applying cool roofs and green roofs in the city scale can effectively reduce heat wave amplitude and duration, whereas the effectiveness of reflective pavements is negligible. However, reflective pavements can be more cost-effective than green roofs because of their low initial and maintenance costs. Electrical power systems are particularly vulnerable to extreme heat. Results indicated that power outage risk caused by temperature rise may increase seventyfold in the Washington metro area by the end of the century. If summer peak load on the electrical grid is cut by three quarters, there would be a twentyfold increase instead. This reduction is achievable by installing solar panels on building roofs, which can add an average generation capacity of 13.02 GW to the existing power system. Increasing the use of rooftop photovoltaics (PV) can increase the level of benefits

A Natural Wind Defrosting, Nano-coated Antibacterial Self-cleaning Energy-saving Health Air-cooled Refrigerator

The air-cooled frost-free household refrigerator is popular in the market because of its large size and frost-free size. However, the evaporator defrost process consumes a large amount of electrical energy to limit the wide spread of this refrigerator, at the same time because of its structural problems, resulting in its evaporator, air duct can not be artificially cleaned, leading to the growth of bacteria, pollution of food storage. This research has developed a self-cleaning energy-saving health refrigerator that uses indoor natural wind defrosting, ultra-hydrophilic nano-titanium dioxide coating photocatalytic sterilization and sterilization. After experimental comparison, under the same operating time of the same operating conditions, the refrigeration mode saves 1.5%, the defrost process saves 95%, reduces the amount of frosting by 23%, the temperature changes of the freezer is less than 7 ℃ , and the desterilization rate of nano-coated reaches 80%

Lattice piecewise affine approximation of explicit nonlinear model predictive control with application to trajectory tracking of mobile robot

To promote the widespread use of mobile robots in diverse fields, the performance of trajectory tracking must be ensured. To address the constraints and nonlinear features associated with mobile robot systems, we apply nonlinear model predictive control (MPC) to realize the trajectory tracking of mobile robots. Specifically, to alleviate the online computational complexity of nonlinear MPC, this paper devises a lattice piecewise affine (PWA) approximation method that can approximate both the nonlinear system and control law of explicit nonlinear MPC. The kinematic model of the mobile robot is successively linearized along the trajectory to obtain a linear time-varying description of the system, which is then expressed using a lattice PWA model. Subsequently, the nonlinear MPC problem can be transformed into a series of linear MPC problems. Furthermore, to reduce the complexity of online calculation of multiple linear MPC problems, we approximate the optimal solution of the linear MPC by using the lattice PWA model. That is, for different sampling states, the optimal control inputs are obtained, and lattice PWA approximations are constructed for the state control pairs. Simulations are performed to evaluate the performance of our method in comparison with the linear MPC and explicit linear MPC frameworks. The results show that compared with the explicit linear MPC, our method has a higher online computing speed and can decrease the offline computing time without significantly increasing the tracking error