Exploiting Stiffness Nonlinearities to Improve the Performance of Galloping Flow Energy Harvesters

Fluid-structure coupling mechanisms such as galloping and wake galloping have recently emerged as effective methods to develop scalable flow energy harvesters (FEHs) that can be used to power remote sensors and sensor networks. The oper-ation concept of these devices is based on coupling the pressure forces culminating from the motion of the fluid past a mechanical oscillator to its natural modes of vibration. As a result, the mechanical oscillator undergoes large-amplitude motions that can be transformed into electricity by utilizing an electromechanical transduc-tion mechanism, which is generally piezoelectric or electromagnetic in nature. Due to their scalability and design simplicity, FEHs are believed to be more effective for micro-power generation than their traditional rotary-type counterparts whose efficiency is known to drop significantly as their size decreases. Furthermore, FEHs can be used to harvest energy from unsteady flow patterns which permits targeting a niche market that traditional rotary-type generators do not address. In the open literature, galloping FEHs have always been designed to possess a linear restoring force. This dissertation considers the design and performance analysis of galloping FEHs with a nonlinear restoring force. Specifically, the objective of this dissertation is three fold. First, it assesses the influence of stiffness nonlinearities on the performance of galloping FEHs under steady and laminar flow conditions. Second, it studies the influence of the nonlinearity on the response of a wake gal-loping FEH to single- and multi-frequency Von Karman vortex streets. Third, for known flow characteristics, the dissertation provides directions for how to choose the restoring force of the harvester to maximize the output power. To achieve the objectives of this dissertation, a nonlinear FEH which consists of a thin piezoelectric cantilever beam augmented with a square-sectioned bluff body at the free end is con-sidered. Two magnets located near the tip of the bluff body are used to introduce the nonlinearity which strength and nature can be altered by changing the distance between the magnets. For a steady laminar flow, three types of nonlinear restoring forces are compared: bi-stable, mono-stable hardening, and mono-stable softening. To study the influ-ence of the restoring force on the performance, a physics-based nonlinear lumped-parameter aero-electromechanical model adopting the quasi-steady assumption for aerodynamic loading is developed. A closed-form solution of the nonlinear response is obtained by employing a multiple-scales perturbation analysis using the Jacobi el-liptic functions. The attained solution is validated experimentally using wind tunnel tests performed at different wind speeds for the three types of restoring forces con-sidered. The validated solution is then used to study the influence of the nonlinearity on the harvesters response. In general, it is shown that, under optimal operating conditions, a harvester designed with a bi-stable restoring force outperforms the other designs. For single- and multi-frequency vortex streets, only linear and bi-stable restoring forces were considered and compared. A nonlinear lumped-parameter model adopt-ing the common uncoupled single-frequency force model for aerodynamics loading is developed and solved using the method of multiple scales. The model is validated against experimental data obtained in a wind tunnel. It is demonstrated that when subjected to a single-frequency periodic wake, the broadband characteristics of wake-galloping FEHs can be dramatically improved by incorporating a bi-stable restoring force. This has the influence of reducing the harvester’s sensitivity to variations in the wind speed around the nominal design value. It is also demonstrated that the shape of the potential function has a considerable influence on the performance of the bi-stable wake galloping FEH. Specifically, it is shown that, for shallower poten-tial wells and smaller separation distances between the wells, the harvester starts performing large inter-well motions at lower wind speeds, but the resulting inter-well motions are generally smaller. On the other hand, for deeper potential wells and larger separation distances between the wells, the harvester starts performing large inter-well motions at higher wind speeds, but the magnitude of the resulting inter-well motions are generally larger. The dissertation also compared the performance of linear and bi-stable wake-galloping FEHs under a multi-frequency vortex street. Results demonstrated that the bi-stable system outperforms the linear harvester as long as the vortices have sufficient time to interact and build a multi-frequency vortical structure. Maximum voltage levels were generated at locations where the interacting vortices result in powerful modes close to the harvesters natural frequency

Ride-sharing Determinants: Spatial and Spatio-temporal Bayesian Analysis for Chicago Service in 2022

The rapid expansion of ride-sharing services has caused significant disruptions in the transpor-tation industry and fundamentally altered the way individuals move from one place to another. Accurate estimation of ride-sharing improves service utilization and reliability and reduces travel time and traffic congestion. In this study, we employ two Bayesian models to estimate ride-sharing demand in the 77 Chicago community areas. We consider demographic, scoio-economic, transportation factors as well as land-use characteristics as explanatory variables. Our models assume conditional autoregression (CAR) prior for the explanatory variables. Moreover, the Bayesian frameworks estimate both the unstructured random error and the struc-tured errors for the spatial and the spatiotemporal correlation. We assessed the performance of the estimated models and the residuals of the spatial regression model have no left-over spatial structure. For the spatiotemporal model, the squared correlation between actual ride-shares and the fitted values is 0.95. Our analysis revealed that the demographic factors (populations size and registered crimes) positively impact the ride-sharing demand. Additionally, the ride-sharing demand increases with higher income and increase in the economically active propor-tion of the population as well as the residents with no cars. Moreover, the transit availability and the walkability indices are crucial determinants for the ridesharing in Chicago

Social learning on the move - A research roadmap

© 2014 IEEE. While teaching using a mobile course content-management, it is not possible to decide if students are confused, bored, frustrated, surprised, focused, exhausted, angry, sad, or happy. These cues are extremely helpful to instructors in deciding whether to speed up, slow down, introduce new materials, or explain a concept in a different way. In this context, there is a strong desire to capture the social interactions between learners (i.e., instructor and student). These interactions could even arise while learners are on the move. This paper\u27s research question is how could social applications like Facebook and Instangram help improve the learning experience? It is clear that these applications are nowadays used for entertainment but someone can tap into the opportunities they offer to improve instructor-student and student-student interactions. These interactions could also occur on the move due to smartphone and tablet widespread, which poses additional challenges on those who aim at providing a positive learning experience

Polyphenols as Tyrosine Kinase Inhibitors for the Treatment of Metastatic Cancers: Current and Future Perspective

Cancer is the world's biggest cause of death as a whole. The higher cancer mortality rate is related to metastasis, which is a major stumbling block in cancer treatment. Polyphenols are a diverse set of antioxidant-rich natural compounds that are often used in cancer treatments as chemopreventives and adjuvants. To find publications that highlight the topic of this review paper, a thorough literature search was conducted in several electronic databases such as PubMed Central, Google Scholar, Scopus, and Medline. Many signaling pathways are involved in the metastatic cascade, including the tyrosine kinase pathway. Tyrosine kinases are a group of enzymes involved in the control of cancer spread. Polyphenols' true role in cancer metastasis remains unappreciated, despite a large body of research proving their antimetastatic effects. As a result, the current work lays out cancer metastasis signaling pathways, stressing the importance of tyrosine kinases in the metastatic process. Polyphenols can suppress tyrosine kinase activity, which contributes to their antimetastatic characteristics. The importance of polyphenols in preventing cancer metastasis by interfering with the tyrosine kinase signaling cascade is highlighted in this work, which could lead to the development of future antimetastatic drugs

An Implemented Approach for Potentially Breast Cancer Detection Using Extracted Features and Artificial Neural Networks

Breast cancer (B-cancer) detection is still complex and challenging problem, and in that case, we propose and evaluate a four-step approach to segment and detect B-cancer disease. Studies show that relying on pure naked-eye observation of experts to detect such diseases can be prohibitively slow and inaccurate in some cases. Providing automatic, fast, and accurate image-processing-and artificial intelligence-based solutions for that task can be of great realistic significance. The presented approach itself scans the whole mammogram and performs filtering, segmentation, features extraction, and detection in a succession mode. The feasibility of the proposed approach was explored on 32 commonly virulent images, and the recognition rate achieved in the detection step is 100 %; further, the approach is able to give reliable results on distorted medical images, since the approach is subjected to a rectification step. Finally, this study is very effectual in decreasing mortality and increasing the quality of treatment of early onset of B-cancer

Symmetrically-private database search in cloud computing

Database outsourcing has gained importance in the past few years due to the emergence of the cloud computing. In Database-as-a-Service (DaaS), which is a category of cloud computing services, the database owner outsources both databases and querying services to a cloud server and clients issue queries over the database to the cloud server. In this context, privacy is a primary challenge and it is necessary to fulfill main privacy requirements of database owners and clients. This paper presents protocols for executing keyword search and aggregate SQL queries that preserve the privacy of both the client and the database owner. Client privacy is preserved such that the database owner and the cloud server cannot infer the constants contained in the query predicates. Database owner privacy is preserved such that the client cannot obtain any additional information beyond the query result. The primitives that are utilized in designing these protocols include symmetric private information retrieval and private integer comparison. We experimentally evaluate the performance of the proposed protocols and report on the experimental results. © 2013 IEEE

Key-and-Signature Compact Multi-Signatures for Blockchain: A Compiler with Realizations

Multi-signature is a protocol where a set of signatures jointly sign a message so that the final signature is significantly shorter than concatenating individual signatures together. Recently, it finds applications in blockchain, where several users want to jointly authorize a payment through a multi-signature. However, in this setting, there is no centralized authority and it could suffer from a rogue key attack where the attacker can generate his own keys arbitrarily. Further, to minimize the storage on blockchain, it is desired that the aggregated public-key and the aggregated signature are both as short as possible. In this paper, we find a compiler that converts a kind of identification (ID) scheme (which we call a linear ID) to a multi-signature so that both the aggregated public-key and the aggregated signature have a size independent of the number of signers. Our compiler is provably secure. The advantage of our results is that we reduce a multi-party problem to a weakly secure two-party problem. We realize our compiler with two ID schemes. The first is Schnorr ID. The second is a new lattice-based ID scheme, which via our compiler gives the first regular lattice-based multi-signature scheme with key-and-signature compact without a restart during signing process

Evaluation and Optimization of Adaptive Cruise Control in Autonomous Vehicles using the CARLA Simulator: A Study on Performance under Wet and Dry Weather Conditions

Adaptive Cruise Control ACC can change the speed of the ego vehicle to maintain a safe distance from the following vehicle automatically. The primary purpose of this research is to use cutting-edge computing approaches to locate and track vehicles in real time under various conditions to achieve a safe ACC. The paper examines the extension of ACC employing depth cameras and radar sensors within Autonomous Vehicles AVs to respond in real time by changing weather conditions using the Car Learning to Act CARLA simulation platform at noon. The ego vehicle controller's decision to accelerate or decelerate depends on the speed of the leading ahead vehicle and the safe distance from that vehicle. Simulation results show that a Proportional Integral Derivative PID control of autonomous vehicles using a depth camera and radar sensors reduces the speed of the leading vehicle and the ego vehicle when it rains. In addition, longer travel time was observed for both vehicles in rainy conditions than in dry conditions. Also, PID control prevents the leading vehicle from rear collision