Evolution of adaptation mechanisms: adaptation energy, stress, and oscillating death

In 1938, H. Selye proposed the notion of adaptation energy and published "Experimental evidence supporting the conception of adaptation energy". Adaptation of an animal to different factors appears as the spending of one resource. Adaptation energy is a hypothetical extensive quantity spent for adaptation. This term causes much debate when one takes it literally, as a physical quantity, i.e. a sort of energy. The controversial points of view impede the systematic use of the notion of adaptation energy despite experimental evidence. Nevertheless, the response to many harmful factors often has general non-specific form and we suggest that the mechanisms of physiological adaptation admit a very general and nonspecific description. We aim to demonstrate that Selye's adaptation energy is the cornerstone of the top-down approach to modelling of non-specific adaptation processes. We analyse Selye's axioms of adaptation energy together with Goldstone's modifications and propose a series of models for interpretation of these axioms. {\em Adaptation energy is considered as an internal coordinate on the `dominant path' in the model of adaptation}. The phenomena of `oscillating death' and `oscillating remission' are predicted on the base of the dynamical models of adaptation. Natural selection plays a key role in the evolution of mechanisms of physiological adaptation. We use the fitness optimization approach to study of the distribution of resources for neutralization of harmful factors, during adaptation to a multifactor environment, and analyse the optimal strategies for different systems of factors

Reconfigurable Reflectarrays and Array Lenses for Dynamic Antenna Beam Control: A Review

Advances in reflectarrays and array lenses with electronic beam-forming capabilities are enabling a host of new possibilities for these high-performance, low-cost antenna architectures. This paper reviews enabling technologies and topologies of reconfigurable reflectarray and array lens designs, and surveys a range of experimental implementations and achievements that have been made in this area in recent years. The paper describes the fundamental design approaches employed in realizing reconfigurable designs, and explores advanced capabilities of these nascent architectures, such as multi-band operation, polarization manipulation, frequency agility, and amplification. Finally, the paper concludes by discussing future challenges and possibilities for these antennas.Comment: 16 pages, 12 figure

Application of Deep Learning Methods in Monitoring and Optimization of Electric Power Systems

This PhD thesis thoroughly examines the utilization of deep learning techniques as a means to advance the algorithms employed in the monitoring and optimization of electric power systems. The first major contribution of this thesis involves the application of graph neural networks to enhance power system state estimation. The second key aspect of this thesis focuses on utilizing reinforcement learning for dynamic distribution network reconfiguration. The effectiveness of the proposed methods is affirmed through extensive experimentation and simulations.Comment: PhD thesi

Satellite swarms for auroral plasma science

With the growing accessibility of space, this thesis work sets out to explore space-based swarms to do multipoint magnetometer measurements of current systems embedded within the Aurora Borealis as an initial foray into concepts for space physics applications using swarms of small spacecraft. As a pathfinder, ANDESITE---a 6U CubeSat with eight deployable picosatellites---was built as part of this research. The mission will fly a local network of magnetometers above the Northern Lights. With the spacecraft due to launch on an upcoming ELaNa mission, here we discuss the details of the science motivation, the mathematical framework for current field reconstruction, the particular hardware implementation selected, the calibration procedures, and the pragmatic management needed to realize the spacecraft. After describing ANDESITE and defining its capability, we also propose a follow-on that uses propulsive nodes in a swarm, allowing measurements that can adaptively change to capture the physical phenomena of interest. To do this a flock of satellites needs to fall into the desired formation and maintain it for the duration of the science mission. A simple optimal controller is developed to model the deployment of the satellites. Using a Monte Carlo approach for the uncertain initial conditions, we bound the fuel cost of the mission and test the feasibility of the concept. To illustrate the system analysis needed to effectively design such swarms, this thesis also develops a framework that characterizes the spatial frequency response of the kilometer-scale filter created by the swarm as it flies through various current density structures in the ionospheric plasma. We then subjugate a nominal ANDESITE formation and the controlled swarm specified to the same analysis framework. The choice of sampling scheme and rigorous basic mathematical analysis are essential in the development of a multipoint-measurement mission. We then turn to a novel capability exploiting current trends in the commercial industry. Magnetometers deployed on the largest constellation to date are leveraged as a space-based magnetometer network. The constellation, operated by Planet Labs Inc., consists of nearly 200 satellites in two polar sun-synchronous orbits, with median spacecraft separations on the order of 375 km, and some occasions of opportunity providing much closer spacing. Each spacecraft contains a magneto-inductive magnetometer, able to sample the ambient magnetic field at 0.1 Hz to 10 Hz with <200 nT sensitivity. A feasibility study is presented wherein seven satellites from the Planet constellation were used to investigate space-time patterns in the current systems overlying an active auroral arc over a 10-minute interval. Throughout the this work advantages, limitations, and caveats in exploiting networks of lower quality magnetometers are discussed, pointing out the path forward to creating a global network that can monitor the space environment

An Overview on Application of Machine Learning Techniques in Optical Networks

Today's telecommunication networks have become sources of enormous amounts of widely heterogeneous data. This information can be retrieved from network traffic traces, network alarms, signal quality indicators, users' behavioral data, etc. Advanced mathematical tools are required to extract meaningful information from these data and take decisions pertaining to the proper functioning of the networks from the network-generated data. Among these mathematical tools, Machine Learning (ML) is regarded as one of the most promising methodological approaches to perform network-data analysis and enable automated network self-configuration and fault management. The adoption of ML techniques in the field of optical communication networks is motivated by the unprecedented growth of network complexity faced by optical networks in the last few years. Such complexity increase is due to the introduction of a huge number of adjustable and interdependent system parameters (e.g., routing configurations, modulation format, symbol rate, coding schemes, etc.) that are enabled by the usage of coherent transmission/reception technologies, advanced digital signal processing and compensation of nonlinear effects in optical fiber propagation. In this paper we provide an overview of the application of ML to optical communications and networking. We classify and survey relevant literature dealing with the topic, and we also provide an introductory tutorial on ML for researchers and practitioners interested in this field. Although a good number of research papers have recently appeared, the application of ML to optical networks is still in its infancy: to stimulate further work in this area, we conclude the paper proposing new possible research directions

Dynamic Voltage Restorer Application for Power Quality Improvement in Electrical Distribution System: An Overview

Dynamic Voltage Restorer (DVR) is a custom power device that is used to improve voltage disturbances in electrical distribution system. The components of the DVR consist of voltage source inverter (VSI), injection transformers, passive filters and energy storage. The main function of the DVR is used to inject three phase voltage in series and in synchronism with the grid voltages in order to compensate voltage disturbances. The Development of (DVR) has been proposed by many researchers. This paper presents a review of the researches on the DVR application for power quality Improvement in electrical distribution network. The types of DVR control strategies and its configuration has been discussed and may assist the researchers in this area to develop and proposed their new idea in order to build the prototype and controller

A generalized optimal power flow program for distribution system analysis and operation with distributed energy resources and solid state transformers

The present distribution system is gradually trending towards a smart grid paradigm with massive development of distributed energy resources (DER), advanced power electronics interfaces, and a digitalized communication platform. Such profound changes bring challenges as well as opportunities for an entity like the distribution network operator (DNO) to optimally operate DERs and other controllable elements to achieve higher levels of energy efficiency, economic benefits, supply reliability and power quality. The major contribution of this dissertation is in the development of a generalized three-phase optimal power flow (OPF) program in a novel control scheme for future distribution system optimization and economic operation. It is developed based on primal-dual interior point method (PDIPM). The program is general enough to model comprehensive system components and topologies. The program can also be customized by user-defined cost functions, system constraints, and new device, such as solid state transformers (SST). An energy storage optimal control using dynamic programming is also proposed to coordinate with the OPF based on a pricing signal called the distribution locational marginal price (DLMP). The proposed OPF program can be used by the DNO in an open access competitive control scheme to optimally aggregate the energy mix by combining the profitability of each resource while satisfying system security constraints --Abstract, page iv

The space physics environment data analysis system (SPEDAS)

With the advent of the Heliophysics/Geospace System Observatory (H/GSO), a complement of multi-spacecraft missions and ground-based observatories to study the space environment, data retrieval, analysis, and visualization of space physics data can be daunting. The Space Physics Environment Data Analysis System (SPEDAS), a grass-roots software development platform (www.spedas.org), is now officially supported by NASA Heliophysics as part of its data environment infrastructure. It serves more than a dozen space missions and ground observatories and can integrate the full complement of past and upcoming space physics missions with minimal resources, following clear, simple, and well-proven guidelines. Free, modular and configurable to the needs of individual missions, it works in both command-line (ideal for experienced users) and Graphical User Interface (GUI) mode (reducing the learning curve for first-time users). Both options have “crib-sheets,” user-command sequences in ASCII format that can facilitate record-and-repeat actions, especially for complex operations and plotting. Crib-sheets enhance scientific interactions, as users can move rapidly and accurately from exchanges of technical information on data processing to efficient discussions regarding data interpretation and science. SPEDAS can readily query and ingest all International Solar Terrestrial Physics (ISTP)-compatible products from the Space Physics Data Facility (SPDF), enabling access to a vast collection of historic and current mission data. The planned incorporation of Heliophysics Application Programmer’s Interface (HAPI) standards will facilitate data ingestion from distributed datasets that adhere to these standards. Although SPEDAS is currently Interactive Data Language (IDL)-based (and interfaces to Java-based tools such as Autoplot), efforts are under-way to expand it further to work with python (first as an interface tool and potentially even receiving an under-the-hood replacement). We review the SPEDAS development history, goals, and current implementation. We explain its “modes of use” with examples geared for users and outline its technical implementation and requirements with software developers in mind. We also describe SPEDAS personnel and software management, interfaces with other organizations, resources and support structure available to the community, and future development plans.Published versio