Review essay: Anthony Howe. Byron and the Forms of Thought (Liverpool: Liverpool UP, 2013) and Carla Pomare. Byron and the Discourse of History (Farnham and Burlington: Ashgate, 2013).

This essay is a comparative review of two recently published books in Byron studies: Anthony Howe's Byron and the Forms of Thought (Liverpool: Liverpool UP, 2013) and Carla Pomare's Byron and the Discourse of History (Farnham and Burlington: Ashgate, 2013)

Data Driven Enhanced VSG Control for Microgrids

This research explores the fusion of deep reinforcement learning and virtual synchronous generator control in a bid to enhance microgrid operations. Microgrids typically consist of multiple inverter-based distributed generators (IBDGs) connected in parallel to mismatched line impedances. This results in unequal reactive power sharing which negatively impacts the performance of IBDGs in microgrids. To achieve enhanced control, a solution utilizing deep reinforcement learning (DRL) is proposed. DRL agents are trained to control variables in each IBDG using a well-designed reward function capable of achieving the following objectives: 1.) ensure output voltage of each IBDG remains within the designated operating boundary and 2.) minimize IBDG RPSE. The proposed DRL method is then compared to the classical droop method under various system disturbances. This exploration into the integration of DRL into microgrid applications holds the potential to revolutionize future grid control methods

Pedestrian Infrastructure Improvements: Effects on Transit Use and Perceptions of the Pedestrian Environment in Portland\u27s Roseway Neighborhood

Over the past two years the Pedestrian Transportation (PTP) of the City of Portland has been engaged in a project to encourage walking and transit use through targeted infrastructure improvements. These improvements are intended to enhance pedestrian access to transit service by aiding street crossing and providing more amenities at bus stops. Other improvements include landscaping, sidewalks, curb extensions and ramps, and improved street lighting. One of the basic assumptions of this project is that the pedestrian environment is related to transportation choices. This report explores that assumption

Making things happen : a model of proactive motivation

Being proactive is about making things happen, anticipating and preventing problems, and seizing opportunities. It involves self-initiated efforts to bring about change in the work environment and/or oneself to achieve a different future. The authors develop existing perspectives on this topic by identifying proactivity as a goal-driven process involving both the setting of a proactive goal (proactive goal generation) and striving to achieve that proactive goal (proactive goal striving). The authors identify a range of proactive goals that individuals can pursue in organizations. These vary on two dimensions: the future they aim to bring about (achieving a better personal fit within one’s work environment, improving the organization’s internal functioning, or enhancing the organization’s strategic fit with its environment) and whether the self or situation is being changed. The authors then identify “can do,” “reason to,” and “energized to” motivational states that prompt proactive goal generation and sustain goal striving. Can do motivation arises from perceptions of self-efficacy, control, and (low) cost. Reason to motivation relates to why someone is proactive, including reasons flowing from intrinsic, integrated, and identified motivation. Energized to motivation refers to activated positive affective states that prompt proactive goal processes. The authors suggest more distal antecedents, including individual differences (e.g., personality, values, knowledge and ability) as well as contextual variations in leadership, work design, and interpersonal climate, that influence the proactive motivational states and thereby boost or inhibit proactive goal processes. Finally, the authors summarize priorities for future researc

Addressing Reactive Power Sharing in Parallel Inverter Islanded Microgrid through Deep Reinforcement Learning

Parallel Inverter Microgrids (MGs) Present a Significant Challenge in the Form of Inverter-Based Distributed Generators (IBDGs) Connected with Varying Line Impedances, Potentially Leading to Substantial Reactive Power-Sharing Errors (RPSE). This Paper Proposes the Fusion of Data-Driven Control into the Conventional Virtual Synchronous Generator in a Bid to Minimize the Sharing Error. First, All State Variables Associated with Each IBDG in the Microgrid Are Sensed and Used as Input Data for a Deep Reinforcement Learning (DRL) Agent. Next, the DRL Agent, motivated by a Unique Reward Function, is Trained to Satisfy Two Objectives: (1) Ensure the Output Voltage of All IBDGs in the System Stays within a Safe Operating Boundary, (2) Ensure the RPSE for the IBDGs is Minimized. the Trained Agent is Deployed in a Simple IBDG Microgrid, and the Performance is Evaluated under Different System Disturbances and Compared with the Traditional Control Methods

Risk-taking, delay discounting, and time perspective in adolescent gamblers: an experimental study

Previous research has demonstrated that adult pathological gamblers (compared to controls) show risk-proneness, foreshortened time horizon, and preference for immediate rewards. No study has ever examined the interplay of these factors in adolescent gambling. A total of 104 adolescents took part in the research. Two equal-number groups of adolescent non-problem and problem gamblers, defined using the South Oaks Gambling Screen-Revised for Adolescents (SOGS-RA), were administered the Balloon Analogue Risk Task (BART), the Consideration of Future Consequences (CFC-14) Scale, and the Monetary Choice Questionnaire (MCQ). Adolescent problem gamblers were found to be more risk-prone, more oriented to the present, and to discount delay rewards more steeply than adolescent non-problem gamblers. Results of logistic regression analysis revealed that BART, MCQ, and CFC scores predicted gambling severity. These novel finding provides the first evidence of an association among problematic gambling, high risk-taking proneness, steep delay discounting, and foreshortened time horizon among adolescents. It may be that excessive gambling induces shortsighted behaviors that, in turn, facilitate gambling involvement

Simultaneous Frequency Regulation and Active Power Sharing in Islanded Microgrid using Deep Reinforcement Learning

This paper presents a novel approach that integrates deep reinforcement learning (DRL) with the conventional virtual synchronous generator (VSG) to address dual objectives of microgrid (MG) control, frequency regulation and precise active power sharing. MGs typically consist of multiple Inverter-Based-Distributed-Generators (IBDGs) connected in parallel through different line impedances. The conventional active power loop (APL) of the VSG encounters significant steady-state frequency errors as load increases/decreases during islanded operation. To mitigate this issue, secondary-level controllers like proportional-integral (PI) control are added to the APL to regulate the frequency of IBDGs. However, PI control compromises power-sharing capabilities when the impedance values of connecting feeders for each IBDG are mismatched. To eliminate frequency errors and achieve accurate power sharing concurrently, this study adopts a DRL-based strategy. The agent collects state information from each IBDG in the microgrid as input and undergoes training using a reward function crafted to satisfy both objectives simultaneously. The performance of the trained agent is demonstrated in a two-inverter microgrid system designed in MATLAB/SIMULINK and is compared against traditional methods

Instantaneous Current and Average Power Flow Characterization of a DC-DC-DC Triple Active Bridge Converter

The Triple Active Bridge (TAB) is a Three-Port Power Converter that Facilitates Bi-Directional Power Flow and Provides Galvanic Isolation, Making It a Subject of Significant Research Attention. This is Attributed to its Diverse Applications in High-Frequency DC-DC Conversion, Electric Vehicles, Renewable Energy Integration, and Micro-Grids. Controlling the System at Run-Time Involves Modification of the Two Phase-Shift Parameters between Respective Bridges. by Analyzing the Fundamental Converter Operating Equations, Future Control Designers Can Use This Framework to Optimize Control Schemes to Mitigate the Under-Determined Nature of the TAB Converter. in This Paper, We Elucidate the Foundational Operational Principles of the TAB and Establish the Defining Equations for Instantaneous Current and Average Power Flow. Furthermore, We Validate These Equations through a Comparative Analysis Involving a Simulation Model of the TAB in PLECS and Hardware Implementation

Active and Reactive Power Flow Control of the Dual Active Bridge Converter

The Dual Active Bridge (DAB) is a reliable and efficient converter capable of providing bi-directional power transfer and galvanic isolation. An ac-ac DAB can control both active and reactive power flow. The present work introduces a combined feedback/feed-forward current control system, utilizing the calculated and measured converter currents translated into the dq reference frame, to control the output power. The system was simulated in PLECS to demonstrate the control algorithm\u27s ability to track the dq currents and provide the necessary output power