A genetic algorithm for simplifying the amino acid alphabet and predicting protein interactions.

A central problem in creating simplified amino acid alphabets is narrow­ing down the massive number of possible simplifications. Since considering all possible simplifications is intractable, effectively creating simplified alphabets is essential. Genetic algorithms have been effective in providing near-optimal solutions for similar combinatorial problems with large solution spaces. This makes them a good candidate for creating simplified alphabets. Simplified amino acid alphabets could uncover hidden relationships in protein sequences, and in turn provide a valuable first step in solving protein-related microbiological problems. The project demonstrates the impact of reducing the alphabet in addressing an important open problem in microbiology, which is predicting protein-protein interactions. Various techniques for predicting protein-protein interactions exist, but are incomplete. No single method can effectively predict more than a small subset of interactions. Hence, a com­prehensive listing all of a cells protein-protein interactions may require many complimentary approaches. The projects results indicate that genetic algo­rithms effectively. create simplified amino acid alphabets, and these alphabets are a useful tool in predicting protein interactions

Detection of False Data Injection Attacks Using the Autoencoder Approach

State estimation is of considerable significance for the power system operation and control. However, well-designed false data injection attacks can utilize blind spots in conventional residual-based bad data detection methods to manipulate measurements in a coordinated manner and thus affect the secure operation and economic dispatch of grids. In this paper, we propose a detection approach based on an autoencoder neural network. By training the network on the dependencies intrinsic in 'normal' operation data, it effectively overcomes the challenge of unbalanced training data that is inherent in power system attack detection. To evaluate the detection performance of the proposed mechanism, we conduct a series of experiments on the IEEE 118-bus power system. The experiments demonstrate that the proposed autoencoder detector displays robust detection performance under a variety of attack scenarios.Comment: 6 pages, 5 figures, 1 table, conferenc

Ars Femina Archive 1500s-1800s: Representing Women Composers through Digital Archives

Representation is one of the most powerful impacts that archives can make on communities. Ensuring that all people’s works, lives, and information is being preserved in an archive is what fuels a many modern day archivist. However, establishing equal representation of minorities and underrepresented groups is not enough to create a more inclusive world, archivists must also create ways for people to access that information. The creation of digital libraries and other online resources, allows for more people to use the resources collected, see themselves and their work represented, and gain an understanding of the artists who have come before them. The Ars Femina Archive (AFA), is housed at Indiana University Southeast, and is a collection of music composed by women from before the 1500s to the 1800s. This archive preserves and celebrates the impact that women in history have had on music. Women are largely underrepresented in the arts and especially in music, the AFA allows for people from around the world to research and access this collection of musical compositions created by women. This presentation will focus on the history of the collection, what is contained in the archive, its mission and how that mission is furthered by digitization, and the impact it has on scholarship and performance

A Constraint Enforcement Deep Reinforcement Learning Framework for Optimal Energy Storage Systems Dispatch

The optimal dispatch of energy storage systems (ESSs) presents formidable challenges due to the uncertainty introduced by fluctuations in dynamic prices, demand consumption, and renewable-based energy generation. By exploiting the generalization capabilities of deep neural networks (DNNs), deep reinforcement learning (DRL) algorithms can learn good-quality control models that adaptively respond to distribution networks' stochastic nature. However, current DRL algorithms lack the capabilities to enforce operational constraints strictly, often even providing unfeasible control actions. To address this issue, we propose a DRL framework that effectively handles continuous action spaces while strictly enforcing the environments and action space operational constraints during online operation. Firstly, the proposed framework trains an action-value function modeled using DNNs. Subsequently, this action-value function is formulated as a mixed-integer programming (MIP) formulation enabling the consideration of the environment's operational constraints. Comprehensive numerical simulations show the superior performance of the proposed MIP-DRL framework, effectively enforcing all constraints while delivering high-quality dispatch decisions when compared with state-of-the-art DRL algorithms and the optimal solution obtained with a perfect forecast of the stochastic variables.Comment: This paper has been submitted to a publication in a journal. This corresponds to the submitted version. After acceptance, it may be removed depending on the journal's requirements for copyrigh

Quantum Neural Networks for Power Flow Analysis

This paper explores the potential application of quantum and hybrid quantum-classical neural networks in power flow analysis. Experiments are conducted using two small-size datasets based on the IEEE 4-bus and 33-bus test systems. A systematic performance comparison is also conducted among quantum, hybrid quantum-classical, and classical neural networks. The comparison is based on (i) generalization ability, (ii) robustness, (iii) training dataset size needed, (iv) training error. (v) training computational time, and (vi) training process stability. The results show that the developed quantum-classical neural network outperforms both quantum and classical neural networks, and hence can improve deep learning-based power flow analysis in the noisy-intermediate-scale quantum (NISQ) era.Comment: 7 pages, 15 figure

Conversations with my washing machine: an in-the-wild study of demand-shifting with self-generated energy

Domestic microgeneration is the onsite generation of low- and zero-carbon heat and electricity by private households to meet their own needs. In this paper we explore how an everyday household routine – that of doing laundry – can be augmented by digital technologies to help households with photovoltaic solar energy generation to make better use of self-generated energy. This paper presents an 8-month in-the-wild study that involved 18 UK households in longitudinal energy data collection, prototype deployment and participatory data analysis. Through a series of technology interventions mixing energy feedback, proactive suggestions and direct control the study uncovered opportunities, potential rewards and barriers for families to shift energy consuming household activities and highlights how digital technology can act as mediator between household laundry routines and energy demand-shifting behaviors. Finally, the study provides insights into how a “smart” energy-aware washing machine shapes organization of domestic life and how people “communicate” with their washing machine