In this paper, we study the behavior of a day-ahead (DA) retail electrical energy market with price-based demand response from air conditioning (AC) loads through a hierarchical multiagent framework, employing a machine learning approach. At the top level of the hierarchy, a retailer agent buys energy from the DA wholesale market and sells it to the consumers. The goal of the retailer agent is to maximize its profit by setting the optimal retail prices, considering the response of the price-sensitive loads. Upon receiving the retail prices, at the lower level of the hierarchy, the AC agents employ a Q-learning algorithm to optimize their consumption patterns through modifying the temperature set-points of the devices, considering both consumption costs and users' comfort preferences. Since the retailer agent does not have direct access to the AC loads' underlying dynamics and decision process (i.e., incomplete information) the data privacy of the consumers becomes a source of uncertainty in the retailer's decision model. The retailer relies on techniques from the field of machine learning to develop a reliable model of the aggregate behavior of the price-sensitive loads to reduce the uncertainty of the decision-making process. Hence, a multiagent framework based on machine learning enables us to address issues such as interoperability and decision-making under incomplete information in a system that maintains the data privacy of the consumers. We will show that using the proposed model, all the agents are able to optimize their behavior simultaneously. Simulation results show that the proposed approach leads to a reduction in overall power consumption cost as the system converges to its equilibrium. This also coincides with maximization in the retailer's profit. We will also show that the same decision architecture can be used to reduce peak load to defer/avoid distribution system upgrades under high penetration of photo-voltaic power in the distribution feeder.

Recently operators of complex systems such as aircraft, power plants, and networks have been emphasizing the need for on-line health monitoring for purposes of maximizing operational availability and safety. The discipline of prognostics and health management (PHM) is being formalized to address the information management and prediction requirements for addressing these needs. Herein, we will explore how standards currently under development within the IEEE can be used to support PHM applications. Particular emphasis will be placed on the role of PHM and PHM-related standards with Department of Defense (DOD) automatic test systems-related research.

Due to uncertainties in generation and load, optimal decision making in electrical energy markets is a complicated and challenging task. Participating agents in the market have to estimate optimal bidding strategies based on incomplete public information and private assessment of the future state of the market, to maximize their expected profit at different time scales. In this paper, we present an agent-based model to address the problem of short-term strategic bidding of conventional generation companies (GenCos) in a power pool. Based on the proposed model, each GenCo agent develops a private probabilistic model of the market (using dynamic Bayesian networks), employs an online learning algorithm to train the model (sparse Bayesian learning), and infers the future state of the market to estimate the optimal bidding function. We show that by using this multiagent framework, the agents will be able to predict and adapt to approximate Nash equilibrium of the market through time using local reasoning and incomplete publicly available data. The model is implemented in MATLAB and is tested on four test case systems: two generic systems with 5 and 15 GenCo agents, and two IEEE benchmarks (9-bus and 30-bus systems). Both the day-ahead (DA) and hour-ahead (HA) bidding schemes are implemented. The results show a drop in market power in the 15-agent system compared to 5-agent system, along with a Pareto superior equilibrium in the HA scheme compared to the DA scheme, which corroborates the validity of the proposed decision making model. Also, the simulations show that introduction of an HA decision making stage as an uncertainty containment tool, leads to a more stable and less volatile price signal in the market, which consequently results in flatter and improved profit curves for the GenCos.

The majority of the clinico-pathological variability observed in patients harboring a repeat expansion in the C9orf72-SMCR8 complex subunit (C9orf72) remains unexplained. This expansion, which represents the most common genetic cause of frontotemporal lobar degeneration (FTLD) and motor neuron disease (MND), results in a loss ofC9orf72expression and the generation of RNA foci and dipeptide repeat (DPR) proteins. The C9orf72 protein itself plays a role in vesicular transport, serving as a guanine nucleotide exchange factor that regulates GTPases. To further elucidate the mechanisms underlyingC9orf72-related diseases and to identify potential disease modifiers, we performed an extensive RNA sequencing study. We included individuals for whom frontal cortex tissue was available: FTLD and FTLD/MND patients with (n = 34) or without (n = 44) an expandedC9orf72repeat as well as control subjects (n = 24). In total, 6706 genes were differentially expressed between these groups (false discovery rate [FDR] < 0.05). The top gene wasC9orf72(FDR = 1.41E-14), which was roughly two-fold lower inC9orf72expansion carriers than in (disease) controls. Co-expression analysis revealed groups of correlated genes (modules) that were enriched for processes such as protein folding, RNA splicing, synaptic signaling, metabolism, and Golgi vesicle transport. Within our cohort ofC9orf72expansion carriers, machine learning uncovered interesting candidates associated with clinico-pathological features, including age at onset (vascular endothelial growth factor A [VEGFA]),C9orf72expansion size (cyclin dependent kinase like 1 [CDKL1]), DPR protein levels (eukaryotic elongation factor 2 kinase [EEF2K]), and survival after onset (small G protein signaling modulator 3 [SGSM3]). Given the fact that we detected a module involved in vesicular transport in addition to a GTPase activator (SGSM3) as a potential modifier, our findings seem to suggest that the presence of aC9orf72repeat expansion might hamper vesicular transport and that genes affecting this process may modify the phenotype ofC9orf72-linked diseases.

Weed control is a major problem in worldwide agriculture, costing an estimated $25.2 billion a year to control.¹ The emergence of herbicide-resistant weeds has compounded this problem, threatening to reduce yields of certain crops by up to 93%.^{2 – 7} In the United States and Canada alone, the combined potential losses from uncontrolled weeds in just corn and soybean would total $43 billion annually,⁸ and production of both crops would be cut in half. Current methods of identifying herbicide resistance require weeks or months,⁹ longer than a complete growing cycle in many cases. To achieve more real-time classification and detection capabilities, we have been developing remote sensing methods based on hyperspectral imaging and machine learning to classify different biotypes of herbicide-resistant weeds.

Factored evolutionary algorithms (FEAs) are a new class of evolutionary search-based optimization algorithms that have successfully been applied to various problems, such as training neural networks and performing abductive inference in graphical models. An FEA is unique in that it factors the objective function by creating overlapping subpopulations that optimize over a subset of variables of the function. In this paper, we give a formal definition of FEA algorithms and present empirical results related to their performance. One consideration in using an FEA is determining the appropriate factor architecture, which determines the set of variables each factor will optimize. For this reason, we present the results of experiments comparing the performance of different factor architectures on several standard applications for evolutionary algorithms. Additionally, we show that FEA's performance is not restricted by the underlying optimization algorithm by creating FEA versions of hill climbing, particle swarm optimization, genetic algorithm, and differential evolution and comparing their performance to their single-population and cooperative coevolutionary counterparts.