Comparing Decision Making Using Expected Utility, Robust Decision Making, and Information-Gap: Application to Capacity Expansion for Airplane Manufacturing

Airplane manufacturing industry is a low-volume high-value industry; however, there is a very high uncertainty associated with it. The industry has long lead times and capacity expansion for such an industry requires huge capital investments. Therefore, capacity planning requires accurate demand forecasting based on the historical data. Various demand forecasting models based on the forecasted demand can serve as an influential tool for the decision making. Based on the profit requirements, cost saving, and the risk attitude of a decision maker, he or she may choose a different strategy. This primary purpose of this research is to model the uncertainty and analyze different decision-making approaches for long-term capacity planning for painting the Boeing 737 airplanes. The first part of the research focusses on identifying the underlying demand trends for the Boeing 737 and Boeing 777 airplane models based on the historical data. Probabilistic models were evaluated for the demand based on model assumptions and statistical analysis. The stochastic processes Brownian motion and a modified geometric Brownian motion were used to predict the demand for the Boeing 737 and Boeing 777 respectively for the next 20 years. The second part of the research focusses on decision making based on the forecasted demand for the Boeing 737 airplanes. The decision is when to construct new hangars to paint new airplanes. Three decision-making approaches were applied to this decision: expected utility, robust decision making, and information gap. Since significant uncertainty exists with the number of airplanes, it is important to compare the decision-making methodologies for different risk tolerances, probabilities, and required profits. The circumstances and assumptions favoring each of the decision-making philosophy under deep uncertainty was discussed and, based on the simulation results, the optimal strategies for the capacity expansion were summarized

Low Degree Metabolites Explain Essential Reactions and Enhance Modularity in Biological Networks

Recently there has been a lot of interest in identifying modules at the level of genetic and metabolic networks of organisms, as well as in identifying single genes and reactions that are essential for the organism. A goal of computational and systems biology is to go beyond identification towards an explanation of specific modules and essential genes and reactions in terms of specific structural or evolutionary constraints. In the metabolic networks of E. coli, S. cerevisiae and S. aureus, we identified metabolites with a low degree of connectivity, particularly those that are produced and/or consumed in just a single reaction. Using FBA we also determined reactions essential for growth in these metabolic networks. We find that most reactions identified as essential in these networks turn out to be those involving the production or consumption of low degree metabolites. Applying graph theoretic methods to these metabolic networks, we identified connected clusters of these low degree metabolites. The genes involved in several operons in E. coli are correctly predicted as those of enzymes catalyzing the reactions of these clusters. We independently identified clusters of reactions whose fluxes are perfectly correlated. We find that the composition of the latter `functional clusters' is also largely explained in terms of clusters of low degree metabolites in each of these organisms. Our findings mean that most metabolic reactions that are essential can be tagged by one or more low degree metabolites. Those reactions are essential because they are the only ways of producing or consuming their respective tagged metabolites. Furthermore, reactions whose fluxes are strongly correlated can be thought of as `glued together' by these low degree metabolites.Comment: 12 pages main text with 2 figures and 2 tables. 16 pages of Supplementary material. Revised version has title changed and contains study of 3 organisms instead of 1 earlie

Representation Learning With Hidden Unit Clustering For Low Resource Speech Applications

The representation learning of speech, without textual resources, is an area of significant interest for many low resource speech applications. In this paper, we describe an approach to self-supervised representation learning from raw audio using a hidden unit clustering (HUC) framework. The input to the model consists of audio samples that are windowed and processed with 1-D convolutional layers. The learned "time-frequency" representations from the convolutional neural network (CNN) module are further processed with long short term memory (LSTM) layers which generate a contextual vector representation for every windowed segment. The HUC framework, allowing the categorization of the representations into a small number of phoneme-like units, is used to train the model for learning semantically rich speech representations. The targets consist of phoneme-like pseudo labels for each audio segment and these are generated with an iterative k-means algorithm. We explore techniques that improve the speaker invariance of the learned representations and illustrate the effectiveness of the proposed approach on two settings, i) completely unsupervised speech applications on the sub-tasks described as part of the ZeroSpeech 2021 challenge and ii) semi-supervised automatic speech recognition (ASR) applications on the TIMIT dataset and on the GramVaani challenge Hindi dataset. In these experiments, we achieve state-of-art results for various ZeroSpeech tasks. Further, on the ASR experiments, the HUC representations are shown to improve significantly over other established benchmarks based on Wav2vec, HuBERT and Best-RQ

An energy-efficient P2P protocol for validating measurements in wireless sensor networks

Wireless sensor networks (WSNs) should collect accurate measurements to reliably capture the state of the environment that they monitor. However, measurement data collected from one or more sensors may drift or become erroneous due to hardware failures or sensor degradation. In WSNs with remote deployments, detecting those measurement errors through a centralized reporting approach can result in a large number of message transmissions, which in turn dramatically decreases the battery life of sensors in the network. In this thesis, we address this issue through three main contributions. First, we propose a protocol in which sensors detect errors in a peer-to-peer (P2P) fashion, and that extends the life of the WSN by minimizing the number of messages transmitted. Second, we propose an e ective anomaly detection approach that has low memory and processing requirements, allowing for easy deployment on low-cost sensor hardware. Third, we develop a trace-driven, discrete-event simulator that allows us to evaluate the developed protocol and approach. In doing so, we use three datasets from real WSN deployments, which include indoor air temperature, sea surface water temperature and seismic wave amplitude sensors. Our results show that our P2P protocol can accurately detect errors and simultaneously extend the e ective WSN lifetime dramatically compared to the centralized protocol

Data-driven methods to improve resource utilization, fraud detection, and cyber-resilience in smart grids

This dissertation demonstrates that empirical models of generation and consumption, constructed using machine learning and statistical methods, improve resource utilization, fraud detection, and cyber-resilience in smart grids. The modern power grid, known as the smart grid, uses computer communication networks to improve efficiency by transporting control and monitoring messages between devices. At a high level, those messages aid in ensuring that power generation meets the constantly changing power demand in a manner that minimizes costs to the stakeholders. In buildings, or nanogrids, communications between loads and centralized controls allow for more efficient electricity use. Ultimately, all efficiency improvements are enabled by data, and it is vital to protect the integrity of the data because compromised data could undermine those improvements. Furthermore, such compromise could have both economic consequences, such as power theft, and safety-critical consequences, such as blackouts. This dissertation addresses three concerns related to the smart grid: resource utilization, fraud detection, and cyber-resilience. We describe energy resource utilization benefits that can be achieved by using machine learning for renewable energy integration and also for energy management of building loads. In the context of fraud detection, we present a framework for identifying attacks that aim to make fraudulent monetary gains by compromising consumption and generation readings taken by meters. We then present machine learning, signal processing, and information-theoretic approaches for mitigating those attacks. Finally, we explore attacks that seek to undermine the resilience of the grid to faults by compromising generators' ability to compensate for lost generation elsewhere in the grid. Redundant sources of measurements are used to detect such attacks by identifying mismatches between expected and measured behavior

Purification and characterisation of endogenous unanchored polyubiquitin

The 76 amino-acid protein ubiquitin is peculiar in its ability to covalently modify substrate proteins. The complexity and diversity in structure and function, of this posttranslational modification is increasingly evident and has been extensively scrutinized. However, the existing notion for ubiquitin to be functionally relevant when covalently linked to substrates was recently found to be dispensable. Pioneering work by Chen’s group has shown that unanchored (non-substrate bound) polyubiquitin chains are just as effective in regulating various cellular processes and presented compelling evidence about the influence of unanchored polyubiquitin chains in regulating cell signalling. That said, not enough emphasis has been placed in developing tools for the direct isolation and characterisation of endogenous unanchored polyubiquitin chains. Our group had previously developed a platform, which employs the Znf-UBP (BUZ) ubiquitin-binding domain of human Isopeptidase T enzyme, to (for the first time) directly purify cellular unanchored polyubiquitin chains of up to 15 ubiquitin moieties long from rat skeletal muscle tissue, also confirming the presence of K48 isopeptide linkages in such chains. Following on, the work described here details further refinement and optimisation of the purification protocol specifically from eukaryotic cell line (HEK293T). We could confirm by western blotting, the presence of K48 and K63 linked endogenous unanchored polyubiquitin chains, some 10 or more ubiquitin moieties long. Our next step was to optimise existing standard protein processing protocols specifically for the study of cellular unanchored ubiquitome. Considering ubiquitin being resistant to trypsin digestion, we found a general lack of consensus in protocols best suited for processing and analysis of the cellular ubiquitome. Extensive optimisations of various protein digestion conditions were performed to arrive on the protocol best suited for unanchored polyubiquitin. The protocol was designed to address the specific problem of maximising (efficient) digestion of ubiquitin, minimising losses in the process to produce samples for quantitative analysis. The purchase of a QqQ mass spectrometer by the group enabled quantitative estimation of polyubiquitin linkages by using AQUA standards although this required significant optimisations as well. Subsequently, we could successfully detect and estimate the abundance of isopeptide linkages purified from HEK293T cell line to be K63 (83%)>>K11 (9%)>K48, K27 (3.7-4.0%) at basal conditions. Moreover, during the course of trying to attain self-sufficiency in this area of work, our knowledge about the pitfalls and caveats in the area also improved. Following the development of a robust in-house platform for the isolation and characterisation of unanchored polyubiquitin chains, we were eager to apply the protocol to study the endogenous unanchored polyubiquitin makeup of cells at basal conditions, and take a step further to determine the change in their levels upon activation of signalling, manipulation of possible regulators and in response to stress. While our attempt at absolute quantification of linkage abundance was not entirely successful, we present data which confirmed increase in K48 and K11 (but not K63) linked unanchored polyubiquitin chains upon proteasomal inhibition and also a strong indication of accumulation of all three linkage types upon suppression of IsoT. Finally, the study has improved our understanding of the regulation of the cellular unanchored ubiquitome

Profile and outcomes of symptomatic pancreatic fluid collections at a tertiary care hospital

Background: Pancreatic fluid collections (PFCs) are seen in about 50% cases of pancreatitis. Most PFCs are usually asymptomatic and resolve spontaneously not requiring intervention. However, symptomatic and complicated PFCs require intervention. In this study we aimed at estimating clinical characteristics, demographics, modalities of treatment and their success rates. Methods: 40 patients with symptomatic PFCs were included in this study. Clinical characteristics, type and location of PFC, indication for treatment, type of intervention, their success and complication rates were recorded. Results: Among 40 patients, 29 were male and 11 females with mean age of 40.2±7.5 years. The most common PFC was pseudocyst (62.5%) followed by walled off necrosis (WON) (25%). The most common indication for intervention was abdominal pain (50%) followed by gastric outlet obstruction (25%), obstructive jaundice (15%) and sepsis (10%). The success rate of EUS guided transmural drainage was 95.6% for pseudocyst and 77.7% for WON. The rate of adverse events was 32% in pseudocysts and 40% in WON. DEN (direct endoscopic necrosectomy) was done in 3 cases of infected WON. Conclusions: The most common PFC seen in practice is pseudocyst followed by WON. Endoscopic (EUS guided) transmural drainage has emerged as the first line intervention for symptomatic PFCs. The rate of complications and associated morbidity is much lesser with endoscopic procedures compared to surgery. The success rate of endoscopic intervention is higher in cases of pseudocyst but complications are higher in necrotic collections