Multi-criteria decision making under uncertainty is a common practice followed in industries and academia. Among several types of uncertainty handling techniques, Chance Constrained Programming (CCP) ...is considered as an efficient and tractable approach provided one has accessibility to distribution of the data for uncertain parameters. However, the assumption that the uncertain parameters must follow some well-behaved probability distribution is a myth for most of the practical applications. This paper proposes a methodology to amalgamate machine learning algorithms with CCP and thereby make it data-driven. A novel fuzzy clustering mechanism is implemented to transcript the uncertain space such that the exact regions of uncertainty are identified. Subsequently, density based boundary point detection and Delaunay triangulation based boundary construction enable intelligent Sobol based sampling in these regions for use in CCP. The Fuzzy clustering mechanism used in the proposed method transforms the existing fuzzy C-means technique such that the decision variables are significantly reduced. This enables evolutionary optimizers to obtain better approximations of the uncertain space by identifying the true clusters. A highly nonlinear real life model for continuous casting from steelmaking industries is considered as a case study for testing the efficiency of data based CCP along with a comprehensive comparison between conventional CCP approach using box uncertainty set and proposed methodology. As the resulting CCP problem is multi-objective in nature, the Pareto solutions are obtained by NSGA II.
Neuronal synchronicity is central in controlling the cognitive functions and disruption in neuronal synchronicity may lead to diseased state. Since the neurons show significant heterogeneity in ...firing pattern in case of in vitro and in vivo cell recordings, automated identification of synchronous and asynchronous neurons in a large population remains challenging. In this context, an efficient data analytics approach is proposed where the time-lapse data is primarily obtained from imaging of intracellular \mathrm{C}\mathrm{a}^{2+} in primary cultures of hippocampal neurons. Here, F1uo-4 is used as the fluorescent indicator for measuring for cytosolic calcium through imaging using confocal microscope. To categorize synchronous response from a set of heterogeneous \mathrm{C}\mathrm{a}^{2+} spiking data, an efficient artificial neural networks based clustering algorithm has been proposed, which proceeds through a variable reduction approach. This algorithm further enables the usage of evolutionary optimizers to solve the optimization problem of Fuzzy C-means (FCM) clustering. Moreover, the novel algorithm estimates the optimal number of cluster and optimal artificial neural network topology, which remains to be a longstanding issue. In order to validate the obtained clustering solution, the cross-correlation coefficient and spiking pattern is measured for the clustered neuron cell data. The obtained solution is compared with that of conventional FCM algorithm such that the efficiency of proposed approach could be tested.
Blisters containing fluid with a high pH were induced in a carbon fiber vinyl ester polymer matrix composite by cathodic polarization in a sodium chloride solution. The oxygen content of the solution ...was varied in order to determine the effect of oxygen on the blister process. It was found that increasing the oxygen content in solution decreased the time for blister initiation, indicating that the reduction of oxygen to form hydroxyl ions was an important reaction. Calculations indicated that a similar cumulative charge density was required for blister initiation for the different oxygen contents, confirming the importance of oxygen to the blister process. A mechanism for blister formation dependent upon oxygen reduction to hydroxyl ions and the subsequent formation of an osmotic cell is outlined from the results of this study
The authors consider a problem related to global routing of multiterminal nets in VLSI layout. They investigate the problem of finding the minimum Steiner tree in the presence of obstacles when the ...terminals lie on the boundary of a rectangle (RSTO) and present two results. The first contribution is an exact solution for finding the rectilinear Steiner tree in the presence of an obstacle when the terminals lie on the boundary of a rectangle. Second, an approximation algorithm for RSTO in the presence of k obstacles is given. It is shown that the algorithm has a tight performance bound. A heuristic algorithm which produces solutions very close to the optimal is given.< >
The ongoing pandemic of the novel Corona Virus Disease 2019 (COVID-19) is an unprecedented challenge to global health, never experienced before.
This study aims to describe the clinical ...characteristics and outcomes of patients with COVID-19 admitted to Mercy Hospitals.
Retrospective, observational cohort study designed to include every COVID-19 subject aged 18 years or older admitted to Mercy Saint (St) Vincent, Mercy St Charles, and Mercy St Anne's hospital in Toledo, Ohio from January 1, 2020 through June 15th, 2020. Primary Outcome Measure was mortality in the emergency department or as an in-patient.
470 subjects including 224 males and 246 females met the inclusion criteria for the study. Subjects with the following characteristics had higher odds (OR) of death: Older age OR 8.3 (95% CI 1.1-63.1, p = 0.04) for subjects age 70 or more compared to subjects age 18-29); Hypertension OR 3.6 (95% CI 1.6-7.8, p = 0.001); Diabetes OR 3.1 (95% CI 1.7-5.6, p<0.001); COPD OR 3.4 (95% CI 1.8-6.3, p<0.001) and CKD stage 2 or greater OR 2.5 (95% CI 1.3-4.9, p = 0.006). Combining all age groups, subjects with hypertension had significantly greater odds of the following adverse outcomes: requiring hospital admission (OR 2.2, 95% CI 1.4-3.4, p<0.001); needing respiratory support in 24 hours (OR 2.5, 95% CI: 1.7-3.7, p<0.001); ICU admission (OR 2.7, 95% CI 1.7-4.4, p<0.001); and death (OR 3.6, 95% CI 1.6-7.8, p = 0.001). Hypertension was not associated with needing vent in 24 hours (p = 0.07).
Age and hypertension were associated with significant comorbidity and mortality in Covid-19 Positive patients. Furthermore, people who were older than 70, and had hypertension, diabetes, COPD, or CKD had higher odds of dying from the disease as compared to patients who hadn't. Subjects with hypertension also had significantly greater odds of other adverse outcomes.
Game theory deals with the strategies of rational players to obtain the best possible outcomes for a player in a game. Quantum game theory is an extension of classical game theory, where players can ...adopt quantum strategies to maximize their payoff advantage. In quantum game theory, we have two prominent quantization schemes, namely the Eisert Wilkens-Lewenstein (EWL) scheme and the Marinatto Weber (MW) scheme. Recently, modified EWL scheme is being used to study the game dynamics. In the era of digital information revolution, scientists have unrestricted access to most of the scientific information. Different scientific disciplines prefer different paths of publication either open access or traditional. Taking this as a framework, we model an open-access game played by scientists with open access and traditional publishing as their strategies. In this work, we study these publication patterns using the quantum game theory approach under the modified EWL quantization scheme. Specifically, we investigate the role of entangling operators and players’ strategies in maximizing the payoff (reputation) for the scientists playing in the open-access game.
•Quantum player outperforms the classical player only when the player adapts identity operation.•For the initial state 01, there is no role of entangling operator when a classical player adapts identity or flip operation.•Payoff advantage for the initial state 00 and |01〉 depends upon the strategies adapted by the players (same or different).•Scientists can get equal reputation by adopting both traditional and open-access publications unanimously. Further, disparities in reputation are sure to arise if scientists do not agree on which publication options to pursue.
Energy efficiency and maximum productivity in ore beneficiation processes can be ensured when integrated grinding circuits function in an optimal fashion. The complexity of first principles based ...models prevents online implementation of control and optimization algorithms, thus, creating the need for the development of accurate data-based models. In this work, deep recurrent neural networks (DRNNs) are implemented for nonlinear system identification of 3 input 6 output integrated grinding circuit from an industrial lead-zinc ore beneficiation set-up. Optimal long short term memory networks (LSTMs) with maximum predictability are obtained by solving a novel multi-objective framework for DRNN architecture design. The optimal LSTMs are trained and validated on pseudo random binary sequence (PRBS) signal with an accuracy of 99%, and tested successfully on unseen random Gaussian sequence (RGS) signal. Comprehensive comparison with conventional tools for nonlinear system identification, such as wavelet networks, is performed to show the efficacy of proposed optimal LSTMs.
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•LSTM network based system identification of integrated grinding circuits.•Industrially validated 3 input 6 output simulator of lead-zinc ore beneficiation.•Novel multiobjective INLP formulation as algorithm for optimal LSTM design.•LSTM trained on PRBS signal is tested on unseen RGS signal with 98% accuracy.•Deep Learning based LSTM model compared with RNNs and Wavelet models.
Induration in steel industries is the process of pelletizing iron ore particles. It is an important unit operation which produces raw materials for a subsequent chemical reduction in Blast Furnace. ...Of the enormous amount of energy consumed by Blast Furnace, a large portion is utilized in processing the raw materials. High-quality raw materials, therefore, ensure less consumption of energy in the Blast Furnace. Thus, optimization of induration process is necessary for conservation of a significant amount of energy in steelmaking industries. To realize this, a highly non-linear, industrially validated, 22 dimensional first principles based model for induration is created and a multi-objective optimization problem is designed. However, the physics-based model being computationally expensive, Multi-layered Perceptron Networks (MLPs) are trained to emulate the induration process. Novelty in this work lies with the optimal architecture design of MLPs through a multi-objective integer non-linear programming (MO-INLP) problem and with simultaneous training size estimation through four different Sobol sampling-based algorithms. Successful emulation of induration process resulted in 10-fold speed increment in optimization through surrogate models. To justify the parsimonious behavior of resultant MLPs, five different tests are performed for checking whether they are over-fitted. Comparison with Kriging adds to other highlights.
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