scholarly journals Improved pseudolikelihood regularization and decimation methods on non-linearly interacting systems with continuous variables

2018 ◽  
Vol 5 (1) ◽  
Author(s):  
Alessia Marruzzo ◽  
Payal Tyagi ◽  
Fabrizio Antenucci ◽  
Andrea Pagnani ◽  
Luca Leuzzi
Keyword(s):  
State Of The Art ◽  
Real Data ◽  
Continuous Variables ◽  
Case Scenario ◽  
Testing Technique ◽  
Worst Case ◽  
Best Value ◽  
Interacting Systems ◽  
Effective Models ◽  
Rule Out

We propose and test improvements to state-of-the-art techniques of Bayeasian statistical inference based on pseudolikelihood maximization with \ell_1ℓ1 regularization and with decimation. In particular, we present a method to determine the best value of the regularizer parameter starting from a hypothesis testing technique. Concerning the decimation, we also analyze the worst case scenario in which there is no sharp peak in the tilded-pseudolikelihood function, firstly defined as a criterion to stop the decimation. Techniques are applied to noisy systems with non-linear dynamics, mapped onto multi-variable interacting Hamiltonian effective models for waves and phasors. Results are analyzed varying the number of available samples and the externally tunable temperature-like parameter mimicing real data noise. Eventually the behavior of inference procedures described are tested against a wrong hypothesis: non-linearly generated data are analyzed with a pairwise interacting hypothesis. Our analysis shows that, looking at the behavior of the inverse graphical problem as data size increases, the methods exposed allow to rule out a wrong hypothesis.

A robust MCDM approach for ERP system selection under uncertain environment based on worst case scenario

2018 ◽  
Vol 31 (3) ◽  
pp. 405-425 ◽  
Author(s):  
Gül Tekin Temur ◽  
Bersam Bolat
Keyword(s):  
State Of The Art ◽  
Selection Problem ◽  
Case Scenario ◽  
Worst Case ◽  
Content Type ◽  
Uncertain Environment ◽  
Erp System ◽  
Worst Case Scenario ◽  
System Selection ◽  
High Uncertainty

Purpose ERP selection is a multi-faceted process and needs to be successful in dealing with high uncertainty. The purpose of this paper is to propose a novel multi-criteria decision making (MCDM) approach, titled as cloud-based design optimization (CBDO), for ERP selection problem to handle high uncertainty with a computationally effective way. Design/methodology/approach CBDO has been utilized as an alternative method to fuzzy set theory and stochastic programming, and proposes robust findings for worst case scenario. In order to assess the proposed methodology, a numerical study is conducted by taking into account existing state-of-the-art study on the ERP selection problem for the small medium enterprises. The outputs of the existing state-of-the-art study are assumed as uncertain and varying across time as it is expected in real life; therefore, different scenarios are created in order to reveal the effect of uncertainty on decisions. Findings In the methodology, the results given under uncertain conditions are compared with the results obtained under stable conditions. It is clearly seen that ERP system selection problem area has high sensitivity to the uncertain environment, and decision makers should not undervalue the unsteadiness of criteria during the ERP system selection process, especially within volatile economies. Originality/value This study contributes to the relevant literature by utilizing CBDO as a MCDM tool in the selection of the ERP software as a first time, and validating the impact of unsteadiness on the ERP selection procedure. It is the first CBDO-based study that validates the effect of distributional differences on uncertainties in the ERP selection processes.

scholarly journals Window of Opportunity for Mitigation to Prevent Overflow of ICU capacity in Chicago by COVID-19

2020 ◽  
Author(s):  
Sergei Maslov ◽  
Nigel Goldenfeld
Keyword(s):  
State Of The Art ◽  
Emergency Situation ◽  
Mitigation Strategies ◽  
List Type ◽  
Window Of Opportunity ◽  
High Confidence ◽  
Case Scenario ◽  
Executive Summary ◽  
Worst Case ◽  
Trade Offs

Executive SummaryWe estimate the growth in demand for ICU beds in Chicago during the emerging COVID-19 epidemic, using state-of-the-art computer simulations calibrated for the SARS-CoV-2 virus. The questions we address are these:Will the ICU capacity in Chicago be exceeded, and if so by how much?Can strong mitigation strategies, such as lockdown or shelter in place order, prevent the overflow of capacity?When should such strategies be implemented?Our answers are as follows:The ICU capacity may be exceeded by a large amount, probably by a factor of ten.Strong mitigation can avert this emergency situation potentially, but even that will not work if implemented too late.If the strong mitigation precedes April 1st, then the growth of COVID-19 can be controlled and the ICU capacity could be adequate. The earlier the strong mitigation is implemented, the greater the probability that it will be successful. After around April 1 2020, any strong mitigation will not avert the emergency situation. In Italy, the lockdown occurred too late and the number of deaths is still doubling every 2.3 days. It is difficult to be sure about the precise dates for this window of opportunity, due to the inherent uncertainties in computer simulation. But there is high confidence in the main conclusion that it exists and will soon be closed.Our conclusion is that, being fully cognizant of the societal trade-offs, there is a rapidly closing window of opportunity to avert a worst-case scenario in Chicago, but only with strong mitigation/lockdown implemented in the next week at the latest. If this window is missed, the epidemic will get worse and then strong mitigation/lockdown will be required after all, but it will be too late.

scholarly journals Unfolding trends of COVID-19 transmission in India: Critical review of available Mathematical models

2020 ◽  
Vol 32 (2 (Supp)) ◽  
pp. 206-214
Author(s):  
Komal Shah ◽  
Ashish Awasthi ◽  
Bhavesh Modi ◽  
Rashmi Kundapur ◽  
Deepak Saxena
Keyword(s):  
Prediction Models ◽  
Real Data ◽  
Sudden Onset ◽  
Policy Implications ◽  
Epidemiological Models ◽  
Case Scenario ◽  
Worst Case ◽  
Social Distancing ◽  
Final Destination ◽  
Data Gaps

Background: There is a surge in epidemiological modeling research due to sudden onset of COVID-19 pandemic across the globe. In the absence of any pharmaceutical interventions to control the epidemic, nonpharmaceutical interventions like containment, mitigation and suppression are tried and tested partners in epidemiological theories. But policy and planning needs estimates of disease burden in various scenarios in absence of real data and epidemiological models helps to fill this gap. Aims and Objectives: To review the models of COVID-19 prediction in Indian scenario, critically evaluate the range, concepts, strength and limitations of these prediction models and its potential policy implications. Results: Though we conducted data search for last three months, it was found that the predictive models reporting from Indian context have started publishing very recently. Majority of the Indian models predicted COVID-19 spread, projected best-, worst case scenario and forecasted effect of various preventive measurements such as lockdown and social distancing. Though the models provided some of the critical information regarding spread of the disease and fatality rate associated with COVID-19, it should be used with caution due to severe data gaps, distinct socio-demographic profiling of the population and diverse statistics of co-morbid condition. Conclusion: Although the models were designed to predict COVID spread, and claimed to be accurate, significant data gaps and need for adjust confounding variables such as effect of lockdown, risk factors and adherence to social distancing should be considered before generalizing the findings. Results of epidemiological models should be considered as guiding beacon instead of final destination.

Detection of Sliding Wheels and Hot Bearings Using Wayside Thermal Cameras

2016 ◽  
Author(s):  
Hanieh Deilamsalehy ◽  
Timothy C. Havens ◽  
Pasi Lautala
Keyword(s):  
Computer Vision ◽  
Detection System ◽  
Automatic Detection ◽  
Real Data ◽  
Detection Algorithm ◽  
Support Vector ◽  
Case Scenario ◽  
Worst Case ◽  
Excessive Heat ◽  
Research Areas

Train car wheels are subjected to different types of damages due to their interactions with the brake shoes and track. If not detected early, these defects can worsen, possibly causing damage to the bogie and rail. In the worst-case scenario, this rail damage can possibly lead to later derailments, a serious concern for the rail industry. Therefore, automatic inspection and detection of wheel defects are high priority research areas. An automatic detection system not only can prevent train and rail damage, but also can reduce operating costs as an alternative for tedious and expensive manned inspection. The main contribution of this paper is to develop a computer vision method for automatically detecting the defects of rail car wheels using a wayside thermal camera. We concentrate on identification of flat-spotted/sliding wheels, which is an important issue for both wheel and suspension hardware and also rail and track structure. Flat spots occur when a wheel locks up and slides while the vehicle is still moving. As a consequence, this process heats up local areas on the metal wheel, which can be observed and potentially detected in thermal imagery. Excessive heat buildup at the flat spot will eventually lead to additional wheel and possibly rail damage, reducing the life of other train wheels and suspension components, such as bearings. Furthermore, as a byproduct of our algorithm, we propose a method for detecting hot bearings. A major part of our proposed hot bearing detection algorithm is common with our sliding wheel detection algorithm. In this paper, we first propose an automatic detection and segmentation method that identifies the wheel and bearing portion of the image. We then develop a computer vision method, using Histogram of Oriented Gradients to extract features of these regions. These feature descriptors are input to a Support Vector Machine classifier, a fast classifier with a good detection rate, which can detect abnormalities in the wheel. We demonstrate our methods on several real data sets taken on a Union Pacific rail line, identifying sliding wheels and hot bearings in these images.

Reducing Probabilistic Weather Forecasts to the Worst Case Scenario: Anchoring Effects

2008 ◽  
Author(s):  
Sonia Savelli ◽  
Susan Joslyn ◽  
Limor Nadav-Greenberg ◽  
Queena Chen
Keyword(s):  
Case Scenario ◽  
Worst Case ◽  
Weather Forecasts ◽  
Worst Case Scenario ◽  
Anchoring Effects

EXPEDITION TO MALI, 2020

2020 ◽  
pp. 12-28
Author(s):  
D. V. Vaniukova ◽  
◽  
P. A. Kutsenkov ◽  
Keyword(s):  
Mass Murder ◽  
The State ◽  
Case Scenario ◽  
Worst Case ◽  
Traditional Art ◽  
Peter The Great ◽  
State Museum ◽  
Bobo Dioulasso ◽  
Oriental Studies ◽  
The Village

The research expedition of the Institute of Oriental studies of the Russian Academy of Sciences has been working in Mali since 2015. Since 2017, it has been attended by employees of the State Museum of the East. The task of the expedition is to study the transformation of traditional Dogon culture in the context of globalization, as well as to collect ethnographic information (life, customs, features of the traditional social and political structure); to collect oral historical legends; to study the history, existence, and transformation of artistic tradition in the villages of the Dogon Country in modern conditions; collecting items of Ethnography and art to add to the collection of the African collection of the. Peter the Great Museum (Kunstkamera, Saint Petersburg) and the State Museum of Oriental Arts (Moscow). The plan of the expedition in January 2020 included additional items, namely, the study of the functioning of the antique market in Mali (the “path” of things from villages to cities, which is important for attributing works of traditional art). The geography of our research was significantly expanded to the regions of Sikasso and Koulikoro in Mali, as well as to the city of Bobo-Dioulasso and its surroundings in Burkina Faso, which is related to the study of migrations to the Bandiagara Highlands. In addition, the plan of the expedition included organization of a photo exhibition in the Museum of the village of Endé and some educational projects. Unfortunately, after the mass murder in March 2019 in the village of Ogossogou-Pel, where more than one hundred and seventy people were killed, events in the Dogon Country began to develop in the worst-case scenario: The incessant provocations after that revived the old feud between the Pel (Fulbe) pastoralists and the Dogon farmers. So far, this hostility and mutual distrust has not yet developed into a full-scale ethnic conflict, but, unfortunately, such a development now seems quite likely.

Risk-Based Robust Bidding Strategies for EVs’ Aggregators in Day-ahead Markets with Uncertainty

2020 ◽  
Author(s):  
Ahmed Abdelmoaty ◽  
Wessam Mesbah ◽  
Mohammad A. M. Abdel-Aal ◽  
Ali T. Alawami
Keyword(s):  
Robust Optimization ◽  
Electricity Market ◽  
Real Life ◽  
Optimization Approach ◽  
Case Scenario ◽  
Worst Case ◽  
Bidding Strategies ◽  
Wind Generators ◽  
Proposed Model ◽  
Optimal Bidding

In the recent electricity market framework, the profit of the generation companies depends on the decision of the operator on the schedule of its units, the energy price, and the optimal bidding strategies. Due to the expanded integration of uncertain renewable generators which is highly intermittent such as wind plants, the coordination with other facilities to mitigate the risks of imbalances is mandatory. Accordingly, coordination of wind generators with the evolutionary Electric Vehicles (EVs) is expected to boost the performance of the grid. In this paper, we propose a robust optimization approach for the coordination between the wind-thermal generators and the EVs in a virtual<br>power plant (VPP) environment. The objective of maximizing the profit of the VPP Operator (VPPO) is studied. The optimal bidding strategy of the VPPO in the day-ahead market under uncertainties of wind power, energy<br>prices, imbalance prices, and demand is obtained for the worst case scenario. A case study is conducted to assess the e?effectiveness of the proposed model in terms of the VPPO's profit. A comparison between the proposed model and the scenario-based optimization was introduced. Our results confirmed that, although the conservative behavior of the worst-case robust optimization model, it helps the decision maker from the fluctuations of the uncertain parameters involved in the production and bidding processes. In addition, robust optimization is a more tractable problem and does not suffer from<br>the high computation burden associated with scenario-based stochastic programming. This makes it more practical for real-life scenarios.<br>

scholarly journals G-Tric: generating three-way synthetic datasets with triclustering solutions

2021 ◽  
Vol 22 (1) ◽  
Author(s):  
João Lobo ◽  
Rui Henriques ◽  
Sara C. Madeira
Keyword(s):  
State Of The Art ◽  
Synthetic Data ◽  
Ground Truth ◽  
Real Data ◽  
Three Dimensions ◽  
Additional Advantage ◽  
Urban Dynamics ◽  
Data Generator ◽  
Real World Datasets ◽  
Synthetic Datasets

Abstract Background Three-way data started to gain popularity due to their increasing capacity to describe inherently multivariate and temporal events, such as biological responses, social interactions along time, urban dynamics, or complex geophysical phenomena. Triclustering, subspace clustering of three-way data, enables the discovery of patterns corresponding to data subspaces (triclusters) with values correlated across the three dimensions (observations $$\times$$ × features $$\times$$ × contexts). With increasing number of algorithms being proposed, effectively comparing them with state-of-the-art algorithms is paramount. These comparisons are usually performed using real data, without a known ground-truth, thus limiting the assessments. In this context, we propose a synthetic data generator, G-Tric, allowing the creation of synthetic datasets with configurable properties and the possibility to plant triclusters. The generator is prepared to create datasets resembling real 3-way data from biomedical and social data domains, with the additional advantage of further providing the ground truth (triclustering solution) as output. Results G-Tric can replicate real-world datasets and create new ones that match researchers needs across several properties, including data type (numeric or symbolic), dimensions, and background distribution. Users can tune the patterns and structure that characterize the planted triclusters (subspaces) and how they interact (overlapping). Data quality can also be controlled, by defining the amount of missing, noise or errors. Furthermore, a benchmark of datasets resembling real data is made available, together with the corresponding triclustering solutions (planted triclusters) and generating parameters. Conclusions Triclustering evaluation using G-Tric provides the possibility to combine both intrinsic and extrinsic metrics to compare solutions that produce more reliable analyses. A set of predefined datasets, mimicking widely used three-way data and exploring crucial properties was generated and made available, highlighting G-Tric’s potential to advance triclustering state-of-the-art by easing the process of evaluating the quality of new triclustering approaches.

scholarly journals Deep Learning for Transient Image Reconstruction from ToF Data

Sensors ◽  
2021 ◽  
Vol 21 (6) ◽  
pp. 1962
Author(s):  
Enrico Buratto ◽  
Adriano Simonetto ◽  
Gianluca Agresti ◽  
Henrik Schäfer ◽  
Pietro Zanuttigh
Keyword(s):  
Deep Learning ◽  
State Of The Art ◽  
Light Response ◽  
Real Data ◽  
Depth Image ◽  
Learning Approach ◽  
Multiple Reflections ◽  
Noisy Input ◽  
Novel Approach ◽  
Incoming Light

In this work, we propose a novel approach for correcting multi-path interference (MPI) in Time-of-Flight (ToF) cameras by estimating the direct and global components of the incoming light. MPI is an error source linked to the multiple reflections of light inside a scene; each sensor pixel receives information coming from different light paths which generally leads to an overestimation of the depth. We introduce a novel deep learning approach, which estimates the structure of the time-dependent scene impulse response and from it recovers a depth image with a reduced amount of MPI. The model consists of two main blocks: a predictive model that learns a compact encoded representation of the backscattering vector from the noisy input data and a fixed backscattering model which translates the encoded representation into the high dimensional light response. Experimental results on real data show the effectiveness of the proposed approach, which reaches state-of-the-art performances.

scholarly journals A Thermally Conductive Pt/AAO Catalyst for Hydrogen Passive Autocatalytic Recombination

Catalysts ◽  
2021 ◽  
Vol 11 (4) ◽  
pp. 491
Author(s):  
Alina E. Kozhukhova ◽  
Stephanus P. du Preez ◽  
Aleksander A. Malakhov ◽  
Dmitri G. Bessarabov
Keyword(s):  
Electron Microscopy ◽  
Particle Size ◽  
Combustion Temperature ◽  
Volume Fraction ◽  
Morphological Characteristics ◽  
Al Alloy ◽  
Impregnation Method ◽  
Case Scenario ◽  
Worst Case ◽  
Thermal Distribution

In this study, a Pt/anodized aluminum oxide (AAO) catalyst was prepared by the anodization of an Al alloy (Al6082, 97.5% Al), followed by the incorporation of Pt via an incipient wet impregnation method. Then, the Pt/AAO catalyst was evaluated for autocatalytic hydrogen recombination. The Pt/AAO catalyst’s morphological characteristics were determined by scanning electron microscopy (SEM) and transmission electron microscopy (TEM). The average Pt particle size was determined to be 3.0 ± 0.6 nm. This Pt/AAO catalyst was tested for the combustion of lean hydrogen (0.5–4 vol% H2 in the air) in a recombiner section testing station. The thermal distribution throughout the catalytic surface was investigated at 3 vol% hydrogen (H2) using an infrared camera. The Al/AAO system had a high thermal conductivity, which prevents the formation of hotspots (areas where localized surface temperature is higher than an average temperature across the entire catalyst surface). In turn, the Pt stability was enhanced during catalytic hydrogen combustion (CHC). A temperature gradient over 70 mm of the Pt/AAO catalyst was 23 °C and 42 °C for catalysts with uniform and nonuniform (worst-case scenario) Pt distributions. The commercial computational fluid dynamics (CFD) code STAR-CCM+ was used to compare the experimentally observed and numerically simulated thermal distribution of the Pt/AAO catalyst. The effect of the initial H2 volume fraction on the combustion temperature and conversion of H2 was investigated. The activation energy for CHC on the Pt/AAO catalyst was 19.2 kJ/mol. Prolonged CHC was performed to assess the durability (reactive metal stability and catalytic activity) of the Pt/AAO catalyst. A stable combustion temperature of 162.8 ± 8.0 °C was maintained over 530 h of CHC. To confirm that Pt aggregation was avoided, the Pt particle size and distribution were determined by TEM before and after prolonged CHC.

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