AHP based Optimal Reasoning of Non-functional Requirements in the i∗ Goal Model

Goal-Oriented Requirements Engineering (GORE) has been found to be a valuable tool in the early stages of requirements engineering. GORE plays a vital role in requirements analysis like alternative design/ goal selection during decision-making. The decision-making process of alternative design/ goal selection is performed to assess the practicability and value of alternative approaches towards quality goals. Majority of the GORE models manage alternative selection based on qualitative approach, which is extremely coarse-grained, making it impossible for separating two alternatives. A few works are based on quantitative alternative selection, yet this does not provide a consistent judgement on decision-making. In this paper, Analytic Hierarchy Process (AHP) is modified to deal with the evaluation of selecting the alternative strategies of inter-dependent actors of i∗ goal model. The proposed approach calculates the contribution degrees of alternatives to the fulfilment of top softgoals. It is then integrated with the normalized relative priority values of top softgoals. The result of integration helps to evaluate the alternative options based on the requirements problem against each other. To clarify the proposed approach, a simple telemedicine system is considered in this paper

Optimal Reasoning of Opposing Non-functional Requirements based on Game Theory

Goal-Oriented Requirement Engineering is a modeling technique that represents software system requirements using goals as goal models. In a competitive environment, these requirements may have opposing objectives. Therefore, there is a requirement for a goal reasoning method, which offers an alternative design option that achieves the opposing objectives of inter-dependent actors. In this paper, a multi-objective zero-sum game theory-based approach is applied for choosing an optimum strategy for dependent actors in the i* goal model. By integrating Java with IBM CPLEX optimisation tool, a simulation model based on the proposed method was developed. A successful evaluation was performed on case studies from the existing literature. Results indicate that the developed simulation model helps users to choose an optimal design option feasible in real-time competitive environments

STint: Self-supervised Temporal Interpolation for Geospatial Data

Supervised and unsupervised techniques have demonstrated the potential for temporal interpolation of video data. Nevertheless, most prevailing temporal interpolation techniques hinge on optical flow, which encodes the motion of pixels between video frames. On the other hand, geospatial data exhibits lower temporal resolution while encompassing a spectrum of movements and deformations that challenge several assumptions inherent to optical flow. In this work, we propose an unsupervised temporal interpolation technique, which does not rely on ground truth data or require any motion information like optical flow, thus offering a promising alternative for better generalization across geospatial domains. Specifically, we introduce a self-supervised technique of dual cycle consistency. Our proposed technique incorporates multiple cycle consistency losses, which result from interpolating two frames between consecutive input frames through a series of stages. This dual cycle consistent constraint causes the model to produce intermediate frames in a self-supervised manner. To the best of our knowledge, this is the first attempt at unsupervised temporal interpolation without the explicit use of optical flow. Our experimental evaluations across diverse geospatial datasets show that STint significantly outperforms existing state-of-the-art methods for unsupervised temporal interpolation

Coupled impacts of the diurnal cycle of sea surface temperature on the Madden–Julian oscillation

Author Posting. © American Meteorological Society, 2014. This article is posted here by permission of American Meteorological Society for personal use, not for redistribution. The definitive version was published in Journal of Climate 27 (2014): 8422–8443, doi:10.1175/JCLI-D-14-00141.1.This study quantifies, from a systematic set of regional ocean–atmosphere coupled model simulations employing various coupling intervals, the effect of subdaily sea surface temperature (SST) variability on the onset and intensity of Madden–Julian oscillation (MJO) convection in the Indian Ocean. The primary effect of diurnal SST variation (dSST) is to raise time-mean SST and latent heat flux (LH) prior to deep convection. Diurnal SST variation also strengthens the diurnal moistening of the troposphere by collocating the diurnal peak in LH with those of SST. Both effects enhance the convection such that the total precipitation amount scales quasi-linearly with preconvection dSST and time-mean SST. A column-integrated moist static energy (MSE) budget analysis confirms the critical role of diurnal SST variability in the buildup of column MSE and the strength of MJO convection via stronger time-mean LH and diurnal moistening. Two complementary atmosphere-only simulations further elucidate the role of SST conditions in the predictive skill of MJO. The atmospheric model forced with the persistent initial SST, lacking enhanced preconvection warming and moistening, produces a weaker and delayed convection than the diurnally coupled run. The atmospheric model with prescribed daily-mean SST from the coupled run, while eliminating the delayed peak, continues to exhibit weaker convection due to the lack of strong moistening on a diurnal basis. The fact that time-evolving SST with a diurnal cycle strongly influences the onset and intensity of MJO convection is consistent with previous studies that identified an improved representation of diurnal SST as a potential source of MJO predictability.The authors gratefully acknowledge support from the Office of Naval Research (N00014-13-1-0133 and N00014-13-1-0139) and National Science Foundation EaSM-3 (OCE-1419235). HS especially thanks the Penzance Endowed Fund for their support of Assistant Scientists at WHOI.2015-05-1

Common NOD2 mutations are absent in patients with Crohn's disease in India

Background: Crohn's disease is being increasingly diagnosed in the Indian subcontinent. Three apparently common mutations in the NOD2 gene are found in up to 30% of sporadic patients with Crohn's disease in western countries. We examined whether such mutations are also found in Indian patients with Crohn's disease. Methods: Venous blood was collected from 82 patients (age range: 7-65 years, 53 men) with Crohn's disease and 149 control subjects; DNA was extracted and subjected to polymerase chain reaction using specific primers. The amplified fragments of size 185, 163 and 151 bp for R702W, G908R and 1007fs, respectively, were digested with MspI, HhaI and ApaI, and the restriction pattern noted after electrophoresis. Results: Twenty-eight patients had ileocolonic disease, 26 ileal disease, 20 colonic disease and 8 had disease limited to proximal small bowel or stomach. None of the 82 patients showed any of the three NOD2 mutations. The control subjects (93 men) had a variety of chronic gastrointestinal disorders (ulcerative colitis 52, irritable bowel syndrome 30, intestinal tuberculosis 20, colon cancer 7, miscellaneous 37). None of the control subjects showed a mutation in any of the three NOD2 mutation analyses. Conclusion: The three NOD2 gene mutations described above are uncommon in Indian patients with Crohn's disease. This study complements information provided by recent studies on NOD2 mutations in Indians

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-Variability-Aware Approach

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change\u27s impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale

Reducing Uncertainty in Sea-level Rise Prediction: A Spatial-variability-aware Approach

Given multi-model ensemble climate projections, the goal is to accurately and reliably predict future sea-level rise while lowering the uncertainty. This problem is important because sea-level rise affects millions of people in coastal communities and beyond due to climate change's impacts on polar ice sheets and the ocean. This problem is challenging due to spatial variability and unknowns such as possible tipping points (e.g., collapse of Greenland or West Antarctic ice-shelf), climate feedback loops (e.g., clouds, permafrost thawing), future policy decisions, and human actions. Most existing climate modeling approaches use the same set of weights globally, during either regression or deep learning to combine different climate projections. Such approaches are inadequate when different regions require different weighting schemes for accurate and reliable sea-level rise predictions. This paper proposes a zonal regression model which addresses spatial variability and model inter-dependency. Experimental results show more reliable predictions using the weights learned via this approach on a regional scale.Comment: 6 pages, 5 figures, I-GUIDE 2023 conferenc

Weakening of Indian Summer Monsoon Synoptic Activity in Response to Polar Sea Ice Melt Induced by Albedo Reduction in a Climate Model

The effect of polar sea ice melt on low latitude climate is little known. To understand the response of the Indian summer monsoon (ISM) synoptic activity to the sea ice melt, we have run a suite of coupled and uncoupled climate model simulations. In one set of simulations, the albedo of sea ice is reduced so that it would melt due to increased absorption of solar radiation. The coupled model simulation with a reduced sea ice albedo resulted in an almost complete melting of the sea ice in summer in both hemispheres. A high-resolution (50&nbsp;km) atmospheric general circulation model (AGCM) is forced with the climatological annual cycles of sea surface temperature (SST) and sea ice concentrations (SIC) from the coupled model outputs to better resolve synoptic scale variability. In the high-resolution AGCM simulations forced with SST and SIC from the sea ice melt experiments, the ISM circulation weakened substantially, and the monsoon low-pressure systems (LPS) activity experienced an overall decline of 23%, with a widespread weakening in the south and a moderate strengthening over the north, in response to a decline of 78% (24%) in SIC over the Arctic (Antarctic) in the June&ndash;September season. The changes in the LPS activity in response to polar sea ice melt are found to be mostly driven by the changes in low-level absolute vorticity and vertical shear over the Bay of Bengal. &nbsp;</p

Machine Learning for Stochastic Parameterization: Generative Adversarial Networks in the Lorenz '96 Model

Stochastic parameterizations account for uncertainty in the representation of unresolved sub-grid processes by sampling from the distribution of possible sub-grid forcings. Some existing stochastic parameterizations utilize data-driven approaches to characterize uncertainty, but these approaches require significant structural assumptions that can limit their scalability. Machine learning models, including neural networks, are able to represent a wide range of distributions and build optimized mappings between a large number of inputs and sub-grid forcings. Recent research on machine learning parameterizations has focused only on deterministic parameterizations. In this study, we develop a stochastic parameterization using the generative adversarial network (GAN) machine learning framework. The GAN stochastic parameterization is trained and evaluated on output from the Lorenz '96 model, which is a common baseline model for evaluating both parameterization and data assimilation techniques. We evaluate different ways of characterizing the input noise for the model and perform model runs with the GAN parameterization at weather and climate timescales. Some of the GAN configurations perform better than a baseline bespoke parameterization at both timescales, and the networks closely reproduce the spatio-temporal correlations and regimes of the Lorenz '96 system. We also find that in general those models which produce skillful forecasts are also associated with the best climate simulations.Comment: Submitted to Journal of Advances in Modeling Earth Systems (JAMES

The skill of atmospheric linear inverse models in hindcasting the Madden–Julian Oscillation

A suite of statistical atmosphere-only linear inverse models of varying complexity are used to hindcast recent MJO events from the Year of Tropical Convection and the Cooperative Indian Ocean Experiment on Intraseasonal Variability/Dynamics of the Madden–Julian Oscillation mission periods, as well as over the 2000–2009 time period. Skill exists for over two weeks, competitive with the skill of some numerical models in both bivariate correlation and root-mean-squared-error scores during both observational mission periods. Skill is higher during mature Madden–Julian Oscillation conditions, as opposed to during growth phases, suggesting that growth dynamics may be more complex or non-linear since they are not as well captured by a linear model. There is little prediction skill gained by including non-leading modes of variability.National Science Foundation (U.S.) (Grant 0731520)United States. Office of Naval Research (Grants N00014-10-1-0541, N00014-13-1-0139 and N00014-13-1-0704)National Science Foundation (U.S.) (Grant OCE-0960770