Optimization using ANN Surrogates with Optimal Topology and Sample Size

Industrial scale process modelling and optimiza tion of long chain branched polymer reaction network is currently an area of extensive research owing to the advantages and growing popularity of branched polymers. The highly complex nature of these reaction networks requires a large set of stiff ordinary differential equations to model them mathematically with adequate precision and accuracy. In such a scenario, where execution time of model is expensive, the idea of making the online optimization and control of these processes seems to be a near impossib le task. Catering to these problems in the ongoing research, the authors presented a novel work where the kinetic model of long chain branched poly vinyl acetate has been utilized to find the optimum processing con ditions of operation using Sobol sequence based ANN as meta models in a fast and highly efficient manner. The article presents a novel generic algorithm, which not only disables the heuristic approach of designing the ANN architecture but also allows the computationally expensive first principle m odel to determine the configuration of the ANN which can emulate it with maximum accuracy along with the size of training samples required. The use of such a fast and efficient Sobol based ANN as surrogate model obtained by the proposed algorithm m akes the optimization process 10 times faster as compared to a case where optimization is carried out with the expensive first principle model

Climatic Drivers for Multi-Decadal Shifts in Solute Transport and Methane Production Zones within a Large Peat Basin

Northern peatlands are an important source for greenhouse gases but their capacity to produce methane remains uncertain under changing climatic conditions. We therefore analyzed a 43-year time series of pore-water chemistry to determine if long-term shifts in precipitation altered the vertical transport of solutes within a large peat basin in northern Minnesota. These data suggest that rates of methane production can be finely tuned to multi-decadal shifts in precipitation that drive the vertical penetration of labile carbon substrates within the Glacial Lake Agassiz Peatlands. Tritium and cation profiles demonstrate that only the upper meter of these peat deposits was flushed by downwardly moving recharge from 1965 through 1983 during a Transitional Dry-to-Moist Period. However, a shift to a moister climate after 1984 drove surface waters much deeper, largely flushing the pore waters of all bogs and fens to depths of 2 m. Labile carbon compounds were transported downward from the rhizosphere to the basal peat at this time producing a substantial enrichment of methane in Delta C-14 with respect to the solid-phase peat from 1991 to 2008. These data indicate that labile carbon substrates can fuel deep production zones of methanogenesis that more than doubled in thickness across this large peat basin after 1984. Moreover, the entire peat profile apparently has the capacity to produce methane from labile carbon substrates depending on climate-driven modes of solute transport. Future changes in precipitation may therefore play a central role in determining the source strength of peatlands in the global methane cycle

Optimization using ANN Surrogates with Optimal Topology and Sample Size

Industrial scale process modelling and optimization of long chain branched polymer reaction network is currently an area of extensive research owing to the advantages and growing popularity of branched polymers. The highly complex nature of these reaction networks requires a large set of stiff ordinary differential equations to model them mathematically with adequate precision and accuracy. In such a scenario, where execution time of model is expensive, the idea of making the online optimization and control of these processes seems to be a near impossible task. Catering to these problems in the ongoing research, the authors presented a novel work where the kinetic model of long chain branched poly vinyl acetate has been utilized to find the optimum processing conditions of operation using Sobol sequence based ANN as meta models in a fast and highly efficient manner. The article presents a novel generic algorithm, which not only disables the heuristic approach of designing the ANN architecture but also allows the computationally expensive first principle model to determine the configuration of the ANN which can emulate it with maximum accuracy along with the size of training samples required. The use of such a fast and efficient Sobol based ANN as surrogate model obtained by the proposed algorithm makes the optimization process 10 times faster as compared to a case where optimization is carried out with the expensive first principle model

TourExplain: A Crowdsourcing Pipeline for Generating Explanations for Groups of Tourists

When a group is traveling together it is challenging to recommendan itinerary consisting of several points of interest (POIs). Thepreferences of individual group members often diverge, but it isimportant to keep everyone in the group satisfied during the entiretrip. We propose a method to consider the preferences of all thepeople in the group. Building on this method, we design expla-nations for groups of people, to help them reach a consensus forplaces to visit. However, one open question is how to best formu-late explanations for such sequences. In this paper, we introduceTourExplain, an automated crowdsourcing pipeline to generate andevaluate explanations for groups with the aim of improving ourinitial proposed explanations by relying on the wisdom of crowds.Accepted author manuscriptWeb Information System

TourExplain: A Crowdsourcing Pipeline for Generating Explanations for Groups of Tourists

When a group is traveling together it is challenging to recommendan itinerary consisting of several points of interest (POIs). Thepreferences of individual group members often diverge, but it isimportant to keep everyone in the group satisfied during the entiretrip. We propose a method to consider the preferences of all thepeople in the group. Building on this method, we design expla-nations for groups of people, to help them reach a consensus forplaces to visit. However, one open question is how to best formu-late explanations for such sequences. In this paper, we introduceTourExplain, an automated crowdsourcing pipeline to generate andevaluate explanations for groups with the aim of improving ourinitial proposed explanations by relying on the wisdom of crowds