General Modeling Approach Research Articles

A Novel Stacked Long Short-Term Memory Approach of Deep Learning for Streamflow Simulation

Rainfall-Runoff simulation is the backbone of all hydrological and climate change studies. This study proposes a novel stochastic model for daily rainfall-runoff simulation called Stacked Long Short-Term Memory (SLSTM) relying on machine learning technology. The SLSTM model utilizes only the rainfall-runoff data in its modelling approach and the hydrology system is deemed a blackbox. Conversely, the distributed and physically-based hydrological models, e.g., SWAT (Soil and Water Assessment Tool) preserve the physical aspect of hydrological variables and their inter-relations while taking a wide range of data. The two model types provide specific applications that interest modelers, who can apply them according to their project specification and objectives. However, sparse distribution of point-data may hinder physical models’ performance, which may not be the case in data-driven models. This study proposes a specific SLSTM model and investigates the SLSTM and SWAT models’ data dependency in terms of their spatial distribution. The study was conducted in the two distinct river basins of Samarahan and Trusan, Malaysia, with over 20 years of hydro-climate data. The Trusan basin’s rain gauges are scattered downstream of the basin outlet and Samarahan’s are located around the basin, with one station within each basin’s limits. The SWAT was developed and calibrated following its general modelling approach, however, the SLSTM performance was also tested using data preprocessing with principal component analysis (PCA). Results showed that the SWAT performance for daily streamflow simulation at Samarahan has been superior to that of Trusan. Both the SLSTM and PCA-SLSTM models, however, showed better performance at Trusan with PCA-SLSTM outperforming the SLSTM. This demonstrates that the SWAT model is greatly affected by the spatial distribution of its input data, while data-driven models, irrespective of the spatial distribution of their entry data, can perform well if the data adequacy condition is met. However, considering the structural difference between the two models, each has its specific application in a water resources context. The study of catchments’ response to changes in the hydrology cycle requires a physically-based model like SWAT with proper spatial and temporal distribution of its entry data. However, the study of a specific phenomenon without considering the underlying processes can be done using data-driven models like SLSTM, where improper spatial distribution of data cannot be a restricting factor.

OD117 - Correlation of peak skin dose with dose indicators and geometric factors in vascular interventional radiology: a general linear model approach

OD117 - Correlation of peak skin dose with dose indicators and geometric factors in vascular interventional radiology: a general linear model approach

Predicting competitions by combining conditional logistic regression and subjective Bayes: An Academy Awards case study

Predicting the outcome of elections, sporting events, entertainment awards and other competitions has long captured the human imagination. Such prediction is growing in sophistication in these areas, especially in the rapidly growing field of data-driven journalism intended for a general audience as the availability of historical information rapidly balloons. Providing statistical methodology to probabilistically predict competition outcomes faces two main challenges. First, a suitably general modeling approach is necessary to assign probabilities to competitors. Second, the modeling framework must be able to accommodate expert opinion which is usually available but difficult to fully encapsulate in typical data sets. We overcome these challenges with a combined conditional logistic regression/subjective Bayes approach. To illustrate the method, we reanalyze data from a recent Time.com piece in which the authors attempted to predict the 2019 Best Picture Academy Award winner using standard logistic regression. Toward engaging and educating a broad readership, we discuss strategies to deploy the proposed method via an online application.

Modeling Epistemic Uncertainty in Offshore Wind Farm Production Capacity to Reduce Risk.

Financial stakeholders in offshore wind farm projects require predictions of energy production capacity to better manage the risk associated with investment decisions prior to construction. Predictions for early operating life are particularly important due to the dual effects of cash flow discounting and the anticipated performance growth due to experiential learning. We develop a general marked point process model for the times to failure and restoration events of farm subassemblies to capture key uncertainties affecting performance. Sources of epistemic uncertainty are identified in design and manufacturing effectiveness. The model then captures the temporal effects of epistemic and aleatory uncertainties across subassemblies to predict the farm availability-informed relative capacity (maximum generating capacity given the technical state of the equipment). This performance measure enables technical performance uncertainties to be linked to the cost of energy generation. The general modeling approach is contextualized and illustrated for a prospective offshore wind farm. The production capacity uncertainties can be decomposed to assess the contribution of epistemic uncertainty allowing the value of gathering information to reduce risk to beexamined.

Building Consensus for Ambitious Climate Action Through the World Climate Simulation

AbstractSociopolitical values are an important driver of climate change beliefs, attitudes, and policy preferences. People with “individualist‐hierarchical” values favor individual freedom, competition, and clearly defined social hierarchies, while “communitarian‐egalitarians” value interdependence and equality across gender, age, heritage, and ethnicity. In the US, individualist‐hierarchs generally perceive less risk from climate change and express lower support for actions to mitigate it than communitarian‐egalitarians. Exposure to scientific information does little to change these views. Here, we ask if a widely used experiential simulation, World Climate, can help overcome these barriers. World Climate combines an engaging role‐play with an interactive computer model of the climate system. We examine pre‐ and post‐World Climate survey responses from 2,080 participants in the US and use a general linear mixed model approach to analyze interactions among participants' sociopolitical values and gains in climate change knowledge, affect, and intent to take action. As expected, prior to the simulation, participants holding individualist‐hierarchical values had lower levels of climate change knowledge, felt less urgency, and expressed lower intent to act than those holding communitarian‐egalitarian values. However, individualist‐hierarchs made significantly larger gains across all constructs, particularly urgency, than communitarian‐egalitarians. Participants' sociopolitical values also shifted: those with individualistic‐hierarchical values before the simulation showed a substantial, statistically significant shift toward a communitarian‐egalitarian worldview. Simulation‐based experiences like World Climate may help reduce polarization and build consensus towards science‐based climate action.

Four Knife-Edge Diffraction With Antenna Gain Model for Generic Blockage Modelling

With the recent adoption of millimeter-wave spectrum in cellular communications, deployment of active antenna arrays and use of beamforming become vital to compensate for the increased path loss. However, directional high-frequency signals may suffer heavy attenuations due to blockage effects. Therefore, blockage modelling that adequately incorporates the effects of beamforming becomes increasingly relevant. We propose a Four Knife-Edge Diffraction with antenna Gain (4KED-G) model, a deterministic approach to model blockage with broad applicability. The 4KED-G model advances upon the existing models in its inclusion of both angular antenna gains and the diffraction from all the four edges of a rectangular screen blocker, leading to a more general and flexible blockage modelling approach compared to existing widely accepted blockage models. We theoretically show that the proposed generalised model incorporates the strengths of these existing models, while overcoming their shortcomings in establishing applicability to wider range of blockage scenarios. We validate the generalised model against known knife-edge diffraction blockage models for specific scenarios.

Global-Local-Comparison Approach: Understanding Marine Mammal Spatial Behavior by Applying Spatial Statistics and Hypothesis Testing to Passive Acoustic Data

Technological innovation in underwater acoustics has progressed research in marine mammal behavior by providing the ability to collect data on various marine mammal biological and behavioral attributes across time and space. But with this comes the need for an approach to distill the large amounts of data collected. Though disparate general statistical and modeling approaches exist, here, a holistic quantitative approach specifically motivated by the need to analyze different aspects of marine mammal behavior within a Before-After Control-Impact framework using spatial observations is introduced: the Global-Local-Comparison (GLC) approach. This approach capitalizes on the use of data sets from large-scale, hydrophone arrays and combines established spatial autocorrelation statistics of (Global) Moran’s I and (Local) Getis-Ord Gi∗(Gi∗) with (Comparison) statistical hypothesis testing to provide a detailed understanding of array-wide, local, and order-of-magnitude changes in spatial observations. This approach was demonstrated using beaked whale foraging behavior (using foraging-specific clicks as a proxy) during acoustic exposure events as an exemplar. The demonstration revealed that the Moran’s I analysis was effective at showing whether an array-wide change in behavior had occurred, i.e., clustered to random distribution, or vice-versa. The Gi∗analysis identified where hot or cold spots of foraging activity occurred and how those spots varied spatially from one analysis period to the next. Since neither spatial statistic could be used to directly compare the magnitude of change between analysis periods, a statistical hypothesis test, using the Kruskal-Wallis test, was used to directly compare the number of foraging events among analysis periods. When all three components of the GLC approach were used together, a comprehensive assessment of group level spatial foraging activity was obtained. This spatial approach is demonstrated on marine mammal behavior, but it can be applied to a broad range of spatial observations over a wide variety of species.

General Approach for Modeling and Control of Multiphase PMSM Drives

This article presents a modeling approach and a control strategy for multiphase surface-mounted permanent magnet synchronous machine drives. The mathematical model is completely general with respect to the machine parameters and to the winding configuration. It also intrinsically considers the effects of eventual constraints for the phase currents, generated from the electrical connections among the phase windings or resulting from faults. The current controller is entirely formalized in the phase variables domain. It is based on a pseudoinverse decoupling algorithm and on a linear decoupled controller. The current references are computed by means of a maximum-torque-per-ampere strategy, which can be also easily adapted for torque sharing purposes. The proposed controller requires minimum changes with respect to system reconfigurations or parameters variations and, therefore, it is suited both for healthy and for faulty operations. An extensive set of experimental results has been conducted to validate the proposed approach in several testing scenarios.

A-120 Comparison of Baseline and Post-Concussion ImPACT Performance in Spanish-English Bilingual and English Monolingual Athletes

Abstract Objective Research comparing English and Spanish administrations of Immediate Post-Concussion Assessment and Cognitive Testing (ImPACT) have consistently found differences in performance on various cognitive composites. Few studies have examined the effect of bilingualism on baseline and post-concussion assessments. This study examined whether monolingual English-speakers (MO) and bilingual Spanish/English speakers (BI) differed in cognitive performance at baseline (BL) and post-concussion (PC). Methods Participants were selected from a larger database of high school athletes administered ImPACT in English at BL and PC. Participants included 86 BI athletes (Mage = 14.53; 65.10% male) and 86 MO athletes matched on age, gender, sport type, and probable concussion as defined by number of PC follow-up assessments (PCF; 0 vs. ≥1). To examine group differences and changes over time in cognitive composites, a general linear modeling approach was used with language and PCF as between-subjects factors and time as a within-subjects factor. Results There was a significant Language x Time x PCF interaction for Visual Memory (VM), Visual Motor Speed (VMS), and Reaction Time (RT). On VM and RT, MO with multiple PCF performed the worst at PC, while MO with 0 PCF performed the best at PC. On VMS, BI with 0 PCF performed the worst at BL. Conclusion Visual memory and reaction time were less affected in bilingual compared to monolingual athletes with probable concussion. However, at baseline, bilingual athletes had slower visual motor speed compared to monolingual athletes. Future research should focus on how bilingualism may serve as both a protective and risk factor for cognitive functioning after probable concussion.

Tone it down or tune it out? Effects of instructor cues on body image, enjoyment, and intentions to return during group exercise for older adults

The use of appearance (versus functionality) cues in group exercise has been linked to negative body image outcomes in young women. This study examined the impact of appearance versus functionality-focused cues during an exercise class on state body image, enjoyment, and intentions to return to a future class, in older adults (60+ years). Participants (N = 107, Mage = 69 years) completed demographic and trait body image measures at a first session and attended either an appearance or functionality-focused full-body strength-based circuit class. Participants completed pre-and post-state body image measures, and measures of enjoyment and intention to return post-class. A General Linear Model approach to a repeated measures multivariate ANCOVA was used to examine pre-to-post-class differences between conditions for state body image; trait body satisfaction with appearance and functionality, BMI, and previous group exercise experience were entered as covariates. ANCOVAs were conducted to examine differences for enjoyment, (gender, BMI, and previous group exercise experience entered as covariates) and intention to return to a similar class, (previous group exercise experience as covariate). No significant differences were found across analyses (ps > .05). These findings contrast with similar studies conducted in younger populations. Future studies should continue to examine body image in aging populations.

Spatial forest vulnerability profile of major forest types in Indian Western Himalaya

Forest vulnerability assessment with ground realities/ data has great implications for adaptation and management planning at local and regional scale. However, such assessments are still limited across globe including the Himalaya. In view of this, present study derived a spatial map of inherent forest vulnerability using ground based observation and information in the western Himalaya. Based on literature and data availability, we selected nine forest vulnerability indicators under four domains i.e., ecological (species richness, and NDVI), climate (temperature, rainfall), topographic (slope, aspect, elevation), disturbances (forest fragmentation) and social (population density). Analytic Hierarchy Process (AHP), which is a multi-criteria decision method, was used for weighting the vulnerability indicators. Based on AHP, forest vulnerability index (FVI) was developed using general linear model approach. The FVI index was further classified into four different categories i.e., low, medium, high and very high. The results revealed maximum (37%) forest grids under medium vulnerable profile followed by 31% (high) and 21% (low) vulnerable grids. In general, temperate and mixed forests show higher forest vulnerability in the region, whereas subtropical pine, broadleaf and subalpine forests falls under lesser vulnerable forest grids. These results exhibited that elevation (15.4%), population density (15.1%), slope (14.5%), rainfall (13.7%), forest fragmentation (12.9%), temperature (12.3), and aspect (11.4%) are the major drivers of forest vulnerability, particularly for the Himalayan region. To validate the results, we compared the developed FVI in present study with ground based FVI already generated in previous studies from the region, and found higher similarity in the assessments. The spatial forest vulnerability maps generated in the present study provides a realistic profile of vulnerable forests in Indian western Himalaya, which can be used for developing adaptation measure and management planning.

Environmental Effects of the African Continental Free Trade Agreement: A Computable General Equilibrium Model Approach

Growth and development in middle- and low-income countries often come at an environmental cost, but is that trade-off always necessary? This study uses a computable general equilibrium model to estimate the macroeconomic and environmental impact of the world’s most significant plurilateral trade agreement, the African Continental Free Trade Agreement (AfCFTA). We build a novel dataset using the Global Trade Analysis Project data, which allows us to estimate the effect on emissions of carbon dioxide (CO2), non-CO2, and other pollutants. In terms of macroeconomic impact, African nations benefit from gross domestic product growth by 1.2% and employment by 2.1%, with less developed economies, such as Togo and Benin, obtaining the largest macroeconomic gains from trade liberalization. On aggregate, we estimate that the agreement will lead to a marginal, 0.3% increase in CO2 emissions, a 19.6% increase in non-CO2 greenhouse gas emissions, and a 21.5% decline in air pollutants. We find considerable heterogeneity across countries. For Nigeria, the rest of Central Africa, and South-Central Africa, the AfCFTA is expected to reduce emissions, while in Ethiopia, Cameroon, and Burkina Faso, estimations show an increase. Transit countries connected to large ports, such as Togo and Benin, are most negatively impacted. We conclude that while the AfCFTA implementation is expected to lead to notable improvements in air quality by reducing air pollutants, the resulting increase in climate-related emissions may require member countries to make concerted efforts to deal with the adverse effects.

Linking molecular composition to proton and copper binding ability of fulvic acid: A theoretical modeling approach based on FT-ICR-MS analysis

Dissolved organic matter (DOM) is one of the most important ligands governing the geochemical cycling of metals in the environment, but recent studies with Fourier transform ion cyclotron resonance mass spectrometry (FT-ICR-MS) have shown enormous complexity and diversity of DOM composition. How the diverse molecular composition of DOM affects the reactivity of DOM with metals is still largely unknown, which precludes us from developing accurate geochemical models for the fate of metals in the environment. In this study, we combined FT-ICR-MS analysis and theoretical modeling approaches and specifically elucidated the link between molecular composition and the proton and Cu binding ability of DOM, using the Suwannee River fulvic acid (FA) as a model humic substance. Batch adsorption experiments were conducted to generate different extents of molecular fractionation of FA samples by ferrihydrite. FT-ICR-MS analyses were employed to investigate the changes of molecular composition while Cu titration and the Windermere Humic Aqueous Model (WHAM) were used to quantify the variations on the Cu binding capacities of FA samples. We developed a general theoretical modeling approach, which integrated a suite of theoretical modeling methods, including the Vienna Soil-Organic-Matter-Modeler (VSOMM), SPARC Performs Automated Reasoning in Chemistry (SPARC), and the linear free energy relationships (LFER), for molecular modeling based on FT-ICR-MS data. Based on the FT-ICR-MS results, we found that, despite of the complex molecular composition of FA, FA molecules can be divided into three representative groups and each group of molecules had distinct chemical properties. Interestingly, molecules within the same group had similar distributions of molecular properties. Based on the chemical properties of the three groups of FA molecules, we successfully constructed three molecular models of FA using VSOMM, and quantified the distributions of proton and Cu binding constants with SPARC and LFER. Those independently determined binding constants were comparable to the WHAM default proton and Cu binding constants, supporting the validity of our modeling approach. Our modeling results suggested that the molecular complexity of DOM may be simplified with representative groups of molecules based on their binding ability with metals in theoretical modeling. Our modeling approach based on FT-ICR-MS data shed light on developing mechanistical models for metal reactions with DOM based on the molecular data, which is helpful for predicting the geochemical cycling of carbon and metals in the environment.

FISCAL POLICY TRANSMISSION CHANNELS AND MACROECONOMIC VARIABLES PERFORMANCE IN NIGERIA

The paper investigates the impact of fiscal policy channels on selected macroeconomic variables in Nigeria over the period of 1970-2018. The study employed the Bayesian approach of the Dynamic Stochastic General Equilibrium Model, after examining the prior and posterior mean values on the models specified. The paper established that channels of transmission from fiscal policy affected the performance of macroeconomic variables in the country that is, the instability on macroeconomic variables performances in Nigeria are highly influenced by the fiscal policy transmission channels. The study concluded that credit to the private sector, exchange rate, government spending and oil revenue were significant variables in Nigeria that need good policy measure for their performances. The paper recommends that there is a need for a sustained reduction in the fiscal policy channels as this helps in achieving sustainable development and improves variables performance. Also, since credit shock is the most active shock through which Fiscal policy channels transmitted to the economy, effort should be made to encourage banks to create more money in the economy to the private sector. And Central Bank of Nigeria should also pursue the government in financing credit availability in the country.

Topic-Oriented Spoken Dialogue Summarization for Customer Service with Saliency-Aware Topic Modeling

In a customer service system, dialogue summarization can boost service efficiency by automatically creating summaries for long spoken dialogues in which customers and agents try to address issues about specific topics. In this work, we focus on topic-oriented dialogue summarization, which generates highly abstractive summaries that preserve the main ideas from dialogues. In spoken dialogues, abundant dialogue noise and common semantics could obscure the underlying informative content, making the general topic modeling approaches difficult to apply. In addition, for customer service, role-specific information matters and is an indispensable part of a summary. To effectively perform topic modeling on dialogues and capture multi-role information, in this work we propose a novel topic-augmented two-stage dialogue summarizer (TDS) jointly with a saliency-aware neural topic model (SATM) for topic-oriented summarization of customer service dialogues. Comprehensive studies on a real-world Chinese customer service dataset demonstrated the superiority of our method against several strong baselines.

Towards Trustworthy Predictions from Deep Neural Networks with Fast Adversarial Calibration

To facilitate a wide-spread acceptance of AI systems guiding decision making in real-world applications, trustworthiness of deployed models is key. That is, it is crucial for predictive models to be uncertainty-aware and yield well-calibrated (and thus trustworthy) predictions for both in-domain samples as well as under domain shift. Recent efforts to account for predictive uncertainty include post-processing steps for trained neural networks, Bayesian neural networks as well as alternative non-Bayesian approaches such as ensemble approaches and evidential deep learning. Here, we propose an efficient yet general modelling approach for obtaining well-calibrated, trustworthy probabilities for samples obtained after a domain shift. We introduce a new training strategy combining an entropy-encouraging loss term with an adversarial calibration loss term and demonstrate that this results in well-calibrated and technically trustworthy predictions for a wide range of domain drifts. We comprehensively evaluate previously proposed approaches on different data modalities, a large range of data sets including sequence data, network architectures and perturbation strategies. We observe that our modelling approach substantially outperforms existing state-of-the-art approaches, yielding well-calibrated predictions under domain drift.

Segmented carbon tax may significantly affect the regional and national economy and environment-a CGE-based analysis for Guangdong Province

Facing the conflicts of climate change, energy consumption, carbon emission, and economic development, it is essential to investigate the impacts of the carbon tax policy implemented in specific regions. A CGE-based multi-dimensional carbon policy (CMDCP) model is developed to i) explore the inter-provincial interdependences by interfering with the economic policies of a single province, and ii) quantify interactive relationships among various components including climate, energy, carbon economy and tax. Integrated approach of computable general equilibrium model and input-output analysis is applied to a series of segmented carbon tax schemes for Guangdong IC modeling and China IE modeling. It is found that when the carbon tax rate is 100 yuan/ton, the GDP of Guangdong will fall by less than 0.5% under three scenario types. At the same time, they could bring 1.3, 1.2 and 1.6 million tons of emission reductions. Levying the carbon tax based on the difference in carbon emission volume is most beneficial for emission intensity reduction. For China, the impact of the segmented carbon tax in specific province has a slight impact on the entire supply chain emissions. It is suggested that a carbon tax of 10–40 yuan/ton could be adopted by Guangdong. Moreover, Guangdong could consider implementing the stepped carbon tax for it can effectively avoid the lack of flexibility of traditional carbon tax policy.

Forecasting value-at-risk and expected shortfall in large portfolios: A general dynamic factor model approach

Beyond their importance from the regulatory policy point of view, Value-at-Risk (VaR) and Expected Shortfall (ES) play an important role in risk management, portfolio allocation, capital level requirements, trading systems, and hedging strategies. However, due to the curse of dimensionality, their accurate estimation and forecast in large portfolios is quite a challenge. To tackle this problem, two procedures are proposed. The first one is based on a filtered historical simulation method in which high-dimensional conditional covariance matrices are estimated via a general dynamic factor model with infinite-dimensional factor space and conditionally heteroscedastic factors; the other one is based on a residual-based bootstrap scheme. The two procedures are applied to a panel with concentration ratio close to one. Backtesting and scoring results indicate that both VaR and ES are accurately estimated under both methods, which both outperform the existing alternatives.

Kinetostatics modeling and analysis of parallel continuum manipulators

Parallel continuum manipulators (PCMs) have increasingly attracted attentions in the field of mechanism and robotics, owing to their inherent merits, such as compliance in structure, reduction of backlash, and easiness in implementation. This paper presents a general approach for the kinetostatics modeling and analysis of spatial PCMs. A discretization-based technique is employed for the nonlinear large-deflection problems of the coupled slender flexible links in these flexible parallel manipulators. Benefiting from the mechanism approximation to the force-deflection characteristics of the flexible links, the kinetostatics models of the PCMs can be established analytically in the realm of robot kinematics/statics, rather than continuum mechanics. Moreover, a closed-form solution to the gradient of these nonlinear algebraic equations can be derived, such that gradient-based searching algorithms can be implemented to efficiently determine the equilibrium configurations in a variety of given conditions. A prototype of a three-limb 6-DOF PCM is developed, on which validation experiments are conducted. And the results verify the effectiveness of the proposed method for the kinetostatics modeling and analysis of parallel continuum manipulators.

Folding analysis for thick origami with kinematic frame models concerning gravity

Thickness and gravity are both the significant problems and challenges for origami structures in the field of building construction. This paper described a numerical folding analysis method to investigate the kinematic characteristics of thick origami using movable frame structural models driven by the external forces. The numeric methodology of the folding analysis is based on the generalized inverse theory. Therefore, both the morphing modes and the folding paths can be obtained in the environment of gravity through introducing the nodal forces directly. A general modeling approach is proposed to establish thick origami models using kinematic frames. To demonstrate the proposed method, single-DOF and multi-DOF examples are investigated. The folding procedures are obtained under the external forces, and the rigid motion modes are discussed. The presented method shows feasibility of analyzing the folding process of thick origami, which can help assess the practical rapid construction tasks in the future.