General Modeling Approach Research Articles

Building energy prediction using artificial neural networks and analysis of covariance in the six thermal zones of Morocco

Energy management is essential in light of the current energy issues, particularly in the building sector, which accounts for a sizable amount of global energy consumption. Incorporating intelligent energy management solutions in this sector is one of the answers that could assist the nation in achieving its goals for environmental changes and overcoming its energy challenges. Energy prediction in buildings is therefore essential for efficient management. The present paper aims to evaluate the performance of two different approaches: Artificial intelligence (AI) and General Linear model (GLM) approaches, in predicting heating energy consumption of an administrative building for which localization concerns the six Moroccan thermal zones. The chosen models for AI and GLM are respectively ANN and ANCOVA. In the TRNSYS environment, building energy simulation was carried out in order to produce a database for the models training and validation. Only two meteorological data were used: External temperature and internal temperature. While the ANN model was trained on mixed data of different thermal zones, ANCOVA model established an equation for each thermal zone separately, with respect of model hypothesis. Results show that ANN model outperforms the ANCOVA model with a correlation coefficient of 0.95 and 0.90 respectively. Thus, the use of Artificial Neural Network for building energy prediction is more accurate than ANCOVA model.

Investigating Infill Wall Configurations on 3-D Structural Systems with a Generalized Modeling Approach

Investigating Infill Wall Configurations on 3-D Structural Systems with a Generalized Modeling Approach

Assessment of light-duty versus heavy-duty diesel on-road mobile source emissions using general additive models applied to traffic volume and air quality data and COVID-19 responses

ABSTRACT Following the outbreak of the COVID-19 pandemic, several papers have examined the effect of the pandemic response on urban air pollution worldwide. This study uses observed traffic volume and near-road air pollution data for black carbon (BC), oxides of nitrogen (NOx), and carbon monoxide (CO) to estimate the emissions contributions of light-duty and heavy-duty diesel vehicles in five cities in the continental United States. Analysis of mobile source impacts in the near-road environment has several health and environmental justice implications. Data from the initial COVID-19 response period, defined as March to May in 2020, were used with data from the same period over the previous two years to develop general additive models (GAMs) to quantify the emissions impact of each vehicle class. The model estimated that light-duty traffic contributes 4–69%, 14–65%, and 21–97% of BC, NOx, and CO near-road levels, respectively. Heavy-duty diesel traffic contributes an estimated 26–46%, 17–63%, and −7–18% of near-road levels of the three pollutants. The estimated mobile source impacts were used to calculate NOx to CO and BC to NOx emission ratios, which were between 0.21–0.32 μg m−3 NOx (μg m−3 CO)−1 and 0.013–0.018 μg m−3 BC (μg m−3 NOx)−1. These ratios can be used to assess existing emission inventories for use in determining air pollution standards. These results agree moderately well with recent National Emissions Inventory estimates and other empirically-derived estimates, showing similar trends among the pollutants. However, a limitation of this study was the recurring presence of an implausible air pollution impact estimate in 41% of the site-pollutant combinations, where a vehicle class was estimated to account for either a negative impact or an impact higher than the total estimated pollutant concentration. The variations seen in the GAM estimates are likely a result of location-specific factors, including fleet composition, external pollution sources, and traffic volumes. Implications: Drastic reductions in traffic and air pollution during the lockdowns of the COVID-19 pandemic present a unique opportunity to assess vehicle emissions. A General Additive Modeling approach is developed to relate traffic levels, observed air pollution, and meteorology to identify the amount vehicle types contribute to near-road levels of traffic-related air pollutants (TRAPs), which is important for future emission regulation and policy, given the significant health and environmental justice implications of vehicle-related pollution along major roadways. The model is used to evaluate emission inventories in the near-road environment, which can be used to refine existing estimates. By developing a locally data-driven method to readily characterize impacts and distinguish between heavy and light duty vehicle effects, local regulations can be used to target policies in major cities around the country, thus addressing local health disbenefits and disparities occurring as a result of exposure to near-road air pollution.

Damage of Infill Masonry Walls due to Vertical Loads in Buildings with Reinforced Concrete Structure

Buildings are subjected to deformations of their reinforced concrete structure due to vertical loads that can lead, in certain cases, to the significant cracking of the unreinforced masonry (URM) infill walls, therefore requiring the development of specific knowledge of these types of deformations in order to minimise them. Aiming the evaluation of buildings with reinforced concrete structures subjected to vertical deformations, the mechanical characteristics of their infill masonry walls, as well as of their interface with the supporting concrete elements, are here analysed. The basic compression behaviour of brick masonry is assessed particularly through the previous analysis of a vertical compression test of a masonry specimen. The mechanical behaviour characteristics of the constituents (bricks, and mortar joints) are analysed to account for their influence on the compression behaviour of masonry infill, aiming particularly for the prevision of their cracking in case of vertical deformations of the supporting reinforced concrete (RC) elements. Based on that evaluation, the analysis of masonry walls subjected to vertical deformations of their supports is made through the assessment of the relevant characteristic behaviour of masonry wall-beam/slab assembly in case of vertical load. A general modelling approach of the behaviour of URM infills and the interaction with their supports is generally accessed. Finally, the preventive control of deformations of masonry wall-beam/slab assembly is discussed.

Dynamic Mortality Modeling: Incorporating Predictions of Future General Population Mortality Into Cost-Effectiveness Analysis

ObjectivesHealth economic models commonly apply observed general population mortality rates to simulate future deaths in a cohort. This is potentially problematic, because mortality statistics are records of the past, not predictions for the future. We propose a new dynamic general population mortality modeling approach, which enables analysts to implement predictions of future changes in mortality rates. The potential implications of moving from a conventional static approach to a dynamic approach are illustrated using a case study. MethodsThe model utilized in National Institute for Health and Care Excellence appraisal TA559, axicabtagene ciloleucel axi for diffuse large B-cell lymphoma, was replicated. National mortality projections were taken from the UK Office for National Statistics. Mortality rates by age and sex were updated each modeled year with the first modeled year using 2022 rates, the second modeled year 2023 and so on. A total of 4 different assumptions were made around age distribution: fixed mean age, lognormal, normal, and gamma. The dynamic model outcomes were compared with those from a conventional static approach. ResultsIncluding dynamic calculations increased the undiscounted life-years attributed to general population mortality by 2.4 to 3.3 years. This led to an increase in discounted incremental life-years within the case study of 0.38 to 0.45 years (8.1%-8.9%), and a commensurate impact on the economically justifiable price of £14 456 to £17 097. ConclusionsThe application of a dynamic approach is technically simple and has the potential to meaningfully affect estimates of cost-effectiveness analysis. Therefore, we call on health economists and health technology assessment bodies to move toward use of dynamic mortality modeling in future.

Re-visiting the causal relationship between tourism and economic growth in India: specific to general modelling approach

PurposeTourism–economy relationship in India has been studied extensively in the past literature using a single equation approach. However, the present paper diverted from this trend and examined the tourism–economy relationship using the specific to general modelling approach over the 1990–2018 time period. The study also accounts for the influence of merchandise trade, capital formation, foreign investment inflows and inflation on economic growth to achieve the robustness of the coefficient estimates.Design/methodology/approachTo achieve the objective, the study utilised a specific to general modelling strategy. First, the regression equation includes only three core variables: gross domestic product (GDP), international tourist receipts and international tourist expenditures. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegration among the variables. As for the estimation technique, the authors employed autoregressive distributed lag (ARDL) approach.FindingsThe paper's findings highlight that tourism receipts and expenditures exert a positively significant impact on economic growth. Moreover, including the additional independent variables does not substantially change the tourism and economic growth relationship. The existence of one-way causality from tourism expenditures to economic growth supports the tourism-led growth hypothesis. These findings highlight the rationale for intervention by the government and policymakers to promote tourism potential and facilities to accelerate the overall growth performance of the country. While the existence of one-way causal effect from economic growth to tourism revenues supports the growth-led tourism development hypothesis, implying that economic expansion is necessary for tourism development.Research limitations/implicationsThis research article tried to present a comprehensive picture of India's tourism–economy relationship. However, the present study is organised as an aggregate economy-level analysis. It assumed that the aggregate tourism sector is homogenous. However, different tourism sectors exert different levels of influence on the economy. The authors expect future research can take the disaggregated analysis of the tourism–economy relationship.Practical implicationsThis study provides valuable insights into the tourism-led growth hypothesis in India. The study highlights comprehensive intervention by the government and policymakers for accelerating tourism development to invigorate the overall growth performance of the country over the long run. The principal recommendation emerging from the present research is that the tourism growth potential can be depended upon to stimulate the economic performance of the Indian economy.Originality/valueThe present study diverted from the previous empirical studies by following a specific to general modelling strategy. First, the regression model includes only three core variables such as economic growth, tourism receipts and tourism expenditure. Next, the authors include other control variables in the regression equation one by one, leading us to test five model types for investigating the cointegrating relationship among the variables. GDP growth rate is used as a dependent variable in all five specifications. The idea is to expand the model to capture every feature of the data generating process.

A Continuous Process for Validation, Verification, and Accreditation of Simulation Models

A simulation model, and more generically, a model, is founded on its assumptions. Assurance of the model’s correctness and correct use is needed to achieve accreditation. Often the exercise of working with a specific code misunderstands the overall process, focusing the resources on the model coding and forgetting the needed resources to ensure the validation of every step of the model definition and coding. The goal of this work is to present a methodology to help in the definition and use of the assumptions in the modeling process. To do so, we present a process to conduct a simulation project, an assumptions taxonomy, and a method that simplifies working with those assumptions. We propose to extend the traditional Validation, Verification, and Accreditation processes to a process composed of eight Validation, Verification, and Accreditation phases that cover the overall life cycle of a model. Although this paper is focused on a simulation model, we can extend the proposed method to a more general modeling approach.

Nitrogen balance is a predictor of farm business performance in the English Farm Business Survey

Global environmental sustainability and food security are fundamental societal issues, and most crop production relies upon inputs from organic or inorganic nitrogen sources. Previous research in the Global North has demonstrated a typical over application of nitrogen across global agriculture with substantial negative impacts on the environment. The objective of this work was to draw on English Farm Business Survey (FBS) data of non-organic General Cropping and Cereal farms to explore the relationship between farm gate nitrogen balance, fertilizer application advice and farm business performance. A mixed effects generalized modeling approach was used to partition the variance into random (such as year, or farm ID) and fixed effects (those of interest). Whilst the financial performance of farm businesses is subject to high variance and multiple drivers, a negative relationship was detected between business performance and farm gate nitrogen balance, we demonstrate that nitrogen lost to the environment of >60 kg per hectare is associated with a significant negative impact on farm performance. Supplier-provided fertilizer advice was also associated with reduced farm performance. These results imply a positive effect on farm performance of enhancing on-farm understanding of crop nutrient requirements through the provision of accredited fertilizer advice. Within the stated bounds our model demonstrates good predictivity on randomly subsetted data, and is presented as a tool for use in scenario modeling of interventions such as agri-environment schemes, Natural Capital and Ecosystems Assessment, and the UN Sustainable Development Goals.

Deciphering the genomic regions governing major agronomic traits and blast resistance using genome wide association mapping in finger millet

Finger millet (Eleusine coracana L.) is climate resilient minor millet of Asia and Africa with wide adaptation and unparallel nutritional profile. To date, genomic resources available in finger millet are scanty and genetic control of agronomic traits remains elusive. Here, a collection of eco-geographically diverse 186 genotypes was quantified for variation in 13 agronomic traits and reaction to blast to identify marker-trait associations (MTAs) using genotyping-by-sequencing (GBS) and genome-wide association study (GWAS). GBS generated 2977 high quality single nucleotide polymorphism (SNPs) markers and identified three subpopulations with varying admixture levels. General linear and mixed model approaches of GWAS to correct for population structure and genetic relatedness identified 132 common MTAs for agronomic traits across the years. The phenotypic variance explained by the makers varied from 4.8% (TP692389-flag leaf width) to 20% (TP714446-green fodder weight). Of these, 26 MTAs showed homology with candidate genes having role in plant growth, development and photosynthesis in the genomes of foxtail millet, rice, maize, wheat and barley. We also found 4 common MTAs for neck blast resistance, which explained 5.9–15.1% phenotypic variance. Three MTAs for neck blast resistance showed orthologues in related genera having putative functions in pathogen defense in plants. The results of this work lay a foundation for understanding the genetic architecture of agronomic traits and blast resistance in finger millet and provide a framework for genomics assisted breeding.

Combining Continuous and Categorical Data Modeling in Developmental Age Estimation Using Hierarchical Bayes

Residual correlations (correlations that persist after accounting for the effect of chronological age) between variables can have a significant impact on final age estimates. Such correlations can result in overly narrow age intervals and high error rates when not accounted for. Modeling correlations can be mathematically problematic across mixed data types. Hierarchical modeling can incorporate continuous and categorical traits into a single model that accounts for correlated variables while reducing computationally expensive calculations. This paper demonstrates a Bayesian hierarchical modeling approach in which trait variables were grouped by data type or bodily system and used to produce separate age estimates with any appropriate model. These age estimates were combined into a single estimate using a multivariate normal model via nested cross-validation. The data used included nine diaphyseal length measurements and 29 epiphyseal fusion and ossification sites from 179 individuals in the publicly available U.S. Subadult Virtual Anthropology Database. Diaphyseal ages were modeled with linear regression and epiphyseal ages with random forest regression. Age estimates from the hierarchical model had reduced bias relative to diaphyseal or epiphyseal maximum likelihood estimates alone. Combined-indicator age intervals from 95% highest density regions (HDRs) were on average 15% narrower than those from diaphyseal 95% HDRs while success rates were 2% lower (91% vs. 93%). Functional example code is provided. A general hierarchical modeling approach may be applicable to other areas of skeletal analysis that employ correlated variables of mixed data types including adult age estimation and ancestry estimation.

Effectiveness of a pharmacist-delivered primary care telemedicine intervention to increase access to pharmacotherapy and specialty treatment for alcohol use problems: Protocol for the alcohol telemedicine consult cluster-randomized pragmatic trial

BackgroundAlcohol use problems are associated with serious medical, mental health and socio-economic consequences. Yet even when patients are identified in healthcare settings, most do not receive treatment, and use of pharmacotherapy is rare. This study will test the effectiveness of the Alcohol Telemedicine Consult (ATC) Service, a novel, personalized telehealth intervention approach for primary care patients with alcohol use problems. MethodsThis cluster-randomized pragmatic trial, supplemented by qualitative interviews, will include adults with a primary care visit between 9/10/21–3/10/23 from 16 primary care clinics at two large urban medical centers within Kaiser Permanente Northern California, a large, integrated healthcare system. Clinics are randomized to the ATC Service (intervention), including alcohol pharmacotherapy and SBIRT (screening, MI (Motivational Interviewing)-based brief intervention and referral to addiction treatment) delivered by clinical pharmacists, or the Usual Care (UC) arm that provides systematic alcohol SBIRT. Primary outcomes include a comparison of the ATC and UC arms on 1) implementation outcomes (alcohol pharmacotherapy prescription rates, specialty addiction treatment referrals); and 2) patient outcomes (medication fills, addiction treatment initiation, alcohol use, healthcare services utilization) over 1.5 years. A general modeling approach will consider clustering of patients/providers, and a random effects model will account for intra-class correlations across patients within providers and across clinics. Qualitative interviews with providers will examine barriers and facilitators to implementation. DiscussionThe ATC study examines the effectiveness of a pharmacist-provided telehealth intervention that combines pharmacotherapy and MI-based consultation. If effective, the ATC study could affect treatment models across the spectrum of alcohol use problems.Clinical Trials Registration: This study has been registered on ClinicalTrials.gov (NCT05252221).

A general defect modelling and simulation-assisted approach for fault isolation in failure analysis

Over the years, defect modelling has demonstrated its effectiveness in intracell defect or root cause estimation via scan test and scan diagnosis methods. This paper describes a novel application of defect modelling in fault isolation and defect estimation in failure analysis (FA). In this article, basic defect models are first created based on failure phenomena and principles of fault isolation tools. Then, considering the corresponding failure phenomena, defect models are inserted into targeted circuits to demonstrate defect impacts via analogue simulation tools. Through this method, unreasonable defect estimations are eliminated, while the remaining estimations can cause failure; thus, defect estimations become more accurate, and further analysis becomes more targeted. To facilitate methodology execution, the analysis flow is optimized and validated in real cases. As such, visible defects are rapidly identified in three real cases, which are suitable examples to reflect the effectiveness of this method in regard to fault isolation. Moreover, further investigation of the three cases proves that how defect modelling and simulation is one powerful approach able to significantly reduce the number of defect candidates and the size of the area of interest (AOI) compared to regular approaches. In addition, via defect modelling and with the assistance of analogue simulation tools, we have an opportunity to continue to isolate defects and enhance the defect estimation accuracy through the validation of each original defect candidate under the condition of insufficient fault isolation data, or we can even meet the resolution limitation of fault isolation tools.

Temporal scaling of human scalp-recorded potentials

Much of human behavior is governed by common processes that unfold over varying timescales. Standard event-related potential analysis assumes fixed-duration responses relative to experimental events. However, recent single-unit recordings in animals have revealed neural activity scales to span different durations during behaviors demanding flexible timing. Here, we employed a general linear modeling approach using a combination of fixed-duration and variable-duration regressors to unmix fixed-time and scaled-time components in human magneto-/electroencephalography (M/EEG) data. We use this to reveal consistent temporal scaling of human scalp-recorded potentials across four independent electroencephalogram (EEG) datasets, including interval perception, production, prediction, and value-based decision making. Between-trial variation in the temporally scaled response predicts between-trial variation in subject reaction times, demonstrating the relevance of this temporally scaled signal for temporal variation in behavior. Our results provide a general approach for studying flexibly timed behavior in the human brain.

Revealing the impact of a water-economy nexus-based joint tax management policy on the environ-economic system: An advanced exploration of double dividend strategies and multi-scale response mechanisms

Economic growth is constrained by water scarcity, and a unified policy analysis framework is required to enhance the coordinated development of the environ-economic system from the perspective of wastewater management. Thus, a multi-scale water-economic double dividend mechanism model (MWED) is first developed to (i) derive strategies for simultaneously achieving water scarcity mitigation and economic increase through compounding wastewater tax (WT) policies and reduced value-added tax (RVT) policies; (ii) explore the internal response mechanisms at the sectoral scale in the central region; and (iii) reveal the responses of economic output and multiple water resources in external regions under the cascading effects. This integrated approach of a computable general equilibrium model, an ecological network analysis, and a multi-regional input-output analysis is applied to Shanxi Province, China, a representative water-scarce region. The double dividend strategies of WT_6 yuan/ton*RVT_2%, WT_8 yuan/ton*RVT_2.5%, and WT_10 yuan/ton*RVT_3% were conducive to alleviating water scarcity and stimulating economic growth by adjusting the production structures or upgrading treatment technologies to reduce wastewater discharge, reducing the use of water-intensive energy sources to condense direct water consumption, and enhancing the connection between low water consumption sectors and advantageous economic sectors to optimize the virtual water network structure. Based on complex inter-regional relationships, these cascading effects can stimulate the economic output of other provinces and alleviate prevailing water scarcities by increasing virtual water inflows in economically developed regions and decreasing direct water consumption in economically undeveloped regions. The ability of water-rich regions to reconcile differences in water resource endowments can be further strengthened.

A general approach for robot pose error compensation based on an equivalent joint motion error model

A general error modelling, measurement, and compensation approach for robot calibration was proposed based on the equivalent joint motion error model from the perspective of vector space. First, the end pose error was expressed as a linear combination of the twists and the equivalent motion errors of the actuated joints, and the latter were described as functions of the ideal configuration. On this basis, the pose error in operating space was decoupled in joint space, and the regression model corresponding to each actuated joint was established. Then, a statistical index with dimensional consistency was proposed to evaluate the predictive capability of the model, and the variation of the prediction accuracy in the presence of system noise was studied. Finally, experiments were conducted adopting the strategy of off-line identification and on-line compensation. After calibration, the average value of robot position and attitude errors can be reduced to 0.046 mm and 0.011 deg, respectively.

A general approach for degradation modeling to enable a widespread use of aging simulations in IC design

The importance of integrated circuit (IC) reliability has been growing to benefit from the potentials of advanced semiconductor technologies in long-living applications, such as automotive electronics. Today, prototypes and products are tested for their reliability. The most important procedures are summarized in the industry standard AEC-Q100 for automotive ICs. There are indications that these tests will not be sufficient for future applications so that simulation-based reliability assessments are expected to complement them soon. Circuit-level aging simulations are one approach to virtually investigate the IC reliability before entering manufacturing. Although they have been available for years, they still appear rarely used. Despite significant progress in research and industry, degradation modeling and validation of aging simulations are still demanding and need improvements. This article focuses on degradation modeling and outlines challenges as well as a solution approach to establish aging simulations as a widespread verification step in future IC design projects.

Methods for the Accurate Real-Time Simulation of High-Frequency Power Converters

This article presents general modeling approaches to achieve an accurate real-time simulation (RTS) of high switching frequency converters.The proposed methods are based on the direct mapped method (DMM) and make use of decoupling techniques when appropriate. The DMM links state variables to diode statuses and provides an exact and noniterative solution to network equations. An electric vehicle battery charger test case comprised of a full-bridge rectifier, an interleaved boost, and a three-phase <italic xmlns:mml="http://www.w3.org/1998/Math/MathML" xmlns:xlink="http://www.w3.org/1999/xlink">LLC</i> is used to demonstrate the high accuracy achieved by the proposed methods compared to a conventional approach. Field-programmable gate array (FPGA) implementations are proposed and shown to achieve from 75- to 175-ns RTS time steps for this test case circuit, allowing its accurate simulation while switched at 200 kHz. To further validate the effectiveness of the FPGA-based simulator, a resonant boost converter is also implemented and simulated in real time.

Neural substrates of saccadic adaptation: Plastic changes versus error processing and forward versus backward learning

Previous behavioral, clinical, and neuroimaging studies suggest that the neural substrates of adaptation of saccadic eye movements involve, beyond the central role of the cerebellum, several, still incompletely determined, cortical areas. Furthermore, no neuroimaging study has yet tackled the differences between saccade lengthening (“forward adaptation”) and shortening (“backward adaptation”) and neither between their two main components, i.e. error processing and oculomotor changes. The present fMRI study was designed to fill these gaps. Blood-oxygen-level-dependent (BOLD) signal and eye movements of 24 healthy volunteers were acquired while performing reactive saccades under 4 conditions repeated in short blocks of 16 trials: systematic target jump during the saccade and in the saccade direction (forward: FW) or in the opposite direction (backward: BW), randomly directed FW or BW target jump during the saccade (random: RND) and no intra-saccadic target jump (stationary: STA). BOLD signals were analyzed both through general linear model (GLM) approaches applied at the whole-brain level and through sensitive Multi-Variate Pattern Analyses (MVPA) applied to 34 regions of interest (ROIs) identified from independent 'Saccade Localizer’ functional data. Oculomotor data were consistent with successful induction of forward and backward adaptation in FW and BW blocks, respectively. The different analyses of voxel activation patterns (MVPAs) disclosed the involvement of 1) a set of ROIs specifically related to adaptation in the right occipital cortex, right and left MT/MST, right FEF and right pallidum; 2) several ROIs specifically involved in error signal processing in the left occipital cortex, left PEF, left precuneus, Medial Cingulate cortex (MCC), left inferior and right superior cerebellum; 3) ROIs specific to the direction of adaptation in the occipital cortex and MT/MST (left and right hemispheres for FW and BW, respectively) and in the pallidum of the right hemisphere (FW). The involvement of the left PEF and of the (left and right) occipital cortex were further supported and qualified by the whole brain GLM analysis: clusters of increased activity were found in PEF for the RND versus STA contrast (related to error processing) and in the left (right) occipital cortex for the FW (BW) versus STA contrasts [related to the FW (BW) direction of error and/or adaptation]. The present study both adds complementary data to the growing literature supporting a role of the cerebral cortex in saccadic adaptation through feedback and feedforward relationships with the cerebellum and provides the basis for improving conceptual frameworks of oculomotor plasticity and of its link with spatial cognition.

Social information-mediated population dynamics in non-grouping prey

Inadvertent social information (ISI) use, i.e., the exploitation of social cues including the presence and behaviour of others, has been predicted to mediate population-level processes even in the absence of cohesive grouping. However, we know little about how such effects may arise when the prey population lacks social structure beyond the spatiotemporal autocorrelation originating from the random movement of individuals. In this study, we built an individual-based model where predator avoidance behaviour could spread among randomly moving prey through the network of nearby observers. We qualitatively assessed how ISI use may affect prey population size when cue detection was associated with different probabilities and fitness costs, and characterised the structural properties of the emerging detection networks that would provide pathways for information spread in prey. We found that ISI use was among the most influential model parameters affecting prey abundance and increased equilibrium population sizes in most examined scenarios. Moreover, it could substantially contribute to population survival under high predation pressure, but this effect strongly depended on the level of predator detection ability. When prey exploited social cues in the presence of high predation risk, the observed detection networks consisted of a large number of connected components with small sizes and small ego networks; this resulted in efficient information spread among connected individuals in the detection networks. Our study provides hypothetical mechanisms about how temporary local densities may allow information diffusion about predation threats among conspecifics and facilitate population stability and persistence in non-grouping animals.Significance statementThe exploitation of inadvertently produced social cues may not only modify individual behaviour but also fundamentally influence population dynamics and species interactions. Using an individual-based model, we investigated how the detection and spread of adaptive antipredator behaviour may cascade to changes in the demographic performance of randomly moving (i.e., non-grouping) prey. We found that social information use contributed to population stability and persistence by reducing predation-related per capita mortality and raising equilibrium population sizes when predator detection ability reached a sufficient level. We also showed that temporary detection networks had structural properties that allowed efficient information spread among prey under high predation pressure. Our work represents a general modelling approach that could be adapted to specific predator-prey systems and scrutinise how temporary local densities allow dynamic information diffusion about predation threats and facilitate population stability in non-grouping animals.

Driving Behavior Modeling Using Naturalistic Human Driving Data With Inverse Reinforcement Learning

Driving behavior modeling is of great importance for designing safe, smart, and personalized autonomous driving systems. In this paper, an internal reward function-based driving model that emulates the human’s decision-making mechanism is utilized. To infer the reward function parameters from naturalistic human driving data, we propose a structural assumption about human driving behavior that focuses on discrete latent driving intentions. It converts the continuous behavior modeling problem to a discrete setting and thus makes maximum entropy inverse reinforcement learning (IRL) tractable to learn reward functions. Specifically, a polynomial trajectory sampler is adopted to generate candidate trajectories considering high-level intentions and approximate the partition function in the maximum entropy IRL framework. An environment model considering interactive behaviors among the ego and surrounding vehicles is built to better estimate the generated trajectories. The proposed method is applied to learn personalized reward functions for individual human drivers from the NGSIM highway driving dataset. The qualitative results demonstrate that the learned reward functions are able to explicitly express the preferences of different drivers and interpret their decisions. The quantitative results reveal that the learned reward functions are robust, which is manifested by only a marginal decline in proximity to the human driving trajectories when applying the reward function in the testing conditions. For the testing performance, the personalized modeling method outperforms the general modeling approach, significantly reducing the modeling errors in human likeness (a custom metric to gauge accuracy), and these two methods deliver better results compared to other baseline methods. Moreover, it is found that predicting the response actions of surrounding vehicles and incorporating their potential decelerations caused by the ego vehicle are critical in estimating the generated trajectories, and the accuracy of personalized planning using the learned reward functions relies on the accuracy of the forecasting model.