Instances Of Model Research Articles

Analysis of Sustainable Efficiency of Freight Transport in Major European Economies: An Integrated Multi-Region Input-Output and DEA Approach

This paper integrates the multi-region input-output model (MRIO) and data envelopment analysis (DEA) methods to analyze the freight transport efficiency in Europe. Social, economic, and environmental influences were combined into a sustainable efficiency rating of the freight transport sector of Germany, France, Italy, Spain, and the Netherlands. First, the freight transport sector's carbon footprint (CFP) was quantified using the MRIO model. The lifecycle-based CFP emissions of freight transport activities were assessed using a dataset from 2000 to 2018. Nineteen stochastic model-based MRIO lifecycle assessments were built for each country. Secondly, sixty instances of DEA models were created using a linear program for each mode in the selected countries. Thirdly, the sustainable efficiency scores were determined for each freight transport mode in each country over four periods: 2000–2004, 2005–2009, 2010–2014, and 2015–2018. The results illustrate that the sustainable efficiency score of inland, water, and air transport modes ranged from 0.38 to 1.

Global Semantic Localization from Abstract Ellipse-Ellipsoid Model and Object-Level Instance Topology

Robust and highly accurate localization using a camera is a challenging task when appearance varies significantly. In indoor environments, changes in illumination and object occlusion can have a significant impact on visual localization. In this paper, we propose a visual localization method based on an ellipse-ellipsoid model, combined with object-level instance topology and alignment. First, we develop a CNN-based (Convolutional Neural Network) ellipse prediction network, DEllipse-Net, which integrates depth information with RGB data to estimate the projection of ellipsoids onto images. Second, we model environments using 3D (Three-dimensional) ellipsoids, instance topology, and ellipsoid descriptors. Finally, the detected ellipses are aligned with the ellipsoids in the environment through semantic object association, and 6-DoF (Degree of Freedom) pose estimation is performed using the ellipse-ellipsoid model. In the bounding box noise experiment, DEllipse-Net demonstrates higher robustness compared to other methods, achieving the highest prediction accuracy for 11 out of 23 objects in ellipse prediction. In the localization test with 15 pixels of noise, we achieve ATE (Absolute Translation Error) and ARE (Absolute Rotation Error) of 0.077 m and 2.70∘ in the fr2_desk sequence. Additionally, DEllipse-Net is lightweight and highly portable, with a model size of only 18.6 MB, and a single model can handle all objects. In the object-level instance topology and alignment experiment, our topology and alignment methods significantly enhance the global localization accuracy of the ellipse-ellipsoid model. In experiments involving lighting changes and occlusions, our method achieves more robust global localization compared to the classical bag-of-words based localization method and other ellipse-ellipsoid localization methods.

Nonlinear Optimization and Adaptive Heuristics for Solving Irregular Object Packing Problems

We review and present several challenging model classes arising in the context of finding optimized object packings (OP). Except for the smallest and/or simplest general OP model instances, it is not possible to find their exact (closed-form) solution. Most OP problem instances become increasingly difficult to handle even numerically, as the number of packed objects increases. Specifically, here we consider classes of general OP problems that can be formulated in the framework of nonlinear optimization. Research experience demonstrates that—in addition to utilizing general-purpose nonlinear optimization solver engines—the insightful exploitation of problem-specific heuristics can improve the quality of numerical solutions. We discuss scalable OP problem classes aimed at packing general circles, spheres, ellipses, and ovals, with numerical (conjectured) solutions of non-trivial model instances. In addition to their practical relevance, these models and their various extensions can also serve as constrained global optimization test challenges.

Access Control Verification in Smart Contracts Using Colored Petri Nets

This paper presents an approach for the verification of access control in smart contracts written in the Digital Asset Modeling Language (DAML). The approach utilizes Colored Petri Nets (CPNs) and their analysis tool CPN Tools. It is a model-driven-based approach that employs a new meta-model for capturing access control requirements in DAML contracts. The approach is supported by a suite of tools that fully automates all of the steps: parsing DAML code, generating DAML model instances, transforming the DAML models into CPN models, and model checking the generated CPN models. The approach is tested using several DAML scripts involving access control extracted from different domains of blockchain applications.

The topology and geometry of neural representations

A central question for neuroscience is how to characterize brain representations of perceptual and cognitive content. An ideal characterization should distinguish different functional regions with robustness to noise and idiosyncrasies of individual brains that do not correspond to computational differences. Previous studies have characterized brain representations by their representational geometry, which is defined by the representational dissimilarity matrix (RDM), a summary statistic that abstracts from the roles of individual neurons (or responses channels) and characterizes the discriminability of stimuli. Here, we explore a further step of abstraction: from the geometry to the topology of brain representations. We propose topological representational similarity analysis, an extension of representational similarity analysis that uses a family of geotopological summary statistics that generalizes the RDM to characterize the topology while de-emphasizing the geometry. We evaluate this family of statistics in terms of the sensitivity and specificity for model selection using both simulations and functional MRI (fMRI) data. In the simulations, the ground truth is a data-generating layer representation in a neural network model and the models are the same and other layers in different model instances (trained from different random seeds). In fMRI, the ground truth is a visual area and the models are the same and other areas measured in different subjects. Results show that topology-sensitive characterizations of population codes are robust to noise and interindividual variability and maintain excellent sensitivity to the unique representational signatures of different neural network layers and brain regions.

Case-crossover designs and overdispersion with application to air pollution epidemiology.

Over the last three decades, case-crossover designs have found many applications in health sciences, especially in air pollution epidemiology. They are typically used, in combination with partial likelihood techniques, to define a conditional logistic model for the responses, usually health outcomes, conditional on the exposures. Despite the fact that conditional logistic models have been shown equivalent, in typical air pollution epidemiology setups, to specific instances of the well-known Poisson time series model, it is often claimed that they cannot allow for overdispersion. This paper clarifies the relationship between case-crossover designs, the models that ensue from their use, and overdispersion. In particular, we propose to relax the assumption of independence between individuals traditionally made in case-crossover analyses, in order to explicitly introduce overdispersion in the conditional logistic model. As we show, the resulting overdispersed conditional logistic model coincides with the overdispersed, conditional Poisson model, in the sense that their likelihoods are simple re-expressions of one another. We further provide the technical details of a Bayesian implementation of the proposed case-crossover model, which we use to demonstrate, by means of a large simulation study, that standard case-crossover models can lead to dramatically underestimated coverage probabilities, while the proposed models do not. We also perform an illustrative analysis of the association between air pollution and morbidity in Toronto, Canada, which shows that the proposed models are more robust than standard ones to outliers such as those associated with public holidays.

A model template for reachability-based containment checking of imprecise observations in timed automata

AbstractVerifying safety requirements by model checking becomes increasingly important for safety-critical applications. For the validity of such proof in practice, the model needs to capture the actual behavior of the real system, which could be tested by containment checks of real observation traces. Basic equivalence checks, however, are not applicable if the system is only partially or imprecisely observable, if the model abstracts from explicit states with symbolic semantics, or if the checks are not expressible in the logics supported by a model checker. In this article, we solve the problem of observation containment checking in timed automata via reachability checking on tester systems. We introduce the logic SRL (sequence reachability logic) to express observations as sequences of delayed reachability properties. Through SBLL (introduced by Aceto et al.) as intermediate logic, we synthesize a set of matcher model templates for partial and imprecise observations and further extend these templates for the case of limited state accessibility in a model. For the obtained matching traces, we define the back-transformation into the original model domain and formally prove the correctness of the transformation. We implemented the observation matching approach, and apply it to a set of 7 demo and 3 case study models with different levels of observability. The results show that all positive and negative observations are correctly classified, and that the most advanced matcher model instance still offers average run times between 0.1 and 1 s in all but 3 scenarios.

INNOVATIVE BUSINESS MODELS IN THE CIRCULAR ECONOMY: AN ANALYSIS OF GOOD PRACTICES FOR ELECTRICAL AND ELECTRONIC EQUIPMENT ON THE EUROPEAN CIRCULAR ECONOMY STAKEHOLDER PLATFORM

The pervasive integration of electronic technologies in the contemporary digital landscape underscores the significance of electrical and electronic equipment (EEE). Beyond their utility in daily life, certain components within EEE, hold classification as critical raw materials (CRMs) and transforms cities into urban mines. This issue highlights the necessity of optimizing its sustainable consumption and production patterns at all stages of the life cycle and across various sectors. The optimization of the complete product life cycle serves as a foundational pillar of the circular economy (CE). While the transition from a linear to a CE has been a longstanding objective promoted by the EU, a comprehensive mapping elucidating potential shortcoming in the effective implementation of this transition in Europe for the field of EEE remains elusive. This paper seeks to investigate the state-of-the-art of CE good practices for EEEs in Europe, shedding light on potential strengths and challenges to be addressed. To obtain an empirical perspective, the European Circular Economy Stakeholder Platform (ECESP) serves as a tool to delineate the trajectory of progress throughout the transition to a CE. The analysis encompasses both life cycle phases and the production sector. The findings underscore a prevailing focus on the product's end-of-life phase and sectors associated with recycling and waste management, while the initial life cycle phase remains underexplored in terms of eco-design solutions or innovative processes. On a positive note, several instances of new business models have emerged, redirecting consumption emphasis towards services.

Discretisation of the Hough parameter space for fitting and recognising geometric primitives in 3D point clouds

Research in recognising and fitting simple geometric shapes has been ongoing since the 1970s, with various approaches proposed, including stochastic methods, parameter methods, primitive-based registration techniques, and more recently, deep learning. The Hough transform is a method of interest due to its demonstrated robustness to noise and outliers, ability to handle missing data, and support for multiple model instances. Unfortunately, one of the main limitations of the Hough transform is how to properly discretise its parameter space, as increasing their number or decreasing the sampling frequency can make it computationally expensive.The relationship between the approximation accuracy and the parameter space’s discretisation is investigated to address this. We present two distinct discretisations to illustrate how the fitting and recognition quality can be improved by selecting an appropriate parameter discretisation. Our parameter-driven space discretisation is shown to significantly improve the parameter recognition quality over the classical method and reduce computational time and space by decreasing the discretisation’s dimension, as demonstrated by an extensive validation on a benchmark of geometric primitives. Preliminary experiments are also presented on segmenting datasets from urban buildings and CAD objects.

Latent Semantic Consensus for Deterministic Geometric Model Fitting.

Estimating reliable geometric model parameters from the data with severe outliers is a fundamental and important task in computer vision. This paper attempts to sample high-quality subsets and select model instances to estimate parameters in the multi-structural data. To address this, we propose an effective method called Latent Semantic Consensus (LSC). The principle of LSC is to preserve the latent semantic consensus in both data points and model hypotheses. Specifically, LSC formulates the model fitting problem into two latent semantic spaces based on data points and model hypotheses, respectively. Then, LSC explores the distributions of points in the two latent semantic spaces, to remove outliers, generate high-quality model hypotheses, and effectively estimate model instances. Finally, LSC is able to provide consistent and reliable solutions within only a few milliseconds for general multi-structural model fitting, due to its deterministic fitting nature and efficiency. Compared with several state-of-the-art model fitting methods, our LSC achieves significant superiority for the performance of both accuracy and speed on synthetic data and real images.

Hierarchical Heuristic Species Delimitation under the Multispecies Coalescent Model with Migration.

The multispecies coalescent (MSC) model accommodates genealogical fluctuations across the genome and provides a natural framework for comparative analysis of genomic sequence data from closely related species to infer the history of species divergence and gene flow. Given a set of populations, hypotheses of species delimitation (and species phylogeny) may be formulated as instances of MSC models (e.g., MSC for one species versus MSC for two species) and compared using Bayesian model selection. This approach, implemented in the program bpp, has been found to be prone to over-splitting. Alternatively heuristic criteria based on population parameters (such as popula- tion split times, population sizes, and migration rates) estimated from genomic data may be used to delimit species. Here we develop hierarchical merge and split algorithms for heuristic species delimitation based on the genealogical divergence index (𝑔𝑑𝑖) and implement them in a python pipeline called hhsd. We characterize the behavior of the 𝑔𝑑𝑖 under a few simple scenarios of gene flow. We apply the new approaches to a dataset simulated under a model of isolation by distance as well as three empirical datasets. Our tests suggest that the new approaches produced sensible results and were less prone to over-splitting. We discuss possible strategies for accommodating paraphyletic species in the hierarchical algorithm, as well as the challenges of species delimitation based on heuristic criteria.

MDAPW3: MDA-based development of blockchain-enabled decentralized applications

Web3.0 Decentralized Application (DApp) is a class of decentralized software in which at least the business logic of the software is implemented using blockchain-based smart contracts. Features such as transparency, decentralized execution environment, no need for a central authority, immutability of data from manipulation, as well as a native transaction-based payment system based on cryptographic tokens are the main advantages of Web3.0 DApps over conventional Web2.0 software in which the business logic and user data are centrally controlled by companies with no transparency. However, the development lifecycle of Web3.0 DApps involves many challenges due to the complexity of blockchain technology and smart contracts as well as the difficulties concerning with the integration of DApp on-chain and off-chain components. To alleviate these challenges, a Model Driven Architecture (MDA) approach for the development of Web3.0 DApps is proposed in this paper that streamlines the development of complex multi-lateral DApps and results in a product that is verifiable, traceable, low-cost, maintainable, less error-prone and in conformance with blockchain platform concepts. Opposed to previous studies in this area that applied MDA only for the development of smart contracts, our proposed MDA-based approach covers the full architecture of Web3.0 DApps: on-chain, off-chain and on-chain/off-chain communication patterns. The method application was demonstrated by implementing a land leasing Dapp where the requirement model (a BPMN choreography model) was transformed into CIM, PIM, and PSM instances successively, and finally, the code-base was generated based on the Ethereum platform technology stack. Epsilon Validation Language (EVL), Epsilon Object Language (EOL), and Epsilon Comparison Language (ECL) were used for the verification/validation of the model instances at each step. Furthermore, by evaluating the quality metrics of the proposed meta-models, we show that they have a better ontology coverage and are more reusable and understandable compared to previous meta-models.

TEMOA-europe: An open-source and open-data energy system optimization model for the analysis of the European energy mix

Energy system modeling tools allow to perform comprehensive analyses for the optimal integration of supply and demand technologies in different scenarios. Open tools, in particular, increase the reliability of such tools and their policy relevance. This work aims to present TEMOA-Europe, an open-data and open-software model instance for OECD Europe. Such model is developed on a time scale up to 2050 and calibrated against acknowledged energy statistics up to 2020. This work is the first to present a net-zero emissions by 2050 trajectory envisaging the absence of Russian energy imports starting from 2030. Despite the stringent constraints, TEMOA-Europe is able to provide results for a decarbonization scenario with growing end-use demands – among which some are reduced for the effect of the elasticity to gas price – considering a larger use of renewable source already starting from the near future and reduced energy intensity. Renewable energy represents more than 60 % of total energy supply by 2050 in the computed pathway. The results are also compared to the projections of the International Energy Agency for the Announced Pledges Scenario (since results for the Net-Zero Emissions Scenario are not publicly available), showing large agreement except for the outcomes concerning wind electricity generation.

Joint Component Estimation for Electricity Price Forecasting Using Functional Models

This work considers the issue of modeling and forecasting electricity prices within the functional time series approach. As this is often performed by estimating and predicting the different components of the price dynamics, we study whether jointly modeling the components, able to account for their inter-relations, could improve prediction with respect to a separate instance of modeling. To investigate this issue, we consider and compare the predictive performance of four different predictors. The first two, namely Smoothing Splines-Seasonal Autoregressive (SS-SAR) and Smoothing Splines-Functional Autoregressive (SS-FAR) are based on separate modeling while the third one is derived from a single-step procedure that jointly estimates all the components by suitably including exogenous variables. It is called Functional Autoregressive with eXogenous variables (FARX) model. The fourth one is a combination of the SS-FAR and FARX predictors. The predictive performances of the models are tested using electricity price data from the northern zone of the Italian electricity market (IPEX), both in terms of forecasting error indicators (MAE, MAPE and RMSE) and by means of the Diebold and Mariano test. The results point out that jointly estimating the components leads to significantly more accurate predictions than using a separate instance of modeling. In particular, the MAE, MAPE, and RMSE values for the best predictor, based on the FARX(3,0,4) model, are 4.25, 9.28, and 5.38, respectively. The percentage error reduction is about 20% with respect to SS-SAR(3,1) and about 10% with respect to SS-FAR(5). Finally, this study suggests that the forecasting errors are generally higher on Sunday and Monday, from hours 3 to 6 in the morning and 14 to 15 in the afternoon, and in June and December. On the other hand, prices are relatively lower on Wednesday, Thursday, and Friday, from hour 20 to 1 a.m., and in January and February.

Robust radiotherapy planning with spatially-based uncertainty sets

Radiotherapy treatment planning is a challenging large-scale optimization problem plagued by uncertainty. Following the robust optimization methodology, we propose a novel, spatially based uncertainty set for robust modeling of radiotherapy planning, producing solutions that are immune to unexpected changes in biological conditions. Our proposed uncertainty set realistically captures biological radiosensitivity patterns that are observed using recent advances in imaging, while its parameters can be personalized for individual patients. We exploit the structure of this set to devise a compact reformulation of the robust model. We develop a row-generation scheme to solve real, large-scale instances of the robust model. This method is then extended to a relaxation-based scheme for enforcing challenging, yet clinically important, dose–volume cardinality constraints. The computational performance of our algorithms, as well as the quality and robustness of the computed treatment plans, are demonstrated on simulated and real imaging data. Based on accepted performance measures, such as minimal target dose and homogeneity, these examples demonstrate that the spatially robust model achieves almost the same performance as the nominal model in the nominal scenario, and otherwise, the spatial model outperforms both the nominal and the box-uncertainty models.

Robust Stochastic Neural Ensemble Learning With Noisy Labels for Thoracic Disease Classification.

Chest radiography is the most common radiology examination for thoracic disease diagnosis, such as pneumonia. A tremendous number of chest X-rays prompt data-driven deep learning models in constructing computer-aided diagnosis systems for thoracic diseases. However, in realistic radiology practice, a deep learning-based model often suffers from performance degradation when trained on data with noisy labels possibly caused by different types of annotation biases. To this end, we present a novel stochastic neural ensemble learning (SNEL) framework for robust thoracic disease diagnosis using chest X-rays. The core idea of our method is to learn from noisy labels by constructing model ensembles and designing noise-robust loss functions. Specifically, we propose a fast neural ensemble method that collects parameters simultaneously across model instances and along optimization trajectories. Moreover, we propose a loss function that both optimizes a robust measure and characterizes a diversity measure of ensembles. We evaluated our proposed SNEL method on three publicly available hospital-scale chest X-ray datasets. The experimental results indicate that our method outperforms competing methods and demonstrate the effectiveness and robustness of our method in learning from noisy labels. Our code is available at https://github.com/hywang01/SNEL.

Evaluating the efficiency of NDVI and climatic data in maize harvest prediction using machine learning

ABSTRACT Accurate anticipation of the maize harvest date is important in the agricultural market, as it ensures the sustainability of food production in response to the increasing global demand for food. This paper proposes a predictive model to determine the optimal harvest time in maize plots using the Normalised Difference Vegetation Index (NDVI) and climatological data. These variables were oversampled and used to train various models, including Random Forest (RF), Gradient Boosting Machine (GBM), Light Gradient Boosting Machine (LGBM), Extreme Gradient Boosting Machine (XGBoost), CatBoost and Support Vector Machine (SVM). Bayesian optimisation has been used to find the best hyperparameters and Shapley values to identify the variables that exert the most significant influence on the prediction in each model instance. As a result of this approach, a model with an accuracy of 92.1% and an Area Under the Curve (AUC) of 0.935 was developed. The variables that determined these results were atmospheric pressure, mean temperature, precipitation, NDVI, and precipitation.

Efficient estimation of true fixed-effects stochastic frontier models

Fixed-effects modeling has become the method of choice in several panel data settings, including models for stochastic frontier analysis. A notable instance of stochastic frontier panel data models is the true fixed-effects model, which allows disentangling unit heterogeneity from efficiency evaluations. While such a model is theoretically appealing, its estimation is hampered by incidental parameters. This note proposes a simple and rather general estimation approach where the unit-specific intercepts are integrated out of the likelihood function. We apply the theory of composite group families to the model of interest and demonstrate that the resulting integrated likelihood is a marginal likelihood with desirable inferential properties. The derivation of the result is provided in full, along with some connections with the existing literature and computational details. The method is illustrated for three notable models, given by the normal-half normal model, the heteroscedastic exponential model, and the normal-gamma model. The results of simulation experiments highlight the properties of the methodology.

Generative Modeling of Semiconductor Devices for Statistical Circuit Simulation

Statistical simulation is a necessary step in integrated circuit design since it provides a realistic picture of the circuit’s behavior in the presence of manufacturing process variations. When some of the circuit components lack an accurate analytical model, as is often the case for emerging semiconductor devices or ones working at cryogenic temperatures, an approximation model is necessary. Such models are usually based on a lookup table or artificial neural network individually fitted to measurement data. If the number of devices available for measurement is limited, so is the number of approximation model instances, which renders impossible a reliable statistical circuit simulation. Approximation models using the device’s physical parameters as inputs have been reported in the literature but are only useful if the end user knows the statistical distributions of those parameters, which is not always the case. The solution proposed in this work uses a type of artificial neural network called the variational autoencoder that, when exposed to a small sample of I-V curves under process variations, captures their essential features and subsequently generates an arbitrary number of similarly disturbed curves. No knowledge of the underlying physical sources of these variations is required. The proposed generative model trained on as few as 20 instances of a MOSFET is shown to precisely reproduce the period and power consumption distributions of a ring oscillator built with these MOSFETs.

Conventional or organic cattle farming? Trade-offs between crop yield, livestock capacity, organic premiums, and government payments

CONTEXTAn important question for farmers is whether to run their farm conventionally or organically. This choice can significantly affect the farm's financial performance and its impact on the environment. OBJECTIVEThe primary objective of this study is to compare the profitability of conventional and organic cattle systems and investigate how it is associated with individual farm characteristics, like forage production capacity, forage quality, milk quota, animal housing capacity, and their relative presences. METHODWe employ a whole farm optimization model, customized for Norwegian cattle farming. The primary goal of this model is to maximize the gross margin by optimizing decisions related to land usage and animal inventory while adhering to a set of constraints. We systematically solve more than 200,000 model instances, with varying farm characteristics. RESULTS AND CONCLUSIONSThe results can be distilled to the following key points: If forage of good quality is readily available, but the livestock operation cannot be expanded due to animal housing and milk quota restrictions, organic may outcompete conventional farming. Otherwise, gross margin is maximized with conventional farming. These findings emphasize the crucial role of forage production capacity and quality in relation to available milk quota and infrastructure when considering the transition from conventional to organic farming. Extensive sensitivity analyses affirm the robustness of these conclusions. Regional regulatory factors, such as government farm payments, also play a significant role, and influence the optimal farming approach. Additionally, we show that increases in organic price premiums can markedly impact the competitiveness of organic farming, even in a system where government payments make out a significant part of the farm revenue. SIGNIFICANCEThe model can support farmers to make informed decisions about converting to organic or conventional farming. It can also be used by policymakers to determine the level of support required to make it worthwhile for different types of farms to convert. We also show that existing government payment schemes give rise to regional differences in the incentives for organic farming in Norway. To ensure equal incentives for organic farming across the country, the organic payments would have to be regionally adjusted, in line with the other already regionally dependent government payments. This insight may be of significant interest to policymakers and other stakeholders.