General Structural Model Research Articles

A conceptual data modeling framework with four levels of abstraction for environmental information

Environmental data generated by observation infrastructures and models is widely heterogeneous in both structure and semantics. The design and implementation of an ad hoc data model for each new dataset is costly and creates barriers for data integration. On the other hand, designing a single data model that supports any kind of environmental data has shown to be a complex task, and the resulting tools do not provide the required efficiency. In this paper, a new data modeling framework is proposed that enables the reuse of generic structures among different application domains and specific applications. The framework considers four levels of abstraction for the data models. Levels 1 and 2 provide general data model structures for environmental data, based on those defined by the Observations and Measurements (O&M) standard of the Open Geospatial Consortium (OGC). Level 3 incorporates generic data models for different application areas, whereas specific application models are designed at Level 4, reusing structures of the previous levels. Various use cases were implemented to illustrate the capabilities of the framework. A performance evaluation using six datasets of three different use cases has shown that the query response times achieved over the structures of Level 4 are very good compared to both ad hoc models and to a direct implementation of O&M in a Sensor Observation Service (SOS) tool. A qualitative evaluation shows that the framework fulfills a collection of general requirements not supported by any other existing solution.

Generalized field inversion strategies for data-driven turbulence closure modeling

Most data-driven turbulence closures are based on the general structure of nonlinear eddy viscosity models. Although this structure can be embedded into the machine learning algorithm and the Reynolds stress tensor itself can be fit as a function of scalar- and tensor-valued inputs, there exists an alternative two-step approach. First, the spatial distributions of the optimal closure coefficients are computed by solving an inverse problem. Subsequently, these are expressed as functions of solely scalar-valued invariants of the flow field by virtue of an arbitrary regression algorithm. In this paper, we present two general inversion strategies that overcome the limitation of being applicable only when all closure tensors are linearly independent. We propose to either cast the inversion into a constrained and regularized optimization problem or project the anisotropy tensor onto a set of previously orthogonalized closure tensors. Using the two-step approach together with either of these strategies then enables us to quantify the model-form error associated with the closure structure independent of a particular regression algorithm. Eventually, this allows for the selection of the a priori optimal set of closure tensors for a given, arbitrary complex test case.

On the role of parameterization in models with a misspecified nuisance component

The paper is concerned with inference for a parameter of interest in models that share a common interpretation for that parameter but that may differ appreciably in other respects. We study the general structure of models under which the maximum likelihood estimator of the parameter of interest is consistent under arbitrary misspecification of the nuisance part of the model. A specialization of the general results to matched-comparison and two-groups problems gives a more explicit and easily checkable condition in terms of a notion of symmetric parameterization, leading to a broadening and unification of existing results in those problems. The role of a generalized definition of parameter orthogonality is highlighted, as well as connections to Neyman orthogonality. The issues involved in obtaining inferential guarantees beyond consistency are briefly discussed.

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Generalized Quasi-Static Mooring System Modeling with Analytic Jacobians

This paper presents a generalized and efficient method for quasi-static analysis of mooring systems, including complex scenarios such as when shared mooring lines interconnect multiple floating wind or wave energy devices. While quasi-static mooring models are well established, most published formulations are focused on specific applications, and no publicly available implementations provide efficient handling of large mooring system networks. The present formulation addresses these gaps by: (1) formulating solutions for edge cases not typically supported by quasi-static models; (2) creating a fully generalized model structure such that any combination of mooring lines, point masses, and floating bodies can be assembled; and (3) deriving analytic expressions for the system Jacobians (stiffness matrices) so that systems with many degrees of freedom can be solved efficiently. These techniques form the theory basis of MoorPy, an open-source mooring analysis library. The model is demonstrated on nine scenarios of increasing complexity with features of interest for offshore renewable energy applications. When compared with steady-state results from a lumped-mass dynamic model, the results show that the quasi-static formulation accurately calculates profiles and tensions and that its analytic approach provides more efficient and reliable computation of system stiffness matrices than finite-differencing methods. These results verify the accuracy of the MoorPy model.

Microphases deriving from Ba2ZrF8 structure type: III: Structural approach of dis-Sr17Zr9F70, Sr23Zr12F94, Pb29Hf15F118. General model of formation of M′2p-1MpF8p-2 microphases

The domain Sr1-xZrxF2+2x studied at 670 °C between the limits 0.340 ≤ x ≤ 0.353 show the presence of a new phase Sr17Zr9F70 and of modulated phases, all deriving from Ba2ZrF8 structure type. In the same domain studied below 600 °C, Sr17Zr9F70 tends to lose its long range ordering between Sr cations and Sr vacancies, tripling the b lattice parameter; moreover a new monoclinic superstructure Sr23Zr12F94 [a = 3sinβ×a (Ba2ZrF8); b = b (Ba2ZrF8); c = c (Ba2ZrF8), β = 105.31°] is also characterized. The homologous Hf phases are isostructural.With Pb, between 555 and 530 °C, another superstructure Pb29Zr15F118 [a = 5 × a (Ba2ZrF8); b = b (Ba2ZrF8); c = c (Ba2ZrF8] and its isotypic Pb29Hf15F118 are evidenced and their structure is solved in great part with a similar cationic disorder between Pb cations and Pb vacancies. Between 530 and 510 °C, a second much more complex superstructure exists but has not been characterized.Moreover, at 630 °C, single crystals of Pb2HfF8 are obtained which allows to confirm that, contrary to Ba2ZrF8, Pb2HfF8 is non-centrosymmetric (Pn21a space group). but the atomic positions deviate only slightly from centrosymmetry.A general structural model for all these M′2p-1MpF8p-2 (p = 6, 9, 12, 15) microphases is developed.

It is a "small world": Relations between performance on five spatial tasks and five mathematical tasks in undergraduate students.

One of the most robust relations in cognition is that between spatial and mathematical reasoning. One important question is whether this relation is domain general or if specific relations exist between performance on different types of spatial tasks and performance on different types of mathematical tasks. In this study, we explore unique relations between performance on five spatial tasks and five mathematical tasks. An exploratory factor analysis conducted on Data Set 1 (N = 391) yielded a two-factor model, one spatial factor and one mathematical factor with significant cross-domain factor loadings. The general two-factor model structure was replicated in a confirmatory factor analysis conducted in a separate data set (N = 364) but the strength of the factor loadings differed by task. Multidimensional scaling and network-based analyses conducted on the combined data sets reveal one spatial cluster, with a central node and one more tightly interconnected mathematical cluster. Both clusters were interconnected via the math task assessing geometry and spatial sense. The unique links identified with the network-based analysis are representative of a "small-world network." These results have theoretical implications for our understanding of the spatial-mathematical relation and practical implications for our understanding of the limitations of transfer between spatial training paradigms and mathematical tasks. (PsycInfo Database Record (c) 2024 APA, all rights reserved).

Thresholding-based cellular automata for transportation network derived future urban growth patterns in a peri-urban area

The present study uses the standalone Cellular Automata (CA) model to predict the transportation network-derived future urban growth patterns of a rapidly expanding peri-urban area in the Mumbai Metropolitan Region. The Landsat satellite images of 1999, 2009, and 2019 are used to deduce the study region's land use/land cover (LULC) patterns. The road networks, suburban railway stations and proposed metro stations are considered the primary drivers of urban growth. The classification results show an increase in built-up area by 48 km2 (104%) from 1999 to 2019, decreasing the open land and vegetation by 21%. The fine-tuned CA gives an area under the relative operating characteristic curve of 0.907, locational accuracy >72% and quantitative accuracy of >89%, indicating a good model fit. The calibrated model is used to simulate the urban growth of 2029 for two scenarios: first a no-metro case and second considering the proposed metro's impact. While the built-up area increases by 28%–120 km2 for the no-metro case, including the proposed metro increases the built-up prediction to 133 km2. The enhanced built-up highlights the role of metro development in triggering significant growth in the region. Although the present study uses Kalyan and contiguous area, the general structure of model and its ability to function with a limited data makes it adaptable across diverse regions at different stages of development worldwide. Such an urban growth model can serve as a practical tool for planners in prompt decision-making.

Proactive Environmental Management's Drive for Green Technology Adoption in MENA Firms: Regulatory Mediation and Financial Moderation

This empirical study explores the determinants of Green Technologies and Practices (GTP) adoption within Middle East and North Africa (MENA) firms, focusing on Proactive Environmental Management (PEM), Environmental Regulation (ER), and Financial Exposure to Environmental Impediments (FEEI). Using World Bank Enterprise Survey data from 5381 firms across six MENA countries, we apply an ordered logit model and General Structured Equation Model (GSEM) to examine the direct and mediated effects of PEM on GTP adoption through ER, and moderation by FEEI. Our analysis reveals a significant impact of PEM on GTP adoption, enhanced by ER mediation. Additionally, FEEI moderation unveils a nuanced relationship between environmental financial exposures and green adoption propensity. The study also investigates control variables, revealing varying GTP adoption propensities across firm characteristics, and offers policy and firm-level recommendations to bolster GTP adoption in the MENA region.

Depth and frequency dependence of geoacoustic properties on the New England Mud Patch from reflection coefficient inversiona).

Muddy sediments cover significant portions of continental shelves, but their physical properties remain poorly understood compared to sandy sediments. This paper presents a generally applicable model for sediment-column structure and variability on the New England Mud Patch (NEMP), based on trans-dimensional Bayesian inversion of wide-angle, broadband reflection-coefficient data in this work and in two previously published reflection-coefficient inversions at different sites on the NEMP. The data considered here include higher frequencies and larger bandwidth and cover lower reflection grazing angles than the previous studies, hence, resulting in geoacoustic profiles with significantly better structural resolution and smaller uncertainties. The general sediment-column structure model includes an upper mud layer in which sediment properties change slightly with depth due to near-surface processes, an intermediate mud layer with nearly uniform properties, and a geoacoustic transition layer where properties change rapidly with depth (porosity decreases and sound speed, density, and attenuation increase) due to increasing sand content in the mud above a sand layer. Over the full frequency band considered in the new and two previous data sets (400-3125 Hz), there is no significant sound-speed dispersion in the mud, and attenuation follows an approximately linear frequency dependence.

The Use of Graph Theory for Modeling and Analyzing the Structure of a Complex System, with the Example of an Industrial Grain Drying Line

This article describes a method for analyzing and modeling a complex agrotechnological system using the example of an industrial grain drying line. Elements of graph theory were used to develop an effective tool for modeling such a system and to formally validate its structure. The proposed method can be applied to transform a general structural model into a set of relational models, to formally evaluate the resulting models’ functionality, and to comprehensively analyze different variants of the process. The method can be deployed at the stage of designing and operating an industrial grain drying line, and it can also be adapted for use in other areas, such as processing lines in the agri-food industry.

The impact of green innovation initiatives on competitiveness and financial performance of the land transport industry

The transportation sector is one of the primary contributors to greenhouse gas emissions that have deteriorating effects on the state of the environment. The implementation of sustainable practices has become one of the most challenging tasks of organizations at present. This study examined the effect of implementing green innovation initiatives on a firm's competitiveness and financial performance of motor vehicle companies in the Philippines. Data were gathered through an online survey questionnaire with a total of 206 respondents composed of employees of various ranks working in companies engaged in the manufacture, distribution, retail, and service of motor vehicles. The theoretical framework presented a hierarchical latent variable model which was validated using the partial least square structural equation modelling (PLS-SEM). The model fit, measurement, general construct fit, discriminant validity, and structural model parameters were examined and found to have acceptable values. The findings indicated that environmental regulations, market demand, government pressure, competitor pressure, corporate social responsibility, and employee conduct were the significant drivers of green innovation initiatives. The study also revealed that the implementation of green innovation initiatives positively affects the firm's competitiveness and financial performance. Motor vehicle companies and other types of organizations are encouraged to demonstrate not only their concern for society or community but also their concern for the environment to acquire better market leverage and financial position.

Parametric heat wave insurance

This paper proposes a flexible framework for structuring and pricing parametric heat wave insurance. The framework is based on a general heat wave definition formulated in terms of an underlying temperature index. The definition can be varied in terms of the heat wave duration, intensity, measurement period and underlying index. This construction makes it straightforward to create contracts tailored to insure against heat events of many different types. A single stochastic model for the underlying index can be used to price all contracts. We consider contracts with payments that depend on the number of heat waves of a certain type occurring in the measurement period and derive the necessary pricing relations based on a general model structure encompassing several popular temperature models in the literature. An empirical case study is performed using data for Berlin where the daily maximum temperature is used as the underlying index. Model implied heat wave probabilities are consistent with historical patterns, point to high likelihoods for short duration heat events of different threshold temperatures and non-negligible risks for future heat waves of extreme temperatures and durations never before observed.

Deep-Learning-Based THz Wireless Channel Property Prediction in Motherboard Desktop Environment

This paper proposes a residual network (ResNet) based feature concatenated neural network model to predict the type of scenario the channel is under and the attribute of the predicted scenario with power delay profile (PDP) as the inputs. The generalized model structure consists of three blocks for feature extraction, scenario prediction, and attribute prediction, respectively. The PDP data is collected from a motherboard desktop environment under five different physical arrangement scenarios. Within each scenario, data is collected several times while varying a different physical attribute for each scenario. Two steps of data augmentation are applied to expand the size and to improve the resolution (difference between the neighbouring attributes) of the measured dataset for the robust training and thorough evaluation of the proposed model. The proposed model is evaluated and compared with an multi-layer perceptron (MLP) based model on an expanded measured and averaged interpolated dataset. It is shown that both models perform very well on the expanded measured dataset with nearly 100% prediction accuracy on both scenarios and attributes. The MLP based model suffers performance degradation on the averaged interpolated dataset with up to a 9% drop of classification accuracy on attribute prediction tasks, while our ResNet based feature concatenated model performs equally in both scenarios. Feature activation map (FAM) and Grad-Class Activation Mapping (Grad-CAM) approaches are applied to provide visual explanations highlighting characteristics of the input PDP used for model decisions. FAM shows that the MLP based model focuses on the multipath generated peaks of the PDP where some interpolated neighboring data points cannot be distinguished. The Grad-CAM shows that the proposed ResNet based feature concatenated model performs better because it has strong attention not only on the multipath peaks, but also on the valleys between those peaks which hold distinguishing information.

Human Activity Recognition Using Attention-Mechanism-Based Deep Learning Feature Combination.

Human activity recognition (HAR) performs a vital function in various fields, including healthcare, rehabilitation, elder care, and monitoring. Researchers are using mobile sensor data (i.e., accelerometer, gyroscope) by adapting various machine learning (ML) or deep learning (DL) networks. The advent of DL has enabled automatic high-level feature extraction, which has been effectively leveraged to optimize the performance of HAR systems. In addition, the application of deep-learning techniques has demonstrated success in sensor-based HAR across diverse domains. In this study, a novel methodology for HAR was introduced, which utilizes convolutional neural networks (CNNs). The proposed approach combines features from multiple convolutional stages to generate a more comprehensive feature representation, and an attention mechanism was incorporated to extract more refined features, further enhancing the accuracy of the model. The novelty of this study lies in the integration of feature combinations from multiple stages as well as in proposing a generalized model structure with CBAM modules. This leads to a more informative and effective feature extraction technique by feeding the model with more information in every block operation. This research used spectrograms of the raw signals instead of extracting hand-crafted features through intricate signal processing techniques. The developed model has been assessed on three datasets, including KU-HAR, UCI-HAR, and WISDM datasets. The experimental findings showed that the classification accuracies of the suggested technique on the KU-HAR, UCI-HAR, and WISDM datasets were 96.86%, 93.48%, and 93.89%, respectively. The other evaluation criteria also demonstrate that the proposed methodology is comprehensive and competent compared to previous works.

A general structural design method, dynamics modeling and application study for built-in sandwich annular traveling wave piezoelectric transducers

This study first summarized a piezoelectric excitation source arrangement guideline for sandwich annular traveling wave transducers, which can excite ideal traveling waves for driving while ensuring consistent circumferential stiffness of the annular transducer. Consequently, a general structural design method was proposed for annular sandwich traveling wave piezoelectric transducers operating with out-of-plane traveling waves based on the above-mentioned arrangement guideline. Moreover, to study the vibration characteristics of the designed transducer based on the proposed method, an electromechanical coupled dynamics model was developed using the dynamic substructure method combined with the Lagrange equation. This model is universal and has guiding significance for the design and optimization of the same type of annular traveling wave piezoelectric transducers. Thereafter, the feasibility of the transducer structure design method and the correctness of the developed dynamics model were verified through appropriate vibration measurement experiments on the transducer prototype. The measured ideal mode shape and regular elliptical motion verified the feasibility of the transducer design method, and the comparison between the vibration measurement and theoretical model calculation results confirmed the correctness of the developed dynamic model. Finally, an application study of the proposed built-in sandwich traveling wave transducer was conducted to investigate its driving characteristics. The proposed transducer was used to drive a rotor to build a traveling wave piezoelectric motor. In addition, output performance experiments of the motor prototype were conducted. The experimental results indicated that under the driving conditions of 300 N pre-pressure, 500 Vpp voltage, and 19 kHz exciting frequency, the no-load speed of the motor was 19.04 RPM, its stall torque was 1.2 N·m, the maximum output power of the entire machine reached 0.6453 W, and its maximum output efficiency was 15.87%. Moreover, the normal operation of the rotating motor further verified the feasibility of the transducer structure design approach and the reliability of the drive application. The structural design method and the corresponding dynamic model proposed in this study are expected to have important guiding significance for the design and optimization of sandwich-type annular traveling wave transducers. Furthermore, the application investigation in this study provides a typical example for the traveling wave transducer.

Dead or alive: carbon as a currency to integrate disease and ecosystem ecology theory

Death is a common outcome of infection, but most disease models do not track hosts after death. Instead, these hosts disappear into a void. This assumption lacks critical realism, because dead hosts can alter host–pathogen dynamics. Here, we develop a theoretical framework of carbon‐based models combining disease and ecosystem perspectives to investigate the consequences of feedbacks between living and dead hosts on disease dynamics and carbon cycling. Because autotrophs (i.e. plants and phytoplankton) are critical regulators of carbon cycling, we developed general model structures and parameter combinations to broadly reflect disease of autotrophic hosts across ecosystems. Analytical model solutions highlight the importance of disease–ecosystem coupling. For example, decomposition rates of dead hosts mediate pathogen spread, and carbon flux between live and dead biomass pools are sensitive to pathogen effects on host growth and death rates. Variation in dynamics arising from biologically realistic parameter combinations largely fell along a single gradient from slow to fast carbon turnover rates, and models predicted higher disease impacts in fast turnover systems (e.g. lakes and oceans) than slow turnover systems (e.g. boreal forests). Our results demonstrate that a unified framework, including the effects of pathogens on carbon cycling, provides novel hypotheses and insights at the nexus of disease and ecosystem ecology.

Создание лингвистического корпуса на основе инструментов обработки естественного языка: планирование программных решений

The paper is aimed at building a model of a linguistic corpus, which is generated according to the rules of the spaCy natural language processing library. Scientific novelty lies in the fact that within the framework of humanities research, the method of modelling is used, which is combined with a corpus approach and takes into account the technological (software) component at the very stage of goal setting. In the research, firstly, a general structural model of a linguistic corpus as a sequence of blocks was determined and standard queries to the database were formulated; secondly, a model of the corpus manager interface able to implement these standard queries was built; thirdly, an analysis of the proposed model with the help of mini-programs that allow assessing the degree of technical feasibility of the queries and their practical value was conducted. At this stage, text arrays of fictional works by German-speaking (F. Kafka, E. M. Remarque) and English-speaking (A. C. Doyle, G. Orwell) writers were involved as linguistic material. The obtained results showed that the constructed model has a number of advantages with a limited number of disadvantages, is flexible in terms of further development and can be programmatically implemented in the short term.

Are low frequency macroeconomic variables important for high frequency electricity prices?

We analyse the importance of low frequency hard and soft macroeconomic information, respectively the industrial production index and the manufacturing Purchasing Managers' Index surveys, for forecasting high-frequency daily electricity prices in two of the main European markets, Germany and Italy. We do that by means of mixed-frequency models, introducing a Bayesian approach to reverse unrestricted MIDAS models (RU-MIDAS). Despite the general parsimonious structure of standard MIDAS models, the RU-MIDAS has a large set of parameters when several predictors are considered simultaneously and Bayesian inference is useful for imposing parameter restrictions. We study the forecasting accuracy for different horizons (from $1$ day ahead to $28$ days ahead) and by considering different specifications of the models. Results indicate that the macroeconomic low frequency variables are more important for short horizons than for longer horizons. Moreover, accuracy increases by combining hard and soft information, and using only surveys gives less accurate forecasts than using only industrial production data.

A Review of The Relationships Between Psychopathy and Facets of Personality Under The Five Factor Model (FFM)

In general personality structure models, the Five Factor Model (FFM) is the primary model which is of the most widely adopted and very well-studied theoretic model of personality. The article argues that the FFM can be use in the understanding of psychopathy and apply in assessing psychopathy by its facets. Researchers have studied that psychopathy traits are paralleled to specific domains of FFM. We raise some related researches as examples to represent the way FFM in assessing psychopathy traits then go deep into five facets of the FFM and specific domains in facets. The utility of FFM in psychopathy study is available for understanding the complicated personality of psychopaths and it renders analysis of the similarity between psychopathy and other personality disorders.