Framework Of Model Research Articles

Temperature-Dependent Oviposition Models for Monochamus saltuarius (Coleoptera: Cerambycidae)

Monochamus saltuarius Gebler is a serious insect pest in Europe and East Asia regions, including Portugal, Spain, China, Japan, and Korea. It transfers the pine wood nematode Bursaphelenchus xylophilus to conifer trees, resulting in pine wilt disease (PWD). As temperature is a key factor influencing insect population dynamics, temperature-dependent models describing M. saltuarius oviposition could estimate population growth potential and evaluate outbreak risks. In this study, the longevity and fecundity of M. saltuarius females were measured under constant temperature conditions ranging from 20 to 32 °C, and temperature-dependent models were constructed. The longevity of M. saltuarius females ranged from 83.36 days to 22.92 days, with a total fecundity of 141 eggs and 52.77 eggs at 20 °C and 32 °C, respectively. To describe oviposition, we used a single-phase simulation describing oviposition as a single model and a two-phase simulation describing sexual maturation and oviposition as two separate models. These models effectively described M. saltuarius oviposition (r2 > 0.96) under constant temperature conditions, with the two-phase simulation demonstrating greater accuracy overall. Such models could facilitate assessments of PWD risks. The modeling framework of this study shows potential for predicting threats from various forestry and agricultural pests.

A business model framework for nature tourism entrepreneurs

ABSTRACT Small-scale tourism entrepreneurs play an important role in the development of nature tourism destinations, and building competitive business models can be crucial for the success of both these entrepreneurs and the nature destinations they inhabit. However, traditional business model frameworks do not adequately capture the unique characteristics of nature tourism and its entrepreneurs, such as their non-economic and nature-based values. Thus, developing knowledge about business models for nature tourism entrepreneurs calls for an alternative theoretical approach. This paper aims to develop such a novel approach by integrating existing knowledge about the unique characteristics of nature tourism and its entrepreneurs with a Service Dominant Logic theory, which incorporates both human and non-human actors such as nature. To illustrate the analytical relevance of the framework, it is applied in a case study of nature tourism entrepreneurs that identifies internal incongruences within the entrepreneurs’ existing business models. The case study also demonstrates how the entrepreneurs attempt to address these incongruences. The framework is relevant also to other small scale tourism entrepreneurs operating in other contexts.

A dependent circular-linear model for multivariate biomechanical data: Ilizarov ring fixator study.

Biomechanical and orthopaedic studies frequently encounter complex datasets that encompass both circular and linear variables. In most cases (i) the circular and linear variables are considered in isolation with dependency between variables neglected and (ii) the cyclicity of the circular variables is disregarded resulting in erroneous decision making. Given the inherent characteristics of circular variables, it is imperative to adopt methods that integrate directional statistics to achieve precise modelling. This paper is motivated by the modelling of biomechanical data, that is, the fracture displacements, that is used as a measure in external fixator comparisons. We focus on a dataset, based on an Ilizarov ring fixator, comprising of six variables. A modelling framework applicable to the six-dimensional joint distribution of circular-linear data based on vine copulas is proposed. The pair-copula decomposition concept of vine copulas represents the dependence structure as a combination of circular-linear, circular-circular and linear-linear pairs modelled by their respective copulas. This framework allows us to assess the dependencies in the joint distribution as well as account for the cyclicity of the circular variables. Thus, a new approach for accurate modelling of mechanical behaviour for Ilizarov ring fixators and other data of this nature is imparted.

Stress Representations for Tensor Basis Neural Networks: Alternative Formulations to Finger–Rivlin–Ericksen

Abstract Data-driven constitutive modeling frameworks based on neural networks and classical representation theorems have recently gained considerable attention due to their ability to easily incorporate constitutive constraints and their excellent generalization performance. In these models, the stress prediction follows from a linear combination of invariant-dependent coefficient functions and known tensor basis generators. However, thus far the formulations have been limited to stress representations based on the classical Finger–Rivlin–Ericksen form, while the performance of alternative representations has yet to be investigated. In this work, we survey a variety of tensor basis neural network models for modeling hyperelastic materials in a finite deformation context, including a number of so far unexplored formulations which use theoretically equivalent invariants and generators to Finger–Rivlin–Ericksen. Furthermore, we compare potential-based and coefficient-based approaches, as well as different calibration techniques. Nine variants are tested against both noisy and noiseless datasets for three different materials. Theoretical and practical insights into the performance of each formulation are given.

Unraveling climate trends in the mediterranean: a hybrid machine learning and statistical approach

AbstractThis study presents a comprehensive spatiotemporal analysis of sea surface temperatures (SST) and surface air temperatures (TAS) across 15 Mediterranean coastal stations, leveraging centennial-scale data to analyze regional climate dynamics. The modeling framework integrates three sequential phases: data preprocessing, statistical analysis, and advanced machine learning techniques, creating a robust analytical pipeline. The data preprocessing phase harmonizes diverse datasets, addresses missing values, and applies transformations to ensure analytical consistency. The statistical modeling employs the Pettitt test for change point detection and linear trend analysis to unveil underlying patterns. The machine learning phase utilizes K-means clustering for climate regime classification and implements tailored Convolutional Neural Networks (CNNs) for cluster-specific future climate anomaly projections. Results unveil a marked anthropogenic climate signal, with contemporary observations consistently surpassing historical baselines. Breakpoint analyses and linear trend assessments reveal heterogeneous climatic shifts, with pronounced warming in the northern Mediterranean. Notably, Nice and Ajaccio exhibit the highest SST increases (0.0119 and 0.0113 °C/decade, respectively), contrasting with more modest trends in Alexandria (0.0052 °C/decade) and Antalya (0.0047 °C/decade) in the eastern Mediterranean. The application of clustering and CNN projections provides granular insights into differential warming trajectories. By 2050, cooler northwestern Mediterranean zones are projected to experience dramatic SST anomalies of approximately 3 °C above the average, with corresponding TAS increases of 2.5 °C. In contrast, warmer eastern and southern regions display more subdued warming patterns, with projected SST and TAS increases of 1.5–2.5 °C by mid-century. This research’s importance is highlighted by its potential to inform tailored adaptation strategies and contribute to the theoretical understanding of climate dynamics, advancing climate modeling and analysis efforts.

Using boundary objects and methodological island (BOMI) modeling in large-scale agile systems development

AbstractLarge-scale systems development commonly faces the challenge of managing relevant knowledge between different organizational groups, particularly in increasingly agile contexts. Here, there is a conflict between coordination and group autonomy, and it is challenging to determine what necessary coordination information must be shared by what teams or groups, and what can be left to local team management. We introduce a way to manage this complexity using a modeling framework based on two core concepts: methodological islands (i.e., groups using different development methods than the surrounding organization) and boundary objects (i.e., artifacts that create a common understanding across team borders). However, we found that companies often lack a systematic way of assessing coordination issues and the use of boundary objects between methodological islands. As part of an iterative design science study, we have addressed this gap by producing a modeling framework (BOMI: Boundary Objects and Methodological Islands) to better capture and analyze coordination and knowledge management in practice. This framework includes a metamodel, as well as a list of bad smells over this metamodel that can be leveraged to detect inter-team coordination issues. The framework also includes a methodology to suggest concrete modeling steps and broader guidelines to help apply the approach successfully in practice. We have developed Eclipse-based tool support for the BOMI method, allowing for both graphical and textual model creation, and including an implementation of views over BOMI instance models in order to manage model complexity. We have evaluated these artifacts iteratively together with five large-scale companies developing complex systems. In this work, we describe the BOMI framework and its iterative evaluation in several real cases, reporting on lessons learned and identifying future work. We have produced a matured and stable modeling framework which facilitates understanding and reflection over complex organizational configurations, communication, governance, and coordination of knowledge artifacts in large-scale agile system development.

Anisotropy-driven magnetic phase transitions in SU(4)-symmetric Fermi gas in three-dimensional optical lattices

Abstract We study SU(4)-symmetric ultracold fermionic mixture in the cubic optical lattice with the variable tunneling amplitude along one particular crystallographic axis in the crossover region from the two- to three-dimensional spatial geometry. To theoretically analyze emerging magnetic phases and physical observables, we describe the system in the framework of the Fermi-Hubbard model and apply dynamical mean-field theory. We show that in two limiting cases of anisotropy there are two phases with different antiferromagnetic orderings in the zero temperature limit and determine a region of their coexistence. We also study the stability regions of different magnetically-ordered states and density profiles of the gas in the harmonic optical trap.

A systematic survey of fuzzy deep learning for uncertain medical data

Intelligent medical industry is in a rapid stage of development around the world, followed by are the expanding market size and basic theories of intelligent medical diagnosis and decision-making. Deep learning models have achieved good practical results in medical domain. However, traditional deep learning is almost calculated and developed by crisp values, while imprecise, uncertain, and vague medical data is common in the process of diagnosis and treatment. It is important and significant to review the contributions of fuzzy deep learning for uncertain medical data, because fuzzy deep learning that originated from fuzzy sets, can effectively deal with uncertain and inaccurate information, providing new viewpoints for alleviating the presence of noise, artifact or high dimensional unstructured information in uncertain medical data. Therefore, taking focus on the intersection of both different fuzzy deep learning models and several types of uncertain medical data, the paper first constructs four types of frameworks of fuzzy deep learning models used for uncertain medical data, and investigates the status from three aspects: fuzzy deep learning models, uncertain medical data and application scenarios. Then the performance evaluation metrics of fuzzy deep learning models are analyzed in details. This work has some original points: (1) four types of frameworks of applying fuzzy deep learning models for uncertain medical data are first proposed. (2) Seven fuzzy deep learning models, five types of uncertain medical data, and five application scenarios are reviewed in details, respectively. (3) The advantages, challenges, and future research directions of fuzzy deep learning for uncertain medical data are critically analyzed, providing valuable suggestions for further deep research.

Transformation of primary care settings implementing a co-located team-based care model: a scoping review

BackgroundIn Canada, primary care reforms led to the implementation of various team-based care models to improve access and provide more comprehensive care for patients. Despite these advances, ongoing challenges remain. The aim of this scoping review is to explore current understanding of the functioning of these care models as well as the contexts in which they have emerged and their impact on the population, providers and healthcare costs.MethodsThe Medline and CINAHL databases were consulted. To be included, team-based care models had to be co-located, involve a family physician, specify the other professionals included, and provide information about their organization, their relevance and their impact within a primary care context. Models based on inter-professional intervention programs were excluded. The organization and coordination of services, the emerging contexts and the impact on the population, providers and healthcare costs were analysed.ResultsA total of 5952 studies were screened after removing duplicates; 15 articles were selected for final analysis. There was considerable variation in the information available as well as the terms used to describe the models. They are operationalized in various ways, generally consistent with the Patient’s Medical Home vision. Except for nurses, the inclusion of other types of professionals is variable and tends to be associated with the specific nature of the services offered. The models primarily focus on individuals with mental health conditions and chronic diseases. They appear to generally satisfy the expectations of the overarching framework of a high-performing team-based primary care model at patient and provider levels. However, economic factors are seldom integrated in their evaluations.ConclusionsThe studies rarely provide an overarching view that permits an understanding of the specific contexts, service organization, their impacts, and the broader context of implementation, making it difficult to establish universal guidelines for the operationalization of effective models. Negotiating the inherent complexity associated with implementing models requires a collaborative approach between various stakeholders, including patients, to tailor the models to the specific needs and characteristics of populations in given areas, and reflection about the professionals to be included in delivering these services.

Multilevel dynamics of matter waves scattering in finite potential wells

We explore the dynamics of matter wave scattering in finite potential wells using analytical solutions of the Schrödinger equation within the framework of a quantum shutter model. We find that the incident wave interferes with the bound states of the quantum well, resulting in time-domain oscillations. These oscillations exhibit Rabi-type frequencies, characterised by the energy differences between the incident wave and the bound states of the quantum well. We show that in systems with double-bound states, the interference pattern is characterised by quantum beats in the time-dependent probability density. The period of these beatings depends on the energy difference between the bound states, which can be tuned by controlling the potential parameters. In the general case where bound, anti-bound, and resonant states coexist in the system spectrum, complex oscillations in the probability density arise from the interactions of the incident wave with different quantum states. We demonstrate that the bound states sector can effectively describe this complex behaviour, providing a simple and reliable analytical expression for the probability density in multilevel systems. This formula highlights the significant role of bound states, whose interaction with the incident wave dominates the transient probability density. This contrasts with conventional systems with potential barriers and wells, where resonances govern the wave dynamics.

Particle Swarm Optimization for a redundant repairable machining system with working vacations and impatience in a multi-phase random environment

With the increasing reliance on cloud computing as the foundational manufacturing systems with intricate dynamics, featuring multiple service areas, varying job arrival rates, diverse service requirements, and the interplay of failures and impatience, significant analytical challenges arise. Queueing networks offer a powerful stochastic modeling framework to capture such complex dynamics. This paper develops a novel, exhaustive queueing model for a finite-capacity redundant multi-server system operating in a multi-phase random environment. The proposed model uniquely integrates real-world factors, including server breakdowns and repairs, waiting servers, synchronous working vacations, and state dependent balking and reneging, into a single queueing model, representing a significant advancement in the field. Using the matrix-analytic method, we establish the steady-state solution and derive key performance metrics. Numerical experiments and sensitivity analyses elucidate the impact of system parameters on performance measures. Additionally, a cost model is formulated, enabling cost optimization analysis using direct search method and Particle Swarm Optimization (PSO) to identify efficient operating configurations.

Universal Correlation for Droplet Fragmentation in a Microfluidic T-Junction.

Despite extensive research on droplet dynamics at microfluidic T-junction, for different droplet lengths and Capillary numbers, there remains limited understanding of their dynamics at different viscosity ratio. In this study, we adopt a modeling framework in a three-dimensional (3D) configuration to numerically investigate the droplet dynamics as it passes through a symmetric T-junction with varying Capillary numbers, droplet lengths, and viscosity ratios. We present a 3D regime map for the first time to demarcate the droplet breakup and no breakup regimes. Herein, we propose a simple surface equation accounting for the critical Capillary number for breakup, in terms of viscosity ratio and dimensionless droplet length. The proposed universal relationship aligns well with experimental and computational findings from the existing literature. Furthermore, we reveal a new droplet breakup characteristic at high viscosity ratio and high Capillary number where the droplet spreads almost twice its initial value before splitting. Overall, this research provides comprehensive understanding of droplet dynamics at the T-junction and has significant implications for several related applications, including the large-scale synthesis of microdroplets using microchannel networks.

Dynamic Operations of a Mobile Charging Crowdsourcing Platform

This paper investigates the operation of a novel electric vehicles (EVs) charging service mode, that is, crowdsourced mobile charging service for EVs, whereby a crowdsourcing platform is established to arrange suppliers (crowdsourced chargers) to deliver charging service to customers’ electric vehicles (parked EVs) at low-battery levels. From the platform operator’s perspective, we aim to determine the optimal operation strategies for mobile charging crowdsourcing platforms to achieve specific objectives. A mathematical modeling framework is developed to capture the interactions among supply, demand, and service operations in the crowdsourced mobile charging market. To design an efficient solution method to solve the formulated model, we first analyze the model properties by rigorously proving that a crucial variable set for operating the mobile charging crowdsourcing system includes charging price, commission control, and period-specific aggregate demand control. Besides, we provide both an equivalent condition and a necessary condition for checking the feasibility of these crucial variables. On top of this, we construct a search tree according to the operation periods in a day to solve the optimal operation strategies, wherein a nondominated principle is adopted as an accelerating technique in the searching process. The solution obtained from the proposed solution algorithm is proved to be sufficiently close to the actual global optimal solutions of the formulated model up to the resolution of the discretization scheme adopted. Numerical examples provide evidence verifying the model’s validity and the solution method’s efficiency. Overall, the research outcome of this work can offer service operators structured and valuable guidelines for operating mobile charging crowdsourcing platforms. Funding: This work was supported by the Singapore Ministry of Education [Grant RG124/21]. Supplemental Material: The online appendices are available at https://doi.org/10.1287/trsc.2023.0126 .

Dynamics of a Discharge Initiated by a Powerful Femtosecond Laser Pulse in Atmospheric Pressure Air in Pre-Breakdown Electrical Fields

Numerical modeling of the dynamics of a discharge initiated by a high-power femtosecond laser pulse in air at atmospheric pressure in pre-breakdown fields was carried out. Calculations were conducted within the framework of a 1D-axisymmetric model that describes the evolution of the radial profiles of the main parameters of the discharge under study. The model includes a system of reaction that determine gas heating and a detailed description of the kinetic processes in a given discharge, as well as a system of gas-dynamic equations to describe the expansions of the heated channel. The results of calculations of the breakdown time of the discharge gap are conсistent with the measurement data over the entire studied range of electric field strengths, E = 9–17 kV/cm. It is shown that one of the key factors determining the evolution of the parameters of a given discharge is the rate of gas heating.

The genetics of phenotypic plasticity. XVIII. Developmental limits restrict adaptive plasticity.

After environmental change, the trait evolution needed to rescue a population depends on the functional form of the plastic change (reaction norm) of that trait. Nearly all previous models of plasticity evolution for continuous traits have assumed that the functional form is linear, i.e., no limits on the range of plasticity. This paper examines the effect of developmental limits, modeled as a sigmoidal reaction norm, on evolutionary rescue after an abrupt environmental change and the subsequent evolution of plasticity, including genetic assimilation. We examined four different scenarios: (1) developmental limits only, (2) developmental limits plus a cost of plasticity, (3) developmental limits with developmental noise, and (4) developmental limits plus environmental variation. The probability of evolutionary rescue increased with an increase in phenotypic variation allowed by plastic development. With a smaller limit to the range of the plastic phenotype, the evolution of adaptive plasticity was limited, meaning the evolution of non-plastic genes was necessary. The addition of developmental constraints to the model did not speed up genetic assimilation, suggesting new theory is needed to understand empirical observations. The modeling framework presented here could be extended to different ecological and evolutionary conditions, alternative reaction norm shapes, the evolution of additional reaction norm parameters such as the range or the location of the inflection point on the environmental axis, or other function-valued traits.

Impact of Fraxinus snag fall on electric distribution and infrastructure stability: An empirical analysis

With increasing climate variability and the movement of exotic pests and diseases, the rate of forest mortality has become an issue of global concern. Emerald Ash Borer (EAB), as one such pest, is causing the mass mortality of ash trees, Fraxinus spp., thus leading to an ongoing surge in the number of snags across North America. Snags are dead-standing trees that pose an extant threat to nearby infrastructure and buildings. In this article, we evaluate the impacts which snags pose to electrical distribution infrastructure. New Jersey, a state with a high degree of urbanization, has an extensive electric grid located in forested areas. New Jersey is currently in the process of upgrading the electric distribution network, which will increase the height of electric distribution lines to increase resiliency and potential capacity. This article demonstrates a yearly risk assessment methodology for Fraxinus snags using an integrated GIS, differential equation, and applied regression model framework under two distribution network parameterizations. The framework is applied to three northern New Jersey counties (Warren, Sussex, and Morris), which are managed by one utility, New Jersey Central Power and Light, and which are home to most of New Jersey’s Fraxinus trees.

Climate change impact on nuclear power outages - Part I: A methodology to estimate hydro-thermic environmental constraints on power generation

To better qualify the ability of nuclear power to contribute to the net-zero emissions transition, the nuclear power sector needs to understand its sensitivity to hydro-climatic and anthropogenic stresses at the local level. While current approaches based on statistical modelling can be used to assess climate change impacts on electricity networks at a global or national level, they are limited when extrapolated to regulatory thresholds at a local level.As part of a series of two papers aimed at assessing the impact of climate change on site-specific nuclear power outages, this paper presents a modelling chain for estimating hydro-climatic-policy scenarios for nuclear power generation. The second one will implement the modelling chain in context of climate change, based on downscaled IPCC scenarios, and applies the method to two contrasted French nuclear power plants.The modelling framework (power outages modelling bias of +7.0 %) appears as a strong basis for investigating the impact of climate change on nuclear power outages in terms of frequency and magnitude. Such work could inform on the evolving sensitivity of the nuclear fleet to support the energy grid, to potentially optimize its management, and to define water-energy nexus levers to better anticipate the effects of climate change.

Innovative Vehicle Design Processes Based on the Integrated Framework for Abstract Physics Modeling (IF4APM)

In industrial vehicle design processes, most companies have implemented model-based systems engineering (MBSE). As a consequence, design processes are nowadays not driven by documents, but by digital models of the vehicle to be developed and its components. These models exist on different levels of abstraction. The models on the requirements level are already well defined as well as the models of the defined product behavior and product properties. In recent years, the specification of models on the level of product functions was largely clarified, and elaborate frameworks already exist. However, this is not yet true for the level between functions and definite properties; this level can be referred to as "abstract physics". The enormous importance of this level, which, amongst others, can represent the physical effect chains which allow a vehicle component to function, is expressed by several researchers. Several research works aim at specifying models on this level, but, until now, no general consensus can be identified, and the existing model specifications are less appropriate for the early stages of vehicle design. This paper explains an Integrated Framework for Abstract Physics Modeling (IF4APM), which incorporates different perspectives of abstract physics and is suited for the early phases. The explanation is based on typical components of several kinds of vehicles. The main advantages of the proposed approach are the consistent interconnection of abstract product models, the clearness and understandability of the resulting matrices, and the aptitude to be used in the early phases of a vehicle design process.

A survey: knowledge graph entity alignment research based on graph embedding

Entity alignment (EA) aims to automatically match entities in different knowledge graphs, which is beneficial to the development of knowledge-driven applications. Representation learning has powerful feature capture capability and it is widely used in the field of natural language processing. Compared with traditional EA methods, EA methods based on representation learning have better performance and efficiency. Hence, we summarize and analyze the representative EA approaches based on representation learning in this paper. We present the problem description and data preprocessing for EA and other related fundamental knowledge. We propose a new EA framework for the latest models, which includes information aggregation module, entity alignment module, and post-alignment module. Based on these three modules, the various technologies are described in detail. In the experimental part, we first explore the effect of EA direction on model performance. Then, we classify the models into different categories in terms of alignment inference strategy, noise filtering strategy, and whether additional information is utilized. To ensure fairness, we perform the comparative analysis of the performance of the models within the categories separately on different datasets. We investigate both unimodal and multimodal EA. Finally, we present future research perspectives based on the shortcomings of existing EA methods.

Liver vessel MRI image segmentation based on dual-path diffusion model

Accurate segmentation of liver blood vessels in magnetic resonance imaging (MRI) images is a challenging task due to the complex tree-like structure and anisotropic diffusion properties of blood vessels. To solve this problem, we propose a new Dual-Path Diffusion Model (DPDM) framework. The framework consists of two collaborative diffusion paths: a local feature learning path based on convolution operations and a global context modeling path based on transform blocks. Local path encodes rich shape priors and preserve spatial details, while global path captures long-distance dependencies and enhance representation. In the decoding phase, the boundary features from the boundary path are fused with the features of the ordinary path decoding, which further enhances the shape sensitivity. In addition, we leverage a multi-task learning scheme to jointly optimize vascular segmentation and boundary prediction tasks in an end-to-end manner. Experiments on retrospective clinical dataset demonstrate that the proposed DPDM framework achieves excellent performance on the liver vessel segmentation task. Compared with state-of-the-art methods, our approach achieved a 6.0% and 7.3% performance improvement in Dice coefficient and IoU index, respectively. Our approach offers a promising solution for automated blood vessel segmentation in precision medicine.