High-level Model Research Articles

An ontology to describe the damage process to built cultural heritage triggered by earthquakes: The CHEDO model

Information on cultural heritage is critical both to understand its value and to support management. Collecting, managing, visualizing, and sharing this information requires standardization. In the last decades, architecture has advanced in information management with the implementation of Building Information Modelling (BIM) methodology. The application of this methodology to cultural heritage has associated challenges due to the complexity and heterogeneity of the information. The integration of ontologies and BIM allows obtaining semantically enriched digital models, which has led to a growing interest in the field of architectural cultural heritage. In this article, the Cultural Heritage Earthquake Damage Ontology (CHEDO) model is presented. This ontology has been designed to document damage and loss caused by earthquakes in cultural heritage, through its integration into BIM systems interconnected with databases, although this integration is not addressed in the present work. The CHEDO model integrates terms and relationships to document the damage process in relation to the building characteristics (architectural, spatial, constructional, and geometrical) and the physical environment of the same. This model, by providing a comprehensive description of the building and its environment, allows for complete and accurate documentation of damage, which supports the planning of post-earthquake restoration and reconstruction. To facilitate interoperability with parallel ontologies in close domains of architectural cultural heritage, CHEDO was designed taking as reference the high-level ontology Conceptual Reference Model (CRM) developed by the International Committee for Documentation (CIDOC), of the International Council of Museums (ICOM). The proposed ontology is structured in four blocks: 'building and surrounding environment', to describe the building and its environment; 'building damage', to represent the damage and its causes; 'location', to describe the location of the building and the external processes; and 'terminology', to categorise and classify certain classes of the ontology.

Dissipation Scaled Internal Wave Drag in a Global Heterogeneously Coupled Internal/External Mode Total Water Level Model

AbstractThis study showcases a global, heterogeneously coupled total water level system wherein salinity and temperature outputs from a coarser‐resolution (12 km) ocean general circulation model are used to calculate density‐driven terms within a global, higher‐resolution (2.5 km) depth‐averaged total water level model. We demonstrate that the inclusion of baroclinic forcing in the barotropic model requires modification of the internal wave drag term to prevent excess degradation of tidal results compared to the barotropic model. By scaling the internal tide dissipation by an easy to calculate dissipation ratio, the resulting heterogeneously coupled model has complex root mean square errors (RMSE) of 2.27 cm in the deep ocean and 12.16 cm in shallow waters for the tidal constituent. While this represents a 10%–20% deterioration as compared to the barotropic model, the improvements in total water level prediction more than offset this degradation. Global median RMSE compared to observations of total water levels, 30‐day sea levels, and non‐tidal residuals improve by 1.86 (18.5%), 2.55 (42.5%), and 0.36 (5.3%) cm respectively. The drastic improvement in model performance highlights the importance of including density‐driven effects within global hydrodynamic models and will help to improve the results of both hindcasts and forecasts in modeling extreme and nuisance flooding. With only an 11% increase in model run time compared to the fully barotropic total water level model, this approach paves the way for high resolution coastal water level and flood models to be used alongside climate models, improving operational forecasting of total water levels.

Opening the black box between performance gaps and business model innovation: the effect of network strategy

ABSTRACT The performance gap, identified as a negative organisational challenge, has significantly attracted the attention of scholars in strategic management. The study examines the relationship mechanism between performance gap, network strategy, and business model innovation. The regression analysis reveals that performance gaps promote enterprises to engage in both network broadening and deepening, with a more pronounced preference for the latter. Moreover, network deepening’s positive impact on business model innovation is found to be greater than that of network broadening. Both network strategies serve as mediators in the relationship between performance gaps and business model innovation, albeit with network deepening playing a more substantial role. Additionally, fsQCA demonstrates that the interaction between performance gaps and network strategies can lead to four distinct configurations for enterprises to achieve high-level business model innovation. The study improves the problem-searching logic, and effectively expands on the scope of inquiry into organisational network governance and innovation strategies to address performance gaps.

Completion of SysML state machines from Given–When–Then requirements

MDE enables the centrality of the models in semi-automated development processes. However, its level of usage in industrial settings is still not adequate for the benefits MDE can introduce. This paper proposes a semi-automatic approach for the completion of high-level models in the lifecycle of critical systems, which exhibit an event-driven behaviour. The proposal suggests a specification guideline that starts from a partial SysML model of a system and on a set of requirements, expressed in the well-known Given–When–Then paradigm. On the basis of such requirements, the approach enables the semi-automatic generation of new SysML state machines model elements. Accordingly, the approach focuses on the completion of the state machines by adding proper transitions (with triggers, guards and effects) among pre-existing states. Also, traceability modelling elements are added to the model. Two case studies demonstrate the feasibility of the proposed approach.

Sensing expectation enables simultaneous proprioception and contact detection in an intelligent soft continuum robot

A high-level perceptual model found in the human brain is essential to guide robotic control when facing perception-intensive interactive tasks. Soft robots with inherent softness may benefit from such mechanisms when interacting with their surroundings. Here, we propose an expected-actual perception-action loop and demonstrate the model on a sensorized soft continuum robot. By sensing and matching expected and actual shape (1.4% estimation error on average), at each perception loop, our robot system rapidly (detection within 0.4 s) and robustly detects contact and distinguishes deformation sources, whether external and internal actions are applied separately or simultaneously. We also show that our soft arm can accurately perceive contact direction in both static and dynamic configurations (error below 10°), even in interactive environments without vision. The potential of our method are demonstrated in two experimental scenarios: learning to autonomously navigate by touching the walls, and teaching and repeating desired configurations of position and force through interaction with human operators.

Development and Evaluation of a Risk Predictive Model for High Level of Self-Reported Symptom Cluster Distress in Patients with Hematologic Malignancies

Development and Evaluation of a Risk Predictive Model for High Level of Self-Reported Symptom Cluster Distress in Patients with Hematologic Malignancies

DeepZoning: Re-accelerate CNN Inference with Zoning Graph for Heterogeneous Edge Cluster

Parallelizing CNN inference on heterogeneous edge clusters with data parallelism has gained popularity as a way to meet real-time requirements without sacrificing model accuracy. However, existing algorithms struggle to find optimal parallel granularity for complex CNNS, the structure of which is a directed acyclic graph (DAG) rather than a chain, and the parallel dimension is inflexible. To distribute the workload of modern CNNs on heterogeneous devices is also proven as NP-hard problem. In this paper, we introduce DeepZoning , a versatile and cooperative inference framework that combines both model and data parallelism to accelerate CNN inference. DeepZoning employs two algorithms at different levels: (1) a low-level Adaptive Workload Partition algorithm that uses linear programming and takes spatial and channel dimensions into optimization during the search for feature map distribution on heterogeneous devices, and (2) a high-level Model Partition algorithm that finds the optimal model granularity and organizes complex CNNs into sequential zones to balance communication and computation during execution. Our experimental evaluations show that DeepZoning is effective, achieving up to a 3.02 × speed improvement on our experimental prototype compared to state-of-the-art algorithms.

Self-aggregation and microhydration mechanisms of monoethanolamine: Far-infrared identification of large-amplitude hydrogen bond libration.

The strong tendency for self-aggregation together with an intriguing mechanism for the microhydration of monoethanolamine (MEA) have been explored by low-temperature far-infrared cluster spectroscopy in doped neon "quantum" matrices at 4K complemented by high-level quantum chemical modeling. In addition to the assignment of new mid-infrared perturbed intramolecular transitions, a distinct far-infrared transition is unambiguously assigned to the concerted large-amplitude hydrogen bond librational motion of the MEA homodimer. This observation confirms a global "head-to-head" intermolecular potential energy minimum associated with the formation of a compact doubly intermolecular OH⋯N hydrogen-bonded cyclic structure, where both monomeric intramolecular OH⋯N hydrogen bonds are broken upon complexation. By means of relative mixing ratio dependencies, dedicated annealing procedures, and selective complexation between MEA and isotopic H216O and H218O samples, distinct far-infrared transitions associated with large-amplitude intra-molecular hindered OH torsional motion and inter-molecular H2O librational (hindered c-type overall rotational) motion of the MEA monohydrate are furthermore assigned unambiguously for the first time. These spectroscopic observations reveal an intriguing metastable conformation, where H2O acts as a OH⋯O hydrogen bond donor to the hydroxy group instead of the amino group of MEA upon microhydration in the cryogenic neon environment, where the microhydration strengthens the intramolecular OH⋯N hydrogen bond of MEA due to hydrogen bond cooperativity.

Reengineering Web applications into pervasive information systems

In today’s mobile-driven world, organizations face the challenge of adapting legacy Web applications to pervasive information systems, such as mobile applications, ensuring seamless accessibility across devices. This paper presents a systematic, three-phase approach to reengineering Web applications into mobile applications: Reverse-engineering, Transformation, and Forward-engineering. In the reverse-engineering phase, high-level conceptual models of the Web application are generated to capture its architecture and functionality. During the transformation phase, these models are refined using Unified Modeling Language (UML) profiles to align with the requirements of mobile systems. The forward-engineering phase utilizes the Acceleo technology, along with predefined mapping rules and code patterns, to automate the generation of mobile application code. A case study is conducted to demonstrate the effectiveness of this framework, revealing a significant reduction in manual effort throughout the reengineering process. This approach offers a scalable and efficient solution for transforming Web applications into mobile applications, contributing to advancements in model-driven engineering and software reengineering.

Organisational cyber resilience: a heuristic for bridging foundations and applications

PurposeThe drive for digitalisation has increased the scope of cyber threats which can exploit the growing footprint of information and communication technology infrastructure supporting modern societies. Despite substantial interest and efforts in researching and building organisational cyber resilience, the resulting body of work is heterogeneous and has yet to reach maturity. This paper aims to address the gap in the conceptualisation of cyber resilience in academic and practice-oriented grey literature.Design/methodology/approachIn this conceptual paper, we firstly seek to explore the available foundations of resilience as a construct and consider how these can be applied to organisational cybersecurity. To that aim, this study employs a targeted literature review approach, incorporating systematic elements to ensure rigour. Literature was identified through comprehensive searches in key academic databases, reference chaining and expert recommendations. Articles were selected based on relevance and contribution to the field, resulting in a thematic analysis to identify gaps and propose a heuristic model for cyber resilience. With this approach, we aim to position the emerging view of cyber resilience relative to risk analysis, while highlighting its domain of “conceptual comparative advantage” – the types of applications it is best suited to address. Finally, a high-level heuristic model for cyber resilience is proposed, which functions across the relevant policy, strategy and operational dimensions while also considering its relationship with cyber risk management.FindingsA conceptual model for organisational cyber resilience is proposed which helps position and frame research contributions in this domain relative to risk analysis, highlighting its domain of comparative advantage. The model integrates policy, strategy and operational dimensions, in a manner conducive to bridging foundations and applications of the concept of cyber risk management. The proposed model provides a critical point of reference to evaluate individual models, frameworks and tools.Originality/valueThis paper is a pioneering effort to overcome the current gaps between conceptual and practical views of cyber resilience. It proposes a new, risk-aligned view of the concept of cyber resilience and provides a structural foundation for further research and practice in the field.

Proving Correctness of Parallel Implementations of Transition System Models

This article addresses the long-standing problem of program correctness for programs that describe systems of parallel executing processes. We propose a new method for proving correctness of parallel implementations of high-level models expressed as transition systems. The implementation language underlying the method is based on the concurrency model of actors and active objects. The method defines program correctness in terms of a simulation relation between the transition system that specifies the program semantics of the parallel program and the transition system that is described by the correctness specification. The simulation relation itself abstracts from the fine-grained interleaving of parallel processes by exploiting a global confluence property of the concurrency model of the implementation language considered in this article. As a proof of concept, we apply our method to the correctness of a parallel simulator of multicore memory systems.

High-Level Feature Fusion Deep Learning Model for Fault Detection in Handling Equipment in Dry Bulk Ports

The flexibility of handling equipment in dry bulk ports is poor, and frequent equipment fault induced by the high-load and high-power working conditions greatly impacts the overall port handling operations, making accurate fault detection play an important role in improving the efficiency and stability of dry bulk port operations. However, as we know, most fault detection methods for port handling equipment depend heavily on monitoring sensor data, which is not applicable in the dry bulk port due to high configuration and maintenance cost, as well as the high false alarm rate of monitoring sensors caused by strong background noise. To solve the problem, this study proposes a High-Level Feature Fusion Deep Learning Model, which uses different deep learning sub-models to extract features of structured and unstructured data. It fuses the extracted feature vectors to achieve fault detection in the handling equipment, establishing the mapping relationship between the fault (e.g., waiting for the pre-loading process, equipment fault, and others) and multi-source heterogeneous operation and maintenance data for the handling equipment, including reclaimers, belt conveyors, dumpers, and ship loaders. To verify the effectiveness of the proposed method, the actual data of a coal port in Northern China is employed as an example. The results show the deep learning model can achieve high prediction accuracy (over 86%) with high efficiency (0.5 s for each sample), which provides decision support for the fault detection in dry bulk port handling equipment.

REFINING THE EMPIRICAL MODEL OF THE HEAT TRANSFER BOUNDARY CONDITIONS OF THE PISTON OF A HIGH-SPEED DIESEL ENGINE

The level of temperature of ICE pistons is a factor that greatly affects their reliability. Therefore, the need to simulate the temperature state of pistons with sufficient accuracy and minimal allocation of resources and time exists in research and during design. Primary approaches for determining the boundary conditions of the problem of thermal conduction of the piston on the basis of criterion equations, high-level mathematical models, and empirical models are considered. The latter ones have become widespread by virtue of simplicity and ease of use in practice; their usability is limited to a certain set of operating modes of one engine or a few ones of a similar design. On the basis of data from four series of experimental tests for a well-studied diesel engine 4ChN12/14 the existing empirical models of boundary conditions, suitable for identifying the temperature state of the piston only within the respective separate series separately, are refined in the paper. Processing of the data set on the measured temperatures in the control zones of the piston was performed, and linear function approximations were found that satisfactorily describe the effect of diesel power on the specified temperatures. The model of boundary conditions for a set of eighteen separate regions on the surfaces of the piston was proposed, which takes into account the dependence of the piston temperature state on the brake power of the ICE, the crank angle of the start of fuel injection, and the conditions of cooling the piston with oil. The influence of the heat insulation layer on the piston crown surface on the heat transfer was accounted for by introducing an additional thermal resistance term into the boundary condition equations. The model made obtaining the temperature field of the piston possible in simulation that is consistent with the results of all series of experiments in the control zones of the piston periphery, the edge of the combustion chamber, the vicinity of the groove of the first piston ring, the cooled surface opposite to the combustion chamber, and the piston skirt. The importance of a gradual change in the temperature of the oil and coolant during the transition of the internal combustion engine from one mode of operation to another regarding the temperature state of the piston is demonstrated. Taking the temperatures of oil from previous modes of operation into account is recommended during the analysis of engine transient processes.

Sustainability challenges of last-mile logistics and transition to online grocery retailing in sparsely populated areas

Purpose This paper focuses on the last-mile logistics (LML) operations in fulfilling online grocery orders and the related sustainability considerations in sparsely populated areas like Australia. It aims to examine how online groceries in sparsely populated areas can benefit from online business. Specifically, this study seeks to investigate whether a centralized order fulfillment approach is better than the existing approach which fulfills online orders from local grocery stores.Design/methodology/approach A multi-method approach is employed to conduct a high level of cost and emission analysis between the existing and the proposed approaches to illustrate the ratios between the two approaches in terms of cost and carbon emissions. Mathematical models are developed with support from the literature. The model is empirically validated with a case study of grocery distribution in the city of Gold Coast, Australia.Findings It finds that the centralized order fulfillment approach in sparsely populated areas can achieve LML sustainability with low cost, high efficiency and less double handling. Meanwhile, the separation of in-store and online retailing processes improves the in-store shopping experience and online shopping visibility, jointly improves customer satisfaction, and consequently achieves a positive effect on long-term sustainability. Additionally, the possibility of automating order picking and dispatching at a central place can make the processes more efficient and help build more sustainable grocery retailing supply chains by using more environmentally friendly systems.Originality/value This paper offers analytical and empirical insights into the sustainability of multi-channel grocery retailing supply chains. The high-level model developed first incorporates the concept of online shopping adoption rates and can serve as a decision-making tool for practitioners to improve supply chain sustainability in LML.

Multi-Level Machine Learning Model to Improve the Effectiveness of Predicting Customers Churn Banks

Abstract This study presents a novel multi-level Stacking model designed to enhance the accuracy of customer churn prediction in the banking sector, a critical aspect for improving customer retention. Our approach integrates four distinct machine-learning algorithms – K-Nearest Neighbor (KNN), XGBoost, Random Forest (RF), and Support Vector Machine (SVM) – at the first level (Level 0). These algorithms generate initial predictions, which are then combined and fed into higher-level models (Level 1) comprising Logistic Regression, Recurrent Neural Network (RNN), and Deep Neural Network (DNN). We evaluated the model through three scenarios: Scenario 1 uses Logistic Regression at Level 1, Scenario 2 employs a Deep Convolutional Neural Network (DNN), and Scenario 3 utilizes a Deep Recurrent Neural Network (RNN). Our experiments on multiple datasets demonstrate significant improvements over traditional methods. In particular, Scenario 1 achieved an accuracy of 91.08%, a ROC-AUC of 98%, and an AUC-PR of 98.15%. Comparisons with existing research further underscore the enhanced performance of our proposed model.

Bayesian Optimization for Anything (BOA): An open-source framework for accessible, user-friendly Bayesian optimization

We introduce Bayesian Optimization for Anything (BOA), a high-level Bayesian Optimization (BO) framework and model wrapping toolkit, which presents a novel approach to simplifying BO, with the goal of making it more accessible and user-friendly, particularly for those with limited expertise in the field. BOA addresses common barriers in implementing BO, focusing on ease of use, reducing the need for deep domain knowledge, and cutting down on extensive coding requirements. A notable feature of BOA is its language-agnostic architecture, which facilitates broader application in various fields and to a wider audience. We showcase BOA's application through three examples: a high-dimensional optimization with 184 parameters of the SWAT + watershed model, a highly parallelized optimization of this intrinsically non-parallel model, and a multi-objective optimization of the FETCH Tree-Crown Hydrodynamics model. These test cases illustrate BOA's effectiveness in addressing complex optimization challenges in diverse scenarios.

Data analytics use in financial due diligence: the influence of accounting and commercial logic

Purpose The purpose of this paper is to contribute to a better, empirically grounded and theoretically informed understanding of data analytics (DA) use and nonuse in accounting for decision-making. To that end, it explores the links between accounting logic, commercial logic and DA use in financial due diligence (FDD). Design/methodology/approach The paper reports the findings of a case study of DA use in the FDD practice of a Big Four accounting firm in Sweden (Pseudonym: DealCo). The primary data comprises semistructured interviews, observations and additional meetings. Institutional logics is mobilized as method theory. Findings First, accounting logic and commercial logic both drove and hindered DA use in DealCo’s FDD practice in different ways. Second, conflicting prescriptions for DA use existed mostly within commercial logic rather than between accounting logic and commercial logic. Third, accounting logic and commercial logic, as perceptual and conceptual filters, seemed to shape DealCo’s advisors’ understanding of DA and give rise to an efficiency-centric DA logic. This logic, in turn, as a high-level model of how to use DA in the context of FDD, governed DA use broadly. Originality/value The paper draws attention to direct and indirect links between accounting logic and commercial logic, on the one hand, and DA conceptions and use, on the other hand. It, thereby, advances prior theorization of DA use in accounting for decision-making.

Effects of sodium lactate injection on meat quality and lactate content in broiler chickens: emphasis on injection method and dosage

This study aims to develop an experimental model of high lactate levels in broilers to mimic the condition of birds under stress or diseases and evaluate its consequent effects on meat quality. The injection sites and dosage effects were compared separately in 2 experiments. Experiment 1 includes 3 injection sites: intraperitoneal injection, intramuscular injection, and subcutaneous injection. Experiment 2 was a dosage experiment based on the results of Experiment 1: sodium lactate intraperitoneal injection group with 1.5, 3, 6 mM concentration. The results showed that injecting sodium lactate intraperitoneally, intramuscularly, or subcutaneously all significantly decreased body weight and breast muscle weight while elevating lactic acid levels in both the blood and breast muscle of broilers. Moreover, all 3 injection methods caused a significant reduction in pH24h and an increase in the shear force value of breast muscle. In addition, dose-response experiments of intraperitoneal injection showed that a concentration of 3 mM and 6 mM were significantly decreased body weight and breast muscle weight in broiler chickens, accompanied by a notable increase in breast muscle lactate content. Compared to the control group, intraperitoneal injections of 1.5 mM, 3 mM, and 6 mM sodium lactate treatments significantly reduced the yellowness values of the breast muscle. As the dose of sodium lactate increased, the shear force value of the breast meat exhibited linear and quadratic increments, while the drip loss decreased linearly. Intraperitoneal injection of 3 mM sodium lactate also significantly reduced the pH24h of broiler breast muscle. In addition, an increased dose of lactate injections up-regulated the glycolytic pathway responsible for endogenous lactate production in the breast muscle by upregulating the expression of phosphofructokinase, pyruvate kinase and lactate dehydrogenase A. In conclusion, intraperitoneal injection of sodium lactate at 3 mM directly increased breast muscle lactate levels, providing a valuable method for establishing a high-level lactate model in poultry.

BIT: A template-based approach to incremental and bidirectional model-to-text transformation

Model-driven development is a model-centric software development paradigm that automates the development process by converting high-level models into low-level code and documents. To maintain synchronization between models and code/documents — which can evolve independently — this paper introduces BIT, a bidirectional language that can serve as a conventional template language for model-to-text transformations. However, a BIT program can function as both a printer, generating text by filling template holes with values from the input model, and a parser, putting parsed values back into the model. BIT comprises a surface language for better usability and a core language for formal definition. We define the semantics of the core language based on the theory of bidirectional transformation, and provide the translation from the surface to the core. We present the proof sketch of the well behavedness of BIT as a formal evidence of soundness. We also conduct three case studies to empirically demonstrate the expressiveness and the effectiveness of BIT. Based on the proof and the case studies, BIT covers the major features of existing template languages, and offers sufficient expressiveness to define real-world model-to-text transformations that can be executed bidirectionally and incrementally.

Growth Characteristics of Ramet System in Phyllostachys praecox Forest under Mulch Management.

The ramet system is a typical structural type in the life history of clonal plants. This massive structure is formed by many similar ramets connected by underground rhizomes, which are independent and mutually influential. Therefore, the ramet system is unique to bamboo forests, and its role in the construction, maintenance, and productivity of bamboo populations is irreplaceable. Mulch management is a high-level cultivation model for bamboo forests that is used to cultivate bamboo shoots. However, the basic conditions of bamboo ramet systems in this managed model are poorly understood. This study analyzed the underground rhizome morphology, bud bank, and branching of bamboo ramets in a Phyllostachys praecox C.D. Chu et C.S. Chao 'Prevernalis' forest to explore the growth patterns of bamboo ramets in high-level management fields. In mulched bamboo forests, the bamboo rhizomes, distributed in intermediate positions of the bamboo ramet system, were long with many lateral buds and branches, and those at the initial and distal ends were short with few lateral buds and branches. The initial end of the ramet system reduced the ramet system, the intermediate part expanded the ramet system, and the distal end promoted ramet system regeneration. Owing to the continuous reduction, expansion, and renewal of ramet systems, the bamboo rhizome system demonstrates mobility and adaptability. This study found that a higher level of bamboo forest management increased the possibility of artificial fragmentation of the ramet system and that improving the efficiency of the ramet system was beneficial for maintaining its high vitality. Thus, this study provides a crucial reference for guiding the precise regulation of bamboo ramet systems in artificial bamboo forests.