Evolutionary Process Model Research Articles

Study on Permeability Evolution Law of Rock Mass under Mining Stress

In order to study the stress–strain–permeability coefficient relationship of overlying strata in a fractured zone after coal mining, taking the Changcun coal mine in the Changzhi basin as an example, the permeability evolution law of coarse sandstone, fine sandstone, siltstone and mudstone during a stress–strain process was analyzed through a triaxial compression permeability test. The generalized model of the rock mass permeability evolution process under mining stress was summarized, and then a coupling model of the stress–water pressure–permeability coefficient of fractured rock was established based on the continuum model of rock mass. The results showed that the maximum permeability coefficient of different coal overburden types was quite different, and the peak strength of the rock mass preceded the maximum permeability coefficient during the rock mass failure process; the permeability coefficient first decreased and then increased, reaching its maximum value after the peak stress, which occurred during the strain-softening stage; the generalized model of rock mass permeability included the compaction stage, elasticity stage, stable fracture stage, unstable fracture stage, macroscopic failure stage and residual strength stage.

Digital technology integration in business model innovation for carbon neutrality: An evolutionary process model for SMEs

This study investigates into the strategies employed by small and medium-sized enterprises (SMEs) to embed digital technology into their business models, aiming to reach carbon neutrality. We offer a comprehensive analysis of five high technology SMEs, unveiling an evolutionary process model that highlights their journey toward integrating technology. The integration process is delineated into three progressive stages. Initially, digital technology is seen as an ‘enabler’ that promotes the birth of an innovative business model, essentially transforming the mechanisms of value creation, delivery, and capture. As SMEs evolve to an intermediate stage, digital technology becomes a ‘disruptor’, reshaping and reinventing the business model in terms of how value is created, delivered, and captured. Entering the maturity stage, digital technology is elevated to the role of ‘expertise’, leading to a sophisticated business model that refines and optimizes these aspects of value. The influence of these business model innovations on carbon neutrality is complex and varies distinctly with each stage, reflecting the dynamic interplay between digital technology and business model evolution.

Predictive process model monitoring using long short-term memory networks

The field of predictive process monitoring focuses on case-level models to predict a single specific outcome such as a particular objective, (remaining) time, or next activity/remaining sequence. Recently, a longer-horizon, model-wide approach has been proposed in the form of process model forecasting, which predicts the future state of a whole process model through the forecasting of all activity-to-activity relations at once using time series forecasting.This paper introduces the concept of predictive process model monitoring which sits in the middle of both predictive process monitoring and process model forecasting. Concretely, by modelling a process model as a set of constraints being present between activities over time, we can capture more detailed information between activities compared to process model forecasting, while being compatible with typical predictive process monitoring objectives which are often expressed in the same language as these constraints. To achieve this, Processes-As-Movies (PAM) is introduced, i.e., a novel technique capable of jointly mining and predicting declarative process constraints between activities in various windows of a process’ execution. PAM predicts what declarative rules hold for a trace (objective-based), which also supports the prediction of all constraints together as a process model (model-based). Various recurrent neural network topologies inspired by video analysis tailored to temporal high-dimensional input are used to model the process model evolution with windows as time steps, including encoder–decoder long short-term memory networks, and convolutional long short-term memory networks. Results obtained over real-life event logs show that these topologies are effective in terms of predictive accuracy and precision.

The evolution process and degradation model of permafrost in the Source Area of the Yellow River on the Qinghai-Tibet Plateau since the Little Ice Age

Permafrost degradation is currently receiving as much attention as global warming. Permafrost evolution is a particularly slow process, making it impossible to examine the entire process by observation. Based on the heat conduction theory, in this study, a nonlinear numerical model for the entire process of permafrost evolution was developed with consideration of phase transition. The numerical simulation model was used to retrospectively analyse the evolution process and degradation pattern of permafrost in Tangchama beach-land in the Source Area of the Yellow River (SAYR) from 1400 to 2100; this analysis was performed using ice core inversion results obtained from the Qinghai-Tibet Plateau since the Little Ice Age and various future climate change scenarios from the IPCC Fifth Assessment Report. The simulation results showed that: 1) since 1900, permafrost temperatures have substantially increased, exceeding the range of change between 1400 and 1900; under the RCP2.6, RCP6.0 and RCP8.5 scenarios, permafrost will degrade and disappear in the next 100 years, and the degradation rate will subsequently accelerate. 2) The permafrost table steadily goes downward at first, and the onset of fast permafrost degradation is signalled by the appearance of a residual thawed layer at a depth of 3–4 m. The permafrost layer then gradually thins out as the permafrost table rapidly descends and the permafrost base slowly ascends. The permafrost will fully deteriorate and vanish once the permafrost table and the permafrost base bounds overlap. 3) Due to permafrost degradation, the ground temperature curve can be divided into the following stages: uniform gradient stage, C-type stage, zero-gradient type stage and phase transition stage. The four stages of the “permafrost life cycle” are initial temperature warming, continuous temperature rising, residual thawed layer, and final permafrost disappearance.

A multi-dimensional evolution modeling method for digital twin process model

Digital twin, visually monitor the dynamic evolution process of part cutting in a digital way, which has attracted widespread attention in smart manufacturing. Under this background, creating a high fidelity and evolvable virtual model has become the key to putting digital twin into practice in the manufacturing field. However, the 3D process model pays scant attention on the dynamically machining process, multi-dimensional evolutionary machining feature, and not even the coupling machining semantics. How to realize the evolution process model creation from the perspective of semantics dimension, time dimension, and feature dimension, an evolutionary concurrent modeling method is proposed based on the process knowledge and machining feature. Firstly, the geometric evolution process of typical features is analyzed, and the association mechanism between processing semantics and evolution features is constructed. Secondly, the mathematical model of the evolution feature is constructed based on the part processing information, and the creation method of processing semantics-driven evolutionary feature is proposed. Thirdly, the association mechanism of "machining semantics-process parameters-geometric features" is built, and the dynamic evolution process model which achieve synchronous evolution with the processing is created based on machining semantics. Finally, a case study is carried out with typical processing feature. The results show that the multi-dimensional evolution modeling of the digital twin process model can realize the synchronous visual monitoring and simulation of product processing. The correlation based on semantic features can improve the efficiency and quality of process model construction and provide solutions for dynamic analysis and decision-making of smart process design.

Explainable AI and Counterfactuals for Test and Evaluation of Intelligent Engineered Systems

AbstractSystems Engineering (SE) based test and evaluation (T&E) approaches have proven crucial for successful realization of most modern‐day complex systems and system‐of‐systems solutions. The design, test, and evaluation of engineered systems with many technologically advanced and complex components hinges on well‐structured integration and life cycle evolution process models, clearly defined requirements, along with controllability, observability, and stability (COS) of components that transcend to the system‐level. However, integration of machine learning (ML), deep learning (DL) and artificially intelligent (AI) components invalidates some SE foundations as the DL algorithms are primarily data‐driven with opaque decision‐making constructs. As a result, the current SE T&E approaches, although necessary, have become insufficient to evaluate the adoption of ML/DL/AI methods in engineered systems. This paper proposes two new approaches—explainable AI (xAI) and counterfactual testing and evaluation (cT&E)—as addition to the SE tool set for T&E of intelligent engineered systems. An example of SE T&E considerations is provided based on a conceptual aircraft control system implementation comparing as a classical controller and a DL‐based reinforcement learning controller.

Study of the Damage Evolution Process and Micromechanics Constitutive Model of Microcrystalline Limestone under Periodic Blasting Load

The cumulative damage to rock and soil mass caused by blasting in tunnel, highway, hydropower, and other projects is very complex, but the process of damage evolution is still unclear. This study took Zhujiapo mining area limestone as the research object and analyzed the change in its strength and mechanical parameters with periodic blasting loads. Combined with damage mechanics theory and microcrack propagation zone theory, a micromechanics constitutive model considering cumulative damage evolution was developed. The results show that (1) periodic blasting loads have a continuous cumulative effect on microcrystalline limestone, the peak strength and elastic modulus of which decrease exponentially with increasing blasting cycles; (2) periodic blasting loads cause crystal fractures, microcrack initiation, and propagation; and (3) the micromechanics damage constitutive model considering confining pressure and periodic blasting loads well describes the micromechanics behavior of microcrystalline limestone in the Zhujiapo mining area. The research results provide a theoretical reference to the study of rock damage mechanical properties under periodic blasting loads.

Intelligent overall planning model of underground space based on digital twin

Against the background of the rapid development of social economy and advanced industrial manufacturing, emerging technologies are sweeping the world. The overall planning of urban space is also gradually developing in the direction of "intelligence". The real estate market has been booming in recent years. As a result, the available land resources on the surface are declining. It first gives a general introduction to the digital twin technology and operating rules, and then introduces the analytic hierarchy process and the complexity evolution process model for specific analysis. It first determines the relative weight vector, and then verifies the consistency of the judgment matrix to obtain the simulated probability density function. For the calculation of underground space safety accident risk assessment, the Bayesian network is used for evaluation. This facilitates disaster warnings of possible and ongoing accidents and reduces the adverse consequences of accidents. Then, the digital twin technology is applied to the analysis of underground space visualization, underground construction and underground disaster prevention monitoring. During the construction stage, the bottom of the upper structure column has changed a lot. The difference between the difference between the foundation difference between the adjacent color spectrum column of the upper structure is relatively mild. When digging a large area of ​​ground soil, the foundation dropped to 5.6 mm. When the soil on the side of the pile was removed, the ground building was basically settle down, 0.8 mm. When the structure of the pile side soil is completed, it indicates that the technology of the pile foundation base of the surface building is basically. Finish. Subsequent sidewalk soil has almost no effect on the upper structure. At this stage, the final deposition difference is 2.427 mm, which meets the requirements of the neighboring column base in the current regulations. Finally, I studied the prevention of disaster prevention in the underground space, simulated the accident scene, and obtained the most suitable imaging equipment for the monitoring of disaster. After the prevention of disaster testing and simulation accidents for underground space, the most suitable imaging equipment for preventing disaster testing was obtained. Its formal digital twin technology.

Aplikasi Panic Buton Untuk Keamanan Warga Berbasis Android

Citizens highly expect environmental security, but criminality is inevitable. The level of crime in the community brings unrest and discomfort, so a security system is needed that is connected with the officers concerned. This study aims to build a security system application connected to environmental security officers. This system can help the public provide reports quickly through the panic button application embedded in Android. System development using the spiral model method. A spiral model is an evolutionary software process model assembling the interactive nature of the prototype using control and systematic aspects of a linear sequential model. Meanwhile, the system design stage uses the Unified Modeling Language (UML). The application is in Android Studio for the design stage, a unique Integrated Development Environment (IDE) that runs on the Android platform. The panic button application is a security system designed to assist the public in providing reports and make it easier for security officers to follow up on the messages given. Accelerate the follow-up process of crimes because it is based on Android.

News Hotspot Event Diffusion Mechanism Based on Complex Network

The wide range of social hot news events on the Internet has made the Internet have a great impact on the public. However, there are few studies on Internet information. In order to improve the efficiency of user network information dissemination of Internet information based on complex network theory and model simulation, this paper makes a more in-depth study on information dissemination on the Internet, constructs a complex network of Internet information dissemination, and analyzes the static topology and dynamic evolution process of the network. Using the attention relationship between Internet users, the Internet information dissemination network, degree, and path were used to select multiple indicators. The static topology of the network is analyzed by using the complex network theory. The study found that the complex network of Internet information is different from other complex networks. The influencing factors of network dynamic evolution are studied from three aspects: overall structure, local structure, and time constraints. The evolution trend of different forms and overall network nodes in the evolution process was explored, and the network dynamic evolution process model was constructed. Practice shows that the model can better describe the evolution process of network information dissemination in complex networks. The degree values of the two networks are positively correlated with the corresponding average clustering coefficients, and the networks have a significant hierarchy. The correlation between news hot events and network nodes is not as good as users’ attention to the network.

Predictions for LISA and PTA based on SHARK galaxy simulations

We present our analysis of a set of populations of massive black hole (MBH) binaries generated in the recent semi-analytic model of galaxy evolution (SHARK). We focus on studying gravitational wave (GW) emission produced during MBH mergers in terms of their detectability with current and future detectors, namely, Pulsar Timing Arrays (PTAs) and Laser Interferometer Space Antenna (LISA). The key advantage of SHARK is that it provides a way to explore a number of distinct models of MBH and galaxy evolution processes within a consistent framework and it was also successfully tested against current constraints from electromagnetic observations. In our work, we studied 12 models that vary in terms of their MBH seed formation scenarios and we tested two different MBH growth and feedback models. Based on our estimates, we find that LISA will be able to detect several to several tens of merger events per year for the most and least massive seed scenarios, respectively. We also show that the strength of this relation depends on the MBH growth model, where in the most extreme case, we find twice as many detected events for the same initial seed masses. Finally, we estimated the amplitude of the GW background at nHz frequencies to be on the order of 1.4 × 10−16 − 1.1 × 10−15. This value depends solely on the time delay between the merger of galaxies and their MBHs.

Characterizing warm atmospheric boundary layer over land by combining Raman and Doppler lidar measurements.

PBL plays a critical role in the atmosphere by transferring heat, moisture, and momentum. The warm PBL has a distinct diurnal cycle including daytime convective mixing layer (ML) and nighttime residual layer developments. Thus, for PBL characterization and process study, simultaneous determinations of PBL height (PBLH) and ML height (MLH) are necessary. Here, new approaches are developed to provide reliable PBLH and MLH to characterize warm PBL evolution. The approaches use Raman lidar (RL) water vapor mixing ratio (WVMR) and Doppler lidar (DL) vertical velocity measurements at the Southern Great Plains (SGP) atmospheric observatory, which was established by the Atmospheric Radiation Measurement (ARM) user facility. Compared with widely used lidar aerosol measurements for PBLH, WVMR is a better trace for PBL vertical mixing. For PBLH, the approach classifies PBL water vapor structures into a few general patterns, then uses a slope method and dynamic threshold method to determine PBLH. For MLH, wavelet analysis is used to re-construct 2-D variance from DL vertical wind velocity measurements according to the turbulence eddy size to minimize the impacts of gravity wave and eddy size on variance calculations; then, a dynamic threshold method is used to determine MLH. Remotely-sensed PBLHs and MLHs are compared with radiosonde measurements based on the Richardson number method. Good agreements between them confirm that the proposed new algorithms are reliable for PBLH and MLH characterization. The algorithms are applied to warm seasons' RL and ML measurements at the SGP site for five years to study warm season PBL structure and processes. The weekly composited diurnal evolutions of PBLHs and MLHs in warm climate were provided to illustrate diurnal and seasonal PBL evolutions. This reliable PBLH and MLH dataset will be valuable for PBL process study, model evolution, and PBL parameterization improvement.

Stress and deformation evolution characteristics of gob-side entry retained by roof cutting and pressure relief

To control the stability of gob-side entry in 110 mining method, the evolution mechanism of surrounding rock stress and deformation in the whole life cycle of the entry must be understood firstly. The Mohr circle analysis model of stress evolution process is established in this study, and the evolution characteristics of the stress and deformation are revealed. Meanwhile, the theoretical analysis results are verified by numerical simulation and engineering data. It can be concluded that the stress and deformation evolution of the gob-side entry can be divided into 3 periods and 11 stages. In these stages, the surrounding rock before mining face is mainly characterized by small deformation, which is mainly caused by the unbalanced stress. The main control idea is to increase the minimum principal stress and reduce the maximum principal stress in the surrounding rock by using the roof cutting and high prestressed support technology, to ensure that the stress circle is far away from the envelope. After mining face excavation, the surrounding rock is mainly characterized by large deformation, which is not only affected by the changes of stress but also by the movement of the overlying roof. Therefore, the main control idea is to adopt materials with high strength and stiffness to temporarily support the entry in the process of roof movement. Although deformation cannot be completely prevented, it can ensure that the stress circle in surrounding rock does not exceed the envelope. It means that the surrounding rock is not damaged, and the surrounding rock is still safe after the roof movement stops. This paper reveals the essence of entry deformation from the perspective of stress evolution and puts forward the corresponding control measures. The achievements can provide theoretical guidance for engineering design of gob-side entry control in 110 mining method.

A Prediction Model of Hydrodynamic Landslide Evolution Process Based on Deep Learning Supported by Monitoring Big Data

Owing to the complex formation mechanism of hydrodynamic landslides and the involvement of multiple influencing factors, the accuracy of the current prediction model of hydrodynamic landslide evolution process is unsatisfactory. This limitation prevents adequate monitoring and early warning on possible landslides in an area. To improve the accuracy of prediction model of hydrodynamic landslide evolution process supported by monitoring big data, the variational mode decomposition (VMD) and support vector regression (SVR) based on deep learning were integrated in the present study. Typical hydrodynamic landslide in the Three Gorges Reservoir Area (TGRA) in China is used as a case study for landslide displacement prediction. First, the VMD was utilized to decompose the cumulative displacement into the trend, periodic, and random terms. Then, external factors were decomposed into subsequences, and those characterized by periodicity and randomness were selected as input datasets. The associated displacement terms were then predicted using the Random Search–Support Vector Regression model. Finally, the total displacement was obtained by superimposing the three predicted components, and this was used to evaluate the performance of the model. The results show that the model improves the performance and accuracy of predicting the displacement associated with a hydrodynamic landslide, and the relative error is ≤2%.

Knowledge Accumulation in Design Science Research

Design science research (DSR) is increasingly prominent in the information systems discipline. However, despite a lively discourse, and methodical and conceptual advances notwithstanding, DSR contributions are rarely cumulative and mostly remain isolated. This lack of cumulative research impedes scientific progress and practical impact. Our goal in this paper is to help establish a cumulative tradition in DSR by proposing (1) a general, consensus-oriented, evolutionary process model of scientific progress based on a synthesis of different streams in the philosophy of science, (2) quality criteria for DSR contributions corresponding to the individual steps in the process model, (3) actions that the IS community as a whole can take to facilitate adherence to these quality criteria. We expect that our work can pave the way towards developing a more cumulative research tradition in DSR and thereby foster genuine scientific progress in the field.

Experimental and theoretical model study on the dynamic mechanical behavior of sintered NdFeB

Sintered neodymium iron boron (NdFeB) is a widely applied permanent magnet, whose dynamic mechanical properties restrict the further development of its application. In order to provide a theoretical basis for the application of NdFeB in the shock field, the dynamic mechanical behavior of sintered NdFeB was studied experimentally and theoretically in this paper. First, the Split Hopkinson Pressure Bar (SHPB) experiments under different strain rates were carried out. The dynamic process was captured by a high-speed camera, and the strain data measured by oscilloscope was processed with the three-wave method. Combined with the electron microscope analysis of the recovered samples, the dynamic failure process and characteristics of NdFeB under high-speed impact were explored, which provided an experimental basis for the following theoretical research. Given the theoretical deficiency of the dynamic mechanical properties of sintered NdFeB and the assumption of microcracks with uniform distribution and no friction, a theoretical model of damage evolution process of brittle materials was established based on the dynamic crack growth criterion. Combining the damage evolution model with Zhu-Wang-Tang (ZWT) constitutive model, the damage constitutive model of sintered NdFeB was established, whose validity was verified by the comparison with the experimental data.

The Co-Evolutionary Process Model of Organizational Identity and Firm Strategy

In our longitudinal study, we investigated organizational identity and strategy related processes that ConsultingCo went through to become a reputable player in Finland in the management consulting market. We investigated the company for seventeen years from its establishment to the first leadership succession. We build an evolutionary process model that illustrates the three-stage -process: (1) emerging, (2) formalizing, and (3) institutionalizing. These stages show how organizational identity emerged and co-evolved together with firm strategy work. Organizational identity work evolved from anti-identity to social identity, and finally to strengths-based identity work. Simultaneously strategy work evolved from visionary to comparative, and finally, as strengths-based strategy work. Our study contributes to the strategy-identity nexus -literature by illustrating the continuous co-evolution of the firm strategy and organizational identity.

Copy Me if You Can? Surviving Institutional Voids Through Business Model Evolution in Africa

The question of how a firm’s business model evolves in an institutionally voided environment still deserves research attention in the business model literature. Bridging the institutional-based view of strategy and interorganizational imitation literature, this study examines how a firm’s business model evolves in an institutionally voided environment in a developing economy. Using a qualitative data on Jumia—an e-commerce giant in Africa, our results suggest that the business model of a firm enfolded by significant institutional voids evolves through the following: first, the firm intentionally seeks to learn vicariously by imitating a successful firm’s business model template. Second, because of the substantial impact of the institutional voids, the intentional pure imitation of the business model template of the successful firm becomes impossible and therefore, the firm begins to modify various components of the imitated business model template through experiential learning that blends the business model imitation process with innovation. Based on our finding, we propose a business model evolution process-model we called “imitate-but-modify”, that explains how a firm’s business model evolves in four unique stages in institutionally voided environments in developing economies. Implication on theory and practice of this finding are discussed.

Modeling and simulation of firm-leading self-organization evolution process in open source product design project

ABSTRACTOpen source product design is characterized by dynamic evolution, autonomous task control, loose organization structure, and frequent collaboration, which shows the process is full of high uncertainty. Firms have used this innovation pattern to obtain ideas. To describe the firm-leading self-organization evolution process, an agent-based modeling and simulation method is applied. We have proposed an agent-based model including designers and firms in light of Complex Adaptive System theory firstly; Secondly, their behaviors, especially the two typical behaviors: task selection and plan screening are illustrated; Finally, the simulation results show that:1) evolution process of agent, product, and community can be described clearly by simulation. Agent-based simulation can support the analysis, evaluation and optimization of the evolution process;2) in task selection phase: firm’s recommendation can improve the evolution efficiency;3)in plan screening phase, firms’ “control” also plays important role in the evolution process, but the impact of designer agents’ self-organization cannot be ignored.

Evolution process, dynamic factors, and agglomeration models of intelligent industrial clusters in the Zhongguancun Science Park

Evolution process, dynamic factors, and agglomeration models of intelligent industrial clusters in the Zhongguancun Science Park