Merger Model Research Articles

Version control for asynchronous BIM collaboration: Model merging through graph analysis and transformation

The design process in construction projects is iterative and multi-disciplinary in nature. In today’s industry practice, several discipline experts concurrently author multiple versions and design variants of BIM models and share them at frequent intervals. Applying a sound version control methodology can significantly enhance automation, enabling the coordination and combination of these model versions into consistent overall models with less extensive manual effort. This paper introduces a diff-and-patch mechanism for transferring changes between model versions, facilitating object-level change tracking using graph representations of BIM model data, and specifically focuses on merging diverging versions through the application of graph transformations. The mechanisms for executing branching and merging of model versions are thoroughly explained and showcased through various illustrative scenarios. The presented method adheres to the established principles of federated BIM collaboration but equips the participating parties with additional means to automate the combination of various model versions, allowing them to focus on the relevant conflicts. The proposed methodology of graph-based version control unlocks the potential of analyzing interdisciplinary dependencies across partial models and enables the more efficient resolution of conflicting model versions.

A Review of Computational Modeling in Wastewater Treatment Processes.

Wastewater treatment companies are facing several challenges related to the optimization of energy efficiency, meeting more restricted water quality standards, and resource recovery potential. Over the past decades, computational models have gained recognition as effective tools for addressing some of these challenges, contributing to the economic and operational efficiencies of wastewater treatment plants (WWTPs). To predict the performance of WWTPs, numerous deterministic, stochastic, and time series-based models have been developed. Mechanistic models, incorporating physical and empirical knowledge, are dominant as predictive models. However, these models represent a simplification of reality, resulting in model structure uncertainty and a constant need for calibration. With the increasing amount of available data, data-driven models are becoming more attractive. The implementation of predictive models can revolutionize the way companies manage WWTPs by permitting the development of digital twins for process simulation in (near) real-time. In data-driven models, the structure is not explicitly specified but is instead determined by searching for relationships in the available data. Thus, the main objective of the present review is to discuss the implementation of machine learning models for the prediction of WWTP effluent characteristics and wastewater inflows as well as anomaly detection studies and energy consumption optimization in WWTPs. Furthermore, an overview considering the merging of both mechanistic and machine learning models resulting in hybrid models is presented as a promising approach. A critical assessment of the main gaps and future directions on the implementation of mathematical modeling in wastewater treatment processes is also presented, focusing on topics such as the explainability of data-driven models and the use of Transfer Learning processes.

A Deep Learning Model for the Normalization of Institution Names by Multisource Literature Feature Fusion: Algorithm Development Study.

The normalization of institution names is of great importance for literature retrieval, statistics of academic achievements, and evaluation of the competitiveness of research institutions. Differences in authors' writing habits and spelling mistakes lead to various names of institutions, which affects the analysis of publication data. With the development of deep learning models and the increasing maturity of natural language processing methods, training a deep learning-based institution name normalization model can increase the accuracy of institution name normalization at the semantic level. This study aimed to train a deep learning-based model for institution name normalization based on the feature fusion of affiliation data from multisource literature, which would realize the normalization of institution name variants with the help of authority files and achieve a high specification accuracy after several rounds of training and optimization. In this study, an institution name normalization-oriented model was trained based on bidirectional encoder representations from transformers (BERT) and other deep learning models, including the institution classification model, institutional hierarchical relation extraction model, and institution matching and merging model. The model was then trained to automatically learn institutional features by pretraining and fine-tuning, and institution names were extracted from the affiliation data of 3 databases to complete the normalization process: Dimensions, Web of Science, and Scopus. It was found that the trained model could achieve at least 3 functions. First, the model could identify the institution name that is consistent with the authority files and associate the name with the files through the unique institution ID. Second, it could identify the nonstandard institution name variants, such as singular forms, plural changes, and abbreviations, and update the authority files. Third, it could identify the unregistered institutions and add them to the authority files, so that when the institution appeared again, the model could identify and regard it as a registered institution. Moreover, the test results showed that the accuracy of the normalization model reached 93.79%, indicating the promising performance of the model for the normalization of institution names. The deep learning-based institution name normalization model trained in this study exhibited high accuracy. Therefore, it could be widely applied in the evaluation of the competitiveness of research institutions, analysis of research fields of institutions, and construction of interinstitutional cooperation networks, among others, showing high application value.

To intervene or not to intervene: young adults’ views on when and how to intervene in online harassment

Abstract Incidents of online harassment are increasing and can have significant consequences for victims. Witnesses (“digital bystanders”) can be crucial in identifying and challenging harassment. This study considered when and how young adults intervene online, with the aim of understanding the applicability of existing theoretical models (i.e., Bystander Intervention Model; Response Decision-Making Framework). Thematic analysis of eight focus groups (UK community sample, N = 67, 18–25 years) resulted in five themes: Noticing and Interpreting the Harassment, Perceived Responsibility for Helping, Consequences of Intervening, Perceived Ability to Make a Difference, and Deciding How to Help. The online context amplified offline preferences, such as greater preference for anonymity and perceived costs of intervention (e.g., social costs). Intervention strategies varied in visibility and effort, preferring “indirect” micro-interventions focused on supporting victims. A new, merged model specific to digital bystanders is proposed, with implications for the design and messaging on social networking sites discussed.

Layer-wise partitioning and merging for efficient and scalable deep learning

Deep Neural Network (DNN) models are usually trained sequentially from one layer to another, which causes forward, backward and update locking problems, leading to poor performance in terms of training time. The existing parallel strategies to mitigate these problems provide suboptimal runtime performance. In this work, we have proposed a novel layer-wise partitioning and merging, forward and backward pass parallel framework to provide better training performance. The novelty of the proposed work consists of (1) a layer-wise partition and merging model which can minimise communication overhead between devices without the memory cost of existing strategies during the training process; (2) a forward pass and backward pass parallelisation to address the update locking problem and minimise the total training cost. The experimental evaluation on real use cases shows that the proposed method outperforms the state-of-the-art approaches in terms of training speed; and achieves almost linear speedup without compromising the accuracy performance of the non-parallel approach.

The Progenitors of Superluminous Type Ia Supernovae

Recent observations of type Ia supernovae (SNe Ia) have discovered a subclass of “super-Chandrasekhar” SNe Ia (SC SNe Ia) whose high luminosities and low ejecta velocities suggest that they originate from the explosions of white dwarfs (WDs) with masses that exceed the Chandrasekhar mass limit. Different models have been proposed to explain the progenitors of these explosions, including a “magnetized WD” model and a “WD merger” model. To test the robustness of these models, we conduct a 1D numerical parameter survey of WD explosions using these models as initial conditions. We follow the explosions using the hydrodynamics code Castro and then use the radiation transport code SuperNu to create light curves and spectra for the models. We find that while both classes of models fall within the range of SC SNe Ia observations on the light-curve width–luminosity relation, only the WD merger models reproduce the observed low ejecta velocities. The light curves of our merger models are more similar photometrically to observations than our magnetized models. Given this, we discuss possible explanations for the brightest SC SNe Ia observations that cannot be reproduced with our WD merger models. This study provides the basis for future SC SNe Ia observations and higher-dimensional numerical models.

Solutions on Establishing and Utilizing BIM for General Contractors in Key Scenarios of Digital Construction

Nowadays, digital construction has become popular when bringing convenience and efficiency to the traditional building construction industry. The primary tool of digital construction is the building information model (BIM). However, from the perspective of general contractors, unresolved puzzles still hinder obtaining the benefits of digital construction. When establishing a unified BIM from submodels provided by subcontractors, there will possibly be incomplete or inconsistent data during model merging. But extracting submodels from unified BIM often includes redundant data, thus making models less usable for subcontractors. It is also difficult for general contractors to effectively and accurately utilize resource information and submodel changes. This paper proposed solutions that depend on the widely adopted industry foundation classes standard to ensure the universality of our methods. First, a model merging algorithm is proposed to support the continuous merge of submodels created by different subcontractors. Second, an instance-level model extraction method based on strongly related entities is proposed, which extracts model instances to the minimum submodel and meets the subcontractor requirements at the same time. Third, the new model storage and indexing method are designed to reduce the complexity of model data and support rapid data retrieval, and a new BIM change detection method based on object metadata is provided. The proposed methods were applied by the general contractor of a large airport project during the construction stage. The application results proved that the proposed methods could ensure the quality of established deepening design models and extracted submodels and significantly reduce human labor by improving efficiency when utilizing BIM, which in turn supported key scenarios throughout the digital construction workflow.

Railway Bridge Geometry Assessment Supported by Cutting-Edge Reality Capture Technologies and 3D As-Designed Models

Documentation of structural visual inspections is necessary for its monitoring, maintenance, and decision about its rehabilitation, and structural strengthening. In recent times, close-range photogrammetry (CRP) based on unmanned aerial vehicles (UAVs) and terrestrial laser scanners (TLS) have greatly improved the survey phase. These technologies can be used independently or in combination to provide a 3D as-is image-based model of the railway bridge. In this study, TLS captured the side and bottom sections of the deck, while the CRP-based UAV captured the side and top sections of the deck, and the track. The combination of post-processing techniques enabled the merging of TLS and CRP models, resulting in the creation of an accurate 3D representation of the complete railway bridge deck. Additionally, a 3D as-designed model was developed based on the design plans of the bridge. The as-designed model is compared to the as-is model through a 3D digital registration. The comparison allows the detection of dimensional deviation and surface alignments. The results reveal slight deviations in the structural dimension with a global average value of 9 mm.

Building an optimal predictive model for imputing tissue-specific gene expression by combining genotype and whole-blood transcriptome data

Accurate imputation of tissue-specific gene expression can be a powerful tool for understanding the biological mechanisms underlying human complex traits. Existing imputation methods can be grouped into two categories according to the types of predictors used. The first category uses genotype data, while the second category uses whole-blood expression data. Both data types can be easily collected from blood, avoiding invasive tissue biopsies. In this study, we attempted to build an optimal predictive model for imputing tissue-specific gene expression by combining the genotype and whole-blood expression data. We first evaluated the imputation performance of each standalone model (using genotype data [GEN model] and using whole-blood expression data [WBE model]) using their respective data types across 47 human tissues. The WBE model outperformed the GEN model in most tissues by a large gain. Then, we developed several combined models that leverage both types of predictors to further improve imputation performance. We tried various strategies, including utilizing a merged dataset of the two data types (MERGED models) and integrating the imputation outcomes of the two standalone models (inverse variance-weighted [IVW] models). We found that one of the MERGED models noticeably outperformed the standalone models. This model involved a fixed ratio between the two regularization penalty factors for the two predictor types so that the contribution of the whole-blood transcriptome is upweighted compared with the genotype. Our study suggests that one can improve the imputation of tissue-specific gene expression by combining the genotype and whole-blood expression, but the improvement can be largely dependent on the combination strategy chosen.

Review: Harnessing extant energy and protein requirement modeling for sustainable beef production.

Numerous mathematical nutrition models have been developed in the last sixty years to predict the dietary supply and requirement of farm animals' energy and protein. Although these models, usually developed by different groups, share similar concepts and data, their calculation routines (i.e., submodels) have rarely been combined into generalized models. This lack of mixing submodels is partly because different models have different attributes, including paradigms, structural decisions, inputs/outputs, and parameterization processes that could render them incompatible for merging. Another reason is that predictability might increase due to offsetting errors that cannot be thoroughly studied. Alternatively, combining concepts might be more accessible and safer than combining models' calculation routines because concepts can be incorporated into existing models without changing the modeling structure and calculation logic, though additional inputs might be needed. Instead of developing new models, improving the merging of extant models' concepts might curtail the time and effort needed to develop models capable of evaluating aspects of sustainability. Two areas of beef production research that are needed to ensure adequate diet formulation include accurate energy requirements of grazing animals (decrease methane emissions) and efficiency of energy use (reduce carcass waste and resource use) by growing cattle. A revised model for energy expenditure of grazing animals was proposed to incorporate the energy needed for physical activity, as the British feeding system recommended, and eating and rumination (HjEer) into the total energy requirement. Unfortunately, the proposed equation can only be solved iteratively through optimization because HjEer requires metabolizable energy (ME) intake. The other revised model expanded an existing model to estimate the partial efficiency of using ME for growth (kg) from protein proportion in the retained energy by including an animal degree of maturity and average daily gain (ADG) as used in the Australian feeding system. The revised kg model uses carcass composition, and it is less dependent on dietary ME content, but still requires an accurate assessment of the degree of maturity and ADG, which in turn depends on the kg. Therefore, it needs to be solved iteratively or using one-step delayed continuous calculation (i.e., use the previous day's ADG to compute the current day's kg). We believe that generalized models developed by merging different models' concepts might improve our understanding of the relationships of existing variables that were known for their importance but not included in extant models because of the lack of proper information or confidence at that time.

Super ensemble based streamflow simulation using multi-source remote sensing and ground gauged rainfall data fusion

Traditional data-driven streamflow predictions usually apply a single model with inconsistent performance in different variability conditions. These days model ensembles or merging the benefits of different models without losing the general character of the data are becoming a trend in hydrology. This study compared three super ensemble learners with eight base models. Twelve years of monthly rolled daily time series data in three river catchments of Ethiopia (Borkena watershed: Awash river basin), (Gummera watershed: Abay river basin), and (Sore watershed: Baro Akobo river basin) is used for single-step daily streamflow simulation using previous thirty-day input timesteps. Five input scenarios are applied: three vegetation indices, three remote sensing-based precipitation products, ground-gauged rainfall, all fused inputs, and selected inputs with the Recursive Feature Elimination (RFE) algorithm. The time series is then divided into training and testing datasets with a ratio of 80:20. The performance of the proposed models was evaluated using the Root Mean Squared Error (RMSE), coefficient of determination (R2), Mean Absolute Error (MAE), and Median Absolute Error (MEDAE). Finally, the result is presented with the corresponding five input scenarios. The catchment's and input scenario's average performance indicated that the three super ensemble learners outperformed the eight base models with relatively stable performance. The top-ranked WASE model exceeded the linear regression baseline by 13.3%. XGB, CNN-GRU, and LSTM proved the highest performance of the base models. This study also revealed that LSTM's key downside is its performance drop in the absence of feature selection criteria. In comparison, XGB showed its superior performance after controlling redundant inputs internally. Moreover, this study uniquely highlights the potential of remote sensing-based vegetation indices in the science of data-driven streamflow modelling for non-gauged catchments with no meteorological time series.

On the Organological Potential of Video Games

The various—and often unpredictable—musical applications given to new digital technologies have fundamentally altered our ways of creating, performing, representing, and understanding musical instruments, giving way to new sounding artifacts that pose unprecedented challenges to musical iconography and organology. The multifaceted nature of digital musical instruments has exposed the limitations of traditional organology, prompting a redefinition of the concept of instrumentality and an upsurge of new methodologies. This renovation, however, has yet to recognize the organological potential of video games, which often make use of visual depictions and reconfigurations of musical instruments in the creation and characterization of virtual universes and characters. Musical instruments play an essential role in regulating the tensions between fiction and reality that underlie videoludic experiences by constituting an active agent in the player’s interaction and emerging relationships with the gameworld and its music. Research on these artifacts has been scarce yet promising thus far, providing this article with a wide-ranging collection of references that have served to inform the proposal of a new cataloguing card model for the identification, examination, and classification of musical instruments represented in, and constituted by, video games and consoles. This model merges traditional organological knowledge inherited from hierarchical systems of classification—such as the Hornbostel-Sachs—with new paradigms and taxonomical categories. In so doing, this model aims to accommodate the dual nature of ludomusical instruments as both iconographical resources of undeniable sociocultural transcendence, and organological artifacts with a set of assigned roles within their virtual worlds. The ultimate goal is to provide a framework that fully recognizes the impact of ludomusical instruments in musical experiences and organological knowledge and expectations both inside and outside the medium.

Stellar-mass black holes in the Hyades star cluster?

ABSTRACT Astrophysical models of binary-black hole mergers in the universe require a significant fraction of stellar-mass black holes (BHs) to receive negligible natal kicks to explain the gravitational wave detections. This implies that BHs should be retained even in open clusters with low escape velocities (≲1 km s−1). We search for signatures of the presence of BHs in the nearest open cluster to the Sun – the Hyades – by comparing density profiles of direct N-body models to data from Gaia. The observations are best reproduced by models with 2–3 BHs at present. Models that never possessed BHs have an half-mass radius $\sim 30~{{\ \rm per\ cent}}$ smaller than the observed value, while those where the last BHs were ejected recently (≲150 Myr ago) can still reproduce the density profile. In 50 per cent of the models hosting BHs, we find BHs with stellar companion(s). Their period distribution peaks at ∼103 yr, making them unlikely to be found through velocity variations. We look for potential BH companions through large Gaia astrometric and spectroscopic errors, identifying 56 binary candidates – none of which is consistent with a massive compact companion. Models with 2–3 BHs have an elevated central velocity dispersion, but observations cannot yet discriminate. We conclude that the present-day structure of the Hyades requires a significant fraction of BHs to receive natal kicks smaller than the escape velocity of $\sim 3 \, \mathrm{km \, s^{-1}}$ at the time of BH formation and that the nearest BHs to the Sun are in, or near, Hyades.

HyFlex pedagogy: six strategies supported by design-based research

PurposeThis study investigates the following research question: What pedagogical strategies are necessary for the success of HyFlex course design? The findings to this question are based in new media literacies and help to further pedagogy in an emerging HyFlex model while also grounding in needed theorization.Design/methodology/approachThis study uses design-based research (DBR) across two iterations and four doctoral, higher education courses, using mixed methods of data collection and analysis.FindingsSix pedagogical strategies influential for HyFlex research are presented, each grounded in a new media literacy skill.Originality/valueThese six pedagogical strategies help practitioners grappling with the HyFlex or blended learning model merge traditional pedagogy with how this might be tailored for students entrenched in a participatory culture.

A MIXED METHOD RESEARCH ON THE INVESTIGATION OF PERFORMANCE CHANGES OF AFFILIATED HOSPITALS

Affiliation is a sectoral merger or partnership model in the health sector. It means integration among institutions. Affiliation was introduced in 2011 and implemented in 2013 by public hospitals in Türkiye. This study had two objectives: (1) addressing the economic and organizational changes in performance in affiliated hospitals and (2) identifying its positive and negative aspects in practice. The study adopted a mixedmethod research design. Quantitative data were analyzed using data envelopment analysis. Qualitative data were analyzed through interviews with hospital administrators. The quantitative sample consisted of ten public hospitals, while the qualitative sample consisted of 30 hospital administrators. The quantitative findings showed that the hospitals had a partial decrease in expenses and a partial increase in revenues. The higher the number of patients, the more the healthcare services. The hospital administrators believed that hospitals needed affiliation. They stated that organizational problems arose in practice, that affiliation had positive and negative aspects, and that all these caused changes in performance. The results indicate that all stakeholders in the affiliation model have great returns. However, they should collaborate to turn affiliation into an ideal structure.

Smart Data Blending Framework to Enhance Precipitation Estimation through Interconnected Atmospheric, Satellite, and Surface Variables

Accurate precipitation estimation remains a challenge, though it is fundamental for most hydrological analyses. In this regard, this study aims to achieve two objectives. Firstly, we evaluate the performance of two precipitation products from the Integrated Multi-satellitE Retrievals for Global Precipitation Measurement (GPM-IMERG) for Sicily, Italy, from 2016 to 2020 by a set of categorical indicators and statistical indices. Analyses indicate the favorable performance of daily estimates, while half-hourly estimates exhibited poorer performance, revealing larger discrepancies between satellite and ground-based measurements at sub-hourly timescales. Secondly, we propose four multi-source merged models within Artificial Neural Network (ANN) and Multivariant Linear Regression (MLR) blending frameworks to seek potential improvement by exploiting different combinations of Soil Moisture (SM) measurements from the Soil Moisture Active Passive (SMAP) mission and atmospheric factor of Precipitable Water Vapor (PWV) estimations, from the Advanced Microwave Scanning Radiometer-2 (AMSR2). Spatial distribution maps of some diagnostic indices used to quantitatively evaluate the quality of models reveal the best performance of ANNs over the entire domain. Assessing variable sensitivity reveals the importance of IMERG satellite precipitation and PWV in non-linear models such as ANNs, which outperform the MLR modeling framework and individual IMERG products.

Merging bioactivity predictions from cell morphology and chemical fingerprint models using similarity to training data

The applicability domain of machine learning models trained on structural fingerprints for the prediction of biological endpoints is often limited by the lack of diversity of chemical space of the training data. In this work, we developed similarity-based merger models which combined the outputs of individual models trained on cell morphology (based on Cell Painting) and chemical structure (based on chemical fingerprints) and the structural and morphological similarities of the compounds in the test dataset to compounds in the training dataset. We applied these similarity-based merger models using logistic regression models on the predictions and similarities as features and predicted assay hit calls of 177 assays from ChEMBL, PubChem and the Broad Institute (where the required Cell Painting annotations were available). We found that the similarity-based merger models outperformed other models with an additional 20% assays (79 out of 177 assays) with an AUC > 0.70 compared with 65 out of 177 assays using structural models and 50 out of 177 assays using Cell Painting models. Our results demonstrated that similarity-based merger models combining structure and cell morphology models can more accurately predict a wide range of biological assay outcomes and further expanded the applicability domain by better extrapolating to new structural and morphology spaces.Graphical

Applications of machine learning to detecting fast neutrino flavor instabilities in core-collapse supernova and neutron star merger models

Neutrinos propagating in a dense neutrino gas, such as those expected in core-collapse supernovae (CCSNe) and neutron star mergers (NSMs), can experience fast flavor conversions on relatively short scales. This can happen if the neutrino electron lepton number ($\nu$ELN) angular distribution crosses zero in a certain direction. Despite this, most of the state-of-the-art CCSN and NSM simulations do not provide such detailed angular information and instead, supply only a few moments of the neutrino angular distributions. In this study we employ, for the \emph{first} time, a machine learning (ML) approach to this problem and show that it can be extremely successful in detecting $\nu$ELN crossings on the basis of its zeroth and first moments. We observe that an accuracy of $\sim95\%$ can be achieved by the ML algorithms, which almost corresponds to the Bayes error rate of our problem. Considering its remarkable efficiency and agility, the ML approach provides one with an unprecedented opportunity to evaluate the occurrence of FFCs in CCSN and NSM simulations \emph{on the fly}. We also provide our ML methodologies on GitHub.

Mining domain-specific edit operations from model repositories with applications to semantic lifting of model differences and change profiling

Model transformations are central to model-driven software development. Applications of model transformations include creating models, handling model co-evolution, model merging, and understanding model evolution. In the past, various (semi-)automatic approaches to derive model transformations from meta-models or from examples have been proposed. These approaches require time-consuming handcrafting or the recording of concrete examples, or they are unable to derive complex transformations. We propose a novel unsupervised approach, called Ockham, which is able to learn edit operations from model histories in model repositories. Ockham is based on the idea that meaningful domain-specific edit operations are the ones that compress the model differences. It employs frequent subgraph mining to discover frequent structures in model difference graphs. We evaluate our approach in two controlled experiments and one real-world case study of a large-scale industrial model-driven architecture project in the railway domain. We found that our approach is able to discover frequent edit operations that have actually been applied before. Furthermore, Ockham is able to extract edit operations that are meaningful—in the sense of explaining model differences through the edit operations they comprise—to practitioners in an industrial setting. We also discuss use cases (i.e., semantic lifting of model differences and change profiles) for the discovered edit operations in this industrial setting. We find that the edit operations discovered by Ockham can be used to better understand and simulate the evolution of models.

Physical and Technological Capital Efficiency for Profit Growth in Small and Medium Enterprises in Gauteng, South Africa: A Descriptive Qualitative Study

The increasing number of businesses closing down and the persistent slow growth of small and medium enterprises (SMEs) within the manufacturing sector is a serious concern globally, including in South Africa. The SMEs that are currently existing are operating below the required efficiency level. Physical and technological capital efficiency integrating manufacturing systematic planning, work study, standardisation and inventory management to improve the profit of SMEs in Gauteng South Africa is inadequate, which is a major concern. This study aimed to explore the physical and technological capital constraints affecting SMEs’ profit growth and develop the physical and technological capital efficiency adoption model merging manufacturing systematic planning, work study, standardisation and inventory management for SME profit growth in Gauteng. The study was qualitative, exploratory and descriptive in nature. Face-to-face interviews were conducted targeting 15 owners/managers amongst SMEs in Gauteng. The findings show the most common causes of slow profit growth, which involve poor material handling, unfavourable workplace layout, unscheduled machine maintenance, network challenges, failure to adhere to workplace standards, insufficient product and material recording systems and poor work methods and procedures. The study revealed major concerns for SMEs that required intervention for these enterprises to sustain their profit growth. Thus, the adoption of the model concerning the incorporation of physical and technological capital efficiency tools to advance manufacturing operations be considered as the contributing insight into the profit growth of SMEs in Gauteng. Thus, this study recommends that the government creates an enabling environment for the adoption of this model for SMEs.