Modeling Strategy Research Articles

Enhancing vibroacoustic reduced order models for digital twin development in industrial applications

In recent years, the growing interest towards the Digital Twin has prompted extensive exploration in both academic and industrial domains. To realize this technology, the development of simulation models that seamlessly run parallel to the physical assets is necessary. Model Order Reduction (MOR) plays a pivotal role, significantly easing the computational demands of 3D models while maintaining high fidelity. Krylov-based methods have proven successful in various vibroacoustic applications. However, state-of-the-art contributions employ suboptimal modeling strategies, leading to computationally inefficient offline phases and limited scalability. The linear Finite Element Method (FEM) exhibits lower efficiency than high-order FEM; Astley-Leis infinite elements, used to enforce non-reflecting conditions, constrain models to spherical domains, proving inefficient for elongated structures. In this work, advanced techniques-FEM Adaptive Order (FEMAO) and flexible infinite elements- are employed to alleviate the computational burden of the MOR process. The advantages of combining MOR, FEMAO, and flexible infinite elements are explored through various numerical examples. Specifically, we highlight the efficiency of output-based MOR in handling models with a high number of inputs, showcasing practical applications, especially in electric motor noise analysis. In conclusion, these numerical techniques reduce the computational burden of the offline phase, unlocking the Digital Twin for large-scale industrial models.

A framework for the acoustic simulation of passing vehicles using variable length delay lines

The sound produced by vehicles driving on roadways constitutes one of the dominant noise sources in urban areas. The impact of traffic noise on human activities and the related investigation on modeling, assessment, and abatement strategies fueled the research on the simulation of the sound produced by individual passing vehicles. Simulators enable in fact to promote a perceptual assessment of the nature of traffic noise and of the impact of single road agents on the overall soundscape. In this work, we present TrafficSoundSim, an open-source framework for the acoustic simulation of vehicles transiting on a road. We first discuss the generation of the sound signal produced by a vehicle, represented as a combination of road/tire interaction noise and engine noise. We then introduce a propagation model based on the use of variable length delay lines, allowing to simulate acoustic propagation and Doppler effect. The proposed simulator incorporates the effect of air absorption and ground reflection, modeled via complex-valued reflection coefficients dependent on the road surface impedance, as well as a model of the directivity of sound sources representing the passing vehicles. The source signal generation and the propagation stages are decoupled, and all effects are implemented using finite impulse response filters. Moreover, no recorded data is required to run the simulation, making the framework flexible and independent on data availability. Finally, to validate the framework capability to accurately simulate passing vehicles, a comparison between synthetic and recorded pass-by events is presented. The validation shows that sounds generated with the proposed method achieve a good match with recorded events in terms of power spectral density and psychoacoustics metrics as well as a perceptually plausible result.

Distinct Element macro-crack networks for expedited discontinuum seismic analysis of large-scale URM structures

Discontinuum analysis is a powerful tool for the detailed seismic assessment of unreinforced masonry (URM) structures, whose widespread use is however still mostly limited to small-scale assemblies or isolated components. Despite their unique capabilities, including the explicit simulation of separation phenomena, collapses and out-of-plane (OOP) failures that are hardly replicable using traditional continuum solutions, the high computational cost entailed by micro-to-meso-scale discontinuum modelling strategies, which are the standard approaches in applied research, presently prevent their employment for building-scale problems. The few available macro-scale discontinuum models specifically conceived for URM, on the other hand, rely on complex geometrical discretization processes that require costly manual work, as well as on less efficient deformable block formulations. In this paper, a new simplified discontinuum macro-model is presented and validated against full-scale laboratory test outcomes on walls, pier-spandrel systems and building specimens, in addition to various meso-scale numerical results, under either in-plane (IP) or OOP actions, and considering quasi-static (monotonic, cyclic) or dynamic loadings. The proposed simulation technique, implemented in a robust Distinct Element Method (DEM) framework, leverages a semi-automated Equivalent Frame discretization algorithm that idealizes masonry piers, spandrels and nodes as an assembly of interlocked rigid macro-blocks connected by a network of zero-thickness interface springs. The latter are herein demonstrated to effectively replicate URM damage at the component-level through fracture energy contact laws, providing macro-scale yet representative failure patterns, as well as adequate predictions of overall strength and deformation capacities. Results obtained show a good agreement between macro- and more sophisticated meso-scale predictions, as well as with experimental outcomes, albeit dissimilarities among measured and computed force-displacement responses – similarly to other simplified strategies – were detected as vertical overburden increases. Notably, for analogous levels of accuracy, the analysis time required by our new macro-models is up to 150 times lower than its meso-counterparts. The use of the proposed simplified strategy also enabled the satisfactory simulation of the quasi-static cyclic and seismic responses of full-scale building-scale specimens, prohibitive tasks using traditional detailed DEM models, while also being up to 10 times faster than previous deformable macro-models.

Advancements and future directions in single-cell Hi-C based 3D chromatin modeling

Single-cell Hi-C data provides valuable insights into the three-dimensional organization of chromatin within individual cells, yet modeling this data poses significant challenges due to its inherent sparsity and variability. This review comprehensively explores the predominant approaches to reconstructing 3D chromatin structures from single-cell Hi-C data, positioning these methods within the broader contexts of single-cell Hi-C research and bulk Hi-C data modeling.We categorize the modeling strategies based on their objective functions, which are framed in terms of force fields, potentials, cost functions, or likelihood probabilities. Despite their diverse methodologies, these approaches exhibit deep underlying similarities. We further dissect the basic components of these models, such as attractive restraint forces and repulsive forces, and discuss additional terms like fluid viscosity and variation penalties.The review also critically evaluates the current state of model validation, highlighting the inconsistencies across various studies and emphasizing the need for a comprehensive validation framework. We detail common validation techniques, including the comparison of distance matrices and the assessment of contact violations.We argue that the future of single-cell Hi-C modeling lies in integrating multiple data modalities and incorporating cell cycle trajectory information. Such integration could significantly advance our understanding of chromatin conformation dynamics during cell cycle progression and cell differentiation. We also foresee the continued growth of optimization-based and molecular dynamics approaches, supported by general molecular dynamics toolkits.

Improving Raman-Based Models for Real-Time Monitoring the CHO Cell Culture Process with Effective Variable Selection Strategies

Research has shown that Raman spectroscopy could be applied to monitor various components in mammalian cell culture in real time. In the process of application, it is necessary to ensure the performance of the Raman-based model. The variable selection strategy is an effective method that significantly influences the model performance and simplification. In this study, different variable selection strategies were evaluated, and the optimal variable selection strategy was determined for monitoring the CHO cell culture process. Firstly, a wide variety of spectral regions involving the Raman fingerprinting region and the C-H stretching region were investigated. Secondly, six different variable selection algorithms were meticulously assessed. Thirdly, the combination of different variable selection algorithms was used to improve model performance and simplify the model. Finally, the monitoring of cell culture processes was implemented. The findings underscored that commonly used spectral regions could improve the model performance but could not simplify the model well. Moving-window partial least square (MWPLS), genetic algorithm (GA), and random frog (RF) are more suitable for Raman modeling of the cell culture process, but they must be used after the spectral region selection. The combination of three variable selection algorithms (MWPLS-GA-RF) improved the model’s performance by 16–70% by selecting 30–60 variables, effectively simplifying the model. For glucose, lactate, viable cell density, and ammonium ion, real-time monitoring was performed well. This study will be helpful for researchers to select suitable variable selection strategies for building models for the real-time monitoring of cell culture.

Metacognitive strategies in mathematical modelling: a case study with engineering students

During the last years, mathematical modelling has been entering the field of engineering education because it is a bridge between mathematics and industry that also promotes the development of skills that require the engineer of the twenty-first century. Nevertheless, the modelling process is involved and requires the deployment of cognitive skills of superior level, particularly metacognitive skills. This contribution presents the results of a multiple qualitative case study on metacognitive strategies used by groups of engineering students associated with each transition of the mathematical modelling process. During the development of the course, observations and retrospective interviews were carried out, which evidenced the use of metacognitive planning strategies from simplification to interpretation; metacognitive monitoring strategies throughout the modelling process, metacognitive regulation strategies from mathematization to mathematical work and metacognitive evaluation strategies during simplification, mathematization and validation. The results expand the classification framework of metacognitive strategies for engineering students.

Challenges of Nigerian Book Publishing Industry in Post-Covid-19 Economy and Survival Strategies

Book publishing, like other sectors of the global economy, has experienced unprecedented challenges in the post-COVID-19 era, which include disrupted supply chains, reduced purchasing power on the part of students and teachers, and increased competition from digital media. The pandemic has also occasioned a change in business model and marketing strategy leading to transition from physical to virtual platforms. In addition, the book publishing industry has equally experienced paper shortages, printing delays, and heightened costs of production thus, encroaching on the publishers’ profit margin. The study, anchored on Free market theory and instinctive theory of motivation, attempted to put book publishing in perspective using qualitative research that relies on secondary data. The study also looked at the effect of Covid-19 on the global economy in general and the book publishing industry in particular. The study discovered the crucial role the advent of digitalization played in the interface of post-COVID-19 economy and book publishing sub-sector. Besides, it revealed the manifold challenges engendered by COVID-19 in the book publishing industry globally and specifically in Nigeria, which include lack of capital, inability to provide adequate numbers of high-quality books, book piracy, proliferation of unqualified author-publishers and poor reading culture, to the extent the sub-sector is struggling to survive. To endure this inclement economic era, the study suggested the following strategies: publishers need to adopt innovative strategies by investing in digital infrastructure so as to be able to publish printed and e-books as well as opening physical and online sales outlets to boost the revenue base, and different publishers can form a consortium to negotiate and minimize the cost of buying raw materials. Additionally, two or more publishing outfits can come together under merger and acquisition arrangements to bolster their capital base and stay competitive. In conclusion, the study made some recommendations that can help to support this industry to stay afloat in this trying time.

Intelligence analysis of membrane distillation via machine learning models for pharmaceutical separation

This study investigates simulation of pharmaceutical separation via membrane distillation process by computational simulation and machine learning modeling strategy. The efficacy of three regression models, i.e., Multi-layer Perceptron (MLP), Gamma Regression, and Support Vector Regression (SVR) in predicting the solute concentration, C(mol/m³), was evaluated. The hyper-parameters were optimized by fine-tuning the models using the Red Deer Algorithm (RDA). Computational analyses were carried out for removal of pharmaceuticals from solution by membrane distillation in continuous mode. Mass transfer and machine learning models were implemented focusing on concentration of solute in the feed section of membrane. Results indicate that the Multi-layer Perceptron model achieved great accuracy with an R2 of 0.9955, an MAE of 0.0084, and an RMSE of 0.0148, effectively capturing complex nonlinear relationships in the data. Gamma Regression also performed acceptably, with fitting R2 of 0.9214, showing its suitability for positively skewed data. The Support Vector Regression model, while capturing the general trend, showed the lowest performance with an R2 of 0.8710. These findings suggest that the Multi-layer Perceptron is the most accurate model for this dataset, followed by Gamma Regression and Support Vector Regression. This underscores the importance of careful model selection and optimization in regression analysis in combination with computational simulation of membrane processes.

Reassessing Cervical Cancer Prevention: Evaluating the NHS Cervical Cancer Screening Programme Through the Health Belief Model and Global Health Promotion Strategies

Cervical cancer remains a significant public health issue in the United Kingdom, with disparities in screening coverage and outcomes persisting despite robust national programs. This critical analysis evaluates the NHS Cervical Screening Programme (CSP) through the lens of the Health Belief Model (HBM) and global health promotion strategies, such as those outlined in the Ottawa Charter. The analysis explores how individual beliefs about susceptibility, severity, benefits, and barriers influence participation in cervical screening, while also critiquing the CSP's reliance on these factors to the potential exclusion of broader social determinants of health. Key findings reveal that while the HBM provides a valuable framework for understanding individual health behaviors, its emphasis on personal responsibility may overlook significant socioeconomic and cultural barriers, leading to inequities in screening uptake. Furthermore, the CSP's approach, though well-intentioned, may inadvertently undermine patient autonomy by promoting a top-down model of health promotion. The analysis is supported by data from the 2022-2023 Cervical Screening Standards Data Report and other contemporary sources, highlighting the need for more culturally sensitive and equitable strategies. Recommendations for policymakers include the enhancement of targeted interventions for high-risk groups, improved communication strategies, and the integration of a social model of health that considers the broader determinants of health. Future studies are encouraged to explore the sociocultural factors influencing screening behaviors and to evaluate the effectiveness of new screening technologies and integrated HPV vaccination programs. This analysis underscores the importance of rethinking cervical cancer prevention strategies to ensure they are inclusive, equitable, and aligned with the principles of health promotion.

Spoofing attack recognition for GNSS-based train positioning using a BO-LightGBM method.

Trustworthy positioning is critical in the operational control and management of trains. For a train positioning system (TPS) based on a global navigation satellite system (GNSS), a spoofing attack significantly threatens the trustworthiness of positioning. However, the influence and recognition of GNSS spoofing attacks are not considered in the existing research on GNSS-enabled TPS. Spoofing attacks affect the performance of GNSS observations and the positioning results, allowing the development of data-driven spoofing recognition solutions. This study aims to achieve effective spoofing recognition for active security protection in TPS. Different features were designed to reflect the effects of a spoofing attack, including GNSS observation-related indicators and odometer-enabled parameters, and a novel Bayesian optimization-light gradient boosting machine (BO-LightGBM) solution was proposed. In particular, a Bayesian optimization technique was introduced into the LightGBM framework to improve the hyperparameter determination capability for recognition model training. Using a GNSS spoofing test platform with a specific GNSS signal generator and the SimSAFE spoofing test tool, different spoofing attack modes were tested to collect sample datasets for model training and evaluation. The results of model establishment and comparison of the model performance indicators illustrated the advantages of the proposed solution, its adaptability to different spoofing attack situations, and its superiority over state-of-the-art modeling strategies.

Estimating and investigating multiple constructs multiple indicators social relations models with and without roles within the traditional structural equation modeling framework: A tutorial.

The present contribution provides a tutorial for the estimation of the social relations model (SRM) by means of structural equation modeling (SEM). In the overarching SEM-framework, the SRM without roles (with interchangeable dyads) is derived as a more restrictive form of the SRM with roles (with noninterchangeable dyads). Starting with the simplest type of the SRM for one latent construct assessed by one manifest round-robin indicator, we show how the model can be extended to multiple constructs each measured by multiple indicators. We illustrate a multiple constructs multiple indicators SEM SRM both with and without roles with simulated data and explain the parameter interpretations. We present how testing the substantial model assumptions can be disentangled from testing the interchangeability of dyads. Additionally, we point out modeling strategies that adhere to cases in which only some members of a group can be differentiated with regards to their roles (i.e., only some group members are noninterchangeable). In the online supplemental materials, we provide concrete examples of specific modeling problems and their implementation into statistical software (Mplus, lavaan, and OpenMx). Advantages, caveats, possible extensions, and limitations in comparison with alternative modeling options are discussed. (PsycInfo Database Record (c) 2022 APA, all rights reserved).

Use of implementation mapping to develop a multifaceted implementation strategy for an electronic prospective surveillance model for cancer rehabilitation

BackgroundElectronic Prospective Surveillance Models (ePSMs) remotely monitor the rehabilitation needs of people with cancer via patient-reported outcomes at pre-defined time points during cancer care and deliver support, including links to self-management education and community programs, and recommendations for further clinical screening and rehabilitation referrals. Previous guidance on implementing ePSMs lacks sufficient detail on approaches to select implementation strategies for these systems. The purpose of this article is to describe how we developed an implementation plan for REACH, an ePSM system designed for breast, colorectal, lymphoma, and head and neck cancers.MethodsImplementation Mapping guided the process of developing the implementation plan. We integrated findings from a scoping review and qualitative study our team conducted to identify determinants to implementation, implementation actors and actions, and relevant outcomes. Determinants were categorized using the Consolidated Framework for Implementation Research (CFIR), and the implementation outcomes taxonomy guided the identification of outcomes. Next, determinants were mapped to the Expert Recommendations for Implementing Change (ERIC) taxonomy of strategies using the CFIR-ERIC Matching Tool. The list of strategies produced was refined through discussion amongst our team and feedback from knowledge users considering each strategy’s feasibility and importance rating via the Go-Zone plot, feasibility and applicability to the clinical contexts, and use among other ePSMs reported in our scoping review.ResultsOf the 39 CFIR constructs, 22 were identified as relevant determinants. Clinic managers, information technology teams, and healthcare providers with key roles in patient education were identified as important actors. The CFIR-ERIC Matching Tool resulted in 50 strategies with Level 1 endorsement and 13 strategies with Level 2 endorsement. The final list of strategies included 1) purposefully re-examine the implementation, 2) tailor strategies, 3) change record systems, 4) conduct educational meetings, 5) distribute educational materials, 6) intervene with patients to enhance uptake and adherence, 7) centralize technical assistance, and 8) use advisory boards and workgroups.ConclusionWe present a generalizable method that incorporates steps from Implementation Mapping, engages various knowledge users, and leverages implementation science frameworks to facilitate the development of an implementation strategy. An evaluation of implementation success using the implementation outcomes framework is underway.

An urban agricultural block construction method based on low-carbon concepts: A case study of the Beijing-Tianjin area in China

With the ongoing progress of urbanization, various issues such as resource scarcity, climate change, and carbon emissions overload have gradually emerged. Urban agriculture (UA) is recognized as a pivotal strategy to promote sustainable urban development. In this context, the study proposes a spatial scale hierarchy, encompassing “single building - building cluster - urban block," to formulate spatial strategies for the theoretical model of urban agricultural blocks. The theoretical model integrates production spaces, production methods, and spatial systems based on relevant design principles and the needs of residents. Nankai District's Qingfeng Road East Block in Tianjin is used as the site for applying and simulating the theoretical model. The results of the study show that UA in a 500m × 500m block can ideally eliminate the need for external food inputs by 100% for starchy vegetables, legumes and fruits, and meat/poultry, while achieving a 254.73% self-sufficiency rate for fungi. Additionally, it can reduce energy inputs by 28.33%, water inputs by 38.13%, and completely eliminate waste output for 8754 people. Carbon emission analysis indicates that the urban agricultural block can reduce approximately 2250.01t CO2 emissions from buildings and waste, with greater carbon-neutral potential in transportation and agriculture, forestry, and other land uses (AFOLU). This is significant for tackling various urban challenges and enhancing future development.

An adaptive learning strategy for surrogate modeling of high-dimensional functions - Application to unsteady hypersonic flows in chemical nonequilibrium

Many engineering applications rely on the evaluation of expensive, non-linear high-dimensional functions. In this paper, we propose the RONAALP algorithm (Reduced Order Nonlinear Approximation with Adaptive Learning Procedure) to incrementally learn, on-the-fly as the application progresses, a fast and accurate reduced-order surrogate model of a target function. First, a combination of nonlinear auto-encoder, community clustering, and radial basis function networks allows us to learn an efficient and compact surrogate model with limited training data. Secondly, an active learning procedure overcomes any extrapolation issues during the online stage by adapting the surrogate model with high-fidelity evaluations that fall outside its current validity range. This approach results in generalizable, fast, and accurate reduced-order models of high-dimensional functions. The method is demonstrated on three direct numerical simulations of hypersonic flows in chemical nonequilibrium. Accurate simulations of these flows rely on detailed thermochemical gas models that dramatically increase the cost of such calculations. Using RONAALP to learn a reduced-order thermodynamic model surrogate on-the-fly, the cost of such simulations was reduced by up to 75% while maintaining an error of less than 10% on relevant quantities of interest.

Generalized field inversion strategies for data-driven turbulence closure modeling

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

Distributed dynamic economic dispatch of biogas-wind-solar-hydrogen multi-microgrid system considering individual selfishness

This paper proposes a biogas-wind-solar-hydrogen multi-microgrid system to address the issues of poor economy and reliability, as well as the waste of wind and solar energy, in single energy-based isolated microgrid systems. The study considers the coupling constraints of multiple time scales and establishes a dynamic economic dispatch model. Furthermore, a consensus-based distributed dynamic economic dispatch strategy is proposed. To tackle the challenge of unified economic dispatch caused by the interaction among multiple microgrids in the joint operation of the biogas-wind-solar-hydrogen multi-microgrid system, a microgrid selfishness impact model and elimination strategy are developed. Simulation results demonstrate the effectiveness and superiority of the proposed distributed dynamic economic dispatch strategy considering individual selfishness in the biogas-wind-solar-hydrogen multi-microgrid system.

Family‐centered creative arts therapies for children with autism: A configurative systematic review

AbstractObjectiveThis configurative systematic review analyzed family‐centered creative arts therapies for children with autism to understand why and how they are done.BackgroundCreative arts therapies and family‐centered practice are both beneficial for children with autism. Research combining both approaches—that is, family‐centered creative arts therapies—is emerging, and reported findings are promising. A deeper understanding of the theoretical influences and mechanisms of these therapies may generate new ways of thinking and working with families with children with autism.MethodsFollowing registration with PROSPERO and in accordance with PRISMA (Preferred Reporting Items for Systematic Reviews and Meta‐Analyses) guidelines, a literature search was performed, resulting in 17 studies. Thematic analysis considered the studies in terms of theoretical influences, creative arts therapy components, and family‐centered components.ResultsThe development‐in‐context perspective was a key theoretical influence. In addition to music and singing, body movement/physical activity was a prominent component of creative arts therapies. Verbal support as well as modeling behaviors and strategies to parents were salient family‐centered components.ConclusionThis systematic review reveals common principles, practices, and gaps in the existing literature and their relevance for families with children with autism.ImplicationsThese findings bear implications for families, therapists, Certified Family Life Educators, and other professionals working with families with children with autism.

MPC Optimization Algorithm and Strategy for HVAC System under Smart City Construction

As a giant in energy consumption, buildings urgently need to optimize control strategies for the main energy consuming equipment inside buildings. It is of great significance to design advanced control algorithms to improve the efficiency of the main energy consuming equipment in buildings, namely air conditioning, in the current energy shortage. The model predictive control algorithms and strategies were used in this study to control the HVAC system to improve the energy utilization of the city. Then linear matrix inequality with robust model predictive feedback controller was used to optimize and get the model predictive control optimization algorithm. The research results showed that, under the influence of different factors, the three regions controlled by the model predictive control optimization algorithm showed a little overshooting in the initial state. But it was quickly corrected after adjustment. Meanwhile, the average tracking error of temperature and humidity in each region was 0.139◦ C and 0.13g/kg dry air, respectively. The average predicted mean vote was 0.32. In actual office buildings, the proposed algorithm controlled the temperature within the reference value range of 0.1◦ C throughout the entire process. The total electricity consumption and electricity price costs were reduced by 12.11% and 22.54%, respectively. In summary, the proposed method has good performance for HVAC system application, which can effectively realize energy saving and emission reduction and improve human comfort. This method makes important contributions to promote the construction of smart cities and the development of green buildings.

Cardiac and skeletal muscle manifestations in the G608G mouse model of Hutchinson-Gilford progeria syndrome.

Hutchinson-Gilford progeria syndrome (HGPS) is a rare premature aging disorder resulting from de novo mutations in the lamin A gene. Children with HGPS typically pass away in their teenage years due to cardiovascular diseases such as atherosclerosis, myocardial infarction, heart failure, and stroke. In this study, we characterized the G608G HGPS mouse model and explored cardiac and skeletal muscle function, along with senescence-associated phenotypes in fibroblasts. Homozygous G608G HGPS mice exhibited cardiac dysfunction, including decreased cardiac output and stroke volume, and impaired left ventricle relaxation. Additionally, skeletal muscle exhibited decreased isometric tetanic torque, muscle atrophy, and increased fibrosis. HGPS fibroblasts showed nuclear abnormalities, decreased proliferation, and increased expression of senescence markers. These findings provide insights into the pathophysiology of the G608G HGPS mouse model and inform potential therapeutic strategies for HGPS.

A PICTURE WORD INDUCTIVE MODEL TO ENHANCING VOCABULARY ACQUISITION AT THE PRIMARY LEVEL

The current research investigates the effect of using the Picture Word Inductive Model (PWIM) Strategy on Vocabulary Acquisition at the Primary Level. The research's null hypotheses have been formulated to be tested in light of the aim. The design of two equivalent groups, namely the experimental and control groups with pre and post-tests, has been adopted. The research sample included 60 female pupils from two primary schools intentionally selected from the center of Nineveh Governorate. They were divided into two groups: the experimental group included 30 female pupils taught according to the PWIM strategy at Al Talai Primary School. The control group included (30) female pupils taught according to the Subscribed method at Al Shaqa'ik Primary School. Both groups were equalized according to a set of variables: pupil's date of birth, pupil's general average of the preceding academic year, pupil's scores in English of the preceding academic year, and parents'. Moreover, the main requirements of this research were prepared, such as determining the teaching materials and preparing the teaching plans as well as; the researcher had prepared an instrument to collect the data based on the dependent variables of this research, namely the vocabulary Acquisition test of a passage followed by five questions. The result reveals a statistically significant difference (0.05) between the experimental and control groups in the Post-Test of Vocabulary Acquisition in favor of The Experimental group. Finally, recommendations and suggestions for further research are presented concerning the necessity of applying the (PWIM) in teaching the English language.