Modeling Techniques Research Articles

Ideotype map research based on a crop model in the context of a climatic gradient

Due to increasing climate uncertainties, optimizing plant traits is essential for sustainable agriculture. This article presents an approach that combines advanced modelling techniques to identify optimal plant traits under various agro-environmental conditions. By integrating a crop model, a climate generator, and our PEQI algorithm (Profile Expected Quantile Improvement), our method aims to create ideotype maps tailored to specific regions.We use the SAMARA model (Simulator of crop trait Assembly, MAnagement Response, and Adaptation), calibrated with trials carried in Sahel on a set of local varieties, to simulate crop growth in diverse environments. The PEQI algorithm adjusts varietal parameters to maximize expected yield, defining precise selection objectives known as ideotypes, which are particularly important in regions with irregular rainfall patterns like the Sahel.With the PEQI algorithm based on a kriging metamodel, we ensure effective adaptation to spatially variable environments. By leveraging a climate generator to simulate meteorological variability, our integrated approach optimizes crop yields in regions such as Senegal, southern Mali, Burkina Faso, and Guinea-Bissau. The outcome is an ideotype map for sorghum, providing breeders with a robust decision-support tool to enhance crop performance amidst climate uncertainty.

Investigation of compressive strength of primitive geometries printed through the fused deposition modeling technique with different path patterns

Fused Deposition Modeling (FDM) is a material-extrusion-based technique used primarily for rapid prototyping and sometimes for an actual servicing part. In the FDM technique, input parent materials are commercial polymers. FDM also has some manufacturing parameters, and the raster pattern significantly affects the mechanical performance of the FDM products. Due to its intrinsic nature, Acrylonitrile Butadiene Styrene (ABS) is widely used in many industries, such as automobiles, medicine, etc. Producing the primitive geometry and selecting the proper infill pattern is challenging. Therefore, the current research paper investigates the effects of various infill patterns on the compressive performance of the three geometries (sphere, 3-side, and 4-side pyramids) printed through the FDM technique out of ABS material. The compressive experiments were conducted on the printed samples and load-displacement curves were evaluated. The results reveal that the concentrate path pattern in the sphere samples has the highest compressive failure load (40127 N). Also, the compressive failure loads in the 3-side and 4-side pyramids fabricated with a 45°/−45° raster pattern are 30444 N and 44396 N, respectively. Finally, comprehensive discussions about the obtained results are stated.

A comparative review of de- and post-growth modeling studies

In recent years, a small but rapidly growing field of modeling alternatives to growth as represented by the de- (DG) and post-growth (PG) discourses has emerged. We compare selected model characteristics of 75 DG and PG related modeling studies, compiled through a systematic literature review (2000−2023), and link model structures and results to different theoretically contested debates surrounding DG/PG. The reviewed studies cover different geographical and temporal scopes, economic theories, modeling techniques and operationalizations of DG/PG. The majority of studies models DG/PG as intentional transition and does not question its compatibility with a capitalist system, while more radical strands of the DG/PG discourse are excluded. Although DG/PG modeling exercises frequently explore the effects of sustainability policies, they represent only a fraction of theoretical DG policy proposals, with the most frequent being: working time reduction, maximum income caps, carbon taxes and a universal basic income. DG/PG modeling studies have demonstrated the importance of integrating biophysical constraints in economic modeling but also have quantitatively assessed the feasibility of environmental integrity and social well-being without growth. Nonetheless, future modeling could be rendered more realistic by paying more attention to the Global South, introducing heterogeneous agents driving sustainability transitions and including multiple planetary boundaries.

Towards explainable oral cancer recognition: Screening on imperfect images via Informed Deep Learning and Case-Based Reasoning

Oral squamous cell carcinoma recognition presents a challenge due to late diagnosis and costly data acquisition. A cost-efficient, computerized screening system is crucial for early disease detection, minimizing the need for expert intervention and expensive analysis. Besides, transparency is essential to align these systems with critical sector applications. Explainable Artificial Intelligence (XAI) provides techniques for understanding models. However, current XAI is mostly data-driven and focused on addressing developers’ requirements of improving models rather than clinical users’ demands for expressing relevant insights. Among different XAI strategies, we propose a solution composed of Case-Based Reasoning paradigm to provide visual output explanations and Informed Deep Learning (IDL) to integrate medical knowledge within the system. A key aspect of our solution lies in its capability to handle data imperfections, including labeling inaccuracies and artifacts, thanks to an ensemble architecture on top of the deep learning (DL) workflow. We conducted several experimental benchmarks on a dataset collected in collaboration with medical centers. Our findings reveal that employing the IDL approach yields an accuracy of 85%, surpassing the 77% accuracy achieved by DL alone. Furthermore, we measured the human-centered explainability of the two approaches and IDL generates explanations more congruent with the clinical user demands.

How CSR communication and value co-creation shape consumer well-being and brand love in the post-COVID-19 pandemic: evidence from the UK

PurposeDrawing upon the service-dominant logic (S-D logic) and elaboration likelihood model (ELM), this paper provides an integrative model to investigate how argument quality and source credibility of CSR communication affects customer value co-creation behavior, resulting in increased brand trust. Additionally, it unveils how brand trust and the perception of COVID-19 risk influence both brand love and subjective well-being.Design/methodology/approachThe data of this study were collected through survey questionnaire from 304 coffee shop customers using convenience sampling and tested using the partial least squares structural equation modeling (PLS-SEM) technique to validate its model.FindingsThe research findings confirm the positive association between source credibility and customer value co-creation (i.e. customer participation and citizenship behavior). By contrast, argument quality is revealed to have a significantly positive effect on customer citizenship behavior (CCB) but a non-significant effect on customer participation behavior (CPB). Additionally, the study identifies that customer value co-creation behavior significantly promotes brand trust. Finally, findings indicate that brand trust and the perception of COVID-19 risk significantly influence brand love and subjective well-being.Originality/valueThis study contributes to the literature on CSR communication and S-D logic and provides new insights for marketers and advertisers to manage brands in the post-pandemic scenario through CSR communications. Furthermore, this study theoretically extends the ELM model to the CSR communication research. Finally, this study expands the relevant literature by clarifying the relationships between the perception of COVID-19 risk, brand love and subjective well-being.

Predicting and optimizing CO2 foam performance for enhanced oil recovery: A machine learning approach to foam formulation focusing on apparent viscosity and interfacial tension

Carbon dioxide foam injection stands as a promising method for enhanced oil recovery (EOR) and carbon sequestration. However, accurately predicting its efficiency amidst varying operational conditions and reservoir parameters remains a significant challenge for conventional modeling techniques. This study explores the application of machine learning (ML) methodologies to develop a robust model for matching experimental values in CO2 foam flooding scenarios. Leveraging a comprehensive dataset encompassing diverse surfactants and rock types, with varied porosity and permeability, our model demonstrates accurate predictions across a wide spectrum of conditions. By focusing on key parameters such as foam apparent viscosity, interfacial tension (IFT), injected foam volume, initial oil saturation, porosity, and permeability, we unveil the pivotal role of these factors in determining CO2 foam EOR performance. Through rigorous analysis, we identify the relative importance of each input parameter, with injected foam volume, apparent viscosity, and IFT emerging as dominant factors. The most accurate model was deep neural network (DNN) (R2 value of 0.99). Higher foam viscosity and lower IFT were found to significantly enhance oil recovery rates, though their effects plateau beyond certain thresholds (apparent viscosities above 1200 cP and IFT values below 0.2 mN/m). The findings underscore the potential of ML-driven approaches in enhancing CO2 foam EOR predictions, offering insights crucial for optimizing foam flooding performance across diverse reservoir settings.

Developing a Framework for Safe AI Model Development on Sensitive Healthcare Data

The Dementias Platform UK (DPUK) Data Portal holds over 60 cohort datasets from over 3 million participants with a range of multi-modal data including neuroimaging and genomics. This has meant we have seen an increasing interest in the development of AI models with the potential for clinical implementation. However, this presents a unique challenge to disclosure control when it comes to assessing these AI models for release due to the risk of attacks such as membership inference, model inversion or even just vulnerabilities in the models like overfitting. This is why we hosted a series of workshops bringing together members of the public, expert researchers, and data owners across the UK to build a framework for allowing the safe development and release of AI models trained on sensitive healthcare data. From this, we have put together recommendations and guidelines for the use of privacy-preserving techniques in AI models to protect patient privacy, and to allow the safe deployment of these models outside of trusted research environments. We also identified the unique challenges to privacy and implementation of privacy-preserving techniques in AI models regarding the use of complex data such as neuroimaging and genomics. This framework has created a path forward for supporting safe AI model development which takes into consideration rapidly evolving ethical, legal and privacy considerations.

Leveraging drone technology for enhanced thermal performance analysis in sustainable architecture: a case study of GBI-certified buildings in Malaysia

ABSTRACT In recent years, the realm of architecture has witnessed a significant transformation marked by a notable surge in focus on sustainable practices and energy efficiency. Architects and engineers increasingly recognize the innovative role of thermal efficiency in enhancing building performance. Such recognition emphasizes the critical importance of thermal analysis in understanding the thermal attributes of building materials and structures. Against this backdrop, this paper aims to bridge a current gap in architectural practices and sustainable design within the Malaysian context. It proposes a case study leveraging drone thermal mapping techniques on Green Building Index (GBI)-certified buildings. The study seeks to examine how architectural designs interact with environmental conditions and identify areas for improvement in sustainable building practices, particularly within Malaysia’s tropical climate context. The primary objective is to assess the effectiveness of drone technology in analysing building thermal performance and to explore how the findings can inform design interventions to enhance sustainability. To achieve this, a structured methodology comprising five procedural phases is employed in a case study focused on three iconic GBI-certified buildings in Putrajaya, namely Putrajaya Holdings Tower, Energy Commission Building, and Heriot-Watt University Malaysia. By integrating thermal imaging with visuospatial data and 3D modeling techniques, the study demonstrates the effectiveness of drones in analysing heat patterns, highlighting their potential as a tool for strategic design interventions. As an exploratory study, this paper serves as a catalyst for further investigations into the integration of drone technologies with architectural design, particularly within the context of tropical climates.

Unveiling the impact of SUMOylation at K298 site of heat shock factor 1 on glioblastoma malignant progression

BackgroundGlioblastoma (GBM) poses a significant medical challenge due to its aggressive nature and poor prognosis. Mitochondrial unfolded protein response (UPRmt) and the heat shock factor 1 (HSF1) pathway play crucial roles in GBM pathogenesis. Post-translational modifications, such as SUMOylation, regulate the mechanism of action of HSF1 and may influence the progression of GBM. Understanding the interplay between SUMOylation-modified HSF1 and GBM pathophysiology is essential for developing targeted therapies. MethodsWe conducted a comprehensive investigation using cellular, molecular, and in vivo techniques. Cell culture experiments involved establishing stable cell lines, protein extraction, Western blotting, co-immunoprecipitation, and immunofluorescence analysis. Mass spectrometry was utilized for protein interaction studies. Computational modeling techniques were employed for protein structure analysis. Plasmid construction and lentiviral transfection facilitated the manipulation of HSF1 SUMOylation. In vivo studies employed xenograft models for tumor growth assessment. ResultsOur research findings indicate that HSF1 primarily undergoes SUMOylation at the lysine residue K298, enhancing its nuclear translocation, stability, and downstream heat shock protein expression, while having no effect on its trimer conformation. SUMOylated HSF1 promoted the UPRmt pathway, leading to increased GBM cell proliferation, migration, invasion, and reduced apoptosis. In vivo studies have confirmed that SUMOylation of HSF1 enhances its oncogenic effect in promoting tumor growth in GBM xenograft models. ConclusionThis study elucidates the significance of SUMOylation modification of HSF1 in driving GBM progression. Targeting SUMOylated HSF1 may offer a novel therapeutic approach for GBM treatment. Further investigation into the specific molecular mechanisms influenced by SUMOylated HSF1 is warranted for the development of effective targeted therapies to improve outcomes for GBM patients.

Performance Properties and Finite Element Modelling of Forest-Based Bionanomaterials/Activated Carbon Composite Film for Sustainable Future

Thanks to its highly crystalline structure and excellent thermal, optical, electrical and mechanical properties, carbon and its derivatives are considered the preferred reinforcement material in composites used in many industrial applications, especially in the forest and forest products sector, including oil, gas and aviation. Since hydroxyethyl cellulose (HEC) is a biopolymer, it has poor mechanical and thermal properties. These properties need to be strengthened with various additives. This study aims to improve the thermal and mechanical properties of hydroxyethyl cellulose by preparing hydroxyethyl cellulose/activated carbon (HEC/AC) composite materials. With this study, composites were obtained for the first time and their mechanical properties were examined using a 3D numerical modeling technique. The thermal stability of the prepared composite materials was investigated via thermal gravimetric analysis (TGA). The samples were heated from 30 °C to 750 °C with a heating rate of 10 °C/min under a nitrogen atmosphere and their masses were measured subsequently. The mechanical properties of the composites were investigated via the tensile test. The viscoelastic properties of the composite films were determined with dynamic mechanical thermal analyses (DMTA) and their morphologies were examined with scanning electron microscopy (SEM) images. According to the results, the best F3 sample (films containing 3 wt.% activated carbon) had an elastic modulus of 168.3 MPa, a thermal conductivity value of 0.068 W/mK, the maximum mass loss was at 328.20 °C and the initial storage modulus at 30 °C was 206.13 MPa. It was determined that the hydroxyethyl cellulose composite films containing 3 wt.% activated carbon revealed the optimum results in terms of both thermal conductivity and viscoelastic response and showed that the obtained composite films could be used in industrial applications where thermal conductivity was required.

Evaluating Prediction Models with Hearing Handicap Inventory for the Elderly in Chronic Otitis Media Patients

Background: This retrospective, cross-sectional study aimed to assess the functional hearing capacity of individuals with Chronic Otitis Media (COM) using prediction modeling techniques and the Hearing Handicap Inventory for the Elderly (HHIE) questionnaire. This study investigated the potential of predictive models to identify hearing levels in patients with COM. Methods: We comprehensively examined 289 individuals diagnosed with COM, of whom 136 reported tinnitus and 143 did not. This study involved a detailed analysis of various patient characteristics and HHIE questionnaire results. Logistic and Random Forest models were employed and compared based on key performance metrics. Results: The logistic model demonstrated a slightly higher accuracy (73.56%), area under the curve (AUC; 0.73), Kappa value (0.45), and F1 score (0.78) than the Random Forest model. These findings suggest the superior predictive performance of the logistic model in identifying hearing levels in patients with COM. Conclusions: Although the AUC for the logistic regression did not meet the benchmark, this study highlights the potential for enhanced reliability and improved performance metrics using a larger dataset. The integration of prediction modeling techniques and the HHIE questionnaire shows promise for achieving greater diagnostic accuracy and refining intervention strategies for individuals with COM.

The role of socially responsible human resource management in improving performance: An analysis of subjective well-being as a mediating variable

The study aims to reveal the dynamic interaction between institutions, organizations, and employees in implementing social responsibility HRM (SRHRM) in SOEs as an institutional pressure. This research utilizes the hypothetic deductive method. Data were collected through a survey of SOE employees, and 524 complete and outlier-free responses were collected. Data analysis used structural equation model techniques. SRHRM strengthens institutional mechanisms to ensure sustainability. Employee job satisfaction and engagement optimize SRHRM functions to support green and non-green performance. The integration of CSR into corporate HR governance affects organizational members' behavior and social structure. It has implications for the organization, society, and a sustainable global world while maintaining balance (triple bottom line). This study provides a comprehensive picture of companies' response mechanisms and proactive behavior in the face of pressure. The success of SRHRM lies in its ability to improve well-being (satisfaction and engagement), which develops based on felt obligation. The felt obligation is vital to employees' engagement with sustainability through green work outcomes.

Demand Forecasting in Supply Chain Using Uni-Regression Deep Approximate Forecasting Model

This research presents a uni-regression deep approximate forecasting model for predicting future demand in supply chains, tackling issues like complex patterns, external factors, and nonlinear relationships. It diverges from traditional models by employing a deep learning strategy through recurrent bidirectional long short-term memory (BiLSTM) and nonlinear autoregressive with exogenous inputs (NARX), focusing on regression-based approaches. The model can capture intricate dependencies and patterns that elude conventional approaches. The integration of BiLSTM and NARX provides a robust foundation for accurate demand forecasting. The novel uni-regression technique significantly improves the model’s capability to detect intricate patterns and dependencies in supply chain data, offering a new angle for demand forecasting. This approach not only broadens the scope of modeling techniques but also underlines the value of deep learning for enhanced accuracy in the fluctuating supply chain sector. The uni-regression model notably outperforms existing models in accuracy, achieving the lowest errors: mean average error (MAE) at 1.73, mean square error (MSE) at 4.14, root mean square error (RMSE) at 2.03, root mean squared scaled error (RMSSE) at 0.020, and R-squared at 0.94. This underscores its effectiveness in forecasting demand within dynamic supply chains. Practitioners and decision-makers can leverage the uni-regression model to make informed decisions, optimize inventory management, and enhance supply chain resilience. Furthermore, the findings contribute to the ongoing evolution of supply chain demand forecasting methodologies.

Design of nonlinear gradient sheet-based TPMS-lattice using artificial neural networks

Gradient triply periodic minimal surface (TPMS) structures are renowned for lightweight design and enhanced performance, but their complex and nonlinear configurations pose challenges in achieving targeted design goals. A new design methodology for the nonlinear gradient structure was proposed in this study, with the aim of achieving efficient and accurate modeling of complex and gradient sheet-based TPMS structures under specific performance objectives. This method utilized automated finite element (FE) simulations to obtain structure topology element densities under various boundary conditions. An artificial neural network (ANN) was then employed to efficiently predict the correspondence between these boundary conditions and topology element densities. A mapping was established between topology element densities and TPMS structural parameters, and the gradient structure was accurately constructed by using the voxel modeling technique. Taking a typical cantilever beam TPMS structure as an example of nonlinear gradient design, the results indicate that the error between the ANN-predicted and FE-simulated structure topology element densities is only 2.73 %, with prediction time being only 0.15 % of the simulation time. The thin regions of the gradient structure align with those geometrically removed in regular topology optimization scheme, achieving up to 65.45 % weight reduction, a 28.72 % improvement over the regular scheme, along with uniform structural stress transition and maximum stress reduction. TC4 alloy nonlinear gradient TPMS structures, printed by metal selective laser melting (SLM) technique, confirm the practical application value of this design method.

Breaking the CO2 Gridlock: Can Renewables Lead the Way for the OECD?

The use of low-carbon energy in power grids is essential for minimizing negative effects on the environment. Energy consumption causes environmental damage to the OECD’s economy. This study aims to investigate the effect of energy consumption, population, and GDP on CO2 emissions using panel data from 17 OECD countries over the period 2000–2023. We use regression approaches, such as partial least squares and principal components, to study the effects of GDP, urban and total population, oil and nuclear use, renewable energy, and industrialization on CO2 emissions. The regression process in this study reduces the data to a two-dimensional representation using a stochastic model and estimation techniques. The findings of this empirical investigation indicate that the United States, Canada, France, Germany, Italy, Korea, Mexico, and the United Kingdom exhibit higher levels of primary energy consumption in comparison to value-added sectors, renewable–geothermal energy, and nuclear energy. We determined the effects of CO2 emissions, GDP, and energy consumption by considering these as the most significant elements. This has made it possible to reduce CO2 emissions by focusing one’s attention and energy on the development of novel technologies, the use of renewable energy sources, and the execution of strategic plans. Attracting increasing attention are technological shifts that deliver enormous quantities of clean energy to combat climate change. Findings from this study can help environmentalists and policymakers better understand the role of structural change and energy consumption processes in the globalization process.

Transport infrastructure: Adaptive choice modelling for deliberative appraisal judgments

Political representation is responsible for choices regarding the supply and the management of transport infrastructure, but its decisions are sometimes in conflict with the will and the general interest expressed by citizens. This situation has progressively prompted the use of specific corrective measures in order to obtain socially sustainable decisions, such as the deliberative procedures for the appraisal of public goods. The standard Stated Choice Modelling Technique (SCMT) can be used to estimate the community appreciation for public goods such as transport infrastructure; but the application of the SCMT in its standard form would be inadequate to provide an estimation that expresses the general interest of the affected community. Hence the need to adapt the standard SCMT on the basis of the operational conditions imposed by deliberative appraisal procedures. Therefore, the general aim of the paper is to outline the basic conditions on which a modified SCMT with deliberative procedure can be set up. Firstly, the elements of the standard SCMT on which to make the necessary adjustments are identified; subsequently, modifications and additions to make to the standard technique are indicated; finally, the contents of an extensive program of experimentation are outlined.

Using an extended technology acceptance model to understand the behavioral adoption intention of mobile commerce among Chinese consumers

The ubiquitous nature of mobile handsets is enhancing consumers’ shopping experience and thus encouraging extensive m-commerce adoption. The personality traits of the users of m-commerce vary and thus understanding the perspective of people to utilize mobile commerce systems is vital. This study thus examined the factors that drive Chinese consumers to adopt m-commerce. The Technology Acceptance Model (TAM) was utilized as the theoretical foundation while the analysis was completed with AMOS using structural equation model techniques. The results have demonstrated that perceived usefulness and perceived ease of use influence m-commerce adoption intentions. Also, service quality along with security, and privacy, were found to significantly drive mobile commerce adoption intentions. Perceived risk showed a significant but negative impact on adoption intentions of m-commerce. Familiarity showed a significant positive three-dimensional impact on PU, PEOU, and m-commerce adoption intentions. The theoretical and managerial repercussions of these results on m-commerce development and diffusion are discussed.

Unravelling the Effect of Consumer Perception of CSR Practices of Multinational Enterprises on Consumer Purchase Intention: Insights from the Sri Lankan FMCG Sector

Due to increased market potential, Multinational enterprises offering fast-moving consumer goods have grown rapidly in developing countries such as Sri Lanka. Accordingly, the main objective of this study is to investigate how CSR practices in the MNE FMCG sector of Sri Lanka affect consumer purchasing intentions through the mediation of brand image, brand attitude, and perceived quality. A positivist research paradigm has been adopted in this study. Accordingly, self-administered questionnaires were distributed among 200 consumers of MNE FMCGs and the structural equation modeling technique was adopted to generate the study's statistical conclusions. It was found that consumer purchase intention is influenced by the CSR practices of MNE FMCG companies, while brand attitude, brand image and perceived quality partially mediate the aforementioned relationship.This study unravels the actual effect of CSR practices of MNEs on consumer purchase intention especially in the context of South Asia based on the theoretical foundations of Social Exchange theory and the Theory of Planned Behaviour. This would be in contrast to extant literature which seems to have inconsistent findings on this relationship.

A Multi‐Modelling Approach for Informing the Conservation of a Cold‐Adapted Terrestrial Amphibian in the Face of Climate Change

ABSTRACTAimWe aimed to assess potential climate change impacts on the distribution of a cold‐adapted terrestrial amphibian that shows strong intraspecific differentiation.LocationAlps and Dinarides.TaxonAlpine salamander (Salamandra atra).MethodsWe built SDMs for each of two major intraspecific lineages (S. a. atra, which is found throughout the Northern Alps, and S. a. prenjensis, which appears sparsely along the Dinarides) and for the entire species, using occurrence points from a carefully curated database, climate data with 1 km2 spatial resolution and eight modelling techniques. We projected climatically suitable areas to 2070 under two climate scenarios (RCP2.6 and RCP8.5), using 24 general circulation models.ResultsFor S. a. atra, under RCP2.6 scenario, we detected a 3% increase in the extent of climatically suitable areas in the Alps (although with low model agreement), yet a 6% decrease under the RCP8.5 scenario. For S. a. prenjensis, we detected a decrease in the extent of climatically suitable areas in the Dinarides between 7% and 45% (RCP2.6 and RCP8.5, respectively). The SDM predictions for the entire species were most in line with those of the S. a. atra model. Although climate variables were not equally strong in predicting the climatic suitability for both lineages, the minimum temperature of the coldest month revealed an important predictor for the entire species, with clear decreases in suitability towards higher temperature.Main ConclusionsAll models reveal a considerable decrease in climate suitability throughout most of the range of S. atra, which is particularly visible in the Dinarides. We highlight the importance of considering intraspecific variation when modelling climate change impacts on geographically differentiated species whose populations vary in their climatic niche. We provide our study results (occurrence data and maps) via a web application that can be useful for guiding conservation efforts.

Peering into the mind: unraveling schizophrenia's secrets using models.

Schizophrenia (SCZ) is a complex mental disorder characterized by a range of symptoms, including positive and negative symptoms, as well as cognitive impairments. Despite theextensive research, the underlying neurobiology of SCZ remain elusive. To overcome this challenge, the use of diverse laboratory modeling techniques, encompassing cellular and animal models, and innovative approaches like induced pluripotent stem cell (iPSC)-derived neuronal cultures or brain organoids and genetically engineered animal models, has been crucial. Immortalized cellular models provide controlled environments for investigating the molecular and neurochemical pathways involved in neuronal function, while iPSCs and brain organoids, derived from patient-specific sources, offer significant advantage in translational research by facilitating direct comparisons of cellular phenotypes between patient-derived neurons and healthy-control neurons. Animal models can recapitulate the different psychopathological aspects that should be modeled, offering valuable insights into the neurobiology of SCZ. In addition, invertebrates' models are genetically tractable and offer a powerful approach to dissect the core genetic underpinnings of SCZ, while vertebrate models, especially mammals, with their more complex nervous systems and behavioral repertoire, provide a closer approximation of the human condition to study SCZ-related traits. This narrative review provides a comprehensive overview of the diverse modeling approaches, critically evaluating their strengths and limitations. By synthesizing knowledge from these models, this review offers a valuable source for researchers, clinicians, and stakeholders alike. Integrating findings across these different modelsmay allow us to build a more holistic picture of SCZ pathophysiology, facilitating the exploration of new research avenues and informed decision-making for interventions.