Development Of Computational Models Research Articles

Recommendation system using bio-inspired algorithms for urban orchards

According to the Food and Agriculture Organization of the United Nations (FAO), climate change is exponentially affecting agricultural production worldwide, with food prices expected to increase by up to 90 percent by 2030 and hunger and malnutrition rates to rise by 2050. This paper presents the development of a platform based on the Internet of Things (IoT) for monitoring urban gardens as a strategy to mitigate hunger, promote food sovereignty and circular economy in areas of food shortage. To this end, an Internet of Things (IoT) architecture is proposed and implemented that involves a social design layer that allows an effective transfer of knowledge to communities and a recommendation system based on evolutionary computation to optimize and maximize the productivity of urban orchards, and thus contribute to the 2030 agenda of the Sustainable Development Goals (SDGs). Finally, three experiments in urban gardens are shown to validate evolutionary computation and artificial intelligence models, such as multiple linear regression, genetic algorithms, ant colony algorithms and spatial estimation and inference algorithms such as the Kriging algorithm. The productivity of urban lettuce orchards is increased between 25 and 45%.

Highly logical and non-emotional decisions in both risky and social contexts: understanding decision making in autism spectrum disorder through computational modeling.

In risky contexts, autism spectrum disorder (ASD) individuals exhibit more logical consistency and non-emotional decisions than do typical adults (TAs). This way of deciding could be also prevailing in social contexts, leading to maladaptive decisions. This evidence is scarce and inconsistent, and further research is needed. Recent developments in computational modeling allow analysis of decisional subcomponents that could provide valuable information to understand the decision-making and help address inconsistencies. Twenty-seven individuals with ASD and 25 TAs were submitted to a framing-task and the ultimatum game (UG). The Rescorla-Wagner computational model was used to analyze UG decisions. Results showed that in the UG, the ASD group exhibited a higher utilitarianism, characterized by lower aversion to unfairness and higher acceptance of offers. Moreover, this way of deciding was predicted by the higher economic rationality found in the framing task, where people with ASD did not manifest emotional biases such as framing effect. These results could suggest an atypical decision making, highly logical and non-emotional, as a robust feature of ASD.

An IoT enabled computational model and application development for monitoring cardiovascular risks

Technological advancement in the rise of the Internet of Things (IoT) and computational modelling and application development has revolutionized medical care. Monitoring cardiovascular risks is faster, easier, and more accurate than ever. Using IoT-enabled computational modelling and application development, medical professionals can detect, monitor, and predict certain conditions more efficiently. That paper explores the Internet of Things (IoT) enabled computational model and application development that can be used to monitor cardiovascular risks effectively. Cardiovascular diseases are the leading cause of death globally, and currently, there is a lack of comprehensive and reliable monitoring systems to assess the risk of such diseases. IoT enables the integration of different data sources, such as physical activity, diet, BMI and the environmental context, to form a comprehensive tracking tool that can provide accurate cardiovascular risk assessment. The developed application can offer personalized health coaching, leveraging machine learning algorithms to identify patterns and adapt a user's healthcare journey. Ultimately, this paper assesses the potential of IoT technology for monitoring cardiovascular risks and integrating it into current healthcare systems.

Importance of Inter-residue Contacts for Understanding Protein Folding and Unfolding Rates, Remote Homology, and Drug Design.

Inter-residue interactions in protein structures provide valuable insights into protein folding and stability. Understanding these interactions can be helpful in many crucial applications, including rational design of therapeutic small molecules and biologics, locating functional protein sites, and predicting protein-protein and protein-ligand interactions. The process of developing machine learning models incorporating inter-residue interactions has been improved recently. This review highlights the theoretical models incorporating inter-residue interactions in predicting folding and unfolding rates of proteins. Utilizing contact maps to depict inter-residue interactions aids researchers in developing computer models for detecting remote homologs and interface residues within protein-protein complexes which, in turn, enhances our knowledge of the relationship between sequence and structure of proteins. Further, the application of contact maps derived from inter-residue interactions is highlighted in the field of drug discovery. Overall, this review presents an extensive assessment of the significant models that use inter-residue interactions to investigate folding rates, unfolding rates, remote homology, and drug development, providing potential future advancements in constructing efficient computational models in structural biology.

Forecasting the phytosanitary state of agrocenoses: innovation developments

Goal. Development and implementation of new theories, methods, technologies and models that would allow more reliable forecasting of the development of pests and pathogens of agricultural crops.
 Methods. Analysis of innovative development of the Institute of Plant Protection of the National Academy of Agrarian Sciences of Ukraine and other institutions of the Scientific and Methodological Center «Protection of Plants» for 2001—2020. Isolation of those relating to the problem forecasting the development of pests and pathogens of plant diseases in agrocenoses of Ukraine.
 Results. The concept of formation of the phytosanitary state of crops and plantations under the conditions of climate change has been developed. The theory of the dynamics of the number of locusts and the prediction of emergency situations in the agricultural sphere of Ukraine has been developed. The widespread use of pheromone traps for diagnosing the phytosanitary state of agrocenoses of Ukraine is recommended. The cyclicity of outbreaks of mass reproduction of the Anisoplia austriaca beetle and the European corn borer was established, depending on the amount of solar activity. The possibility of applying GIS-technologies for the analysis of long-term data was studied. Predictive computer programs (models) of harvest shortages of rapeseed, sunflower, corn, sugar beet, and winter wheat from a complex of pests have been developed. An algorithm for the application of information technology programs for the analysis of multi-year dynamics of the phytosanitary state of agrocenoses has been developed. The role of the developed computer model of the development of leaf spots of grain crops in determining the moment for the most effective application of fungicides has been established. Methods of forecasting the development of pathogens of grape diseases in the South of Ukraine have been developed. A system of forecasting the phytosanitary state of agrocenoses of Ukraine in the conditions of the year and planning the implementation of protective measures has been developed.
 Conclusions. Innovative developments of the Scientific and Methodological Center «Plant Protection» make it possible to solve the following issues at a modern level: forecasting changes in the agrosphere based on the analysis of a long-term database of hydrothermal conditions and indicators of the phytosanitary state of agrocenoses; prevention of emergency situations in the agricultural sector on the basis of a modern monitoring system using GPS-navigation and the development of regulations for carrying out protective measures; forecasting the development of dangerous pests and diseases of agricultural crops; forecasting crop failures and determining the economic feasibility of chemical plant protection; forecasting the appearance and spread of quarantine pests.

Deep Reinforcement Learning and Auto-Differential Evolution Co-Guided Coal Washing

Background. Coal washing is a complicated process and difficult to control, which has many controlling parameters with strong coupling relationship. It is still a challenge to realize the self-perception, self-adjustment, and self-evaluation of coal washing machine, improve the quality of coal washing, ensure production safety, and reduce labor cost. Methods. Through the intelligent transformation of jig, this paper proposes an intelligent washing method with cooperated deep reinforcement learning and evolutionary computation. First, it designs a fault warning method based on statistical analysis, helping to recover the normal running state of jig with manual maintenance. Then, it constructs a regulation strategy generation method with deep reinforcement learning supported by the fusion of artificial experience and historical data. Last, for the lack of monitoring data caused by poor communication quality and environment, the regulation strategy prediction method with evolutionary computation and surrogate model is proposed. Results. In practice, this method shows accurate fault warning accuracy and rapid cleaned coal ash adjustment response ability. Conclusions. This shows that the method proposed in this paper is of great significance for intelligent washing and can better cope with the special situation when the washing equipment sensing data are missing.

Development of quantum computational model for analysis of drug delivery: Loading of human beta-defensin 3 and mitoxantrone onto composite polymers

A molecular dynamics simulation study evaluated the co-loading of mitoxantrone (MTX) and human beta-defensin 3 (HBD3) on PLGA and chitosan chains. Simulations were performed on four different polymeric systems. Electrostatic and vdW interactions between MTX and HBD3 are examined. According to the obtained results, MTX strongly interacts with PLGA. In addition, HBD3 interacts strongly with CHI. Moreover, the radial distribution function (RDF) was estimated for the compounds. The structural changes of HBD3 and the interaction potentials were considered when selecting a suitable polymeric carrier for HBD3. For this purpose, HBD3 in a water medium was considered a reference system. RMSD, RMSF, radius of gyration, and the secondary structure of HBD3 were analyzed. According to the results, HBD3 has a secondary structure similar to water when adsorbed on 10C and 5PC. CHI could stabilize the structure of HBD3. 5PC (5 PLGA and 5 CHI chains) is suitable for co-loading MTX and HBD3. Polymeric carriers can be designed using these molecular results.

Electro-thermal performance evaluation of a prismatic battery pack for an electric vehicle

Abstract In recent years, electric vehicles (EVs) have grown in popularity as a viable way to reduce greenhouse gas emissions by replacing conventional vehicles. The need for EV batteries is steadily increasing. An essential and expensive part of electric transportation is the battery. The operating temperature of the lithium-ion (Li-ion) battery significantly impacts the performance of the EV battery pack. Battery packs undergo temperature fluctuations during the charging and discharging procedures due to internal heat generation, necessitating an examination of the temperature distribution of the battery pack. The geometrical spacing between cells is considered larger and identical and is kept open on two sides for free air circulation. A novel battery thermal management system (BTMS) design is required to effectively dissipate heat from the prismatic battery pack module. The electro-thermal behaviour of the prismatic Li-ion battery pack module was investigated based on the high charge/discharge rate. This study presents the development of a three-dimensional free open-source OpenFOAM computational fluid dynamics model for prismatic cell battery packs that simulates heat generation, air flow field, and temperature distribution across the width and depth of the battery pack module. The prismatic battery pack simulation results are compared with the experimental and simulation results of the cylindrical battery pack. It was also revealed that prismatic cells generate more heat on the backside, requiring battery packs to have increased cooling and space between individual cells to ensure sufficient air circulation for cooling and gas removal. The BTMS is improved by designing with increased space among the prismatic battery cells as compared with the conventional prismatic cell battery pack design.

Tool-pin profile effects on thermal and material flow in friction stir butt welding of AA2219-T87 plates: computational fluid dynamics model development and study

Tool-pin profile effects on thermal and material flow in friction stir butt welding of AA2219-T87 plates: computational fluid dynamics model development and study

Multiscale physics-based in silico modelling of nanocarrier-assisted intravascular drug delivery

A rational design of drug nanocarriers supported by in silico modelling tools can improve the efficacy of nanosystem-based intravascular drug delivery (IVDD). Computational model development stems from the vision of replacing conventional (pre)clinical trials with advanced simulations and applies to the development of more efficient nanocarriers for intravascular therapies. To establish a standardized framework for in silico preclinical trials, it is necessary to include in silico tools that can model each experimental stage of a preclinical trial for a respective nanocarrier system and give accurate and verifiable results. This review paper highlights the status of intravascular drug delivery supported by nanocarriers and discusses the modelling stages of a physics-based multiscale modelling framework that should be developed, validated and exploited to address the need for an effective preclinical assessment of nanocarriers for IVDD.

Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement in MRI Imaging.

Background: The development of advanced computational models for medical imaging is crucial for improving diagnostic accuracy in healthcare. This paper introduces a novel approach for virtual contrast enhancement (VCE) in magnetic resonance imaging (MRI), particularly focusing on nasopharyngeal cancer (NPC). Methods: The proposed model, Pixelwise Gradient Model with GAN for Virtual Contrast Enhancement (PGMGVCE), makes use of pixelwise gradient methods with Generative Adversarial Networks (GANs) to enhance T1-weighted (T1-w) and T2-weighted (T2-w) MRI images. This approach combines the benefits of both modalities to simulate the effects of gadolinium-based contrast agents, thereby reducing associated risks. Various modifications of PGMGVCE, including changing hyperparameters, using normalization methods (z-score, Sigmoid and Tanh) and training the model with T1-w or T2-w images only, were tested to optimize the model's performance. Results: PGMGVCE demonstrated a similar accuracy to the existing model in terms of mean absolute error (MAE) (8.56 ± 0.45 for Li's model; 8.72 ± 0.48 for PGMGVCE), mean square error (MSE) (12.43 ± 0.67 for Li's model; 12.81 ± 0.73 for PGMGVCE) and structural similarity index (SSIM) (0.71 ± 0.08 for Li's model; 0.73 ± 0.12 for PGMGVCE). However, it showed improvements in texture representation, as indicated by total mean square variation per mean intensity (TMSVPMI) (0.124 ± 0.022 for ground truth; 0.079 ± 0.024 for Li's model; 0.120 ± 0.027 for PGMGVCE), total absolute variation per mean intensity (TAVPMI) (0.159 ± 0.031 for ground truth; 0.100 ± 0.032 for Li's model; 0.153 ± 0.029 for PGMGVCE), Tenengrad function per mean intensity (TFPMI) (1.222 ± 0.241 for ground truth; 0.981 ± 0.213 for Li's model; 1.194 ± 0.223 for PGMGVCE) and variance function per mean intensity (VFPMI) (0.0811 ± 0.005 for ground truth; 0.0667 ± 0.006 for Li's model; 0.0761 ± 0.006 for PGMGVCE). Conclusions: PGMGVCE presents an innovative and safe approach to VCE in MRI, demonstrating the power of deep learning in enhancing medical imaging. This model paves the way for more accurate and risk-free diagnostic tools in medical imaging.

Effects of glucose and lactate on Streptococcus mutans abundance in a novel multispecies oral biofilm model.

The oral microbiome plays an important role in protecting oral health. Here, we established a controlled mixed-species in vitro biofilm model and used it to assess the impact of glucose and lactate on the ability of Streptococcus mutans, an acidogenic and aciduric species, to compete with commensal oral bacteria. A chemically defined medium was developed that supported the growth of S. mutans and four common early colonizers of dental plaque: Streptococcus gordonii, Actinomyces oris, Neisseria subflava, and Veillonella parvula. Biofilms containing the early colonizers were developed in a continuous flow bioreactor, exposed to S. mutans, and incubated for up to 7 days. The abundance of bacteria was estimated by quantitative polymerase chain reaction (qPCR). At high glucose and high lactate, the pH in bulk fluid rapidly decreased to approximately 5.2, and S. mutans outgrew other species in biofilms. In low glucose and high lactate, the pH remained above 5.5, and V. parvula was the most abundant species in biofilms. By contrast, in low glucose and low lactate, the pH remained above 6.0 throughout the experiment, and the microbial community in biofilms was relatively balanced. Fluorescence in situ hybridization confirmed that all species were present in the biofilm and the majority of cells were viable using live/dead staining. These data demonstrate that carbon source concentration is critical for microbial homeostasis in model oral biofilms. Furthermore, we established an experimental system that can support the development of computational models to predict transitions to microbial dysbiosis based on metabolic interactions.IMPORTANCEWe developed a controlled (by removing host factor) dynamic system metabolically representative of early colonization of Streptococcus mutans not measurable in vivo. Hypotheses on factors influencing S. mutans colonization, such as community composition and inoculation sequence and the effect of metabolite concentrations, can be tested and used to predict the effect of interventions such as dietary modifications or the use of toothpaste or mouthwash on S. mutans colonization. The defined in vitro model (species and medium) can be simulated in an in silico model to explore more of the parameter space.

Development and Validation of Computational Fluid Dynamics Model of Ladle Furnace with Electromagnetic Stirring System.

Numerical methods are crucial to supporting the development of new technology in different industries, especially steelmaking, where many phenomena cannot be directly measured or observed under industrial conditions. As a result, further designing and optimizing steelmaking equipment and technology are not easy tasks. At the same time, numerical approaches enable modeling of various phenomena controlling material behavior and, thus, understanding the physics behind the processes occurring in different metallurgical devices. With this, it is possible to design and develop new technological solutions that improve the quality of steel products and minimize the negative impact on the environment. However, the usage of numerical approaches without proper validation can lead to misleading results and conclusions. Therefore, in this paper, the authors focus on the development of the CFD-based (computational fluid dynamics) approach to investigate the liquid steel flow inside one metallurgical device, namely a ladle furnace combined with an EMS (electromagnetic stirring) system. First, a numerical simulation of electromagnetic stirring in a scaled mercury model of a ladle furnace was carried out. The numerical results, such as stirring speed and turbulent kinetic energy, were compared with measurements in the mercury model. It was found that the results of the transient multiphase CFD model achieve good agreement with the measurements, but a free surface should be included in the CFD model to simulate the instability of the flow pattern in the mercury model. Based on the developed model, a full-scale industrial ladle furnace with electromagnetic stirring was also simulated and presented. This research confirms that such a coupled model can be used to design new types of EMS devices that improve molten steel flow in metallurgical equipment.

The Dynamic Characteristics of Railway Portal Frame Bridges: A Comparison between Measurements and Calculations

Railway bridges are subjected to significant dynamic loads. A numerical model of the bridge structure that captures its dynamic characteristics as accurately as possible is essential for the simulation of train crossings. However, most existing Calculation Models either do not consider the dynamic interaction between the structure and the soil, known as the soil–structure interaction (SSI), or give it only secondary importance. As a result, the accuracy of the predicted dynamic characteristics is affected. This paper illustrates how the dynamic interactions of abutments impact the portal frame bridge’s SSI. This influence prompts the question of incorporating the frequency-dependent influence of the structure–soil–structure interaction (SSSI) into the modelling process. We propose a conservative estimation of the frequency range influenced by the shear wave interference of the SSSI and recommend using it as an application limit in the development of computational models. Based on this estimation, a Calculation Model is presented. In this approach, the SSI is considered using the well-known quasi-static spring–damper method from foundation vibration analysis, adhering to limitations based on the SSSI. For the application of the presented Calculation Model, four concrete portal frame bridges with spans between 9 m and 17 m along the high-speed line from Nuremberg to Munich, Germany, are investigated by analyzing the dynamic characteristics and comparing them with the prediction of the proposed numerical Calculation Model. The presented method shows good calculation accuracy.

‘Do not parade your ignorance’: Negation as a power tool of toxic geek masculinity

Catering to a primarily male user population, Reddit is often said to be a breeding ground for toxic language use, which has led to the development of multiple computational classification models for the automated identification of toxicity in online conversations. Since negation can play a key role in cyberbullying, functioning as a popular stance marker in the dialogical exchange of dominating views in the digital sphere, it should not be ignored. Likewise, its use as a rhetorical strategy deserves more linguistic attention. Described as Engagement resources of disclaim in Appraisal theory, negated constructions may be employed by Reddit users to disalign themselves from divergent perspectives through emotion-driven ad hominem attacks, not only resulting in a potentially detrimental impact on the users’ mental health, but also perpetuating toxic power relationships. Therefore, this paper examines the use of negation, in combination with various other interpersonal resources, within the Redditors’ dialogical interactions, to explore its possible motivations and role in shaping the online discourse. A computer-assisted appraisal analysis was conducted on 2637 most upvoted comments and replies (49,866 words), extracted from the subreddit of r/AustralianPolitics between 11–14 April and 7–10 May 2022, during the period leading up to the so-called federal climate change election in Australia. The study shows evidence of a seemingly dominant negation pattern characterised by negative judgement and its findings tentatively suggest that some of the male Australian users may use negation as a discursive strategy driven by ideology and emotion. This raises the possibility that the language use encountered on r/AustralianPolitics may be influenced by covert toxic geek masculinity.

Abstract IA019: Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit

Abstract Breast tumors arise and progress via processes that involve intrinsic deregulation of epithelial cells and that also alter the composition and function of associated stromal and immune cells. Together, these tumor-intrinsic and microenvironmental changes enable malignant epithelial cells in the tumor to acquire key cancer hallmarks, including proliferation, migration, immune evasion and further evolution. The resulting collection of cancer and stromal cells comprise a complex, adaptive tumor ecosystem. Long-term therapeutic success will require that drugs attack malignant cells, favorably activate the immune system, and create a tumor microenvironment that does not support tumor (re)growth. Creating a process for treatment selection will require an integrated systems biology approach to develop, test and refine a robust computational framework that takes into account the states, signaling relationships, and spatial organization that determine the emergent properties inherent in complex interacting systems. Toward this goal, we have developed a coordinated experimental-computational approach designed to enable identification of effective treatment strategies. To examine therapeutic responses of diverse aspects of the tumor ecosystem, we have deployed a novel drug delivery microdevice that enables rapid, high-throughput assessment of the effects of multiple therapies on tumor cells and the surrounding microenvironment. When coupled with multiplex tissue imaging, this platform provides a comprehensive assessment of the state and spatial organization of the tumor ecosystem as it adapts to therapy. Initial studies demonstrated that many drugs designed to target malignant epithelial cells strongly impact stromal and immune cells, providing new insights into the importance of considering multiple aspects of the tumor ecosystem when designing effective therapeutic strategies. The resultant data are being used to guide development of a multiscale computational model that considers the functional relationships between different tumor components and can be used to predict the effects of therapies on diverse aspects of tumor ecosystems. In total, our approach has the potential to transform cancer treatments through rational identification of effective therapeutic strategies that lead to long-term durable control of tumors. Citation Format: Laura M. Heiser. Multiscale systems approach to target tumor ecosystem responses for therapeutic benefit [abstract]. In: Proceedings of the AACR Special Conference in Cancer Research: Translating Cancer Evolution and Data Science: The Next Frontier; 2023 Dec 3-6; Boston, Massachusetts. Philadelphia (PA): AACR; Cancer Res 2024;84(3 Suppl_2):Abstract nr IA019.

Development of 3D computational model of membrane-bound cell for the study of cell contraction

Development of 3D computational model of membrane-bound cell for the study of cell contraction

The effectiveness of using interactive computer models when teaching physics in modern school

Relevance. This paper concerns the use of interactive methods and technologies in the distance educational process when studying physics in secondary school.Purpose. The purpose of the paper is to identify the possibilities of interactive computer models and to develop didactic and practical bases of the methodology of their effective application in teaching physics to secondary school students.Methodology. The following pedagogical methods of literature analysis and statistical processing of the results are used in the work. Conversation, observation, experience, practical work with the predominance of the heuristic nature of the cognitive activity of students are the main methods of organising work with students.Results. Special attention is paid to the forms and types of educational activities in information networks, as well as problems that arise during distance learning. The article reveals the need to develop methodological foundations for the use of interactive computer models in teaching a physics course in secondary school. The paper considers the main provisions of the methodology for using interactive computer models in the educational process, methodological and didactic requirements for its organisation. This article discusses the prospects of using interactive methods and technologies in the distance learning process. This paper unlocks the potential of interactive computer models and justifies the expediency of their use in the process of teaching physics in secondary school.Conclusions. The article substantiates the importance of the relationship between methodological and technological approaches to the development of interactive educational computer models. The paper shows the effectiveness of interactive computer models in physics for self-training of students to increase their level of practical and applicable skills.

In vivo functional phenotypes from a computational epistatic model of evolution

Computational models of evolution are valuable for understanding the dynamics of sequence variation, to infer phylogenetic relationships or potential evolutionary pathways and for biomedical and industrial applications. Despite these benefits, few have validated their propensities to generate outputs with in vivo functionality, which would enhance their value as accurate and interpretable evolutionary algorithms. We demonstrate the power of epistasis inferred from natural protein families to evolve sequence variants in an algorithm we developed called sequence evolution with epistatic contributions (SEEC). Utilizing the Hamiltonian of the joint probability of sequences in the family as fitness metric, we sampled and experimentally tested for in vivo [Formula: see text]-lactamase activity in Escherichia coli TEM-1 variants. These evolved proteins can have dozens of mutations dispersed across the structure while preserving sites essential for both catalysis and interactions. Remarkably, these variants retain family-like functionality while being more active than their wild-type predecessor. We found that depending on the inference method used to generate the epistatic constraints, different parameters simulate diverse selection strengths. Under weaker selection, local Hamiltonian fluctuations reliably predict relative changes to variant fitness, recapitulating neutral evolution. SEEC has the potential to explore the dynamics of neofunctionalization, characterize viral fitness landscapes, and facilitate vaccine development.

Development of novel computational models based on artificial intelligence technique to predict the viscosity of ionic liquids-water mixtures

This paper delves into the practical application of K-Nearest Neighbors (KNN), Kernel Ridge Regression (KRR), and Lasso Regression for the prediction of viscosity of ionic liquids in a dataset characterized by categorical variables (Cation, Anion) and numeric variables (T(K), xIL(mol%)). Indeed, mole percentage of ionic liquids and temperature were considered as inputs for the models. The models' effectiveness is rigorously assessed, with K-Nearest Neighbors notably exhibiting exceptional predictive performance. To enhance model accuracy, Tabu Search is employed as an optimization tool for hyperparameter tuning. Numeric results showcase KNN's superiority, supported by a remarkable R2 test score of 0.91628 and the lowest RMSE among the models. Tabu Search optimization further refines model performance, emphasizing the critical role of hyperparameter tuning in achieving robust regression models in predicting the viscosity of ionic liquid-water mixtures. This study contributes valuable insights into the optimization process, demonstrating tangible improvements in predictive accuracy for viscosity predictions in similar contexts.