Advanced Modeling Techniques Research Articles

A comparative study of two data-driven modeling approaches to predict drug release from ER matrix tablets.

The pharmaceutical industry is striving to develop innovative and promising tools, increasingly embracing new data-driven approaches, to understand, improve and accelerate the drug product development process. While extended release (ER) oral formulations offer a number of advantages, including maintenance of therapeutic drug levels, a reduction in dosing frequency, and minimization of side effects, achieving consistent drug release profiles remains a significant challenge. As a critical attribute for drug absorption into systemic circulation, in vitro dissolution testing represents a time-consuming and complex method for the evaluation of such formulations. The main objective of this study was to develop a model for predicting drug dissolution in the quality by design (QbD)-based development of ER oral hydrophilic matrix tablets comprising polyethylene oxide (PEO). Two main modeling approaches are conducted and compared: (i) model screening to fit and compare multiple predictive machine learning (ML) models and then deploy the best model, in this case, artificial neural networks (ANN), and (ii) functional data analysis (FDA) combined with the design of experiments (DoE) that fit a smoothing model to each dissolution curve as a continuous function. A dataset comprising 91 ER matrix tablet formulations was analyzed, with the dissolution data split into training, validation, and test sets (70%, 20%, and 10%, respectively). The results demonstrated that both ANN and functional DoE (FDOE) models achieved high similarity with the experimental dissolution profiles, as indicated by f2 values ranging from 48 to 88 for the FDOE and 52 to 88 for ANN. This work highlights the potential of integrating advanced data-driven modeling techniques into ER drug development to enhance dissolution prediction accuracy and streamline the formulation process, thus reducing time and costs.

Structural insights into the recognition of RALF peptides by FERONIA receptor kinase during Brassicaceae pollination.

Ensuring species integrity and successful reproduction is pivotal for the survival of angiosperms. Members of Brassicaceae family employ a "lock and key" mechanism involving stigmatic (sRALFs) and pollen RALFs (pRALFs) binding to FERONIA, a Catharanthus roseus receptor-like kinase 1-like (CrRLK1L) receptor, to establish a prezygotic hybridization barrier. In the absence of compatible pRALFs, sRALFs bind to FERONIA, inducing a lock state for pollen tube penetration. Conversely, compatible pRALFs act as a key, facilitating successful fertilization. Competing pRALFs reduce the sRALFs binding to FERONIA in a dose-dependent manner, enabling pollen tube penetration. Despite its crucial role in Brassicaceae hybridization, the structural basis of this binding remains elusive owing to the highly flexible nature of RALF peptides. Using advanced structural modeling techniques and flexible peptide molecular docking, this study reveals that pRALFs and sRALFs bind to negatively charged pockets in FERONIA with varying binding affinities. Our study unveils the structural basis of this binding, shedding light on the molecular mechanism underlying hybridization barriers in Brassicaceae.

Transfer Learning for Thickener Control

Thickener control is a key area of focus in the minerals processing industry, particularly due to its crucial role in water recovery, which is essential for sustainable resource management. The highly nonlinear nature of thickener dynamics presents significant challenges in modeling and optimization, making it a strong candidate for advanced surrogate modeling techniques. However, traditional data-driven approaches often require extensive datasets, which are frequently unavailable, especially in new plants or unexplored operational domains. Developing data-driven models without enough data representative of the dynamics of the system could result in incorrect predictions and consequently, unstable response of the controller. This paper proposes the application of a methodology that leverages transfer learning to address these data limitations to enhance surrogate modeling and model predictive control (MPC) of thickeners. The performance of three approaches—a base model, a transfer learning model, and a physics-informed neural network (PINN)—are compared to demonstrate the effectiveness of transfer learning in improving control strategies under limited data conditions.

Performance Analysis of a Waste-Gated Turbine for Automotive Engines: An Experimental and Numerical Study

In this article, the results of an experimental investigation and a 1D modeling activity on the steady-state performance of a wastegated turbocharger turbine for spark ignition engines are presented. An experimental campaign to analyze the turbine performance for different waste-gate valve openings was conducted at the test bench for components of propulsion systems of the University of Genoa. Thanks to the experimental activity, a 1D model is developed to assess the interaction between the flow through the impeller and the by-pass port. Advanced modeling techniques are crucial for improving the assessment of turbocharger turbines performance and, consequently, enhancing the engine–turbocharger matching calculation. The initial tuning of the model is based on turbine characteristic maps obtained with the by-pass port kept closed. The study then highlights the waste-gate valve behavior considering its different openings. It was found that a more refined model is necessary to accurately define the mass flow rate through the waste-gate valve. After independently tuning the 1D models of the turbine and the waste-gate valve, their behavior is analyzed in parallel-flow conditions. The results highlight significant interactions between the two components that must be taken into account to reduce inaccuracies in the engine-turbocharger matching calculation. These interactions lead to a reduced swallowing capacity of the turbine impeller. This reduction has an impact on the power delivered to the compressor, the boost pressure, and, consequently, the engine backpressure. The results suggest that methods generally adopted that consider the by-pass valve and the turbine as two nozzles working in parallel under the same thermodynamic condition could be insufficient to accurately assess the turbocharger behavior.

Bio-lubricants as viable replacements for mineral oils in automotive engines: A review

This review paper explores the potential of bio-lubricants as sustainable alternatives to conventional mineral oils for the lubrication of automotive engines. Bio-lubricants, which are derived from renewable sources such as vegetable oils, provide substantial environmental advantages, such as reduced toxicity and biodegradability. The review summarizes the results of recent research, emphasizing the superior lubricity, oxidative stability, and wear resistance of bio-lubricants, particularly those that have been formulated with additives to improve performance. The chemical structure and fatty acid composition of vegetable oils, including soybean, palm, and cottonseed oils, are the subject of discussion, as they have a significant impact on their lubrication properties. The efficacy of advanced modeling techniques, such as genetic algorithm (GA) and artificial neural networks (ANN), in the prediction and optimization of bio-lubricant formulations is assessed. Despite their promising qualities, bio-lubricants encounter obstacles such as fluctuations in raw material quality and increased production costs. The review underscores the necessity of ongoing research and development to confront these obstacles, with an emphasis on enhancing performance consistency and cost-effectiveness. It is argued that the transition to bio-lubricants is a crucial step in the pursuit of sustainable automotive lubrication, as it reduces dependence on non-renewable resources and mitigates environmental damage. Future research directions and recommendations are suggested to further improve the applicability and adoption of bio-lubricants in the automotive industry.

Building Resilience: A Holistic Approach for Revitalizing Existing Infrastructure Assets

This presented research delves into the challenges and approaches encompassed in allotting available resources for the maintenance and renovation of crucial infrastructure assets, focusing intently on buildings in Old Cairo earmarked for relocation to the new administrative capital, manipulating an integral empirical assessment and advanced modeling techniques. Amidst intensifying imperatives for robust infrastructure against a backdrop of financial constraints and hastened asset deterioration, this study proposes a cutting-edge, thorough approach to fund distribution. The developed approach incorporates asset hierarchy and classification, condition assessment via pilot survey, Markov chain model for degradation anticipation, stakeholder perception analysis through questionnaires for comparative analysis, and constructing an optimization model for fund allocation. The findings unveiled the dire condition of the selected infrastructure assets and the pressing requisite for strategic maintenance and renovation interventions. The developed approach offered a pragmatic evidence-based approach concerning decision-makers, improving resource allocation efficacy and fostering urban development schemes. This paper calls for protractible presented methodologies to a broad spectrum of assets and geographies and integrating incipient technologies for dynamic asset management approaches.

Predictive modeling of microplastic adsorption in aquatic environments using advanced machine learning models.

This study addresses the critical environmental concerns surrounding microplastics, aiming to elucidate the intricate factors influencing their behavior and interactions with organic pollutants. Utilizing advanced artificial neural network modeling techniques, including GRU, LSTM, RNN, and CNN, a comprehensive analysis of microplastic sorption capacity and underlying mechanisms is conducted. The research relies on a meticulously curated dataset encompassing fundamental parameters such as organic compound composition, n-octanol/water partition coefficient, covalent acidity, covalent basicity, molecular polarizability to volume ratio, and the logarithm of the partition coefficient. Findings underscore the significance of understanding the n-octanol/water distribution coefficient (Log D) in predicting organic pollutant fate in aquatic environments, with compounds displaying higher Log D values exhibiting heightened affinity for microplastics, posing substantial ecological and human health risks. Additionally, the study highlights the importance of selecting appropriate models to accurately capture complex sorption processes, especially in varied aquatic environments. Furthermore, the profound impact of acidity, molecular polarizability to volume ratio, and covalent basicity on microplastic behavior is elucidated. Notably, machine learning models and CNNs demonstrate remarkable speed in prediction generation. A comparative analysis of four robust machine learning models establishes fundamental alignment between model predictions and empirical findings. Particularly noteworthy is the RNN model, emerging as the most accurate with an impressive accuracy of 0.967 and a minimum absolute error of 0.38. These results underscore the efficacy of the RNN model in predicting microplastic dynamics, holding promise for significant contributions to addressing microplastic pollution.

Enhancing air quality predictions in Chile: Integrating ARIMA and Artificial Neural Network models for Quintero and Coyhaique cities.

In this comprehensive analysis of Chile's air quality dynamics spanning 2016 to 2021, the utilization of data from the National Air Quality Information System (SINCA) and its network of monitoring stations was undertaken. Quintero, Puchuncaví, and Coyhaique were the focal points of this study, with the primary objective being the construction of predictive models for sulfur dioxide (SO2), fine particulate matter (PM2.5), and coarse particulate matter (PM10). A hybrid forecasting strategy was employed, integrating Autoregressive Integrated Moving Average (ARIMA) models with Artificial Neural Networks (ANN), incorporating external covariates such as wind speed and direction to enhance prediction accuracy. Vital monitoring stations, including Quintero, Ventanas, Coyhaique I, and Coyhaique II, played a pivotal role in data collection and model development. Emphasis on industrial and residential zones highlighted the significance of discerning pollutant origins and the influence of wind direction on concentration measurements. Geographical and climatic factors, notably in Coyhaique, revealed a seasonal stagnation effect due to topography and low winter temperatures, contributing to heightened pollution levels. Model performance underwent meticulous evaluation, utilizing metrics such as the Akaike Information Criterion (AIC), Ljung-Box statistical tests, and diverse statistical indicators. The hybrid ARIMA-ANN models demonstrated strong predictive capabilities, boasting an R2 exceeding 0.90. The outcomes underscored the imperative for tailored strategies in air quality management, recognizing the intricate interplay of environmental factors. Additionally, the adaptability and precision of neural network models were highlighted, showcasing the potential of advanced technologies in refining air quality forecasts. The findings reveal that geographical and climatic factors, especially in Coyhaique, contribute to elevated pollution levels due to seasonal stagnation and low winter temperatures. These results underscore the need for tailored air quality management strategies and highlight the potential of advanced modeling techniques to improve future air quality forecasts and deepen the understanding of environmental challenges in Chile.

Numerical Investigation of Fracture Behavior for Glass Fiber Composite Concretes

Waste aggregate-based composite cementitious materials preserve natural resources and reduce environmental concerns throughout green concepts. In this view, recycled waste aggregate-based concrete and its innovative design require advanced computational modeling techniques. Accordingly, this comprehensive and computational research is conducted to investigate the fracture behavior of notched glass fiber reinforced concrete beam modeled with finite element method. The impacts of substitution ratios of the waste on strains and crack opening displacements of validated beam models are investigated and argued.

Fate and Transport of Heavy Metals in Soil, Surface Water, and Groundwater: Implications for Environmental Management

Critical challenges caused by heavy metals, such as hexavalent chromium, arsenic, and cadmium, emanate from their persistence and toxicity. This work discusses the fate and transport of heavy metals in the soil, surface water, and groundwater, focusing on their sources, pathways, and mechanisms of mobility. The research, therefore, underlines the key processes governing the behavior of heavy metals in environmental media by using a comprehensive review of field data and advanced modeling techniques. It becomes evident that mining, discharge of industrial waste, and agricultural activities are the predominant anthropogenic sources of contamination. Besides these factors, speciation and, further, the bioavailability of metals depend upon the climatic and soil type, which affects the paths of transport of these species. This research has shown a dire need for some effective environmental management strategies using remediation technologies and regulatory frameworks that might help minimize the risks to ecosystems and human health.

ANALYSIS OF THE EFFECT OF U-TURN ON TRAFFIC FLOW PERFORMANCE

This paper aims to explore the impact of U-turn motions on traffic flow and service levels. The research employs the Guidebook (PKJI, 2023) to assess observations related to traffic volume, vehicle speed, and both disturbed and undisturbed flow, which are gathered during the field survey. The data used for analysis was obtained by collecting primary data and secondary data according to the needs of the research. In addition, the data was obtained from a number of reports and documents that had been prepared, as well as the results of other literature studies. The results show that the number of vehicles on a road section is significantly increased, resulting in a significant increase in traffic volume and a decrease in vehicle speed. Moreover, the frequency of the use of the maneuvers affects the level of congestion on the road section, highlighting the need for early and strategic planning in the development of transportation infrastructure, particularly land transportation, which is crucial for facilitating everyday mobility and economic activities. This paper also highlights the importance of incorporating advanced traffic modeling techniques and simulations to enhance prediction of traffic behavior under different scenarios, informing infrastructure improvements and traffic management strategies. Finally, comparing findings with similar studies in other regions could yield valuable benchmarks for assessing traffic efficiency and service quality.

Assessment of Seismic Vulnerability for a Hospital Building Using Field Data and Various Numerical Analyses Considering Bidirectional Ground Motion Effects

For the assessment of seismic effects on RC buildings, the real structural condition has to be modelled as accurately as possible. Medical facilities and hospitals have to resist seismic actions and remain operational after seismic events. For this reason, a detailed seismic vulnerability assessment of a hospital building located in Orihuela, Spain, is presented in this paper using a combination of field monitoring data and numerical analysis. Ambient noise measurements from field monitoring using Raspberry Shake-based sensors are used to capture dynamic characteristics that describe the building behaviour. Data from these sensors were used to update and refine the finite element model of the structure for a detailed analysis of the building’s seismic performance. The different analytical procedures included both elastic and inelastic modelling, as well as static and dynamic assessments, to provide an exhaustive evaluation of the building’s behaviour under seismic loads. In the numerical model, the effect of masonry infill walls is considered, taking into account detailed material properties and structural configurations. Furthermore, the study carefully selects ground motion records representing two limit states—Damage Limitation (DL) and Severe Damage (SD)—to conduct an extensive seismic analysis. In each limit state applied to the structure, there are 14 bidirectional ground motions with components alternately directed along the two principal directions of the building. This analysis evaluated the structural response, focusing on torsional effects, inter-storey drift ratios, and the seismic performance of individual components. The results were compared to other analysis types, considering both overall and localised behaviour, to determine the reliability of different approaches. The findings support the idea that field monitoring data should be combined with advanced modelling techniques to achieve a more accurate evaluation of the building’s seismic vulnerability, considering bidirectional effects.

Hyperspectral Remote Sensing Estimation of Rice Canopy LAI and LCC by UAV Coupled RTM and Machine Learning

Leaf chlorophyll content (LCC) and leaf area index (LAI) are crucial for rice growth and development, serving as key parameters for assessing nutritional status, growth, water management, and yield prediction. This study introduces a novel canopy radiative transfer model (RTM) by coupling the radiation transfer model for rice leaves (RPIOSL) and unified BRDF model (UBM) models, comparing its simulated canopy hyperspectra with those from the PROSAIL model. Characteristic wavelengths were extracted using Sobol sensitivity analysis and competitive adaptive reweighted sampling methods. Using these wavelengths, rice phenotype estimation models were constructed with back propagation neural network (BPNN), extreme learning machine (ELM), and broad learning system (BLS) methods. The results indicate that the RPIOSL-UBM model’s hyperspectra closely match measured data in the 500–650 nm and 750–1000 nm ranges, reducing the root mean square error (RMSE) by 0.0359 compared to the PROSAIL model. The ELM-based models using the RPIOSL-UBM dataset proved most effective for estimating the LAI and LCC, with RMSE values of 0.6357 and 6.0101 μg · cm−2, respectively. These values show significant improvements over the PROSAIL dataset models, with RMSE reductions of 0.1076 and 6.3297 μg · cm−2, respectively. The findings demonstrate that the proposed model can effectively estimate rice phenotypic parameters from UAV-measured hyperspectral data, offering a new approach to assess rice nutritional status and enhance cultivation efficiency and yield. This study underscores the potential of advanced modeling techniques in precision agriculture.

Three-Dimensional Prospective Modeling and Deep Metallogenic Prediction of the Lintan Gold Deposit in Guizhou Province, China

The Lintan gold deposit, located in the “gold triangle” of Qianxinan, Guizhou Province, has become a focal point for implementing the “exploring near existing deposits” strategy, aiming to identify another large-scale gold deposit within the region. This study addresses the challenges of deep-edge mineral exploration in the Lintan gold deposit by adopting a metallogenic system perspective. Using a multidisciplinary approach, it integrates geological, geophysical, and geochemical datasets to construct various three-dimensional (3D) visualization and prospectivity models. The research leverages geostatistical theories and methods, multisource digital information analysis, and advanced 3D modeling and visualization techniques to verify mineralization anomalies. These efforts expand the scope of prospectivity evaluation for the deep-edge regions of the Lintan gold deposit into 3D space. In the 3D spatial framework, this study elucidates the metallogenic geological characteristics, geophysical anomalies, and geochemical signatures within the study area. Building upon this foundation, it conducts a comprehensive analysis and evaluation of geological, geochemical, and geophysical prospecting indicators under multisource geoscience datasets. This approach transitions from known to unknown domains, effectively reducing the ambiguities and uncertainties associated with single-source data interpretations. The findings demonstrate that, under the guidance of geological prospectivity models, the effective integration and synthesis of geological, geophysical, and geochemical data can reveal the interrelationships between metallogenic geological bodies and the contributing factors of the metallogenic system. This enables the identification of anomalous information associated with metallogenic geological bodies and facilitates the spatial localization and prediction of target areas for deep-edge mineral resources. The proposed methodology provides novel insights and techniques for deep-edge mineral exploration. Comprehensive analysis indicates significant prospectivity for mineral resource exploration in the deep-edge regions of the Lintan gold deposit.

ANN-QSAR, Molecular Docking, ADMET Predictions, and Molecular Dynamics Studies of Isothiazole Derivatives to Design New and Selective Inhibitors of HCV Polymerase NS5B.

Background/Objectives: RNA polymerase (NS5B), serves as a crucial target for pharmaceutical interventions aimed at combating the hepatitis C virus (HCV), which poses significant health challenges worldwide. The present research endeavors to explore and implement a variety of advanced molecular modeling techniques that aim to create and identify innovative and highly effective inhibitors that specifically target the RNA polymerase enzyme. Methods: In this study, a QSAR investigation was carried out on a set of thirty-eight isothiazole derivatives targeting NS5B inhibition and thus hepatitis C virus (HCV) treatment. The research methodology made use of various statistical techniques including multiple linear regression (MLR) and artificial neural networks (ANNs) to develop satisfactory models in terms of internal and external validation parameters, indicating their reliability in predicting the activity of new inhibitors. Accordingly, a series of potent NS5B inhibitors is designed, and their inhibitory potential is confirmed through molecular docking simulations. Results: These simulations showed that the interactions between these inhibitors and the active site 221 binding pocket of the NS5B protein are hydrophobic and hydrogen bond interactions, as well as carbon-hydrogen bonds and electrostatic interactions. Additionally, these newly formulated compounds displayed favorable ADMET characteristics, with molecular dynamics investigations revealing a stable energetic state and dynamic equilibrium. Conclusions: Our work highlights the importance of NS5B inhibition for the treatment of HCV.

Machine Learning Methods in Student Mental Health Research: An Ethics-Centered Systematic Literature Review

This study conducts an ethics-centered analysis of the AI/ML models used in Student Mental Health (SMH) research, considering the ethical principles of fairness, privacy, transparency, and interpretability. First, this paper surveys the AI/ML methods used in the extant SMH literature published between 2015 and 2024, as well as the main health outcomes, to inform future work in the SMH field. Then, it leverages advanced topic modeling techniques to depict the prevailing themes in the corpus. Finally, this study proposes novel measurable privacy, transparency (reporting and replicability), interpretability, and fairness metrics scores as a multi-dimensional integrative framework to evaluate the extent of ethics awareness and consideration in AI/ML-enabled SMH research. Findings show that (i) 65% of the surveyed papers disregard the privacy principle; (ii) 59% of the studies use black-box models resulting in low interpretability scores; and (iii) barely 18% of the papers provide demographic information about participants, indicating a limited consideration of the fairness principle. Nonetheless, the transparency principle is implemented at a satisfactory level with mean reporting and replicability scores of 80%. Overall, our results suggest a significant lack of awareness and consideration for the ethical principles of privacy, fairness, and interpretability in AI/ML-enabled SMH research. As AI/ML continues to expand in SMH, incorporating ethical considerations at every stage—from design to dissemination—is essential for producing ethically responsible and reliable research.

Advancing economic impact models for data-driven decision-making in strategic procurement practices

This paper explores the advancement of economic impact models to enhance data-driven decision-making in strategic procurement practices. Traditional models, while foundational, often fail to address the complexities of modern supply chains and the dynamic nature of today’s economic environments. This study highlights the historical evolution of economic impact modeling, examines current trends in data analytics integration, and identifies gaps in existing approaches. Proposed advancements include leveraging real-time data, predictive analytics, and artificial intelligence to improve model accuracy, scalability, and relevance. The discussion underscores the importance of addressing challenges such as data quality, integration, and regulatory compliance to fully unlock the potential of economic impact models. The implications for practitioners and policymakers are significant, emphasizing the role of advanced modeling techniques in fostering transparency, resilience, and sustainability in procurement. Recommendations for future research include standardized methodologies, technological innovations, and collaborative implementation strategies, ensuring the models remain robust and adaptable in dynamic economic landscapes. Keywords: Economic Impact Modeling, Strategic Procurement, Data-Driven Decision-Making, Predictive Analytics, Artificial Intelligence in Procurement, Sustainable Supply Chains.

Linking Water Quality Indicators in Stable Reservoir Ecosystems: Correlation Analysis and Ecohydrological Implications

This research was conducted to determine the connections between dissolved oxygen (DO), chemical oxygen demand (COD), permanganate index (CODMn), five-day biochemical oxygen demand (BOD5), and ammonia nitrogen (NH3-H) across five reservoirs of Yunmeng County, China, from January to November 2022. Each month, water samples were collected and subjected to analysis using standard methods. The samples were collected and analyzed using standard methods: dissolved oxygen was determined using the electrochemical probe method, COD was measured via the rapid digestion spectrophotometric method, CODMn was detected using the potassium permanganate oxidation method, BOD5 was determined using the dilution and inoculation method, and NH3-N was measured by using the Nessler reagent spectrophotometry method. The results confirmed strong positive correlations between COD and CODMn, with different intensities from reservoir to reservoir. More specific and demanding COD parameters were used to estimate the level of oxygen consumption; hence, a more variable correlation strength was observed between BOD5 and the other two parameters. Thus, BOD5 was found to be the main indicator of biodegradable organic matter and bacterial oxygen consumption. However, the results were negative, showing a decreasing trend. This means that the oxygen content was lower in the majority of reservoirs, which is attributed to the decomposition of ammonia nitrogen and the presence of organic matter. These findings significantly contribute to the development of appropriate programs for efficient water quality monitoring and the development of reservoir-specific management strategies. This study suggests that there is a need for continuous monitoring of these parameters, together with the extension of the program to additional reservoirs and water quality indicators, along with the use of advanced modeling techniques to clarify the underlying factors that connect water quality parameters in these complex reservoir ecosystems.

A Fault Displacement Model Based on the FDHI Database

Fault displacement models are a key component of probabilistic fault displacement hazard analysis (PFDHA), providing the displacement probability density function. We present a new fault displacement model developed using the comprehensive database compiled for the Fault Displacement Hazard Initiative (FDHI) project. The model predicts the total net displacement across multi-stranded surface ruptures as a function of moment magnitude and position along the rupture length. We provide separate models for strike-slip, reverse, and normal faulting. A bilinear magnitude scaling is used to capture the steeper magnitude scaling for smaller magnitudes observed in the data for strike-slip and normal faulting. The scaling for reverse faulting approaches log-linear but still uses a bilinear form. Our flexible location scaling functional form captures the asymmetric profile shapes common in the data for dip-slip events and more symmetrical elliptical profiles in the strike-slip data. We applied a Box-Cox transformation to the displacement dataset so that the data resemble a normal distribution, which allows the transformed displacement to be conveniently modeled as normally distributed. Between- and within-event aleatory variability are modeled separately. All model parameters are estimated using Bayesian regression, which allows within-model epistemic uncertainty to be quantified. Compared with earlier models, the new model produces less aleatory variability and smaller upper-tail displacements for magnitudes greater than about 6.5 for all styles of faulting and all locations along the rupture. Data dispersion at the rupture end-points in the strike-slip and reverse faulting resulted in large aleatory variability. Large aleatory variability for smaller magnitude strike-slip and normal events is driven by a lack of data at smaller magnitudes. The use of a large, high-quality database and advanced statistical modeling techniques in our new model provides improved predictions of fault displacement compared with earlier models.

Evaluation of the Integration of Topological Optimisation in the Process Chain for Manufacturing Customised Orthopaedic Devices via Additive Manufacturing

Purpose: The effectiveness of the customised solutions compared to the conventional ones and the emergence of advanced production technologies, such as Additive Manufacturing (AM) techniques, strengthened the trend towards an enhanced individualization of the clinical treatments. In the present research, the value of topological optimisation (TO) in the manufacturing process of tailor-made orthopaedic appliance (upper-limb orthosis) was analysed. Methodology: From the morphology of a patient's arm, orthotic models were developed. Nonparametric optimization (Simulia Tosca) was performed, based on the Finite Element Analysis (FEA) program (Abaqus), and contributed to the development of TO orthotic models with diverse levels of volume reduction fraction. The modelling and manufacturing framework for customising orthotic solutions was evaluated with a discussion on the feasibility of lightweight and high-performance products, encompassing production time and cost. Pilot products were produced with a Material Extrusion (MEX) printer. Findings: TO proved to be a practical and valuable approach for the advanced customisation of orthopaedic devices, offering lightweight solutions able to withstand stresses also during patient rehabilitation and remission. From the rapid prototyping perspective, specific strategies must be adopted to prevent the escalation of production costs and time. Originality: The research delves into the overall benefit of implementing an advanced modelling technique within the context of manufacturing highly customised orthoses, analysing how TO activity impacts the rapid prototyping process. Beyond product evaluation, the analysis explores broader implications, including the assessment of feasibility and the development of strategies for integrating the approach into clinical workflows and hospital settings.