Comprehensive Tool Research Articles

Statistical Predictive Hybrid Choice Modeling: Exploring Embedded Neural Architecture

This study introduces an enhanced version of the discrete choice model combining embedded neural architecture to enhance predictive accuracy while preserving interpretability in choice modeling across temporal dimensions. Unlike the traditional architectures, which directly utilize raw data without intermediary transformations, this study introduces a modified approach incorporating temporal embeddings for improved predictive performance. Leveraging the Phones Accelerometer dataset, the model excels in predictive accuracy, discrimination capability and robustness, outperforming traditional benchmarks. With intricate parameter estimates capturing spatial orientations and user-specific patterns, the model offers enhanced interpretability. Additionally, the model exhibits remarkable computational efficiency, minimizing training time and memory usage while ensuring competitive inference speed. Domain-specific considerations affirm its predictive accuracy across different datasets. Overall, the subject model emerges as a transparent, comprehensible, and powerful tool for deciphering accelerometer data and predicting user activities in real-world applications.

Advancing Heart Disease Forecasting: Integrating CNN-LSTM Models with Feature Enhancement for Improved Predictive Accuracy

Background: Heart failure (HF) is the leading cause of death worldwide, with accurate early diagnosis being challenging due to the disease's subtle symptoms and the requirement for extensive medical expertise. The development of predictive models is crucial for early intervention. Objective: This study proposes a method for Dynamic Forecasting (DF) of Heart Diseases (HD) using a Deep Learning (DL) model that integrates Convolutional Neural Networks (CNN) and Long Short-Term Memory (LSTM) networks. The aim is to create an automatic, accurate, and cost-effective system for predicting cardiac valve failure. Methods: The study introduces a Hybrid DL model combining CNN and LSTM with Feature Enhancement (FE) and Comprehensible Artificial Intelligence (CAI) techniques. The CNN extracts features from clinical data, which are then processed by the LSTM model to handle temporal dependencies. The model's effectiveness was evaluated using a freely available cardiovascular disease dataset. Accuracy was assessed with and without FE. Results: The CNN-LSTM model achieved an accuracy of 83.5% with FE and 88.2% without FE. The model demonstrated superior performance in terms of sensitivity (80.04% with FE), specificity (87.11% with FE), and overall accuracy compared to other methods including Support Vector Machines (SVM), Decision Trees (DT), and Random Forests (RF). Conclusion: The proposed CNN-LSTM model offers a significant improvement in forecasting heart disease dynamics by effectively integrating CNN for feature extraction and LSTM for temporal analysis. This hybrid approach, combined with advanced feature enhancement and explainable AI techniques, provides a more accurate and comprehensible prediction tool for early heart disease detection.

Patients’ perceptions matter: Risk communication and psychosocial factors in orthodontics

Effective risk communication is essential for achieving patient-centered oral health care, but the limited understanding of patients' subjective perceptions of orthodontic-related risks hinders this process. This study aimed to investigate adults' awareness, concerns, and risk-avoidance behaviors about long-term orthodontic risks, exploring their relationship with psychosocial factors. We included 498 adult patients (mean age, 27.3 ± 6.8 years; women, 75.5%) during their initial visits to the orthodontic department at a hospital in Chengdu, China. Participants' understanding of orthodontic risks was gauged before and after exposure to the Oral Health Education Comics (OHEC), a specifically designed digital tool. Concurrently, we used logistic regression models to investigate the associations between patients' depression, anxiety, self-esteem, perfectionism, and dentofacial esthetics with risk perceptions. Approximately 79.5% of participants initially reported low awareness of orthodontic risks, with most knowledge from online sources. Notably, the percentage of participants with high awareness increased to 64.8% after OHEC. The negative facial soft-tissue change was most concerning for participants: 53.4% showed high concerns, and 28.1% showed high avoidance. Furthermore, linear regression indicated positive associations between depression (β= 0.42 [95% confidence interval {CI}, 0.07-0.77]) and anxiety (β= 0.76 [95% CI, 0.35-1.18]) with orthodontic risk concerns, whereas risk avoidance was positively associated with depression (β= 0.62 [95% CI, 0.27-0.97]), anxiety (β= 1.09 [95% CI, 0.68-1.50]), and perfectionism (β= 0.24 [95% CI, 0.02-0.46]). Findings emphasize the imperative of streamlined risk communication in orthodontics. By incorporating comprehensible tools such as OHEC and integrating psychosocial evaluations, more refined patient-practitioner communication and psychosomatic-based dental care can be achieved.

A Transferability-Based Method for Evaluating the Protein Representation Learning.

Self-supervised pre-trained language models have recently risen as a powerful approach in learning protein representations, showing exceptional effectiveness in various biological tasks, such as drug discovery. Amidst the evolving trend in protein language model development, there is an observable shift towards employing large-scale multimodal and multitask models. However, the predominant reliance on empirical assessments using specific benchmark datasets for evaluating these models raises concerns about the comprehensiveness and efficiency of current evaluation methods. Addressing this gap, our study introduces a novel quantitative approach for estimating the performance of transferring multi-task pre-trained protein representations to downstream tasks. This transferability-based method is designed to quantify the similarities in latent space distributions between pre-trained features and those fine-tuned for downstream tasks. It encompasses a broad spectrum, covering multiple domains and a variety of heterogeneous tasks. To validate this method, we constructed a diverse set of protein-specific pre-training tasks. The resulting protein representations were then evaluated across several downstream biological tasks. Our experimental results demonstrate a robust correlation between the transferability scores obtained using our method and the actual transfer performance observed. This significant correlation highlights the potential of our method as a more comprehensive and efficient tool for evaluating protein representation learning.

Aggregation operators-based divergence measures for single-valued neutrosophic sets with their applications to pattern recognition

A Single-valued neutrosophic set (SVNS) has recently been explored as a comprehensive tool to assess uncertain information due to varied human cognition. This notion stretches the domain of application of the classical fuzzy set and its extended versions. Various comparison measures based on SVNSs like distance measure, similarity measure, and, divergence measure have practical significance in the study of clustering analysis, pattern recognition, machine learning, and computer vision-related problems. Existing measures have some drawbacks in terms of precision and exclusion of information and produce unreasonable results in categorization problems. In this paper, we propose a generic method to define new divergence measures based on common aggregation operators and discuss some algebraic properties of the proposed divergence measures. To further appreciate the proposed divergence measures, their application to pattern recognition has been investigated in conjunction with the prominent existing comparison measures based on SVNSs. The comparative assessment sensitivity analysis of the proposed measures establishes their edge over the existing ones because of appropriate classification results.

InVADo: Interactive Visual Analysis of Molecular Docking Data.

Molecular docking is a key technique in various fields like structural biology, medicinal chemistry, and biotechnology. It is widely used for virtual screening during drug discovery, computer-assisted drug design, and protein engineering. A general molecular docking process consists of the target and ligand selection, their preparation, and the docking process itself, followed by the evaluation of the results. However, the most commonly used docking software provides no or very basic evaluation possibilities. Scripting and external molecular viewers are often used, which are not designed for an efficient analysis of docking results. Therefore, we developed InVADo, a comprehensive interactive visual analysis tool for large docking data. It consists of multiple linked 2D and 3D views. It filters and spatially clusters the data, and enriches it with post-docking analysis results of protein-ligand interactions and functional groups, to enable well-founded decision-making. In an exemplary case study, domain experts confirmed that InVADo facilitates and accelerates the analysis workflow. They rated it as a convenient, comprehensive, and feature-rich tool, especially useful for virtual screening.

Remote field-programmable gate array laboratory for signal acquisition and design verification

A remote laboratory utilizing field-programmable gate array (FPGA) technologies enhances students’ learning experience anywhere and anytime in embedded system design. Existing remote laboratories prioritize hardware access and visual feedback for observing board behavior after programming, neglecting comprehensive debugging tools to resolve errors that require internal signal acquisition. This paper proposes a novel remote embedded-system design approach targeting FPGA technologies that are fully interactive via a web-based platform. Our solution provides FPGA board access and debugging capabilities beyond the visual feedback provided by existing remote laboratories. We implemented a lab module that allows users to seamlessly incorporate into their FPGA design. The module minimizes hardware resource utilization while enabling the acquisition of a large number of data samples from the signal during the experiments by adaptively compressing the signal prior to data transmission. The results demonstrate an average compression ratio of 2.90 across three benchmark signals, indicating efficient signal acquisition and effective debugging and analysis. This method allows users to acquire more data samples than conventional methods. The proposed lab allows students to remotely test and debug their designs, bridging the gap between theory and practice in embedded system design.

Comparative Analysis of Water Sustainability Indices: A Systematic Review

The achievement of water sustainability necessitates the development and application of comprehensive assessment tools to monitor and evaluate the impact of water resource management. This article presents a comprehensive comparative analysis of various water sustainability indices, emphasizing their underlying principles, methodologies, and potential applications. Our study reveals the diverse landscape of existing indices, illustrating that even indices with similar names can vary significantly in scope and methodology. Via a systematic review of 124 publications, this study provides insights into existing composite indices related to water sustainability, highlighting their specific applications and potential contributions to water resource management and sustainability. The information gathered from the selected papers was synthesized and analyzed thematically to identify common patterns through keyword co-occurrence mapping, SDG mapping, standard review protocols, and cluster analyses. Through a cluster analysis, we identified six distinct clusters of indices, highlighting the need for careful consideration in selecting appropriate ones. Moreover, our analysis of co-occurring keywords underscores the close relationship between sustainable development, water resources, water supply, and water conservation within the context of water-related indices. Notably, these indices address not only sustainable development goal six but also a number of other interconnected goals. It was also found that “sustainability index” is a common name for different nature water indices. This review also identifies research gaps in the existing literature. However, significant limitations exist, including a lack of historical application and future projections for many current water sustainability indicators. Without the ability to track changes over time and project the future, identifying areas of improvement and measuring progress toward long-term water sustainability goals becomes challenging. Furthermore, many indices are complex and designed for watershed or regional levels, limiting their adaptability to different contexts. Despite these challenges, indices remain valuable tools for assessing and managing water resources sustainably, addressing various dimensions of sustainability, and supporting decision-making processes across different sectors and contexts.

Demographic, Clinical and Language Characteristics of Malaysian Patients with Aphasia: A Retrospective Study

Limited research has been conducted in Malaysia on the characteristics of patients with aphasia. To address this gap, a retrospective study was conducted at Hospital Rehabilitasi Cheras (HRC). The study aimed to profile the demographic, clinical, and language characteristics of patients who underwent speech therapy for aphasia. Medical records from 2014 to 2017 were reviewed, focusing on 1,259 adult patients who were referred to the Speech Therapy Unit. Analysis included demographics, clinical aspects, and linguistic features. The Western Battery of Aphasia-Revised (WAB-R), a comprehensive assessment tool, was used to evaluate the language abilities. Among the patients' files analysed, a total of 211 contained WAB-R test results. The demographic profile revealed a higher proportion of male patients compared to females, with Malay patients comprising the majority, followed by Chinese and Indians. Age distribution included 41.7% older adults (aged 55 and above), 37.9% middle-aged (aged 36-55), and 20.4% younger adults (aged 18- 35). Notably, 44.5% had completed high school education. Clinically, right- sided hemiparesis was prevalent as noted with 67.8% of patients, while 14.7% had left-sided hemiparesis. The primary cause of aphasia was stroke, accounting for 71.1% of cases, with ischemic stroke being the dominant subtype (56.9%). Fluent aphasia was presented in 50.2% of the cases, compared to 49.3% of non-fluent aphasia. Anomic aphasia was the most common subtype (35.5%), followed by global aphasia (27.5%) and Broca’s aphasia (17.5%). The language profile provided a detailed description, encompassing linguistic aspects relevant to different aphasia types. These preliminary findings yielded valuable insights and hold significant implications for the clinical management of aphasia patients in Malaysia.

A Qualitative and Comparative Performance Assessment of Logically Centralized SDN Controllers via Mininet Emulator

An alternative networking approach called Software Defined Networking (SDN) enables dynamic, programmatically efficient network construction, hence enhancing network performance. It splits a traditional network into a centralized control plane and a configurable data plane. Because the core component overseeing every data plane action is the controller in the control plane, which may contain one or more controllers and is thought of as the brains of the SDN network, controller functionality and performance are crucial to achieve optimal performances. There is much controller research available in the existing literature. Nevertheless, no qualitative comparison study of OpenFlow-enabled distributed but logically centralized controllers exists. This paper includes a quantitative investigation of the performance of several distributed but logically centralized SDN controllers in custom network scenarios using Mininet, as well as a thorough qualitative comparison of them. More precisely, we give a qualitative evaluation of their attributes and classify and categorize 13 distributed but logically centralized SDN controllers according to their capabilities. Additionally, we offer a comprehensive SDN emulation tool, called Mininet-based SDN controller performance assessment, in this study. Using six performance metrics—bandwidth, round-trip time, delay, jitter, packet loss, and throughput—this work also assesses five distributed but logically centralized controllers within two custom network scenarios (uniform and non-uniform host distribution). Our analysis reveals that the Ryu controller outperforms the OpenDayLight controller in terms of latency, packet loss, and round-trip time, while the OpenDayLight controller performs well in terms of throughput, bandwidth, and jitter. Throughout the entire experiment, the HyperFlow and ONOS controllers performed worst in all performance metrics. Finally, we discuss detailed research findings on performance. These experimental results provide decision-making guidelines when selecting a controller.

Uncertainty assessment of aquifer hydraulic parameters from pumping test data

Data from pumping tests is a noisy process, and therefore, performing the pumping test numerous times will not get the same drawdown values. As a consequence, various pumping experiments lead to different values for aquifer parameter estimates. The data of pumping tests are usually analyzed using traditional methods (aquifer tests and AQtesolv software), which depend on trial and error technique. During these methods, non-unique values of hydraulic parameters are selected, which usually have a high level of uncertainty. Uncertainty must be taken into account in determining aquifer parameters, especially when using groundwater models for decision makers. The main goal of this study is to build a comprehensive tool for quantifying uncertainty associated with hydraulic parameter estimation from different pumping test conditions for fully penetrating wells in confined and semi-confined aquifers. To achieve the previous objective, a FORTRAN code was developed to apply the Markov Chain Monte Carlo (MCMC) algorithm using different likelihood functions (exponential, inverse, and log). This developed tool can be used to detect the most probable range of aquifer parameters that are consistent with pumping test data with a high degree of confidence. The tool was successfully used to several hypothetical cases to demonstrate the uncertainty in the quantification of aquifer parameters and compare the findings to the standard method's results. Also, the concept was verified numerically (using Modflow program) with satisfactory results using a hypothetical case with well-known aquifer parameters. Finally, the tool was applied for actual pumping test data with good results.

Hysterolaparoscopy as a comprehensive diagnostic and therapeutic tool in modern ART

The aim of the study is to study Hysterolaparoscopy as a comprehensive diagnostic and therapeutic tool in modern art. The scheduled investigations were done to determine fitness for surgery. Patient was admitted one day prior to the procedure, and preanesthetic checkup was done. Hysterolaparoscopy was scheduled in preovulatory period between day 5 and day 10 of cycle and done under general anesthesia. Diagnostic laparoscopy was performed using a 30-degree deflection angle telescope powered with a fiber-optic cable for light source. Panoramic view of the abdomen was obtained a stepwise evaluation was made. Hysterolaproscopy may appear to be invasive, but is more beneficial, as diagnosis and therapeutic interventions can be done at the same sitting. Furthermore, it is possible to identify patients who will need ART at the earliest, thus expediting treatment. This helps in preventing further compromise in ovarian reserve and progression of disease and thus reducing financial burden to the couples.

Aleatoric and epistemic uncertainty extraction of patient-specific deep learning-based dose predictions in LDR prostate brachytherapy.

Objective.In brachytherapy, deep learning (DL) algorithms have shown the capability of predicting 3D dose volumes. The reliability and accuracy of such methodologies remain under scrutiny for prospective clinical applications. This study aims to establish fast DL-based predictive dose algorithms for low-dose rate (LDR) prostate brachytherapy and to evaluate their uncertainty and stability.Approach.Data from 200 prostate patients, treated with125I sources, was collected. The Monte Carlo (MC) ground truth dose volumes were calculated with TOPAS considering the interseed effects and an organ-based material assignment. Two 3D convolutional neural networks, UNet and ResUNet TSE, were trained using the patient geometry and the seed positions as the input data. The dataset was randomly split into training (150), validation (25) and test (25) sets. The aleatoric (associated with the input data) and epistemic (associated with the model) uncertainties of the DL models were assessed.Main results.For the full test set, with respect to the MC reference, the predicted prostateD90metric had mean differences of -0.64% and 0.08% for the UNet and ResUNet TSE models, respectively. In voxel-by-voxel comparisons, the average global dose difference ratio in the [-1%, 1%] range included 91.0% and 93.0% of voxels for the UNet and the ResUNet TSE, respectively. One forward pass or prediction took 4 ms for a 3D dose volume of 2.56 M voxels (128 × 160 × 128). The ResUNet TSE model closely encoded the well-known physics of the problem as seen in a set of uncertainty maps. The ResUNet TSE rectum D2cchad the largest uncertainty metric of 0.0042.Significance.The proposed DL models serve as rapid dose predictors that consider the patient anatomy and interseed attenuation effects. The derived uncertainty is interpretable, highlighting areas where DL models may struggle to provide accurate estimations. The uncertainty analysis offers a comprehensive evaluation tool for dose predictor model assessment.

Chronic Stress Indicator: A Novel Tool for Comprehensive Stress Analysis.

Extensive research has highlighted the strong association between chronic stress and negative health outcomes. This relationship is influenced by various factors, including sociobehavioral, environmental, and genetic and epigenomic forces. To comprehensively assess an individual's stress levels, we propose the development of the Chronic Stress Indicator (CSI), a novel comprehensive multifaceted tool that incorporates key biological, anthropometric, behavioral, and socioeconomic factors. The objective of this study is to assess the effectiveness of the CSI compared to Allostatic Load (AL), a type of chronic stress, in identifying health issues related to stress. The objective of this research is to evaluate the performance of the Chronic Stress Indicator (CSI) versus Allostatic Load (AL) in detecting adverse health outcomes within the U.S. demographic aged 20-49. The information used for this study was sourced from the National Health and Nutrition Examination Survey (NHANES), carried out from 2001 to 2004. Logistic regression modeling was employed to calculate odds ratios and confidence intervals. The Wilcoxon rank-sum test was employed to assess differences in means, whereas the chi-square test, accompanied by Cramer's V statistic, was used to examine the association among categorical variables. Additionally, the relationship between continuous variables was analyzed using Pearson's correlation coefficient. Our association tests show that the length of occupation activity and health status were among the strongest associations to CSI risk. Based on our logistic regression models, age and sex were found to be significant factors in determining AL. We also found that age, smoking, and longest occupation activity were significant factors of CSI risk. These findings suggest a need for individuals to limit smoking as it may lead to higher overall stress despite its common use as a coping mechanism for stress. We should also review the level of occupational activity a job has before continuously working on it as this may also lead to higher cumulative stress.

Applicability of electromyography (EMG) as a prospective technique for textural evaluation of different types of biscuits

Bakery industry is an important segment of the food industry, worldwide, and texture is an important determinant in the consumer acceptability of baked products especially biscuits. In this study, six different textured biscuits namely, whole wheat flour biscuit, chocolate biscuit, cumin biscuit, cardamom biscuit, pistachio biscuit and cashew biscuit, procured from local bakery, were analyzed for their proximate composition and color characteristics. The biscuits were having significantly (p ≤ 0.05) different values for proximate composition and color parameters. This study primarily aimed at investigating the application of electromyography (EMG) as a prospective technique for texture evaluation of these different types of biscuits. EMG is an upcoming technique for in vivo food texture evaluation which gives real-time information about the dynamic textural changes occurring during mastication of a food product. The EMG study was conducted on ten human subjects who were served six samples of biscuits in duplicate. To eliminate subject factor variance, two-way ANOVA was applied on the relative values of EMG variables. Two recordings of same subject in an EMG session for a biscuit sample showed significant (p ≤ 0.05) degree of similarity indicating that human mastication behavior is reproducible. Initially, twenty-eight EMG parameters were obtained which were reduced to five representative parameters using cluster analysis. Principal component analysis further classified these five masticatory parameters into two independent principal components (PC1 and PC2) for explaining the data variance. Cumulative proportions for PC1 and PC2 were 37.6% and 70.8%, respectively. The results showed that mastication time obtained from electromyographic analysis can evaluate sensory fracturability, hardness and chewiness. While middle interburst duration can effectively evaluate sensory cohesiveness and instrumental flexural strength. The study concluded that EMG can serve as a comprehensive texture evaluation tool in the bakery industry.

The sensitivity of radiobiological models in carbon ion radiotherapy (CIRT) and its consequences on the clinical treatment plan: Differences between LEM and MKM models.

Carbon ion radiotherapy (CIRT) relies on relative biological effectiveness (RBE)-weighted dose calculations. Japanese clinics predominantly use the microdosimetric kinetic model (MKM), while European centers utilize the local effect model (LEM). Despite both models estimating RBE-distributions in tissue, their physical and mathematical assumptions differ, leading to significant disparities in RBE-weighted doses. Several European clinics adopted Japanese treatment schedules, necessitating adjustments in dose prescriptions and organ at risk (OAR) constraints. In the context of these two clinically used standards for RBE-weighted dose estimation, the objective of this study was to highlight specific scenarios for which the translations between models diverge, as shortcomings between them can influence clinical decisions. Our aim was to discuss planning strategies minimizing those discrepancies, ultimately striving for more accurate and robust treatments. Evaluations were conducted in a virtual water phantom and patient CT-geometry, optimizing LEM RBE-weighted dose first and recomputing MKM thereafter. Dose-averaged linear energy transfer (LETd) distributions were also assessed. Results demonstrate how various parameters influence LEM/MKM translation. Similar LEM-dose distributions lead to markedly different MKM-dose distributions and variations in LETd. Generally, a homogeneous LEM RBE-weighted dose aligns with lower MKM values in most of the target volume. Nevertheless, paradoxical MKM hotspots may emerge (at the end of the range), potentially influencing clinical outcomes. Therefore, translation between models requires great caution. Understanding the relationship between these two clinical standards enables combining European and Japanese based experiences. The implementation of optimal planning strategies ensures the safety and acceptability of the clinical plan for both models and therefore enhances plan robustness from the RBE-weighted dose and LETd distribution point of view. This study emphasizes the importance of optimal planning strategies and the need for comprehensive CIRT plan quality assessment tools. In situations where simultaneous LEM and MKM computation capabilities are lacking, it can provide guidance in plan design, ultimately contributing to enhanced CIRT outcomes.

Diagnosis and clinical management of thrombotic thrombocytopenic purpura (TTP): a consensus statement from the TTP Catalan group.

Thrombotic thrombocytopenic purpura (TTP) is a low prevalence disease characterized by severe deficiency of the enzyme ADAMTS13, leading to the development of thrombotic microangiopathy (TMA) and often resulting in severe organ disfunction. TTP is an extremely serious condition and, therefore, timely and appropriate treatment is critical to prevent life-threatening complications.Over the past 25 years, significant advances in the understanding of the pathophysiology of immune TTP have led to the development of readily available techniques for measuring ADAMTS13 levels, as well as new drugs that are particularly effective in the acute phase and in preventing relapses. These developments have improved the course of the disease.Given the complexity of the disease and its various clinical and laboratory manifestations, early diagnosis and treatment can be challenging.To address this challenge, a group of experienced professionals from the Catalan TTP group have developed this consensus statement to standardize terminology, diagnosis, treatment and follow up for immune TTP, based on currently available scientific evidence in the field. This guidance document aims to provide healthcare professionals with a comprehensive tool to make more accurate and timely diagnosis of TTP and improve patient outcomes.

RATIONALE OF FINANCIAL STABILITY IN SOUTH AFRICA: CONSTRUCTING A FINANCIAL STRESS INDEX

The need for financial stability became apparent after the devastating events of the 2008 financial crisis. It was around this time that central banks around the world started to construct and implement scientific methods to assess financial stability. This research gives a comprehensive understanding of financial stability and makes use of scientific methodology by constructing the financial stress index for South Africa from periods 2006 through 2015 to predict and attain financial stability. After the 2008 global financial crisis, South African policymakers have put in place some policies and tools to enhance financial stability. To complement these policies, a new financial framework was developed, that is, the twin peaks model to continuously gather data and asses financial stability on a continuous basis. To put this framework into practice the financial stress indicator or index acted as a single comprehensive tool to measure and monitor stability. Included in this index are indicators that capture the different dimensions of the banking sector. The results gathered from this index contribute to the understanding of financial stability and how it is measured. It was found that the volatility or patterns of the financial stress index could be used as predictions and early warning tools for policymakers or the Central Bank to step in and contain potential risks. Moreover, from the empirical data collected the financial stability policies in South Africa are effective, as the financial stability index after the period of crises remained stable.

Nanoparticle migration in nanofluid film boiling: A numerical analysis using the continuous-species-transfer method

Investigating the boiling behavior of nanofluids remains a topic of considerable research interest. The existing theoretical framework often underscores Brownian motion and thermophoresis among the predominant mechanisms that influence nanoparticles' movement within nanofluids. However, most current numerical models addressing nanofluid boiling either neglect these mechanisms or incorporate them inadequately. A comprehensive review of the literature reveals two primary gaps. The larger set of numerical studies on nanofluids boiling does not account for any governing equation for nanoparticle concentration, neglecting the primary mechanisms of nanoparticle movement. The smaller set includes such an equation but limits its application to the vapor domain, thereby overlooking the liquid nanofluid phase. Moreover, in the context of film boiling involving nanofluids, an insulating vapor film forms between the nanofluid and the heated surface. This vapor film is dynamic, experiencing evaporation at its outer boundary, leading to an increase in nanoparticle concentration at the vapor-liquid interface—another aspect always neglected in existing numerical models. To address these research gaps, this study introduces a specialized governing equation employing the Continuous-Species-Transfer method within the framework of Computational Multi-Fluid Dynamics. Importantly, it also incorporates governing equations for thermophoresis and Brownian motion to account for the motion of nanoparticles. The method's efficacy is further corroborated by solving a two-dimensional axisymmetric film boiling problem on a vertical cylinder, with results aligning well with analytical studies that consider Brownian motion and thermophoretic behavior. Overall, the study fills a critical gap in the literature by providing a comprehensive tool for examining complex interfacial behaviors in nanofluid film boiling.

Modeling of compressive stress in AlSi10Mg alloys using feed-forward neural networks

This study addresses the challenge of modeling compressive stress in AlSi10Mg composites by introducing a method that employs feedforward artificial neural networks (ANNs) and their interpretability, which helps to simulate and analyze material behavior under various conditions. The main objective is to develop a predictive ANN model that can effectively simulate material responses under several factors, incorporating diverse testing parameters and material specifications related with its synthesis. An optimized ANN model, featuring eleven neurons in its hidden layer, was used and demonstrated high predictive accuracy, achieving R 2 values exceeding 0.94. Additionally, a SHAP interpretability analysis was conducted to assess the influence of key factors such as strain and material conditions on the stress response. The results highlight the significant role of material synthesis processes, compared to the strain rate, in the stress response. In conclusion, this method presents a comprehensive tool for studying complex stress behaviors in AlSi10Mg-based composites , offering insights that could guide future material development and research.