Predictive Approach Research Articles

Do traffic flow states follow Markov properties? A high-order spatiotemporal traffic state reconstruction approach for traffic prediction and imputation

Assessing traffic states accurately is challenging due to the complex, high-dimensional, and nonlinear nature of traffic systems. This study introduces the innovative High-Order Spatiotemporal Traffic State Reconstruction (HOSTSR) algorithm, designed to track and predict traffic flow dynamics effectively. It combines phase space reconstruction with time delays and high-order neighborhood concepts from graph theory to improve traffic state assessments' accuracy. The algorithm's effectiveness is validated using chi-square tests and the Chapman-Kolmogorov equation to confirm the Markovian properties of traffic flows. A lean autoencoder, informed by prior Markov knowledge of traffic states, is developed for mapping traffic states to real traffic data, proving highly effective for traffic data imputation due to the Markov model's memoryless property. Experimental results from the PeMSD04 and PeMSD08 datasets show that HOSTSR outperforms traditional state reconstruction methods based on delayed coordinate embedding in predicting future traffic flow state based on four key metrics. The autoencoder framework, guided by prior Markov knowledge, shows significant advantages in addressing traffic data gaps in different cases over six baseline models. Gradient sensitivity analysis further evaluates the impact of prior knowledge on improving the autoencoder's interpretability for interpolation efforts.

Climate model selection via conformal clustering of spatial functional data

AbstractClimate model selection stands as a critical process in climate science and research. It involves choosing the most appropriate climate models to address specific research questions, simulating climate behaviour, or making projections about future climate conditions. This paper proposes a new approach, using spatial functional data analysis, to asses which of the 18 EURO CORDEX simulation models work better for predicting average temperatures in the Campania region (Italy). The method involves two key steps: first, using functional data analysis to process climate variables and select optimal models by a hierarchical clustering procedure; second, validating the chosen models by proposing a new conformal prediction approach to the anomalies associated to each cluster.

Explainable machine learning approach for cancer prediction through binarilization of RNA sequencing data.

In recent years, researchers have proven the effectiveness and speediness of machine learning-based cancer diagnosis models. However, it is difficult to explain the results generated by machine learning models, especially ones that utilized complex high-dimensional data like RNA sequencing data. In this study, we propose the binarilization technique as a novel way to treat RNA sequencing data and used it to construct explainable cancer prediction models. We tested our proposed data processing technique on five different models, namely neural network, random forest, xgboost, support vector machine, and decision tree, using four cancer datasets collected from the National Cancer Institute Genomic Data Commons. Since our datasets are imbalanced, we evaluated the performance of all models using metrics designed for imbalance performance like geometric mean, Matthews correlation coefficient, F-Measure, and area under the receiver operating characteristic curve. Our approach showed comparative performance while relying on less features. Additionally, we demonstrated that data binarilization offers higher explainability by revealing how each feature affects the prediction. These results demonstrate the potential of data binarilization technique in improving the performance and explainability of RNA sequencing based cancer prediction models.

An efficient CNN-transformer hybrid approach for water turbine unit failure prediction

Water Turbine Unit is the core equipment of water power generation. It is the most important research object in hydroelectric power. The analysis of daily monitoring data of Water Turbines can evaluate its running state and avoid the loss caused by failure. In this paper, A hybrid neural network architecture which CNN combines Transformer is proposed to evaluate the health state of water turbines based on multivariate long time series data. It has two core components: (i) dilated convolution applies to capture low-level and local semantic information, then the output of convolutional layers is divided into subseries-level patches by time, these patches are regarded as input tokens of Transformer layers; (ii) utilizing self-attention of Transformer to extract high-level and global semantic information. Patching design naturally has benefit as follow: (i) local semantic information is retained in Transformer input tokens and high-level semantic confirm to common sense of human understanding through low-level construction; (ii) the length of Transformer input sequence is greatly shorted and attention can be more concentrated compared with point-wise form. Meanwhile, the computation and memory usage reduces at the same time. The experimental result indicates that the hybrid architecture can achieves excellent performance in time series understanding.

Mapping surface sediment characteristics in enclosed shallow‐marine environments using spatially balanced designs and the random forest algorithm

AbstractMapping the sedimentary character of the seafloor in large water‐filled basins is fundamental for understanding landform dynamics to inform research, management, intervention and conservation actions. Seabed mapping methods have undergone considerable development in the last two decades, including the uptake of machine learning approaches for sediment size prediction and classification. However, predictions of surficial sediment characteristics are often hindered by the availability of ground truthing data, their arrangement in space and the modelling approach chosen. Spatially informed sampling designs provide an opportunity to significantly improve the accuracy and uncertainty of predicted sediment distributions. In this study, we apply a machine learning algorithm to predict sediment distributions across Port Phillip Bay, a large (1930 km2) structurally controlled estuary on the southeast coast of Victoria, Australia. Surface sediment samples (n = 252) were collected using a spatially balanced design, ensuring that sampling effort was spread evenly within the embayment with increased sampling intensity placed in more heterogeneous areas. Surficial textural metrics were modelled using the random forest algorithm with bathymetric and hydrodynamic predictor variables. Models highlighted trends in sediment grain size, sorting and composition consistent with predicted wave‐ and current‐induced sediment mobilisation. Model predictions were accurate (normalised‐root mean squared error [NRMSE]: 0.14–0.16); however, standard error was not homogeneous across the study area. Uncertainty maps highlighted areas where additional sampling effort may be needed, including areas where transitional bathymetry impacted surficial sediment character and areas of anthropogenic modifications to the seabed. This study shows the benefits of undertaking spatially informed sample design, block cross‐validation during model fitting and quantifying spatial uncertainty in predictive maps to accurately quantify the fundamental boundary conditions of sediment size. The results of this study are intended to inform local coastal management, including beach renourishment activities. However, approaches outlined are applicable to any study where the seafloor grain size is a fundamental variable in understanding landscape change.

Twin-RSA: deep learning-based automated heterogeneous data fusion approach for patient progression prediction using EHR data

Twin-RSA: deep learning-based automated heterogeneous data fusion approach for patient progression prediction using EHR data

Predicting the Spread of a Pandemic Using Machine Learning: A Case Study of COVID-19 in the UAE

Pandemics can result in large morbidity and mortality rates that can cause significant adverse effects on the social and economic situations of communities. Monitoring and predicting the spread of pandemics helps the concerned authorities manage the required resources, formulate preventive measures, and control the spread effectively. In the specific case of COVID-19, the UAE (United Arab Emirates) has undertaken many initiatives, such as surveillance and contact tracing by introducing mobile apps such as Al Hosn, containment of spread by limiting the gathering of people, online schooling and remote work, sanitation drives, and closure of public places. The aim of this paper is to predict the trends occurring in pandemic outbreak, with COVID-19 in the UAE being a specific case study to investigate. In this paper, a predictive modeling approach is proposed to predict the future number of cases based on the recorded history, taking into consideration the enforced policies and provided vaccinations. Machine learning models such as LASSO Regression and Exponential Smoothing, and deep learning models such as LSTM, LSTM-AE, and bi-directional LSTM-AE, are utilized. The dataset used is publicly available from the UAE government, Federal Competitiveness and Statistics Centre (FCSC) and consists of several attributes, such as the numbers of confirmed cases, recovered cases, deaths, tests, and vaccinations. An additional categorical attribute is manually added to the dataset describing whether an event has taken place, such as a national holiday or a sanitization drive, to study the effect of such events on the pandemic trends. Experimental results showed that the Univariate LSTM model with an input of a five-day history of Confirmed Cases achieved the best performance with an RMSE of 275.85, surpassing the current state of the art related to the UAE by over 30%. It was also found that the bi-directional LSTMs performed relatively well. The approach proposed in the paper can be applied to monitor similar infectious disease outbreaks and thus contribute to strengthening the authorities’ preparedness for future pandemics.

Estimating kill intervals for a specific prey species using location clusters from GPS-collared Eurasian lynx (Lynx lynx)

An increasing number of GPS telemetry studies have helped to gain important insights into predator-prey relationships in recent years. However, considerable time and effort is needed to evaluate whether GPS location clusters (GLCs) reflect predation events. To reduce field effort, predictive models are being developed to calculate predator kill intervals, but few studies have attempted to do this for a specific species of prey. Between 2013 and 2018, we studied predation by 13 GPS-collared Eurasian lynx (Lynx lynx) on Alpine chamois (Rupicapra rupicapra) in the northwestern Swiss Alps. Our objectives were to predict the total number of killed chamois, including potential kills in unchecked GLCs, and to evaluate if model predictions were sufficiently accurate. We built a set of generalized linear models (GLM) predicting the occurrence of GLCs containing lynx-killed chamois (1) versus GLCs containing other prey types or no prey (0) and compared their predictive performance by means of k-fold cross-validation. We found that model performance was very similar for all candidate models, with the full model yielding the best cross-validation result (accuracy = 0.83, sensitivity = 0.43, specificity = 0.94). Female lynx killed on average one chamois every 11.9 days (10.6–13.0 days, 95% CI); male lynx killed one chamois every 7.2 days (6.7–7.6 days, 95% CI). Our model showed high specificity for detecting non-chamois GLCs, but sensitivity for detection of GLCs with actual chamois kills was low. We conclude that the sensitivity of the models should be further improved, but the results can be sufficient for practical application. Predictive modelling approaches do not replace extensive fieldwork but require large sets of field data, high individual variability and thorough knowledge of a predator’s ecology and prey community. Our approach may provide useful results for binary classifications in rather simple predator-prey systems, but extrapolations from one study system to another might be difficult.

Artificial neural network-based approach for prediction of nanomechanical properties of anodic coating on additively manufactured Al–10Si–Mg alloy

Nowadays, anodic coating on additively manufactured (AM) or 3D printed Al–10Si–Mg alloy are used for various components in spacecraft such as antenna feeds, wave guides, structural brackets, collimators, thermal radiators etc. In this study, artificial neural network (ANN) and power law-based models are developed from experimental nanoindentation data for predicting elastic modulus and hardness of anodized AM Al–10Si–Mg at any desired loads. Data from nanoindentation experiments conducted on plan- and cross-sections of anodized coating on AM Al–10Si–Mg alloy was considered for modeling. Apart from nanomechanical properties, load and displacement curves were predicted using Python software from ANN and the Power law model of nanoindentation. It is observed that the ANN model of 50 mN nanoindentation experimental data can accurately predict the loading pattern at any desired load below 50 mN. Elastic modulus and hardness of anodized AM Al–10Si–Mg computed from ANN and the power law model of the unloading curve are also comparable with the values obtained from Weibull distribution analysis reported elsewhere. The derived models were also used to predict nanomechanical properties at 25 and 35 mN, for which no experimental data was available. The computed hardness of plan section of the anodic coating is 3.99 and 4.02 GPa for 25 and 35 mN, respectively. The computed hardness of cross-section of the anodic coating of is 7.16 and 6.61 GPa for 25 and 35 mN, respectively. Thus, the ANN and Power law model of nanoindentation can predict elastic modulus and hardness at different loads by conducting the minimum number of experiments. The novel approach to predict nanomechanical properties using ANN resulted in determining realistic and design specific data on hardness and modulus of the anodized coating on AM Al–10Si–Mg alloy.

A novel approach of stability and topography prediction in five-axis ball-end milling process through workpiece-edge-coupling

By studying the workpiece-edge-coupling (WEC) in five-axis ball-end milling, the contact characteristics between the workpiece and edge curve are analyzed, and the chip model is extracted and simplified. The edge curve involved in the cutting process of each edge are calculated at each time and a new instantaneous numerical chip thickness model is established. Then the milling forces and stability lobe diagram (SLD) are calculated in following cut process with lead and tilt angle. The milling forces and SLD of lead angle at 15° and tilt angle at −15° are verified by comparing with Otzurk model as references, and it is found the SLD of WEC model can reflect the unstable points more properly than that of Otzurk model. Also the vibration in [Formula: see text] and [Formula: see text] directions show a divergence trend, which proves the high precision for the new algorithm adapted to the stability prediction of five-axis ball-end milling process. In addition, the surface topography is acquired considering lead and tilt angle as well as forced vibration, and the result is consistent with the experiment in the existing literature. It is found that the milling forces, SLD and surface topography show the same variation trend with the increase of lead and tilt angle. Besides, the stability region significantly expands and the surface topography improves by applying positive tilt angle other than the negative. Then, under the conditions of positive lead and tilt angle, increasing lead angle and decreasing tilt angle reduces the milling force, expands the stability region of SLD and improve the surface topography. The optimized tool posture is acquired by the coincident analysis of milling force, SLD and surface topography under different lead and tilt angle.

A Deep Learning Approach for Accurate Path Loss Prediction in LoRaWAN Livestock Monitoring

The agricultural sector is amidst an industrial revolution driven by the integration of sensing, communication, and artificial intelligence (AI). Within this context, the internet of things (IoT) takes center stage, particularly in facilitating remote livestock monitoring. Challenges persist, particularly in effective field communication, adequate coverage, and long-range data transmission. This study focuses on employing LoRa communication for livestock monitoring in mountainous pastures in the north-western Alps in Italy. The empirical assessment tackles the complexity of predicting LoRa path loss attributed to diverse land-cover types, highlighting the subtle difficulty of gateway deployment to ensure reliable coverage in real-world scenarios. Moreover, the high expense of densely deploying end devices makes it difficult to fully analyze LoRa link behavior, hindering a complete understanding of networking coverage in mountainous environments. This study aims to elucidate the stability of LoRa link performance in spatial dimensions and ascertain the extent of reliable communication coverage achievable by gateways in mountainous environments. Additionally, an innovative deep learning approach was proposed to accurately estimate path loss across challenging terrains. Remote sensing contributes to land-cover recognition, while Bidirectional Long Short-Term Memory (Bi-LSTM) enhances the path loss model’s precision. Through rigorous implementation and comprehensive evaluation using collected experimental data, this deep learning approach significantly curtails estimation errors, outperforming established models. Our results demonstrate that our prediction model outperforms established models with a reduction in estimation error to less than 5 dB, marking a 2X improvement over state-of-the-art models. Overall, this study signifies a substantial advancement in IoT-driven livestock monitoring, presenting robust communication and precise path loss prediction in rugged landscapes.

Incorporating the Third Law of Geography with Spatial Attention Module–Convolutional Neural Network–Transformer for Fine-Grained Non-Stationary Air Quality Predictive Learning

Accurate air quality prediction is paramount in safeguarding public health and addressing air pollution control. However, previous studies often ignore the geographic similarity among different monitoring stations and face challenges in dynamically capturing different spatial–temporal relationships between stations. To address this, an air quality predictive learning approach incorporating the Third Law of Geography with SAM–CNN–Transformer is proposed. Firstly, the Third Law of Geography is incorporated to fully consider the geographical similarity among stations via a variogram and spatial clustering. Subsequently, a spatial–temporal attention convolutional network that combines the spatial attention module (SAM) with the convolutional neural network (CNN) and Transformer is designed. The SAM is employed to extract spatial–temporal features from the input data. The CNN is utilized to capture local information and relationships among each input feature. The Transformer is applied to capture time dependencies across long-distance time series. Finally, Shapley’s analysis is employed to interpret the model factors. Numerous experiments with two typical air pollutants (PM2.5, PM10) in Haikou City show that the proposed approach has better comprehensive performance than baseline models. The proposed approach offers an effective and practical methodology for fine-grained non-stationary air quality predictive learning.

Towards determining perceived audience intent for multimodal social media posts using the theory of reasoned action

Increasing use of social media has resulted in many detrimental effects in youth. With very little control over multimodal content consumed on these platforms and the false narratives conveyed by these multimodal social media postings, such platforms often impact the mental well-being of the users. To reduce these negative effects of multimodal social media content, an important step is to understand creators’ intent behind sharing content and to educate their social network of this intent. Towards this goal, we propose Intent-o-meter, a perceived human intent prediction model for multimodal (image and text) social media posts. Intent-o-meter models ideas from psychology and cognitive modeling literature, in addition to using the visual and textual features for an improved perceived intent prediction model. Intent-o-meter leverages Theory of Reasoned Action (TRA) factoring in (i) the creator’s attitude towards sharing a post, and (ii) the social norm or perception towards the multimodal post in determining the creator’s intention. We also introduce Intentgram, a dataset of 55K social media posts scraped from public Instagram profiles. We compare Intent-o-meter with state-of-the-art intent prediction approaches on four perceived intent prediction datasets, Intentonomy, MDID, MET-Meme, and Intentgram. We observe that leveraging TRA in addition to visual and textual features—as opposed to using only the latter–results in improved prediction accuracy by up to 7.5%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$7.5\\%$$\\end{document} in Top-1 accuracy and 8%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$8\\%$$\\end{document} in AUC on Intentgram. In summary, we also develop a web browser application mimicking a popular social media platform and show users social media content overlaid with these intent labels. From our analysis, around 70%\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$70\\%$$\\end{document} users confirmed that tagging posts with intent labels helped them become more aware of the content consumed, and they would be open to experimenting with filtering content based on these labels. However, more extensive user evaluation is required to understand how adding such perceived intent labels mitigate the negative effects of social media.

Evaluation of an automated clinical decision system with deep learning dose prediction and NTCP model for prostate cancer proton therapy.

To evaluate the feasibility of using a deep learning dose prediction approach to identify patients who could benefit most from proton therapy based on the normal tissue complication probability (NTCP) model.
Approach: Two 3D UNets were established to predict photon and proton doses. A dataset of 95 patients with localized prostate cancer was randomly partitioned into 55, 10, and 30 for training, validation, and testing, respectively. We selected NTCP models for late rectum bleeding and acute urinary urgency of grade 2 or higher to quantify the benefit of proton therapy. Propagated uncertainties of predicted ΔNTCPs resulting from the dose prediction errors were calculated. Patient selection accuracies for a single endpoint and a composite evaluation were assessed under different ΔNTCP thresholds.
Main results: Our deep learning-based dose prediction technique can reduce the time spent on plan comparison from approximately 2 days to as little as 5 seconds. The expanded uncertainty of predicted ΔNTCPs for rectum and bladder endpoints propagated from the dose prediction error were 0.0042 and 0.0016, respectively, which is less than one-third of the acceptable tolerance. The averaged selection accuracies for rectum bleeding, urinary urgency, and composite evaluation were 90%, 93.5%, and 93.5%, respectively.
Significance: Our study demonstrates that deep learning dose prediction and NTCP evaluation scheme could distinguish the NTCP differences between photon and proton treatment modalities. In addition, the dose prediction uncertainty does not significantly influence the decision accuracy of NTCP-based patient selection for proton therapy. Therefore, automated deep learning dose prediction and NTCP evaluation schemes can potentially be used to screen large patient populations and to avoid unnecessary delays in the start of prostate cancer radiotherapy in the future.

JARVIS-Leaderboard: a large scale benchmark of materials design methods

Lack of rigorous reproducibility and validation are significant hurdles for scientific development across many fields. Materials science, in particular, encompasses a variety of experimental and theoretical approaches that require careful benchmarking. Leaderboard efforts have been developed previously to mitigate these issues. However, a comprehensive comparison and benchmarking on an integrated platform with multiple data modalities with perfect and defect materials data is still lacking. This work introduces JARVIS-Leaderboard, an open-source and community-driven platform that facilitates benchmarking and enhances reproducibility. The platform allows users to set up benchmarks with custom tasks and enables contributions in the form of dataset, code, and meta-data submissions. We cover the following materials design categories: Artificial Intelligence (AI), Electronic Structure (ES), Force-fields (FF), Quantum Computation (QC), and Experiments (EXP). For AI, we cover several types of input data, including atomic structures, atomistic images, spectra, and text. For ES, we consider multiple ES approaches, software packages, pseudopotentials, materials, and properties, comparing results to experiment. For FF, we compare multiple approaches for material property predictions. For QC, we benchmark Hamiltonian simulations using various quantum algorithms and circuits. Finally, for experiments, we use the inter-laboratory approach to establish benchmarks. There are 1281 contributions to 274 benchmarks using 152 methods with more than 8 million data points, and the leaderboard is continuously expanding. The JARVIS-Leaderboard is available at the website: https://pages.nist.gov/jarvis_leaderboard/

Improved nonparametric survival prediction using CoxPH, Random Survival Forest & DeepHit Neural Network

In recent times, time-to-event data such as time to failure or death is routinely collected alongside high-throughput covariates. These high-dimensional bioinformatics data often challenge classical survival models, which are either infeasible to fit or produce low prediction accuracy due to overfitting. To address this issue, the focus has shifted towards introducing a novel approaches for feature selection and survival prediction. In this article, we propose a new hybrid feature selection approach that handles high-dimensional bioinformatics datasets for improved survival prediction. This study explores the efficacy of four distinct variable selection techniques: LASSO, RSF-vs, SCAD, and CoxBoost, in the context of non-parametric biomedical survival prediction. Leveraging these methods, we conducted comprehensive variable selection processes. Subsequently, survival analysis models—specifically CoxPH, RSF, and DeepHit NN—were employed to construct predictive models based on the selected variables. Furthermore, we introduce a novel approach wherein only variables consistently selected by a majority of the aforementioned feature selection techniques are considered. This innovative strategy, referred to as the proposed method, aims to enhance the reliability and robustness of variable selection, subsequently improving the predictive performance of the survival analysis models. To evaluate the effectiveness of the proposed method, we compare the performance of the proposed approach with the existing LASSO, RSF-vs, SCAD, and CoxBoost techniques using various performance metrics including integrated brier score (IBS), concordance index (C-Index) and integrated absolute error (IAE) for numerous high-dimensional survival datasets. The real data applications reveal that the proposed method outperforms the competing methods in terms of survival prediction accuracy.

Establishing subdivisions of M1 stage nasopharyngeal carcinoma based on decision tree classification: A multicenter retrospective study

ObjectivesTo meet the demand for personalized treatment, effective stratification of patients with metastatic nasopharyngeal carcinoma (mNPC) is essential. Hence, our study aimed to establish an M1 subdivision for prognostic prediction and treatment planning in patients with mNPC. Materials and methodsThis study included 1239 patients with mNPC from three medical centers divided into the synchronous mNPC cohort (smNPC, n = 556) to establish an M1 stage subdivision and the metachronous mNPC cohort (mmNPC, n = 683) to validate this subdivision. The primary endpoint was overall survival. Univariate and multivariate Cox analyses identified covariates for the decision-tree model, proposing an M1 subdivision. Model performance was evaluated using time-dependent receiver operating characteristic curves, Harrell’s concordance index, calibration plots, and decision curve analyses. ResultsThe proposed M1 subdivisions were M1a (≤5 metastatic lesions), M1b (>5 metastatic lesions + absent liver metastases), and M1c (>5 metastatic lesions + existing liver metastases) with median OS of 34, 22, and 13 months, respectively (p < 0.001). This M1 subdivision demonstrated superior discrimination (C-index = 0.698; 3-year AUC = 0.707) and clinical utility over those of existing staging systems. Calibration curves exhibited satisfactory agreement between predictions and actual observations. Internal and mmNPC cohort validation confirmed the robustness. Survival benefits from local metastatic treatment were observed in M1a, while immunotherapy improved survival in patients with M1b and M1c disease. ConclusionThis novel M1 staging strategy provides a refined approach for prognostic prediction and treatment planning in patients with mNPC, emphasizing the potential benefits of local and immunotherapeutic interventions based on individualized risk stratification.

Radiation Safety Assessment in Prostate Cancer Treatment: A Predictive Approach for I-125 Brachytherapy

This study uses Monte Carlo simulation and experimental measurements to develop a predictive model for estimating the external dose rate associated with permanent radioactive source implantation in prostate cancer patients. The objective is to estimate the accuracy of the patient’s external dose rate measurement. First, I-125 radioactive sources were implanted into Mylar window water phantoms to simulate the permanent implantation of these sources in patients. Water phantom experimental measurement was combined with Monte Carlo simulation to develop predictive equations, whose performance was verified against external clinical data. The model’s accuracy in predicting the external dose rate in patients with permanently implanted I-125 radioactive sources was high (R2 = 0.999). A comparative analysis of the experimental measurements and the Monte Carlo simulations revealed that the maximum discrepancy between the measured and calculated values for the water phantom was less than 5.00%. The model is practical for radiation safety assessments, enabling the evaluation of radiation exposure risks to individuals around patients with permanently implanted I-125 radioactive sources.

Transfer learning-based confinement strength prediction of concrete confined by FRP transverse reinforcements

Although several confinement strength models have been proposed for concrete confined by fiber-reinforced polymer (FRP) transverse reinforcements, these models show considerable scatter and discrepancies in the estimation of experimental data. Machine learning (ML) techniques may present an alternative prediction approach, but challenges arise due to insufficient experimental data. This study, therefore, develops a transfer learning-based model for predicting the confinement strength of concrete confined by FRP transverse reinforcements. Firstly, a literature review was conducted to collect a target dataset of FRP-confined concrete columns and a source dataset of steel-confined concrete columns used for knowledge transfer. Subsequently, a transfer learning algorithm was proposed to construct an ML confinement strength model for FRP-confined concrete. Finally, the performance of the proposed model was compared to that of six ML models and seven physics-based models. The model evaluation based on one-time random split indicated that the proposed model provided more accurate predictions for the confinement strength than other models considered in this study, achieving an R2 of 0.9089 on the test dataset. Shuffle split evaluation demonstrated that the proposed model exhibited superior stability and robustness compared to other models considered. Based on the proposed transfer learning-based model, the importance of the input parameters was obtained, further confirming the robustness of the proposed model.

Enhancing tuberculosis vaccine development: a deconvolution neural network approach for multi-epitope prediction

Tuberculosis (TB) a disease caused by Mycobacterium tuberculosis (Mtb) poses a significant threat to human life, and current BCG vaccinations only provide sporadic protection, therefore there is a need for developing efficient vaccines. Numerous immunoinformatic methods have been utilized previously, here for the first time a deep learning framework based on Deconvolutional Neural Networks (DCNN) and Bidirectional Long Short-Term Memory (DCNN-BiLSTM) was used to predict Mtb Multiepitope vaccine (MtbMEV) subunits against six Mtb H37Rv proteins. The trained model was used to design MEV within a few minutes against TB better than other machine learning models with 99.5% accuracy. The MEV has good antigenicity, and physiochemical properties, and is thermostable, soluble, and hydrophilic. The vaccine's BLAST search ruled out the possibility of autoimmune reactions. The secondary structure analysis revealed 87% coil, 10% beta, and 2% alpha helix, while the tertiary structure was highly upgraded after refinement. Molecular docking with TLR3 and TLR4 receptors showed good binding, indicating high immune reactions. Immune response simulation confirmed the generation of innate and adaptive responses. In-silico cloning revealed the vaccine is highly expressed in E. coli. The results can be further experimentally verified using various analyses to establish a candidate vaccine for future clinical trials.