Outstanding Precision Research Articles

Dot-array porous medium model for windscreen and its simulation accuracy analysis

Porous medium models have long been prevalent numerical computation tools. Although they exhibit swift computational speed, their accuracy in simulating windscreen perforation structures is challenged. This paper introduces the innovative dot-array porous medium (DAPM) model, which accurately portrays the perforation structure and material characteristics of a windscreen by establishing virtual holes on the porous medium. Not only does it simplify modeling by eliminating complex perforation processes, but it also adeptly simulates the flow behavior of the windscreen. The comprehensive comparison between the DAPM model and the physical mesh model, traditional porous medium model, as well as wind tunnel test results, demonstrates that the DAPM model not only possesses rapid computational speed but also delivers outstanding precision in results. In terms of velocity distribution, vortex distribution, and flow intensity in the flow field, the model indicates a high level of accuracy, clearly exceeding that of the porous medium model. Moreover, the DAPM model showcases high versatility and adjustability in practical applications. By adjusting dimension parameters, it demonstrates the capability to precisely simulate any windscreen with holes arranged in a matrix pattern. This research provides an efficient and reliable tool for the numerical simulation of windscreens, with broad application prospects.

Teor de água de equilíbrio: Uso de sensores de umidade relativa do ar intergranular em silos

ABSTRACT Grains are hygroscopic materials and, during storage, they are subject to exchanging moisture with the environment according to temperature and relative air humidity, making it important to monitor these factors. Digital temperature and relative air humidity sensors appear as an alternative for monitoring the moisture of grain mass inside silos; they are simple to install and use. Digital sensors present outstanding precision in temperature measurements. The objective in this study was to assess the efficiency and applicability of intergranular relative air humidity sensors linked to digital temperature sensors in the thermometry system of silos storing soybeans. Two samples of soybeans were analyzed in the upper, middle and lower thirds of the silo, for grain mass temperature, intergranular relative air humidity, estimated equilibrium moisture content according to collected data, and moisture content of the sampled grains. Moisture contents obtained from sensor measurements using the hygroscopic equilibrium equation and determined using the oven method were compared. The equilibrium moisture content estimated by the data provided by the sensors did not differ by the Tukey test (p ≤ 0.05) from the moisture content determined by the oven method. Digital temperature and relative air humidity sensors have proven to be efficient, as they contribute to estimating the equilibrium moisture content with satisfactory precision.

Machine Learning Approach for Early Diagnosis of Dyslexia Among Primary School Children: A Scoping Review and Model Development

Dyslexia, a prevalent learning disorder among primary school children, often goes undetected until later stages, hindering academic progress and socio-emotional development. Early diagnosis is crucial for effective intervention. Machine Learning (ML) offers promise in developing accurate diagnostic tools. However, there's a scarcity of comprehensive reviews focusing on ML approaches for dyslexia diagnosis in this demographic. In this scoping review, we consolidate existing literature and present the development of a novel ML model that was customized for early dyslexia diagnosis. Utilizing Decision Tree, K-Nearest Neighbors (KNN), Logistic Regression, Naive Bayes, and Random Forest. The comparative analysis of ML methods for dyslexia detection in elementary school children reveals distinct strengths. Decision Tree shows robust precision: 92.31% for dyslexia-prone, 90.62% for diagnosed dyslexia, and 86.67% for no dyslexia detected, with corresponding high recall values of 90.57%, 87.88%, and 100%, respectively. KNN excels with an overall accuracy of 94.00% and perfect precision for undetected dyslexia (100%), with high precision and recall for dyslexia-prone and diagnosed dyslexia. Logistic Regression highlights significant predictors and achieves precision of 95.38% for dyslexia-prone and 88.24% for diagnosed dyslexia, with recall rates of 93.34% and 90.91%, respectively. Naive Bayes exhibits outstanding precision for no dyslexia and dyslexia-prone categories (100%), with slightly lower precision for diagnosed dyslexia (82.5%), but perfect recall for undetected and diagnosed dyslexia. Random Forest demonstrates balanced performance with precision ranging from 91.18% to 94.23% and recall from 92.31% to 93.94%, achieving an overall accuracy of 93.00%. These results underscore ML's potential in enabling early dyslexia detection, facilitating timely interventions to improve outcomes for affected children and advancing dyslexia diagnosis.

A Systematic Literature Review on Leaf Disease Recognition Using Computer Vision and Deep Learning Approach

Background: Plant diseases affect agricultural output, quality and profitability, making them serious obstacles for agriculture. It is essential to detect diseases early in order to reduce losses while retaining sustainable practices. Plant disease detection has benefited greatly from the use of computer vision and deep learning in recent years because of their outstanding precision and computing capability. Objective: In this paper, we intend to investigate the role of deep learning in computer vision for plant disease detection while looking into how these techniques address complex disease identification problems. A variety of deep learning architectures were reviewed, and the contribution of frameworks such as Tensorflow, Keras, Caffe and PyTorch to the researchers' model construction was studied as well. Additionally, the usage of open repositories such as PlantVillage and Kaggle along with the customized datasets were discussed. Methods: We gathered the most recent developments in deep learning techniques for leaf disease detection through a systematic literature review of research papers published over the past decade, using reputable academic databases like Scopus and Web of Science, following the Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) method for guidance. Results: This study finds that researchers consistently enhance existing deep learning architectures to improve prediction accuracy in plant disease detection, often by introducing novel architectures and employing transfer learning methods. Frameworks like TensorFlow, Keras, Caffe, and PyTorch are widely favored for their efficiency in development. Additionally, most studies opt for public datasets such as PlantVillage, Kaggle, and ImageNet, which offer an abundance of labelled data for training and testing deep learning models. Conclusion: While no singular ‘best' model emerges, the adaptability of deep learning and computer vision demonstrates the dynamic nature of plant disease recognition area, and this paper provides a comprehensive overview of deep learning's transformative impact on plant disease recognition by bringing together information from different studies. Keywords: Deep learning, Computer vision, Plant disease, Systematic literature review

Evaluation of Various Deep Learning Algorithms for Landslide and Sinkhole Detection from UAV Imagery in a Semi-arid Environment

AbstractSinkholes and landslides occur due to soil collapse in different slope types, often triggered by heavy rainfall, presenting challenges in the semi-arid Golestan province, Iran. This study primarily focuses on the detection of these phenomena. Recent advancements in unmanned aerial vehicle (UAV) image acquisition and the incorporation of deep learning (DL) algorithms have enabled the creation of semi-automated methods for highly detailed soil landform detection across large areas. In this study, we explored the efficacy of six state-of-the-art deep learning segmentation algorithms—DeepLab-v3+, Link-Net, MA-Net, PSP-Net, ResU-Net, and SQ-Net—applied to UAV-derived datasets for mapping landslides and sinkholes. Our most promising outcomes demonstrated the successful mapping of landslides with an F1-Score of 0.95% and sinkholes with an F1-Score of 89% in a challenging environment. ResUNet exhibited an outstanding Precision of 0.97 and Recall of 0.92, culminating in the highest F1-Score of 0.95, indicating the best landslide detection model. MA-Net and SQ-Net resulted in the highest F1-Score for sinkhole detection. Our study underscores the significant impact of DL segmentation algorithm selection on the accuracy of landslide and sinkhole detection tasks. By leveraging DL segmentation algorithms, the accuracy of both landslide and sinkhole detection tasks can be significantly improved, promoting better hazard management and enhancing the safety of the affected areas.

Evaluations of an Early Change in Tumor Pathophysiology in Response to Radiotherapy with Oxygen Enhanced Electron Paramagnetic Resonance Imaging (OE EPRI).

Electron Paramagnetic Resonance Imaging (EPRI) can image the partial pressure of oxygen (pO2) within in vivo tumor models. We sought to develop Oxygen Enhanced (OE) EPRI that measures tumor pO2 with breathing gases of 21% O2 (pO221%) and 100% O2 (pO2100%), and the differences in pO2 between breathing gases (ΔpO2). We applied OE EPRI to study the early change in tumor pathophysiology in response to radiotherapy in two tumor models of pancreatic cancer. We developed a protocol that intraperitoneally administered OX071, a trityl radical contrast agent, and then acquired anatomical MR images to localize the tumor. Subsequently, we acquired two pO221% and two pO2100% maps using the T1 relaxation time of OX071 measured with EPRI and a R1-pO2 calibration of OX071. We studied 4T1 flank tumor model to evaluate the repeatability of OE EPRI. We then applied OE EPRI to study COLO 357 and Su.86.86 flank tumor models treated with 10Gy radiotherapy. The repeatability of mean pO2 for individual tumors was ± 2.6Torr between successive scans when breathing 21% O2 or 100% O2, representing a precision of 9.6%. Tumor pO221% and pO2100% decreased after radiotherapy for both models, although the decreases were not significant or only moderately significant, and the effect sizes were modest. For comparison, ΔpO2 showed a large, highly significant decrease after radiotherapy, and the effect size was large. MANOVA and analyses of the HF10 hypoxia fraction provided similar results. EPRI can evaluate tumor pO2 with outstanding precision relative to other imaging modalities. The change in ΔpO2 before vs. after treatment was the best parameter for measuring the early change in tumor pathophysiology in response to radiotherapy. Our studies have established ΔpO2 from OE EPRI as a new parameter, and have established that OE EPRI is a valuable new methodology for molecular imaging.

From optimal observables to machine learning: an effective-field-theory analysis of e+e−→ W+W− at future lepton colliders

We apply machine-learning techniques to the effective-field-theory analysis of the e+e− → W+W− processes at future lepton colliders, and demonstrate their advantages in comparison with conventional methods, such as optimal observables. In particular, we show that machine-learning methods are more robust to detector effects and backgrounds, and could in principle produce unbiased results with sufficient Monte Carlo simulation samples that accurately describe experiments. This is crucial for the analyses at future lepton colliders given the outstanding precision of the e+e− → W+W− measurement (~ 10−4 in terms of anomalous triple gauge couplings or even better) that can be reached. Our framework can be generalized to other effective-field-theory analyses, such as the one of e+e− → tt¯\\documentclass[12pt]{minimal} \\usepackage{amsmath} \\usepackage{wasysym} \\usepackage{amsfonts} \\usepackage{amssymb} \\usepackage{amsbsy} \\usepackage{mathrsfs} \\usepackage{upgreek} \\setlength{\\oddsidemargin}{-69pt} \\begin{document}$$ t\\overline{t} $$\\end{document} or similar processes at muon colliders.

Evaluation and Drivers of Four Evapotranspiration Products in the Yellow River Basin

Evapotranspiration is a key driver of water and energy exchanges between terrestrial surfaces and the atmosphere, significantly influencing ecosystem balances. This study focuses on the Yellow River Basin (YRB), where evapotranspiration impacts both ecological dynamics and human activities. By analyzing actual evapotranspiration data from 1982 to 2017, this research provides insights into its spatial and temporal patterns within the YRB. Furthermore, a comprehensive assessment and comparative analysis were performed on four distinct evapotranspiration product datasets: GLDAS-Noah, ERA5-Land, GLEAM v3.8a, and MOD16A2. Employing the Geodetector model, the research identified seven key influencing factors—the digital elevation model (DEM), slope, aspect, precipitation, temperature, soil moisture, and normalized difference vegetation index (NDVI)—and analyzed their impact on evapotranspiration variations, yielding the following insights: (1) Based on the monthly-scale actual evapotranspiration dataset from 1982 to 2017, the annual average evapotranspiration in the YRB fluctuated between 375 and 473 mm, with an average value of 425 mm. A declining trend in the region’s overall evapotranspiration was discerned using the Theil–Sen median slope estimator and Mann–Kendall trend test. (2) The datasets from GLDAS-Noah, ERA5-Land, and GLEAM exhibited the highest correlation with the observed datasets, all exceeding a correlation coefficient of 0.96. In contrast, the MOD16A2 dataset showed the least favorable performance. The ERA5-Land dataset was particularly noteworthy for its close alignment with observational benchmarks, as evidenced by the lowest recorded root mean square error (RMSE) of 5.09 mm, indicative of its outstanding precision. (3) Employing the Geodetector model, a thorough analysis was conducted of the interactions between evapotranspiration and seven critical determinants. The findings revealed that precipitation and the NDVI were the most significant factors influencing evapotranspiration, with q-values of 0.59 and 0.42 in 2010, and 0.71 and 0.59 in 2015, respectively. These results underscore their pivotal role as the main drivers of evapotranspiration variability within the YRB. Conversely, the q-values for slope in 2010 and 2015 were only 0.01 and nearly zero, respectively, indicating their minimal impact on the dynamics of evapotranspiration in the YRB.

Enhancing aspect-based multi-labeling with ensemble learning for ethical logistics.

In the dynamic domain of logistics, effective communication is essential for streamlined operations. Our innovative solution, the Multi-Labeling Ensemble (MLEn), tackles the intricate task of extracting multi-labeled data, employing advanced techniques for accurate preprocessing of textual data through the NLTK toolkit. This approach is carefully tailored to the prevailing language used in logistics communication. MLEn utilizes innovative methods, including sentiment intensity analysis, Word2Vec, and Doc2Vec, ensuring comprehensive feature extraction. This proves particularly suitable for logistics in e-commerce, capturing nuanced communication essential for efficient operations. Ethical considerations are a cornerstone in logistics communication, and MLEn plays a pivotal role in detecting and categorizing inappropriate language, aligning inherently with ethical norms. Leveraging Tf-IDF and Vader for feature enhancement, MLEn adeptly discerns and labels ethically sensitive content in logistics communication. Across diverse datasets, including Emotions, MLEn consistently achieves impressive accuracy levels ranging from 92% to 97%, establishing its superiority in the logistics context. Particularly, our proposed method, DenseNet-EHO, outperforms BERT by 8% and surpasses other techniques by a 15-25% efficiency. A comprehensive analysis, considering metrics such as precision, recall, F1-score, Ranking Loss, Jaccard Similarity, AUC-ROC, sensitivity, and time complexity, underscores DenseNet-EHO's efficiency, aligning with the practical demands within the logistics track. Our research significantly contributes to enhancing precision, diversity, and computational efficiency in aspect-based sentiment analysis within logistics. By integrating cutting-edge preprocessing, sentiment intensity analysis, and vectorization, MLEn emerges as a robust framework for multi-label datasets, consistently outperforming conventional approaches and giving outstanding precision, accuracy, and efficiency in the logistics field.

A novel approach to macular edema detection: DeepLabv3+ segmentation and VGG with vision transformer classification

The domain of deep learning has seen significant advancements, particularly in the context of detecting macular edema from images of the retina, in recent times. This study introduces an innovative model for identifying macular edema, employing two deep learning models: Deeplabv3 + and VGG with a vision transformer. The Deeplabv3 + model is used to segment the macula region in the retinal images. The segmented macula region is then fed into the VGG for feature extraction with a vision transformer model for detection. This approach leverages the strengths of both models in detecting accurately and efficiently. The Deeplabv3 + model can accurately segment the macula region, which is crucial for accurate detection. The VGG combined with a vision transformer model proves highly efficient in detecting even subtle changes in the macular region, signifying the existence of macular edema. The results of our experiments with the dataset show that the proposed method outperforms current cutting-edge techniques. With an outstanding precision rate of 99.53%, the suggested approach firmly solidifies its superiority. The results highlight the effectiveness of the proposed technique in precisely and effectively detecting pathological fluid accumulation in retina images. This ability can have a substantial influence on the early detection and management of eye disorders.

Comprehensive analysis of the interaction of antigen presentation during anti-tumour immunity and establishment of AIDPS systems in ovarian cancer.

There are hundreds of prognostic models for ovarian cancer. These genes are based on different gene classes, and there are many ways to construct the models. Therefore, this paper aims to build the most stable prognostic evaluation system known to date through 101 machine learning strategies. We combined 101 algorithm combinations with 10 machine learning algorithms to create antigen presentation-associated genetic markers (AIDPS) with outstanding precision and steady performance. The inclusive set of algorithms comprises the elastic network (Enet), Ridge, stepwise Cox, Lasso, generalized enhanced regression model (GBM), random survival forest (RSF), supervised principal component (SuperPC), Cox partial least squares regression (plsRcox), survival support vector machine (Survival-SVM). Then, in the train cohort, the prediction model was fitted using a leave-one cross-validation (LOOCV) technique, which involved 101 different possible combinations of prognostic genes. Seven validation data sets (GSE26193, GSE26712, GSE30161, GSE63885, GSE9891, GSE140082 and ICGC_OV_AU) were compared and analysed, and the C-index was calculated. Finally, we collected 32 published ovarian cancer prognostic models (including mRNA and lncRNA). All data sets and prognostic models were subjected to a univariate Cox regression analysis, and the C-index was calculated to demonstrate that the antigen presentation process should be the core criterion for evaluating ovarian cancer prognosis. In a univariate Cox regression analysis, 22 prognostic genes were identified based on the expression profiles of 283 genes involved in antigen presentation and the intersection of genes (p < 0.05). AIDPS were developed by our machine learning-based integration method, which was applied to these 22 genes. One hundred and one prediction models are fitted using the LOOCV framework, and the C-index is calculated for each model across all validation sets. Interestingly, RSF + Lasso was the best model overall since it had the greatest average C-index and the highest C-index of any combination of models tested on the validated data sets. In comparing external cohorts, we found that the C-index correlated AIDPS method using the RSF + Lasso method in 101 prediction models was in contrast to other features. Notably, AIDPS outperformed the vast majority of models across all data sets. Antigen-presenting anti-tumour immune pathways can be used as a representative gene set of ovarian cancer to track the prognosis of patients with cancer. The antigen-presenting model obtained by the RSF + Lasso method has the best C-INDEX, which plays a key role in developing antigen-presenting targeted drugs in ovarian cancer and improving the treatment outcome of patients.

Sea hedgehog-inspired surface-enhanced Raman scattering biosensor probe for ultrasensitive determination of Staphylococcus aureus in food supplements

Staphylococcus aureus contamination in food supplements poses substantial challenges to public health and large-scale production but the sensitive detection in a timely manner remains a bottleneck. Drawing inspiration from the sea hedgehog, gold nanostars (AuNSs) were leveraged to design an ultrasensitive surface-enhanced Raman scattering (SERS) biosensor for the determination of Staphylococcus aureus in food supplements. Besides the surface enhancement furnished by the AuNSs, Raman reporter molecules and specific aptamers sequentially self-assembled onto these AuNSs to construct the “three-in-one” SERS biosensor probe for label-based quantitation of Staphylococcus aureus. Following incubation with contaminated health product samples, the gold nanostars@Raman reporter-aptamer specifically recognize and assemble around Staphylococcus aureus cells, forming a distinctive sea hedgehog structure. This unique configuration results in an amplified Raman signal at 1338 cm−1 and an enhancement factor of up to 6.71 × 107. The entire quantitative detection process can be completed within 30 min, boasting an exceptional limit of detection as low as 1.0 CFU mL−1. The method exhibits a broad working range for the determination of Staphylococcus aureus, with concentrations spanning 2.15 CFU mL−1 to 2.15 × 105 CFU mL−1. Furthermore, it demonstrates outstanding precision, with relative standard deviation values consistently below 5.0%. As a showcase to validate the practicality of the SERS method, we conducted tests on determining Staphylococcus aureus in a herbal food supplement, i.e., Ginkgo Biloba extract (GBE); the results align closely with those obtained through the conventional lysogeny broth agar plate method, pointing to the potential applicability in real-world scenarios.

Ensemble 3D CNN and U-Net-based brain tumour classification with MKKMC segmentation

Advanced brain cancer is the deadliest type with just a few months survival rate. Existing technologies hinder the objective of forecasting cancer. This work aims to fulfil the pressing requirement for timely and precise identification of advanced-stage brain tumours, which are notorious for their markedly reduced life expectancy. It presents an innovative hybrid approach for predicting brain tumours and improves diagnostic capabilities. The Multiple Kernel K-Means Cluster Algorithm (MKKCA) is used to segment brain MRI images effectively, differentiating healthy and tumorous tissues. After segmentation, a hybrid approach with 3D-Convolutional Neural Network (CNN) and U-Net has been utilized for classification. The objective is to effectively and accurately distinguish normal and pathological brain images. To enhance the efficiency, we include the Improved Whale Optimization Algorithm (IWOA), which guarantees accurate and dependable performance via location updates. The methodology demonstrates outstanding precision with 98.5% accuracy rate, 98.56% specificity, 91% sensitivity, 87.45% precision and a recall rate of 96% with the F-Measure at 96.02%. These findings, obtained using MATLAB, demonstrate a substantial performance improvement compared to current approaches. This development not only represents a significant addition to diagnostic imaging but also a crucial role in the prediction and treatment of brain cancers.

A bilateral feature fusion network for defect detection on mobile cameras

Defect detection in mobile phone cameras constitutes a critical aspect of the manufacturing process. Nonetheless, this task remains challenging due to the complexities introduced by intricate backgrounds and low-contrast defects, such as minor scratches and subtle dust particles. To address these issues, a Bilateral Feature Fusion Network (BFFN) has been proposed. This network incorporates a bilateral feature fusion module, engineered to enrich feature representation by fusing feature maps from multiple scales. Such fusion allows the capture of both fine and coarse-grained details inherent in the images. Additionally, a Self-Attention Mechanism is deployed to garner more comprehensive contextual information, thereby enhancing feature discriminability. The proposed Bilateral Feature Fusion Network has been rigorously evaluated on a dataset of 12,018 mobile camera images. Our network surpasses existing state-of-the-art methods, such as U-Net and Deeplab V3+, particularly in mitigating false positive detection caused by complex backgrounds and false negative detection caused by slight defects. It achieves an F1-score of 97.59%, which is 1.16% better than Deeplab V3+ and 0.99% better than U-Net. This high level of accuracy is evidenced by an outstanding precision of 96.93% and recall of 98.26%. Furthermore, our approach realizes a detection speed of 63.8 frames per second (FPS), notably faster than Deeplab V3+ at 57.1 FPS and U-Net at 50.3 FPS. This enhanced computational efficiency makes our network particularly well-suited for real-time defect detection applications within the realm of mobile camera manufacturing.

Empowering Sustainable Agriculture: An Enhanced Deep Learning Model for PD Detection in Agricultural Operation System

A country’s financial growth is prejudiced by its rate of agricultural output. Nevertheless, Plant Diseases (PD) pose a substantial obstacle to the cultivation and value of foodstuff. The timely detection of PDs is paramount for public wellness and Sustainable Agriculture (SA) promotion. The conventional diagnostic procedure entails a pathologist’s visual evaluation of a particular plant through in-person visits. Nevertheless, the manual inspection of crop diseases is limited due to its low level of accuracy and the limited availability of skilled workers. To address these concerns, there is a need to develop automated methodologies capable of effectively identifying and classifying a wide range of PDs. The precise detection and categorization of PDs pose a challenging task due to various factors. These include the presence of low-intensity data in both the image’s backdrop and the forefront, the significant similarity in color between normal and diseased plant regions, the presence of noise in the specimens, and the variations in the location, chrominance, framework, and dimensions of plant leaves. This paper presents a novel approach for identifying and categorizing PDs using a Deep Convolutional Neural Network - Transfer Learning (DCNN-TL) technique in the Agricultural Operation System (AOS). The proposed method aims to enhance the capabilities of SA in accurately identifying and categorizing PDs. The improved Deep Learning (DL) methodology incorporates a TL technique based on fine-tuned Visual Geometry Group 19 (VGG19) architecture. The revised system accurately detects and diagnoses five distinct PD categories. Among the evaluated methods, the proposed DCNN-TL in this study shows outstanding precision, recall, and accuracy values of 0.996, 0.9994, and 0.9998, respectively.

Toward robust and privacy-enhanced facial recognition: A decentralized blockchain-based approach with GANs and deep learning.

In recent years, the extensive use of facial recognition technology has raised concerns about data privacy and security for various applications, such as improving security and streamlining attendance systems and smartphone access. In this study, a blockchain-based decentralized facial recognition system (DFRS) that has been designed to overcome the complexities of technology. The DFRS takes a trailblazing approach, focusing on finding a critical balance between the benefits of facial recognition and the protection of individuals' private rights in an era of increasing monitoring. First, the facial traits are segmented into separate clusters which are maintained by the specialized node that maintains the data privacy and security. After that, the data obfuscation is done by using generative adversarial networks. To ensure the security and authenticity of the data, the facial data is encoded and stored in the blockchain. The proposed system achieves significant results on the CelebA dataset, which shows the effectiveness of the proposed approach. The proposed model has demonstrated enhanced efficacy over existing methods, attaining 99.80% accuracy on the dataset. The study's results emphasize the system's efficacy, especially in biometrics and privacy-focused applications, demonstrating outstanding precision and efficiency during its implementation. This research provides a complete and novel solution for secure facial recognition and data security for privacy protection.

Optimizing Skin Cancer Survival Prediction with Ensemble Techniques.

The advancement in cancer research using high throughput technology and artificial intelligence (AI) is gaining momentum to improve disease diagnosis and targeted therapy. However, the complex and imbalanced data with high dimensionality pose significant challenges for computational approaches and multi-omics data analysis. This study focuses on predicting skin cancer and analyzing overall survival probability. We employ the Kaplan-Meier estimator and Cox proportional hazards regression model, utilizing high-throughput machine learning (ML)-based ensemble methods. Our proposed ML-based ensemble techniques are applied to a publicly available dataset from the ICGC Data Portal, specifically targeting skin cutaneous melanoma cancers (SKCM). We used eight baseline classifiers, namely, random forest (RF), decision tree (DT), gradient boosting (GB), AdaBoost, Gaussian naïve Bayes (GNB), extra tree (ET), logistic regression (LR), and light gradient boosting machine (Light GBM or LGBM). The study evaluated the performance of the proposed ensemble methods and survival analysis on SKCM. The proposed methods demonstrated promising results, outperforming other algorithms and models in terms of accuracy compared to traditional methods. Specifically, the RF classifier exhibited outstanding precision results. Additionally, four different ensemble methods (stacking, bagging, boosting, and voting) were created and trained to achieve optimal results. The performance was evaluated and interpreted using accuracy, precision, recall, F1 score, confusion matrix, and ROC curves, where the voting method achieved a promising accuracy of 99%. On the other hand, the RF classifier achieved an outstanding accuracy of 99%, which exhibits the best performance. We compared our proposed study with the existing state-of-the-art techniques and found significant improvements in several key aspects. Our approach not only demonstrated superior performance in terms of accuracy but also showcased remarkable efficiency. Thus, this research work contributes to diagnosing SKCM with high accuracy.

Multi-step influenza forecasting through singular value decomposition and kernel ridge regression with MARCOS-guided gradient-based optimization

This research delves into the significance of influenza outbreaks in public health, particularly the importance of accurate forecasts using weekly Influenza-like illness (ILI) rates. The present work develops a novel hybrid machine-learning model by combining singular value decomposition with kernel ridge regression (SKRR). In this context, a novel hybrid model known as H-SKRR is developed by combining two robust forecasting approaches, SKRR and ridge regression, which aims to improve multi-step-ahead predictions for weekly ILI rates in Southern and Northern China. The study begins with feature selection via XGBoost in the preprocessing phase, identifying optimal precursor information guided by importance factors. It decomposes the original signal using multivariate variational mode decomposition (MVMD) to address non-stationarity and complexity. H-SKRR is implemented by incorporating significant lagged-time components across sub-components. The aggregated forecasted values from these sub-components generate ILI values for two horizons (i.e., 4-and 7-weekly ahead). Employing the gradient-based optimization (GBO) algorithm fine-tunes model parameters. Furthermore, the deep random vector functional link (dRVFL), Ridge regression, and gated recurrent unit neural network (GRU) models were employed to validate the MVMD-H-SKRR-GBO paradigm's effectiveness. The outcomes, assessed using the MARCOS (Measurement of alternatives and ranking according to compromise solution) method as a multi-criteria decision-making method, highlight the superior accuracy of the MVMD-H-SKRR-GBO model in predicting ILI rates. The results clearly highlight the exceptional performance of the MVMD-H-SKRR-GBO model, with outstanding precision demonstrated by impressive R, RMSE, IA, and U95 % values of 0.946, 0.388, 0.970, and 1.075, respectively, at t + 7.

Developing optical and thermal models with experimental validation of parabolic trough collector for moroccan industrial heat applications

This work presents the development of advanced optical and thermal models for parabolic trough collector systems (PTCs) through an extensive experimental study utilizing a PTMx-24 collector from Soltigua company, installed at Green Energy Park (GEP) research facility. The optical model leverages Tonatiuh software, employing the Monte Carlo Ray Tracing (MCRT) method. Moreover, a MATLAB script was adjusted to account for Incidence Angle Modifier (IAM) fluctuations in the optical analysis, while incorporating receiver thermal parameters for the thermal analysis. This study assesses the potential of PTCs in the territory of Solar Heat for Industrial Processes (SHIP) over various seasons within a semi-arid climate, offering invaluable insights for sustainable energy utilization. The validation of the models was conducted across four distinct days characterized by diverse meteorological conditions. The optical model demonstrates a remarkable correlation of 97.04 % between the MATLAB script and experimental results. Additionally, results from Tonatiuh software exhibited an important accuracy of 95.92 %. The thermal model showcased outstanding precision, revealing a correlation of 98.5 % compared to experimental results, signifying its adaptability to collectors of varying geometries and parameters. These findings highlight the predictive potential of these models, aiding in the decision-making process to determine the system's performance on specific days with specific meteorological conditions.

Empowering nuclear reactor analysis: OpenNode for accurate 3D core simulation through nodal expansion neutron diffusion equations solving

OpenNode is a sophisticated reactor core simulation tool, meticulously designed for educational purposes. Its main function is to solve the complex partial differential equation governing multi-group neutron diffusion in a Cartesian geometry. OpenNode relies on the second- and fourth-order Nodal Expansion Method (NEM) to provide accurate solutions to three-dimensional steady-state problems, encompassing both the direct and adjoint modes. At the heart of this method, the neutron balance equation serves as the fundamental framework for calculating the nodal neutron flux and the effective multiplication factor, enabling the complex evaluation of neutron flux distributions within individual nodes.The NEM methodology, carefully programmed in Fortran-90 in OpenNode, meticulously decomposes the reactor core into nodes of reasonable size. This approach balances computational efficiency and accuracy, achieving outstanding precision even when using a reduced number of computational meshes. To further increase the number of equations while maintaining the balance between unknowns, OpenNode judiciously incorporates two essential approximations: the Weighted Residual Method (WRM) and the Quadratic Transverse Leakage (QTL) approximation.OpenNode transcends simple computational prowess by adopting an intuitive Graphical User Interface (GUI), meticulously developed by combining Python 3, Qt Designer, and Blender software. This Python-powered graphical interface offers users a seamless, user-friendly experience, facilitating tasks such as data entry, execution of complex simulations, calculation of results, data visualization, 3D design of nuclear core geometry, and in-depth analysis of results. One of OpenNode’s key features is its innate flexibility, enabling it to solve problems in both 2D and 3D Cartesian geometries. This adaptability makes it a versatile tool suitable for a wide range of research and power reactor applications, offering users the ability to model and design reactors with unrivaled ease.To substantiate OpenNode’s reliability, comprehensive criticality calculations are carried out in various scenarios, including single-cell verification, BIBLIS, KOEBERG cores in two dimensions (2D), and IAEA cores in three dimensions (3D). The results clearly demonstrate OpenNode’s robustness and reliability in multidimensional criticality analysis. In particular, OpenNode adheres to an open-source philosophy, making it readily available for download. This commitment to open access fosters collaboration and paves the way for wider adoption within the education and research communities, nurturing a thriving ecosystem of shared knowledge and exploration.