Model Predictions Research Articles

Capillary rise in partially saturated rigid porous media

We explore predictions of two models of one-dimensional capillary rise in rigid and partially saturated porous media. One is an existing one from the literature and the second is a free-boundary model based on Richards’ equation with two moving boundaries of the evolving partially saturated region. Both models involve the specification of saturation-dependent functions for local capillary pressure and permeability and connect to classical models for saturated porous media. Existing capillary-rise experiments show two notable regimes: (i) an early-time regime typically well-described by classical capillary-rise theory in a fully saturated porous media, and (ii) a long-time regime that has anomalous dynamics in which the capillary-rise height may scale with a non-classical power law in time or have more complicated dynamics. We demonstrate that the predictions of both models compare well with experimental capillary-rise data over early- and long-time regimes gathered from three independent studies in the literature. The model predictions also shed light on recent scaling laws that relate the capillary pressure and permeability of the partially saturated media to the capillary-rise height. We use these models to probe computationally observed permeability relationships to capillary-rise height. We demonstrate that a recently proposed permeability scaling for the anomalous capillary-rise regime is indeed realized and is particularly apparent in the lower portion of the partially saturated media. For our free-boundary model we also compute capillary pressure measures and show that these reveal the linear relation between the capillary pressure and capillary-rise height expected for a capillarity–gravity balance in the upper portion of the partially saturated porous media.

An improved method for evaluating fracture density using pulsed neutron capture logging

Fracture density is a critical fracture evaluation parameter for the optimization and production prediction of hydraulic fracturing models. Non-radioactive tracer techniques (NRT) have successfully used a quantitative fracture density method based on neutron-induced gamma ray from formation elements. Furthermore, fracture density can be calculated using the formation capture cross-section before and after hydraulic fracturing based on pulsed neutron capture logging. However, the response sensitivity of the macroscopic scattering cross section to the fractures will decrease when the fracture density is high due to the neutron self-shielding phenomenon. Therefore, an improved fracture density evaluation method is applied, which combines the macroscopic capture cross section and the neutron self-shielding correction factor. In the new method, the peak area of titanium from the captured gamma spectrum was used to obtain a neutron self-shielding correction factor, in order to improve the sensitivity of fracture density determination. Furthermore, the response of capture cross-section variation to fracture density at various tagged proppant concentrations and formation backgrounds was investigated. The findings indicate that the tagged proppant concentration influences the detection limit of fracture density and the sensitivity of fracture density identification. The accurate calculation range of fracture density using the new method has been extended from 5% to 10% under the condition that the tagged proppant concentration is 0.2%. Meanwhile, water salinity significantly impacts capture cross-section variation, while the effects of porosity, lithology, and fluid type on capture cross-section variation are negligible. A simulated fracturing example demonstrates the method's applicability in various measuring environments. The results show that fracture density and height are consistent with the model settings after correcting for water salinity, and the fracture density calculation error is less than 3%. Therefore, our proposed evaluation method for fracture density corrected for the neutron self-shielding effect improves response sensitivity and fracture density calculation accuracy.

Acoustic metamaterials characterization via laser plasma sound sources

Phononic crystals and acoustic metamaterials are expected to become an important enabling technology for science and industry. Currently, various experimental methods are used for evaluation of acoustic meta-structures, such as impedance tubes and anechoic chambers. Here we present a method for the precise characterization of acoustic meta-structures that utilizes rapid broadband acoustic pulses generated by point-like and effectively massless laser plasma sound sources. The method allows for broadband frequency response and directivity evaluations of meta-structures with arbitrary geometries in multiple sound propagation axes while also enabling acoustic excitation inside the structure. Experimental results are presented from acoustic evaluations of various phononic crystals with band gaps in the audible range, notably also in the very low frequencies, validating the predictions of numerical models with high accuracy. The proposed method is expected to boost research and commercial adoption of acoustic metamaterials in the near future.

A Novel Approach to Surface Roughness Virtual Sample Generation to Address the Small Sample Size Problem in Ultra-Precision Machining.

Surface roughness is one of the main bases for measuring the surface quality of machined parts. A large amount of training data can effectively improve model prediction accuracy. However, obtaining a large and complete surface roughness sample dataset during the ultra-precision machining process is a challenging task. In this article, a novel virtual sample generation scheme (PSOVSGBLS) for surface roughness is designed to address the small sample problem in ultra-precision machining, which utilizes a particle swarm optimization algorithm combined with a broad learning system to generate virtual samples, enriching the diversity of samples by filling the information gaps between the original small samples. Finally, a set of ultra-precision micro-groove cutting experiments was carried out to verify the feasibility of the proposed virtual sample generation scheme, and the results show that the prediction error of the surface roughness prediction model was significantly reduced after adding virtual samples.

Empathy in undergraduate medical students: a multi-center cross-sectional study in China

BackgroundFostering empathy has been continuously emphasized in the global medical education. Empathy is crucial to enhance patient-physician relationships, and is associated with medical students’ academic and clinical performance. However, empathy level of medical students in China and related influencing factors are not clear.MethodsThis was a cross-sectional study among medical students in 11 universities. We used the Jefferson Scale of Empathy Student-version of Chinese version to measure empathy level of medical students. Factors associated with empathy were identified by the univariate and multivariate logistic regression analyses. Based on the variables identified above, the nomogram was established to predict high empathy probability of medical students. Receiver operating characteristic curve, calibration plot and decision curve analysis were used to evaluate the discrimination, calibration and educational utility of the model.ResultsWe received 10,901 samples, but a total of 10,576 samples could be used for further analysis (effective response rate of 97.02%). The mean empathy score of undergraduate medical students was 67.38 (standard deviation = 9.39). Six variables including gender, university category, only child or not, self-perception doctor-patient relationship in hospitals, interest of medicine, Kolb learning style showed statistical significance with empathy of medical students (P < 0.05). Then, the nomogram was established based on six variables. The validation suggested the nomogram model was well calibrated and had good utility in education, as well as area under the curve of model prediction was 0.65.ConclusionsWe identify factors influencing empathy of undergraduate medical students. Moreover, increasing manifest and hidden curriculums on cultivating empathy of medical students may be needed among medical universities or schools in China.

Optimizing motor imagery BCI models with hard trials removal and model refinement

Deep learning models have demonstrated remarkable performance in the classification of motor imagery BCI systems. However, these models exhibit sensitivity to challenging trials, often called hard trials, leading to performance degradation. In this paper, we address this issue by proposing two novel methods for identifying and mitigating the impact of hard trials on model performance. The first method leverages model prediction scores to discern hard trials. The second approach employs a quantitative explainable artificial intelligence (XAI) approach, enabling a more transparent and interpretable means of hard trial identification. The identified hard trials are removed from the entire motor imagery training and validation dataset, and the deep learning model is further re-trained using the dataset without hard trials. To evaluate the efficacy of these proposed methods, experiments were conducted on the Open BMI dataset. The results for hold-out analysis show that the proposed quantitative XAI- based hard trial removal method has statistically improved the average classification accuracy of the baseline deep CNN model from 63.77% to 68.70%, with p-value = 7.66e−11 for the subject-specific MI classification. Additionally, analyzing the scalp map representing the average relevance scores of correctly classified trials compared to a misclassified trial provides a deeper insight into identifying hard trials. The results indicate that the proposed quantitaive-based XAI approach outperformes the prediction-score-based approach in hard trial identification.

Does high-latitude ionospheric electrodynamics exhibit hemispheric mirror symmetry?

Abstract. Ionospheric electrodynamics is a problem of mechanical stress balance mediated by electromagnetic forces. Joule heating (the total rate of frictional heating of thermospheric gases and ionospheric plasma) and ionospheric Hall and Pedersen conductances comprise three of the most basic descriptors of this problem. More than half a century after identification of their central role in ionospheric electrodynamics, several important questions about these quantities, including the degree to which they exhibit hemispheric symmetry under reversal of the sign of dipole tilt and the sign of the y component of the interplanetary magnetic field (so-called “mirror symmetry”), remain unanswered. While global estimates of these key parameters can be obtained by combining existing empirical models, one often encounters some frustrating sources of uncertainty: the measurements from which such models are derived, usually magnetic field and electric field or ion drift measurements, are typically measured separately and do not necessarily align. The models to be combined moreover often use different input parameters, different assumptions about hemispheric symmetry, and/or different coordinate systems. We eliminate these sources of uncertainty in model predictions of electromagnetic work J⋅E (in general not equal to Joule heating ηJ2) and ionospheric conductances by combining two new empirical models of the high-latitude ionospheric electric potential and ionospheric currents that are derived in a mutually consistent fashion: these models do not assume any form of symmetry between the two hemispheres; are based on Apex magnetic coordinates (denoted Apex), spherical harmonics, and the same model input parameters; and are derived exclusively from convection and magnetic field measurements made by the Swarm and CHAMP satellites. The model source code is open source and publicly available. Comparison of high-latitude distributions of electromagnetic work in each hemisphere as functions of dipole tilt and interplanetary magnetic field clock angle indicates that the typical assumption of mirror symmetry is largely justified. Model predictions of ionospheric Hall and Pedersen conductances exhibit a degree of symmetry, but clearly asymmetric responses to dipole tilt and solar wind driving conditions are also identified. The distinction between electromagnetic work and Joule heating allows us to identify where and under what conditions the assumption that the neutral wind corotates with the Earth is not likely to be physically consistent with predicted Hall and Pedersen conductances.

Predicting radiation-induced immune suppression in lung cancer patients treated with stereotactic body radiation therapy.

Stereotactic body radiation therapy (SBRT) is known to modulate the immune system and contribute to the generation of anti-tumor T cells and stimulate T cell infiltration into tumors. Radiation-induced immune suppression (RIIS) is a side effect of radiation therapy that can decrease immunological function by killing naive T cells as well as SBRT-induced newly created effector T cells, suppressing the immune response to tumors and increasing susceptibility to infections. RIIS varies substantially among patients and it is currently unclear what drives this variability. Models that can accurately predict RIIS in near real time based on treatment plan characteristics would allow treatment planners to maintain current protocol specific dosimetric criteria while minimizing immune suppression. In this paper, we present an algorithm to predict RIIS based on a model of circulating blood using early stage lung cancer patients treated with SBRT. This Python-based algorithm uses DICOM data for radiation therapy treatment plans, dose maps, patient CT data sets, and organ delineations to stochastically simulate blood flow and predict the doses absorbed by circulating lymphocytes. These absorbed doses are used to predict the fraction of lymphocytes killed by a given treatment plan. Finally, the time dependence of absolute lymphocyte count (ALC) following SBRT is modeled using longitudinal blood data up to a year after treatment. This model was developed and evaluated on a cohort of 64 patients with 10-fold cross validation. Our algorithm predicted post-treatment ALC with an average error of cells/L with 89% of the patients having a prediction error below 0.5×109 cells/L. The accuracy was consistent across a wide range of clinical and treatment variables. Our model is able to predict post-treatment ALC<0.8 (grade 2 lymphopenia), with a sensitivity of 81% and a specificity of 98%. This model has a ∼38-s end-to-end prediction time of post treatment ALC. Our model performed well in predicting RIIS in patients treated using lung SBRT. With near-real time model prediction time, it has the capability to be interfaced with treatment planning systems to prospectively reduce immune cell toxicity while maintaining national SBRT conformity and plan quality criteria.

Explaining deep learning-based activity schedule models using SHapley Additive exPlanations

ABSTRACT Artificial neural networks are often criticized for their black box nature in travel behavior literature. The lack of understanding of variable influence induces little confidence in model predictions, significantly affecting their practical utility. This study aims to address this issue by employing SHapley Additive exPlanations to understand the influence of different variables in a deep learning-based activity schedule model. The activity schedule is represented as a time series which enables the study of temporal variations in the influence of each variable at much finer resolutions compared to earlier approaches. The findings reveal that variables such as the day-of-week, month of the year, and social participation wield significant influence over the activity schedule, while household structure and urban class also exert noticeable impacts. This proposed methodology enhances our understanding of variable influences at different times of the day, instilling confidence in the deep learning model’s results, advancing its practical application.

Potential distribution and ecological impacts of Acmella radicans (Jacquin) R.K. Jansen (a new Yunnan invasive species record) in China

Background Acmella radicans(Jacquin) R.K. Jansen is a new invasive species record for Yunnan Province, China. Native to Central America, it has also been recently recorded invading other parts of Asia. To prevent this weed from becoming a serious issue, an assessment of its ecological impacts and potential distribution is needed. We predicted the potential distribution of A. radicans in China using the MaxEnt model and its ecological impacts on local plant communities and soil nutrients were explored.ResultsSimulated training using model parameters produced an area under curve value of 0.974, providing a high degree of confidence in model predictions. Environmental variables with the greatest predictive power were precipitation of wettest month, isothermality, topsoil TEB (total exchangeable bases), and precipitation seasonality, with a cumulative contribution of more than 72.70% and a cumulative permutation importance of more than 69.20%. The predicted potential suitable area of A. radicans in China is concentrated in the southern region. Projected areas of A. radicans ranked as high and moderately suitable comprised 5425 and 26,338 km2, accounting for 0.06 and 0.27% of the Chinese mainland area, respectively. Over the 5 years of monitoring, the population density of A. radicans increased while at the same time the population density and importance values of most other plant species declined markedly. Community species richness, diversity, and evenness values significantly declined. Soil organic matter, total N, total P, available N, and available P concentrations decreased significantly with increasing plant cover of A. radicans, whereas pH, total K and available K increased.ConclusionOur study was the first to show that A. radicans is predicted to expand its range in China and may profoundly affect plant communities, species diversity, and the soil environment. Early warning and monitoring of A. radicans must be pursued with greater vigilance in southern China to prevent its further spread.

Production of microalgae in wastewater and brackish waters: kinetic, lipid content, bioremediation and cost analysis studies

ABSTRACT The cultivation of microalgae in domestic wastewater offers a sustainable solution for the treatment of effluents, while at the same time producing biomass rich in lipids, potentially usable in the production of biofuels. Furthermore, reuse contributes to the treatment of wastewater, transforming a byproduct into a valuable source of nutrients for the production of microalgae biomass. This study involves the production of microalgae in open cultivation, using domestic effluents as a source of nutrients in brackish environments, to study the potential for biodiesel production. Intracellular lipids were between 17 and 20%. As for the bioremediation capacity, the results showed removal levels greater than 95% of nutrients, as well as bacterial and pollutant load reduction. The growth kinetics and the prediction of theoretical kinetic models through the use of computational tools show significant differences, due to the lack of control of process parameters in open cultivations. Based on the literature review and market research, a cost analysis for large-scale production in open crops was made, comparing with closed crops and finding lower costs in the implementation, maintenance and production of biodiesel in the production open.

Deep learning in the precise assessment of primary Sjögren's Syndrome based on ultrasound images.

This study aimed to investigate the value of a deep learning (DL) model based on greyscale ultrasound (US) images for precise assessment and accurate diagnosis of primary Sjögren's syndrome (pSS). This was a multicentre prospective analysis. All pSS patients were diagnosed according to 2016 ACR/EULAR criteria. 72 pSS patients and 72 sex- and age-matched healthy controls recruited between January 2022 and April 2023, together with 41 patients and 41 healthy controls recruited from June 2023 to February 2024 were used for DL model development and validation, respectively. DL model was constructed based on the ResNet 50, input with preprocessed all participants' bilateral submandibular glands (SMGs), parotid glands (PGs), and lacrimal glands (LGs) greyscale US images. Diagnostic performance of the model was compared with two radiologists. The accuracy of prediction and identification performance of DL model were evaluated by calibration curve. 864 and 164 greyscale US images of SMGs, PGs, and LGs were collected for development and validation of the model. The AUCs of DL model in the SMG, PG, and LG were 0.92, 0.93, 0.91 in the model cohort, and were 0.90, 0.88, 0.87 in the validation cohort respectively, outperforming both radiologists. Calibration curves showed the prediction probability of DL model were consistent with the actual probability in both model cohort and validation cohort. DL model based on greyscale US images showed diagnostic potential in the precise assessment of pSS patients in the SMG, PG, and LG, outperforming conventional radiologist evaluation.

Combining Neural Networks and CMIP6 Simulations to Learn Windows of Opportunity for Skillful Prediction of Multiyear Sea Surface Temperature Variability

AbstractWe use neural networks and large climate model ensembles to explore predictability of internal variability in sea surface temperature (SST) anomalies on interannual (1–3 years) and decadal (1–5 and 3–7 years) timescales. We find that neural networks can skillfully predict SST anomalies at these lead times, especially in the North Atlantic, North Pacific, Tropical Pacific, Tropical Atlantic and Southern Ocean. The spatial patterns of SST predictability vary across the nine climate models studied. The neural networks identify “windows of opportunity” where future SST anomalies can be predicted with more certainty. Neural networks trained on climate models also make skillful SST predictions in reconstructed observations, although the skill varies depending on which climate model the network was trained. Our results highlight that neural networks can identify predictable internal variability within existing climate data sets and show important differences in how well patterns of SST predictability in climate models translate to the real world.

New Model and Public Online Prediction Platform for Risk Stratification of Vocal Cord Leukoplakia.

To extract texture features from vocal cord leukoplakia (VCL) images and establish a VCL risk stratification prediction model using machine learning (ML) techniques. A total of 462 patients with pathologically confirmed VCL were retrospectively collected and divided into low-risk and high-risk groups. We use a 5-fold cross validation method to ensure the generalization ability of the model built using the included dataset and avoid overfitting. Totally 504 texture features were extracted from each laryngoscope image. After feature selection, 10 ML classifiers were utilized to construct the model. The SHapley Additive exPlanations (SHAP) was employed for feature analysis. To evaluate the model, accuracy, sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were utilized. In addition, the model was transformed into an online application for public use and further tested in an independent dataset with 52 cases of VCL. A total of 12 features were finally selected, random forest (RF) achieved the best model performance, the mean accuracy, sensitivity, specificity, and AUC of the 5-fold cross validation were 92.2 ± 4.1%, 95.6 ± 4.0%, 85.8 ± 5.8%, and 90.7 ± 4.9%, respectively. The result is much higher than the clinicians (AUC between 63.1% and 75.2%). The SHAP algorithm ranks the importance of 12 texture features to the model. The test results of the additional independent datasets were 92.3%, 95.7%, 90.0%, and 93.3%, respectively. The proposed VCL risk stratification prediction model, which has been developed into a public online prediction platform, may be applied in practical clinical work. 3 Laryngoscope, 2024.

Interaction between clinicians and artificial intelligence to detect fetal atrioventricular septal defects on ultrasound: how can we optimize collaborative performance?

Artificial intelligence (AI) has shown promise in improving the performance of fetal ultrasound screening in detecting congenital heart disease (CHD). The effect of giving AI advice to human operators has not been studied in this context. Giving additional information about AI model workings, such as confidence scores for AI predictions, may be a way of further improving performance. Our aims were to investigate whether AI advice improved overall diagnostic accuracy (using a single CHD lesion as an exemplar), and to determine what, if any, additional information given to clinicians optimized the overall performance of the clinician-AI team. An AI model was trained to classify a single fetal CHD lesion (atrioventricular septal defect (AVSD)), using a retrospective cohort of 121 130 cardiac four-chamber images extracted from 173 ultrasound scan videos (98 with normal hearts, 75 with AVSD); a ResNet50 model architecture was used. Temperature scaling of model prediction probability was performed on a validation set, and gradient-weighted class activation maps (grad-CAMs) produced. Ten clinicians (two consultant fetal cardiologists, three trainees in pediatric cardiology and five fetal cardiac sonographers) were recruited from a center of fetal cardiology to participate. Each participant was shown 2000 fetal four-chamber images in a random order (1000 normal and 1000 AVSD). The dataset comprised 500 images, each shown in four conditions: (1) image alone without AI output; (2) image with binary AI classification; (3) image with AI model confidence; and (4) image with grad-CAM image overlays. The clinicians were asked to classify each image as normal or AVSD. A total of 20 000 image classifications were recorded from 10 clinicians. The AI model alone achieved an accuracy of 0.798 (95% CI, 0.760-0.832), a sensitivity of 0.868 (95% CI, 0.834-0.902) and a specificity of 0.728 (95% CI, 0.702-0.754), and the clinicians without AI achieved an accuracy of 0.844 (95% CI, 0.834-0.854), a sensitivity of 0.827 (95% CI, 0.795-0.858) and a specificity of 0.861 (95% CI, 0.828-0.895). Showing a binary (normal or AVSD) AI model output resulted in significant improvement in accuracy to 0.865 (P < 0.001). This effect was seen in both experienced and less-experienced participants. Giving incorrect AI advice resulted in a significant deterioration in overall accuracy, from 0.761 to 0.693 (P < 0.001), which was driven by an increase in both Type-I and Type-II errors by the clinicians. This effect was worsened by showing model confidence (accuracy, 0.649; P < 0.001) or grad-CAM (accuracy, 0.644; P < 0.001). AI has the potential to improve performance when used in collaboration with clinicians, even if the model performance does not reach expert level. Giving additional information about model workings such as model confidence and class activation map image overlays did not improve overall performance, and actually worsened performance for images for which the AI model was incorrect. © 2024 The Authors. Ultrasound in Obstetrics & Gynecology published by John Wiley & Sons Ltd on behalf of International Society of Ultrasound in Obstetrics and Gynecology.

ARFGCN: Adaptive Receptive Field Graph Convolutional Network for Urban Crowd Flow Prediction

Urban crowd flow prediction is an important task for transportation systems and public safety. While graph convolutional networks (GCNs) have been widely adopted for this task, existing GCN-based methods still face challenges. Firstly, they employ fixed receptive fields, failing to account for urban region heterogeneity where different functional zones interact distinctly with their surroundings. Secondly, they lack mechanisms to adaptively adjust spatial receptive fields based on temporal dynamics, which limits prediction performance. To address these limitations, we propose an Adaptive Receptive Field Graph Convolutional Network (ARFGCN) for urban crowd flow prediction. ARFGCN allows each region to independently determine its receptive field size, adaptively adjusted and learned in an end-to-end manner during training, enhancing model prediction performance. It comprises a time-aware adaptive receptive field (TARF) gating mechanism, a stacked 3DGCN, and a prediction layer. The TARF aims to leverage gating in neural networks to adapt receptive fields based on temporal dynamics, enabling the predictive network to adapt to urban regional heterogeneity. The TARF can be easily integrated into the stacked 3DGCN, enhancing the prediction. Experimental results demonstrate ARFGCN’s effectiveness compared to other methods.

Measurement of ZZ production cross-sections in the four-lepton final state in pp collisions at [formula omitted] with the ATLAS experiment

This paper reports cross-section measurements of ZZ production in pp collisions at s=13.6TeV at the Large Hadron Collider. The data were collected by the ATLAS detector in 2022, and correspond to an integrated luminosity of 29 fb−1. Events in the ZZ→4ℓ (ℓ=e, μ) final states are selected and used to measure the inclusive and differential cross-sections in a fiducial region defined close to the analysis selections. The inclusive cross-section is further extrapolated to the total phase space with a requirement of 66 <mZ< 116 GeV for both Z bosons, yielding 16.8±1.1 pb. The results are well described by the Standard Model predictions.

Optimizing Lithium-Ion Battery Performance: Integrating Machine Learning and Explainable AI for Enhanced Energy Management

Managing the capacity of lithium-ion batteries (LiBs) accurately, particularly in large-scale applications, enhances the cost-effectiveness of energy storage systems. Less frequent replacement or maintenance of LiBs results in cost savings in the long term. Therefore, in this study, AdaBoost, gradient boosting, XGBoost, LightGBM, CatBoost, and ensemble learning models were employed to predict the discharge capacity of LiBs. The prediction performances of each model were compared based on mean absolute error (MAE), mean squared error (MSE), and R-squared values. The research findings reveal that the LightGBM model exhibited the lowest MAE (0.103) and MSE (0.019) values and the highest R-squared (0.887) value, thus demonstrating the strongest correlation in predictions. Gradient boosting and XGBoost models showed similar performance levels but ranked just below LightGBM. The competitive performance of the ensemble model indicates that combining multiple models could lead to an overall performance improvement. Furthermore, the study incorporates an analysis of key features affecting model predictions using SHAP (Shapley additive explanations) values within the framework of explainable artificial intelligence (XAI). This analysis evaluates the impact of features such as temperature, cycle index, voltage, and current on predictions, revealing a significant effect of temperature on discharge capacity. The results of this study emphasize the potential of machine learning models in LiB management within the XAI framework and demonstrate how these technologies could play a strategic role in optimizing energy storage systems.

Enhanced prediction of cement raw meal oxides by near-infrared spectroscopy using machine learning combined with chemometric techniques.

The component analysis of raw meal is critical to the quality of cement. In recent years, near-infrared (NIR) has been emerged as an innovative and efficient analytical method to determine the oxide content of cement raw meal. This study aims to utilize NIR spectroscopy combined with machine learning and chemometrics to improve the prediction of oxide content in cement raw meal. The Savitzky-Golay convolution smoothing method is applied to eliminate noise interference for the analysis of calcium carbonate ( ), silicon dioxide ( ), aluminum oxide ( ), and ferric oxide ( ) in cement raw materials. Different wavelength selection techniques are used to perform a comprehensive analysis of the model, comparing the performance of several wavelength selection techniques. The back-propagation neural network regression model based on particle swarm optimization algorithm was also applied to optimize the extracted and screened feature wavelengths, and the model prediction performance was checked and evaluated using and RMSE. In conclusion, the results indicate that NIR spectroscopy in combination with ML and chemometrics has great potential to effectively improve the prediction performance of oxide content in raw materials and highlight the importance of modeling and wavelength selection techniques. By enabling more accurate and efficient determination of oxide content in raw materials, NIR spectroscopy coupled with meta-modeling has the potential to revolutionize quality assurance practices in cement manufacturing.

Detection method of potato leaf disease based on YOLOv5s

An improved leaf target detection method based on the YOLOv5s network is proposed to address the issues of low model detection accuracy and slow detection speed in potato leaf image target detection. Firstly, a deformable convolution replaces the standard convolution in YOLOv5s to ensure that the convolution region always covers the target region. Secondly, CBAM attention module is introduced into the convolutional module to enhance local feature extraction and fusion capability of the network, while WIoU_Loss serves as Bounding box loss function SRN-DeblurNet deblurnet is combined with YOLOv5s network to convert part of fuzzy images into clear ones before being integrated with multi-scale features for model prediction. To verify its effectiveness, we trained our model using Pytorch deep learning framework and achieved an accuracy rate of 90.3% and recall rate of 88%, which are respectively 8.6% and 8.9% higher than those obtained by YOLOv5s.