High Prediction Accuracy Research Articles

Soft sensor modeling method for Pichia pastoris fermentation process based on substructure domain transfer learning

BackgroundAiming at the problem that traditional transfer methods are prone to lose data information in the overall domain-level transfer, and it is difficult to achieve the perfect match between source and target domains, thus reducing the accuracy of the soft sensor model.MethodsThis paper proposes a soft sensor modeling method based on the transfer modeling framework of substructure domain. Firstly, the Gaussian mixture model clustering algorithm is used to extract local information, cluster the source and target domains into multiple substructure domains, and adaptively weight the substructure domains according to the distances between the sub-source domains and sub-target domains. Secondly, the optimal subspace domain adaptation method integrating multiple metrics is used to obtain the optimal projection matrices Ws and Wt that are coupled with each other, and the data of source and target domains are projected to the corresponding subspace to perform spatial alignment, so as to reduce the discrepancy between the sample data of different working conditions. Finally, based on the source and target domain data after substructure domain adaptation, the least squares support vector machine algorithm is used to establish the prediction model.ResultsTaking Pichia pastoris fermentation to produce inulinase as an example, the simulation results verify that the root mean square error of the proposed soft sensor model in predicting Pichia pastoris concentration and inulinase concentration is reduced by 48.7% and 54.9%, respectively.ConclusionThe proposed soft sensor modeling method can accurately predict Pichia pastoris concentration and inulinase concentration online under different working conditions, and has higher prediction accuracy than the traditional soft sensor modeling method.

Construction and control parameter matching method of hydraulic pile hammer surrogate model combining transfer algorithm and knowledge embedding

Due to its complex application environment, hydraulic pile hammers are difficult to model and accurately grasp their motion laws in automation control. Therefore, to address the high-precision prediction problem of the motion law of hydraulic pile hammer systems under complex working conditions, a knowledge embedding surrogate model based on a dual neural network is designed, which can be divided into two parts: feature extraction network and prediction network. On this basis, the model introduces physics knowledge embedding technology, adds inequality constraints based on physical laws to the loss function, and introduces transfer learning algorithms to maintain high accuracy of the model under limited data conditions. The results showed that the PSM (γ = 0.5) model designed in the study had a median of -0.001 and a variance of 0.038 under undisturbed conditions, demonstrating high prediction stability. Under perturbation conditions, the median of the PSM (γ = 1.5) model was -0.003, with a variance of 0.031, indicating the highest prediction accuracy. From this, the research model has good prediction accuracy under complex working conditions, which can lay a technical foundation for predicting the motion law of hydraulic pile hammers and automatic control.

Online variational Gaussian process for time series data

Gaussian processes (GPs) are a powerful and popular framework for addressing machine learning problems, particularly for time-dependent data such as that generated by the Internet of Things (IoT). GPs offer a compelling choice for constructing real-valued nonlinear models due to their inherent flexibility and ability to quantify uncertainty. However, traditional GP methods are often hindered by cubic computational complexity, making them impractical for the massive and potentially unbounded datasets commonly encountered in IoT applications. To address this issue, researchers have developed various sparse approximation methods that significantly reduce the computational burden of GPs. Among these, pseudo-point approximations have proven to be highly influential, leveraging a subset of the training data to represent the entire observation space. The variational sparse GP is a state-of-the-art approach that approximates the posterior distribution of GP models, enabling faster and more efficient predictions in real-time data series. However, integrating variational inference into the GP framework for sequentially arriving data remains a significant challenge, particularly when dealing with time series data where the underlying data distribution evolves over time. In this paper, we propose the OnLine Variational Gaussian Process (OLVGP) algorithm, which introduces a novel approach for dynamically managing the number of inducing points based on the concept of eigenfunction inducing features. Unlike traditional methods that rely on a fixed number of inducing points, OLVGP adaptively adjusts the number of inducing points as new data arrives and optimizes them from the model, ensuring that the model remains computationally efficient while maintaining high predictive accuracy. Our method capitalizes on the sophisticated design of the online variational inference framework, ensuring a technically robust derivation and implementation. We validate the effectiveness and efficiency of our approach through both synthetic examples and real-world experiments. The results demonstrate that OLVGP not only substantially reduces computational costs compared to traditional sparse GP methods but also dynamically adapts to the evolving data, delivering improved performance in time series prediction.

Machine Learning‐Assisted Hybrid Package of White Light‐Emitting Diodes Employing Quantum Dots and Phosphor

AbstractWhite light‐emitting diodes (WLEDs), known for their high brightness, high efficiency, and long lifetime, are widely utilized in the backlight of liquid crystal displays. However, it is still difficult to improve the color gamut of WLEDs while maintaining the L50 lifetime. The luminous characteristics of WLEDs employing different combinations of quantum dots and phosphor are investigated in this work. Additionally, investigations on the L50 lifetime for WLEDs are carried out by employing a two‐step accelerated stress method. Finally, an ensemble machine learning model is proposed to predict the L50 lifetime of WLEDs, achieving high predictive accuracy with R2 of 0.986.

Plasma extracellular vesicles carry immune system-related peptides that predict human longevity.

Extracellular vesicles (EVs) play crucial roles in aging. In this National Institutes on Aging-funded study, we sought to identify circulating extracellular vesicle (EV) biomarkers indicative of longevity. The plasma EV proteome of 48 older adults (mean age 77.2 ± 1.7 years [range 72-80]; 50% female, 50% Black, 50% < 2-year survival, 50% ≥ 10-year survival) was analyzed by high-resolution mass spectrometry and flow cytometry. The ability of EV peptides to predict longevity was evaluated in discovery (n = 32) and validation (n = 16) datasets with areas under receiver operating characteristic curves (AUCs). Longevity-associated large EV (LEV) plasma subpopulations were mainly related to immune cells (HLA-ABC+, CD9+, and CD31+) and muscle cells (MCAD+ and RyR2+). Of 7960 identified plasma EV peptides (519 proteins), 46.4% were related to the immune system and 10.1% to muscle. Compared with short-lived older adults, 756 EV peptides (131 proteins) had a higher abundance, and 130 EV peptides (78 proteins) had a lower abundance in long-lived adults. Among longevity-associated peptides, 437 (58 proteins) were immune system related, and 12 (2 proteins) were muscle related. Using just three to five plasma EV peptides (mainly complement components C2-C6), we achieved high predictive accuracy for longevity (AUC range 0.91-1 in a hold-out validation dataset). Our findings suggest that immune cells produce longevity-associated plasma EVs and elucidate fundamental mechanisms regulating aging and longevity. EV longevity predictors suggest there may be merit in targeting complement pathways to extend lifespan, for instance, with any one of the multiple complement inhibitors currently available or in clinical development.

Machine learning-driven simplification of the hypomania checklist-32 for adolescent: a feature selection approach

BackgroundThe Hypomania Checklist-32 is widely used to screen for bipolar disorder, but its length can be challenging for adolescents with manic symptoms. This study aimed to develop a shortened version of the HCL-32 tailored for adolescents using machine learning techniques.MethodsData from 2,850 adolescents (mean age 15.50 years, 68.81% female) who completed the HCL-32 were analyzed. Random forest (RF) and gradient boosting machine (GBM) algorithms were employed for feature selection. The area under the curve (AUC) was used to evaluate model performance. Receiver operating characteristic (ROC) analysis was conducted to determine optimal cutoff points for the shortened scale.ResultsAn 8-item version of the HCL-32 was derived, maintaining high predictive accuracy (AUC = 0.97). The selected items captured core symptoms of adolescent mania, including increased energy, risk-taking, and irritability. Two cutoff points were identified: a score of 3 offered high specificity (0.98) and positive predictive value (0.98), while a score of 4 provided balanced sensitivity (0.87) and specificity (0.94) with the highest overall accuracy (0.91).ConclusionsThe machine learning-driven 8-item version of the HCL-32 demonstrates strong diagnostic utility for adolescent bipolar disorder, offering a more efficient screening tool without sacrificing clinical sensitivity. This shortened scale may improve assessment feasibility and accuracy in clinical settings, addressing the unique challenges of diagnosing bipolar disorder in adolescents.

Real-time surrogate compensation architecture for machine-tool thermal error compensation with high-performance model

Thermal error compensation based on high-performance (high-accuracy prediction of thermal errors under complex machining conditions and environments) models is of great significance to high-precision CNC machining. Currently, the high-performance thermal error models can only run at the edge computer due to the computing power limitation of CNC system under real-time constraints, Therefore, the timeliness of the thermal error compensation instruction cannot be guaranteed; delayed and mutated compensation instructions cause defects in the machining surface, which is the main obstacle that restricts the application of thermal error compensation based on high-performance models. In order to solve this challenge, this research proposes a novel real-time surrogate compensation architecture for machine tool thermal error compensation with high-performance model. Based on the look-ahead machine tool control command, the thermal error of next stage could be predicted according to the high-performance thermal error prediction model running on the edge, and the parameters of thermal error surrogate compensation model running on the CNC system could be fitted for high accuracy real-time compensation of the thermal error during the processing process. The verification experimental results on the CK40S CNC lathe show that the proposed surrogate method can retain more than 95% of the accuracy of the original high performance thermal error prediction model. With the surrogate thermal error compensation, the machining surface quality of the machine tool is flawless, and the batch machining error has been reduced from 18.3 μm to 4.5 μm. Compared with the traditional Multi Liner Regression (MLR) model compensation method, the machining accuracy under complex working conditions is further improved by 50%.

A Case–Control Study on the Usefulness of Serum Lecithin: Cholesterol Acyltransferase Activity as a Predictor of Retained Placenta in Close-Up Dairy Cows

The purpose of this study was to investigate the usefulness of the activity of lecithin:cholesterol acyltransferase (LCAT), the enzyme responsible for esterification of cholesterol in plasma, as a predictor of retained placenta (RP) in close-up cows, compared with the non-esterified fatty acids (NEFA) concentration. This study was conducted as a case–control study between February 2010 and February 2016, on a single farm with approximately 200 Holstein parous cows in Hokkaido, Japan. Of the 1187 dairy cattle that calved, 835 dairy cattle were enrolled that underwent routine regular health examinations including blood sampling, body condition score (BCS) and the rumen fill score (RFS) at the close-up stage between 2 and 21 days before their expected calving dates. Of these, 27 cows that were multiparous and had RP were designated as the RP group. The controls were 60 clinically healthy cows that did not develop RP and were matched for the sampling period and parity with the RP group. The LCAT activity and NEFA concentration were significantly (p &lt; 0.01) lower and higher, respectively, in the RP group than in controls. There was no significant difference in cholesteryl esters, free cholesterol concentrations and BCS between the two groups. However, RFS was significantly (p &lt; 0.01) lower in the RP group than in the controls. Cows with LCAT activity of &lt;450 U were 3.6 times more likely to develop RP than those with higher values, whereas those with NEFA levels above 0.4 mEq/L were 5.4 times more likely to. The area under the curve of receiver operator characteristic curves showed that LCAT activity was as efficient as the NEFA concentration in the diagnostic prediction of RP, suggesting it to be a useful predictor. Logistic regression analysis with LCAT or NEFA and RFS as explanatory variables resulted in a model with higher predictive accuracy than with each alone, indicating RFS to be a possible factor in predicting RP.

Construction of a risk prediction model of diquat poisoning based on clinical indicators.

This study aims to explore the clinical characteristics and prognostic factors in patients with diquat (DQ) poisoning and to develop a clinical risk assessment model to improve diagnosis and treatment strategies. Data from 60 patients with DQ poisoning, including basic characteristics, poisoning severity, and inflammatory response indicators, were collected. The plasma concentration of DQ was measured using liquid chromatography-mass spectrometry. The included patients were categorized into survival and death groups based on their 30-day outcomes. Fisher's exact test was used to identify statistically significant clinical indicators (p<.05), and logistic regression within a generalized linear model (GLM) framework was employed to analyze these indicators alongside the severity index of diquat poisoning (SIDP), followed by the construction of a prognostic model. The performance of the model was evaluated through receiver operating characteristic (ROC) analysis, and the accuracy of the model was assessed. Additionally, two independent sample Wilcoxon tests compared the clinical indicators between high-risk and low-risk groups. Fisher's exact test identified significant differences in variables such as oral drug dosage (ODD), time from poisoning to admission (TFPTA), state of consciousness (SOC), Glasgow Coma Scale (GCS), white blood cells (WBC), myoglobin (Myo), high-flow nasal cannula (HFNC), invasive mechanical ventilation (IMV), acute kidney injury (AKI), and acute lung injury (ALI) (p<.05) between the survival and death groups. The GLM-based risk assessment model demonstrated high predictive accuracy, with an area under the ROC curve (AUC) of 0.97 (SE 0.017, 95% CI 0.939-1.001), indicating potent prognostic capability. The Wilcoxon test revealed that ODD, Myo, SIDP, aspartate transferase (AST), creatine kinase (CK), hemoglobin (Hb), cardiac troponin (cTnT), and serum creatinine (Cr) levels were significantly higher in the high-risk group. The clinical risk assessment model effectively predicts the prognosis of patients with DQ poisoning, aiding clinicians in personalizing treatment strategies to improve patient outcomes.

A Typical Infrared Background Radiation Prediction Model Based on RF-VMD and Optimized Hybrid Neural Network

ABSTRACT The short-term prediction and adjustment of a target’s infrared radiation hold significant value in military camouflage applications. Existing radiation prediction models generally require real-time environmental and meteorological data support, resulting in lag in active camouflage. To meet the demand for active camouflage of background infrared (IR) radiation, a short-term background IR radiation prediction method based on historical data is proposed. First, a random forest (RF) is used to filter the collected multidimensional meteorological parameters. Variational mode decomposition (VMD) is applied for time-frequency analysis on these parameters, optimizing them with Bayesian algorithms and decomposing them into multivariate intrinsic mode functions (IMFs) with similar frequencies to reduce the impact of nonlinearity in the data. Based on the superimposed IMFs as inputs, a hybrid deep neural network prediction model is established. The model optimizes the CNN-LSTM network with residual connections and introduces a multi-head self-attention mechanism to enhance spatiotemporal feature extraction of the multidimensional meteorological parameters, focusing on key temporal feature regions. According to the experimental results, the constructed model demonstrates high prediction accuracy and adaptability across different background environments, with a low parameter count and fast prediction capability, meeting the practical application needs for various complex backgrounds.

Mitigating saturation effects in rice nitrogen estimation using Dualex measurements and machine learning

Nitrogen is essential for rice growth and yield formation, but traditional methods for assessing nitrogen status are often labor-intensive and unreliable at high nitrogen levels due to saturation effects. This study evaluates the effectiveness of flavonoid content (Flav) and the Nitrogen Balance Index (NBI), measured using a Dualex sensor and combined with machine learning models, for precise nitrogen status estimation in rice. Field experiments involving 15 rice varieties under varying nitrogen application levels collected Dualex measurements of chlorophyll (Chl), Flav, and NBI from the top five leaves at key growth stages. Incremental analysis was performed to quantify saturation effects, revealing that chlorophyll measurements saturated at high nitrogen levels, limiting their reliability. In contrast, Flav and NBI remained sensitive across all nitrogen levels, accurately reflecting nitrogen status. Machine learning models, particularly random forest and extreme gradient boosting, achieved high prediction accuracy for leaf and plant nitrogen concentrations (R2 &amp;gt; 0.82), with SHAP analysis identifying NBI and Flav from the top two leaves as the most influential predictors. By combining Flav and NBI measurements with machine learning, this approach effectively overcomes chlorophyll-based saturation limitations, enabling precise nitrogen estimation across diverse conditions and offering practical solutions for improved nitrogen management in rice cultivation.

Development and Validation of a Nomogram for Predicting Systemic Inflammatory Response Syndrome Following Percutaneous Nephrolithotomy.

Objective: To develop and validate a nomogram for predicting the occurrence of systemic inflammatory response syndrome (SIRS) following percutaneous nephrolithotomy (PCNL), aiming to enhance clinical decision-making and treatment planning. Methods: Clinical data of 1,047 patients undergoing PCNL at a single-center hospital between 2017 and 2023 were retrospectively analyzed. Independent risk factors influencing SIRS occurrence were identified through multi-variable logistic regression analysis, and a predictive model was constructed. The model's accuracy and reliability were evaluated through internal training and validation set. Results: Multi-variable regression analysis identified six key predictive factors: gender, diabetes, urine culture results, stone surface, staghorn stones, and operative time, leading to the establishment of a nomogram predictive model. Internal validation and validation set data demonstrated the model's high predictive accuracy and reliability, with areas under the receiver operating characteristic curve of 0.718 and 0.723, respectively. Conclusions: A nomogram predictive model for assessing SIRS following PCNL was successfully developed and validated. This model provides clinicians with a valuable tool for personalized treatment planning and implementing preventive measures.

Rapid diagnosis of bacterial vaginosis using machine-learning-assisted surface-enhanced Raman spectroscopy of human vaginal fluids.

The accurate and rapid diagnosis of bacterial vaginosis (BV) is crucial due to its high prevalence and association with serious health complications, including increased risk of sexually transmitted infections and adverse pregnancy outcomes. Although widely used, traditional diagnostic methods have significant limitations in subjectivity, complexity, and cost. The development of a novel diagnostic approach that integrates SERS with ML offers a promising solution. The CNN model's high prediction accuracy, cost-effectiveness, and extraordinary rapidity underscore its significant potential to enhance the diagnosis of BV in clinical settings. This method not only addresses the limitations of current diagnostic tools but also provides a more accessible and reliable option for healthcare providers, ultimately enhancing patient care and health outcomes.

Risk prediction of computer investment database information management system based on machine learning algorithms

In recent years, with the continuous development of the financial market, the risk prediction of computer investment database information management systems (IMS) has high practical value. At present, there are risk issues in the information management system, which may cause drawbacks to investment data processing. To address these issues, this article used Machine Learning (ML) algorithms to analyze the risk prediction of computer investment database IMS. This article introduced and utilized typical Self-Organizing Map (SOM) and Artificial Neural Network (ANN) combination algorithms, regression algorithms, and Gradient Boosting Decision Tree (GBDT) algorithms to compare and analyze the prediction accuracy of these three algorithms. This article found that the GBDT algorithm has the highest prediction accuracy. Through a large amount of experimental data, it has been proven that the average testing accuracy using regression algorithms was 3.5% higher than that using neural network algorithms. It was found that the average test accuracy using the GBDT algorithm was 7.2% higher than the average test accuracy using the regression algorithm. The study also explores the combination of physiological and behavioral data collected by wearable devices to provide more comprehensive risk assessment and decision support, which provides an important reference for the optimization of enterprise risk management. Through this innovative data source integration, this paper provides a new perspective for the application and development of machine learning algorithms in computer investment database IMS.

Establishment of multiple machine learning prognostic model for gene differences between primary tumors and lymph nodes in luminal breast cancer.

This study aimed to explore the correlation between primary tumors (PT) and paired metastatic lymph nodes (LN) and to develop a predictive model to provide evidence for forecasting patient prognoses. We obtained single-cell and bulk transcriptome data from the Gene Expression Omnibus database. Furthermore, mRNA transcriptomic data, encompassing 112 normal tissues and 1066 breast cancer samples, along with survival, clinical, and mutation information for breast cancer patients, were acquired from The Cancer Genome Atlas (TCGA). Employing a machine learning integration framework incorporating ten distinct algorithms, we developed and validated a prognostic model. We constructed a prognostic model named Lymph Node Metastasis-Related Scores (LMRS) using 26 differentially expressed genes trained on eight TCGA datasets. Across validation sets, the model demonstrated a high C-index, signifying its stability and effectiveness, outperforming 64 models from other studies. Notably, cytolytic activity and T cell co-stimulation were downregulated in the high LMRS group, alongside a downregulation of immune cells, including B cells, CD8 + T cells, iDCs, and TILs. Similarly, most immune checkpoints exhibited a decreasing trend with high LMRS expression. Finally, we selected the hub biomarkers PGK1 and HSP90 for pathological verification. Results indicated higher expression levels in PT and LN compared to normal and benign tumors, with higher expression levels in LN than in PT. This comprehensive analysis sheds light on gene expression differences between PT and LN in breast cancer, culminating in the development of a multiple-gene prognostic model with high clinical accuracy for prognosis prediction.

Non-Destructive Detection and Visualization of Chlorophyll Content in Cherry Tomatoes Based on Hyperspectral Technology and Machine Learning

The cherry tomato has an important economic value and an increasingly broad market, and the chlorophyll content of cherry tomato leaves can directly reflect the plant’s photosynthetic ability, thus indirectly reflecting its growth status. Therefore, this paper proposes a regression detection method for chlorophyll in cherry tomato leaves by combining machine learning and hyperspectral technology to realize non-destructive, fast, and more accurate detection. Firstly, Moving-Average (MA) preprocessing was chosen as the pretreatment method for this paper, and three regression models of principal component regression (PCR), random forest (RF), and partial least squares regression (PLSR) were established with leaf chlorophyll under different nitrogen concentrations. The CARS_PLSR algorithm has the highest prediction accuracy with accuracy, precision, RMSEC, and RMSEP of 0.8790, 0.9187, 2.9581, and 2.5578, respectively. The study examined the impact of various nitrogen concentrations on the chlorophyll content of cherry tomato leaves, and it was found that the high concentration of nitrogen inhibited the SPAD value of cherry tomato leaves more than that of the low concentration, and the optimal concentration of nitrogen fertilization for tomatoes was 300 mg·L−1. Finally, a regression model was established by using CARS-PLSR combined with the pseudo-color map technology, and a distribution map of chlorophyll content in different SPAD value gradients of cherry tomato leaves was obtained, which could visualize the distribution of chlorophyll and its distribution sites in the leaves and understand the growth status of cherry tomatoes. The distribution of chlorophyll content in different SPAD values of cherry tomato leaves was obtained by using the CARS-PLSR regression model combined with pseudo-color map technology, which can visualize the distribution of chlorophyll in the leaves and the parts of distribution and understand the growth condition of cherry tomatoes. Finally, the optimal model is applied in conjunction with a sprayer to automate fertilizer application.

Checkpoint data-driven GCN-GRU vehicle trajectory and traffic flow prediction

With the development of information technology, massive traffic data-driven short-term traffic situation analysis of urban road networks has become a research hotspot in urban traffic management. Accurate vehicle trajectory and traffic flow prediction can provide technical support for vehicle path planning and road congestion warning. Unlike most studies that use GPS data to predict vehicle trajectories, this paper combines the broad coverage, high reliability, and lighter weight of traffic checkpoint data to propose a method that uses trajectory prediction technology to forecast the traffic flow in urban road networks accurately. The method adopts a checkpoint data-driven approach for data collection, combines graph convolutional neural network (GCN) and gated recurrent unit (GRU) models to more effectively learn and extract spatiotemporal correlation features of vehicle trajectories, which significantly improves the accuracy of vehicle trajectory prediction, and uses the output of the trajectory prediction model to forecast traffic flow more accurately. Firstly, transforming the checkpoint data into daily vehicle trajectories with time series characteristics, realizing the vehicle trajectory travel chain division. Secondly, the adjacency matrix is established by using the spatial relationship of each checkpoint, and the feature matrix of the vehicle’s driving trajectory over time is established, which is used as the input of GCN to learn the spatial characteristics of the vehicle while driving on the road network, and then GRU is added to further process the data after GCN training, constructing a GCN-GRU vehicle trajectory prediction model for vehicle trajectory prediction. Finally, the traffic flow of each checkpoint is calculated based on the prediction result of vehicle trajectory and compared with the real checkpoint flow. This paper conducts many experiments on the Qingdao City Shinan district checkpoint dataset. The results show that compared with the single models GCN, GRU, BiGRU, and BiLSTM, the GCN-GRU model has reduced the MAE by 0.75, 0.46, 0.52, and 0.57, and the RMSE by 0.76, 0.52, 0.58, and 0.68, respectively, demonstrating stronger spatial and temporal correlation characteristics and higher prediction accuracy. The MAPE between the forecasted flow and the real flow is 0.18, which verifies the reliability of the proposed method.

All‐Sky Microwave Radiance Observation Operator Based on Deep Learning With Physical Constraints

AbstractSatellite data assimilation relies on the radiative transfer models (RTMs) to establish the relationships between model state variables and satellite radiances. However, atmospheric radiative transfer calculations are computationally expensive, especially when involving multiple‐scattering calculations in cloudy areas. In recent years, deep learning (DL) models have been increasingly applied to emulate and accelerate physical models. This study, for the first time, explores DL techniques to emulate all‐sky radiative transfer in microwave bands. The FengYun‐3E (FY‐3E) Microwave Humidity Sounder‐2 (MWHS‐2) was selected as the target instrument due to its comprehensive spectral coverage, with the radiative transfer for TOVS scattering module (RTTOV‐SCATT) serving as the reference model. Three DL architectures were trained and compared, including multilayer perceptron (MLP), Bidirectional Long Short‐Term Memory with Attention (BiLSTM‐Attention), and Transformer. The BiLSTM‐Attention architecture demonstrated superior performance in both clear‐sky and cloudy radiance simulations. This may be attributed to its bidirectional recurrent structure resembling physical radiative transfer processes and the attention mechanism's ability to link MWHS‐2 channels with corresponding vertical layers. Although DL models achieve high accuracy in forward prediction, they often struggle with instability in Jacobian calculations. To address this issue, the trained BiLSTM‐Attention model was fine‐tuned using the reference model Jacobians as physical constraints. The fine‐tuned BiLSTM‐Attention model accurately characterized radiance sensitivities to temperature, water vapor, and hydrometeors under different cloud conditions, indicating its potential to serve as a radiance observation operator in data assimilation and physical retrieval applications.

Explainable AI: Enhancing Interpretability of Machine Learning Models

Explainable Artificial Intelligence (XAI) is emerging as a critical field to address the “black box” nature of many machine learning (ML) models. While these models achieve high predictive accuracy, their opacity undermines trust, adoption, and ethical compliance in critical domains such as healthcare, finance, and autonomous systems. This research explores methodologies and frameworks to enhance the interpretability of ML models, focusing on techniques like feature attribution, surrogate models, and counterfactual explanations. By balancing model complexity and transparency, this study highlights strategies to bridge the gap between performance and explainability. The integration of XAI into ML workflows not only fosters trust but also aligns with regulatory requirements, enabling actionable insights for stakeholders. The findings reveal a roadmap to design inherently interpretable models and tools for post-hoc analysis, offering a sustainable approach to democratize AI.

Integrative bioinformatics analysis reveals novel insights into osteoarthritis pathogenesis and diagnostic biomarkers

BackgroundOsteoarthritis (OA) is a prevalent joint disorder characterized by degeneration and inflammation. Understanding its molecular mechanisms is crucial for diagnosis and treatment.MethodsWe employed bioinformatics analyses to study OA using gene expression data. Differential expression analysis, weighted gene co-expression network analysis (WGCNA), and protein-protein interaction (PPI) network analysis were conducted. Enrichment analyses were performed to elucidate the biological significance of identified genes. Additionally, signature genes were identified using LASSO regression analysis, and a diagnostic nomogram was developed. qRT-PCR was conducted to confirm the expression levels of signature genes.ResultsWe identified 200 differentially expressed genes (DEGs) and a lightgreen module strongly correlated with OA. Within this module, 97 core genes were identified. Fifteen core lipopolysaccharide-related genes (LRGs) were found, enriched in immune and inflammatory pathways. Three hub genes (CCL3, ZFP36, and CCN1) emerged as potential biomarkers for OA diagnosis, with a nomogram showing high predictive accuracy, and validated by using clinical samples. Gene set enrichment analysis (GSEA) revealed distinct signaling pathways associated with the signature genes. Immunological analysis indicated altered immune profiles in OA, with the signature genes influencing immune cell infiltration and immune response pathways.ConclusionOur study provides insights into OA pathogenesis and identifies potential diagnostic biomarkers. The developed nomogram shows promise for accurate OA diagnosis. Furthermore, the signature genes play crucial roles in modulating the immune microenvironment in OA, suggesting their therapeutic potential.Clinical trial numberNot applicable.