Search Results (137)

This study evaluates the competitiveness and sustainable development prospects of French-speaking African countries by constructing a comprehensive framework integrating the TOPSIS method and adaptive LASSO algorithm. Using multivariate data from sources such as the World Bank, 30 indicators covering core, basic, and auxiliary competitiveness were selected to quantitatively analyze the competitiveness of 26 French-speaking African countries. Results show that their comprehensive competitiveness exhibits spatial patterns of “high in the north and south, low in the east and west” and “high in coastal areas, low in inland areas”. Algeria, Morocco, and six other countries demonstrate high competitiveness, while Central African countries generally show low competitiveness. The adaptive LASSO algorithm identifies three key influencing factors, including the proportion of R&D expenditure to GDP, high-tech exports, and total reserves, as well as five secondary key factors, including the number of patent applications and total number of domestic listed companies, revealing that scientific and technological investment, financial strength, and innovation transformation capabilities are core constraints. Based on these findings, sustainable development strategies are proposed, such as strengthening scientific and technological research and development and innovation transformation, optimizing financial reserves and capital markets, and promoting China–Africa collaborative cooperation, providing decision-making references for competitiveness improvement and regional cooperation of French-speaking African countries under the background of the “Belt and Road Initiative”. Full article

Principal component analysis (PCA) is a widely applied multivariate statistical technique across scientific disciplines, with forestry being one of its most dynamic areas of use. Its primary strength lies in reducing data dimensionality and classifying parameters within complex ecological datasets. This study provides the first comprehensive bibliometric and literature review focused exclusively on PCA applications in forestry. A total of 96 articles published between 1993 and 2024 were analyzed using the Web of Science database and visualized using VOSviewer software, version 1.6.20. The bibliometric analysis revealed that the most active scientific fields were environmental sciences, forestry, and engineering, and the most frequently published journals were Forests and Sustainability. Contributions came from 198 authors across 44 countries, with China, Spain, and Brazil identified as leading contributors. PCA has been employed in a wide range of forestry applications, including species classification, biomass modeling, environmental impact assessment, and forest structure analysis. It is increasingly used to support decision-making in forest management, biodiversity conservation, and habitat evaluation. In recent years, emerging research has demonstrated innovative integrations of PCA with advanced technologies such as hyperspectral imaging, LiDAR, unmanned aerial vehicles (UAVs), and remote sensing platforms. These integrations have led to substantial improvements in forest fire detection, disease monitoring, and species discrimination. Furthermore, PCA has been combined with other analytical methods and machine learning models—including Lasso regression, support vector machines, and deep learning algorithms—resulting in enhanced data classification, feature extraction, and ecological modeling accuracy. These hybrid approaches underscore PCA’s adaptability and relevance in addressing contemporary challenges in forestry research. By systematically mapping the evolution, distribution, and methodological innovations associated with PCA, this study fills a critical gap in the literature. It offers a foundational reference for researchers and practitioners, highlighting both current trends and future directions for leveraging PCA in forest science and environmental monitoring. Full article

Partial Least Squares (PLS) regression has been widely used to model the relationship between predictors and responses. However, PLS may be limited in its capacity to handle complex spectral data contaminated with significant noise and interferences. In this paper, we propose a novel filter learning-based PLS (FPLS) model that integrates an adaptive filter into the PLS framework. The FPLS model is designed to maximize the covariance between the filtered spectral data and the response. This modification enables FPLS to dynamically adapt to the characteristics of the data, thereby enhancing its feature extraction and noise suppression capabilities. We have developed an efficient algorithm to solve the FPLS optimization problem and provided theoretical analyses regarding the convergence of the model, the prediction variance, and the relationships among the objective functions of FPLS, PLS, and the filter length. Furthermore, we have derived bounds for the Root Mean Squared Error of Prediction (RMSEP) and the Cosine Similarity (CS) to evaluate model performance. Experimental results using spectral datasets from Corn, Octane, Mango, and Soil Nitrogen show that the FPLS model outperforms PLS, OSCPLS, VCPLS, PoPLS, LoPLS, DOSC, OPLS, MSC, SNV, SGFilter, and Lasso in terms of prediction accuracy. The theoretical analyses align with the experimental results, emphasizing the effectiveness and robustness of the FPLS model in managing complex spectral data. Full article

Logistic regression is often used to solve classification problems. This article combines the advantages of Bayesian methods and spike-and-slab Lasso to select variables in high-dimensional logistic regression. The method of introducing a new hidden variable or approximating the lower bound is used to solve the problem of logistic functions without conjugate priors. The Laplace distribution in spike-and-slab Lasso is expressed as a hierarchical form of normal distribution and exponential distribution, so that all parameters in the model are posterior distributions that are easy to deal with. Considering the high time cost of parameter estimation and variable selection in high-dimensional models, we use the variational Bayesian algorithm to perform posterior inference on the parameters in the model. From the simulation results, it can be seen that it is an adaptive prior that can perform parameter estimation and variable selection well in high-dimensional logistic regression. From the perspective of algorithm running time, the method proposed in this article also has high computational efficiency in many cases. Full article

Information regarding the impact of polymetallic exposure on metabolic syndrome (MetS) among residents living near abandoned Pb-Zn mines is limited. Our objective was to investigate the impact of co-exposure to metal mixtures on the prevalence of MetS among residents. ICP-MS was used to measure the levels of 24 metals in the urine of 1744 participants, including 723 participants living near abandoned Pb-Zn mines, labeled as exposed area, and 1021 participants from other towns, labeled as reference area in the same city. Multivariable generalized linear regression, adaptive LASSO penalized regression, and BKMR were used to assess the associations between metals and MetS. The levels of eleven metals were higher, while those of nine metals were lower in the exposed area than those in the reference area. Mg, Cd, Ti, TI, Zn, Rb, and Pb were selected as important MetS predictors using LASSO regression. In exposed area, urinary Zn and TI were positively associated with MetS, whereas Mg was negatively associated with MetS. In the reference area, urinary Zn was positively associated with MetS, whereas Mg and Ti were negatively associated with MetS. The BKMR model indicates a statistically significant positive overall effect of the seven metal mixtures on MetS in the exposed area. Polymetallic exposure was positively associated with MetS risk in the abandoned Pb-Zn mining areas, suggesting that excessive Zn and TI may be associated with a higher MetS risk among residents living near abandoned Pb-Zn mines. Full article

Variable selection is important for developing accurate and interpretable prediction models. While classical and penalized methods are widely used, few simulation studies provide meaningful comparisons. This study compares their predictive performance and model complexity in low-dimensional data. Three classical methods (best subset selection, backward elimination, and forward selection) and four penalized methods (nonnegative garrote (NNG), lasso, adaptive lasso (ALASSO), and relaxed lasso (RLASSO)) were compared. Tuning parameters were selected using cross-validation (CV), Akaike information criterion (AIC), and Bayesian information criterion (BIC). Classical methods performed similarly and produced worse predictions than penalized methods in limited-information scenarios (small samples, high correlation, and low signal-to-noise ratio (SNR)), but performed comparably or better in sufficient-information scenarios (large samples, low correlation, and high SNR). Lasso was superior under limited information but was less effective in sufficient-information scenarios. NNG, ALASSO, and RLASSO outperformed lasso in sufficient-information scenarios, with no clear winner among them. AIC and CV produced similar results and outperformed BIC, except in sufficient-information settings, where BIC performed better. Our findings suggest that no single method consistently outperforms others, as performance depends on the amount of information in the data. Lasso is preferred in limited-information settings, whereas classical methods are more suitable in sufficient-information settings, as they also tend to select simpler models. Full article

Aiming at tackling the challenges of traditional classification methods, which are labor-intensive, time-consuming, and inefficient, a nitrogen-efficient wheat variety classification method using support vector machine-extreme gradient boosting (SVM-XGBoost) based on unmanned aerial vehicle (UAV) hyperspectral remote sensing was proposed in this study. First, eight agronomic indicators closely related to wheat nitrogen efficiency were analyzed using t-SNE dimensionality reduction and hierarchical clustering, enabling the classification of 12 wheat varieties into nitrogen-efficient and nitrogen-inefficient varieties under different nitrogen stress conditions. Second, a hyperspectral feature band selection method based on least absolute shrinkage and selection operator-competitive adaptive reweighted sampling (Lasso-CARS) was employed using hyperspectral canopy data collected during the wheat heading stage with an UAV to extract feature bands relevant to nitrogen-efficient wheat classification. This approach aimed to mitigate the impact of high collinearity and noise in high-dimensional hyperspectral data on model construction. Furthermore, the SVM-XGBoost method integrated the extracted feature bands with the support vectors and decision function outputs from the preliminary SVM classification. It then leveraged XGBoost to capture nonlinear relationships and construct the final classification model using gradient-boosted trees, achieving intelligent classification of nitrogen-efficient wheat varieties. The model also selected nitrogen fertilization strategies based on the characteristics of different wheat varieties. The results demonstrated robust performance under low, high, and no nitrogen stress, with average overall accuracies of 74%, 83%, and 70% (Kappa coefficients: 0.67, 0.80, and 0.48), respectively. This study provided an efficient and accurate UAV hyperspectral remote sensing-based method for nitrogen-efficient wheat variety classification, offering a technological foundation to accelerate precision breeding. Full article

Background: Prostate cancer (PCa) remains one of the most prevalent malignancies in men, with diagnostic challenges arising from the limited specificity of current biomarkers, like PSA. Improved stratification tools are essential to reduce overdiagnosis and guide personalized patient management. Objective: This study aimed to identify and validate clinical and hematological biomarkers capable of differentiating PCa from benign prostatic hyperplasia (BPH) and precancerous lesions (PL) using univariate and multivariate statistical methods. Methods: In a cohort of 514 patients with suspected PCa, we performed a univariate analysis (Kruskal–Wallis and ANOVA) with preprocessing via adaptive Box–Cox transformation and missing value imputation through probabilistic principal component analysis (PPCA). LASSO regression was used for variable selection and classification. An ROC curve analysis assessed diagnostic performance. Results: Five variables—age, PSA, Index %, hemoglobin (HGB), and the International Index of Erectile Function (IIEF)—were consistently significant across univariate and multivariate analyses. The LASSO regression achieved a classification accuracy of 70% and an AUC of 0.74. Biplot and post-hoc analyses confirmed partial separation between PCa and benign conditions. Conclusions: The integration of multivariate modeling with reconstructed clinical data enabled the identification of blood-based biomarkers with strong diagnostic potential. These routinely available, cost-effective indicators may support early PCa diagnosis and patient stratification, reducing unnecessary invasive procedures. Full article

Skeletal muscle health and function are closely linked to long-term aerobic exercise, particularly in enhancing muscle metabolism and regulating gene expression. Regular endurance training can significantly ameliorate metabolic dysfunction and prevent chronic diseases. However, the precise molecular mechanisms underlying skeletal muscle adaptations to long-term aerobic exercise require further clarification. To address this, we integrated transcriptomic and single-cell omics datasets from multiple long-term aerobic exercise models retrieved from the GEO database. After merging and batch correction, differential expression analysis identified 204 DEGs, including 110 upregulated and 94 downregulated genes. Key feature genes were screened using Lasso regression, SVM-RFE, and Random Forest machine learning algorithms, validated by RT-qPCR, and refined through PPI network analysis. Among them, Fos and Tnfrsf12a were significantly downregulated following long-term aerobic exercise. Notably, Fos exhibited a more pronounced decrease than Tnfrsf12a, and was strongly associated with inflammation and muscle regeneration. PPI network analysis indicated that Fos interacted with genes such as Casp3, Egr1, Aft3, Hspa5, Src, and Igf2. GO, KEGG, and GSEA enrichment analyses revealed that Fos is involved in skeletal muscle differentiation, tissue remodeling, and the NF-κB inflammatory pathway. ssGSEA analysis further showed that samples with low Fos expression had significantly elevated Th1/Th2 and Treg cell infiltration. Single-cell analysis confirmed preferential Fos expression in muscle fiber/adipocyte progenitors, satellite cells, and tenocytes, all critical for myogenesis. In summary, our findings suggest that long-term aerobic exercise downregulates Fos, potentially alleviating inflammation and enhancing satellite cell-mediated muscle regeneration. Fos may serve as a central regulator of skeletal muscle remodeling during long-term aerobic exercise. Full article

This paper presents a data-driven framework for optimizing energy-efficient locomotion in a bio-inspired soft inchworm robot. Leveraging a feedforward neural network, the proposed approach accurately models the nonlinear relationships between actuation parameters (pressure, frequency) and environmental conditions (surface friction). The neural network achieves superior velocity prediction performance, with a coefficient of determination (R²) of 0.9362 and a root mean squared error (RMSE) of 0.3898, surpassing previously reported models, including linear regression, LASSO, decision trees, and random forests. Particle Swarm Optimization (PSO) is integrated to maximize locomotion efficiency by optimizing the velocity-to-pressure ratio and adaptively minimizing input pressure for target velocities across diverse terrains. Experimental results demonstrate that the framework achieves an average 9.88% reduction in required pressure for efficient movement and a 6.45% reduction for stable locomotion, with the neural network enabling robust adaptation to varying surfaces. This dual optimization strategy ensures both energy savings and adaptive performance, advancing the deployment of soft robots in diverse environments. Full article

This study includes a comprehensive evaluation of six penalty estimation strategies for partially linear models (PLRMs), focusing on their performance in the presence of multicollinearity and their ability to handle both parametric and nonparametric components. The methods under consideration include Ridge regression, Lasso, Adaptive Lasso (aLasso), smoothly clipped absolute deviation (SCAD), ElasticNet, and minimax concave penalty (MCP). In addition to these established methods, we also incorporate Stein-type shrinkage estimation techniques that are standard and positive shrinkage and assess their effectiveness in this context. To estimate the PLRMs, we consider a kernel smoothing technique grounded in penalized least squares. Our investigation involves a theoretical analysis of the estimators’ asymptotic properties and a detailed simulation study designed to compare their performance under a variety of conditions, including different sample sizes, numbers of predictors, and levels of multicollinearity. The simulation results reveal that aLasso and shrinkage estimators, particularly the positive shrinkage estimator, consistently outperform the other methods in terms of Mean Squared Error (MSE) relative efficiencies (RE), especially when the sample size is small and multicollinearity is high. Furthermore, we present a real data analysis using the Hitters dataset to demonstrate the applicability of these methods in a practical setting. The results of the real data analysis align with the simulation findings, highlighting the superior predictive accuracy of aLasso and the shrinkage estimators in the presence of multicollinearity. The findings of this study offer valuable insights into the strengths and limitations of these penalty and shrinkage strategies, guiding their application in future research and practice involving semiparametric regression. Full article

(1) Background: Predicting hospitalization length for COVID-19 patients is crucial for optimizing resource allocation and patient management. Radiomics, combined with machine learning (ML), offers a promising approach by extracting quantitative imaging features from CT scans. The aim of the present study is to use and adapt machine learning (ML) architectures, exploiting CT radiomics information, and analyze algorithms’ capability to predict hospitalization at the time of patient admission. (2) Methods: The original CT lung images of 168 COVID-19 patients underwent two segmentations, isolating the ground glass area of the lung parenchyma. After an isotropic voxel resampling and wavelet and Laplacian of Gaussian filtering, 92 intensity and texture radiomics features were extracted. Feature reduction was conducted by applying a last absolute shrinkage and selection operator (LASSO) to the radiomic features set. Three ML classification algorithms, linear support vector machine (LSVM), medium neural network (MNN), and ensemble subspace discriminant (ESD), were trained and validated through a 5-fold cross-validation technique. Model performance was assessed using accuracy, sensitivity, specificity, precision, F1-score, and the area under the receiver operating characteristic curve (AUC-ROC). (3) Results: The LSVM classifier achieved the highest predictive performance, with an accuracy of 86.0% and an AUC of 0.93. However, reliable outcomes are also registered when MNN and ESD architecture are used. (4) Conclusions: The study shows that radiomic features can be used to build a machine learning framework for predicting patient hospitalization duration. The findings suggest that radiomics-based ML models can accurately predict COVID-19 hospitalization length. Full article

To achieve a more accurate assessment of water resource carrying capacity (WRCC), the indicators of water resources, social resources, and ecological environment were selected to construct the WRCC system on the basis of the combinatorial assignment method with advantages. Moreover, the incorporation of key water quality influences into water quality predictions facilitated the performance of predictive models. Adaptive Lasso Regression was used to select key factors affecting water quality, whereas the CatBoost algorithm ranked the importance of the key factors selected by Adaptive Lasso in the prediction model. The CatBoost Convolutional Neural Network-Bidirectional Long Short-Term Memory-Attention (CNN-BiLSTM-Attention) model was used to forecast WQI. The research results propose a new WRCC evaluation and water quality prediction model method. The results show that the average barrier levels for water resources, socio-economic development, and ecological environment were 34.97%, 34.93%, and 30.10%, respectively. Compared to other system layers of WRCC, the obstacle degree of the ecological environment system layer has always been lower. The total sewage treatment, greening coverage in built-up areas, and per capita green space in parks were the main obstacle factors to the WRCC within the Min River Basin. Based on the results of the key factor screening, it can be seen that dissolved oxygen is positively correlated with the water quality of the watershed, while the other key influencing factors are negatively correlated with the WQI. Total nitrogen had the greatest impact on water quality conditions in the watershed, with a regression coefficient of −1.7532. From the comparison of the prediction results, it is known that the hybrid model can make the MAE value of 45% monitoring points reach the minimum, and the RMSE value of 35% monitoring points reach the minimum. The percentages of the remaining prediction models that reached the lowest values for MAE and RMSE were 15% to 20% and 15% to 30%, respectively. Compared with other prediction models, the MSE and RMSE values of the hybrid model were relatively small, which was more conducive to predicting water quality in the Min River Basin. Full article

Rice (Oryza sativa L.), a staple crop vital to global food security, faces escalating threats from climate change and inefficient nitrogen management. This study employed least absolute shrinkage and selection operator (LASSO) regression to analyze the stage-specific impacts of nitrogen application, temperature, and rainfall on rice yield and nitrogen use efficiency (NUE) across three growing seasons (2020–2022) in Jiangsu Province, China. The key findings revealed the following: (1) the reproductive stages (flowering and filling stages) exhibited extreme thermal sensitivity, with high temperatures (>35 °C) causing substantial yield losses (33.1% average) and reducing nitrogen recovery efficiency (NRE: 22.4–60.5% loss) and the nitrogen translocation ratio (NTR: 26.3–61.6% loss); (2) the vegetative stages (tillering and jointing and booting stages) were highly rainfall-sensitive, with rainfall during tillering (2.1–9.7 mm/day) influencing 50% of the traits, including four NUE types; (3) appropriate nitrogen management (250–350 kgN·ha⁻¹) mitigated the heat-induced losses, increasing physiological nitrogen use efficiency (PNUE) by 30.0–41.8% under extreme heat and alleviating the losses of yield. This study further verified the generalizability of LASSO. Compared with the traditional models, LASSO overcomes the issue of multicollinearity and can more effectively identify the key factors driving climate change across different spatial gradients. These findings provide actionable insights for optimizing nitrogen application timing, improving climate-resilient breeding, and developing stage-specific adaptation strategies to safeguard rice productivity under global warming. Full article

Regularization methods such as LASSO, adaptive LASSO, Elastic-Net, and SCAD are widely employed for variable selection in statistical modeling. However, these methods primarily focus on variables with strong effects while often overlooking weaker signals, potentially leading to biased parameter estimates. To address this limitation, Gao, Ahmed, and Feng (2017) introduced a corrected shrinkage estimator that incorporates both weak and strong signals, though their results were confined to linear models. The applicability of such approaches to survival data remains unclear, despite the prevalence of survival regression involving both strong and weak effects in biomedical research. To bridge this gap, we propose a novel class of post-selection shrinkage estimators tailored to the Cox model framework. We establish the asymptotic properties of the proposed estimators and demonstrate their potential to enhance estimation and prediction accuracy through simulations that explicitly incorporate weak signals. Finally, we validate the practical utility of our approach by applying it to two real-world datasets, showcasing its advantages over existing methods. Full article