Search Results (2,197)

As organizations strive to balance environmental stewardship with economic competitiveness, understanding the performance implications of Green Innovation (GI) has become increasingly important. Although the nexus between Green Product Innovation (GPI), Green Process Innovation (GPrI), and organizational outcomes has attracted sustained scholarly attention, empirical evidence remains inconclusive. To reconcile these inconsistencies and delineate boundary conditions, this study synthesizes data from 48 empirical investigations (2012–2025) via a random-effects meta-analysis with the Hartung–Knapp adjustment and trim-and-fill procedures to strengthen statistical inference. Results reveal significant small-to-moderate positive associations between GI and environmental (r = 0.172), financial (r = 0.191), and innovation performance (r = 0.143). Notably, moderator analyses demonstrate a synergy premium, where Integrated GI measures significantly outperform isolated GPI or GPrI approaches (r = 0.353). Substantial heterogeneity exists (I² = 91.2%), which is significantly moderated by innovation type, industry pollution intensity, geographic region, and research design. Our findings reinforce the Natural-Resource-Based View (NRBV) and the Dynamic Capabilities framework, highlighting that strategic returns depend on asset orchestration and contextual factors. We conclude that firms should adopt a holistic approach, integrating both product and process innovations to enhance competitive advantage in an incremental and context-contingent manner, while interpreting innovation-performance results cautiously given the limited evidence base. Full article

This study presents an integrated geophysical–geomatic approach for the investigation of archaeological sites, combining low-frequency Ground-Penetrating Radar (GPR) and close-range photogrammetry at the Archaeological Park of Abellinum (southern Italy). Unlike conventional applications using high-frequency antennas, the low-frequency GPR system employed in this study enabled deep subsurface imaging, allowing reconstruction of buried stratigraphic and architectural features to depths of several metres. This enhanced penetration capacity facilitated a more comprehensive understanding of the investigated environments, by complementing rather than replacing high-frequency surveys and expanding the interpretable volume in complex urban and peri-urban contexts. GPR reflection data were integrated with high-resolution photogrammetric surface models, enabling direct comparison between visible structures and subsurface geometries. The combined dataset provided precise correlations between surface features and subsurface anomalies, demonstrating the potential of this integrated methodology for detailed archaeological interpretation. Overall, this approach offers a scalable, non-invasive framework applicable to other complex archaeological landscapes, supporting both research objectives and long-term heritage management. By systematically combining low-frequency GPR with high-resolution photogrammetry, the study introduces a methodological contribution that extends interpretative depth well beyond the limits of conventional surveys. Full article

The performance degradation of electronic power components during long-term operation can compromise system reliability and safety. Therefore, accurately predicting their remaining useful life (RUL) is critical for the reliability of safety-critical systems that utilize these components. This paper proposes a hybrid model integrating bidirectional long short-term memory networks (BiLSTM) and Gaussian process regression (GPR) for RUL prediction of electronic power components. The BiLSTM module provides high-precision point predictions, while the GPR module leverages the sequence features and trend information extracted by BiLSTM to deliver reliable interval predictions and high-confidence probabilistic outputs. The model’s predictive accuracy was validated using NASA’s publicly available lithium-ion battery dataset. Experimental results demonstrate that, compared to existing models, the proposed model achieves at least a 9.6% improvement in point prediction performance and a 63% improvement in interval prediction performance, fully validating the reliability and accuracy of the BiLSTM-GPR approach. The model was further applied to predict the RUL of DC-DC power modules. The predicted Continuous Ranked Probability Score (CRPS) reached a maximum of 0.050405, while the Probability Integral Transform (PIT) results exhibited a uniform distribution within the (0,1) range, further demonstrating the model’s high reliability and predictive confidence. Full article

Background: Routine admission inflammatory and metabolic biomarkers have been proposed as adjunctive tools in mild traumatic brain injury (mTBI). However, their association with specific traumatic intracranial lesion types remains unclear. Methods: We conducted a prospective observational study including adult patients with isolated mTBI who underwent head computed tomography (CT) on admission. Admission laboratory parameters included the platelet-to-lymphocyte ratio (PLR), systemic immune-inflammation index (SII), and glucose-to-potassium ratio (GPR). Two predefined endpoints were assessed. The first compared biomarker values between CT-positive and CT-negative patients. The second evaluated associations between biomarkers and individual intracranial lesion subtypes, including analyses restricted to isolated lesions. Results: A total of 125 patients were included, of whom 95 (76%) were CT-positive. No significant differences were observed between CT-positive and CT-negative patients for PLR (p = 0.793), GPR (p = 0.531), or SII (p = 0.291). In lesion-specific analyses including all intracranial injuries, subdural hematoma (SDH) was associated with higher GPR compared with patients without SDH (p = 0.016). In analyses restricted to patients with isolated lesions, SDH was associated with higher PLR (p = 0.018) and higher GPR (p = 0.015). No significant associations were observed between any biomarker and intraparenchymal hemorrhage, subarachnoid hemorrhage, or epidural hematoma (all p > 0.05). Patients with multiple intracranial injuries exhibited higher PLR (p = 0.012) and higher SII (p = 0.021) compared with those with isolated lesions. After correction for multiple comparisons, none of the observed associations remained statistically significant. Conclusions: These findings suggest that routine systemic biomarkers have limited global discriminatory value in mTBI. Exploratory lesion-specific associations with SDH did not remain significant after correction for multiple comparisons, underscoring the preliminary nature of these findings. Full article

Monitoring the α-biodiversity indicators of mangrove forests and understanding their spatiotemporal trends can guide mangrove restoration strategies. Taking Qinglan Port in Hainan Province, China, as our study area, we compared multiple machine learning methods to predict the spatial distribution of α-biodiversity indicator Shannon’s diversity index (SHDI) by integrating LiDAR points and Worldview-2 images. In addition, the relationship between mangrove forests’ SHDI values and growth years was analyzed. The study extracted 28 spectral features and 99 LiDAR features from Worldview-2 and LiDAR data, respectively. The RReliefF method was adopted to select informative features. Four machine learning methods, including support vector machines (SVMs), extreme gradient boosting (XGBoost), deep neural networks (DNNs), and Gaussian process regression (GPR), were used to establish SHDI prediction models. The leave-one-out cross-validation (LOOCV) method was used to evaluate prediction accuracy, and the optimal model was adopted to generate a spatial map of SHDI. Based on Google Earth and Worldview-2 images, the spatial regions of mangrove forests in 2008, 2013, 2018, and 2023 were identified. The SHDI values within different restoration periods were statistically analyzed by using the mangroves’ spatiotemporal distributions. The results showed that RReliefF selected a total of 30 features, including 13 spectral features and 17 LiDAR features. Using preferred features, GPR had the highest prediction accuracy, with an LOOCV R² of 0.51, followed by SVM (R² = 0.44) and DNN (R² = 0.32); the accuracy of XGBoost (R² = 0.29) was relatively poor. The increased areas of rehabilitated mangrove forests in the periods of 2008–2013, 2013–2018, and 2018–2023 were 0.31 km², 0.13 km², and 1.35 km², respectively. Mangroves growing before 2008 owned the highest mean SHDI value of 0.74, followed by mangroves in 2008–2013 and 2013–2018; mangrove forests restored in 2018–2023 had the lowest mean SHDI value of 0.63. The results indicated that mangrove SHDI can be predicted by integrating LiDAR and Worldview-2. The mangrove population exhibited more diverse α-biodiversity characteristics as growth time increased. In subsequent mangrove restoration processes, planting mangroves of diverse species is beneficial to ensure the stability of the mangrove community. Full article

Factor of Safety (FOS) is a significant index to measure the stability condition of a rock slope in mining or civil engineering. In this paper, we evaluate and compare four different machine learning models, Gaussian Process Regressor (GPR), Support Vector Regressor (SVR), Random Forest (RF), and a hybrid genetic algorithm–multi-layer perceptron (GA-MLP), using two separate real-world datasets. The two separate datasets used in this study are from a previously conducted study on highway excavation with rock cutting in China, and another one in a mining site in Peru, with five geotechnical properties used as inputs, including slope height, slope angle, unit weight, cohesion, and friction angle. The two separate datasets were separated into training, validation, and testing datasets. The testing dataset of the models is unseen data used to assess model performance in an unbiased manner. The result shows that the SVR had the highest prediction accuracy, followed by GPR for the mining dataset, and GPR had the highest performance among all the models for the highway excavation dataset. From the boxplot, we can see that SVR, while having the highest predictive accuracy, has a larger variance in prediction compared to GPR for the mining dataset. Full article

Oversized rock fragments (boulders) produced during bench blasting adversely affect the efficiency of mining downstream processes such as loading, hauling, and crushing, thus leading to regularly requiring costly secondary breakage and the use of mechanized rock breakers. This study presents a probabilistic framework for forecasting boulder size in surface mining operations by employing Gaussian Process Regression (GPR), benchmarked against the Kuznetsov–Cunningham–Ouchterlony (KCO) empirical fragmentation model and a Multivariate Regression Analysis (MVRA) equation. The research study has analyzed blasting datasets, comprising Geological Strength Index (GSI), number of holes (NH), hole depth (HD), maximum charge per delay (MCPD), total explosive mass (TEM), and boulder size determined by Split-Desktop image analysis. Eight Gaussian Process Regression kernels—squared exponential, rational quadratic, matern with ν = 3/2, and matern with ν = 5/2, both with and without automatic relevance determination (ARD)—were assessed. The GPR model with the ARD matern 3/2 kernel attained superior validation performance of R² = 0.9016 and RMSE = 4.2482, outperforming the KCO and MVRA models, which displayed significant prediction errors for boulder size. In addition, the sensitivity analysis results demonstrated that GSI and HD were the most influential parameters on boulder size, followed by NH, MCPD, and TEM, accordingly. The findings indicate that GPR, especially when employing ARD matern kernels, precisely estimates the boulder size, and thus can serve as a viable method for optimizing blast design and facilitate efficient boulder management in surface mining operations. Full article

This article first investigates the single-factor effects in water-jet guided laser (WJGL) machining of diamond via experiments, analyzing how processing performance responds to laser energy and machining control parameters to define their optimization ranges. Subsequently, an Optimal Latin Hypercube Sampling (OLHD) is adopted to collect experimental data points, enabling exploration of the interaction mechanisms between process parameters and their compatibility with machining performance indicators. A surrogate model based on Gaussian Process Regression (GPR) with combined kernel functions is constructed to capture the complex nonlinear mapping between process parameters and response metrics. To address inherent uncertainties in the optimization model, an improved NSGA-III algorithm integrating the Expected Improvement dominance partition strategy (EIS) is proposed, using Expected Improvement (EI) to determine dominance relationships under WJGL processing uncertainties and derive matched process parameters. Validation via test functions and machining experiments demonstrate that the proposed method outperforms traditional NSGA-III (T-NSGA-III) with significantly lower prediction deviations. The optimized parameters achieved remarkable performance improvements: cutting depth (Nd) increased by 48.21%, kerf width (Kw) reduced by 1.44%, line roughness average (Ra) decreased by 43.09%, and cutting speed (Cs) improved by 78.40%. This research provides a viable process optimization approach for WJGL technology, enabling high-quality, efficient, and robust diamond machining. Full article

Google Earth Engine (GEE) has revolutionised remote sensing. The GEE cloud platform lets users quickly analyse large satellite imagery datasets with custom programmes, enhancing global-scale analysis. Crop condition monitoring using GEE would greatly help in decision-making and precision agriculture. Estimating canopy chlorophyll content (CCC) is an effective way to monitor crops using remote sensing because leaf chlorophyll is a key indicator. A hybrid model that combines radiative transfer models (RTMs), such as PROSAIL, with Gaussian Process Regression (GPR) can effectively estimate crop biophysical parameters using remote sensing images. GPR has proven to be one of the best methods for this purpose. This study aimed to develop a hybrid model to estimate CCC from S2 imagery and transfer it to the GEE platform for efficient data processing. In this work, the CCC (g/cm²) data from the S2 biophysical processor toolbox for the S2 imagery of the ICAR-Indian Agricultural Research Institute (IARI) on 23 February 2023 were used as observation data to train the hybrid algorithm. The hybrid model was successfully validated against the 155 input data with an R2 of 0.94, RMSE of 10.02, and NRMSE of 5.04%. The model was integrated into GEE to successfully generate a CCC-estimated map of IARI using S2 imagery from 23 February 2023. An R2 value of 0.96 was observed when GEE-estimated CCC values were compared against CCC values estimated locally. This establishes that the GEE-based CCC estimation with the PROSAIL + GPR hybrid model is an effective and accurate method for monitoring vegetation and crop conditions over large areas and extended periods. Full article

Ground penetrating radar (GPR) data inversion, especially in parallel-layered homogeneous media with multiple subsurface targets, still faces challenges in accurately reconstructing geometric structures due to weak reflections and complex target–medium interactions. To address these limitations, this paper proposes a novel multi-scale inversion framework named GPRGAN-SCSE (Ground Penetrating Radar Generative Adversarial Network with Spatial-Channel Squeeze and Excitation). Built upon the Pix2Pix Generative Adversarial Network (Pix2PixGAN), the proposed model incorporates a Spatial-Channel Squeeze and Excitation (SCSE) module into a residual U-Net generator to adaptively enhance target features embedded in layered media. Furthermore, a tri-scale discriminator ensemble is designed to enforce structural consistency and suppress layer-induced artifacts. The network is optimized using a composite loss integrating adversarial loss, L1 loss, and gradient difference loss to jointly improve structural continuity and boundary sharpness. Experiments conducted on a simulation dataset of parallel-layered homogeneous media with multiple targets demonstrate that GPRGAN-SCSE substantially outperforms existing inversion networks. The proposed method reduces the MAE by 63.8% and achieves a Structural Similarity Index (SSIM) of 99.96%, effectively improving the clarity of subsurface edges and the fidelity of geometric contours. These results confirm that the proposed framework provides a robust and high-precision solution for non-destructive subsurface imaging under layered media conditions. Full article

Spatial predictions of forest biomass at regional scale in forests are critical to evaluate the effects of management practices across environmental gradients. Although multi-source remote sensing combined with machine learning has been widely applied to estimate forest biomass, these approaches often rely on complex data acquisition and processing workflows that limit their scalability for large-area assessments. To improve the efficiency, this study evaluates the potential of annual multi-sensor satellite embeddings derived from the AlphaEarth Foundations model for forest biomass prediction. Using field inventory data from 89 forest plots at the Yunhe Forestry Station in Zhejiang Province, China, we assessed and compared the performance of four machine learning algorithms: Random Forest (RF), Support Vector Regression (SVR), Multi-Layer Perceptron Neural Networks (MLPNN), and Gaussian Process Regression (GPR). Model evaluation was conducted using repeated 5-fold cross-validation. The results show that SVR achieved the highest predictive accuracy in broad-leaved and mixed forests, whereas RF performed best in coniferous forests. When all forest types were modeled together, predictive performance was consistently limited across algorithms, indicating substantial heterogeneity (e.g., structure, environment, and topography) among forest types. Spatial prediction maps across Yunhe Forestry Station revealed ecologically coherent patterns, with higher biomass values concentrated in intact forests with less human disturbance and lower biomass primarily occurring in fragmented forests and near urban regions. Overall, this study highlights the potential of embedding-based remote sensing for regional forest biomass estimation and suggests its utility for large-scale forest monitoring and management. Full article

In recent years, digital technologies have become increasingly prevalent in the field of heritage protection. In addition to geomatic techniques like laser scanning (LiDAR) and Structure-from-Motion (SfM), geophysical methods, especially Ground-Penetrating Radar (GPR), offer added value for investigating protected buildings and objects. Additionally, chemical analysis (e.g., X-ray fluorescence, XRF) and mineral magnetic methods can be utilized to investigate specific research topics. All these methods are completely non-invasive and leave the heritage site untouched. Furthermore, they are cost-efficient and fast to use. Within this paper, we want to present an integrated study of a medieval sarcophagus in Bamberg Cathedral. The geophysical surveys via GPR and magnetic susceptibility (MS) measurements should answer open questions regarding the construction and internal layout of the sandstone sarcophagus, dated to the Early or High Middle Ages. The susceptibility data indicated an inner lead coffin in the lower part behind the stone slabs due to an unusual diamagnetic response in these parts. In contrast, the GPR data gave no such indication and revealed that the interior is too small for a direct burial of the bishop. Hence, an additional XRF survey was conducted to help solve this contradiction. The latter data indicate that the lead could be due to remains of a former painting on the sarcophagus with colours containing lead white pigments. Due to the porous sandstone, the moist environmental conditions, and the high weight of the lead elements, these could have accumulated at the bottom of the sarcophagus, creating the diamagnetism detected by the magnetic susceptibility measurements. Full article

Intramuscular fat (IMF) is a crucial determinant of pork quality, influencing tenderness, flavor, and consumer preferences, yet selective breeding has reduced its levels in modern pigs. This review explores the molecular and cellular mechanisms of IMF deposition, including progenitor cell differentiation via pathways like Wnt/β-catenin and PPARγ, and advances in non-invasive detection methods such as hyperspectral imaging and Raman spectroscopy. It highlights correlations and causal links between the gut microbiota composition and IMF, established through omics analyses, fecal microbiota transplantation, and germ-free models. Key microbial metabolites, including short-chain fatty acids (SCFAs) and bile acids, modulate lipid metabolism bidirectionally via signaling receptors like GPR43, FXR, and TGR5. Future research should integrate multi-omics and develop probiotics to enhance IMF efficiency for sustainable pork production. Full article

Lithium-ion batteries are widely applied in transportation, communication, and other fields. Nevertheless, during prolonged cycling operation, internal electrochemical reactions inevitably lead to the degradation of the state-of-health (SOH). To ensure the reliability and safety of lithium-ion batteries, accurate SOH estimation is of critical importance. Nevertheless, under practical operating conditions, obtaining fully recorded charge–discharge data is often impractical. Motivated by the practical charging behaviors of lithium-ion batteries, this paper proposes a practical SOH estimation method based on incremental capacity analysis, dynamic time warping (DTW), and gradient-boosting regression trees (GBRTs). Three health indicators—interval incremental capacity features, local capacity–voltage curve similarity, and segmented voltage curve similarity—are extracted. The proposed method requires only 0.13 V and 0.07 V voltage windows on the Oxford and CALCE datasets. The effectiveness of the proposed model is verified across both public datasets and laboratory test data. Experimental results demonstrate RMSE values of approximately 2.5% and 2.0%, respectively. Compared with mainstream SOH estimation algorithms, the proposed approach delivers comparable accuracy while achieving training time reductions of up to 57.6% and 91.9% relative to GPR and SVM, making it suitable for real-time battery management systems. Full article

This study compares three detectors—Single Shot MultiBox Detector (SSD), Faster Region-based Convolutional Neural Network (Faster R-CNN), and Only Look Once v11 (YOLOv11)—for detecting subgrade distress in GPR radargrams. SSD converges fastest but shows weaker detection performance, while Faster R-CNN achieves higher localization accuracy at the cost of slower convergence. YOLOv11 offers the best overall performance. To push YOLOv11 further, we introduce three enhancements: a Multi-Scale Edge Enhancement Module (MEEM), a Multi-Feature Multi-Scale Attention (MFMSA) mechanism, and a hybrid configuration that combines both. On a representative dataset, YOLOv11_MEEM yields a 0.2 percentage-point increase in precision with a 0.2 percentage-point decrease in recall and a 0.3 percentage-point gain in mean Average Precision@0.5:0.95, indicating improved generalization and efficiency. YOLOv11_MFMSA achieves precision comparable to MEEM but suffers a substantial recall drop and slower inference. The hybrid YOLOv11_MEEM+MFMSA underperforms on key metrics due to gradient conflicts. MEEM reduces electromagnetic interference through dynamic edge enhancement, preserving real-time performance and robust generalization. Overall, MEEM-enhanced YOLOv11 is suitable for real-time subgrade distress detection in GPR radargrams. The research findings can offer technical support for the intelligent detection of subgrade engineering while also promoting the resilient development and sustainable operation and maintenance of urban infrastructure. Full article