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Reports of illegal logging are increasing globally, driving a need for tools that can effectively identify wood products at the species level. This identification is crucial for regulatory purposes, certifying legal lumber, preventing environmental crimes, and protecting ecosystems and society. Current wood identification methods are primarily based on anatomical observation of wood tissues, chemical profiling using direct analysis in real-time time-of-flight mass spectrometry (DART-TOFMS), and DNA-based analyses. While these approaches have their advantages, they also present challenges, particularly when species-level identification is required for enforcement. As an alternative, metabolite profiling using separation techniques coupled with mass spectrometry shows potential as a robust species-level wood identification method. Here, we present a method for classifying wood at the species level through chemical profiling of the wood metabolome using comprehensive two-dimensional gas chromatography coupled to time-of-flight mass spectrometry (GC×GC-TOFMS) combined with chemometric analysis. In this study, different tissues, including sapwood, heartwood, and branches from seven wood species collected from genetically characterized trees representing three distinct genera (Quercus, Acer, Picea) were investigated. Principal component analysis (PCA) was used to visualize differences between species and tissue types, while partial least squares-discriminant analysis (PLS-DA) and feature selection were used to construct classification models for species-level wood identification. The classification models were built using data from wood cores, branches, or a mixture of wood cores and branch samples. Each classification model was tested with an external validation set, and the performance of the classification model was evaluated based on the prediction of the external validation data. Our results show that classification modelling using wood metabolomic data is promising, especially with the same tissue type, presenting accuracies of 100%, 100%, and 93.2% in the prediction of wood core samples at the species level for Quercus, Acer, and Picea, respectively.

In a recent reply to my criticisms (Carcassi et al. in Found Phys 55:5, 2025), Carcassi, Oldofredi, and Aidala (COA) admitted that their no-go result for (psi )-ontic models is based on the implicit assumption that all states are equally distinguishable, but insisted that this assumption is a part of the (psi )-ontic models defined by Harrigan and Spekkens, thus maintaining their result’s validity. In this note, I refute their argument again, emphasizing that the ontological models framework (OMF) does not entail this assumption. I clarify the distinction between ontological distinctness and experimental distinguishability, showing that the latter depends on dynamics absent from OMF, and address COA’s broader claims about quantum statistical mechanics and Bohmian mechanics.

Ga_1−xIn_xSb (0 < x < 1) single crystals can be used as the substrates to epitaxially grow a variety of components required for high-performance infrared detectors and lasers. Ga_0.92In_0.08Sb crystals (25 mm diameter, 120 mm length) were prepared with both the vertical Bridgman (VB) and the traveling heater method (THM). The effects of the maximum furnace temperature on the structures and photoelectric properties of GaInSb crystal were investigated. Compared with the VB, the THM reduced the In element segregation and dislocation density of the GaInSb crystal, and improved its photoelectric properties. Moreover, the crystallization quality of GaInSb crystal improved with the decrease of the maximum furnace temperature of the THM. The axial segregation of In element in the GaInSb crystals decreased from 0.110 mol%/mm to 0.081 mol%/mm, while the radial segregation increased from 0.057 mol%/mm to 0.089 mol%/mm. And the dislocation density reduced from 3.295 × 10³ cm⁻² to 2.604 × 10³ cm⁻². The carrier concentration increased from 1.254 × 10¹⁸ cm⁻³ to 1.463 × 10¹⁸ cm⁻³, the carrier mobility increased from 1422 cm²/(V·s) to 1676 cm²/(V·s), and the resistivity reduced from 1.617 × 10^–3 Ω cm to 1.393 × 10^– Ω cm. And the infrared transmittance of GaInSb crystal improved from 43 to 47%.

In the context of intensifying competition within the power market, power companies face the dual challenges of enhancing customer loyalty and optimizing marketing strategies. This study addresses these challenges by employing the long-term and short-term memory (LSTM) network model to analyze data-driven power marketing strategies and their impact on customer loyalty. The LSTM model is trained on a dataset combining time-series power consumption data with customer interaction scores and market response rates. This enables the model to predict and explain customer responses to marketing efforts with greater accuracy. Unlike traditional marketing models, which lack the capacity to capture dynamic customer behavior over time, the LSTM model accounts for both the temporal nature of consumption patterns and static customer feedback, offering a more holistic view. Key findings indicate that improving the quality of customer service interaction and accurately targeting marketing activities significantly boosts customer loyalty. In particular, customer interaction scores and market response rates are the most influential factors driving customer loyalty, providing critical insights for companies to adjust their strategies effectively. This study’s novelty lies in its application of advanced machine learning methods, such as LSTM, to the power industry—a sector traditionally slower to adopt such innovations. By bridging this gap, the research provides actionable recommendations on how power companies can implement data-driven marketing strategies to improve service quality, increase customer retention, and enhance their competitive position in the market. Additionally, the results underscore the model’s effectiveness in forecasting and optimizing marketing outcomes, offering a scalable solution for the evolving power sector. In the power market, companies face challenges in enhancing customer loyalty and optimizing strategies. This study employs the LSTM network model, trained on combined time-series power consumption, customer interaction scores and market response rates data. Unlike traditional models that struggle with dynamic customer behavior capture, LSTM accounts for consumption pattern temporality and static feedback. It outperforms other techniques like Random Forest and XGBoost in handling time-dependent consumption data. The key findings highlight the importance of customer interaction and targeted marketing. By applying LSTM, power companies can better predict customer responses, optimize marketing, improve service quality and enhance competitive position, providing a scalable solution for the evolving power sector.

Heavy-duty diesel trucks (HDDTs) contributed significantly to on-road air pollution. This study measured the traffic flow of HDDTs on seven national highways in Beijing-Tianjin-Hebei (BTH) region to estimate air pollutants of HDDTs from 2016 to 2020. The emission factors are corrected by the International Vehicle Emissions (IVE) model, traffic flow, and road length. Compared with 2016, traffic flow in 2019 increased by 41%, carbon monoxide (CO) and nitrogen oxide (NOx) emissions increased by 0.3% and 4%, while hydrocarbons (HC) and particulate matter (PM) emissions decreased by about 26% and 18%. The COVID-19 lockdowns did not cause significant annual changes in traffic flow and pollution emissions in 2020 compared with 2019, but there were substantial monthly variations in pollutant emissions. Our study showed that although control measures were implemented during the "Thirteenth Five-Year Plan" period, the substantial increase in regional HDDT transport posed a continuing challenge in reducing road traffic emissions.

We study the RTT orthosymplectic super Yangians and present their Drinfeld realizations for any parity sequence, generalizing the results of Jing et al. (Commun Math Phys 361(3):827–872, 2018) for non-super case, Molev (Algebras Representation Theory, 26, 2023) for a standard parity sequence, and Peng (Commun Math Phys 346(1):313–347, 2016), Tsymbaliuk (Lett Math Phys 110(8):2083–2111, 2020) for the super A-type.

This paper proposes a novel approach for transformer fault diagnosis. Initially, a high-dimensional feature set comprising 19 features related to five gas concentrations is constructed to reflect the gas-fault relationship. Subsequently, the Shapley Additive Explanations (SHAP) method is employed to evaluate feature importance and select a subset that significantly influences model predictions, thereby simplifying the model and enhancing its interpretability. Following this, the bald eagle search (BES) intelligent optimization algorithm is utilized to optimize the hyperparameters of the light gradient boosting machine (LGBM) model, further improving its predictive capability. Comparative experiments with various traditional machine learning models validate the effectiveness of the proposed method. The SHAP-BES-LGBM model achieves the highest accuracy of 0.9509 and an f1 score of 0.9606 on the test set, with only 11 samples misclassified, demonstrating superior classification performance and underscoring the advantages of this integrated approach in transformer fault diagnosis.

This paper examined the thermal gelation of yak myofibrillar protein (MP) incubated in a Fenton oxidation system at different pH values for 24 h. The effect of protein oxidation on the gel properties at different pH levels was explored by studying the water-holding capacity (WHC), solubility, texture, and other characteristics, while the chemical force, rheological, and microstructural variation in these conditions were analyzed. The results showed that protein oxidation negatively impacted the yak meat MP gel characteristics (p < 0.05). Increasing the H₂O₂ interval at different pH levels significantly decreased (p < 0.05) the WHC, solubility, and texture of the protein gel, the average reduction was 7.5%, 27.5%, 12.5% respectively. The H₂O₂ concentration and pH level substantially affected ionic and hydrogen bond formation (p < 0.05). Oxidation had the most obvious impact on the gel characteristics at pH 5.0. The MP gel displayed a loose, disordered microstructure with the lowest WHC, textural strength, storage modulus (G’), and intermolecular protein force. Oxidation had the least impact on the gel properties at pH 6.0. The MP gel exhibited the highest textural strength, G’, and intermolecular forces, characterized by a compact, orderly microstructure with small, uniformly distributed pores. The gel displayed the best WHC after oxidation at pH 8.0.

We consider the general theory of 6-parameter shells, in which material points on the midsurface are endowed with 3 translational and 3 rotational degrees of freedom. In this framework, we derive quasiconvexity conditions and rank-one convexity conditions. These inequalities represent necessary conditions for energy minimizers; they are the two-dimensional counterparts of the well-known relaxed convexity conditions in three-dimensional finite elasticity. As a specific feature, the quasiconvexity inequality for shells contains the gradients in the tangent plane of the variation fields associated to deformation and microrotation. Finally, we also deduce the Legendre-Hadamard condition for shells, as a consequence of the rank-one convexity inequality.