Search Results (7,402)

Aimed at the problems of traditional wood species identification relying on manual experience, slow identification speed, and insufficient robustness, this study takes hyperspectral images of cross-sections of 10 typical wood species commonly found in Puer, Yunnan, China, as the research object. It comprehensively applies various spectral and texture feature extraction technologies and proposes an intelligent wood species identification method based on the fusion of multimodal texture-dominated features and deep learning. Firstly, an SOC710-VP hyperspectral imager is used to collect hyperspectral data under standard laboratory lighting conditions, and a hyperspectral database of wood cross-sections is constructed through reflectance calibration. Secondly, in the spectral space construction stage, a comprehensive similarity matrix is built based on four types of spectral similarity indicators. Representative bands are selected using two Max–Min strategies: partitioned quota and coverage awareness. Multi-scale wavelet fusion is performed to generate high-resolution fused images and extract interest point features. Thirdly, in the texture space construction stage, three types of texture feature matrices are generated based on the PCA first principal component map, and interest point features are extracted. Fourthly, in the complementary collaborative learning stage, the ST-former model is constructed. The weights of the trained SpectralFormer++ and TextureFormer are imported, and only the fusion weights are optimized and learned to realize category-adaptive spectral–texture feature fusion. Experimental results show that the overall classification accuracy of the proposed joint model reaches 90.27%, which is about 8% higher than that of single-modal models on average. Full article

Solar thermophotovoltaic (STPV) systems can break the Shockley–Queisser (SQ) limit through selective absorbers and emitters, whose ideal emissivity is crucial as a design target. In this paper, we systematically analyze the ideal selective spectrum and solve the conflict between energy and efficiency in photothermal conversion efficiency by modifying the corresponding equation. The ideal emissivity of the absorber is one in [$E_{\mathrm{c}}$, ∞] and zero out of this range. For actual design, if spectra deviation cannot be avoided, $E_{\mathrm{c}}$ is preferable to redshifting. The ideal emissivity of the emitter is one in $[E_{\mathrm{g}},E_{\max }]$ and the decrease in $E_{\min }$ should be suppressed relative to $E_{\max }$. Besides, the optimal bandwidth of the emitter is about 0.08 eV for different photovoltaic cells and working conditions, which gives a rough and valuable guide in practical design. The analytical progress and design strategies will give a reference and direction for the future design of STPV. Full article

Artificial intelligence (AI) is a core technology driving the Fourth Industrial Revolution and serves as a foundation for sustainable technological competitiveness. Despite the rapid growth of AI-related patent filings in Korea, the overall quality of these patents remains relatively low. This study examines the determinants of patent quality in university–industry (UI) collaboration and investigates how firms’ R&D capability moderates this relationship. Using 90,782 AI patents filed with the Korean Intellectual Property Office (KIPO) between 2013 and 2023, the Patent Quality Index (PQI) was constructed by integrating forward citations, patent-family size, and the number of claims through min–max normalization. Regression analyses reveal that UI collaboration per se has no significant average effect on PQI, but firms with stronger R&D capability achieve higher patent quality through collaboration. In addition, greater collaboration depth and accumulated prior experience significantly enhance PQI, while the negative effect of technological cognitive distance is mitigated by absorptive capacity. These findings demonstrate that sustainable innovation outcomes depend not merely on the quantity of collaboration but on the synergy between qualitative collaboration structures and internal R&D capabilities. By linking open innovation theory with absorptive capacity, this study provides empirical evidence for fostering sustainable innovation ecosystems in which universities and firms co-create technological value. Full article

Climate change poses a serious threat to amphibians and reptiles, which are especially vulnerable because of limited thermoregulatory capacity and restricted dispersal. We used an ensemble species distribution modeling framework to assess habitat determinants, niche breadth, and climate-driven distribution changes for eight legally protected endangered amphibian and reptile species in South Korea. Occurrence records collected between 1997 and 2021 were combined with ten bioclimatic, topographic, and hydrological predictors, and 11 species distribution modeling algorithms (SDMs), including Random Forest and MaxEnt, were implemented and combined into weighted ensemble predictions. The weighted ensemble model showed high predictive performance (mean ROC–AUC = 0.897; overall mean across all SDMs = 0.843). Variable-importance analysis revealed clear taxonomic contrasts: reptiles exhibited approximately 1.7-fold greater dependence on temperature variables than amphibians, whereas amphibians were more strongly associated with precipitation and topographic context. Environmental niche-breadth analysis identified Sibynophis chinensis, Hynobius yangi, and Dryophytes suweonensis as narrow- or moderate-niche specialists largely constrained by precipitation of the driest month and a small set of climatic variables. Under moderate (SSP2-4.5) and high (SSP5-8.5) emission scenarios, areas of high species richness are projected to decline by 22% and 45%, respectively, by the 2070s, with distribution centroids shifting northeastward and pronounced habitat loss in western lowland plains. Priority conservation targets include S. chinensis, D. suweonensis, and H. yangi, which combine narrow niches, restricted ranges, and high climate vulnerability. These findings provide a quantitative basis for climate-adaptive conservation planning for threatened herpetofauna in South Korea. Full article

This study presents a machine learning framework for predicting the axial compressive strength of circular concrete-filled steel tube (CFST) columns subjected to concentric and eccentrically applied axial loads. A harmonized database of 1287 test specimens was compiled, encompassing diverse material strengths, geometric configurations, and eccentricity levels. Among the trained models, the CatBoost (CatB) algorithm exhibited the highest predictive performance. A 300-run Monte Carlo simulation yielded a mean R² of 0.966 (Min: 0.804; Max: 0.996), with a mean RMSE of 588.8 kN and MAPE of 8.36%, demonstrating accuracy and robustness across repeated randomized splits. Comparative benchmarking against current design equations revealed that CatBoost substantially reduced prediction scatter, improving the mean ratio and reducing the COV from 70–75% (ACI/AIJ/Wang) to 5.43%, while maintaining a nearly unbiased mean prediction ratio of 1.00. In addition, inverse prediction models based on CatBoost achieved test-set R² values of 0.908 for compressive strength and 0.945, 0.900, and 0.816 for key design parameters (D, t, L), indicating promising capability for supporting preliminary sizing and parameter selection. The outcomes of this study highlight the potential of data-driven modelling to complement existing design provisions and assist engineers in early-stage decision-making for axially loaded circular CFST columns. Full article

This paper presents an integrated approach to the structural decomposition of concurrent control systems using exact transversal hypergraphs (XT-hypergraphs). The proposed method combines formal properties of XT-hypergraphs with invariant-based Petri net analysis to enable automatic partitioning of complex, concurrent specifications into deterministic and independent components. The approach focuses on preserving behavioral correctness while minimizing inter-component dependencies and computational complexity. By exploiting the uniqueness of minimal transversal covers, reducibility, and structural stability of XT-hypergraphs, the method achieves a deterministic decomposition process with polynomial-delay generation of exact transversals. The research provides practical insights into the construction, reduction, and classification of XT structures, together with quality metrics evaluating decomposition efficiency and structural compactness. The developed methodology is validated on representative real-world control and embedded systems, showing its applicability in deterministic modeling, analysis, and implementation of concurrent architectures. Future work includes the integration of XT-hypergraph algorithms with adaptive decomposition and verification frameworks to enhance scalability and automation in modern system design and integration with currently popular AI and machine learning methods. Full article

The uptake and accumulation of heavy metals by wild-grown mushrooms is raising health concerns for consumers worldwide with respect to variability conditioned by species and harvesting site specificity. This study aims to evaluate the concentration of elements (Zn) and heavy metals (Cd, Pb, Hg) in wild-growing edible mushroom samples (n = 200) collected from industrial and non-industrial areas in Poland. Over half of the analyzed mushroom samples (51%) exceeded EU limits for Cd, Pb, or Hg. Xerocomellus chrysenteron and X. subtomentosus (XCS) showed the highest accumulation, with median Cd and Pb concentrations of 3.53 mg/kg and 0.63 mg/kg fresh mass, respectively, in industrial areas. Spatial factors, including distance from emission sources and wind direction, significantly influenced element accumulation, with Cd levels in XCS up to 20 times higher than in Suillus species. A high-consumption scenario (96 g/day) indicated a substantial non-carcinogenic risk (HQ > 1) from Cd exposure via XCS consumption, both in industrial (HQ up to 9.01) and non-industrial areas (HQ max = 1.80), with cumulative hazard index (HI) ranging from 1.21 to 11.01. It is imperative to select the optimal regions for mushroom harvesting and to refrain from consuming species that accumulate elements to the greatest extent. Full article

This study presents a controlled benchmarking analysis of min–max scaling, Z-score normalization, and an adaptive preprocessing pipeline that combines percentile-based ROI cropping with histogram standardization. The evaluation was conducted across four public chest X-ray (CXR) datasets and three convolutional neural network architectures under controlled experimental settings. The adaptive pipeline generally improved accuracy, F1-score, and training stability on datasets with relatively stable contrast characteristics while yielding limited gains on MIMIC-CXR due to strong acquisition heterogeneity. Ablation experiments showed that histogram standardization provided the primary performance contribution, with ROI cropping offering complementary benefits, and the full pipeline achieving the best overall performance. The computational overhead of the adaptive preprocessing was minimal (+6.3% training-time cost; 5.2 ms per batch). Friedman–Nemenyi and Wilcoxon signed-rank tests confirmed that the observed improvements were statistically significant across most dataset–model configurations. Overall, adaptive normalization is positioned not as a novel algorithmic contribution, but as a practical preprocessing design choice that can enhance cross-dataset robustness and reliability in chest X-ray classification workflows. Full article

Background: Phosphorus is an essential nutrient for plant growth and development, playing a multifaceted and vital role in plants. Phosphate Transporter 1 (PHO1) is a class of important functional genes involved in plant phosphorus uptake and transport. We identify PHOSPHATE 1 (PHO1) members in mung beans and investigate their response to low phosphorus stress, thereby aiding in the development of stress-tolerant, high-yielding mung bean varieties. Methods: A bioinformatic analysis was performed, which led to the identification of the PHO1 homologue sequence in mung beans. This analysis also elucidated its gene and protein structural characteristics alongside its phylogenetic relationships. qRT-PCR was used to analyze the expression patterns of genes in roots and leaves in response to conditions of prolonged low-phosphorus and phosphorus-deprivation stress. Results: Total PHO1 homologues were identified in mung beans, which can be grouped into 3 groups (Group I-III). Phylogenetic analysis indicates that VrPHO1s shares closer evolutionary relationships with PHO1 in legumes, and exhibits 6 collinear gene pairs with Glycine max (soybean), all with Ka/Ks ratios below 1, suggesting they have undergone purifying selection. The gene promoter region contains multiple cis-acting elements capable of participating in plant growth and development, stress responses, and plant hormone responses. Expression analysis revealed that more VrPHO1 genes responded to phosphorus stress in roots than in leaves; of these, the expression of VrPHO1; H2, VrPHO1; H3, and VrPHO1; H5 genes was significantly induced by continuous phosphorus-deficient stress. Conclusions: This study provides a comprehensive genome-wide identification of the PHO1 family in mung bean and provides valuable candidate gene resources for the future study of their biological functions and regulatory roles in phosphate-deficient stress. Full article

Loop closure detection is essential for improving the long-term consistency and robustness of simultaneous localization and mapping (SLAM) systems. Existing LiDAR-based loop closure approaches often rely on limited or partial geometric features, restricting their performance in complex environments. To address these limitations, this paper introduces a Density Triangle Descriptor (DTD). The proposed method first extracts keypoints from density images generated from LiDAR point clouds, and then constructs a triangle-based global descriptor that is invariant to rotation and translation, enabling robust structural representation. Furthermore, to enhance local discriminative ability, the neighborhood around each keypoint is modeled as a Gaussian distribution, and a local descriptor is derived from the entropy of its probability distribution. During loop closure detection, candidate matches are first retrieved via hash indexing of triangle edge lengths, followed by entropy-based local verification, and are finally refined by singular value decomposition for accurate pose estimation. Extensive experiments on multiple public datasets demonstrate that compared to STD, the proposed DTD improves the average F1 max score and EP by 18.30% and 20.08%, respectively, while achieving a 50.57% improvement in computational efficiency. Moreover, DTD generalizes well to solid-state LiDAR with non-repetitive scanning patterns, validating its robustness and applicability in complex environments. Full article

This study proposes a unified stochastic framework for approximating and computing the gradient of every smooth function evaluated at non-independent variables, using ${\mathcal{\ell }}_p$-spherical distributions on ${\mathbb{R}}^d$ with $d,p\ge 1$. The upper-bounds of the bias of the gradient surrogates do not suffer from the curse of dimensionality for any $p\ge 1$. Additionally, the mean squared errors (MSEs) of the gradient estimators are bounded by $K_0{N}^{-1}d$ for any $p\in [1,2]$, and by $K_1{N}^{-1}{d}^{2/p}$ when $2\le p\ll d$ with N the sample size and $K_0,K_1$ some constants. Taking $max\left\{2,log\left(d\right)\right\}<p\ll d$ allows for achieving dimension-free upper-bounds of MSEs. In the case where $d\ll p<+\infty$, the upper-bound $K_2{N}^{-1}{d}^{2-2/p}/{(d+2)}^2$ is reached with $K_2$ a constant. Such results lead to dimension-free MSEs of the proposed estimators, which boil down to estimators of the traditional gradient when the variables are independent. Numerical comparisons show the efficiency of the proposed approach. Full article

This paper advances two main theses: The first overarching thesis is that the Slavic perfective/imperfective distinction is predominantly of a lexical-derivational nature. Among the categories of the tense–modality–aspect (TMA) system, Slavic aspect systems represent marginal categories, rather than core ones, which are realized by means of inflectional morphology. The second, and related, thesis concerns the status of Slavic verb prefixes in Slavic aspect systems, given that prefixed verbs constitute the bulk of their perfective verbs. I will provide some arguments, also defended elsewhere, that Slavic verb prefixes are not perfective markers, e.g., do not spell out a functional head/feature in the dedicated aspect structure, as is often assumed in syntactic theories of aspect, and neither do they carry a uniform semantic function for the interpretation of perfective aspect. Instead, Slavic verb prefixes are best treated as separate from perfectivity, on both formal and semantic grounds. This separation, however, does not mean that the two are unrelated. Here, the semantics of perfectivity is represented by means of the maximalization operator (max_e). The most fundamental requirement for its application, and for any maximalization operator for that matter, is that it respect some ordering criterion. It is the role of Slavic verb prefixes to contribute to its specification. They do so by virtue of having common uses/meanings that can be analyzed as extensive or intensive measure functions or vague quantifiers over arguments of verbs to which they are attached. Such meanings are reducible to a uniform scalar-based representation, from which the requisite ordering criterion can be extracted. Full article

Climate change is expected to alter the distribution of many threatened mammals, yet national-scale identification of climate refugia and conservation priorities remains limited for South Korea. This study aimed to map current hotspots and future refugia for 10 endangered mammal species and evaluate conservation implications under SSP climate scenarios. We compiled occurrence records from nationwide field surveys and protected-area monitoring and fitted ten species distribution models (GLM, GAM, GBM, CTA, ANN, SRE, FDA, MARS, RF, and MaxEnt) using biomod2 with climatic, topographic, and anthropogenic predictors at 1 km resolution. A weighted ensemble model achieved strong predictive performance (mean AUC = 0.840). Current richness hotspots were concentrated along the Baekdudaegan mountain range, and several national parks emerged as core multi-species areas. Variable-importance analysis indicated that topographic constraints (elevation and slope) dominated for most species, consistent with mountain-dependent habitat use. Future projections showed relatively stable richness patterns under SSP2–4.5 but pronounced contractions under SSP5–8.5 by the 2070s, with persistent high-suitability areas converging in the northern Baekdudaegan. The resulting suitability and richness layers provide spatial decision-support for protected-area strengthening, connectivity-oriented management, and targeted monitoring to support national climate-adaptation planning. Full article

Avocado cultivation is expanding rapidly in East Africa, driven by growing market demand, yet planning often relies on farmers’ experience rather than systematic spatial analysis, raising risks of inefficient land and resource use. Therefore, this study applied four species distribution models (SDMs), Generalized Additive Models (GAM), Boosted Regression Trees (BRT), Maximum Entropy (MaxEnt), and Random Forest (RF), along with an ensemble model to map potential avocado suitability in Tanzania. The models were calibrated using 199 Variance Inflation Factor (VIF)-depurated occurrence records from which climatic, edaphic, and topographic predictor variables were extracted. BRT and RF had the best predictive abilities, with AUC values ranging from 0.77 ± 0.20 to 0.81 ± 0.13. The individual models identified Njombe, Iringa, Songwe, Kigoma, Rukwa, Kagera, and Morogoro as regions with high suitability, with more than 30% of each region’s total area predicted to be suitable for avocado production. Moderate suitability (15% to ≤30% of the regional area) was recorded for Kilimanjaro, Arusha, Dodoma, Manyara, Mara, Mbeya, Ruvuma, Tanga, and Katavi, whereas negligible suitability was forecasted for most of the remaining regions by the majority of the models. These findings suggest that heavy investments in avocado production and value chain additions should be directed primarily to regions with high suitability in order to use resources efficiently and minimize investment risks. More targeted, site-specific management should be encouraged in moderately suitable regions, with a focus on helping farmers identify and manage the best avocado sites rather than promoting broad expansion across the country. The findings generated by the ensemble model could be incorporated in the Tanzania Agriculture Climate Adaptation Technology Deployment Programme (TACATDP) to enhance sustainable crop investment, lower production risks, and strengthen the resilience of the avocado sector in the country. Full article

Background/Objectives: Patient preoperative cardiorespiratory physical fitness measured by maximal oxygen consumption (VO2max) is highly relevant to postoperative outcomes, with low VO2max associated with a greater symptom burden and a greater prevalence of long-term treatment-related cardiovascular disease risk factors in patients undergoing surgery. A network meta-analysis (NMA) was conducted to determine the effects of different components of prehabilitation, including exercise, nutrition, psychological intervention, and different combinations of the aforementioned interventions, on oxygen consumption in people undergoing major abdominal or cardiothoracic surgery. Methods: A literature search was conducted from inception to December 2025. Randomized controlled trials on the effectiveness of prehabilitation programmes on pre-surgery VO2max were included. The risk of bias was assessed via the Cochrane risk of bias (RoB 2.0) tool, and the quality of evidence was assessed via the Grading of Recommendations, Assessment, Development, and Evaluation (GRADE) tool. Pairwise meta-analyses and NMAs were conducted for direct and indirect evidence. Results: Fourteen studies were included in this NMA. The highest effect (ES) for VO2max scores was for the exercise group versus the control group (ES: 0.44; 95% CI: 0.11, 0.78). When exercise was categorized according to intensity, the highest effect was for high-intensity interval training (HIIT) versus the control (ES: 0.51; 95% CI: 0.04, 0.97). Conclusions: Exercise HIIT should be considered the most effective strategy for improving exercise capacity in patients undergoing major abdominal or cardiothoracic surgery. Given the importance of VO2 as a predictor of morbidity, mortality, and the potential occurrence of adverse events after the procedure in surgical patients, it is essential to include its measurement in future studies to estimate both the risk of procedures and the effect of prehabilitation programmes. Full article