Search Results (203)

Crabapples (Malus spp.) are important ornamental trees in northern temperate regions. However, their phenotypic diversity and ornamental values remain poorly characterized, due to a lack of systematic comparison between introduced and domestically bred cultivars/lines. This knowledge gap limits the effective utilization of their germplasm. In this study, 111 floral, foliar, fruit, and tree architectural traits were measured across 93 introduced (North American) and 118 domestically bred (Chinese) cultivars/lines. Comparative analyses using Shannon–Wiener (H′) and Pielou’s evenness (J) indices revealed that floral traits exhibited the highest phenotypic diversity, followed by fruits, leaves, and tree architecture. Among these, 51 key traits (e.g., budlet color, leaf area, and fruit shape) showed above-average diversity, while others (e.g., flower type, leaf cracking, and exocarp color) were less uniform, indicating rare phenotypes. Domestically bred cultivars showed significant improvements in flower color and type, mature leaf shape and size, and fruit characteristics, including novel budlet, bud and petal colors, increased stamen numbers, semi-double or double flowers, and diverse fruit colors. A multi-dimensional ornamental evaluation (Analytic Hierarchy Process) identified 26 superior genotypes and several organ-specific selections for flower- (26), fruit- (25), foliage- (21), and tree architecture-viewing (14) purposes. These findings provide a theoretical basis for updating Malus distinctness, uniformity, and stability (DUS) guidelines, targeted breeding, and strategic landscape applications, highlighting the potential of both introduced and domestic germplasm for ornamental improvement. Full article

Crabapples (Malus spp.) are widely planted ornamental and multipurpose trees in temperate regions and represent an important component of forest and landscape resources. However, the absence of a standardized classification framework has led to nomenclatural confusion, hindering germplasm conservation, breeding, and international exchange. In this study, 80 introduced crabapple cultivars preserved in the germplasm repository of Nanjing Forestry University were systematically evaluated using 55 morphological traits of flowers, leaves, fruits, and tree architecture. A hierarchical framework was established based on flower type and corolla color, dividing cultivars into Single, Semidouble, and Double Flower groups, with further subdivisions of Single cultivars by color. Numerical taxonomy (R- and Q-type clustering) validated the robustness of this framework, identifying petal number and corolla color as the most consistent traits across cultivars and seasons (inter-cultivar CV < 10%), serving as reliable diagnostic indicators, although within-cultivar variation was not quantified. The proposed system resolved frequent misidentifications (e.g., M. ‘Kelsey’ and M. ‘Molten Lava’) and provided standardized descriptors for cultivar identification. Beyond taxonomy, the framework enhances germplasm management, supports nursery production and landscape forestry, and facilitates international exchange of ornamental resources. These findings highlight the potential of integrating morphological and numerical approaches for germplasm diversity assessment and contribute to the development of a unified global classification system for ornamental crabapples. Full article

Unrecognized mechanical faults and emergency sounds in vehicles can compromise safety, particularly for individuals with hearing impairments and in sound-insulated or autonomous driving environments. As intelligent transportation systems (ITSs) evolve, there is a growing need for inclusive, non-intrusive, and real-time diagnostic solutions that enhance situational awareness and accessibility. This study introduces an interpretable, sound-based machine learning framework to detect vehicle faults and emergency sound events using acoustic signals as a scalable diagnostic source. Three purpose-built datasets were developed: one for vehicular fault detection, another for emergency and environmental sounds, and a third integrating both to reflect real-world ITS acoustic scenarios. Audio data were preprocessed through normalization, resampling, and segmentation and transformed into numerical vectors using Mel-Frequency Cepstral Coefficients (MFCCs), Mel spectrograms, and Chroma features. To ensure performance and interpretability, feature selection was conducted using SHAP (explainability), Boruta (relevance), and ANOVA (statistical significance). A two-phase experimental workflow was implemented: Phase 1 evaluated 15 classical models, identifying ensemble classifiers and multi-layer perceptrons (MLPs) as top performers; Phase 2 applied advanced feature selection to refine model accuracy and transparency. Ensemble models such as Extra Trees, LightGBM, and XGBoost achieved over 91% accuracy and AUC scores exceeding 0.99. SHAP provided model transparency without performance loss, while ANOVA achieved high accuracy with fewer features. The proposed framework enhances accessibility by translating auditory alarms into visual/haptic alerts for hearing-impaired drivers and can be integrated into smart city ITS platforms via roadside monitoring systems. Full article

Camphora officinarum Nees is a significant economic tree because of its aromatic, medicinal, and ornamental attributes. The diverse flavonoids present within the leaves of C. officinarum have been neglected for an extended period, hindering a comprehensive understanding of the molecular mechanisms responsible for color transformation and resistance to adverse environmental conditions. In this study, multi-omics analyses were conducted to systematically compare the relative contents of flavonoid metabolites and the expression profiles of flavonoid-related genes across three developmental stages of C. officinarum leaves. A total of 175 flavonoid compounds were detected via metabolomics, with flavonols being the most abundant. Through weighted gene co-expression network analysis, 25 key DEGs encoding CHS, DFR, FLS, ANS, F3′H, and LAR genes are predicted to be involved in anthocyanin biosynthesis for color change during leaf development. Notably, ten MYB, seven bHLH, and three ERF factors are potentially implicated in the regulation of key genes, underscoring their significant contributions to the color mechanisms underlying flavonoid biosynthesis. Other flavonoids, e.g., apigenin, isorhamnetin glycosides, sakuranetin, and sakuranin, may facilitate the adaptation of C. officinarum for protective purposes against adverse environmental conditions. These findings lay a theoretical foundation for resource exploration and the ornamentation improvement of C. officinarum. Full article

Urochloa brizantha (Brizantha) is cultivated under varying altitudinal and management conditions. Twelve full-sun (monoculture) plots and twelve shaded (silvopastoral) plots were established, proportionally distributed at 170, 503, 661, and 1110 masl. Evaluations were conducted 15, 30, 45, 60, and 75 days after establishment. The conservation and integration of trees in silvopastoral systems reflected a clear anthropogenic influence, evidenced by the preference for species of the Fabaceae family, likely due to their multipurpose nature. Although the altitudinal gradient did not show direct effects on soil properties, intermediate altitudes revealed a significant role of CaCO₃ in enhancing soil fertility. These edaphic conditions at mid-altitudes favored the leaf area development of Brizantha, particularly during the early growth stages, as indicated by significantly larger values (p < 0.05). However, at the harvest stage, no significant differences were observed in physiological or productive traits, nor in foliar chemical components, underscoring the species’ high hardiness and broad adaptation to both soil and altitude conditions. In Brizantha, a significant reduction (p < 0.05) in stomatal size and density was observed under shade in silvopastoral areas, where solar radiation and air temperature decreased, while relative humidity increased. Nonetheless, these microclimatic variations did not lead to significant changes in foliar chemistry, growth variables, or biomass production, suggesting a high degree of adaptive plasticity to microclimatic fluctuations. Foliar ash content exhibited an increasing trend with altitude, indicating greater efficiency of Brizantha in absorbing calcium, phosphorus, and potassium at higher altitudes, possibly linked to more favorable edaphoclimatic conditions for nutrient uptake. Finally, forage quality declined with plant age, as evidenced by reductions in protein, ash, and In Vitro Dry Matter Digestibility (IVDMD), alongside increases in fiber, Neutral Detergent Fiber (NDF), and Acid Detergent Fiber (ADF). These findings support the recommendation of cutting intervals between 30 and 45 days, during which Brizantha displays a more favorable nutritional profile, higher digestibility, and consequently, greater value for animal feeding. Full article

This review examines the principal preclinical and clinical findings assessing the effects of White Mulberry (Morus Alba Linn) plant extract supplementation currently available. Since it is one of the most cultivated species of mulberry tree, it has caught the eye of researchers for its rich phytochemical profile as well as multi-purpose usages. The leaves, fruits, and other parts of the White Mulberry plant take on the role of valuable sources of bioactive compounds, including flavonoids, phenolic acids, terpenoids, and alkaloids. These secondary metabolites have a wide range of health benefits, such as antioxidant, anti-inflammatory, and antidiabetic properties. Commonly used as dietary supplements, White Mulberry plant extracts have shown their great capacity in improving metabolic profile, decreasing the cardiovascular risk, and supporting overall health. Full article

Soil moisture content (SMC) is a critical parameter for agricultural productivity, particularly in semi-arid regions, where irrigation practices are extensively used to offset water deficits and ensure decent yields. Yet, the socio-economic and remote context of these regions prevents sufficiently dense SMC monitoring in space and time to support farmers in their work to avoid unsustainable irrigation practices and preserve water resource availability. In this context, our study addresses the challenge of high spatial resolution (i.e., 20 m) SMC estimation by integrating remote sensing datasets in machine learning models. For this purpose, a dataset made of 166 soil samples’ SMC along with corresponding SMC, precipitation, and radar signal derived from Soil Moisture Active Passive (SMAP), Integrated Multi-satellitE Retrievals for GPM (IMERG), and Sentinel-1 (S1), respectively, was used to assess four machine learning models’ (Decision Tree—DT, Random Forest—RF, Gradient Boosting—GB, Extreme Gradient Boosting—XGB) reliability for SMC mapping. First, each model was trained/validated using only the coarse spatial resolution (i.e., 10 km) SMAP SMC and IMERG precipitation estimates as independent features, and, second, S1 information (i.e., 20 m) derived from single scenes and/or composite images was added as independent features to highlight the benefit of information (i.e., S1 information) for SMC mapping at high spatial resolution (i.e., 20 m). Results show that integrating S1 information from both single scenes and composite images to SMAP SMC and IMERG precipitation data significantly improves model reliability, as R² increased by 12% to 16%, while RMSE decreased by 10% to 18%, depending on the considered model (i.e., RF, XGB, DT, GB). Overall, all models provided reliable SMC estimates at 20 m spatial resolution, with the GB model performing the best (R² = 0.86, RMSE = 2.55%). Full article

In view of the low efficiency and slow development of fruit and vegetable picking in China, the picking sequence and obstacle avoidance of clustered crabapples were studied with them as the picking target. The multi-objective picking sequence of crabapples was planned, and the adaptive pheromone factor, heuristic function, and volatile factor were used to improve the ant colony (ACO) algorithm, so as to improve the convergence speed, adaptability, and global search ability of the algorithm. In order to avoid the collision between the robotic arm and the branches of the fruit tree, the three-dimensional reconstruction of the fruit tree was carried out, the shape and position information of the obstacle branch was determined, the artificial potential field was fused with the RRT, the search orientation of the RRT algorithm was enhanced, the inflection point was reduced, and the convergence speed was improved. The results showed that the average success rate of picking was 89.58%, and the robotic arm did not collide with the branches according to the planned picking sequence during the picking process, so as to achieve the picking purpose and picking effect. Full article

This review paper synthesizes the application of knowledge graphs (KGs) in Alzheimer’s disease (AD) research, based on two basic questions, as follows: what types of input data are available to construct these knowledge graphs, and what purpose the knowledge graph is intended to fulfill. We synthesize results from existing works to illustrate how diverse knowledge graph structures behave in different data availability settings with distinct application targets in AD research. By comparative analysis, we define the best methodology practices by data type (literature, structured databases, neuroimaging, and clinical records) and application of interest (drug repurposing, disease classification, mechanism discovery, and clinical decision support). From this analysis, we recommend AD-KG 2.0, which is a new framework that coalesces best practices into a unifying architecture with well-defined decision pathways for implementation. Our key contributions are as follows: (1) a dynamic adaptation mechanism that adapts methodological elements automatically according to both data availability and application objectives, (2) a specialized semantic alignment layer that harmonizes terminologies across biological scales, and (3) a multi-constraint optimization approach for knowledge graph building. The framework accommodates a variety of applications, including drug repurposing, patient stratification for precision medicine, disease progression modeling, and clinical decision support. Our system, with a decision tree structured and pipeline layered architecture, offers research precise directions on how to use knowledge graphs in AD research by aligning methodological choice decisions with respective data availability and application goals. We provide precise component designs and adaptation processes that deliver optimal performance across varying research and clinical settings. We conclude by addressing implementation challenges and future directions for translating knowledge graph technologies from research tool to clinical use, with a specific focus on interpretability, workflow integration, and regulatory matters. Full article

Moringa oleifera (LAM) is a multipurpose tree species with extensive pharmacological and ethnomedicinal properties. Production of important medicinal plants is facing decline under changing climatic conditions, which brings along exacerbated abiotic stresses like salinity and intraspecific competition, particularly high planting densities. Increasing plant density is seen as a strategy to increase production; however, the intraspecific competition and a lack of arable land limit productivity. Salinity has been estimated to harm approximately six percent of the Earth’s landmass. This leads to a loss of over 20% of agricultural output annually. These stressors can significantly curtail moringa’s growth and yield potential. Literature designates that Arbuscular Mycorrhizal Fungi (AMF), ubiquitous soil microorganisms forming symbiotic associations with plant roots, offer a promising avenue for mitigating these stresses. This narrative review aims to investigate the utilization of AMF to alleviate the detrimental effects of salinity and high planting density on Moringa oleifera. The different adaptive strategies M. oleifera undergoes to mitigate both stressors are explored. The review found that AMF inoculation enhances plant tolerance to these stressors by improving nutrient acquisition, water relations, and activating stress response mechanisms. By facilitating improved nutrient and water absorption, AMF enhance root architecture, modulate ROS scavenging mechanisms, and promote optimal biomass allocation, ensuring better survival in high-density plantings. Furthermore, AMF-mediated stress alleviation is linked to enhanced physiological efficiency, including increased chlorophyll content, root–shoot biomass balance, and ion homeostasis. This review is important because it could provide insights into a sustainable, natural solution for improving the resilience of Moringa oleifera under adverse environmental conditions, with potential applications in global agriculture and food security. Future research should prioritize identifying and characterizing moringa-specific AMF species and evaluate the long-term efficacy, feasibility, and economic viability of AMF application in real-world moringa cultivation systems to fully harness the potential of AMF in moringa cultivation. Full article

This study investigated the effects of Bradyrhizobium elkanii inoculation and nitrogen (N) fertilization on the growth of Pseudalbizzia niopoides seedlings in a nursery and their subsequent performance in soil. P. niopoides is a legume tree native to Latin American tropical forests, known to nodulate but with no previously identified rhizobial partner. Seedlings were grown in a nursery under varying N fertilization rates (0, 250, 500, 1000, and 2000 mg L⁻¹) with and without B. elkanii inoculation. Morphological traits, nodulation, and post-planting growth were assessed. Both inoculation and N fertilization significantly enhanced seedling growth in the nursery. However, high N rates suppressed nodulation and caused root toxicity. Inoculated seedlings exhibited improved growth after planting, particularly at lower N rates. Notably, inoculated seedlings without added N demonstrated vigorous new root proliferation after three months, highlighting the beneficial effects of the symbiosis. In terms of nitrogen fertilization in nurseries, a N rate up to 500 mg L⁻¹ produced satisfactory plant growth and no prejudicial effects on the symbiosis establishment. However, it is possible to raise seedlings even in the 0 mg L⁻¹ N rate, with a vigorous root emission during the post-planting growth. This study provides valuable insights into the interaction between a specific rhizobia strain and P. niopoides, with implications for nursery practices and sustainable agroforestry systems. Full article

Abnormal phenomena on urban roads, including uneven surfaces, garbage, traffic congestion, floods, fallen trees, fires, and traffic accidents, present significant risks to public safety and infrastructure, necessitating real-time monitoring and early warning systems. This study develops Urban Road Anomaly Visual Large Language Models (URA-VLMs), a generative AI-based framework designed for the monitoring of diverse urban road anomalies. The InternVL was selected as a foundational model due to its adaptability for this monitoring purpose. The URA-VLMs framework features dedicated modules for anomaly detection, flood depth estimation, and safety level assessment, utilizing multi-step prompting and retrieval-augmented generation (RAG) for precise and adaptive analysis. A comprehensive dataset of 3034 annotated images depicting various urban road scenarios was developed to evaluate the models. Experimental results demonstrate the system’s effectiveness, achieving an overall anomaly detection accuracy of 93.20%, outperforming state-of-the-art models such as InternVL2.5 and ResNet34. By facilitating early detection and real-time decision-making, this generative AI approach offers a scalable and robust solution that contributes to a smarter, safer road environment. Full article

The Internet of Things is used in many application areas in our daily lives. Ensuring the security of valuable data transmitted over the Internet is a crucial challenge. Hash functions are used in cryptographic applications such as integrity, authentication and digital signatures. Existing lightweight hash functions leverage task parallelism but provide limited scalability. There is a need for lightweight algorithms that can efficiently utilize multi-core platforms or distributed computing environments with high degrees of parallelization. For this purpose, a data-parallel approach is applied to a lightweight hash function to achieve massively parallel software. A novel structure suitable for data-parallel architectures, inspired by basic tree construction, is designed. Furthermore, the proposed hash function is based on a lightweight block cipher and seamlessly integrated into the designed framework. The proposed hash function satisfies security requirements, exhibits high efficiency and achieves significant parallelism. Experimental results indicate that the proposed hash function performs comparably to the BLAKE implementation, with slightly slower execution for large message sizes but marginally better performance for smaller ones. Notably, it surpasses all other evaluated algorithms by at least 20%, maintaining a consistent 20% advantage over Grostl across all data sizes. Regarding parallelism, the proposed PLWHF achieves a speedup of approximately 40% when scaling from one to two threads and 55% when increasing to three threads. Raspberry Pi 4-based tests for IoT applications have also been conducted, demonstrating the hash function’s effectiveness in memory-constrained IoT environments. Statistical tests demonstrate a precision of ±0.004, validate the hypothesis in distribution tests and indicate a deviation of ±0.05 in collision tests, confirming the robustness of the proposed design. Full article

South American wood and wood-based products play major roles in the global forest sector. Most research related to Paraguayan wood is focused on forest restoration, urban arborization, silviculture, and botanical taxonomy. Often overlooked but of major importance is the cellular structure of the trees that comprise remaining forests in Paraguay. Wood greatly contributes to forest value, yet wood anatomy studies remain novel in the country. To further document Paraguayan wood anatomy, two downed species of multipurpose Myrtaceae trees were sampled from a subtropical semi-deciduous forest in Areguá, Central Paraguay. In this article, heartwood xylem anatomy was observed and documented using low-cost methodology to support the regional realities of the emerging field in rural communities, especially local Paraguayan peoples. This included specific gravity, density, and basic light microscopic features. Sample material was processed near the pith at breast height to display cellular features in the transverse, radial, and tangential planes. Four features were measured with light microscopy and ImageJ: tangential vessel element diameter, vessel element length, ray seriation, and ray height. Results showed structural similarity between species, with diffuse porosity, solitary pores, simple perforation plates, alternate intervessel pits, and apotracheal diffuse parenchyma in aggregates. These results represent the first sampling of Myrtaceae from Paraguay in a methodology that can be easily replicated by the native population, thereby enabling further wood anatomy studies in the region. Full article

In tree-based algorithms like random forest and deep forest, due to the presence of numerous inefficient trees and forests in the model, the computational load increases and the efficiency decreases. To address this issue, in the present paper, a model called Automatic Deep Forest Shrinkage (ADeFS) is proposed based on shrinkage techniques. The purpose of this model is to reduce the number of trees, enhance the efficiency of the gcforest, and reduce computational load. The proposed model comprises four steps. The first step is multi-grained scanning, which carries out a sliding window strategy to scan the input data and extract the relations between features. The second step is cascade forest, which is structured layer-by-layer with a number of forests consisting of random forest (RF) and completely random forest (CRF) within each layer. In the third step, which is the innovation of this paper, shrinkage techniques such as LASSO and elastic net (EN) are employed to decrease the number of trees in the last layer of the previous step, thereby decreasing the computational load, and improving the gcforest performance. Among several shrinkage techniques, elastic net (EN) provides better performance. Finally, in the last step, the simple average ensemble method is employed to combine the remaining trees. The proposed model is evaluated by Monte Carlo simulation and three real datasets. Findings demonstrate the superior performance of the proposed ADeFS-EN model over both gcforest and RF, as well as the combination of RF with shrinkage techniques. Full article