Search Results (3,947)

This study examines the effects of drought timing on height growth and seasonal leaf phenology in pedunculate oak (Quercus robur L.) seedlings. Drought represents a significant threat to long-lived tree species, impacting growth, phenology, and recovery potential. This research aims to assess whether the timing of drought stress influences height growth and leaf phenology while also investigating possible compensatory mechanisms. The experiment involved five groups of seedlings: four exposed to drought at different periods during the 2022 and 2023 growing seasons, and one regularly irrigated control group. The key monitored parameters included height growth, spring flushing, autumn leaf senescence, and photosynthesis. Preliminary results revealed that late-spring and summer drought had a significant negative impact on height growth and delayed autumn senescence, whereas mid-spring drought allowed for compensatory growth. Spring leaf phenology remained largely unaffected by drought treatments. None of the drought-stressed plants showed increased photosynthesis during the recovery phase compared to the control. These findings highlight the critical role of drought timing in determining growth and phenological outcomes. Relatively late-season droughts were particularly detrimental, limiting recovery and resource allocation, while early-season droughts provided better opportunities for compensation. Further research on drought recovery mechanisms and nutrient interactions is needed to refine forestry management strategies under climate change. Full article

Urbanization replaces natural vegetation for city expansion, impacting environmental and climatic variables that affect the health of the human population and fauna. These changes affect important groups such as birds, given their greater sensitivity to anthropogenic alterations, especially when we understand these effects on a large scale, considering countries such as Brazil, which represents the third country with the greatest diversity of bird species in the world. Conversely, green spaces like urban parks, tree-lined avenues, and riparian forests seem to foster biodiversity conservation. Here, we analyze the effects of vegetation on bird communities and bird–plant interactions in urban riparian areas that have undergone ecological restoration. The study was carried out between January and October 2019 in two restored urban areas of Uberlândia, Brazil. Results showed that the richness of birds observed between the two areas was Praia Clube (n = 86) and Parque Linear Rio Uberabinha (n = 80). The most representative trophic guilds in the areas, with the highest proportion in their relative abundances during both seasons, were granivores, omnivores, insectivores, and frugivores. Composition varied significantly between areas as a function of the plant community, particularly when considering the interaction between season and area (ANOSIM: R = 0.19; Stress = 0.10; p = 0.008). In environments dominated by generalist and synanthropic species (Eared Dove, Picazuro Pigeon), effective planning and management of green areas are crucial. It is important to acknowledge that certain bird species depend on specific habitats, such as riparian forests, and that specific plant species within these areas are vital for specialized bird species, such as species endemic to the Brazilian Savanna or Cerrado and restricted to Brazil (White-striped Warbler) and species in vulnerable categories globally (Bare-faced Curassow). Therefore, restoration efforts in degraded areas should be carefully planned to restore interactions and conserve biodiversity effectively. Full article

Olive cultivation is still concentrated within the Mediterranean basin, although the last thirty years have seen an expansion into geographical areas outside it. Traditional olive groves, with large planting distances and centuries-old trees, still predominate. However, more and more space is being given over to modern plantations, which allow an ever-increasing degree of mechanisation, although some legal restrictions, often related to the monumental nature of the plantations, make the conversion of old plantations into new ones not always easy. The extreme case is super-intensive olive growing, where the very concept of olive growing has been rethought. In this context, harvesting is the most time-consuming and costly of the cultivation operations. Without it, or rather without a high degree of mechanisation, it is still not possible to produce high-quality oils. A leading role is always played by the trunk shakers, who are still the undisputed protagonists in this sector. This review looks at trunk shakers in olive groves, showing the latest models, and their strengths and weaknesses, based on the research carried out in recent decades. Full article

Phytophthora sojae is the causal agent of the Phytophthora root and stem rot in soybean, which has resulted in a significant increase in the incidence of the disease and substantial yield losses on a global scale. The proliferation of Phytophthora sojae can be mitigated through the development of Phytophthora-resistant soybean cultivars. A fundamental understanding of the genetic diversity and dynamic changes within the P. sojae population is essential for disease management and the development of new P. sojae-resistant varieties. Although a large number of pathogen samples can lead to more comprehensive interpretations and better conclusions, only six indigenous P. sojae isolates were available in the Republic of Korea at the time of the experiments. Due to the limited availability, this study preliminarily aimed to assess the pathotypes and genetic variation of the six P. sojae isolates collected in the Republic of Korea. The virulence patterns of all the six P. sojae isolates differed based on the 15 soybean differentials known for P. sojae resistance. The six isolates displayed high levels of pathotype complexities, ranging from 8 to 15, which is notably higher than those observed in other countries. Furthermore, 18 of the 21 simple sequence repeat markers used exhibited polymorphisms. The mean allele number (3.8) shows higher genetic variability compared to that (2.5) of isolates from the USA. The gene diversity (0.624) and the mean polymorphic information content (0.579) also displayed high levels of variation among the six isolates. A low mean heterozygosity (0.019) indicated a rare but possible outcrossing between the isolates, which was detected by the SSR marker PS07. Genetic dissimilarity assessments were employed to categorize the six P. sojae isolates into three groups using a neighbor-joining phylogenetic tree and principal component analysis. Although on a small scale, the phenotypic and genotypic assay results obtained indicated a significant variability in the pathotypes and genetic variation within the P. sojae isolates in the Republic of Korea. Though limited in scope, these results will be a cornerstone for elucidating the virulence pathotype and genetic diversity of the P. sojae population in future analyses. These findings also have the potential to improve the soybean breeding strategies aimed at enhancing resistance to P. sojae in the Republic of Korea. Full article

This study explores the application of advanced Recurrent Neural Network (RNN) architectures—specifically Long Short-Term Memory (LSTM) and Gated Recurrent Unit (GRU)—for classifying chicken egg fertility based on embryonic development detected in egg images. Traditional methods, such as candling, are labor-intensive and often inaccurate, making them unsuitable for large-scale poultry operations. By leveraging the capabilities of LSTM and GRU models, this research aims to automate and enhance the accuracy of egg fertility classification, thereby contributing to agricultural automation. A dataset comprising 240 high-resolution egg images was employed, resized to 256 × 256 pixels for optimal processing efficiency. LSTM and GRU models were trained to discern fertile from infertile eggs by analyzing the sequential data represented by the pixel rows in these images. The LSTM model demonstrated superior performance, achieving a validation accuracy of 89.58%, significantly surpassing the GRU model (66.67%). Compared to classical methods such as Decision Tree (85%), Logistic Regression (88.3%), the LSTM model demonstrated superior performance, achieving a validation accuracy of 89.58%, significantly surpassing the GRU model (66.67%). Compared to Decision Tree (85%), Logistic Regression (88.3%), SVM (84.57%), K-means (82.9%), and R-CNN (70%), the LSTM model achieved the highest classification accuracy. Unlike classical machine learning approaches that rely on handcrafted features and predefined decision rules, LSTM effectively learns complex sequential dependencies within images, improving fertility classification accuracy in real-world poultry farming applications. In contrast, GRU models, while more computationally efficient, may struggle with generalization under constrained data conditions. This study underscores the potential of advanced RNNs in enhancing the efficiency and accuracy of automated farming systems, paving the way for future research to further optimize these models for real-world agricultural applications. Full article

Reinforced concrete shear walls (RCSWs) are complicated to compute their shear capacity due to their large cross-sectional height-to-thickness ratios and the fact that they are subjected to vertical loads. Numerous factors influence RCSWs’ shear strength capacity, and the analytical models find it challenging to fully account for each factor’s impact on RCSWs’ shear-bearing capacity. Machine learning (ML) technology can deeply capture the mapping relationship between each input feature and the target value, and provide a more flexible and effective prediction method for RCSW shear-bearing capacity. To this end, a shear capacity test database containing 583 RCSW specimens was first established and characterized, and then the database was employed to train single, ensemble, and deep learning models for the shear strength of shear walls and combined with hyper-parameter tuning to enhance each model’s prediction performance, after which the prediction performance of each model was compared. Then, the ML models were contrasted with conventional techniques founded on the mechanical premise. Finally, in order to improve the prediction accuracy and reliability of the ML methods, the individually trained models were integrated into a stacking model using the stacking method, and the stacking model’s prediction performance was assessed. The results of this study show that in the single model, the test set R² of the decision tree (DT) reaches 0.94, showing good trend-capturing ability. Among the ensemble models, Gradient Boosting (GB) performs the best and is comparable to DT in terms of RMSE and R² and significantly outperforms other ensemble methods, such as Random Forest (RF) and Bagging. Deep Neural Networks (DNNs) show the strongest predictive ability among all models, with the lowest RMSE (263 kN) and a test R² of 0.95, which is much better than the majority of ensemble models. The ML models show high accuracy and reliability compared to the traditional RC shear wall shear capacity models. The stacking model has an R² of 0.98 and a CoV of 0.147 in the test set, and it is much better than other independent ML models (R² = 0.88~0.95, CoV = 0.179~0.651). Full article

Desert and semi-desert ecosystems cover a large proportion of global land area, but their tree-ring materials had traditionally been studied less intensively than that of forest ecosystems. In this study, we presented the time series of growth rings from eight typical shrub species of the semi-desert region in the northern foot of Yinshan Mountain, Inner Mongolia, China. The results showed that all those shrub species had recognizably demarcated annual rings of main stems, and tree-ring chronologies could been constructed successfully. The climate-growth analysis indicated that the chronologies was positively correlated with precipitation and PDSI but negatively correlated with temperature variables, indicating that drought stress had primary importance in the control of the relative ring width from year to year for those shrub species. Interestingly, the annual growth rate of those shrub species had no noticeable downward trend in recent decades, indicating that shrub growth had not negatively impacted the recently developed warm–dry climate in the sample sites. Our results provide evidence that growth rings in the main stems of shrub species in the northern foot of Yinshan Mountain should be a reliable proxy of annual fluctuation in the semi-desert environment of China. Full article

Urban streets are critical public spaces that significantly influence the thermal comfort of city dwellers. However, the issue of summer thermal discomfort in severely cold regions has been largely overlooked. This study focuses on Youth Street in Shenyang, a city in a severely cold region, to explore the impact of various street spaces and vegetation layouts on the thermal environment and comfort using ENVI-met modeling and correlation analysis. The study varied the aspect ratio (AR) of the street, street tree species, and plant spacing across 60 scenarios and simulated thermal comfort over a 10-h period on a typical summer day. Results show that air temperature (Ta), mean radiant temperature (Tmrt) and sky view factor (SVF) are positively correlated with physiologically equivalent temperature (PET). Street trees effectively reduce Ta, increase RH and lower wind speed (WS), but plant spacing has minimal impact on WS. Higher AR values lead to greater improvements in pedestrian thermal comfort. Specifically, the highest heat mitigation rate (HMR) is observed at low AR (9.87% at AR = 0.5 and 9.94% at AR = 1.0), while it is lower at high AR (8.16% at AR = 2.0). Conversely, larger plant spacing of street trees diminishes the effectiveness of thermal comfort improvements. The improvement effect of plant spacing is more pronounced in street spaces with smaller AR. In these spaces, closer plant spacing significantly enhances thermal comfort by providing more shade and reducing Ta and Tmrt. However, in street spaces with higher AR, overly dense plant configurations can reduce WS and limit the cooling effect of ventilation, thereby diminishing overall heat mitigation ability. Conclusions suggest that urban planners should consider both street space and vegetation layouts to optimize thermal comfort. For urban main streets in severely cold regions, an AR of 1:1 with deciduous broadleaf trees and hedges planted at 6 m spacing is recommended. In high-AR streets, dense plant configurations should be avoided. This study provides valuable insights for improving the thermal comfort and sustainable design of urban street spaces, supporting new construction and development in similar climate environments. Full article

Biodiversity maintenance mechanisms have been central to the study of community ecology, and the negative density dependence effect plays an important role in maintaining species diversity in forest communities. However, the strength and direction of the negative density dependence effect may change at different latitudinal gradients, and theory predicts that the negative density dependence effect increases with decreasing latitude. Using three provinces in northeastern China as the study target, we selected forest ecosystems in 15 locations according to the latitude gradient and analyzed the mixing of large- and small-diameter trees and adjacent tree species at different latitudinal gradients by the second-order characteristic function of mark mingling (The species mingling was used as “constructed marks” and we developed a second-order characteristic function of mark mingling useful for comparing spatial species mingling via random assignment of species patterns at specific ecological scales). It was found that the tree species mixed level of the large trees was higher, that of the small trees was lower in the stands at the middle and low latitudes (40, 41, and 43), and the tree species mixed level of the large or small trees was lower in the stands at high latitudes (45 and 46). Also, the level of mixing of large trees with surrounding tree species was significantly different among latitudes within the small scale (0–5 m). More importantly, the peak value of the difference in the second-order characteristic function of mark mingling (Δv(r)) of the stand increased gradually with decreasing latitude. The results indicated that the difference in tree species mixing degree between large and small trees was increasing, and this phenomenon was more obvious at the small scale (0–10 m). In general, we found that the negative density dependence effect in the late successional forest system showed a variation trend with latitude gradient, which showed that with the decrease in latitude, the negative density dependence effect in the stands was increasing. The results showed that in temperate forests, in low-latitude stands (40–43° N), there is significant peak in species mingling differences at small scales (0–10 m). Spatial heterogeneity thinning should be prioritized, and rare tree species should be replanted within a 10 m radius to alleviate intraspecific competition. In contrast, in high-latitude stands (45–46° N), human disturbance should be reduced to maintain the natural community structure. These measures can provide precise management strategies for regional biodiversity conservation. This study revealed the response of the intensity of the negative density dependence effect to changes in latitudinal gradients, and provides new ideas for maintaining and controlling regional species diversity. Full article

Automatic glacier extraction from remote sensing images is the most important approach for large scale glacier monitoring. Commonly used band calculation indices to enhance glacier information are not effective in identifying shadowed glaciers and debris-covered glaciers. In this study, we used the Kolmogorov–Smirnov test as the theoretical basis and determined the most suitable band calculation indices to distinguish different land cover classes by comparing inter-sample separability and reasonable threshold range ratios of different indices. We then constructed a glacier classification decision tree. This approach resulted in the development of a method to automatically extract glacier areas at given spatial and temporal scales. In comparison with the commonly used indices, this method demonstrates an improvement in Cohen’s kappa coefficient by more than 3.8%. Notably, the accuracy for shadowed glaciers and debris-covered glaciers, which are prone to misclassification, is substantially enhanced by 108.0% and 6.3%, respectively. By testing the method in the Qilian Mountains, the positive prediction value of glacier extraction was calculated to be 91.8%, the true positive rate was 94.0%, and Cohen’s kappa coefficient was 0.924, making it well suited for glacier extraction. This method can be used for monitoring glacier changes in global mountainous regions, and provide support for climate change research, water resource management, and disaster early warning systems. Full article

Water scarcity significantly challenges agricultural systems worldwide, especially in tropical areas such as the Dominican Republic. This study explores integrating satellite-based remote sensing technologies and field-based soil moisture sensors to assess water stress and optimize irrigation management in avocado orchards in Puerto Escondido, Dominican Republic. Using multispectral imagery from the Landsat 8 and 9 satellites, key vegetation indices (NDVI and SAVI) and NDWI, a water-related index that specifically indicates changes in crop water contents, rather than vegetation vigor, were derived to monitor vegetation health, growth stages, and soil water contents. Crop coefficient (Kc) values were calculated from these vegetation indices and combined with reference evapotranspiration (ETo) estimates derived from three meteorological models (Hargreaves–Samani, Priestley–Taylor, and Blaney–Criddle) to assess crop water requirements. The results revealed that soil moisture data from sensors at 30 cm depth strongly correlated with satellite-derived estimates, reflecting avocado trees’ critical root zone dynamics. Additionally, seasonal patterns in the vegetation indices showed that NDVI and SAVI effectively tracked vegetative growth stages, while NDWI indicated changes in the canopy water content, particularly during periods of water stress. Integrating these satellite-derived indices with field measurements allowed a comprehensive assessment of crop water requirements and stress, providing valuable insights for improving irrigation practices. Finally, this study demonstrates the potential of remote sensing technologies for large-scale water stress assessment, offering a scalable and cost-effective solution for optimizing irrigation practices in water-limited regions. These findings advance precision agriculture, especially in tropical environments, and provide a foundation for future research aimed at enhancing data accuracy and optimizing water management practices. Full article

Mango (Mangifera Indica L.), a key horticultural crop, particularly in Pakistan, has been primarily studied locally using low- to medium-resolution satellite imagery, usually focusing on a particular phenological stage. The large canopy size, complex tree structure, and unique phenology of mango trees further accentuate intrinsic challenges posed by low-spatiotemporal-resolution data. The absence of mango-specific vegetation indices compounds the problem of accurate health classification and yield estimation at the tree level. To overcome these issues, this study utilizes high-resolution multi-spectral UAV imagery collected from two mango orchards in Multan, Pakistan, throughout the annual phenological cycle. It introduces MangiSpectra, an integrated two-staged framework based on Long Short-Term Memory (LSTM) networks. In the first stage, nine conventional and three mango-specific vegetation indices derived from UAV imagery were processed through fine-tuned LSTM networks to classify the health of individual mango trees. In the second stage, associated data such as the trees’ age, variety, canopy volume, height, and weather data were combined with predicted health classes for yield estimation through a decision tree algorithm. Three mango-specific indices, namely the Mango Tree Yellowness Index (MTYI), Weighted Yellowness Index (WYI), and Normalized Automatic Flowering Detection Index (NAFDI), were developed to measure the degree of canopy covered by flowers to enhance the robustness of the framework. In addition, a Cumulative Health Index (CHI) derived from imagery analysis after every flight is also proposed for proactive orchard management. MangiSpectra outperformed the comparative benchmarks of AdaBoost and Random Forest in health classification by achieving 93% accuracy and AUC scores of 0.85, 0.96, and 0.92 for the healthy, moderate and weak classes, respectively. Yield estimation accuracy was reasonable with $R^2=0.21$, and $\mathrm{RMSE}=50.18$. Results underscore MangiSpectra’s potential as a scalable precision agriculture tool for sustainable mango orchard management, which can be improved further by fine-tuning algorithms using ground-based spectrometry, IoT-based orchard monitoring systems, computer vision-based counting of fruit on control trees, and smartphone-based data collection and insight dissemination applications. Full article

Background: Machine learning methods for clinical assistance require a large number of annotations from trained experts to achieve optimal performance. Previous work in natural language processing has shown that it is possible to automatically extract annotations from the free-text reports associated with chest X-rays. Methods: This study investigated techniques to extract 49 labels in a hierarchical tree structure from chest X-ray reports written in Danish. The labels were extracted from approximately 550,000 reports by performing multi-class, multi-label classification using a method based on pattern-matching rules, a classic approach in the literature for solving this task. The performance of this method was compared to that of open-source large language models that were pre-trained on Danish data and fine-tuned for classification. Results: Methods developed for English were also applicable to Danish and achieved similar performance (a weighted F1 score of 0.778 on 49 findings). A small set of expert annotations was sufficient to achieve competitive results, even with an unbalanced dataset. Conclusions: Natural language processing techniques provide a promising alternative to human expert annotation when annotations of chest X-ray reports are needed. Large language models can outperform traditional pattern-matching methods. Full article

Radio Frequency Identification (RFID) is a key component in automatic systems that address challenges in environment monitoring. However, tag collision continues to be an essential challenge in such applications due to high-density RFID deployments. This paper addresses the issue of RFID tag collision in large-scale intensive tags, particularly in industrial membrane contamination monitoring systems, and improves the system performance by minimizing collision rates through an innovative collision-avoiding algorithm. This research improved the Predictive Framed Slotted ALOHA–Collision Tracking Tree (PRFSCT) algorithm by cooperating probabilistic and deterministic methods through dynamic frame length adjustment and multi-branch tree processes. After simulation and validation in MATLAB R2023a, we performed a hardware test with the RFM3200 and UHFReader18 passive tags. The method’s efficiency is evaluated through collision slot reduction, delay minimization, and enhanced throughput. PRFSCT significantly reduces collision slots when the number of tags to identify is the same for PRFSCT, Framed Slotted ALOHA (FSA), and Collision Tracking Tree (CTT); the PRFSCT method needs the fewest time slots. When identifying more than 200 tags, PRFSCT has 225 collision slots for 500 tags compared to FSA and CTT, which have approximately 715 and 883 for 500 tags, respectively. It demonstrates exceptional stability and adaptability under increased density needs while improving tag reading at distances. Full article

ATP synthase is a key enzyme in photophosphorylation in photosynthesis and oxidative phosphorylation in respiration, which can catalyze the synthesis of ATP and supply energy to organisms. ATP synthase has been well studied in many animal species but has been poorly characterized in plants. This research identified forty ATP synthase family members in the rubber tree, and the phylogenetic relationship, gene structure, cis-elements, and expression pattern were analyzed. These results indicated that the ATP synthase of mitochondria was divided into three subgroups and the ATP synthase of chloroplast was divided into two subgroups, respectively. ATP synthase in the same subgroup shared a similar gene structure. Evolutionary relationships were consistent with the introns and exons domains, which were highly conserved patterns. A large number of cis elements related to light, phytohormones and stress resistance were present in the promoters of ATP synthase genes in rubber trees, of which the light signal accounts for the most. Transcriptome and qRT-PCR analysis showed that HbATP synthases responded to cold stress and hormone stimulation, and the response to ethylene was most significant. HbMATPR3 was strongly induced by ethylene and salicylic acid, reaching 122-fold and 17-fold, respectively. HbMATP7-1 was 41 times higher than the control after induction by jasmonic acid. These results laid a foundation for further studies on the function of ATP synthase, especially in plant hormone signaling in rubber trees. Full article