Search Results (37,339)

The quantitative inversion of the leaf area index (LAI) of green plum trees is crucial for orchard field management and yield prediction. The data on the relative content of chlorophyll (SPAD) in leaves and environmental data from orchards show a significant correlation with LAI. Effectively integrating these two data types for LAI inversion is important to explore. This study proposes a multi−source decision fusion LAI inversion model for green plums based on their adjusted determination coefficient (MDF−ADRS). First, three statistical methods—Pearson, Spearman rank, and Kendall rank correlation analyses—were used to measure the linear relationships between variables, and the six environmental factors most highly correlated with LAI were selected from the orchard’s environmental data. Then, using multivariate statistical analysis methods, LAI inversion models based on environmental feature factors (EFs−PM) and SPAD (SPAD−PM) were established. Finally, a weight optimization allocation strategy was employed to achieve a multi−source decision fusion LAI inversion model for green plums. This strategy adaptively allocates weights based on the predictive performance of each data source. Unlike traditional models that rely on fixed weights or a single data source, this approach allows the model to increase the influence of a key data source when its predictive strength is high and reduce noise interference when it is weaker. This dynamic adjustment not only enhances the model’s robustness under varying environmental conditions but also effectively mitigates potential biases when a particular data source becomes temporarily unreliable. Our experimental results show that the MDF−ADRS model achieves an R2 of 0.88 and an RMSE of 0.39 in the validation set, outperforming other fusion methods. Compared to the EFs−PM and SPAD−PM models, the R2 increased by 0.19 and 0.26, respectively, and the RMSE decreased by 0.16 and 0.22. This model effectively integrates multiple sources of data from green plum orchards, enabling rapid inversion and improving the accuracy of green plum LAI estimation, providing a technical reference for monitoring the growth and managing the production of green plums. Full article

Scorpion stings are a significant public health concern globally, particularly in tropical and subtropical regions. Scorpion venoms contain a diverse array of bioactive peptides, and different scorpion species around the world typically exhibit varying venom profiles, resulting in a wide range of envenomation symptoms. Despite their harmful effects, scorpion venom peptides hold immense potential for drug development due to their unique characteristics. Therefore, the establishment of a comprehensive database that catalogs scorpions along with their known venom peptides and proteins is imperative in furthering research efforts in this research area. We hereby present ScorpDb, a novel database that offers convenient access to data related to different scorpion species, the peptides and proteins found in their venoms, and the symptoms they can cause. To this end, the ScorpDb database has been primarily advanced to accommodate data on the Iranian scorpion fauna. From there, we propose future community efforts to include a larger diversity of scorpions and scorpion venom components. ScorpDb holds the promise to become a valuable resource for different professionals from a variety of research fields, like toxinologists, arachnologists, and pharmacologists. The database is available at https://www.scorpdb.com/. Full article

Based on the field survey and reference data of the sea area of the Zhongjieshan Islands from 2021 to 2022, the Ecopath model was used to analyze the energy flow structure of the marine ecosystem of the sea area of the Zhongjieshan Islands; the energy structure of the marine ecosystem was divided into 21 functional groups, and its nutrient structure, energy flow, and total system characteristics were analyzed. The results show that the credibility of the model is 0.414, which is at a medium level. The trophic level of each functional group of the ecosystem in the sea area of Zhongjieshan Islands was 1–3.48, the energy flow structure of the system was mainly concentrated in the first five grades, and the trophic level was relatively simple, with the average energy transfer efficiency of the system being 8.11%, the energy flow range being 2.81–13.04%, the energy transfer efficiency of the primary producers of the system being 7.25%, and the energy conversion efficiency of the system debris being 9.12%. The total system throughput was 2125.96 t·km⁻²; The analysis of the overall characteristics of the ecosystem showed that the system connectance index and the system omnivory index were 0.45 and 0.24, respectively, while the Finn’s cycling index was 8.24, the Finn’s mean path length of the system was 2.72, and the total primary production/total respiration was 1.71. In this study, the marine ecosystem model of the sea area of the Zhongjieshan Islands was studied to understand the trophic structure and ecosystem status of the sea area, which is conducive to the sustainable utilization and scientific management of fishery resources in the sea area. Full article

This study develops a progressive navigation and guidance model for the route selection of cyclists executed in a designated area. The route selection of cyclists is modeled as a Pareto multi-objective optimization problem which is solved with the NSGA-II algorithm. The study aims to contribute to the ongoing efforts to create efficient and cyclist-friendly navigation tools to promote sustainable urban mobility. Data collection methods include GPS tracking, field measurements, and qualitative approaches to understand cyclists’ behavior and preferences. Nine objective functions are constructed based on criteria related to safety and comfort, incorporating decision variables related to cyclists riding on sidewalks, capturing the complexity of urban cycling infrastructure. Tests are performed in a defined area in the center of Athens, Greece. The NSGA-II algorithm is executed with modifications and the Pareto front is constructed, which consists of 28 alternative routes between two origin–destination points. The four routes that optimize the nine criteria of the objective functions are presented, with most routes passing through the Zappeion Gardens. The NSGA-II algorithm is proven to be a suitable approach for applications in networks with complex characteristics and for capturing cyclists’ choices when they face conflicting options. The study presents how a novel approach for the multi-objective optimization of cyclists’ route choice, which considers a wide range of cyclists’ needs and preferences, can be implemented in an urban environment with a lack of cycle infrastructure. Full article

The challenge of fracture segmentation remains a significant obstacle in imaging logging interpretation within the current oil and gas exploration and development field. However, existing image segmentation algorithms still encounter issues related to accuracy, speed, and robustness, as well as a tendency to misdetect or overlook small fractures when applied to logging image fracture segmentation tasks. To address these challenges comprehensively, this paper proposes an end-to-end fracture segmentation algorithm named SWSDS-Net. This algorithm is built upon the UNet architecture and incorporates the SimAM with slicing (SWS) attention mechanism along with the deformable strip convolution (DSCN) module. The SWS introduces a fully 3D attention mechanism that effectively learns the weights of each neuron in the feature map, enabling better capture of fracture features while ensuring fair attention and enhancement for both large and small objects. Additionally, the deformable properties of DSCN allow for adaptive sampling based on fracture shapes, effectively tackling challenges posed by varying fracture shapes and enhancing segmentation robustness. Experimental results demonstrate that SWSDS-Net achieves optimal performance across all evaluation metrics in this task, delivering superior visual results in fracture segmentation while successfully overcoming limitations present in existing algorithms such as complex shapes, noise interference, and low-quality images. Moreover, serving as a lightweight network solution enables SWSDS-Net’s deployment on mobile devices at remote sites—an advancement that lays a solid foundation for interpreting logging data and promotes deep learning technology application within traditional industrial scenarios. Full article

We evaluated the long-term impacts of various forest management practices on the structure and biodiversity of Estonian hemiboreal forests, a unique ecological transition zone between temperate and boreal forests, found primarily in regions with cold winters and moderately warm summers, such as the northern parts of Europe, Asia, and North America. The study examined 150 plots across stands of different ages (65–177 years), including commercial forests and Natura 2000 habitat 9010* “Western Taiga”. These plots varied in stand origin—multi-aged (trees of varying ages) versus even-aged (uniform tree ages), management history—historical (practices before the 1990s) and recent (post-1990s practices), and conservation status—protected forests (e.g., Natura 2000 areas) and commercial forests focused on timber production. Data on forest structure, including canopy tree diameters, deadwood volumes, and species richness, were collected alongside detailed field surveys of vascular plants and bryophytes. Management histories were assessed using historical maps and records. Statistical analyses, including General Linear Mixed Models (GLMMs), Multi-Response Permutation Procedures (MRPP), and Indicator Species Analysis (ISA), were used to evaluate the effects of origin, management history, and conservation status on forest structure and species composition. Results indicated that multi-aged origin forests had significantly higher canopy tree diameters and deadwood volumes compared to even-aged origin stands, highlighting the benefits of varied-age management for structural diversity. Historically managed forests showed increased tree species richness, but lower deadwood volumes, suggesting a biodiversity–structure trade-off. Recent management, however, negatively impacted both deadwood volume and understory diversity, reflecting short-term forestry consequences. Protected areas exhibited higher deadwood volumes and bryophyte richness compared to commercial forests, indicating a small yet persistent effect of conservation strategies in sustaining forest complexity and biodiversity. Indicator species analysis identified specific vascular plants and bryophytes as markers of long-term management impacts. These findings highlight the ecological significance of integrating historical legacies and conservation priorities into modern management to support forest resilience and biodiversity. Full article

Rice (Oryza) is a genus in the Gramineae family, which has grown widely all over the world and is a staple food source for people’s survival. The genetic information of rice has garnered significant attention in recent years, prompting numerous researchers to conduct extensive investigations in this field. But rice mitochondrial codon usage patterns have received little attention. The present study systematically analyzed the codon usage patterns and sources of variance in the mitochondrial genome sequences of five rice species by the CodonW and R software programs. Our results revealed that the GC content of codons in rice mitochondrial genome genes was determined to be 43.60%. Notably, the individual codon positions exhibited distinct GC contents: 48.00% for position 1, 42.65% for position 2, and 40.16% for position 3. These findings suggest the preference of the rice mitochondrial genome for codons ending in A or U. A weak codon bias was observed, with the effective number of codons (ENC) varying between 40.02 and 61.00, with an average value of 54.34. Subsequently, we identified 25 identical high-frequency codons in five rice mitochondrial genomes, with 11 codons ending in A and 12 codons ending in U. The regression lines in the neutrality plot exhibited slopes of less than 0.5 in five rice species, indicating a predominant role of natural selection, while mutation pressure remained relatively insignificant. In the PR2-plot analysis, most of the genes were located in the right half of the plot, indicating that the third base of the synonymous codon was preferred to end in G than C. Additionally, the ENC plot and ENC ratio analysis unveiled that codon preferences in the rice mitochondrial genome were predominantly influenced by natural selection rather than mutational pressure. The analysis of correspondence revealed distinct variations in the codon usage pattern across five rice mitochondrial genomes. Based on the RSCU values of species, a cluster tree was inconsistent with the mitochondrial genetic data, indicating that RSCU data could not be used as a basis for classification at the species level in the Oryza genus. These results will help decide the specific types of natural selection pressures influencing codon usage and improve the expression of exogenous genes in rice mitochondrial genomes by optimizing their codons. Full article

Electromagnetic technology is widely applied in numerous fields, and precise electromagnetic characteristic fitting technology has become a crucial part for enhancing system performance and optimizing design. However, it faces challenges such as high computational complexity and the difficulty in balancing the accuracy and generalization ability of the model. For example, the Radar Cross Section (RCS) distribution characteristics of a single corner reflector model or Luneberg lens provide a relatively stable RCS value within a certain airspace range, which to some extent reduces the difficulty of radar target detection and fails to truly evaluate the radar performance. This paper aims to propose an innovative multi-parameter optimization method for electromagnetic characteristic fitting based on deep learning. By selecting common targets such as reflectors and Luneberg lens reflectors as optimization variables, a deep neural network model is constructed and trained with a large amount of electromagnetic data to achieve high-precision fitting of the target electromagnetic characteristics. Meanwhile, an advanced genetic optimization algorithm is introduced to optimize the multiple parameters of the model to meet the error index requirements of radar target detection. In this paper, by combining specific optimization variables such as corner reflectors and Luneberg lenses with the deep learning model and genetic algorithm, the deficiencies of traditional methods in handling electromagnetic characteristic fitting are effectively addressed. The experimental results show that the 60° corner reflector successfully realizes the simulation of multiple peak characteristics of the target, and the Luneberg lens reflector achieves the simulation of a relatively small RCS average value with certain fluctuations in a large space range, which strongly proves that this method has significant advantages in improving the fitting accuracy and optimization efficiency, opening up new avenues for research and application in the electromagnetic field. Full article

The natural caving method, as a new technique in underground mining, has been promoted and applied in several countries worldwide. The destruction of the bottom rock mass structure directly impacts the structural stability of underground engineering, resulting in damage and collapse of underground tunnels. Therefore, based on the principles of explosion theory and field monitoring data, a scaled three-dimensional numerical simulation model of underground blasting was constructed using LS-DYNA19.0 software to investigate the attenuation patterns of underground blasting vibrations and their impact on nearby tunnels. The results show that the relative error range between the simulated blasting vibration velocities based on the FEM-SPH (Finite Element Method–Smoothed Particle Hydrodynamics) algorithm and the measured values is between 7.75% and 9.85%, validating the feasibility of this method. Significant fluctuations in blasting vibration velocities occur when the blast center increases to within a range of 10–20 m. As the blast center distance exceeds 25 m, the vibration velocities are increasingly influenced by the surrounding stress. Additionally, greater stress results in higher blasting vibration velocities and stress wave intensities. Fitting the blasting vibration velocities of various measurement points using the Sadovsky formula yields fitting correlation coefficients ranging between 0.92 and 0.97, enabling the prediction of on-site blasting vibration velocities based on research findings. Changes in propagation paths lead to localized fluctuations in the numerical values of stress waves. These research findings are crucial for a deeper understanding of underground blasting vibration patterns and for enhancing blasting safety. Full article

Most fatigue-loading research has concentrated on constant-amplitude tests, which seldom represent actual service conditions. Because of the significant time and expense associated with variable-amplitude experiments, researchers often employ block/step-loading tests to evaluate the effects of variable-amplitude loading. These tests utilize various sequences of low-to-high and high-to-low loads to simulate real-world scenarios. Empirical investigations have shown inconsistencies in the damage accumulation under different load sequences. Although literature reviews exist for simulation and experimental methods, there is limited research examining the impact of step/variable-amplitude loading on adhesive joints in composite materials. This review aims to address this gap by comprehensively analyzing the effects of load sequence and block loading on fatigue damage progression in fiber-reinforced polymer composites. Additionally, the applicability of various step-loading fatigue damage accumulation models to adhesive materials is evaluated through numerical simulation to study its suitability in predicting fatigue failure. This review also explores recent theoretical advancements in this field over the past few years, examining more than 100 fatigue damage accumulation models categorized into seven subcategories: (i) linear damage rules, (ii) nonlinear damage curve and two-stage linearization models, (iii) life curve modification models, (iv) models based on crack growth concepts, (v) continuum damage mechanics-based models, (vi) material degradation models, and (vii) energy-based models. Finally, numerical simulations using the most common nonlinear cumulative fatigue damage accumulation models were conducted to predict fatigue failure in adhesively bonded joints under four step-loading tests, and the results were compared with the experimental data. Numerical simulations revealed the need and scope of further development of a fatigue failure model under step/variable loading. This comprehensive review offers valuable insights into the complex nature of fatigue failure in adhesive joints under variable loading conditions and highlights current state-of-the-art nonlinear fatigue damage accumulation models for adhesive materials. Full article

This study applied modified linear regression in machine learning (ML) to predict the direction of arrival (DoA) in cellular networks using field measurements and radiofrequency parameters. Models were developed from base station data, with preprocessing for pattern identification and formula adjustments to improve the accuracy across angle ranges. Machine learning, tested here as an additional method to traditional techniques, achieved a root mean square error (RMSE) of 3.63 to 17.93, demonstrating enhanced adaptability. While requiring substantial data and computational resources, this approach highlights machine learning’s potential as a valuable tool for DoA estimation in cellular networks. Full article

Modern, large bridges and tunnels represent important nodes in transportation arteries and have a significant impact on the development of transportation. The health and safety monitoring of these structures has always been a significant concern and is reliant on various types of sensors. Distributed acoustic sensing (DAS) with telecommunication fibers is an emerging technology in the research areas of sensing and communication. DAS provides an effective and low-cost approach for the detection of various resources and seismic activities. In this study, field experiments are elucidated, using DAS for the Hong Kong–Zhuhai–Macao Bridge, and for studying vehicle trajectories, earthquakes, and other activities. The basic signal-processing methods of filtering and normalization are adopted for analyzing the data obtained with DAS. With the proposed DAS technology, the activities on shore, vehicle trajectories on bridges and in tunnels during both day and night, and microseisms within 200 km were successfully detected. Enabled by DAS technology and mass fiber networks, more studies on sensing and communication systems for the monitoring of bridge and tunnel engineering are expected to provide future insights. Full article

This study evaluates the habitat of the Bocachico fish (Prochilodus magdalenae) in the Ciénaga de Betancí, Colombia, using a habitat suitability index (HSI) model. Wetlands like the Ciénaga de Betancí are under significant pressure from anthropogenic activities, affecting biodiversity and ecosystem health. The Bocachico, a species of immense cultural and economic importance, faces habitat degradation and fragmentation. Using hydrodynamic and water quality data, a numerical model (EFDC+ Explorer 11.5), and field data collected from multiple sampling campaigns, we assessed habitat suitability based on five key parameters: water temperature, dissolved oxygen, ammonia nitrogen, velocity, and depth. The model results indicated that environmental conditions in the wetland remained relatively stable during the dry season, with an average HSI score of 0.67, where 9% of the wetland area displayed acceptable conditions, and the remaining 91% displayed medium conditions. The wet season, on the other hand, had an average HSI score of 0.64, with 7.2% of the area in the acceptable suitability range, and the remaining 92.8% in the medium category. Variations in HSI were primarily driven by ammonia nitrogen levels, water velocity, and depth. Despite limited fluctuations in the HSI, areas of low suitability were identified, particularly in regions impacted by human activities. These findings have practical implications for conservation strategies, providing valuable insights for the sustainable management and conservation of the Ciénaga de Betancí, informing strategies for improving habitat conditions for the Bocachico, and supporting wetland restoration efforts. Full article

Nanotechnology and machine learning (ML) are rapidly emerging fields with numerous real-world applications in medicine, materials science, computer engineering, and data processing. ML enhances nanotechnology by facilitating the processing of dataset in nanomaterial synthesis, characterization, and optimization of nanoscale properties. Conversely, nanotechnology improves the speed and efficiency of computing power, which is crucial for ML algorithms. Although the capabilities of nanotechnology and ML are still in their infancy, a review of the research literature provides insights into the exciting frontiers of these fields and suggests that their integration can be transformative. Future research directions include developing tools for manipulating nanomaterials and ensuring ethical and unbiased data collection for ML models. This review emphasizes the importance of the coevolution of these technologies and their mutual reinforcement to advance scientific and societal goals. Full article

The paper explores the impact of digital payment systems, blockchain technology, and AI/machine learning on innovation and sustainability in financial organizations. As part of the analysis, the study has adopted an explanatory research design and has used SmartPLS in order to analyze the data collected from 230 professionals of different fields through a structured questionnaire. The results show positive effects of digital payment systems and blockchain technology on organizations’ innovations with the impact of digital payments being the most pronounced. Empirical results suggest that these technologies are important to improve sustainability performance, depending on measures of internal consistency and discriminant validity among the proposed constructs. Al, also machine learning, has the highest relevance with environmental sustainability, thereby underlining the importance and work of such measures. Based on the Resource-Based View (RBV) theory, the study also explains the need for the organization to assimilate these innovations to enhance the organizational operations, customer satisfaction, and compliance with the laws. The study highlights fintech’s potential to address environmental issues and enhance societal goals, but geographical limitations may obstruct its transportability. Full article