Search Results (81,158)

As our comprehension of cyanobacterial classification in diverse ecosystems broadens, it becomes essential to explore the biodiversity of lesser-known areas for a thorough understanding of both global and local diversity. This research, which is part of a larger investigation into soil biocrust algae diversity in the Sanyang Wetland located in Zhejiang Province, China, introduces a novel taxon of non-heterocystous filamentous cyanobacteria employing a polyphasic approach for cyanobacterial classification, integrating morphological, molecular, ecological, and biogeographical considerations. The findings from morphological analysis, 16S rRNA gene sequencing, and the identification of the 16S-23S ITS rRNA region have led to the discovery of a new genus, Paludothrix, which is categorized within the family Coleofasciculaceae. The proposed generic name and specific epithet of these new taxa adhere completely to the guidelines established by the International Code of Nomenclature for algae, fungi, and plants. The modern taxonomic system of cyanobacteria is constantly being updated and improved. The description of new taxa using the polyphasic approach can enrich the relevant knowledge in the field of cyanobacteria classification. The results of this study will increase our understanding of terrestrial cyanobacteria within wetland environments. Full article

Maize lodging is a prevalent stress that can significantly diminish corn yield and quality. Unmanned aerial vehicles (UAVs) remote sensing is a practical means to quickly obtain lodging information at field scale, such as area, severity, and distribution. However, existing studies primarily use machine learning (ML) methods to qualitatively analyze maize lodging (lodging and non-lodging) or estimate the maize lodging percentage, while there is less research using deep learning (DL) to quantitatively estimate maize lodging parameters (type, severity, and direction). This study aims to introduce advanced DL algorithms into the maize lodging classification task using UAV-multispectral images and investigate the advantages of DL compared with traditional ML methods. This study collected a UAV-multispectral dataset containing non-lodging maize and lodging maize with different lodging types, severities, and directions. Additionally, 22 vegetation indices (VIs) were extracted from multispectral data, followed by spatial aggregation and image cropping. Five ML classifiers and three DL models were trained to classify the maize lodging parameters. Finally, we compared the performance of ML and DL models in evaluating maize lodging parameters. The results indicate that the Random Forest (RF) model outperforms the other four ML algorithms, achieving an overall accuracy (OA) of 89.29% and a Kappa coefficient of 0.8852. However, the maize lodging classification performance of DL models is significantly better than that of ML methods. Specifically, Swin-T performs better than ResNet-50 and ConvNeXt-T, with an OA reaching 96.02% and a Kappa coefficient of 0.9574. This can be attributed to the fact that Swin-T can more effectively extract detailed information that accurately characterizes maize lodging traits from UAV-multispectral data. This study demonstrates that combining DL with UAV-multispectral data enables a more comprehensive understanding of maize lodging type, severity, and direction, which is essential for post-disaster rescue operations and agricultural insurance claims. Full article

The utilization of covalent triazine frameworks (CTFs) as photocatalysts has witnessed rapid advancements in the field of photocatalysis. However, the presence of residual components in certain CTFs materials is widely ignored as regards their influence on photocatalytic performance. In this study, we find that trifluorosulfonic acid (TfOH) molecules stably exist in the amino-functionalized CTF-NH₂ framework, which enhance the affinity for water. The experimental results indicate that the residual TfOH elevates the VB position of CTF-NH₂, facilitating the oxidization of both water and sacrificial agents. Moreover, the present of TfOH accelerates the separation and transfer of photogenerated charge carriers to the Pt cocatalyst. Consequently, CTF-NH₂-F containing residual TfOH molecules demonstrates a significant enhancement in the photocatalytic hydrogen evolution, achieving about 250 µmol over a duration of 3 h of illumination, which represents a 2.5-fold increase compared to that observed for CTF-NH₂. This research underscores the substantial impact that residues exert on photocatalytic performance. Full article

With urbanization, ozone (O₃) pollution and the urban heat island (UHI) effect have become increasingly prominent. UHI can affect O₃ production and its dilution and dispersion, but the underlying mechanisms remain unclear. This study investigates the spatial and temporal distribution of O₃ pollution and the UHI effect, as well as the influence of UHI on O₃ pollution in the Sichuan Basin. Atmospheric pollution data for O₃ and NO₂ from 2020 were obtained from local environmental monitoring stations, while temperature and single-layer wind field data were sourced from ERA5-Land, a high-resolution atmospheric reanalysis dataset provided by the European Centre for Medium-Range Weather Forecasts (ECMWF). The results indicate the following: (1) O₃ concentrations in the Sichuan Basin exhibit distinct seasonal variations, with the highest levels in spring, followed by summer and autumn, and the lowest in winter. In terms of spatial variation, the overall distribution is highest in western Sichuan, second highest along the Sichuan River, and lowest in central Sichuan. (2) There are significant regional differences in UHII across Sichuan, with medium heat islands (78.63%) dominating western Sichuan, weak heat islands (82.74%) along the Sichuan River, and no heat island (34.79%) or weak heat islands (63.56%) in central Sichuan. Spatially, UHII is mainly distributed in a circular pattern. (3) Typical cities in the Sichuan Basin (Chengdu, Chongqing, Nanchong) show a positive correlation between UHII and O₃ concentration (0.071–0.499), though with an observed temporal lag. This study demonstrates that UHI can influence O₃ concentrations in two ways: first, by altering local heat balance, thereby promoting O₃ production, and second, by generating local winds that contribute to the diffusion or accumulation of O₃, forming distinct O₃ concentration zones. Full article

This paper presents a novel dual-band Gysel filtering power divider (FPD) with an excellent isolation performance and a significantly wide isolation bandwidth. Although Gysel power dividers have been extensively studied in the field of radio frequency (RF), the integration of filtering functionality and the expansion of isolation bandwidth remain challenging. The proposed design addresses these challenges by incorporating frequency transform resonators (FTRs) and a microstrip/slotline (M/S) phase inverter into the classic Gysel topology. The FTR is directly connected to the output port to provide a dual-band response, enabling the Gysel FPD to operate without external coupling between the resonator and the port. The M/S phase inverter is a dual-layer 180${}^{\circ }$ phase shifter, designed to replace the conventional 180${}^{\circ }$ transmission lines loaded between the two isolation resistors of the Gysel FPD, achieving a wide isolation bandwidth. To validate the proposed design method, a dual-band Gysel FPD with center frequencies of 1.4 GHz and 1.7 GHz is designed, fabricated, and measured. The measured results show that the in-band return loss is greater than 20 dB, and the in-band insertion loss is about 0.6 dB, and the amplitude and phase imbalance characteristics are good. In addition, the 20 dB-isolation fractional bandwidth achieves 97% (0.78–2.25 GHz). The measured results show excellent agreement with the simulation results, validating the effectiveness of the proposed design methodology. Full article

Background: Atrial fibrillation (AF) is a common cardiac arrhythmia associated with left atrial dysfunction. The impact of pulmonary vein isolation (PVI) using pulsed field ablation (PFA) on left atrial function has not been previously quantified. This study aims to evaluate the effects of PVI using PFA on left atrial function in patients with AF. Methods: Thirty-four patients undergoing PVI with PFA between July 2022 and November 2023 were included. The left atrial function was assessed using echocardiography pre-procedure and at 6 months post-procedure. Results: The mean age of the patients was 66.5 ± 9.76 years, with 70.6% being male. The cohort included 44% of patients with paroxysmal AF. PVI was successfully achieved in all patients, with a significant improvement in all aspects of left atrial strain at an average of six-month follow-up. The left atrial strain reservoir (LASr) increased from 12.5 ± 5.8% to 21.7 ± 8.1% (p < 0.001). Notably, patients with paroxysmal AF exhibited a greater increase in LASr compared to those with persistent AF. Additionally, pre-procedural sinus rhythm was a significant predictor of better LASr outcomes. Conclusions: PFA is associated with significant improvement in left atrial reservoir strain, suggesting a positive impact on atrial function. These findings have important implications for the therapeutic management of AF and warrant further research. Full article

This paper comprehensively summarizes and discusses the latest research progress in the underwater concrete structure damage repair technology of infrastructures. The prompt application of underwater concrete structure repair technology can effectively deal with the damaged parts of underwater concrete structures, and it can ensure the safe and stable operation of infrastructure and extend its service life. Firstly, this study uses bibliometric methods to analyze the characteristics of the literature on research into underwater concrete repair in the past 30 years (1993–2023), and expounds the research status and hotspots of this field. Then, we conduct a comprehensive classification and discussion of the underwater concrete structure damage repair technologies at the current stage. This technology can be divided into two major types: direct underwater type and dry environment type. Further, the development history of these technologies is systematically sorted out and, combined with practical engineering application cases, the operation processes, applicability, limitations, and economy of these technologies are analyzed. Finally, the challenges and future development trends of the current underwater concrete structure damage repair technology are pointed out, which provides a direction for future research on the intelligent maintenance of underwater concrete structures. Full article

Cavity flows have a wide range of low-speed applications ($M\le 0.3$), such as aircraft wheel wells, ground transportations, and pipelines. They induce strong flow oscillations which can substantially increase noise, drag, vibration, and lead to structural fatigue. In the current study, a steady jet was forced from the cavity trailing edge with different momentum fluxes (J = 0.11 kg/m·s², 0.44 kg/m·s², and 0.96 kg/m·s²). The aim of this study was to investigate the impact of the steady jet on the time-averaged flow field and the cavity separated shear layer oscillations for an open cavity with a length-to-depth ratio of $L/D=4$ at $R{e}_{\theta }=1.28\times {10}^3$. Particle image velocimetry, surface oil flow visualisation, constant temperature anemometry, and pressure measurements were performed. The study found that increasing the jet momentum flux caused a significant increase in thickness and deflection of the cavity separated shear layer. Due to the counterflow interaction between the jet and cavity separated shear layer, the growth rate ($d{\delta }_{\omega }/dx$) of the cavity separated shear layer increased significantly from 0.193 for the no-jet case to 0.273 for the J = 0.96 kg/m·s² case. As a result, the return flow rate increased, causing the separation point on the cavity floor to shift upstream from $x/L\approx 0.2$ for the no-jet case to $x/L\approx 0.1$ for the J = 0.96 kg/m·s² case. Furthermore, increasing the jet momentum flux increased the broadband level of the cavity separated shear layer oscillations. Full article

The application of smart materials in robots has attracted considerable research attention. This study developed an inchworm robot that integrates smart materials and a bionic design, using the unique properties of magnetorheological elastomers (MREs) to improve the performance of robots in complex environments, as well as their adaptability and movement efficiency. This research stems from solving the problem of the insufficient adaptability of traditional bionic robots on different surfaces. A robot that combines an MRE foot, an electromagnetic control system, and a bionic motion mechanism was designed and manufactured. The MRE foot was made from silicone rubber mixed with carbonyl iron particles at a specific ratio. Systematic experiments were conducted on three typical surfaces, PMMA, wood, and copper plates, to test the friction characteristics and motion performance of the robot. On all tested surfaces, the friction force of the MRE foot was reduced significantly after applying a magnetic field. For example, on the PMMA surface, the friction force of the front leg dropped from 2.09 N to 1.90 N, and that of the hind leg decreased from 3.34 N to 1.75 N. The robot movement speed increased by 1.79, 1.76, and 1.13 times on PMMA, wooden, and copper plate surfaces, respectively. The MRE-based intelligent foot design improved the environmental adaptability and movement efficiency of the inchworm robot significantly, providing new ideas for the application of intelligent materials in the field of bionic robots and solutions to movement challenges in complex environments. Full article

In May 2008, Chaitén volcano entered an eruptive process, leading to one of the world’s largest eruptions in recent decades. The magnitude of tephra ejected by the eruption left different types of disturbances and caused diverse forms of environmental damage that were heterogeneously distributed across the surrounding area. We went to the field to assess the early vegetation responses a year after the eruption in September 2009. We evaluated the lateral-blast disturbance zone. We distributed a set of plots in three disturbed sites and one in an undisturbed site. In each of these sites, in a rectangular plot of 1000 m², we marked all standing trees, recording whether they were alive, resprouting, or dead. Additionally, in each site of 80 small plots (~4 m²), we tallied the regenerated plants, their coverage, and the log volume. We described whether the plant regeneration was occurring on a mineral or organic substrate (i.e., ash or leaf litter, respectively). In the blast zone, the eruption created a gradient of disturbance. Close to the crater, we found high levels of devastation marked by no surviving species, scarcely standing-dead trees and logs, and no tree regeneration. At the other extreme end of the disturbance zone, the trees with damaged crowns were resprouting, small plants were regrowing, and seedlings were more dispersed. The main form of regeneration was the resprouting of trunks or buried roots; additionally, a few seedlings were observed in the small plots and elsewhere in disturbed areas. The results suggest that the early stages of succession are shaped by life history traits like dispersion syndrome and regeneration strategy (i.e., vegetative), as was found after other volcanic eruptions. Likewise, the distribution of biological legacies, which is related to disturbance intensity, can cause certain species traits to thrive. For instance, in the blow-down zone, surviving species were chiefly those dispersed by the wind, while in the standing-dead zone, survivors were those dispersed by frugivorous birds. Additionally, we suggest that disturbance intensity variations are related to the elevation gradient. The varying intensities of disturbance further contribute to these ecological dynamics. The early succession in the blast zone of Chaitén volcano is influenced by the interaction between species-specific life history, altitudinal gradient, and biological legacies. Further studies are required to observe the current successional patterns that occur directly in the blast zone and compare these results with those obtained following other volcanic disturbances. Full article

The visual perception of interior architecture plays a crucial role in real estate marketing, influencing the decisions of buyers, interior architects, and real estate agents. These professionals rely on personal assessments of space, often drawing from their experience of using décor to influence how interiors are perceived. While intuition may validate some approaches, this study explores an under-examined aspect of interior design using a mobile eye-tracking device. It investigates how decorative elements affect spatial perception and offers insights into how individuals visually engage with interior environments. By integrating décor into the analysis of interior architecture, this study broadens the traditional scope of the field, demonstrating how décor composition can modulate spatial perception using eye-tracking technology. Results show that effective styling can redirect attention from key architectural elements, sometimes causing them to be overlooked during the critical first moments of observation commonly known as the “first impression”. These findings have important implications for interior design practice and architectural education. Full article

Background/Objectives: Modern sequencing technologies have transformed the identification of medicinal plant species and varieties, overcoming the limitations of traditional morphological and chemical approaches. This review explores the key DNA-based techniques, including molecular markers, DNA barcoding, and high-throughput sequencing, and their contributions to enhancing the accuracy and reliability of plant identification. Additionally, the integration of multi-omics approaches is examined to provide a comprehensive understanding of medicinal plant identity. Methods: The literature search for this review was conducted across databases such as Google Scholar, Web of Science, and PubMed, using keywords related to plant taxonomy, genomics, and biotechnology. Inclusion criteria focused on peer-reviewed studies closely related to plant identification methods and techniques that contribute significantly to the field. Results: The review highlights that while sequencing technologies offer substantial improvements, challenges such as high costs, technical expertise, and the lack of standardized protocols remain barriers to widespread adoption. Potential solutions, including AI-driven data analysis and portable sequencers, are discussed. Conclusions: This review provides a comprehensive overview of molecular techniques, their transformative impact, and future perspectives for more accurate and efficient medicinal plant identification. Full article

Precisely predicting vegetation backscatter involves various challenges, such as complex vegetation structure, soil–vegetation interaction, and data availability. Deep learning (DL) works as a powerful tool to analyze complex data and approximate the nonlinear relationship between variables, thus exhibiting potential applications in microwave scattering problems. However, few DL-based approaches have been developed to reproduce vegetation backscatters owing to the lack of acquiring a large amount of training data. Motivated by a relatively accurate single-scattering radiative transfer model (SS-RTM) and radar measurements, we, for the first time to our knowledge, introduce a transfer learning (TL)-based approach to estimate the radar backscatter of vegetation canopy in the case of soybean fields. The proposed approach consists of two steps. In the first step, a simulated dataset was generated by the SS-RTM. Then, we pre-trained two baseline networks, namely, a deep neural network (DNN) and long short-term memory network (LSTM), using the simulated dataset. In the second step, limited measured data were utilized to fine-tune the previously pre-trained networks on the basis of TL strategy. Extensive experiments, conducted on both simulated data and in situ measurements, revealed that the proposed two-step TL-based approach yields a significantly better and more robust performance than SS-RTM and other DL schemes, indicating the feasibility of such an approach in estimating vegetation backscatters. All these outcomes provide a new path for addressing complex microwave scattering problems. Full article

The glaciers of the Himalayas are essential for water resources in South Asia and the Qinghai–Tibet Plateau, but they are undergoing accelerated mass loss, posing risks to water security and increasing glacial hazards. This study examines long-term changes in the geometry and flow speeds of both land- and lake-terminating glaciers at the headwaters of the Yarlung Zangbo River, using field measurements, remote sensing, and numerical ice flow modeling. We observed significant heterogeneity in glacier behaviors across the region, with notable differences between glacier terminus types and even among neighboring glaciers of the same type. Between 1974 and 2020, glacier thinning and mass loss rates doubled in the early 21st century ($-0.57\pm 0.05$ m w.e. a⁻¹) compared to 1974–2000 ($-0.24\pm 0.11$ m w.e. a⁻¹). While lake-terminating glaciers generally experienced more rapid retreat and mass loss, the land-terminating N241 Glacier displayed comparable mass loss rates. Lake-terminating glaciers retreated by over 1000 m between 1990 and 2019, while land-terminating glaciers retreated by less than 750 m. The ITS_LIVE velocity dataset showed higher and more variable flow speeds in lake-terminating glaciers. Numerical modeling from 2000 to 2017 revealed divergent changes in flow regimes, with lake-terminating glaciers generally experiencing acceleration, while land-terminating glaciers showed either a slowing down or stable flow behavior. Our findings underscore the significant role of lake-terminating glaciers in contributing to ice mass loss, emphasizing the need for advanced glacier models that incorporate dynamic processes such as frontal calving and longitudinal coupling. Full article

In geoenergy science and engineering, well placement optimization is the process of determining optimal well locations and configurations to maximize economic value while considering geological, engineering, economic, and environmental constraints. This complex multi-million-dollar problem involves optimizing multiple parameters using computationally intensive reservoir simulations, often employing advanced algorithms such as optimization algorithms and machine/deep learning techniques to find near-optimal solutions efficiently while accounting for uncertainties and risks. This study proposes a hybrid workflow for determining the locations of production wells during primary oil recovery using a multi-modal convolutional neural network (M-CNN) integrated with an evolutionary optimization algorithm. The particle swarm optimization algorithm provides the M-CNN with full-physics reservoir simulation results as learning data correlating an arbitrary well location and its cumulative oil production. The M-CNN learns the correlation between near-wellbore spatial properties (e.g., porosity, permeability, pressure, and saturation) and cumulative oil production as inputs and output, respectively. The learned M-CNN predicts oil productivity at every candidate well location and selects qualified well placement scenarios. The prediction performance of the M-CNN for hydrocarbon-prolific regions is improved by adding qualified scenarios to the learning data and re-training the M-CNN. This iterative learning scheme enhances the suitability of the proxy for solving the problem of maximizing oil productivity. The validity of the proxy is tested with a benchmark model, UNISIM-I-D, in which four oil production wells are sequentially drilled. The M-CNN approach demonstrates remarkable consistency and alignment with full-physics reservoir simulation results. It achieves prediction accuracy within a 3% relative error margin, while significantly reducing computational costs to just 11.18% of those associated with full-physics reservoir simulations. Moreover, the M-CNN-optimized well placement strategy yields a substantial 47.40% improvement in field cumulative oil production compared to the original configuration. These findings underscore the M-CNN’s effectiveness in sequential well placement optimization, striking an optimal balance between predictive accuracy and computational efficiency. The method’s ability to dramatically reduce processing time while maintaining high accuracy makes it a valuable tool for enhancing oil field productivity and streamlining reservoir management decisions. Full article