Dense Distribution Research Articles

A method of nucleus image segmentation and counting based on TC-UNet++ and distance watershed

Nucleus segmentation and counting play a crucial role in many cell analysis applications. However, the dense distribution and blurry boundaries of nucleus make nucleus segmentation tasks challenging. This paper proposes a novel segmentation and counting method. Firstly, TC-UNet++ is proposed to achieve a global segmentation. Then, the distance watershed method is used to finish local segmentation, which separate the adhesion and overlap part of the image. Finally, counting method is performed to obtain information on the counting number, area and center of mass of nucleus. TC-UNet++ achieved a Dice coefficient of 89.95% for cell instance segmentation on the Data Science Bowl dataset, surpassing the original U-Net++ by 0.23%. It also showed a 5.09% improvement in counting results compared to other methods. On the ALL-IDB dataset, TC-UNet++ reached a Dice coefficient of 83.97%, a 7.93% increase over the original U-Net++. Additionally, its counting results improved by 16.82% compared to other approaches. These results indicate that our method has a more complete and reasonable nucleus segmentation and counting scheme compared to other methods.

The Decontamination Effect of an Oscillating Chitosan Brush Compared With an Ultrasonic PEEK-Tip: An InVitro Study Using a Dynamic Biofilm Model.

This study aimed to assess the effect of an oscillating chitosan brush (OCB) compared with an ultrasonic device with PEEK tip (US-PEEK) for mechanical implant surface decontamination using an invitro model combining 3D models and a validated dynamic multispecies biofilm. A multispecies biofilm using six bacterial strains (Streptococcus oralis, Veillonella parvula, Actinomyces naeslundii, Fusobacterium nucleatum, Porphyromonas gingivalis, and Aggregatibacter actinomycetemcomitans) was seeded on dental implants with machined and sandblasted, large-grit and acid-etched (SLA) surfaces. These were installed in 3D models depicting peri-implant defect. Mechanical decontamination was performed for 120 s using either an OCB or a US-PEEK. A negative control group received no treatment. Scanning electron microscopy (SEM) was used to evaluate the bacterial composition and quantitative PCR (qPCR) analyzed the number of each bacterial species [colony-forming units per milliliter (CFU/mL)]. Well-structured biofilms with a dense microbial distribution were observed on the negative control implants after 72 h. qPCR following mechanical decontamination showed a scarce bacterial reduction in the OCB group. The US-PEEK group exhibited a significant decrease in bacterial species compared to both OCB and control groups (p < 0.05). A biofilm removal effect was also observed in the OCB group for the machined implant surfaces. In vitro assessment using an anatomical 3D model showed that mechanical decontamination effectively reduced biofilm. The US-PEEK group demonstrated biofilm reduction on the SLA surface, while the OCB group showed a reduction on the machined implant surface. Additionally, the US-PEEK group demonstrated greater efficacy in reducing bacterial numbers.

Bioinspired core-shell microparticle for dual-delivery of prebiotic and probiotic for the treatment of ulcerative colitis

Lactiplantibacillus plantarum (LP) is a well-known probiotic strain that has a beneficial effect in preventing ulcerative colitis. However, delivering a sufficient number of viable LP to the colon still face challenges due to its vulnerability to the highly complex intestinal flora ecosystem. Herein, we present a centrifuge-driven micronozzle system designed for double-layered core-shell alginate microcapsules (DAM), which can serve as an effective carrier for dual delivery of resistant starch nanoparticles (RSNP, prebiotic) and LP (probiotics) for the treatment of colitis. This system enables precise loading of LP and RSNP within the core and shell regions of DAM, respectively. The resulting LP/RS@DAM exhibited a high encapsulation efficiency of LP (108 CFU per bead), in which the dense distribution of RSNP in the shell effectively protected LP against acidic conditions (pH 2) and maintained the cell viability up to 52 % even after long-term storage for 30 days. Furthermore, LP/RS@DAM effectively enhances the production of short-chain fatty acids, leading to a reduction in inflammatory cytokines and restoration of intestinal microbial diversity in dextran sulfate sodium (DSS)-induced colitis. We believe that this innovative approach would offer a potential solution for improving colitis management and paving the way for tailored therapeutic interventions in gastrointestinal disorders.

Deep Learning-based Enhancement of Fluorescence Labeling for Accurate Cell Lineage Tracing During Embryogenesis.

Automated cell lineage tracing throughout embryogenesis plays a key role in the study of regulatory control of cell fate differentiation, morphogenesis and organogenesis in the development of animals, including nematode Caenorhabditis elegans. However, automated cell lineage tracing suffers from an exponential increase in errors at late embryo because of the dense distribution of cells, relatively low signal-to-noise ratio (SNR) and imbalanced intensity profiles of fluorescence images, which demands a huge amount of human effort to manually correct the errors. The existing image enhancement methods are not sensitive enough to deal with the challenges posed by the crowdedness and low signal-to-noise ratio. An alternative method is urgently needed to assist the existing detection methods in improving their detection and tracing accuracy, thereby reducing the huge burden for manual curation. We developed a new method, termed as DELICATE, that dramatically improves the accuracy of automated cell lineage tracing especially during the stage post 350 cells of C. elegans embryo. DELICATE works by increasing the local SNR and improving the evenness of nuclei fluorescence intensity across cells especially in the late embryos. The method both dramatically reduces the segmentation errors by StarryNite and the time required for manually correcting tracing errors up to 550-cell stage, allowing the generation of accurate cell lineage at large-scale with a user-friendly software/interface. All images and data are available at https://doi.org/10.6084/m9.figshare.26778475.v1. The code and user-friendly software are available at https://github.com/plcx/NucApp-develop. Supplementary data are available at Bioinformatics online.

BioSAM: Generating SAM Prompts From Superpixel Graph for Biological Instance Segmentation.

Proposal-free instance segmentation methods have significantly advanced the field of biological image analysis. Recently, the Segment Anything Model (SAM) has shown an extraordinary ability to handle challenging instance boundaries. However, directly applying SAM to biological images that contain instances with complex morphologies and dense distributions fails to yield satisfactory results. In this work, we propose BioSAM, a new biological instance segmentation framework generating SAM prompts from a superpixel graph. Specifically, to avoid over-merging, we first generate sufficient superpixels as graph nodes and construct an initialized graph. We then generate initial prompts from each superpixel and aggregate them through a graph neural network (GNN) by predicting the relationship of superpixels to avoid over-segmentation. We employ the SAM encoder embeddings and the SAM-assisted superpixel similarity as new features for the graph to enhance its discrimination capability. With the graph-based prompt aggregation, we utilize the aggregated prompts in SAM to refine the segmentation and generate more accurate instance boundaries. Comprehensive experiments on four representative biological datasets demonstrate that our proposed method outperforms state-of-the-art methods.

Enhancing whipped cream anti-freeze properties: A dual plant system with aquafaba and mung bean protein

The objective of this study was to substitute sodium caseinate (SC) with a dual plant-based alternative to produce a refreshing sour whipped cream at pH 3.0, which could maintain stability when paired with low-pH foods. Aquafaba (AQ) was utilized as a novel foam booster, enhancing the whipped properties of the whipped cream, while pH12-shifting treated mung bean protein (BMBP) functioned as an interfacing stabilizer, improving its stability. Freeze-thaw stability of the aerated emulsion and the protein-protein interactions in the emulsion were characterized by analyzing water distribution, thermodynamics, serum loss, and protein retention rates. The high bound water content and dense bubble distribution of whipped cream prepared by AQ and native mung bean protein (NMBP) delivered superior freeze-thaw stability, shown as reduced serum loss, higher protein retention, and increased hardness. In addition, the internal fat network of the whipped cream made from a 1:1 combination of NMBP and BMBP showed moderate agglomeration with a comparable γmax value as SC-based whipped cream, indicating enhanced resistance to deformation and improved short-term shape retention. In conclusion, the combination of AQ and MBP provided viable options for the replacement of SC in commercial whipped cream.

Investigating the microstructure and high-temperature wear resistance of TiAl/WC coating modified via scanning electron beam

In this study, TiAl/WC cladding coatings were modified to improve high-temperature wear resistance by scanning electron beam treatment. Results of the microstructure reveal that the modified coatings are composed of an α2-Ti3Al matrix, with a high density of TiC reinforced phase and Ti3AlC2 MAX phase. At a scanning speed of 6 mm/s, TiAl/WC coating exhibits superior microhardness and high-temperature wear resistance. After the wear test at 800 °C, the minimum wear volume of modified TiAl/WC coating is 0.084 mm3, which is 4.47 times smaller than that of the TC21 substrate. It is mainly attributed to the dense and uniform distribution of hard TiC with a rigid supporting role and Ti3AlC2 MAX phases with a self-lubricating effect. Furthermore, due to the effect of frictional heat, the decomposition of Ti3AlC2 promoted the formation of a dense Al2O3 protective film. The wear mechanism of modified TiAl/WC coatings exhibits a synergistic occurrence of slight adhesive wear, abrasive wear, and oxidative wear. Scanning electron beam technology shows significant potential for extending the service life of the coatings in high-temperature environments.

Dual-functional core–shell microneedle patches for oral ulcers treatment

Microneedle patches have been widely used in transdermal drug delivery due to their painless and enhanced drug delivery. Despite their established success, their application within the oral cavity, characterized by a dense distribution of nociceptor, remains largely unexplored. Here, we introduce a well-designed dual-functional core–shell microneedle patches to address oral ulcers. The outer shell of the microneedle tips is crafted from hyaluronic acid (HA) and loaded with lidocaine, while the inner core, composed of gelatin methyl methacrylate (GelMA) encapsulates dexamethasone. The HA shell rapidly dissolves upon application, facilitating the immediate release of lidocaine to induce anesthesia at the ulcer site, thereby alleviating the associated painful sensations. Simultaneously, GelMA imparts mechanical strength and ensured the sustained release of dexamethasone. In vivo experiments further demonstrated that the Dex@MN group exhibiting the most rapid healing rate also displayed the highest levels of collagen deposition and the lowest concentrations of inflammatory factors. This collective evidence underscores the considerable potential of dual-functional core–shell microneedle patches in the treatment of oral diseases.

Probabilistic Air Traffic Complexity Analysis Considering Prediction Uncertainties in Traffic Scenarios

This article presents a methodology for analyzing probabilistic air traffic complexity by integrating prediction uncertainties in convective weather scenarios. With the Performance Review Unit (PRU) model as a base, this method modifies the original framework by incorporating a weather-related complexity indicator. The approach was tested in Austrian airspace using ensemble weather forecasts and historical flight plan data. The results demonstrated that a probabilistic model effectively assesses traffic complexity and captures trends in complexity over time, providing greater reliability in high-complexity sectors. Validation revealed a strong alignment between simulator complexity values and probabilistic complexity, especially in sectors characterized by dense data distributions. In contrast, sectors with more elongated distributions tended to overestimate complexity. Quantitative analysis indicated that the error between the probabilistic mean complexity and the simulator complexity values ranged from 12% to 23%, with higher errors in sectors with lower complexity. This validation confirmed the model’s ability to predict complexity trends, thereby assisting flow manager positions (FMPs) in traffic flow and airspace management. Overall, this study demonstrated that probabilistic complexity assessment provides a deeper understanding of traffic behaviour, facilitating more effective air traffic flow management in uncertain and dynamic conditions.

CFD–DEM modeling of particle segregation behavior in a simulated flash smelting furnace

Particle behavior plays a crucial role in flash smelting furnaces, but limited understanding is hindering further development of this modern pyrometallurgical process. This study presents a two-way coupled CFD–DEM approach to investigate particle distribution, collision, and segregation behaviors within a furnace. The simulation results are validated against experimental data, demonstrating good agreement. The effects of the particle mass–flow rate and size distribution are then analyzed. The results indicate that small particles are carried by airflows to the center of the particle-dense region, whereas large particles remain at the outer part, worsening segregation. Increasing the mass–flow rate significantly increases the particle number density and number of particle collisions but barely influences size-induced segregation. In contrast, enlarging the size of fed particles reduces particle number density and broadens dense distribution within a safe range. This ensures the necessary space and high mixing index of the particles, which benefits high-rate smelting processes.

DETR-ORD: An Improved DETR Detector for Oriented Remote Sensing Object Detection with Feature Reconstruction and Dynamic Query

Optical remote sensing images often feature high resolution, dense target distribution, and uneven target sizes, while transformer-based detectors like DETR reduce manually designed components, DETR does not support arbitrary-oriented object detection and suffers from high computational costs and slow convergence when handling large sequences of images. Additionally, bipartite graph matching and the limit on the number of queries result in transformer-based detectors performing poorly in scenarios with multiple objects and small object sizes. We propose an improved DETR detector for Oriented remote sensing object detection with Feature Reconstruction and Dynamic Query, termed DETR-ORD. It introduces rotation into the transformer architecture for oriented object detection, reduces computational cost with a hybrid encoder, and includes an IFR (image feature reconstruction) module to address the loss of positional information due to the flattening operation. It also uses ATSS to select auxiliary dynamic training queries for the decoder. This improved DETR-based detector enhances detection performance in challenging oriented optical remote sensing scenarios with similar backbone network parameters. Our approach achieves superior results on most optical remote sensing datasets, such as DOTA-v1.5 (72.07% mAP) and DIOR-R (66.60% mAP), surpassing the baseline detector.

Construction of COFs@MoS2-Pd Hierarchical Tubular Heterostructures for Enhanced Catalytic Performance.

Here, we report ternary COFs@MoS2-Pd hybrids with an innovative self-sacrificial approach. MoO3@Covalent organic frameworks (COFs) microcables were first prepared and then two-dimensional MoS2 nanosheets (NSs) were integrated onto the surface of COFs, as COFs@MoS2, after treatment with hydrothermal reaction. The MoS2 NSs were used as an excellent support to introduce Pd nanoparticles (NPs) thanks to their reducing ability for the formation of the ternary COFs@MoS2-Pd hybrids. While COF microtubes improved the electrical conductivity of the hybrid materials, they also decreased the aggregation of MoS2 NSs, as a contribution to the enhanced catalytic performance. The mild reaction between MoS2 and Pd2+ ions realized the dense distribution of Pd NPs onto COFs@MoS2 for abundant active sites to further improve the catalytic performance. Thus, the hierarchical MoS2-based ternary hybrids were prepared with the enhanced catalytical performance as validated with the enzyme-like catalysis and the reduction of 4-nitrophenol.

Regulating precipitate characteristics in a rolled AZ80 alloy based on twinning and de-twinning deformation

The effect of twinning and de-twinning on precipitation behavior of a rolled AZ80 alloy has been examined. Results showed that both twinning and de-twinning deformation can promote the precipitation of continuous precipitates. And only dense continuous precipitates can be formed inside twinned and de-twinned regions. The high density crystal defects existing in the twinned and de-twinned regions increase the diffusion rate of solute elements and provide more nucleation sites for precipitation, resulting in a homogeneous and dense distribution of continuous precipitates in these regions. Comparing to direct aging, both post-twinning aging and post-de-twinning aging can remarkably increase the yield strength and peak strength by promoting the precipitation of continuous precipitates. For tension and compression, both direct aging and post-de-twinning aging reduce the strain corresponding to peak strength (εp) and static toughness (UT) to a close level. In contrast, post-twinning aging exhibits little loss in εp and largely increases UT. The strengthening and toughening mechanisms were also discussed in detail.

497 Amylase supplementation influences nutrient digestibility regardless of corn type in broiler chicken diets

Abstract Corn is the primary energy supply in the broiler chicken diet. Physical and nutritional characteristics related to corn endosperm can affect nutrient utilization, as well as the inclusion of exogenous enzymes. This study aimed to evaluate two different corn types classified according to endosperm traits in diets for broiler chickens supplemented or not with amylase. Male broiler chickens (n = 360; 1-d old Cobb 500) were allocated in metabolic battery cages and randomly assigned in a 2 x 2 factorial arrangement (2 corn types; 2 amylase doses: 0 and 90 g/t of diet). A hardness test was performed to categorize and classify each corn type according to breakage force. Corn endosperms were assessed through scanning electron microscopy to investigate structure variations of starch granules. From 19 to 21 d, excreta samples were collected to estimate apparent total tract retention (ATTR) of nutrients and apparent metabolizable energy (AME). On d 26, ileal digesta content was collected to determine apparent ileal nutrients digestibility (AID) and ileal digestible energy (IDE). Data were analyzed using the Student t-test for hardness test (P &amp;lt; 0.05). The Tukey test was performed with a significance declared of 5% to compare the averages obtained for ATTR and AID using the GLM procedure on R Software. Differences were observed in the hardness test (P &amp;lt; 0.05), which classified each corn type either as hard (217 N) or soft (166 N; Table 1). The SEM highlighted structural variations between the floury and vitreous endosperm of each corn. In soft corn, starch granules were more dispersed in the floury endosperm, with a less dense distribution observed in the vitreous endosperm compared with hard corn (Figure 1). However, hard corn exhibited a more compact starch granules distribution in both floury and vitreous endosperm. No differences were observed (P &amp;gt; 0.05) in ATTR of dry matter, crude protein, or AME attributed to corn type, but amylase supplementation improved ATTR of dry matter (P = 0.015) and crude protein (P = 0.020; Table 2). On AID, corn type affected (P = 0.041) dry matter coefficient, and amylase supplementation significantly improved dry matter (P = 0.019), crude protein ileal digestibility (P = 0.012), and IDE (P = 0.018). No interactions between corn type and amylase supplementation were observed (P &amp;gt; 0.05). These findings demonstrated that amylase supplemented in broiler diets impacts nutrient utilization regardless of the corn type used.

AID-YOLO: An Efficient and Lightweight Network Method for Small Target Detector in Aerial Images

The progress of object detection technology is crucial for obtaining extensive scene information from aerial perspectives based on computer vision. However, aerial image detection presents many challenges, such as large image background sizes, small object sizes, and dense distributions. This research addresses the specific challenges relating to small object detection in aerial images and proposes an improved YOLOv8s-based detector named Aerial Images Detector-YOLO(AID-YOLO). Specifically, this study adopts the General Efficient Layer Aggregation Network (GELAN) from YOLOv9 as a reference and designs a four-branch skip-layer connection and split operation module Re-parameterization-Net with Cross-Stage Partial CSP and Efficient Layer Aggregation Networks (RepNCSPELAN4) to achieve a lightweight network while capturing richer feature information. To fuse multi-scale features and focus more on the target detection regions, a new multi-channel feature extraction module named Convolutional Block Attention Module with Two Convolutions Efficient Layer Aggregation Net-works (C2FCBAM) is designed in the neck part of the network. In addition, to reduce the sensitivity to position bias of small objects, a new function, Normalized Weighted Distance Complete Intersection over Union (NWD-CIoU_Loss) weight adaptive loss function, was designed in this study. We evaluate the proposed AID-YOLO method through ablation experiments and comparisons with other advanced models on the VEDAI (512, 1024) and DOTAv1.0 datasets. The results show that compared to the Yolov8s baseline model, AID-YOLO improves the mAP@0.5 metric by 7.36% on the VEDAI dataset. Simultaneously, the parameters are reduced by 31.7%, achieving a good balance between accuracy and parameter quantity. The Average Precision (AP) for small objects has improved by 8.9% compared to the baseline model (YOLOv8s), making it one of the top performers among all compared models. Furthermore, the FPS metric is also well-suited for real-time detection in aerial image scenarios. The AID-YOLO method also demonstrates excellent performance on infrared images in the VEDAI1024 (IR) dataset, with a 2.9% improvement in the mAP@0.5 metric. We further validate the superior detection and generalization performance of AID-YOLO in multi-modal and multi-task scenarios through comparisons with other methods on different resolution images, SODA-A and the DOTAv1.0 datasets. In summary, the results of this study confirm that the AID-YOLO method significantly improves model detection performance while maintaining a reduced number of parameters, making it applicable to practical engineering tasks in aerial image object detection.

Choke Anastomosis: A Key Element Acting as a Shunt Converter Between Adjacent Angiosomes.

Choke anastomosis is commonly recognized as a resistance factor that detrimentally affects the hemodynamics of the skin flap; however, its additional potential physiological roles in normal skin function are currently not fully understood. Ten cadaveric forehead flap specimens pedicled with unilateral STAs were perfused with lead oxide-gelatin mixture, and then dissected into 3 layers, including the super temporal fascia-frontalis-galea aponeurotica layer, the subcutaneous adipose tissue layer, and the "super-thin flap" layer. The forehead flap and stratified specimens underwent molybdenum target x-ray and subsequent transparent processing to effectively visualize the microscopic spatial architecture of arterial vessels across all levels. Based on the different anastomoses near the midline area of the flap, 2 types of arterial perfusion were identified: choke anastomosis type (8/10) and true anastomosis type (2/10). The former formed multiple choke anastomoses near the midline. In the "super-thin flap" layer, arterial perfusion density on the ipsilateral side was significantly higher compared to that on the contralateral side. The arterioles on the ipsilateral side exhibited a dense and uninterrupted distribution, whereas those on the contralateral side appeared sparse and dispersed. The latter exhibited an alternative perfusion pattern; the bilateral arterial vessels were connected with 3 to 5 true anastomoses near the midline. Furthermore, the microscopic architecture confirmed a uniform distribution of arterioles that remained continuous from ipsilateral to contralateral sides in the "super-thin flap" layer. This study demonstrated that choke anastomosis not only impairs blood perfusion in the adjacent angiosomes but also acts as a shunt converter to impact the blood supply of distal skin flaps at different levels through the "trans-territory diversion phenomenon." This results in necrosis of the superficial dermis while preserving survival of the deep subcutaneous adipose tissue.

Extraction of Origanum onites L. using an industrial-type microwave-assisted distillation (MWAD) system: Increasing energy saving and essential oil yield compared to conventional steam distillation

Oregano species are commonly utilized for different medical purposes as well as for other essential oil products. One of the oregano species, Origanum onites L. has a dense natural distribution in the Aegean and Mediterranean regions of Turkey and Greece and is cultivated mainly in Denizli province. Turkey meets 80 % of the world's oregano market. Since Origanum onites L., whose essential oils are of high quality, has a high economic value, assessing the quality and production of essential oils extracted using various methods is important. Microwave-assisted distillation (MWAD) processes are generally carried out under laboratory conditions using clevenger or small-scale specialized instruments made for research in the lab. Thus, designing industrial-scale distillation systems with microwave power is necessary. For this reason, an industrial-type MWAD system with 12 magnetrons (power output of 1 kW) was developed in this work, and both conventional steam distillation (SD) and MWAD of oregano plant were realized, as a novelty. This study experimentally investigated the effects of microwave power and applied time on both energy saving (%) and essential oil yield (%). System was designed to distill 9 kg of dried plants chopped at once and the results obtained were used to obtain reference data for MWAD systems that can be used in industrial systems. The obtained essential oil samples were subjected to GC-MS analysis for chemical analysis. A statistical analysis of the experimental data was performed via Design Expert software to elucidate the impact of microwave power and time on essential oil yield and energy savings. When the results were evaluated, the essential oil yield of oregano plant increased by 4–22 % with the MWAD system compared to SD. The highest increase in yield was achieved with the test (500 W + 40 min). Also, the distillation time with MWAD system was 50 % shorter than with SD system. The MWAD system consumed at least 25 % less energy. According to the GC-MS analysis results, carvacrol increased steadily depending on the time applied microwave power as well as oxygenated monoterpenes. The chemical composition of the essential oils obtained from the proposed MWAD system did not show any incompatible value according to the ISO-7925 standard. As a result, this proposed method can be safely used in industrial systems.

Development of novel Gd-Fe/Ti composites with tunable thermal expansion property

Abstract Titanium alloys are considered one of the most promising materials. However, their poor thermal expansion property remains a major obstacle to their widespread application. In this study, we explored an innovative design strategy to tune the thermal expansion properties of titanium alloy. Specifically, we used rare earth iron intermetallic compounds (Gd-Fe IMCs) with low coefficient of thermal expansion (CTE) as expansion inhibitors to prepare composites with the required thermal expansion properties via in situ reaction. The morphology and size of Gd-Fe IMCs can be effectively controlled by electromagnetic and ultrasonic fields, resulting in a dense distribution of micro/nano-structured Gd-Fe IMCs and strong interfacial bonding of the composites. This alloy has an excellent CTE of 6.8 × 10−6/K and a high ultimate tensile strength of 921 MPa. The improvement in the physical properties (especially in thermal expansion properties) of titanium alloy can be attributed to the synergistic effect between Gd-Fe IMCs and Ti matrix. This design strategy can also be extended to other titanium alloys as a reference for designing low thermal expansion titanium alloys.

Real-Time Detection and Counting of Wheat Spikes Based on Improved YOLOv10

Wheat is one of the most crucial food crops globally, with its yield directly impacting global food security. The accurate detection and counting of wheat spikes is essential for monitoring wheat growth, predicting yield, and managing fields. However, the current methods face challenges, such as spike size variation, shading, weed interference, and dense distribution. Conventional machine learning approaches have partially addressed these challenges, yet they are hampered by limited detection accuracy, complexities in feature extraction, and poor robustness under complex field conditions. In this paper, we propose an improved YOLOv10 algorithm that significantly enhances the model’s feature extraction and detection capabilities. This is achieved by introducing a bidirectional feature pyramid network (BiFPN), a separated and enhancement attention module (SEAM), and a global context network (GCNet). BiFPN leverages both top-down and bottom-up bidirectional paths to achieve multi-scale feature fusion, improving performance in detecting targets of various scales. SEAM enhances feature representation quality and model performance in complex environments by separately augmenting the attention mechanism for channel and spatial features. GCNet captures long-range dependencies in the image through the global context block, enabling the model to process complex information more accurately. The experimental results demonstrate that our method achieved a precision of 93.69%, a recall of 91.70%, and a mean average precision (mAP) of 95.10% in wheat spike detection, outperforming the benchmark YOLOv10 model by 2.02% in precision, 2.92% in recall, and 1.56% in mAP. Additionally, the coefficient of determination (R2) between the detected and manually counted wheat spikes was 0.96, with a mean absolute error (MAE) of 3.57 and a root-mean-square error (RMSE) of 4.09, indicating strong correlation and high accuracy. The improved YOLOv10 algorithm effectively solves the difficult problem of wheat spike detection under complex field conditions, providing strong support for agricultural production and research.

Modulating the valence states of transition metals in FeCoNiMnW high-entropy alloys for enhanced oxygen evolution reaction activity

Amorphous high-entropy alloys (HEA) are promising electrocatalysts with multi-element active sites and dense defect distribution, high valences of 3d transition metals (3d TMs) are beneficial for the oxygen evolution reaction (OER). However, it is still unknown how to enhance the 3d TMs valence state in amorphous HEA to improve OER catalytic activity. Herein, we successfully boosted the valence state of 3d TMs in HEA through electronegativity. The prepared FeCoNiMnW HEA exhibited significant amorphous characteristics. X-ray photoelectron spectroscopy analysis confirmed the formation of Ni3+ sites after adding electronegative W and the widespread occurrence of oxygen vacancies through amorphization. The enhanced oxidation state of Ni reduced the overpotential, while the abundant oxygen vacancies significantly facilitated electron transfer and thus boosting the rate of catalytic reactions. These characteristics lead to the amorphous FeCoNiMnW HEA exhibiting excellent OER performance. Amorphous FeCoNiMnW HEA films deposited on carbon cloth loaded with carbon nanotubes (FeCoNiMnW@CCC) only require an overpotential of 253 mV to generate a current density of 10 mA cm−2, which is much lower than RuO2 films (399 mV) and commercial RuO2 (309 mV), and shows excellent durability for 700 h at 20 mA cm−2 and 100 h at 200mA cm−2. This work provides a novel strategy for designing high-performance OER catalysts through structural and electronic modulation.