Search Results (3,370)

Fructooligosaccharides (FOS) are short fructans with different degrees of polymerization (DP) and bonds in their structure, generated by the distinct activities of fructosyltransferase enzymes, which produce distinct types of links. FOS are in high demand on the market, mainly because of their prebiotic effects. In recent years, depending on the link type in the FOS structure, prebiotic activity has been shown to be increased. Studies on β-fructanofuranosidases (Ffasa), enzymes with fructosyltransferase activity in yeasts, have reported the production of ¹F-FOS, ⁶F-FOS, and ⁶G-FOS. The aims of this investigation were to evaluate the capability of fifteen different yeasts to grow in Agave sp. juices and to determine the potential of these juices as substrates for FOS production. Additionally, the research aimed to corroborate and analyze the fructosyltransferase activity of enzymatic extracts obtained from agave yeasts by distinct induction media and to identify the role and optimal parameters (time and sucrose and glucose concentrations) for FOS and disaccharides production through Box–Behnken designs. To carry out such a task, different techniques were employed: FT-IR, TLC, and HPAEC-PAD. We found two yeasts with fructosyltransferase activity, P. kudriavzevii ITMLB97 and C. lusitaniae ITMLB85. In addition, within the most relevant results, the production of the FOS 1-kestose, 6-kestose, and neokestose, as well as disaccharides inulobiose, levanobiose, and blastose, molecules with potential applications, was determined. Overall, FOS production requires suitable yeast species, which grow in a medium under optimal conditions, from which microbial enzymes with industrial potential can be obtained. Full article

Straw fiber curtain contains a plant fiber blanket woven from crop straw, which is mainly used to protect embankment slopes from rainwater erosion. To investigate the erosion control performance of slopes covered with straw fiber curtains of different structural configurations, physical model tests were conducted in a 95 cm × 65 cm × 50 cm (length × height × width) test box with a slope ratio of 1:1.5 under controlled artificial rainfall conditions (20 mm/h, 40 mm/h, and 60 mm/h). The study evaluated the runoff characteristics, sediment yield, and key hydrodynamic parameters of slopes under the coverage of different straw fiber curtain types. The results show that the A-type straw fiber curtain (woven with strips of straw fiber) has the best effect on water retention and sediment reduction, while the B-type straw fiber curtain (woven with thicker straw strips) with vertical straw fiber has a better effect regarding water retention and sediment reduction than the B-type transverse straw fiber curtain. The flow of rainwater on a slope covered with straw fiber curtain is mainly a laminar flow. Straw fiber curtain can promote the conversion of water flow from rapids to slow flow. The Darcy-Weisbach resistance coefficient of straw fiber curtain increases at different degrees with an increase in rainfall time. Full article

The presence of nest boxes not only increases the reproductive success of many passerine birds in transformed forest habitats, but they also constitute important artificial microhabitats for many groups of invertebrates. One of such groups which have been often found in this microhabitat is saprophagous mites from the suborder Uropodina (Acari: Mesostigmata). The current study was conducted in October 2023 and 2024 in Bory Tucholskie National Park (BTNP) (northern Poland), where material from 137 tit (Paridae) and nuthatch (Sitta europaea) nest boxes was collected. The aim of this study was to analyse the stability of the communities of Uropodina in nest boxes in the park in two seasons and to determine whether the mite community structure within these nest boxes is similar in each year. The second aim was to analyse the abundance of Uropodina in relation to the composition of the nest box bedding material. This study revealed that the community in the scrutinised nest boxes was formed in both seasons by two species of nidicolous Uropodina species, i.e., Leiodinychus orbicularis (C.L. Koch, 1839) and Chiropturopoda nidiphila (Wiśniewski and Hirschmann, 1993), and that the species composition and the community structure were also very similar in both years. This study revealed that Ch. nidiphila dominated in the nest boxes with moss and grass, whereas L. orbicularis was most abundant in the boxes where the bedding was a mixture of mammalian hair and grass. However, no statistically significant differences in the abundance of these two mite species in both cases were revealed. Full article

Focusing on the problem of insufficient detection accuracy caused by blurred target boundaries, variable scales, and severe noise interference in side-scan sonar images, this paper proposes a high-precision detection network named ESL-YOLO, which integrates edge perception and adaptive quality assessment. Firstly, an Edge Fusion Module (EFM) is designed, which integrates the Sobel operator into depthwise separable convolution. Through a dual-branch structure, it realizes effective fusion of edge features and spatial features, significantly enhancing the ability to recognize targets with blurred boundaries. Secondly, a Self-Calibrated Dual Attention (SCDA) Module is constructed. By means of feature cross-calibration and multi-scale channel attention fusion mechanisms, it achieves adaptive fusion of shallow details and deep-rooted semantic content, improving the detection accuracy for small-sized targets and targets with elaborate shapes. Finally, a Location Quality Estimator (LQE) is introduced, which quantifies localization quality using the statistical characteristics of bounding box distribution, effectively reducing false detections and missed detections. Experiments on the SIMD dataset show that the mAP@0.5 of ESL-YOLO reaches 84.65%. The precision and recall rate reach 87.67% and 75.63%, respectively. Generalization experiments on additional sonar datasets further validate the effectiveness of the proposed method across different data distributions and target types, providing an effective technical solution for side-scan sonar image target detection. Full article

Suspension bridges, as a type of long-span bridge, usually have a larger longitudinal displacement at the end of the beam (LDBD). LDBD can be used to evaluate the safety of bridge components at the end of the beam. However, due to factors such as sensor failure and system maintenance, LDBD in the bridge health monitoring system is often missing. Therefore, this study reconstructs the missing part of LDBD based on the long short-term memory network (LSTM) and various data. Specifically, first, the monitoring data that may be related to LDBD in a suspension bridge is analyzed, and the temperature and beam end rotation angle data (RDBD) at representative locations are selected. Then, the temperature data at different places of the bridge are used as the input of the LSTM model to compare and analyze the prediction effect of LDBD. Next, RDBD is used as the input of the LSTM model to observe the prediction effect of LDBD. Finally, temperature and RDBD are used as the input of the LSTM model to observe whether the prediction effect of the LSTM model is improved. The results show that compared with other parts of the bridge, the prediction effect of the temperature inside the box girder in the main span as the model input is better; when RDBD is used as the input of the LSTM model, it is better than the prediction effect of temperature as the model input; temperature and RDBD have higher prediction accuracy when used as the input of the LSTM model together than when used separately as the input of the LSTM model. Full article

Fish coloration plays an important role in reproduction and camouflage, yet capturing color variation under field conditions remains challenging. We present a standardized, semi-automated protocol for measuring body coloration in the popular model fish threespine stickleback (Gasterosteus aculeatus). Individuals are photographed in a controlled light box within minutes of capture, and color is sampled from eight anatomically defined standard sites in human-perception-based CIELAB space. Analyses combine univariate color metrics, multivariate statistics, and the ΔE* perceptual difference index to detect subtle shifts in hue and brightness. Validation on pre-spawning fish shows the method reliably distinguishes males and females well before full breeding colors develop. Although it currently omits ultraviolet signals and fine-scale patterning, the approach scales efficiently to large sample sizes and varying lighting conditions, making it well suited for population-level surveys of camouflage dynamics, sexual dimorphism, and environmental influences on coloration in sticklebacks. Full article

To enhance the perception capability of radar in complex electromagnetic environments, this paper proposes an intelligent jamming recognition and parameter estimation method based on deep learning. The core idea of the method is to reformulate the jamming perception problem as an object detection task in computer vision, and we pioneer the application of oriented object detection to this problem, enabling simultaneous jamming classification and key parameter estimation. This method takes the time–frequency spectrogram of jamming signals as input. First, it employs the oriented object detection network YOLOv8-OBB (You Only Look Once Version 8–oriented bounding box) to identify three types of classic suppression jamming and five types of Interrupted Sampling Repeater Jamming (ISRJ) and outputs the positional information of the jamming in the time–frequency spectrogram. Second, for the five ISRJ types, a post-processing algorithm based on boxes fusion is designed to further extract features for secondary recognition. Finally, by integrating the detection box information and secondary recognition results, parameters of different ISRJ are estimated. In this paper, ablation experiments from the perspective of Non-Maximum Suppression (NMS) are conducted to simulate and compare the OBB method with the traditional horizontal bounding box-based detection approaches, highlighting OBB’s detection superiority in dense jamming scenarios. Experimental results show that, compared with existing jamming detection methods, the proposed method achieves higher detection probabilities under the jamming-to-noise ratio (JNR) ranging from 0 to 20 dB, with correct identification rates exceeding 98.5% for both primary and secondary recognition stages. Moreover, benefiting from the advanced YOLOv8 network, the method exhibits an absolute error of less than 1.85% in estimating six types of jamming parameters, outperforming existing methods in estimation accuracy across different JNR conditions. Full article

Structural cracks significantly threaten the safety and longevity of historical buildings, which are essential parts of cultural heritage. Conventional inspection techniques, which depend heavily on manual visual evaluations, tend to be inefficient and subjective. This research introduces an automated framework for crack and damage detection using advanced YOLO (You Only Look Once) models, aiming to improve both the accuracy and efficiency of monitoring heritage structures. A dataset comprising 2500 high-resolution images was gathered from historical buildings and categorized into four levels of damage: no damage, minor, moderate, and severe. Following preprocessing and data augmentation, a total of 5000 labeled images were utilized to train and evaluate four YOLO variants: YOLOv5, YOLOv8, YOLOv10, and YOLOv11. The models’ performances were measured using metrics such as precision, recall, mAP@50, mAP@50–95, as well as losses related to bounding box regression, classification, and distribution. Experimental findings reveal that YOLOv10 surpasses other models in multi-target detection and identifying minor damage, achieving higher localization accuracy and faster inference speeds. YOLOv8 and YOLOv11 demonstrate consistent performance and strong adaptability, whereas YOLOv5 converges rapidly but shows weaker validation results. Further testing confirms YOLOv10’s effectiveness across different structural components, including walls, beams, and ceilings. This study highlights the practicality of deep learning-based crack detection methods for preserving building heritage. Future advancements could include combining semantic segmentation networks (e.g., U-Net) with attention mechanisms to further refine detection accuracy in complex scenarios. Full article

Reliable adaptation in Class II resin-based composite (RBC) restorations with margins on cementum remains challenging. This study compared the internal adaptation (IA) and closed porosity (CP) of three restorative strategies for such cavities, using either total-etch or self-etch adhesive approaches. Standardized box-only cavities were prepared on both proximal surfaces of 30 extracted molars, applying self-etch on mesial and total-etch on distal cavities. Group 1 used a layered microhybrid RBC; Group 2 used a flowable RBC base beneath a layered microhybrid RBC; and Group 3 used a thermoviscous RBC in a 4 mm bulk increment. Micro-computed tomography was employed to assess IA and CP. ANOVA, Tukey post hoc, and univariate analyses were used to evaluate group differences and the effects of adhesive/restorative strategies. Group 2 demonstrated the best adaptation (0.10%), whereas Group 3 exhibited the highest internal gap ratio (0.63%) and the lowest CP (p = 0.006). Total-etch adhesive significantly improved IA compared to self-etch (p < 0.001). These findings emphasize the impact of material selection and adhesive technique on the quality of restorations in cementum-located Class II cavities. Full article

The biggest challenge in cancer therapy is tumor resistance to the classical approach. Thus, research interest has shifted toward the cancer stem cell population (CSC). CSCs are a small subpopulation of cancer cells within tumors with self-renewal, differentiation, and metastasis/malignant potential. They are involved in tumor initiation and development, metastasis, and recurrence. Method. A narrative review of significant scientific publications related to the topic and its applicability in endometrial cancer (EC) was performed with the aim of identifying current knowledge about the identification of CSC populations in endometrial cancer, their biological significance, prognostic impact, and therapeutic targeting. Results: Therapy against the tumor population alone has no or negligible effect on CSCs. CSCs, due to their stemness and therapeutic resistance, cause tumor relapse. They target CSCs that may lead to noticeable persistent tumoral regression. Also, they can be used as a predictive marker for poor prognosis. Reverse transcription–polymerase chain reaction (RT-PCR) demonstrated that the cultured cells strongly expressed stemness-related genes, such as SOX-2 (sex-determining region Y-box 2), NANOG (Nanog homeobox), and Oct 4 (octamer-binding protein 4). The expression of surface markers CD133+ and CD44+ was found on CSC as stemness markers. Along with surface markers, transcription factors such as NF-kB, HIF-1a, and b-catenin were also considered therapeutic targets. Hypoxia is another vital feature of the tumor environment and aids in the maintenance of the stemness of CSCs. This involves the hypoxic activation of the WNT/b-catenin pathway, which promotes tumor survival and metastasis. Specific antibodies have been investigated against CSC markers; for example, anti-CD44 antibodies have been demonstrated to have potential against different CSCs in preclinical investigations. Anti-CD-133 antibodies have also been developed. Targeting the CSC microenvironment is a possible drug target for CSCs. Focusing on stemness-related genes, such as the transcription pluripotency factors SOX2, NANOG, and OCT4, is another therapeutic option. Conclusions: Stemness surface and gene markers can be potential prognostic biomarkers and management approaches for cases with drug-resistant endometrial cancers. Full article

Mabroom dates (Phoenix dactylifera L.) are recognized as one of the most important crops in Qatar. Fresh fruit dates are susceptible to mould and post-harvest spoilage, resulting in a significant financial loss. Octanoic fatty acid (OFA) has been shown to regulate the growth of mould-causing organisms such as fungi and bacteria. It is known to have antibacterial properties. The objective of the current study was to evaluate the in vitro effect of OFA on the post-harvest pathogens of Mabroom fruits. Fresh, apparently healthy, and fully ripe Mabroom dates were obtained from the National Agriculture and Food Corporation (NAFCO). The chosen fruits were packed in sterile, well-ventilated plastic boxes and transported to the lab under controlled conditions. The fruits were distributed into five groups (G1 to G5). The groups G1, G2, and G3 received 1%, 2%, and 3.5% OFA, respectively, while G4 was left untreated and G5 was washed only with tap water as a positive control treatment. Each group contained 200 g of fresh and healthy semi-soft dates. The samples were then dried and incubated in a humidity chamber at 25 °C ± 2 for seven days. The signs and symptoms of decay were monitored and recorded. The presence of pathogens was confirmed via phenotypic and microscopic-based methods. The results showed a significant difference (p ≤ 0.05) among the groups. OFA at 3.5% had the strongest inhibitory action against post-harvest pathogens, followed by OFA2%. However, there were no differences (p ≤ 0.05) between OFA1% and the control groups. Aspergillus spp., Penicillium spp., Rhizopus spp., and Botrytis spp. were most abundant in the control group, followed by OFA2% and OFA1%, respectively. In conclusion, octanoic fatty acid at 3.5% may improve the quality of date fruits through its high antimicrobial activity, reduce the effect of post-harvest decay, minimize the loss of date fruits during storage, and improve the sustainability of date fruits. Further experiments are necessary to confirm the effectiveness of OFA as a green solution for sustainable date fruit production. Full article

Deep Reinforcement Learning (DRL) algorithms often exhibit significant performance variability across different training runs, even with identical settings. This paper investigates the hypothesis that a key contributor to this variability is the divergence in the observation spaces explored by individual learning agents. We conducted an empirical study using Proximal Policy Optimization (PPO) agents trained on eight Atari environments. We analyzed the collected agent trajectories by qualitatively visualizing and quantitatively measuring the divergence in their explored observation spaces. Furthermore, we cross-evaluated the learned actor and value networks, measuring the average absolute TD-error, the RMSE of value estimates, and the KL divergence between policies to assess their functional similarity. We also conducted experiments where agents were trained from identical network initializations to isolate the source of this divergence. Our findings reveal a strong correlation: environments with low-performance variance (e.g., Freeway) showed high similarity in explored observation spaces and learned networks across agents. Conversely, environments with high-performance variability (e.g., Boxing, Qbert) demonstrated significant divergence in both explored states and network functionalities. This pattern persisted even when agents started with identical network weights. These results suggest that differences in experiential trajectories, driven by the stochasticity of agent–environment interactions, lead to specialized agent policies and value functions, thereby contributing substantially to the observed inconsistencies in DRL performance. Full article

Strawberry planting requires different amounts of soil water-holding capacity and fertilizer at different growth stages. Determining the stages of strawberry growth has important guiding significance for irrigation, fertilization, and picking. Quick and accurate identification of strawberry plants at different stages can provide important information for automated strawberry planting management. We propose an improved multistage identification model for strawberry based on the YOLOv9 algorithm—the ASHM-YOLOv9 model. The original YOLOv9 showed limitations in detecting strawberries at different growth stages, particularly lower precision in identifying occluded fruits and immature stages. We enhanced the YOLOv9 model by introducing the Alterable Kernel Convolution (AKConv) to improve the recognition efficiency while ensuring precision. The squeeze-and-excitation (SE) network was added to increase the network’s capacity for characteristic derivation and its ability to fuse features. Haar wavelet downsampling (HWD) was applied to optimize the Adaptive Downsampling module (Adown) of the initial model, thereby increasing the precision of object detection. Finally, the CIoU function was replaced by the Minimum Point Distance based IoU (MPDIoU) loss function to effectively solve the problem of low precision in identifying bounding boxes. The experimental results demonstrate that, under identical conditions, the improved model achieves a precision of 97.7%, a recall of 97.2%, mAP50 of 99.1%, and mAP50-95 of 90.7%, which are 0.6%, 3.0%, 0.7%, and 7.4% greater than those of the original model, respectively. The parameters, model size, and floating-point calculations were reduced by 3.7%, 5.6% and 3.8%, respectively, which significantly boosted the performance of the original model and outperformed that of the other models. Experiments revealed that the model could provide technical support for the multistage identification of strawberry planting. Full article

This study evaluated birch and oak ash extracts as alternative extractants for isolating humic substances (HSs) from peat and lignite. The effects of ultrasound intensity, extraction time, and temperature were optimized using a Box–Behnken design and validated statistically. The highest HSs yields were obtained from peat with oak ash extract (pH 13.18), compared to birch ash extract (pH 12.09). Optimal process parameters varied by variant, falling within 309–391 mW∙cm⁻², 116–142 min, and 67–79 °C. HSs extracted under optimal conditions were fractionated into humic acids (HAs) and fulvic acids (FAs), and then analyzed by elemental analysis, Fourier Transform Infrared Spectroscopy (FTIR), and Cross-Polarization Magic Angle Spinning Carbon-13 Nuclear Magnetic Resonance Spectroscopy (CP/MAS ¹³C NMR). The main differences in HSs quality were influenced by raw material and fraction type. However, the use of birch ash extract consistently resulted in a higher proportion of carboxylic structures across all fractions. Overall, wood ash extract, especially from oak, offers a sustainable and effective alternative to conventional extractants, particularly for HSs isolation from lignite. Notably, HSs yield from lignite with oak ash extract (29.13%) was only slightly lower than that achieved with 0.5 M NaOH (31.02%), highlighting its practical potential in environmentally friendly extraction technologies. Full article

As a high-market-value sector, the consumer electronics industry is particularly vulnerable to reputational damage from surface defects in shipped products. However, the high level of automation and the short product life cycles in this industry make defect sample collection both difficult and inefficient. This challenge has led to a severe shortage of publicly available, comprehensive datasets dedicated to surface defect detection, limiting the development of targeted methodologies in the academic community. Most existing datasets focus on general-purpose object categories, such as those in the COCO and PASCAL VOC datasets, or on industrial surfaces, such as those in the MvTec AD and ZJU-Leaper datasets. However, these datasets differ significantly in structure, defect types, and imaging conditions from those specific to consumer electronics. As a result, models trained on them often perform poorly when applied to surface defect detection tasks in this domain. To address this issue, the present study introduces a specialized optical sampling system with six distinct lighting configurations, each designed to highlight different surface defect types. These lighting conditions were calibrated by experienced optical engineers to maximize defect visibility and detectability. Using this system, 14,478 high-resolution defect images were collected from actual production environments. These images cover more than six defect types, such as scratches, plain particles, edge particles, dirt, collisions, and unknown defects. After data acquisition, senior quality control inspectors and manufacturing engineers established standardized annotation criteria based on real-world industrial acceptance standards. Annotations were then applied using bounding boxes for object detection and pixelwise masks for semantic segmentation. In addition to the dataset construction scheme, commonly used semantic segmentation methods were benchmarked using the provided mask annotations. The resulting dataset has been made publicly available to support the research community in developing, testing, and refining advanced surface defect detection algorithms under realistic conditions. To the best of our knowledge, this is the first comprehensive, multiclass, multi-defect dataset for surface defect detection in the consumer electronics domain that provides pixel-level ground-truth annotations and is explicitly designed for real-world applications. Full article