Search Results (413)

Multi-object tracking (MOT) in satellite videos is a challenging task due to the small size and blurry features of objects, which often lead to intermittent detection and tracking instability. Many existing object detection and tracking models often struggle with these issues, as they are not designed to effectively handle the unique characteristics of satellite videos. To address these challenges, we propose LocaLock, a joint detection and tracking framework for MOT that incorporates feature matching concepts from single object tracking (SOT) to enhance tracking stability and reduce intermittent tracking results. Specifically, LocaLock utilizes an anchor-free detection backbone for efficiency and employs a local cost volume (LCV) module to perform precise feature matching in the local area. This provides valuable object priors to the detection head, enabling the model to “lock” onto objects with greater accuracy and mitigate the instability associated with small object detection. Additionally, the local computation within the LCV module ensures low computational complexity and memory usage. Furthermore, LocaLock incorporates a novel motion flow (MoF) module to accumulate and exploit temporal information, further enhancing feature robustness and consistency across frames. Rigorous evaluations on the VISO dataset demonstrate the superior performance of LocaLock, surpassing existing methods in tracking accuracy and precision within the demanding satellite video analysis domain. Notably, LocaLock achieved state-of-the-art performance on the VISO benchmark, achieving a multi-object tracking accuracy (MOTA) of 62.6 while ensuring fast running speed. Full article

The genus Ancistrohaptor was proposed to accommodate monopisthocotylans flatworms parasitic on the gills of species of the genus Triportheus in Manaus, Amazonas state, Brazil. Its main characteristics are (a) an accessory piece of the male copulatory organ composed of two distinct parts; (b) dextral or dextroventral vaginal openings; and (c) large ventral anchors with elongated shafts. A new species of Ancistrohaptor was found to parasitize the gills of Triportheus signatus collected from the Salgado River, Ceará State, Brazil. A new species of Monopisthocotyla was collected and described. Ancistrohaptor forficata sp. n. is primarily characterized by having a male copulatory organ with less than one turn, the presence of an articulated accessory piece with a concave rod-shaped termination, and a free accessory piece that is clamp shaped and bifurcated, as well as a dorsal bar with shading present in its medial part. This is the fourth species description of the genus Ancistrohaptor for fish of the genus Triportheus and the first record for T. signatus and the aquatic ecosystems of the Caatinga domain. Full article

Properties of surface anchoring depend on the absorbed exposure energy and various potential interactions associated with liquid crystal and azo dye layers. In this study, we investigate a model of dispersion, steric and photoinduced interactions with the goal of providing a qualitative and quantitative description of orientationally ordered hard uniaxial liquid crystals and azo dye molecules. By using the Onsager theory, we estimated the effect of excluded volume. Typical repulsive potentials between liquid crystal and azo dye molecules are displayed graphically. The presence of statistical dispersion in molecular alignment of liquid crystals leads to potential wells in dipole–dipole interactions. Our mean field theory investigation of dipole–dipole interactions shows that the anchoring free energy is governed by the net interaction energy associated with the averaged dipole moments of liquid crystal and azo dye molecules, photoaligned surface dipole moments, and local charge densities. We also use the Fokker–Planck equation to show that rotational diffusion is described by the effective mean field potential, which includes photoinduced and van der Waals interactions. Our findings underscore the potential of mean field theory for intermolecular couplings in photoaligned surfaces, opening up new pathways of molecular design for a broad range of parameters. Full article

We report the design and development of a novel multifunctional nanostructure, RB-AuSiO₂_HSA-DOX, where tri-modal cancer treatment strategies—photothermal therapy (PTT), photodynamic therapy (PDT), chemotherapy—luminescent properties and targeting are integrated into the same scaffold. It consists of a gold core with optical and thermo-plasmonic properties and is covered by a silica shell entrapping a well-known photosensitizer and luminophore, Rose Bengal (RB). The nanoparticle surface was decorated with Human Serum Albumin (HSA) through a covalent conjugation to confer its targeting abilities and as a carrier of Doxorubicin (DOX), one of the most effective anticancer drugs in clinical chemotherapy. The obtained nanostructure was fully characterized through transmission electron microscopy (TEM), dynamic light scattering (DLS) and UV-visible spectroscopy, with a homogeneous and spherical shape, an average diameter of about 60 nm and negative ζ-potential value Singlet oxygen generation and photothermal properties were explored under green light irradiation. The interaction between DOX-HSA anchored on the nanoplatform was investigated by fluorescence spectroscopy and compared to that of DOX-HSA, pointing out different accessibility of the drug molecules to the HSA binding sites, whether the protein is free or bound to the nanoparticle surface. To the best of our knowledge, there are no studies comparing a drug–HSA interaction with that of the same protein anchored to nanoparticles. Furthermore, the uptake of RB-AuSiO₂_HSA-DOX into MDA-MB-231 mammary cells was assessed by confocal imaging, highlighting—at early time of incubation and as demonstrated by the increased DOX luminescence displayed within cells—a better internalization of the carried anticancer drug compared to the free one, making the obtained nanostructure a suitable and promising platform for an anticancer multimodal approach. Full article

Giant freshwater prawn (Macrobrachium rosenbergii) farming in Bangladesh began in the 1970s and has become a significant export industry. Despite its potential, there are concerns about the environmental sustainability of prawn farming due to its high greenhouse gas (GHG) footprint, but implementation of integrated multi-trophic aquaculture (IMTA) may help minimize the GHG emission. A key element in IMTA is using plants to take up inorganic nutrients released by the prawns, producing valuable plant products and cleaning the water. Using a quadrat sampling method, we conducted a field study in combined prawn and shrimp ponds, aquaculture fishponds, and non-aquaculture waters in south- west Bangladesh to characterize plant diversity and identify suitable species for IMTA in prawn farms. A total of 38 plant species were identified with densities ranging from 4.5–6.1 plants/m² in the aquaculture ponds to 11.6–17.1 plants/m² in the prawn/shrimp and the non-aquaculture ponds. Free-floating plants were the most abundant, followed by emergent, floating anchored, and submerged plants. Most plants have commercial values as food, fodder, fish feed, fertilizer, or medicines to local people. Our results suggest that species within the Oxalis, Ipomoea, Azolla, and Lemna genera are suitable extractive aquatic plants for the implementation of IMTA in prawn farms and may improve the sustainability of prawn production. Full article

The accuracy of node localization plays a crucial role in the performance and reliability of wireless sensor networks (WSNs), which are widely utilized in fields like security systems and environmental monitoring. The integrity of these networks is often threatened by the presence of malicious nodes that can disrupt the localization process, leading to erroneous positioning and degraded network functionality. To address this challenge, we propose the security-aware localization using bat-optimized malicious anchor prediction (BO-MAP) algorithm. This approach utilizes a refined bat optimization algorithm to improve both the precision of localization and the security of WSNs. By integrating advanced optimization with density-based clustering and probabilistic analysis, BO-MAP effectively identifies and isolates malicious nodes. Our comprehensive simulation results reveal that BO-MAP significantly surpasses six current state-of-the-art methods—namely, the Secure Localization Algorithm, Enhanced DV-Hop, Particle Swarm Optimization-Based Localization, Range-Free Localization, the Robust Localization Algorithm, and the Sequential Probability Ratio Test—across various performance metrics, including the true positive rate, false positive rate, localization accuracy, energy efficiency, and computational efficiency. Notably, BO-MAP achieves an impressive true positive rate of 95% and a false positive rate of 5%, with an area under the receiver operating characteristic curve of 0.98. Additionally, BO-MAP exhibits consistent reliability across different levels of attack severity and network conditions, highlighting its suitability for deployment in practical WSN environments. Full article

Due to the numerous disadvantages that come with having anchors in the detection process, a lot of researchers have been concentrating on the design of object detectors that do not rely on anchors. In this work, we use anchor-free object detectors in the field of computer vision for surface defect detection. First, we constructed a surface defect detection dataset about real wind turbine blades, which was supplemented with several methods due to the lack of natural data. Next, we used a number of popular anchor-free detectors (CenterNet, FCOS, YOLOX-S, and YOLOV8-S) to detect surface defects in this blade dataset. After experimental comparison, YOLOV8-S demonstrated the best detection performance, with a high accuracy (79.55%) and a short detection speed (9.52 fps). All the upcoming experiments are predicated on it. Third, we examined how the attention mechanism added to various YOLOV8-S model positions affected the two datasets—our blade dataset and the NEU dataset—and discovered that the insertion methods on the two datasets are the same when focusing on comprehensive performance. Lastly, we carried out a significant amount of experimental comparisons. Full article

Fasciola hepatica is a parasitic trematode that causes fasciolosis in sheep, provoking a decrease in their reproductive capacity, weight gain, meat and milk production, and wool quality. In the pathogenesis of F. hepatica, the penetration and migration of parasitic stages through the liver provoke intense inflammatory immune responses and tissue damage. The aim of this study was to investigate the cytotoxic effects of Fascila hepatica-induced ovine NETs in exposed hepatocytes in vitro, and to analyze whether F. hepatica antigens (FhAg) trigger the release of ovine NETs under hypoxic conditions as well as the roles of matrix metalloproteinase-9 (MMP-9) and CD11b in this cellular process in vitro. Here, isolated ovine PMNs were co-cultured with FhAg under hypoxia (5% O₂) and NETs were visualized via immunofluorescence analyses, confirming their classical characteristics. The quantification of NETs in response to FhAg in hypoxic conditions significantly enhanced the formation of anchored and cell-free NETs (p < 0.01), and NADPH oxidase (NOX) inhibitor diphenylene iodonium (DPI) significantly reduced their production (p < 0.05). Furthermore, the cytotoxic effect of NETs on hepatic cells was determined by using a live/dead-staining with Sytox Orange, thereby demonstrating that FhAg-induced NETs are cytotoxic for hepatic cells (p = 0.001). We additionally analyzed PMN supernatants to determine the enzymatic activity of MMP-9, observing that FhAg exposure enhances MMP-9 release in ovine PMNs (p < 0.05) but not in bovine PMNs. Interestingly, by using flow cytometric analysis, we determined that the exposure of PMNs to FhAg does not increase the CD11b surface expression of ovine PMNs. This could be an effect of the activation of other surface receptors or transcription factors involved in F. hepatica-induced NETosis. Consequently, we hypothesize that F. hepatica-induced NETs play a role in the pathogenesis of fasciolosis, contributing to liver tissue damage if released in an uncontrolled manner. Full article

You Only Look Once (YOLO) and its variants have emerged as the most popular real-time object detection algorithms. They have been widely used in real-time smart transportation applications due to their low-latency detection and high accuracy. However, because of the diverse characteristics of YOLO models, selecting the optimal model according to various applications and environments in smart transportation is critical. In this article, we conduct an empirical evaluation and analysis study for most YOLO versions to assess their performance in smart transportation. To achieve this, we first measure the average precision of YOLO models across multiple datasets (i.e., COCO and PASCAL VOC). Second, we analyze the performance of YOLO models on multiple object categories within each dataset, focusing on classes relevant to road transportation such as those commonly used in smart transportation applications. Third, multiple Intersection over Union (IoU) thresholds are considered in our performance measurement and analysis. By examining the performance of various YOLO models across datasets, IoU thresholds, and object classes, we make six observations on these three aspects while aiming to identify optimal models for road transportation scenarios. It was found that YOLOv5 and YOLOv8 outperform other models in all three aspects due to their novel performance features. For instance, YOLOv5 achieves stable performance thanks to its cross-stage partial darknet-53 (CSPDarknet53) backbone, auto-anchor mechanism, and efficient loss functions including IoU loss, complete IoU loss, focal loss, gradient harmonizing mechanism loss. Similarly, YOLOv8 outperforms others with its upgraded CSPDarknet53 backbone, anchor-free mechanism, and efficient loss functions like complete IoU loss and distribution focal loss. Full article

In recent years, anchor-free object detectors have become predominant in deep learning, the YOLOv8 model as a real-time object detector based on anchor-free frames is universal and influential, it efficiently detects objects across multiple scales. However, the generalization performance of the model is lacking, and the feature fusion within the neck module overly relies on its structural design and dataset size, and it is particularly difficult to localize and detect small objects. To address these issues, we propose the FL-YOLOv8 object detector, which is improved based on YOLOv8s. Firstly, we introduce the FSDI module in the neck, enhancing semantic information across all layers and incorporating rich detailed features through straightforward layer-hopping connections. This module integrates both high-level and low-level information to enhance the accuracy and efficiency of image detection. Meanwhile, the structure of the model was optimized and designed, and the LSCD module is constructed in the detection head; adopting a lightweight shared convolutional detection head reduces the number of parameters and computation of the model by 19% and 10%, respectively. Our model achieves a comprehensive performance of 45.5% on the COCO generalized dataset, surpassing the benchmark by 0.8 percentage points. To further validate the effectiveness of the method, experiments were also performed on specific domain urine sediment data (FCUS22), and the results on category detection also better justify the FL-YOLOv8 object detection algorithm. Full article

Plasmonic-based biosensors have gained prominence as potent optical biosensing platforms in both scientific and medical research, attributable to their enhanced sensitivity and precision in detecting biomolecular and chemical interactions. However, the detection of low molecular weight analytes with high sensitivity and specificity remains a complex and unresolved issue, posing significant limitations for the advancement of clinical diagnostic tools and medical device technologies. Notably, abnormal cholesterol levels are a well-established indicator of various pathological conditions; yet, the quantitative detection of the free form of cholesterol is complicated by its small molecular size, pronounced hydrophobicity, and the necessity for mediator molecules to achieve efficient sensing. In the present study, a novel strategy for cholesterol quantification was developed, leveraging a plasmonic optical readout in conjunction with a highly specific cholesterol-binding peptide (C-pept) as a biorecognition element, anchored on a functionalized silica substrate. The resulting biosensor exhibited an exceptionally low detection limit of 21.95 µM and demonstrated a linear response in the 10–200 µM range. This peptide-integrated plasmonic sensor introduces a novel one-step competitive method for cholesterol quantification, positioning itself as a highly sensitive biosensing modality for implementation within the AVAC platform, which operates using reflective dark-field microscopy. Full article

In some location scenarios where the location information of nodes cannot be mastered in advance, the anchor-free location technology is particularly important. In order to reduce the complicated calculation and eliminate the accumulated error in the traditional anchor-free location algorithm, a new anchor-free location algorithm based on transition coordinates is proposed in this paper. This algorithm is different from the traditional methods such as minimum cost function or inverse matrix. Instead, N initial coordinates are randomly generated as the starting position of the transition coordinates, and the position increment between the transition coordinates and the real coordinates of the node is constantly modified. After K iterations, the convergent position coordinates are finally infinitely close to the real position coordinates of N nodes, and the computational complexity is less than most existing algorithms. As follows, the factors that affect the performance of the algorithm are investigated in the simulation experiment, including the topology structure, positioning accuracy and the total number of nodes, etc. The results show great advantages compared with the traditional anchor-free positioning algorithm. When the topology structure of the initial coordinates changes from a square to a random graph, the number of iterations increases by 15.79%. When the positioning accuracy increased from 1% to 1‰, the number of iterations increased by 36.84%. When the number of nodes N is reduced from 9 to 4, the number of iterations is reduced by 63.16%. In addition, the algorithm can also be extended to the field of moving coordinates or three-dimensional spatial positioning, which has broad application prospects. Full article

Nowadays, cotton boll detection techniques are becoming essential for weaving and textile industries based on the production of cotton. There are limited techniques developed to segment, detect, and count cotton bolls precisely. This analysis identified several limitations and issues with these techniques, including their complex structure, low performance, time complexity, poor quality data, and so on. A proposed technique was developed to overcome these issues and enhance the performance of the detection and counting of cotton bolls. Initially, data were gathered from the dataset, and a pre-processing stage was performed to enhance image quality. An adaptive Gaussian–Wiener filter (AGWF) was utilized to remove noise from the acquired images. Then, an improved Harris Hawks arithmetic optimization algorithm (IH²AOA) was used for segmentation. Finally, an anchor-free compact central attention cotton boll detection network (A-frC²AcbdN) was utilized for cotton boll detection and counting. The proposed technique utilized an annotated dataset extracted from weakly supervised cotton boll detection and counting, aiming to enhance the accuracy and efficiency in identifying and quantifying cotton bolls in the agricultural domain. The accuracy of the proposed technique was 94%, which is higher than that of other related techniques. Similarly, the precision, recall, F1-score, and specificity of the proposed technique were 93.8%, 92.99%, 93.48%, and 92.99%, respectively. Full article

Cysteine plays a crucial role in the development of an efficient copper-catalyst system, where its thiol group serves as a strong anchoring site for metal coordination. By immobilizing copper onto cysteine-modified, polydopamine-coated magnetite particles, this advanced catalytic platform exhibits exceptional stability and catalytic activity. Chemical modification of the polydopamine (PDA) surface with cysteine enhances copper salt immobilization, leading to the formation of the Fe₃O₄@PDA-Cys@Cu platform. This system was evaluated in palladium-free, copper-catalyzed Sonogashira coupling reactions, effectively catalyzing the coupling of terminal acetylenes with aryl halides. Additionally, the Fe₃O₄@PDA-Cys@Cu platform was employed in click reactions, confirming the enhanced catalytic efficiency due to increased copper content. The reusability of the platform was further investigated, demonstrating improved performance, especially in recyclability tests in click reaction, making it a promising candidate for sustainable heterogeneous catalysis. Full article

This study aimed to investigate the feasibility of using geopolymer paste (GP) as an adhesive agent for (i) anchoring steel bars in concrete substrates, (ii) repairing concrete, and (iii) repairing limestone and granite masonry blocks commonly found in historic buildings. In this investigation, seven cement-free GP mixes were developed with different combinations of binder materials (slag, silica fume, and metakaolin). The mechanical properties, adhesive performance, and production cost of the developed GP mixes were compared to those of a commercially epoxy adhesive mortar (EAM). The results obtained from this study indicated that the use of GPs enhanced the bonding between steel bars and concrete substrates, achieving bonding strengths that were 19.7% to 49.2% higher than those of control specimens with steel bars directly installed during casting. In concrete repairs, the GPs were able to restore about 60.6% to 87.9% of the original capacity of the control beams. Furthermore, GPs exhibited a promising performance in repairing limestone and granite masonry blocks, highlighting their potential suitability for masonry structures. The best adhesive performance was observed when a ternary binder material system consisting of 70% slag, 20% metakaolin and 10% silica fume was used. This combination, compared to the investigated EAM, showed comparable adhesive properties at a significantly low cost, indicating the viability of GPs as a cost-effective, eco-friendly adhesive agent. Full article