Search Results (58)

In this work, ethyl cellulose nanoparticles loaded with rosemary extract (RCL-NPs) were synthesized and utilized to reinforce high-density polyethylene (HDPE) packaging bags as a nanotechnological alternative for rabbit meat preservation. The synthesized RCL-NPs were characterized by DLS and for their stability. The analyzed variables of rabbit meat packaged samples included drained liquid, weight loss, color, pH, texture, and hardness. The total phenolic content (TPC) and antioxidant capacity of rosemary extract were also investigated. The results demonstrated that RCL-NPs were 117.30 nm in size with a negative surface charge (−24.59 mV) and low PDI (0.12). According to the Higuchi model, the release rate of RCL-NPs was sustained from 0 to 24 h. The encapsulation efficiency of the implemented synthesis route was 99.97%. The TPC of rosemary extract was 566.13 ± 1.72 mg GAE/L, whereas their antioxidant activity utilizing the DPPH and FRAP assays was 27.86 ± 0.32 mM Trolox/L and 0.31 mM Trolox/L, respectively. Contrary to control samples, rabbit meat samples conserved in HDPE packaging bags reinforced with RCL-NPs prevent drained liquid and weight loss, while preserving *L (60 ± 2.5–66.10 ± 2.0) and *b (10.67 ± 2.28–11.62 ± 2.39), pH (5.22 ± 0.05–5.80 ± 0.03), and texture (10.37 ± 0.82–0.70 ± 0.50). In the same regard, the developed material conserved the hardness of rabbit meat samples, exhibiting values that ranged from 27.79 ± 7.23 to 27.60 ± 3.05 N during the evaluated period (0–13 days). The retrieved data demonstrate the efficacy of RCL in preserving the quality of rabbit meat when integrated with additional food packaging materials. Full article

Given the complexity of near-surface soil moisture retrieval, a single machine learning algorithm often struggles to capture the intricate relationships among multiple features, resulting in limited generalization and robustness. To address this issue, this study proposes a multi-satellite fusion GNSS-IR soil moisture retrieval method based on heterogeneous ensemble machine learning models. Specifically, two heterogeneous ensemble learning strategies (Bagging and Stacking) are combined with three base learners, Back Propagation Neural Network (BPNN), Random Forest (RF), and Support Vector Machine (SVM), to construct eight ensemble GNSS-IR soil moisture retrieval models. The models are validated using data from GNSS stations P039, P041, and P043 within the Plate Boundary Observatory (PBO) network. Their retrieval performance is compared against that of individual machine learning models and a deep learning model (Multilayer Perceptron, MLP), enabling an optimized selection of algorithms and model architectures. Results show that the Stacking-based models significantly outperform those based on Bagging in terms of retrieval accuracy. Among them, the Stacking (BPNN-RF-SVM) model achieves the highest performance across all three stations, with R of 0.903, 0.904, and 0.917, respectively. These represent improvements of at least 2.2%, 2.8%, and 2.1% over the best-performing base models. Therefore, the Stacking (BPNN-RF-SVM) model is identified as the optimal retrieval model. This work aims to contribute to the development of high-accuracy, real-time monitoring methods for near-surface soil moisture. Full article

To address issues commonly observed during the inference phase of large language models—such as inconsistent labels, formatting errors, or semantic deviations—a series of targeted strategies has been proposed. First, a relation label refinement strategy based on semantic similarity and syntactic structure has been designed to calibrate the model’s outputs, thereby improving the accuracy and consistency of label prediction. Second, to meet the contextual modeling needs of different types of instance bags, a multi-level contextual augmentation strategy has been constructed. For multi-sentence instance bags, a graph-based retrieval enhancement mechanism is introduced, which integrates intra-bag entity co-occurrence networks with document-level sentence association graphs to strengthen the model’s understanding of cross-sentence semantic relations. For single-sentence instance bags, a semantic expansion strategy based on term frequency-inverse document frequency is employed to retrieve similar sentences. This enriches the training context under the premise of semantic consistency, alleviating the problem of insufficient contextual information. Notably, the proposed multi-granularity framework captures semantic symmetry between entities and relations across different levels of context, which is crucial for accurate and balanced relation understanding. The proposed methodology offers practical advancements for semantic analysis applications, particularly in knowledge graph development. Full article

The autonomous positioning of drone-based remote sensing plays an important role in navigation in urban environments. Due to GNSS (Global Navigation Satellite System) signal occlusion, obtaining precise drone locations is still a challenging issue. Inspired by vision-based positioning methods, we proposed an autonomous positioning method based on multi-view reference images rendered from the scene’s 3D geometric mesh and apply a bag-of-words (BoW) image retrieval pipeline to achieve efficient and scalable positioning, without utilizing deep learning-based retrieval or 3D point cloud registration. To minimize the number of reference images, scene coverage quantification and optimization are employed to generate the optimal viewpoints. The proposed method jointly exploits a visual-bag-of-words tree to accelerate reference image retrieval and improve retrieval accuracy, and the Perspective-n-Point (PnP) algorithm is utilized to obtain the drone’s pose. Experiments are conducted in urban real-word scenarios and the results show that positioning errors are decreased, with accuracy ranging from sub-meter to 5 m and an average latency of 0.7–1.3 s; this indicates that our method significantly improves accuracy and latency, offering robust, real-time performance over extensive areas without relying on GNSS or dense point clouds. Full article

In river and lake ecosystem management, comprehensive water quality monitoring is crucial. Traditional in situ water quality monitoring is costly, and it is challenging to cover entire water bodies. Remote sensing imagery offers the possibility of efficient monitoring of water quality over large areas. However, remote sensing data typically contain a large amount of noise and redundant information, making it difficult for models to capture the effective spectral information and the relationships in the water quality in the remote sensing data. Consequently, this hinders the achievement of high-precision water quality inversion performance. Therefore, this study proposes a comprehensive water quality inversion framework based on a multilayer denoising autoencoder that automatically extracts effective spectral features, utilizing a multilayer denoising autoencoder to extract effective features from Sentinel-2 remote sensing data, thereby reducing noise in the subsequent model input data and mitigating the overfitting problem in subsequent models. A bagging ensemble learning model was established to invert the total phosphorus concentration in Taihu Lake. This model reduces the prediction bias generated by a single machine learning model and was compared with decision tree, random forest, and linear regression models. The research results indicate that compared to a single model, the bagging ensemble learning model achieved better water quality retrieval results, with a coefficient of determination of 0.9 and an MAE of 0.014, while the linear regression model performed the worst, with a coefficient of determination of 0.42. Additionally, models trained using spectral effective information extracted by multilayer denoising autoencoders showed improved water quality retrieval accuracy compared to those trained with raw data, with the coefficient of determination for the bagging model increasing from 0.62 to 0.9. This study provides a rapid and accurate method for large-scale watershed water quality monitoring using remote sensing data, offering technical support for applying remote sensing data to watershed environmental management and water resource protection. Full article

Vegetation water content (VWC) is a crucial parameter for evaluating vegetation growth, climate change, natural disasters such as forest fires, and drought prediction. Spaceborne global navigation satellite system reflectometry (GNSS-R) has become a valuable tool for soil moisture (SM) and biomass remote sensing (RS) due to its higher spatial resolution compared with microwave measurements. Although previous studies have confirmed the enormous potential of spaceborne GNSS-R for vegetation monitoring, the utilization of this technology to fuse multiple RS parameters to retrieve VWC is not yet mature. For this purpose, this paper constructs a local high-spatiotemporal-resolution spaceborne GNSS-R VWC retrieval model that integrates key information, such as bistatic radar cross section (BRCS), effective scattering area, CYGNSS variables, and surface auxiliary parameters based on five ensemble machine learning (ML) algorithms (i.e., bagging tree (BT), gradient boosting decision tree (GBDT), extreme gradient boosting (XGBoost), random forest (RF), and light gradient boosting machine (LightGBM)). We extensively tested the performance of different models using SMAP ancillary data as validation data, and the results show that the root mean square errors (RMSEs) of the BT, XGBoost, RF, and LightGBM models in VWC retrieval are better than 0.50 kg/m². Among them, the BT and RF models performed the best in localized VWC retrieval, with RMSE values of 0.50 kg/m². Conversely, the XGBoost model exhibits the worst performance, with an RMSE of 0.85 kg/m². In terms of RMSE, the RF model demonstrates improvements of 70.00%, 52.00%, and 32.00% over the XGBoost, LightGBM, and GBDT models, respectively. Full article

With the increasing use of unmanned aerial vehicles (UAVs) in various fields, achieving the precise localization of UAV images is crucial for enhancing their utility. Photogrammetry-based techniques, particularly bundle adjustment, serve as foundational methods for accurately determining the spatial coordinates of UAV images. The effectiveness of bundle adjustment is significantly influenced by the selection of input data, particularly the composition of overlapping image sets. The selection process of overlapping images significantly impacts both the accuracy of spatial coordinate determination and the computational efficiency of UAV image localization. Therefore, a strategic approach to this selection is crucial for optimizing the performance of bundle adjustment in UAV image processing. In this context, we propose an efficient methodology for determining overlapping image sets. The proposed method selects overlapping images based on image similarity, leveraging the complementary strengths of the bag of visual words and number of matches techniques. Essentially, our method achieves both high accuracy and high speed by utilizing a Bag of Visual Words for candidate selection and the number of matches for additional similarity assessment for overlapping image-set determination. We compared the performance of our proposed methodology with the conventional number of matches and bag-of-visual word-based methods for overlapping image-set determination. In the comparative evaluation, the proposed method demonstrated an average precision of 96%, comparable to that of the number of matches-based approach, while surpassing the 62% precision achieved by both bag-of-visual-word methods. Moreover, the processing time decreased by approximately 0.11 times compared with the number of matches-based methods, demonstrating relatively high efficiency. Furthermore, in the bundle adjustment results using image sets, the proposed method, along with the number of matches-based methods, showed reprojection error values of less than 1, indicating relatively high accuracy and contributing to the improvement in accuracy in estimating image positions. Full article

DNA analysis of traces from commonly found objects like knives, smartphones, tapes and garbage bags related to crime in aquatic environments is challenging for forensic DNA laboratories. The amount of recovered DNA may be affected by the water environment, time in the water, method for recovery, transport and storage routines of the objects before the objects arrive in the laboratory. The present study evaluated the effect of four storage conditions on the DNA retrieved from bloodstains, touch DNA, fingerprints and hairs, initially deposited on knives, smartphones, packing tapes, duct tapes and garbage bags, and submerged in lake water for three time periods. After retrieval, the objects were stored either through air-drying at room temperature, freezing at −30 °C, in nitrogen gas or in lake water. The results showed that the submersion time strongly influenced the amount and degradation of DNA, especially after the longest submersion time (21 days). A significant variation was observed in success for STR profiling, while mtDNA profiling was less affected by the submersion time interval and storage conditions. This study illustrates that retrieval from water as soon as possible and immediate storage through air-drying or freezing before DNA analysis is beneficial for the outcome of DNA profiling in crime scene investigations. Full article

During the past four decades, recurrent outbreaks of various arthropod-borne viruses have been reported in Mauritania. This review aims to consolidate the current knowledge on the epidemiology of the major arboviruses circulating in Mauritania. Online databases including PubMed and Web of Science were used to retrieve relevant published studies. The results showed that numerous arboviral outbreaks of variable magnitude occurred in almost all 13 regions of Mauritania, with Rift Valley fever (RVF), Crimean–Congo hemorrhagic fever (CCHF), and dengue (DEN) being the most common infections. Other arboviruses causing yellow fever (YF), chikungunya (CHIK), o’nyong-nyong (ONN), Semliki Forest (SF), West Nile fever (WNF), Bagaza (BAG), Wesselsbron (WSL), and Ngari (NRI) diseases have also been found circulating in humans and/or livestock in Mauritania. The average case fatality rates of CCHF and RVF were 28.7% and 21.1%, respectively. RVF outbreaks have often occurred after unusually heavy rainfalls, while CCHF epidemics have mostly been reported during the dry season. The central and southeastern regions of the country have carried the highest burden of RVF and CCHF. Sheep, cattle, and camels are the main animal reservoirs for the RVF and CCHF viruses. Culex antennatus and Cx. poicilipes mosquitoes and Hyalomma dromedarii, H. rufipes, and Rhipicephalus everesti ticks are the main vectors of these viruses. DEN outbreaks occurred mainly in the urban settings, including in Nouakchott, the capital city, and Aedes aegypti is likely the main mosquito vector. Therefore, there is a need to implement an integrated management strategy for the prevention and control of arboviral diseases based on sensitizing the high-risk occupational groups, such as slaughterhouse workers, shepherds, and butchers for zoonotic diseases, reinforcing vector surveillance and control, introducing rapid point-of-care diagnosis of arboviruses in high-risk areas, and improving the capacities to respond rapidly when the first signs of disease outbreak are identified. Full article

Multiple-instance learning has become popular over recent years due to its use in some special scenarios. It is basically a type of weakly supervised learning where the learning dataset contains bags of instances instead of a single feature vector. Each bag is associated with a single label. This type of learning is flexible and a natural fit for multiple real-world problems. MIL has been employed to deal with a number of challenges, including object detection and identification tasks, content-based image retrieval, and computer-aided diagnosis. Medical image analysis and drug activity prediction have been the main uses of MIL in biomedical research. Many Algorithms based on MIL have been put forth over the years. In this paper, we will discuss MIL, the background of MIL and its application in multiple domains, some MIL-based methods, challenges, and lastly, the conclusions and prospects. Full article

Irrigation is one of the key agricultural management practices of crop cultivation in the world. Irrigation practice is traceable on satellite images. Most irrigated area mapping methods were developed based on time series of NDVI or backscatter coefficient within the growing season. However, it has been found that winter irrigation out of growing season is also dominating in north China. This kind of irrigation aims to increase the soil moisture for coping with spring drought and reduce the wind erosion in spring. This study developed a remote sensing-based classification approach to identify irrigated fields out of growing season with Radom Forest algorithm. Four spectral bands and all Normalized Difference Vegetation Index (NDVI) like indices computed from any two of these four bands for each of the seven scenes of GF-1 satellite data were used as the input features in the building of separated RF models and in applying the built models for the classification. The results showed that the mean of the highest out-of-bag accuracies for seven RF models was 94.9% and the mean of the averaged out-of-bag accuracies in the plateau for seven RF models was 94.1%; the overall accuracy for all seven classified outputs was in the range of 86.8–92.5%, Kappa in the range of 84.0–91.0% and F1-Score in the range of 82.1–90.1%. These results showed that the classification was neither overperformed nor underperformed as the accuracies of all classified images were lower than the model ones. This study also found that irrigation started to be applied as early as in November and irrigated fields were increased and suspended in December and January due to freezing conditions. The newly irrigated fields were found again in March and April when the temperature rose above zero degrees. The area of irrigated fields in the study area were increasing over time with sizes of 98.6, 166.9, 208.0, 292.8, 538.0, 623.1, 653.8 km² from December to April, accounting for 6.1%, 10.4%, 12.9%, 18.2%, 33.4%, 38.7%, and 40.6% of the total irrigatable land in the study area, respectively. The results showed that the method developed in this study performed well. This study found on the satellite images that 40.6% of irrigatable fields were already irrigated before the sowing season and the irrigation authorities were supposed to improve their water supply capacity in the whole year with this information. This study may complement the traditional consideration of retrieving irrigation maps only in growing season with remote sensing images for a large area. Full article

Uterine fibromas are the most common benign uterine tumors. Although the majority of leiomyomas remain asymptomatic, they can cause serious clinical problems, including abnormal uterine bleeding, pelvic pain, and infertility, which require effective gynecological intervention. Depending on the symptoms as well as patients’ preferences, various treatment options are available, such as medical therapy, non-invasive procedures, and surgical methods. Regardless of the extent of the surgery, the preferred option is the laparoscopic approach. To reduce the risk of spreading occult malignancy and myometrial cells associated with fragmentation of the specimen before its removal from the peritoneal cavity, special systems for laparoscopic contained morcellation have been developed. The aim of this review is to present the state-of-the-art contained morcellation. Different types of available retrieval bags are demonstrated. The advantages and difficulties associated with contained morcellation are described. The impact of retrieval bag usage on the course of surgery, as well as the effects of the learning curve, are discussed. The role of contained morcellation in the overall strategy to optimize patient safety is highlighted. Full article

With the increasing demand for remote sensing image applications, extracting the required images from a huge set of remote sensing images has become a hot topic. The previous retrieval methods cannot guarantee the efficiency, accuracy, and interpretability in the retrieval process. Therefore, we propose a bag-of-words association mapping method that can explain the semantic derivation process of remote sensing images. The method constructs associations between low-level features and high-level semantics through visual feature word packets. An improved FP-Growth method is proposed to achieve the construction of strong association rules to semantics. A feedback mechanism is established to improve the accuracy of subsequent retrievals by reducing the semantic probability of incorrect retrieval results. The public datasets AID and NWPU-RESISC45 were used to validate these experiments. The experimental results show that the average accuracies of the two datasets reach 87.5% and 90.8%, which are 22.5% and 20.3% higher than VGG16, and 17.6% and 15.6% higher than ResNet18, respectively. The experimental results were able to validate the effectiveness of our proposed method. Full article

Anaerobic soil disinfestation (ASD) has been adopted in over 900 ha in California strawberry production as an alternative to chemical fumigation. Rice bran, the predominant carbon source for ASD, has become increasingly expensive. In 2021–22 and the 2022–23 field studies, we evaluated 20–30% lower-priced wheat middlings (Midds) and dried distillers’ grain (DDG) at 21,800 kg ha⁻¹ (in 2021) and 17,000 kg ha⁻¹ (in 2022) as alternative carbon sources to rice bran. The study was placed at Santa Paula, California in September of each season in preparation for strawberry planting in October. Soil and air temperatures were 18–26 °C during that time. After the incorporation of carbon sources into the top 30 cm of bed soil, beds were reshaped, and irrigation drip lines were installed and covered with totally impermeable film (TIF) to prevent gas exchange. Beds were irrigated to saturate the bed soil within 48 h after TIF installation. Anaerobic conditions were measured with soil redox potential (Eh) sensors placed at 15 cm depth in all plots. Both DDG and Midds plots maintained Eh at −180 to 0 mV during the two ASD weeks, while untreated soil was aerobic at 200 to 400 mV. Permeable bags with inocula of Macrophomina phaseolina, a lethal soil-borne pathogen of strawberry, and tubers of a perennial weed Cyperus esculentus were placed 15 cm deep in the soil at ASD initiation and retrieved two weeks later for analyses. Two weeks after that, holes were cut to aerate beds and ‘Victor’ or ‘Fronteras’ bare-root strawberries were transplanted into them. ASD with DDG reduced viable microsclerotia of M. phaseolina by 49% in the first season and 75 to 85% with both carbon sources in the second season. Both ASD treatments reduced tuber germination of C. esculentus 86–90% compared to untreated soil in one of two years. Additionally, Midds and DDG provided greater sufficiency of plant-available nitrogen and phosphorus compared to untreated soil with synthetic pre-plant fertilizer and improved fruit yields by 11–29%. ASD with these carbon sources can suppress soil pathogens and weeds and help sustain organic strawberry production in California. Full article

Contained electromechanical morcellation has emerged as a safety approach for laparoscopic myomatous tissue retrieval. This retrospective single-center analysis evaluated the bag deployment practicability and safety of electromechanical in-bag morcellation when used for big surgical benign specimens. The main age of patients was 39.3 years (range 21 to 71); 804 myomectomies, 242 supracervical hysterectomies, 73 total hysterectomies, and 1 retroperitoneal tumor extirpation were performed. A total of 78.7% of specimens weighed more than 250 g (n = 881) and 9% more than 1000 g. The largest specimens, weighing 2933 g, 3183 g, and 4780 g, required two bags for complete morcellation. Neither difficulties nor complications related to bag manipulation were recorded. Small bag puncture was detected in two cases, but peritoneal washing cytology was free of debris. One retroperitoneal angioleiomyomatosis and three malignancies were detected in histology (leiomyosarcoma = 2; sarcoma = 1); therefore, patients underwent radical surgery. All patients were disease-free at 3 years follow-up, but one patient presented multiple abdominal metastases of the leiomyosarcoma in the third year; she refused subsequent surgery and was lost from follow-up. This large series demonstrates that laparoscopic bag morcellation is a safe and comfortable method to remove large and giant uterine tumors. Bag manipulation takes only a few minutes, and perforations rarely occur and are easy to detect intraoperatively. This technique did not result in the spread of debris during myoma surgery, potentially avoiding the additional risk of parasitic fibroma or peritoneal sarcoma. Full article