Search Results (237)

The accurate identification of seismic faults, which serve as crucial fluid migration pathways in hydrocarbon reservoirs, is of paramount importance for reservoir characterization. Traditional interpretation is inefficient. It also struggles with complex geometries, failing to meet the current exploration demands. Deep learning boosts fault identification significantly but struggles with edge accuracy and noise robustness. To overcome these limitations, this research introduces SwiftSeis-AWNet, a novel lightweight and high-precision network. The network is based on an optimized MedNeXt architecture for better fault edge detection. To address the noise from simple feature fusion, a Semantics and Detail Infusion (SDI) module is integrated. Since the Hadamard product in SDI can cause information loss, we engineer an Attention-Weighted Semantics and Detail Infusion (AWSDI) module that uses dynamic multi-scale feature fusion to preserve details. Validation on field seismic datasets from the Netherlands F3 and New Zealand Kerry blocks shows that SwiftSeis-AWNet mitigates challenges like the loss of small-scale fault features and misidentification of fault intersection zones, enhancing the accuracy and geological reliability of automated fault identification. Full article

The root of Plumbago indica L. is commercially available in herbal markets in both crude and powdered forms. P. indica root is a key ingredient in numerous polyherbal formulations. However, P. indica has two closely related species, P. zeylanica L. and P. auriculata Lam. Since only P. indica is traditionally used in Thai polyherbal products, adulteration with other species could potentially compromise the therapeutic efficacy and overall effectiveness of these formulations. To address this issue, a MassARRAY iPLEX assay was developed to accurately identify and differentiate P. indica from its closely related species. Five single nucleotide polymorphism (SNP) sites—positions 18, 112, 577, 623, and 652—within the internal transcribed spacer (ITS) region were selected as genetic markers for species identification. The assay demonstrated high accuracy in identifying P. indica and was capable of detecting the species at DNA concentrations as low as 0.01 ng/µL. Additionally, the assay successfully identified P. zeylanica in commercial crude drug samples, highlighting potential instances of adulteration. Furthermore, it was able to distinguish P. indica in mixed samples containing P. indica, along with either P. zeylanica or P. auriculata. The developed MassARRAY iPLEX assay proves to be a reliable and effective molecular tool for authenticating P. indica raw materials. Its application holds significant potential for ensuring the integrity of herbal products by preventing misidentification and adulteration. Full article

Quickly and accurately identifying weed areas is of great significance for improving weeding efficiency, reducing pesticide residues, protecting soil ecological environment, and increasing crop yield and quality. Targeting low detection efficiency in complex agricultural environments and inability of multispectral input in weed recognition of minor grain based on unmanned aerial vehicles (UAVs), a semantic segmentation model for buckwheat weeds based on MSU-Net (multispectral U-shaped network) was proposed to explore the influence of different band optimizations on recognition accuracy. Five spectral features—red (R), blue (B), green (G), red edge (REdge), and near-infrared (NIR)—were collected in August when the weeds were more prominent. Based on the U-net image semantic segmentation model, the input module was improved to adaptively adjust the input bands. The neuron death caused by the original ReLU activation function may lead to misidentification, so it was replaced by the Swish function to improve the adaptability to complex inputs. Five single-band multispectral datasets and nine groups of multi-band combined data were, respectively, input into the improved MSU-Net model to verify the performance of our method. Experimental results show that in the single-band recognition results, the B band performs better than other bands, with mean pixel accuracy (mPA), mean intersection over union (mIoU), Dice, and F1 values of 0.75, 0.61, 0.87, and 0.80, respectively. In the multi-band recognition results, the R+G+B+NIR band performs better than other combined bands, with mPA, mIoU, Dice, and F1 values of 0.76, 0.65, 0.85, and 0.78, respectively. Compared with U-Net, DenseASPP, PSPNet, and DeepLabv3, our method achieved a preferable balance between model accuracy and resource consumption. These results indicate that our method can adapt to multispectral input bands and achieve good results in weed segmentation tasks. It can also provide reference for multispectral data analysis and semantic segmentation in the field of minor grain crops. Full article

Background: Trichophyton indotineae, a new emerging pathogen according to the WHO, is known to cause severe forms of tinea. Given that traditional identification methods rely on morphological characteristics, and the morphological distinctions among T. indotineae, T. mentagrophytes, and T. interdigitale are minimal, the adoption of alternative diagnostic techniques, such as RT-PCR or gene sequencing, has become critically important to prevent misidentification. The purpose of this study was firstly to analyze the local epidemiology of dermatophytes isolated and secondly to investigate the presence of T. indotineae among the isolated strains. Methods: Between January 2021 and June 2024, 1096 samples of skin adnexa were analysed. The isolated strains belonging to the genus Trichophyton were submitted to molecular identification by ITS sequencing, and T. indotineae strains were subjected to squalene epoxidase (SQLE) sequencing analysis. Results: Trichophyton rubrum and Trichophyton interdigitale appear to be the most prevalent pathogenic species. Molecular identification reveals four T. indotineae strains (4/87; 4.61%) from Asian patients, which were also characterized by gene mutations associated with terbinafine resistance. Conclusions: This study has made it clear that there is a need to implement basic mycological diagnostics with molecular methods to avoid misidentifications, ensure the correct identification, and evaluate the presence of mutations associated with antifungal drug resistance. Full article

In this paper we propose a novel set of techniques for real-time Multi-Target Multi-Camera (MTMC) tracking of vehicles in congested, low speed environments, such as those of drive-thru scenarios, where metrics such as the number of vehicles, time of stay, and interactions between vehicles and staff are needed and must be highly accurate. Traditional methods of tracking based on Intersection over Union (IoU) and basic appearance features produce fragmented trajectories of misidentifications under these conditions. Furthermore, detectors, such as YOLO (You Only Look Once) architectures, exhibit different types of errors due to vehicle proximity, lane changes, and occlusions. Our methodology introduces a new tracker algorithm, Multi-Object Tracker based on Corner Displacement (MTCD), that improves the robustness against bounding box deformations by analysing corner displacement patterns and several other factors involved. The proposed solution was validated on real-world drive-thru footage, outperforming standard IoU-based trackers like Nvidia Discriminative Correlation Filter (NvDCF) tracker. By maintaining accurate cross-camera trajectories, our framework enables the extraction of critical operational metrics, including vehicle dwell times and person–vehicle interaction patterns, which are essential for optimizing service efficiency. This study tackles persistent tracking challenges in constrained environments, showcasing practical applications for real-world surveillance and logistics systems where precision is critical. The findings underscore the benefits of incorporating geometric resilience and delayed decision-making into MTMC architectures. Furthermore, our approach offers the advantage of seamless integration with existing camera infrastructure, eliminating the need for new deployments. Full article

Software requirements engineering is one of the most critical and time-consuming phases of the software-development process. The lack of communication with stakeholders and the use of natural language for communicating leads to misunderstanding and misidentification of requirements or the creation of ambiguous requirements, which can jeopardize all subsequent steps in the software-development process and can compromise the quality of the final software product. Natural Language Processing (NLP) is an old area of research; however, it is currently undergoing strong and very positive impacts with recent advances in the area of Machine Learning (ML), namely with the emergence of Deep Learning and, more recently, with the so-called transformer models such as BERT and GPT. Software requirements engineering is also being strongly affected by the entire evolution of ML and other areas of Artificial Intelligence (AI). In this article we conduct a systematic review on how AI, ML and NLP are being used in the various stages of requirements engineering, including requirements elicitation, specification, classification, prioritization, requirements management, requirements traceability, etc. Furthermore, we identify which algorithms are most used in each of these stages, uncover challenges and open problems and suggest future research directions. Full article

Precise identification and quantification of fish movement states are of significant importance for conducting fish behavior research and guiding aquaculture production, with object tracking serving as a key technical approach for achieving behavioral quantification. The traditional DeepSORT algorithm has been widely applied to object tracking tasks; however, in practical aquaculture environments, high-density cultured fish exhibit visual characteristics such as similar textural features and frequent occlusions, leading to high misidentification rates and frequent ID switching during the tracking process. This study proposes an underwater fish object tracking method based on the improved DeepSORT algorithm, utilizing ResNet as the backbone network, embedding Deformable Convolutional Networks v2 to enhance adaptive receptive field capabilities, introducing Triplet Loss function to improve discrimination ability among similar fish, and integrating Convolutional Block Attention Module to enhance key feature learning. Finally, by combining the aforementioned improvement modules, the ReID feature extraction network was redesigned and optimized. Experimental results demonstrate that the improved algorithm significantly enhances tracking performance under frequent occlusion conditions, with the MOTA metric improving from 64.26% to 66.93% and the IDF1 metric improving from 53.73% to 63.70% compared to the baseline algorithm, providing more reliable technical support for underwater fish behavior analysis. Full article

Striking belief distortions may accompany various disorders of awareness that are predominantly associated with right hemispheric cerebral dysfunction. Distortions may range on a continuum of pathological severity, from the unawareness of paralysis in anosognosia for hemiplegia, to a more startling disturbance in denial of paralysis where belief may starkly conflict with reality. The patients’ beliefs about their limitations typically represent attempts to make sense of limitations or to impart meaning to incongruous facts. These beliefs are often couched in recollections from past memories or previous experience, and are hard to modify even given new information. Various explanations of unawareness have been suggested, including sensory, cognitive, monitoring and feedback operations, feedforward mechanisms, disconnection theories, and hemispheric asymmetry hypotheses, along with psychological denial, to account for the curious lack of awareness in anosognosia and other awareness disorders. This paper addresses these varying explanations of the puzzling beliefs regarding hemiparesis in anosognosia. Furthermore, using the multi-dimensional nature of unawareness in anosognosia as a model, some startling belief distortions in other right-hemisphere associated clinical syndromes are also explored. Other neurobehavioral disturbances, though perhaps less common, reflect marked psychopathological distortions. Startling disorders of belief are notable in somatic illusions, non-recognition or delusional misattribution of limb ownership (asomatognosia, somatoparaphrenia), or delusional identity (Capgras syndrome) and misidentification phenomena. Difficulty in updating beliefs as a source of unawareness in anosognosia and other awareness disorders has been proposed. Processes of belief development are considered to be patterns of thought, memories, and experience, which coalesce in a sense of the bodily and personal self. A common consequence of such disorders seems to be an altered representation of the self, self-parts, or the external world. Astonishing nonveridical beliefs about the body, about space, or about the self, continue to invite exploration and to stimulate fascination. Full article

Accurate species identification of small aquatic insects remains challenging due to their morphological similarities. This study addresses this issue by developing a DNA barcode reference library for the globally distributed Brillia (Diptera: Chironomidae). We analyzed cytochrome c oxidase subunit I (COI) sequences of 241 specimens belonging to 13 Brillia species from 18 countries, including 56 newly generated and 185 publicly available COI barcodes. Our integrated approach included genetic distance analysis, haplotype network construction, and ecological niche modeling. The results revealed remarkable cryptic diversity, with sequences clustering into 30 Barcode Index Numbers and 158 unique haplotypes, most being region-specific. Notably, East Asian and North American populations showed complete genetic distinctness, suggesting long-term isolation. Environmental factors, particularly temperature and precipitation gradients, were identified as key drivers of this diversification. The study also corrected several misidentifications in existing databases. These findings significantly advance our understanding of Brillia diversity and provide a reliable molecular tool for freshwater ecosystem monitoring, with important implications for biodiversity conservation and environmental assessment. Full article

Candida auris (C. auris) is an emerging multidrug-resistant fungal pathogen recognized by the World Health Organization (WHO) as a critical global health threat. Its rapid transmission, high mortality rate, and frequent misidentification in clinical laboratories present significant challenges for diagnosis and infection control. This review provides a comprehensive overview of current and emerging diagnostic methods for C. auris detection, including culture-based techniques, biochemical assays, matrix-assisted laser desorption/ionization time-of-flight mass spectrometry (MALDI-TOF MS), and molecular diagnostics such as PCR and loop-mediated isothermal amplification (LAMP). We evaluate each method’s sensitivity, specificity, turnaround time, and feasibility in clinical and surveillance settings. While culture remains the diagnostic gold standard, it is limited by slow turnaround and phenotypic overlap with related species. Updated biochemical platforms and MALDI-TOF MS with expanded databases have improved identification accuracy. Molecular assays offer rapid, culture-independent detection. Antifungal susceptibility testing (AFST), primarily using broth microdilution, is essential for guiding treatment, although standardized breakpoints remain lacking. This review proposes an integrated diagnostic workflow and discusses key innovations and gaps in current practice. Our findings aim to support clinicians, microbiologists, and public health professionals in improving early detection, containment, and management of C. auris infections. Full article

Under the influence of extreme climatic conditions, landslide disasters occur frequently in the Loess Plateau due to complex geological structures, loose soil, and frequent intense rainfall. These events are often concealed, posing significant challenges for disaster prevention. High-resolution optical remote sensing combined with field surveys can improve identification accuracy; however, concerns persist regarding issues such as omission and misidentification during hazard identification and monitoring processes. To address these challenges, this study proposes an integrated remote-sensing identification approach, focusing specifically on the central region of Tianshui, a typical landslide-prone area within the Loess Plateau. Utilizing Sentinel-1 and JL1LF01A remote-sensing imagery collected from 2022 to 2023, we conducted ground deformation monitoring through the Small Baseline Subset Interferometric Synthetic Aperture Radar (SBAS-InSAR) technique. By integrating deformation results with optical imagery features indicative of potential landslide sites, a comprehensive identification method was developed to precisely detect potential landslide hazards. Verification of the identified sites was subsequently performed using the Google Earth platform, resulting in the establishment of a final dataset of potential landslide hazards within the study area. This outcome clearly demonstrates the high applicability and accuracy of the integrated remote-sensing identification method in the context of landslide hazard assessment. Furthermore, this research provides a solid scientific foundation for geological hazard identification efforts and plays a critical guiding role in disaster prevention and mitigation in Tianshui City, thereby enhancing the region’s capacity to withstand disaster risks and effectively safeguarding local lives and property. Full article

The generation of road interchange networks benefits various applications, such as vehicle navigation and intelligent transportation systems. Traditional methods often focus on common road structures but fail to fully utilize long-term trajectory continuity and flow information, leading to fragmented results and misidentification of overlapping roads as intersections. To address these limitations, we propose a forward and reverse tracking method for high-accuracy road interchange network generation. First, raw crowdsourced trajectory data is preprocessed by filtering out non-interchange trajectories and removing abnormal data based on both static and dynamic characteristics of the trajectories. Next, road subgraphs are extracted by identifying potential transition nodes, which are verified using directional and distribution information. Trajectory bifurcation is then performed at these nodes. Finally, a two-stage fusion process combines forward and reverse tracking results to produce a geometrically complete and topologically accurate road interchange network. Experiments using crowdsourced trajectory data from Shenzhen demonstrated highly accurate results, with 95.26% precision in geometric road network alignment and 90.06% accuracy in representing the connectivity of road interchange structures. Compared to existing methods, our approach enhanced accuracy in spatial alignment by 13.3% and improved the correctness of structural connections by 12.1%. The approach demonstrates strong performance across different types of interchanges, including cloverleaf, turbo, and trumpet interchanges. Full article

In this study, we combined automated annotation tools with targeted dereplication based on MS/MS fragmentation pathway studies to identify polycyclic polyprenylated acylphloroglucinols (PPAPs) in Hypericum species, using H. patulum and H. hookeranium as a case study. These species, extensively used in traditional medicine, exhibit morphological similarities that often result in misidentification. Following UHPLC-HRMS/MS analysis of plant extracts, a molecular network approach facilitated a comprehensive comparison of their chemical composition, assigning specific clusters to O-glycosylated flavonoids and PPAPs. Eight peaks, including quercitrin, isoquercitrin, procyanidins, chlorogenic acid, quercetin, and glycosylated derivatives, were annotated from the GNPS database. For PPAPs, despite the structural complexity posing challenges for automated annotation using public databases, our targeted-dereplication strategy, relying on in-house spectral data, led to the putative identification of 22 peaks for H. patulum and H. hookeranium. Key compounds such as hyperforin, hyperscabrone K, and garcinialliptone M were detected in both species, underscoring their chemical similarity. MS/MS fragmentation pathways, particularly the successive losses of isobutene and isoprenyl units, emerged as a consistent signature for PPAP detection and may be useful for selecting PPAP-enriched extracts or fractions for further phytochemical investigations. Full article

To enhance the accuracy of product recognition in non-store retail sales and address misidentification and missed detection caused by occlusion in densely placed goods, we propose an improved YOLOv8-based network: Dense-YOLO. We first introduce an enhanced multi-scale feature extraction module (EMFE) in the feature extraction layer and employ a lightweight feature fusion strategy (LFF) in the feature fusion layer to improve the network’s performance. Next, to enhance the performance of dense product recognition, particularly when handling small and multi-scale objects in complex settings, we propose a novel multi-scale dynamic interaction attention mechanism (MDIAM). This mechanism combines dynamic channel weight adjustment and multi-scale spatial convolution to emphasize crucial features, while avoiding overfitting and enhancing model generalization. Finally, a cross-layer feature interaction mechanism is introduced to strengthen the interaction between low- and high-level features, further improving the model’s expressive power. Using the public COCO128 dataset and over 2000 daily smart retail cabinet product images compiled in our laboratory, we created a dataset covering 50 product categories for ablation and comparison experiments. The experimental results indicate that the accuracy under MDIAM is improved by 1.6% compared to other top-performing models. The proposed algorithm achieves an mAP of 94.9%, which is a 1.0% improvement over the original model. The enhanced algorithm not only significantly improves the recognition accuracy of individual commodities but also effectively addresses the issues of misdetection and missed detection when multiple commodities are recognized simultaneously. Full article

An annotated list of liverworts of the Murmansk Region is compiled based on a critical compilation of publications and label data available in the information system CRIS (L.). It includes 210 species, 2 subspecies and 8 varieties, which is 59 species more than in the list of species published in 1982. Ten taxa were excluded through comparison with the previous list and later publications, due to misidentifications or new taxonomical treatments. Annotations for each species include the synonyms under which they were listed for the region; the category of threat in the Red Data Books of Europe, Russia and the Murmansk Region; links to the most representative publications on occurrence in each of nine accepted biogeographic provinces of the region; and at least one specimen number of the KPABG or INEP herbaria in the case of the absence of published data. In total, we provide 259 new records for different provinces based on herbaria KPABG (205 records) and INEP (52 records). Additionally, there are links to publications on the nucleotide sequence data of 149 specimens obtained for 82 species and for 1 variety from the Murmansk Region, including 14 specimens (11 species), published here for the first time. Species threatened in Europe, Russia and the Murmansk Region are discussed and future perspectives of liverwort study in the Murmansk Region are outlined. Full article