Identification Precision Research Articles

Forensic Entomology in Jeddah: Molecular Identification of Emerging Insect Strains

Background: The precise identification of forensic insects is one of the most important aspects in forensic entomology since it is the basis for determining post-mortem intervals (PMI) and providing other valuable forensic evidence in criminal investigations. Methods: This study demonstrates the utility of DNA barcoding in accurately identifying new strains of forensic insects collected from the Jeddah Governorate. Result: High similarity percentages ranging from 98.74% to 99.68% were observed, underscoring the precision of molecular identification. The identified species included members of the Diptera (flies) and Coleoptera (beetles) orders, commonly associated with forensic cases. Notably, Phoridae sp. (Forensic_JED_21) was identified with 98.74% similarity, highlighting their role in providing valuable post-mortem interval (PMI) estimations. Physiphora alceae was identified twice (Forensic_JED_22 and Forensic_JED_24) with similarities of 99.22% and 99.07%, respectively, demonstrating the robustness of DNA barcoding. Philonthus discoideus (Forensic_JED_23) and Physiphora demandata (Forensic_JED_25) were identified with 99.68% similarity, confirming the presence of these species in the Jeddah region. Phylogenetic analysis using MEGA11 placed these strains in well-defined clades, confirming their taxonomic accuracy and evolutionary relationships. These findings align with previous research on the genetic diversity of forensic insects, adding crucial data to forensic entomology databases. This study enhances the forensic entomology framework by providing accurate species identification, essential for legal investigationsand supports the development of region-specific forensic tools through the addition of new genetic data from Jeddah.

AET-net: A framework for subtype classification based on the multi-omics data of breast cancer

Breast cancer (BC) is one of the most prevalent cancers worldwide and remains a significant global public health challenge. The biomechanical characteristics of tumor microenvironments provide critical insights into cellular interactions and mechanical stress responses that potentially influence cancer progression. The integration and analysis of multi-omics data for BC subtype classification present substantial challenges, including high-dimensional data complexity and difficulties in integrating heterogeneous omics data characteristics. To address these challenges, we propose an Autoencoder and Transformer integrated neural network (AET-net) classification framework. The experimental results demonstrate that our model achieves significant performance improvements in predicting BC subtypes based on integrated multi-omics datasets, with an Accuracy of 0.912 and an AUC of 0.9862. These results not only validate the high accuracy of our model in BC subtype classification, providing a valuable tool for diagnostic decision support, but also demonstrate the potential of integrated multi-omics data analysis in enhancing the precision and efficiency of BC subtype identification.

SMART Model: A Robust Approach for Cyber Criminal Identification using Smartphone Data

The SMART (Smartphone Metadata Analysis for Recognizing Threats) model is a novel approach to the identification of prospective cyber criminals by analyzing smartphone data, with a particular emphasis on social media interactions, messages, and call logs. The SMART model, in contrast to conventional methods that depend on a wide variety of features, prioritizes critical parameters to ensure more precise and effective analysis. This model exhibits exceptional adaptability and robustness in a variety of data environments by employing sophisticated feature extraction and classification algorithms. This targeted approach not only improves the precision of threat identification but also offers a practicable solution for real-world cybersecurity applications, where data quality and consistency may vary.

Plant Leaf Disease Detection Using Integrated Color and Texture Features

In the realm of precision agriculture, a pivotal challenge lies in the detection, identification, and grading of crop diseases. This multifaceted task necessitates the involvement of expert human resources and time-sensitive actions aimed at mitigating the risks of production losses and the rapid spread of diseases. The effectiveness of the majority of developed systems in this domain hinges on the quality of image features and disease segmentation accuracy. This paper presents a comprehensive research endeavor in the domain of Content-Based Image Retrieval (CBIR), specifically tailored to detect and classify leaf diseases. The proposed system integrates both color and texture features to underpin its functionality, providing a robust framework for accurate disease detection. By leveraging advanced image processing techniques, the system enhances the precision of disease identification, which is crucial for timely and effective intervention in agricultural practices. To evaluate the system’s performance, maize leaves afflicted by rust and blight serve as prime candidates for testing. These diseases were chosen due to their prevalence and significant impact on crop yield. The experimental results demonstrate that the developed system consistently excels in its disease detection and identification tasks, boasting an impressive efficiency rate of 98.33%. This high level of accuracy underscores the potential of the system to be a valuable tool in precision agriculture, aiding farmers and agricultural experts in maintaining healthy crops and optimizing production. The integration of color and texture features not only improves the detection accuracy but also provides a comprehensive understanding of the disease characteristics. This dual-feature approach ensures that the system can distinguish between different types of diseases with high precision, making it a versatile solution for various agricultural applications. The findings of this research highlight the importance of advanced image analysis techniques in enhancing the capabilities of disease detection systems, paving the way for more efficient and effective agricultural practices.

Model decomposition method for minimizing the consumption of support structure for FFF

Fused filament fabrication (FFF) often requires additional support structures for manufacturing parts with overhanging areas, leading to substantial material and time wastage. Although several model decomposition techniques have been developed to tackle this issue, most focus solely on the constraint of overhang surface area. As a result, these techniques often struggle with parts that have intricate overhang characteristics, causing reduced printing surface accuracy and quality. This study introduces a precise method for detecting overhang regions and support-relevant overhang regions to improve the accuracy of overhang region identification and printing precision. Additionally, a model decomposition strategy leveraging genetic algorithm optimization is proposed to minimize the need for support structures, aiming for nearly support-free printing. Moreover, for intricate structures with a high topology where support structures are unavoidable, a novel projection support based on the distribution of periodic points in triangular form is developed to address this printing challenge. Experimental results demonstrate that the implementation of the algorithm outlined in this work on an FFF printer has achieved nearly support-free printing. Moreover, the proposed method for generating support structures has the potential to reduce material usage by 27 %.

Exploring acetylation-related gene markers in polycystic ovary syndrome: insights into pathogenesis and diagnostic potential using machine learning

Objective Polycystic ovary syndrome (PCOS) is a prevalent cause of menstrual irregularities and infertility in women, impacting quality of life. Despite advancements, current understanding of PCOS pathogenesis and treatment remains limited. This study uses machine learning-based data mining to identify acetylation-related genetic markers associated with PCOS, aiming to enhance diagnostic precision and therapeutic efficacy. Methods Advanced machine learning techniques were used to improve the precision of key gene identification and reveal their biological mechanisms. Validation on an independent dataset (GSE48301) confirmed their diagnostic value, assessed through ROC curves and nomograms for PCOS risk prediction. Molecular mechanisms of acetylation-related gene regulation in PCOS were further examined through clustering, immune-environmental, and gene network analyses. Results Our analysis identified 15 key acetylation-regulated genes differentially expressed in PCOS, including SGF29, NOL6, KLF15, and INO80D, which are relevant to PCOS pathogenesis. ROC curve analyses on training and validation datasets confirmed the model’s high diagnostic accuracy. Additionally, these genes were associated with immune cell infiltration, offering insights into the inflammatory aspect of PCOS. Conclusion The identified acetylation gene markers offer novel insights into the molecular mechanisms underlying PCOS and hold promise for enhancing the development of precise diagnostic and therapeutic strategies.

CNV-Finder: Streamlining Copy Number Variation Discovery.

Copy Number Variations (CNVs) play pivotal roles in the etiology of complex diseases and are variable across diverse populations. Understanding the association between CNVs and disease susceptibility is of significant importance in disease genetics research and often requires analysis of large sample sizes. One of the most cost-effective and scalable methods for detecting CNVs is based on normalized signal intensity values, such as Log R Ratio (LRR) and B Allele Frequency (BAF), from Illumina genotyping arrays. In this study, we present CNV-Finder, a novel pipeline integrating deep learning techniques on array data, specifically a Long Short-Term Memory (LSTM) network, to expedite the large-scale identification of CNVs within predefined genomic regions. This facilitates the efficient prioritization of samples for subsequent, costly analyses such as short-read and long-read whole genome sequencing. We focus on five genes-Parkin (PRKN), Leucine Rich Repeat And Ig Domain Containing 2 (LINGO2), Microtubule Associated Protein Tau (MAPT), alpha-Synuclein (SNCA), and Amyloid Beta Precursor Protein (APP)-which may be relevant to neurological diseases such as Alzheimer's disease (AD), Parkinson's disease (PD), or related disorders such as essential tremor (ET). By training our models on expert-annotated samples and validating them across diverse cohorts, including those from the Global Parkinson's Genetics Program (GP2) and additional dementia-specific databases, we demonstrate the efficacy of CNV-Finder in accurately detecting deletions and duplications. Our pipeline outputs app-compatible files for visualization within CNV-Finder's interactive web application. This interface enables researchers to review predictions and filter displayed samples by model prediction values, LRR range, and variant count in order to explore or confirm results. Our pipeline integrates this human feedback to enhance model performance and reduce false positive rates. Through a series of comprehensive analyses and validations using both short-read and long-read sequencing data, we demonstrate the robustness and adaptability of CNV-Finder in identifying CNVs with regions of varied sparsity, noise, and size. Our findings highlight the significance of contextual understanding and human expertise in enhancing the precision of CNV identification, particularly in complex genomic regions like 17q21.31. The CNV-Finder pipeline is a scalable, publicly available resource for the scientific community, available on GitHub (https://github.com/GP2code/CNV-Finder; DOI 10.5281/zenodo.14182563). CNV-Finder not only expedites accurate candidate identification but also significantly reduces the manual workload for researchers, enabling future targeted validation and downstream analyses in regions or phenotypes of interest.

Parameter Identification of Solid Oxide Fuel Cell Using Elman Neural Network and Dynamic Fitness Distance Balance-Manta Ray Foraging Optimization Algorithm

An accurate solid oxide fuel cell model is a prerequisite for optimizing the operation and state estimation of subsequent cell systems. Hence, this work aimed to utilize a vigoroso algorithmic tool, i.e., Elman neural network, for data prediction to enrich cell measurement data and employ the trained network model for noise reduction of voltage–current data. Furthermore, to obtain reliable cell parameters, a novel parameter identification model based on the dynamic fitness distance balance-manta ray foraging optimization (dFDB-MRFO) algorithm is proposed. Two datasets were applied to extract the electrochemical model and simple electrochemical model parameters of the solid oxide fuel cell model. To verify adequately the superiority of this method, which is compared with another seven conventional heuristic algorithms, four performance indicators were selected as evaluation criteria. Comprehensive case studies demonstrated that through data processing, the precision and robustness of identification could be effectively heightened. In general, the model fitting data obtained via parameter identification using dFDB-MRFO have excellent fitting precision contrast with the measured voltage–current data. Notably, the fitting degree obtained by dFDB-MRFO in the simple electrochemical model reached 99.95% and 99.91% under the two datasets, respectively.

Establishing an Interactive Sequence Database for Shiitake Cultivar Identification.

Shiitake mushrooms (Lentinula edodes) hold significant cultural and economic value, particularly in Asia where they are extensively cultivated. The diversification of shiitake cultivars, driven by the need to adapt to various climatic conditions and cultivation methods, has resulted in over 200 distinct cultivars. Reliable identification of these cultivars is crucial for breeding, intellectual property protection, and effective genetic resource management. Traditional morphological methods are inadequate due to their subjectivity and labor-intensive nature. This study leverages nanopore high-throughput sequencing to comprehensively analyze the rDNA regions (SSU, ITS, LSU, IGS) of 41 shiitake strains from Taiwan's Bioresource Collection and Research Center (BCRC), comprising 5 wild strains, 33 commercial strains, and 3 wild-commercial hybrids. Our results identified the IGS1 region as the most variable and suitable for cultivar differentiation. Consequently, we developed an interactive online database (https://github.com/Raingel/ShiitakeIGS1) that integrates 317 IGS1 sequences from Taiwan, Japan, and China. This platform allows users to upload their IGS1 sequences and identify similar cultivars through a user-friendly interface, enhancing the precision and efficiency of shiitake cultivar identification.

Leap-Type Response of Redox/Photo-Active Lanthanide-Based Metal-Organic Frameworks for Early and Accurate Screening of Prostate Cancer.

The development of high-accuracy technologies to distinguish the quite tiny concentration change of tumor markers between negative and positive is of vital significance for early screening and diagnosis of cancers, but is still a great challenge for the conventional biosensors because of their "gradual" detection mode. Herein, a unique "leap-type" responsive lanthanide MOF-based biosensor (designated as Tb-CeMOF-X) with defect-mediated redox-/photo-activities is developed for precisely identifying acid phosphatase (ACP), an early pathological marker of prostate cancer (PCa) in serum. The engineered Tb-CeMOF-X probe achieves a bursting switch-on luminescence at the critical concentration of ACP (9 U ⋅ L-1), while keeping silent below this threshold, undergoing a qualitative signal change from "zero" to "one" between negative and positive indicators and thus significantly improving the identification precision. Significantly, such "leap-type" response performance can be further edited and amplified by rational defect engineering in the crystal structure to improve the accessibility of active centers, consequently maximizing the detection sensitivity toward ACP in the complex biological media. This study proposes the first paradigm for the development of "leap-type" biosensors with ultra-sensitive differentiation capability between negative and positive, and provides a potentially valuable tool for early and accurate screening of PCa.

Ehsan’s Three Tables Model: A comprehensive guide for identifying research gaps and conducting systematic literature reviews in business and economics studies

The aim of this study is to introduce and assess Ehsan’s Three Tables Model as a novel framework for conducting systematic literature reviews in business and economics studies. The primary challenge in this field lies in the inconsistent identification of research gaps, which often impedes knowledge advancement. To address this, Ehsan’s Three Tables Model offers a structured and comprehensive approach designed to improve the precision and clarity of research gap identification. Methods employed include a systematic review of existing literature, deductive analysis, and the application of the Three Tables Model to categorize and analyze research gaps across various industries and regions. Results of the study demonstrate that this model offers a more rigorous methodology for organizing and analyzing literature, ensuring that identified gaps are both relevant and actionable. It moves beyond conventional approaches by employing a three-step process: compiling relevant studies, categorizing research gaps, and outlining how the current study fills these gaps. The author concluded that the proposed model contributes to the field of business and economics studies by presenting a fresh, structured perspective on literature reviews, providing researchers with an innovative tool to conduct more impactful and targeted studies.

Spatially resolved metabolomics: From metabolite mapping to function visualising.

Mass spectrometry imaging (MSI)-based spatially resolved metabolomics addresses the limitations inherent in traditional liquid chromatography-tandem mass spectrometry (LC-MS)-based metabolomics, particularly the loss of spatial context within heterogeneous tissues. MSI not only enhances our understanding of disease aetiology but also aids in the identification of biomarkers and the assessment of drug toxicity and therapeutic efficacy by converting invisible metabolites and biological networks into visually rendered image data. In this comprehensive review, we illuminate the key advancements in MSI-driven spatially resolved metabolomics over the past few years. We first outline recent innovations in preprocessing methodologies and MSI instrumentation that improve the sensitivity and comprehensiveness of metabolite detection. We then delve into the progress made in functional visualization techniques, which enhance the precision of metabolite identification and annotation. Ultimately, we discuss the significant potential applications of spatially resolved metabolomics technology in translational medicine and drug development, offering new perspectives for future research and clinical translation. HIGHLIGHTS: MSI-driven spatial metabolomics preserves metabolite spatial information, enhancing disease analysis and biomarker discovery. Advances in MSI technology improve detection sensitivity and accuracy, expanding bioanalytical applications. Enhanced visualization techniques refine metabolite identification and spatial distribution analysis. Integration of MSI with AI promises to advance precision medicine and accelerate drug development.

Investigating the Spatial Distribution and Influencing Factors of Non-Grain Production of Farmland in South China Based on MaxEnt Modeling and Multisource Earth Observation Data.

Excessive non-grain production of farmland (NGPF) seriously affects food security and hinders progress toward Sustainable Development Goal 2 (Zero Hunger). Understanding the spatial distribution and influencing factors of NGPF is essential for food and agricultural management. However, previous studies on NGPF identification have mainly relied on high-cost methods (e.g., visual interpretation). Furthermore, common machine learning techniques have difficulty in accurately identifying NGPF based solely on spectral information, as NGPF is not merely a natural phenomenon. Accurately identifying the distribution of NGPF at a grid scale and elucidating its influencing factors have emerged as critical scientific challenges in current literature. Therefore, the aims of this study are to develop a grid-scale method that integrates multisource remote sensing data and spatial factors to enhance the precision of NGPF identification and provide a more comprehensive understanding of its influencing factors. To overcome these challenges, we combined multisource remote sensing images, natural/anthropogenic spatial factors, and the maximum entropy model to reveal the spatial distribution of NGPF and its influencing factors at the grid scale. This combination can reveal more detailed spatial information on NGPF and quantify the integrated influences of multiple spatial factors from a microscale perspective. In this case study of Foshan, China, the area under the receiver operating characteristic curve is 0.786, with results differing by only 1.74% from the statistical yearbook results, demonstrating the reliability of the method. Additionally, the total error of our NGPF identification result is lower than that of using only natural/anthropogenic information. Our method enhances the spatial resolution of NGPF identification and effectively detects small and fragmented farmlands. We identified elevation, farming radius, and population density as dominant factors affecting the spatial distribution of NGPF. These results offer targeted strategies to mitigate excessive NGPF. The advantage of our method lies in its independence from negative samples. This feature enhances its applicability to other cases, particularly in regions lacking high-resolution grain crop-related data.

CellSAM: Advancing Pathologic Image Cell Segmentation via Asymmetric Large-Scale Vision Model Feature Distillation Aggregation Network.

Segment anything model (SAM) has attracted extensive interest as a potent large-scale image segmentation model, with prior efforts adapting it for use in medical imaging. However, the precise segmentation of cell nucleus instances remains a formidable challenge in computational pathology, given substantial morphological variations and the dense clustering of nuclei with unclear boundaries. This study presents an innovative cell segmentation algorithm named CellSAM. CellSAM has the potential to improve the effectiveness and precision of disease identification and therapy planning. As a variant of SAM, CellSAM integrates dual-image encoders and employs techniques such as knowledge distillation and mask fusion. This innovative model exhibits promising capabilities in capturing intricate cell structures and ensuring adaptability in resource-constrained scenarios. The experimental results indicate that this structure effectively enhances the quality and precision of cell segmentation. Remarkably, CellSAM demonstrates outstanding results even with minimal training data. In the evaluation of particular cell segmentation tasks, extensive comparative analyzes show that CellSAM outperforms both general fundamental models and state-of-the-art (SOTA) task-specific models. Comprehensive evaluation metrics yield scores of 0.884, 0.876, and 0.768 for mean accuracy, recall, and precision respectively. Extensive experiments show that CellSAM excels in capturing subtle details and complex structures and is capable of segmenting cells in images accurately. Additionally, CellSAM demonstrates excellent performance on clinical data, indicating its potential for robust applications in treatment planning and disease diagnosis, thereby further improving the efficiency of computer-aided medicine.

Triboelectric signal enhancement via interface structural design and integrated with deep learning for real-time online material recognition

Triboelectric nanogenerator (TENG) is a promising technology for material recognition due to a large amount of distinguishable feature information in triboelectric signal when the TENG device touches different materials. Nevertheless, the identification precision has only improved from the perspective of sensor construction and operation of design, rather than from the electrodes and the triboelectric interface structure. To prevent such issues, a design strategy of electrode/triboelectric material interface structure is applied to enhance the output performance and recognition accuracy. Electroless nickel was plated on a sponge to fabricate a TENG device electrode with a roughness of 179.1 µm. A coarse triboelectric material/electrode interface can improve the output performance of corresponding device. Hence, the nickel layer surface generates more the triboelectric charges, further leading to characteristic triboelectric signals, which can be distinguished by deep machine learning. As expected, the collected TENG signal has the unique characteristic of being able to identify the touched material with a recognition accuracy of 96 %. Based on this, combined with deep machine learning and the triboelectric effect, a material awareness system integrating TENG-based sensors, data collation, and display components was also designed and built for real-time material type identification with superior accuracy. The potential application advantages of the manufactured TENG device in nonvisual intelligent recognition can be foreseen.

A line scanning monitoring method for conveyor belt deviation using point cloud

Abstract A precise conveyor belt deviation monitoring method using line array point cloud data is proposed and demonstrated, which can ensure the healthy running of the conveyor system. The point cloud data characterizing the surface of the conveyor belt is collected in a line scanning way. Then, using a unique soft extraction method that weighted fusing three key features (cross-sectional variation, belt’s horizontal width, and previous frame) to process this data, the edge information of the conveyor belt can be accurately and robustly identified in real-time. Furthermore, the point cloud processing mode enables a belt-segmented deviation analysis method based on a standard sequence query. This can accurately determine the offset value and deviation trend of the conveyor belt, thereby achieving early warning of deviation faults. Experimental results show that the belt edge identification precision can reach 0.3 mm, and an early warning can be provided at least 57 m before the occurrence of a belt deviation fault. This belt deviation monitoring method can be widely applied in various working environments, especially in harsh conditions like mines and ports. It also has potential applications in automated production lines within Industry 4.0.

A mental health monitoring system based on intelligent algorithms and biosensors: Algorithm behaviour analysis and intervention strategies

Introduction: Mental health monitoring encompasses the systematic observation and assessment of an individual’s psychological well-being, aiming to detect, understand and manage mental health conditions. It involves various techniques and interventions tailored to support individuals in achieving and maintaining optimal mental wellness. Recent advancements include the use of biosensors and biomechanics to analyze physiological signals that correlate with mental states, providing a more comprehensive understanding of psychological well-being. Aim: The aim of this research is to construct an innovative mental health monitoring system established on intelligent algorithms and biomechanical data through behaviour analysis and intervention strategies. Methodology: We propose a novel Snow Ablation-driven Bi-directional Fine-tuned Recurrent Neural Network (SA-BFRNN) to identify the state of mental health. In addition to behavioural data, biosensors are employed to collect real-time physiological signals such as heart rate variability, skin conductance, and muscle tension, offering objective markers of mental stress and anxiety. These biomechanical inputs are integrated into the system for multi-modal analysis. We employ the SA algorithm, iteratively removing less influential connections and nodes based on their impact on model performance. This process enhances network efficiency and generalization capabilities, refining the BFRNN for mental health state identification. Utilizing a questionnaire with 25 questions, administered to a selected group of 756 individuals, we validate our proposed model. Biosensor data is synchronized with questionnaire responses to improve the precision of mental state identification. Clustering-derived labels are validated with mean opinion score. These labels inform classifiers for individual mental health prediction, aligning with our objective of robust mental health assessment through data-driven approaches. SA-BFRNN integrates both forward and backward temporal information, enhancing its ability to discern subtle patterns in behaviour. Through iterative fine-tuning, our network learns to adapt to diverse datasets, enabling precise identification of mental health states. Research findings: In the result evaluation phase, we thoroughly examine how well our proposed SA-BFRNN model recognizes various states of mental health across different parameters. Our findings also highlight the significance of incorporating biomechanics, where biosensor data showed a strong correlation with mental health indicators, thereby augmenting the accuracy of the system. Our findings emphasize the efficacy of the SA-BFRNN technique, as demonstrated by its overall performance in terms of recall (92.56%), accuracy (90.13%), F1-score (88.16%) and precision (89.23%). Our experimental results unequivocally demonstrate that our proposed model performed better than other traditional approaches in classifying contents from multimodal data, showing notable enhancements in accuracy and robustness, particularly under dynamic conditions.

Deep Learning for Robust Facial Expression Recognition: A Resilient Defense Against Adversarial Attacks

Adversarial attacks can be extremely dangerous, particularly in scenarios where the precision of facial expression identification is of utmost importance. Hiring adversarial training methods proves effective in mitigating these threats. Although effective, this technique requires large computing resources. This study aims to strengthen deep learning model resilience against adversarial attacks while optimizing performance and resource efficiency. Our proposed method uses adversarial training techniques to create adversarial examples, which are permanently stored as a separate dataset. This strategy helps the model learn and enhances its resilience to adversarial attacks. This study also evaluates models by subjecting them to adversarial attacks, such as the One Pixel Attack and the Fast Gradient Sign Method, to identify any potential vulnerabilities. Moreover, we use two different model architectures to see how well they are protected against adversarial attacks. It compared their performances to determine the best model for making systems more resistant while still maintaining good performance. The findings show that the combination of the proposed adversarial training technique and an efficient model architecture outcome in increased resistance to adversarial attacks. This also improves the reliability of the model and saves more resources for computation. This is evidenced by the high accuracy results achieved at 98.81% accuracy on the CK+ datasets. The adversarial training technique proposed in this study offers an efficient alternative to overcome the limitations of computational resources. This fortifies the model against adversarial attacks, resulting in significant increases in model resilience without loss of performance.

Ship visual trajectory exploitation via an ensemble instance segmentation framework

Maritime traffic safety is of utmost importance in the shipping industry. It is common to avoid potential traffic accident via the support of varied kinematic maritime data (e.g., ship position, ship speed). The study aims to fulfill maritime traffic safety enhancement by extracting multiple ship trajectories from maritime video data. The proposed framework is implemented with three steps. Firstly, the framework obtains initial ship imaging positions by proposing an ensemble YOLOX (You Only Look Once X) with CenterNet model. Secondly, ship contours from each maritime frame are explored with the deep snake module. Thirdly, consecutive ship imaging trajectory is identified with an enhanced Bytetrack multi-ship target tracking algorithm. Experimental results demonstrate that our model outperforms other ship trajectory extraction-like models considering that the identification F-Score (IDF1), IDP (identification Precision), IDR (identification recall), and MOTA (multi-object tracking accuracy) for our proposed framework were 0.965, 0.995, 0.933, and 0.925, respectively. The research findings can help ship crew better aware on-site traffic situations, and thus make optimal ship maneuvering operations for the purpose of enhancing maritime traffic system safety and sustainability.

Improving fluid identification in well logging using Continuous Wavelet Transform and Vision Transformers: An innovative approach

Well logging fluid prediction is one of the key steps in assessing oil and gas reserves. By analyzing downhole logging data, different types of fluids contained in underground rocks, such as crude oil, natural gas, and water, can be determined. This information is crucial for assessing the abundance and recoverable reserves of oil and gas resources and helps guide oil and gas exploration and development work. We have introduced a novel model called CWT (Continuous Wavelet Transform)-ViT (Vision Transformer). CWT can simultaneously provide frequency information at different scales, enabling the model to analyze downhole logging data more comprehensively and accurately at different scales. Underground rock structures often exhibit features at multiple scales, and CWT can effectively capture these features, aiding in better differentiation of different types of fluids. The ViT model utilizes the Transformer architecture, allowing for global attention over input sequences without being limited by sequence length. This enables the model to comprehensively understand the overall information of downhole logging data and extract richer features. For complex geological structures and fluid distributions in geological exploration, the global attention mechanism helps the model better grasp the overall situation, thereby improving the accuracy of fluid prediction. When we used the CWT-ViT method for well logging fluid prediction, we achieved a high accuracy rate of 97.50% in the first dataset, which further improved to 97.77% in the second dataset. These results demonstrate the significant robustness and efficiency of the CWT-ViT method in lithology prediction using well logging data. We also conducted blind well experiments, and our CWT-ViT model outperformed other models, achieving a blind well prediction accuracy of 97.36%. Therefore, the experiments indicate that the key to improving accuracy in well logging fluid prediction with CWT lies in its multiscale analysis capability, effectively capturing different fluid characteristic frequencies. Additionally, CWT enhances signal features and removes noise, increasing the precision of fluid identification. Finally, the integration with ViT further optimizes fluid prediction performance, making it outstanding in complex geological environments. The advantages of ViT in fluid prediction include its excellent sequence modeling capability, effective handling of long-distance dependencies, and enhanced ability to capture fluid characteristics in complex well logging data.