Traditional Identification Methods Research Articles

Socio-cultural factors associated with mushroom consumption among tea garden population of Dibrugarh, Assam

Background: Major challenge with climate change in India is sustaining food supply for economically disadvantaged leading to widespread malnutrition and related illnesses. Aim of the study was to assess the socio-cultural factors associated with mushroom consumption among tea garden population of Dibrugarh, Assam. Methods: Community based cross-sectional study was conducted in which 6 tea gardens were selected based on high mushroom consumption. From each selected tea garden, 35 households were covered using simple random sampling technique and data was collected using a semi structured questionnaire. Results: Majority (61.72%) of the respondents were males, 69.04% belonged to lower middle socioeconomic status (class IV). Easy availability (80%) during monsoon was common reason for consumption. Most of them (95.71%) used traditional identification methods. Traditional cooking methods were followed, with the assumption that those rendered all mushroom edible. Majority believed that lemon could dilute toxins in case of accidental poisoning. Conclusions: There was a need to make community aware of scientific identification of edible mushroom to prevent morbidities and mortalities arising out of it.

Advancing Element Identification for Web and Mobile Automation

Element identification is a cornerstone of automation testing, directly influencing the reliability, scalability, and efficiency of test scripts. With the rapid evolution of web and mobile applications, traditional element identification methods—such as XPath, CSS Selectors, and static attributes—are increasingly challenged by dynamic DOM structures, shadow DOMs, virtual DOMs, and hybrid frameworks. These challenges are further amplified in mobile environments, where gestures, native elements, and platform-specific attributes add layers of complexity. This paper explores the advancements in element identification techniques that address these challenges for both web and mobile automation testing. It presents a comparative analysis of traditional methods and emerging solutions, highlighting their limitations in modern application scenarios. The study introduces hybrid strategies, including context-aware locators, heuristic-based identification, and early implementations of adaptive and self-healing locators. It also examines the role of cross-platform tools like Selenium, Appium, and Cypress in tackling identification issues. Furthermore, the paper emphasizes the importance of unified approaches that bridge web and mobile testing, particularly in hybrid applications with embedded web views. Future trends, such as collaborative tagging standards between development and testing teams, are discussed to provide a forward-looking perspective on overcoming element identification bottlenecks. By addressing key pain points and proposing innovative techniques, this paper aims to guide testers, developers, and tool creators in adopting resilient, scalable, and adaptable element identification strategies that meet the demands of modern software applications.

A New Method for Non-Destructive Identification and Tracking of Multi-Object Behaviors in Beef Cattle Based on Deep Learning.

The method proposed in this paper provides theoretical and practical support for the intelligent recognition and management of beef cattle. Accurate identification and tracking of beef cattle behaviors are essential components of beef cattle production management. Traditional beef cattle identification and tracking methods are time-consuming and labor-intensive, which hinders precise cattle farming. This paper utilizes deep learning algorithms to achieve the identification and tracking of multi-object behaviors in beef cattle, as follows: (1) The beef cattle behavior detection module is based on the YOLOv8n algorithm. Initially, a dynamic snake convolution module is introduced to enhance the ability to extract key features of beef cattle behaviors and expand the model's receptive field. Subsequently, the BiFormer attention mechanism is incorporated to integrate high-level and low-level feature information, dynamically and sparsely learning the behavioral features of beef cattle. The improved YOLOv8n_BiF_DSC algorithm achieves an identification accuracy of 93.6% for nine behaviors, including standing, lying, mounting, fighting, licking, eating, drinking, working, and searching, with average 50 and 50:95 precisions of 96.5% and 71.5%, showing an improvement of 5.3%, 5.2%, and 7.1% over the original YOLOv8n. (2) The beef cattle multi-object tracking module is based on the Deep SORT algorithm. Initially, the detector is replaced with YOLOv8n_BiF_DSC to enhance detection accuracy. Subsequently, the re-identification network model is switched to ResNet18 to enhance the tracking algorithm's capability to gather appearance information. Finally, the trajectory generation and matching process of the Deep SORT algorithm is optimized with secondary IOU matching to reduce ID mismatching errors during tracking. Experimentation with five different complexity levels of test video sequences shows improvements in IDF1, IDS, MOTA, and MOTP, among other metrics, with IDS reduced by 65.8% and MOTA increased by 2%. These enhancements address issues of tracking omission and misidentification in sparse and long-range dense environments, thereby facilitating better tracking of group-raised beef cattle and laying a foundation for intelligent detection and tracking in beef cattle farming.

Pipeline multi-type high consequence area identification based on mask R-CNN with fused attention mechanisms

Identification of high consequence areas is an important task in pipeline integrity management. However, traditional identification methods are generally characterized by low efficiency, high cost and low accuracy. For this reason, this paper proposes a recognition method based on the improved algorithm Mask Region-based Convolutional Neural Network. Coordinate attention mechanism module is introduced into the traditional Mask R-CNN algorithm to improve the recognition accuracy and reduce the training time. For the identification results, GIS tools are utilized to establish high consequence zones along both sides of the pipeline, and the grade and scope of the high consequence zones are determined according to relevant specifications.In this paper, this method is used to identify the high-consequence area of a pipeline section in Guangdong Province, the results show that: 1, the improved algorithm in the identification of densely populated, geologic hazards, flammable and explosive high consequence zones of the average accuracy of the identification of 1.7%, 3.4%, 3.9%. 2, The method in this paper identifies 8 more building elements and 0.311 more kilometers of pipeline mileage compared to traditional identification methods. The method of this paper can provide a reference for the early identification of high consequence areas of pipelines.

Comparative analysis of complete chloroplast genomes of Synotis species (Asteraceae, Senecioneae) for identification and phylogenetic analysis

BackgroundThe Synotis (C. B. Clarke) C. Jeffrey & Y. L. Chen is an ecologically important genus of the tribe Senecioneae, family Asteraceae. Because most species of the genus bear similar morphology, traditional morphological identification methods are very difficult to discriminate them. Therefore, it is essential to develop a reliable and effective identification method for Synotis species. In this study, the complete chloroplast (cp.) genomes of four Synotis species, S. cavaleriei (H.Lév.) C. Jeffrey & Y.L. Chen, S. duclouxii (Dunn) C. Jeffrey & Y.L. Chen, S. nagensium (C.B. Clarke) C. Jeffrey & Y.L. Chen and S. erythropappa (Bureau & Franch.) C. Jeffrey & Y. L. Chen had been sequenced using next-generation sequencing technology and reported here.ResultsThese four cp. genomes exhibited a typical quadripartite structure and contained the large single-copy regions (LSC, 83,288 to 83,399 bp), the small single-copy regions (SSC, 18,262 to 18,287 bp), and the inverted repeat regions (IR, 24,837 to 24,842 bp). Each of the four cp. genomes encoded 134 genes, including 87 protein-coding genes, 37 tRNA genes, 8 rRNA genes, and 2 pseudogenes (ycf1 and rps19). The highly variable regions (trnC-GCA-petN, ccsA-psaC, trnE-UUC-rpoB, ycf1, ccsA and petN) may be used as potential molecular barcodes. The complete cp. genomes sequence of Synotis could be used as the potentially effective super-barcode to accurately identify Synotis species. Phylogenetic analysis demonstrated that the four Synotis species were clustered into a monophyletic group, and they were closed to the Senecio, Crassocephalum and Dendrosenecio in tribe Senecioneae.ConclusionsThis study will be useful for further species identification, evolution, genetic diversity and phylogenetic studies within this genus Synotis and the tribe Senecioneae.

An automatic recognition method for power quality disturbances based on CAB-Net

Abstract The accurate identification of power quality disturbances (PQDs) is crucial for maintaining the stability and reliability of power systems. In this paper, an automatic identification method for PQDs based on CNN-BiGRU-Attention Network (CAB-Net) is proposed. To handle the temporal characteristics and complexity of composite PQDs signals, the proposed model leverages the bidirectional information transmission of the BiGRU model to efficiently extract temporal features. Furthermore, to boost recognition accuracy, an attention mechanism is incorporated, the key information can be intelligently locked in the feature extraction stage, and the information can be efficiently screened and utilized, thereby enhancing its ability to identify power quality disturbance signals. To confirm the validity of the method, we use the simulation data for experimental proof. Experimental results demonstrate that compared to traditional power quality disturbance identification methods, the method has excellent recognition ability and stronger robustness.

Multiple-model state-space system identification with time delay using the EM algorithm

For a dynamic process identification throughout the whole operating range under diverse operating conditions, it is difficult to capture the process dynamics by a single process model in which the traditional identification method can be adopted to implement parameter estimation. By using the multiple dual-rate state-space models to approach the parameter-varying time-delay systems with different operating conditions, this paper explores an EM algorithm to simultaneously estimate the hidden variable, the parameter vector, the state variable and the time-delay by introducing hidden variables and by using a Kalman smoother.

Identification of MIMO Nonlinear Gaussian Time-Varying System Based on Multi-Dimensional Taylor Network Multi-Level Approximation

Aiming at the problems of identification difficulties and low identification accuracy in modelling and identification of multiple-input multiple-output (MIMO) nonlinear Gaussian time-varying systems, this paper proposes an identification scheme based on the step-by-step approximation of multidimensional Taylor network (MTN). The aim of this paper is to improve the modelling of complex nonlinear systems so as to improve the prediction performance and control effect of the system. Different from the traditional multidimensional Taylor network identification method, this method adopts an order-by-order approximation strategy, which seeks its parameters sequentially from the lower order to the higher order, and continuously optimises the parameter weights during the parameter seeking process. Firstly, the nonlinear function model is approximated as a polynomial form by the order-by-order Taylor expansion, and then the weight parameters of each order of the Taylor expansion are calculated and updated step by step by using the algorithm based on the Variable Forgetting Factor Recursive Least Squares (VFF-RLS) method. Through iterative optimized of these parameters, dynamic weight assignment to each order of the Taylor expansion is achieved. A parameter-identified nonlinear function model is finally obtained, which can more accurately describe the dynamic behaviour and characteristics of the system. Finally, an arithmetic simulation is carried out through an example to verify the effectiveness of the proposed method.

Precision Detection of Salt Stress in Soybean Seedlings Based on Deep Learning and Chlorophyll Fluorescence Imaging.

Soil salinization poses a critical challenge to global food security, impacting plant growth, development, and crop yield. This study investigates the efficacy of deep learning techniques alongside chlorophyll fluorescence (ChlF) imaging technology for discerning varying levels of salt stress in soybean seedlings. Traditional methods for stress identification in plants are often laborious and time-intensive, prompting the exploration of more efficient approaches. A total of six classic convolutional neural network (CNN) models-AlexNet, GoogLeNet, ResNet50, ShuffleNet, SqueezeNet, and MobileNetv2-are evaluated for salt stress recognition based on three types of ChlF images. Results indicate that ResNet50 outperforms other models in classifying salt stress levels across three types of ChlF images. Furthermore, feature fusion after extracting three types of ChlF image features in the average pooling layer of ResNet50 significantly enhanced classification accuracy, achieving the highest accuracy of 98.61% in particular when fusing features from three types of ChlF images. UMAP dimensionality reduction analysis confirms the discriminative power of fused features in distinguishing salt stress levels. These findings underscore the efficacy of deep learning and ChlF imaging technologies in elucidating plant responses to salt stress, offering insights for precision agriculture and crop management. Overall, this study demonstrates the potential of integrating deep learning with ChlF imaging for precise and efficient crop stress detection, offering a robust tool for advancing precision agriculture. The findings contribute to enhancing agricultural sustainability and addressing global food security challenges by enabling more effective crop stress management.

Bridge damage identification based on synchronous statistical moment theory of vehicle–bridge interaction

AbstractConsidering the weak noise resistance and low identification efficiency of traditional bridge damage identification methods, a data‐driven approach based on synchronous statistical moment theory and vehicle–bridge interaction vibration theory is proposed. This method involves two main steps. First, a two‐axle test vehicle is used to collect acceleration response signals synchronously from adjacent designated measurement points while stationary. This operation is repeated to calculate the second‐order statistical moment curvature (SOSMC) difference of entire bridge points corresponding signals in different states. By comparing with the reference value, the preliminary damage location of the bridge can be obtained. Second, the first‐order modal shape curve is constructed using the second‐order statistical moment (SOSM). The refined identification of bridge damage is then based on an improved direct stiffness back calculation of the bridge's stiffness. This article proposes the synchronization theory for the first time and combines it with the statistical moment clustering method, forming an innovative approach to obtaining structural vibration modes. The effectiveness of this method has been well validated through numerical simulations with different parameters and on‐site bridge tests. The research results indicate that SOSMC indicators have better noise resistance and higher recognition efficiency in identifying damage locations, compared to modal curvature and flexibility curvature indicators. Additionally, compared to transfer rate and random subspace methods, the SOSM method results in smaller error and higher identification efficiency.

Hydrolytic Response of Beta-Amylase to Selected Starches when Adsorptively Immobilized on Agarose Gel

Fermentative microorganisms bring about transformative processes in food, thereby enhancing flavor and appearance indices, inhibiting undesirable components, improving shelf and storage life, as well as improving health and nutritional quality parameters. Traditional methods for identification of fermentative microorganisms involve cultivating and analyzing phenotypic characteristics, which are labor-intensive and time-consuming. Advanced molecular techniques such as polymerase chain reaction, whole gene sequencing, next generation sequencing, fluorescent in situ hybridization (FISH), Matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), fourier transform infrared spectroscopy and hyper-spectral imaging offer rapid, cost-effective, and reliable identification of bacteria and fungi cultured on agar plates or in liquid media. Techniques, such as genotyping, examine the DNA sequence to determine the genetic makeup of microorganisms, alongside the grouping of isolates via application of phylogenetic tree similarity indices. Overall, modern molecular techniques have revolutionized the identification of fermentative microorganisms, providing efficient and more elaborate alternatives to traditional methods. In this review work, an attempt is made to examine the historical and analytical trends from the conventional methods to the most recent modern tools for effective identification of industrially-important fermentative microorganisms.

Recent Advances in the Identification and Characterization of Fermentative Microorganisms: An Exploratory Review

Fermentative microorganisms bring about transformative processes in food, thereby enhancing flavor and appearance indices, inhibiting undesirable components, improving shelf and storage life, as well as improving health and nutritional quality parameters. Traditional methods for identification of fermentative microorganisms involve cultivating and analyzing phenotypic characteristics, which are labor-intensive and time-consuming. Advanced molecular techniques such as polymerase chain reaction, whole gene sequencing, next generation sequencing, fluorescent in situ hybridization (FISH), Matrix assisted laser desorption ionization time of flight mass spectrometry (MALDI-TOF MS), fourier transform infrared spectroscopy and hyper-spectral imaging offer rapid, cost-effective, and reliable identification of bacteria and fungi cultured on agar plates or in liquid media. Techniques, such as genotyping, examine the DNA sequence to determine the genetic makeup of microorganisms, alongside the grouping of isolates via application of phylogenetic tree similarity indices. Overall, modern molecular techniques have revolutionized the identification of fermentative microorganisms, providing efficient and more elaborate alternatives to traditional methods. In this review work, an attempt is made to examine the historical and analytical trends from the conventional methods to the most recent modern tools for effective identification of industrially-important fermentative microorganisms.

LEVERAGING ARTIFICIAL INTELLIGENCE (AI) IN PUBLIC SECTOR FINANCIAL RISK MANAGEMENT: INNOVATIONS, CHALLENGES, AND FUTURE DIRECTIONS

This paper examines the transformative role of Artificial Intelligence (AI) in enhancing financial risk management practices within the public sector in different context. With the increasing complexity of financial markets and the growing demand for transparency and efficiency in public finance management, traditional methods of risk identification and mitigation are proving to be inadequate. AI and machine learning technologies offer a promising alternative, with their ability to process vast amounts of data, recognize patterns, and predict future trends with a high degree of accuracy. We delve into the application of predictive models for identifying various financial risks, including credit, market, liquidity, and operational risks, and explore how AI algorithms can optimize decision-making processes through scenario analysis, resource allocation, and policy impact analysis. The paper also addresses the challenges associated with integrating AI into public financial management systems, such as data privacy concerns, the need for skilled personnel, and the importance of developing regulatory frameworks that ensure ethical use and transparency. By presenting case studies and real-world applications, this study highlights both the successes and failures of AI implementations in the public sector, providing valuable lessons learned and best practices. As AI technologies continue to evolve, this paper underscores the need for ongoing research and adaptation to fully realize their potential in making public sector financial risk management more proactive, efficient, and resilient.

Development of a multiplex PCR assay and quantification of microbial markers by ddPCR for identification of saliva and vaginal fluid

The identification of biological fluids at crime scenes contributes to crime scene reconstruction and provides investigative leads. Traditional methods for body fluid identification are limited in terms of sensitivity and are mostly presumptive. Emerging methods based on mRNA and DNA methylation require high quality template source. An exploitable characteristic of body fluids is their distinct microbial profiles allowing for the discrimination of body fluids based on microbiome content. Microbial DNA is highly abundant within the body, robust and stable and can persist in the environment long after human DNA has degraded. 16S rRNA sequencing is the gold standard for microbial analysis; however, NGS is costly, and requires intricate workflows and interpretation. Also, species level resolution is not always achievable. Based on the current challenges, the first objective of this study was to develop a multiplex conventional PCR assay to identify vaginal fluid and saliva by targeting species-specific 16S rRNA microbial markers. The second objective was to employ droplet digital PCR (ddPCR) as a novel approach to quantify bacterial species alone and in a mixture of body fluids. Lactobacillus crispatus and Streptococcus salivarius were selected because of high abundance within vaginal fluid and saliva respectively. While Fusobacterium nucleatum and Gardnerella vaginalis, though present in healthy humans, are also frequently found in oral and vaginal infections, respectively. The multiplex PCR assay detected L. crispatus and G. vaginalis in vaginal fluid while F. nucleatum and S. salivarius was detected in saliva. Multiplex PCR detected F. nucleatum, S. salivarius and L. crispatus in mixed body fluid samples while, G. vaginalis was undetected in mixtures containing vaginal fluid. For samples exposed at room temperature for 65 days, L. crispatus and G. vaginalis were detected in vaginal swabs while only S. salivarius was detected in saliva swabs. The limit of detection was 0.06 copies/µl for F. nucleatum (2.5 ×10−9 ng/µl) and S. salivarius (2.5 ×10−6 ng/µl). L. crispatus and G. vaginalis had detection limits of 0.16 copies/µl (2.5 ×10−4 ng/µl) and 0.48 copies/µl (2.5 ×10−7 ng/µl). All 4 bacterial species were detected in mixtures and aged samples by ddPCR. No significant differences were observed in quantity of bacterial markers in saliva and vaginal fluid. The present research reports for the first time the combination of the above four bacterial markers for the detection of saliva and vaginal fluid and highlights the sensitivity of ddPCR for bacterial quantification in pure and mixed body fluids.

Uncover the Unusual Near‐Infrared Luminescence in Stannates – Interplay of Cr3 + with Codopants

AbstractThe unintentional incorporation of various unknown trace amounts of impurities poses challenges and controversies in identifying luminescent mechanisms of certain systems. A common approach to confirm or exclude the involvement of a certain activation center is to monitor the changes in luminescence by intentionally doping the inferred species into the crystal. Here, an intriguing luminescent phenomenon resulting from the hidden interplay between the dopants and trace impurities, is presented. In alkaline‐earth stannate perovskites, near‐infrared luminescence is observed. By combining density‐functional calculations with the well‐designed doping and codoping experimental strategies, the mechanisms behind this anomaly is successfully uncovered. It originates from the stabilization of Cr3 + by codopants such as Bi and rare‐earth (RE) ions, acting as electron donors. The luminescence is almost unobservable when chromium is doped alone, and thus the traditional identification method does't work. This work emphasizes the important role of trace impurity in materials, especially the subtle change of valence state of trace impurity upon doping, which provides enlightening insights and prompts reassessments of numerous reported anomalous luminescent phenomena.

A Transformer-based approach to highly granular source code authorship attribution

Traditional source code authorship identification methods often rely on features such as textual similarity, programming style or metadata, however, these methods often struggle to extract the precise source code authoring style when dealing with large-scale code bases or complex programming patterns, resulting in poor performance. Therefore, this paper proposes a Transformer-based high fine-grained source code author attribution method.Aiming at the problem of roughness of existing literature on word segmentation, this paper proposes a high fine-grained source code segmentation method to extract higher fine-grained features. Aiming at the problem of feature dimension redundancy, this paper adopts the Transformer network to locate sensitive features that can characterise the author's style.To verify the effectiveness of the model, it was tested on GCJ-C++ and GCJ-Java datasets. The experimental results show that the proposed method model achieves higher recognition accuracy in the source code authorship attribution problem compared to the traditional methods.

A Fault Identification Method of Hybrid HVDC System Based on Wavelet Packet Energy Spectrum and CNN

Aiming at the shortcomings of traditional fault identification methods in fault information acquisition, In the scenario of hybrid HVDC transmission system, a new fault identification method is proposed by using wavelet packet energy spectrum and convolutional neural network (CNN), which effectively solves the problem of complex fault feature extraction of hybrid HVDC transmission system. This method effectively improves the accuracy of fault identification. Firstly, tThe frequency-domain characteristics of the fault transient signal are extracted by wavelet packet transform, and the feature differences are reflected in the form of energy spectrum. Secondly, according to the extracted energy feature information, the order of fault line and fault type is identified by CNN. Finally, through example verification and algorithm comparison, it is concluded that, the mentioned model has a strong ability to identify faults, and has strong anti-noise interference and tolerance to transition resistance.

A Transformer-based approach to highly granular source code authorship attribution

Traditional source code authorship identification methods often rely on features such as textual similarity, programming style or metadata, however, these methods often struggle to extract the precise source code authoring style when dealing with large-scale code bases or complex programming patterns, resulting in poor performance. Therefore, this paper proposes a Transformer-based high fine-grained source code author attribution method.Aiming at the problem of roughness of existing literature on word segmentation, this paper proposes a high fine-grained source code segmentation method to extract higher fine-grained features. Aiming at the problem of feature dimension redundancy, this paper adopts the Transformer network to locate sensitive features that can characterise the author's style.To verify the effectiveness of the model, it was tested on GCJ-C++ and GCJ-Java datasets. The experimental results show that the proposed method model achieves higher recognition accuracy in the source code authorship attribution problem compared to the traditional methods.

Overflow Identification and Early Warning of Managed Pressure Drilling Based on Series Fusion Data-Driven Model

Overflow is one of the complicated working conditions that often occur in the drilling process. If it is not discovered and controlled in time, it will cause gas invasion, kick, and blowout, which will bring inestimable accidents and hazards. Therefore, overflow identification and early warning has become a hot spot and a difficult problem in drilling engineering. In the face of the limitations and lag of traditional overflow identification methods, the poor application effect, and the weak mechanisms of existing models and methods, a method of series fusion of feature data obtained from physical models as well as sliding window and random forest machine learning algorithm models is proposed. The overflow identification and early warning model of managed pressure drilling based on a series fusion data-driven model is established. The research results show that the series fusion data-driven model in this paper is superior to the overflow identification effect of other feature data and algorithm models, and the overflow recognition accuracy on the test samples reaches more than 99%. In addition, when the overflow is identified, the overflow warning is performed through the pop-up window and feature information output. The research content provides guidance for the identification of drilling overflow and the method of model fusion.

A Deep Learning Approach Considering Image Background for Pneumonia Identification Using Explainable AI (XAI).

Pneumonia mainly refers to lung infections caused by pathogens, such as bacteria and viruses. Currently, deep learning methods have been applied to identify pneumonia. However, the traditional deep learning methods for pneumonia identification take less account of the influence of the lung X-ray image background on the model's testing effect, which limits the improvement of the model's accuracy. In this paper, we propose a deep learning method that considers image background factors and analyzes the proposed method with explainable deep learning for explainability. The essential idea is to remove the image background, improve the pneumonia recognition accuracy, and apply the Grad-CAM method to obtain an explainable deep learning model for pneumonia identification. In the proposed approach, (1) preliminary deep learning models for pneumonia X-ray image identification without considering the background are built; (2) deep learning models for pneumonia X-ray image identification with background consideration are built to improve the accuracy of pneumonia identification; (3) Grad-CAM method is employed to analyze the explainability. The proposed approach improves the accuracy of pneumonia identification, and the highest accuracy of VGG16 reaches 95.6%. The proposed approach can be applied to real pneumonia identification for early detection and treatment.