Traditional Identification Research Articles

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.

An Intelligent Parameter Identification Algorithm of Linear Fuzzy Fractional Differential Equation

Abstract The traditional linear generation fuzzy fractional differential equation parameter identification algorithm lacks the update of parameter identification process, has large amount of calculation, slow convergence speed of parameter identification and strong dependence on initial values. In this paper, a new intelligent recognition algorithm for linearly generated fuzzy fractional differential equations is proposed. The parameters of the equation are re expressed by the constant level set. The piecewise constant level set algorithm based on equation parameter intelligent identification is used to solve the steady-state solution of fractional differential equation, and the non convergence problem caused by too much calculation is solved. A new algorithm scheme for linearly generating fuzzy fractional differential equations is established, the constraints of the level set of the differential equations are calculated, and the updated algorithm for parameter identification of the equation is obtained. The evolutionary algorithm is used to solve the updating algorithm to realize the intelligent identification algorithm of linear fractional fuzzy differential equations. Experimental results show that the algorithm has the advantages of fast convergence speed, high calculation accuracy and low initial value.

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.

Masculinity and Homosociality in Western Cinema: Taking Close (2022) as a Case Study

Masculinity and homosociality in Western cinema undergo continuous evolution, transitioning from the construction of traditional ideals to contemporary, more diverse expressions, thereby presenting challenges and deconstructions. To contribute to the theorization of masculinity and homosociality in Western films, this study examines Lukas Dhont's Close to analyze how masculinity and homosociality are represented and negotiated to reproduce gender structures and heteronormative ideologies, and offer implications for future film presentations. Employing textual and formal analyses, along with relevant theories of masculinity and homosociality, this paper identifies different forms of masculinities embodied in hierarchical and horizontal homosociality. In doing so, this paper finds that homosociality provides space for some masculinities to assert and solidify their dominance while relegating others to subordinated or marginalized positions. The dynamic shifts in masculinity also correspondingly affect homosocial dynamics. Further exploration is warranted in presenting and constructing positive narratives surrounding non-hegemonic masculinity and horizontal homosociality, so that a diverse, anti-homophobic, and gender equality-supporting masculinity on screen may have the potential to achieve hegemony.

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.

Intelligent warehousing performance management based on Internet of Things and automation technology in the context of green manufacturing

At present, there is a significant imbalance in the development level of warehousing in China, and most enterprises still use traditional material identification technology, resulting in many unnecessary manual operations in the management process, thereby reducing efficiency and causing waste. Therefore, in the context of green manufacturing, we propose an intelligent warehousing performance management solution based on Internet of Things technology and automation technology. By conducting research on the development and research status of the Internet of Things and intelligent warehousing at home and abroad, this paper delves into the various requirements of intelligent warehousing systems and formulates a targeted architecture for this system. Through experiments, it has been proven that the intelligent warehouse performance evaluation model improved by dual technology can effectively improve warehouse management level, reduce enterprise costs, and enhance enterprise efficiency. After in-depth analysis, it is concluded that this method has considerable adequacy and practicality in evaluating the performance management of intelligent warehousing. Therefore, in the context of green manufacturing, the intelligent warehouse performance management system built by adding IoT technology and automation technology has achieved automation and intelligence in performance management evaluation and warehouse entry and exit management, and has extremely high practical value.

An insight into the cryptic diversity of Fragilariaceae (Bacillariophyta), with the description of a new Antarctic species, Gedaniella antarctica sp. nov.

ABSTRACT Fragilariaceae is a paraphyletic family of araphid diatoms commonly used as bio-indicators, in environmental assessments and in paleoenvironmental reconstructions, and with various potential industrial applications. Recent molecular taxonomic research has highlighted significant limitations in traditional morphology-based investigations of these diatom species. Most descriptions of species and genera of the Fragilariaceae present broad morphological character definitions and many diagnostic characters are indistinguishable by light microscopy. In this sense, taxon misidentification is common and could have serious implications for environmental surveys and laboratory experiments. To better understand the diversity of the Fragilariaceae, we (1) performed phylogenetic analyses of the small subunit ribosomal RNA (18S rRNA), rbcL and psbC gene sequences, (2) inferred a cladogram from key morphological features used in the traditional identification of Fragilariaceae, (3) tested if the topologies of the tree recovered from the molecular phylogeny, of the tree based on morphology and of the trees constraining to monophyly the genera Sarcophagodes, Pseudostaurosira and Nanofrustulum (together and separately) were significantly different and (4) mapped morphological character states on the ML tree and inferred their evolution based on maximum parsimony. Our results supported the monophyly of a group of Fragilariaceae within small araphid diatoms including the genera Cratericulifera, Plagiostriata, Castoridens, Opephora, Staurosira, Staurosirella, Punctastriata, Psammotaenia, Hendeyella, Stauroforma, Pseudostaurosira sensu Li, Nanofrustulum sensu Li, Serratifera sensu Li and Gedaniella sensu Li. Molecular phylogeny and topology tests suggested that the latest circumscriptions of the genera Sarcophagodes, Pseudostaurosira and Nanofrustulum sensu Morales were not monophyletic. Analyses of the Antarctic strain IMA070A collected during the XXXIV Italian Antarctic Expedition using fine structural features of the frustule and molecular data revealed that this diatom belongs to a distinct lineage within Gedaniella, which we describe here as Gedaniella antarctica sp. nov.

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.

Exploring Listeria monocytogenes in Ewe Milk: ssrA Gene-based Real-time PCR Identification, Phylogenetic Analysis, and Antibacterial Assessment of Magnesium Oxide Nanoparticles Synthesized with Myrtus communis Leaf Extract

Background: Listeria monocytogenes, a zoonotic pathogen affecting humans and animals, exhibits a global distribution, including Iraq. This study focused on the rapid identification and phylogenetic analysis of L. monocytogenes in freshly collected ewe milk samples from various farms in Al-Qadisiyah province, Iraq.Methods: The study was conducted with care and precision, involving 150 milk samples. These samples were subjected to traditional bacterial isolation and identification using the enrichment culture method and biochemical tests, with the PCR technique confirming the results. The antibacterial activity of MgONPs was then assessed using the disc diffusion method, ensuring a comprehensive and reliable approach to the study.Result: The results show that 150 ewe milk samples underwent real-time PCR (RT-PCR) targeting the ssrA gene, followed by partial 16S rRNA gene sequencing (PSGS) of purified conventional PCR products. Furthermore, the study entails the biosynthesis of magnesium oxide nanoparticles using Myrtus communis leaf extract, followed by a comprehensive characterization utilizing UV-spectra, FTIR, SEM, and TEM techniques. The Agar well diffusion method assessed the antibacterial efficacy of these Biosynthesized nanoparticles against L. monocytogenes. The RT-PCR results revealed the presence of L. monocytogenes in 36 out of 150 samples (24%). Subsequent PCR analysis confirmed the presence of the pathogen in 30 out of these 36 positive samples (83.33%). Sequencing of two purified PCR products demonstrated 100% nucleotide identity with global isolates from Iraq and Turkey. Furthermore, the study demonstrated that L. monocytogenes exhibited substantial sensitivity (24.66 ± 0.3) to the biosynthesized magnesium oxide nanoparticles. These findings underscore the speed and precision of the RT-PCR method for detecting L. monocytogenes in fresh ewe milk samples.Conclusion: This comprehensive investigation enhances our understanding of L. monocytogenes prevalence in ewe milk and highlights the potential of Myrtus communis -derived nanoparticles for combating this pathogen.Keywords: Antibacterial nanoparticles; Listeria monocytogenes; Magnesium oxide; Myrtus communis sheep milk; ssrA gene

Accuracy of plant identification applications to identify plants in suspected poisoning cases referred to the Queensland Poisons Information Centre

Accidental exposure to potentially poisonous plants is a common reason for contacting poisons information centres. Although most exposures are mild, early identification can be critical for severe cases. Smartphone apps that use image recognition can serve as an alternative to traditional botanist identification. This study investigates the accuracy of smartphone applications compared to botanist identification in real-life plant exposure cases referred to the Queensland Poisons Information Centre. This prospective observational study analysed data from 25 plant exposure cases between February 2022 and January 2023. In these cases, images were referred to a botanist for identification and reviewed by six researchers using six different plant identification applications (Google Lens™, PictureThis, PlantSnap, LeafSnap, Plant Identifier, and Pl@ntnet™). The researchers’ identifications were then compared to the botanist’s identification. The accuracy of the applications varied considerably, PictureThis was the best, with 74% correct identification per exposure, followed by Plant Identifier and Pl@ntnet™ (both 72%). PlantSnap had the lowest accuracy (38%). Agreement between researchers using the same application also showed variation. Current smartphone plant identification applications are not reliable enough for poison information centres to use in guiding advice for plant exposure calls. Botanist identification remains the gold standard. Clinicians should be wary of their use in real-world scenarios.

ENHANCE DOCUMENT VALIDATION UIPATH POWERED SIGNATURE VERIFICATION

Abstract—Signatures are widely used as a means of personal identification and verification. Many documents like bank cheques and legal transactions require signature verification. Signature-based verification of a large number of documents is a very difficult and time-consuming task. Consequently, an explosive growth has been observed in biometric personal verification and authentication systems that are connected with quantifiable physical unique characteristics (finger prints, hand geometry, face, ear, iris scan, or DNA) or behavioural features (gait, voice etc.). As traditional identity verification methods such as tokens, passwords, pins etc suffer from some fatal flaws and are incapable to satisfy the security necessities, the paper aims to consider a more reliable biometric feature, signature verification for the considering. We present a survey of signature verification systems. We classify and give an account of the various approaches that have been proposed for signature verification.

Ready, Steady, No? The Contested Legitimacy of Weapon Deliveries to Ukraine in German Foreign Policy Discourse

AbstractHow were arms deliveries to Ukraine (de)legitimized in the German Bundestag? This paper is based on a discourse analysis of parliamentary debates held between December 2021 and January 2023 on the question of military support for Ukraine. It shows how the legitimization of arms deliveries and various identity narratives of German foreign policy are mutually constitutive but also reconfigured. Members of Alliance 90/The Greens, the Free Democratic Party, and the Christian Democratic Union/Christian Social Union legitimized arms deliveries by referring to Germany’s identity as a European “shaping power” from which a “responsibility” to provide military support was derived. Ukraine had become part of the Western European community; therefore, arms deliveries were necessary to defend the European “we” against the Russian aggression. Criticism of arms deliveries was voiced by The Left, who emphasized Germany’s pacifist identity, which had been established as a lesson from National Socialism and should not be abandoned. The Alternative for Germany party also rejected any military support, pointing to the negative consequences for economic relations with Russia and openly questioning the legitimacy of Ukrainian statehood, as Putin derives geopolitical claims over Ukraine that “must be respected.” The most radical change was observed within the Social Democratic Party parliamentary group. Just a few weeks after his appearance in the Bundestag, Scholz and his parliamentary group mobilized a vocabulary that was less associated with “Zeitenwende” and more with Germany’s traditional identity as a “civilian power” in order to legitimize a “balanced” approach so that “Russia does not win” but Germany is not dragged into the war either.

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.

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.

Інституційний простір сфери оплати праці.

The problem of adequate performance of economic and social functions of labor remuneration and its compliance with the criteria of decent work is extremely relevant for Ukraine. The problem cannot be solved only by adjusting the existing and adopting new normative legal acts of labor law, since the corresponding legislative amendments must be accepted by the key parties to social and labor relations. The purpose of the article is to investigate the institutional environment in the field of labor remuneration in Ukraine in its various manifestations, to identify the most common institutional constructions, structures and practices that need correction and should become a priority object of state policy. The research was conducted using the methods of desk study. In the absence of official statistical data on the labor market and wages, indirect assessment methods were used based on available administrative data, materials of international organizations, results of sociological surveys, etc. The authors abandoned the traditional identification of institutions exclusively with formal organizations, which are mainly created by the government. The novelty is provided by the study of widespread informal institutional structures and practices, their main manifestations and socio-economic consequences. he study revealed that due to the low efficiency of controlling and punitive instruments of state regulation, several informal and implicit institutional structures have taken root in the institutional environment of the sphere of labor remuneration in addition to or instead of formal norms and rules, in particular, the shadowing of labor remuneration, labor remuneration based on individual agreements, unpaid overtime work, fictitious individual entrepreneurs, etc. Manifestations, main causes and consequences of the spread of informal practices are analysed. The impact of the full-scale military aggression against Ukraine on the labor market and labor force patterns in terms of working conditions and wages is assessed, and the risk of deepening shortage of workers for the needs of the post-war reconstruction is emphasized. The role of the government as the largest employer and the main transformations of the wage institution in the public sector are revealed. Conclusions have been made regarding the priority functions of the state in the sphere of wages, based on the need to ensure the resilience of the national economy.

Forensic intelligence: Expanding the potential of forensic document examination

AbstractForensic document examination is characterized by its longevity, diversity, and evolution over time. Predominantly, published research within this field has focused on handwriting examination, the articulation of forensic conclusions, and the development of technical instrumental advancements, focusing on the use of document examination in the resolution of casework. This is a persistent and common problem within forensic science that Kirk identified in 1963 and that other authors have reaffirmed more recently. Ultimately, this has resulted in the potential of forensic intelligence, remaining relatively underexplored in the field of document examination. Forensic intelligence is a different way to view and analyze traces, shifting the focus from the traditional identification of source and activity, to instead identifying trends in criminal activity to assist in the reduction, prevention, and proactive disruption of crime. Despite a distinct disparity between these strands of research, there has been a persevering evolution toward the implementation of a systematic forensic intelligence method for the examination of fraudulent identity documents. Since its initial inception into the research community, this method has expanded and been implemented across Europe, and Canada, with tests also being conducted in Australia. These first tangible steps toward a forensic intelligence capacity within document examination have also inspired new work using forensic intelligence and systematic comparisons within the field of handwriting examination, as well as the recognition of the transversal potential of this method, with it being applied to both physical and digital documents. In this review, the fields of document examination and forensic intelligence will first be introduced, along with a subsequent examination of the research that has led to the creation of a forensic intelligence model within the field of document examination. It should be noted that this review has largely been limited to a review of research that has been published in English and French due to the language of the authors.This article is categorized under: Crime Scene Investigation > From Traces to Intelligence and Evidence Forensic Chemistry and Trace Evidence > Emerging Technologies and Methods Crime Scene Investigation > Epistemology and Method

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.