Traditional Identification Research Articles

The cluster analysis of traditional Chinese medicine authenticity identification technique assisted by chemometrics

This study explore the authenticity identification technique of traditional Chinese medicine (TCM) using chemometrics in conjunction with cluster analysis. A clustering Gaussian mixture model was constructed and applied for the data clustering analysis of four types of TCM. Chemical measurements combined with discrete wavelet transform (DWT), Fourier transform infrared spectroscopy (FTIR), and Fourier self-deconvolution (FSD) were utilized for the detailed differentiation of Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium. Differences in the attenuated total reflection-FTIR (ATR-FTIR) spectra among the four TCMs were observed. Utilizing clustering algorithms, the one-dimensional DWT of the infrared spectra of samples was employed for the authentication of Chinese herbal medicines. The model demonstrates optimal performance throughout 2000 rounds of network training. The accuracy (88.6 %), sensitivity (86.5 %), and specificity (82.7 %) of the model constructed in this study significantly surpassed those of the CNN model: accuracy (67.7 %), sensitivity (70.4 %), and specificity (68.5 %) (P < 0.05). By setting the cluster size K = 5 and the number of Gaussian mixture model components to 5, the model effectively fits the actual number of categories within the dataset. Infrared spectroscopy analysis revealed distinct carbon-oxygen stretching vibration absorption peaks between 1025 and 1200 cm−1 for Bupleurum scorzonerifolium, Bupleurum yinchowense, Bupleurum marginatum, and Bupleurum smithii Wolff var. parvifolium, indicating strong absorption peaks of carbohydrates. A comprehensive structural information analysis revealed a similarity of above 0.982 among the four types of TCM. Combined with chemometrics and intelligent algorithm-based cluster analysis, successful and accurate authentication of TCM authenticity was achieved, providing an effective methodology for quality control in TCM.

Design and Optimization of the Shape of a Single-person Submersible based on ANSYS

Due to the complex growth environment of wild sea cucumbers, the difficulty and low efficiency of traditional ROV identification, at present, wild sea cucumber fishing is mainly obtained through manual fishing, in order to ensure the life safety of divers and improve the fishing efficiency of sea cucumbers, new ideas and solutions for single-man submersibles suitable for shallow water operations are proposed. Under the condition of ensuring the driver's operating space, the shape and structure of an easy-to-process manned submersible are designed to optimize its resistance performance. Creo was used to establish a model of a single-man submersible for shape design, and three design schemes were proposed: scheme 1 flat spindle type, scheme 2 imitation unmanned boat type, and scheme 3 imitation airfoil type. The drag performance of the single-man submersible is simulated and analyzed by using the Fluent module in the analytical fluid dynamics simulation software Ansys, and the results show that the drag performance of scheme 3 imitation airfoil type is better than that of other schemes.The conclusion was verified by simple experiments.It provides effective reference information for the subsequent structural optimization of single-man submersibles.

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.

Karihwaientáhkwen: Conceptualizing Morality in Indigenous Consciousness

Many public institutions, including universities and colleges, have committed to Indigenous engagement – the institutional effort to engage with experiences, histories, and perspectives of First Nations, Inuit, and Métis peoples in meaningful ways. In many areas of institutional endeavour, these commitments have frequently included a focus upon spiritual orientations of Indigenous peoples as a central aspect of their knowledge and worldviews. In this study, we sought to acquire knowledge on moral understandings that were resident in the consciousness of Indigenous Elders and Knowledge Keepers situated at universities across Canada. Findings revealed that personal and communal experiences informed a developed sense of responsibility that may be situated in the unique manifestations of their respective knowledge systems, heritages, and consciousnesses. Although non-Indigenous orientations such as those of Christianity informed the narratives of some, a prevailing sense of traditional identity emerged from participants.

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.

Traditional identity contents predict women’s amusement with sexist jokes about men through benevolent but not hostile sexism

Abstract Two preregistered studies (N = 679) tested the links between women’s gender identity and their amusement with sexist jokes targeting men. We hypothesized that the interaction between the strength (i.e., level of in-group identification) and contents (traditional/progressive) of women’s gender identity would be associated with their amusement with jokes targeting men and that this relationship would be mediated by sexism toward men. Although Study 1 provided no support for this hypothesis, we found positive associations between women’s traditional identity contents and their amusement with benevolent and hostile jokes about men, explained by the higher endorsement of benevolent (but not hostile) sexism toward men. Study 2 replicated this effect, suggesting that women’s enjoyment of jokes targeting men might stem from their beliefs that justify the existing power (im)balance between sexes and not their hostility toward men.

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.