This paper presents an enrichment method for the case of re-entrant corner singularities in linear elastic problems but with no enrichment of the finite element solution space. Instead an a posteriori error estimator, with gradient descent, is used to determine the approximate solution of the coefficients for the enrichment functions about each re-entrant corner. The method then approximately removes the singularities from the problem, increasing its regularity. As a result is that exponential convergence of the error can be achieved with uniform refinement in polynomial order. Almost no improvement in the error is expected or observed if uniform refinement in p is used. The approach is termed the Celatus method as the singularities are hidden from view . As problems are made regular it is shown that exponential convergence rates are observed when the Celatus method is combined with h p -adaptivity ( h p -Celatus), requiring far fewer degrees of freedom compared, by orders of magnitude, to traditional finite element analysis with h p -adaptivity. Furthermore each term for the enrichment functions for every re-entrant corner can be evaluated independently. Therefore the method can be implemented in an inherently parallel way. The proposed approach offers an ≈ 10 times reduction in computation time for the same accuracy compared to standard finite element analysis with h p -adaptivity. Additionally, since the solution space is not enriched and is always polynomial, the issue of having near singular matrices does not exist for the Celatus method. The discontinuous Galerkin finite element method is used here, but all equations and methodology are equally applicable to the continuous Galerkin method. • Enrichment of solution achieved without enriching the finite element solution space. • Each corner’s enrichment terms are evaluated independently for parallel execution. • Requires orders of magnitude fewer degrees of freedom than traditional hp-adaptive FEM. • Singularities are removed, converting the problem from non-smooth to smooth, providing convergent results with increasing polynomial order only. • Method avoids issues with near-singular matrices common in enriched methods.

Team-based learning (TBL) has shown promise in promoting active learning and clinical reasoning in health professions education, yet its implementation in veterinary curricula remains seemingly variable and at times, intimidating. This teaching tip demonstrates how TBL can be adapted and implemented to meet an instructor's specific needs. It uses a third-year veterinary elective course to illustrate how the approach reinforces core curriculum content and promotes experiential learning through small-group clinical reasoning and discussion. Despite initial challenges, including student resistance to pre-session work, technical difficulties with digital platforms, and variability in faculty familiarity with TBL, using a modified TBL approach yielded positive outcomes in student engagement and faculty collaboration. The course redesign emphasized a stepwise introduction to TBL, allowing for gradual adoption while maintaining alignment with core learning objectives. Some limitations noted during the initial redesign included logistical challenges such as the Canvas Learning Management System (LMS) platform and manual grading of free-text responses. Group dynamics also varied, particularly among students with differing clinical interests, underscoring the importance of thoughtful team composition. These insights have informed future directions, including expanded faculty training, increased use of traditional TBL elements such as Readiness Assurance Tests (RATs), and improved digital infrastructure. Overall, this initiative represents a meaningful step toward a more scalable and effective TBL model in veterinary education, with the potential to enhance both student learning and faculty engagement.

In recent years, Chinese animation has increasingly embraced traditional cultural elements, with Chan (Zen) Buddhism emerging as a rich source of philosophical and aesthetic inspiration. Existing research on the manifestation of Chan aesthetics in Chinese animation has explored the topic from diverse perspectives, yet analyses from a systematic semiotic perspective remain limited. Most symbolic studies reduce Chan elements to isolated visual signs with one-to-one meaning correspondences, neglecting the synergistic operation of narrative, visual, and auditory symbols in animation as an integrated system. Drawing on Roland Barthes’ theory of myth, this study employs a qualitative semiotic analysis to examine how Chan aesthetics are reconstructed and naturalised in Chinese animated works across different periods and genres. The analysis demonstrates that core Chan concepts are reconfigured into secularised audiovisual symbol systems. These systems translate abstract philosophy into tangible aesthetic forms and narrative structures, with meaning generated through the interplay of denotation, connotation, and myth. Furthermore, the representation of Chan aesthetics evolves across eras. Early animation relies on minimalist ink-wash visuals and implicit narrative; contemporary commercial animated film employs causal storytelling to embed Chan values in modern contexts; and Ye Youtian ‘poetic animation’ emphasises personal spiritual expression through non-linear imagery.

ABSTRACT Research Findings: This scoping study examined the global state of artificial intelligence (AI) curricula in early childhood education (ECE) for young children (ages 3–8). Nine studies from four regions met the inclusion criteria, with research conducted primarily in the United States, followed by China. Across these studies, all 10 traditional curriculum elements proposed by van den Akker were evident, either explicitly or inferentially. A typology of three key AI curricular approaches was identified (AI-as-Objective, AI-as-Content, and AI-as-Pedagogy). AI-as-Pedagogy was the most common approach, followed by curricula combining AI-as-Objective and AI-as-Content, and then those integrating all three. Furthermore, most AI curricula encompassed all three competencies (AI Knowledge, AI Skills, and AI Dispositions), with consistent emphasis on the development of AI skills. However, these curricula do not explicitly engage with the evolving ethical landscape surrounding AI, highlighting the absence of AI ethics as a core competency. Teacher scaffolding emerged as the primary source of developmental support. Practice or Policy: As the primary implementers of AI curricula, teachers would benefit from adequate professional development and institutional support to better understand the what, why, and how of designing and implementing AI curricula to foster children’s key AI competencies, including the integration of AI ethics across different AI curricular approaches.

In modern China, the introduction of Science from the West posed a significant challenge to Chinese Buddhism, which was already in a state of decline. The intellectual currents of the New Culture Movement (1915–1923) and the subsequent anti-religious movement, which initially targeted Christianity but expanded to include all religions, subjected Buddhism to severe criticism and pressure for reform. In response, Buddhist intellectuals developed the idea of “Buddhism being scientific” as a defensive strategy. On the one hand, they direct parallels between Buddhist concepts, such as the microscopic world described in scriptures, and modern scientific discoveries like microbiology and the theory of relativity, aiming to demonstrate Buddhism’s empirical validity and superiority. On the other hand, they argued that Buddhism could supplement the shortcomings of science, particularly in addressing spiritual and moral needs, thus positioning it as a necessary complement to a purely materialistic worldview. Under the dominant influence of the scientific paradigm, Buddhism underwent a profound academic transformation. Its teachings were systematically integrated into modern disciplinary frameworks, such as Buddhist history, philosophy, and psychology, shifting from a primarily faith-based practice to an object of scholarly study. This scientization process stripped many traditional elements of their sacred character, reinterpreting them through a rational lens and ultimately redirecting the course of modern Chinese Buddhism.

Partial differential equations (PDEs) are fundamental to modeling complex and nonlinear physical phenomena, but their numerical solution often requires significant computational resources, particularly when a large number of forward full solution evaluations are necessary, such as in design, optimization, sensitivity analysis, and uncertainty quantification. Recent advances in artificial intelligence – particularly operator learning – have enabled surrogate models that efficiently predict full-field PDE solutions; however, these models often struggle with accuracy and robustness when faced with highly nonlinear responses driven by sequential input functions. To address these challenges, we propose the Sequential Neural Operator Transformer (S-NOT), an architecture that combines gated recurrent units (GRUs) with the self-attention mechanism of transformers to address time-dependent, nonlinear PDEs. Unlike sequential-deep operator networks(S-DON), which use a dot product to merge encoded outputs from the branch and trunk sub-networks, S-NOT leverages attention to better capture intricate dependencies between sequential inputs and spatial query points. We benchmark S-NOT on three challenging datasets from real-world applications with plastic and thermo-viscoplastic highly nonlinear material responses: multiphysics steel solidification, a three dimensional (3D) lug specimen, and a dogbone specimen under temporal and path-dependent loadings. The results show that S-NOT yields prediction errors up to 4.5 times smaller than S-DON even for data outliers. Furthermore, S-NOT provides an acceleration of 4 orders-of-magnitude compared to traditional finite element method simulations, demonstrating its accuracy and robustness for drastically accelerating computational frameworks in scientific and engineering applications.

Learning batik motif design at Vocational High Schools requires learning media that can facilitate conceptual understanding visually and interactively in accordance with students’ characteristics. However, the use of interactive learning media that supports the​ improvement of students’cognitive abilities at SMKN 3 Pekalongan is still limited. This study aims to develop interactive learning media using Genially and to analyze its effectiveness in improving students’ cognitive abilities in traditional and contemporary batik elements at SMKN 3 Pekalongan. This study employed a Research and Development (R&D) method using the ADDIE model. The research subjects consisted of 61 eleventh-grade KKBT students, who were divided into an experimental class and a control class to examine the effectiveness of the developed learning media. The efectiveness test showed an increase in the learning outcomes in the experimental class from 74.45 to 89.55, with N-gain value 0.657 (moderate category), while the control class obtained an N-gain of 0.21 (low category). The Independent Sample Test results indicated a significant difference in learning outcomes between the experimental class and the control class. Therefore, the Genially-based interactive learning media is declared feasible, practical, and effective for use in learning.

This paper uses constructivist learning theory and media ecology theory as the guiding ideas, combines the mixed research method, and takes a quasi-experimental research approach to explore the effects of digital means in traditional culture education. Taking high school students as a sample for empirical research, the experimental class implemented teaching incorporating digital means such as VR technology, AR technology and multimedia interaction, while the control class conducted regular classroom lecture-style teaching, and carried out a 12-week teaching reform experiment involving three aspects of teaching ancient poems, teaching literary texts and traditional festival culture. This project used the questionnaire survey method, case interview method, case tracking method, and platform access statistics to obtain information data, and analyzed the data using a combination of qualitative and quantitative methods with the assistance of SPSS statistical software, which confirmed that the digital curriculum resources can effectively improve the students' level of knowledge of culture as well as their ability to understand culture, and enhance their interest in learning; at the same time, it has enlarged the scope of dissemination, improved the At the same time, the scope of dissemination has been expanded and the efficiency of dissemination has been improved, and the number of visitors has increased by nearly 13 times. Expanding from three provinces to the whole of China has increased the efficiency of dissemination by nearly 10 times. The case study also shows how VR, AR and multimedia technologies can be used in teaching and learning contexts.

The reliability of machines largely depends on the accuracy of predicting the stress–strain state of components in tribofatigue systems, especially under high operating loads. Traditional finite element analysis provides high accuracy but requires significant computational resources and offers limited flexibility for rapid parameter variation. In recent years, machine learning methods have been increasingly applied in engineering practice. Among them, neural networks are of particular interest, as they allow nonlinear relationships between loads and stresses to be captured while significantly reducing computation time compared to traditional models. This work proposes an approach for predicting maximum stresses in the “shaft–insert” system by combining three-dimensional finite element modeling with subsequent neural network training. A database was created containing the results of numerical experiments for different combinations of bending and contact loads. A fully connected neural network with three hidden layers and different activation functions was used for training. The quality of the model was assessed using standard metrics: Mean Squared Error, Mean Absolute Error (MAE), and the coefficient of determination R2. The trained neural network demonstrated high accuracy in predicting maximum stresses both in the shaft and in the insert. For the training set, the R2 value reached 0.99991, and for the test set it was 0.99984, confirming minimal deviations from finite element results. The MAE was less than 0.006, while the maximum relative error in the test set did not exceed 3.2 %. The developed neural network model demonstrated the ability to reproduce the results of finite element analysis for the “shaft–insert” system while providing a substantial reduction in computation time compared to traditional finite element simulations. The model was constructed for a limited range of loads; therefore, further research should focus on expanding the dataset and including additional materials, which will make it possible to evaluate the scalability of the approach and its robustness under more complex conditions.

This study describes the representation of Indonesian Generation Z's national identity on IDN Times' Instagram. The results show that Indonesian Generation Z prefers content that combines traditional cultural elements with modernity, in creative and easily accessible forms, such as through social media, short videos, and digital platforms. The research design used mixed methods, namely by conducting quantitative content analysis on Indonesian Generation Z's national identity content on IDN Times' Instagram from January to September 2024. Qualitative data were obtained through in-depth interviews with 20 Generation Z individuals aged 18 to 25, representing all provinces in Indonesia. The qualitative approach was carried out using virtual anthropology methods, focusing on how information and communication technology influences social life, culture, and individual identity in virtual environments. Three elements studied include the representation of traditional symbols, local-global hybridization, and digital activities. Content featuring traditional music with a modern touch, or memes that reflect Indonesia's rich culture in a lighthearted and humorous manner, is more engaging. The presence of social media has become a primary space for expressing, criticizing, and capturing global issues. Generation Z also tends to support environmentally friendly products and engage in activities that can reduce negative impacts on the Earth. They use social media for positive change to maintain their Indonesian identity, but still adapt to global trends.

The mechanical behavior of aqueduct structures exhibits highly complex characteristics during prestress tensioning, making it difficult for the traditional double-control method to accurately predict and real-time control the key stresses. To improve the construction safety of prestressed tensioning and the prediction accuracy of structural prestress responses, this study develops a rapid structural mechanical property prediction method based on machine learning. Taking prestressed aqueducts as the research object, a system of “finite element simulation—sample generation—machine learning prediction” is established. Firstly, multiple groups of tensioning parameter combinations are designed via Latin hypercube sampling, and the stress responses are obtained through finite element analysis to form a high-quality training sample library. Subsequently, critical structural features are extracted based on mesh reconstruction, and stress prediction models are established using the K-Nearest Neighbors (KNN) and Random Forest algorithms respectively; the prediction performance of both models is compared and validated against finite element simulation results. Furthermore, the prediction outputs of the optimal machine learning model were used to analyze the stress distribution and potential stress concentration issues of the structure during the tensioning process. The comparative analysis results indicate that the Random Forest model performs best in terms of stress prediction accuracy and stability, and its prediction results are highly consistent with those of the finite element method. Compared with traditional finite element condition analysis, the machine learning model can complete multi-condition stress prediction in a shorter time. Leveraging its high-efficiency prediction capability, local high-stress areas of the structure in the tensioning construction scheme can be identified, thereby providing effective optimization schemes to improve the stress distribution. The mechanical response prediction method for the prestress tensioning process of aqueducts, with machine learning as the core, constructed in this paper realizes the rapid and reliable prediction of key stresses throughout the entire prestress tensioning process. This method can be applied to assist in optimizing tensioning construction schemes and construction monitoring, providing a practical technical solution for safety control of aqueduct structures during the prestress construction stage.

The digital age has transformed culinary marketing strategies, with social media, particularly Instagram, becoming the primary platform for building audience engagement through visual content. Hyperlocality in visual communication not only emphasizes geographic location, but also integrates elements of culture, tradition, and community identity. Previous studies have shown that visual storytelling can increase engagement, but research on how hyperlocal visual content specifically affects audience engagement is still limited. This study uses a qualitative approach with visual analysis methods, in-depth interviews, and participatory online observation to explore hyperlocal visual content strategies in culinary marketing on Instagram. A comparative study was conducted on two Instagram accounts, @jajan.sawangan and @sawangankuliner, to understand how visual elements such as composition, color, typography, and narrative influence engagement. The results showed that using visual elements that are more aesthetically pleasing, consistent, and reflective of local identity can significantly increase audience engagement. Accounts with more structured visual strategies and stronger narratives have higher levels of interaction than accounts with less cohesive visual approaches. Based on these findings, the study developed a conceptual model of audience engagement with hyperlocal visual content that can be used to guide community-based digital branding strategies. This study contributes to the study of digital communication, hyperlocal marketing, and social media branding, and provides recommendations for culinary business professionals to optimize visual strategies on Instagram.

Purpose The current study examines the factors that influence attitudes towards renewable energy and the implications that such attitudes have for the intention to adopt renewable energy systems based on the theory of planned behaviour (TPB) as the theoretical framework. In particular, the study uses the TPB through the incorporation of moral identity and sustainable knowledge to fill a gap in the existing body of knowledge regarding the inadequacy of ethical self-concept and sustainability-associated cognition regarding the adoption of renewable energy. Design/methodology/approach The structured survey data were collected, and partial least squares structural equation modelling was used to analyse the data. The measurement model was first established in terms of reliability and discriminant validity. Subsequently, the PLS bootstrapping was carried out to examine the relationship between variables in the structural model. Findings The results show that moral identity and sustainable knowledge act as major predictors of positive attitudes to renewable energy, which predicts the intention to adopt it with significant power. Besides this, subjective norms and perceived behavioural control have a direct influence on attitudes. Originality/value This study advances the TPB theory by integrating moral identity and sustainable knowledge as novel predictors of renewable energy attitudes and adoption intention. This study extends the TPB beyond its traditional attitude, normative and control elements by adding to its explanatory power in the adoption of renewable energy. The empirical findings provide theory-based suggestions to practitioners and policymakers, related to the development of ethically based and knowledge-based interventions, to strengthen pro-adoption attitudes and accelerate a shift to renewable energy.

In recent years, China has strategically leveraged its fashion industry to bolster its soft power on the global stage. This article explores how Chinese fashion brands and events have become potent instruments of cultural diplomacy. Focusing on Shanghai Tang as a case study, the article examines the brand’s ability to fuse traditional Chinese elements with modern design to promote Chinese culture worldwide. Unlike western fashion industries, in which government involvement is minimal, Chinese fashion benefits from state initiatives aimed at projecting national identity and pride. This article conceptualizes fashion as a distinct modality of soft power and identifies three mechanisms through which it operates: materialization (embedding national narratives in garments and retail spaces), embodiment (incorporating stylized “Chineseness” into everyday dress and performances of taste), and positionality (repositioning China within a global fashion field historically dominated by European luxury houses). Drawing on first-hand brand materials and policy directives, the study demonstrates how Chinese fashion enhances China’s global influence and fosters cross-cultural appreciation.

The rising demand for cooling in hot semi-arid cities like Jaipur is putting increasing pressure on energy infrastructure and urban resilience. This study investigates the potential of Jaali, a traditional perforated screen used in Indian architecture, as a passive strategy to reduce energy demand in a contemporary office building through data-driven optimisation and computational analysis. Using detailed energy simulations in DesignBuilder, this research explores how variations in orientation, cavity depth, perforation ratio and screen thickness affect cooling performance during the summer months through a systematic parametric study generating 84 simulation configurations. The model is based on a 12-storey office building designed according to local energy codes. The results show that the optimal configuration differs by orientation. On the south façade, the optimal combination is a 100 mm Jaali with 20% perforation and a 1.5 m cavity, which delivers the best performance. The west façade performs best with a thicker 150 mm screen, the same 20% perforation ratio, and a 1.0 m cavity depth. On the east façade, the strongest performance is achieved with a 150 mm Jaali, 50% perforation, and a 1.5 m cavity, with cooling demand reduction of up to 8.71%. These findings demonstrate that traditional design elements, when optimised for modern use, can offer measurable energy savings through predictive modelling frameworks. More importantly, their widespread adoption could support urban cooling strategies, reduce peak electricity loads and contribute to sustainable development across rapidly growing cities in hot climates. The comprehensive dataset generated provides a foundation for future AI-enhanced building energy optimisation applications.

This study focuses on the costume culture depicted in Tang Dynasty Dunhuang murals, exploring its digital translation and integration into contemporary fashion design. It aims to address the research question of how ancient mural art can merge with digital fashion design. A combination of iconographic analysis, practice-based research, and 3D digital reconstruction was employed to systematically examine and reinterpret the form, color, and structure of Dunhuang mural costumes. The findings reveal that these costumes feature flowing lines, balanced silhouettes, and vivid color palettes, reflecting the Tang Dynasty’s aesthetic ideals of grandeur, vitality, and harmony with nature. Through digital reconstruction and creative experimentation, the study successfully transforms traditional Dunhuang costume elements into digital fashion works that embody both cultural depth and modern aesthetics. The innovation of this study lies in introducing a digital fashion design perspective to systematically analyze the aesthetic structure of Dunhuang mural costumes, establishing a translation pathway from traditional imagery to digital fashion, and demonstrating that the fusion of traditional aesthetics and digital technologies not only broadens the expressive language of contemporary fashion design but also contributes to the preservation, inheritance, and global dissemination of cultural heritage.

The paper presents the results of the development and study of activating flux paste compositions based on traditional oxide elements for argon arc welding (A-TIG). The main attention is paid to the effect of the flux composition on the ionization of the arc and the ratio of the penetration depth to the weld width. Experiments were carried out with various compositions of flux pastes containing fluorspar (CAF₂) and welding flux AN-60, while the ratio of components in the composition of the pastes varied. During the experiments, the coating was carried out on steel plates with a thickness of 10 mm, steel grade – 09G2C. The greatest penetration depth was achieved by compositions No. 1 and No. 2, where the penetration depth was 6.5 mm and 7 mm respectively. It is found out that the optimal fluorspar content in the paste should not exceed 25%, which makes it possible to achieve the maximum penetration depth. The main ionizers of the welding arc are identified: Si, Ca, Mg and Mn, which content can be increased or changed to optimize the paste compositions. Based on the study results, it is found that the developed activating fluxes can be used to optimize argon arc welding of thick-walled structures, as well as to provide surface hardening of steels instead of traditional chemical and thermal treatment methods. The study objective: to develop and conduct experimental studies of complex activating fluxes based on widespread components to increase the penetration depth when welding thick-sheet structures with butt and lap weld joints. The study tasks: to study the effect of the developed activating fluxes on the ionization of the welding arc and the penetration depth of09G2C steel when performing surfacing by argon arc method. The research methods. Preparation of the initial components and activating flux pastes based on them; argon arc surfacing on the steel samples; sample preparation, measurement of the weld width and penetration depth by metallographic method; calculations of the ionization degree of the flux pastes components and assessment of their effect on the arc during surfacing. The novelty of the work. Application of the developed compositions of activating flux pastes for argon arc surfacing/welding (A-TIG) of thick-walled steels. An increase in the penetration depth and a narrowing of the weld width during active ionization of the arc. The use of compositions of activating fluxes as a basic component of flux pastes for surface hardening of steels. The study results. Activating flux pastes of No. 1 and No. 2 compositions showed the best penetration results, with their use the greatest penetration depth of 6.5-7 mm was achieved, which is 1.5-1.8 times higher than without the use of activating fluxes. When studying the composition of the fluxes, the optimal content of the components was determined, where the total amount of CaF2 should not exceed 25%. No defects were found in the macrostructure during surfacing. Conclusion: Compositions of activating flux pastes No. 1 and No. 2 for the argon arc surfacing/welding (A-TIG) method provide an increase in the penetration depth of steel samples, are recommended for further use in flux pastes for surface hardening of steels and further modification of chemical composition.

This research aims to explore the concept of folkism as a framework for analyzing African choral music performances, with a specific focus on Leke Joel's folk cantata titled "370" as a study. Folkism, characterized by the preservation and promotion of cultural traditions and heritage, serves as a lens to explore the cultural expression and identity embedded in African choral music. The study employs a combination of musicological analysis, ethnographic research, and critical interpretation to delve into the integration of traditional elements, including melodies, rhythms, vocal techniques, language, and performance structures, within the context of African choral performance. By analyzing the performance of Leke Joel's "370," this research aims to elucidate how folkism is manifested in the delivery of the musical performance, showcasing the ways in which performers engage with and embody the cultural heritage. Findings reveal that the cantata incorporates distinct African musical features, such as the use of call and response, and polyphony, creating a complex and dynamic musical texture. These elements contribute to the immersive nature of the composition, engaging the audience with the vibrancy of African musical traditions. In conclusion, this study emphasizes the importance of embracing compositions like Leke Joel's "370" Cantata, which celebrates African cultural heritage through music. It is therefore recommended that more African composers embark on similar journeys, creating compositions that promotes the continent's rich culture and ensure the continuity of African heritage for future generations.

This study aims to explore a scientific and systematic design method for product packaging by integrating perceptual engineering, interactive genetic algorithms, and traditional cultural elements. A case study on daily necessities packaging was conducted, in which the semantic difference method was used to collect perceptual evaluations from 20 subjects. The proposed model was verified through 800 iterations of a genetic algorithm. The results show that the proposed method significantly improves the gene matching rate of packaging designs compared to traditional approaches. After optimization, the interactive genetic algorithm achieved a satisfaction score of approximately 9 points out of 10. The integration of enterprise information systems reduced material procurement approval cycles by 50% and enhanced the accuracy of market acceptance prediction for new designs. This study provides a novel paradigm for modern packaging design, demonstrating that the integration of perceptual engineering, genetic algorithms, and enterprise information systems can effectively balance visual appeal and market feasibility.

This paper presents a conformal phased array antenna suitable for unmanned aerial vehicle (UAV) wings that features wide bandwidth, wide-angle scanning, and narrow elevation beamwidth. Unlike the traditional planar dipole elements with balun feeding, the proposed antenna element adopts a dipole pair with two-wire parallel feeding, thereby eliminating the need for a complex balun. To suppress impedance mismatch during large-angle scanning, two matching stubs are integrated into the ground plane, which effectively eliminates scanning blind spots. Additionally, by loading three directors, the elevation beamwidth at the center frequency is compressed to 69[Formula: see text] while the gain is improved, without degrading the active impedance bandwidth. Based on this element, a 1×12 linear wing-conformal array prototype is fabricated. Both simulated and measured results verify that the array realizes a relative bandwidth of 24.8% with no grating lobes even when scanned to ±65[Formula: see text] in the azimuth plane, and the gain fluctuation at the key frequency points is all less than 3 dB. These results validate the effectiveness of the design and highlight its potential for UAV radar applications.