Style Patterns Research Articles

Analyzing Learning Style Patterns in Higher Education: A Bibliometric Examination Spanning 1984 to 2022 Based on the Scopus Database

Analyzing Learning Style Patterns in Higher Education: A Bibliometric Examination Spanning 1984 to 2022 Based on the Scopus Database

Learning Styles of Medical Students, Surgical Residents, Medical Staff, and General Surgery Teachers When Learning Surgery: Protocol for a Scoping Review.

Learning styles are biological and developmental configurations of personal characteristics that make the same teaching method effective for some and ineffective for others. Studies support a relationship between learning style and career choices in medicine, resulting in learning style patterns being observed in different residency programs, including in general surgery, from medical school to the last stages of training. The methodologies, populations, and contexts of the few studies pertinent to the matter are very different from one another, and a scoping review on this theme will enhance and organize what is already known. The goal of this study is to identify and map out data from studies on the learning styles of medical students, surgical residents, medical staff, and surgical teachers. The review will consider studies on the learning styles of medical students in a clinical cycle or internship, surgical residents with no restriction on year of residency, medical staff in general surgery, or general surgery's medical faculty. Primary studies published in English, with no specific time frame, will be considered. The search will be carried out in four databases, and reference lists will be searched for additional studies. Duplicates will be removed, and two independent reviewers will screen the titles, abstracts, and full texts of the selected studies. Data collection will be performed using a tool developed by the researchers. A results summary will be presented with figures, narratives, and tables. A quantitative and qualitative analysis will be carried out and further results will be shared. The search was funded on September 25, 2023. Data collection was performed in the two following months. Of the 213 articles found, 135 were excluded due to duplication. The remaining 78 articles will have their titles and abstracts analyzed by three of the researchers independently to select those that meet the eligibility criteria. This data is expected to be published in the first semester of 2025. Conducting a scoping review is the best way to map what is known about a subject. Understanding how students, residents, staff, and even teachers prefer to learn surgery is key to staying up to date and knowing how to best educate those pursuing a surgical career. Open Science Framework 75ku4; https://osf.io/75ku4. DERR1-10.2196/57229.

Interim analysis of the tasty trial: A Mediterranean diet style pattern improves joint ultrasound score and inflammation in rheumatoid arthritis

Interim analysis of the tasty trial: A Mediterranean diet style pattern improves joint ultrasound score and inflammation in rheumatoid arthritis

Analysis of Student Learning Styles as an Implementation of Differentiated Learning Strategies in the Indonesian Education Merdeka Curriculum

The aim of this research is to explore the relationship between student learning styles which have implications for student learning outcomes in elementary schools, with a focus on the implementation of differentiated learning in the Independent Curriculum. Qualitative research methods were used to collect data from class IV students at SDN 1 Waleddesa. Data was collected through observation and interviews regarding student learning styles. The research results revealed that students' learning style patterns resulted in visual (4 people), auditory (4 people) and kinesthetic (2 people) learning styles. Observations show that with the strategic abilities of teachers who are skilled in designing responsive learning, they are able to provide implications for students' diverse learning experiences according to their needs and are able to increase student achievement under the Independent Curriculum Framework in Elementary Schools. With differentiated learning strategies, students will be able to achieve their optimal learning potential, according to the characteristics and tendencies of each individual student as implemented by teachers at SDN 1 Waleddesa. The implications of this research emphasize that to meet students' learning styles, teachers need to learn models, approaches, strategies and methods that lead to differentiated learning as in the Merdeka curriculum.

ST2SI: Image Style Transfer via Vision Transformer using Spatial Interaction

While retaining the original content structure, image style transfer uses style image to render it to obtain stylized images with artistic features. Because the content image contains different detail units and the style image has various style patterns, it is easy to cause the distortion of the stylized image. We proposes a new Style Transfer based on Vision Transformer using Spatial Interaction (ST2SI), which takes advantage of Spatial Interactive Convolution (SIC) and Spatial Unit Attention (SUA) to further enhance the content and style representation, so that the encoder can not only better learn the features of the content domain and the style domain, but also maintain the structural integrity of the image content and the effective integration of style features. Concretely, the high-order spatial interaction ability of Spatial Interactive Convolution can capture complex style patterns, and Spatial Unit Attention can balance the content information of different detail units through the change of attention weight, thus solving the problem of image distortion. Comprehensive qualitative and quantitative experiments prove the efficacy of our approach.

Arbitrary style transfer via multi-feature correlation

Recent research in arbitrary style transfer has highlighted challenges in maintaining the balance between content structure and style patterns. Moreover, the improper application of style patterns onto the content image often results in suboptimal quality. In this paper, a novel style transfer network, called MCNet, is proposed. It is based on multi-feature correlations. To better explore the intrinsic relationship between the style image and the content image and to transfer the most suitable style onto the content image, a novel Global Style-Attentional Transfer Module, named GSATM, is introduced in this work. GSATM comprises two parts: Forward Adaptive Style Transformation (FAST) and Delayed Style Transformation (DST). The former analyzes the relationship between style and content features and fine-tunes the style features, whereas the latter transfers the content features based on the fine-tuned style features. Moreover, a new encoding and decoding structure is designed to effectively handle the output of GSATM. Extensive quantitative and qualitative experiments fully demonstrate the superiority of our algorithm. Project page: https://github.com/XiangJinCherry/MCNet.

Arbitrary stylized light-field generation for three-dimensional light-field displays based on radiance fields

Three-dimensional (3D) light-field display technology can reconstruct the spatial characteristics of 3D scenes and provide users with an immersive visual experience without the need for any additional external devices. Here, an arbitrary stylized light-field generation method for 3D light-field displays is presented, and the tripartite constraints are addressed by conducting style transformation in a refined feature space. A multi-dimensional feature refinement module is designed to learn which aspects and regions should be prioritized within the high-level feature grid of the scene, which allows content and style patterns to be better matched and blended. To preserve more structural details and reduce artifacts, a novel global quality and local structure joint loss function is used for optimization. A mask-guided light-field coding method based on ray-casting is employed to accelerate the synthesis of stylized light-field images. Experimental results demonstrate that the proposed method can generate higher-quality stylized 3D light-field content with any given style in a zero-shot manner. Additionally, the method provides two user control extensions to further enrich the ways for the personalized editing of 3D content displayed on 3D light-field displays.

Exploring the Temporal Consistency of Arbitrary Style Transfer: A Channelwise Perspective.

Arbitrary image stylization by neural networks has become a popular topic, and video stylization is attracting more attention as an extension of image stylization. However, when image stylization methods are applied to videos, unsatisfactory results that suffer from severe flickering effects appear. In this article, we conducted a detailed and comprehensive analysis of the cause of such flickering effects. Systematic comparisons among typical neural style transfer approaches show that the feature migration modules for state-of-the-art (SOTA) learning systems are ill-conditioned and could lead to a channelwise misalignment between the input content representations and the generated frames. Unlike traditional methods that relieve the misalignment via additional optical flow constraints or regularization modules, we focus on keeping the temporal consistency by aligning each output frame with the input frame. To this end, we propose a simple yet efficient multichannel correlation network (MCCNet), to ensure that output frames are directly aligned with inputs in the hidden feature space while maintaining the desired style patterns. An inner channel similarity loss is adopted to eliminate side effects caused by the absence of nonlinear operations such as softmax for strict alignment. Furthermore, to improve the performance of MCCNet under complex light conditions, we introduce an illumination loss during training. Qualitative and quantitative evaluations demonstrate that MCCNet performs well in arbitrary video and image style transfer tasks. Code is available at https://github.com/kongxiuxiu/MCCNetV2.

Investigation of learning style patterns: A case study of basic sciences medical students at Tabriz University of Medical Sciences

Investigation of learning style patterns: A case study of basic sciences medical students at Tabriz University of Medical Sciences

Recognition of propaganda techniques in newspaper texts: Fusion of content and style analysis

Public texts aiming at reader manipulation for propaganda or disinformation purposes pose a significant threat to society. The ability to detect the presence of a specific manipulative technique in a text offers an informed warning to readers and guides them to carefully judge the actual statement.In this article, we address the problem of developing new models capable of analyzing newspaper articles for propagandistic features. We introduce a new large dataset of manipulative techniques obtained via gathering and human annotation of 8,646 newspaper articles in Czech, which represents one of the former Soviet influence area languages. The dataset allows both to train new methods to recognize propaganda and disinformation and offer a general comparable benchmark for the techniques. We evaluate the dataset against selected state-of-the-art machine learning approaches to provide high-performing baselines for detecting seventeen annotated manipulative techniques. We also present thorough measurements of inter-annotator agreements that approximate the difficulty level of each of the attributes.As a new finding, we propose a set of text style analysis features that lean on the assumption that each manipulation leads to a specific style pattern. We show that the style analysis improves the detection results for most of the manipulative techniques. The viability of the approach is also confirmed on the well-known QProp propaganda dataset, providing new state-of-the-art results.

Rhetorical Features of National Newspaper Headlines

Headlines wield immense influence in newspapers, being the primary conveyors of key messages while concurrently piquing reader interest and visually conveying innovative concepts. Therefore, our study analyzes the stylistic intricacies within National Newspaper Headlines, with specific emphasis on Rhetorical Features. To realize this objective, we adopted a Stylistic Analysis research framework, employing a lottery method and an observational checklist as our data collection tools. Employing a simple random sampling method, we carefully selected headlines from diverse national media company websites. To ensure validity, we determined the necessary sample size using the reliable Slovin formula. Upon analysis of our gathered data, a distinct pattern emerged. The use of metonymy emerged as the predominant stylistic element within national newspaper headlines. Remarkably, journalists from these media entities used this feature to deliver clarity and enhance overall readability, effectively underscoring its role in facilitating communication. In conclusion, our study delves into the detailed patterns of style found in national newspaper headlines. Our research highlights how journalists skillfully use these style elements not only to share information but also to grab readers’ attention and affect how people interpret news headlines.

PNeSM: Arbitrary 3D Scene Stylization via Prompt-Based Neural Style Mapping

3D scene stylization refers to transform the appearance of a 3D scene to match a given style image, ensuring that images rendered from different viewpoints exhibit the same style as the given style image, while maintaining the 3D consistency of the stylized scene. Several existing methods have obtained impressive results in stylizing 3D scenes. However, the mod- els proposed by these methods need to be re-trained when applied to a new scene. In other words, their models are cou- pled with a specific scene and cannot adapt to arbitrary other scenes. To address this issue, we propose a novel 3D scene stylization framework to transfer an arbitrary style to an ar- bitrary scene, without any style-related or scene-related re- training. Concretely, we first map the appearance of the 3D scene into a 2D style pattern space, which realizes complete disentanglement of the geometry and appearance of the 3D scene and makes our model be generalized to arbitrary 3D scenes. Then we stylize the appearance of the 3D scene in the 2D style pattern space via a prompt-based 2D stylization al- gorithm. Experimental results demonstrate that our proposed framework is superior to SOTA methods in both visual qual- ity and generalization.

MAPPING TRADITION AND MODERNIZATION: A STUDY ON THE EVOLUTION OF COSTUMES OF THE TRIPURI AND REANG WOMEN IN TRIPURA

Tripura is a land-locked state in Northeast India that comprises of multiple communities. Majorly dominated by the Tribal and Bengali communities, this state has witnessed drastic changes in its culture and heritage. Since partition, a huge demographic and ethnographic change has ushered in major challenges for the tribal communities. One such major change was witnessed in the evolution of Tripuri and Reang costumes. The traditional dresses of the communities faced historic challenges that questioned their personal and collective identity. The impact of westernization and modernization on Tripuri dressing culture shall be analysed in this paper. This paper shall also try to study how patterns of cloth and style of dressing have evolved due to the impact of global culture over the local culture. The reasons for the evolution shall be mapped through qualitative data collected from different people (of different communities) belonging to different parts of the state.

Inverting Geodetic Strain Rates for Slip Deficit Rate in Complex Deforming Zones: An Application to the New Zealand Plate Boundary

AbstractThe potential for future earthquakes on faults is often inferred from inversions of geodetically derived surface velocities for locking on faults using kinematic models such as block models. This can be challenging in complex deforming zones with many closely spaced faults or where deformation is not readily described with block motions. Furthermore, surface strain rates are more directly related to coupling on faults than surface velocities. We present a methodology for estimating slip deficit rate directly from strain rate and apply it to New Zealand for the purpose of incorporating geodetic data in the 2022 revision of the New Zealand National Seismic Hazard Model. The strain rate inversions imply slightly higher slip deficit rates than the preferred geologic slip rates on sections of the major strike‐slip systems including the Alpine Fault, the Marlborough Fault System and the northern part of the North Island Fault System. Slip deficit rates are significantly lower than even the lowest geologic estimates on some strike‐slip faults in the southern North Island Fault System near Wellington. Over the entire plate boundary, geodetic slip deficit rates are systematically higher than geologic slip rates for faults slipping less than one mm/yr but lower on average for faults with slip rates between about 5 and 25 mm/yr. We show that 70%–80% of the total strain rate field can be attributed to elastic strain due to fault coupling. The remaining 20%–30% shows systematic spatial patterns of strain rate style that is often consistent with local geologic style of faulting.

The Hybrid Musical Landscape of Ligeti's Late Concertos

AbstractDiscussing his Horn Trio, György Ligeti imaginatively describes the second movement as a dance that was “inspired by different folk musics of nonexistent people, as if Hungary, Romania, and all of the Balkans were located somewhere between Africa and the Caribbean.” And in more general remarks about his works, he goes on to suggest that, rather than overtly referencing their stylistic features, he abstracted technical principles from various traditions and combined them into an idiosyncratically amalgamated musical language. This essay shows an expansive approach to hybridity in Ligeti's Violin Concerto and Hamburg Concerto, going well beyond previous remarks about African rhythmic influences. Ligeti's practice encompasses not only rhythm, but also texture, pitch, and tuning systems; it spans a wider breadth of traditions as well, including newly identified sources such as flute and panpipe ensembles from New Guinea and yodeling traditions from across the globe. An analysis of passages from these late concertos – undertaken alongside evidence from his ethnomusicological sources, recordings, and sketches housed at the Paul Sacher Stiftung – demonstrates the intricacies and patterns of Ligeti's late style and the compelling statement it makes about the role of hybridity and globalization in contemporary life.

Prevalence of metabolic disorders among women in reproductive age group in hilly rural area of North India.

Metabolic syndrome consists of cardiometabolic risk factors that promote the development of atherosclerotic cardiovascular disease, type 2 'DM' and obesity. These are associated with increased cardiovascular mortality and morbidity. Metabolic disorders (MD) are becoming more prevalent both in developing countries and developed countries and are now considered as lifestyle diseases. In women of reproductive age group, especially pregnancy, the blood glucose level is increasing which adversely affects the health of mother and child. Similarly, high blood pressure also precipitates the problems. This study was carried out to find the prevalence of hypertension, diabetes mellitus, obesity and 'MD' among the women living in remote rural set-up. This cross-sectional study was done among women of reproductive age group in 15 villages from 5 panchayats of field practice area of Maharishi Markandeshwar Medical College and Hospital, Kumarhatti, Solan. They were screened for 'MD' through investigative procedures (weight, height, BMR, abdominal girth, blood pressure through sphygmomanometer, blood glucose through the glucometer method), serum HDL and triglycerides. Respondents from the family were asked about the common/general information of house. The tool used for collecting general and relevant information from the respondent was a questionnaire, which was pretested for validity before being used in the field. Four-hundred and sixty-seven women of reproductive age group participated in the study. Half of the participants were with qualification of matriculate and 9.2% participants were illiterate. Three-fourths of the participants were married women and 89% were vegetarian. Sixty-four per cent of participants were housewives. Half of the participants had a normal BMI, whereas 28.9% were overweight and 10% were obese. The prevalence of hypertension and diabetes among the participants were 12.5% and 9.8%, respectively. Forty-seven per cent participants had a waist circumference above 80 cm. The level of non-communicable diseases is related with the MD which has the adverse effect on the various systems and organs of the subjects. The MD can be controlled with the certain changes in the life style pattern. The GOI is also concerned with such scenarios in the country. It is recommended that women of reproductive age group undergo regular blood pressure and blood sugar screenings to detect hypertension and diabetes early and take appropriate measures to manage them.

Consistent Arbitrary Style Transfer Using Consistency Training and Self-Attention Module.

Arbitrary style transfer (AST) has garnered considerable attention for its ability to transfer styles infinitely. Although existing methods have achieved impressive results, they may overlook style consistencies and fail to capture crucial style patterns, leading to inconsistent style transfer (ST) caused by minor disturbances. To tackle this issue, we conduct a mathematical analysis of inconsistent ST and develop a style inconsistency measure (SIM) to quantify the inconsistencies between generated images. Moreover, we propose a consistent AST (CAST) framework that effectively captures and transfers essential style features into content images. The proposed CAST framework incorporates an intersection-of-union-preserving crop (IoUPC) module to obtain style pairs with minor disturbance, a self-attention (SA) module to learn the crucial style features, and a style inconsistency loss regularization (SILR) to facilitate consistent feature learning for consistent stylization. Our proposed framework not only provides an optimal solution for consistent ST but also outperforms existing methods when embedded into the CAST framework. Extensive experiments demonstrate that the proposed CAST framework can effectively transfer style patterns while preserving consistency and achieve the state-of-the-art performance.

Non-parametric style transfer: Correlation-aware exact distribution matching

Early feed-forward neural methods of arbitrary image style transfer utilized the encoded feature map up to its second-order statistics, i.e., mean and variance (or covariance) of the encoded feature map. Recent methods have begun to utilize feature statistics beyond the 2nd-order statistics, e.g., a higher-order moment, slicing-based method, and histogram matching for better style representation. However, even in the distribution matching of the latest method, considering the channel-correlation and the order of duplicated values in the feature map is missing. This resulted in insufficient style transfer or artifacts on style-transferred images e.g., unexpected color or pattern co-occurrence, skewed color or style pattern into one side, noise between background and foreground, etc. In this work, we show how to make a feature transform layer to consider both channel-correlation and duplicated values in the feature map for exact feature distribution matching in style transfer. Our experimental results prove that the stylized images obtained with our method are more similar to the target style images qualitatively without losing content clearness and superior to the existing methods quantitatively in all possible style measures, e.g., 0.0311 lower sliced Wasserstein distance than the previous method and the most favored choice with 25.54% preference.

The Dispute Between Thinking Styles and Thinking Skills Among Technical Students

Thinking style is one thing that can help students in solving problems by knowing the students' own style. The style of thinking is not an ability but more towards how to use the existing abilities of each individual. Similarly, thinking skills can help students to solve problems and can make students smart in thinking about things. Thinking skills can be formulated as the process of using the mind to find a solution or understanding of something. Thinking skills also allow students to integrate each newly experienced experience into each individual’s mind box. The aim of this study was to identify the thinking style patterns and the level of thinking skills between the demographic factors among technical students. The design of this study was descriptive and inferential with a quantitative approach. A total of 273 technical students from the Faculty of Technical and Vocational Education (FPTV) at Universiti Tun Hussein Onn Malaysia (UTHM) were randomly selected as a sample covering the fields of Building Construction (BBB), Catering (BBC), Welding and Metal Fabrication (BBD), Electrical and Electronics (BBE), Creative Multimedia (BBF) and Refrigeration and Air Conditioning (BBG). A set of modified questionnaires from past studies were used as research instruments. Majority of technical students have a balanced thinking style and follow a critical and creative thinking style. The majority of all technical students at FPTV has a level of mastery of all three thinking skills analytical, practical and creative. Among the three types of skills, the majority of technical students have a level of mastery of high practical thinking skills, followed by creative thinking skills and analytical thinking skills. It is hoped that this study can provide guidance to students, lecturers and institutions to improve the Way of teaching and learning in accordance with the thinking style and thinking skills of students.

FineStyle: Semantic-Aware Fine-Grained Motion Style Transfer with Dual Interactive-Flow Fusion.

We present FineStyle, a novel framework for motion style transfer that generates expressive human animations with specific styles for virtual reality and vision fields. It incorporates semantic awareness, which improves motion representation and allows for precise and stylish animation generation. Existing methods for motion style transfer have all failed to consider the semantic meaning behind the motion, resulting in limited controls over the generated human animations. To improve, FineStyle introduces a new cross-modality fusion module called Dual Interactive-Flow Fusion (DIFF). As the first attempt, DIFF integrates motion style features and semantic flows, producing semantic-aware style codes for fine-grained motion style transfer. FineStyle uses an innovative two-stage semantic guidance approach that leverages semantic clues to enhance the discriminative power of both semantic and style features. At an early stage, a semantic-guided encoder introduces distinct semantic clues into the style flow. Then, at a fine stage, both flows are further fused interactively, selecting the matched and critical clues from both flows. Extensive experiments demonstrate that FineStyle outperforms state-of-the-art methods in visual quality and controllability. By considering the semantic meaning behind motion style patterns, FineStyle allows for more precise control over motion styles. Source code and model are available on https://github.com/XingliangJin/Fine-Style.git.