Search Results (8,584)

Although there is growing agreement between scholars about the crucial role of Maraimalai Adigal in the early stage of the Tamil nationalist movement, the state of current understanding of this “religious phase of Tamil nationalism” is far from satisfactory. Authors of this article focused on three important claims in the currently accepted view on the character and goals of Adigal’s religious reform. The first stance portrays his efforts for purification of the Tamil language from foreign influences as “anti-Aryan” and “anti-Sanskritic.” The second claim describes the reformer’s efforts as a move from polytheism to “Shaiva monotheism”, and builds on ideas of the early Orientalists and Christian missionaries in India who formulated the “Sanskritic hegemony” thesis. As an assumption running through the debates about Adigal’s reforms, there is conviction that the Tamil intellectual basically accepted the crystallizing Aryan Invasion Theory as true description of both Ancient India and roots of the social problems in Tamilnadu of his times. In their thorough analysis of Adigal’s work and scholarly debates, authors of this article disclose the role of unexamined assumption about religious competition being the main form of cultural encounters in India, and argue for very different understanding of Adigal’s efforts to revive Shaivism. Full article

Factor analysis is a well-known statistical method to describe the variability of observed variables in terms of a smaller number of unobserved latent variables called factors. Even though latent factors are conceptually independent of each other, their influence on the observed variables is often joint and synergistic. We propose to quantify the synergy of the joint influence of factors on the observed variables using O-information, a recently introduced metric to assess high-order dependencies in complex systems; in the proposed framework, latent factors and observed variables are jointly analyzed in terms of their joint informational character. Two case studies are reported: analyzing resting fMRI data, we find that DMN and FP networks show the highest synergy, consistent with their crucial role in higher cognitive functions; concerning HeLa cells, we find that the most synergistic gene is STK-12 (AURKB), suggesting that this gene is involved in controlling the HeLa cell cycle. We believe that our approach, representing a bridge between factor analysis and the field of high-order interactions, will find wide application across several domains. Full article

Early printed books, particularly incunabula, are invaluable archives of the beginnings of modern educational systems. However, their complex layouts, antique typefaces, and page degradation caused by bleed-through and ink fading pose significant challenges for automatic transcription. In this work, we present a modular pipeline that addresses these problems by combining modern layout analysis and language modeling techniques. The pipeline begins with historical layout-aware text segmentation using Kraken, a neural network-based tool tailored for early typographic structures. Initial optical character recognition (OCR) is then performed with Kraken’s recognition engine, followed by post-correction using a fine-tuned ByT5 transformer model trained on manually aligned line-level data. By learning to map noisy OCR outputs to verified transcriptions, the model substantially improves recognition quality. The pipeline also integrates a preprocessing stage based on our previous work on bleed-through removal using robust statistical filters, including non-local means, Gaussian mixtures, biweight estimation, and Gaussian blur. This step enhances the legibility of degraded pages prior to OCR. The entire solution is open, modular, and scalable, supporting long-term preservation and improved accessibility of cultural heritage materials. Experimental results on 15th-century incunabula show a reduction in the Character Error Rate (CER) from around 38% to around 15% and an increase in the Bilingual Evaluation Understudy (BLEU) score from 22 to 44, confirming the effectiveness of our approach. This work demonstrates the potential of integrating transformer-based correction with layout-aware segmentation to enhance OCR accuracy in digital humanities applications. Full article

Environmentally friendly coated urea prepared using a waterborne polymer coating material is essential for promoting green and sustainable practices in modern agriculture. However, significant efforts are still urgently needed to address the undesirable properties of waterborne polymer coatings, i.e., poor hydrophobic properties and numerous micropores. Herein, dual nano-SiO₂ and siloxane-modified waterborne-polymer-coated urea was successfully developed. The characteristics of waterborne-polymer-coated urea before and after modification were compared. The results demonstrate that nano-SiO₂ and siloxane modification improved the hydrophobicity (water absorption decreased from 119.86% to 46.35%) and mechanical strength (tensile strength increased from 21.09 to 31.29 MPa, and the elongation at break exhibited an increase of 22.42%) of the waterborne polymer coatings. Furthermore, the –OH number of the modified coatings was decreased, while the coating surface formed a nano-scale rough structure, prolonging the nitrogen (N)-controlled release period from 7 to 28 days. Overall, the proposed novel dual-modification technique utilizing waterborne polymer coatings highlights the significant potential of eco-friendly coated urea with renewable coatings in modern agriculture. Full article

According to the latest Verizon DBIR report, credential abuse, including password reuse and human factors in password creation, remains the leading attack vector. It was revealed that most users change their passwords only when they forget them, and 35% of respondents find mandatory password rotation policies inconvenient. These findings highlight the importance of combining technical solutions with user-focused education to strengthen password security. In this research, the “human factor in the creation of usernames and passwords” is considered a vulnerability, as identifying the patterns or rules used by users in password generation can significantly reduce the number of possible combinations that attackers need to try in order to gain access to personal data. The proposed method based on an LSTM model operates at a character level, detecting recurrent structures and generating generalized masks that reflect the most common components in password creation. Open datasets of 31,000 compromised passwords from real-world leaks were used to train the model and it achieved over 90% test accuracy without signs of overfitting. A new method of evaluating the individual password creation habits of users and automatically fetching context-rich keywords from a user’s public web and social media footprint via a keyword-extraction algorithm is developed, and this approach is incorporated into a web application that allows clients to locally fine-tune an LSTM model locally, run it through ONNX, and carry out all inference on-device, ensuring complete data confidentiality and adherence to privacy regulations. Full article

The landscapes of China’s multi-ethnic areas are rich in natural and cultural value, but they are threatened by homogenization and urbanization. This study aims to establish a method for identifying and classifying the landscape characters in China’s multi-ethnic areas to support the protection and sustainable development of the landscape in these areas. Taking the Miao Frontier Corridor as an example, the study optimized a parameterization method of landscape character assessment (LCA), integrated relevant cultural and natural elements, and used the K-means clustering algorithm to determine the landscape character types and regions of the Miao Frontier Corridor. The results show that (1) the natural conditions, ethnic exchanges, and historical institutions of the Miao Frontier Corridor have had a significant impact on its overall landscape; and (2) using ethnic group culture as a cultural element in LCA helps to reveal the unique cultural value of areas with different landscape characters. This study expands the LCA framework and applies it to multi-ethnic areas in China, thereby establishing a database that can serve as the basis for cross-regional landscape protection, management, and development planning in these areas. The research methods can be widely used in other multi-ethnic areas in China. Full article

I use Shakespeare to teach acting to students. A key to my work is impression management: what Shakespeare called reputation. I view the management of reputation as a route into Shakespearean character, which I present to students as a mask attuned to sacred values. The physical basis from which the actor can discover the mask is what Hamlet calls ‘smoothness’, which I explain with an acting exercise. We discover the force of sacred values by noticing the ubiquity of keywords in the text such as honor, virtue, reason, shame and faith. By holding characters to the fire of their sacred values, I shift the actor’s attention from an individualist idea of authentic representation towards a sense of character as a battleground of mind-shaping. The resulting performance work is scaled up to a more expansive and energized degree than the actor may be used to delivering in a social media-saturated environment in which what is often prioritized is a quasi-confessional self-revelation. The revelation of an inner life then emerges through a committed exploration of antithetical relations, a strategy basic both to mask work and to Shakespeare’s poetics. The actor finds their personal connection to the material by facing the contradiction between the objective standards of behavior demanded of the character and the character’s attempt to control their status, that is, how they are seen. The final value of the performance work is that the actor learns how to manage their reputation so that they come to appear like a giant who is seen from a distance. Full article

This paper explores emotional analysis in Hebrew texts, focusing on improving machine learning techniques for depression detection by integrating psychological feature lexicons. Hebrew’s complex morphology makes emotional analysis challenging, and this study seeks to address that by combining traditional machine learning methods with sentiment lexicons. The dataset consists of over 350,000 posts from 25,000 users on the health-focused social network “Camoni” from 2010 to 2021. Various machine learning models—SVM, Random Forest, Logistic Regression, and Multi-Layer Perceptron—were used, alongside ensemble techniques like Bagging, Boosting, and Stacking. TF-IDF was applied for feature selection, with word and character n-grams, and pre-processing steps like punctuation removal, stop word elimination, and lemmatization were performed to handle Hebrew’s linguistic complexity. The models were enriched with sentiment lexicons curated by professional psychologists. The study demonstrates that integrating sentiment lexicons significantly improves classification accuracy. Specific lexicons—such as those for negative and positive emojis, hostile words, anxiety words, and no-trust words—were particularly effective in enhancing model performance. Our best model classified depression with an accuracy of 84.1%. These findings offer insights into depression detection, suggesting that practitioners in mental health and social work can improve their machine learning models for detecting depression in online discourse by incorporating emotion-based lexicons. The societal impact of this work lies in its potential to improve the detection of depression in online Hebrew discourse, offering more accurate and efficient methods for mental health interventions in online communities. Full article

This article presents studies on the degradation susceptibility of two commercially available gear oils used in electric passenger vehicle transmissions. A series of aging tests were conducted using selected research methods. Due to the lack of a recommended methodology for testing the thermal oxidation stability of such oils, standardized methods were applied: ASTM D5704, ASTM D8206, ASTM D2272, PN-EN 16091, and PN-C-04080. To determine the degree of degradation, changes in physicochemical parameters (kinematic viscosity at 40 °C and 100 °C and acid number) and changes in the chemical character of oil components, based on FTIR spectra, were evaluated. Significant changes in properties were found in the tested oils, which were confirmed by spectral analysis. It was found that all the mentioned methods for assessing thermal oxidation stability are suitable for evaluating such oils, but they differ in the aggressiveness of the method towards the tested oil. These methods can be ranked according to their impact on the degradation of the tested oil. Full article

Ne Zha 2 (Ne Zha: Mo Tong Nao Hai, 哪吒之魔童闹海, 2025) is a prime example of the modernization of traditional literary intellectual property (IP). It has achieved the highest box office gross in Chinese cinematic history and ranks among the top five highest-grossing films globally. This article uses a case study approach to examine the adaptation strategies of Ne Zha 2 (2025), offering strategic insights for future film adaptations. The analysis focuses on four key dimensions—character, plot, theme, and esthetics—to explore how these elements contribute to the film’s adaptation. The findings reveal that the film strikes a balance between intertextuality and innovation, achieved through multidimensional character development, narrative subversion, contemporary thematic reinterpretation, and sophisticated esthetic techniques. By maintaining the emotional connection to the classical IP, the adaptation not only delivers stunning visual spectacles but also provides a culturally immersive experience, revitalizing traditional mythology with contemporary relevance. Full article

To improve the accuracy of Japanese finger-spelled character recognition using an RGB camera, we focused on feature design and refinement of the recognition method. By leveraging angular features extracted via MediaPipe, we proposed a method that effectively captures subtle motion differences while minimizing the influence of background and surrounding individuals. We constructed a large-scale dataset that includes not only the basic 50 Japanese syllables but also those with diacritical marks, such as voiced sounds (e.g., “ga”, “za”, “da”) and semi-voiced sounds (e.g., “pa”, “pi”, “pu”), to enhance the model’s ability to recognize a wide variety of characters. In addition, the application of a change-point detection algorithm enabled accurate segmentation of sign language motion boundaries, improving word-level recognition performance. These efforts laid the foundation for a highly practical recognition system. However, several challenges remain, including the limited size and diversity of the dataset and the need for further improvements in segmentation accuracy. Future work will focus on enhancing the model’s generalizability by collecting more diverse data from a broader range of participants and incorporating segmentation methods that consider contextual information. Ultimately, the outcomes of this research should contribute to the development of educational support tools and sign language interpretation systems aimed at real-world applications. Full article

This study addresses the understanding of fungal diversity and their bioremediation roles in an integrated aquaculture wastewater bioremediation system, an area less explored compared to bacteria, viruses, and protozoa. Despite the rapid advancement and affordability of molecular tools, insights into fungal communities remain vague, and interpreting environmental studies in an ecologically meaningful manner continues to pose challenges. To bridge this knowledge gap, we developed an integrated aquaculture wastewater bioremediation system, incorporating photosynthetic bacteria, and utilizing internal transcribed spacer (ITS) sequencing to analyze fungal community composition. Our findings indicate that the fungal community in aquaculture wastewater is predominantly composed of the phyla Ascomycota and Chytridiomycota, with dominant genera including Aspergillus, Hortea, and Ciliphora. FUNGuild, a user-friendly trait and character database operating at the genus level, facilitated the ecological interpretation of fungal functional groups. The analysis revealed significant negative correlations between nutrient levels (COD_mn, NH₄⁺-N, NO₃⁻-N, NO₂⁻-N, and PO₄⁻³-P) and specific fungal functional groups, including epiphytes, animal pathogens, dung saprotrophs, plant pathogens, and ectomycorrhizal fungi. The removal rate for the COD_mn, NH₄⁺-N, NO₃⁻-N, NO₂⁻-N, and PO₄⁻³-P were 71.42, 91.37, 88.80, 87.20, and 91.72% respectively. This study highlights the potential role of fungal communities in bioremediation processes and provides a framework for further ecological interpretation in aquaculture wastewater treatment systems. Full article

Small towns in mountainous regions face significant challenges in formulating effective landscape zoning strategies due to pronounced landscape fragmentation, which is driven by both the dominance of large-scale forest resources and the lack of coordination between administrative planning departments. To tackle this problem, this study focused on Yuqian, a quintessential small mountainous town in Hangzhou, Zhejiang Province. The town’s layout was divided into a grid network measuring 70 m × 70 m. A two-step cluster process was employed using ArcGIS and SPSS software to analyze five landscape variables: altitude, slope, land use, heritage density, and visual visibility. Further, eCognition software’s semi-automated segmentation technique, complemented by manual adjustments, helped delineate landscape character types and areas. The overlay analysis integrated these areas with administrative village units, identifying four landscape character types across 35 character areas, which were recategorized into four planning and management zones: urban comprehensive service areas, agricultural and cultural tourism development areas, industrial development growth areas, and mountain forest ecological conservation areas. This result optimizes the current zoning types. These zones closely match governmental sustainable development zoning requirements. Based on these findings, we propose integrated landscape management and conservation strategies, including the cautious expansion of urban areas, leveraging agricultural and cultural tourism, ensuring industrial activities do not impact the natural and village environment adversely, and prioritizing ecological conservation in sensitive areas. This approach integrates spatial and administrative dimensions to enhance landscape connectivity and resource sustainability, providing key guidance for small town development in mountainous regions with unique environmental and cultural contexts. Full article

Background/Objectives: Increasing evidence suggests that lncRNAs are core regulators in the field of tumor progression, with context-specific functions in oncogenic tumorigenesis. LINC01133, a lncRNA that has been identified as both an oncogene and a tumor suppressor, remains largely unexplored in terms of its molecular mechanisms. The purpose of this study was to conduct an in silico analysis, incorporating literature research on various cancer types, to investigate the structural and functional duality of LINC01133. This analysis aimed to identify pathways influenced by LINC01133 and evaluate its mechanism of action as a potential therapeutic target and diagnostic biomarker. Methods: In silico analyses and a narrative review of the literature were performed to predict conserved structural elements, functional internal loops, and overall conservation of the LINC01133 sequence among different vertebrate organisms, summarizing the empirical evidence regarding its roles as a tumor suppressor and tumor-promoting roles in various types of tumors. Results: LINC01133 harbors the evolutionarily conserved structural regions that might allow for binding to relevant driver signaling pathways, substantiating its specific functionality. Its action extends beyond classical tumor mechanisms, affecting proliferation, migration, invasion, and epigenetic pathways in various types of tumors, as indicated by the in silico results and narrative review of the literature we present here. Clinical outcome associations pointed to its potential as a biomarker. Conclusions: The dual character of LINC01133 in tumor biology further demonstrates its prospective therapeutic value, but complete elucidation of its mechanisms of action requires further investigation. This study establishes LINC01133 as a multifaceted lncRNA, supporting context-specific strategies in targeting its pathways, and calls for expanded research to harness its full potential in oncology. Full article

Detecting manipulated document images is essential for verifying the authenticity of official records and preventing document forgery. However, forgery artifacts are often subtle and localized in fine-grained regions, such as text boundaries or character outlines, where visual symmetry and structural regularity are typically expected. These manipulations can disrupt the inherent symmetry of document layouts, making the detection of such inconsistencies crucial for forgery identification. Conventional CNN-based models face limitations in capturing such edge-level asymmetric features, as edge-related information tends to weaken through repeated convolution and pooling operations. To address this issue, this study proposes an edge-focused method composed of two components: the Edge Attention (EA) layer and the Edge Concatenation (EC) layer. The EA layer dynamically identifies channels that are highly responsive to edge features in the input feature map and applies learnable weights to emphasize them, enhancing the representation of boundary-related information, thereby emphasizing structurally significant boundaries. Subsequently, the EC layer extracts edge maps from the input image using the Sobel filter and concatenates them with the original feature maps along the channel dimension, allowing the model to explicitly incorporate edge information. To evaluate the effectiveness and compatibility of the proposed method, it was initially applied to a simple CNN architecture to isolate its impact. Subsequently, it was integrated into various widely used models, including DenseNet121, ResNet50, Vision Transformer (ViT), and a CAE-SVM-based document forgery detection model. Experiments were conducted on the DocTamper, Receipt, and MIDV-2020 datasets to assess classification accuracy and F1-score using both original and forged text images. Across all model architectures and datasets, the proposed EA–EC method consistently improved model performance, particularly by increasing sensitivity to asymmetric manipulations around text boundaries. These results demonstrate that the proposed edge-focused approach is not only effective but also highly adaptable, serving as a lightweight and modular extension that can be easily incorporated into existing deep learning-based document forgery detection frameworks. By reinforcing attention to structural inconsistencies often missed by standard convolutional networks, the proposed method provides a practical solution for enhancing the robustness and generalizability of forgery detection systems. Full article