Search Results (1,132)

Rahul Sankrityayan, a twentieth-century Indian polymath, is known for his contributions to Buddhism, Marxism, and Hindi literature. While his writing has been analyzed for its engagement with Buddhism and Tibet, he is also credited with inaugurating Hindi travel-writing. Though his contributions to this genre are well-recognized, one crucial work—ghummakaṛa śāstra (1945; lit. The Treatise of a Wanderer)—has received insufficient scholarly attention. This article investigates the intersection of religion, travel-writing, and satire in two chapters of Sankrityayan’s treatise: athāto ghummakaṛa jijñāsā (lit. Thus, the Curiosity of a Wanderer) and dharma aur ghummakaṛī (lit. Religion and Wandering). It argues that Sankrityayan employs the figure of the Wanderer to critique religions, religious ideals, and religious figures in two key ways. First, by framing his work as a śāstra (treatise) in the classical sense, he appropriates authoritative discourse to contest religious ideas. Second, the Wanderer functions as a transcendental subject who pervades history. Blending satire with polemic, the text subverts traditional religious hermeneutics. Through close analysis, this paper demonstrates how Sankrityayan’s unconventional form—a dialogic interplay between treatise and satire—invites readers to interrogate religious authority, offering a model for engaging with religion beyond doctrinal frameworks. Full article

This paper introduces a novel approach to tree modeling architecture integrated with blockchain technology, aimed at enhancing landscape spatial planning and forest monitoring systems. The primary objective is to develop a low-cost, automated tree CAD modeling methodology combined with blockchain functionalities to support smart forest projects and collaborative design processes. The proposed method utilizes a parametric tree CAD model consisting of four 2D tree-frames with a 45° division angle, enriched with recorded tree-leaves’ texture and color. An “AI Text-by-Voice CAD Programming” technique is employed to create tangible tree-model NFT tokens, forming the basis of a thematic “Internet-of-Trees” blockchain. The main results demonstrate the effectiveness of the blockchain/Merkle hash tree in tracking tree geometry growth and texture changes through parametric transactions, enabling decentralized design, data validation, and planning intelligence. Comparative analysis highlights the advantages in cost, time efficiency, and flexibility over traditional 3D modeling techniques, while providing acceptable accuracy for metaverse projects in smart forests and landscape architecture. Core contributions include the integration of AI-based user voice interaction with blockchain and behavioral data for distributed and collaborative tree modeling, the introduction of a scalable and secure “Merkle hash tree” for smart forest monitoring, and the facilitation of fintech adoption in environmental projects. This framework offers significant potential for advancing metaverse-based landscape architecture, smart forest surveillance, sustainable urban planning, and the improvement of citizen involvement in sustainable forestry paving the way for a greener future. Full article

Background/Objectives: While the prevalence of depression increases during the peri- and post-menopausal periods, the potential of diet as both a modifiable risk factor and complementary treatment option has received limited research attention in this population. To address this gap, we conducted a scoping review aiming to map and synthesize the existing literature on diet and depression in peri- and post-menopause. Methods: Studies were identified through Medline, EMBASE, PsycINFO, CENTRAL, Web of Science, and Scopus. After deduplication in Covidence, two reviewers independently screened titles, abstracts, and full texts using predefined eligibility criteria. Data were extracted using standardized forms and presented in tables and figures. Methodological quality was assessed using the Cochrane RoB-2 for intervention studies and NHLBI tools for observational studies. Results: Thirty-eight studies met the inclusion criteria, including 29 observational and 9 interventional studies. Dietary patterns showed the most consistent associations with depressive symptoms, whereas findings for foods, nutrients, and other food components were inconsistent. Most observational studies had a moderate to high risk of bias, while over half of experimental studies were rated as low risk. Conclusions: Although limited by volume and poor methodological quality, existing evidence suggests that healthy diets may be protective against depressive symptoms in peri- and post-menopausal women, while unhealthy diets may increase risk. High-quality cohort studies and clinical trials are needed to guide future research and inform professionals working at the intersection of nutrition, psychiatry, and women’s health. Protocol registration: osf.io/b89r6. Full article

Jñānakīrti (Tib. Ye shes grags pa), an eminent monk of the late Indian Buddhist period, composed the Tattvāvātara (De kho na nyid la ’jug pa, Realizing Reality), of which only a Tibetan translation exists in the Tibetan Tripiṭaka-Tanjur. The treatise is considered an exposition of the Mahāmudrā teachings, with the chapter entitled “Practices of the Secret Mantra Approach” (gSang sngags kyi sgo’i spyod pa) forms a large part. However, this part has been less frequently discussed. This chapter guides the practice of Mahāmudrā non-dual yoga, which is intended for practitioners with superior faculties. The core content of this chapter can be subsumed under the following two aspects: Mahāmudrā teachings involve practicing insight (prajñā), which represents the theory of meditation, i.e., the idea of emptiness (śūnyatā); it also involves practicing skillful means (upāya), which includes the methods of cultivation, such as tantric rituals such as Vajradhātu maṇḍala visualization. From the perspective of compositional length, the first half of the text contains numerous quotations of verses, with several being from Nāgārjuna’s Mūlamadhyamakakārikā, while the second half mainly draws references from the Tattvasaṃgrahatantra and the Guhyasamājatantra. More attention should be paid to the juxtaposition of the Mahāmudrā teachings with the Tattvasaṃgrahatantra and the Guhyasamājatantra, which reflect the early form of the Mahāmudrā teachings as they were introduced into Tibetan Buddhism. Full article

This article investigates the role of hagiography as a mediating genre between Scripture and liturgy. Hagiographical readings for saints (legenda) have been featured in the office of Matins in Rome from at least the eighth century. By the early modern period, these texts came under scrutiny for a lack of historical credibility, a concern echoed in the reform of the breviary after the Second Vatican Council which pruned the office of much legendary material. Yet recent scholarship on hagiography suggests that the dominant postconciliar concern—historicity—failed to fully understand the genre. Legenda were not bad history, but forms of narrative exegesis, a means to “display to the faithful fitting examples for their imitation” (SC 111). The liturgical function of the hagiographical readings emerges clearly in four case studies comparing Matins of the Breviarum Romanum 1568 to the Liturgia Horarum of 1971 for Agatha, Cecilia, Agnes, and Lucy. These feasts demonstrate both the motivation and the result of the directive to reform the readings of the saints to accord with the “facts of history” (SC 92c). This study demonstrates the need for further work on these understudied hagiographical readings, which use the liturgical and Scriptural context to propose saints as living extensions of the Gospel, rendered concrete and attractive through narrative. Full article

Research articles are valuable resources for Information Retrieval and Natural Language Processing (NLP) tasks, offering opportunities to analyze key components of scholarly content. This study investigates the presence of methodological terminology in psychology research over the past 30 years (1995–2024) by applying a novel NLP and Machine Learning pipeline to a large corpus of 85,452 abstracts, as well as the extent to which this terminology forms distinct thematic groupings. Combining glossary-based extraction, contextualized language model embeddings, and dual-mode clustering, this study offers a scalable framework for the exploration of methodological transparency in scientific text via deep semantic structures. A curated glossary of 365 method-related keywords served as a gold-standard reference for term identification, using direct and fuzzy string matching. Retrieved terms were encoded with SciBERT, averaging embeddings across contextual occurrences to produce unified vectors. These vectors were clustered using unsupervised and weighted unsupervised approaches, yielding six and ten clusters, respectively. Cluster composition was analyzed using weighted statistical measures to assess term importance within and across groups. A total of 78.16% of the examined abstracts contained glossary terms, with an average of 1.8 term per abstract, highlighting an increasing presence of methodological terminology in psychology and reflecting a shift toward greater transparency in research reporting. This work goes beyond the use of static vectors by incorporating contextual understanding in the examination of methodological terminology, while offering a scalable and generalizable approach to semantic analysis in scientific texts, with implications for meta-research, domain-specific lexicon development, and automated scientific knowledge discovery. Full article

A pervasive cognitive asymmetry exists between designers and users, and contemporary furniture form design often struggles to accommodate and balance multi-dimensional user affective requirements. To address these challenges, this study proposes an intelligent optimization design method for furniture form that enhances the universality of user research and the balance of design decision-making. First, representative URs are extracted from online user review texts collected through web crawling. These URs are then classified into three-dimensional quality attributes using the refined Kano’s model, thereby identifying the key URs. Second, a decomposition table of furniture design characteristics (DCs) is constructed. Third, the multi-objective red-billed blue magpie optimizer (MORBMO) is employed to automatically generate a Pareto solution set that satisfies the multi-dimensional key URs, from which the final optimal solution is determined. The proposed method improves the objectivity and granularity of user research, assists furniture enterprises in prioritizing product development, and enhances user satisfaction across multiple affective dimensions. Furthermore, it provides enterprises with flexible choices among diverse alternatives, thereby mitigating the asymmetry inherent in furniture form design. Full article

This study aims to conduct a comprehensive comparative evaluation of three transformer-based models, PEGASUS, BART, and T5 variants (SMALL and BASE), for the task of abstractive text summarization. The evaluation spans across three benchmark datasets: CNN/DailyMail (long-form news articles), Xsum (extreme single-sentence summaries of BBC articles), and Samsum (conversational dialogues). Each dataset presents unique challenges in terms of length, style, and domain, enabling a robust assessment of the models’ capabilities. All models were fine-tuned under controlled experimental settings using filtered and preprocessed subsets, with token length limits applied to maintain consistency and prevent truncation. The evaluation leveraged ROUGE-1, ROUGE-2, and ROUGE-L scores to measure summary quality, while efficiency metrics such as training time were also considered. An additional qualitative assessment was conducted through expert human evaluation of fluency, relevance, and conciseness. Results indicate that PEGASUS achieved the highest ROUGE scores on CNN/DailyMail, BART excelled in Xsum and Samsum, while T5 models, particularly T5-Base, narrowed the performance gap with larger models while still offering efficiency advantages compared to PEGASUS and BART. These findings highlight the trade-offs between model performance and computational efficiency, offering practical insights into model scaling—where T5-Small favors lightweight efficiency and T5-Base provides stronger accuracy without excessive resource demands. Full article

Introduction: People who exercise outdoors in urban environments may inhale increased amounts of polluted air due to temporary respiratory changes induced by physical activity. The objective of this scoping review was to map the physiological, morphological, and/or functional responses of the respiratory system to air pollution in healthy adults who exercise outdoors in urban environments. Methods: This review was conducted following the guidelines of the Preferred Reporting Items Extension for Scoping Reviews (PRISMA-ScR). A comprehensive search of Medline (PubMed), Redalyc, Scielo, and Web of Science was conducted to identify clinical trials, quasi-experimental studies, and cross-sectional studies published in the last 10 years in English. Studies with healthy adult participants engaged in outdoor physical activity in urban environments were included. Texts with participants with preexisting respiratory diseases, elite athletes, animal models, and computer simulations were excluded. Results: The most frequently reported air pollutants were PM_2.5, PM₁₀, and ozone (O₃); the most common forms of exercise were walking, running, and cycling. Exposure to air pollutants during physical activity was associated with reductions in forced vital capacity (FVC) and forced expiratory volume in 1 second (FEV₁), as well as increases in the fraction of exhaled nitric oxide (FeNO) and proinflammatory biomarkers. Conclusion: The findings indicated that there are modifications in lung function in those who exercise outdoors. However, the association between these respiratory responses and air pollution was not statistically significant in most cases. Some authors suggested that the health benefits of physical activity could mitigate the harmful effects of air pollution. Full article

This article investigates the case of Jewish Spiritual Renewal (JSR) to illuminate the evolving landscape of Jewish textuality and therapeutic literacies shaped by spiritual, neoliberal, and therapeutic discourses. Based on qualitative methodology, utilizing case studies and long-term field work, it highlights the continuing importance of texts in contemporary Jewish expressions of devotion. Despite JSR’s primary focus on embodied spiritual experience, the article shows how Jewish texts are integrated into study sessions, workshops, courses, and rituals, to promote therapeutic insights and spiritual development. Aligning with other modern forms of Jewish spirituality, JSR posits that Jewish texts are valuable insofar as they facilitate personal growth and self-transformation. This is achieved through the intentional selection and performance of texts, particularly those from Jewish esoteric traditions, Kabbalah, Hasidism, and ethical literature (Musar), often interpreted in a psychological context. Full article

This work is devoted to the development of dialogical hermeneutics. As a special field of research, hermeneutics was formed as a result of the efforts of Schleiermacher, Dilthey, Heidegger, and Gadamer. The first source of hermeneutics is Aristotle’s treatise “On Interpretation”, which formulates the special type of speech—‘logos apophantikos’—that aligns speech with the identification of thinking and being. However, this approach is challenged by the hermeneutics of the sophists, for whom speech is a command, a prayer, a question, an answer, or a narrative. The second source of hermeneutics is the predominantly Protestant tradition of interpreting biblical texts. This paper examines the hermeneutic strategies of Jewish classical texts, which differ significantly from the Christian tradition of understanding text. Jewish classical texts, from Tanakh and Talmud to Jewish mysticism and philosophy, are more focused not on propositions, but on commands, prayers, questions, answers, dialogue, and narrative. Thus, the hermeneutic strategy of Jewish texts converges with investigations of the Greek sophists. Particular emphasis is placed on the medieval Jewish philosophy. The paper examines three works: “Emunot ve-deot” by Saadia Gaon, “Kuzari” by Halevi, and “Guide of the Perplexed” by Maimonides. In this regard, we discuss the system of dual argumentation, the relation between halakha and aggadah, and the strategy of concealment and revelation in language—approaches that in many ways present an alternative to the hermeneutics of understanding. The Study of rabbinic tradition leads us to the development of dialogical hermeneutics that forms the methodological foundation of humanistic culture. Full article

Stock price forecasting remains a central challenge in financial modeling due to the non-stationarity, noise, and high dimensionality of market dynamics, as well as the growing importance of unstructured textual information. In this work, we propose a multimodal prediction framework that combines FinBERT-based financial sentiment extraction with technical and statistical indicators to forecast short-term stock price movement. Contextual sentiment signals are derived from financial news headlines using FinBERT, a domain-specific transformer model fine-tuned on annotated financial text. These signals are aggregated and fused with price- and volatility-based features, forming the input to a gradient-boosted decision tree classifier (XGBoost). To ensure interpretability, we employ SHAP (SHapley Additive exPlanations), which decomposes each prediction into additive feature attributions while satisfying game-theoretic fairness axioms. In addition, we integrate differential privacy into the training pipeline to ensure robustness against membership inference attacks and protect proprietary or client-sensitive data. Empirical evaluations across multiple S&P 500 equities from 2018–2023 demonstrate that our FinBERT-enhanced model consistently outperforms both technical-only and lexicon-based sentiment baselines in terms of AUC, F1-score, and simulated trading profitability. SHAP analysis confirms that FinBERT-derived features rank among the most influential predictors. Our findings highlight the complementary value of domain-specific NLP and privacy-preserving machine learning in financial forecasting, offering a principled, interpretable, and deployable solution for real-world quantitative finance applications. Full article

This article offers a descriptive analysis of a specific form (uṣūl) of prophetic eulogy (ṣalawāt) as vocally performed within Sufi orders such as the Rifāʿiyya, Qādiriyya, and Jarrahiyya of today’s Türkiye. It combines a music–theoretical and music–sociological as well as ritual–theoretical perspective to examine how the structured performance of these chants functions both as a spiritual practice and as a means of social formation. Drawing on this dual perspective, the article analyses the underlying musical structures and elements of the ṣalawāt chant, such as melody, rhythm, harmony, modal frameworks, and dynamics. By examining how these formal aspects shape the aesthetic experience, emotional resonance, and theological significance of the eulogy, the study aims to highlight its performative and affective potential within Sufi devotional practice. Within the ritual framework of Sufi orders (ṭarīqa), this rhythmic and collective performance acts as a practice of tazkiya an-nafs (self-purification), cultivating attentiveness, moral refinement, and communal belonging through synchronized voice, breath, and bodily presence. The repeated invocation of the Prophet Muḥammad, venerated as the perfect human (al-insān al-kāmil), thus becomes a means of fostering inner transformation and spiritual proximity. In this way, ṣalawāt chanting mediates religious meaning not only through text but through embodied experience and performative devotion. Full article

This article identifies different forms of poetic transport—understood in the sense of metaphor, transition, transfer, crossing and translation—in Hölderlin’s poem “Patmos”. There are several motifs scattered throughout the poem that semantically express a transition using highly metaphorical language: motifs reflecting on the mediation between the divine and the human, signalling the hybridization of Greek and Christian religion, and indicating transfer from ancient to modern thought. Initially, this article examines the metaphorical quality of language in contrast to its analytical capacity and proposes that the former—by seeking forms of transitions—enables mediation between the associative-affective reading of the text and the critical-analytic method of the scientific view. Hölderlin reflects on this fundamental issue as a result of his spatial transition to Regensburg. The article will further show that various forms of transfer sustain the entire poem: motifs ranging from an epochal transfer to the transition from a topographical space into the text, the superimposition of different figures and the transformation of the biblical narrative, as well as the crossing between the different layers of the draft and the poet’s task of a creative translation of various forms of encountering the world, all describe issues central to Patmos. Full article

Visual grounding for remote sensing (RSVG) is the task of localizing the referred object in remote sensing (RS) images by parsing free-form language descriptions. However, RSVG faces the challenge of low detection accuracy due to unbalanced multi-scale grounding capabilities, where large objects have more prominent grounding accuracy than small objects. Based on Faster R-CNN, we propose Faster R-CNN in Visual Grounding for Remote Sensing (FR-RSVG), a two-stage method for grounding RS objects. Building on this foundation, to enhance the ability to ground multi-scale objects, we propose Faster R-CNN with Adaptive Vision-Language Fusion (FR-AVLF), which introduces a layered Adaptive Vision-Language Fusion (AVLF) module. Specifically, this method can adaptively fuse deep or shallow visual features according to the input text (e.g., location-related or object characteristic descriptions), thereby optimizing semantic feature representation and improving grounding accuracy for objects of different scales. Given that RSVG is essentially an expanded form of RS object detection, and considering the knowledge the model acquired in prior RS object detection tasks, we propose Faster R-CNN with Adaptive Vision-Language Fusion Pretrained (FR-AVLF_PRE). To further enhance model performance, we propose Faster R-CNN with Cascaded Hierarchical Attention Grounding and Multi-Level Adaptive Vision-Language Fusion Pretrained (FR-CHAGAVLF_PRE), which introduces a cascaded hierarchical attention grounding mechanism, employs a more advanced language encoder, and improves upon AVLF by proposing Multi-Level AVLF, significantly improving localization accuracy in complex scenarios. Extensive experiments on the DIOR-RSVG dataset demonstrate that our model surpasses most existing advanced models. To validate the generalization capability of our model, we conducted zero-shot inference experiments on shared categories between DIOR-RSVG and both Complex Description DIOR-RSVG (DIOR-RSVG-C) and OPT-RSVG datasets, achieving performance superior to most existing models. Full article