Search Results (234)

Background/Objectives: Obesity is associated with cognitive decline, and metabolic bariatric surgery (MBS) can improve both physical and cognitive outcomes. However, cognitive improvements post-surgery are variable. This study explores the role of skeletal muscle health, specifically muscle strength and mass, in cognitive performance after MBS, aiming to identify factors that influence cognitive recovery. Methods: In this prospective study, 55 patients scheduled for MBS were assessed 1 month before and 12 months after surgery. Body composition, including appendicular lean mass (ALM) and fat mass, was measured using dual-energy X-ray absorptiometry (DXA). Handgrip strength (HGS) was assessed via dynamometry, and neurocognitive performance was evaluated using a standardized test battery. Results: Significant improvements in cognitive performance were observed at 12 months post-surgery in attention, memory, language, executive functions and overall cognitive performance. Regarding body composition, significant reductions were observed in fat mass and body mass index (BMI). A decrease in ALM and no changes in HGS were observed; however, when adjusted for body size, both showed an increase. Regression analysis identified baseline HGS, but not muscle mass, as a significant predictor of cognitive performance at 12 months post-surgery. Conclusions: MBS leads to significant improvements in cognitive function. Our results suggest that baseline HGS may play a role in predicting cognitive outcomes post-surgery, highlighting the need for comprehensive assessments of both physical and cognitive health in patients undergoing MBS. Further longitudinal studies are needed to explore causal relationships and the potential reversibility of cognitive deficits post-surgery. Full article

The Secret of the Golden Flower (Taiyi Jinhua Zongzhi 太乙金華宗旨), a Qing dynasty spirit-writing (fuji扶乩) text, is widely known through the Wilhelm–Jung translation lineage, where jinhua 金華 is rendered as “Golden Flower” and read as mandala-like symbolism. Based on a close reading of the Daozang Jiyao 道藏輯要version, this article argues that in the Chinese text jinhua is not primarily a floral image but a technical and experiential term for luminosity in Daoist inner-alchemical cultivation. Hua 華 is resemanticized from botanical “flower/flourishing” into “radiance,” and the work explicitly defines the key term as “jinhua is light”. The text further organizes cultivation into a three-stage trajectory—“sudden emergence”, “circulation”, and “great condensation”, through which qi 氣 is refined into light and luminosity stabilizes as spirit (shen 神). Finally, the analysis situates this luminous grammar within the work’s explicit Three Teachings (sanjiao 三教) framing: Confucian “illuminating virtue” (mingde 明德) and Buddhist idioms of luminous mind-nature (xin-xing guangming 心性光明) and dharma-body language function as a shared vocabulary for describing non-grasping awareness and embodied realization. On this basis, jinhua is best understood not as a decorative metaphor or a purely psychological symbol but as a practice-oriented mechanism of ontological luminosity, clarifying both the inner-alchemical logic of The Secret and the stakes of its modern reception. Full article

Chatbots have emerged as a promising interface for facilitating access to complex datasets, allowing users to pose questions in natural language rather than relying on specialized technical workflows. At the same time, remote sensing has transformed archaeological practice by producing vast amounts of imagery from LiDAR, drones, and satellites. While these advances have created unprecedented opportunities for discovery, they also pose significant challenges due to the scale, heterogeneity, and interpretative demands of the data. In related scientific domains, multimodal conversational systems capable of integrating natural language interaction with image-based analysis have advanced rapidly, supported by a growing body of survey and review literature documenting their architectures, datasets, and applications across multiple fields. By contrast, archaeological applications of chatbots remain limited to text-based prototypes, primarily focused on education, cultural heritage mediation or archival search. This review synthesizes the historical development of chatbots, examines their current use in remote sensing, and evaluates the barriers to adapting such systems for archaeology. Four major challenges are identified: data scale and heterogeneity, scarcity of training datasets, computational costs, and uncertainties around usability and adoption. By comparing experiences across domains, this review highlights both the opportunities and the limitations of integrating conversational AI into archaeological workflows. The central conclusion is that domain-specific adaptation is essential if multimodal chatbots are to become effective analytical partners in archaeology. Full article

This study examines how farm workers working with cattle talk to and interact with these non-human animals. This study presents linguistic animal studies and multi-species pragmatics, and it is based on fieldwork, interviews, and video recordings from several types of Finnish dairy farms. This study concentrates especially on one facet of human–cattle interaction: how humans use dung pushers and other sticks when communicating with cows. Thus, it draws on the materiality of language. It is shown how objects, bodies, and spaces, as well as words and linguistic constructions, are meaningful in human–animal interaction. Videoed recordings are analysed with multimodal conversation analysis. It is shown how dung pushers and snow stakes are used when steering cows, making them stand up, and pointing at things. It is then shown how these objects become ‘meaning-carriers’ for humans and for cows. For example, the dung pusher acquires four different meaning qualities for the human participants in the cattle barns: floor-cleaner quality, shepherd’s-crook quality, pointer quality, and weapon quality. The study examines how the cows’ and humans’ Umwelts, the subjective meaning universes of these species and their constituent individuals, influence interaction on farms and how and why the dung pusher becomes a semiotic resource. Full article

Code coverage-guided unit test generation (CGTG) and large language model-based test generation (LLMTG) are two principal approaches for the generation of unit tests. Each of these approaches has its inherent advantages and drawbacks. Tests generated by CGTG have been shown to exhibit high code coverage and high executability. However, they lack the capacity to comprehend code intent, which results in an inability to identify deviations between code implementation and design intent (i.e., functional defects). Conversely, although LLMTG demonstrates an advantage in terms of code intent analysis, it is generally characterized by low executability and necessitates iterative debugging. In order to enhance the ability of unit test generation to identify functional defects, a novel framework has been proposed, entitled the intent analysis-guided unit test generation and refinement (IGTG&R) model. The IGTG&R model consists of a two-stage process for test generation. In the first stage, we introduce coverage path entropy to enhance CGTG to achieve high executability and code coverage of test cases. The second stage refines the test cases using LLMs to identify functional defects. We quantify and verify the interference of incorrect code implementation on intent analysis through conditional entropy. In order to reduce this interference, the focal method body is excluded from the code context information during intent analysis. Using these two-stage process, IGTG&R achieves a more profound comprehension of the intent of the code and the identification of functional defects. The IGTG&R model has been demonstrated to achieve an identification rate of functional defects ranging from 65% to 89%, with an execution success rate of 100% and a code coverage rate of 75.8%. This indicates that IGTG&R is superior to the CGTG and LLMTG approaches in multiple aspects. Full article

The accurate measurement of audience engagement in real-world live events remains a significant challenge, with the majority of existing research confined to controlled environments like classrooms. This paper presents a comprehensive survey of Computer Vision AI-driven methods for real-time audience engagement monitoring and proposes a novel, holistic architecture to address this gap, with this architecture being the main contribution of the paper. The paper identifies and defines five core constructs essential for a robust analysis: Attention, Emotion and Sentiment, Body Language, Scene Dynamics, and Behaviours. Through a selective review of state-of-the-art techniques for each construct, the necessity of a multimodal approach that surpasses the limitations of isolated indicators is highlighted. The work synthesises a fragmented field into a unified taxonomy and introduces a modular architecture that integrates these constructs with practical, business-oriented metrics such as Commitment, Conversion, and Retention. Finally, by integrating cognitive, affective, and behavioural signals, this work provides a roadmap for developing operational systems that can transform live event experience and management through data-driven, real-time analytics. Full article

Structural problems are a common topic among several optimization works; with the use of finite element analysis (FEA), the aim of these works is to improve the mechanical behavior of the distinct elements or bodies involved in these optimization problems. However, the impact of the meshing discretization on the outcome of the optimization process has not been studied in previous works. The present work investigates the effect of mesh element size on the mechanical optimization of two cases of study; the first one is about a modal optimization on a cantilever beam, and the second one is about a cellular beam, where the aim is to reduce the weight of the beam under static load. In these two optimization problems, variables commonly used in the literature were employed, while additionally including the mesh size as an extra variable. The computational framework is implemented on MATLAB R2022a, and the modal and weight optimizations are carried out through APDL (ANSYS Parametric Design Language) executed in batch mode. The results demonstrate that the consideration of the mesh size element can improve the computational time that is required to perform this mechanical optimization, achieving a 96% percentage of time reduction instead of making the analysis with the finest element size (in case 1) and a 90 percent time reduction for the second case of study. Full article

Background/Objectives: Amputation often leads to decreased body image satisfaction and self-acceptance, affecting mental health and social integration. The Amputee Body Image Scale (ABIS) is a validated tool designed to assess satisfaction and measure body image acceptance. The scale has been translated into many languages to improve rehabilitation services for individuals with amputation worldwide; however, a validated Arabic version of this scale does not currently exist. The study aims to cross-culturally adapt and validate the ABIS for Arabic-speaking individuals with lower-limb amputation. Methods: A cross-cultural adaptation was performed according to the International Test Commission (ITC) guidelines. The adapted Arabic version of the scale was completed by 100 Jordanian individuals with lower-limb amputations. Their responses were then statistically analyzed using factor analysis to assess content and construct validity and Cronbach’s α to assess internal consistency (reliability). Results: The Arabic version of the ABIS demonstrated strong construct validity (KMO = 0.898; Bartlett’s test p < 0.001) and high internal consistency (Cronbach’s alpha = 0.92), while factor analysis suggested the multidimensionality of the scale after cross-cultural adaptation. Conclusions: The findings suggest the preliminary validity and reliability of the Arabic version of the ABIS, supporting its potential use in rehabilitation centres to assess body image satisfaction among Arabic-speaking individuals with amputation. Nevertheless, further improvements are warranted to adapt the scale to Arabic culture. Full article

Background: Obesity and sleep issues are pressing health challenges that are closely tied together. Poor sleep can lead to weight gain, and in turn, obesity can make sleep problems worse. In Saudi Arabia, lifestyle habits—like having late-night meals, excessive screen time, and a lack of physical activity—might worsen these relationships at every age. Objectives: This review explores the relationship between obesity and sleep disorders across different stages of life in Saudi populations. Methods: We conducted a systematic review following the PRISMA 2020 guidelines. From December 2023 to March 2024, we searched PubMed, Scopus, Web of Science, and ScienceDirect for English-language observational studies related to obesity and sleep among Saudi populations. We evaluated the risk of bias using the ROBINS-I tool. Results: We included fourteen studies, involving 14,876 participants (49.8% male). The findings consistently indicated that obesity is linked to poor sleep quality, shorter sleep duration, insomnia, excessive daytime sleepiness, and breathing issues during sleep such as obstructive sleep apnea. Most studies showed a moderate risk of bias, mainly due to their cross-sectional designs and reliance on self-reported sleep data. Conclusions: Obesity and sleep disturbances are closely linked across all age groups in Saudi Arabia. Most studies reported associations between higher body weight and poorer sleep outcomes, including shorter duration, lower quality, and increased risk of sleep-disordered breathing. These findings underscore the need for age-specific public health strategies that encourage healthy sleep habits and lifestyle behaviors. Limitations: Variability in measurement methods across studies prevented us from conducting a meta-analysis. Funding/Registration: No funding; review not registered. Full article

Geological interfaces are crucial elements governing deposit formation, such as silica–calcium surfaces, intrusive contact interfaces, and unconformities can serve as key symbols for mineral exploration prediction. Geological maps provide relatively detailed representations of primary geological interfaces and their interrelationships. However, in previous mineral resource predictions, the type differences in different geological interfaces were ignored, and the types of different geological interfaces vary greatly, thus affecting the validity of the mineral prediction results. Manual interpretation and analysis of geological interfaces involve substantial workloads and make it difficult to effectively apply the rich geological information depicted on geological maps to mineral exploration prediction processes. Therefore, this study proposes a model for intelligent identification of geological interface types based on deep learning. The model extracts the attribute information, such as the age and lithology of the geological bodies on both sides of the geological boundary arc, based on the digital geological map of the Gouli gold mining area in Dulan County, Qinghai Province, China. The learning dataset comprising 5900 sets of geological interface types was constructed through manual annotation of geological interfaces. The arc segment is taken as the basic element; the model adopts natural language processing technology to conduct word vector embedding processing on the text attribute information of geological bodies on both sides of the geological interface. The processed embedding vectors are fed into the convolutional neural network (CNN) for training to generate the geological interface type recognition model. This method can effectively identify the type of geological interface, and the identification accuracy can reach 96.52%. Through quantitative analysis of the spatial relationship between different types of geological interfaces and ore points, it is known that they have a good correlation in spatial distribution. Experimental results show that the proposed method can effectively improve the accuracy and efficiency of geological interface recognition, and the accuracy of mineral prediction can be improved to some extent by adding geological interface type information in the process of mineral prediction. Full article

Recently, human–robot interaction (HRI) with social robots has attracted significant attention. Among them, companion robots, which exhibit pet-like behaviors and interact with people primarily through non-verbal means, particularly require the generation of appropriate gestures. This paper presents the design and implementation of a companion cat robot, named PEPE, with a large language model (LLM)-based hierarchical emotional motion generation method. To design the cat-like companion robot, an analysis of feline emotional behaviors was conducted to identify the body parts and motions essential for effective emotional expression. Based on this analysis, the required degrees of freedom (DoFs) and structural configuration for PEPE were derived. To generate expressive gestures efficiently and reliably, a hierarchical LLM-based emotional motion generation method was proposed. The process defines the robot’s structural features, establishes a gesture generation code format, and incorporates emotion-based guidelines grounded in feline behavioral analysis to mitigate LLM hallucination and ensure physical feasibility. The proposed method was implemented on the physical robot, and eight emotional gestures were generated—Happy, Angry, Sad, Fearful, Joyful, Excited, Positive Feedback, and Negative Feedback. A user study with 15 participants was conducted to validate the system. The high-arousal gestures—Angry, Joyful, and Excited—were rated significantly above the neutral clarity threshold (p < 0.05), demonstrating clear user recognition. Meanwhile, low-arousal gestures exhibited neutral-level perceptions consistent with their subtle motion profiles. These results confirm that the proposed LLM-based framework effectively generates expressive, physically executable gestures for a companion robot. Full article

Anorexia nervosa is among the most lethal psychiatric conditions. Online pro-anorexia (“pro-ana”) communities may frame starvation and restriction in moral or spiritual terms. This study explored how pro-ana discourse on X (formerly Twitter) encodes values, spirituality, and identity through language, with attention to clinical practice. A dataset of 2396 English-language tweets (2020–2025) was collected using dual criteria (pro-ana hashtags plus eating-disorder keywords). Only U.S.-based English tweets were included to maintain linguistic and cultural coherence with LIWC-22 norms and counseling frameworks developed in U.S. contexts. Tweets were separated into three corpora (full, hashtags, and tweet bodies) and analyzed using Linguistic Inquiry and Word Count 2022 (LIWC-22), supplemented with custom spirituality and pro-ana dictionaries, and keyword/keyness analysis against a 36-billion-token web reference corpus. Religious language appeared consistently higher in hashtags compared with tweets and Twitter norms. Tweets contained more authenticity and self-disclosure, while hashtags functioned as collective markers of identity and practice. Body and food terms were strongly elevated, and affiliation terms appeared comparatively suppressed. Keyness analysis identified distinctive items such as prayer fast, fasting prayer (Luke), OMAD fast, hunger hurt, and I’m punching, illustrating how sacred, cultural, and diet-related slogans were combined within pro-ana discourse. Pro-ana rhetoric may function as a sacralized identity frame that can provide existential meaning to disordered practices. These findings contribute to behavioral science by highlighting how online communities linguistically construct health-related identities and values. They also suggest that effective clinical interventions should address eating disorders not only at behavioral and cognitive levels but also at the level of values and spirituality. Full article

Background and Objectives: This systematic review synthesized somatic and psychosocial determinants of postpartum sexual dysfunction (PSD) during the first 12 months after childbirth. Methods: Following PRISMA 2020 guidelines, we searched PubMed, Web of Science, and Scopus from inception to 4 August 2025 without language limits for the indexed records. Eligible studies enrolled postpartum women (≤12 months) and reported validated sexual outcomes (FSFI/FSFI-6, PISQ-12), dyspareunia, or sexual activity, examining breastfeeding, partner support/body image, perineal trauma/instrumentation, or postpartum perineal/musculoskeletal pain. Two reviewers independently screened and extracted data; risk of bias was assessed with a modified Newcastle–Ottawa Scale. Results: Of 1127 records screened, 15 studies were included. Perineal morbidity and early pain consistently tracked with worse sexual outcomes; assisted vaginal birth increased 6-month dyspareunia odds (OR 2.5). Breastfeeding was often associated with lower early sexual function and higher dyspareunia (6-month dyspareunia OR 4.4), with attenuation by 12 months. Higher partner/family support and more positive body image correlated with better FSFI scores. Heterogeneity in timing, measures, and adjustment precluded meta-analysis; results were narratively synthesized. Conclusions: Perineal trauma and early pain are dominant risk signals; breastfeeding-related symptoms exert early and context-dependent effects; psychosocial resources are protective. Full article

This article explores how fashion, as a cultural system, constructs and circulates dominant imaginaries of the body, focusing on the visual narratives presented on the covers of Vogue Italia. Moving beyond a purely esthetic or biological notion of beauty, this study adopts an embodiment perspective to analyze how socio-cultural contexts shape representations of bodies, identities, and subjectivities within fashion media. Through a qualitative longitudinal analysis of Vogue Italia covers spanning over sixty years, this study explores how visible diversity is mediated through the visual and symbolic codes of fashion, revealing significant changes in esthetic sensibility and editorial strategies throughout the course of time. Rather than interpreting these representations as a straightforward response to growing demands for inclusion, we argue that fashion’s engagement with diversity operates through a mimetic logic that simultaneously displays alterity while reasserting existing hierarchies of power, desirability, and visibility. The results suggest that editorial choices have been decisive in determining the visual trajectory of the magazine, sometimes anticipating broader cultural debates on gender, race, and identity. We contend that Vogue Italia does not simply reflect social transformations, but actively contributes to shaping a cultural script in which inclusion becomes both commodified and contained. Ultimately, this study highlights the ambivalent role of fashion media in negotiating inclusion and exclusion through the visual construction of bodies emphasizing how the language of diversity can either challenge or reproduce hegemonic visual imaginaries. Full article

This study proposes a methodological approach to the redesign of a 1980s vehicle, the Lancia Beta HPE, integrating the TRIZ (Theory of Inventive Problem Solving) and the Industrial Design Structure (IDeS) frameworks within the design process. The redesign process focused on both the external morphology of the vehicle and its propulsion system, aligning the outcome with contemporary trends in market evolution, societal shifts, and environmental considerations. The objective of the project was to reinterpret stylistic elements that were typical of 1980s automotive design through a contemporary lens, while incorporating characteristics of the current aesthetic of electric vehicles (EVs). A pivotal element of the research involved a comparative stylistic analysis of past and present vehicle design languages. This facilitated the identification of design guidelines for adapting formal and stylistic details to the electric mobility paradigm, with emphasis on contemporary aesthetics and energy efficiency. The transition from internal combustion to electric propulsion necessitated a comprehensive re-evaluation of the vehicle’s key exterior features, encompassing the front end, body shape, and lighting systems, in order to reflect a novel ecological identity and convey technological advancement. In order to inform stylistic choices, an in-depth exploration of electric propulsion principles was conducted, leveraging AI-based tools such as GPT to support TRIZ-guided problem-solving. Full article