Search Results (296)

Remote photoplethysmography (rPPG) extracts physiological signals from facial videos by analyzing subtle skin color variations caused by blood flow. While this technology enables contactless health monitoring, it also raises privacy concerns because facial videos reveal both identity and sensitive biometric information. Existing privacy-preserving techniques, such as blurring or pixelation, degrade visual quality and are unsuitable for practical rPPG applications. This paper presents rePPG, a framework that inserts a desired rPPG signal into facial videos while preserving the original facial appearance. The proposed method disentangles facial appearance and physiological features, enabling replacement of the physiological signal without altering facial identity or visual quality. Skin segmentation restricts modifications to skin regions, and a cycle-consistency mechanism ensures that the injected rPPG signal can be reliably recovered from the generated video. Importantly, the extracted rPPG signals are evaluated against the injected target physiological signals rather than the subject’s original physiological state, ensuring that the evaluation measures signal rewriting accuracy. Experiments on the PURE and UBFC datasets show that rePPG successfully embeds target PPG signals, achieving 1.10 BPM MAE and 95.00% PTE6 on PURE while preserving visual quality (PSNR 24.61 dB, SSIM 0.638). Heart rate metrics are computed using a 5-second temporal window to ensure a consistent evaluation protocol. Full article

Research on ChatGPT GPT-4 and GPT-5 in higher education has focused on quantitative adoption models (intention to use and predictors) and fragmented effects (writing, performance, well-being, dependence, or ethics). However, this approach keeps the debate stuck in an outdated phase of debate about the tool’s acceptance, even though ChatGPT is part of the academic ecosystem. The objective of the study is to understand, from students’ voices, how the quality of academic interaction with ChatGPT is configured, and to identify patterns of decision-making, validation, ethical regulation, and communication (transparency/concealment) in university contexts. An interpretive qualitative approach was followed. A total of 418 university students participated, all of whom provided qualitative data through semi-structured virtual interviews. The data were analyzed using reflective thematic analysis in six phases, with the support of ATLAS.ti software for rooting and density calculations. The results revealed ten categories that structure the phenomenon (adoption, attitudes, writing, translation, performance, cross-cutting skills, integrity, well-being, disciplinary use, and institutional integration). A continuum was observed between high-quality interaction (verification, rewriting, appropriation, and responsible authorship) and low-quality interaction (cognitive delegation, overconfidence, dependence, and concealment). The quality of student interaction with ChatGPT requires critical, ethical, and institutional regulation to guide and legitimize the academic process. Full article

Human health and well-being are dependent on natural environments, which is the core foundation of the growing discipline of outdoor therapies. However, as with psychotherapy research in general, the field of outdoor therapies lacks descriptive process-oriented theoretical frameworks that precisely reflect this multi-faceted practice. Therapeutic work, whether this takes place indoors or outdoors, comprises numerous implicit relational and environmental dimensions. Implicit aspects are largely sensed, embodied and intuitive, and therefore hard to pin down and describe accurately. In this exploratory study, a survey mapped implicit guiding frameworks amongst outdoor therapy practitioners (n = 68) representing 18 nations. A constructivist grounded theory analysis resulted in the proposition of a practice theory, called the implicit ecosystem of outdoor therapies, made up of eight interrelated components: (1) joint engagement and co-creating agendas; (2) a foundation of safety and trust; (3) being in parallel and not fix; (4) awareness and attunement here-now; (5) the dynamic of outer and inner landscapes; (6) a constantly moving and meaning-making endeavor; (7) creativity, play, and whole-body activation; and (8) working through natural barriers and rewriting narratives. This grounded theory offers a preliminary blueprint of a practice-guiding framework developed from within the outdoor therapy discipline intended to advance theory, training, and research. Full article

Cyber–Physical Systems of Systems (CPSoS) integrate autonomous constituent systems to accomplish complex missions. Nonetheless, decentralized coordination and continuous evolution create intricate dependencies that make behavior difficult to analyze. Current semi-formal modeling approaches, despite being easy to understand and widely accessible, lack semantic precision and are not computationally checkable to guarantee time-critical properties. Furthermore, current formal methods are often fragmented: they analyze behavior either at the individual CPS level or the collective CPSoS level, failing to provide a multi-level specification. To address these limitations, we propose an integrated framework combining SysML and Maude rewriting logic. SysML provides structural and behavioral specification capabilities, while Maude enables rigorous semantics, executable models, and formal verification. First, our approach proposes MM-CPSoS, a meta-model that unifies CPS and CPSoS entities with explicit temporal constraints. Dynamic behavior is captured through evolution patterns governing mission progression across both levels. Then, we encode SysML models into Maude as object-oriented configurations and conditional rewrite rules, enabling linear temporal logic (LTL) model checking of temporal properties. Finally, we demonstrate our approach through a Time-Aware Road Crisis Management System (TaRCiMaS²). Full article

In recent years, imaginative techniques have effectively addressed the growing demand for brief, evidence-based treatments applicable in various contexts. Among these, Imagery with Rescripting (ImRs) was developed within the Schema Therapy model. ImRs can be applied individually or in combination with other protocols, demonstrating significant outcomes even after just one session. This narrative review aims to provide an overview of the applications of ImRs, with a specific focus on its effectiveness in trauma-related disorders. The search string used was “(‘imagery with Rescripting’) AND ((‘Trauma’ OR ‘PTSD’ OR ‘dissociation’))”. The following databases were utilized: PubMed, Scopus, Web of Science, Medline, Embase, and PsychInfo. The research included English-language and Italian-language studies, encompassing experimental and observational designs, case reports, and case series. Samples consisted of healthy participants or clinical populations aged 18 years and older, with no temporal limitations. A total of 56 articles were selected. The results highlight the efficacy of this intervention, whether administered individually or as part of combined protocols, across a wide range of diagnostic categories, including healthy samples, post-traumatic stress disorder (PTSD), borderline personality disorder (BDP), sleep disorders, psychotic spectrum disorders, chronic pain, anxiety disorders, depression, and eating disorders. The studies also support hypotheses about the mechanisms underlying the technique: ImRs facilitates the reprocessing of the meaning associated with mental representations and reduces the occurrence of negative intrusive images related to past events. This process alters and rewrites the individual’s negative memories and images. The narrative review supports the effectiveness of ImRs in treating various psychopathological disorders, both trauma-related and non-trauma-related. In addition to highlighting the effectiveness of ImRs when appropriately integrated with other techniques, the review emphasizes the importance of conducting efficacy studies on larger samples to evaluate ImRs as a standalone intervention model. Full article

Large Language Models (LLMs) have achieved substantial progress in knowledge-intensive tasks, particularly through Retrieval-Augmented Generation (RAG) frameworks. However, existing RAG systems often suffer from performance degradation when input queries are misaligned with retrieval requirements, and effectively coordinating retrieval with reasoning remains challenging—especially for multi-hop questions requiring iterative retrieval steps. To address these challenges, we propose ReWriteGen, a unified framework that integrates query rewriting, retrieval augmentation, and complementary generation within a coordinated architecture, optimized using reinforcement learning (Group Relative Policy Optimization, GRPO) and Direct Preference Optimization (DPO). ReWriteGen introduces a retrieval-aware query rewriting mechanism to better align input queries with external knowledge. The framework optimizes retrieval-augmented answers without requiring supervised reasoning annotations.Our experiments show that ReWriteGen consistently outperforms traditional RAG baselines across three multi-hop QA benchmarks: HotpotQA, MuSiQue, and 2Wiki. On HotpotQA, ReWriteGen achieves improvements of 5.32 and 5.10 percentage points in EM and LLM-based evaluation, respectively, compared to the strongest baseline. Corresponding gains of 11.90 and 7.18 are observed on MuSiQue, and 15.45 and 18.60 on 2Wiki.ReWriteGen enhances the coordination between retrieval and reasoning in LLMs, delivering consistent performance gains while reducing reliance on supervised reasoning annotations and extensive task-specific engineering. Full article

We introduce and study a new class of nonlinear operators on metric spaces, called rational $(a,p)-$quasicontractions. Within this framework, we establish Greguš-type fixed-point theorems for closed, convex subsets of Banach spaces. The results establish the existence and uniqueness of fixed points, as well as the convergence of the Picard iteration for every initial guess. We show that rational $(a,p)-$quasicontractions strictly extend several classical contractive classes, including Hardy-Rogers, Kannan, Chatterjea, and rational contractions, and we provide explicit examples exhibiting the properness of these inclusions. As an application, we consider a nonlocal boundary value problem for a Caputo fractional differential equation of order $\alpha \in (1,2)$ with distributed delay and mixed nonlocal boundary conditions. By rewriting the problem as a Hammerstein-Volterra integral equation on a cone, and imposing natural growth and rational Lipschitz conditions on the delayed nonlinearity, we show that the associated Hammerstein operator is a rational $(a,p)-$quasicontraction. This yields the existence, uniqueness, and global attractivity of a positive solution. Two model fractional nonlinearities with delayed feedback are discussed in detail, along with a numerical scheme that illustrates the predicted geometric convergence of the discrete Picard iteration in the Caputo fractional setting. Full article

This work is axiomatic and structural in nature and is not intended as a phenomenological physical theory, but as a framework clarifying minimal informational primitives from which geometric and dynamical descriptions may emerge. We present a background-independent framework in which physical geometry, interaction-like forces, and spacetime arise as effective descriptions of constrained relational information rather than as fundamental entities. The only primitive structure is a network of degrees of freedom linked by admissible informational relations, each subject to quantifiable constraints on accessibility or flow. The motivation is to identify whether a single minimal relational primitive can account jointly for the emergence of geometry, forces, and spacetime, without presupposing a manifold, fields, or fundamental interactions. The framework is formalized using weighted relational graphs in which constraint weights encode limitations on information flow between degrees of freedom. Effective geometry is defined operationally through minimal constraint cost along relational paths, yielding an emergent metric without assuming spatial embedding. Relational evolution is modeled via a minimal configuration-space dynamics defined by local rewrite moves, and a statistical description is introduced through an informational action that governs coarse-grained response rather than serving as a fundamental dynamical law. Curvature-like observables are defined using transport-based comparisons of local accessibility structure. Within this setting, metric structure emerges from constrained relational accessibility, while curvature-like behavior arises from heterogeneity in constraint structure. Effective forces appear as entropic or informational action gradients with respect to coarse-grained control parameters that modulate relational constraints, and are interpreted as emergent responses rather than primitive interactions. A finite worked example explicitly demonstrates the emergence of nontrivial distance, curvature proxies, and an effective force via geodesic switching under constraint variation, without assuming fundamental spacetime, fields, or particles. The results support an interpretation in which geometry, forces, and spacetime are representational features of constrained information flow rather than fundamental elements of physical law. The framework clarifies conceptual distinctions and points of compatibility with existing approaches to emergent spacetime, and it outlines qualitative expectations for regimes in which smooth geometric descriptions are expected to break down. The work delineates the scope and limits of geometric description without proposing a complete phenomenological theory. Full article

Extracellular matrix (ECM) stiffening is a defining biophysical feature of solid tumors that reshape gene regulation through mechanotransduction. Increased collagen crosslinking and stromal remodeling enhance integrin engagement, focal-adhesion signaling and force transmission to the nucleus, where key hubs such as lysyl oxidase (LOX), focal adhesion kinase (FAK) and the Hippo co-activators YAP1 and TAZ (WWTR1) promote proliferation, invasion, stemness and therapy resistance. Here, we synthesize evidence that quantitative changes in matrix stiffness remodel the miRNome and lncRNome in both tumor and stromal compartments, including extracellular vesicle cargo that reprograms metastatic niches. To address heterogeneity in experimental support, we classify mechanosensitive ncRNAs into studies directly validated by stiffness manipulation (e.g., tunable hydrogels/AFM) versus indirect associations based on mechanosensitive signaling, and we summarize physiological versus pathophysiological stiffness ranges across tissues discussed. We further review competing endogenous RNA (ceRNA) networks converging on mechanotransduction nodes and ECM remodeling enzymes, and discuss translational opportunities and challenges, including targeting mechanosensitive ncRNAs, combining ncRNA modulation with anti-stiffening strategies, delivery barriers in dense tumors, and the potential of circulating/exosomal ncRNAs as biomarkers. Overall, integrating ECM mechanics with ncRNA regulatory circuits provides a framework to identify feed-forward loops sustaining aggressive phenotypes in rigid microenvironments and highlights priorities for validation in physiologically relevant models. Full article

RNA editing is a way to diversify, regulate expression, and expand the cell transcriptome. The most common RNA editing is the reversible conversion of adenosine (A) to inosine (I) driven by double-stranded RNA-binding adenosine deaminases (ADARs). As inosine is recognized as guanosine (G) during translation, the RNA editing may result in non-synonymous codon changes. For this reason, ADARs have gained attention as promising enzymes to rewrite mRNA. Many efforts were undertaken to engineer a precise, effective, and controllable ADAR-based system to target certain Adenines on RNA to repair pathological mutations. This review summarizes the advances in ADAR-mediated RNA editing, evolving from systems using antisense oligonucleotides as guide RNA to recruit endogenous or overexpressed ADARs, through more complex setups additionally expressing other RNA-binding proteins, to rational designs harnessing ADARs to convert other nucleotides and amplify the low initial signal. Increasing the specificity and yield of RNA editing, expanding the number of targetable sites, and reducing off-target and bystander activity remain key challenges for these technologies. Improving delivery efficiency across a broad range of cell types, as well as optimizing delivery routes in in vivo studies are also critical to harness them as advantageous tools for both research and therapy. Full article

Lesson preparation plays a crucial role in structuring and organizing the teaching process. However, traditional lesson design and presentation creation require teachers to spend a considerable amount of time reviewing the literature and organizing materials. Therefore, developing an intelligent and multimodal technology capable of automatically generating lesson materials holds great significance. Such technology can potentially reduce teachers’ workloads and improve the efficiency and quality of lesson preparation, as indicated by teacher satisfaction and preference judgments. In this paper, we introduce LessonAgent, a multimodal and interactive pipeline that leverages large language models (LLMs) to generate lesson plans, presentations, and podcasts. Our system enhances the quality of generated materials through diverse input modalities, refined generation mechanisms, and interactive feedback with teachers. Specifically, we present the Plan10k dataset—a high-quality bilingual collection of lesson plans—and employ it to train and evaluate our framework. The pipeline consists of three main modules: a query rewriting module that handles multimodal teacher inputs (e.g., textual concepts, images, or textbook excerpts), a lesson plan generation module that produces structured content, and a chapter correction module that integrates retrieval-based tools to improve factual accuracy and contextual relevance. Furthermore, teachers can interact with intermediate results, allowing adaptive refinement throughout the generation process. Based on the generated lesson plans, the framework further produces corresponding visual presentations and podcasts, forming a comprehensive multimodal teaching assistant system. Extensive experiments and teacher evaluations demonstrate the superior performance and satisfaction of our approach. Full article

The ability to revise texts to meet the needs and expectations of the target audience requires sustained and deliberate practice. Revision becomes more complex when working on somebody’s else text and in a second language. Against this background, we conducted an exploratory and descriptive case study qualitatively shedding light on the characteristics of the processes and the products of revision. We collected data from three graduate students revising a business text in English (their second language) and from an experienced writer/editor, native English speaker, revising the same text in his first language. Using keystroke logging, screen recording, and text analysis, we observed an alternation between revision and rewriting, as well as a combination of expert features (e.g., inclusion of reader-oriented explanations) and less expert features (e.g., fewer rounds of revision) among graduate students. There were also differences between the students and the expert in the way in which they spatially organised their tasks. We interpreted these results within the context of cognitive and sociocultural models of writing, and especially the notion of agency. Full article

Information asymmetry between complex source texts and general-audience comprehension remains a critical challenge in Artificial Intelligence. However, existing supervised simplification methods suffer from the scarcity of parallel training data, while standard text summarization methods often discard essential details to reduce length. Furthermore, zero-shot large language models frequently lack fine-grained controllability over linguistic complexity. To address these technical limitations, we present a framework to resolve information asymmetry by casting text simplification as a controllable denoising language modeling task. Unlike summarization, our approach preserves full semantic coverage while reducing difficulty. Our algorithm targets the problem of identifying and rewriting complex spans without labeled data through three mechanisms: (1) Asymmetry-Aware Masking, which uses model-based reconstruction difficulty (Negative Log-Likelihood) to isolate high-complexity terms; (2) paraphrase context prompting to enforce semantic invariance; and (3) an adaptive decoding strategy that dynamically penalizes complex tokens based on input difficulty. On ASSET (Abstractive Sentence Simplification Evaluation and Tuning dataset), our best setting reaches SARI (System output Against References and against the Input) 42.90 with FKGL (Flesch–Kincaid Grade Level) 7.10 (Sentence Similarity 0.948), and performs consistently on TurkCorpus (SARI 41.10), while requiring no parallel data or fine-tuning. Full article

Desire, a transformative force, is one of contemporary serial narratives’ most intricate and multifaceted dimensions. Far from being reducible to a mere representation of sexual attraction, desire in television seriality operates as a prism through which to explore issues of intimacy, identity, and power. This paper seeks to analyze how desire is staged and problematised within a set of emblematic series that have significantly shaped contemporary cultural imagination. Grey’s Anatomy explores the entanglement of desire with professional life, emotional fragility, and collective trauma, constructing narratives where eros intersects with affective labour and the negotiation of identity within high-pressure contexts. Sex and the City proposes a very different model, placing female desire at the centre as a space of autonomy, experimentation, and confrontation with the normative frameworks of late capitalist society. By contrast, The Handmaid’s Tale reimagines desire within a dystopian theocracy, assigning it an overtly political function: here, erotic impulses and affective attachments become acts of resistance against systemic repression and biopolitical control. More recently, Sex Education embodies a cultural shift, presenting desire through a plural and inclusive lens that embraces diversity of sexual orientation, gender identity, and relational practices. These case studies, when viewed collectively, reveal how television series rework cultural codes of sexuality and intimacy, producing new imaginaries of the body, pleasure, and identity. In this perspective, serial narratives emerge as key cultural laboratories, reproducing and challenging dominant ideologies of desire while offering audiences opportunities for recognition, critique, and affective engagement beyond the screen. Full article

This article explores the reclamation of Khoisan identities in South Africa as a multifaceted process of cultural, spatial, and political resurgence. Framed within the country’s constitutional vision of a “Nation of Nations,” the research examines how Khoisan communities—historically marginalised and classified under apartheid as “Coloured”—are reasserting their Indigenous heritage through acts of cultural revival and place-based activism. Centred on Cape Town, the ancestral homeland and symbolic epicentre of both colonial encounter and Indigenous resurgence, the article theoretically investigates how creativity, heritage, and activism intersect in processes of reimagining, renaming, and retaking of place. Drawing on theories of visual sovereignty and re-placement, it analyses how visual and performative practices—ranging from protest art and language revitalisation to heritage occupations—function as decolonial acts that reclaim both the image and meaning of place. The article situates the Khoisan revival within broader global movements of Indigenous self-representation and argues that reclaiming place constitutes a living form of sovereignty, restoring relational networks between people, land, and identity. Ultimately, it demonstrates that contemporary Khoisan activism transforms visibility into agency, using culture and creativity as tools to rewrite belonging and to decolonise South Africa’s cultural landscape. Full article