Search Results (113)

The overall prevalence of dyslipidemia among adults continues to rise. However, limited attention has been devoted to the association between free sugar intake and dyslipidemia. This study aimed to explore the association between free sugar intake and dyslipidemia, as well as its subtypes, in Chinese adults. A total of 38,656 adults from the Chinese Food Consumption Survey (CFCS) and 3974 from the China Health and Nutrition Survey (CHNS) were included in the final analysis. Dietary intake data were collected by 24 h dietary recalls and the weighing of household foods and condiments. For the CHNS, multivariable Cox proportional hazards regression models were used to examine the association between free sugar intake and the risk of dyslipidemia and its subtypes. Restricted cubic spline (RCS) regression was used to evaluate the nonlinear relationship between free sugar intake and dyslipidemia. Stratified analyses and interaction tests were performed across key covariates to explore potential effect modifiers of the association between free sugar intake and dyslipidemia risk. For the CFCS, the mean free sugar intake among Chinese adults was 6.4 g/d. For the CHNS, a total of 1052 individuals were diagnosed with dyslipidemia, corresponding to an incidence rate of 26.5% during a total of 24,531 person-years of follow-up (median follow-up, 6.0 years) in the CHNS cohort. After multivariable adjustment, the risk of dyslipidemia was increased by 38% with >20 g/d free sugar intake (HR = 1.38, 95% CI: 1.04–1.84, p for trend = 0.006), with <5 g/d free sugar intake as a reference. For different subtypes of dyslipidemia, with <5 g/d free sugar intake as a reference, the risk of hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia was increased by 91% (HR = 1.91, 95% CI: 1.34–2.74, p for trend < 0.001) and 52% (HR = 1.52, 95% CI: 1.01–2.29, p for trend = 0.004) with > 20g/d free sugar intake, respectively. Stratified analyses demonstrated that the impact of high free sugar intake on the development of hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia was significantly amplified among urban residents and those with insufficient physical activity. While linear dose–response associations were observed between free sugar intake and the risk of dyslipidemia (p for nonlinearity > 0.05), a nonlinear, inverted U-shaped association emerged for hyper-LDL-cholesterolemia (p for nonlinearity = 0.007). Our study demonstrates that free sugar intake levels in Chinese adults were relatively low compared to the recommended intake level (<50 g/d) in the Chinese Dietary Guidelines 2022. Nevertheless, increased free sugar intake was positively associated with a higher risk of incident dyslipidemia, as well as incident hypo-HDL-cholesterolemia and hyper-LDL-cholesterolemia. Targeted nutrition education is recommended to promote balanced dietary patterns and enhance overall dietary quality, thereby effectively reducing the risk of dyslipidemia. Full article

This review explores the hypothesis that schizophrenic symptoms may be understood not as isolated deficits, but as interconnected manifestations of a structural reorganization of consciousness. The premises of this work are grounded in a comparative matrix that suggests an underlying “consanguinity” between the philosopher’s voluntary epoché—the suspension of the natural attitude performed to study the inner workings of consciousness—and the involuntary “unworlding” passively experienced in schizophrenia. By exploring this shared ontological ground, the text suggests how specific phenomenological shifts, such as the collapse of the “vital drive,” may manifest as clinical markers; this process may eventually lead to an involuntary “transcendental reduction” where the mind’s internal machinery becomes an object of forced awareness. Building on these premises, the review tentatively outlines several key achievements. It addresses the substrate-subjectivity gap by linking biological sensory-binding failures to the onset of involuntary hyper-reflexivity. Regarding structural loss and gain of function, it suggests that the psychotic transition involves a simultaneous erosion of common-sense coherence and an intensified receptivity to unfiltered perceptual fragments, which may trigger a search for metaphysical meanings. In terms of a therapeutic synthesis, it proposes exploring the conversion of “artless decentering” into a manageable, strategic distance through mindfulness and person-centered position-taking. Finally, it discusses a potential nosographic evolution, advocating for future diagnostic classifications that prioritize the experiencing self and qualitative insights to support a more translational and empathetic approach to psychiatry. Full article

Home automation is undergoing a paradigm shift from connected IoT environments with rule-based control to intelligent homes exhibiting ambient intelligence and proactive adaptation. Artificial intelligence, privacy-preserving sensing, and converging connectivity standards are the primary forces driving this transition. This systematic literature review synthesizes the technological foundations, architectural developments, emerging paradigms, and socio-technical challenges characterizing the next generation of smart homes, evaluated against the original Ambient Intelligence (AmI) vision. Following PRISMA 2020 guidelines, searches were conducted across four databases—IEEE Xplore, ACM Digital Library, Scopus, and Web of Science—covering studies published between January 2020 and June 2025. From 3450 records, 113 studies were selected through a two-reviewer screening procedure with inter-rater reliability assessments. Quality was assessed using a modified JBI Critical Appraisal Checklist, and findings were synthesized through thematic analysis. Three converging technological pillars were identified: multi-modal privacy-preserving sensing including mmWave radar; a hierarchical cloud-edge-TinyML intelligence engine; and unified connectivity through the Matter/Thread standard. Emerging paradigms include LLM-based cognitive orchestration, hyper-personalization, Digital Twin simulation, and grid-interactive prosumer energy management. Realizing that the intelligent home vision requires addressing the privacy–security–trust trilemma, algorithmic bias, system reliability, and human–agent collaboration, a research roadmap encompassing explainable AI, privacy-by-design, lifelong learning, and standardized ethical auditing is proposed. Full article

As modern society grows increasingly complex, access to essential services such as healthcare, legal aid, tailored education, and psychological support remains heavily gated by socio-economic, neurological, and systemic barriers. This paper explores the transformative potential of Large Language Models (LLMs) and Generative Artificial Intelligence not merely as industrial productivity enhancers, but as vital “social scaffolds” capable of fostering a more inclusive society. Crucially, we propose a paradigm shift in the concept of Ethical Computing—moving from a passive defensive framework of non-maleficence (“do no harm”) to an active mandate of beneficence, where AI systems are explicitly developed to serve marginalized and un(der)served populations. Through this expanded ethical lens, we systematically analyze the democratizing impact of AI across four primary axes of inclusivity: socio-economic (providing zero-cost medical triage and legal translation for undocumented populations), neurospicy (acting as a non-judgmental communicative bridge for individuals with Autism Spectrum Disorder), pedagogical (delivering hyper-personalized executive function support for Special Educational Needs), and psychological (serving as an accessible, first-level triage system for mental health crises). By framing LLMs as a modern social safety net, we outline a clear trajectory for future research, advocating for an “ethical-by-design” development paradigm that explicitly prioritizes equity, accessibility, and the active dismantling of historical barriers for the digitally and socially disenfranchised. Full article

Purpose: With the advent of sixth-generation (6G) communication technology, systematic mapping of its use cases to associated technical requirements has become essential for accelerating standardization, guiding R&D investment, and informing policy formulation. Methods: This study consolidated 65 use case scenarios from key academic and institutional 6G sources into 21 representative cases. A three-round Delphi-based expert assessment, employing a five-point Likert scale and interquartile-range-based consensus monitoring, was used to assign primary and secondary technical requirements across six core dimensions: immersive communication, massive communication, hyper-reliable low-latency communication, integrated sensing and communication, integrated artificial intelligence and communication (IAAC), and ubiquitous connectivity. A three-dimensional (3D) prism-based visualization framework was subsequently developed to represent the interdependencies among these requirements. Results: IAAC and massive communication emerged as the most critical requirements, each functioning as a primary or secondary driver across most use cases. The prism framework revealed hierarchical and complementary relationships among the six dimensions that conventional 2D wheel diagrams cannot adequately capture. Furthermore, a nine-criterion multidimensional classification framework, encompassing data transmission mode, decision-making mode, communication flow, interaction type, device type, deployment type, human activity innovation, user type, and personalization level, was developed, offering industry-specific guidance for service design. Collectively, the proposed framework supports user-centric design, informs strategic technology planning, and contributes to policy development while acknowledging existing limitations in quantitative mapping and economic analysis. Full article

The digital transformation of the tourism industry faces a dual challenge: the fragmentation of data across platforms and the lack of immersive “try-before-you-buy” experiences. While Large Language Models (LLMs) have revolutionized information synthesis, they typically lack real-time visual verification capabilities. This paper proposes a novel, multimodal AI Agent architecture that integrates advanced natural language planning with photorealistic 3D visualization. We present a system where a conversational agent, powered by Gemini 2.5 Flash, orchestrates a suite of dynamic tools to build structured travel itineraries (flights, hotels, activities) while simultaneously deploying a neural rendering engine. This engine utilizes a modular Structure-from-Motion (SfM) pipeline feeding into 3D Gaussian Splatting (3DGS) to render navigable, high-fidelity digital twins of hotel facilities directly within the chat interface. Positioned as a Technology Readiness Level 4 (TRL 4) proof of concept (PoC), this work demonstrates the technical feasibility of the multimodal integration between conversational logic and automated visual synthesis. The results demonstrate the technical feasibility of a pipeline that dynamically binds LLM inference to 3D spatial data, providing a foundation for high-fidelity, interactive travel consultancy. Full article

This qualitative study employs interpretive phenomenology and Actor–Network Theory (ANT) to examine the evolving role of AI as an agent within European marketing contexts. Drawing on semi-structured interviews with 36 senior executives from the tourism, fintech, professional services, and digital media sectors, the study identifies four interconnected themes: (1) ambivalent human–AI co-agency, where AI operates as a “co-strategist” influencing budgets and decisions; (2) infrastructural and regulatory challenges arising from legacy systems and GDPR/EU AI Act constraints; (3) ethical issues concerning opacity, bias, and exclusion in hyper-personalization; and (4) the redefinition of professional identities towards hybrid socio-technical roles. The findings underscore AI’s role as a co-creator of strategy, governance, and power, highlighting the necessity of balanced co-agency, robust infrastructure, ethical safeguards, and adaptable skill sets. The AI-MARC framework (Agency, Infrastructure, Responsibility, Capability) provides a practical framework for governance of sustainable AI integration. This work addresses gaps in qualitative AI marketing research by emphasising reflexive practices amid evolving regulations, with the aim of fostering equitable networks that align innovation, fairness, and accountability. Full article

In social virtual reality (VR) and metaverse platforms, users express their identity through both avatar appearance and on-avatar textual cues, such as speech balloons. However, little is known about how the harmony between these cues influences self-representation and social impressions. We propose that when avatar appearance and text design, including color, font, and tone, are consistent, users experience a stronger self-expression fit and elicit greater interpersonal affinity. A within-subject study ($N=21$) in VRChat manipulated the social context, color harmony between avatar hair and text, and style or content consistency between tone and font. Questionnaires provided composite indices for perceived congruence, self-expression fit, and affinity. Analyses included repeated-measures ANOVA, linear mixed-effects models, and mediation tests. Results showed that congruent pairings increased both self-expression fit and affinity compared to mismatches, with mediation analyses indicating that self-expression fit fully mediated the effect. These findings integrate theories of avatar influence and computer-mediated communication into a framework for metaverse design, highlighting the value of consistent avatar and text styling. Full article

Sensory processing differences, reported in up to 97% of individuals with autism spectrum disorder (ASD), are increasingly recognized as a defining feature of the condition, shaping perception, cognition, and adaptive behavior. Atypical sensory responsivity, ranging from hyper- to hypo-reactivity and sensory seeking, emerges early in development and contributes to the clinical and neurobiological heterogeneity of autism. Alterations in neural connectivity, the balance of excitation and inhibition, and multisensory integration are thought to underlie these sensory profiles, influencing emotional regulation, attention, and social interaction. Sensory features also interact with co-occurring conditions such as anxiety, attention deficit hyperactivity disorder, and sleep and feeding difficulties, thereby shaping developmental trajectories and influencing adaptive behavior. Clinically, these sensory dysfunctions have a significant impact on daily participation and quality of life, extending their effects to family functioning. Understanding individual sensory phenotypes is therefore essential for accurate assessment and personalized intervention. Current therapeutic approaches include Sensory Integration Therapy, Sensory-Based Interventions, Sequential Oral Sensory Approach, and structured physical activity programs, often complemented by behavioral and mindfulness-based techniques. Emerging neuroplasticity-oriented methods for targeted modulation of sensory processing networks include neurofeedback and non-invasive brain stimulation. Overall, current evidence highlights the central role of sensory processing in ASD and underscores the need for multidisciplinary, individualized approaches to optimize developmental trajectories and enhance adaptive functioning. This review provides an updated synthesis of sensory processing in ASD, integrating neurobiological, developmental, and clinical evidence to highlight established knowledge, unresolved questions, and priorities for future research. Full article

Amid rapid digital transformation and shifting consumption models, the tailoring industry faces a dual challenge: preserving its artisanal essence while adapting to the expectations of an increasingly digital-oriented clientele. This study introduces a methodological framework for designing buyer personas suited to the contemporary artisanal tailoring ecosystem, offering a structured approach to understanding modern consumer behavior within hybrid physical–digital environments. Using a mixed-methods design and Sastrería Jorge Ospina (Caicedonia, Colombia) as a case study, 378 online surveys—117 from current clients and 261 from potential clients—were analyzed using descriptive and inferential statistical techniques (Pearson’s χ², p < 0.05). Managerial priorities were concurrently assessed using a multi-criteria decision-making model (TOPSIS) with entropy-based weighting. The analysis identified two consumer archetypes: (1) the Classic Segment—mature clients motivated by tradition, loyalty, and reliability, who value tangible elegance and experiential craftsmanship; and (2) the Digital Segment—young consumers driven by aesthetic trends, convenience, and immediacy, who prioritize online interaction and personalized digital consumption. TOPSIS results highlighted older men (Cᵢ = 1.000) and young women (Cᵢ = 0.870) as the most strategically valuable customer groups. These findings redefine the post-digital tailoring consumer as a hybrid entity guided by artisanal value, hyper-personalization, and digital engagement. Full article

This article proposes a conceptual reformulation of the phenomenon of boredom by carefully distinguishing ordinary situational or psychologically driven boredom from what is here termed existential boredom: a specific mode of disconnection in which the subject’s capacity to inhabit a meaningful horizon of possibilities becomes temporarily suspended. Rather than interpreting boredom as a mere lack of stimulation, momentary dissatisfaction, or simple emotional discomfort, the study argues that certain forms of boredom reveal a phenomenological contraction of possibility, involving disturbances in lived temporality, value orientation, imaginative projection, and embodied intentionality. Through a critical analysis of key thinkers, the article clarifies the tensions and limitations within classical accounts and delineates the proposed concept of existential boredom from adjacent phenomena such as Unheimlichkeit, Frankl’s “existential vacuum”, clinical apathy, and everyday boredom. It is argued that existential boredom functions as a phenomenological indicator of existential disconnection, not as an ontological diagnosis of meaninglessness but as a liminal experience that renders visible the temporary suspension of the structures that normally sustain meaningful world-disclosure. This conceptualisation also illuminates the contemporary prevalence of this affective state within contexts of hyper-stimulation, attentional fragmentation, and the erosion of meaning frameworks. By offering an integrated analytical framework, the article contributes to a more rigorous understanding of boredom as an existential phenomenon, with implications for philosophy, contemporary psychology, and the study of human experience in modern life. This approach not only expands our understanding of boredom but also invites us on a journey of self-discovery and personal growth. Full article

This study evaluated the applicability of the dual drainage model, Hyper Connected–Solution for Urban Flood (HC-SURF), for real-time urban flood forecasting. The model was applied to the extreme rainfall event of August 2022 in the Sillim and Daerim drainage basins in Seoul. Its accuracy and computational efficiency were quantitatively compared with those of two widely used commercial models, the Personal Computer Storm Water Management Model (PC-SWMM) and InfoWorks Integrated Catchment Modelling (ICM). Accuracy was assessed by measuring spatial agreement with observed inundation trace maps using binary indicators, including the Critical Success Index (CSI), Probability of Detection (POD), and False Alarm Ratio (FAR). Computational efficiency was evaluated by comparing simulation times under identical conditions. In terms of accuracy against observations, HC-SURF achieved CSI values ranging from 0.26 to 0.45, with POD values from 0.37 to 0.81 and FAR values from 0.49 to 0.53 across the two basins. In inter-model comparisons, the model showed high hydraulic consistency, demonstrating CSI values between 0.72 and 0.88, POD between 0.82 and 0.99, and FAR between 0.08 and 0.15. In terms of computational efficiency, HC-SURF reduced calculation times by approximately 9% and 44% compared with InfoWorks ICM and PC-SWMM, respectively, for a 48 h simulation. The model also completed a 6 h rainfall simulation in approximately 8 min, meeting the lead time requirements for rapid urban flood forecasting. Overall, these findings show that HC-SURF effectively balances simulation accuracy with computational efficiency, demonstrating its suitability for real-time urban flood forecasting. Full article

Background/Objectives: Computational studies using drift diffusion models on go/no-go escape tasks consistently show that individuals with suicidal ideation (SI) preferentially engage in active escape from negative emotional states. This study extends these findings by examining how individuals with SI update beliefs about action–outcome contingencies and uncertainty when trying to escape an aversive state. Methods: Undergraduate students with (n = 58) and without (n = 62) a lifetime history of SI made active (go) or passive (no-go) choices in response to stimuli to escape or avoid an unpleasant state in a laboratory-based negative reinforcement task. A Hierarchical Gaussian Filter (HGF) was used to estimate trial-by-trial trajectories of contingency and volatility beliefs, along with their uncertainties, prediction errors (precision-weighted), and dynamic learning rates, as well as fixed parameters at the person level. Bayesian mixed-effects models were used to examine the relationship between trial number, SI history, trial type, and all two-way interactions on HGF parameters. Results: We did not find an effect of SI history, trial type, or their interactions on perceived volatility of reward contingencies. At the trial level, however, participants with a history of SI developed progressively stronger contingency beliefs while simultaneously perceiving the environment as increasingly stable compared to those without SI experiences. Despite this rigidity, they maintained higher uncertainty during escape trials. Participants with an SI history had higher dynamic learning rates during escape trials compared to those without SI experiences. Conclusions: Individuals with an SI history showed a combination of cognitive inflexibility and hyper-reactivity to prediction errors in escape-related contexts. This combination may help explain difficulties in adapting to changing environments and in regulating responses to stress, both of which are relevant for suicide risk. Full article

Generative Artificial Intelligence (GenAI) is rapidly transforming industries and organizations through automation and augmentation. Personalization of human–system interaction is a key area that can be significantly advanced through the effective implementation of GenAI. GenAI, positioned as an intermediary between humans and systems, can transform the human experience from the pre-defined, conventional notions of personalization into a dynamic and integrated hyperpersonalization capability. This article presents the zero-shot-to-head-shot hyperpersonalization (Z2H2) framework, which aims to achieve this through the effective adoption of GenAI techniques. It is a domain-neutral framework of three incremental stages named zero-shot, few-shot, and head-shot that gradually increase the level of hyperpersonalization of the human–system interaction. The framework is further represented in a layered system design and the Z2H2 Data Modality Matrix (ZDMM), which systematically maps data types, AI capabilities, and personalization objectives for each stage. The capabilities of the framework are demonstrated in an educational setting, followed by an empirical evaluation using the Open University Learning Analytics Dataset (OULAD). Although this dataset only contains demographic and aggregated clickstream data, which is a subset of attributes relevant to the entire framework, the gradual development of zero-shot-to-head-shot hyperpersonalization is effectively demonstrated and validated on these student interactions. Full article

Obesity and depression are two of the most prevalent diseases with increasing trends worldwide; it has been some time since the first epidemiological associations were first described. Currently, there is abundant evidence showing the physiology and the molecular aspects that intersect the biology of both ailments. This narrative review aims to synthesize current evidence on the epidemiology and shared pathophysiology of obesity and major depressive disorder, emphasizing convergent inflammatory, neuroendocrine, metabolic, genetic, and gut–brain mechanisms. We aggregate evidence for a bidirectional relationship mediated by: (1) chronic low-grade inflammation (elevated CRP, IL-6, TNF-α; microglial activation); (2) HPA axis dysregulation (hyper/corticosteronemia, impaired feedback, altered CRH/ACTH signaling); (3) metabolic and neurotrophic signaling deficits (insulin and leptin resistance, dysregulated adipokines such as leptin/adiponectin, impaired BDNF and synaptic plasticity); (4) lipid-derived neurotoxicity and mitochondrial stress (saturated fatty acids, ceramides, oxidative stress); and (5) gut–brain axis perturbations (microbiota dysbiosis, increased intestinal permeability, LPS-driven endotoxemia, altered short-chain fatty acids and tryptophan–kynurenine metabolism). We highlight how these convergent pathways promote neuroinflammation and mood dysregulation in individuals with obesity and summarize clinical consequences for screening, integrated management, and targeted interventions that modulate immune, neuroendocrine, metabolic, and microbial processes. Finally, we outline priorities for identifying shared biomarkers and advancing personalized strategies via multi-omics and systems medicine to improve prevention, diagnosis, and treatment. Full article