Search Results (95)

This essay argues for the reciprocal benefits of joining the new theories of matter emerging out of critical posthumanism and the psychedelic drugs currently experiencing a so-called “renaissance” in global north societies. While the former’s twin emphasis on relationality and embodiment is perfectly suited to capture and ground the ontological, epistemological, and ethical implications of psychedelic experiences of interconnectedness and transformation, these substances are in turn powerful companions through which to enact a “posthuman phenomenology” that helps us with the urgent task to “access, amplify, and describe” our deep imbrication with our more-than-human environments. In other words, I argue that while the “new materialism” emerging out of posthumanism can help elaborate a psychedelic rationality, psychedelics can in turn operate as educators in materiality. It is from this materialist perspective that we can best make sense of psychedelics’ often touted potential for social transformation and the enduring suspicion that they are somehow at odds with the “ontoepistemological order” of modernity. From this point of view, I contend that a crucial critical move is to push against the common trope that this opposition is best expressed as a turn from the narrow scientific and “consumerist materialism” of modern Western societies to more expansive “spiritual” worldviews. Pushing against this science-–spirituality binary, which in fact reproduces modern “indivi/dualism” by confining psychedelic experience inside our heads, I argue instead that what is in fact needed to think through and actualize such potentials is an increased attention to our material transcorporeality. In a nutshell, if we want psychedelics to inform social change, we must be more, not less, materialist—albeit by redefining matter in a rather “weird”, non-reductive way and by redefining consciousness as embodied. By the end of the essay, attaching psychedelics to a new materialism will enable us to formulate a “material spirituality” that establishes psychedelics’ political value less in an idealistic or cognitive “politics of consciousness” and more in a “materialization of critique”. Full article

This paper presents an integrated digital training twin framework for adaptive aircraft maintenance education, combining real-time competence modeling, algorithmic orchestration, and cloud–edge deployment architectures. The proposed system dynamically evaluates learner skill gaps and assigns individualized training resources through a multi-objective optimization function that balances skill alignment, Bloom’s cognitive level, fidelity tier, and time efficiency. A modular orchestration engine incorporates reinforcement learning agents for policy refinement, federated learning for privacy-preserving skill analytics, and knowledge graph-based curriculum models for dependency management. Simulation results were conducted on the Pneumatic Systems training module. The system’s validation matrix provides full-cycle traceability of instructional decisions, supporting regulatory audit-readiness and institutional reporting. The digital training twin ecosystem offers a scalable, regulation-compliant, and data-driven solution for next-generation aviation maintenance training, with demonstrated operational efficiency, instructional precision, and extensibility for future expansion. Full article

Cognitive Disengagement Syndrome (CDS) is a clinical construct characterized by symptoms such as excessive daydreaming, mental confusion, slowed behavior, and reduced cognitive and motor activity. Increasing evidence suggests a potential overlap between CDS and Emotional Dysregulation (ED), a transdiagnostic construct associated with difficulties in regulating emotional responses. This narrative review synthesizes current empirical findings and theoretical perspectives on the co-occurrence of CDS and ED, with a particular focus on insights provided by behavioral genetics—especially twin studies. We describe the core principles and models used in twin research and evaluate how they have been applied to disentangle genetic and environmental contributions to these phenotypes and their overlap. While some studies support a shared etiology between CDS and ED, particularly through non-shared environmental influences, research in this area remains limited and conceptually fragmented. The review identifies critical knowledge gaps, including the lack of longitudinal studies, inconsistent definitions of ED, and limited exploration of developmental trajectories. We argue that future twin studies are essential for clarifying these unresolved issues. Nonetheless, limitations include the scarcity of twin-based studies directly examining the CDS–ED association and methodological inconsistencies across the existing literature. Full article

Deep learning (DL) approaches to natural language processing (NLP) offer powerful tools for creating digital twins (DTs) of patients in psychiatry and neurological rehabilitation by processing unstructured textual data such as clinical notes, therapy transcripts, and patient-reported outcomes. Techniques such as transformer models (e.g., BERT, GPT) enable the analysis of nuanced language patterns to assess mental health, cognitive impairment, and emotional states. These models can capture subtle linguistic features that correlate with symptoms of degenerative disorders (e.g., aMCI) and mental disorders such as depression or anxiety, providing valuable insights for personalized treatment. In neurological rehabilitation, NLP models help track progress by analyzing a patient’s language during therapy, such as recovery from aphasia or cognitive decline caused by neurological deficits. DL methods integrate multimodal data by combining NLP with speech, gesture, and sensor data to create holistic DTs that simulate patient behavior and health trajectories. Recurrent neural networks (RNNs) and attention mechanisms are commonly used to analyze time-series conversational data, enabling long-term tracking of a patient’s mental health. These approaches support predictive analytics and early diagnosis by predicting potential relapses or adverse events by identifying patterns in patient communication over time. However, it is important to note that ethical considerations such as ensuring data privacy, avoiding bias, and ensuring explainability are crucial when implementing NLP models in clinical settings to ensure patient trust and safety. NLP-based DTs can facilitate collaborative care by summarizing patient insights and providing actionable recommendations to medical staff in real time. By leveraging DL, these DTs offer scalable, data-driven solutions to promote personalized care and improve outcomes in psychiatry and neurological rehabilitation. Full article

Embodied Conversational Agents (ECAs) serve as digital twins (DTs), visually and behaviorally mirroring human counterparts in various roles, including healthcare coaching. While existing research primarily focuses on single-coach ECAs, our work explores the benefits of multi-coach virtual health sessions, where users engage with specialized diet, physical, and cognitive coaches simultaneously. ECAs require verbal relational cues—such as empowerment, affirmation, and empathy—to foster user engagement and adherence. Our study integrates Generative AI to automate the embedding of these cues into coaching dialogues, ensuring the advice remains unchanged while enhancing delivery. We employ ChatGPT to generate empathetic and collaborative dialogues, comparing their effectiveness against manually crafted alternatives. Using three participant cohorts, we analyze user perception of the helpfulness of AI-generated versus human-generated relational cues. Additionally, we investigate whether AI-generated dialogues preserve the original advice’s semantics and whether human or automated validation better evaluates their lexical meaning. Our findings contribute to the automation of digital health coaching. Comparing ChatGPT- and human-generated dialogues for helpfulness, users rated human dialogues as more helpful, particularly for working alliance and affirmation cues, whereas AI-generated dialogues were equally effective for empowerment. By refining relational cues in AI-generated dialogues, this research paves the way for automated virtual health coaching solutions. Full article

With the growing demand for diverse and high-volume manufacturing of composite material parts in aerospace applications, traditional machining methods have faced significant challenges due to their low efficiency and inconsistent quality. To address these challenges, digital twin (DT) technology offers a promising solution for developing automated production systems by enabling optimal configuration of manufacturing parameters. However, despite its potential, the widespread adoption of DT in complex manufacturing systems remains hindered by inherent limitations in adaptability and inter-system collaboration. This paper proposes an integrated framework that combines Model-Based Systems Engineering (MBSE) with deep learning (DL) to develop a digital twin system capable of adaptive machining. The proposed system employs three core components: machine vision-based process quality inspection, cognition-driven reasoning mechanisms, and adaptive optimization modules. By emulating human-like cognitive error correction and learning capabilities, this system enables real-time adaptive optimization of aerospace manufacturing processes. Experimental validation demonstrates that the cognition-driven DT framework achieves a defect recognition accuracy of 99.59% in aircraft cable fairing machining tasks. The system autonomously adapts to dynamic manufacturing conditions with minimal human intervention, significantly outperforming conventional processes in both efficiency and quality consistency. This work underscores the potential of integrating MBSE with DL to enhance the adaptability and robustness of digital twin systems in complex manufacturing environments. Full article

Xishu Gardens, an exemplary narrative of classical Chinese gardens, faces challenges in preserving its commemorative spatial structures while accommodating modern visitors’ needs. While trajectory analysis is critical, existing studies struggle to interpret multi-dimensional perception-preference data owing to spatiotemporal mismatches in multi-source datasets. This study adopted an improved Ward–K-medoids hybrid clustering algorithm to analyze 885 trajectory samples and 34,384 synchronized data points capturing emotional valence, cognitive evaluations, and dwell time behaviors via panoramic digital twins across three heritage sites (Du Fu Thatched Cottage, San Su Shrine, and Wangjiang Tower Park). Our key findings include the following: (1) Axial bimodal patterns: Type I high-frequency looping paths (27.6–68.9% recurrence) drive deep exploration, in contrast to Type II linear routes (≤0.5% recurrence), which enable intensive node coverage. (2) Layout-perception dynamics: single-axis layouts maximize behavioral engagement (DFTC), free-form designs achieve optimal emotional-cognitive integration (WTP), and multi-axis systems amplify emotional-cognitive fluctuations (SSS). (3) Spatial preference hierarchy: entrance and waterfront zones demonstrate dwell times 20% longer than site averages. Accordingly, the proposed model synchronizes Type II peak-hour throughput with Type I off-peak experiential depth using dynamic path allocation algorithms. This study underscores the strong spatial guidance mechanisms of Xishu Gardens, supporting tourism management and heritage conservation. Full article

The first 1000 days of life starts from conception to a child’s second birthday. Research suggests that the period is critical for cognitive, physical, and emotional development. Investments in maternal and child healthcare during this period have a profound impact on long-term health, educational attainment, and economic productivity. This study examined the impact of such investments on child health outcomes in Kenya, using data from the 2015/2016 Kenya Integrated Household Budget Survey (KIHBS). Key areas of focus included maternal healthcare, early antenatal care, skilled delivery, exclusive breastfeeding, proper weaning practices, immunization, and the timely treatment of childhood illnesses. Using the Cox regression hazard model, the study revealed that twins faced a higher risk of mortality compared to single births, while firstborns were less likely to die before their fifth birthday; larger household sizes were associated with reduced child mortality, and children in female-headed households had a lower likelihood of dying, likely due to better adherence to proper health and nutritional practices. Maternal health conditions, the place of delivery, and assistance during childbirth significantly influenced survival, with government health facility deliveries yielding better outcomes than homebirths. This study emphasizes the importance of educating pregnant women and mothers on health risks and public health protocols during this critical period. Strengthening healthcare systems and promoting equitable access to essential services during the first 1000 days could improve child survival rates and enhance long-term economic productivity. Full article

To address the challenges of dynamic adversarial scenario modeling distortion, insufficient cross-institutional data privacy protection, and simplistic evaluation systems in collegiate basketball tactical education, this study proposes and validates an immersive instructional system integrating digital twin and federated learning technologies. The four-tier architecture (sensing layer, digital twin layer, federated layer, and interaction layer) synthesizes multimodal data (motion trajectories and physiological signals) with Multi-Agent Reinforcement Learning (MARL) to enable virtual–physical integrated tactical simulation and real-time error correction. Experimental results demonstrate that the experimental group achieved 35.2% higher tactical execution accuracy (TEA) (p < 0.01), 1.8 s faster decision making (p < 0.05), and 47% improved team coordination efficiency compared to the controls. The hierarchical federated learning framework (trajectory ε = 0.8; physiology ε = 0.3) maintained model precision loss at 2.4% while optimizing communication efficiency by 23%, ensuring privacy preservation. A novel three-dimensional “Skill–Creativity–Load” evaluation system revealed a 22% increase in unconventional tactical applications (p = 0.013) through the Tactical Creativity Index (TCI). By implementing lightweight federated architecture with dynamic cognitive offloading mechanisms, the system enables resource-constrained institutions to achieve 87% of the pedagogical effectiveness observed in elite programs, offering an innovative solution to reconcile educational equity with technological ethics. Future research should focus on long-term skill transfer, multimodal adaptive learning, and ethical framework development to advance intelligent sports education from efficiency-oriented paradigms to competency-based transformation. Full article

We investigated the molecular and clinical profile of five boys carrying the fragile X messenger ribonucleoprotein 1 (FMR1) mutation and who suffered from the effects of prenatal alcohol exposure. Fragile X syndrome (FXS) testing was performed using PCR and Southern Blot analysis, and fragile X messenger ribonucleoprotein protein (FMRP) expression levels were measured by Western blot analysis. Clinical evaluation included cognitive functions, adaptive skills, autism phenotype, and severity of behavior measures. Fetal Alcohol Spectrum Disorder (FASD) was also assessed. Five adopted male siblings were investigated, four of which (cases 1, 2, 3, and 4) were diagnosed with FXS, FASD, and ASD, and one, the fraternal triplet (case 5), was diagnosed with FASD and ASD and no FXS. The molecular profile of case 1 and 2 showed the presence of a hypermethylated full mutation (FM) and the resulting absence of FMRP. Cases 3 and 4 (identical twins) were FM-size mosaics (for the presence of an FM and a deleted allele), resulting in 16% and 50% FMRP expression levels, respectively. FMRP expression level was normal in case 5 (fraternal twin). Severe behavioral problems were observed in all cases, including aggression, tantrum, self-harming, anxiety, and defiant behavior, due to different mutations of the FMR1 gene, in addition to biological exposure, home environmental factors, and potentially to additional background gene effects. Full article

With the movement of the construction industry towards Construction 5.0, Digital Twin (DT) has emerged in recent years as a pivotal and comprehensive management tool for predictive strategies for infrastructure assets. However, its effective adoption and conceptual implementation remain limited in this domain. Current review works focused on applications and potentials of DT in general infrastructures. This review focuses on interpreting DT’s conceptual foundation in the flexible pavement asset context, including core components, considerations, and methodologies. Existing pavement DT implementations are evaluated to uncover their strengths, limitations, and potential for improvement. Based on a systematic review, this study proposes a comprehensive cognitive DT framework for pavement management. It explores the extent of enhanced decision-making and a large-scale collaborative DT environment. This study also identifies current and emerging challenges and enablers, as well as highlights future research directions to advance DT implementation and support its alignment with the transformative goals of Construction 5.0. Full article

In light of the remarkable success of large language models (LLMs) in natural language understanding and generation, a trend of applying LLMs to professional domains with specialized requirements stimulates interest across various fields. It is desirable to further understand the level of intelligence that can be achieved by LLMs in solving domain-specific problems, as well as the resources that need to be invested accordingly. This paper studies the problem of generating high-quality test questions with specified knowledge points and target cognitive levels in AI-assisted teaching and learning. Our study shows that LLMs, even those as immense as GPT-4 or Bard, can hardly fulfill the design objectives, lacking clear focus on cognitive levels pertaining to specific knowledge points. In this paper, we explore the opportunity of enhancing the capability of LLMs through system design, instead of training models with substantial domain-specific data, consuming mass computing and memory resources. We propose a novel design scheme that orchestrates a dual-LLM engine, consisting of a question generation model and a cognitive-level evaluation model, built with fine-tuned, lightweight baseline models and prompting technology to generate high-quality test questions. The experimental results show that the proposed design framework, TwinStar, outperforms the state-of-the-art LLMs for effective test question generation in terms of cognitive-level adherence and knowledge relevance. TwinStar implemented with ChatGLM2-6B improves the cognitive-level adherence by almost 50% compared to Bard and 21% compared to GPT-4.0. The overall improvement in the quality of test questions generated by TwinStar reaches 12.0% compared to Bard and 2% compared with GPT-4.0 while our TwinStar implementation consumes only negligible memory space compared with that of GPT-4.0. An implementation of TwinStar using LLaMA2-13B shows a similar trend of improvement. Full article

In the ever-changing world of modern manufacturing, maintaining product quality is of great importance, yet extremely difficult due to complexities and the dynamic production paradigm. Currently, quality is rather reactively measured through periodic inspections and manual assessments. Traditional quality management systems (QMS), through these reactive measures, are often inefficient because of their higher operational cost and delayed defect detection and mitigation. The paper introduces a novel cognitive twin (CT) framework, which is the next evolved version of digital twin (DT). It is designed to advance the current quality management in flexible manufacturing systems (FMSs) through real-time, data-driven, and predictive optimization. This proposed framework uses four data types, namely feedstock quality (Q_f), machine degradation (Q_m), product processing quality (Q_p), and quality inspection (Q_i). By utilizing the power of machine learning algorithms, the cognitive twin constantly monitors and then analyzes real-time data. The cognitive twin optimizes the above quality components. This enables a very proactive decision making through an augmented reality (AR) interface by providing real-time visual insights and alerts to the operators. Thorough experimentation was conducted on the aforementioned FMS. Through the experiments, it was revealed that the proposed cognitive twin outperforms conventional QMSs by a great margin. The cognitive twin achieved a 2% improvement in the total quality scores. A 60% decrease in defects per unit (DPU) is observed as well as a sharp 40% decrease in scrap rate. Furthermore, the overall equipment efficiency (OEE) increased to 93–96%. The overall equipment efficiency increased by 11.8%, on average, from 82% to 93%, and the scrap rate decreased by 33.3% from 60% to 40%. The excellent results showcase the effectiveness of cognitive twin quality management via minimum wastage, continuous quality improvement, and enhancement in operational efficiency in the paradigm of smart manufacturing. This research study contributes to the field of industry 4.0 by providing a comprehensive, scalable, and adaptive quality management solution, thus leading the way for further advancements in intelligent manufacturing systems. Full article

This paper focuses on the application of human–computer interaction technology in construction project safety management. Through bibliometric methods, we carried out an in-depth analysis of 286 relevant papers from Web of Science and Google Scholar from 2000 to 2024. The research results indicate that human–computer interaction technology has achieved remarkable development in four aspects: intelligent monitoring systems, risk assessment and management, ergonomics and cognitive psychology, as well as computer simulation and virtual reality. Meanwhile, this research has given rise to a series of new research topics, such as the safety operation decision-making method for intelligent construction machinery, the application of human action behavior recognition technology, and the application of Internet of Things technology in the safety control of smart construction sites. Additionally, future research modules have been identified, including personalized safety training, digital twin technology, and multimodal data analysis. This study not only summarizes the existing research achievements but also puts forward targeted suggestions for future development trends in the field of construction safety management from a practical perspective, aiming to promote the in-depth application and development of human–computer interaction technology in construction safety management. Full article

Predictive maintenance of built assets often relies on scheduled routine practices that are disconnected from real-time stress assessment, degradation and defects. However, while Digital Twin (DT) technology within building and urban studies is maturing rapidly, its use in predictive maintenance is limited. Traditional preventive and reactive maintenance strategies that are more prevalent in facility management are not intuitive, not resource efficient, cannot prevent failure and either underserve the asset or are surplus to requirements. City Information Modeling (CIM) refers to a federation of BIM models in accordance with real-world geospatial references, and it can be deployed as an Urban Digital Twin (UDT) at city level, like BIM’s deployment at building level. This study presents a systematic review of 105 Scopus-indexed papers to establish current trends, gaps and opportunities for a cognitive predictive maintenance framework in the architecture, engineering, construction and operations (AECO) industry. A UDT framework consisting of the CIM of a section of the University of Florida campus is proposed to bridge the knowledge gap highlighted in the systematic review. The framework illustrates the potential for CNN-IoT integration to improve predictive maintenance through advance notifications. It also eliminates the use of centralized information archiving. Full article