Search Results (1,219)

Commercial operations across trading, import/export, logistics, and technology distribution are being reshaped by the convergence of artificial intelligence (AI), machine learning, multi-agent robotics, and Extended Reality (XR). Small and Medium-sized Enterprises (SMEs) feel this shift acutely: they face the same pressures as their larger competitors; labor shortages, high-SKU inventories that resist tidy categorization, narrow margins, and customer expectations set by Amazon-grade fulfilment, but rarely command the capital or the structured warehouse environments that make industrial automation straightforward. Existing frameworks for AI-driven automation and digital twins have been developed primarily for large-scale industrial settings and do not account for the capital, infrastructure, and organizational constraints specific to SMEs, leaving a gap in SME-scoped integration models. This research addresses that gap by asking how AI-driven robotics and immersive technologies can be integrated to optimize commercial workflows in SMEs operating in dynamic logistics and trading environments. The proposed framework is grounded in Sociotechnical Systems Theory, which treats technology and organizational workflows as jointly designed and mutually adapting, and follows a Design Science orientation in which the architecture itself is constructed as an evaluable artifact rather than a purely descriptive model. Methodologically, the study conducts a narrative synthesis of literature on embodied AI, computer vision, digital twins, VR training, and AR-assisted operations, combined with workflow analysis to identify where SMEs lose the most time and money. These are translated into a four-layer system architecture (perception, cognition, execution, integration) deployed through a four-phase implementation model: needs assessment, digital-twin and VR pre-training, controlled hardware pilot, and AR-supported scaling. The contribution of the study is twofold: conceptually, it brings together several technologies that are often discussed separately in the literature, while focusing specifically on the needs and constraints of SMEs while practically, it proposes a phased roadmap that can help SMEs adopt these technologies gradually, reducing both financial and operational risks. The approach also emphasizes human–robot collaboration rather than replacing human workers. The study does not include experimental validation, it presents a conceptual architecture and implementation roadmap consistent with a Design Science artifact-construction stage that can serve as a basis for empirical testing in real commercial environments. Full article

Genetic neurodevelopmental disorders (NDDs) encompass a heterogeneous group of conditions characterized by impaired cognitive, behavioral, and neurological development resulting from pathogenic variants affecting brain development and synaptic function. Advances in molecular genetics and next-generation sequencing have significantly expanded the understanding of the genetic architecture underlying disorders such as Rett syndrome (RTT), Fragile X syndrome (FXS), Angelman syndrome (AS), and autism spectrum disorders. Beyond these classical neurodevelopmental disorders, spinal muscular atrophy (SMA) is included as a paradigmatic example of successful RNA-based therapeutic translation. Concurrently, RNA-based therapeutics have emerged as promising precision medicine strategies capable of modulating gene expression at the transcriptional and post-transcriptional levels. These approaches include antisense oligonucleotides (ASOs), small interfering RNAs (siRNAs), messenger RNA (mRNA) therapies, RNA editing technologies, and splice-modulating agents. Recent clinical successes, particularly in spinal muscular atrophy, have demonstrated the transformative potential of RNA therapeutics in neurological disease. However, substantial challenges remain, including BBB penetration, long-term safety, immune activation, and genotype-specific variability in therapeutic response. This review summarizes current advances in RNA-based therapeutics for genetic NDDs, highlighting molecular mechanisms, disease-specific therapeutic strategies, translational progress, delivery challenges, and future directions. Overall, continued progress will depend on the integration of disease biology, rational RNA therapeutic design, and effective CNS-targeted delivery, supporting the broader implementation of precision RNA medicine for genetic neurodevelopmental disorders. Full article

Green consumption is a crucial pathway for promoting ecological environmental improvement and fostering sustainable economic and social development. Although policies have been continuously implemented and residents’ environmental awareness is gradually increasing, there remains a significant gap in the transformation from cognition to behavior. Promoting green consumption is a systematic project that requires deep coordination among three key actors: the government, enterprises, and residents. However, existing studies have mostly focused on interactions between the government and enterprises, paying insufficient attention to the long-term strategic interactions among all three parties when residents are incorporated into the game system. To address this gap, this study develops a tripartite evolutionary game model that includes the government, enterprises, and residents to analyze the intrinsic driving forces behind the development of green consumption, thereby extending the research perspective to multi-agent dynamic strategic interaction. Through mathematical derivation of the replicator dynamic equations and the Jacobian matrix for each party, this study finds that the system has a unique evolutionarily stable equilibrium point, namely a tripartite synergistic state characterized by active government intervention, green production by enterprises, and green consumption by residents. Sensitivity analysis examines the impact of changes in key parameters on the evolutionary trajectory of the system, providing parameter-level evidence for differentiated policy design. Simulation results indicate that efforts should be directed toward building a collaborative governance system based on government guidance, enterprise responsibility, and resident participation, so as to promote the transformation of green consumption governance from single-dimensional management to pluralistic co-governance. Full article

Akerlof’s classical theory of the “Market for Lemons,” which conceptualizes adverse selection driven by information asymmetry, established the foundation of information economics. While the traditional model assumes static equilibria among a limited number of agents, analyzing its behavioral dynamics within large-scale, complex network environments remains a highly relevant task in computational social science. This study extends the classical lemon market model into a nonlinear dynamical system on adaptive networks. We mathematically elucidate macro-level social phase transitions—specifically structural exclusion such as scapegoating and collective ostracism—induced by computational cognitive limits, and evaluate optimal intervention strategies to mitigate these systemic failures. Multi-agent simulations utilizing large-scale tensor operations demonstrate that autonomous edge rewiring under incomplete information does not merely result in the uniform displacement of high-quality goods as predicted by static theory. Instead, the network self-organizes into an irreversible structural division: a core group of influential agents monopolizes high-quality information, while marginalized agents are isolated into a peripheral “lemon echo chamber” where only low-quality information circulates. To address this structural pathology under a resource constraint limiting intervention to 10% of the total agents, we evaluated two distinct approaches. The results indicate that providing informational support to influential hubs functions as a trap that exacerbates systemic inequality, superficially elevating the overall market evaluation but permanently fixing the exclusion gap. Conversely, the forced maintenance and protection of “weak ties” bridging disconnected clusters constitutes the mathematically optimal solution to dissolve fragmentation, effectively eliminating the price gap and facilitating social inclusion. Furthermore, this study demonstrates that the mechanism of social exclusion exhibits strong hysteresis effects. A distinct tipping point governs the progression toward a fragmented lemon echo chamber. Interventions implemented after crossing this critical threshold fail to restore the system to its baseline state despite identical resource expenditure, confirming the presence of an irreversible phase transition. These findings establish that the collapse dynamics outlined in the classical lemon market serve as a generalized model for explaining contemporary collective ostracism driven by information cascades. Consequently, the analysis highlights the necessity of early intervention prior to critical thresholds and the systemic preservation of structural bypasses rather than post-hoc remediation. Full article

Journaling can support mental health and self-reflection, but traditional journaling requires users to act simultaneously as reflector, facilitator, and recorder, which may increase cognitive load and potentially hinder sustained practice or contribute to rumination. This study proposes the Reflective Conversational Journal (RCJ), an AI-based system in which AI supports facilitation and recording while users focus on reflection. Grounded in cognitive load theory, Rogers’ person-centered counseling principles, and Socratic questioning, RCJ was designed around three principles: contextual connectivity, structured recording, and empathy and questioning. A prototype integrating an AI agent, a template engine, and a client application was developed as a context-aware human–AI interaction system. Four experts in journaling and psychological counseling evaluated RCJ over one week and completed a post-use evaluation comprising Likert-scale items and open-ended questions. The mean score across the nine design-validity and implementation-fidelity items was 4.67/5 (SD = 0.48). Experts perceived contextual linking as useful for recognizing behavioral patterns and automatic structuring as helpful for reducing recording burden. However, limited depth in questions and interaction fatigue from frequent questioning were identified as areas for improvement. The findings provide preliminary evidence of design validity and implementation fidelity rather than objective evidence of cognitive-load reduction or clinical effectiveness. RCJ operationalizes a complementary human–AI role structure in which AI supports facilitation and recording while the user retains the reflector role. Full article

Large Language Models (LLMs) are increasingly integrated into agentic workflows that require extended reasoning, persistent state management, coordinated tool use, and controlled execution. As this operational scope expands, a central question emerges: whether probabilistic generation alone can reliably support coherent behavior across interacting system components. This paper addresses that question through a structural diagnostic review of contemporary agentic systems. Starting from LLM-based tutoring as an analytically demanding entry point and extending toward structurally related agent architectures, the paper draws on a five-phase review of $N=145$ research records. The analysis is organized through the Agentic Structure Taxonomy (AST), which structures the literature across four dimensions: Cognition, Interaction, Orchestration, and Governance. The review identifies five recurrent empirical problem patterns and uses them as abductive diagnostic cues for formulating seven cross-dimensional transition gaps that capture recurrent discontinuities at the boundaries between reasoning, state, control, and execution. From these gaps, fourteen structural constraints are derived across three control domains: state isolation, control alignment, and execution governance. These constraints are interpreted not as prescriptive design mandates, but as analytically derived conditions associated with reducing error propagation across subsystem transitions. The paper argues that reliability in agentic systems is shaped not only by model performance or prompt design, but also by whether the boundaries linking probabilistic reasoning to persistent state, orchestration, and execution are governed by explicit structural conditions. Full article

Stroke remains one of the leading causes of death and long-term neurological disability worldwide, while currently available therapeutic strategies are limited by narrow treatment windows and incomplete neuroprotection. In this context, plant-derived polyphenols have attracted increasing attention as potential adjunctive agents because of their multimodal biological activity. This review focuses on resveratrol and curcumin, two of the most extensively investigated polyphenols, and evaluates their potential role in the prevention and treatment of ischaemic and haemorrhagic stroke. Evidence from in vitro studies, animal models, and early clinical trials indicates that both compounds may attenuate key mechanisms involved in stroke-related brain injury, including oxidative stress, neuroinflammation, mitochondrial dysfunction, apoptosis, autophagy dysregulation, blood–brain barrier disruption, and microglial activation. Emerging evidence further suggests that interactions with the gut microbiota and modulation of the gut–brain axis may contribute to their biological effects by influencing intestinal barrier integrity, microbial metabolite production, systemic inflammation, and vascular risk. Preclinical studies show that resveratrol and curcumin can reduce infarct volume, limit cerebral oedema, preserve neuronal viability, promote angiogenesis and neurogenesis, and improve neurological and cognitive outcomes. Their beneficial effects have been reported both when administered before stroke onset and after cerebral injury, suggesting potential relevance for both prevention and post-stroke therapy. However, interpretation of these findings requires consideration of the translational limitations of experimental stroke models, which do not fully reproduce the heterogeneity, comorbidities, age profile, and variable reperfusion patterns characteristic of human stroke. Although commonly used models such as middle cerebral artery occlusion provide important mechanistic and therapeutic insights, preclinical efficacy should therefore not be regarded as a direct predictor of clinical benefit. Resveratrol and curcumin may also complement established and emerging treatment strategies, including thrombolysis, endovascular interventions, antihypertensive therapy, and stem cell-based approaches. Nevertheless, their clinical translation remains limited by poor solubility, low bioavailability, rapid metabolism, and insufficient clinical evidence. Novel formulations, including nanoparticles, exosome-based delivery systems, and structurally modified analogues, may help overcome these barriers by improving brain targeting and therapeutic efficacy. Overall, resveratrol and curcumin represent promising but still investigational candidates for adjunctive stroke therapy, requiring further well-designed clinical trials to define their optimal dosing, timing, safety, and clinical value. Full article

The connected operating room captures, transmits, and displays data, but it does not learn. This perspective presents Chirurgie 4.0 (C40), a French-led initiative born within the Commission Innovation of the Académie nationale de chirurgie, which proposes not only a concept but a method for the safe adoption of artificial intelligence (AI) in surgery. At its core is the C40 Maturity Model of the Operating Room, a human-governed “surgical world model” describing the transition from the Smart OR to the Learning OR across six levels, from the conventional operating room to a sovereign, federated network of surgical world models. We situate surgical autonomy on an explicit six-level scale, show that autonomous devices are already an accepted clinical reality in fields such as interventional cardiology, ophthalmology, neuro- and orthopedic surgery, and argue that governance must be native rather than retrofitted, through a Cognitive Governance Layer resting on human oversight, explainability, auditability and agent governance. We describe the economic and sovereignty stakes specific to intelligent surgical technologies and set out the design of the 2026 C40 field survey, whose results will feed a Livre Blanc for public decision-makers. C40 offers five steps that can genuinely be climbed, and a method for climbing them safely, with the surgeon retaining final clinical authority at every step. Full article

The rapid integration of artificial intelligence (AI) into educational settings presents a profound challenge for teacher education. This study examines how pioneering teacher educators in Israeli colleges of education perceive their role in training student teachers for AI-integrated teaching. Using a qualitative, interpretive phenomenological approach, semi-structured interviews were conducted with 13 participants—seven pedagogical advisors and six lecturers—from seven teacher training institutions representing diverse educational streams. Data were analyzed using a hybrid approach combining human thematic analysis and AI-assisted dialogic analysis. Findings revealed that participants perceived themselves as agents of change responsible for modeling critical AI use, while raising epistemic concerns regarding cognitive atrophy and the erosion of expertise. A crisis of trust triggered by students’ uncritical submission of AI-generated outputs prompted a shift toward process-based and in-class assessment. Pre-service teachers used AI for differentiated lesson planning, visual aids, and simulations, with experiences ranging from heightened self-efficacy to frustration. Persistent challenges included rapid technological change, the absence of institutional policy, ethical and privacy concerns, cultural factors, and economic barriers. Drawing on post-digital theory and AI literacy frameworks, this study argues that effective preparation for the AI era requires a fundamental reimagining of teacher education—one that redefines literacy, pedagogy, and human agency. Full article

To address the challenges of complex multi-objective trade-offs, tightly coupled physical constraints in reservoir dam safety dispatching, and fulfill the significant cognitive gaps in human–machine interaction, a framework with four deep cognitive layers and a physical computation layer is proposed which integrates large language models (LLMs) with multi-agent collaboration. The framework stratifies cognitive intelligence into interface translation, strategic cognition, tactical reasoning, and operational understanding layers; performs computation in the physical computation layer; and achieves deep coupling among agents in different layers through the Blackboard information sharing mechanism. The physical computation layer consists of the gate-opening discharge, water-level storage capacity, runoff and inflow, downstream risk calculation agents and a Pareto multi-objective optimizer to realize non-dominated sorting of multi-dimensional objectives encompassing dam safety, ecological loss, downstream risk, and operational complexity. Illustrative case analysis indicates that this framework can effectively parse user requirements expressed in natural language, generate dispatching schemes conforming to physical constraints, achieve error control and quantify the downstream risk. This research provides a scalable framework for the implementation of intelligent reservoir dispatching and can enhance the intelligence of digital twins of river basins. Full article

Post-exertional malaise (PEM) and cognitive dysfunction (hereafter “cognitive dysfunction”, including the patient-reported syndrome often described as “brain fog”) are among the most disabling features of Long COVID; yet, approved disease-modifying treatments remain lacking. Emerging evidence implicates interacting disturbances in mitochondrial bioenergetics, redox regulation and neurovascular inflammation, although much of the supporting evidence remains indirect and derives from acute COVID-19, myalgic encephalomyelitis/chronic fatigue syndrome (ME/CFS), primary mitochondrial disease, inflammatory biology and mechanistic pharmacology rather than from direct Long COVID intervention trials. This hypothesis-generating narrative review develops a mechanism-based translational framework: that a pathway-targeted nutraceutical programme may modulate selected elements of these three axes, subject to prior demonstration of formulation quality, pharmacokinetic feasibility, target engagement and safety. Candidate modules comprise coenzyme Q10 and alpha-lipoic acid for bioenergetic/redox support; selenium, sulforaphane and resveratrol for Nrf2–thioredoxin-related redox regulation; and Boswellia serrata, luteolin and eicosapentaenoic acid for putative prostaglandin/resolution-pathway modulation. Sonlicromanol provides a conceptual mechanistic precedent for combined redox and prostaglandin-directed pharmacology, but it is not considered pharmacologically equivalent to an eight-agent nutraceutical combination. We summarise the mechanistic rationale, distinguish direct from indirect evidence, define qualitative evidence-grading criteria, outline safety and interaction considerations, and propose a staged translational research programme. This framework is intended to generate falsifiable hypotheses for future Long COVID studies, not to imply established clinical efficacy. Full article

Background: Chronic obstructive pulmonary disease (COPD) disproportionately affects older adults. While targeted biologic therapies have shown efficacy in type 2 eosinophilic endotypes, elderly patients remain underrepresented in pivotal clinical trials. This narrative review examines the pathophysiological rationale, efficacy data, and unique geriatric challenges of biologic therapies in older adults. Methods: A literature search was conducted in PubMed and Scopus for phase II and III randomized controlled trials (RCTs) and observational studies evaluating biologic agents (anti-IL-5, anti-IL-4/IL-13, and anti-alarmins) in COPD, focusing on populations aged ≥ 65 years, comorbidities, and frailty. Discussion: Phase III data for dupilumab and mepolizumab indicate significant reductions in annualized exacerbations in patients with blood eosinophils ≥ 300 cells/μL. However, the mean age in the landmark trials (65 years) is a decade lower than the real-world average. Age-related immune remodeling—specifically immunosenescence and inflammaging—coexists with disease-specific pathways, potentially altering therapeutic responses. Furthermore, clinical trials systematically exclude older individuals with severe multimorbidity, physical frailty, cognitive impairment, and malnutrition. Conclusions: Biologics provide a precise, mechanism-based approach, yet evidence in the oldest-old remains limited. Future management should utilize the treatable traits framework, shifting selection criteria from chronological age to biological age metrics, including frailty status and functional reserve. Full article

Methamphetamine (METH) is a known proinflammatory agent; however, the impacts of inflammasomes on its neurotoxic effects are not fully understood. In the present study, we assessed the impact of prolonged METH administration on the hippocampal inflammasome profile in male and female mice and determined alterations of the inflammasome profile in response to METH. In addition to inflammasome activation, METH induced both systemic and hippocampal-specific inflammatory responses, leading to cognitive impairment, reduced hippocampal cell proliferation, and altered proteomic profiles. Importantly, the responses to METH exposure exhibited important sexual dimorphism. Treatment with inflammasome inhibitor MCC950 attenuated METH-induced inflammatory events; however, we also observed several off-target effects of this inhibitor affecting mouse anxiety-like behavior and cognitive function. Overall, our results indicate the preventive potential of MCC950 in METH-related neurotoxicity, while underscoring its limitations due to distinct sex-dependent differences in response to both METH and MCC950 and highlighting significant sexual dimorphism. Full article

The growing capabilities of artificial intelligence (AI) have not translated straightforwardly into organisational value. A persistent disconnect—the “last-mile problem”—arises from structural gaps between idealised AI tasks and real-world organisational contexts. Synthesising insights from organisational theory, cognitive science, and computer science, we have developed a five-dimensional diagnostic framework that maps the challenges of human-AI collaboration across Integration, Representation, Scale, Temporality, and Adequacy gaps. These gaps illuminate how socio-technical complexity, contextualised problem representations, interdependencies among agents, dynamic environments, and limitations in current AI reasoning collectively constrain full automation and demand human judgement. By reviewing the historical evolution of AI—from symbolic systems to machine learning, generative models, and emerging agentic approaches—we show that augmentation remains the dominant and most viable mode of use in complex environments. An illustrative system-dynamics example demonstrates how improvements in algorithmic performance do not automatically yield proportional system-level gains. Overall, our framework provides researchers with a conceptual lens and practitioners with a diagnostic tool for assessing complementarities and informing the design of human-AI collaborations. The framework is offered as a conceptual synthesis and diagnostic instrument rather than an empirically validated model. Full article

Permanent lunar habitation will require robotic systems that can maintain infrastructure, recover from local failures, and acquire new operational capabilities under limited Earth supervision. Existing planetary robots are largely fixed-function specialists, while end-to-end foundation-model policies remain difficult to validate and extend for safety-critical surface operations. We present SELENE (Self-Evolving Lunar Embodied ageNt Ecosystem), an architectural proposal for contract-governed lunar robotic autonomy centered on a shared Atomic Action Library $\mathbb{A}$. The key abstraction is the Atomic Action Contract: a typed skill interface that specifies parameters, preconditions, goal predicates, execution bindings, safety envelopes, runtime reports, and validation metadata. Through this contract, a VLM-driven Cognitive Agent plans over executable skills, a multi-modal Execution Agent realizes them through optimization-based controllers, Vision–Language–Action (VLA) policies, Vision–Language–Navigation (VLN) policies, or reinforcement-learned policies, and an offline Evolutionary Agentic Framework synthesizes and registers new candidate contracts without modifying the planner or the execution interface. This paper presents an architecture-level validation of that contract mechanism. We instantiate SELENE across two heterogeneous pathways on LunarBot and its simulation counterpart, with optimization-based control supported as a third execution modality. A pre-trained VLA policy adapted from 100 teleoperated demonstrations achieves 29/30 task success (96.7 percent) in in-domain trials on the physical LunarBot. A curriculum–RL policy instantiates the traversal pathway in simulated lunar-gravity terrain. Together, these results show that the Atomic Action Contract can serve as a common registration and dispatch interface across heterogeneous control modalities. The same contract layer also defines the path toward runtime gap-triggered self-evolution, mission-grade admission, and lunar-environment validation in subsequent system-level studies. Full article