Search Results (1,514)

Digital twins are becoming vital decision-making infrastructures across critical infrastructure sectors such as smart city urban services, transportation, energy, and healthcare. As digital twins become autonomous and gain real-time intervention capabilities, their governance becomes increasingly essential. Yet existing governance mechanisms remain largely procedural: they emphasize compliance without operationalizing the cognitive practices required to question assumptions, detect algorithmic harms, or support legitimate multi-actor deliberation. Drawing on a systematic scoping review, this study synthesizes the literature on digital twin autonomy, algorithmic risks, epistemic foundations, and governance mechanisms. The review reveals a fundamental gap: current governance mechanisms lack institutionalized cognitive capacities for continuous validation, proactive detection of emerging harms, and structured multi-stakeholder deliberation. This gap is corroborated by a limited but growing body of empirical studies on governance in deployed DT settings. To address this gap, the paper proposes the skeptical intelligence framework, developed through design science research. The framework integrates three cognitive functions: validation, detection, and deliberation supported by operational principles, governance artifacts, and distributed accountability roles. The framework advances digital twin governance beyond compliance toward a model rooted in critical epistemology, reflexivity, transparency, and democratic legitimacy. Consistent with design science research, the framework is delivered and evaluated at design time; empirical implementation and outcome evaluation are planned across multi-actor digital twin infrastructure contexts, including smart city governance, port logistics, and energy networks, where DT-mediated decisions redistribute opportunities and risks across heterogeneous stakeholders. Empirical validation in an operational setting is planned as the next phase of this research. Full article

Background: Combat sports require athletes to repeatedly perform explosive actions under conditions of fatigue, demanding high levels of neuromuscular coordination, reactive agility, and intermittent effort capacity. This study examined changes in biomechanical and neuromuscular performance over a 15-week period during which competitive Sambo athletes completed a CrossFit-based functional training program alongside their regular sport-specific training. Methods: Forty male Sambo athletes (22.4 ± 2.6 years) completed a longitudinal repeated-measures intervention consisting of three weekly CrossFit-based sessions integrated into regular sport-specific training. The program was periodized across four progressive mesocycles emphasizing functional strength, explosive power, reactive agility, and fatigue resistance. Pre- and post-intervention assessments included upper- and lower-limb reaction speed, vertical jump height, repeated jump endurance, reactive agility, grip endurance, body-weight squat performance, Romanian deadlift repetitions, and the Special Judo Fitness Test (SJFT). Statistical analyses included paired-samples t-tests or Wilcoxon signed-rank tests with Benjamini–Hochberg false discovery rate correction (α = 0.05). Results: Significant post-intervention improvements were observed in most performance variables. Upper- and lower-limb reaction performance improved by 22.9% and 9.7%, respectively (p < 0.001), while vertical jump height increased by 11.4% and repeated jump endurance by 16.6% (p < 0.001). Reactive agility time decreased by 6.6% (p < 0.001), indicating faster cognitive-motor responsiveness. Functional strength improved substantially, with increases of 43.7% in body-weight squat performance and 71.9% in Romanian deadlift repetitions (p ≤ 0.001). Grip endurance improved by 6.3% (p = 0.007), whereas maximal handgrip strength showed no significant changes. The SJFT index decreased by 5.0% (p < 0.001), reflecting enhanced intermittent effort efficiency and fatigue tolerance. Large effect sizes were identified for upper-limb reaction speed (d = 1.42), explosive endurance (d = 1.46), and reactive agility (d = 0.96). Conclusions: Over the 15-week study period, participation in a CrossFit-based functional training program integrated into regular Sambo practice was associated with improvements in several biomechanical and neuromuscular performance outcomes, including reaction performance, explosive power, agility, muscular endurance, and sport-specific work capacity. However, because the study did not include a control group, these changes cannot be attributed exclusively to the CrossFit-based intervention. Controlled studies are required to distinguish intervention-specific effects from seasonal training adaptations, continued sport-specific practice, and test familiarization. Full article

Working memory (WM) capacity predicts a wide range of cognitive abilities, yet little is known about how individuals with different WM capacities regulate memory use during natural tasks in which memory use is voluntarily determined. To address this question, we conducted two experiments (N = 120) in which participants completed a copying task and a change-detection task. Experiment 1 manipulated sampling cost to increase reliance on internal memory storage, whereas Experiment 2 manipulated time pressure to increase demands for maintaining consistent memory use. Increasing sampling cost led participants to use more memory but did not make individual differences in WM capacity more apparent in mean memory usage. In contrast, time pressure revealed a reliable association between WM capacity and the consistency of memory usage across sampling episodes. Higher-capacity individuals exhibited more consistent memory use. These findings suggest that WM capacity shapes voluntary memory use not simply by increasing how much information people remember, but by influencing how consistently they regulate memory use under different task demands. Full article

Sleep supports cognition across the lifespan, but its significance may differ developmentally: in adults, it largely maintains and restores functioning within mature neural systems, whereas in children and adolescents it may also support the formation of cognitive, emotional, and self-regulatory capacities. This systematic review and evidence and gap map, prospectively registered in PROSPERO and conducted according to PRISMA 2020, searched PubMed, PsycINFO, Scopus, and Web of Science for studies of sleep duration, quality/fragmentation, timing/regularity, and daytime sleepiness in relation to executive function/self-regulation, attention/vigilance, emotion/behavior regulation, and academic functioning among typically developing children and adolescents. Twenty-five studies contributed 46 study–outcome links. Across nearly all populated evidence cells, more adequate sleep profiles were associated with better developmental outcomes. The strongest and most experimentally supported evidence concerned sleep duration in relation to executive function, emotion/behavior regulation, and academic functioning; evidence for sleep timing/regularity and daytime sleepiness was weaker and predominantly cross-sectional. Unlike adults, for whom sleep primarily restores a mature system, children may lose not only today’s functioning but tomorrow’s developmental trajectory when sleep is chronically curtailed. Sleep duration currently provides the strongest basis for intervention, whereas the long-term developmental effects of sleep quality, timing, regularity, and sleepiness require further longitudinal and experimental investigation during this formative period. Full article

While artificial intelligence (AI) is expected to drive paradigm-shifting transformations, many initiatives result in merely incremental optimization. Anchored in strategic human capital theory, this study shifts the analytical focus from the scale of elite technical talent, conceptualized as AI stars, to the configuration of their knowledge structures to unpack this paradox. Using a dataset of 1270 medical AI patents from corporate R&D teams, we employed high-dimensional fixed-effects models to examine these dynamics. The results reveal that while the knowledge diversity of AI stars acts as a potent engine for breakthrough innovation, this generative capacity is attenuated by excessive domain familiarity. Specifically, direct domain familiarity (derived from internal experience) and indirect domain familiarity (absorbed through external collaborative networks) negatively moderate this relationship, a dynamic theorized to operate through internal cognitive entrenchment and external relational conformity, respectively. Extending the efficiency-driven consensus regarding bilingual expertise, these findings demonstrate that excessive domain embeddedness transforms from an informational bridge into a restrictive constraint during paradigm-shifting innovations, particularly within highly institutionalized environments. Full article

Background: Parkinson’s disease (PD) exhibits remarkable clinical heterogeneity, with substantial variability in motor progression, cognitive decline, and the development of non-motor manifestations. Although the propagation of pathological alpha-synuclein is considered a central mechanism of PD pathogenesis, it does not fully explain why specific neuronal populations demonstrate differential susceptibility to degeneration. Emerging evidence suggests that selective neuronal vulnerability is determined by the interaction of multiple biological domains, including calcium homeostasis, mitochondrial bioenergetic capacity, lysosomal function, axonal architecture, synaptic resilience, and neuroinflammatory responses. Methods: A structured critical narrative review was performed using PubMed/MEDLINE, Scopus, and Web of Science databases from inception to March 2025. Only full-text articles published in English and peer-reviewed journals were included. Evidence from human post-mortem studies, genetic analyses, biomarker investigations, experimental models, induced pluripotent stem cell studies, and longitudinal clinical cohorts were systematically synthesised to identify the principal mechanisms underlying selective neuronal vulnerability and their potential translational application. To capture evidence published after this electronic cut-off, a supplementary manual review of reference lists of key systematic reviews and landmark publications was conducted up to the date of manuscript submission; references with 2025 or 2026 publication dates entered through this supplementary process. The supplementary manual review was conducted as a targeted scan of reference lists of key systematic reviews and high-impact publications identified during the primary search and did not constitute an independent updated systematic search. Results: Five interconnected biological domains emerged as key determinants of neuronal susceptibility in PD: calcium-mediated metabolic stress associated with autonomous pacemaker activity, mitochondrial dysfunction and energetic failure, impaired lysosomal degradation and proteostasis (particularly involving the GBA1–glucocerebrosidase pathway), vulnerability related to extensive axonal and synaptic architecture, and neuroinflammatory mechanisms involving microglial and astrocytic activation. Among these domains, the lysosomal pathway currently provides the strongest translational link between molecular mechanisms and measurable clinical outcomes. Based on this integrated framework, we propose the Parkinson’s Vulnerability Index (PVI), a hypothesis-generating multidimensional model combining genetic, enzymatic, alpha-synuclein seeding, cognitive, olfactory, and neuroimaging biomarkers to facilitate biological stratification and improve the design of mechanism-targeted clinical trials. Conclusions: Selective neuronal vulnerability provides a complementary framework to alpha-synuclein propagation models for understanding the heterogeneity of PD. The proposed PVI is not intended as a diagnostic or prognostic clinical instrument but as a research tool requiring prospective validation. Future precision medicine approaches in PD may benefit from integrating vulnerability-related biomarkers with existing biological staging systems to identify patients most likely to benefit from targeted disease-modifying therapies. 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

Consumer food waste is a major challenge for food system sustainability and a governance problem that cannot be addressed by individual behavioral change alone. Previous reviews have examined the causes of food waste or evaluated specific intervention strategies, but less attention has been paid to how behavioral drivers, intervention strategies, and the roles of governance actors can be examined within an integrated analytical framework. Against this background, this review aims to synthesize the behavioral drivers of consumer food waste, classify the main types of behavioral interventions, and develop a multi-level governance perspective that connects these elements. Using a narrative review approach supported by a structured literature search, the review identifies three broad categories of behavioral drivers, namely cognitive biases, motivational drivers, and environmental and situational influences. It then classifies behavioral interventions into five types, namely information-based interventions, social norm interventions, choice architecture and environmental design interventions, incentive-based interventions, and education and capacity-building interventions. The review indicates that no single intervention type is likely to be effective across all settings. Instead, intervention outcomes are closely linked to the alignment among targeted behavioral drivers, intervention strategies, consumption contexts, and governance support. From a multi-level governance perspective, this review views consumers as the actors engaged in everyday food-related practices, businesses as the designers of the consumption environments and operational routines, and policymakers as the providers of institutional, informational, and economic support. By bringing behavioral drivers, intervention strategies, and governance roles into a shared analytical framework, this review moves beyond fragmented, individual-centered approaches to reducing consumer food waste. The framework suggests that food waste reduction efforts may be more effectively implemented and maintained when consumer practices, business environments, and policy conditions are mutually supportive. Full article

Intimate partner violence-related brain injury is the most recent condition in a 150-year arc in which biological brain injury has been misattributed to psychological or moral causes before formal clinical recognition emerged. Earlier conditions in this pattern were each recognized only after decades of delay. In each prior case, that delay was constrained by limits in available diagnostic technology. Intimate partner violence-related brain injury is the first condition in which diagnostic technology, including computed tomography, magnetic resonance imaging, diffusion tensor imaging, and neurocognitive assessment, has been continuously available throughout the period of non-recognition. This review identifies three structural barriers that sustain this recognition gap: a diagnostic barrier that leaves the injury without formal criteria, an administrative coding barrier that leaves it absent from ICD architecture, and a population surveillance barrier that leaves it indistinguishable from broader assault categories. Each barrier reinforces the others, limiting visibility, resource allocation, and access to care. Across these conditions, this review argues that deferred recognition reflected an institutional dynamic that shaped which injured populations became clinically legible. Recent neuroimaging and cognitive studies make the biological imperative explicit. A cognitive entrapment framework reframes the reduced capacity to engage the cognitive and material resources leaving requires as injury-driven rather than as ambivalence or motivational deficit. The framework offers a mechanistic account of how brain injury may disrupt the multistep planning that leaving demands. Intimate partner violence-related brain injury is not only underdiagnosed but structurally underserved; correcting the mechanisms of recognition failure is necessary for access to treatment and rehabilitation. Full article

Background: Studies indicate that depression and cognitive functions are important issues of mental health, a serious health issue worth evaluating with possible alternative non-medical treatment options in MS patients. Aim: The aim of this study was to investigate the effect of practice on fatigue, respiratory closure, depression and cognition in patients diagnosed with MS and whether the benefit of Hatha Yoga training is affected by personality structure. Methods: The study included 25 participants between the ages of 18 and 70 who were diagnosed with MS, with an EDSS of 6 and below and who did not show symptoms of depression and did not use antidepressants. Any previous cerebrovascular disease, hypertension (HTN), diabetes mellitus (DM), thyroid dysfunction, or vitamin B12 deficiency was determined as an exclusion criterion. In the study, patients received Hatha Yoga practice 2 days a week for 8 weeks. The MOCA (Montreal Cognitive Assessment) and Beck Depression Scale, personality analysis test, pulmonary function test, and Fatigue Impact Scale were applied to all patients fulfilling the inclusion criteria at the beginning and after the completion of Yoga practice. Results: According to the results obtained, a statistically significant decrease in Beck Depression Inventory scores (p = 0.020) and a significant increase in MOCA Cognitive Test scores (p < 0.001) were detected following the yoga intervention. Although an increase was observed in respiratory capacity (FVC), this change did not reach statistical significance (p = 0.082). These findings demonstrate that yoga practice significantly improves depression levels and cognitive functions. The relationship between the benefits derived from yoga training and personality traits was evaluated using the participants’ Temperament and Character Inventory (TCI) profiles. Following the yoga intervention, a statistically significant increase was observed in novelty seeking (p < 0.001) and persistence (p < 0.001). Conversely, participants exhibited a significant decrease in harm avoidance post-yoga (p < 0.001). However, no statistically significant changes were found in reward dependence (p = 0.766) or cooperation (p = 0.181) scores after the intervention. Full article

Despite substantial progress in biomarker discovery and multi-omics profiling, several features of Alzheimer’s disease (AD), including prolonged compensated states, heterogeneous clinical trajectories, and marked stage-dependent therapeutic responses, remain difficult to integrate into a single mechanistic framework. In this review, we propose an integrative and testable conceptual framework that reframes AD as a single, progressive multi-layer network disorder whose dynamics arise from hierarchical constraint propagation and progressive loss of cross-scale coordination. Integrating evidence from human genetics, epigenomics, transcriptomics, proteomics, metabolomics, spatial biology, connectomics, and longitudinal biomarker studies, we examine how molecular, cellular, and circuit-level processes interact over time to shape disease progression. Within this framework, different omics measurements are interpreted as complementary representations of disease-related changes, rather than as independent molecular signatures. Disease progression reflects the gradual convergence of immune, metabolic, proteostatic, cytoskeletal, and synaptic stress, with overt cognitive impairment emerging when compensatory capacity is exceeded, producing threshold-like network destabilization. By explicitly linking biological scale, temporal hierarchy, and network structure, this synthesis extends prior network-medicine, connectomic, and multi-omics approaches into a testable framework for state-aware stratification, integrative analysis, and stage-appropriate therapeutic investigation in AD. Full article

Frailty syndrome and Alzheimer’s disease are prevalent conditions in the elderly that are associated with aging, decreased quality of life, and a significant healthcare burden. Evidence for a relationship between physical frailty and neurodegenerative decline is accumulating. This study analyzed two independent datasets, a frailty dataset based on gait and mobility parameters and an AD dataset with clinical, functional and lifestyle variables, in order to evaluate and compare their classification performance using machine learning. Features were optimized using dimensionality reduction techniques to keep predictors of clinical significance and hyperparameter optimized Random Forest models were built to develop the best model. Evaluation was performed with Accuracy, F1-score, Matthews Correlation Coefficient and Area Under the Curve. The results showed that the models constructed on the whole AD dataset achieved maximum predictive power with an accuracy of 0.946, which was slightly increased to an accuracy of 0.948 after the selection of significant features. Diagnostic models based on frailty were able to demonstrate an ACC predictive capacity of 0.6418, and in terms of feature selection, improvements appeared in all indicators. Regarding the features derived from Alzheimer’s disease associated with geriatric frailty, they managed to surpass the ACC frailty features of 0.741 alone, suggesting some intercalation mechanisms between neurodegeneration and physical vulnerability. These findings show that machine learning algorithms accompanied by feature selection improve clinical discrimination and prediction of frailty and neurodegenerative disorders, which offers a promising aspect for geriatric assessment. The frailty models analyzed demonstrated an ACC predictive capacity of 0.6418, even though feature selection improved all indicators. Alzheimer’s disease-derived features associated with frailty outperformed features in the frailty dataset with an ACC of 0.741, suggesting the mechanism of overlap between neurodegeneration and physical vulnerability. These results support the theory of a motor-cognitive aging continuum, indicating that algorithmic machine learning techniques coupled with feature selection mainly provide computational validation for the biological intersection of neurodegeneration and physical frailty, rather than forming an independent predictive clinical model. Using these algorithms the study highlights shared pathophysiological mechanisms, providing a significant insight into systemic geriatric deterioration. Full article

Ocean deoxygenation is intensifying globally, and hypoxia is increasingly documented on tropical coral reefs; yet, its consequences for the physiology and social functioning of reef fishes remain poorly understood. Here, we experimentally exposed the bluestreak cleaner wrasse (Labroides dimidiatus) to two repeated nocturnal severe hypoxia events (<2 mg O₂ L⁻¹), reflecting the upper range of currently observed reef conditions. We quantified aerobic performance using intermittent flow respirometry across three phases: baseline (n = 24, prior to exposure), post-hypoxia (n = 24, the morning following the first hypoxia event), and post-recovery (n = 22, three days later). Control fish were followed over the same time frame and tested with the same protocol to account for handling or timing effects. Cooperative performance (n = 22) was assessed separately using an ecologically relevant Feeding Against Preference (FAP) task conducted after the second hypoxic night, allowing us to test whether inhibitory control is sensitive to acute oxygen limitation. Brain morphology (n = 12) was subsequently quantified in a subset of individuals to assess potential changes in relative brain size and regional investment. Repeated hypoxia was associated with persistent reductions in aerobic performance. Maximum and resting metabolic rates declined across phases in hypoxia-exposed fish, resulting in reduced absolute and factorial aerobic scope relative to controls. Hypoxia also altered performance in the FAP task, with exposed fish feeding more often against their food preference, a result consistent with enhanced inhibitory control. In contrast, hypoxia exposure was associated with a significant reduction in relative telencephalon size, while other brain regions and overall brain size were unaffected. These findings suggest that severe nocturnal hypoxic events can impose lasting constraints on aerobic capacity and selectively alter brain structure, while also modifying self-control during cooperative interactions. Together, these results indicate that oxygen limitation can affect cognition both directly and through underlying physiological and neural mechanisms. As hypoxic events intensify under ongoing climate change, such constraints may have important implications for the stability of cleaner–client mutualisms, which could ultimately affect broader reef-level ecosystem stability. Full article

Krill oil (KO) is a marine-derived nutritional ingredient distinguished by its unique structural composition and increasing relevance in human nutrition, particularly in the context of healthy aging and preservation of intrinsic capacity. Approximately 30–65% of its fatty acids are esterified to phospholipids, a molecular configuration associated with enhanced bioavailability and efficient tissue incorporation of omega-3 polyunsaturated fatty acids, even at relatively low doses. Antarctic krill (Euphausia superba) originates from minimally impacted marine ecosystems, contributing to a favorable safety profile characterized by inherently low levels of environmental contaminants, including heavy metals and persistent organic pollutants. Beyond its structural and safety attributes, KO provides a complex nutrient matrix comprising approximately 25% total n-3 polyunsaturated fatty acids (PUFAs), including ~14% eicosapentaenoic acid (EPA) and ~6.5% docosahexaenoic acid (DHA), together with choline and astaxanthin. This compositional synergy may modulate inflammatory pathways, redox balance, membrane dynamics, and cellular signaling, thereby influencing physiological systems implicated in aging biology. This narrative review critically examines how these distinctive characteristics translate into clinically relevant outcomes supporting the multifunctional applications of KO in human nutrition. The analysis is structured within the framework of intrinsic capacity, encompassing locomotion, vitality, sensory, cognition and psychological dimensions, as a conceptual model for healthy aging. Considerations on bioavailability, tolerability, and sustainability are also discussed in the context of nutritional strategies promoting healthy longevity. Full article

Environmental regulation is widely regarded as an institutional driver of corporate green transformation, yet how it operates on firms and for whom remains unsettled. Treating the 2015 entry into force of China’s revised Environmental Protection Law as a quasi-natural experiment, this study estimates a difference-in-differences (DID) model on 38,910 firm-year observations covering 4447 Shanghai and Shenzhen A-share firms over 2010–2024. Corporate green transformation is measured along three dimensions—the length-normalized intensity of green-transformation language in annual reports, green patent output, and green total factor productivity—and combined into a composite index. The regulation raises green-transformation intensity by 0.156 (about 14.8% of the sample mean) and green total factor productivity by 0.0021; pre-reform event-study coefficients are jointly insignificant, no randomized placebo reaches the observed estimate, and the result survives propensity-score matching and province-by-year fixed effects. It is accompanied by falling greenwashing and rising disclosure specificity, indicating that the additional green language is not merely talk. Bootstrap mediation tests support transmission through executive green cognition and through green exploratory, but not exploitative, innovation. Contrary to the received view, the law did not loosen financing constraints; instead, pre-reform financial capacity bounds the response, and firms that were more constrained respond substantially less. Effects are larger for digitally capable, better-governed firms and in more marketized regions, and command-and-control instruments outperform market-based pilots over this window. Full article