Search Results (1,566)

Manufacturing sectors and ecosystems can improve their innovation performance through digital platforms, connected intelligence, and organisational settings that enable collaboration among experts and ecosystem members. The convergence of skills and capabilities distributed across humans, organisations, communities, and AI agents creates intelligent environments that can support ecosystemic and transformative innovation. To examine this hypothesis, we follow a three-stage methodology. First, we develop a modelling framework based on a vector autoregressive model, in which a weighted matrix representing directed binary couplings among human, collective, and machine intelligence drives the transition of a manufacturing ecosystem from a baseline innovation state to a more advanced one. Second, we present the SmartGreenEcos experiment, which develops an intelligent environment adapted to a specific manufacturing ecosystem. The experiment demonstrates the feasibility of the model’s abstract architecture by implementing digital platforms, e-services, and AI agents that facilitate inter-company collaboration, experimentation, and innovation. Third, we use simulations and analyse the eigenvalues and eigenvectors of the weighted matrix to examine the internal dynamics of intelligent environments and identify key thresholds and drivers of change. The results of this three-stage methodology provide insights into the design of intelligent environments and the interaction parameters through which connected intelligence can improve innovation performance. Full article

This paper proposes a mismatch-index-driven coordinated flexible-step terminal-free distributed model predictive control (DMPC) scheme with an adaptive prediction horizon for asynchronous multi-agent systems (MASs) subject to bounded disturbances. The proposed approach explicitly exploits the inherent symmetry of the undirected communication topology among the agents, which ensures reciprocal information exchange, balanced cooperative interactions, and facilitates the rigorous analysis of consensus under asynchrony. By extending the generalized discrete-time control Lyapunov function (g-dclf) framework to the perturbed setting, we introduce a robust g-dclf together with a robust average decrease constraint that explicitly accounts for the worst-case effect of disturbances. A coordinated self-triggering mechanism, built upon the cost prediction mismatch index and the flexible-step execution strategy, is developed to simultaneously determine the inter-execution times and the number of control steps to be applied in each iteration. In addition, an adaptive shrinking prediction horizon strategy is incorporated to further reduce the computational complexity of the local optimization control problems (OCPs) as the agents approach consensus. The resulting robust flexible-step terminal-free DMPC (RFSTDMPC) algorithm is fully distributed, handles asynchronous communication, and operates without any stability-related terminal constraint. Recursive feasibility of each local OCP and input-to-state stability (ISS) of the overall closed-loop MAS are rigorously established under the symmetric network structure. Simulation results on the consensus problem of three perturbed nonholonomic vehicles demonstrate the effectiveness of the proposed scheme in achieving practical full-state stabilization while significantly alleviating the online computational burden. Full article

This paper investigates distributed optimization-assisted output consensus for nonlinear multi-agent systems with mutually incommensurate Caputo orders, unavailable velocity-like states, bounded disturbances, measurement noise, and actuator saturation. A mixed-power exact penalty flow generates practical optimal references from local costs and intermittent neighbor broadcasts. The penalty gain and a smoothing bias bound are determined from a public interval, topology information, and certified local gradient data without prior knowledge of the aggregate optimizer. An autonomous decaying threshold provides event-triggered communication, an initial condition-independent fixed-time practical certificate for the integer-order optimizer, and exclusion of finite-time event accumulation. The physical layer is analyzed with established Caputo quadratic inequalities and agentwise Mittag–Leffler comparison. Fractional reference and command filters, a composite observer, and two-gain anti-saturation compensation form the output feedback controller, while the physical result is formulated as a finite-horizon regional verification certificate. Numerical studies include same-model and communication budget comparisons, a recent method-inspired optimizer benchmark, certificate tightening, and robustness tests for initialization, the fractional order, measurement noise, and the integration step size. Full article

Reinforcement learning techniques have been widely applied to multi-UAV cooperative path-planning tasks. However, existing multi-agent reinforcement learning methods are still affected by environmental non-stationarity, cooperation difficulties among agents, and low utilization efficiency of experience samples in complex obstacle environments. These issues often lead to slow convergence and unstable training performance. To address these problems, an Improved Experience Replay Multi-Agent Deep Deterministic Policy Gradient (IER-MADDPG) algorithm is proposed for multi-UAV cooperative path planning. First, a cooperative path-planning model is established under the Centralized Training Distributed Execution framework. Second, a dual-layer replay buffer structure consisting of a global replay buffer and a local replay buffer is designed to preserve both global cooperative information and individual experience. Third, a fusion experience sampling mechanism is introduced by combining prioritized experience replay and random uniform sampling to improve sample utilization efficiency and training stability. Finally, training experiments were conducted in environments with different obstacle configurations to evaluate the proposed method. Experimental results demonstrate that IER-MADDPG outperforms other comparison algorithms in terms of convergence speed, training stability, and path-planning performance. Full article

As deep neural networks continue to scale and enable emerging applications such as agentic AI systems, training increasingly relies on distributed paradigms across heterogeneous edge devices. However, this shift introduces significant security challenges, particularly model poisoning attacks, which are largely underexplored in model-parallel settings. To address these challenges, we propose a trusted and attack-resilient mechanism for distributed DNN training that supports both data and model parallelism. The mechanism leverages a blockchain-enabled infrastructure to ensure the tamper-resistant and auditable execution of security-critical operations. It introduces a Loss-aware Credit Evaluation mechanism to assess agent reliability based on group-level training dynamics and a Shuffling-based Isolation Mechanism to progressively cluster and isolate malicious agents across training epochs. In addition, Byzantine-tolerant aggregation (BTA) is employed to further mitigate adversarial influence during model aggregation. Extensive experiments demonstrate that the proposed mechanism achieves superior robustness and efficiency compared with state-of-the-art methods under diverse poisoning attack scenarios. Full article

Background: Lung adenocarcinoma (LUAD) remains a leading cause of cancer-related mortality worldwide, and effective biomarkers for prognosis and therapeutic guidance are still lacking. TRPM4, a sodium-related ion channel, has been implicated in tumor progression; however, its role in LUAD, particularly at the single-cell level, remains unclear. Methods: Single-cell RNA sequencing data were used to analyze the cellular distribution of TRPM4 in LUAD. TRPM4-related genes were identified through correlation analysis in the TCGA cohort, followed by construction of a prognostic model using Cox and LASSO regression analyses. The model was validated in an independent GEO dataset. Functional enrichment and drug sensitivity analyses were performed to explore the underlying mechanisms and therapeutic implications. Random forest analysis was applied to identify key genes, and in vitro experiments were conducted to validate their biological functions. Results: A TRPM4-related prognostic signature was established, demonstrating robust predictive performance in both training and validation cohorts. High-risk patients were characterized by activation of cell cycle and DNA replication pathways, showing differences in computationally predicted sensitivity to multiple chemotherapeutic and targeted agents. Furthermore, RNPEPL1 was identified as a key gene and experimentally validated to promote LUAD cell proliferation, migration, and invasion. Conclusions: The TRPM4-related signature and RNPEPL1 may provide novel insights for risk stratification and personalized therapeutic strategies. Full article

To reveal the chemical composition and concentration distribution, spatial distribution characteristics, source composition, and health impacts of volatile organic compounds (VOCs) in the two multi-industry industrial parks in Tianjin, 57 VOC species were determined by using SUMMA canister sampling with preconcentration gas chromatography/mass spectrometry. Based on these measurements, the Positive Matrix Factorization (PMF) model was used for source analysis to achieve quantitative identification and contribution analysis of different source factors. The health risks of VOC concentrations were analyzed based on the assessment framework recommended by the United States Environmental Protection Agency (USEPA). The results showed that the average concentrations of TVOCs in A-1 (Industrial Park and Cluster A, 5 m), A-2 (Industrial Park and Cluster A, 10 m), B-1 (Industrial Park and Cluster B, 5 m), and B-2 (Industrial Park and Cluster B, 10 m) were 59.72 ppbv, 45.35 ppbv, 32.44 ppbv, and 21.65 ppbv, respectively. TVOC concentrations were higher at A-1 and B-1 than at A-2 and B-2, and were generally higher at site A than at site B. The VOC components were mainly alkanes (53.5%–71.4%), followed by aromatic hydrocarbons (15.6%–36.2%), alkenes (4.6%–9.9%), and alkynes (3.1%–7.6%). The proportion of aromatic hydrocarbons in A was higher (22.13% and 36.22%), while alkanes dominated absolutely in B (71.4% and 66.9%). n-Hexane was the key species driving the spatial differences (a typical source of VOCs in solvent usage). The concentration in A-1 was 3.1 times that of A-2, and B-1 was 3.3 times that of B-2. It was mainly controlled by local solvent unorganized emissions. The differences in species between the two points at site A for toluene, xylene, etc., were relatively gentle, while at site B, the concentration of the same aromatic hydrocarbons at B-1 was significantly higher than that at B-2, presenting a clearer spatial characteristic, indicating differences in the spatial distribution of organic solvent-related industrial activities in different sites. The source analysis showed that A-1 was dominated by solvent usage (38.64%) and natural gas/LPG sources (36.96%); A-2 by vehicle exhaust (40.20%) and natural gas/LPG sources (36.15%); B-1 by cleaning-agent usage (38.81%) and fuel combustion (30.33%); and B-2 by plastic and rubber production (36.90%) and fuel combustion (31.84%). The health risk assessment showed that benzene, n-hexane, and xylene dominated the non-carcinogenic risks, but the overall non-carcinogenic (HI < 1) and carcinogenic (CR < 1 × 10⁻⁶) risks were both below the threshold. This study reveals inter-site differences in VOC concentrations, compositions, and source contributions across two mixed industrial parks, providing a basis for site-specific VOC control priorities, source-targeted monitoring strategies, and risk management in mixed industrial areas. Full article

Territorial ecological restoration involves a continuous chain of tasks, including degradation diagnosis, target identification, zoning-based governance, process monitoring, effectiveness assessment and adaptive management. The relevant knowledge is widely distributed across policy plans, monitoring reports, restoration cases and the academic literature, and is characterized by multi-source heterogeneity, cross-scale associations and dynamic change. Existing knowledge organization approaches mainly rely on textual synthesis, indicator systems or general-purpose knowledge graph construction tools, and therefore struggle to simultaneously handle cross-context implicit relations, domain-rule constraints, inconsistent entity expressions and evidence traceability in ecological restoration knowledge. To address these limitations, this paper proposes EcoRestore-KG, a multi-agent knowledge graph construction framework for territorial ecological restoration. The framework unifies heterogeneous inputs through controlled evidence representation and adaptive context segmentation, and organizes ontology-guided triple mining, cross-context relation inference, graph quality control, entity canonicalization, relation endpoint remapping and evidence binding into a progressive workflow for the automatic extraction, auditing and assembly of ecological restoration knowledge. Experimental results show that EcoRestore-KG outperforms general-purpose large language models and existing knowledge graph construction baselines in relation extraction, entity coverage and semantic-quality evaluation. It achieves relation precision, recall and F1 scores of 71.4% ± 0.3%, 69.5% ± 4.4% and 70.3% ± 2.3%, respectively, improving relation F1 by 9.9 percentage points over the strongest baseline. Its entity F1 reaches 79.8% ± 0.7%, and its LLM-S score reaches 8.48 ± 0.11. Single-module and combined ablation experiments further demonstrate that evidence representation, context segmentation, cross-context relation inference, relation quality auditing and entity canonicalization jointly support the performance gains of the framework. This study provides a verifiable methodological pathway for structured organization, quality auditing, evidence tracing and subsequent integration of newly available knowledge. Full article

Artificial intelligence (AI) increasingly mediates leadership-relevant judgment through models, dashboards, metrics, decision-support systems, and autonomous agents. This conceptual article develops a socio-technical systems theory of systemically mediated leadership, defined as a nested system-level condition and recurrent process configuration through which human actors, AI systems, organizational routines, governance institutions, and affected stakeholders jointly produce and revise direction, meaning, consequential judgment, legitimacy, and accountability through recursive feedback. A problem-driven conceptual synthesis was updated through 3 August 2026. A structured discovery pass yielded 97 candidate records; 85 sources were retained after relevance screening, citation chaining, concept mapping, and comparison of eight candidate mechanism families. Four proposed qualification conditions jointly define the construct within the present framework: AI mediation, leadership relevance, distributed judgment, and recurrent institutional embedding. Five mechanism families explain transformations in responsibility, legitimacy, control, attention, and feedback timing: moral delegation, interpretive laundering, ceremonial oversight, metric-driven sensemaking, and ethical latency. A causal-loop model specifies justificatory reinforcement, capability atrophy, power insulation, and accountable correction. Their relative dominance produces three ideal-type dynamic regimes: accountable adaptation, stabilized trade-offs, and destructive drift. The theory predicts that organizations using equally accurate models may produce divergent leadership and accountability outcomes because their feedback, power, and oversight architectures differ. Responsible AI leadership thus depends on system architecture and contestable institutional practice, not leader intention, formal human approval, or model accuracy alone. Full article

Echinophora sibthorpiana Guss. (Apiaceae) is an aromatic plant of considerable ethnobotanical significance, traditionally employed as a flavouring and preservative agent in food and valued for its medicinal properties across the Eastern Mediterranean region, yet its non-volatile phytochemistry and biosafety profile remain insufficiently characterized. In the present study, the n-butanol fraction of the 80% methanolic extract of the aerial parts of E. sibthorpiana was fractionated using chromatographic methods and the structures of the isolated compounds were elucidated by 1D and 2D-NMR spectroscopy and HR-ESI-MS. The distribution of Echinophora metabolites isolated by our group across six species in Türkiye was assessed by LC-qTOF-MS. Antioxidant capacity was evaluated by CUPRAC, FRAP, and TEAC assays; DNA-protective activity by the pBR322 plasmid model; genotoxic and antigenotoxic potential in the Drosophila melanogaster wing SMART assay. Four secondary metabolites, known as vicenin-2 (1), rutin (2), isoquercitrin (3) and betulalbuside A (4), were isolated and reported from E. sibthorpiana for the first time. Notably, the C-glycoside flavone vicenin-2 and the acyclic monoterpene glucoside betulalbuside A also represent the first isolation of these compounds from the genus Echinophora. LC-qTOF-MS profiling identified widely distributed flavonoid constituents together with more restricted metabolites that may have chemotaxonomic relevance. The n-BuOH fraction and the flavonol glycosides rutin and isoquercitrin exhibited the highest antioxidant and DNA-protective activities. None of the tested samples displayed genotoxic activity, while all showed antigenotoxic effects against ethyl methanesulfonate-induced DNA damage, with total spot frequency inhibition ranging from 47% to 83%. Overall, these findings provide a first comprehensive characterization of the non-volatile phytochemistry and biosafety profile of E. sibthorpiana and support the further investigation of its constituents as safe antioxidant and chemopreventive agents with potential nutraceutical applications. Full article

Wireless Sensor Network (WSN) underpins numerous applications, including environmental surveillance, industrial control, and smart cities, where reliable connectivity is vital for continuous sensing and data transmission. On the other hand, a small number of failed or compromised Critical Nodes (CNs) can badly fragment the network topology, interfere with communication, and impair performance. Consequently, devising effective solutions to the CN Detection Problem (CNDP) while taking connectivity, energy, and reliability into account remains a serious research endeavor. To tackle this challenge, this work proposes a Genetic Algorithm-assisted Damped Yo-Yo Grey Wolf Optimization framework with a Revitalized Boltzmann Distribution connectivity model (GA-DY-RBD-GWO). The CNDP is formulated as a node-elimination optimization problem that identifies the top-(k) CNs, where each search agent represents a candidate subset of k nodes. To realistically characterize network connectivity, a Revitalized Boltzmann Distribution (RBD)-based pairwise connectivity model is developed by jointly considering hop distance, residual path energy, and distance-based link reliability. Based on the resulting connectivity matrix, Total Pairwise Connectivity (TPC) is computed, and node criticality is quantified by the reduction in TPC after removing a candidate node set. To effectively explore the combinatorial search space, the Grey Wolf Optimization is augmented with a damped Yo-Yo control mechanism that adaptively balances exploration and exploitation during the optimization process. Furthermore, Genetic Algorithm-inspired crossover and mutation operators improve population diversity and avoid premature convergence, while elitist retention keeps the best-so-far candidate solution. By integrating realistic RBD-based connectivity modeling with an adaptive hybrid metaheuristic, GA-DY-RBD-GWO accurately identifies CNs whose deletion induces maximal TPC degradation. Extensive experiments under diverse network topologies, deployment scenarios, and spatial distributions demonstrate that the GA-DY-RBD-GWO exhibits superior performance over representative baselines, revealing that it is an efficient topology-aware solution for CNDP to improve the reliability of WSNs. Full article

Indolo[2,3-b]quinoxalines, along with their N-substituted derivatives, exhibit pronounced anticancer activity, although the mechanisms of their biological action may vary. Herein, a panel of sixty-five 6-(2-aminoethyl)-6H-indolo[2,3-b]quinoxaline derivatives comprising eight series with distinct amine moieties connected to the tetracyclic indoloquinoxaline core via a dimethylene linker was evaluated as drug-like candidates for kinase binding and cytotoxic activity. The ADME (Absorption, Distribution, Metabolism, and Excretion) properties of the compounds included in this set were preliminarily determined using the SwissADME tool. Analysis revealed that the library of quinoxaline derivatives largely complies with the drug-likeness rule for kinase-targeted compounds. As part of the biological screening, the compounds were initially tested on two cell lines MonoMac-6 and THP-1 (both derived from patients with acute monocytic leukemia) using sunitinib, a known antitumor agent acting as a multi-target receptor tyrosine kinase inhibitor, as a reference compound. Compound 3g, which demonstrated the highest activity in the cytotoxicity analysis (IC₅₀ = 1.9 and 3.5 μM for the MonoMac-6 and THP-1 cell lines, respectively), was screened using the Eurofins DiscoverX scanEDGE panel, comprising 97 distinct kinases representing all known kinase families. Subsequently, the compound was tested using the Eurofins DiscoverX scanTK™ panel, covering 135 distinct receptor and non-receptor tyrosine kinases. Based on initial screening results, compound 3g exhibits relatively high binding activity against fourteen tyrosine kinases, including TYK2, ZAP70, eight mutant forms of ABL1, two mutant forms of FLT3, and one mutant form of ALK, and demonstrates relatively high binding selectivity with respect to non-mutant tyrosine kinases (S-score: 0.024). Secondary screening of nine selected analogs of compound 3g led to the identification of compound 3h, which demonstrates relatively high binding affinity for FLT3 (D835V) (K_d = 0.41 μM). Molecular modeling suggested modes of binding interaction of the compounds 3h and 3g in the FLT3 (D835V) catalytic site. Our results demonstrate that 6-(2-aminoethyl)-6H-indolo[2,3-b]quinoxaline derivatives could be potential candidates for developing anticancer drugs. Full article

Fossil human footprints provide direct evidence of past presence and behaviour, yet the processes governing their preservation remain poorly understood. In particular, it remains unclear whether repeated footprint-bearing surfaces reflect population abundance and occupation continuity, or instead arise through environmental and taphonomic filtering. This study addresses the problem using an agent-based model (ABM) of footprint formation and preservation within a dynamic lake-margin environment. The model simulates interactions between human activity, basin morphology, and time-varying hydrological conditions under contrasting climate forcing regimes and occupation structures. Preservation is treated as a probabilistic process occurring when wetting events coincide with sufficient footprint availability, allowing analysis within an environmental–behavioural state space. Results show that the footprint record is highly sensitive to the temporal organisation of climate variability, with different forcing regimes producing distinct preservation architectures even under comparable mean environmental conditions. Basin geometry exerts a fundamental control, with basins that are not too steep or shallow maximising shoreline mobility and preservation potential, while behavioural organisation shapes how footprints are distributed relative to preservation windows. In contrast, population size alone exerts comparatively limited influence. Comparison with artefact accumulation highlights a key contrast: artefacts integrate behaviour across time, whereas footprints are preserved episodically, capturing short-lived snapshots of activity. Footprint-bearing surfaces should therefore not be interpreted as direct proxies for population size or occupation continuity, but as discrete sampling events generated through the intersection of human activity and transient preservation opportunity. More broadly, the model suggests that fossil footprint assemblages are emergent products of coupled behavioural, geomorphic, and environmental systems, providing a framework for more robust interpretation of the human footprint record. Full article

Cataracts are the leading cause of reversible blindness worldwide; this condition results from the aggregation of lens proteins. Currently, surgery remains the only treatment; however, there is growing interest in non-surgical approaches, including the use of bioactive compounds incorporated into nanostructured systems designed to enhance solubility, enable controlled release, and improve bioavailability and bioactivity. Among the bioactive compounds investigated, curcumin has attracted considerable attention due to its antioxidant and anti-inflammatory properties, positioning it as a potential anticataractogenic agent. In the present study, curcumin-loaded nanoemulsion was developed via ultrasonication and characterized by average particle size, D90 percentile, ζ potential, and rheological behavior. In addition, its anti-cataract efficacy was evaluated both using an in vivo model in rats and an ex vivo model employing human cataract samples. The resulting curcumin-loaded nanoemulsion exhibited an average particle size of 152 ± 19.79 nm with a monomodal distribution, along with good physical stability over time. The nanoemulsion exhibited apparent viscosity between 30 and 25 mPa·s, at shear rate values (100 to 0 s⁻¹), indicating slight shear-thinning behavior. Regarding the effect on cataracts, in the in vivo model, cataract reversal was observed. Furthermore, ex vivo isothermal titration calorimetry (ITC) analyses indicated exothermic heat exchange between the curcumin nanoemulsions and cataract fragments, consistent with binding interactions occurring within lens components, likely involving crystallin proteins. These findings provide biophysical and in vivo evidence that intravitreally administered curcumin-loaded nanoemulsions not only prevent but actively reverse lens opacity, positioning them as a promising non-surgical therapeutic approach for cataract treatment. Full article

Efficient treatment of diabetic wounds (DW) remains a major clinical challenge worldwide owing to vascular insufficiency, multiple bacterial infections, and overactivation of pro-inflammatory M1 macrophages caused by hyperglycemia. The development of novel pharmaceutical agents with multiple biological functions is urgently needed. Coptis chinensis Franch. (CC) has been used to treat diabetes for thousands of years in China, but the curative effects and underlying mechanisms of CC in DW remain uncertain. Herein, a homogeneous heteropolysaccharide component, namely CCP, was isolated and purified from CC, which exhibited a molecular weight of 39,697 Da and was primarily composed of Glc, GalA, Ara, Gal, and Xyl. CCP has a light yellowish color and is distributed in a block shape with small surface granulations. In vitro experiments revealed that CCP dose-dependently mitigated high glucose-induced suppression of viability, migration, and tube formation in HUVECs. Meanwhile, CCP promotes the polarization of M1 macrophages toward the M2 phenotype to exert anti-inflammatory effects, while possessing certain antibacterial properties. In addition, a composite hydrogel system was successfully constructed by introducing sodium carboxymethyl cellulose and carbomer 940 for CCP delivery. The obtained hydrogels exhibited reasonable moisturizing, swelling, and drug release capacities, along with favorable rheological behaviors and certain antibacterial activity. More importantly, the in vivo wound healing model evaluation in diabetic rats demonstrated that CCP hydrogel dressings could effectively promote wound healing by reducing inflammation, accelerating collagen deposition, upregulating the expression of VEGF and key angiogenesis-related factors. In addition, composite hydrogels demonstrated excellent cytocompatibility and hemocompatibility, which holds great promise for clinical application in DW treatment. Full article