Multi-echelon supply chains and inventory networks are inherently complex, characterized by stochastic demand, variable lead times, and interdependent transportation links. Traditional queueing–inventory models often assume independence among nodes and edges, limiting their applicability in realistic, interconnected supply networks. In this study, we develop a graph-theoretic framework for multi-echelon queueing–inventory systems, where nodes represent warehouses, suppliers, or retailers and edges represent stochastic transportation or information flows with temporal correlations. By integrating queueing theory, stochastic inventory control, and graph spectral analysis, the framework captures propagation of demand signals, stockouts, and service delays throughout the network, enabling the identification of critical bottlenecks and optimization of inventory allocation. Simulation results demonstrate that the model effectively accounts for node and edge dependencies, reflecting how upstream disruptions or fluctuating lead times influence downstream inventory levels and service performance. The proposed methodology provides actionable insights for complex logistics systems, including shipyards, multi-warehouse distribution networks, and manufacturing supply chains, supporting resilient, efficient, and responsive inventory management strategies under uncertainty.

Involution, as a rising sociocultural phenomenon in contemporary Chinese society, has raised concerns about its potential psychological impact on young people. However, little is known about how perceived involution relates to university students’ well-being and whether such patterns differ by gender and academic stage. The present study investigated perceived involution and subjective well-being among 5,235 university students using network analysis. Distinct characteristics of involution perception were identified across gender and academic year. Network structures revealed largely negative associations between perceived involution and well-being, with female students displaying denser and more interconnected networks than males. Additionally, comparisons across grade levels indicated differentiated psychological profiles: lower-grade students were more affected by academic stress, while upper-grade students showed stronger links between competitive norms and future-related concerns. These findings contribute to a nuanced understanding of how involution manifests psychologically and underscore the value of network analysis in mapping complex relationships between cultural stressors and mental health in youth populations.

Distributed systems, integral to contemporary computing, involve the coordinated functioning of autonomous nodes across interconnected networks. Architectural models like client-server and peer-to-peer configurations define their structure, presenting both advantages and challenges. Challenges stem from the intricacies of managing geographically dispersed nodes, encompassing issues of data consistency, fault tolerance, scalability, and security. Overcoming these hurdles demands advanced algorithms and protocols, especially for achieving consistency and resilience in the face of failures. Scalability is a critical consideration, and security concerns add complexity to their design. Current trends in distributed systems, such as edge computing, serverless architectures, and blockchain technologies, aim to address these challenges and enhance system capabilities. Edge computing optimizes proximity to data sources, serverless architectures streamline resource utilization, and blockchain offers decentralized and tamper-resistant solutions. Understanding these architectures, addressing challenges, and embracing emerging trends are pivotal for constructing robust and efficient distributed systems that align with the demands of the interconnected digital landscape.

The integration of artificial intelligence (AI) into surgical education is transforming the way surgical skills and knowledge are developed. This article examines how AI aligns with key educational theories-behaviourism, cognitivism, constructivism, humanism, and connectivism-to enhance learning through personalised simulations, adaptive feedback, and networked platforms. A review of literature and theoretical frameworks was conducted to analyse AI's applications in surgical training. Key features include AI-driven tools for structured feedback, cognitive optimisation, experiential learning, individual growth, and collaboration through interconnected networks. The article also identifies ethical challenges, including data privacy, algorithmic bias, and equitable access. AI has the potential to revolutionise surgical education by fostering critical thinking, improving training outcomes, and expanding access to learning resources. However, risks such as over-reliance on automation, loss of hands-on experience, and superficial AI use ("AI theatre") highlight the need for thoughtful and ethical implementation. With a balanced and collaborative approach among educators, technologists, and healthcare professionals, AI can create dynamic, learner-centred environments. By addressing challenges, AI can support the development of skilled, compassionate surgeons equipped to navigate the complexities of modern medical practice.

Multi-objective coordinated optimization of flexible interconnected distribution networks under rainstorm conditions

A biotemplated in situ growth method was employed to fabricate self-supporting metal-organic framework (MOF) aerogels using bacterial nanocellulose (BNC) and collagen foam as templates. The one-step synthesis method enables uniform and dense coating of MOF crystals (ZIF-8 and ZIF-L) on nanocellulose and collagen nanofibers, resulting in an interconnected 3D open porous network. Integrating plasmonic nanostructures with metal-organic frameworks (MOFs) in three-dimensional (3D) aerogels enables the realization of multifunctional materials that combine high porosity, thermal stability, electromagnetic field enhancement, and photothermal properties, therefore simultaneously supporting surface-enhanced Raman scattering (SERS)-based sensing and antimicrobial functions. The plasmonic/MOF hybrid aerogels allow highly sensitive vapor-phase detection of toxic volatile organics (TVOs) including p-aminothiophenol (p-ATP), formalin, and aniline, harnessing the synergistic effects of MOF-assisted analyte trapping and electromagnetic field enhancement from the plasmonic nanostructures. The photothermal properties of the plasmonic/MOF aerogels together with Zn2+/Ag+ ion release resulted in high antibacterial efficacy (>99%) against Escherichia coli and Staphylococcus aureus under low-power laser irradiation. The simple, scalable, and versatile method demonstrated here can be extended to other functional nanomaterials and MOFs for realizing multifunctional materials with a 3D open porous architecture.

Mycobacteria possess a uniquely complex cell envelope and rely on a diverse array of secretion systems to interact with their environment, ensure survival, and modulate host immune responses. This review provides a comprehensive overview of these secretion pathways, from the universally conserved Sec and Tat systems to the specialized ESX/type VII secretion systems, as well as lipid transporters of the MmpL family, with particular emphasis on Mycobacterium tuberculosis and other clinically relevant members of the M. tuberculosis complex and non-tuberculous mycobacteria. By integrating findings from historical literature and the most recent experimental and bioinformatic studies, we outline the genetic organization, structure, regulation, and functional interplay of these pathways. Emphasis is placed on how these systems are not isolated entities but form a highly interconnected network that coordinates protein and lipid export essential for virulence, immune modulation, and cell wall integrity. We also explore the translational potential of secreted effectors and their transport machineries, discussing their relevance as targets for therapeutic interventions, including novel inhibitors, diagnostic biomarkers, and vaccine candidates. We highlight critical knowledge gaps and propose avenues for future research, particularly those that leverage multidisciplinary approaches. By drawing connections across secretion systems and emphasizing their shared and distinct roles, this work aims to provide an integrated framework that supports both fundamental understanding and biomedical innovation in mycobacterial pathogenesis.

Volcanic outgassing through interconnected bubble networks controls eruption dynamics. Frameworks formed by crystals may facilitate gas escape, but the relative spatial arrangements of bubbles and crystals remain overlooked. We conducted multi-resolution X-ray computed tomography (CT) imaging to examine vesicle-crystal spatial relationships in a dacite bomb, a lava block in a pyroclastic flow deposit, and spine lavas from the 1990-1995 Unzen eruption. We acquired micro-CT, computed laminography (CL), and nano-CT images with progressively higher resolution. Reconstructed 3D images reveal that large vesicles are consistently connected to crystals across all sample types and analysis scales. Size distribution analysis demonstrates preferential connectivity between large vesicles and large crystals. Vesicles in the bread crust bomb that appear isolated actually form interconnected networks, while vesicles in shear-deformed dome samples occupy narrow inter-crystal gaps as sheet-like structures. Compaction timescale calculations and gas flow modelling indicate that interconnected bubble networks enable efficient initial outgassing at depths of 0.5-0.8 km, while crystal-supported pathways subsequently transport ascending gas through shallow conduit regions. These analyses suggest that compaction of the crystal-bearing, interconnected bubbles causes outgassing at depth, thereby developing the crystal frameworks that act as pathways for outgassing during the final ascent to form the dome.

Path congestion detection and disjoint path for improving communication efficiency in dragonfly interconnection networks

A complex network of processors and communication channels, known as an interconnection network, is used by components in a parallel computing system to share data. In computer networks, where positioning various modules on the integrated circuit is one of the primary cost requirements, performing concurrent algorithms in these intricate, organized circuits requires the use of graph embedding. The placement issue in circuit designs, without any deterministic algorithms, can be resolved by determining the most suitable layout. Such a network allocation process can be carried out using graph embedding. This study establishes the optimal arrangement of processors to obtain minimum wirelength for embedding the Cartesian product of complete graphs into paths and grids.

Lung surfactant (LS) plays an essential role in preventing lung collapse due to physical forces by forming surface-active lipid-protein membranous films at the respiratory air-liquid interface. Throughout its biological cycle, LS exists in a variety of metabolically related, conspicuous morphological forms. Epithelial alveolar type II cells store LS as intracellular, tightly packed, multilayered organelles known as lamellar bodies. These are secreted as still-condensed material in the form of lamellar body-like particles, which, upon adsorption, give rise to the interfacial film and surface-associated structures. Surfactant material purified from bronchoalveolar lavage fluids has been extensively examined by conventional transmission electron microscopy (TEM), providing important information about LS ultrastructure. However, potential artifacts associated with classical TEM preparation methods-such as staining, dehydration, resin embedding, and sectioning-hinder the observation of surfactant biological samples in their truly native state. In this work, we have taken advantage of cutting-edge cryo-microscopy techniques to visualize the structural complexity present in LS preparations without fixation, in a frozen-hydrated state, and thus closer to physiological conditions. The implementation of cryo-preservation approaches has allowed us to unveil unprecedented ultrastructural details of the diverse morphological states in which LS is present in the alveolar spaces, such as the presence of a protein-based pore connecting the lumen of the LBPs with the external milieu, and an onion-like structure that suggests a mechanism that uses the energy accumulated upon LB assembly in the pneumocytes for a rapid release of the membranous complexes to the exterior. These morphological features shed light on the dynamic processes by which LS is unpacked from secreted condensed states to the more disorganized, interconnected membranous networks that sustain breathing mechanics.

Abstract Effective heat dissipation is crucial for the optimal performance and safety of electronic devices. Electronic products and energy-efficient home equipment generate more heat during operation and continuous use. Therefore, it is essential to implement efficient heat dissipation methods to prevent problems such as component malfunctions, increased failure rates, and safety hazards. Polymer composites have recently garnered attention due to their unique properties and versatility. These composites achieve improved thermal conductivity by incorporating ceramic nanoparticles, making them suitable for heat dissipation applications. However, challenges arise from the electrical conductivity of ceramic fillers, which can interfere with the performance of precision electronic components. Additionally, the thermal conductivity of ceramic nanoparticles is typically low (less than 10 W/(m·K)), requiring high filler content (above 50 vol.%), which increases the cost and weight of the composite materials. Nanofiber-based heat dissipation sheets, particularly those fabricated through electrospinning, offer a promising alternative. Ceramic nanofibers provide continuous thermal pathways within the polymer matrix, enhancing heat conduction. However, while in-plane thermal conductivity can be significantly improved, the thermal conductivity in the thickness direction remains limited due to the insufficient heat conduction network in this direction. Advanced techniques such as freeze-drying offer promising solutions to this problem, enabling the formation of three-dimensional (3D) interconnected nanofiber networks. These 3D structures demonstrate superior performance over conventional one-dimensional nanofibers. This review provides a comprehensive overview of the synthesis and integration of ceramic nanofibers, including aluminum oxide (Al 2 O 3 ), aluminium nitride (AlN), silicon dioxide (SiO 2 ), titanium dioxide (TiO 2 ), and beryllium oxide (BeO), into polymer matrices for heat dissipation applications. It discusses the thermal and electrical performance of these materials alongside emerging strategies to optimize thermal conductivity in both in-plane and thickness directions. Furthermore, this review highlights recent advances in freeze-drying techniques for fabricating 3D nanofiber structures and outlines future directions for overcoming the challenges in synthesizing high-performance ceramic nanofibers.

ABSTRACTAimAutism spectrum disorder (ASD) is a neurodevelopmental disorder characterized by significant heterogeneity in clinical symptoms and underlying neurobiology. This study aimed to identify distinct ASD biotypes and uncover their neurobiological underpinnings using a novel graph‐based subtyping approach.MethodsResting‐state fMRI and clinical data from 443 males with ASD (17.22 ± 8.63 years) were analyzed. We proposed a population graph‐based dual autoencoder for subtyping (PG‐DAS), a deep clustering framework that integrates imaging data and nonimaging data to extract deep features for biotype identification. Statistical analyses were conducted to compare clinical scores and functional connectivity patterns between biotypes. Correlation analyses examined the associations between intra‐ and internetwork connectivity and clinical symptoms. Predictive modeling using support vector regression assessed the ability of network connectivity to predict clinical scores.ResultsTwo distinct ASD biotypes were identified. ASD1 exhibited significantly lower clinical scores and reduced network integration, characterized by weaker intra‐ and internetwork connectivity, particularly in core networks such as the cingulo‐opercular network, linked to communication symptom scores. In contrast, ASD2 exhibited greater network segregation, with internetwork connectivity in sensorimotor‐related networks correlating with total symptom scores. Predictive modeling further revealed biotype‐specific brain‐behavior associations, with ASD1 and ASD2 showing positive correlations with social and communication scores, respectively.ConclusionThis study underscores the critical role of biotype‐specific brain network patterns in understanding ASD heterogeneity. The proposed PG‐DAS framework proved effective in ASD subtyping and holds promise for broader applications in exploring other neuroheterogeneous disorders.

Influences of cations on the gelation-promoting effect of konjac glucomannan on the phase transition of κ-carrageenan.

Purpose: To identify significant genes responsible for altering immune response in viral infections, including SARS, H1N1, Influenza, and Rhinovirus, as there are no previous studies that have analyzed these viral infections together. Methods: Viral infection datasets pertaining to SARS, H1N1, Influenza, and Rhinovirus were obtained from the NCBI Gene Expression Omnibus. We have used three GEO datasets with accession numbers: GSE47962, GSE48466, and GSE71766. The Differentially Expressed Genes (DEG’s) were identified from each of the datasets, and then common DEGs were extracted. Protein-ProteinInteraction (PPI) network was constructed for the common DEGs obtained in all the virus datasets. Finally, we analyzed the PPI network to identify the hub genes that have high interconnectivity with other genes. The significantly enriched pathways are reported. Results: By performing the comparative analysis, we identified 463 common DEG’s among the viral infection datasets under study. The highly interconnected PPI network constructed from these genes contained 3396 edges with an average node degree of 14.7 and an average local clustering coefficient of 0.406. There were 51 nodes with degree>50. The highest interconnected node, STAT1 had degree 113. Conclusion: STAT 1 gene is identified as the most significant hub gene related to the immune response in all four viral infections, including SARS, H1N1, Influenza, and Rhinovirus. Its trivial role is already known in different viral infections, but being most significant in the four viruses together is a novel finding. It is thus identified as a central gene that is a potential therapeutic drug and vaccine target for viral infections.

Dynamic regulation of cell death signaling.

Thermoresponsive and reusable chitosan/hydroxypropyl cellulose monolithic hydrogel with interconnected porosity engineered via freeze-thaw processing for selective dyes removal.

Dual electronic-ionic conductive 3D current collector for stable lithium metal anodes.

Low-strain spherical Na2.5Fe1.75(SO4)3 cathode enabled by morphology control for long-cycle sodium-ion batteries.

Interactions in starch-lipid complex systems improve 3D printing accuracy and form resistant starch structures: Based on complex properties and molecular simulations.