Abstract: Structural Health Monitoring (SHM) is essential for the safety and long-term performance of civil and mechanical infrastructure, yet traditional vibration-based approaches often struggle with nonlinear behavior and environmental variability. Koopman operator theory provides a promising alternative by enabling linear analysis of nonlinear structural dynamics through observable functions. This review examines 67 peer-reviewed studies published between 2010 and 2025 and selected using Preferred Reporting Items for Systematic Reviews and Meta-Analysis (PRISMA) guidelines. We outline the development of Koopman-based methods from Dynamic Mode Decomposition (DMD) and Extended-DMD (EDMD) to recent applications in civil, mechanical, and aerospace systems. The review clarifies the mathematical foundations of Koopman analysis and its relationship to structural dynamics. It also identifies major research gaps, including limited damage-sensitive observable design, insufficient use of structural mechanics constraints, the absence of quantitative links between Koopman spectra and physical damage, inadequate benchmarking, and the need for real-time deployment strategies. We conclude by outlining a hybrid Koopman framework that integrates physics-based information with data-driven learning to support interpretable and scalable SHM.

Abstract: Background: Sleep disorders like insomnia, obstructive sleep apnea (OSA), REM behavior disorder etc. are nowadays diagnosed through the Internet of Things (IoT)-enabled sys-tems that monitor and analyze the subject's sleep data. Health IoT networks are rife with communications of sensitive physiological data from wearable EEG, ECG, SpO₂ and res-piratory sensors. However, these networks face threats from anomalous traffic flows, sig-nal sabotage and data integrity violation. In this paper, an AI-based hybrid detection and classification framework is proposed for secure Sleep Health IoT (S-HIoT) networks. The integrated CNN, BiLSTM and RF model provides a proposed framework for joint sleep-stage classification and network anomaly detection. To this end, a multi-objective loss function is proposed for jointly optimizing the physiological state prediction and se-cure traffic monitoring. Experimental validation using the Sleep-EDF and CICIoMT2024 datasets demonstrate a classification accuracy of 97.8% for sleep staging, and 98.6% for network detection with low inference latency (

Abstract: Differential Liberation Expansion (DLE) and viscosity tests are core elements of the Pressure–Volume–Temperature (PVT) laboratory suite used to characterize reservoir oils under depletion and to support compositional modeling and reservoir simulation. Nevertheless, both DLE and viscosity testing remain expensive and time-consuming due to specialized equipment, strict operating procedures, and the need for experienced laboratory personnel.Building on our prior work that introduced the proximity-informed Local Interpolation Model (LIM) framework for Constant Composition Expansion (CCE), this study demonstrates how the same end-to-end, neighborhood-based workflow is applied to DLE and viscosity test data. A target fluid is embedded in a compositional–thermodynamic descriptor space and paired with a small set of thermodynamically similar fluids drawn from a PVT data archive. Within this locality, LIM is used to infer DLE behavior by combining local interpolation for key scalar quantities (e.g., saturation-point and endpoint PVT values) with shape-preserving reconstruction of pressure-dependent curves. For viscosity, the same approach reconstructs the oil-viscosity curve across the undersaturated and saturated regions. Evaluation on a proprietary database of DLE and viscosity tests shows strong agreement across diverse fluids for both DLE and oil viscosity trends. This supports reducing reliance on new DLE and viscosity measurements while maintaining engineering-grade fidelity in reservoir-engineering and simulation workflows. This approach has been fully automated through software so it can be set up and directly utilized by the field operators on their own databases to significantly reduce their fluid sampling and laboratory analysis costs. Moreover, the proposed AI model does not use others’ data while respecting data privacy and data ownership.

Abstract: Over the last two decades, there has been a paradigm shift in geotechnical engineering driven by advances in sensing, communication, and data-driven techniques. These advancements enhanced the safety and reliability of geotechnical infrastructure through real-time monitoring and automated decision-making. In recent times, Large Language Models (LLMs) have emerged as advanced data-driven techniques contributing to automated risk assessment of geotechnical infrastructure. LLMs are advanced deep learning models widely used to solve complex numerical problems, analyze large volumes of data, and generate human language. This paper presents a comprehensive review of the application of LLM in geotechnical engineering. The integration of LLMs into geotechnical engineering has demonstrated significant advances in slope stability analysis, bearing capacity computation, numerical analysis, soil-structure interaction, and underground infrastructure. By summarizing the latest research findings and practical applications, this research paper underscores the potential of LLMs to advance and automate various processes in geotechnical engineering. The findings presented in this paper not only provide insights into the current LLM-based geotechnical practices but also emphasize the instrumental role LLM can play in advancing geotechnical engineering, ultimately ensuring a safer and more sustainable future. Lastly, this paper highlights the different LLM capabilities which can be used to empower geotechnical engineers.

Abstract: This study presents, for the first time, a comprehensive multi-fidelity aero-thermo-fluid framework (spanning 0D-analytical, 1D and 3D domains) applied to the analysis of a structural oil-to-air Fan Outlet Guide Vane Cooler (FOGVC) in a jet engine. Addressing the need for efficient thermal management in next-generation engines, a hierarchical approach is established to characterize both thermal dissipation and pressure drop performance. The framework compares five simulation levels—ranging from high-fidelity conjugate heat transfer to 0D analytical models—across two distinct internal geometries (a rectangular inverted-U and a circular coil) covering different flow regimes. The research quantifies the trade-offs between physical fidelity and computational cost, establishing a decision-making criterion for the design of complex structural coolers. Results demonstrate that while 0D analytical methods provide high accuracy-to-speed ratios for temperature prediction, they exhibit significant deviations in pressure drop estimation and lack of capture local thermal gradients critical to structural integrity, where high-fidelity fully coupled 3D simulations are indispensable.Furthermore, the analysis reveals fundamental limitations in current passive heat exchanger designs under extreme operating conditions, suggesting a paradigm shift toward active or adaptive components is required to meet future dissipation targets.

Abstract: To investigate the interaction between mine ventilation and the thermal en-vironment in a fully mechanized longwall face, a Computational Fluid Dy-namics (CFD) model was developed for the 11-3107 working face of Menkeqing Coal Mine based on field-measured data. The model was used to analyze the effects of ventilation mode, electromechanical equipment layout, roadway length, airflow velocity, and inlet air temperature on the thermal environment of the working face. The results show that changing the ventilation mode alone has only a limited effect on reducing the maximum face temperature, although the U-shaped system provides a comparatively practical ventilation arrange-ment under the studied conditions. Locating major electromechanical equipment in the return airway helps reduce the temperature in the intake airway and working face. Shorter ventilation routes, higher airflow velocity, and lower inlet air temperature all contribute to improved thermal conditions. Considering both simulation results and operational constraints, cooling equipment should be installed near the intake airway to effectively lower the working-face temper-ature. Based on psychrometric analysis and ventilation parameters, the required cooling load for the 11-3107 fully mechanized working face was determined to be 2417 kW under normal conditions and 3082 kW under critical conditions, in-cluding a 20% safety margin. The study provides a numerical basis for venti-lation optimization, cooling-system design, and heat-hazard control in deep underground coal mines.

Abstract: The automation of Integrated Circuit (IC) physical layout optimization remains a critical challenge, primarily due to the complex interplay between electrical and physical constraints. We propose ChipForm, a framework that reframes this task as a constraint-driven, reinforcement learning-guided graph optimization problem. Unlike perception-based approaches, ChipForm directly processes circuit netlists using a Hierarchical Graph Encoder (HGE) to extract features and predict timing, power, and density constraints. Subsequently, a Reinforcement Learning Placement Agent (RLPA) performs sequential cell placement, optimizing for minimal wirelength while explicitly satisfying these predicted constraints. A key contribution is a unified, end-to-end training strategy that jointly optimizes constraint prediction and placement policy. Extensive experiments on the CircuitNet benchmark demonstrate state-of-the-art performance: ChipForm achieves an 85.2% physical executability rate (DRC/LVS pass) and reduces constraint prediction errors (e.g., 0.11 OOD timing criticality error) compared to prior methods. Ablation studies confirm the necessity of each component, showing that explicit constraint prediction heads improve OOD generalization by 5.7% in executability, and the RL agent outperforms a greedy baseline by 3.9%. ChipForm thus provides a robust, data-driven approach for generating high-quality, manufacturable chip layouts directly from netlist specifications.

Abstract: Environmental governance is no longer shaped only by expert judgement or statutory procedure. In recent years, algorithmic systems have begun to mediate how data are interpreted, to shape the scoring of risk, and to influence the way policy priorities are established. These systems now affect regulatory analysis. They also inform climate adaptation modelling and guide decisions on land use while supporting sustainability monitoring. Although artificial intelligence (AI) is often presented as a means to improve environmental outcomes, its deployment introduces lifecycle emissions while raising concerns about institutional opacity and exposing risks related to public legitimacy that remain insufficiently embedded in current governance frameworks. This article advances the concept of algorithmic sustainability and treats it as a condition of governance rather than a technical attribute of computational tools. Drawing on a structured qualitative synthesis of interdisciplinary research, the study identifies three conditions required for sustainable AI use in environmental decision systems. One concerns lifecycle carbon integrity. Another addresses institutional accountability. A third focuses on alignment with public value. These conditions are translated into a tiered Environmental AI Impact Assessment model (EAIA) designed to support regulatory oversight while remaining institutionally feasible. By separating computing-related effects from operational consequences and from wider systemic implications, the framework clarifies how algorithmic applications may improve environmental performance while still generating rebound pressures that threaten broader sustainability goals.

Abstract: Right‑ventricular (RV) remodeling is a decisive determinant of symptoms, decompensation, and survival across pulmonary arterial hypertension, chronic thromboembolic pulmonary hypertension, chronic lung disease, left‑heart disease with secondary pulmonary hypertension, congenital heart disease, and selected post–myocardial infarction (MI) phenotypes in which RV dysfunction emerges through infarction, ischemia, or ventriculo‑pulmonary interactions. Compared with the left ventricle (LV), RV remodeling mechanics is less often reviewed as a coherent multiscale field that links fiber architecture and extracellular matrix remodeling to constitutive parameters, imaging‑derived deformation, and clinically interpretable endpoints. This review unifies these layers with a specific aim that is useful to both cardiovascular mechanicians and medical imaging researchers: to clarify what RV mechanics quantities are measured, what are inferred, and what must be assumed. We synthesize RV geometry and microstructure, pressure–volume based coupling metrics, tissue‑scale passive and active mechanics, and the dominant constitutive modeling families used in RV finite element studies. We then map imaging observables from echocardiography and cardiac magnetic resonance (CMR) to mechanical interpretation, focusing on deformation (strain, strain‑rate), chamber performance (volumes, ejection fraction), afterload characterization, and tissue substrate proxies (late gadolinium enhancement and mapping methods). Throughout, we show how septal mechanics and pericardial constraint shape RV stress–strain relationships and can confound biomarker interpretation if omitted. We propose an implementable mechanics‑aware interpretation framework that decomposes RV remodeling into load, pump–arterial coupling, passive stiffness/substrate, and activation/coordination components, each tied to measurable quantities and model parameters. Finally, we argue that transferable “reference ranges” for RV mechanics should be expressed as physiology‑conditioned envelopes that specify loading state, acquisition protocol, and analysis software rather than as single numbers. The review concludes with a practical research agenda centered on multi‑modal datasets with synchronized pressures, transparent segmentation and region definitions, uncertainty reporting, and open modeling pipelines that enable prospective prediction of decompensation and therapy response.

Abstract: Background: Myocardial infarction (MI) produces regionally heterogeneous loss of contractility and progressive extracellular matrix remodeling that reshapes left ventricular mechanics from hours to months. This review links infarct, border zone, and remote myocardium microstructure to organ-scale remodeling and patient-specific finite-element and growth-and-remodeling models. Methods: We synthesise experimental, computational, and translational studies on post-MI constitutive behavior, imaging-informed personalization, and inverse inference, emphasizing parameter identifiability and uncertainty quantification. Results: Contemporary models can reproduce volumes and strain patterns and support counterfactual simulations, but decision-grade prediction is limited by weak in vivo observability of regional stiffness and contractility, confounding with loading, and incomplete treatment of measurement and model-form uncertainty. Conclusions: Clinically credible prediction will require simplified, context-of-use-aligned models constrained by microstructure-informed priors, paired pressure-volume-strain datasets, longitudinal validation, and routine reporting of identifiability and uncertainty.

Abstract: Engineering design increasingly uses generative AI to explore large form spaces, yet concept-driven generation is only useful if observers consistently perceive the intended attribute. We propose a ranking-based human validation layer that tests whether AI-generated concept-intensity gradients are interpretable, reliable, and usable. For each Product–Concept pair, a controlled generative workflow produced six variants intended to increase concept expression (A–F). In an online study, 26 design engineers ranked the variants by perceived intensity, with an optional not-applicable (NA) flag when category recognition failed. We analyse rankings with heatmap diagnostics, inter-observer agreement, monotonic alignment with the intended order, and Plackett–Luce aggregation with uncertainty, while using NA trends to bound operational ranges. Across nine pairs, most gradients aligned with the intended direction, but performance depended on the concept and product context, revealing both stable and failure-prone segments. The approach provides an evidence-based gate for concept implementation in AI-generative design.

Abstract: Blast furnace gas (BFG) must be deeply purified when it is as fuel for combined-cycle power generation. To improve collection efficiency of the fine particulate dust in BFG by wet electrostatic precipitators (WESPs), this study implemented measures such as optimizing nozzle atomization performance and spatial distribution of droplets, along with adding chemical agglomeration agents and surfactants, These approaches pro-moted the chemical agglomeration of fine dust and enhanced dust collection efficiency. The results show that under overlapping spray conditions, the 1/8 solid cone nozzle produced the smallest droplets size with the most uniform spatial distribution, exhib-iting a d50 of 141.17 μm. When this nozzle was used in combination with guar gum (GG) as a chemical agglomerant, the d50 of BFG dust increased from 8.46 μm to 14.75 μm. The synergistic application of 5 mg/m³ sesbania gum (SBG) and 5 mg/m³ oc-tylphenol ethoxylate (OP-10) further increased the dust d50 to 19.08 μm. Using the 1/8 solid cone nozzle and with an XTG concentration of 5 mg/m³, resulted in the highest dust collection efficiency of 96.76%, while the synergistic use of SBG/OP-10 achieved an efficiency of 97.69%. This study elucidates the influence of nozzle atomization charac-teristics and spray liquid type on dust agglomeration and collection efficiency, providing both theoretical and practical foundations for the deep purification of blast furnace gas.

Abstract: Digital twins are becoming an important tool in biomedical systems. They help with real time monitoring, prediction, and control. They work well only when they can combine many types of physiological data. They must also stay closely in sync with the real system.This paper describes a digital twin framework that uses a Unified Namespace. The UNS acts as a central data hub. It collects signals from sensors, organ level models, and patient information. It keeps all data in one clear and interoperable structure. It separates data producers from data users. This makes the system easier to scale. It also supports fast data flow and constant model updates.A multiscale computational model sits at the center of the twin. It joins physiological behavior with predictive methods. It supports real time decisions in a closed loop system. A sample biomedical case shows how the UNS improves system speed, prediction quality, and control actions. The results show that UNS based digital twins can support personalized medicine. They can also improve biomedical workflows and help build advanced cyber physical healthcare systems.

Abstract: Hybrid robotic manufacturing systems integrating additive and subtractive processes enable fabrication of complex, high-value components but are typically executed sequentially, resulting in long cycle times. Concurrent execution of Directed Energy Deposition (DED) and milling promises productivity gains but introduces coupled thermal, mechanical and spatial interactions that challenge conventional process planning. This work addresses the methodological problem of planning milling operations in the presence of an ongoing DED process. The concurrent planning task is formulated as a mixed-integer, nonlinear, multi-objective optimisation problem capturing sequencing and orientation decisions, cutting parameters and temporal coupling to the deposition trajectory. A hierarchical, surrogate-assisted optimisation framework is proposed, combining unified decision-variable encoding, deterministic decoding and staged feasibility enforcement to ensure robotic executability. Disturbance mechanisms such as thermal interaction, particulate interference and pose-dependent dynamic compatibility are incorporated as modular objective abstractions, enabling systematic trade-offs between machining productivity and preservation of deposition process integrity. The proposed framework is demonstrated on a large-scale hybrid manufacturing case study with sparsely distributed machining segments, illustrating interaction between spatial sequencing, temporal feasibility and disturbance-aware optimisation under stated assumptions. The framework is methodological and provides a transferable foundation for future development and validation of disturbance-aware planning strategies for concurrent additive-subtractive manufacturing.

Abstract: Reliable and resilient communication systems are essential for first responders, enabling quick coordination and effective emergency responses. However, traditional communication networks often encounter congestion, interoperability problems, and failures during large-scale disasters. To address these challenges, specialized networks like FirstNet have been developed, leveraging advancements in LTE and 5G, as well as priority access mechanisms, to enhance reliability and coverage. This paper examines the technological advancements in first-responder communication systems, highlighting the limitations of legacy networks and the enhancements offered by modern solutions. We examine key components, including network prioritization, spectrum allocation, and integration with AI-driven traffic management. Additionally, this study assesses the role of digital twins in bolstering network resilience and fault tolerance for emergency communications. By synthesizing recent advancements, this research provides insights into future developments and policy considerations necessary to ensure a seamless and robust communication infrastructure for first responders.

Abstract: This review paper explores two primary methodologies for modeling flexible multi-body systems, namely, the Assumed Mode Method and the Lumped Pa-rameter Method. Flexible multi-body systems models, which involve small elastic deformations, are critical for simulating and optimizing the behavior of structures in various engineering applications, such as robotic arms, space structures, and rotating machinery. The Assumed Mode Method decomposes a system’s motion into rigid-body movements and elastic deformations using predefined mode shapes, providing an efficient alternative to the Finite Element Method. The Lumped Parameter Method simplifies flexible systems by modeling them as rigid segments connected by springs and dampers, which capture elasticity and damp-ing effects. This review focuses on the basic use, implementation of these two approaches. Additionally, a carne with flexible pendulum load is modeled using both approaches to demonstrate their effectiveness in capturing system dynamics.

Abstract: Growing disruptions, uncertainties, and complex risks such as pandemics, extreme weather, and geopolitical conflicts imperil the under-examined construction supply chain, a network that occupies a pivotal nexus in the broader economy. Therefore, it is vital to map its relationships and pinpoint where disruptions concentrate, and recovery can be accelerated. Guided by three research questions on network emergence, positional vulnerability, and how pressures steer technology adoption, this exploratory study maps how construction supply chain networks both create and alleviate operational strain. To address this problem, this study combines empirical, semi-structured interviews with social network analytics. Purposive and snowball sampling yield semi-structured interviews that span all major supply chain roles. Thematic coding translates reported interactions into nodes and edges of a complex network and groups challenges into thematic categories. Furthermore, degree, betweenness, and eigenvector metrics outlined structural vulnerabilities and leverage points. The results show how six main challenge categories (comprising 16 open codes) concentrate systematically at specific network positions. Relationship and contract issues accumulate at high-centrality brokers (degree centrality 0.818) while external pressures affect peripheral suppliers. Technology adoption preferences emerge from structural roles, with central coordinators seeking predictive analytics and peripheral actors prioritizing traceability systems in networks with moderate density (0.591). The research provides a replicable framework for identifying structural vulnerabilities and designing position-based interventions in construction supply chains. The network-theoretic framework opens new research directions for dynamic network analysis, multi-project supply webs, and stakeholder-centered technology integration strategies.

Abstract: Gypsum based fire protection relies on thermally activated dehydration, where chem-ically bound water is released and evaporated, producing an endothermic heat sink and delaying heat penetration through assemblies. In parallel, non-organic hydrated salts are increasingly used as flame retardant additives in gypsum-based systems to enhance heat absorption over targeted temperature ranges. Fire simulation tools and performance-based fire engineering methods require dehydration kinetics and reaction enthalpies that can be implemented as coupled thermal chemical source terms. How-ever, additive specific kinetic datasets suitable for such implementation remain limited, especially under restricted vapor exchange conditions representative of porous con-struction materials. The present study investigates the thermal decomposition behavior and dehydration kinetics of selected non-organic hydrated salts—aluminium trihydrate (ATH), magne-sium hydroxide (MDH), calcium aluminate sulphate (CAS), and magnesium sulphate heptahydrate (ESM)—commonly used as flame-retardant additives in gypsum-based construction materials. Differential scanning calorimetry (DSC) experiments were conducted at three heating rates (10, 20, and 30 K/min for MDH, CAS and ESM and 20, 40 and 60 K/min for GB-ATH) up to 600 °C using pinhole crucibles to simulate autogenous vapor pressure. Thermal analysis revealed that ATH, MDH, and CAS undergo single-step dehydration, while ESM exhibits a complex multi-step mechanism involving the formation of in-termediate meta-stable hydrates. Kinetic parameters were determined using both model-free (Starink) and model-fitting approaches. The derived activation energy profiles confirmed the single-step nature of ATH and MDH and identified CAS and ESM as multi-stage systems. All reactions were well described using the Avrami–Erofeev model, indicating nucleation-and-growth mechanisms. The extracted kinetic triplets were validated through numerical simulation, showing close agreement with experimental α(t) and dα/dt(T) data. The resulting kinetic triplets and dehydration enthalpies form a directly usable dataset for coupled heat transfer and dehydration models of gypsum-based assemblies, enabling improved parameter-ization of endothermic heat sinks and bound water release in fire safety engineering simulations.

Abstract: The Zeta-Minimizer Theorem formalizes the minimization of a phase functional derived from compressibility factor expansions and exponential resummations, yielding convergence to the Riemann zeta function ζ(s). In a symmetric measure space (Xⓜ,μⓜ,G) equipped with helical operators, constraints of rational signed cosines, positive integer representation dimensions, non-zero integer differences, and prime-modulated exponential decays ensure prime emergence as indivisible cycles in representation graphs (via Hilbert's irreducibility and Maschke's theorem). Corollaries derive stacked phases as stratified orbifolds with hyperbolic tendencies, emergent geometries as layered manifolds, bounded prime descent, dimensional resistance, and RH Theorem via spectral centering at Re(s)=1/2. Axioms abstract thermodynamic intuitions purely: Axiom I as concave entropy maximization on measures; Axiom II as spectral Gibbs minima with explicit frequency forms; Axiom III as covariance projections and flux conservation. The framework generates number-theoretic structures as shadows of optimization processes, with complex numbers/polynomials as projected artifacts and quantization implicit in multiphase triads. Applications include atomic stratification (quantized shells from phase jumps), angular momentum tensors (minimized over strata), fine structure invariant (α ̂^(-1)=4π^3+π^2+π≈137.036 from cycle sums with β=5 leaps), and covariant mappings to arbitrary variables via category theory (functors and RG universality for Gear discretization). This provides rigorous deduction for analytic number theory, algebraic geometry, and spectral theory, demoting elementary constructs to derived descriptions.

Abstract: This study evaluates the technical feasibility of deploying containerized oxy-combustion power modules with integrated CO₂ capture in remote Ecuadorian Amazon oil fields. Associated petroleum gas is conditioned with a 35 wt.% diethano-lamine (DEA) sweetening stage specifically implemented to remove H₂S and reduce acid-gas loading prior to combustion, improving fuel quality and protecting down-stream equipment while increasing methane mole fraction for combustion. System ef-ficiency is governed by stoichiometric oxygen demand, with methane requiring 2 mol O₂/mol fuel and hexane requiring 11 mol O₂/mol fuel; favoring methane-rich streams reduces ASU energy demand, enhances combustion performance, and lowers separa-tion costs. The combined oxy-combustion cycle attains a thermal efficiency of 33.10% and an exergetic efficiency of 39.98%. Major energy penalties arise from the cryogenic air separation unit and the CCS train, yet operational tuning of CO₂ recirculation and steam flow could raise thermal efficiency by up to 2%. The ASU produces oxygen at 96.67% purity with an energy consumption of 0.385 kWh/kg O₂, while the CCS achieves 99.99% CO₂ capture at 0.41 kWh/kg CO₂. Sourcing gas from three production blocks provides flexibility to accommodate supply variability. The modular 272 MW unit demonstrates viability for off-grid power supply, routine flaring reduction, and scalable acid-gas valorization in frontier oilfields.