Abstract: Non-dilute emulsions are emulsions where the concentration of the droplets is high enough for the neighboring droplets to interact with each other hydrodynamically but is still smaller than the packed bed concentration where the droplets are packed and deformed against each other. Thus, they cover a broad range of droplet concentration. Many emulsions encountered in industrial applications fall under this category. Non-dilute emulsions exhibit rich rheological behavior from a simple Newtonian fluid to a highly non-Newtonian fluid reflecting shear-thinning, shear-thickening, yield stress, viscoelasticity, etc. In this article, the rheology of non-dilute emulsions is re-viewed comprehensively. Emulsions of hard-sphere type droplets and deformable droplets, with and without surfactants, are covered. The mathematical models de-scribing the rheological behavior of non-dilute emulsions are discussed. The influences of electric charge and interfacial rheology on the rheological behavior of emulsions are covered in detail. The flocculation of droplets caused by different mechanisms such as depletion and bridging induced by additives and their effect on emulsion rheology are investigated thoroughly. Finally, the dynamic rheology of non-dilute emulsions is dis-cussed covering both pure oil-water interfaces and additive-laden interfaces. The mathematical models describing the dynamic rheological behavior of non-dilute emulsions are described. Based on the existing theoretical and empirical models, it is possible to a priori predict the rheology of non-dilute emulsions. However, serious gaps in the existing knowledge on non-dilute emulsion rheology remain. This review identifies the gaps in existing knowledge and points out future directions in research related to non-dilute emulsion rheology.

Abstract: The aerodynamic performance of an aircraft wing is influenced by the angle of attack (AoA), which directly affects lift, drag, and overall efficiency. This study presents a theoretical analysis of the effect of AoA on a symmetric thin aerofoil using aerodynamic models including Thin Aerofoil Theory and Lifting-Line Theory are discussed. Results are discussed under three regimes; linear AoA range where lift increases proportionally while maintaining steady improvement in lift to drag ratio, pre-stall regime where partial flow separation takes place by reducing lift growth, near-stall where flow separation causes deterioration in aerodynamic efficiency. The study highlights the importance of wing geometry including aspect ratio, camber, and sweep angle. Understanding complex interactions between AoA, lift generation, drag forces, and wing geometry is crucial for optimizing aircraft design and improving aerodynamic performance.

Abstract: The effect of high-gravity fields, generated by rapid rotation, on CO₂ adsorption in activated carbon beds is examined. Adsorption-desorption kinetics is monitored before, during, and after short rotation periods at up to 5,000rpm. Rotation induced a reproducible transient bump in headspace pressure, quantitatively attributed to a centrifugal free energy shift (~12.2 J/mol) that overfilled weak adsorption sites beyond their static equilibrium. The bump mechanism is described by fold catastrophe theory, with a critical angular velocity (ωc=3,500rpm) triggering a sudden transition to a high-occupancy branch. Post-rotation, constant-rate zero-order desorption from shallow sites overlapped with a slower pseudo-first-order adsorption process as deep, previously inaccessible pores became available, increasing CO₂ capacity by 18.4%. Kinetic modelling produced an apparent diffusivity of 1.2x10-5m2/s and a structural accessibility time constant of ~25h. Thermodynamic analysis showed that rotation improved the overall free energy of adsorption and altered entropy in a manner consistent with the observed adsorption-desorption sequence. These results demonstrate that rotational fields can enhance CO₂ uptake, modify kinetic pathways, and trigger threshold phenomena in porous adsorbents.

Abstract: This study addresses the need for intelligent condition monitoring in high-complexity medical imaging systems by proposing a smart sensing architecture for the Revolution EVO Computed Tomography (CT) scanner. Ensuring operational reliability and minimizing unexpected downtime remain critical challenges in advanced CT platforms, motivating the integration of distributed sensing and data-driven analytics. The proposed framework combines Smart Sensor Networks with Machine Learning (ML)-based analysis to enable continuous acquisition and synchronization of heterogeneous operational data from key subsystems, including the X-ray tube assembly, detector array, rotational gantry mechanism, and data acquisition and processing unit. Multivariate feature extraction and sensor-level data fusion are employed to support anomaly detection and predictive assessment of system behavior. The methodology is informed by technical documentation and system specifications provided by GE HealthCare, together with established approaches in intelligent sensing and predictive analytics. The results demonstrate that structured integration of multi-sensor data and ML-based inference can enhance diagnostic sensitivity and enable early identification of abnormal operational patterns. It is concluded that a sensor-centric monitoring architecture provides a feasible pathway toward improved reliability, reduced unplanned interruptions, and more efficient lifecycle management of CT imaging systems.

Abstract: Accurate replication of road signal effects over the vehicles in laboratory environments is critical for vehicle durability testing and development. However, the traditional signal reconstruction methods often suffer from the inclusion of noise in the collected acceleration data. Thus, there is a limitation on the fidelity of hydraulic road simulations. This study proposes a comprehensive experimental-analytical framework for motorcycle testing in a laboratory environment. In the study, the integration of Fourier-based curve fitting with nonlinear adaptive control algorithms was done. Experimental signals were initially collected from a motorcycle on three different road surfaces. The displacement reference signals for the hydraulic actuators were generated using a harmonic curve-fitting approach from these signals. The performance analysis of the reconstruction signals was investigated in both the time and frequency domains. To ensure accurate trajectory tracking performance under parametric uncertainties, an adaptive backstepping control algorithm was designed. Experimental results revealed the superior performance of the proposed controller at all three road profiles, achieving Root Mean Square Errors (RMSE) as low as 1.3 mm. The controller exhibited robustness, maintaining consistent tracking precision with negligible performance variance across significantly different road characteristics, thereby validating the framework's utility for fatigue analysis.

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.