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This article reviews the evolution and current status of using blockchain technology to improve Smart Grid security and trust. It also summarizes the main challenges faced by blockchain-based Smart Grid systems. The survey provides a clear explanation of the various risks that threaten the blockchain-based Smart Grid and explores how AI technologies can support intrusion detection systems in identifying anomalies. It further highlights the importance of emerging quantum-computing risks and the challenges of adopting post-quantum cryptography in resource-constrained smart meters. The current AI evidence is concentrated on offline detection of false data and traffic anomalies, while operational validation, uncertainty calibration, adversarial robustness, and deployment-cost reporting remain limited, partly due to the lack of publicly available real-time BSG datasets and the complexity of realistic Smart Grid simulations. Further, post-quantum migration is constrained by the memory, bandwidth, latency, and energy budgets of long-lived smart-meter hardware. The abstract also identifies end-to-end evaluation of hybrid classical/post-quantum signatures as a priority. These insights support the development of more secure and resilient Smart Grid systems. By addressing these challenges and implementing robust countermeasures, this article aims to enhance the overall security of Smart Grids and promote their long-term resilience. Full article

Molecular dynamics (MD) simulations rely on accurate numerical integration of the equations of motion, where the choice of the timestep (Δt) critically affects stability and precision. Rather than aiming to recover an exact atomic trajectory, second-order integrators, such as those implemented in LAMMPS with the Nosé–Hoover thermostat, yield a global error scaling as O(Δt²). However, practical results deviate from this ideal behavior. In this work, we systematically analyze the effect of Δt on the accuracy of MD simulations using a polarizable force field applied to battery-relevant systems. Different errors were quantified for a range of timesteps. We evaluate whether timestep-induced numerical deviations affect physically meaningful observables, including diffusion coefficients and radial distribution functions, and examine the role of different integration schemes in solid and liquid components. The results show that decreasing Δt beyond the stability threshold leads to only marginal improvements in accuracy while significantly increasing computational cost at medium and long timescales. Conversely, excessively large Δt values produce numerical instability and unphysical behavior. These findings indicate that the expected ideal error scaling does not directly translate into practical accuracy gains and highlight the need for balanced timestep selection based on physical robustness rather than trajectory convergence alone. Full article

Triple Friction Pendulum (TFP) bearings are widely employed as seismic isolation systems to reduce seismic force and deformation demands in structures. However, the combined influence of vertical mass-distribution irregularity, soil–structure interaction (SSI), structural eccentricity, and nonstationary earthquake excitation on the nonlinear torsional response of TFP-isolated buildings remains insufficiently understood. This study investigates the seismic response of a five-storey reinforced concrete (RCC) building equipped with TFP bearings subjected to nonstationary spectrum-compatible horizontal earthquake ground motions. The investigated structure represents a low-to-medium-rise isolated building with a superstructure period Ts ≤ 0.5 s, and the conclusions are applicable within the examined structural configuration, adopted TFP properties, and equivalent SSI modelling assumptions. A nonlinear time-history analysis framework combined with Monte Carlo-based spectrum-compatible ground-motion simulations is employed to evaluate the effects of SSI, structural eccentricity, and vertical mass-distribution irregularity. Validation against OpenSees benchmark simulations demonstrates strong agreement in the predicted structural responses. Parametric analyses show that increasing the soil stiffness ratio from 0.5 to 2.0 reduces the normalized isolation displacement by approximately 46.5% and the corner rotational response by approximately 36.7%. Conversely, increasing the vertical mass-irregularity ratio from 1.0 to 1.5 increases the inter-storey drift ratio by approximately 60% and corner-displacement magnification by approximately 48.6% within the investigated cases. The results demonstrate that seismic response is governed not only by the magnitude of vertical mass irregularity but also by its location due to changes in inertia-force distribution, modal participation, and lateral–torsional coupling. These findings highlight the importance of considering SSI and vertical mass distribution simultaneously when assessing the seismic performance of torsionally asymmetric TFP-isolated RCC buildings. Further studies incorporating taller structures, multidirectional excitation, detailed soil–foundation interaction models, and experimentally calibrated TFP properties are required to extend the applicability of the findings. Full article

Despite extensive research on the abrasive properties of lunar regolith, the use of in-process tribological signals for wear assessment remains insufficiently studied. The aim of this work is to evaluate the relationship between torque response and abrasive wear severity in rotating shaft–seal systems exposed to lunar regolith simulants. Rotating EN 1.4404 stainless-steel shafts and spring-loaded natural polytetrafluoroethylene (PTFE) lip seals were tested under three-body abrasive wear conditions. Five particle-size fractions of LX-M100 Lunar Mare and LX-TH100 simulants were investigated at test durations of 15 min, 30 min and 1440 min. Frictional torque was continuously recorded, while post-test shaft surface roughness was used to characterize wear severity and Scanning Electron Microscope (SEM) analysis of the PTFE counterface was used to identify wear mechanisms. The results showed that torque response systematically depended on particle-size fraction and simulant type. Furthermore, the torque-response descriptors provide in-process information related to abrasive interaction severity, with peak torque events ($T_{max}$) showing the strongest relationship with abrasive surface modification. The statistical analysis was based on 27 complete observations, and considering the prematurely terminated test runs and the resulting limitations of the dataset, the obtained relationships are interpreted as exploratory. These findings provide a physical basis for torque-based wear monitoring in sealed rotating mechanisms for future lunar applications. Full article

The efficient use of existing road infrastructure has become increasingly important in densely developed urban areas where large-scale roadway expansion is constrained. Variable directional lanes (VDLs) and traffic signal control can reallocate roadway capacity and improve network performance. However, most existing studies optimize lane configurations and signal timing under a fixed route-flow distribution and therefore do not capture the feedback between control decisions and travelers’ route choices. To address this limitation, this study proposes a bi-level framework for coordinating VDLs and traffic signals at multiple intersections. The upper-level model determines the VDL functions and signal-control parameters to minimize total system travel time, while the lower-level static Logit-based stochastic user equilibrium model endogenously redistributes fixed origin–destination (OD) demand among candidate paths. Thus, OD demand remains fixed within each analysis period, whereas the route-flow distribution responds endogenously to the interaction between traffic control and aggregate route-choice responses. A hybrid solution procedure combining the Non-dominated Sorting Genetic Algorithm II and the Method of Successive Averages is used to solve the coupled control–assignment problem. Numerical experiments on a hypothetical network showed that incorporating route-choice feedback improved coordinated VDL–signal control under the tested conditions. In a supplementary comparison with the pre-optimization BPR-based reference scenario, the average route travel time decreased by 7.67–12.84% across the five representative demand periods, including reductions of 12.52% and 12.84% during the morning and evening peak periods, respectively. Microscopic simulation provided an additional numerical consistency check, with average discrepancies of 4.66% before optimization and 4.38% after optimization between the analytical and simulation results. These findings indicate that incorporating aggregate route-choice feedback can support sustainable urban traffic management by reducing travel time and congestion and improving the utilization of existing transportation infrastructure. However, further validation using real-world data and larger-scale networks is required, and environmental benefits should be evaluated explicitly using energy-consumption and emission indicators. Full article

Maintaining stable dissolved oxygen (DO) and pH conditions is critical for reliable operation of bioreactor systems used in cell culture and bioprocess manufacturing. However, accurate regulation of DO and pH remains challenging due to nonlinear process dynamics and variations in operating conditions. In particular, temperature fluctuations can affect gas solubility, gas–liquid mass transfer, and CO₂ buffering equilibrium, resulting in deviations in DO and pH. Existing control methods often rely on predefined mechanistic models or reactor-specific parameter identification, which may limit adaptability under changing operating conditions. This paper proposes a disturbance-compensated data-driven adaptive predictive control framework for DO and pH stabilization in bioreactor systems under dynamic temperature disturbances. Based on dynamic linearization, the proposed framework establishes an online input–output representation using measured gas composition, temperature disturbance, and environmental responses. An adaptive gain adjustment mechanism and pseudo-partial-derivative estimation method are developed to update the control relationship online without requiring an explicit process model or iterative optimization. Furthermore, temperature variations are incorporated as measurable disturbances to achieve real-time compensation of their effects on DO and pH dynamics. The proposed framework was evaluated through simulations and experiments using a 3 L bioreactor platform. Compared with a PID controller with temperature feedforward and conventional model-free adaptive predictive control, the proposed method reduced DO and pH tracking errors and improved recovery performance under temperature disturbances. The results demonstrate that the proposed data-driven adaptive predictive control strategy provides an effective approach for DO and pH stabilization in bioreactor systems under temperature-varying conditions. Full article

Coal self-heating in longwall goaf areas results from strongly coupled gas flow, heat transfer, mass transport, and chemical reactions occurring within a porous medium containing residual coal. This study presents a mathematical and numerical model for analysing these transient and non-isothermal processes with spatially variable permeability based on in-situ mining data. The model accounts for gas filtration through the porous goaf, heat and mass transfer between the gas and solid phases, heterogeneous coal oxidation, homogeneous gas-phase reactions, continuous methane emission, and the possibility of nitrogen inertisation. The governing equations form a strongly coupled non-linear system and are solved using the finite volume method. Numerical simulations were performed for U-type and Y-type ventilation layouts. The results provide spatial distributions of methane, oxygen, and carbon monoxide concentrations, gas temperature, solid-phase temperature, pressure, and gas velocity. The simulations demonstrate that ventilation configuration affects oxygen penetration, gas composition, and temperature development within the goaf. In particular, the Y-type ventilation system promotes deeper oxygen ingress into the porous zone, which may increase the extent of regions susceptible to coal self-heating. The proposed approach provides a framework for analysing coupled thermal and transport phenomena associated with spontaneous coal combustion and for assessing the influence of ventilation conditions on the development of thermal hazards in longwall goaf areas. Full article

To address spatial confusion and semantic collapse in maintenance knowledge graph construction for long-distance belt conveyor systems, this study proposes a linear topology-aware alignment framework driven by large language models. The framework constructs a Physical–Space–Fault–Rule ontology and introduces a normalized linear topology mapping mechanism to assign unique spatial anchors to highly homogeneous components. For noisy and colloquial maintenance logs, a Condition–Constraint–Action–Space (CCAS) extraction mechanism is developed to reconstruct implicit fault causal chains and spatial information from unstructured text. To prevent erroneous merging of components with similar semantic descriptions but different physical locations, the LTA-Alignment algorithm integrates Gaussian topological penalties with LLM-based grey-zone arbitration. A structured gold-standard annotation protocol is further established using maintenance logs, BOM records, spatial anchors, and closed-loop repair evidence. Experiments over three predefined runs show that CCAS achieves a global F1-score of 94.10 ± 0.56%, exceeding T5 by 6.20 percentage points. LTA-Alignment achieves a Merge F1-score of 95.75 ± 0.53% and an entity cluster purity of 96.70 ± 0.53%, exceeding SCSA by 3.36 and 3.90 percentage points, respectively. In downstream fault traceability, the dynamic spatiotemporal knowledge graph achieves an Accuracy@1 of 92.78 ± 0.36%, 10.16 percentage points higher than R-GCN, together with an estimated 68.50 ± 0.73% reduction in rule-based simulated troubleshooting time relative to Keyword Retrieval. The results demonstrate that combining normalized spatial topology with LLM reasoning improves entity disambiguation, fault-causal reconstruction, and traceability performance under the evaluated non-branching belt conveyor maintenance setting. Full article

This paper presents a real-time browser-based platform for thermohydraulic characterization of a compact laboratory heating system, developed using the PolyVR research-grade virtual reality engine. Experimental measurements are retrieved at 1 Hz from a cloud-based database and processed via browser-native computational framework that continuously performs thermophysical modeling, hydraulic analysis and energy balance evaluation. The system calculates the rate of heat transfer (h), overall heat transfer coefficient (U), dimensionless numbers (Re, Pr, Gr, Nu), pump performance, heater efficiency and cumulative thermal energy. PolyVR provides the immersive environment in which the partial digital twin functionality is integrated alongside the browser-based thermohydraulic calculations. The whole system includes support for animations regarding flow diagrams, valve state indicators, thermal field visualization and manipulation of system elements. The system architecture is designed to work on desktops, head-mounted devices, as well as in CAVE (cave automatic virtual environment) systems with remote connection made possible via using ngrok tunnels. The experiments were separated into three categories (steady-state, dynamic and validation). Steady-state and dynamic datasets show that the browser computation with PolyVR achieves high-fidelity thermohydraulic analysis similar to that done in laboratory settings. The steady-state and transient datasets illustrate that browser-based computation provides highly accurate thermohydraulic simulation close to that of the laboratory reference computations. For all experiments performed on the platform, the deviation of measurements does not exceed ±0.5 K in temperature, ±5% in flow rate and ±1% in pressure. The energy balance is closed with a deviation of ±2–3%. Full article

Agentic artificial intelligence is gaining significance in chemical engineering. Many process decisions involve coordinated actions rather than isolated predictions. These decisions are constrained by physical limitations, uncertain measurements, safety protocols, and human supervision. This PRISMA-guided systematic review asked where AI agents are applied in chemical engineering and process control-related problems. It also asked which agent families are used, what evidence supports their contributions, and what limitations condition deployment. Scopus was searched in the title, abstract, and keyword fields on 20 April 2026. Eligible records were peer-reviewed articles published from 2022 to 2026, indexed in the Chemical Engineering subject area, and explicitly relevant to AI agents. Evidence maturity, reported limitations, and risk of overinterpretation were extracted for each study. The included studies were synthesized into four domains. These are safety and risk; digitalization and process systems engineering workflows; control, scheduling, and operations; and molecular, reaction, and materials design. Most evidence still comes from simulations, computational studies, or prototypes rather than from plants in operation. The review therefore identifies six conditions for deployment backed by auditable engineering evidence. These are industrial validation, safety guarantees, digital twins grounded in ontologies, reproducible evaluation of LLM agents, governance of the interaction between engineers and agents, and integration across scales. Full article

The premature degradation of aluminium extrusion dies remains one of the major challenges affecting process efficiency, product quality, and tooling costs. Although numerous studies have investigated wear mechanisms and proposed solutions such as surface treatments, coatings and lubrication, the absence of a generic and reproducible laboratory methodology for evaluating tribological performance under representative extrusion die contact conditions limits the objective and systematic comparison of alternative tribological solutions. This study proposes and experimentally validates a generic tribological methodology for laboratory simulation of aluminium extrusion die contacts. Rather than reproducing the complete extrusion process, the methodology isolates the dominant physical mechanisms governing die degradation and reproduces their essential characteristics under controlled laboratory conditions, providing a representative platform for systematic tribological investigations. The methodology was developed through the selection and scaling of representative contact parameters, including contact geometry, normal load, sliding velocity and operating temperature. The experimental programme incorporated physical similarity principles, a controlled run-in procedure and repeated use of the same hardened steel counterface to reproduce cumulative die exposure under successive aluminium contacts. Two aluminium alloys (AA6063 and AA6082) were evaluated using a small ring-on-disc configuration against a hardened GCr15 steel counterface. Experimental validation was carried out using two extrusion lubricant systems, complemented by three additional commercial lubricants to assess the robustness and general applicability of the proposed methodology. The experimental results demonstrate that the proposed methodology provides repeatable and sufficiently sensitive measurements of friction and wear, allowing clear differentiation between lubricant systems and aluminium alloy–lubricant combinations while maintaining representative contact conditions. The study also demonstrates that steady-state friction should be identified from the actual friction evolution rather than by applying a fixed averaging interval. Although lubricant evaluation is employed here as the experimental validation case, the proposed methodology is intended as a generic experimental framework applicable to the assessment of surface treatments, coatings, tool materials, lubrication systems, and other tribological strategies aimed at extending extrusion die service life. Full article

Achieving net-zero energy and zero-emission buildings is a critical pathway toward decarbonizing the built environment, particularly in cooling-dominated regions where operational energy demand remains exceptionally high. Existing university buildings in hot–humid climates face significant challenges due to intensive cooling requirements, limited passive cooling potential, and the economic burden associated with large-scale renewable energy deployment. This study develops and evaluates a climate-responsive retrofit framework that integrates sequential energy optimization, CFD-based passive-cooling analysis, and on-site renewable energy systems to transform an operational university building in Jeddah, Saudi Arabia, into a nearly net-zero energy building (NZEB). A high-fidelity DesignBuilder–EnergyPlus model was calibrated using three years of monthly measured electricity consumption data, achieving strong agreement with utility records (NMBE = 2.19%, CV(RMSE) = 7.93%). The proposed framework prioritizes demand-side load reduction through optimized HVAC operation, envelope enhancement, daylight-responsive lighting control, natural ventilation, and Passive Downdraught Evaporative Cooling (PDEC) before renewable energy integration. The baseline building exhibited an Energy Use Intensity (EUI) of 613 kWh/m²·year, with cooling accounting for approximately 70% of total electricity consumption. Sequential optimization reduced annual energy demand by 58%, while CFD-supported passive cooling strategies provided an additional 17% reduction in cooling energy and improved indoor airflow performance. Crucially, nearly 80% of total energy savings were realized prior to photovoltaic (PV) deployment. A 1586-kW rooftop photovoltaic system subsequently offset the residual annual demand, achieving a nearly net-zero annual energy balance. Over 25 years, the proposed retrofit pathway reduced life-cycle costs from 7.51 million SAR to 3.13 million SAR. The findings demonstrate that climate-responsive demand reduction is the primary enabler of NZEBs in hot–humid regions, substantially reducing renewable energy requirements and long-term economic costs while providing a scalable pathway to decarbonize existing campus infrastructure. Full article

Packaging geometry determines how products occupy pallet space and, consequently, how efficiently transport capacity is used, yet these effects are rarely evaluated during early design. This study develops a parametric framework linking secondary packaging geometry to transport-related environmental, economic and logistics performance through multi-criteria decision analysis. Indicators are generated from box dimensions, board grammage, packaging costs, and pallet configurations. Criterion weights are derived using the Analytic Hierarchy Process, and alternatives are ranked using TOPSIS. Twelve packaging configurations were evaluated across seven criteria, followed by an industrial application involving six real configurations from a food supplement packaging project. The analysis showed that packaging geometry produced differences of up to 31.2% in CO₂ emissions per unit and 6.2% in cost under identical transport conditions. Increasing packing density did not necessarily improve system performance, and the highest-density configuration ranked last. Sensitivity analysis under four weighting structures and a Monte Carlo simulation using 10,000 randomly generated weight vectors showed that the main ranking patterns remained robust under preference uncertainty. The results indicate that packaging performance cannot be assessed independently of the logistics system in which it operates. The framework provides a reproducible basis for early packaging and supply chain decisions. Full article

Conventional air stripping for ammonia–nitrogen wastewater is often hampered by packing clogging and low mass transfer efficiency. To address these limitations, this study proposes a jet impact negative pressure reactor (JI-NPR) featuring an optimized scatter-pattern (D7) multi-orifice configuration. Computational Fluid Dynamics (CFD) simulations were employed to systematically investigate the effects of Reynolds number (Re = 5503.4~9651.0, corresponding to 2.76~4.84 m/s) on the hydrodynamic characteristics and deamination performance. Results indicate that increasing jet velocity significantly enhances the water volume fraction, resultant velocity, and pressure core intensity within the impact zone. Notably, these enhancements are maximized at the second row (z = 146 mm), attributed to reduced interference from the negative-pressure flash evaporation region. While a higher Re promotes interfacial renewal and vortex evolution, thereby enhancing mass transfer, it also intensifies energy dissipation and reduces the uniformity of the turbulent kinetic energy distribution. This work elucidates a critical trade-off between mass transfer enhancement and energy consumption, establishing a quantitative structure: the Re–flow field-performance relationship. The findings provide a theoretical foundation for the design and optimization of energy-efficient, high-performance wastewater treatment systems. Full article

Measles virus infection couples nonlinear processes across biological scales, including within-host viral amplification, immune-cell depletion, delayed adaptive control, persistent viral RNA, heterogeneous host severity and vaccination-dependent population spread. A multiscale mathematical framework is developed by coupling a seven-variable within-host ordinary differential equation model to a stochastic cellular automaton. The within-host system extends a four-variable measles immunodynamics core by including IFN-$\gamma$-dominant and IL-17-associated immune responses, persistent viral RNA and neutralizing antibodies. Six host archetypes are represented as structured parameter perturbations of this common dynamical core. The principal novelty is an explicit cross-scale coupling operator that separates genuinely ODE-derived host descriptors from hybrid epidemiological mapping rules and independently specified population-level contact and susceptibility assumptions, allowing within-host heterogeneity to propagate transparently into a spatial stochastic epidemic model. An explicit ODE-to-cellular-automaton map translates within-host trajectories into infectious timing, daily infectivity profiles and an illustrative ODE-informed severity-to-death transition mapping used internally by the cellular automaton. The mortality map depends on viral burden, infectious duration, IFN-$\gamma$ deficit, cumulative infectivity and an immune-deficit–infectivity interaction term. Population simulations show a nonlinear reduction in attack rate with increasing vaccination coverage, reduced modeled death burden under targeted high-risk in silico perturbations and additional suppression under reactive vaccination campaigns. A direct local cellular-automaton secondary-infection estimate is reported instead of interpreting cumulative infectivity burden as a reproduction number. A targeted contact-structure sensitivity further shows that matching the expected local direct-secondary-infection potential does not imply equivalent population-level attack rates, emphasizing that the quantitative CA outcomes are geometry specific. Sobol sensitivity analysis with convergence up to $N_{\mathrm{base}}=4096$ identifies core viral and immune parameters as dominant drivers of within-host and multiscale outputs. The framework provides an explicit dynamical-systems approach for coupling differential-equation immunodynamics to spatial stochastic population models in mathematical biology. Full article