Abstract: This study proposes a surrogate-assisted evolutionary optimization framework for small dataset that integrates machine learning–based surrogate models with evolutionary algorithms for the aerodynamic optimization of a spiked blunt body in supersonic flow. A database of simulated cases covering a range of Mach numbers, spike length ratios (L/D), and diameter ratios (d/D) was used to train regression models and identify optimal geometries. Among the tested algorithms, the Gradient Boosting Regressor (GBR) achieved the best predictive performance (R² = 0.8909, RMSE = 0.00775), accurately capturing the nonlinear dependencies of the drag coefficient (C_d). Evolutionary optimization methods, including Differential Evolution (DE), Covariance Matrix Adaptation Evolution Strategy (CMA-ES), and Genetic Algorithm (GA), consistently converged to near-optimal configurations, with DE exhibiting the most stable behavior across Mach regimes. SHapley Additive exPlanations (SHAP) analysis revealed that (L/D) is the most influential parameter on C_d, followed by Mach number and (d/D), highlighting the dominant effect of geometric slenderness in drag reduction. The integration of data-driven modeling with evolutionary computation provides a robust framework for aerodynamic optimization, offering both predictive accuracy and physical interpretability. These results demonstrate the potential of hybrid Machine Learning-Evolutionary algorithms and CFD approaches to accelerate the design of high-performance configurations in supersonic applications.

Abstract: We develop a method for constructing Lyapunov functions via Semidefinite Programming (SDP) that certifies the stability of oscillatory systems with both Cartesian and angular variables. We utilize the theory of hybrid polynomials (also called mixed trigonometric-polynomials) introduced by Dumitrescu. We use this theory to convert Lyapunov and dual Lyapunov stability conditions for oscillatory systems into SDP problems. Solving these problems using standard convex programming solvers leads to expressions of Lyapunov densities and local Lyapunov functions for these systems, even without apriori knowing the invariant attracting set. To illustrate the applicability of our method, we consider the analysis of Kuramoto models and the state feedback design problem for an inverted pendulum on a cart. Specifically, we establish certificates of almost global synchronization (phase locking) for second-order Kuramoto models. The paper concludes by developing an SDP certificate that enables the design of a swing-up control for an inverted pendulum on a cart. For the analysis, we use our program vSOS-hybrid, based on CVX in MATLAB, openly available on GitHub.

Abstract: This study integrates in-situ Quantitative Infrared Thermography (QIRT) and Building Energy Simulation (BES) to optimize the energy performance of an existing multi-story residential building in a temperate climate. QIRT was utilized to diagnose thermal anomalies at the interfaces of uninsulated walls, RC skeleton and fenestration junctions, revealing significant thermal bridging and air infiltration while enabling the calculation of the Temperature Index (TI) at critical interfaces. A key finding of the non-destructive diagnostic phase was the discrepancy between in-situ (UINSITU) and theoretical (UCALC) thermal transmittance values, providing an empirical baseline for subsequent optimi-zations. A multi-objective analysis was conducted using genetic algorithms to evaluate 192 retrofit combinations, involving three insulation materials at four thicknesses and 16 glazing types. The impacts on primary energy consumption, CO₂ emissions, and 30-year global costs (per EN 15459-1:2017) were quantified under the volatile economic conditions. Findings indicate that the energy-optimal solution reduces primary energy by 53% and CO₂ emissions by 51%, while the cost-optimal configuration reduces global costs by 52% relative to the reference case. The Pareto analysis reveals a robust convergence between financial and energy efficiency targets, proving that deep retrofitting is an economically imperative strategy for achieving national decarbonization goals and the 2053 net-zero vision.

Abstract: This study investigates the mechanical response of bifacial glass-glass photovoltaic modules subjected to snow-type loading, with a particular focus on the influence of silicon cell spacing on global deformation and local stress distributions in the silicon layer. Five computational finite element models were developed which explicitly represent all laminate layers and discrete cell layout. The numerical results are interpreted within the framework of partial interaction and shear transfer between the glass plies, and are validated against previously obtained home conducted experimental observations. The results demonstrate that silicon cell layout has a pronounced effect on local tensile stresses in silicon cells and on the curvature distribution within the laminate, while its influence on the global kinematic response is less critical. The numerical analysis indicates that the relative displacements between the glass layers resulting from the flexibility of the adhesive bond play a critical role.

Abstract: Maleic anhydride (MAH) grafting is widely employed to compatibilise polylactic acid (PLA) in fibre-reinforced composites; however, the influence of reactant addition sequence during melt processing varies widely across the literature, with no clear consensus on an optimal approach. In this study, the effect of reactant addition sequence on the graft yield of MAH onto PLA was investigated using dicumyl peroxide (DCP) as an initiator. Four loading protocols were examined in which the order of addition of PLA, DCP, and MAH was varied using approaches commonly reported in the literature, while all other processing conditions were held constant. A strong dependence of grafting yield on addition sequence was observed, with values ranging from 0.12% to 0.51%, corresponding to more than a four-fold variation under otherwise identical processing conditions. Simultaneous addition of PLA, DCP, and MAH produced the highest grafting yield, attributed to a more effective utilisation of peroxide-derived radicals. These results demonstrate that reactant addition sequence is a critical processing variable governing MAH grafting efficiency.

Abstract: The sustainable utilization of industrial by-products in concrete production has recently become a priority in modern construction engineering due to its global availability, environmental benefits, and potential engineering properties. The use of steel slag (SS) in fine and coarse sizes with mineral admixtures, including micro silica (MS), in concrete design must be thoroughly examined for durability and efficiency, considering several variables, including their types and contents. The combined impacts of MS and SS must also be well investigated. This article examined the mechanical and durability properties of concrete after adding SS and MS separately and in combination. In an experimentally based investigation, fine steel slag (FSS) and coarse steel slag (CSS) was substituted for natural fine and coarse aggregate, respectively in varying ratios (20% - 70%) in the same mixture with and without MS (10%) as partial cement replacement. The concrete mixtures' workability, density, compressive strength, flexural strength, splitting tensile strength, capillary water absorption rate (sorptivity), and freeze-thaw resistance were assessed. Results indicate that compressive strength increased progressively up to 40% SS replacement, achieving 42.1 MPa compared with 36.4 MPa for plain concrete. Beyond 50% replacement, strength declined despite continuous increases in density. According to the mechanical properties and durability investigated in the present study, the optimum performance was observed in the replacement of 30–40% SS along with 10% MS, which confirmed its modification. The findings provide engineers, researchers, and decision-makers in the construction industry with valuable guidance on the practical benefits and elements to consider when including SS and MS into concrete mixtures. This application maximizes resource efficiency and reduces environmental impact while enhancing the mechanical and durability properties of concrete.

Abstract: This paper presents a novel hybrid learning-based control scheme for position control of robot manipulators whose structure is based on a closed-kinematic-chain mechanism (CKCM). The developed control scheme integrates two complementary control components: The Feedback Controller and the Learning Controller. The Feedback Controller is designed using linearization about a desired trajectory and a PID control law whose gains are select-ed by a tuning algorithm to guarantee semi-global stability of the closed-loop system. The Learning Controller incorporates PID-type iterative learning strategy to generate additional control inputs to compensate for modeling uncertainties and unmodeled dynamics. By up-dating the control input iteratively from trial to trial, the Learning Controller progressively improves the overall control performance. The effectiveness of the developed control scheme is demonstrated through computer simulations conducted on a six-degree-of-freedom CKCM robot manipulator. Simulation results are presented and discussed to evaluate the tracking accuracy and robustness of the developed approach.

Abstract: Building code waiver assessments in Singapore remain largely discretionary, relying on case officers' subjective judgment with limited decision-support tooling. This study presents the first machine learning framework for predicting building code waiver outcomes, trained on 197 historically decided cases from the Building and Construction Authority (BCA) across five waiver categories: barrier-free accessibility (n = 45), ventilation (n = 61), staircase design (n = 37), safety provisions (n = 30), and structural modifications (n = 24), spanning 2021 to 2023. Fourteen engineered features, including documentation completeness, technical justification quality, and compliance history, were extracted through domain-expert annotation. Four models were evaluated: L2-regularised logistic regression, random forest, gradient boosting (XGBoost), and a weighted ensemble. The ensemble achieved the highest predictive accuracy of 83.7% (95% CI: 79.2-88.1%) with an area under the receiver operating characteristic curve (AUC) of 0.891 (95% CI: 0.854-0.928), significantly outperforming all individual models (McNemar's test, p < 0.05). SHAP analysis revealed that documentation completeness and technical justification quality collectively account for 55% of prediction variance. A companion five-by-five risk assessment matrix, combining predicted rejection probability with consequence severity, stratified cases into actionable risk tiers correlating with observed approval rates from 90.3% (very low risk) to 10.0% (very high risk; Spearman rho = -0.71, p < 0.001). The framework offers a transparent, data-driven complement to regulatory judgment and demonstrates feasibility for integration into Singapore's Corenet X digital building submission platform.

Abstract: The increasing complexity of modern infrastructure projects necessitates a digital transformation in project delivery processes. This study uses the Sibiu-Făgăraș Highway project in Romania as a qualitative case study to investigate the implementation of an integrated digital delivery framework. The research analyzes the synergistic application of key technologies—including a Common Data Environment (CDE), model-based fabrication, 4D/5D simulation, drone-based photogrammetry, and Business Intelligence (BI)—within a unified Plan-Do-Check-Act (PDCA) cycle. The core finding is that their integration creates a comprehensive digital ecosystem that functions as a human-in-the-loop digital twin for project delivery. This ecosystem significantly enhances coordination, enables data-driven decision-making, and reduces project risks by transforming traditional, reactive controls into a proactive, cyclical management system.

Abstract: The construction industry is among the most resource-intensive and environmentally damaging sectors, largely due to reliance on energy-intensive materials such as cement and steel. Growing concerns over climate change and resource depletion have increased interest in sustainable building materials as drivers of environmentally friendly architecture. This study examines the role of bamboo and unfired clay bricks in promoting sustainable architectural practices. A mixed-methods approach was adopted, combining literature review, comparative case studies, questionnaire surveys, and semi-structured interviews with architects, engineers, and construction professionals. Data from 150 respondents and interviews with 20 professionals were analysed to evaluate environmental performance, feasibility, user perception, and barriers to adoption. Findings indicate that bamboo and unfired clay bricks are widely regarded as environmentally preferable due to their low carbon footprint, renewability, biodegradability, and reduced production energy. Key factors influencing their eco-friendliness include carbon emission reduction, biodegradability, and availability of renewable resources. However, limited awareness, regulatory challenges, resistance to change, technical concerns, and skill requirements remain major obstacles to widespread adoption. The study concludes that bamboo and unfired clay bricks hold strong potential to advance environmentally friendly architecture, particularly in developing countries, if supported by appropriate policies, technical standards, capacity building, and increased stakeholder awareness.

Abstract: Rapid access to building intelligence is critical for emergency response, yet paper Fire Safety Instructions (FSi) often provide limited utility under stress. This structured narrative review addresses the "information gap" between unit arrival and decision-making by analyzing legal admissibility, technological requirements, and security risks of digital FSi across Poland, Germany, France, Belgium, and Hungary. While no explicit prohibition of digital forms was identified, enforcement practices prioritize paper as the evidentiary master. Consequently, we propose a hybrid model: a paper master for compliance and redundancy, supplemented by a digital operational overlay accessed via "zero-install" offline-first Progressive Web Apps (PWA). The review defines a Minimum Operational Dataset (MOD)—prioritizing critical data like utility shut-offs and hazards over full documentation—and addresses cybersecurity threats, specifically QR-phishing ("quishing"). We conclude that the hybrid model minimizes legal and operational risks while significantly reducing time-to-information, provided that strict content identity and change management protocols are maintained.

Abstract: Designing long-range aircraft for operation in 2050 represents a complex multidisciplinary challenge that requires integrating technical performance with broader sustainability objectives, including environmental responsibility, economic viability, circular economy principles, and social acceptance. Although previous studies have explored stakeholder needs in aviation, they typically focus on limited stakeholder groups, emphasize technical and operational requirements, or address specific aircraft concepts, resulting in a fragmented and insufficiently systematic understanding of sustainability-driven needs for future long-range aircraft. This study addresses this gap by providing a comprehensive and structured identification of stakeholders that directly or indirectly influence the development of long-range aircraft, together with a systematic derivation and classification of their needs. The analysis is based on an extensive review of academic literature, grey literature, regulatory documents, and industry sources. Stakeholders were organized into coherent categories and subgroups capturing the full ecosystem—including manufacturers, operators, passengers, regulators, communities, and energy suppliers—and a total of 191 stakeholder needs were identified and analyzed across technical, environmental, economic, circular, and social dimensions. The resulting needs establish a holistic and reusable foundation to inform the conceptual design and design parameters of future long-range aircraft within the ongoing European EXAELIA project, which focuses on conceptualizing disruptive long-range aircraft to inform and drive the development of flying testbeds. By integrating multidimensional stakeholder expectations at the earliest design stages, this work supports the development of aircraft that are not only technically robust but also environmentally sustainable, economically viable, circular, and socially inclusive.

Abstract: Hydrogen is a versatile energy carrier essential for decarbonizing hard-to-abate sectors and long-duration storage. This study presents a unified techno-economic comparison of major production pathways—grey/blue steam methane reforming, biomass gasification, thermochemical cycles, biological methods, and solar-powered electrolysis—using 2025 benchmarks. Focus is on a 100 kW off-grid PV-electrolyzer system with realistic assumptions (PV performance ratio 0.85, electrolyzer efficiency 70% LHV). In Iran's high-insolation regions (PSH ≥ 5.15 kWh/kWp/day), annual yields reach 3.2–3.4 tonnes H₂—55–60% higher than northern Europe—with round-trip efficiency of 23.8%. Solar electrolysis offers zero direct emissions and 51–55 kWh/kg H₂ consumption. Scaling to multi-MW coastal hybrids with renewable desalination projects LCOH of 3.0–4.0 USD/kg by 2030, positioning Iran as a competitive exporter. A reproducible model and phased roadmap provide actionable insights.

Abstract: This paper presents a tunable mode - locked fiber laser that employs a carbon - nanotube - based saturable absorber and a commercially available tunable filter. The operating wavelength of this laser is 1550 nanometers. The erbium - doped fiber (EDF) has a wide gain range, enabling the laser to achieve ultrafast mode - locking. Meanwhile, the tunable filter offers a broad wavelength selection range. The operating wavelength range of the mode - locking technology is from 1532.6 nanometers to 1569.9 nanometers. This tunable mode - locked fiber laser has a simple structure and a wide operating wavelength range. Therefore, it is highly suitable for applications in fields such as optical communication, sensing, and laser processing.

Abstract: The global energy transition aims to decarbonise both transportation and electricity generation to mitigate climate change and reduce reliance on fossil fuels. Electrification of private transportation, through the adoption of electric vehicles (EVs), presents a promising pathway to achieving the first objective. Concurrently, the rapid advancement and cost reduction of photovoltaic (PV) technology have positioned solar energy as a viable solution for renewable electricity production. This review paper synthesises recent modelling and empirical studies examining the synergies and challenges of coupling EV charging with PV electricity production. It explores the multifaceted benefits of this integration across various contexts: residential, workplace, highways, and public parking infrastructures. Additionally, the paper delves into practical considerations essential for real-world implementation, such as political incentives, charging stations, and tariff structures. By offering an overview of the cost effectiveness and implementation challenges across the four corners of the world, in a diversity of climate, solar irradiance and mobility behaviours, the review bridges the gap identified in the previous reviews on the potential of EV-PV coupling.

Abstract: The COVID-19 pandemic and the resulting mobility restrictions significantly disrupted urban traffic patterns. This study quantitatively assesses the impact of these restrictions on vehicle flow at a signalized central intersection in Cluj-Napoca, Romania, through an integrated methodology combining continuous radar-based traffic measurements and AI-assisted video analysis. Traffic data were collected before the pandemic (November 2019) and during the lockdown period (April 2020), enabling a comparative evaluation of flow characteristics and vehicle arrival patterns. Under constrained observational conditions, vehicle arrivals were modeled using a probabilistic framework grounded in the Poisson distribution. The findings indicate a dramatic contraction of mobility demand, with traffic volumes declining in 2020 to 9.55% of pre-pandemic levels. The probabilistic assessment highlights the predominance of free-flow regimes under reduced demand and confirms the adequacy of the Poisson model in low-density traffic scenarios. The proposed framework is transferable to other urban contexts and supports resilience-oriented, data-driven traffic management under extreme mobility disruptions.

Abstract: The increasing complexity of contemporary ship design, driven by multidisciplinary integration, dense spatial constraints, and stringent regulatory frameworks, poses significant limitations to traditional Computer-Aided Design (CAD)-based engineering workflows. While Artificial Intelligence (AI) techniques have been applied to isolated marine optimization problems, their systematic and governance-compliant integration into regulated CAD environments remains underdeveloped. This paper proposes a governance-aware methodological framework for the integration of Cognitive AI into marine CAD systems. The framework defines a layered architecture that combines structured data management, engineering corpus modeling, hybrid reasoning mechanisms (rule-based systems, machine learning models, and multi-objective optimization), and real-time CAD interaction. A human-in-the-loop cognitive cycle is embedded to ensure traceability, regulatory compliance, decision transparency, and professional accountability. To quantitatively assess engineering impact, a normalized performance evaluation model is introduced, incorporating indicators for design cycle time reduction, iteration convergence, compliance enhancement, and rework minimization. The framework is validated through a scenario-based application to pipe routing, demonstrating its analytical consistency and integration feasibility within operational design workflows. The proposed methodology establishes a reproducible and certification-aligned foundation for AI-augmented ship design, contributing to the structured digital transformation of Shipyard 4.0 environments.

Abstract: This study compares the accuracy of empirical and regression models for predicting the size distribution of blasted material at drawpoints in sublevel caving. Field data were sourced from Ernest Henry mine (EHM). At EHM, full-scale blasting trials were conducted under controlled conditions by varying explosive density and burden size, with fragmentation measured using laser scanning at different extraction tonnages. To minimise scanning inaccuracies, scan results were combined to represent the particle-size distribution for the EHM data. Five models were evaluated: Kuz-Ram, extended Kuz-Ram, Two Component Model (TCM), Kuznetsov-Cunningham-Ouchterlony (KCO), and a regression-based Underground Ring Blasting Model (URBM) developed in our previous work. Models were assessed for predicting P20, P50, and P80 passing sizes. Results show the extended Kuz-Ram and TCM perform better for finer fragment sizes, with Mean Absolute Percentage Error (MAPE) of 8%. URBM was the most accurate for median and coarse sizes (MAPE 5% for P50 and 3% for P80) and showed the least variability in errors. A SHAP-based sensitivity analysis of the three most accurate models identified key variables for median size prediction, highlighting the importance of rock properties and ring design parameters.

Abstract: Adaptive façade systems are increasingly used to mitigate glare in daylit spaces; however, their performance is often evaluated using illuminance-based metrics or uncalibrated simulations, limiting the reliability of visual comfort assessment. This study proposes a calibrated experimental–simulation framework for assessing glare reduction achieved by a kinetic horizontal shading system (KSS )under real daylight conditions. The approach integrates reduced-scale physical measurements with Radiance-based simulations using a digitally reconstructed twin of the experimental setup. Two geometrically identical test chambers positioned side-by-side —a static reference chamber and a kinetic chamber equipped with six 0.63 m-deep adaptive fins—were investigated using a 1:20 scale mock-up. Internal illuminance measurements were normalised between chambers, and a sky-scaling procedure was applied to calibrate simulated sky luminance distributions against measured data on an hourly basis. This enabled the use of photometrically validated HDR renderings for glare evaluation. Glare performance was analysed for three representative clear-sky days during periods of maximum solar exposure (11:00–17:00). Visual comfort was assessed using Daylight Glare Probability (DGP), Daylight Glare Index (DGI), and veiling luminance (Lveil). The KSS reduced mean DGP from 0.57 to 0.35 (−38%) and peak glare values by nearly half compared to the static configuration. Veiling luminance was reduced by 73%, confirming a substantial physiological improvement in visual comfort. These results demonstrate that adaptive fin movement effectively suppresses both perceptual and physiological glare during critical daylight hours. The proposed calibrated experimental–simulation workflow offers a robust and transferable methodology for evaluating glare performance of adaptive façade systems under real-world daylight conditions.

Abstract: This research explores the development of high-strength biofiber cement boards with good thermal insulation, using recycled biofiber derived from cement packaging combined with a water-based adhesive and Portland cement through a cementation-curing process. This approach reduces cement packaging and other biofiber waste, promoting environmental sustainability without requiring chemical additives or incineration of waste biofibers. Recycled biofiber from cement bags, composed primarily of cellulose (60 wt%), lignin (15 wt%), and hemicellulose (10 wt%), serves as a reinforcing phase, while the cement and adhesive mixture provide a strong binding matrix. The physical, mechanical, chemical, and thermal properties of the biofiber cement boards were evaluated in accordance with relevant industrial standards, including TISI 878:2023, BS 874, ASTM D570, ASTM C518, ISO 8301, and JIS A1412. The findings show that a higher ratio of water-based adhesive to cement mortar (1:2), combined with a higher content of biofiber sheets, significantly enhances performance, particularly in Formulas 7, 8, and 9. The resulting boards demonstrate strong bonding ability, improved fire resistance, better moisture and weather resistance, and longer service life compared to lower ratios. These outcomes highlight the potential of recycled biofiber composites as sustainable alternatives for thermal insulation and structural applications, such as ceilings and walls in building construction.