Abstract: Lower-limb rehabilitation exoskeletons must balance biomechanical compatibility, structural safety, and low mass to enable practical, repeatable gait assistance. This paper proposes a planar pantograph-derived exoskeleton leg driven by a Chebyshev Lambda linkage and develops an integrated workflow from mechanism synthesis to manufac-turable optimization and experimental verification. A mannequin-coupled multibody model was built in MSC ADAMS to evaluate joint kinematics, end-point (foot) trajec-tories, and joint reaction forces under multiple scenarios (fixed-frame, ramp, stair as-cent, and inclined-plane walking). The extracted joint loads were transferred to a par-ametric finite element model in ANSYS Workbench, where response-surface surrogates and a multi-objective genetic algorithm (MOGA) were used to minimize mass under stiffness and strength constraints. For the optimized load-bearing link, the selected minimum-mass design reached a component mass of 0.542 kg while respecting the imposed structural limits, i.e., a maximum total deformation below 0.2 mm and a maximum equivalent (von Mises) stress below 55 MPa (e.g., ~0.188 mm deformation and ~39 MPa stress in the optimal candidate). A rapid prototype was manufactured by 3D printing and experimentally evaluated using CONTEMPLAS high-speed video tracking, providing measured X_M(t) and Y_M(t) trajectories and joint-angle histories for quantita-tive comparison with simulations via RMSE metrics.

Abstract: Virtual Power Plants (VPPs) face significant challenges in managing the uncertainty and variability of distributed energy resources (DERs), which can result in high trading risk and deter investment. This paper proposes and evaluates two advanced optimisation techniques—stochastic programming and robust optimisation—to derive risk-aware bidding strategies for VPP participation in the day-ahead and balancing electricity markets. These methods are benchmarked against a deterministic, expectation-based model. The novelty of this work lies in the comparative application of stochastic and robust frameworks to VPP bidding strategy design under real-world uncertainty, the introduction of scenario-based wind and conventional generation models, and the integration of energy storage into the optimisation framework to assess its impact on profitability and risk mitigation. Through a series of simulations using actual market data from the UK (Elexon), we evaluate three generation portfolio configurations—conventional, renewable, and aggregated. The results show that while stochastic optimisation consistently achieves the highest expected profit, the robust model ensures the highest minimum profit under worst-case conditions. Moreover, combining DER types and integrating battery storage further enhances profitability and reduces exposure to imbalance penalties. These findings provide valuable insights for the development of intelligent, risk-aware trading strategies for VPP operators.

Abstract:

Requirements for robustness and performance in the frequency domain in control theory are usually formulated as constraints on the modulus of complex functions describing the open-loop system, the sensitivity function, and the complementary sensitivity function. These constraints generate circular sets that can be interpreted as admissible or forbidden regions in the complex plane. In engineering practice, they are often treated as method-specific constructions, without clarifying the general geometric mechanism by which they arise. This study develops a geometric approach in which a broad class of frequency domain robustness constraints is represented as level sets of analytic and fractional-linear functions. The resulting circular sets in the Nyquist plane are characterized in a unified manner and transferred to admissible regions in the s-plane through preimage mappings. The approach is formulated entirely using complex transfer functions, without state-space representations, linear matrix inequalities, or optimization methods. Classical robustness measures, including gain margin, phase margin, and constraints on sensitivity and complementary sensitivity, are shown to be special cases of the same geometric structure. This interpretation establishes a direct link between frequency domain constraints and closed-loop pole locations, allowing a qualitative assessment of robustness and dynamic properties of control systems without introducing new stability criteria or design procedures.

Abstract: Experimental validation of dissolved oxygen (DO) control in aquaculture is often limited by biological variability, environmental factors, and pond hydrodynamics, which reduce reproducibility and hinder reliable assessment. To address this, we developed a laboratory-scale, control-oriented platform that minimizes external disturbances and enhances statistical reliability. Oxygen demand was emulated via chemical deoxygenation with sodium sulfite, so aeration experiments begin from near‑zero dissolved oxygen (DO). Sodium sulfite is added only during initialization; any residual persists briefly into the early closed-loop phase. Using this framework, On-Off control with hysteresis and discrete-time PID control were compared in terms of overshoot, rise time, settling time, and steady-state error. Under a confidence criterion, the PID controller required fewer repetitions than the On-Off strategy to achieve comparable reliability.

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.