Abstract: A sustainable city requires a sustainable means of transportation. This ambition is leading towards a higher penetration of electric vehicles (EVs) in our cities, in both the private and commercial sectors, putting more and more burden on the existing power grid. Modern deregulated power grids vary electricity tariffs from location to location and from time to time, to compensate for any additional burden. In this paper, we propose a profit-aware solution to strategically manage the movements of EVs in the city to support the grid while exploiting these locational, time-varying prices. This work is divided into three parts: M1) Profit-aware charging location and optimal route selection, M2) Profit-aware charging & discharging location and optimal route selection, and M2b) Profit-aware charging & discharging location and optimal route selection considering the demand-side flexibility. This work is tested on the MATLAB programming platform using the Gurobi optimisation solver. From the extensive case study, it is found that M1 can yield profits up to 2 times more than those of its competitors, whereas M2 can achieve profits up to 2.5 times higher and simultaneously provide substantial grid support. Additionally, M2b extension has made M2 more efficient in terms of grid support.

Abstract: Metal additive manufacturing (AM) has emerged as a transformative route for producing lightweight, high-precision, and geometrically complex components in aerospace, biomedical, and microelectronic sectors. Among AM technologies, Laser Powder Bed Fusion (LPBF) offers exceptional design freedom; however, its widespread adoption particularly for titanium alloys remains constrained by two persistent challenges: shrinkage-induced dimensional deviation and porosity-related performance loss. In LPBF-processed Ti-6Al-4V, residual linear deviation typically falls within 0.1–0.8% when geometric compensation, preheating, and support strategies are implemented, while raw, uncompensated shrinkage is more commonly reported in the range of 1.2–2.0%, especially for thin-wall or thermally constrained geometries. Volumetric contraction (approximately 2–6%) may remain significant depending on part architecture and localized thermal accumulation. Concurrently, gas-induced and lack-of-fusion pores continue to undermine fatigue resistance and dimensional reliability. Research into process optimization, thermal management, and post-processing such as Hot Isostatic Pressing (HIP), vacuum sintering, and stress-relief annealing has improved density and mechanical integrity, while recent developments in AI-assisted monitoring, physics-informed models, and digital-twin frameworks are redefining defect prediction and control. Drawing on more than 100 peer-reviewed studies, this review synthesizes mechanism-driven insights and outlines a forward-looking roadmap, demonstrating how hybrid processing, real-time sensing, and data-centric control collectively advance the pathway toward defect-minimized, industrial-scale manufacturing of titanium components.

Abstract: The integration of Generative AI into civil engineering is currently constrained by the susceptibility of Large Language Models (LLMs) to hallucination and their inherent lack of physics-based knowledge. To address these limitations, this paper presents a conceptual framework for the integration of Agentic Artificial Intelligence (AI) into the complete lifecycle of seismic-resistant structural engineering. The proposal employs a modular software architecture built on the Model Context Protocol (MCP), enabling distributed collaboration among specialised AI agents across six critical stages: (1) seismic hazard assessment, (2) structural modelling and analysis, (3) design and optimisation, (4) construction quality control, (5) structural health monitoring (SHM), and (6) ethical audit and explainability. In this architecture, agents operate as autonomous MCP Clients within a standardised context, orchestrating workflows by communicating directly with deterministic MCP Servers and the human user. This structure strictly manages tool execution through synchronous, verifiable MCP calls, ensuring that stochastic agentic reasoning remains decoupled from immutable numerical execution. By grounding generative outputs in physics-based engines and Retrieval-Augmented Generation (RAG), the framework ensures traceable reasoning, transparency, and professional accountability, offering a pathway for the ethical deployment of AI systems in civil and structural engineering.

Abstract: Bridge decks are exposed to chloride ingress from de-icing salts, freeze-thaw cycling, and repeated wetting and drying, which gradually degrades the concrete over time. Many existing models treat concrete conditions as static and do not capture time-varying chloride exposure. This study develops deterioration envelopes for concrete bridge decks that predict loss of compressive strength and internal integrity by combining accelerated laboratory testing with in-situ bridge core data extracted from Delaware bridges. The model is supported by three data sources: accelerated laboratory tests, cores from in-service bridges provided by the Delaware Department of Transportation (DelDOT), and climate and asset datasets from the National Oceanic and Atmospheric Administration (NOAA) and the Federal Highway Administration's (FHWA) InfoBridge™ database. Laboratory specimens (n = 300) were reproduced based on Delaware mix designs from the 1970s and 1980s and were tested in accordance with ASTM and ACI protocols. Environmental conditioning applied wet-dry and freeze-thaw cycles at chloride contents of 0, 3, and 15 percent to replicate field exposure within a shortened test period. Measured properties included compressive strength, modulus of elasticity, resonance frequency, and chloride penetration. Results show a gradual, near-linear reduction in compressive strength and resonance frequency with increasing chloride content over 160 cycles, which corresponds to about 2 to 5 years of service exposure. Resonance frequency was the most sensitive indicator of internal damage across the tested chloride contents. By combining test results, core data, and bridge inspection history into a single durability index, the deterioration envelopes forecast long-term degradation under different chloride exposures, providing a basis for prediction that extends beyond visual inspection.

Abstract: This paper presents the development and validation of a 3D CFD model of a heave plate under forced oscillations using a Lattice-Boltzmann, LES software, which has never been used for industrial applications in this context. The main objective of the model is to be versatile enough to maintain accuracy in extreme cases of amplitudes and frequencies. The validation is carried out with experimental results from previous research, with some results also compared with the ones obtained using a finite-volume software. A lattice and time step convergence is achieved along with a symmetry study. Once the optimal model has been selected it is tested under 4 extreme cases, analyzing the results yielded for the force, added mass and damping coefficients and also assessing its limitations. Results show good correlation between the model and the experimentation, especially in cases of higher force values, and also with the results from the finite-volume software. Further-more, a vorticity field study will be carried out to better understand the behavior of the heave plate in these extreme cases. Finally, an assessment of the dominance of pres-sure-induced forces over viscous forces under low KC numbers is carried out using radial and surface integration.

Abstract: The most important step for the installation of a wind farm is to know the wind regime in the region, since an error in estimating this wind speed causes an error proportional to the cube of power, resulting in financial losses for investors. Therefore, knowing the methods used for predicting wind energy becomes important and the knowledge of how research and studies in this area are going help map the subject and outline strategies for developing research in strategic areas. For this purpose, a *** using the Scopus database considering some keywords, such as ("forecast" OR "prevision") AND "wind" AND ("turbine" OR "power" OR "energy" or "velocity" or "speed"), considering the period since 2020, and analyzing the data of the documents found using the Bibliometrix package. With the results found, it was possible to map researchers, and institutions that are developing work in this area, in addition to the most cited articles, as an indication parameter. Future works could include CFD simulation models most applied in different wind speed analysis reviews.

Abstract: This study compares the publication performance of Open-Access (OA) and subscrip-tion-based (SB) journals in Engineering, using bibliometric indicators from Scopus (2023 view). A total of 3012 active Engineering journals were analysed, of which 757 are OA, and 2255 are SB. Four metrics were examined for the period 2020–2023: CiteScore, total citations, number of published documents, and the percentage of cited articles, stratified by CiteScore quartiles (Q1–Q4) and the top 10% CiteScore group. SB journals concentrate most citations and tend to achieve higher mean CiteScores, larger publication volumes, and higher percentages of cited articles in the upper strata, with statistically significant differences on several indicators. At the same time, OA journals display CiteScore medians that are very similar to those of SB journals and lower var-iance for several indicators, particularly within the top 10% group, indicating more consistent performance among well-established OA titles. OA journals represent around one quarter of Engineering journals in Scopus, but remain underrepresented in the most highly cited segment. These findings suggest a hybrid configuration in which SB journals retain dominance at the top of the impact hierarchy. In contrast, OA jour-nals offer competitive and more homogeneous outlets with implications for publication strategies and open-access policies in Engineering.

Abstract: The complexity of modern cyber-physical systems is steadily increasing as their functional scope expands and as regulations become more demanding. To cope with this complexity, organizations are adopting methodologies such as Model-based Systems Engineering (MBSE). By creating system models MBSE promises significant advantages such as improved traceability, consistency, and collaboration. On the other hand, the adoption of MBSE faces challenges in both the introduction and the operational use. In the introduction phase, challenges include high initial effort and steep learning curves. In the operational use phase, challenges arise from the difficulty of retrieving and reusing information stored in system models. Research on the support of MBSE through Artificial Intelligence (AI), especially Generative AI, has so far focused mainly on easing the introduction phase, for example by using Large Language Models (LLM) to assist in creating system models. However, Generative AI could also support the operational use phase by helping stakeholders access the information embedded in existing system models. This study introduces an LLM-based multi-agent system that applies a Graph-Retrieval-Augmented-Generation (GraphRAG) strategy to access and utilize information stored in MBSE system models. The system’s capabilities are demonstrated through a chatbot that answers questions about the underlying system model. This solution reduces the complexity and effort involved in retrieving system model information and improves accessibility for stakeholders who lack advanced knowledge in MBSE methodologies. The chatbot was evaluated using the architecture of a battery electric vehicle as a reference model and a set of 100 curated questions and answers. When tested across four large language models, the best-performing model achieved an accuracy of 93 percent in providing correct answers.

Abstract: Trichloroethylene (TCE) and tetrachloroethylene (PCE) are chlorinated organic liquids widely employed in various industrial processes. However, due to their high toxicity and cancerogenic proprieties, these compounds are recognized as environmental pollutants. Therefore, the removal of TCE and PCE from wastewater is a crucial objective for environmental protection. This work investigated the adsorption capacity of syndiotactic polystyrene (sPS) fibers, activated in the nanoporous crystalline δ form, to remove volatile organic compounds from aqueous solutions. TCE can be adsorbed in the nanoporous crystalline δ form of sPS, leading to the formation of a clathrate structure, in which it acts as the guest molecule. This adsorption mechanism allows for high process selectivity, as well as the capture of even trace amounts (in the ppb range) of the pollutants under consideration, in relatively short times (e.g., 67 hours). Also, a process with two successive adsorption tests was performed replacing the solid used for the first contact with the contaminated solution with fresh δ-sPS fibers. This approach allowed the reduction of TCE concentration down to 8 ppb. In conclusion, δ-sPS nanoporous fibers demonstrated a great potential for the efficient removal of chlorinated organic compounds from wastewater, providing a promising alternative to conventional adsorption processes.

Abstract: Reaction time (RT) is a key indicator of cognitive and motor processing speed, and its age-related decline has important implications for everyday activities such as driving. However, conventional Psychomotor Vigilance Tests (PVTs) assess hand responses and do not capture lower-limb reaction characteristics relevant to pedal operations. This study used a foot-response version of the PVT (Foot PVT) to compare RTs between younger and older adults and to examine the influence of height, sleep factors, and physical activity level (PAL). Twenty younger adults (24 ± 3 years) and twenty-four older adults (73 ± 5 years) performed a 10-minute Foot PVT between 11:00 and 14:00. Participants responded to visual stimuli by moving the right foot laterally from a central pedal to the left or right pedals. RT mean, RT median, RT SD, skewness, and kurtosis were calculated, and correlation and multiple regression analyses were conducted using height, five OSA Sleep Inventory factors, and PAL as predictors. RT mean was significantly slower in older adults (818 ± 105 ms) than in younger adults (700 ± 73 ms), indicating an age-related delay of approximately 120 ms. Older adults showed lower skewness and kurtosis, suggesting more homogeneous responses and a cautious response strategy. In younger adults, height correlated negatively with RT (r = −0.593), and multiple regression identified height as the only significant predictor (adjusted R² = 0.316). No significant predictors were found in older adults. In the combined sample, height and age jointly explained 37.2% of RT variance. These findings indicate that Foot PVT performance reflects both biomechanical characteristics and age-related declines in reaction speed. Height strongly influences RT in younger adults, whereas RT in older adults appears to be shaped by multifactorial age-related changes. The Foot PVT provides a practical tool for assessing lower-limb reaction capabilities relevant to driving and aging.

Abstract: The challenge of insufficient monitoring accuracy in vision-based multi-point dis-placement measurement of bridges using Unmanned Aerial Vehicles (UAVs) stems from camera motion interference and the limitations in camera performance. Existing methods for UAV motion correction often fall short of achieving the high precision necessary for effective bridge monitoring, and there is a deficiency of high-performance cameras that can function as adaptive sensors. To address these challenges, this paper proposes a UAV vision-based method for multi-point displacement measurement of bridges and introduces a monitoring system that includes a UAV-mounted camera, a computing terminal, and targets. The proposed technique was applied to monitor the dynamic displacements of the Lunzhou Highway Bridge in Qingyuan City, Guangdong Province, China. The research reveals the deformation behavior of the bridge under vehicle traffic loads. Field test results show that the system can accurately measure vertical multi-point displacements across the entire span of the bridge, with monitoring results closely matching those obtained from a Scheimpflug camera. With a root mean square error (RMSE) of less than 0.3 mm, the proposed method provides essential data necessary for bridge displacement monitoring and safety assessments.

Abstract: This paper presents an outline of the problems facing the Polish energy sector. It high-lights the significant role of wind energy in the National Power System, while limiting the possibility of installing new wind farms. It is suggested that repowering and ex-tending the operational life of wind turbines will be an important solution to this problem. The possibility of using data from existing turbines to inform operational strategies was analyzed. Historical data was obtained for selected wind turbines and statistically analyzed. The main goal of the study was to develop regression models for wind conditions and electricity production. The best fit between the actual distribu-tions of the analyzed variables and selected theoretical distributions was determined. It was demonstrated that in the analyzed case, the Log-Normal distribution provided a better fit than the Weibull distribution, preferred by the energy industry.

Abstract: This study investigates the differences in flexural behavior of ultra-high-performance concrete (UHPC) arising from variations in test methods and key experimental parameters. Flexural tensile tests were conducted on 51 specimens representing 17 combinations of test variables, including steel fiber length (13 mm and 19.5 mm), specimen cross-sectional dimensions (75×75 mm, 100×100 mm, and 150×150 mm), presence or absence of a notch, and loading configuration (three-point and four-point loading). The tests were performed in accordance with ASTM C1609 and EN 14651, and both deflection and crack mouth opening displacement (CMOD) were normalized by the span length to compare the influence of each parameter. The notched specimens demonstrated significantly improved reliability, exhibiting up to an 8.4-fold reduction in standard deviation due to the consistent initiation of cracking. Regarding size effects, the 75×75 mm specimens showed an overestimation of flexural performance due to the wall effect of fiber distribution, whereas the 100×100 mm and 150×150 mm specimens exhibited similar flexural responses. The comparison of loading configurations revealed that three-point loading produced up to 11.7% higher flexural tensile strength than four-point loading, attributable to concentrated moment–shear interaction and the combined effects of fiber bridging and shear resistance mechanisms. In addition, specimens with longer steel fibers (19.5 mm) exhibited 5.2–9.7% higher flexural performance than those with shorter fibers (13 mm), which is attributed to enhanced interfacial bonding and improved crack dispersion capacity.

Abstract: This study investigates the influence of array height, irradiance, and wind speed on temperature difference and thermal gradients in photovoltaic (PV) arrays operating in hot, arid conditions. A field experiment was conducted in Mesa, Arizona (latitude 33° N), using two fixed-tilt PV module arrays installed at different elevations—one at 1 m and the other at 2 m above ground level. Each array comprised seven monocrystalline PV modules arranged in a single row with an 18° tilt angle optimized for summer performance. Data were collected between June and September 2025 and the analysis was restricted to 10:00–13:00 h to avoid shading and ensure uniform irradiance exposure on both arrays. Measurements included module backsheet temperatures at the center and edge modules, ambient temperature, plane-of-array (POA) irradiance, and wind speed. By maintaining identical orientation, tilt, and exposure conditions, the evaluation isolated the effect of height on module operating temperature and intra-array thermal gradients. Results indicate that the 2 m array consistently operated 1–3°C cooler than the 1 m array, confirming the positive impact of elevation on convective cooling. This reduction corresponds to a 0.4–0.9 % improvement in module efficiency or power based on standard temperature coefficients of crystalline silicon modules. The 1 m array exhibited a mean edge–center temperature gradience of −1.54°C, while the 2 m array showed −2.47°C, indicating stronger edge cooling in the elevated configuration. The 1 m array displayed a broader temperature range (−7 °C to +3°C) compared to the 2 m array (−5°C to +2°C), reflecting greater variability and weaker convective uniformity near ground level. The temperature gradience became more negative as irradiance increased, signifying intensified edge cooling under higher solar loading. Conversely, wind speed inversely affected ΔT, mitigating thermal gradients at higher airflow velocities. Overall, elevating PV arrays enhances convective heat transfer, reduces module temperature, and improves reliability and power output. These findings highlight the importance of array height, array length, irradiance, and wind conditions in optimizing PV system thermal and electrical performance.

Abstract: Against the backdrop of the global energy transition, the efficient exploitation of marine renewable energy has become a key pathway toward carbon neutrality. Wind–wave hybrid systems (WWHSs) have attracted increasing attention due to their resource complementarity, efficient spatial utilization, and shared infrastructure. However, most existing studies focus on single components or local optimization. A systematic integration of the full technology chain remains limited, hindering the transition from demonstration projects to commercial deployment. This review provides a comprehensive overview of the technological evolution and key characteristics of offshore wind turbine (OWT) foundations and wave energy converters (WECs). Fixed-bottom foundations remain the mainstream solution for near-shore development. Floating offshore wind turbines (FOWTs) represent the core direction for deep-sea deployment. Among WEC technologies, oscillating buoy (OB) WECs are the dominant research pathway. Yet high costs and poor performance under extreme sea states remain major barriers to commercialization. On this basis, the paper summarizes three major integration modes of WWHSs. Among them, hybrid configurations have become the research focus due to their structural sharing, hydrodynamic coupling, and significant cost and energy synergies. Furthermore, the review synthesizes optimization strategies for both technology design and spatial layout, aiming to enhance energy capture, structural stability, and overall economic performance. Finally, the paper critically identifies current research gaps and bottlenecks, and outlines key technological pathways required for future commercial viability. These include the development of high-performance adaptive power take-off (PTO) systems, deeper understanding of multi-physics coupling mechanisms, intelligent operation and maintenance enabled by digital twins, and comprehensive life-cycle techno-economic and environmental assessments. This review aims to provide a systematic reference for the advancement of multi-energy offshore systems and to support future integrated energy development in deep-sea environments.

Abstract: Unexploded ordnance (UXO) poses a significant hazard in military training areas. This paper assesses the effectiveness of aerial drone-mounted magnetometry for detecting buried UXO located outside the designated impact zones of the National Training Center (CENAD) of San Gregorio (Zaragoza, Spain), considered the largest maneuver area in Europe. To this end, a high-resolution aeromagnetic survey was conducted using a GEM GSMP-35U proton magnetometer mounted on a hexacopter drone. Data were collected at flight altitudes of 7 m and 2 m above ground level along a grid with 1-m line spacing. For its validation, eleven UXOs were deliberately buried at known coordinates to evaluate the system’s sensitivity and spatial resolution under operational conditions. The results demonstrate the capability of aerial drone-based magnetometry to detect small magnetic anomalies (with amplitudes between 2 and 18 nT) associated with buried UXO in complex environments characterised by high ferromagnetic noise, achieving signal-to-noise ratios greater than 5 (SNR > 5) at 2-m altitude and a geolocation accuracy of approximately 0.5 m. These findings support the use of unmanned aerial magnetometry as a viable tool for identifying hazardous remnants in military training ranges and operational scenarios, enabling coverage of 0.53 ha in less than one hour of effective flight time.

Abstract: This study develops and validates a simulation driven, human centric lighting framework for UK residential buildings that integrates circadian and biophilic design, daylight harvesting, and dynamic smart controls using DIALux Evo. A comparative quantitative design was adopted to evaluate traditional manual calculation versus simulation based optimisation across twenty lighting scenes in one bedroom flats, under identical spatial and environmental conditions and in compliance with EN 12464 1 and CIBSE LG standards. Performance was assessed using electrical energy consumption (kWh), average illuminance (lux), and luminous efficacy (lm/W), with statistical validation via paired t tests. The optimised design reduced mean energy consumption from 10.25 kWh to 8.68 kWh (t = 5.12, p = 1.2×10⁻⁵), increased mean illuminance from 94.36 lux to 116.93 lux (t = 7.095, p = 1.0×10⁻⁶), and improved luminous efficacy from 57.2–65.65 lm/W to 98.25–105.35 lm/W across living, kitchen, bedroom, and bathroom areas. Although a minority of scenes showed neutral or adverse energy outcomes, the dominant trend evidences statistically significant reductions in demand and enhanced lighting quality. The contribution is a reproducible and standards aligned methodology that advances best practice for low carbon residential lighting, with actionable guidance for architects, engineers, and policymakers pursuing Net Zero targets and occupant well being.

Abstract: Transmission error (TE) is one of the most important sources of gear noise and vibration. Manufacturing tolerances and assembly shifts introduce deviations in tooth geometry that produce periodic mesh disturbances; these disturbances excite the drivetrain and radiate as airborne sound. While many studies have modelled individual tolerance effects in deterministic simulations, few have evaluated the combined influence of realistic tolerance distributions using open Monte–Carlo data. This work analyses the public Gear Statistical tolerance analysis dataset (≈40 k samples) to answer the following questions: (Q1) How accurately can the kinematic transmission error be predicted from measured tolerance and process‑shift data? (Q2) Which individual tolerances and their interactions have the greatest impact on TE? (Q3) Can a TE‑derived proxy quantify noise‑critical excitation without an acoustic model? (Q4) What tolerance combinations minimise TE and noise while respecting manufacturing cost? (Q5) Is there a quantifiable trade‑off between tolerance tightening cost and noise reduction? To address these questions we formulate hypotheses: H1 Non‑linear machine‑learning models achieve high predictive accuracy for TE (R² > 0.85) compared with linear baselines. H2 A small subset of tolerances—profile and lead errors—dominate the TE variance and interact non‑linearly. H3 A relative noise proxy (RNP) derived from TE faithfully ranks noise‑critical excitation across tolerance combinations. H4 A Pareto front exists in the cost–noise plane, enabling cost‑effective tolerance optimisation. H5 Targeted adjustment of the top two tolerances yields larger noise‑reduction per cost than uniform tightening. The following sections describe the data, the modelling framework and the results that support these hypotheses.

Abstract: The rapid mobilization of sediment stored behind dams, in amounts that are large relative to mean annual sediment loads can jump start river restoration but can also adversely impact habitat, infrastructure, land, and water use upstream of, within, and downstream of the former impoundment. A wide range of geomorphic and engineering assessment tools were applied to help manage sediment-related risks associated with the removal of two dams from the Elwha River in Washington State and the release of roughly 21 million m3 of sediment. Each of these tools had their strengths and weaknesses, which are explored here. The processes of sediment erosion, transport and deposition were complex. No one model was able to fully simulate all these with the accuracy necessary for predicting the magnitude and timing of coarse and fine sediment release from the reservoir. Collectively, however, the model outputs provided enough information to guide the adaptive sediment management process during dam removal. When the complexity of the morphodynamic responses to dam removal and the associated risks exceeded the capacity of any one tool to adequately assess, synoptic forecasting proved useful. The lessons learned on the Elwha have provided insights into how to use a variety of modeling techniques to address sediment management issues such as dam removal scale, complexity and risk increase.

Abstract:

The increasing demand for sustainable materials in electrical engineering has encouraged the substitution of conventional fillers in epoxy insulation with recycled industrial by-products. This study investigates the potential use of waste tire rubber particles and zinc oxide recovered from electric arc furnace dust as eco-friendly fillers for epoxy resins in high-voltage insulation applications. Four material variants were fabricated: pure epoxy, epoxy with 10 wt% ZnO, epoxy with 10 wt% tire rubber, and epoxy with 20 wt% tire rubber. The breakdown voltage of each composite was measured under AC voltage. Results indicate that the incorporation of recycled fillers influences the breakdown voltage depending on both the type and concentration of filler. The 10 wt% ZnO-filled epoxy exhibited a moderate enhancement in breakdown voltage compared to pure epoxy, attributable to interfacial polarization and charge trapping at the epoxy-ZnO interface. Conversely, tire rubber fillers introduced localized field distortion and interfacial voids, resulting in a gradual reduction of breakdown voltage with increasing filler content. The results show that ZnO from metallurgical waste can function as an effective additive to improve dielectric performance. This approach supports circular-economy principles and offers a sustainable option for future high-voltage insulation materials.