Abstract: Complementing the steady state modeling of an ideal solar evaporation pond, the present study is concerned with the dynamic modeling of the ideal solar evaporation pond for Li extraction from brines. The approach conceptualizes the pond as a sequence of discrete particles of brine of suitable size with continuous feeding and discharging considering water evaporation events over time. The discrete dynamic model produces a dynamic concentration profile over time, which converges to a steady-state concentration profile equal to that predicted by the ideal solar evaporation law. This discrete dynamic particle model allows for variations in feed flow rate, evaporation rate per unit area, feed concentration, and pond area. This approach allows the visualization of concentration perturbations propagating at a finite speed through the system exhibiting a hyperbolic nature. The model was validated using average lithium, boron, and magnesium concentrations obtained from an industrial pond system at the Salar de Atacama, Chile, in the year 2023. Therefore, this discrete dynamic model constitutes a versatile tool for analyzing and optimizing the performance of solar evaporation pond systems under variable operating conditions.

Abstract: Thorium has been recognized as a potentially viable alternative fuel for Molten Salt Reactors, Advanced Heavy Water Reactors, and High Temperature Reactors, and it is also being considered for Accelerator Driven Systems. Modular thorium reactor designs are expected to generate electrical outputs ranging from 100 to 3000 MW, underscoring their adaptability to national energy strategies. The role of thorium in nuclear reactors and fuel cycles, with a particular focus on advanced pressurized water reactors (PWRs), is reviewed. Currently, most Generation II nuclear reactors still depend on uranium within once-through fuel cycles. In nuclear-producing countries, growing interest in thorium reflects a wider effort to diversify energy generation, strengthen national nuclear programs, and align technological development with environmental goals. Key priorities include preserving limited uranium reserves, addressing rising global energy demand, and facilitating rural electrification. Domestic thorium-based power plants offer the potential to support sustainable development by curbing carbon dioxide and nitrogen oxide emissions. Furthermore, their resilience to seasonal variability enhances electricity supply stability, a crucial consideration for agriculture and irrigation. This study aims to explore current and future R&D priorities for innovative modular thorium reactors in nuclear-producing countries while presenting preliminary design concepts and simulation results that illustrate their prospective role in advanced nuclear energy systems. A preliminary electricity production cost estimate of US$20 per MWh was informed by Copenhagen Atomics' work on thorium molten salt reactors.

Abstract: Financial fraud represents a growing challenge for financial institutions and e-commerce, requiring increasingly sophisticated detection methods. Traditional machine learning models, while effective, can reach limitations when facing complex fraud patterns and highly imbalanced datasets. This paper proposes a novel ensemble approach, KAN-XGBoost, which combines the power of Kolmogorov-Arnold Networks (KAN) for learning complex relationships with the robustness of the Extreme Gradient Boosting (XGBoost) algorithm for high-performance classification. Using the synthetic PaySim dataset, we demonstrate the effectiveness of our approach. To address the severe class imbalance, the Synthetic Minority Oversampling Technique (SMOTE) was applied to the training data. Our experimental results show that the KAN-XGBoost ensemble model, in soft voting configuration, significantly outperforms the individual models, achieving a performance metrics of 99%. This high performance suggests that the hybridization of KANs with established boosting algorithms constitutes a promising avenue for enhancing the security of financial transactions.

Abstract: This project aims to develop a basic predictive maintenance model for aviation sensors, especially the ones that are directly related to flight safety. The study first uses SCAPS-1D to simulate a simple semiconductor structure and observe how electrical parameters such as Voc and Jsc change with temperature and material conditions. After getting these results, a simple state-space model is built in MATLAB to represent the relation between temperature input and device output. The idea is that many aircraft sensors, like the Angle of Attack (AOA) sensor, also contain small pn junction electronics inside, and their failures often begin with small drifts in electrical behavior. By comparing the SCAPS results with the state-space model, the project shows how these changes can be used as early indicators of degradation. This work gives a starting framework for future predictive maintenance studies by connecting semiconductor modeling with aviation sensor health monitoring.

Abstract: Advanced Air Mobility (AAM), having drones (aerial robots) as a core, is becoming an integral additional part of Disaster Management (DM) systems in metropolitan regions, and of future smart urban development and systems. Unmanned Aircraft Systems (UAS) are able to transport efficiently high added value goods and to provide efficient monitoring during the disaster events and their development. This paper provides a foresight on the overall System of Systems (SoS) needed including the UAS application in the studied use cases, as well as the crucial integration of relevant advanced communication systems (ACS), for the safe and sustainable UAS operation. In the evolving AAM, ACS is a crucial enabler and core element of a functioning SoS -- for the purposes of UAS navigation and operations safety, DM data collection and processing. The emphasised SoS enables emergency goods deliveries and the complete and efficient deployment and operation of an entire DM system (meant for monitoring, search and rescue, and decision making support), where UASs are used as logistic tools, and simultaneously for the monitoring of the environment and the disaster events in the affected regions. AAM is being operated mainly in the third dimension (airspace), which enables us to be minimally dependent on any types of ground transport infrastructure. Due to this, its precise navigation and management as well as relevant data streams transfer are crucial for the operational efficiency and safety. This foresight study provides a comprehensive, SUDEM (EU), REGUAS (DE), 5G!Drones (EU), and ETHER (EU) projects' lessons learnt-based path for understanding and efficiently deploying the SUDEM SoS including AAM and ACS for the purpose of the described 2 combined use cases: (i) high-added value goods transport and (ii) live monitoring, and the necessary educational model.

Abstract: The growing demand for high-strength and low-core-loss soft magnetic materials in high-efficiency energy conversion devices necessitates the development of novel alloys that combine excellent mechanical and soft magnetic properties. This work investigated the effect of Ta content on the microstructure and properties of as-cast (Fe₇Co₆Ni₆)93-xTaxAl7 (x=3, 5, 7) multiprincipal element alloys (MPEAs). The alloys featured an FCC matrix, in which Ta addition led to the precipitation of a Ta-rich Laves phase and significant grain refinement. The Ta5 alloy demonstrated an optimal balance of properties, with a yield strength approaching 1 GPa, an elongation of ~10%, a saturation magnetization of 92.88 emu/g, and a coercivity of 446.43 A/m, indicating good strength, ductility, and soft magnetic performance. An appropriate amount of Ta enhanced strength via precipitation and grain-boundary strengthening, while the saturation magnetization showed only a moderate reduction. The coercivity was effectively kept low by the fine, dispersed Co2Ta Laves phases, which minimized domain wall pinning.

Abstract: Laboratories across educational levels have traditionally required in-person attendance, limiting practical activities to specific times and physical spaces. This paper presents a technological architecture based on a system-on-chip (SoC) and a connectivist model, grounded in Connectivism Learning Theory, for implementing a remote laboratory in digital logic design using FPGA devices. The architecture leverages an Internet of Things (IoT) environment to provide applications and servers that enable remote access, programming, manipulation, and visualization of FPGA-based development boards located in the institution’s laboratory, from anywhere and at any time. The connectivist model allows learners to interact with multiple nodes for attending synchronous classes, performing laboratory exercises, managing the remote laboratory, and accessing educational resources asynchronously. This approach aims to enhance learning, knowledge transfer, and skills development. A four-year evaluation was conducted, including one experimental group using an e-learning approach and three in-person control groups from a Digital Logic Design course. The experimental group achieved an average performance score of 9.777, surpassing the control groups, suggesting improved academic outcomes with the proposed system. Additionally, a Technology Acceptance Model–based survey showed very high acceptance among learners. This paper presents a novel connectivist model, which we have called the Massive Open Online Laboratory.

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.