Abstract: The design, fabrication, and characterization of a highly transparent and flexible monopole antenna optimized for the 3–6 GHz frequency band is presented in this paper. In traditional Transparent Conductive Oxide (TCO) designs, there is always a trade-off between the RF efficiency and transparency. Therefore, an Aerosol Jet® 5X system was used to directly print a silver nanoparticle mesh over a 50-µm polyimide (PI) substrate. With this fabrication method, a durable structure was yield which works well both electrically and mechanically with 85% transparency and a gain of −2.5 dBi. In order to demonstrate how the antenna is flexible and compatible with other devices, it was bent over a cylindrical body and was integrated with a commercial solar panel. The results show that impedance matching and radiation characteristics of the antenna remain stable under bending conditions, and no critical decrease was observed in solar energy harvesting. Consequently, this design presents a suitable solution for energy-autonomous IoT systems, smart windows, and CubeSat applications.

Abstract:

The transition to hydrogen-fueled gas turbines is vital for decarbonising power systems, especially in space- and weight-constrained applications such as offshore FLNG and FPSO. While hydrogen offers zero-carbon emissions at the point of use, its use in gas turbines faces technical challenges due to high flame speed, flammability limits, low energy density, and high flame temperature. These increase the risks of flashback and NOₓ formation, especially when retrofitting existing combustors. Developing hydrogen-ready combustors for both pure hydrogen and blends is an ongoing research area. This study investigates a can-type, annular gas turbine combustor for use with pure hydrogen and blends. Using CFD simulations in ANSYS Fluent, it analyses flow, flame, temperature, and stability across hydrogen ratios from 0% to 100%. The model employs RANS equations, a realizable k–ε turbulence model, non-premixed combustion, and species transport; thermal radiation is modelled with the P-1 method, and NOₓ with the Zeldovich mechanism. Results show hydrogen increases flame reactivity, shortens flame length, and enhances recirculation zones, maintaining stability at ~50% hydrogen. Higher fractions increase flame temperature and velocity, increasing the risk of flashback. Pure hydrogen produces compact, high-temperature flames that require advanced designs for stability. Model predictions match experimental and published data from NASA, Siemens SGT-800, GE LM6000, and Kawasaki, confirming credibility. This CFD assessment offers insights into hydrogen combustor design, supporting the move towards hydrogen-ready turbines and low-carbon offshore power generation.

Abstract: Nature-like fishways (NLFs) are a key restoration measure for fragmented rivers at low-head barriers, yet their economic and functional performance is poorly documented. This study provides a comprehensive analysis of 134 NLF projects in Spain (2003–2025), classifying them by typology, energy dissipation elements, and construction method. We quantified construction costs using standardized indicators and assessed available hydraulic and biological efficiency data. Results show a predominance of public funding schemes and a strong geographical concentration of NLFs in the northern half of the country, with ramps (76.1%) being more frequent than bypass channels. Construction costs varied markedly among designs, with concrete boulder ramps consistently representing the most cost-intensive NLF configurations, while also being strongly influenced by local site conditions and construction constraints. Only a small fraction of projects (13.4%) underwent post-construction efficiency assessment, but those evaluated generally showed favorable performance for multiple fish species. Our findings provide a state-of-the-art overview of NLFs in Spain, together with a practical classification framework and standardized cost indicators to support the planning and prioritization of river connectivity restoration projects.

Abstract: In this paper, we propose an correntropy weighted extended Kalman filter (CWEKF) method to address the challenges of low estimation accuracy and poor robustness in sensorless rotor speed estimation for doubly-fed induction generators (DFIGs). Firstly, based on Faraday's law of electromagnetic induction and the mechanical motion equation, we derive a DFIG nonlinear state-space model. This model quantifies the sources of nonlinearity arising from cross-coupling terms and product terms, providing a precise model foundation for rotor speed estimation. Secondly, we introduce correntropy theory to design a residual dynamic weighting scheme. By quantifying the local similarity between current and historical residuals, the scheme adaptively adjusts the noise covariance estimation weights, suppressing the interference of outdated data. Combined with the Chi-squared test, we derive an adaptive kernel bandwidth mechanism, balancing the response speed to noise variations and the estimation accuracy in steady-state. Additionally, we further integrate Huber robust weighting and regularization techniques for constructing a hybrid weighting mechanism and optimizing the covariance positive-definiteness correction to address the numerical stability deficiencies of the original algorithm. Using the Lipschitz condition and Lyapunov theory, we prove the mean-square exponential boundedness of the CWEKF estimation error. Finally, we build a DFIG vector control model using MATLAB. Comparative experiments are conducted with EKF, AEKF, and RWEKF under three operating conditions. The results show that the CWEKF has a maximum rotor speed estimation error \( \leq \) 5 r/min, and the response time has been reduced by 65% compared to the traditional EKF, exhibiting significantly improved robustness under parameter variations and strong noise conditions.

Abstract: Ulaanbaatar, the capital of Mongolia, operates one of the world’s largest district heating (DH) systems in the coldest national capital (Heating Degree Days ~5800). Despite serving over 60% of the city’s 1.6 million residents, the current 3rd generation DH system suffers from high thermal losses (~17–18%) and relies on coal-fired combined heat and power plants. Transitioning to 4th generation district heating (4GDH) with lower supply temperatures could reduce these losses while enabling future low-temperature renewable energy integration. This GIS-based spatial heat load density (HLD) analysis uses operational data from the Ulaanbaatar District Heating Company, encompassing 13,500 buildings with a total connected capacity of 3,924 MW. Grid-based spatial analysis was performed at two resolutions (1 km² and 2 km²). Threshold sensitivity analysis was conducted across HLD criteria of 1–5 MW/km². Results indicate that median HLD values exceed the European reference threshold of 3 MW/km², with log-normal distributions confirmed by Shapiro–Wilk tests. Three candidate pilot zones were identified. A hybrid temperature strategy (65/35 °C above −25 °C; 90/60 °C below) further contextualizes the findings. These results suggest spatially favorable conditions for 4GDH development, providing a quantitative foundation for subsequent techno-economic feasibility studies.

Abstract: Thermoelectric generators (TEGs) enable compact waste-heat energy harvesting but require high-gain DC–DC conversion due to their low output voltage for microgrid interfacing. This work proposes a novel TEG-supplied two-stage architecture consisting of a perturb and observe (P&O)-based MPPT boost converter followed by a modified Z-source converter regulated through an advanced control strategy. The modified Z-source topology enables high voltage gain without extreme duty ratios and mitigates switching losses by eliminating diode-related reverse-recovery effects via synchronous operation. To enhance dynamic performance, an advanced model predictive control (MPC) approach is adopted and benchmarked against conventional MPC and sliding mode control (SMC). Simulation results under hot-surface temperature variations demonstrate that the proposed system maintains stable 400-V DC bus regulation at a 100-W output level. In contrast, conventional MPC exhibits switching-frequency deviations that increase switching losses during transients, while conventional SMC suffers from significant voltage deviations. After the temperature variation tests, the proposed control strategy is subjected to a ±20% load test, in which it maintains 400-V regulation with nearly fixed-frequency operation, confirming its superior dynamic suitability for TEG-based systems operating at 50 kHz. The proposed innovative design provides a new perspective for TEG researchers while supporting sustainable waste-heat energy utilization.

Abstract: The development of cutting-edge vehicle communication technology targeted at enhancing road safety, traffic efficiency, and autonomous mobility has been expedited by the emergence of intelligent transportation systems. Direct wireless connection between vehicles is made possible via vehicle-to-vehicle (V2V) communication, which speeds up the exchange of vital safety data like speed, trajectory, braking status, and road conditions. By incorporating Artificial Intelligence (AI) into V2V networks, predictive skills are improved, enabling cars to foresee possible risks and react proactively as opposed to reactively.With an emphasis on its operational architecture, supporting technologies, recent research advancements, and future network paradigms, this study provides a thorough scientific overview of V2V communication. The report demonstrates how next-generation wireless technologies, edge computing, and AI-driven analytics are converting vehicle networks into intelligent safety ecosystems. Important issues are also looked at, such as interoperability, scalability, cyber security hazards, and latency limitations. The study comes to the conclusion that AI-enabled V2V communication will be a key component of completely autonomous and accident-free transportation systems.

Abstract: The sixth generation of mobile networks (6G) is envisioned as an AI-native and computa-tion-driven infrastructure capable of supporting ultra-low latency, massive connectivity and intelligent services across highly heterogeneous environments. Achieving these objec-tives challenges traditional centralised architectures and motivates a shift towards dis-tributed computing and intelligence at the network edge. This study presents a structured computational analysis of architectural approaches that integrate distributed computing paradigms and Edge Artificial Intelligence (Edge AI) as core enablers of 6G networks. The methodology follows PRISMA guidelines for systematic reviews and is based on a com-prehensive analysis of peer-reviewed literature, architectural proposals and standardisa-tion documents retrieved from major scientific databases, including IEEE Xplore, Scopus, Web of Science, MDPI and arXiv, as well as reports from ITU-R, 3GPP and ETSI. The analysis examines the evolution from cloud-centric to edge-centric computing, key Edge AI techniques—such as Federated Learning, Split Learning and edge-adapted large AI models—and their role in enabling intelligent orchestration, resource optimisation and context-aware services. The results indicate that the tight integration of distributed com-puting and Edge AI enhances network responsiveness, scalability and adaptability, while also revealing persistent challenges related to orchestration complexity, resource con-straints, security and interoperability. The study concludes that holistic computational architectures and AI-native design principles are essential for the effective realisation of 6G networks and for guiding future research and standardisation efforts.

Abstract: The removal of nitrogen and phosphorus from wastewater with low organic carbon content requires the addition of an external carbon source. The objective of this study was to assess the influence of hydraulic retention time (HRT) on the efficiency of external carbon source utilization and on nitrogen and phosphorus removal in a Rotating Electro-Biological Disc Contactor (REBDC). The energy demand was evaluated based on energy consumption (E) and current efficiency (CE). Hydroponic tomato wastewater was treated in the REBDC at a constant current density of 2.5 A/m². Sodium acetate was used as the carbon source. Two C/N ratios were tested: 2.0 and 3.0, under HRT conditions of 24 h and 48 h. For both C/N ratios, extending the HRT resulted in decreased nitrogen removal efficiency. At HRT = 48 h and C/N = 3.0, the nitrogen concentration in the effluent was more than three times lower compared with C/N = 2.0. The highest phosphorus removal efficiency was achieved at C/N = 3.0 and HRT= 48h (98.8%). Increasing the HRT led to reduced TOC utilization for both C/N ratios. As a consequence of extended HRT, lower CE values and higher E values were observed, indicating increased energy demand for nutrient removal.

Abstract: Trajectory planning algorithms are essential in human-robot collaboration (HRC), as they must generate efficient trajectories for seamless interaction. Given the risks and complexity of testing in real-world scenarios, a virtual environment was developed in Unity 3D, integrating a digital twin of the UR3 robot that delivers workpieces to a user equipped with a Meta Quest device. The RRT, RRT-Star (RRTS), and RRT-Connect (RRTC) algorithms were evaluated using ANOVA and Tukey post-hoc tests, considering the following response variables: safety, feasibility, smoothness, and computation time across three experimental scenarios characterized by (i) low, (ii) medium, and (iii) high levels of movement of the participant’s left hand. The statistical results indicate that RRTC exhibited the best performance in terms of smoothness and computation time. Based on these findings, a multicriteria decision-making analysis was conducted using the Analytic Hierarchy Process (AHP), combining quantitative evidence derived from the statistical analysis with expert judgments supported by bibliographic references. This multicriteria analysis enabled the coherent integration of the different evaluation criteria and concluded that RRTC is the most suitable alternative for collaborative assembly tasks in CHR environments.

Abstract: Micro-electro-mechanical systems (MEMS) sensors offer the benefits of compact size, lightweight design, and low cost, making them widely applicable in consumer electronics, vehicles, health, defense, and communications. With enhanced performance, MEMS sensors have also found applications in oil exploration and geophysical studies. For years, pressure and temperature sensing during hydraulic fracturing has been employed to enhance the down-hole conductivity of oil and gas extraction. Nevertheless, the pursuit of developing high-precision MEMS sensors for oil exploration continues to be an active area of research. This paper details the design, fabrication, packaging, and characterization of a silicon-on-insulator (SOI) MEMS piezoresistive pressure sensor integrated with a temperature sensor. It also describes the design of a chamber that simulates real conditions at the bottom of oil exploration wells. The sensors were successfully designed and fabricated based on physics-based simulations, deep reactive ion etching and anodic bonding techniques. The pressure sensors, along with the signal conditioning system, demonstrated a linear response, with a maximum linear error of 4.95% at 7 MPa and a reduced linear error of less than 3.5% at 24 MPa. Additionally, a quadratic approximation for the temperature sensors was achieved, showing a maximum resistance change of 8.5% at 140 ºC.

Abstract: An alternative MEMS navigation method for determining latitude, longitude, and altitude is proposed, based on Velocity-Aided Navigation, when global navigation satellite system (GNSS) or Starlink signals are unavailable for various reasons. Currently, GNSS receivers are the primary navigation systems that meet consumer demand for location accuracy. However, GNSS receivers are not autonomous and are susceptible to jamming and spoofing. Strapdown inertial navigation systems (SINS), unlike GNSS, are autonomous. Their operating principle is based on double integration of accelerometer output signals. However, they have a significant drawback: SINS errors increase significantly over time. Two approaches are used to improve accuracy. The first involves using expensive, high-precision gyroscopes and accelerometers. The other involves correcting the SINS by integrating it with navigation systems built on physical principles different from those of the SINS. An alternative method based on MEMS IMU for determining navigation parameters is proposed that does not require double integration of accelerometer output signals. Analytical expressions for the errors of the new method are derived. Calculations showed that the errors of the new method are significantly smaller than those of the SINS. Experimental testing confirmed the calculation results and demonstrated that the errors of the new method are comparable to those of the SINS integrated with GNSS and a Kalman filter. The proposed alternative NEMS navigation method for determining latitude, longitude, and altitude can be used independently, as an alternative to GNSS for integration with the SINS, and can also serve as a backup navigation system.

Abstract: Developing a solar hybrid banana dryer designed explicitly for South Pacific is a significant step forward in addressing both environmental sustainability and economic efficiency in the region. This project encompasses the design and implementation of a sophisticated drying system tailored to meet local conditions, featuring an array of components such as solar panels, batteries, a solar collector, an air filter, a heat exchanger, a drying chamber, electric heaters, forced ventilation, and a control unit. This paper details the various phases of the project, including design objectives, options, literature review findings, design selection processes, detailed design calculations, and forward-looking recommendations. Project, bringing diverse expertise to ensure the dryer met South Pacific economic and environmental needs. The primary aim was to create a low-cost, sustainable, portable dryer that is simple to construct and maintain and capable of upholding hygiene standards despite variable weather conditions. When designing the dryer banana was used as the model material to produce banana powder but it can be used for crops like breadfruit, pineapple, tomatoes and cassava and whatever else can benefit. By adopting this innovative solution, south pacific can benefit from an environmentally friendly, economically viable method of banana drying, supporting local agriculture and contributing to sustainable development in the region.

Abstract: Plug-in hybrid electric vehicles (PHEVs) are expected to reduce fleet CO₂ emissions, yet their real-world performance often deviates substantially from type-approval expectations. This study examines whether traction battery capacity provides an independent explanatory signal for the test-to-reality CO₂ gap (gap%), or whether it primarily acts as a proxy for market segmentation and usage patterns. Using European on-board fuel and energy consumption monitoring (OBFCM) records for 457,555 PHEV observations (2021–2023) from 14 manufacturers, we estimate nested fixed-effects models and introduce engineered usage proxies describing charge-depleting operation (EUR), hybrid utilization intensity (HI), energy-into-battery intensity (EDE), and a real-world to type-approval fuel-consumption ratio proxy (ELP). Battery capacity alone explains limited variation in gap% (R² = 0.075), while adding segment/year/manufacturer fixed effects increases R² to 0.203 and adding usage proxies increases it to 0.826, with the battery coefficient attenuating from 19.6 to 8.9 percentage points per kWh. Allowing non-linear battery terms via cubic B-splines yields only a modest additional improvement (R² ≈ 0.829), although the conditional shape is non-monotonic. Importantly, the battery–gap association is strongly segment-dependent, ranging from −22.1 pp/kWh in medium vans to +10.5 pp/kWh in large cars. Robustness checks using model-identifier fixed effects (MS_Cn) with standard errors clustered by MS_Cn further attenuate the battery effect (p ≈ 0.085), whereas ELP remains strongly associated with gap%. Overall, battery capacity is informative for compliance analytics mainly as a proxy variable capturing segmentation and real-world usage, rather than a universal lever of PHEV CO₂ performance.

Abstract: Deep metamorphic rock reservoir permeability prediction faced great challenge due to strong heterogeneity. Fractures were widely developed, but the traditional models did not involve into the contribution of fractures. To establish an effective model to predict permeability, parameters related to fracture needed to be taken into account. In this study, taking the Archaeozoic Formation in BZ 19-6 Region - a typical deep metamorphic rock reservoir and located in southwestern Bohai Bay Basin - as an example, the porosity frequency spectra were first extracted from electrical imaging logging, and relationships between the shape of porosity frequency spectrum and rock pore structure were analyzed. Two parameters, which were defined as the logarithmic mean (φgm) and standard deviation between two golden section points (φgsr), were used to reflect the main position and wide of porosity frequency spectrum, and a novel model to predict permeability was established. After the target formations were classified into two types based on the difference of pore type and pore-fracture configuration relationship, the involved model coefficients were calibrated. Consecutive permeability curves were derived from our raised model in the intervals with which porosity frequency spectra were acquired. Comparisons of predicting permeabilities with core-derived results illustrated that this proposed model was much reasonable, and the resolution of predicted permeability curve was high. Except for the deep metamorphic rock reservoirs, the estimated model could be widely used in other types of formations (e.g., volcanic rock reservoirs, carbonate rock reservoirs) where secondary pores were developed.

Abstract: This research proposes a robust multi-objective NSGA-II heuristic optimization framework for CFRP (Carbon Fiber Reinforced Polymer) wrapping to promote seismic-resilient and low-carbon high-rise infrastructure. The method integrates nonlinear time-history analysis with a multi-objective genetic algorithm to determine optimal CFRP application conditions, including whether floors require CFRP wrapping, mortar jacketing, steel jacketing, or combinations thereof. It further optimizes CFRP thickness, orientation, design pattern (unilinear, bidirectional, hybrid), coverage, and anchorage details. Optimization simultaneously minimizes overall cost, torsional irregularity index, Park–Ang damage index, and seismic sensitivity while maintaining structural reliability under seismic loading. Simulation results indicate that proposed CFRP framework reduces torsional impacts by approximately 35%, enhancing seismic resilience. Hybrid CFRP configurations combined with 20–40 MPa mortar and optional 10–40 mm steel jacketing showed improved structural performance. Anchorage of 0–2 per end per face reduced torsional drift to ≤0.5–1.0%. For a 10-storey building, lower floors benefit from CFRP with mortar/steel jacketing up to ±45°, mid-level floors from hybrid (0/±45°) configurations, and upper floors from predominantly ±45° CFRP with occasional 90° bands. CFRP thickness of 0–3 mm (0–6 plies) achieved improved seismic resilience, cost efficiency, and structural reliability, supporting its potential for seismic-resilient infrastructure policy and design.

Abstract: Adaptive Traffic Signal Control (ATSC) remains a critical challenge for urban mobility. In this direction, Deep Reinforcement Learning (DRL) has been widely investigated for ATSC, showing promising improvements in simulated environments. However, a noticeable gap remains between simulation-based results and practical implementations, due to reward formulations that do not address phase instability. Stochastic variations may trigger premature phase changes ("flickers”), affecting signal behavior and potentially limiting deployment in real scenarios. Although several works have examined delay, queues, and decentralized coordination, stability-focused variables remain comparatively less explored, particularly in single yet complex intersections. This study proposes a decentralized DRL model for ATSC with Noise Injection (ATSC-DRLNI) applied to a single intersection, introducing a stability-oriented reward function that integrates flickers, queue length, and Advantage Actor-Critic (A2C) learning feedback. The model is evaluated in Simulation of Urban MObility (SUMO) platform and compared against seven baseline methods, using real traffic data from a Mexican city for calibration and validation. Results suggest that penalizing flickers may contribute to more stable phase transitions, while reductions of up to 40\% in queue length were observed in heavy-traffic scenarios. These findings indicate that incorporating stability-related variables into reward functions may help bridge the gap between DRL-based ATSC studies.

Abstract: We present a high-fidelity aerodynamic shape optimization framework for the NREL S809 airfoil using Free-Form Deformation (FFD) parameterization coupled with a RANS CFD solver and a discrete adjoint for gradient computation. The design objective is to maximize aerodynamic efficiency (lift-to-drag ratio, CL/CD) under single- and multi-point operating conditions relevant to horizontal-axis wind turbines. Geometry changes are controlled by 40 FFD surface control points, and the Sequential Least Squares Programming (SLSQP) algorithm enforces lift, thickness and volume constraints. We validate our solver against NREL Phase VI wind-tunnel data and perform mesh-convergence and gradient-verification studies. Single-point optimization yields a CL/CD increase of ≈22.5% relative to the baseline at Re = 3.48×10⁶, while multi-point optimizations (2–4 points) produce robust improvements across the operating envelope. We discuss sensitivity to turbulence/transition modeling, show gradient check results (finite-difference vs adjoint), and provide mesh-independence evidence. The optimized shapes reduce maximum thickness and move camber forward, improving lift while reducing drag; however, structural implications of thickness reduction are discussed. The framework and validation illustrate a reproducible path for high-fidelity airfoil optimization targeted at wind-energy applications.

Abstract: Alkaline electrolysers are a proven and cost-efficient technology for large-scale hydrogen production. While established, improving their performance remains an active research area. A significant but less recognized factor is the internal geometry of electrode supports, which influences gas removal and mass transport, thereby affecting efficiency. This study details the design, fabrication, and experimental validation of three unique alkaline electrolysers, each featuring a modified internal electrode-support geometry. Their performance was comprehensively assessed through polarization curve analysis in individual, partial, and global configurations. The development followed the Advanced Product Quality Planning (APQP) methodology, employing pure nickel electrodes for stability. The research experimentally demonstrates how specific geometric alterations directly influence electrochemical performance and overall efficiency. By operating three electrolysers simultaneously, the system achieved an overall efficiency of 42% and a maximum oxyhydrogen production rate of 10 L min-1 with minimal electrolyte carryover This systematic work establishes essential design guidelines aimed at advancing the technology from Technology Readiness Level (TRL) 5 to TRL 6, facilitating the development of a reliable 5 kW hydrogen production system.

Abstract: Fractured reservoir characterization is a complex and challenging task due to its depositional nature and high uncertainty in the spatial distribution of fractures, typically when well data is limited and interpolation algorithms are employed. This paper introduces an alternative workflow designed to enhance fracture modeling between well locations by incorporating seismic attributes, using publicly released data from the Teapot Dome field. The paper's objective is to create a fracture model for the Tensleep reservoir in the Teapot Dome Anticline by employing seismic attributes sensitive to fault and fracture features, while also demonstrating the limitations of interpolation-based models such as Gaussian simulation. The approach uses artificial neural networks to predict fracture intensity by analyzing seismic data and well logs, training supervised probabilistic artificial networks to identify the seismic attributes that most closely correlate with the fracture intensity property derived from well log data. The validated network successfully transformed the 3D seismic data into 3D fracture intensity data, achieving a high correlation coefficient between the selected seismic attributes and the training wells. The research findings are extremely valuable because they help address the lack of information on fractures, improve reservoir management, and optimize well placement.