Search Results (2,186)

Lithium-ion batteries (LIBs) are essential for electric vehicles, consumer electronics, and grid storage, but their rapidly increasing demand is paralleled by growing waste volumes. Current disposal methods remain costly, complex, energy-intensive, and environmentally unsustainable. This pilot study investigates a scalable, low-impact disposal method by incorporating LIB waste into concrete, evaluating both the structural and environmental effects of LIB waste on concrete performance. Several cement–mortar cube specimens were cast and tested under compression using the cement–mortar mix with varying battery waste components, such as black mass and varied metals. All mortar mixes maintained an identical water-to-cement ratio. The compressive strength of the cubes was measured at 3, 7, 14, 21, and 28 days after casting and compared. The mix containing black mass exhibited a 35% reduction in compressive strength on day 28, whereas the mix containing varied metals showed a 55% reduction relative to the control mix without LIB waste. A case study was conducted to evaluate the combined structural and environmental performance of a concrete specimen incorporating LIB waste by estimating the embodied carbon (EC) for each mix and comparing the strength-to-net EC ratio. Selective incorporation of LIB waste into concrete provides a practical, low-carbon upcycling pathway, reducing both embodied carbon and landfill burden while enabling greener, non-structural construction materials. This sustainable approach simultaneously mitigates battery waste and lowers cement-related CO₂ emissions, delivering usable concrete for non-structural and low-strength structural applications. Full article

Reducing fuel costs, maximizing waste utilization, and improving energy efficiency are critical challenges in agricultural thermal processes. This study addresses these issues by developing and evaluating a mixed-fuel burner and furnace system for steaming mushroom substrate cubes using crude glycerol and recycled vegetable oil as low-cost alternative energy sources. The experimental investigation assessed boiler thermal efficiency, combustion efficiency, exhaust-gas composition, temperature distribution, steam generation, and combustion-gas dispersion within the furnace. In parallel, analytical modeling of pressure, temperature, and gas-flow behavior was performed to validate the experimental observations. Five fuel compositions were examined, including 100% used vegetable oil, 100% crude glycerol, and blended ratios of 50/50, 25/75, and 10/90 (glycerol/vegetable oil), with all tests conducted in accordance with DIN EN 203-1 standards. The results demonstrate that blending used vegetable oil with glycerol significantly improves flame stability, increases peak combustion temperatures, and suppresses incomplete-combustion byproducts compared with pure glycerol operation. Combustion efficiencies of 90–99% and boiler thermal efficiencies of 72–73% were achieved. Among the tested fuels, the optimal balance between combustion stability, efficiency, and cost was achieved with a 25% glycerol and 75% used vegetable oil mixture. Economic analysis revealed that the proposed mixed-fuel system offers superior viability compared with LPG, reducing annual fuel costs by approximately 50%, shortening steaming time by 2 h per batch, and achieving a payback period of only 3.26 months. These findings confirm the feasibility of the proposed waste-to-energy system for small- and medium-scale agricultural applications. To further enhance sustainability and renewable fuel utilization, future work should focus on improving air–fuel mixing for higher glycerol fractions, scaling the system for larger farms, and extending its application to other agricultural thermal processes. Full article

Fe-Ni-based alloys have attracted attention due to their potential for applications such as transmission line de-icing, where the core requirements include a Curie temperature near the freezing point and sufficient saturation magnetization. Accordingly, this study designed an Fe-29Ni-2Cr-1.5Mn (at.%) alloy with a Curie temperature around the freezing point, aiming to investigate the correlation between microstructural evolution and magnetic properties after cold rolling and annealing. The alloy was cold-rolled by 65% and subsequently annealed at 873 K for 0 to 60 min. The study reveals systematic evolutions in the alloy’s microstructure and magnetic properties. During the initial annealing stage, recovery substructures predominantly formed within the deformed grains, accompanied by a reduction in dislocation density and lattice constant. In the later annealing stage, the recrystallized fraction increased, although complete recrystallization was not achieved. Texture analysis indicates that the intensity of the Cube texture strengthened from 0.48 to 1.13. Correspondingly, the saturation magnetization and Curie temperature increased by approximately 9.76% and 10.25%, respectively, in the early annealing period, and then stabilized thereafter. The early-stage improvement in properties is likely related to stress relief and lattice distortion relaxation during the recovery stage. The calculated magnetocrystalline anisotropy constant of this alloy at 273 K is K₁ = 126 ± 18 J/m³, indicating that the <100> direction is its easy magnetization axis. This study provides insights into optimizing the magnetic properties of this alloy through controlled annealing. Full article

The new mineral polyarsite, ideally Na₇CaMgCu₂(AsO₄)₄F₂Cl, was discovered in high-temperature incrustations of the active Arsenatnaya fumarole at the Second scoria cone of the Northern Breakthrough of the Great Tolbachik Fissure Eruption, Tolbachik volcano, Kamchatka, Russia. It is associated with aegirine, sanidine, ferrisanidine, hematite, halite, sylvite, cassiterite, evseevite, axelite, badalovite, johillerite, arsmirandite, aphthitalite, tridymite, potassic-magnesio-fluoro-arfvedsonite and litidionite. Polyarsite forms short-prismatic, equant or tabular crystals up to 0.15 mm across, their clusters up to 0.3 mm in size or crusts up to 0.5 mm across and up to 0.03 mm thick. Polyarsite is transparent, sky-blue to light blue, with vitreous lustre. It is brittle, no cleavage is observed and the fracture is uneven. D_calc. = 3.592 g cm⁻³. Polyarsite is optically biaxial (+), α = 1.624 (4), β = 1.645 (4), γ = 1.682 (4) (589 nm), 2V_meas. = 70 (10)°. The empirical chemical formula calculated based on 19 O+F+Cl apfu is Na_7.04Ca_1.00Mg_0.92Cu_2.06Fe³⁺_0.06(As_3.96S_0.05)_Σ4.01O_16.28F_1.66Cl_1.06. Polyarsite is monoclinic, space group I2/m, a = 8.4323(4), b = 10.0974(4), c = 10.7099(6) Å, β = 90.822(4)°, V = 911.79(8) ų and Z = 2. The crystal structure was determined based on SCXRD data, R = 0.0391. Polyarsite demonstrates a novel structure type. The structure is based on the (1 0 1) heteropolyhedral layers formed by Cu₂O₈Cl dimers built by CuO₄Cl tetragonal pyramids sharing common Cl vertex, AsO₄ tetrahedra and MgO₄F₂ octahedra. Adjacent layers are linked via CaO₈ cubes to form a pseudo-framework which hosts octahedrally coordinated Na cations. Polyarsite was named based on the Greek words πολύς, poly, “many” and due to belonging to arsenates: this arsenate contains many chemical components ordered between different positions in crystal structure. Full article

This study determined the elastic properties of “green” concrete with cement partially replaced by oil shale ash (OSA) and reinforced with short basalt integral fibers (BIFs). Commercially available Deutsche Basalt Faser (DBF) GmbH Turbobuild Integral basalt fibers were used. There is currently a high demand both for strengthening concrete and applying ecological approaches with respect to circular economy. Oil shale ash is the byproduct of oil shale combustion. Basalt fiber is produced by melting basalt rock. Both BIF and OSA are used as additives in concrete. Cement replacement by OSA non-linearly changes the concrete’s strength properties, and the addition of BIF improves them. An experimental investigation was conducted using four-point bending tests and cube sample compression tests. Theoretical methods such as Voigt and Reuss boundaries, the Halpin–Tsai method, and the Mori–Tanaka method were used to predict the elastic properties of the fabricated samples. The theoretical models can provide useful information, although they may not fully capture the real properties observed experimentally. The results show that BIFs protect against instant brittle destruction. The experiments demonstrated an optimal OSA concentration for a fixed amount of BIF, resulting in the highest load-bearing capacity of the concrete. The addition of either 15% OSA only or 20% OSA and CBF can increase the stiffness of the concrete. This article provides guidance to the construction sector on using OSA and CBF together. Full article

Cloud-based Earth observation platforms, such as data cubes, enable reproducible analyses of long-term satellite time series for climate and urban studies. In parallel, Essential Climate Variables (ECVs) provide a standardised framework for monitoring climate dynamics, with urban land cover and temperature being particularly relevant in historic urban contexts. This study analyses long-term trends and statistical associations between satellite-based ECVs and urbanisation indicators within the Historic Urban Landscape (HUL) of Sfax (Tunisia) from 1985 to 2021. Using the Digital Earth Africa (DEA) data cube, we derived six urban spectral indices (USIs), land surface temperature, air temperature at 2 m, wind characteristics, and precipitation from Landsat and ERA5 reanalysis data. An automated and reproducible Python-based workflow was implemented to assess USI behaviour, evaluate their performance against the Global Human Settlement Layer (GHSL), and explore spatio-temporal co-variations between urbanisation and climate variables. Results reveal a consistent increase in air and surface temperatures alongside a decreasing precipitation trend over the study period. The USIs demonstrate comparable accuracy levels (≈88–90%) in delineating urban areas, with indices based on SWIR and NIR bands (NDBI, BUI, NBI) showing the strongest statistical associations with temperature variables. Correlation and multivariate regression analyses indicate that temporal variations in USIs are more strongly associated with air temperature than with land surface temperature; however, these relationships reflect statistical co-variation rather than causality. By integrating satellite-based ECVs within a data cube framework, this study provides an operational methodology for long-term monitoring of urban-climate interactions in historic Mediterranean cities, supporting both climate adaptation strategies and the objectives of the UNESCO HUL approach. Full article

In a situation where cities are increasingly being developed vertically and complexly, a novel approach for analyzing vertical urban functional patterns is proposed. For this purpose, a multi-dimensional GDC (Geo Data Cube) consisting of spatial and temporal data x, y, z, t, and f dimensions containing layer information was created. At this time, the size of the GDC cell (interval in x, y, z dimensions) is calculated by cell point data using the three-dimensional (3D) Moran’s I index value calculated with the 3D Diversity Factor (DF) based on information entropy proposed to reduce the uncertainty of information for each cell. In other words, the cell with the smallest index value was chosen to minimize the influence of Modifiable Areal Unit Problem (MAUP) that occurs when mapping. The 3D land use index (3D LUI) is calculated as a linearly weighted sum of the spatial accessibility of uses between cells (3D KDF) and the enrichment of uses (3D EF), taking into account the first law of geography. Finally, the 3D LUI value for each use was calculated for each cell of the GDC, and the use with the highest value was determined as the urban function of the cell. As a result of applying this to Seocho-gu, Seoul, Republic of Korea (ROK) in June 2024 and visually evaluating it using the street view provided by Kakao Map, it was confirmed that commercial and residential functions were vertically separated in buildings with residential–commercial complexes or shops on the ground floor. It was also confirmed that such characteristics did not appear in the two-dimensional (2D) urban functional patter analysis. Full article

This study proposes a neural-augmented Libor Market Model (LMM) for swaption surface calibration that enhances expressive power while maintaining the interpretability, arbitrage-free structure, and numerical stability of the classical framework. Classical LMM parametrizations, based on exponential decay volatility functions and static correlation kernels, are known to perform poorly in sparsely quoted and long-tenor regions of swaption volatility cubes. Machine learning–based diffusion models offer flexibility but often lack transparency, stability, and measure-consistent dynamics. To reconcile these requirements, the present approach embeds a compact neural network within the volatility and correlation layers of the LMM, constrained by structural diagnostics, low-rank correlation construction, and HJM-consistent drift. Empirical tests across major currencies (EUR, GBP, USD) and multiple quarterly datasets from 2024 to 2025 show that the neural-augmented LMM consistently outperforms the classical model. Improvements of approximately 7–10% in implied volatility RMSE and 10–15% in PV RMSE are observed across all datasets, with no deterioration in any region of the surface. These results reflect the model’s ability to represent cross-tenor dependencies and surface curvature beyond the reach of classical parametrizations, while remaining economically interpretable and numerically tractable. The findings support hybrid model designs in quantitative finance, where small neural components complement robust analytical structures. The approach aligns with ongoing industry efforts to integrate machine learning into regulatory-compliant pricing models and provides a pathway for future generative LMM variants that retain an arbitrage-free diffusion structure while learning data-driven volatility geometry. Full article

The microstructure of polyurethane (PU) grouting material is the key determinant of its macroscopic dielectric properties. In this study, based on its microscopic fractal characteristics and combined with effective medium theory and the Menger sponge structure, an n-stage fractal dielectric model was constructed. This model correlates the material’s dielectric response with its fractal dimension and porosity. The fractal dimensions of PU specimens with densities ranging from 0.29734 g/cm³ to 0.41817 g/cm³ were calculated using the box-counting method. Within this density range, the fractal dimension of the PU specimens showed no significant variation, with a calculated value of approximately 2.7355. By approximating the microscopic unit as an n-stage fractal cube based on the Menger sponge structure and incorporating series-parallel dielectric models, an analytical expression for the dielectric constant was derived. A comparison with experimental data shows that the model’s predictions are in good agreement with the measured values, with a mean relative error (MRE) of only 4%. Full article

Recent progress in Reinforcement Learning (RL), especially deep RL, has created new possibilities for autonomous control in complex and uncertain environments. This study explores these possibilities through a practical approach, implementing an RL agent on a custom-built CubeSat. The CubeSat, equipped with a reaction wheel for active attitude control, serves as a physical testbed for validating RL-based strategies. To mimic space-like conditions, the CubeSat was placed on a custom air-bearing platform that allows near-frictionless rotation along a single axis, simulating microgravity. Unlike simulation-only research, this work showcases real-time hardware-level implementation of a Double Deep Q-Network (DDQN) controller. The DDQN agent receives real system state data and outputs control commands to orient the CubeSat via its reaction wheel. For comparison, a traditional PID controller was also tested under identical conditions. Both controllers were evaluated based on response time, accuracy, and resilience to disturbances. The DDQN outperformed the PID, showing better adaptability and control. This research demonstrates the successful integration of RL into real aerospace hardware, bridging the gap between theoretical algorithms and practical space applications through a hands-on CubeSat platform. Full article

Active Debris Removal (ADR) missions demand precise and rapid controllers that lower collision risks specifically in the capture phase of tumbling objects. Sliding Mode Control (SMC), in general, offers robustness against model uncertainties. However, traditional reaching laws often face slow convergence when the chaser is too far from the target state. In this paper, we address this particular limitation and present the first application of Power Rate Reaching Law Sliding Mode Control (PRRL-SMC) to the 6-DOF coupled dynamics of a CubeSat-based debris capture mission in both the pre-capture tracking and post-capture stabilization phases in the case of tumbling debris. To show the strength of our work, we evaluate the proposed controller against Proportional–Derivative (PD), Linear Quadratic Regulator (LQR), second-order SMC (SOSMC), and terminal SMC (TSMC) for the pre-capture tracking and post-capture stabilization phases. By numerical simulations we show that PRRL-SMC reduces convergence time extremely and achieves stable capture in 7.6 s. This time it is 24.6 s for LQR and 28.1 s for SOSMC. The controller also handles the abrupt inertia variations of the combined stack post-capture successfully. This is efficient for proximity operations because of their importance in timing and fuel conservation. Full article

This study presents an innovative approach by combining magnetized water (MW) with Bacillus megaterium to improve the sustainability of concrete under various curing conditions. These enhancements contribute directly to reduced cement use and improved durability, both essential factors in sustainable construction. An experimental program with 27 distinct mixes analyzed variables such as the type of water (tap water/TW and two magnetization sequences/MW1 and MW2), bacterial dosage (0%, 2.5%, and 5% relative to cement weight), and curing methods (traditional water curing/C1, thermal shock/C2, freeze–thaw/C3). The primary discovery is a synergistic relationship between MW and bacteria: the MW1 treatment (1.5 T followed by 0.9 T) paired with a 2.5% bacterial dosage significantly improved the mechanical and self-healing properties of the concrete. This combination led to significant improvements in workability and compressive strength, achieving an increase of as much as 46.5% compared to the control. There was also an impressive recovery of strength in pre-cracked specimens, particularly under thermal shock curing (C2), where some healed cubes exceeded the strength of the uncracked ones. On the other hand, a 5% bacterial dosage was less effective, often resulting in reduced returns due to variations in microstructure. SEM and XRD analyses confirmed a more compact matrix and increased calcite precipitation with 2.5% bacteria, illustrating the combined effects of microbial activity and microwave treatment for sustainable concrete. Full article

Pneumatic soft robotic devices are emerging as promising tools for assisting hand rehabilitation in individuals with post-stroke motor impairment. However, evidence regarding their immediate functional impact remains limited, particularly across different impairment levels. This study presents a pilot validation of the YAD_V2 pneumatic finger rehabilitation robot and evaluates acute changes in finger range of motion (ROM) and task performance during a single intervention session. Twenty stroke participants were categorized into two groups based on the Fugl-Mayer Hand sub score: severe impairment (FMA-Hand < 10) and mild-to-moderate impairment (FMA-Hand ≥ 10). ROM was measured using integrated bending sensors during voluntary flexion–extension before, during, and after a 10-min pneumatic actuation session. A mixed 2 × 3 repeated-measure ANOVA revealed a significant Group × Time interaction (F(2, 36) = 4.628, p = 0.016, partial η² = 0.205). In the severe group, ROM increased from 8.53° to 28.46° during actuation (p = 0.002), and partially returned to baseline afterward. In the mild–moderate group, no significant ROM changes were observed; however, cube-transfer time improved significantly (mean improvement: 0.88 s, p = 0.039). These findings indicate that pneumatic assistance induces distinct acute effects depending on impairment severity. This study provides preliminary evidence supporting the feasibility of the YAD_V2 robotic system and highlights the need for multi-session clinical trials to determine therapeutic efficacy. Full article

With the rapid growth of multimodal remote sensing (RS) data, there is an increasing demand for intelligent onboard computing to alleviate the transmission and latency bottlenecks of traditional orbit-to-ground downlinking workflows. While many lightweight AI algorithms have been widely developed and deployed for onboard inference, their limited generalization capability restricts performance under the diverse and dynamic conditions of advanced Earth observation. Recent advances in remote sensing foundation models (RSFMs) offer a promising solution by providing pretrained representations with strong adaptability across diverse tasks and modalities. However, the deployment of RSFMs onboard resource-constrained devices such as nano satellites remains a significant challenge due to strict limitations in memory, energy, computation, and radiation tolerance. To this end, this review proposes the first comprehensive survey of onboard RSFMs deployment, where a unified deployment pipeline including RSFMs development, model compression techniques, and hardware optimization is introduced and surveyed in detail. Available hardware platforms are also discussed and compared, based on which some typical case studies for low Earth orbit (LEO) CubeSats are presented to analyze the feasibility of onboard RSFMs’ deployment. To conclude, this review aims to serve as a practical roadmap for future research on the deployment of RSFMs on edge devices, bridging the gap between the large-scale RSFMs and the resource constraints of spaceborne platforms for onboard computing. Full article

Incremental computing is becoming increasingly important for processing large-scale datasets. In satellite imagery, spatial resolution, temporal depth, and large files pose significant computational challenges, requiring efficient architectures to manage processing time and resource usage. Accordingly, in this study, we propose a dynamic architecture, termed Multi-Cube, for optical satellite time series. The framework introduces a modular and baseline-aware approach that enables scalable subdivision, incremental growth, and consistent management of spatiotemporal data. Built on NetCDF, xarray, and Zarr, Multi-Cube automatically constructs stable multidimensional data cubes while minimizing redundant reprocessing, formalizing automated internal decisions governing cube subdivision, baseline reuse, and incremental updates to support recurrent monitoring workflows. Its performance was evaluated using more than 83,000 Sentinel-2 images (covering 2016–2024) across multiple areas of interest. The proposed approach achieved a 5.4× reduction in end-to-end runtime, decreasing execution time from 53 h to 9 h, while disk I/O requirements were reduced by more than two orders of magnitude compared with a traditional sequential reprocessing pipeline. The framework supports parallel execution and on-demand sub-cube extraction for responsive large-area monitoring while internally handling incremental updates and adaptive cube management without requiring manual intervention. The results demonstrate that the Multi-Cube architecture provides a decision-driven foundation for integrating dynamic Earth observation workflows with analytical modules. Full article