Search Results (396)

The northward shift in climate zones and the urban heat island effect demand passive cooling for building roofs in northern regions. Artificial turf is a lightweight candidate, but existing studies treat it as homogeneous material, overlooking blade morphology and roof-scale thermal performance. This study conducted a scaled indoor experiment using a 1 m³ building model. Three artificial turfs with different blade lengths (Type A long, Type B medium, Type C short) were compared against concrete and XPS roofs under simulated summer solar radiation. Results show that blade morphology governs thermal performance. Type A exhibited the lowest peak surface temperature (48.9 °C vs. 53.4 °C and 60.6 °C), and its interface temperature (37.0 °C) was 15.1–19.0 °C lower than Types B and C, attributed to a static air insulation layer and enhanced convection. Its cooling rate (0.98 °C/min) was 1.69–2.33 times faster. Compared to concrete and XPS, Type A had lower surface temperature, less downward heat conduction, and a 29.3 °C drop in 30 min (concrete: 22.3 °C; XPS: 21.7 °C), showing urban heat island mitigation potential. Its heat flux reduction ratio reached 42.9%, with equivalent thermal resistance of ~0.40 m²·K/W, reducing summer peak indoor temperature by 3–6 °C in aging buildings. Double-layer stacking underperformed a single long-blade layer due to heat accumulation. Optimised long-blade turf challenges the view that low albedo inevitably causes high temperature, offering dual benefits of insulation and rapid dissipation for passive cooling in urban renewal. Full article

Soil moisture (SM) governs land–atmosphere exchanges and strongly influences agricultural management and hydrological assessment, yet high-resolution mapping remains challenging due to sensor-specific confounding effects and limited field observations. This study develops a practical workflow for point-scale SM estimation and wall-to-wall mapping by integrating multi-sensor remote sensing predictors with ensemble learning. A compact predictor set was constructed from Sentinel-2 optical indices, Sentinel-1 SAR descriptors (σVV and the polarization ratio σVH/σVV), and topographic information, collocated with in situ SM measurements along a transect in the study area. Three tree-based regressors—Random Forest, XGBoost, and CatBoost—were trained under an identical feature configuration and evaluated using R², Root Mean Square Error (RMSE), and Mean Absolute Error (MAE) together with predicted–observed diagnostics. A stacking ensemble was then implemented using leakage-controlled K-fold out-of-fold predictions to generate meta-features, with a Decision Tree as the meta-learner tuned via a grid search. Results show that base learners achieve comparable skill (R² ≈ 0.60–0.62; RMSE ≈ 0.038–0.039), while stacking improves test accuracy (RMSE = 0.0346) and provides a stable mapping-ready model. The trained framework was transferred to stacked raster predictors to produce spatially continuous SM maps, revealing coherent moisture heterogeneity across the region. Accordingly, the objective of this study is to develop a compact and application-oriented point-to-map workflow for high-resolution soil moisture estimation by integrating Sentinel-1/2-derived predictors with stacking-based model fusion, rather than to propose a new physically based retrieval model. Full article

In the Arabian Gulf region, saltwater desalination is considered to be a significant process in producing clean water. This paper presents a sustainable, combined process for upgrading a Doha reverse osmosis (RO) plant in Kuwait. A pilot-scale microbial desalination cell (MDC) stack is proposed as a pre-treatment unit prior to the RO process in order to improve plant performance. A cost–benefit analysis is conducted for the combined system to emphasize the significance of the MDC–RO process. In RO, the expected energy consumption is 2.6–13 kWh per m³ of desalinated water, whereas using MDC can reduce this to about 0.52–5.3 kWh/m³. Moreover, this new technology using catalytic MDCs can help in improving electric current production and reducing the amount of rejected brine and membrane fouling in the RO process. The electric current is improved by reducing MDCs’ internal resistance using a reduced graphene oxide/polyaniline composite-coated stainless steel mesh cathode electrode. Layer-by-layer electro-deposition can be applied to achieve these coatings. An intermediate zeolite filter is proposed to mitigate RO membrane fouling. The combined system’s natural zeolite-membrane filter improves water purification. In this study, we assessed the combined MDC–RO process for upgrading the Doha plant’s performance in terms of quality, cost, and time. The suggested catalytic MDC, using efficient, low-cost materials as cathode electrodes with an equivalent daily cost of 0.01 USD/m³ and a desalination efficiency of about 40%, acts as an alternative to high-cost platinum metal electrodes. The results also indicate that the equivalent daily cost of energy consumption using the MDC process is about 0.03 USD/m³, whereas the investment cost is about 0.4 USD/m³ daily for one year of cell operation. Full article

Open-cathode Proton Exchange Membrane Fuel Cells (PEMFCs) are a promising technology for increasing the endurance of small Unmanned Aerial Vehicles (UAVs), ground robots, e-bikes, and light electric vehicles. However, their performance under realistic operating conditions is strongly influenced by rapid variations in load, temperature, and ambient pressure, which are often neglected in design-oriented or quasi-steady-state analyses. This study experimentally investigates a 1 kW open-cathode PEMFC system, including its balance of plant and a passive supercapacitor buffer, under a representative UAV flight power profile. Steady-state and dynamic tests were conducted to assess polarization characteristics, thermal behavior, parasitic power consumption, and hydrogen utilization. Results revealed significant thermal inertia and hysteresis effects during load transients, causing voltage deviations from steady-state performance and stabilization times exceeding 90 s. The supercapacitor effectively reduced stack current ramp rates, although some high-frequency oscillations remained. Under flight-representative conditions, the system achieved stable operation with average voltaic efficiency ranging from 55.3% to 60.7% and net efficiency ranging from 50.2% to 54.2%. Auxiliary components had a measurable impact on overall performance: cooling fans accounted for 2–6% of stack power during steady operation and approximately 2.5% of total mission energy, while hydrogen purge losses can significantly reduce vehicle endurance. The findings demonstrate the importance of energy-based performance assessment, including auxiliary loads and purge losses, to obtain realistic estimates of efficiency and endurance in dynamic PEMFC-powered applications. Full article

Background/Objectives: Cell-free DNA (cfDNA) end-motifs (EDMs) are promising fragmentomic features for noninvasive cancer detection; however, their diagnostic utility may be limited by background signals from abundant hematopoietic-derived cfDNA fragments. Existing EDM-based approaches, including the Motif Diversity Score (MDS) and classifiers based on raw motif frequencies, often show limited robustness across different datasets. Methods: To address this limitation, we developed a frequency-domain analytical framework based on the Discrete Fourier Transform (DFT), converting k-mer EDM frequency profiles into amplitude spectral features. We further constructed a stacking-based Ensemble Spectral Model (ESM) integrating multi-scale spectral features from 4–6-mer EDMs. Results: The framework was evaluated using 1782 plasma cfDNA samples from four independent studies comprising six datasets. Raw EDM profiles showed extremely high similarity between cancer and non-cancer samples (mean Spearman R = 0.999). Following DFT transformation, amplitude spectra showed improved separability between groups. Across datasets, the ESM achieved a mean AUC of 0.843, representing a 15.0% improvement over raw 4-mer EDM-based SVM models and a 56.4% improvement over the MDS. At 95% specificity, mean sensitivity reached 0.585, exceeding those of the raw EDM (0.418) and MDS (0.195). Frequency-guided motif attribution further linked spectral features to sequence-level motif patterns and potential regulatory programs. Conclusions: Frequency-domain transformation improves the representation of cfDNA EDM profiles and provides a robust analytical framework for cross-dataset cancer detection. Full article

To address the challenges posed by complex Cretaceous(K) deep structural deformation and the poorly understood decoupling mechanism between deep and shallow structural layers in the foreland thrust belt of the Kuqa depression, Tarim Basin, this study integrates high-precision 3D seismic interpretation with balanced cross-section restoration techniques to systematically elucidate the controlling role of rheological heterogeneity within the Shushanhe Formation (K₁s) mudstone on the stress–lithology–structure coupling mechanism. Our findings demonstrate that variations in thickness and rheological properties of the Shushanhe Formation mudstone govern the structural segmentation along the Zhongqiu–Dongqiu transect. In the Dongqiu area, an exceptionally thick and highly ductile mudstone layer induces principal stress deflection and horizontal shearing, effectively absorbing vertical strain transmitted from deep-seated tectonic wedges. This results in pronounced decoupling between deep and shallow strata, giving rise to broad, gentle anticlines and ramp-flat imbricate structures at depth. Conversely, in the Zhongqiu area, the mudstone thins significantly and becomes more brittle, increasing the friction coefficient and impeding vertical stress transmission. Consequently, near-vertical stacking occurs in the proximal compressional segment, leading to the development of high-angle thrust faults and strike-slip-modified fault-bend folds. This study clarifies the genetic mechanism of non-coaxial structures controlled by the mudstone detachment layer and confirms that the plastic flow of this layer not only enhances lateral sealing capacity but also acts as an effective rheological barrier, thereby preserving the deep overpressured hydrocarbon reservoirs in the Yageliemu Formation (K₁y). These insights provide a robust theoretical foundation for shifting exploration strategies from shallow structural traps to deep, subtle lithologic–structural composite plays, offering critical guidance for sweet spot prediction in ultra-deep settings. Full article

Laser beam welding (LBW) of aluminium busbar-to-terminal connections for electric-vehicle battery packs requires precise in-process monitoring. Membrane-free optical microphones provide a high-bandwidth (DC–MHz) acoustic channel that captures keyhole, melt-pool, and plume dynamics. This study proposes Acoustic Signal Intelligence (ASI), a deep learning framework for LBW state classification from a single optical microphone, evaluated on an open dataset (183 AA1050 welds, $f_s$ = 2.5 MHz) under a five-class taxonomy: lack of fusion, lack of connection, sound, marginal, and piercing. The contributions are: (i) a compact 1-D CNN encoder on a mel-scale STFT spectrogram, reaching the highest macro-F1 (0.72 mean across three-fold replicate-out cross-validation) and 100% piercing recall in every fold—a multi-representation fusion variant adding a wavelet-packet decomposition and a 24-feature library targeting the 8, 63 and 110 kHz keyhole-resonance peaks was evaluated as an ablation arm and did not survive cross-validation, so the proposed model is mel-only; (ii) a systematic benchmark against six classical-ML and four deep learning baselines in which Transformer-hybrid ablations and ACGAN-style augmentation underperform compared to the compact CNN on the 122-sample training set, with the Transformer underperformance confirmed by a 30-configuration grid search over learning rate, weight decay, and dropout (best tuned macro-F1 = 0.441 vs. CNN 0.724); and (iii) a Grad-CAM analysis that recovers the keyhole-resonance bands without prior knowledge. A single optical microphone is thus a viable real-time alternative to multi-sensor stacks for battery-pack laser welding. Full article

Ceramic nanofiber-based materials have wide applicability in high-temperature management and protection. The transformation of conventional two-dimensional ceramic nanofibrous membranes into three-dimensional nanofiber-based bulks can effectively improve their thermal insulation performance and expand their range of applications. Herein, lamellar-structured Al₂O₃-SiO₂ nanofibrous aerogels (LASO NFAs) with varying inorganic binder contents were prepared via a sequence of processes involving face-to-face stacking, impregnation, and calcination, using flexible Al₂O₃-SiO₂ nanofibrous membranes (ASO NFMs) as building units and aluminum dihydrogen phosphate as an inorganic binder. Varying the inorganic binder content in the aerogel matrix enables effective control over the compressive properties and interlayer spacing of the resulting aerogels. Specifically, the optimized LASO-20 NFAs demonstrated relatively good compression resilience, with a plastic deformation of 22.1% after undergoing 500 compressive cycles at a compressive strain of 50%. Moreover, profiting from the high-temperature resistance of ASO NFMs and substantial air content present within nanofiber interlayers, the LASO-20 NFAs with a thickness of 20 mm could effectively insulate against surface temperatures of 1000 °C down to 224 °C. Moreover, LASO-20 NFAs exhibited a room-temperature thermal conductivity of approximately 0.043 W·m⁻¹·K⁻¹, illustrating a favorable high-temperature thermal insulation characteristic. Furthermore, the LASO-20 NFAs presented promising service performance in extreme environments, providing a novel perspective in the development of new types of ceramic aerogels. Full article

Flexible pressure sensors with a porous architecture are highly desirable for wearable health monitoring and intelligent human–machine interaction, owing to their excellent comfort and conformability to human motion. However, conventional porous sensors often suffer from poor signal accuracy and unstable output, which limit their capability for precision sensing. To address these challenges, we designed and fabricated a flexible pressure sensor with exceptional linearity by mimicking the unique surface structure of Iron Cross Begonia (Begonia masoniana) leaves. The sensor is constructed using a readily available melamine foam as the backbone: a porous sensing scaffold is first obtained via a simple dip-coating process, and a film featuring bioinspired protrusions is fabricated by repeated replica molding. Lamination of these two components yields a stacked sensor device. Characterization demonstrates that the sensor achieves a broad pressure detection range of up to 350 kPa, with a minimum resolvable pressure of 250 Pa, and exhibits an excellent linearity of 0.999 over its entire working range (0–350 kPa). Moreover, the sensor shows stable responses under varying loading frequencies, is capable of detecting low-frequency signals, and retains its performance without notable degradation even after 5000 repeated loading-unloading cycles. In practical applications, the sensor accurately monitors flexion and extension movements of the wrist, finger, neck, and knee, capturing human motion signals with high fidelity. Furthermore, it enables information encoding and transmission through finger gestures. The proposed bioinspired structural design strategy effectively enhances the overall performance of porous pressure sensors, offering a new paradigm for the development of flexible sensing devices with promising applications in wearable health monitoring, human motion detection, and human–machine interaction. Full article

Coal has been Malaysia’s primary energy source for electricity generation for the past few decades, resulting in increased greenhouse gas emissions and irreversible environmental damage. Solid Oxide Fuel Cells (SOFCs) have emerged as a viable clean-energy alternative to mitigate these environmental effects. There has been significant emphasis on developing pollution-free technology, with limited attention given to the environmental impact of SOFC. Research and development efforts have primarily focused on the design and technical aspects of SOFC. Prior to the introduction of SOFC to market, quantifying the environmental footprint of SOFC manufacturing is necessary to support a sustainable energy transition. This study conducts a comprehensive Life Cycle Assessment (LCA) of SOFC manufacturing in accordance with ISO 14040 and 14044 standards. The analysis focuses on a planar electrolyte-supported SOFC with a lifespan of 4.57 years, using a functional unit of 1 kWh electrical output. The Environmental Footprint (EF) 3.1 method implemented in GaBi Software was used for the impact assessment. Key environmental impact categories considered in the LCA include Climate Change (CC), Acidification Potential (AP), Eutrophication Potential (EP), Ozone Depletion Potential (ODP), Photochemical Ozone Formation (POF), and Human Toxicity Potential (HTP). The total climate change impact is approximately 19.674 kg CO₂ eq./kWh, with the Balance of Plant (BoP) phase contributing 91% of this impact, while the fuel cell stack phase contributes 1.25%. The study identifies key areas for improvement, primarily related to BoP and other high-impact processes, and emphasizes the importance of targeted measures to effectively reduce the environmental impacts associated with SOFC manufacturing. Full article

This paper presents a current-reused vertically stacked (CRVS) topology for a high-dynamic-range amplifier (HDRA) implemented in a 0.1 μm GaAs pHEMT process, targeting wideband millimeter-wave (mm-wave) receiver front-ends. The proposed design breaks the inherent trade-off between noise figure (NF), linearity, and bandwidth, achieving simultaneous enhancement of transconductance efficiency, Miller effect suppression, and wideband matching. The fabricated prototype operates over a continuous 20–43 GHz bandwidth (covering K- and Ka-bands), demonstrating state-of-the-art performance: a flat gain of 24 ± 0.6 dB, a minimum NF of 2.2 dB, a maximum output 1 dB compression point (OP1dB) of 15.8 dBm and a low power consumption of 5 V/65 mA, with both input and output return losses better than −10 dB across the entire band. The results validate the effectiveness of the CRVS topology and highlight the competitiveness of GaAs pHEMT technology for high-performance wideband mm-wave front-ends, making the design suitable for applications including 5G/6G communication, satellite systems, and mm-wave test equipment. Full article

The growing energy demand in rural areas such as the ejido Boca del Cerro, located in Tenosique, Tabasco (Mexico), near the Usumacinta River, calls for sustainable energy solutions such as microgrids. This study proposes an energy management system combining renewable energy forecasting and fuzzy control for a simulated small autonomous rural microgrid scenario designed to supply a fixed priority load of 5 kW and a variable flexible load ranging from 1 to 10 kW. Three LSTM architectures (vanilla, stacked, and bidirectional) are compared for predicting solar irradiance, wind speed, and river flow. The vanilla model is optimized using Hyperband to improve prediction accuracy, particularly for flow rate, which is rarely addressed in similar studies. Forecasts feed into models of photovoltaic, wind, and hydro systems within the microgrid. Energy dispatch is managed through fuzzy logic control. The fuzzy controller supports load prioritization, battery charge/discharge management, and surplus energy redirection to an absorbing load. The final vanilla LSTM achieved RMSE values of 25.741, 0.302, and 12.644 for solar irradiance, wind speed, and river flow, respectively, with NSE values above 0.949 in all cases. These results indicate high forecasting accuracy for solar irradiance and river flow, with limited improvement for wind speed. Overall, the proposed EMS enables effective energy flow management, while the integration of hydrokinetic turbines with AI-based forecasting represents a novel contribution. Full article

To address the difficulty of simultaneously achieving effective heat dissipation and adequate humidification in open-cathode air-cooled proton exchange membrane fuel cells (PEMFCs) under medium and high power operation, this study proposes a hydrothermal management strategy based on coordinated ultrasonic atomization humidification and fan speed regulation. A three-dimensional single-cell multiphysics model is developed and validated using a 300 W experimental platform. The effects of atomization frequency and water temperature on stack performance and internal hydrothermal distribution are systematically investigated. Results show that ultrasonic atomization provides inlet precooling, latent heat absorption, and active region humidification, thereby improving hydrothermal uniformity within the stack. Under the optimal condition of 100 kHz and 55 °C, the peak stack power increases by 21.0% to 319.00 W, while voltage consistency and surface temperature uniformity are also improved. Analysis based on the Stokes number and Dalton’s law of partial pressures indicates that the optimum results from a balance between suppressing droplet agglomeration and inertial deposition, and limiting oxygen dilution caused by excessive water vapor. The proposed strategy provides a compact and practical approach for improving the stability, uniformity, and efficiency of air-cooled PEMFCs. Full article

Solving geometry problems is a critical challenge in education, for it demands the integration of textual semantic descriptions, mathematical formula logic and spatial graphical information, as well as rigorous geometric theorem application and stepwise logical deduction. These are core capabilities that underpin the realization of personalized intelligent tutoring and efficient educational resource allocation. Traditional geometry problem solving methods often suffer from deficiencies in accuracy and the fusion of text, formula and image features. Hence, this paper proposes a method of solving geometry problems based on a text–formula–image (TFI) multimodal parsing and fusion model. The TFI parser employs a self-attention multilayer Transformer to enhance the extraction of logical relations among geometric text expressions. Meanwhile, it parses formulas into tree structures to overcome the loss of formula structural features, which utilizes symbolic embedding and tree-structured encoding to preserve hierarchical logical information and yields unified formula representations via a multi-granularity fusion module. The TFI parser also leverages a Feature Pyramid Network (FPN) for the accurate detection of geometric and non-geometric instances, resolves the issues of blurred segmentation for slender geometric elements and the inaccurate localization of small-sized symbols through mask averaging and RoIAlign, and generates high-dimensional image features using DenseNet-121. The TFI multimodal fusion model integrates a contrastive learning mechanism and constructs fused feature representations by stacking self-attention and cross-attention layers. This design effectively narrows the semantic gap between text, formula, and image features, addressing the inadequacy of traditional fusion approaches in deep cross-modal feature alignment. An attention-augmented Gated Recurrent Unit (GRU) network processes the fused TFI features to produce target operation trees and geometry solutions, ensuring interpretable and precise reasoning performance. The proposed method is evaluated on the PGDP5K and GeoEval datasets, and it achieves an average accuracy of 59.63% in geometry problem solving, which validates its effectiveness. This paradigm offers a viable technical approach for uniformly modeling complex educational tasks, including geometry problem solving and timetable scheduling. Full article

Accurate prediction of electricity demand during hot seasons is essential for maintaining power system reliability, particularly in humid subtropical regions such as Guangdong, China, where high temperatures strongly influence consumption. However, many models rely primarily on air temperature and may not fully capture combined atmospheric effects. This study evaluates the potential of biometeorological thermal indices for improving summer electricity load forecasting. Daily maximum load and meteorological data during May–September 2019–2021 were analyzed using Back-Propagation Neural Network (BP), Random Forest (RF), and a Stacking ensemble model. Three indices—Effective Temperature (ET), Physiological Equivalent Temperature (PET), and the Universal Thermal Climate Index (UTCI)—were introduced as predictors. The ensemble model achieved the best performance, with Ensemble–UTCI yielding the highest accuracy (R² = 0.559, RMSE = 60.96 × 10⁴ kW, MAE = 45.10 × 10⁴ kW). Compared with temperature-based models, biometeorological indices consistently improved predictions, with UTCI performing best (average RMSE = 62.81 × 10⁴ kW). Bayesian analysis shows strong evidence of improvement in RF and ensemble models, but not in BP or linear models, indicating model dependence. During the July 2021 heat event, RF showed greater robustness, with PET–RF achieving the lowest error (MAPE = 3.03%). These results demonstrate the value of biometeorological indices for load forecasting in humid subtropical regions. Full article