Search Results (86)

The advancement of sustainable construction requires the development of earthen materials compatible with 3D printing (additive manufacturing), along with specified engineering standards. Many existing studies improve workability and early strength using chemical stabilizers such as cement; however, these additives increase embodied carbon and undermine sustainability objectives. Challenges remain in the formulation of an earthen mixture that satisfies both printability and structural requirements for large-scale construction. Previous earth-based mixes have reported excessive shrinkage and inadequate compressive strength. This study presents the systematic optimization of a low-carbon, 3D-printable earthen mixture using locally sourced clay-loam soil from Belén, New Mexico (NM). The soil was modified with graded concrete sand and rice hull fiber to improve printing parameters such as buildability, extrudability, and printability while meeting the NM Earthen Building Code requirements for compressive and flexural strength. Soil characterization tests (particle size distribution, consistency, optimal water content) guided iterative refinement to enhance dimensional stability and mechanical performance. A baseline 2:1 soil-to-sand ratio (max aggregate size No. 4) was established. Incorporating $2\%$ rice hull fiber and reducing max aggregate size to No. 16 (S67F2) early-age shrinkage was reduced from $12.33\%$ to $3.48\%$ ($72\%$ reduction) while maintaining a 28-day compressive strength exceeding 660 psi, more than twice the code minimum. The optimized mixture supported 24 printed layers without deformation, achieved 189 psi flexural strength (three times the code minimum), and produced a stable 2-ft-diameter dome with minimal cracking. Full article

Urban traffic noise poses escalating environmental challenges in rapidly urbanizing regions with high-density buildings, yet systematic investigations into its spatiotemporal characteristics remain relatively scarce. This study addresses this research gap via the synchronized on-site monitoring of traffic noise and traffic flow on a representative arterial road in Guangzhou, China. The analysis reveals that nighttime equivalent continuous A-weighted sound levels (L_Aeq) are 3.0–4.0 dB(A) higher than those during the congested daytime peak, a phenomenon primarily driven by higher vehicle speeds under nighttime free-flow traffic conditions. The spatial analysis uncovers complex three-dimensional noise propagation dynamics specific to urban street canyons. Vertical profiling demonstrates a counterintuitive pattern where noise levels do not attenuate with building height, and upper floors experience marginally higher noise exposure than the ground floor, which is attributed to the canyon effect, where multiple sound wave reflections offset the natural distance attenuation. A validated three-dimensional computational model was further employed to evaluate the efficacy of noise mitigation strategies, showing that an integrated intervention combining porous asphalt pavement and acoustic barriers achieves a maximum noise attenuation of 19.9 dB(A) at ground-level receptors. This significant reduction stems from a synergistic effect: porous asphalt reduces noise at the source on a global scale, while acoustic barriers provide localized shielding for the lower floors of adjacent buildings. This research concludes that effective traffic noise control in high-density urban areas requires three-dimensional, multi-faceted strategies addressing noise source characteristics, transmission pathways, and receptor vulnerabilities. Full article

Acoustic precipitation enhancement (APE) is an emerging non-chemical weather-modification technique, yet quantitative three-dimensional evidence of its impact on rainy clouds remains scarce. This study investigates a stratiform precipitation event over the Bayinbuluke Basin in the central Tianshan Mountains of northwestern China, 29–30 August 2024, using an X-band phased-array weather radar (X-PAR) coordinated with an upward-directed acoustic source. Rapid volumetric scans and sector-aligned range-height indicators were combined to reconstruct the three-dimensional cloud structure before, during, and after acoustic operation. During acoustic operation, the results were stronger and more persistent than during the non-operation period, with localized values exceeding 40 dBZ. Within the 3 km influence zone, low-level reflectivity increased across all azimuthal sectors with clear directional dependence. Dual-ratio analysis showed statistically significant enhancement in the windward sector (247°, $DR$ = 1.91, $p$ = 0.0004) and the leeward sector (137°, $DR$ = 1.51, $p$ = 0.008), indicating that acoustic-induced responses extended beyond the primary radiation sector and propagated downstream with cloud advection. These results, based on a single stratiform precipitation case, demonstrate that volumetric X-PAR observations can detect localized cloud-structure responses during acoustic operation. Full article

This study evaluates energy conversion and heat transfer in a germicidal chamber employing gold nanorods (AuNRs) irradiated with an infrared laser (808 nm, 0.8 W) to generate heat via localized surface plasmon resonance. The investigation focused on the preliminary selection of chamber materials and the geometry of the bottom surface supporting the AuNRs as the heat source in a photothermoablation application. A one-way multiphysics and multiscale approach was applied, integrating nanoscale heating phenomena with a macroscale fluid and heat flow. The validated 2D numerical model shows satisfactory agreement with experimental data and is suitable for further design analyses. Computational Fluid Dynamics (CFD) simulations were conducted to determine temperature and entropy distributions, mean and maximum temperatures, and Nusselt numbers, allowing the assessment of the energy conversion process under different configurations and AuNR dimensions. The results indicate that a configuration with a gradually descending stepped structure enhances interactions between nanoparticles and the fluid, increasing the internal energy and producing elevated temperatures. Under optimal conditions, a temperature rise of approximately 75 °C was achieved. These findings demonstrate that integrating material selection, surface geometry, and nanoparticle absorbance optimization can significantly improve the efficiency of bacterial inactivation in germicidal chambers. This study provides a framework for future investigations on fully three-dimensional multiscale and multiphysical modeling, as well as a targeted AuNR design to maximize the thermal performance. Full article

Cough sounds are a key acoustic indicator for detecting respiratory diseases in laying hens, which have become increasingly prevalent with the intensification of poultry housing systems. As an important early signal, cough sounds play a vital role in disease prevention and precision health management through timely recognition and spatial localization. In this study, an improved BiLSTM–TDOA method was proposed for the accurate recognition and localization of laying hen cough sounds. Nighttime audio data were collected and preprocessed to extract 81 acoustic features, including formant parameters, MFCC, LPCC, and their first and second derivatives. These features were then input into a BiLSTM-Attention model, which achieved a precision of 97.50%, a recall of 90.70%, and an F1-score of 0.9398. An improved TDOA algorithm was then applied for three-dimensional sound source localization, which resulted in mean absolute errors of 0.1453 m, 0.1952 m, and 0.1975 m along the X, Y, and Z axes across 31 positions. The results demonstrated that the proposed method enabled accurate recognition and 3D localization of abnormal vocalizations in laying hens, which will provide a novel approach for early detection, precise control, and intelligent health monitoring of respiratory diseases in poultry houses. Full article

In practical engineering geophysics, anomalous bodies are typically three-dimensional (3-D) structures, making it inaccurate to represent the subsurface geoelectric model using a two-dimensional (2-D) assumption. Furthermore, the underlying mechanism of the telluric electrical field frequency selection method (TEFSM) remains insufficiently understood. To address these limitations, this study presents a 3-D forward modeling algorithm based on the edge-based finite element method to solve the TEFSM forward problem. This paper also investigates the application of TEFSM in mineral water exploration, striving to minimize the influence of strong electromagnetic interference sources such as high-voltage power lines. Specifically, the paper presents the forward theory of TEFSM and analyzes the causes of galvanic distortion, particularly static shift. Numerical simulations examine the response characteristics of anomalous bodies and the influence of galvanic distortion. The results indicate that galvanic distortion enhances shallow local anomalies in the modulus of the electric field while masking deeper targets. In contrast, the phase of the electric field effectively reflects deeper anomalous bodies and is minimally affected by galvanic distortion. Future improvements in frequency selectors may enable reliable phase measurements, thereby enhancing data interpretability. Subsequently, the TEFSM was applied to field data collected during mineral water exploration. The field test results confirm the effectiveness of TEFSM and demonstrate that it is a portable, simple, low-cost, and highly efficient method for groundwater detection. Full article

The Internet of Things (IoT) and Edge Computing (EC) play an essential role in today’s communication systems, supporting diverse applications in industry, healthcare, and environmental monitoring; however, these technologies face a major challenge in accurately determining the geographic origin of sensed data, as such data are meaningful only when their source location is known. The use of Global Positioning System (GPS) is often impractical or inefficient in many environments due to limited satellite coverage, high energy consumption, and environmental interference. This paper recruits the Distance Vector-Hop (DV-Hop), Jellyfish Search (JS), and Artificial Rabbits Optimization (ARO) algorithms and presents an innovative GPS-free positioning framework for three-dimensional (3D) EC environments. In the proposed framework, the basic DV-Hop and multi-angulation algorithms are generalized for three-dimensional environments. Next, both algorithms are structurally modified and integrated in a complementary manner to balance exploration and exploitation. Furthermore, a Lévy flight-based perturbation phase and a local search mechanism are incorporated to enhance convergence speed and solution precision. To evaluate performance, sixteen 3D IoT environments with different configurations were simulated, and the results were compared with nine state-of-the-art localization algorithms using MSE, NLE, ALE, and LEV metrics. The quantitative relative improvement ratio test demonstrates that the proposed method is, on average, 39% more accurate than its competitors. Full article

Vertical line source irrigation is a localized water-saving technique suitable for deep-rooted crops, but the geometric structure of the wetted bulb lacks a systematic analytical modeling method. This study established a simplified three-dimensional (3D) analytical model to predict the wetted volume under vertical line source irrigation conditions. First, the model determined boundary points based on an empirical wetting-front equation and fitted the wetting profile with ellipse–parabola functions to derive analytical expressions for area and volume. Then, using aeolian sandy soil as the research object, the model predicted that during 0–250 min of irrigation, the wetted pattern area increased from 80.0 cm² to 5050.6 cm², and the wetted volume increased from 251.3 cm³ to 208,014.4 cm³. At 250 min, the lower, middle, and upper volume components accounted for 67.3%, 24.2%, and 8.4%, respectively. Finally, the model was validated using loam soil, and the results showed good agreement between the calculated and measured values. The model requires only simple input and enables fast computation. It effectively characterizes the three-dimensional spatiotemporal variation of the wetted bulb and provides a theoretical reference for the design of pipe spacing and irrigation quota. Full article

Sparse arrays can effectively reduce antenna cost and implementation complexity. However, most existing research in sparse array design mainly focuses on far-field scenarios, which cannot be directly applied to near-field (NF) source localization, where the delay term and source incident parameters exhibit a nonlinear relationship. In this paper, employing a symmetric enhanced nested array, a high-precision underdetermined three-dimensional (3D) NF localization method is proposed. Firstly, the symmetry of the array and the fourth-order cumulant are utilized to construct the equivalent virtual far-field (FF) received data. Then, a gridless, sparse, and parametric approach combined with an l₁-singular value decomposition-based pairing procedure is employed to obtain estimates of two paired angles. Finally, a one-dimensional (1D) spectral estimator is applied to obtain the estimate of the range parameter. By analyzing the virtual aperture, the optimal parameter configuration for a given number of elements is obtained. As shown by simulation results, the proposed method can handle underdetermined estimation. Compared with the other algorithms, the proposed algorithm achieves significant improvements in both angular and distance accuracy, with enhancements of 65% and 61.7%, respectively. Full article

Effective soil salinity monitoring is crucial for sustainable land management in arid regions. Most current studies face limitations by relying solely on single-source data. This study presents a novel three-dimensional (3D) optical-radar feature space model combining Gaofen-3 polarimetric synthetic aperture radar (SAR) and Sentinel-2 multispectral data for China’s Yutian Oasis. The random forest (RF) feature selection algorithm identified three optimal parameters: Huynen_vol (volume scattering component), RVI_Freeman (radar vegetation index), and NDSI (normalized difference salinity index). Based on the interactions of these three optimal features within the 3D feature space, we constructed the Optical-Radar Salinity Inversion Model (ORSIM). Subsequent validation using measured soil electrical conductivity (EC) data (May–June 2023) demonstrated strong model performance, with ORSIM achieving R² = 0.75 and RMSE = 7.57 dS/m. Spatial analysis revealed distinct salinity distribution patterns: (1) Mildly salinized areas clustered in the central oasis region, and (2) severely salinized zones predominated in northern low-lying margins. This spatial heterogeneity strongly correlated with local topography-higher elevation (south) to desert depression (north) gradient. The 3D feature space approach advances soil salinity monitoring by overcoming traditional 2D limitations while providing an accurate, transferable framework for arid ecosystem management. Furthermore, this study significantly expands the application potential of SAR data in soil salinization research. Full article

With growing global concerns over sustainable wastewater treatment, there is a pressing need for low-cost, eco-friendly filtration solutions. This study conducted a life cycle assessment (LCA) to evaluate the potential of improving slow sand filtration efficiency by integrating alternative materials like clay and oyster shell powder (OSP), while minimizing the environmental footprint. Additionally, the adaptability of three-dimensional (3D) printing was explored to incorporate these materials into innovative filter designs, assessing scalability for broader wastewater applications. Ten filter configurations, including a slow sand filter (SSF) enhanced with OSP (90:10) and 3D-printed clay–OSP bricks (ratios of 90:10, 85:15, 80:20), were assessed across three sourcing distances: local (in situ), regional (161 km), and distant (1609 km). The results showed that SSFs with OSP consistently delivered lower environmental impacts, reducing freshwater ecotoxicity, eutrophication, and human toxicity by up to 4% compared to conventional SSFs, particularly when transport was minimized. Among brick-based systems, single-brick columns offered the best balance of performance and impact, while three-brick columns had the highest environmental burden, largely due to the increased electricity use. Economic analysis reinforced the environmental findings: SSFs with OSP were the most cost-effective option, followed closely by SSFs, while brick-based systems were slightly more expensive, with costs rising sharply when sourcing distances exceeded 161 km. Overall, integrating OSP into SSFs offers an optimal balance of sustainability and affordability, while single-brick columns (90:10) present a promising alternative. Future research should further optimize material blends and design configurations to align with long-term environmental and economic goals. Full article

Radar target near-field scattering characteristics are essential for the identification of target properties and the improvement of target recognition. Nevertheless, the efficiency of current precision three-dimensional (3D) imaging algorithms in near-field scattering measurements is restricted by their substantial computational complexity. To resolve this matter, we propose a hybrid 3D imaging algorithm that is optimized for cylindrical sampling and operates in both the wavenumber domain and time domain (WDTD). Wavenumber domain algorithms are initially utilized for the rapid localization of strong scattering sources. Subsequently, morphological image analysis techniques are employed to delineate the regions containing strong scattering sources. Ultimately, accurate calculations are performed utilizing backpropagation (BP) in time domain algorithms. This method significantly reduces the computational burden while maintaining imaging accuracy by integrating rapid scattering source extraction with precise computation for critical regions. The proposed capacity to accomplish efficient and precise 3D imaging is effectively demonstrated by the experimental results, which effectively mitigate the computational challenges associated with traditional algorithms. Furthermore, the method effectively reconstructs near-field echoes of scattering sources, underscoring its potential for decoupling target–background interactions. The versatility of this method is further demonstrated by its ability to be applied to other 3D imaging configurations, which illustrates its potential to advance radar imaging technologies and near-field scattering research. Full article

To address the challenges of low optimization efficiency and premature convergence in existing algorithms for unmanned aerial vehicle (UAV) 3D path planning under complex operational constraints, this study proposes an enhanced honey badger algorithm (LRMHBA). First, a three-dimensional terrain model incorporating threat sources and UAV constraints is constructed to reflect the actual operational environment. Second, LRMHBA improves global search efficiency by optimizing the initial population distribution through the integration of Latin hypercube sampling and an elite population strategy. Subsequently, a stochastic perturbation mechanism is introduced to facilitate the escape from local optima. Furthermore, to adapt to the evolving exploration requirements during the optimization process, LRMHBA employs a differential mutation strategy tailored to populations with different fitness values, utilizing elite individuals from the initialization stage to guide the mutation process. This design forms a two-population cooperative mechanism that enhances the balance between exploration and exploitation, thereby improving convergence accuracy. Experimental evaluations on the CEC2017 benchmark suite demonstrate the superiority of LRMHBA over 11 comparison algorithms. In the UAV 3D path planning task, LRMHBA consistently generated the shortest average path across three obstacle simulation scenarios of varying complexity, achieving the highest rank in the Friedman test. Full article

Tropospheric aerosols play an important role in the notable warming phenomenon and climate change occurring in the Arctic. The accuracy of Cloud–Aerosol Lidar with Orthogonal Polarization (CALIOP) aerosol optical depth (AOD) and the distribution of Arctic AOD based on the CALIOP Level 2 aerosol products and the Aerosol Robotic Network (AERONET) AOD data during 2006–2021 were analyzed. The distributions, trends, and three-dimensional (3D) structures of the frequency of occurrences (FoOs) of different aerosol subtypes during 2006–2021 are also discussed. We found that the CALIOP AOD exhibited a high level of agreement with AERONET AOD, with a correlation coefficient of approximately 0.67 and an RMSE of less than 0.1. However, CALIOP usually underestimated AOD over the Arctic, especially in wet conditions during the late spring and early summer. Moreover, the Arctic AOD was typically higher in winter than in autumn, summer, and spring. Specifically, polluted dust (PD), dust, and clean marine (CM) were the dominant aerosol types in spring, autumn, and winter, while in summer, ES (elevated smoke) from frequent wildfires reached the highest FoOs. There were increasing trends in the FoOs of CM and dust, with decreasing trends in the FoOs of PD, PC (polluted continental), and DM (dusty marine) due to Arctic amplification. In general, the vertical distribution patterns of different aerosol types showed little seasonal variation, but their horizontal distribution patterns at various altitudes varied by season. Furthermore, locally sourced aerosols such as dust in Greenland, PD in eastern Siberia, and ES in middle Siberia can spread to surrounding areas and accumulate further north, affecting a broader region in the Arctic. Full article

This study proposes a novel technique for detecting aerial moving targets using multiple satellite radars. The approach enhances the image contrast of fused local three-dimensional (3D) profiles. Exploiting global navigation satellite system (GNSS) satellites as illuminators of opportunity (IOs) has brought remarkable innovations to multistatic radar. However, target detection is restricted by radiation sources since IOs are often uncontrollable. To address this, we utilize satellite radars operating in an active self-transmitting and self-receiving mode for controllability. The main challenge of multiradar target detection lies in effectively fusing the target echoes from individual radars, as the target ranges and Doppler histories differ. To this end, two periods, namely the integration period and detection period, are precisely designed. In the integration period, we propose a range difference-based positive and negative second-order Keystone transform (SOKT) method to make range compensation accurate. This method compensates for the range difference rather than the target range. In the detection period, we develop two weighting functions, i.e., the Doppler frequency rate (DFR) variance function and smooth spatial filtering function, to extract prominent areas and make efficient detection, respectively. Finally, the results from simulation datasets confirm the effectiveness of our proposed technique. Full article