Search Results (45)

The aging water distribution system in the United States, constructed mainly during the 1970s with some pipes dating back 125 years, is experiencing significant deterioration leading to substantial water losses. Along with the potential for water loss savings, improvements in the distribution system by using leak detection technologies can create net energy and cost savings. In this work, a new framework has been presented to calculate the economic level of leakage within water supply and distribution systems for two primary leak detection technologies (acoustic vs. satellite). In this work, a new framework is presented to calculate the economic level of leakage (ELL) within water supply and distribution systems to support smart infrastructure in smart cities. A case study focused using water audit data from Atlanta, Georgia, compared the costs of two leak mitigation technologies: conventional acoustic leak detection and artificial intelligence–assisted satellite leak detection technology, which employs machine learning algorithms to identify potential leak signatures from satellite imagery. The ELL results revealed that conducting one survey would be optimum for an acoustic survey, whereas the method suggested that it would be expensive to utilize satellite-based leak detection technology. However, results for cumulative financial analysis over a 3-year period for both technologies revealed both to be economically favorable with conventional acoustic leak detection technology generating higher net economic benefits of USD 2.4 million, surpassing satellite detection by 50%. A broader national analysis was conducted to explore the potential benefits of US water infrastructure mirroring the exemplary conditions of Germany and The Netherlands. Achieving similar infrastructure leakage index (ILI) values could result in annual cost savings of $4–$4.8 billion and primary energy savings of 1.6–1.9 TWh. These results demonstrate the value of combining economic modeling with advanced leak detection technologies to support sustainable, cost-efficient water infrastructure strategies in urban environments, contributing to more sustainable smart living outcomes. Full article

This study explores the archaeoacoustics of rock imagery at Site 26LN211, the northernmost petroglyph site in the White River Narrows (WRN) Archaeological District, Nevada, USA. The research examines the relationship between rock writing placement and acoustic properties, considering their potential significance to indigenous groups such as the Southern Paiute and Western Shoshone. Fieldwork conducted in 2024 employed impulse response recordings to analyze sound behavior in various spatial configurations, including near and distant measurements. The results indicate that, unlike other WRN sites with strong echoes and reverberation, Site 26LN211 exhibits clear sound transmission with limited acoustic reflections. This suggests its suitability for oral storytelling, song recitatives, and ritual practices rather than sound-enhanced ceremonial performances. Additionally, the presence of vision quest structures above the site implies spiritual significance, although the results do not show a significant acoustic relationship between them and the petroglyph zone. Comparative studies with other indigenous sites reinforce the role of acoustics in shaping cultural landscapes. These findings contribute to broader discussions on the interplay between rock writing, sound, and indigenous traditions, emphasizing the need for preservation. Full article

Unmanned aerial and autonomous surface vehicles (UAVs and ASVs, respectively) are two emerging technologies for the mapping of coastal and marine environments. Using UAV photogrammetry, the sea-bottom composition can be resolved with very high fidelity in shallow waters. At greater depths, acoustic methodologies have far better propagation properties compared to optics; therefore, ASVs equipped with multibeam echosounders (MBES) are better-suited for mapping applications in deeper waters. In this work, a sea-bottom classification methodology is presented for mapping the protected habitat of Mediterranean seagrass Posidonia oceanica (habitat code 1120) in a coastal subregion of Heraklion (Crete, Greece). The methodology implements a machine learning scheme, where knowledge obtained from UAV imagery is embedded (through training) into a classifier that utilizes acoustic backscatter intensity and features derived from the MBES data provided by an ASV. Accuracy and precision scores of greater than 85% compared with visual census ground-truth data for both optical and acoustic classifiers indicate that this hybrid mapping approach is promising to mitigate the depth-induced bias in UAV-only models. The latter is especially interesting in cases where the studied habitat boundaries extend beyond depths that can be studied via aerial devices’ optics, as is the case with P. oceanica meadows. Full article

Underwater dynamic targets often display significant blurriness in their forward-looking sonar imagery, accompanied by sparse feature representation. This phenomenon presents several challenges, including disturbances in the trajectories of underwater targets and alterations in target identification throughout the tracking process, thereby complicating the continuous monitoring of moving targets. This research proposes a new framework for underwater acoustic data interpolation and underwater object tracking. Considering the character of underwater acoustic images, a Swin Transformer is integrated in the architecture of the YOLOv5 network; then, an improved Deep Simple Online and Real-time Tracking method is developed. By enlarging the bounding box output generated by the detector and subsequently integrating it into the tracker, the sensing horizon of the tracker is broadened. This strategy enables the extraction of noise features surrounding the target, thereby augmenting the target’s characteristics and improving the stability of the tracking process. The experimental results demonstrate that the proposed method effectively reduces the frequency of changes in target identification numbers, minimizes the occurrence of trajectory interruptions, and decreases the overall percentage of trajectory interruptions. Additionally, it significantly enhances tracking stability, particularly in scenarios involving intersecting target paths and encounters. Full article

The concept of synthetic aperture radar (SAR) has the advantage of being able to obtain high-quality images even when the target area is at night or covered with obstacles such as clouds or fog. These imaging capabilities have led to a rapid increase in demand for space SAR imagery across a variety of sectors, including government, military, and commercial sectors. The SAR-based deployable reflector antenna was developed in this series of paper. The satellite performance is influenced by the aperture size of an antenna. To improve the image acquisition performance, the SAR antenna has the configuration of several foldable CFRP reflectors. In this paper, the experimental investigation of the Structural-thermal model deployable reflector antenna is performed. During the launch condition, the satellite and payload are subjected to the dynamic load. In the STM phase, the acoustic test was conducted to evaluate the structural stability of the deployable reflector antenna within the acoustic environment. The sinusoidal vibration test was implemented to investigate the fundamental frequency for inplane/normal directions and evaluate the structural stability of reflector antenna. By using experimental data obtained from the thermal-balance test, the well-correlated thermal analysis model was established to execute the orbital thermal analysis. The experimental results of the environmental test in STM phase show that the deployable reflector antenna has structural stability for the structural/thermal environments. The configuration of the deployable reflector antenna determined in STM phase can be applied to the qualification model. Full article

There are numerous lakes in the Tibetan Plateau (TP) that significantly impact regional climate and aquatic ecosystems, which often freeze seasonally owing to the high altitude. However, the special warming mechanisms of lake water under ice during the frozen period are poorly understood, particularly in terms of solar radiation penetration through lake ice. The limited understanding of these processes has posed challenges to advancing lake models and improving the understanding of air–lake energy exchange during the ice-covered period. To address this, a field experiment was conducted at Qinghai Lake, the largest lake in China, in February 2022 to systematically examine thermal conditions and radiation transfer across air–ice–water interfaces. High-resolution remote sensing technologies (ultrasonic instrument and acoustic Doppler devices) were used to observe the lake surface changes, and MODIS imagery was also used to validate differences in lake surface conditions. Results showed that the water temperature under the ice warmed steadily before the ice melted. The observation period was divided into three stages based on surface condition: snow stage, sand stage, and bare ice stage. In the snow and sand stages, the lake water temperature was lower due to reduced solar radiation penetration caused by high surface reflectance (61% for 2 cm of snow) and strong absorption by 8 cm of sand (absorption-to-transmission ratio of 0.96). In contrast, during the bare ice stage, a low reflectance rate (17%) and medium absorption-to-transmission ratio (0.86) allowed 11% of solar radiation to penetrate the ice, reaching 11.70 W·m⁻², which increased the water temperature across the under-ice layer, with an extinction coefficient for lake water of 0.39 (±0.03) m⁻¹. Surface coverings also significantly influenced ice temperature. During the bare ice stage, the ice exhibited the lowest average temperature and the greatest diurnal variations. This was attributed to the highest daytime radiation absorption, as indicated by a light extinction coefficient of 5.36 (±0.17) m⁻¹, combined with the absence of insulation properties at night. This study enhances understanding of the characteristics of water/ice temperature and air–ice–water solar radiation transfer through effects of different ice coverings (snow, sand, and ice) in Qinghai Lake and provides key optical radiation parameters and in situ observations for the refinement of TP lake models, especially in the ice-covered period. Full article

Mycelium-based composites (MBCs) are highly valued for their ability to transform low-value organic materials into sustainable building materials, offering significant potential for decarbonizing the construction sector. The properties of MBCs are influenced by factors such as the mycelium species, substrate materials, fabrication growth parameters, and post-processing. Traditional fabrication methods involve combining grain spawn with loose substrates in a mold to achieve specific single functional properties, such as strength, acoustic absorption, or thermal insulation. However, recent advancements have focused on digital biofabrication to optimize MBC properties and expand their application scope. Despite these developments, existing research predominantly explores the use of grain spawn inoculation, with little focus on liquid spawn. Liquid spawn, however, holds significant potential, particularly in digital biofabrication, due to its ease of deposition and greater precision compared with grains. This paper, part of a digital biofabrication framework, investigates the growth kinetics of Ganoderma lucidum and Pleurotus ostreatus on hemp non-woven mats, offering flexibility and mold-free fabrication using liquid inoculation. By integrating mycelium growth kinetics into digital biofabricated materials, researchers can develop more sustainable, efficient, and specialized solutions using fewer resources, enhancing the adaptability and functionality of MBCs. The experiment involved pre-cultivating P. ostreatus and G. lucidum in yeast peptone dextrose (YPD) and complete yeast media (CYM) under static (ST) and shaking (SH) conditions. Four dilutions (1:10, 1:2, 1:1, and 2:1) were prepared and analyzed through imagery to assess growth kinetics. Results showed that lower dilutions promoted faster growth with full coverage, while higher dilutions offered slower growth with partial coverage. SH conditions resulted in slightly higher coverage and faster growth. To optimize the control of material properties within the digital biofabrication system, it is recommended to use CYM ST for P. ostreatus and YPD SH for G. lucidum, as their growth curves show clear separation between dilutions, reflecting distinct growth efficiencies and speeds that can be selected for desired outcomes. Full article

Bottom-grab samplers have long been the standard to describe nearshore marine habitats both qualitatively and quantitively. However, sediment samplers are designed to collect specific grain sizes and therefore have biases toward those sediments. Here, we discuss seafloor characterizations based on grain size analysis alone vs. grain size analysis augmented with quantitative benthic imagery. We also use both datasets to inform a prevalent benthic habitat classification system. The Coastal and Marine Ecological Classification Standard (CMECS) was used to test this hypothesis. CMECS was adopted by the federal government to standardize habitat classification in coastal U.S. waters. CMECS provides a hierarchal framework to define and interpret benthic habitats but does not prescribe specific sampling methods. Photography has been utilized for many decades in benthic ecology but has rarely been employed in habitat classification using CMECS. No study to date has quantitatively examined the benefit of incorporating benthic imagery into the classification of biotopes using CMECS. The objective of this study is to classify a roughly 1 km² subtidal area within Herring Cove in Provincetown, MA with CMECS and quantify the benefit of augmenting classification with low-cost imagery. A benthic habitat survey of the study area included grab sampling for grain-size analysis and invertebrate taxonomy, benthic imagery, water quality sampling at 24 sampling stations, and acoustic mapping of the study area. Multivariate statistical analyses were employed to classify biotic communities and link environmental and biological data to classify biotopes. The results showed that benthic imagery improved the classification and mapping of CMECS components. Furthermore, the classification of habitats and biotopes was improved using benthic imagery data. These findings imply that the incorporation of low-cost benthic imagery is warranted in coastal benthic biotope classification and mapping studies and should be regularly adopted. This study has implications for coastal benthic ecologists classifying benthic habitats within the CMECS framework. Full article

We present a data-driven, in situ proximal multi-sensor digital soil mapping approach to develop digital twins for multiple agricultural fields. A novel Digital Soil Core^TM (DSC) Probe was engineered that contains seven sensors, each of a distinct modality, including sleeve friction, tip force, dielectric permittivity, electrical resistivity, soil imagery, acoustics, and visible and near-infrared spectroscopy. The DSC System integrates the DSC Probe, DSC software (v2023.10), and deployment equipment components to sense soil characteristics at a high vertical spatial resolution (mm scale) along in situ soil profiles up to a depth of 120 cm in about 60 s. The DSC Probe in situ proximal data are harmonized into a data cube providing vertical high-density knowledge associated with physical–chemical–biological soil conditions. In contrast, conventional ex situ soil samples derived from soil cores, soil pits, or surface samples analyzed using laboratory and other methods are bound by a substantially coarser spatial resolution and multiple compounding errors. Our objective was to investigate the effects of the mismatched scale between high-resolution in situ proximal sensor data and coarser-resolution ex situ soil laboratory measurements to develop soil prediction models. Our study was conducted in central California soil in almond orchards. We collected DSC sensor data and spatially co-located soil cores that were sliced into narrow layers for laboratory-based soil measurements. Partial Least Squares Regression (PLSR) cross-validation was used to compare the results of testing four data integration methods. Method A reduced the high-resolution sensor data to discrete values paired with layer-based soil laboratory measurements. Method B used stochastic distributions of sensor data paired with layer-based soil laboratory measurements. Method C allocated the same soil analytical data to each one of the high-resolution multi-sensor data within a soil layer. Method D linked the high-density multi-sensor soil data directly to crop responses (crop performance and behavior metrics), bypassing costly laboratory soil analysis. Overall, the soil models derived from Method C outperformed Methods A and B. Soil predictions derived using Method D were the most cost-effective for directly assessing soil–crop relationships, making this method well suited for industrial-scale precision agriculture applications. Full article

Geometrical models of the airways offer a comprehensive perspective on the complex interplay between lung structure and function. Originating from mathematical frameworks, these models have evolved to include detailed lung imagery, a crucial enhancement that aids in the early detection of morphological changes in the airways, which are often the first indicators of diseases. The accurate representation of airway geometry is crucial in research areas such as biomechanical modeling, acoustics, and particle deposition prediction. This review chronicles the evolution of these models, from their inception in the 1960s based on ideal mathematical constructs, to the introduction of advanced imaging techniques like computerized tomography (CT) and, to a lesser degree, magnetic resonance imaging (MRI). The advent of these techniques, coupled with the surge in data processing capabilities, has revolutionized the anatomical modeling of the bronchial tree. The limitations and challenges in both mathematical and image-based modeling are discussed, along with their applications. The foundation of image-based modeling is discussed, and recent segmentation strategies from CT and MRI scans and their clinical implications are also examined. By providing a chronological review of these models, this work offers insights into the evolution and potential future of airway geometry modeling, setting the stage for advancements in diagnosing and treating lung diseases. This review offers a novel perspective by highlighting how advancements in imaging techniques and data processing capabilities have significantly enhanced the accuracy and applicability of airway geometry models in both clinical and research settings. These advancements provide unique opportunities for developing patient-specific models. Full article

Three-section landslides are renowned for their immense size, concealed development process, and devastating impact. This study conducted physical model tests to simulate one special geological structure called a three-section-within landslide. The failure process and precursory characteristics of the tested samples were meticulously analyzed using video imagery, micro-seismic (MS) signals, and acoustic emission (AE) signals, with a focus on event activity, intensity, and frequency. A novel classification method based on AE waveform characteristics was proposed, categorizing AE signals into burst signals and continuous signals. The findings reveal distinct differences in the evolution of these signals. Burst signals appeared exclusively during the crack propagation and failure stages. During these stages, the cumulative AE hits of burst signals increased gradually, with amplitude rising and then declining. High-amplitude burst signals were predominantly distributed in the middle- and high-frequency bands. In contrast, cumulative AE hits of continuous signals escalated rapidly, with amplitude monotonously increasing, and high-amplitude continuous signals were primarily distributed in the low-frequency band. The emergence of burst signals and high-frequency AE signals indicated the generation of microcracks, serving as early-warning indicators. Notably, the early-warning points of AE signals were detected earlier than those of video imagery and MS signals. Furthermore, the early-warning point of burst signals occurred earlier than those of continuous signals, and the early-warning point of the classification method preceded that of overall AE signals. Full article

The digital representation of seafloor, a challenge in UNESCO’s Ocean Decade initiative, is essential for sustainable development support and marine environment protection, aligning with the United Nations’ 2030 program goals. Accuracy in seafloor representation can be achieved through remote sensing measurements, including acoustic and laser sources. Ground truth information integration facilitates comprehensive seafloor assessment. The current seafloor mapping paradigm benefits from the object-based image analysis (OBIA) approach, managing high-resolution remote sensing measurements effectively. A critical OBIA step is the segmentation process, with various algorithms available. Recent artificial intelligence advancements have led to AI-powered segmentation algorithms development, like the Segment Anything Model (SAM) by META AI. This paper presents the SAM approach’s first evaluation for seafloor mapping. The benchmark remote sensing dataset refers to Puck Lagoon, Poland and includes measurements from various sources, primarily multibeam echosounders, bathymetric lidar, airborne photogrammetry, and satellite imagery. The SAM algorithm’s performance was evaluated on an affordable workstation equipped with an NVIDIA GPU, enabling CUDA architecture utilization. The growing popularity and demand for AI-based services predict their widespread application in future underwater remote sensing studies, regardless of the measurement technology used (acoustic, laser, or imagery). Applying SAM in Puck Lagoon seafloor mapping may benefit other seafloor mapping studies intending to employ AI technology. Full article

CORAL (Catamaran fOr UndeRwAter expLoration) is a compact, unmanned catamaran-type vehicle designed and developed to assist the scientific community in exploring marine areas such as inshore regions that are not easily accessible by traditional vessels. This vehicle can operate in different modalities: completely autonomous, semi-autonomous, or remotely assisted by the operator, thus accommodating various investigative scenarios. CORAL is characterized by compact dimensions, a very low draft and a total electric propulsion system. The vehicle is equipped with a single echo-sounder, a 450 kHz Side Scan Sonar, an Inertial Navigation System assisted by a GPS receiver and a pair of high-definition cameras for recording both above and below the water surface. Here, we present results from two investigations: the first conducted in the tourist harbour in Pozzuoli Gulf and the second in the Riomaggiore-Manarola marine area within the Cinque Terre territory (Italy). Both surveys yielded promising results regarding the potentiality of CORAL to collect fine-scale submarine elements such as anthropic objects, sedimentary features, and seagrass meadow spots. These capabilities characterize the CORAL system as a highly efficient investigation tool for depicting shallow bedforms, reconstructing coastal dynamics and erosion processes and monitoring the evolution of biological habitats. Full article

An innovative payload containing a sensitive mid-wave infrared camera was flown on an uncrewed aircraft system (UAS) to acquire thermal imagery along a reach of the Sacramento River, California, USA. The imagery was used as input for an ensemble particle image velocimetry (PIV) algorithm to produce near-continuous maps of surface flow velocity along a reach approximately 1 km in length. To assess the accuracy of PIV velocity estimates, in situ measurements of flow velocity were obtained with an acoustic Doppler current profiler (ADCP). ADCP measurements were collected along pre-planned cross-section lines within the area covered by the imagery. The PIV velocities showed good agreement with the depth-averaged velocity measured by the ADCP, with $R^2$ values ranging from 0.59–0.97 across eight transects. Velocity maps derived from the thermal image sequences acquired on consecutive days during a period of steady flow were compared. These maps showed consistent spatial patterns of velocity vector magnitude and orientation, indicating that the technique is repeatable and robust. PIV of thermal imagery can yield velocity estimates in situations where natural water-surface textures or tracers are either insufficient or absent in visible imagery. Future work could be directed toward defining optimal environmental conditions, as well as limitations for mapping flow velocities based on thermal images acquired via UAS. Full article

Brain imaging studies have recently provided some evidence in favor of covert cognitive processes that are ongoing in patients with disorders of consciousness (DoC) (e.g., a minimally conscious state and vegetative state/unresponsive wakefulness syndrome) when engaged in passive sensory stimulation or active tasks such as motor imagery. In this exploratory study, we used transcranial magnetic stimulation (TMS) of the motor cortex to assess modulations of corticospinal excitability induced by action observation in eleven patients with DoC. Action observation is known to facilitate corticospinal excitability in healthy subjects, unveiling how the observer’s motor system maps others’ actions onto her/his motor repertoire. Additional stimuli were non-biological motion and acoustic startle stimuli, considering that sudden and loud acoustic stimulation is known to lower corticospinal excitability in healthy subjects. The results indicate that some form of motor resonance is spared in a subset of patients with DoC, with some significant difference between biological and non-biological motion stimuli. However, there was no covariation between corticospinal excitability and the type of DoC diagnosis (i.e., whether diagnosed with VS/UWS or MCS). Similarly, no covariation was detected with clinical changes between admission and discharge in clinical outcome measures. Both motor resonance and the difference between the resonance with biological/non-biological motion discrimination correlated with the amplitude of the N20 somatosensory evoked potentials, following the stimulation of the median nerve at the wrist (i.e., the temporal marker signaling the activation of the contralateral primary somatosensory cortex). Moreover, the startle-evoking stimulus produced an anomalous increase in corticospinal excitability, suggesting a functional dissociation between cortical and subcortical circuits in patients with DoC. Further work is needed to better comprehend the conditions in which corticospinal facilitation occurs and whether and how they may relate to individual clinical parameters. Full article