Search Results (5,827)

Rare earth elements (REEs) play an important role in emerging renewable energy technology, the production of advanced materials, energy conservation, and high-end manufacturing industries, making them an irreplaceable strategic resource. The diagnostic spectral absorption features of REEs in the visible and near-infrared spectrum can be effectively used for identifying the occurrences of REEs on the Earth’s surface. This study systematically compared three airborne hyperspectral sensors—HyMap, CASI-1500h, and AisaFENIX 1K—for detecting REEs in the Bayan Obo area of Inner Mongolia, China. The CASI-1500h imagery performed most effectively in identifying the locations of REEs among the three sensors evaluated here. Additionally, this study proposed a hyperspectral workflow for REE identification, which enabled the detection of REE-bearing minerals regardless of the host rock types—including carbonatites and associated dikes, fenite-syenites, and metamorphic feldspar-quartz sandstone. Laboratory-based spectroscopy and mineral chemistry analyses indicated that the absorption features of the REE-bearing mineral monazite within the 400‒1000 nm range can be ascribed to Nd³⁺. This study demonstrates the potential of airborne hyperspectral technology for efficient and large-scale exploration of REE deposits. Full article

Intentional and unintentional GNSS interference can greatly affect the performance of precise timing and localization in areas such as automated driving or aviation. Nevertheless, reports show that jamming occurs near many European airports that are located close to a highway or in heavy industry areas due to broadcasting of interfering signals. To assess the impact of such potential risks, we investigated interference occurring on a section of highway located both near to an airport and close to logistics centers as part of the Austrian Security Research Program project CATCH-IN. This section of highway is of particular interest, as the highway runs in parallel to the approach path of aircraft and crosses the approach path 3.7 km before the aircraft touches down (the flight altitude is only 200 m above the ground). For this experiment, we distributed six Septentrio Mosaic x5 GNSS receivers as sensors along the highway and monitored this section for five months. We analyzed the data with AGC monitoring, CN0 monitoring, and baseband sample monitoring to identify interference along the highway that could affect sensors along the descending flight trajectory. During the period of this experiment, we saw events that we believe could cause potential safety risks and problems for aviation safety. In our analysis, we focused on the statistical evaluation of the temporal repetitions, in particular the times of day that see more interference and the frequencies at which more interference occurs. Additionally, we analyzed the performance of different algorithms for dealing with large datasets. The results provide new insight into potential monitoring stations near airports and raise awareness of potential risks and vulnerabilities in aviation safety as well as automated driving along highways. Full article

Lasers are a key enabling technology across numerous engineering and scientific fields, especially in high-energy laser systems for defense, materials processing, and fusion research, where precise characterization of high-power, large-divergence-angle laser spots is critical. However, the inherent properties of high-power, large-divergence-angle lasers—such as large spot area and strong intensity contrast—pose real obstacles to existing methods, which often suffer from low accuracy and inefficiency. In this paper, a flat-field correction technique was proposed for the CCD to reduce the distortions produced by the non-uniform response of the sensor in spot measurements. Then, a spot recognition algorithm based on support vector machines was developed, which can effectively and accurately locate and identify laser spots with limited training samples and computational resources, achieving a classification accuracy of over 98.11%. Additionally, an efficient correction approach is proposed to assess the spot intensity and shape with high accuracy even at large tilt angles. Experimental results show that this proposed approach can measure the high-power laser spot with a large divergence angle precisely and efficiently, and improves both the measurement precision and operational efficiency remarkably. Full article

Building on prior evidence that biomass cooking drives personal air pollution in rural and peri-urban Bangladesh, we measured kitchen pollution alongside personal exposure and examined the influence of outdoor industrial and traffic emissions on personal and indoor air quality. In an mHealth based-behavior change communication (BCC) intervention study (NCT05570552), 400 women were enrolled from rural Matlab and peri-urban Araihazar in Bangladesh. We measured 24 h personal exposure to fine particulate matter 2.5 (PM_2.5) and black carbon (BC) using personal monitors (UPAS V2), and 72–120 h PM_2.5 in 200 kitchens and outdoors of households using air quality sensors (PurpleAir Flex). Compared to clean fuel users, biomass users showed greater personal and kitchen exposure to PM_2.5, showing good correlation between personal and indoor PM_2.5 measurements (R² = 0.722). Daily average personal PM_2.5 and kitchen PM_2.5 during both cooking and non-cooking periods were higher in rural than peri-urban areas. Geographic information system mapping revealed that personal PM_2.5 was inversely related to the distance of factories from households when below <300 m in both rural and urban areas. Only in Araihazar, personal BC was higher in households located near factories or roads (<200–300 m) compared to those situated further away. Higher personal BC exposure was found in peri-urban women than rural women (p < 0.001). Higher levels of PM_2.5 and increased BC were found in rural and peri-urban households, respectively, which were located in close proximities to formal/informal factories and main roads. These findings highlight the need for sustainable household energy transitions and improved air quality management to reduce air pollution exposure in Bangladesh. Full article

Abandoned oil and gas wells pose significant risks to human health and the environment by emitting air pollutants, contaminating groundwater, and leaving behind hazardous debris. In the United States, approximately 3.9 million documented wells vary widely in the accuracy of their recorded locations and plugging status, creating major challenges for detection, mapping, and remediation. Existing well detection methods show some promise but often lose effectiveness under complex conditions, such as vegetation occlusion or construction without metal components. In this study, we propose a drone-based approach equipped with a highly sensitive methane sensor to identify statistical anomalies in methane concentrations around abandoned oil and gas well sites. To address the noisy and variable nature of environmental sensor data, statistical methods were developed that enable reliable anomaly detection under field conditions. Controlled release experiments with known emission points validated the method’s ability to statistically detect methane anomalies that may indicate nearby emission sources. We further tested the approach at a field site containing three abandoned wells with known locations and sparse emission profiles. The results demonstrate that the proposed drone-based sensing method can serve as a rapid survey approach to identify areas with elevated methane signals around well sites, helping to reduce the scope of the ground survey area, and supporting prioritization of follow-up ground investigations. This approach provides a practical means to support targeted monitoring and prioritization of remediation efforts, while supporting the future development of source attribution and localization methods. Full article

Accurate identification of damage in simply supported girder bridges and the timely implementation of protective measures are crucial to preventing structural failure. Rotation influence line-based methods offer a straightforward and cost-effective approach for bridge monitoring; however, their validation has primarily relied on numerical simulations, with a lack of rigorous theoretical explanation. To address this limitation, an analytical relationship between the rotation difference at cross-sections before and after damage and the moving load position is first derived using the principle of virtual work, thereby clarifying the theoretical mechanism underlying damage identification. On this theoretical basis, a novel generalized information entropy index of rotation difference is proposed by incorporating information entropy theory to quantify the local nonlinear response induced by damage. The proposed method is validated through numerical simulations conducted on a simply supported steel girder bridge model developed in ANSYS, as well as through comparisons with existing experimental datasets. The results demonstrate that the proposed index can accurately and stably identify both single and multiple damage locations in bridges, while requiring only two inclination sensors installed at the supports. Under varying damage locations and severity levels, the entropy response curves consistently exhibit distinct peaks corresponding to the actual damage positions, thereby confirming the physical consistency and practical applicability of the method. The strategy of combining a minimal sensor configuration with information entropy analysis significantly reduces system complexity and cost while maintaining identification accuracy, providing an efficient and economical solution for practical bridge health monitoring. Full article

This study presents a structured methodology for optimizing the placement and selection of accelerometer sensors for structural health monitoring in civil infrastructures. The approach integrates both ambient and forced vibration testing data, followed by a unified analysis of sensor energy distribution through singular value decomposition of the cross power spectral density. The energy associated with each sensor is normalized and decomposed into its vertical, longitudinal, and transversal components, allowing for detailed ranking and visualization across different structural elements such as the deck and supporting piers. A comparative analysis between the energy distributions obtained from ambient and forced vibrations is conducted to identify consistent sensor locations. The sensor configuration is then iteratively refined using a combination of global dynamic criteria and local spatial constraints to ensure both stability and optimal spatial distribution. The resulting framework enables the systematic design of sensor layouts that combine energy-based robustness with optimal spatial distribution across all three spatial components, while significantly reducing the number of required sensors, ensuring the preservation of damage detection capability and long-term structural health monitoring. Full article

This research presents a comprehensive multi-domain environmental assessment of Delos Island, a UNESCO World Heritage Site, through integration of long-term atmospheric and satellite remote sensing datasets. A significant methodological contribution of this research is the development of a cross-mission harmonization approach that enables the reconstruction of a continuous, multi-decadal atmospheric record. By implementing a hierarchical calibration pipeline to harmonise Ozone Monitoring Instrument (OMI) and Tropospheric Monitoring Instrument (TROPOMI) observations, the study effectively eliminated a 6.61-fold systematic instrument offset, producing a 21-year time series (2004–2025) of tropospheric NO₂ concentrations. Simultaneously, a 24-year analysis (2000–2024) of coastline dynamics was conducted using the Landsat archive to quantify land area changes across the island and within a 1.03 km² Archaeological Area of Interest (AOI). Results indicate that atmospheric NO₂ concentrations stabilised following a 2015 peak, while coastal erosion represents a measurable risk to structural integrity. Net land loss of 18,400 m² was documented within the AOI, driven by localised geomorphological factors and exposure to Meltemi winds. The results indicate that these environmental processes are physically independent yet collectively require a multilayered conservation strategy to protect vulnerable archaeological heritage from atmospheric pollution and coastal retreat. Furthermore, the research highlights the value of long-term satellite datasets spanning more than two decades for supporting heritage monitoring and management, especially in remote or hard-to-reach locations. Through the analysis of the spatial and temporal characteristics of these sensors, the research enables the identification of hazard proxies that can inform risk-aware decision-making. Full article

Indoor exposure to particulate matter (PM) depends on ventilation-driven transport, yet sensor placement in real rooms is often based on limited point data. This study develops and experimentally validates a transient CFD framework, using RANS airflow coupled with Lagrangian discrete phase tracking, to map PM_2.5 and PM₁₀ in a full-scale 2.0 × 3.0 × 2.5 m bedroom with a fixed, non-oscillating pedestal fan and an open window. Airflow was verified by grid independence and validated against 10-point velocity measurements (RMSE = 0.108 m·s⁻¹). Incense experiments (≈31 min burn) provided PM time series over the first 60 min at 16 locations on two heights; emission rate, burning time, and air-change rate (1.96–5.39 ACH) were calibrated so that accepted models achieved aggregate R² > 0.90. Spatial mapping on a 0.5 m grid shows that PM behavior is governed primarily by airflow-defined accumulation pockets rather than by source proximity alone. A near-source region consistently captured strong early-time peaks, whereas remote low-exchange pockets remained elevated during the decay phase. For PM_2.5, the most persistent hotspot is a ceiling-adjacent recirculation pocket, while for PM₁₀, gravitational settling shifted the dominant hotspots toward floor-layer, low-velocity regions. An exposure score combining normalized peak and time-averaged concentrations, interpreted together with particle-track persistence metrics, distinguished transiently traversed regions from true retention pockets. The results show that sensor placement should follow the monitoring objective: near-source regions are more responsive to peak events, ceiling pockets are more suitable for persistent PM_2.5 monitoring, and floor hotspots are more critical for PM₁₀. No single fixed sensor location adequately represents both particle sizes in the present bedroom and ventilation configuration. Full article

The Advanced Meteorological Imager (AMI) on GEO-KOMPSAT-2A (GK-2A) lacks a 2.1 μm shortwave infrared channel, precluding the dark target surface reflectance estimation that other geostationary aerosol retrievals rely on. We propose an improved land aerosol optical depth (AOD) retrieval in which background surface reflectance (BSR) is derived entirely from pixel-level bidirectional reflectance distribution function (BRDF) inversion using the scaled Ross-Thick Li-Sparse (sRTLS) kernel model fitted to geostationary time-series observations. Unlike existing approaches, the algorithm inverts the BRDF independently at each retrieval channel without relying on spectral reflectance relationships or external surface reflectance products; it assumes a low-background AOD during an initial accumulation period and then iteratively refines both BRDF coefficients and AOD. Two aerosol models—generic and dust—are supported, with a geographic dust-zone mask activating two-model selection during spring. Validation against 74 Aerosol Robotic Network sites over 2023 yields R = 0.86, RMSE = 0.15, and bias = −0.02, compared with R = 0.59, RMSE = 0.25, and bias = −0.04 for the National Meteorological Satellite Center (NMSC) GK-2A AOD product. The largest improvements appear at AOD ≤ 0.1 (bias: +0.03 versus +0.11) and AOD > 0.8 (bias: −0.12 versus −0.85). The full March–May (MAM) evaluation yields bias = −0.06 across all 74 sites. As a separate parallel retrieval restricted to matchups inside the geographic dust-zone mask, the proposed algorithm (dust model included) gives bias = −0.03, which worsens to −0.11 when only the generic model is applied—nearly a fourfold increase. A comparison against Himawari-9/Advanced Himawari Imager (AHI)—a co-located geostationary sensor carrying a 2.3 μm shortwave infrared (SWIR) channel—shows that the proposed algorithm (R = 0.897) outperforms Himawari-9/AHI (R = 0.855) across all metrics, demonstrating competitive accuracy without relying on a SWIR channel. Full article

Low-cost air quality sensors enable dense urban monitoring networks but require calibration against reference-grade instruments. While machine learning calibration is well-established for co-located sensor pairs, applying these calibrations to sensors deployed far from any reference station—the operational reality for most network sensors—lacks systematic methodology. We address this gap using 24 months of hourly data (August 2023–July 2025) from Sofia, Bulgaria, where five official reference stations (Executive Environmental Agency) operate alongside 22 AirThings low-cost sensors, four of which are co-located. Random Forest models achieved $R^2\in (0.53,0.75)$ across PM_2.5, PM₁₀, NO₂, and O₃, representing from 40% (for O₃) to 408% (for PM_2.5) improvement over Multiple Linear Regression baselines. Using leave-one-station-out spatial cross-validation, we derived pollutant-specific uncertainty growth rates ($\alpha$) from 3.84% to 5.62% per km, characterizing how calibration uncertainty increases with distance from reference stations (statistically significant for PM₁₀ and O₃, $p<0.05$). Applied to 18 non-co-located sensors, the framework generated 1.2 million calibrated hourly measurements with 95% prediction intervals over the study period. Co-location sites spaced 6 km apart achieve a less than 30% uncertainty increase at network midpoints, within EU Air Quality Directive thresholds for indicative monitoring. These empirically derived $\alpha$ parameters enable network planners to predict measurement reliability at arbitrary sensor locations without ground-truth validation, providing evidence-based guidance for cost-effective hybrid monitoring network design. Full article

Background: Solid waste collection is a relevant issue for municipalities and can be improved by installing volumetric sensors inside dumpsters. Sensors generate a maintenance cost but provide additional information to decide which dumpsters to empty in a given day when visiting all of them is expensive. Moreover, dumpsters close to each other are expected to follow similar filling trends, as they serve the same catchment area; hence, equipping them all with sensors may be inconvenient. This leads to the problem of finding sensor locations that minimize routing, waste overflow, and sensor maintenance costs. Methods: We tackle the problem using a heuristic based on adaptive large neighborhood search and a one-step look-ahead policy, performed through a rolling horizon method to approximate the multi-stage stochastic programming problem, in order to compute the number and locations of sensors to be installed, minimizing the total cost. Results: We apply the proposed approach to a realistic setting with 50 dumpsters in Torino. The results show that placing sensors in 21 dumpsters at optimized locations allowed saving about 17,000 € per year and reduced vehicle emissions by 15.5%. Conclusions: The proposed approach enables more cost-effective and sustainable waste collection operations. Full article

In Canada, early fire detection is an important component of wildfire management, and it utilizes a combined effort approach including public reports, aviation patrols, and satellite observations. The role of ground-based continuous smoke sensors has not been formally assessed in Canadian wildfire management detection systems. Dense networks of ground-based, internet-enabled continuous smoke sensors were deployed at three locations across southern Canada during 2023 and 2024, in concert with planned prescribed fire in grass fuels as well as incidental wildfire ignitions. Smoke sensor detection of fires was compared to polar orbiting and geostationary fire detection. Large fire events (50–600 ha) with a ground smoke detector distance of 1–2 km were observed on most occasions (n = 7), but the detection rate dropped to 30% for fires 1 ha or smaller. Follow-up smoke monitoring after the initial detection offered valuable information on smoke production and dispersion across multiple sensors. This typically nighttime smoldering smoke production fell below the threshold for geostationary satellite fire observation and is otherwise only captured sparingly by polar orbiting satellites. Thus, ground-based smoke detection systems likely fit an important niche for monitoring low-energy (i.e., smoldering) smoke events from fully contained fires or to monitor fires considered recently extinguished. Full article

Background: Freezing of gait (FoG) is a common symptom of Parkinson’s disease, especially in its later stages of progression. Characterized by involuntary stopping during normal gait patterns, FoG greatly increases fall risk, reducing quality of life. Given the complex presentation and etiology of FoG, current treatments have proven ineffective in managing episodes. In recent years, machine learning algorithms have been leveraged to derive actionable clinical insights from biomedical datasets. As a manifestation of neuromechanical dysfunction, impending FoG episodes may be characterized through data collected by wearable devices and sensors. Objective: This scoping review evaluates the current landscape of machine and deep learning-derived biomarkers to enhance the personalized management of FoG. Methods: This scoping review was conducted using established methodological frameworks for scoping reviews and is reported in accordance using the PRISMA-ScR checklist. Three databases were queried, with screening yielding 60 studies. Results: Thirty-nine papers reported on deep learning techniques, with the most common architectures being convolutional neural networks and long short-term memory models. Conclusions: Inertial measurement units, which can be worn on various locations, may be a promising modality for practical implementation. To generate closed-loop FoG therapies, algorithms can be integrated into real-time systems like robotic exoskeletons or adaptive deep brain stimulation. Future work in generating datasets from ambulatory devices, as well as distributed computing strategies, may lead to real-time FoG management. Full article

Snow avalanches pose a serious hazard in snow-covered, mountainous areas. In order to protect inhabited areas and infrastructure such as roads and railway lines, avalanche protection measures need to be taken. In addition to permanent, technical protection measures, temporary, organizational measures, which are based on risk assessments by local avalanche warning commissions, are utilized. These avalanche risk assessments rely on regional avalanche bulletins, weather forecasts, local expertise, and information on current snowpack conditions. Our research seeks to enhance knowledge of current snowpack and avalanche conditions by providing in situ monitoring of potential avalanche slopes. Therefore, we developed a novel sensor box array, peakr, consisting of multiple sensor units deployed by hand or by drone at key avalanche slope locations throughout the winter season. The sensors continuously measure temperature, humidity, position, and snowpack movement. Data are transmitted via LoRaWAN and GSM, stored locally, and accessed through a web platform. Automated analysis using a decision tree and event-detection algorithm triggers immediate alerts to responsible personnel via SMS and email. This paper presents an overview of the peakr sensor array and web platform, focusing on data analysis and avalanche events from the Arlberg ski resort in winter 2023/2024, supported by webcam time-lapse validation. Full article