Search Results (849)

Hurricane Florence (2018) proved to be a damaging tropical cyclone that formed off the coast of the Cabo Verde Islands. On 12 UTC 14 September 2018, Florence made landfall as a weakened category 1 Hurricane in Wrightsville Beach, NC. In the midst of landfall, Florence’s ground speed stalled considerably to near zero. Because of this stall, Florence continued to accumulate feet of rain along the coastline, and the inundation of seawater became extreme. Due to the impacts of Florence, the Weather Research and Forecasting Model (WRF-ARW) was used to simulate the tropical cyclone and provide insight into the thermodynamics and dynamics that played a significant role at the time of landfall. After the control case, several sensitivity experiments were conducted. The historical sensitivity experiments utilize the thermodynamic and moisture fields of ERA5 reanalysis data from 1968 and 1998, respectively, to modify the thermodynamic and moisture fields in the initial conditions of the WRF–ARW control case. In addition, to study the potential future climate impacts of Florence, the NCAR CESM Global Bias-Corrected CMIP5 Output to Support WRF/MPAS Research dataset was utilized. The same approach was taken as the historical versions of Florence for sensitivity experiments for future climate, i.e., thermodynamic and moisture fields for both 2038 and 2068 under the RCP6.0 and RCP8.5 climate scenarios, respectively. Results suggest a corresponding intensity shift with minor track deflections. Based on these modifications, synoptic and mesoscale dynamics will be studied to provide insight into how Florence-like hurricanes may change based on certain climate scenarios. Full article

Chirped fiber Bragg gratings (CFBGs) are robust diagnostic sensors that are widely used to track detonation-driven and shock wave propagation. CFBGs are inscribed with a linearly chirped periodic index of refraction changes that alter the Bragg wavelength along the length of the probe. The light return of each individual Bragg element is captured by a detector at a unique time to map the full reflected spectrum. The CFBG spectrum is measured with a dispersive Fourier transform of the reflected light that temporally stretches the spectrum to increase spatial resolution and make a one-to-one map of the wavelength on a time axis. Here, we propose an improvement of CFBG temporal resolution by incorporating two co-linear laser pulses with orthogonal polarization states and a 5 ns time offset. The two separate signals were split and tracked by two separate detectors. An oscilloscope captured good separation in the signals, and two separate spectrograms were generated and interleaved in the post-processing of the data. This novel technique doubled the CFBG temporal resolution and led to a doubled location resolution. As a proof-of-concept of this technique, the resolution improvement was compared between standard CFBG measurements and the two polarization states method on a position-sensitive CFBG sensor. CFBG resolution doubling will advance sensor capabilities and will have a direct impact on improving capture and analysis in dynamic, high-explosive experiments. Full article

Water age reflects water sources, storage, and pathways, and regulates the solute retention and dissolution associated with biogeochemical processes, highlighting its hydrological and ecological importance. However, accurate water age estimation in tracer-aided models depends heavily on the quality and spatio-temporal resolution of precipitation isotopic signals. This study investigates how distributed rainfall δ²H signals affect the simulation of young water fraction (F_yw) via the Storage Age Selection (SAS) model in topographically complex subtropical mountain catchments. Eight precipitation δ²H scenarios were generated using two temporal approaches (stepwise and sinewave) and four spatial interpolation methods: (1) raw data, (2) reversed effective recharge elevation method (rERE), (3) linear regression with elevation (ER), and (4) regression-kriging (RK). Later on, the time-variant SAS model was calibrated against observed stream water δ²H collected from the year 2022 to the year 2024. Results show that the SAS model consistently produced similar F_yw estimates for catchments (8%~40%) across all eight scenarios, demonstrating strong robustness to input uncertainty and validating the dominant role of catchment characteristics in regulating water age. The combined stepwise temporal and rERE spatial approach provided better agreement with observed stream δ²H, particularly in the eastern, steeper catchments, yielding superior model efficiency along with better constrained uncertainty. This study highlights the sensitivity of age-tracking models to precipitation isotopic inputs and provides practical guidance for selecting an interpolation strategy in data-limited mountainous environments. Full article

We present a systematic evaluation of the positional and rotational tracking accuracy of the Meta Quest 3 mixed-reality headset using a TECHMAN TM5-900 collaborative robot (±0.05 mm repeatability) as a highly repeatable robot-driven reference. The headset was rigidly attached to the robot’s tool flange and subjected to single-axis translational motions (200 mm along X, Y, and Z) and rotational motions (Roll ± 65°, Pitch ± 85°, and Yaw ± 85°). Each test was repeated three times, and the resulting trajectories were averaged to improve statistical robustness. Both data sources were integrated into a single Python-based application running on the same computer. The headset streamed its data via UDP, while the robot, implemented as an ROS2 node, published its data to the same host. This configuration enabled simultaneous acquisition of both streams, ensuring temporal consistency without the need for offline interpolation. All comparisons were performed in a relative reference frame, thereby avoiding the need for absolute hand–eye calibration. Coordinate-frame alignment was achieved using Singular Value Decomposition (SVD)-based rigid-body Procrustes analysis. Over 2848 synchronized samples spanning 151.46 s, the Meta Quest 3 achieved a mean translational RMSE of 0.346 mm (3D RMSE = 0.621 mm) and a mean rotational RMSE of 0.143°, with Pearson correlation coefficients greater than 0.9999 on all axes. These results show sub-millimeter positional tracking and sub-degree rotational tracking under controlled conditions, supporting the potential of the Meta Quest 3 for precision-oriented mixed-reality applications in industrial and research settings. Full article

Tropospheric delays can be the leading source of error in spaceborne interferometric synthetic aperture radar (InSAR) measurements. Here, we find that the non-uniform troposphere delay features are dependent on the squint angles used for repeat-pass InSAR data acquisitions. Large squint angles cause large along-track shifts in these non-uniform troposphere delay features. By processing the airborne L-band uninhabited aerial vehicle SAR (UAVSAR) data with three different squint angles, we were able to see various non-uniform delay structures of different sizes with varying delays of up to a few centimeters across the observed interferograms. We were also able to estimate the altitude of the effective troposphere delay layers. The understanding of the squint-dependent troposphere delay can help us separate the surface deformation component from the atmosphere delay component in the InSAR phase measurements. A number of methods are proposed for this separation. We used the UAVSAR data and simulated surface deformations to verify these methods. This technique can also be used for spaceborne cases. Full article

Nature-based tourism in protected areas brings economic benefits but can also lead to negative environmental impacts, such as trail degradation. This study aimed to quantify hiking-induced degradation on the Mvubu and Nkonkoni trails in Enseleni Nature Reserve, South Africa. Data were collected through systematic sampling at 20 points along each trail, with 50-m intervals between sampling locations. Several trail degradation indicators were recorded, including: trail grade (TG), landform grade (LG), cross-sectional area (CSA), soil compaction, surface composition, soil texture, and soil moisture. Maximum incision depth (MID) and trail width (WID) were treated as response variables. Statistical relationships between degradation indicators and response variables were analysed using linear regression and partial least squares regression (PLSR). The results indicated significant differences (p < 0.05) between the two trails for several degradation indicators, including surface composition (specifically soil cover), soil compaction, soil texture, and soil moisture. PLSR models explained 19–20% of the variance in MID and 12–55% of the variance in WID. Such weak model performance suggests that trail degradation may be influenced by additional factors not measured in this study. In particular, human behavioural factors, such as hiker avoidance of muddy sections, may play an important role in shaping patterns of trail degradation beyond the measured environmental variables. Early signs of rill erosion were observed on the Mvubu Trail, while informal trail formation was evident on the Nkonkoni Trail. Consequently, the study recommends a dual-track strategy involving revegetation along with the installation of water bars and check dams on the Mvubu Trail to prevent rilling, and “Leave-No-Trace” visitor education for the Nkonkoni Trail to reduce informal path formation. Full article

This study experimentally evaluates the thermal performance of a compact parabolic trough solar collector (PTSC) operating under actual solar conditions in Chachapoyas, a high-Andean city in northern Peru characterized by frequent cloud cover and variable irradiance. Despite the growing interest in solar thermal systems, few studies have assessed PTC behavior under high-altitude, diffuse radiation conditions typical of Andean regions. The PTSC, aligned along the north–south axis and equipped with a manual solar tracking system, was monitored for 30 consecutive days. Solar irradiance, ambient temperature, and water inlet/outlet temperatures were recorded at 30 min intervals using a DAVIS Vantage Pro Plus weather station and infrared thermometers (±0.5 °C accuracy). Thermal efficiency was determined from the ratio of useful heat gain to incident solar energy, based on instantaneous irradiance data. Results showed peak irradiance values of 1000 W m⁻² and maximum outlet water temperatures of 85 °C, achieving an average efficiency of 68 ± 2.5%. The collector maintained stable operation even under fluctuating radiation, confirming its suitability for domestic hot-water and low-temperature industrial applications. These findings provide the first experimental evidence of efficient solar-thermal conversion in cloudy highland environments of Peru, supporting the deployment of decentralized renewable energy systems in the Andean region. Full article

Synthetic aperture radar altimeter (SARAL) directs the radar beam toward the nadir point of the flight trajectory. It is capable of capturing elevation variations in the terrain of interest. To ensure that the nadir point remains within the beam coverage under complicated flight attitudes, a wide beamwidth is necessary. However, the wide beamwidth introduces a spatial-variant delay problem with respect to different scatters in the along-track direction, which degrades the accuracy in obtaining the terrain elevation contour. To this end, a spatial-variant Delay-Doppler (SVDD) algorithm is proposed in this paper. The core advantage of the proposed algorithm is that an analytical spectrum is obtained through rigorous mathematical derivation for the wide-beam SARAL geometry. Accordingly, all correction functions are implemented via complicated multiplications without interpolation operations. High computational efficiency is therefore ensured. To address the spatial-variant delay problem, a direct geometric relationship is first established between the Doppler frequency and the azimuthal position. Based on this relationship, the spatial-variant characteristic is mapped from the spatial domain to the Doppler domain. This mapping is then directly employed to construct the spatial-variant delay correction function. At the same time, range walk correction and range curve correction are carried out. In such cases, the variation of the undulating terrain can be recovered from the Delay-Doppler Map (DDM). Both simulated and raw data of the radar altimeter are applied to verify the effectiveness of the proposed SVDD algorithm. Comparisons with the conventional algorithm are also performed to demonstrate the superiority of the SVDD algorithm. Full article

Rapid urbanization has promoted socioeconomic growth but has exacerbated spatial-structure imbalances. This study investigates 41 prefecture-level cities in the Yangtze River Delta (YRD) from 2010 to 2022. Using nighttime light data, we compute the Comprehensive Nighttime Light Index (CNLI) to track urbanization dynamics and delineate built-up areas. Furthermore, we apply random-forest recursive feature elimination with cross-validation (RF-RFECV) and a Shapley additive explanations (SHAP)-based interpretation framework to quantify the spatiotemporal evolution of urbanization drivers. The results indicate that urbanization in the YRD increased steadily overall during the study period. Shanghai maintained its core leadership, Jiangsu and Zhejiang advanced steadily, and Anhui rapidly caught up driven by regional integration policies. Although regional disparities generally converged, persistent absolute gaps in small and medium-sized cities and inland areas remain a prominent challenge to balanced development. Spatially, urbanization exhibits a gradient differentiation of “higher in the east and lower in the west, and higher along rivers and coasts than inland.” The regional spatial structure gradually shifted from an early “pole-core–belt” pattern to a polycentric and networked urban agglomeration system, with metropolitan areas and economic belts serving as important carriers for promoting spatial balance. Furthermore, built-up areas exhibit a trajectory of “core agglomeration, corridor-oriented expansion, and intensive transition.” The shrinking coverage of the standard deviational ellipse and a slowdown in expansion rates suggest a shift from extensive outward sprawl to more concentrated development. Regarding driving mechanisms, YRD urbanization has evolved from early-stage factor-scale expansion to a later-stage efficiency- and innovation-driven trajectory. While population density remained the dominant driver, early-stage reliance on transport infrastructure and fiscal decentralization was largely replaced by the strengthening effects of per capita output and green innovation. Overall, these findings provide empirical evidence for optimizing spatial patterns and designing differentiated policies for high-quality urbanization in the YRD. Full article

In ecologically fragile and geomorphologically complex mountainous regions, ensuring a smooth transition from poverty alleviation to multidimensional sustainable rural development remains a key issue in regional governance. Focusing on the Qinba Mountains, a typical former contiguous poverty-stricken region in China covering 18 prefecture-level cities in six provinces, this study uses 2009–2023 prefecture-level panel data to examine the spatiotemporal evolution and driving mechanisms of coordinated rural revitalization. An integrated framework of “multi-dimensional evaluation–spatiotemporal tracking–attribution diagnosis” is developed by combining the improved AHP–entropy-weight TOPSIS method, the Coupling Coordination Degree (CCD) model, spatial Markov chains, spatial autocorrelation, and the Geodetector. The results show pronounced subsystem asynchrony. Livelihood and Well-being Security (U5) improves steadily, while Level of Industrial Development (U1), Civic Virtues and Cultural Vibrancy (U3), and Rural Governance (U4) also rise but with clear spatial differentiation; by contrast, Quality of Human Settlements (U2) fluctuates in stages under ecological fragility. Overall, the coupling coordination level advances from the Verge of Imbalance to Intermediate Coordination, yet the regional pattern remains uneven, with eastern basin cities leading and western deep mountainous cities lagging. State transitions display both policy responsiveness and path dependence: the probability of retaining the original state ranges from 50.0% to 90.5%; low-level neighborhoods reduce the upward transition probability to 25%, whereas medium-to-high-level neighborhoods raise the upward transition probability of low-level cities from 36.36% to 53.33%. Spatial dependence is also evident, with Global Moran’s I increasing, with fluctuations, from 0.331 in 2009 to 0.536 in 2023; high-value clusters extend along the Guanzhong Plain–Han River Valley corridor, while low-value clusters remain relatively locked in mountainous border areas. Driving mechanisms show clear stage-wise succession. At the single-factor level, the explanatory power of Road Network Density (F6) declines from 0.639 to 0.287, whereas Terrain Relief Amplitude (F1) becomes the dominant background constraint in the later stage (q = 0.772). Multi-factor interactions are generally enhanced. In particular, the traditional infrastructure-led pathway weakens markedly, with F1 ∩ F6 = 0.055 in 2023, while the interaction between terrain and consumer market vitality becomes dominant, with F1 ∩ F7 = 0.987 in 2023. On this basis, three major pathways are identified: government fiscal intervention and transportation accessibility improvement, capital agglomeration and market demand stimulation, and human–earth system adaptation and ecological value realization. These findings provide quantitative evidence for breaking spatial lock-in and improving cross-regional resource allocation in ecologically constrained mountainous regions. Full article

Background: Breakpoints in the pre-examination processes and at organizational interfaces are a significant source of failures in specimen identification and tracking in anatomic pathology. While ISO 15189 emphasizes end-to-end traceability and risk-based quality management, implementing these principles in complex, multi-actor specimen pathways remains challenging. This study explores expert perspectives on specimen process chains, tracking mechanisms, and ISO 15189-oriented quality and risk management in pathology. Methods: We conducted 10 semi-structured expert interviews across three settings. Interviews were audio-recorded, transcribed, pseudonymized, and analyzed using structured qualitative content analysis (Mayring) supported by MAXQDA. A deductive category system derived from the theoretical framework and interview guide comprised six main categories and twelve subcategories. Results: Across 512 coded text segments, participants identified several factors as critical for effective implementation, including: (i) interface management along the specimen pathway, with recurrent vulnerabilities at handovers between operating theater/ward/transport and accessioning; (ii) the central role of barcode-based identification and the need for closed-loop traceability; (iii) the importance of measurable quality indicators and incident learning systems to operationalize risk management; (iv) persistent paper–digital handoffs and heterogeneous IT landscapes that undermine data integrity; (v) the need for clearly assigned responsibilities, training, and SOP governance; and (vi) implementation barriers including resources, change management, and vendor integration, alongside practical enablers such as incremental roll-out and cross-professional governance. Conclusions: Experts converge on a pragmatic ISO 15189-aligned roadmap: prioritize interface risks, standardize identifiers and handover rules, define a minimal KPI set for tracking and misidentification events, and reduce paper–digital handoffs by interoperable IT. Future work should quantify baseline error rates and evaluate the impact of digital tracking interventions on patient safety and turnaround times. Full article

Spatial light field metrics, such as cylindrical illuminance, provide essential information for qualitative lighting evaluation, yet they remain far less common in practice than horizontal illuminance. To address this gap, we present a multi-sensor prototype that simultaneously measures horizontal illuminance E_h and approximates mean cylindrical illuminance E_z from a set of vertical illuminances uniformly distributed around a cylindrical surface. The device uses a flexible PCB wrapped around a support barrel, along with an inertial and magnetic measurement unit for orientation tracking. The measurements enable direct calculation of the modelling factor defined in the technical standard EN 12 464 and the visualization of the directional light distribution using polar plots and an illuminance solid. Results show that the prototype approximates mean cylindrical illuminance with high accuracy while preserving directional information, allowing the illuminance solid to be decomposed into vector and symmetric components. Compared with conventional approximation methods, the proposed multi-sensor approach reduces spatial error and yields richer data for lighting analysis. These findings indicate that multi-sensor systems can bridge the gap between theoretical spatial metrics and practical photometry and support the improved modelling evaluation and integration of qualitative lighting parameters into routine workflows. Full article

Precise track measurement of the geometric state of high-speed railways is a prerequisite for their smooth and safe operation. Current track inspection trolleys, which integrate only an inertial navigation system (INS) and a total station (TS), rely entirely on the track control network (CPIII) deployed along the track when calibrating their absolute location to avoid INS errors. Due to the high dependency on the surrounding CPIII points, this method faces severe challenges in terms of operational efficiency and cost control. To address this issue, this study utilizes the fast and precise positioning capability of the Chinese Beidou System (BDS) and proposes a novel adaptive location calibration approach using tightly integrated BDS/TS data. Using the Kalman filtering framework, this approach integrates BDS observations with the TS distance measurements in the observation domain, and the number of CPIII points to be observed is adaptively reduced according to the surrounding environments. Thus, the absolute location of track inspection trolleys can be quickly and accurately calibrated without INS data, greatly reducing dependency on CPIII points. Experiments were conducted under two typical scenarios: open-sky and blocked BDS signals. The results demonstrate that, under open-sky scenarios, the adopted BDS-only solution achieves positioning errors of less than 1.0 cm in the north, east, and up directions within 5 min, completely getting rid of the reliance on the control network, while in obstructed scenarios, where the BDS-only solution fails to converge at the 1 cm level within 5 min, the tightly integrated BDS/TS approach, combined with CPIII data, enables fast convergence in the northward and eastward, with positioning errors of less than 1 cm. The proposed approach provides a novel location calibration scheme in the track geometric states measurement under different environments, effectively reducing the dependence of track measurement operations on CPIII points and significantly enhancing measurement efficiency and flexibility. Full article

Phosphate mining companies in Morocco pose many environmental and occupational safety risks, especially through the release of airborne particulates, gas pollutants, and heavy metals. While there is increased implementation of monitoring systems within industrial mining contexts, current methodologies are still predominantly founded on rule-based systems or classical statistics that presume linearity in relationships between an arbitrary set of environmental parameters and the likelihood of an incident. Conversely, mining operations are characterized by intricately dynamic nonlinear combinations of numerous environmental and operational variables. As a result, a potential research opportunity exists for the application of sophisticated machine learning techniques that provide the ability to detect various levels of operational risk within phosphate mining scenarios. This study has three objectives. First, to examine the mining environmental and operational data from the phosphate mining sites to determine the mining operational conditions that present the highest risk. Second, to create a machine learning classification model which utilizes a Feedforward Neural Network (FNN) to identify operational states that are prone to incidents based on multivariate sensor data. Third, to assess the validity and reliability of the model using machine learning validity and reliability evaluation techniques along with statistical validation methods. In this study, an artificial intelligence-based approach for AI-based safety monitoring was proposed by using a Feedforward Neural Network (FNN) on a detailed data set of 1536 hourly measurements, directly recorded onsite at OCP plants in Benguerir and Khouribga. Environmental and industrial parameters (dust concentration, gas emissions, temperature, and toxic metal content) were measured using industrial-grade sensors certified for such a type of application. By means of training the proposed FNN model with adaptive gradient descent and dropout regularization with early stopping, a test mean squared error of 0.057 and over 85% accuracy on incident detection were obtained. Gradient tracking and m-adaptive validation proved the stability and convergence of the model. Emissions and dust were identified as the main risk classifiers in a variable importance analysis. The findings demonstrate that the mining sector may move from reactive to proactive safety management and validate the incorporation of AI into a real-time monitoring infrastructure inside the OCP ecosystem. Practical concerns of industrial data gathering, model interpretability, and the moral application of AI in high-risk settings are also addressed by the study. Full article

Residential heating is a major contributor to atmospheric pollution, especially in populated areas. Traditional methods for measuring emissions, such as chimney probes, are limited due to the need for prior owner consent, which can compromise the reliability of results—particularly when detecting the illegal burning of materials like plastic or waste oil. This study introduces a mobile air pollution monitoring system using compact sensor modules installed on vehicles and drones. These autonomous modules are equipped with gas, particulate matter, and environmental sensors, along with Global Positioning System (GPS) tracking to record pollutant concentrations in real time and associate them with specific geographic locations. Field experiments conducted in Hungary and Uzbekistan demonstrated the system’s effectiveness in detecting elevated pollutant levels in rural areas with solid fuel heating and in urban zones affected by industrial activity and traffic. For instance, PM2.5 concentrations ranged from 15 μg/m³ in forested areas to as high as 160 μg/m³ in industrial zones, while CO₂ levels near chimneys exceeded background values by 15–25 ppm. Drone-based measurements enabled vertical profiling and direct analysis of emissions from individual chimneys, providing detailed spatial distribution data. The proposed mobile sensing approach allows for the accurate localization of pollution sources and the assessment of air quality variations within small-scale environments. This method overcomes limitations of stationary or pre-announced inspections and supports proactive environmental monitoring and enforcement. Full article