Search Results (342)

Flow velocity is a key environmental factor that exerts multifaceted effects on fish growth and adaptation. Through long-term natural selection, fish have evolved adaptability to specific flow conditions, which not only relate to oxygen supply and food acquisition but also play a decisive role in reproduction, development, and population maintenance. To investigate the genomic mechanisms through which hydrodynamic environments drive divergence in closely related species, we focused on two sister species, Hemiculter bleekeri and Hemiculter leucisculus, which are adapted to contrasting flow regimes. We generated high-quality, chromosome level telomere-to-telomere (T2T) genomes and integrated comparative genomic analyses, we investigated the genetic basis underlying body shape regulation and reproductive strategies, aiming to decipher the adaptive evolutionary patterns of these species in response to differing hydrodynamic conditions from an integrated genotype phenotype perspective. We integrated PacBio HiFi, Hi-C, and Oxford Nanopore Technologies (ONT) ultra-long read sequencing data to construct high-quality T2T reference genomes for both species. The final genome assemblies are 0.998 Gb for H. bleekeri and 1.05 Gb for H. leucisculus, with each species possessing 24 chromosomes and all chromosomal sequences assembled into single contigs. Contig N50 values reached 40.45 Mb and 40.66 Mb, respectively, and both assemblies are gap-free. BUSCO assessments yielded completeness scores of 99.34% for both genomes, confirming their high continuity and accuracy. Integrated morphometric and genomic analyses revealed distinct adaptive strategies in two Hemiculter Species. H. bleekeri has evolved a streamlined body, underpinned by expansions in body shape related genes, and a pelagic egg strategy. In contrast, the adhesive egg strategy of H. leucisculus is supported by expansions in adhesion-related gene families. This divergence reflects adaptation to distinct flow velocity. By combining high-quality chromosome-level T2T genomes with morphometric and comparative genomic approaches, this study establishes a comprehensive framework for understanding the molecular mechanisms underlying adaptive evolution in freshwater fishes inhabiting contrasting flow velocity. Full article

The present study presents long-term monitoring data on the dynamics of bedload transport processes in alpine gravel-bed river systems in Austria (Urslau, Strobler-Weißenbach) using radio frequency identification (RFID) technology. The detection of embedded RFID tracers was facilitated by the use of stationary antennas. This methodology enabled the acquisition of high-resolution data on particle transport velocities, transport distances, and sediment dynamics. Monitoring has been in operation permanently over a period of 8 years, including several intense flood events. In total, 1612 RFID-tagged stones were deployed, and the maximum measured particle velocity was 2.47 m s⁻¹. The measurements at the Urslau stream revealed seasonal variability and long-term trends, while targeted short-term measurements at the Strobler-Weißenbach stream provided valuable insights into the dynamics of flood events. The results underscore the significance of environmental factors, including the grain size, river gradient, and hydraulic parameters, in the dynamics of bedload transport in alpine gravel bed streams. Furthermore, the efficiency of stationary antennas was optimised to ensure uninterrupted monitoring. This study underscores the importance of contemporary monitoring technologies in analysing river processes and addressing challenges, including those brought about by climate change. Full article

Reliable assessments of dam stability require the continuous acquisition and interpretation of deformation data, as monitoring technologies provide essential information for evaluating structural behavior. Surface displacement measurements are particularly valuable for identifying instability within the dam embankment and adjacent slopes. While terrestrial surveying networks can provide accurate point-based observations, they are often time-consuming and costly to maintain. Satellite radar interferometry (InSAR) offers a complementary, cost-effective means of monitoring surface displacement with wide spatial coverage; however, careful analysis is required to avoid misinterpreting superficial motions of riprap and cover materials as true dam settlement. In this study, we use multi-platform SAR datasets, including Sentinel-1A (2014–2019) and high-resolution TerraSAR-X (2018), to investigate the deformation behavior of the Taleqan Dam. We compare LOS displacement derived from InSAR with independent measurements from a terrestrial surveying network spanning the same period. TerraSAR-X data indicate up to ~20 mm of LOS displacement over three months (May–August 2018), and the displacement pattern is consistent with the Sentinel-1 time series. Despite lower spatial resolutions, Sentinel-1 provided dense, temporally continuous coverage, with LOS velocities reaching ~4 mm/yr on the downstream slope. The combined datasets demonstrate that the observed deformation predominantly reflects the ongoing lateral movement of downstream riprap materials rather than the vertical settlement of the dam’s core. These results highlight both the utility of InSAR for long-term dam monitoring and the importance of integrating multi-sensor observations to ensure accurate interpretations of dam deformation signals. Full article

With the continuous development of cities and the increasing utilization of underground space, ambient noise seismic imaging has become an essential approach for exploring and monitoring the urban subsurface. The integration of Distributed Acoustic Sensing (DAS) with ambient noise imaging offers a more convenient and effective solution for investigating shallow subsurface structures in urban environments. To overcome the limitations of conventional ambient noise seismic nodes, which are costly and incapable of achieving high-density data acquisition, this work makes use of existing urban telecommunication fibers to record ambient noise and perform sliding-window cross-correlation on it. Then the Phase-Weighted Stack (PWS) technique is applied to enhance the quality and stability of the cross-correlation signals, and fundamental-mode Rayleigh wave dispersion curves are extracted from the cross-correlation functions through the High-Resolution Linear Radon Transform (HRLRT). In the inversion stage, an adaptive regularization strategy based on automatic L-curve corner detection is introduced, which, in combination with the Preconditioned Steepest Descent (PSD) method, enables efficient and automated dispersion inversion, resulting in a well-resolved near-surface S-wave velocity structure. The results indicate that the proposed workflow can extract useful surface-wave dispersion information under typical urban noise conditions, achieving a feasible level of subsurface velocity imaging and providing a practical technical means for urban underground space exploration and utilization. Full article

Monitoring the complex, three-dimensional flow within centrifugal compressor diffusers remains a major challenge due to geometric confinement, high rotational speeds, and strong unsteadiness near surge and stall. This review provides a comprehensive assessment of contemporary instrumentation strategies for diffuser flow characterization, spanning pressure, temperature, velocity, vibration, and acoustic measurements. The article outlines the standards governing compressor instrumentation, compares conventional probes with emerging high-resolution and high-bandwidth sensor technologies, and evaluates the effectiveness of pressure- and temperature-based diagnostics, optical methods, and advanced dynamic sensing in capturing diffuser behavior. Case studies from industrial compressors, research rigs, and high-speed experimental facilities illustrate how sensor layout, bandwidth, and synchronization influence the interpretation of flow stability, performance degradation, and surge onset. Collectively, these examples demonstrate that high-frequency pressure and temperature probes remain indispensable for instability detection, while optical techniques such as PIV, LDV, and PSP/TSP offer unprecedented spatial resolution for understanding flow structures. The findings highlight the growing integration of hybrid sensing architectures, digital acquisition systems, and data-driven analysis in diffuser research. Overall, the review identifies current limitations in measurement fidelity and accessibility while outlining promising paths toward more robust, real-time monitoring solutions for reliable centrifugal compressor operation. Full article

Background/Objectives: Children with Developmental Coordination Disorder (DCD) frequently experience handwriting difficulties, or dysgraphia. The association between DCD and dysgraphia has long been observed and described. However, few studies have examined the acquisition and transfer of graphomotor skills in these children, i.e., their ability to learn new graphic gestures and reuse them in new tasks. The objective of this study was to evaluate the acquisition of pseudo-letters and their transfer to different types of tasks in children with DCD. Methods: Three case studies of children with DCD, with or without an associated dysgraphia, were compared to an age-matched control group. Participants learned to produce six pseudo-letters during an acquisition phase, then transferred their learning to two tasks: the first assessed the transfer of learned strokes to new pseudo-letters, and the second assessed the transfer of stroke sequences to combinations of two or three pseudo-letters. Performances were analyzed on the basis of four variables: handwritten product quality, and three measures reflecting the handwriting process, i.e., velocity, fluency, and the number of stops during writing. Results: Acquisition and transfer abilities differed depending on the presence and severity of dysgraphia. Only the presence of a severe dysgraphia associated with DCD led to a lower quality and a greater on-paper velocity than typically developing children during the learning test. As to transfer, DCD children were able to transfer their learning, even in the presence of a dysgraphia. Only in the case of the second, more distant, transfer task, the presence of a severe dysgraphia led to an increase in velocity and in fluency, and a decrease in the number of stops, in addition to the lower quality. This pattern is typical of handwriting in DCD children with dysgraphia. Conclusions: The acquisition of de novo graphomotor skills depends on the presence and severity of a dysgraphia associated with DCD, but not on the severity of other motor impairments. The further transfer of these skills is preserved in DCD children. Full article

To maintain a high standard of environmental quality, industrial plants must be able to foresee and control the impacts resulting from their activities. One of the most challenging issues for the metallurgical and mining industry when it comes to protecting the environment is the measurement of particulate matter emissions generated by the wind action over the erodible surfaces of stockpiles of granular materials. It is known that the emissive phenomenon starts from a specific threshold friction velocity, which is an inherent characteristic of each material. This parameter can be derived from relationships available in the scientific and technical literature, which, however, only provide qualitative estimations. Therefore, the threshold friction velocity of the specific materials under investigation must be assessed through laboratory tests. This article discusses the results obtained for nine raw materials sampled in a metallurgical plant by applying three different procedures, (1) the sieve-based analysis suggested by U.S. EPA; (2) the laboratory tests performed with an Environmental Wind Tunnel; and (3) the PI-SWERL tests (i.e., tests performed with a Portable In-Situ Wind ERosion Lab), and presents a comparative analysis of the three methods. Findings indicate that the EPA methodology tends to be less accurate than the wind tunnel and PI-SWERL tests, though its accuracy can be slightly improved by adding an additional sieve size for materials with finer aggregates. The wind tunnel and PI-SWERL provided comparable results, with PI-SWERL offering practical advantages due to its portability and an effective synchronization between its data acquisition systems. Full article

Glacier mass balance and ice flow dynamics, strongly influenced by climatic variability, topography, and geological–structural controls, can be precisely characterized through in situ GPS measurements of surface ice velocity, though such data remain limited due to logistical challenges in field acquisition. This study presents direct measurements of surface ice velocity on Grey Glacier, a major outlet glacier of the Southern Patagonian Icefield (SPI) in Chile. Ice flow was monitored over a one-week period in late 2002 by tracking the displacement of six stakes installed on the glacier surface. The resulting velocity data reveal spatial patterns of surface flow that provide significant information for the comparison and validation of remote sensing observations, which is particularly relevant considering that the ice mass from which the data were collected has since disappeared due to glacier retreat. The combined use of ground-based and remote sensing methods is essential for advancing our understanding of glacier motion and behavior, particularly in the context of climate forcing. Full article

To address the issues of poor universality, high cost, and difficulty in parameter acquisition associated with existing methods for estimating crop evapotranspiration (ET_c) in greenhouses, this study focused on tomato plants in a Venlo-type greenhouse (Zhenjiang, Jiangsu Province, from 20 November 2024 to 9 January 2025) to explore the applicability of the surface renewal (SR) method in greenhouses. Micrometeorological data were collected by deploying high-frequency temperature sensors and other equipment. The accuracy of sensible heat flux (H) estimation by the traditional Snyder method and the Chen method was compared. Based on the latent heat flux (LE) measured by the evaporimeter method, the actual sensible heat flux was derived through an energy balance model, which was then used for comparative verification with the estimation results of the two methods. The results showed that the Chen method, which incorporates friction velocity (u*) and does not rely on the empirical calibration coefficient α, is adaptable to the characteristics of non-uniform airflow in greenhouses. Under sunny conditions (R² = 0.722 during the day and R² = 0.712 at night) and cloudy conditions (R² = 0.7558 during the day and R² = 0.754 at night), the estimation accuracy of the Chen method was significantly higher than that of the Snyder method. Moreover, for the entire experimental period, the R² value reached 0.733, the Pearson’s r coefficient was 0.856, and the outlier rate was as low as 9.1%. This study innovatively applies the SR method to semi-closed heated greenhouses, making a clear distinction from previous SR studies conducted in open-field environments and providing a new approach for the accurate estimation of greenhouse heat flux. Full article

Real-time prediction of the instantaneous fuel consumption rate (FCR) of any vehicle is the key to improving energy efficiency and reducing emissions. The conventional prediction methods, which include an on-board diagnostic (OBD) system, require the specific vehicle parameters and environmental conditions such as air density. We propose a data-driven Bayesian optimization and Monte Carlo (MC) Dropout methods-based long short-term memory (BMC-LSTM) network FCR prediction model using only the vehicle’s throttle position, velocity, and acceleration data. The cost-effective LSTM network-based solution enhances the high-resolution prediction accuracy within a deep learning framework. The network is integrated with the Bayesian optimization and MC-Dropout methods to ensure a probabilistically optimal hyperparameter set and robust networks. The proposed method presents an FCR model that provides calibrated predictions and reliability against distribution drift by probabilistically tuning hyperparameters with Bayesian optimization and quantifying epistemic uncertainty with the MC-Dropout. Our approach requires only vehicle speed, longitudinal acceleration, and throttle position at inference time. Note, however, that the reference FCR used to train and validate the models was obtained from OBD during data acquisition. The performance of the proposed method is compared with a conventional LSTM and Bidirectional LSTM-based multidimensional models, XGBoost and support vector regression-based models, and first- and fourth-order polynomials, which are derived using the least-squares method. The prediction performance of the method is evaluated using Mean Squared Error, Root Mean Squared Error, Mean Absolute, and R-squared statistical metrics. The proposed method achieves a superior $R^2$ score and substantially reduces the conventional error metrics. Full article

In marine seismic exploration, ghost waves distort reflection waveforms and narrow the frequency band of seismic records. Traditional deghosting methods are susceptible to practical limitations from sea surface fluctuations and velocity variations. This paper proposes a τ-p domain deghosting method based on the Hybrid Least Squares Residual (HyBR LSMR) algorithm. We first establish a linear forward model in the τ-p domain that describes the relationship between the total wavefield and upgoing wavefield, transforming deghosting into a linear inverse problem. The method then employs the hybrid LSMR algorithm with Tikhonov regularization to address the inherent ill-posedness. A key innovation is the integration of the Generalized Cross Validation (GCV) criterion to adaptively determine regularization parameters and iteration stopping points, effectively avoiding the semi-convergence phenomenon and enhancing solution stability. Applications to both synthetic and field data demonstrate that the proposed method effectively suppresses ghost waves under various acquisition conditions, significantly improves the signal-to-noise ratio and resolution, broadens the effective frequency band, and maintains good computational efficiency, providing a reliable solution for high-precision seismic data processing in complex marine environments. Full article

The parameterization of vegetation indices (VIs) is crucial for sustainable irrigation and horticulture management, specifically for urban green infrastructure (GI) management. However, the constraints of roadside traffic, motor and industrially related pollution, and potential public vandalism compromise the efficacy of conventional in situ monitoring systems. The shortcomings of prevalent satellites, UAVs, and manual/automated sensor measurements and monitoring systems have already been reviewed. This research proposes a novel urban GI monitoring system based on an integration of gas exchange and various VIs obtained from computer vision algorithms applied to data acquired from three novel sources: (1) Integrated gas sensor data using nine different volatile organic compounds using an electronic nose (E-nose), designed on a PCB for stable performance under variable environmental conditions; (2) Plant growth parameters including effective leaf area index (LAIe), infrared index (Ig), canopy temperature depression (CTD) and tree water stress index (TWSI); (3) Meteorological data for all measurement campaigns based on wind velocity, air temperature, rainfall, air pressure, and air humidity conditions. To account for spatial and temporal data acquisition variability, the integrated cameras and the E-nose were mounted on a vehicle roof to acquire information from 172 Elm trees planted across the Royal Parade, Melbourne. Results showed strong correlations among air contaminants, ambient conditions, and plant growth status, which can be modelled and optimized for better smart irrigation and environmental monitoring based on real-time data. Full article

The accurate acquisition and real-time calculation of the attitude angle of precision firefighting extinguishing projectiles are essential for ensuring stable flight and precise extinguishing agent release. However, measuring the roll attitude angle in such projectiles is challenging due to their highly dynamic nature and environmental disturbances such as fire smoke, high temperature, and electromagnetic interference. Traditional methods for measuring attitude angles rely on multi-sensor fusion schemes, which suffer from complex structure and high cost. This paper proposes a single-gyro attitude calculation method based on micro-electromechanical inertial measurement units (MIMUs). This method integrates Fourier transform time-frequency analysis with a second-order Infinite Impulse Response (IIR) bandpass filtering algorithm optimized by dynamic coefficients. Unlike conventional fixed-coefficient filters, the proposed algorithm adaptively updates filter parameters according to instantaneous roll angular velocity, thereby maintaining tracking capability under time-varying conditions. This theoretical contribution provides a general framework for adaptive frequency-tracking filtering, beyond the specific engineering case of firefighting projectiles. Through joint time-frequency domain processing, it achieves high-precision dynamic decoupling of the roll angle, eliminating the dependency on external sensors (e.g., radar/GPS) inherent in conventional systems. This approach drastically reduces system complexity and provides key technical support for low-cost and high-reliability firefighting projectile attitude control. The research contributes to enhancing the effectiveness of urban firefighting, forest fire suppression, and public safety emergency response. Full article

The development of automated systems for real-time material evaluation is becoming increasingly critical for structural engineering applications, infrastructure diagnostics and advanced material research. This work introduces a novel, fully automated nonlinear acoustics monitoring platform that employs Bulk Wave excitation in combination with non-contact Laser Doppler Vibrometry (LDV) detection to continuously assess the microstructural evolution of cement-based composites. Unlike conventional approaches—such as ultrasonic velocity measurements or compressive strength tests—which lack sensitivity to early-stage changes and also require manual operation, the proposed system enables unsupervised, high-precision monitoring of the material by leveraging the second and third harmonic generation (β₂, β₃) as nonlinear indicators of internal material changes. A specialized LabVIEW-based software manages excitation control, signal acquisition, frequency-domain analysis, and real-time feedback. As an initial step, the system’s stability, linearity, and measurement reliability were validated on metallic samples, and verified through long-duration experiments. Subsequently, the system was used to monitor hydration in cement-based specimens with varying water-to-cement and carbon nanotube (CNT) reinforcement ratios, thereby demonstrating its capability to resolve subtle nonlinear responses. The results highlight the system’s enhanced sensitivity, repeatability, and scalability, demonstrating that it as a powerful tool for structural health monitoring, smart infrastructure, and predictive maintenance applications. Full article

China’s sinuous coastline is increasingly threatened by land subsidence driven by complex geological conditions and intensive human activity. Using year-round Sentinel-1A acquisitions for 2023 and SBAS-InSAR processing, we generated the first millimetre-resolution subsidence velocity field covering the 50 km coastal buffer of mainland China. We elucidated subsidence patterns and their drivers and quantified the associated socio-economic risks by integrating 1 km GDP and population data. Our analysis shows that ~55.77% of the coastal zone is subsiding, exposing 97.42 million residents and CNY 16.41 billion of GDP. Four hotspots—Laizhou Bay, northern Jiangsu, the Yangtze River Delta (YRD) and the Pearl River Delta (PRD)—exhibit the most pronounced deformation. Over-extraction of groundwater is identified as the primary driver. The 15 m resolution subsidence product provides an up-to-date, high-precision dataset that effectively supports sustainable development research in coastal hazard prevention, territorial spatial planning, and sea-level rise studies. Full article