High-accuracy Data Research Articles

Enhanced detection of surface deformations in LPBF using deep convolutional neural networks and transfer learning from a porosity model

Our previous research papers have shown the potential of deep-learning models for real-time detection and control of porosity defects in 3D printing, specifically in the laser powder bed fusion (LPBF) process. Extending these models to identify other defects like surface deformation poses a challenge due to the scarcity of available data. This study introduces the use of Transfer Learning (TL) to train models on limited data for high accuracy in detecting surface deformations, marking the first attempt to apply a model trained on one defect type to another. Our approach demonstrates the power of transfer learning in adapting a model known for porosity detection in LPBF to identify surface deformations with high accuracy (94%), matching the performance of the best existing models but with significantly less complexity. This results in faster training and evaluation, ideal for real-time systems with limited computing capabilities. We further employed Gradient-weighted Class Activation Mapping (Grad-CAM) to visualize the model’s decision-making, highlighting the areas influencing defect detection. This step is vital for developing a trustworthy model, showcasing the effectiveness of our approach in broadening the model’s applicability while ensuring reliability and efficiency.

High-accuracy bathymetric method fusing ICESAT-2 datasets and the two-media photogrammetry model

Improving the accuracy of nearshore bathymetric measurements is essential for understanding coastal environments, resource management, and navigation. The Ice, Cloud, and Land Elevation Satellite-2 (ICESat-2) is the first laser satellite that uses the photon-counting technique. The ICESat-2 is equipped with the Advanced Topographic Laser Altimeter System (ATLAS), which enables higher-accuracy measurements of water, ice, and land elevation on Earth. Two-media photogrammetric bathymetry is a type of nearshore bathymetric technology that uses the geometrical characteristics of light rays. With this technique, the accuracy and reliability mainly depend on eliminating systematic errors and ensuring accurate spatial photogrammetric positioning relative to the object being measured. To improve the bathymetric accuracy of two-media photogrammetry, we integrated high-accuracy elevation data from photon datasets as constraining and control parameters. The improved method effectively eliminated systematic errors in two-media photogrammetry during the established joint-block adjustment model. To improve its accuracy and reliability, we employed multispectral WorldView-2 stereo images in our experiments. Furthermore, the bathymetric results were validated and assessed using in situ and photon data. The experimental results show that the highest accuracy achieved with the bathymetric measurements in our study area was a root mean square error (RMSE) of 0.96 m and a mean absolute error of 0.57 m. Using the proposed fusion method, the bathymetric accuracy (as measured using the RMSE) was 1 m higher than that of two-media photogrammetry without the photon datasets.

Multi-beam Depth Coverage Modeling and Solution Based on Geometric Relationships

In recent years, precise measurements have become increasingly important in various fields. Multibeam bathymetry, known for its wide coverage, high data accuracy, and efficiency, has been widely adopted in scientific research and engineering projects. However, its precision and efficiency are influenced by depth, slope, and survey line direction. Thus, optimizing survey line coverage and overlap is a pressing issue. This study establishes a mathematical model to explore the coverage width and overlap rate of multibeam bathymetry under different depth conditions. Using geometric relationships and trigonometric functions, the model relates measurement range to depth and slope, and its effectiveness is validated with actual survey data. The study proposes optimized survey routes, providing theoretical support and technical guidance for practical measurement work.

Nurses' experiences using electronic medical record (EMR): A systematic review

Background: The evolution of information and communication technology has led to significant changes in nursing care and the health care system, particularly through the use of Electronic Medical Records (EMR). This literature review focuses on the pivotal role of nurses in the development and implementation of EMR for the documentation of nursing care. Purpose: To investigate nurses' experiences with electronic medical records (EMR) for documenting nursing care. Method: A systematic review approach in line with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. Articles were sourced from online databases such as PubMed, Google Scholar, Garuda, and Science and Technology Index (SINTA). The review was organized according to the PICOS framework. In this article, the PICOS criteria were defined as follows: P: Nurse and health worker, I: Nursing documentation uses electronic medical records, C: Explains the influence, impact of electronic documentation, O: Efficiency, quality of health care and safety of patient care, S: qualitative research methods. The keywords used in the search included “documentation of nursing”, “electronic medical record” and “nurse performance”. Articles were selected based on specific inclusion criteria: publication within the last five years (2019-2024), English language, use of qualitative research methodology, the nurse uses electronic medical records, and with full-text availability. Results: The literature review found that most nurses felt a sense of ease and efficiency in filling out documentation, higher data accuracy, increased work productivity, and ease in identifying patient data to carry out nursing diagnoses regarding patient care, thereby underscoring the integral role of nurses in the development and use of EMR. Conclusion: Computer-based nursing care documentation provides various conveniences and helps nurses deliver their care. Thus, considering the use of EMR in nursing documentation may increase the quality of care.

IoT-based framework for optimizing energy efficiency and reliability in acoustic sensor networks using mobile sinks

In today’s world, there is an increasing demand for environmental monitoring, surveillance, and oceanographic research, which poses challenges in improving energy efficiency and data transfer reliability in Acoustic Sensor Networks. Existing methods face hurdles due to limited energy resources and unreliable data transmission. We propose a Reliable and Energy-Efficient Framework with Sink Mobility (REEFSM) to address these issues. This framework optimizes energy consumption and enhances data reliability by incorporating advanced energy management strategies such as adaptive duty cycling and efficient data transmission mechanisms by minimizing forwarding nodes. Simulation results demonstrate that REEFSM reduces energy consumption by up to 43% and increases data reliability by 35% compared to protocols like EERBCR and DEADS. REEFSM ensures zero dead nodes, minimizes packet drops, and maintains high data accuracy throughout the simulation. This research outcome provides a sustainable and reliable solution for energy-efficient data collection in underwater environments. The future research directions, including integrating autonomous path planning, energy harvesting, and machine learning techniques, hold great potential for further advancements in the field.

Encyclopædia Inflationaris: Opiparous Edition

The current flow of high-accuracy astrophysical data, among which are the Cosmic Microwave Background (CMB) measurements by the Planck satellite, offers an unprecedented opportunity to constrain the inflationary theory. This is however a challenging project given the size of the inflationary landscape which contains hundreds of different scenarios. Given that there is currently no observational evidence for primordial non-Gaussianities, isocurvature perturbations or any other non-minimal extension of the inflationary paradigm, a reasonable approach is to consider the simplest models first, namely the slow-roll single-field models with minimal kinetic terms. This still leaves us with a very populated landscape, the exploration of which requires new and efficient strategies. It has been customary to tackle this problem by means of approximate model-independent methods while a more ambitious alternative is to study the inflationary scenarios one by one. We have developed the publicly available runtime library ASPIC11https://github.com/cosmicinflation/aspic. to implement this last approach. The ASPIC code provides all routines needed to quickly derive reheating-consistent observable predictions within this class of scenarios. ASPIC has been designed as an evolutive code which presently supports 118 different models. In this paper, for each of the ASPIC models, we present and collect new results in a systematic manner, thereby constituting the first Encyclopædia Inflationaris.

Analysis of Deformation and Flood Inundation Spatial Distribution Using Sentinel-1 SAR Data for The 2021 South Kalimantan Floods

Abstract Major flood disasters that have occurred in Indonesia, one of which occurred in South Kalimantan in early 2021. This flood disaster was caused by high rainfall intensity. There are several relationships between rainfall, flood, and deformation. The objective of this study is to monitor deformation in flood inundation that occurred during the flood disaster in South Kalimantan. Observations with direct measurements in the field provide high data accuracy, but in its implementation there are limitations. Therefore, we utilize the development of Synthetic Aperture Radar (SAR) technology in observing flood inundation spatial distribution and deformation in South Kalimantan. Using SAR cannot be as accurate as direct measurements in the field, but it is effective in saving time, can be applied to wide area coverage, and can be observed multitemporally. The SAR data used is Sentinel-1 imagery with C-band which can be freely accessed and has high spatial and temporal resolution. In flood mapping with backscatter using change detection, the flood inundation distribution was 2022.558 km2. The results show deformation variations in the form of land subsidence of up to 15.9 cm in the flood distribution area.

Assessing the Potential of UAV for Large-Scale Fractional Vegetation Cover Mapping with Satellite Data and Machine Learning

Fractional vegetation cover (FVC) is an essential metric for valuating ecosystem health and soil erosion. Traditional ground-measuring methods are inadequate for large-scale FVC monitoring, while remote sensing-based estimation approaches face issues such as spatial scale discrepancies between ground truth data and image pixels, as well as limited sample representativeness. This study proposes a method for FVC estimation integrating uncrewed aerial vehicle (UAV) and satellite imagery using machine learning (ML) models. First, we assess the vegetation extraction performance of three classification methods (OBIA-RF, threshold, and K-means) under UAV imagery. The optimal method is then selected for binary classification and aggregated to generate high-accuracy FVC reference data matching the spatial resolutions of different satellite images. Subsequently, we construct FVC estimation models using four ML algorithms (KNN, MLP, RF, and XGBoost) and utilize the SHapley Additive exPlanation (SHAP) method to assess the impact of spectral features and vegetation indices (VIs) on model predictions. Finally, the best model is used to map FVC in the study region. Our results indicate that the OBIA-RF method effectively extract vegetation information from UAV images, achieving an average precision and recall of 0.906 and 0.929, respectively. This method effectively generates high-accuracy FVC reference data. With the improvement in the spatial resolution of satellite images, the variability of FVC data decreases and spatial continuity increases. The RF model outperforms others in FVC estimation at 10 m and 20 m resolutions, with R2 values of 0.827 and 0.929, respectively. Conversely, the XGBoost model achieves the highest accuracy at a 30 m resolution, with an R2 of 0.847. This study also found that FVC was significantly related to a number of satellite image VIs (including red edge and near-infrared bands), and this correlation was enhanced in coarser resolution images. The method proposed in this study effectively addresses the shortcomings of conventional FVC estimation methods, improves the accuracy of FVC monitoring in soil erosion areas, and serves as a reference for large-scale ecological environment monitoring using UAV technology.

Monitoring JUICE deployment operations with high-accuracy accelerometer data

The Jupiter Icy Moons Explorer (JUICE) mission is a cornerstone of ESA's Cosmic Vision 2015–2025 program devoted to the scientific exploration of Jupiter and its icy moons. Launched on April 14, 2023, from French Guiana, JUICE will perform multiple gravity assists, including an unprecedented lunar-Earth double gravity assist, in order to shape its trajectory to Jupiter, where it will arrive in July 2031. Thanks to a suite of 10 scientific instruments, JUICE will carry out a comprehensive investigation of the jovian system, with special focus on Ganymede. Among these experiments is the Gravity and Geophysics of Jupiter and the Galilean Moons (3GM) radio science and planetary geodesy experiment, supported by the onboard High Accuracy Accelerometer (HAA). The HAA, a three-axis spring mass accelerometer, measures the non-gravitational perturbations acting on the spacecraft, that need to be measured in order to fully exploit the highly accurate range and range rate measurements of 3GM. This paper analyzes the HAA data collected in the very early phase of the mission, to monitor the initial deployment of the spacecraft's moving appendages. The vibration modes of the magnetometer boom and solar arrays were clearly detected during the appendages' deployment, as well as the latching of the Langmuir probe hinges. The detected resonance frequencies of the first and second magnetometer boom bending modes are equal to 0.44 and 0.46 Hz, respectively. The HAA data contributed to identifying the root-cause of the anomalous Radar for Icy Moon Exploration (RIME) antenna deployment. The strong external perturbations due to the actuators' activation have been used to characterize the spacecraft tranquillization time. Despite the significant increase in the spacecraft noise floor during deployment events, the full sensitivity of the instrument was regained within 10 min. This information can be used to plan the spacecraft operations during the scientific phase of the mission.

Integrating low-cost GNSS and MEMS accelerometer for precise dynamic displacement monitoring

We assess the performance of the self-developed receiver coupling dual-frequency low-cost GNSS chipset and IMU MEMS sensor. The system also comprises software dedicated to vibration monitoring based on relative kinematic positioning in a post-processing mode. The observation assessment reveals high accuracy of low-cost GNSS and MEMS accelerometer data. The Septentrio Mosaic X5 chipset exhibits competitive to high-grade receivers’ phase observation noise, outperformance in code noise level and observation auto-correlation at a level that may be neglected even in high-rate applications. The evaluation of the MEMS accelerometer data proves that the sensor accuracy is adequate for vibration monitoring. With the shake table experiment, we examine the performance of vibration recovery. An advantage of the coupled solution over a GNSS-only one is documented with a 15 % reduction of the amplitude error. The backward smoothing of coupled solutions drives the next 20 % error drop. Finally, the high performance of the coupled solution based on low-cost GNSS & accelerometer is confirmed by a mean amplitude error of 0.4 mm. The frequency analyses indicate that both coupled and single-sensor solutions precisely detect the vibration frequencies; however, they also confirm the outperformance of the former over the latter. The power spectra of the recovered displacement time series accentuate the benefits of GNSS + accelerometer fusion and backward filtering for reducing displacement noise.

Forest Canopy Height Estimation Combining Dual-Polarization PolSAR and Spaceborne LiDAR Data

Forest canopy height data are fundamental parameters of forest structure and are critical for understanding terrestrial carbon stock, global carbon cycle dynamics and forest productivity. To address the limitations of retrieving forest canopy height using conventional PolInSAR-based methods, we proposed a method to estimate forest height by combining single-temporal polarimetric synthetic aperture radar (PolSAR) images with sparse spaceborne LiDAR (forest height) measurements. The core idea of our method is that volume scattering energy variations which are linked to forest canopy height occur during radar acquisition. Specifically, our methodology begins by employing a semi-empirical inversion model directly derived from the random volume over ground (RVoG) formulation to establish the relationship between forest canopy height, volume scattering energy and wave extinction. Subsequently, PolSAR decomposition techniques are used to extract canopy volume scattering energy. Additionally, machine learning is employed to generate a spatially continuous extinction coefficient product, utilizing sparse LiDAR samples for assistance. Finally, with the derived inversion model and the resulting model parameters (i.e., volume scattering power and extinction coefficient), forest canopy height can be estimated. The performance of the proposed forest height inversion method is illustrated with L-band NASA/JPL UAVSAR from AfriSAR data conducted over the Gabon Lope National Park and airborne LiDAR data. Compared to high-accuracy airborne LiDAR data, the obtained forest canopy height from the proposed approach exhibited higher accuracy (R2 = 0.92, RMSE = 6.09 m). The results demonstrate the potential and merit of the synergistic combination of PolSAR (volume scattering power) and sparse LiDAR (forest height) measurements for forest height estimation. Additionally, our approach achieves good performance in forest height estimation, with accuracy comparable to that of the multi-baseline PolInSAR-based inversion method (RMSE = 5.80 m), surpassing traditional PolSAR-based methods with an accuracy of 10.86 m. Given the simplicity and efficiency of the proposed method, it has the potential for large-scale forest height estimation applications when only single-temporal dual-polarization acquisitions are available.

Functional and dynamic profiling of transcript isoforms reveals essential roles of alternative splicing in interferon response

Type I interferon (IFN-I) plays an important role in the innate immune response through inducing IFN-I-stimulated genes (ISGs). However, how alternative splicing (AS) events, especially over time, affect their function remains poorly understood. We generated an annotation (113,843 transcripts) for IFN-I-stimulated human B cells called isoISG using high-accuracy long-read sequencing data from PacBio Sequel II/IIe. Transcript isoform profiling using isoISG revealed that isoform switching occurred in the early response to IFN-I so that ISGs would gain functional domains (e.g., C4B) or higher protein production (e.g., IRF3). Conversely, isoforms lacking functional domains increased during the late phase of IFN-I response, mainly due to intron retention events. This suggests that isoform switching both triggers and terminates IFN-I responses at the translation and protein levels. Furthermore, genetic variants influencing the isoform ratio of ISGs were associated with immunological and infectious diseases. AS has essential roles in regulating innate immune response and associated diseases.

Modeling the Impacts of Climate Change on Potential Distribution of Betula luminifera H. Winkler in China Using MaxEnt

Betula luminifera H. Winkler, a fast-growing broad-leaved tree species native to China’s subtropical regions, possesses significant ecological and economic value. The species’ adaptability and ornamental characteristics make it a crucial component of forest ecosystems. However, the impacts of global climate change on its geographical distribution are not well understood, necessitating research to predict its potential distribution shifts under future climate scenarios. Our aims were to forecast the impact of climate change on the potential suitable distribution of B. luminifera across China using the MaxEnt model, which is recognized for its high predictive accuracy and low sample data requirement. Geographical coordinate data of B. luminifera distribution points were collected from various databases and verified for redundancy. Nineteen bioclimatic variables were selected and screened for correlation to avoid overfitting in the model. The MaxEnt model was optimized using the ENMeval package, and the model accuracy was evaluated using the Akaike Information Criterion Correction (delta.AICc), Training Omission Rate (OR10), and Area Under the Curve (AUC). The potential distribution of B. luminifera was predicted under current and future climate scenarios based on the Shared Socio-economic Pathways (SSPs). The optimized MaxEnt model demonstrated high predictive accuracy with an AUC value of 0.9. The dominant environmental variables influencing the distribution of B. luminifera were annual precipitation, minimum temperature of the coldest month, and standard deviation of temperature seasonality. The potential suitable habitat area and its geographical location were predicted to change significantly under different future climate scenarios, with complex dynamics of habitat expansion and contraction. The distribution centroid of B. luminifera was also predicted to migrate, indicating a response to changing climatic conditions. Our findings underscore the importance of model optimization in enhancing predictive accuracy and provide valuable insights for the development of conservation strategies and forest management plans to address the challenges posed by climate change.

Utilizing deep transfer learning to discover changes in landscape patterns in urban wetland parks based on multispectral remote sensing

Urban wetland parks are essential for protecting ecosystems and alleviating urban heat island effects. Owing to the impact of urban sprawl and human activities, habitats in wetland parks have become increasingly fragmented, evoking an urgent need to accurately monitor and analyze such changes. In this study, a transfer learning-based ResNet-18 method was proposed to classify the landscape patterns of urban wetland parks by integrating the advantages of remote sensing technologies, i.e., long-time series of Gaofen-2 and high-accuracy data of unmanned aerial vehicle remote sensing. The proposed method solves the dual problems of low precision and sparse sample data in landscape pattern classification. By employing the proposed method, we realized long-time-series, high-accuracy analysis of landscape pattern changes in a national wetland park. Our results showed that the overall accuracy was 90.69–97.96 % and Kappa coefficient was stable between 0.865 and 0.968, fully verifying the effectiveness and reliability of our method. We revealed a shrinking trend in the area of water bodies along with an expanding trend in the area of other land types. Thus, our findings reflect the significant impact of urban sprawl on the landscape patterns of wetland parks.

Impact of Atmospheric River Reconnaissance Dropsonde Data on the Assimilation of Satellite Radiance Data in GFS

Abstract Satellites provide the largest dataset for monitoring the earth system and constraining analyses in numerical weather prediction models. A significant challenge for utilizing satellite radiances is the accurate estimation of their biases. High-accuracy nonradiance data are commonly employed to anchor radiance bias corrections. However, aside from the impacts of radio occultation data in the stratosphere, the influence of other types of “anchor” observation data on radiance assimilation remains unclear. This study provides an assessment of impacts of dropsonde data collected during the Atmospheric River (AR) Reconnaissance program, which samples ARs over the northeast Pacific, on the radiance assimilation using the Global Forecast System (GFS) and Global Data Assimilation System at the National Centers for Environmental Prediction. The assimilation of this dropsonde dataset has proven crucial for providing enhanced anchoring for bias corrections and improving the model background, leading to an increase of ∼5%–10% in the number of assimilated microwave radiance in the lower troposphere/midtroposphere over the northeast Pacific and North America. The impact on tropospheric infrared radiance is not only small but also beneficial. Impacts of dropsondes on the use of stratospheric channels are minimal due to the absence of dropsonde observations at certain altitudes, such as aircraft flight levels (e.g., 150 hPa). Results in this study underscore the usefulness of dropsondes, along with other conventional data, in optimizing the assimilation of satellite radiance. This study reinforces the importance of a diverse observing network for accurate weather forecasting and highlights the specific benefits derived from integrating dropsonde data into radiance assimilation processes. Significance Statement This study aims to evaluate the impact of aircraft reconnaissance dropsondes on the assimilation of satellite radiance data, utilizing observations from the 2020 Atmospheric River Reconnaissance program. The key findings reveal a substantial enhancement in the model first guess and improved estimates of radiance biases. Notably, there is a significant 5%–10% increase in microwave radiance observations over the northeastern Pacific and North America, with positive yet modest effects observed in tropospheric infrared radiance. These findings underscore the crucial role of atmospheric river reconnaissance dropsondes as anchor data, enhancing the assimilation of radiance observations. In essence, the inclusion of these dropsondes in routine networks is particularly valuable for optimizing data assimilation in regions with sparse observational data.

A historical Llewellyn Ivor Price fossil fish collection from the Middle to Late Miocene of the Brazilian Amazon

Brazilian Miocene fluvial and lacustrine deposits are mainly found in the southwestern Amazon basin in the state of Acre, where fossiliferous sections from the Middle to Late Miocene of the Solimões Formation (Fm.) emerge in riverbanks during the dry season and low river-water level periods. The Solimões Fm. is notable for its high fossil vertebrate diversity (Myliobatiformes, Siluriformes, Ceratodontiformes, Osteoglossiformes, Characiformes, Acregoliathidae, Squamata, Crocodilia, Testudines, Suliformes, Rodentia, Notoungulata, Litopterna, Astrapotheria, Proboscidea, Didelphimorphia, Pilosa, Sirenia, Cetacea, Primates, Chiroptera, and Artiodactyla) deposited in the paleo-Amazon context. This fossiliferous faunal assemblage reflects the major geological and paleoclimatic Miocene/Pliocene transition events that triggered the greatest extinction of megafauna and other groups of animals in the Paleo-Amazon and, alongside the establishment of the largest Amazon transcontinental river in South America, led to species turnover and the diversity explosion to current levels. Paleontological expeditions carried out between 1950s and 1970s at Solimões Fm. outcrops along the Acre and Upper Juruá rivers allowed the recovery of an extraordinary vertebrate paleontological collection, albeit published in fragmentary contributions. Despite supporting the largest continental fish fauna, comprising ca. 2400 valid extant species, the Amazon hydrographic system harbors a still poorly known fossil ichthyofauna. This study describes a fossil fish assemblage from the Solimões Fm. for the first time based on high accuracy data acquired by improved microCT techniques, computerized systems, and examinations of the best recent and fossil fish collections. This fish assemblage is employed to elucidate ancestral Amazonian fauna paleodiversity and paleoenvironmental context interpretations and explore the elements that led to coeval paleo-ichthyofauna from the northwestern paleo-Amazon region. Miocene species Phractocephalus (†P. acreornatus and †P. nassi) are proposed as fossil guides.

Short high-accuracy tritium data time series for assessing groundwater mean transit times in the vadose and saturated zones of the Luxembourg Sandstone aquifer

Abstract. Among the manifold of environmental tracers at hand, tritium is the only one that can give information on groundwater age within the timescale of 100 years for the entire flow system, i.e., unsaturated and saturated. However, while in the Southern Hemisphere, a single water sample is sufficient for tritium-based young groundwater dating, several tritium measurements spanning multiple years are still needed in the Northern Hemisphere to disentangle the natural cosmogenic tritium input from that caused by the atmospheric thermonuclear weapons tests mainly carried out in the early 1960s. Although it is advised to focus tritium dating on sites where long chronicles of tritium data are available, in this study we tested the potential for short high-accuracy tritium data series (∼4 years) to date groundwater from 35 springs draining the Luxembourg Sandstone aquifer (central western Europe). We determined groundwater mean transit times using the lumped-parameter model approach in a Monte Carlo uncertainty estimation framework to provide uncertainty ranges inherent to the low number of tritium data at hand and their related analytical errors. Our results show that unambiguous groundwater mean transit time assessments cannot be determined solely based on such recent short tritium time series, given that several ranges of mean transit times appeared theoretically possible. Nonetheless we succeeded in discriminating groundwater mean transit times in the vadose and saturated zones of the aquifer through a stepwise decision process guided with several supplementary data. The mean transit time required for water to cross the vadose zone was estimated to be between 0.5±0.5 and 8.1±1.2 years depending on the spring, while for water to flow through the saturated zone, it varied from 5.7±2.4 to 18.9±4.6 years (median ± half of the 5–95 percentile range). Our findings are consistent with both the tritium measurements of individual springs and the hydrogeological context of the study area. We specifically corroborated the dating results using the known hydrogeological properties of the Luxembourg Sandstone aquifer, the hydrochemistry of the studied springs, and their discharge dynamics. When translated into water velocities (which average ∼12 and ∼170 m yr−1 for the vadose and the saturated zones, respectively), the tritium dating results mirrored the horizontal–vertical anisotropy of the aquifer's hydraulic properties caused by the bedded character of the Luxembourg Sandstone. In addition to improving our understanding of water transit times in the Luxembourg Sandstone aquifer, this study demonstrates how it is currently possible to use short tritium time series to date young groundwater bodies at new sites in central Europe.

Real Time Traffic Sign Recognition Using Integrated Camera Sensors and YOLOV8 Algorithm

Traffic signs are an important element in maintaining smoothness and safety on the road. However, there are still many drivers who violate them, causing various negative impacts. Traffic Sign Recognition (TSR) is a technology that has the ability to detect and identify various types of traffic signs by utilizing artificial intelligence in the computer vision domain. TSR has been applied in various vehicle applications, such as Advance Driver Assistance System (ADAS) and Autonomous Driving System (ADS). This system is made by integrating camera sensors with the YOLOv8 algorithm which has high accuracy and fast data processing. The data used were 2093 images and annotated through Roboflow. Then the data is trained through Google Collaboratory with a mAP evaluation result of 95.5% which shows that the system can detect objects accurately. The level of precision and success of the model in detecting objects is 93.5% and the success is 93.3%. The results of system testing can use images, videos, and through cameras in real time.

Light-averse behaviour of attic-dwelling bats when commuting through urban areas

Light pollution is spreading around the world, causing a variety of problems, particularly for nocturnal animals such as bats. As urban areas expand, attic-dwelling bats face the problem of being exposed to more and more artificial light as they move to their dark foraging areas. Avoiding street lights could force bats to make energetically costly detours, which could eventually lead to the collapse of urban colonies. We therefore investigated the response of attic-dwelling bats to street lighting, using the brown long-eared bat (Plecotus auritus) as an example. Using radio-tracking, we followed 17 bats during their commutes through two urban areas: a small town and a village in rural Germany. After a conservative filtering process to ensure a high spatial accuracy of the tracking data, we analysed the data of nine bats in detail, using a use-versus-availability approach for the small town and a descriptive approach for the smaller dataset obtained from the village. The best-fitting model confirmed that the bats responded to artificial light at night. Close to the roost (∼10 m), bats preferred areas with low light intensities, but seemed to tolerate some light levels. We speculate that this pattern was caused by bats being forced to leave the roost into an illuminated area. At medium distances from roosts (∼400 m), most bats avoided street lights. In fact, all bats avoided being in close proximity to street lights, even when the lanterns produced only low levels of light. We conclude that brown long-eared bats avoid street lights when moving through urban areas. We recommend the establishment of dark corridors in urban areas to support high bat diversity, including all species along the continuum of light-opportunistic and light-sensitive species.

A new CNN-BASED object detection system for autonomous mobile robots based on real-world vehicle datasets

Recently, autonomous mobile robots (AMRs) have begun to be used in the delivery of goods, but one of the biggest challenges faced in this field is the navigation system that guides a robot to its destination. The navigation system must be able to identify objects in the robot's path and take evasive actions to avoid them. Developing an object detection system for an AMR requires a deep learning model that is able to achieve a high level of accuracy, with fast inference times, and a model with a compact size that can be run on embedded control systems. Consequently, object recognition requires a convolutional neural network (CNN)-based model that can yield high object classification accuracy and process data quickly. This paper introduces a new CNN-based object detection system for an AMR that employs real-world vehicle datasets. First, we create original real-world datasets of images from Banda Aceh city. We then develop a new CNN-based object identification system that is capable of identifying cars, motorcycles, people, and rickshaws under morning, afternoon, and evening lighting conditions. An SSD Mobilenetv2 FPN Lite 320 × 320 architecture is employed for retraining using these real-world datasets. Quantitative and qualitative performance indicators are then applied to evaluate the CNN model. Training the pre-trained SSD Mobilenetv2 FPN Lite 320 × 320 model improves its classification and detection accuracy, as indicated by its performance results. We conclude that the proposed CNN-based object detection system has the potential for use in an AMR.