Search Results (4,244)

Land subsidence caused by human engineering activities is a serious problem worldwide. We selected Qian’an County as the study area to explore the evolution of land subsidence and predict its deformation trend. This study utilized synthetic aperture radar interferometry (InSAR) technology to process 64 Sentinel-1 data covering the area, and high-precision and high-resolution surface deformation data from January 2017 to December 2021 were obtained to analyze the deformation characteristics and evolution of land subsidence. Then, land subsidence was predicted using the intelligence neural network theory, machine learning methods, time-series prediction models, dynamic data processing techniques, and engineering geology of ground subsidence. This study developed three time-series prediction models: a support vector regression (SVR), a Holt Exponential Smoothing (Holt) model, and multi-layer perceptron (MLP) models. A time-series prediction analysis was conducted using the surface deformation data of the subsidence funnel area of Zhouzi Village, Qian’an County. In addition, the advantages and disadvantages of the three models were compared and analyzed. The results show that the three developed time-series data prediction models can effectively capture the time-series-related characteristics of surface deformation in the study area. The SVR and Holt models are suitable for analyzing fewer external interference factors and shorter periods, while the MLP model has high accuracy and universality, making it suitable for predicting both short-term and long-term surface deformation. Ultimately, our results are valuable for further research on land subsidence prediction. Full article

Construction work and regular inspection work at nuclear power plants involve many special tasks, unlike general on-site work. In addition, the opportunity to transfer knowledge from skilled workers to unskilled workers is limited due to the inability to easily enter the plant and various security and radiation exposure issues. Therefore, in this study, we considered the application of virtual reality (VR) as a method to increase opportunities to learn anytime and anywhere and to transfer knowledge more effectively. In addition, as an interactive learning method to improve comprehension, we devised a system that uses hand tracking and eye tracking to allow participants to experience movements and postures that are closer to the real work in a virtual space. For hand-based work, three actions, “pinch”, “grab”, and “hold”, were reproduced depending on the sizes of the parts and tools, and visual confirmation work was reproduced by the movement of the gaze point of the eyes, faithfully reproducing the special actions of the inspection work. We confirmed that a hybrid learning process that appropriately combines the developed active learning method, using experiential VR, with conventional passive learning methods, using paper and video, can improve the comprehension and retention of special work at nuclear power plants. Full article

After-school programs in Romania are not mandatory, and most of the time, they are funded by parents. In Romania, over 41.5% of students come from disadvantaged families that cannot afford to finance after-school activities. In recent years, there have been only a few free after-school programs for disadvantaged students. Our study aimed to measure the impact of such an after-school program, which mostly uses alternative teaching methods, on several aspects of learning improvement at the level of disadvantaged students in primary and secondary education. The research results revealed a significant improvement in the education of children after they participated in after-school programs for a large range of learning results. Among the intervention actions, mathematical competencies, basic competencies in science and technology, and digital competencies were identified as the main predictors of high learning performance. The results also revealed that a longer length of such programs had a positive effect on educational performance and the socio-emotional development of disadvantaged students. It was also shown that the impact of intervention is more pronounced in the case of young students and adults who followed the “Second Chance” programs. The results support the effectiveness and importance of such projects in promoting holistic and sustainable education and in providing valuable information to decision-makers regarding the impact of after-school programs on the education of disadvantaged people to improve social sustainability. Full article

Fresh water lakes are vulnerable assets that need to be protected against manmade/natural challenges like climate change and anthropogenesis activities. This study addresses the predictability of the lake water level changes based on the knowledge acquired directly from the climate data. Two fresh water lakes named Lake Iznik and Uluabat, located in Turkey, are addressed. Time series of the lake water levels during October 1990–September 2019 at a monthly scale, along with the corresponding anomalies of 24 Large-Scale Atmospheric–Oceanic Oscillations (LSAOOs) from around the globe, are used in the analysis. The relationship between variables and the structure of the models are initially acquired based on the significance of the dependence between climate indices and lake water levels with consideration of the significance of the Spearman rank-order coefficient. Then, the time series are divided into training (80%) and testing (20%) sets. The Extreme Learning Method (ELM), enhanced with the genetic algorithm (ELM-GA) and Invasive Weed Optimization (ELM-IWO), is then used in the predictive models. Based on the results, Lake Uluabat showed a stronger teleconnection with LSAOOs, while the ELM-GA for Lake Iznik and ELM-IWA for Lake Uluabat depicted the best performance in the prediction of lake water levels. Comparison of the enhanced ELM-IWO to the corresponding ELM-GA illustrates that the ELM-IWO reveals more acceptable results owing to its flexible nature. Full article

The prediction of the ablation rate of silicone rubber-based composites is of great significance to accelerate the development of flexible thermal protection materials. Herein, a method which combines uniform design experimentation, active learning, and virtual sample generation was proposed to establish a prediction model of the mass ablation rate based on a small dataset. Briefly, a small number of sample points were collected using uniform design experimentation, which were marked to construct the initial dataset and primitive model. Then, data points were acquired from the sample pool and iterated using various integrated algorithms through active learning to update the above dataset and model. Finally, a large number of virtual samples were generated based on the optimal model, and a further optimized prediction model was achieved. The results showed that after introducing 300 virtual samples, the average percentage error of the gradient boosting decision tree (GBDT) prediction model on the test set decreased to 3.1%, which demonstrates the effectiveness of the proposed method in building prediction models based on a small dataset. Full article

Wheat spike count is one of the critical indicators for assessing the growth and yield of wheat. However, illumination variations, mutual occlusion, and background interference have greatly affected wheat spike detection. A lightweight detection method was proposed based on the YOLOv5s. Initially, the original YOLOv5s was improved by combing the additional small-scale detection layer and integrating the ECA (Efficient Channel Attention) attention mechanism into all C3 modules (YOLOv5s + 4 + ECAC3). After comparing GhostNet, ShuffleNetV2, and MobileNetV3, the GhostNet architecture was finally selected as the optimal lightweight model framework based on its superior performance in various evaluations. Subsequently, the incorporation of five different activation functions into the network led to the identification of the RReLU (Randomized Leaky ReLU) activation function as the most effective in augmenting the network’s performance. Ultimately, the network’s loss function of CIoU (Complete Intersection over Union) was optimized using the EIoU (Efficient Intersection over Union) loss function. Despite a minor reduction of 2.17% in accuracy for the refined YOLOv5s + 4 + ECAC3 + G + RR + E network when compared to the YOLOv5s + 4 + ECAC3, there was a marginal improvement of 0.77% over the original YOLOv5s. Furthermore, the parameter count was diminished by 32% and 28.2% relative to the YOLOv5s + 4 + ECAC3 and YOLOv5s, respectively. The model size was reduced by 28.0% and 20%, and the Giga Floating-point Operations Per Second (GFLOPs) were lowered by 33.2% and 9.5%, respectively, signifying a substantial improvement in the network’s efficiency without significantly compromising accuracy. This study offers a methodological reference for the rapid and accurate detection of agricultural objects through the enhancement of a deep learning network. Full article

Power transformers are vital and costly components in power systems, essential for ensuring a reliable and uninterrupted supply of electrical energy. Their protection is crucial for improving reliability, maintaining network stability, and minimizing operational costs. Previous studies have introduced differential protection schemes with harmonic restraint to detect internal transformer faults. However, these schemes often struggle with computational inaccuracies in fault detection due to neglecting current transformer (CT) saturation and associated uncertainties. CT saturation during internal faults can produce even harmonics, disrupting relay operations. Additionally, CT saturation during transformer energization can introduce a DC component, leading to incorrect relay activation. This paper introduces a novel feature extracted through advanced wavelet transform analysis of differential current. This feature, combined with differential current amplitude and bias current, is used to train a deep learning system based on long short-term memory (LSTM) networks. By accounting for existing uncertainties, this system accurately identifies internal transformer faults under various CT saturation and measurement uncertainty conditions. Test and validation results demonstrate the proposed method’s effectiveness and superiority in detecting internal faults in power transformers, even in the presence of CT saturation, outperforming other recent modern techniques. Full article

Electrocardiogram (ECG) signals are a measure minute electrical signals generated during the cardiac cycle, a biometric signal that occurs during vital human activity. ECG signals are susceptible to various types of noise depending on the data acquisition conditions, with factors such as sensor placement and the physiological and mental states of the subject contributing to the diverse shapes of these signals. When the data are acquired in a single session, the environmental variables are relatively similar, resulting in similar ECG signals; however, in subsequent sessions, even for the same person, changes in the environmental variables can alter the signal shape. This phenomenon poses challenges for person identification using ECG signals acquired on different days. To improve the performance of individual identification, even when ECG data is acquired on different days, this paper proposes an ensemble deep neural network for person identification by comparing and analyzing the ECG recognition performance under various conditions. The proposed ensemble deep neural network comprises three streams that incorporate two well-known pretrained models. Each network receives the time-frequency representation of ECG signals as input, and a stream reuses the same network structure under different learning conditions with or without data augmentation. The proposed ensemble deep neural network was validated on the Physikalisch-Technische Bundesanstalt dataset, and the results confirmed a 3.39% improvement in accuracy compared to existing methods. Full article

With the rapid development of the Internet of Things (IoT) technology, the number and types of devices accessing the Internet are increasing, leading to increased network security problems such as hacker attacks and botnets. Usually, these attacks are related to the type of device, and the risk can be effectively reduced if the type of network device can be efficiently identified and controlled. The traditional network device identification method uses active detection technology to obtain information about the device and match it with a manually defined fingerprint database to achieve network device identification. This method impacts the smoothness of the network and requires the manual establishment of fingerprint libraries, which imposes a large labor cost but only achieves a low identification efficiency. The traditional machine learning method only considers the information of individual packets; it does not consider the timing relationship between packets, and the recognition effect is poor. Based on the above research, in this paper, we considered the packet temporal relationship, proposed the TCN model of the Inception structure, extracted the packet temporal relationship, and designed a multi-head self-attention mechanism to fuse the features to generate device fingerprints for device identification. Experiments were conducted on the publicly available UNSW dataset, and the results showed that this method achieved notable improvements compared to the traditional machine learning method, with F1 reaching 96.76%. Full article

In several applications of text classification, training document labels are provided by human evaluators, and therefore, gathering sufficient data for model creation is time consuming and costly. The labeling time and effort may be reduced by active learning, in which classification models are created based on relatively small training sets, which are obtained by collecting class labels provided in response to labeling requests or queries. This is an iterative process with a sequence of models being fitted, and each of them is used to select query articles to be added to the training set for the next one. Such a learning scenario may pose different challenges for machine learning algorithms and text representation methods used for text classification than ordinary passive learning, since they have to deal with very small, often imbalanced data, and the computational expense of both model creation and prediction has to remain low. This work examines how classification algorithms and text representation methods that have been found particularly useful by prior work handle these challenges. The random forest and support vector machines algorithms are coupled with the bag of words and FastText word embedding representations and applied to datasets consisting of scientific article abstracts from systematic literature review studies in the biomedical domain. Several strategies are used to select articles for active learning queries, including uncertainty sampling, diversity sampling, and strategies favoring the minority class. Confidence-based and stability-based early stopping criteria are used to generate active learning termination signals. The results confirm that active learning is a useful approach to creating text classification models with limited access to labeled data, making it possible to save at least half of the human effort needed to assign relevant or irrelevant class labels to training articles. Two of the four examined combinations of classification algorithms and text representation methods were the most successful: the SVM algorithm with the FastText representation and the random forest algorithm with the bag of words representation. Uncertainty sampling turned out to be the most useful query selection strategy, and confidence-based stopping was found more universal and easier to configure than stability-based stopping. Full article

This study addresses the potential of machine learning (ML) algorithms in geophysical and geodetic research, particularly for enhancing GNSS time series analysis. We employed XGBoost and Long Short-Term Memory (LSTM) networks to analyze GNSS time series data from the tectonically active Anatolian region. The primary objective was to detect discontinuities associated with seismic events. Using over 13 years of daily data from 15 GNSS stations, our analysis was conducted in two main steps. First, we characterized the signals by identifying linear trends and seasonal variations, achieving $R^2$ values of 0.84 for the XGBoost v.2.1.0 model and 0.81 for the LSTM model. Next, we focused on the residual signals, which are primarily related to tectonic movements. We applied various threshold values and tested different hyperparameters to identify the best-fitting models. We designed a confusion matrix to evaluate and classify the performance of our models. Both XGBoost and LSTM demonstrated robust performance, with XGBoost showing higher true positive rates, indicating its superior ability to detect precise discontinuities. Conversely, LSTM exhibited a lower false positive rate, highlighting its precision in minimizing false alarms. Our findings indicate that the best fitting models for both methods are capable of detecting seismic events (Mw ≥ 4.0) with approximately 85% precision. Full article

In recent years, several automated and noninvasive methods for wildlife monitoring, such as passive acoustic monitoring (PAM), have emerged. PAM consists of the use of acoustic sensors followed by sound interpretation to obtain ecological information about certain species. One challenge associated with PAM is the generation of a significant amount of data, which often requires the use of machine learning tools for automated recognition. Here, we couple PAM with BirdNET, a free-to-use sound algorithm to assess, for the first time, the precision of BirdNET in predicting three tropical songbirds and to describe their patterns of vocal activity over a year in the Brazilian Pantanal. The precision of the BirdNET method was high for all three species (ranging from 72 to 84%). We were able to describe the vocal activity patterns of two of the species, the Buff-breasted Wren (Cantorchilus leucotis) and Thrush-like Wren (Campylorhynchus turdinus). Both species presented very similar vocal activity patterns during the day, with a maximum around sunrise, and throughout the year, with peak vocal activity occurring between April and June, when food availability for insectivorous species may be high. Further research should improve our knowledge regarding the ability of coupling PAM with BirdNET for monitoring a wider range of tropical species. Full article

Marine litter poses a significant global threat to marine ecosystems, primarily driven by poor waste management, inadequate infrastructure, and irresponsible human activities. This research investigates the application of image preprocessing techniques and deep learning algorithms for the detection of seafloor objects, specifically marine debris, using unmanned aerial vehicles (UAVs). The primary objective is to develop non-invasive methods for detecting marine litter to mitigate environmental impacts and support the health of marine ecosystems. Data was collected remotely via UAVs, resulting in a novel database of over 5000 images and 12,000 objects categorized into 31 classes, with metadata such as GPS location, wind speed, and solar parameters. Various image preprocessing methods were employed to enhance underwater object detection, with the Removal of Water Scattering (RoWS) method demonstrating superior performance. The proposed deep neural network architecture significantly improved detection precision compared to existing models. The findings indicate that appropriate databases and preprocessing methods substantially enhance the accuracy and precision of underwater object detection algorithms. Full article

Registered nurses in the operating room require specialized competencies that surpass basic educational training. Existing national and international documents attempt to outline these competencies but often lack comprehensive details. To address this, a repertoire of technical and professional competencies for operating room nurses, aligned with European and National Qualifications Frameworks, is proposed. Aim: Develop a repertoire of technical and professional competencies for perioperative and perianesthesiological specialist nursing roles. Methods: An at-home ethnography design was employed, utilizing participant observation, interviews to the double, and focus groups. Convenience sampling included 46 participants from a university and a public hospital in northern Italy. Data were collected from September 2021 to June 2023 and analyzed using inductive content analysis and data triangulation. Results: Identified 17 specialized technical professional competencies for perioperative and perianesthesiological nursing, divided into 6 areas of activity. These competencies encompass 19 learning outcomes, 152 tasks, 222 knowledge elements, and 218 skills. Conclusions: This competency repertoire aids in the public recognition of qualifications and serves as a valuable tool for identifying, validating, and certifying competencies. Future research should focus on exploring the competencies of central sterilization nurses and transversal competencies. Full article

Three-dimensional point clouds, as an advanced imaging technique, enable researchers to capture plant traits more precisely and comprehensively. The task of plant segmentation is crucial in plant phenotyping, yet current methods face limitations in computational cost, accuracy, and high-throughput capabilities. Consequently, many researchers have adopted 3D point cloud technology for organ-level segmentation, extending beyond manual and 2D visual measurement methods. However, analyzing plant phenotypic traits using 3D point cloud technology is influenced by various factors such as data acquisition environment, sensors, research subjects, and model selection. Although the existing literature has summarized the application of this technology in plant phenotyping, there has been a lack of in-depth comparison and analysis at the algorithm model level. This paper evaluates the segmentation performance of various deep learning models on point clouds collected or generated under different scenarios. These methods include outdoor real planting scenarios and indoor controlled environments, employing both active and passive acquisition methods. Nine classical point cloud segmentation models were comprehensively evaluated: PointNet, PointNet++, PointMLP, DGCNN, PointCNN, PAConv, CurveNet, Point Transformer (PT), and Stratified Transformer (ST). The results indicate that ST achieved optimal performance across almost all environments and sensors, albeit at a significant computational cost. The transformer architecture for points has demonstrated considerable advantages over traditional feature extractors by accommodating features over longer ranges. Additionally, PAConv constructs weight matrices in a data-driven manner, enabling better adaptation to various scales of plant organs. Finally, a thorough analysis and discussion of the models were conducted from multiple perspectives, including model construction, data collection environments, and platforms. Full article