Search Results (759)

The high rate of depredation of forest resources causes major climatic changes that affect the economic activities and health of populations, plunging them into poverty and social problems. The state is responsible for dealing with these problems, because it has the technical, legal, and economic power to do so. This research aims to identify the factors that limit the design and formulation of public policies for the conservation of natural resources at the level of local district and provincial governments in Peru. For this study, we used qualitative methodology and non-probabilistic sampling, as well as techniques such as unstructured interviews, focus groups, and documentary review; moreover, for data analysis, we applied the theoretical saturation design in grounded theory. The results show that the conservation of natural resources is not positioned as a priority public policy in municipal administrations; furthermore, the organic units of natural resources suffer financial restrictions, and not because of a lack of budget availability, but because of a lack of will and decision-making capacity of authorities and civil servants, as well as a lack of coordination between the different national governments. It concludes that there are conflicts of interest in public policy making, with abuse of power and corruption predominating. Furthermore, the complexity of addressing sustainability criteria and the inability to confront the environmental crisis mean that international summits and national norms are attenuated in the face of the problems of environmental degradation. Deforestation should be considered a major public priority problem because of its environmental, economic, social, and health impacts. These problems require a holistic approach that combines local, national, and international policies and fosters effective and participatory governance. Full article

Background/Objectives: YAT2150 is a first-in-class antiplasmodial compound that has been recently proposed as a new interesting drug for malaria therapy. Methods/Results: The fluorescence of YAT2150 rapidly increases upon its entry into Plasmodium, a property that can be of use for the design of highly sensitive diagnostic approaches. YAT2150 blocks the in vitro development of the ookinete stage of Plasmodium and, when added to an infected blood meal, inhibits oocyst formation in the mosquito. Thus, the compound could possibly contribute to future transmission-blocking antimalarial strategies. Cell influx/efflux studies in Caco-2 cells suggest that YAT2150 is internalized by endocytosis and also through the OATP2B1 transporter, whereas its main export route would be via OSTα. YAT2150 has an overall favorable drug metabolism and pharmacokinetics profile, and its moderate cytotoxicity can be significantly reduced upon encapsulation in immunoliposomes, which leads to a dramatic increase in the drug selectivity index to values close to 1000. Although YAT2150 binds amyloid-forming peptides, its in vitro fluorescence emission is stronger upon association with peptides that form amorphous aggregates, suggesting that regions enriched in unstructured proteins are the preferential binding sites of the drug inside Plasmodium cells. The reduction of protein aggregation in the parasite after YAT2150 treatment, which has been suggested to be directly related to the drug’s mode of action, is also observed following treatment with quinoline antimalarials like chloroquine and primaquine. Conclusions: Altogether, the data presented here indicate that YAT2150 can represent the spearhead of a new family of compounds for malaria diagnosis and therapy due to its presumed novel mode of action based on the interaction with functional protein aggregates in the pathogen. Full article

This entry reviews MongoDB’s fundamentals, architectural features, advantages, and limitations, providing a comprehensive understanding of its capabilities. MongoDB’s impact on the database landscape is profound, challenging traditional relational databases and influencing the adoption of NoSQL solutions globally. With its continued growth, innovation, and commitment to addressing evolving market needs, MongoDB remains a pivotal player in modern data management, empowering organizations to build scalable, efficient, and high-performance applications. Full article

The interest in the enhancement of innovative solutions in the geospatial data classification domain from integrated aerial methods is rapidly growing. The transition from unstructured to structured information is essential to set up and arrange geodatabases and cognitive systems such as digital twins capable of monitoring territorial, urban, and general conditions of natural and/or anthropized space, predicting future developments, and considering risk prevention. This research is based on the study of classification methods and the consequent segmentation of low-altitude airborne LiDAR data in highly forested areas. In particular, the proposed approaches investigate integrating unsupervised classification methods and supervised Neural Network strategies, starting from unstructured point-based data formats. Furthermore, the research adopts Machine Learning classification methods for geo-morphological analyses derived from DTM datasets. This paper also discusses the results from a comparative perspective, suggesting possible generalization capabilities concerning the case study investigated. Full article

Objectives: This study investigated workplace violence (WPV) toward frontline health workers under comprehensive interventions to improve the occupational safety and health management system in a Chinese infectious disease hospital. Methods: The risk assessment of WPV using an international questionnaire was conducted in 2018 and 2021 to compare the perceived levels of exposure to WPV and intervention measures before and after the intensification of anti-violence measures in the hospital context. Additionally, qualitative data were collected in 2021 through semi-structured and unstructured interviews, providing complementary information about WPV toward frontline health workers (HWs). Results: After establishing the occupational safety and health management system (OSHMS), the total incidence rate of WPV decreased from 60.90% in 2018 to 34.44% in 2021. Psychological violence declined significantly from 60.90% in 2018 to 33.89% in 2021. The endorsement of precautionary measures increased significantly from 2018 to 2021, including patient screening recognition, patient protocol, shift or rota changes, etc. A thematic analysis of several subthemes shows that HWs had an in-depth understanding of WPV, recognizing its multifaceted consequences in the context of complex risk factors. Conclusions: This study demonstrates a significant decrease in WPV, psychological violence, verbal abuse, bullying/mobbing, and ethnic discrimination after implementing the comprehensive OSHMS. Full article

To address the challenges of change detection tasks, including the scarcity and dispersion of labeled samples, the difficulty in efficiently extracting features from unstructured image objects, and the underutilization of high-order correlation information, we propose a novel architecture based on hypergraph convolutional neural networks. By characterizing superpixel vertices and their high-order correlations, the method implicitly expands the number of labels while assigning adaptive weight parameters to adjacent objects. It not only describes changes in vertex features but also uncovers local and consistent changes within hyperedges. Specifically, a vertex aggregation mechanism based on superpixel segmentation is established, which segments the difference map into superpixels of diverse shapes and boundaries, and extracts their significant statistical features. Subsequently, a dynamic hypergraph structure is constructed, with each superpixel serving as a vertex. Based on the multi-head self-attention mechanism, the connection probability between vertices and hyperedges is calculated through learnable parameters, and the hyperedges are generated through threshold filtering. Moreover, a framework based on hypergraph convolutional neural networks is customized, which models the high-order correlations within the data through the learning optimization of the hypergraph, achieving change detection in remote sensing images. The experimental results demonstrate that the method obtains impressive qualitative and quantitative analysis results on the three remote sensing datasets, thereby verifying its effectiveness in enhancing the robustness and accuracy of change detection. Full article

In the field of forestry ecology, image data capture factual information, while literature is rich with expert knowledge. The corpus within the literature can provide expert-level annotations for images, and the visual information within images naturally serves as a clustering center for the textual corpus. However, both image data and literature represent large and rapidly growing, unstructured datasets of heterogeneous modalities. To address this challenge, we propose cross-modal embedding clustering, a method that parameterizes these datasets using a deep learning model with relatively few annotated samples. This approach offers a means to retrieve relevant factual information and expert knowledge from the database of images and literature through a question-answering mechanism. Specifically, we align images and literature across modalities using a pair of encoders, followed by cross-modal information fusion, and feed these data into an autoregressive generative language model for question-answering with user feedback. Experiments demonstrate that this cross-modal clustering method enhances the performance of image recognition, cross-modal retrieval, and cross-modal question-answering models. Our method achieves superior performance on standardized tasks in public datasets for image recognition, cross-modal retrieval, and cross-modal question-answering, notably achieving a 21.94% improvement in performance on the cross-modal question-answering task of the ScienceQA dataset, thereby validating the efficacy of our approach. Essentially, our method targets cross-modal information fusion, combining perspectives from multiple tasks and utilizing cross-modal representation clustering of images and text. This approach effectively addresses the interdisciplinary complexity of forestry ecology literature and the parameterization of unstructured heterogeneous data encapsulating species diversity in conservation images. Building on this foundation, intelligent methods are employed to leverage large-scale data, providing an intelligent research assistant tool for conducting forestry ecological studies on larger temporal and spatial scales. Full article

In this research, we analyzed narratives from the National Electronic Injury Surveillance System (NEISS) dataset to predict the top two injury codes using a comparative study of ensemble machine learning (ML) models. Four ensemble models were evaluated: Random Forest (RF) combined with Logistic Regression (LR), K-Nearest Neighbor (KNN) paired with RF, LR combined with KNN, and a model integrating LR, RF, and KNN, all utilizing a probabilistic likelihood-based approach to improve decision-making across different classifiers. The combined KNN + LR ensemble achieved an accuracy of 90.47% for the top one prediction, while the KNN + RF + LR model excelled in predicting the top two injury codes with a very high accuracy of 99.50%. These results demonstrate the significant potential of ensemble models to enhance unstructured narrative classification accuracy, particularly in addressing underrepresented cases, and the potential of the proposed probabilistic ensemble framework ML models in improving decision-making in public health and safety, providing a foundation for future research in automated clinical narrative classification and predictive modeling, especially in scenarios with imbalanced data. Full article

As with the broad field of deep learning, autonomy is a research topic that has experienced a heavy explosion in attention from both the scientific and commercial industries due to its potential for the advancement of humanity in many cross-cutting disciplines. Recent advancements in computer vision-based autonomy has highlighted the potential for the realization of increasingly sophisticated autonomous ground vehicles for both commercial and non-traditional applications, such as grocery delivery. Part of the success of these technologies has been a boon in the abundance of training data that is available for training the autonomous behaviors associated with their autonomy software. These data abundance advantage is quickly diminished when an application moves from structured environments, i.e., well-defined city road networks, highways, street signage, etc., into unstructured environments, i.e., cross-country, off-road, non-traditional terrains. Herein, we aim to present insights, from a dataset perspective, into how the scientific community can begin to expand autonomy into unstructured environments, while highlighting some of the key challenges that are presented with such a dynamic and ever-changing environment. Finally, a foundation is laid for the creation of a robust off-road dataset being developed by the Engineer Research and Development Center and Mississippi State University’s Center for Advanced Vehicular Systems. Full article

This paper proposes an evaluating and mapping methodology of terrain traversability for off-road navigation of autonomous vehicles in unstructured environments. Terrain features are extracted from RGB images and 3D point clouds to create a traversal cost map. The cost map is then employed to plan safe trajectories. Bayesian generalized kernel inference is employed to assess unknown grid attributes due to the sparse raw point cloud data. A Kalman filter also creates density local elevation maps in real time by fusing multiframe information. Consequently, the terrain semantic mapping procedure considers the uncertainty of semantic segmentation and the impact of sensor noise. A Bayesian filter is used to update the surface semantic information in a probabilistic manner. Ultimately, the elevation map is utilized to extract geometric characteristics, which are then integrated with the probabilistic semantic map. This combined map is then used in conjunction with the extended motion primitive planner to plan the most effective trajectory. The experimental results demonstrate that the autonomous vehicles obtain a success rate enhancement ranging from 4.4% to 13.6% and a decrease in trajectory roughness ranging from 5.1% to 35.8% when compared with the most developed outdoor navigation algorithms. Additionally, the autonomous vehicles maintain a terrain surface selection accuracy of over 85% during the navigation process. Full article

Background: The increasing rate of intensive care unit (ICU) readmissions poses significant challenges in healthcare, impacting both costs and patient outcomes. Predicting patient readmission after discharge is crucial for improving medical quality and reducing expenses. Traditional analyses of electronic health record (EHR) data have primarily focused on numerical data, often neglecting valuable text data. Methods: This study employs a hybrid model combining BERTopic and Long Short-Term Memory (LSTM) networks to predict ICU readmissions. Leveraging the MIMIC-III database, we utilize both quantitative and text data to enhance predictive capabilities. Our approach integrates the strengths of unsupervised topic modeling with supervised deep learning, extracting potential topics from patient records and transforming discharge summaries into topic vectors for more interpretable and personalized predictions. Results: Utilizing a comprehensive dataset of 36,232 ICU patient records, our model achieved an AUROC score of 0.80, thereby surpassing the performance of traditional machine learning models. The implementation of BERTopic facilitated effective utilization of unstructured data, generating themes that effectively guide the selection of relevant predictive factors for patient readmission prognosis. This significantly enhanced the model’s interpretative accuracy and predictive capability. Additionally, the integration of importance ranking methods into our machine learning framework allowed for an in-depth analysis of the significance of various variables. This approach provided crucial insights into how different input variables interact and impact predictions of patient readmission across various clinical contexts. Conclusions: The practical application of BERTopic technology in our hybrid model contributes to more efficient patient management and serves as a valuable tool for developing tailored treatment strategies and resource optimization. This study highlights the significance of integrating unstructured text data with traditional quantitative data to develop more accurate and interpretable predictive models in healthcare, emphasizing the importance of individualized care and cost-effective healthcare paradigms. Full article

In low-altitude, GNSS-denied scenarios, Unmanned aerial vehicles (UAVs) rely on sensor fusion for self-localization. This article presents a resilient multi-sensor fusion localization system that integrates light detection and ranging (LiDAR), cameras, and inertial measurement units (IMUs) to achieve state estimation for UAVs. To address challenging environments, especially unstructured ones, IMU predictions are used to compensate for pose estimation in the visual and LiDAR components. Specifically, the accuracy of IMU predictions is enhanced by increasing the correction frequency of IMU bias through data integration from the LiDAR and visual modules. To reduce the impact of random errors and measurement noise in LiDAR points on visual depth measurement, cross-validation of visual feature depth is performed using reprojection error to eliminate outliers. Additionally, a structure monitor is introduced to switch operation modes in hybrid point cloud registration, ensuring accurate state estimation in both structured and unstructured environments. In unstructured scenes, a geometric primitive capable of representing irregular planes is employed for point-to-surface registration, along with a novel pose-solving method to estimate the UAV’s pose. Both private and public datasets collected by UAVs validate the proposed system, proving that it outperforms state-of-the-art algorithms by at least 12.6%. Full article

Coastal lagoons are important wetland sites for migratory species and the local flora and fauna population. The Stagnone Lagoon is a coastal lagoon located on the west edge of Sicily between the towns of Marsala and Trapani. The area is characterized by salt-harvesting plants and several archaeological sites and is affected by microtidal excursion. Two mouths allow exchange with the open sea: one smaller and shallower in the north and one larger and deeper in the south. This study aims to understand the lagoon’s hydrodynamics, in terms of circulation and involved forces. The circulation process appears to be dominated mainly by tide excursions and wind forces. Wind velocity, water levels, and water velocity were recorded during different field campaigns in order to obtain a benchmark value. The hydrodynamic circulation has been studied with a 2DH (two-dimensional in the horizontal plane) unstructured mesh model, calibrated with data collected during the 2006 field campaign and validated with the data of the 2007 campaign. Rapid changes in averaged velocity have been found both in Vx and Vy components, showing the strong dependence on seiches. This study tries to identify the main factor that domains the evolution of the water circulation. Sensitivity analyses were conducted to estimate the correct energy transfer between the forcing factors and dissipating ones. A Gauckler–Strickler roughness coefficient between 20 and 25 m^1/3/s is found to be the most representative in the lagoon. To enhance the knowledge of this peculiar lagoon, the MIKE 21 model has been used, reproducing all the external factors involved in the circulation process. Nash–Sutcliffe coefficient of efficiency (NSE) values up to 0.92 and 0.79 are reached with a Gauckler–Strickler coefficient equal to 20 m^1/3/s related to water depth and the Vy velocity component. The Vx velocity component NSE has never been satisfying, showing the limits of the 2D approach in reproducing the currents induced by local morphological peculiarities. Comparing the NSE value of water depth, there is a loss of up to 70% in model predictivity capability between the southern and the northern lagoon areas. This study aims to support the local decision-makers to improve the management of the lagoon itself. Full article

Diagnosing complex air compressor systems with traditional data-driven deep learning models often results in isolated fault diagnosis, ignoring correlations between concurrent faults. This paper introduces a knowledge graph construction approach for the air compressor fault diagnosis field, using after-sales business data as the source. We propose a model based on Robustly Optimized Bidirectional Encoder Representations from Transformers (RoBERTa), specifically tailored for constructing a knowledge graph for air compressor fault diagnosis. By integrating Whole Word Masking (WWM) technology, Bidirectional Long Short-Term Memory (BiLSTM), and Conditional Random Fields (CRFs), our approach effectively extracts specific entities from unstructured data. On our dataset, the model achieved an average accuracy of 0.7962 and an F1 score of 0.7956, demonstrating notable improvements in both accuracy and recall for entity recognition tasks. The extracted entities were subsequently stored in a Neo4j graph database, facilitating the construction of a domain-specific knowledge graph for air compressor fault diagnosis. Full article

Promoting the deployment and application of embedded systems in complex forest scenarios is an inevitable developmental trend in advanced intelligent forestry equipment. Unstructured roads, which lack effective artificial traffic signs and reference objects, pose significant challenges for driverless technology in forest scenarios, owing to their high nonlinearity and uncertainty. In this research, an unstructured road parameterization construction method, “DeepLab-Road”, based on tight coupling of multisource information is proposed, which aims to provide a new segmented architecture scheme for the embedded deployment of a forestry engineering vehicle driving assistance system. DeepLab-Road utilizes MobileNetV2 as the backbone network that improves the completeness of feature extraction through the inverse residual strategy. Then, it integrates pluggable modules including DenseASPP and strip-pooling mechanisms. They can connect the dilated convolutions in a denser manner to improve feature resolution without significantly increasing the model size. The boundary pixel tensor expansion is then completed through a cascade of two-dimensional Lidar point cloud information. Combined with the coordinate transformation, a quasi-structured road parameterization model in the vehicle coordinate system is established. The strategy is trained on a self-built Unstructured Road Scene Dataset and transplanted into our intelligent experimental platform to verify its effectiveness. Experimental results show that the system can meet real-time data processing requirements (≥12 frames/s) under low-speed conditions (≤1.5 m/s). For the trackable road centerline, the average matching error between the image and the Lidar was 0.11 m. This study offers valuable technical support for the rejection of satellite signals and autonomous navigation in unstructured environments devoid of high-precision maps, such as forest product transportation, agricultural and forestry management, autonomous inspection and spraying, nursery stock harvesting, skidding, and transportation. Full article