Search Results (37)

The term “vaccine” has been broadly and inconsistently applied to a range of products with widely divergent immunological outcomes, leading to the erosion of public trust and confusion among both medical professionals and the public. Historically, and by broad public understanding, a vaccine is expected to prevent infection, transmission, and disease through the induction of sterilizing, or true neutralizing immunity, specifically, the prevention of pathogen entry and replication in vivo. This ideal extends beyond the mere elicitation of neutralizing antibodies demonstrable in vitro. This paper proposes a three-tier classification system designed to differentiate products currently designated as “vaccines,” specifically to address the distinction between those that meet the traditional, highest-expectation definition (Tier 1), therapeutic vaccines that primarily prevent disease (Tier 2), and immunomodulatory therapeutics that primarily reduce disease severity (Tier 3). By detailing the mechanism of action of each product and emphasizing the urgent need for this refined classification, our aim is to restore public confidence in vaccination programs, improve understanding of vaccine-induced immunity among healthcare professionals, and empower informed decision-making by the public. We argue that a clearer understanding of vaccine capabilities will ultimately lead to increased vaccine uptake for those vaccines that do prevent infection, transmission, and disease. Full article

Coastal health assessment and diagnosis are important tools for decision-making and coastal management. In this paper, the concept of ecosystem health, which uses medical terminology to define the state of coastal health, was applied to examine and diagnose the state of the physical coastal systems of 120 coastal sites along the Moroccan Mediterranean coastline. Based on this assessment approach, five categories are defined: (1) “Good Health”, with two subdivisions: (1a) “Health Warning” and (1b) “Surface Wounds”; (2) “Minor Injury”; (3) “Major Injury”; (4) “On Life Support” and (5) “Deceased”. According to the results obtained, 38 sites (32%) were classified in the “Good Health” category, with 35 sites (29%) in the “Health Warning” and 11 (9%) in the “Surface Wounds” subdivisions; 14 sites (12%) in each of the “Minor Injury” and “Major Injury” categories; no sites (0%) in the “On Life Support” category; and 8 sites (7%) in the “Deceased” category. The considerable percentage of sites in the alert and lower categories highlights the level of degradation and ongoing loss of coastal ecosystems along the Moroccan Mediterranean coast due to the significant impact of anthropogenic processes and inadequate coastal management practices, highlighting the current degradation of its physical state and its capacity to function naturally, i.e., its ability to respond to various present and future environmental changes. The results and proposals presented in this paper offer important perspectives for the governance, preservation, and management of coastal systems and are very useful in limiting and preventing the degradation of coastal systems linked to natural processes and the development of future anthropogenic activities. In addition, they stress the importance of protecting sites classified as “healthy” and restoring those classified as “alert” or “unhealthy”, using sound management strategies based on reliable scientific data. Full article

Continuous use of agricultural chemicals and fertilizers, sporadic sewer overflow events, and an increase in urbanization have led to significant nutrient/pollutant loadings into the semi-arid Arroyo Colorado River basin, which is located in South Texas, U.S. Priority nutrients that require reduction include phosphorus and nitrogen and to mitigate issues of low dissolved oxygen, in some of its river segments. Consequently, the river’s potential to support aquatic life has been significantly reduced, thus highlighting the need for restoration. To achieve this restoration, a watershed protection plan was developed, comprising several preventive mitigation measures, including installing green infrastructure (GI) practices. However, for effective reduction of excessive nutrient loadings, there is a need to study the effects of different combinations of GI practices under current and future land use scenarios to guide decisions in implementing the cost-effective infrastructure while considering factors such as the existing drainage system, topography, land use, and streamflow. Therefore, this study coupled the Soil and Water Assessment Tool (SWAT) model with the System for Urban Stormwater Treatment and Analysis Integration (SUSTAIN) model to determine the effects of different combinations of GI practices on the reduction of nitrogen and phosphorus under changing land use conditions in three selected Arroyo Colorado subwatersheds. Two land use maps from the U.S. Geological Survey (USGS) Forecasting Scenarios of land use (FORE-SCE) model for 2050, namely, A1B and B1, were implemented in the coupled SWAT-SUSTAIN model in this study, where the urban area is projected to increase by 6% and 4%, respectively, with respect to the 2018 land use scenario. As expected, runoff, phosphorus, and nitrogen slightly increased with imperviousness. The modeling results showed that implementing either vegetated swales or wet ponds reduces flow and nutrients to meet the Total Maximum Daily Loads (TMDLs) targets, which cost about USD 1.5 million under current land use (2018). Under the 2050 future projected land use changes (A1B scenario), the cost-effective GI practice was implemented in vegetated swales at USD 1.5 million. In contrast, bioretention cells occupied the least land area to achieve the TMDL targets at USD 2 million. Under the B1 scenario of 2050 projected land use, porous pavements were most cost effective at USD 1.5 million to meet the TMDL requirements. This research emphasizes the need for collaboration between stakeholders at the watershed and farm levels to achieve TMDL targets. This study informs decision-makers, city planners, watershed managers, and other stakeholders involved in restoration efforts in the Arroyo Colorado basin. Full article

Minimal facet and lateral mass fractures of the subaxial cervical spine (C3–C7) are a distinct subset of spinal injuries that present diagnostic and therapeutic challenges. These fractures often result from low-energy trauma or hyperextension mechanisms. They are frequently stable. However, subtle fracture instability and associated soft tissue injuries may lead to delayed instability, neurological compromise, and/or chronic severe pain if not properly identified. Accurate diagnosis relies on a combination of plain radiography, high-resolution computed tomography (CT), and magnetic resonance imaging (MRI) to assess bony and ligamentous integrity. Treatment strategy is determined based on fracture stability, neurological status, and radiographic findings. Most stable fractures can be effectively treated with conservative treatment, allowing for natural healing while minimizing complications. However, when instability is suspected—such as those with significant disc and ligamentous injuries, progressive deformity, or neurological deficits—surgical stabilization may be considered. The presence of vertebral artery injury (VAI) can further complicate management. To mitigate the risk of stroke, a multidisciplinary approach that includes neurosurgery, vascular surgery, and interventional radiology is needed. Surgical treatment aims to restore spinal alignment, maintain stability, and prevent further neurological deterioration with approaches tailored to individual fracture patterns and patient-specific factors. Advances in surgical techniques, perioperative management, and endovascular interventions for VAI continue refining treatment options to improve clinical outcomes while minimizing complications. Despite increasing knowledge of these fractures and associated vascular injuries, optimal treatment strategies remain unclear due to limited high-quality evidence. This review provides a comprehensive analysis of the anatomy, biomechanics, classification, imaging modalities, and treatment strategies for minimal facet and lateral mass fractures in the subaxial cervical spine, highlighting recent advancements in diagnostic tools, therapeutic approaches, and managing vertebral artery injuries. A more precise understanding of the natural history and optimal management of these injuries will help spine specialists refine clinical decision-making and improve patient outcomes. Full article

We present a study that addresses the critical need for a prototype Decision Support System for forest fire information and management in Uttarakhand, India. The study’s main objective was to carry out statistical analysis of large fire incident datasets to understand trends of fires in the region and develop essential spatial decision support tools. These tools address the necessary fire management decision-making along with comprehensive datasets that can enable a decision maker to exercise better management. Moreover, this DSS addresses three major components of forest fire decision support: (i) pre-fire (forest information visualization) tools, (ii) during-fire terrain-based spatial decision support tools, and (iii) post-fire restoration tools. The efforts to develop this DSS included satellite lidar dataset-based fuel load estimations, the Keetch–Byram Drought Index, and the integration of spatial tools that ensure better spatial decisions in fire suppression planning. In addition, based on the bibliographic literature, the study also uses ecological and community-based knowledge, including financial aspects, for fire prevention and post-fire restoration planning. The development of this DSS involves an open-source R Shiny framework, enabling any decision maker at the execution or planning level to access these key datasets and simulate the spatial solutions cost-effectively. Hence, this study aimed to internalize key decision support tools and datasets based on extensive statistical analysis for data-driven forest fire planning and management. Full article

Due to the differences in the green water (GW) budget patterns of different vegetation, improper vegetation restoration may not only fail to improve the ecological environment but also cause irreversible damage to ecologically vulnerable areas, especially when vegetation restoration continues to be implemented in the future, and the pressure on water scarcity increases further. However, there is a lack of standardized research on the differences in the patterns of recharge, consumption, and efficient use of GW in typical vegetation. This makes the research results vary and cannot provide direct support for water management decision-making. Therefore, in this study, 30-year-old woodlands (R. pseudoacacia and P. orientalis) and two typical grasslands (I. cylindrican and M. sativa) that are similar to each other except for species were selected in a headwater catchment in the rain-fed agricultural area. A new GW concept and assessment framework was constructed to study the GW of long-term revegetation using a combination of field experiments and model simulations during the 2019–2020 growing season. The study findings comprise the following: (1) High-efficiency green water (GW_H), low-efficiency green water (GW_L), ineffective green water (GW_I), and available green water storage (GW_A) in the four sample plots during the study period were defined, separated, and compared. (2) An analysis of GW_A variations under different water scenarios. (3) The establishment of GW_H and GW_L thresholds. (4) Strategies to reduce GW_I and optimize GW potential while maintaining soil erosion prevention measures. (5) Suggestions for vegetation restoration species based on diverse factors. This research enhances comprehension of the impact of vegetation restoration on green water dynamics in ecologically vulnerable areas such as the rain-fed agricultural zone of the Loess Plateau. Full article

Early fault detection technologies play a decisive role in preventing equipment failures in industrial production. The primary challenges in early fault detection for industrial applications include the severe imbalance of time-series data, where normal operating data vastly outnumber anomalous data, and in some cases, anomalies may be virtually absent. Additionally, the frequent changes in operational modes during machinery operation further complicate the detection process, making it difficult to effectively identify faults across varying conditions. This study proposes a bearing early anomaly detection method based on contrastive learning and reconstruction approaches to address the aforementioned issues. The raw time-domain vibration data, which were collected from sensors mounted on the bearings of the machinery, are first preprocessed using the Ricker wavelet transform to effectively remove noise and extract useful signal components. These processed signals are then fed into a BYOL-based contrastive learning network to learn more discriminative global feature representations. In addition, we design the reconstruction loss to complement contrastive learning. By reconstructing the masked original data, the reconstruction loss forces the model to learn detailed information, thereby emphasizing the preservation and restoration of local details. Our model not only eliminates the reliance on negative samples found in mainstream unsupervised methods but also captures data features more comprehensively, achieving superior fault detection accuracy under different operating conditions compared to related methods. Experiments on the widely used CWRU multi-condition-bearing fault dataset demonstrate that our method achieves an average fault detection accuracy of 96.97%. Moreover, the experimental results show that on the full-cycle IMS dataset, our method detects early faults at least 2.3 h earlier than the other unsupervised methods. Furthermore, the validation results for the full-cycle XJTU-SY dataset further demonstrate its excellent generalization ability. Full article

Trauma-induced shoulder dislocations and sprains rank among the most common upper extremity injuries, with contact sports accounting for the majority of cases. These injuries often lead to substantial impairments in joint function and long recovery times, requiring targeted therapeutic interventions to restore mobility and prevent recurrent injuries. Given the pivotal role of exercise therapy in restoring shoulder function, this study systematically reviews the literature on the comparative effectiveness of supervised versus self-managed exercise therapy following acute shoulder trauma. PubMed, Cochrane CENTRAL, Embase, Web of Science, and Science Direct were searched up to 13 December 2024. Conservative and post-surgical treatment modes were analyzed separately. Five studies with a total 689 participants were included (conservative: n = 538 across two studies; post-surgical: n = 151 across three studies). Both treatment modes showed similar pooled effects (standardized mean difference, SMD_conservative: $-0.35$, 95% CI [$-1.39$, 0.69]; SMD_{post-surgical}: $-0.23$, 95% CI [$-1.21$, 0.75]), with a marginal improvement in shoulder function favoring supervised therapy. Four studies had some risk of bias, and one had serious risk; GRADE certainty was low. Supervised exercise therapy may offer slightly greater functional improvements over self-managed training, but evidence is limited by heterogeneity and low certainty. Further high-quality trials with standardized protocols and improved adherence tracking are needed to establish more definitive conclusions and guide clinical decision-making. Full article

Revealing geological environment resilience (GER) under seawater intrusion (SWI) hazards is a prerequisite for solving groundwater resource depletion, land salinization, and ecological degradation in coastal cities. This study applies the resilience design approach based on urban complex adaptive systems theory to understand the impact of SWI on the geological environment. Taking SWI as the research object, the GER evaluation method under SWI disaster was established by selecting five elastic indexes: disturbance intensity, geological environment vulnerability, stress resistance, recovery, and adaptability. This method is used to evaluate the GER level of the coastal areas of Shenzhen in recent years under the impact of SWI hazards. The study found that there is a negative correlation between the intensity of disturbance and precipitation amount. The vulnerability is greater the closer the distance to the coastline and the shallower the depth of bedrock burial. Resistance is composed of early warning ability and disaster prevention ability, and the result is 10.07, which belongs to the medium level. The recovery is 1.49, which is at a relatively high level, indicating a high capacity for restoration ability. The adaptability increased from 3.03 to 3.13, so that the area of seawater intrusion is becoming smaller. GER is affected by precipitation amount and depth of bedrock burial; the greater the precipitation and the shallower the bedrock burial, the lower the GER. Precipitation amount significantly impacts the SWI situation in the eastern coastal area of Shenzhen. In the central region, the impact of precipitation on GER is less significant. However, in the western region, the depth of bedrock burial primarily affects GER. Compared to completely weathered granite, Pleistocene fluvial plain sediments are more susceptible to SWI effects in freshwater environments. This study contributes to a deeper understanding of the impact of SWI on the geological environment in coastal areas, providing decision-makers with the necessary knowledge to develop targeted and effective governance and prevention strategies. Full article

Concrete is currently the most used construction material, mainly due to its mechanical strength, chemical stability, and low cost. This material is affected by wear processes caused by the environment, which lead to a reduction in the useful life of the infrastructure in the long term. These wear processes can cause cracks, corrosion of reinforcing steel, loss of load capacity, and loss of concrete section, among other problems. Considering the above, it is necessary to carry out durability studies on concrete to determine the integrity conditions in which the infrastructure is found, the reasons for its deterioration, the environmental factors that affect it, and its useful life under these conditions, and develop restoration or protection plans. Generally, the durability studies include non-destructive testing such as ultrasonic pulse velocity, electrical resistivity, porosity measurement, and capillary absorption rate. These techniques make it possible to characterize the concrete and obtain information such as the total volume of pores, susceptibility to corrosion of the reinforcing steel, decrease in mechanical resistance, cracks, presence of humidity, and aggressive ions inside the concrete. In this work, two durability studies are presented with non-destructive tests carried out on active piers that are 20 and 40 years old. These are located in coastal areas in southern Mexico on the Gulf of Mexico side, with 80% average annual relative humidity, temperatures above 33 °C on average, high concentrations of salts, load handling, vibrations, flora and fauna typical of the marine ecosystem, etc. The results obtained reveal important information about the current state of the piers and the damage caused by the environment over time. This information allowed us to make decisions on preventive actions and develop appropriate and specific restoration projects for each pier. Full article

A rapid dynamic security assessment (DSA) is crucial for online preventive and restoration decision-making. The deep learning-based DSA models have high efficiency and accuracy. However, the complex model structure and high training cost make them hard to update quickly. This paper proposes a dynamic security partition assessment method, aiming to develop accurate and incrementally updated DSA models with simple structures. Firstly, the power grid is self-adaptively partitioned into several local regions based on the mean shift algorithm. The input of the mean shift algorithm is a symmetric electrical distance matrix, and the distance metric is the Chebyshev distance. Secondly, high-level features of operating conditions are extracted based on the stacked denoising autoencoder. The symmetric electrical distance matrix is modified to represent fault locations in local regions. Finally, DSA models are constructed for fault locations in each region based on the radial basis function neural network (RBFNN) and Chebyshev distance. An online incremental updating strategy is designed to enhance the model adaptability. With the simulation software PSS/E 33.4.0, the proposed dynamic security partition assessment method is verified in a simplified provincial system and a large-scale practical system in China. Test results demonstrate that the Chebyshev distance can improve the partition quality of the mean shift algorithm by approximately 50%. The RBFNN-based partition assessment model achieves an accuracy of 98.96%, which is higher than the unified assessment with complex models. The proposed incremental updating strategy achieves an accuracy of over 98% and shortens the updating time to 30 s, which can meet the efficiency of online application. Full article

Monolithic zirconia (MZ) crowns are widely utilized in dental restorations, particularly for substantial tooth structure loss. Inspection, tactile, and radiographic examinations can be time-consuming and error-prone, which may delay diagnosis. Consequently, an objective, automatic, and reliable process is required for identifying dental crown defects. This study aimed to explore the potential of transforming acoustic emission (AE) signals to continuous wavelet transform (CWT), combined with Conventional Neural Network (CNN) to assist in crack detection. A new CNN image segmentation model, based on multi-class semantic segmentation using Inception-ResNet-v2, was developed. Real-time detection of AE signals under loads, which induce cracking, provided significant insights into crack formation in MZ crowns. Pencil lead breaking (PLB) was used to simulate crack propagation. The CWT and CNN models were used to automate the crack classification process. The Inception-ResNet-v2 architecture with transfer learning categorized the cracks in MZ crowns into five groups: labial, palatal, incisal, left, and right. After 2000 epochs, with a learning rate of 0.0001, the model achieved an accuracy of 99.4667%, demonstrating that deep learning significantly improved the localization of cracks in MZ crowns. This development can potentially aid dentists in clinical decision-making by facilitating the early detection and prevention of crack failures. Full article

Artistic image completion assumes a significant role in the preservation and restoration of invaluable art paintings, marking notable advancements through the adoption of deep learning methodologies. Despite progress, challenges persist, particularly in achieving optimal results for high-resolution paintings. The intricacies of complex structures and textures in art paintings pose difficulties for sophisticated approaches like Generative Adversarial Networks (GANs), leading to issues such as small-scale texture synthesis and the inference of missing information, resulting in distortions in lines and unnatural colors. Simultaneously, patch-based image synthesis, augmented with global optimization on the image pyramid, has evolved to enhance structural coherence and details. However, gradient-based synthesis methods face obstacles related to directionality, inconsistency, and the computational burdens associated with solving the Poisson equation in non-integrable gradient fields. This paper introduces a pioneering approach, integrating Weighted Similarity-Confidence Laplacian Synthesis to comprehensively address these challenges and advance the field of artistic image completion. Experimental results affirm the effectiveness of our approach, offering promising outcomes for the preservation and restoration of art paintings with intricate details and irregular missing regions. The integration of weighted Laplacian synthesis and patch-based completion across multi-regions ensures precise and targeted completion, outperforming existing methods. A comparative analysis underscores our method’s superiority in artifact reduction and minimizing blurriness, particularly addressing challenges related to color discrepancies in texture areas. Additionally, the incorporation of pyramid blending proves advantageous, ensuring smoother transitions and preventing noticeable seams or artifacts in blended results. Based on empirical results, our method consistently outperforms previous methods across both high and low resolutions. Responding to these insights, our approach emerges as an invaluable guide for both curators and artists. The algorithm’s performance yields insights that underscore the central role of thoughtful decision making in the creation of art paintings. This guidance extends to informing choices related to color selection, brushstrokes, and various other elements integral to the artistic process. During the creation phase, employing these insights enables artists and curators to optimize not only the digitization but also the subsequent restoration process. This proves especially vital when dealing with the intricacies involved in physically restoring damaged original art paintings. Importantly, our approach not only streamlines the restoration process but also contributes significantly to the preservation and enhancement of the digital representations of these distinctive and often intricate works of art. Full article

Mangrove forests are a biodiverse ecosystem known for a wide variety of crucial ecological services, including carbon sequestration, coastal erosion control, and prevention of saltwater intrusion. Given the ecological importance of mangrove forests, a comprehensive and up-to-date mangrove extent mapping at broad geographic scales is needed to define mangrove forest changes, assess their implications, and support restoration activities and decision making. The main objective of this study is to evaluate mangrove classifications derived from a combination of Landsat-8 OLI, Sentinel-2, and Sentinel-1 observations using a random forest (RF) machine learning (ML) algorithm to identify the best approach for monitoring Guyana’s mangrove forests on an annual basis. Algorithm accuracy was tested using high-resolution planet imagery in Collect Earth Online. Results varied widely across the different combinations of input data (overall accuracy, 88–95%; producer’s accuracy for mangroves, 50–87%; user’s accuracy for mangroves, 13–69%). The combined optical–radar classification demonstrated the best performance with an overall accuracy of 95%. Area estimates of mangrove extent ranged from 908.4 to 3645.0 hectares. A ground-based validation exercise confirmed the extent of several large, previously undocumented areas of mangrove forest loss. The results establish that a data fusion approach combining optical and radar data performs marginally better than optical-only approaches to mangrove classification. This ML approach, which leverages free and open data and a cloud-based analytics platform, can be applied to mapping other areas of mangrove forests in Guyana. This approach can also support the operational monitoring of mangrove restoration areas managed by Guyana’s National Agricultural and Research Extension Institute (NAREI). Full article

Extreme events are increasing in frequency and severity due to climate change, making the littoral zone even more vulnerable and requiring continuous monitoring for its optimized management. The low-lying Ebro Delta ecosystem, located in the NW Mediterranean, was subject to Storm Gloria in the winter of 2020, the most severe coastal storm registered in the area in decades and one of the most intense ever recorded in the Mediterranean. This event caused intense rainfall, severe flooding, the erosion of beaches, and the destruction of coastal infrastructures. In this study, the Landsat-8 and Sentinel-2 satellites were used to monitor the flooding impact and water quality status, including chlorophyll-a, suspended particulate matter, and turbidity, to evaluate the pre-, syn-, and post-storm scenarios. Image processing was carried out using the ACOLITE software and the on-the-cloud Google Earth Engine platform for the water quality and flood mapping, respectively, showing a consistent performance for both satellites. This cost-effective methodology allowed us to characterize the main water quality variation in the coastal environment during the storm and detect a higher flooding impact compared to the one registered three days later by the Copernicus Emergency Service for the same area. Moreover, the time series revealed how the detrimental impact on the water quality and turbidity conditions was restored two weeks after the extreme weather event. While transitional plumes of sediment discharge were formed, no phytoplankton blooms appeared during the study period in the delta. These results demonstrate that the workflow implemented is suitable for monitoring extreme coastal events using open satellite imagery at 10–30 m spatial resolution, thus providing valuable information for early warning to facilitate timely assistance and hazard impact evaluation. The integration of these tools into ecological disaster management can significantly improve current monitoring strategies, supporting decision-makers from the local to the national level in prevention, adaptation measures, and damage compensation. Full article