Search Results (599)

An open question in radiobiology concerns whether low doses of radiation are harmful or if cells are able to tolerate such exposure with minimal or no disruption. This issue is relevant for evaluating public health risks associated with the increasing number of medical computed tomography (CT) diagnostic procedures. This study evaluated the impact of CT scan-level exposure on human adipose mesenchymal stem cells (hMSCs) by measuring DNA damage responses (γH2AX, 53BP1, pATM foci), proliferation (Ki-67), senescence (β-galactosidase), and multiple gene expressions. Responses to one or five CT exposures were compared to a 2 Gy X-ray dose at intervals from 1 h to 10 passages post-irradiation. It was shown that CT scan briefly increased DNA damage markers but showed no significant long-term effects. A high dose of 2 Gy X-ray exposure caused sustained DNA damage, decreased proliferation, increased senescence, and significant changes in hundreds of genes even after several cell generations. After a single CT exposure, gene expression changes were minimal, while high-dose exposure led to strong activation of DNA repair and stress response pathways. Five CT scans caused a slight activation of LIF and HSPA1B genes, but these effects were minor compared to the high-dose group. All detected effects from CT scans were not observed by ten cell passages, whereas high-dose effects persisted. In conclusion, typical CT scan exposures have only short-term, mild effects on hMSCs, while high-dose radiation causes lasting cellular and genetic changes. Full article

Background/Objectives: Post-activation performance enhancement (PAPE) is an acute neuromuscular phenomenon influenced by training status, yet evidence regarding its response to isometric conditioning activity (ICA) across different athletic populations remains inconclusive. This study investigated the acute effects of ICA on countermovement jump (CMJ) performance in trained (T) versus highly trained (HT) male participants. Methods: A total of 32 participants (T: n = 16; HT: n = 16) completed two randomized sessions: a control condition (CTRL) and an isometric protocol (ICA; three sets of three maximal isometric back squat contractions, 3 s each). CMJ height was assessed at baseline and at 3-, 6- and 9-min post-intervention using a force platform. Repeated-measures ANOVA examined interactions between time, condition, and training status. Results: A significant improvement in jump height was observed only in the HT-ISO group at 3 min post-ICA (mean difference: +3.0 ± 2.3 cm; p < 0.005; d = 0.65). No significant changes were detected in the T group across conditions. Peak power and modified reactive strength index showed no significant differences, though effect trends favored the HT group. Conclusions: ICA elicits short-term PAPE effects in highly trained, but not moderately trained, individuals. These findings underscore the importance of tailoring warm-up protocols to the athlete’s training level for optimal performance enhancement. Full article

Mechanical damage reduces the marketability of Korla fragrant pears, severely restricting industry development. To enhance the commercial value of pears, this study investigated the effects of impact, compressive, and combined impact-compressive damage types on the weight loss rate, L*, a*, and b* of pears, and constructed a multi-output prediction model for the weight loss rate, L*, a*, and b* of damaged pears during storage by integrating partial least squares regression (PLSR), support vector regression (SVR), and long short-term memory (LSTM), from which the optimal prediction model was selected to achieve synchronous detection of the physical quality of damaged pears during storage. The results indicated that during storage, the weight loss rate, a*, and b* of pears subjected to different damage types gradually increased with prolonged storage time, while L* gradually decreased. Under the same damage volume situation, pears subjected to impact-static pressure combined action exhibited the fastest storage quality change speed, followed by impact action, static pressure action. The SVR multi-output model demonstrated optimal performance in predicting the weight loss rate, L*, a*, and b* of damaged pears during storage, achieving mean coefficient of determination R², root mean square error (RMSE), and residual prediction deviation (RPD) values of 0.988, 0.513, and 10.072, respectively, for these four quality indicators. These results establish a theoretical foundation for the development of simultaneous monitoring techniques for fruit storage quality. Full article

The global shift toward clean production, like using renewable energy, has significantly decreased the use of synchronous machines (SMs), which help maintain stability and control, causing serious frequency stability issues in power systems with low inertia. Fractional order controller-based virtual synchronous machines (FOC-VSMs) have become a promising option, but they rely on communication networks to work together in real time, causing them to be at risk of cyberattacks, especially from false data injection attacks (FDIAs). This paper suggests a new way to detect FDI attacks using a federated physics-informed long short-term memory (PI-LSTM) network. Each FOC-VSM uses its data to train a PI-LSTM, which keeps the information private but still helps it learn from a common model that understands various operating conditions. The PI-LSTM incorporates physical constraints derived from the FOC-VSM swing equation, facilitating residual-based anomaly detection that is sensitive to minor deviations in control dynamics, such as altered inertia or falsified frequency signals. Unlike traditional LSTMs, the physics-informed architecture minimizes false positives arising from benign disturbances. We assessed the proposed method on an IEEE 9-bus test system featuring two FOC-VSMs. The results show that our method can successfully detect FDI attacks while handling regular changes, proving it could be a strong solution. Full article

Wetlands are an important part of coastal ecosystems but are under increasing pressure from climate change-induced sea-level rise and flooding, in addition to development pressures associated with increasing human populations. The change in tidal events and their intensity due to sea-level rise is also reshaping and challenging the vitality of existing wetland systems, requiring more intensive localized studies to identify future-focused restoration and conservation strategies. To support this endeavor, this study utilizes tide gauge datasets from the Australian Bureau of Meteorology (BOM) for maximum sea-level (Hmax) prediction and Landsat Collection surface reflectance datasets obtained from the United States Geological Survey (USGS) database to detect and project patterns of change in the Maroochy River floodplain of Queensland, Australia. This study developed an efficient hybrid deep learning model combining a Convolutional Neural Network and Bidirectional Long Short-Term Memory (CNNBiLSTM) architecture for the prediction of maximum sea-level and tidal events. The proposed model significantly outperformed three benchmark models (Multiple Linear Regression (MLR), Support Vector Regression (SVR), and CatBoost) in achieving a high correlation coefficient (r = 0.9748) for maximum sea-level prediction. To further address the increasing frequency and intensity of tidal events linked to sea-level rise, a CNNBiLSTM classification model was also developed, achieving 96.72% accuracy in predicting extreme tidal occurrences. This study identified a significant positive linear increase in sea-level rise of 0.016 m/year between 2014 and 2024. Wetland change detection using Landsat imagery along the Maroochy River floodplain also identified a substantial vegetation loss of 395.64 hectares from 2009 to 2023. These findings highlight the strong potential of integrating deep learning and remote sensing for improved prediction and assessment of sea-level extremes and coastal ecosystem changes. The study outcomes provide valuable insights for informing not only conservation and restoration activities but also for providing localized projections of future change necessary for the progression of effective climate adaptation and mitigation strategies. Full article

Lubricating oil degradation directly impacts friction coefficient, wear rate, and lubrication regime transitions, making precise health quantification essential for predictive tribological maintenance. However, conventional evaluation methods fail to capture subtle tribological changes preceding lubrication failure, often oversimplifying complex multi-parameter relationships critical to friction and wear performance. To address this challenge, this study proposes Seasonal–Trend decomposition using Loess, a Factor Attention Network, a Temporal Convolutional Network, and an Informer with Long Short-Term Memory Variational Autoencoder (SFTI-LVAE) framework for continuous tribological health assessment of diesel engine lubricants. The approach integrates Seasonal–Trend decomposition using Loess (STL) for trend–seasonal separation, a Factor Attention Network (FAN) for multidimensional feature fusion, and a Temporal Convolutional Network (TCN)-enhanced Informer for capturing long-term tribological dependencies. By combining Long Short-Term Memory (LSTM) temporal modeling with Variational Autoencoder (VAE) reconstruction, the method quantifies lubricant health through reconstruction error, establishing a direct correlation between data deviation and tribological performance degradation. Additionally, permutation importance-based feature evaluation and parameter contribution quantification techniques enable deep mechanistic analysis and fault source tracing of lubricant health degradation. Experimental validation using multi-sensor monitoring data demonstrates that SFTI-LVAE achieves a 96.67% fault detection accuracy with zero false alarms, providing early warning 6.47 h before lubrication failure. Unlike traditional anomaly detection methods that only classify conditions as abnormal or normal, the proposed continuous health index reveals gradual tribological degradation processes, capturing subtle viscosity–temperature relationships and wear particle evolution indicating early lubrication regime transitions. The health index correlates strongly with tribological performance indicators, enabling a transition from reactive maintenance to predictive tribological management, providing an innovative solution for equipment health evaluation in the digital tribology era. Full article

Short-term channel adjustments are a research topic of great relevance in the framework of fluvial geomorphology, but studies on this topic have been quite scarce in Southern Italy, at least since the 2010s, notwithstanding the fact that this area is strongly representative of a much wider morphoclimatic context, i.e., the Mediterranean area, which particularly suffers from the effects of current climate change. Currently, different interpretations still exist about the type and role of controlling factors, and a common morphoevolutionary trend is quite far from being defined; so, new case studies are needed. In this paper, the geomorphological changes experienced by the Sabato R. (Southern Italy) over a period of ~150 years were investigated. A reach-scale geomorphological analysis in a GIS environment of raster data, consisting of four topographic maps (from 1870, 1909, 1941 and 1955) and five sets of orthophotos (from 1998, 2004, 2008, 2011 and 2014), was carried out, integrated with field-surveyed data. Land-use changes, in-channel anthropic disturbances, floods and rainfall variations were selected as possible controlling factors. The study highlighted four morphoevolutionary phases of the studied river. Phase 1 (1870s–1910s) was characterized by a relative channel stability in terms of both mean width and pattern, while channel widening dominated during Phase 2 (1910s–1940s). In contrast, Phase 3 (1940s–1990s) was characterized by intense and diffuse narrowing. Finally, during Phase 4 (from the 1990s onward), an alternation in channel narrowing and flood-induced widening was detected. During all phases, changes in both channel pattern and riverbed elevation were less evident than those in channel width. Land-use changes and, later, floods, in addition to in-channel human disturbances at a local scale, were the main controlling factors. The obtained results have profound implications for rivers located outside Italy as well, as they provide new insights into the role played by the considered controlling factors in the geomorphological evolution of a typical Mediterranean river. Understanding this role is fundamental in regional-scale river management, hazard mitigation and environmental planning, as proved by the vast pre-existing scientific literature. Full article

Background and Objectives: Periodontitis is a chronic inflammatory disease that leads to progressive destruction of periodontal tissues and remains a significant global health burden. While conventional therapies such as scaling and root planning offer short-term improvements, they often fall short in maintaining long-term microbial control, underscoring the need for adjunctive strategies. This study evaluated the clinical and microbiological effects of a novel essential oil (EO)-based gel—SmartGel OV—formulated with Origanum vulgare. Materials and Methods: Thirty adults with periodontitis were enrolled in a 4-month observational study, during which SmartGel OV was applied daily via gingival massage. Clinical outcomes and bacterial profiles were assessed through probing measurements and real-time PCR analysis. Additionally, a pilot AI-based tool was explored as a supplemental method to monitor inflammation progression through intraoral images. Results: Significant reductions were observed in Fusobacterium nucleatum and Capnocytophaga spp., accompanied by improvements in clinical markers, including probing depth, bleeding on probing, and plaque index. The AI framework successfully identified visual inflammation changes and supported early detection of non-responsiveness. Conclusions: SmartGel OV demonstrates promise as a natural adjunctive treatment for periodontitis and AI monitoring was included as an exploratory secondary tool to assess feasibility for future remote tracking. Full article

Given the intricate operating environment of steel truss bridges, data anomalies are frequently initiated by faults in the sensor monitoring system itself during the monitoring process. This paper utilizes a steel truss bridge as a case study in engineering, with a primary focus on the deflection of the main girder. The paper establishes an Attention Mechanism-based Bidirectional Long Short-Term Memory Neural Network (ATT-BiLSTM) model, with the objective of accurately repairing abnormal monitoring data. Firstly, correlation heat maps and Gray correlation are employed to detect anomalies in key measurement point data. Subsequently, the ATT-BiLSTM and Support Vector Machine (SVR) models are established to repair the anomalous monitoring data. Finally, various evaluation indexes, including Pearson’s correlation coefficient, mean squared error, and coefficient of determination, are utilized to validate the repairing accuracy of the ATT-BiLSTM model. The findings indicate that the repair efficacy of ATT-BiLSTM on anomalous data surpasses that of SVR. The repaired data exhibited a tendency to decrease in amplitude at the anomalous position, while maintaining the prominence of the data at abrupt deflection change points, thereby preserving the characteristics of the data. The repair rate of anomalous data attained 93.88%, and the mean square error of the actual complete data was only 0.0226, leading to substantial enhancement in the integrity and reliability of the data. Full article

In the context of intelligent coal mine safety monitoring, an integrated prediction and early-warning method named MTGNN-Bayesian-IF-DBSCAN (Multi-Task Graph Neural Network–Bayesian Optimization–Isolation Forest–Density-Based Spatial Clustering of Applications with Noise) is proposed to address the challenges of gas concentration prediction and anomaly detection in coal mining faces. The MTGNN (Multi-Task Graph Neural Network) is first employed to model the spatiotemporal coupling characteristics of gas concentration and wind speed data. By constructing a graph structure based on sensor spatial dependencies and utilizing temporal convolutional layers to capture long short-term time-series features, the high-precision dynamic prediction of gas concentrations is achieved via the MTGNN. Experimental results indicate that the MTGNN outperforms comparative algorithms, such as CrossGNN and FourierGNN, in prediction accuracy, with the mean absolute error (MAE) being as low as 0.00237 and the root mean square error (RMSE) maintained below 0.0203 across different sensor locations (T0, T1, T2). For anomaly detection, a Bayesian optimization framework is introduced to adaptively optimize the fusion weights of IF (Isolation Forest) and DBSCAN (Density-Based Spatial Clustering of Applications with Noise). Through defining the objective function as the F1 score and employing Gaussian process surrogate models, the optimal weight combination (w_if = 0.43, w_dbscan = 0.52) is determined, achieving an F1 score of 1.0. By integrating original concentration data and residual features, gas anomalies are effectively identified by the proposed method, with the detection rate reaching a range of 93–96% and the false alarm rate controlled below 5%. Multidimensional analysis diagrams (e.g., residual distribution, 45° diagonal error plot, and boxplots) further validate the model’s robustness in different spatial locations, particularly in capturing abrupt changes and low-concentration anomalies. This study provides a new technical pathway for intelligent gas warning in coal mines, integrating spatiotemporal modeling, multi-algorithm fusion, and statistical optimization. The proposed framework not only enhances the accuracy and reliability of gas prediction and anomaly detection but also demonstrates potential for generalization to other industrial sensor networks. Full article

Dams play a vital role in securing water and electricity supplies for households and industry, and they contribute significantly to flood protection. Regular monitoring of dam deformations holds fundamental socio-economic and ecological importance. Traditionally, this has relied on time-consuming in situ techniques that offer either high spatial or temporal resolution. Persistent Scatterer Interferometry (PSI) addresses these limitations, enabling high-resolution monitoring in both domains. Sensors such as TerraSAR-X (TSX) and Sentinel-1 (S-1) have proven effective for deformation analysis with millimeter accuracy. Combining TSX and S-1 datasets enhances monitoring capabilities by leveraging the high spatial resolution of TSX with the broad coverage of S-1. This improves monitoring by increasing PS point density, reducing revisit intervals, and facilitating the detection of environmental deformation drivers. This study aims to investigate two objectives: first, we evaluate the benefits of a spatially and temporally densified PS time series derived from TSX and S-1 data for detecting radial deformations in individual dam segments. To support this, we developed the TSX2StaMPS toolbox, integrated into the updated snap2stamps workflow for generating single-master interferogram stacks using TSX data. Second, we identify deformation drivers using water level and temperature as exogenous variables. The five-year study period (2017–2022) was conducted on a gravity dam in North Rhine-Westphalia, Germany, which was divided into logically connected segments. The results were compared to in situ data obtained from pendulum measurements. Linear models demonstrated a fair agreement between the combined time series and the pendulum data ($R^2$ = 0.5; MAE = 2.3 mm). Temperature was identified as the primary long-term driver of periodic deformations of the gravity dam. Following the filling of the reservoir, the variance in the PS data increased from 0.9 mm to 3.9 mm in RMSE, suggesting that water level changes are more responsible for short-term variations in the SAR signal. Upon full impoundment, the mean deformation amplitude decreased by approximately 1.7 mm toward the downstream side of the dam, which was attributed to the higher water pressure. The last five meters of water level rise resulted in higher feature importance due to interaction effects with temperature. The study concludes that integrating multiple PS datasets for dam monitoring is beneficial particularly for dams where few PS points can be identified using one sensor or where pendulum systems are not installed. Identifying the drivers of deformation is feasible and can be incorporated into existing monitoring frameworks. Full article

In recent decades, fishery stock enhancement has been increasingly utilized as a restoration tool to mitigate population declines and enhance the resilience of marine fisheries. Nevertheless, persistent enhancement efforts risk eroding the evolutionary potential of wild populations via genetic homogenization and maladaptive gene flow. Using long-term monitoring data (2017–2023), we quantified the effects of large-scale Penaeus penicillatus stock enhancement (~10⁸ juveniles/yr) on wild population dynamics and genetic integrity in the Beibu Gulf ecosystem. Temporal genetic changes were assessed using eight highly polymorphic microsatellite loci, comparing founder (2017) and enhanced (2024) populations to quantify stocking impacts. Insignificantly lower expected heterozygosity was observed in the stocked population (He = 0.60, 2024) relative to natural populations (He = 0.62–0.66; p > 0.1), indicating genetic dilution effects from enhancement activities. No significant erosion of genetic diversity was detected post-enhancement, suggesting current stocking practices maintain short-term population genetic integrity. Despite conserved heterozygosity, pairwise Fst analysis detected significant genetic shifts between temporal cohorts (pre-enhancement—2017 vs. post-enhancement—2024; Fst = 0.25, p < 0.05), demonstrating stocking-induced population restructuring. Genetic connectivity analysis revealed that while the enhanced Beihai population (A-BH) maintained predominant self-recruitment (>90%), it experienced substantial stocking-derived gene flow (17% SW → A-BH). The post-stocking period showed both reduced genetic exchange with adjacent populations and increased asymmetric dispersal from A-BH (e.g., 5% to YJ), indicating that hatchery releases simultaneously enhanced population isolation while altering regional genetic structure. Our findings revealed the paradoxical dual effects of stock enhancement and allelic diversity while disrupting natural genetic architecture. This underscores the need for evolutionary-impact assessments in marine resource management. Full article

Coastal blue carbon ecosystems (traditional types such as mangroves, salt marshes, and seagrass meadows; emerging types such as tidal flats and mariculture) play pivotal roles in capturing and storing atmospheric carbon dioxide. Reliable assessment of the spatial and temporal variation and the carbon storage potential holds immense promise for mitigating climate change. Although previous field surveys and regional assessments have improved the understanding of individual habitats, most studies remain site-specific and short-term; comprehensive, multi-decadal assessments that integrate all major coastal blue carbon systems at the national scale are still scarce for China. In this study, we integrated 30 m Landsat imagery (1992–2022), processed on Google Earth Engine with a random forest classifier; province-specific, literature-derived carbon density data with quantified uncertainty (mean ± standard deviation); and the InVEST model to track coastal China’s mangroves, salt marshes, tidal flats, and mariculture to quantify their associated carbon stocks. Then the GeoDetector was applied to distinguish the natural and anthropogenic drivers of carbon stock change. Results showed rapid and divergent land use change over the past three decades, with mariculture expanded by 44%, becoming the dominant blue carbon land use; whereas tidal flats declined by 39%, mangroves and salt marshes exhibited fluctuating upward trends. National blue carbon stock rose markedly from 74 Mt C in 1992 to 194 Mt C in 2022, with Liaoning, Shandong, and Fujian holding the largest provincial stock; Jiangsu and Guangdong showed higher increasing trends. The Normalized Difference Vegetation Index (NDVI) was the primary driver of spatial variability in carbon stock change (q = 0.63), followed by precipitation and temperature. Synergistic interactions were also detected, e.g., NDVI and precipitation, enhancing the effects beyond those of single factors, which indicates that a wetter climate may boost NDVI’s carbon sequestration. These findings highlight the urgency of strengthening ecological red lines, scaling climate-smart restoration of mangroves and salt marshes, and promoting low-impact mariculture. Our workflow and driver diagnostics provide a transferable template for blue carbon monitoring and evidence-based coastal management frameworks. Full article

Accurate ship berthing behavior prediction (BBP) is essential for enabling collision warnings and support decision-making. Existing methods based on Automatic Identification System (AIS) data perform well in the task of ship trajectory prediction over long time-series and large scales, but struggle with addressing the fine-grained and highly dynamic changes in berthing scenarios. Therefore, the accuracy of BBP remains a crucial challenge. In this paper, a novel BBP method based on Light Detection and Ranging (LiDAR) data is proposed. To test its feasibility, a comprehensive dataset is established by conducting on-site collection of berthing data at Dalian Port (China) using a shore-based LiDAR system. This dataset comprises equal-interval data from 77 berthing activities involving three large ships. In order to find a straightforward architecture to provide good performance on our dataset, a cascading network model combining convolutional neural network (CNN), a bi-directional gated recurrent unit (BiGRU) and bi-directional long short-term memory (BiLSTM) are developed to serve as the baseline. Experimental results demonstrate that the baseline outperformed other commonly used prediction models and their combinations in terms of prediction accuracy. In summary, our research findings help overcome the limitations of AIS data in berthing scenarios and provide a foundation for predicting complete berthing status, therefore offering practical insights for safer, more efficient, and automated management in smart port systems. Full article

Understanding flood scenes is essential for effective disaster response. Previous research has primarily focused on computer vision-based approaches for analyzing flood scenes, capitalizing on their ability to rapidly and accurately cover affected regions. However, most existing methods emphasize static image analysis, with limited attention given to dynamic video analysis. Compared to image-based approaches, video analysis in flood scenarios offers significant advantages, including real-time monitoring, flow estimation, object tracking, change detection, and behavior recognition. To address this gap, this study proposes a computer vision-based multi-object tracking (MOT) framework for intelligent flood scene understanding. The proposed method integrates an optical-flow-based module for short-term undetected mask estimation and a deep re-identification (ReID) module to handle long-term occlusions. Experimental results demonstrate that the proposed method achieves state-of-the-art performance across key metrics, with a HOTA of 69.57%, DetA of 67.32%, AssA of 73.21%, and IDF1 of 89.82%. Field tests further confirm its improved accuracy, robustness, and generalization. This study not only addresses key practical challenges but also offers methodological insights, supporting the application of intelligent technologies in disaster response and humanitarian aid. Full article