Search Results (111)

Accurate detection and characterization of surface defects in concrete dams is vital for ensuring safe operation. To address the limitations of existing research focused solely on cracks and the challenges traditional convolutional networks face in adapting to deformation and multiscale features, this study introduces DCN-YOLO, a deformable convolution-augmented framework for the simultaneous detection and classification of multiple defect types from UAV-acquired imagery. The model outputs bounding box localizations and categorical labels. Based on YOLOv12, this proposed model integrates DCNv4 deformable convolutions with the C3k2 module. By leveraging adaptive sampling offsets and dynamic modulation, the proposed model enhances geometric modeling for irregular defects, improving the detection of small and medium defects while achieving an acceptable trade-off in inference efficiency. To address multiple defect coexistence, we adopt Binary Cross-Entropy (BCE) loss to decouple classification and localization, improving training stability in multi-label scenarios. A Multi-defects dataset was created using UAV images, and performance was validated on the CrackSeg public dataset. The proposed model achieved 77.4% ± 0.2% overall precision under complex conditions, exceeding the YOLOv12l baseline by 7.1% and improving mAP_50-95 by 4.2%. It demonstrated competitive performance in detecting cracks, aggregate exposure, and construction joints, thereby providing a potentially robust and efficient approach for intelligent inspection of concrete dam surface defects. Full article

Monitoring dam deformation is critical for ensuring structural integrity and identifying long-term settlement trends. However, traditional InSAR techniques often face limitations in tropical environments due to severe temporal decorrelation. This study addresses these challenges at Jatiluhur Dam, Indonesia, by implementing an integrated framework using Sentinel-1 InSAR, in situ leveling, GNSS, and reservoir water-level data from 2019 to 2024. To overcome the observation bottlenecks, Tracy–Widom-guided PSI (TW-PSI) was employed and compared against SBAS and conventional PSI. The TW-PSI approach successfully increased on-structure measurement point density by approximately 40%, supporting a first-order ascending–descending decomposition into east–west and quasi-vertical components. The analysis reveals a persistent settlement bowl at the central crest (C7–C12), consistent with long-term leveling observations and supported by regional GNSS trend checking. While the 2022 Mw 5.6 Cianjur earthquake showed no statistically significant co-seismic crest deformation, a strong correlation (r = −0.709) was identified between crest deformation and reservoir water-level variations, suggesting an observational association between reservoir level and crest settlement tendency. Furthermore, the application of the Annual Structural Deformation Tolerance Ratio (ASDTR) identified specific priority monitoring zones. These findings demonstrate that the proposed integrated framework can support operational dam deformation monitoring by linking satellite-derived measurements with in situ observations and engineering-oriented interpretation. Full article

Occupational stress in dentistry, particularly during endodontic procedures, impacts practitioners’ health and performance. This study introduces a novel approach using real-time Heart Rate (HR) data from Apple Watch technology to assess physiological responses potentially indicative of occupational stress among endodontists. Materials and Methods: Twelve endodontists participated in this study, treating 119 patients. In a controlled clinical environment, HR was continuously monitored with the Apple Watch, and data were recorded via the Cardiogram app, capturing HR variations across treatment stages. Results: Significant HR fluctuations were observed during procedurally demanding stages such as local anesthesia and rubber dam placement. The endodontist’s HR, as a physiological proxy for stress, was significantly influenced by the treatment difficulty level, stage, and the patient’s age. Conclusions: Using Apple Watch technology, our study revealed significant HR variations during different endodontic treatment stages, suggesting fluctuating physiological responses that may reflect occupational stress. Elevated HR was noted during patient examination and rubber dam placement, particularly in complex cases. These preliminary findings suggest that HR monitoring via wearable technology may serve as a useful, albeit indirect, indicator of occupational stress during endodontic procedures. Future studies with larger samples and additional validated stress biomarkers are needed to confirm these observations. Full article

Crack opening and reinforcement stress are two complementary indicators of the service state of reinforced concrete hydraulic structures, yet they are often predicted separately. This study develops a data-driven multi-task temporal fusion framework for joint 48 h ahead prediction of dam crack responses and rebar stress using multi-source monitoring data. The measured data comprise five crack-monitoring series, five rebar stress series, local temperature channels, reservoir water level, antecedent rainfall, and an auxiliary environmental signal over approximately four years. Target responses are aligned only at common measured timestamps; no synthetic target observations are introduced. A simplified engineering layout and plan-based crack–rebar distances are further used to examine whether an explicit spatial prior can strengthen the shared temporal representation without introducing synthetic target values. A residual multi-task temporal fusion network (MTTF-Net) is proposed with a shared Transformer encoder, attention pooling, task-specific decoders, and a response-continuity regularization term. The model is compared with persistence, Ridge regression, random forest, Extra Trees, XGBoost, and GRU baselines under a chronological train/validation/test split. For the independent test period, Ridge regression obtains the lowest overall RMSE (2.2968), whereas MTTF-Net provides the lowest crack RMSE (0.0141), the lowest overall MAE (1.0035), and the second-best overall RMSE (2.3813). Distance-informed ablation, denoted as MTTF-Net-S, remains close to MTTF-Net in macro-averaged $R^2$ but is not superior in the overall test metrics, indicating that the available horizontal distances are valuable engineering metadata but cannot replace richer three-dimensional structural connectivity. These results indicate that the monitoring data contain a strong linear autoregressive component, while multi-task temporal fusion improves nonlinear crack response prediction and remains competitive for stress forecasting. The source code is prepared as a public implementation package, whereas the measured monitoring dataset is subject to data owner restrictions. Full article

The present investigation was designed to assess how perinatal contact with the pyrethroid insecticide cyfluthrin (CY) influences cognitive performance in developing rat progeny and to clarify the contributing cellular events through examination of neuroinflammatory processes alongside pyroptotic and apoptotic pathways. An experimental framework involving CY administration during gestation was implemented using Sprague–Dawley (SD) dams, with subsequent monitoring of placental parameters and neonatal outcomes. Once offspring reached postnatal day twenty-one, their behavior was characterized via a battery consisting of the open field paradigm, novel object recognition task, and the Morris water navigation test. Hippocampal tissue architecture and fine structural details were visualized by employing hematoxylin–eosin (HE) staining and Nissl substance labeling. Protein and transcript abundances for pro-inflammatory mediators (TNF-α, IL-6), synaptic constituents (postsynaptic density protein-95, PSD-95; synaptophysin, SYP), and pyroptotic machinery components (NLRP3, GSDMD, Caspase-1) within hippocampal homogenates were quantified through immunoblotting and real-time quantitative PCR procedures, and the spatial distribution of these molecules was validated via immunohistochemical detection. Neuronal apoptosis was assessed by TUNEL staining. The results demonstrated that gestational CY exposure led to reduced placental weight and diameter, decreased blood sinus area in the labyrinth zone, lower offspring birth weight, and impaired catch-up growth. Behavioral tests revealed that CY-exposed offspring exhibited diminished spontaneous locomotor activity, impaired novel object recognition memory, and significant deficits in spatial learning and memory. Pathological analysis showed disorganized neuronal arrangement and reduced Nissl bodies in the hippocampal CA1 region. Compared to the control group, CY exposure markedly upregulated the protein expression of TNF-α and IL-6, downregulated PSD-95 and SYP, activated the NLRP3/GSDMD/Caspase-1-mediated pyroptotic pathway, and increased the expression of the apoptotic protein Caspase-3, culminating in a significant increase in hippocampal neuronal apoptosis. In conclusion, early-life exposure to cyfluthrin impairs cognitive function in offspring, an effect closely associated with the induction of hippocampal neuroinflammation and the activation of pyroptotic and apoptotic pathways. These findings provide novel toxicological evidence for a more comprehensive assessment of the potential health risks posed by CY exposure in human populations. Full article

Civil infrastructure plays a critical role in ensuring societal safety and economic development. However, structural damages such as cracks inevitably occur during long-term service. Traditional manual inspection methods are insufficient to meet the demands of large-scale and routine monitoring. Unmanned Aerial Vehicles (UAV) remote sensing has become an important approach for Structural Health Monitoring (SHM), owing to its high spatial resolution imaging capability and superior operational flexibility. Nevertheless, existing studies focus on optimizing individual algorithms, lacking a systematic analysis oriented toward multi-scenario engineering applications. Therefore, we present a comprehensive review of UAV-based crack detection techniques for infrastructure using remote sensing imagery. First, publicly available datasets, UAV platforms, and evaluation metrics are systematically summarized. Then a multi-level visual analysis framework for UAV inspection is established. The framework categorizes existing methodologies into five levels: image-level classification, object-level detection, pixel-level segmentation, geometric quantification, and three-dimensional (3D) reconstruction, followed by a systematic evaluation of representative methods. Furthermore, the applicability of different methods across diverse scenarios, including bridges, pavements, dams, building facades and wind turbine blades, is systematically explored. Finally, the key challenges and future research directions are discussed. This review aims to provide a systematic theoretical foundation and methodological reference for advancing UAV-based infrastructure crack inspection from algorithm development toward practical multi-scenario engineering applications. Full article

Water security in rapidly urbanising river basins is increasingly threatened by untreated city effluents, industrial discharges, and legacy agricultural contamination. The Tula River basin in central Mexico illustrates this issue, absorbing the majority of Mexico City’s effluent while sustaining a heavily exploited aquifer beneath one of the nation’s largest irrigation districts. This study provides an integrated assessment of surface water and groundwater quality throughout the basin, including the Endhó Dam and its associated aquifer. Water quality analysis revealed severe surface water contamination (WQI > 300), driven by untreated sewage and inadequate sanitation infrastructure. Elevated COD, BOD, and nutrient concentrations indicate significant organic loading and eutrophication risk. Near Tula City, arsenic, copper, and zinc were detected at levels posing direct risks to human health. Groundwater quality was comparatively favourable, with 71% of wells recording WQI < 100; however, arsenic exceeded permissible limits more than twentyfold in select wells, attributed to geological sources. The detection of SVOCs in both hydrological compartments confirms cross-compartment contamination. Point-source reduction alone is insufficient for aquifer recovery; comprehensive sanitation strategies and long-term monitoring are urgently required. These findings carry direct relevance for water governance in megacity-dependent basins globally, where urban, agricultural, and geological stressors demand integrated management approaches. Full article

Background/Objectives: Bordetella bronchiseptica is a Gram-negative bacterium that, either acting alone or in concert with other bacterial or viral pathogens, is a major cause of the canine infectious respiratory disease (CIRD) complex in dogs. Most currently available vaccines are given intranasally or orally and, whilst providing satisfactory reduction in disease severity, can be difficult to use especially in aggressive or anxious dogs. Whilst a small number of injectable B. bronchiseptica vaccines have been developed, little is known about their characteristics with regard to the age at first vaccination, the onset of immunity, duration of immunity, induction of antibody responses, concurrent use with the core vaccines used in most dogs, efficacy in the face of maternally derived antibodies (MDAs) or existing immunity and safety in pregnant animals. Here we describe the development of a safe and efficacious injectable B. bronchiseptica vaccine that utilises a novel process to purify fimbriae. Methods: The fimbrial antigen was formulated with a vitamin E-based oil-in-water adjuvant known to be safe in dogs (Nobivac^® Respira Bb). To evaluate dose response, thirty-nine naïve 5–6-week-old Beagle puppies were allocated to four groups and vaccinated subcutaneously with Nobivac^® Respira Bb at 69 U, 25 U, and 7 U (with a booster at two weeks). All groups were challenged with B. bronchiseptica two weeks after the booster. To evaluate the onset of immunity at 5–6 weeks of age, twenty-one naïve Beagle dogs were split into two groups: group 1 received Nobivac Respira Bb (88 U/dose) plus Nobivac DHPPi and Nobivac L4; group 2 received DHPPi and L4 only. Both groups were challenged with B. bronchiseptica two weeks after the second vaccination. Safety in pregnancy was evaluated by vaccinating pregnant dams and monitoring whelping outcomes and puppy health. Protection in puppies with maternally derived antibodies (MDAs) was studied in 28 pups (11 MDA-negative and 17 MDA-positive from vaccinated and unvaccinated dams). Pups were vaccinated at 5–6 weeks; one group remained unvaccinated to monitor MDA kinetics. All puppies were challenged with B. bronchiseptica at 19 weeks, after MDAs became undetectable. Serology was monitored throughout; daily clinical observations and nasal swabs post-challenge assessed protection and bacterial shedding. Results: Nobivac Respira Bb (MSD Animal Health), was safe for use in 5–6-week-old puppies alongside other Nobivac core canine vaccines without vaccine interference. The vaccine has an onset of immunity of two weeks and significantly reduces both the clinical signs of B. bronchiseptica-induced disease and bacterial excretion into the environment. Furthermore, the vaccine is equally efficacious in puppies with maternally derived antibodies derived from vaccinated dams and can be used safely in pregnant bitches. Conclusions: This vaccine represents a convenient, safe and efficacious alternative to vaccines delivered via the oral or intranasal routes and is a positive addition to the range of vaccines targeted at reducing disease induced by B. bronchiseptica. Full article

Suspended sediment plays a critical role in river ecosystem health, nutrient transport, and water quality, while also affecting navigation infrastructure and reservoir sedimentation in regulated rivers. A sound understanding of sediment dynamics in complex river systems consisting of natural lakes and engineered navigation structures remains a critical challenge for river management and water quality assessment. This study investigates the longitudinal patterns of suspended sediment concentration (SSC) along a ~500-km reach of the Upper Mississippi River containing Lake Pepin and multiple lock-and-dam structures. In this study, we analyze remotely sensed SSC estimates from the RivSED database (2001–2019). The SSC datasets were then integrated with in situ streamflow measurements and potential soil erosion to characterize sediment supply and transport dynamics and relate with upstream contributing watershed’s attributes. Results reveal distinct sediment behavior patterns: (1) Lake Pepin functions as a significant sediment trap, creating a clear discontinuity in SSC with mean concentrations decreasing from ~25 mg/L upstream to ~13 mg/L within the lake; (2) longitudinal SSC profiles show re-establishment patterns downstream of the lake, reaching ~23 mg/L approximately 100 km below the outlet; (3) strong positive correlation (r = 0.80, R² = 0.64, p < 0.001) exists between watershed sediment export and river-reach-scale sediment fluxes. Temporal analysis across these upstream monitoring stations shows sediment export rates ranging from 10,000 to 200,000 tons/year, with notable inter-annual variability driven by discharge patterns. This research demonstrates the utility of combining a spectrum of datasets for exploring sediment dynamics in complex riverine systems. Though the current study is a case study, the study results provide crucial insights for navigation management, ecosystem health assessment, and watershed management strategies in similar settings. Full article

Background: Maternal immune activation (MIA) during pregnancy is a known risk factor for several neurodevelopmental and psychiatric disorders, including schizophrenia. In rodent models, MIA is commonly induced using polyinosinic/polycytidylic acid (Poly(I/C)), a viral mimetic that activates Toll-like receptor 3 (TLR3) signaling and elicits an inflammatory response in both the dam and the fetuses. MIA results in various behavioral abnormalities in offspring, including deficits in social interaction. Recent studies have shown that MIA decreases the ability to maintain mitochondrial membrane potential (ΔΨm), the electrical component of the electrochemical gradient required for ATP production and alters mitochondrial protein expression in brain tissue isolated from adult offspring. Methods: In the present study, we monitor ΔΨm non-invasively in vivo using a previously published bioluminescence probe in juvenile and adult MIA offspring. We then investigated gene expression in the medial prefrontal cortex of MIA offspring by analyzing a previously published RNA sequencing dataset in combination with MitoCarta3.0, a comprehensive inventory of genes involved in mitochondrial function. Finally, we tested the hypothesis that this mitochondrial dysfunction contributes to the behavioral deficits observed in MIA offspring. Results: We have observed impaired ΔΨm maintenance in juvenile MIA offspring that persists into adulthood. Also, we found that MIA alters the expression of many genes associated with mitochondrial energy production. We demonstrated that nicotinamide riboside, a precursor to NAD⁺ known to restore ΔΨm, significantly attenuates MIA-induced social interaction deficits. Conclusions: Together, these findings highlight mitochondrial function as a promising therapeutic target for symptoms associated with schizophrenia and support the potential for drug discovery aimed at enhancing mitochondrial health. Full article

Underwater crack detection is critical for the structural health monitoring of concrete dams; however, complex turbid environments and limited computational resources on underwater robots pose significant challenges. This study proposes HAIS-SegFormer, a lightweight segmentation network utilizing a Mix Transformer backbone. We introduce a tandem Hybrid Attention mechanism—cascading Coordinate Attention (CoordAtt) and Convolutional Block Attention Modules (CBAM)—to preserve long-range topological connectivity and refine local edge details. Furthermore, a Feature Inhibition Module (FIM), modeled after biological lateral inhibition, is designed to actively suppress high-frequency background noise such as water plants. Experimental results on an underwater crack dataset demonstrate that HAIS-SegFormer achieves a favorable trade-off between segmentation accuracy (71.66% mIoU) and computational efficiency (73 FPS, 3.80 M parameters). The proposed framework provides a robust and resource-efficient solution for automated underwater inspections. Full article

Accurate assessment of water quality is crucial for protecting public health and promoting environmental sustainability. Conventional laboratory-based methods for evaluating microbial contaminants are often time-consuming, resource-intensive, and reactive in nature, limiting their effectiveness for real-time water quality monitoring and management. This study examines the application of data-driven machine learning models to predict E. coli concentrations in Midmar Dam, utilizing readily available physicochemical parameters. A comparative analysis was conducted using five classical standalone ML algorithms: Random Forest (RF), Support Vector Machine (SVM), k-Nearest Neighbors (kNN), Artificial Neural Network (ANN), and Extreme Gradient Boosting (XGBoost). These models were assessed based on their predictive performance using standard error metrics, including Mean Absolute Error (MAE), Mean Squared Error (MSE), and Root Mean Squared Error (RMSE). Among the models evaluated, the kNN algorithm demonstrated superior performance, achieving the lowest MSE and RMSE values, thereby highlighting its effectiveness in capturing the complex relationships between physicochemical indicators and microbial contamination levels. The findings demonstrate the potential of ML-based approaches to serve as efficient, scalable, and proactive tools for sustainable water-quality monitoring and management in dams. Full article

Classical instrumental technologies still remain important among the geodetic methods of dam monitoring, but periodic observations are often insufficient for timely detection of hazardous deformations. Therefore, the integration of continuous and remote sensing technologies into a multi-level system of observation improves the assessment of a structural condition. This research work evaluates the integrated approach that combines the GNSS data, robotic total station measurements, and satellite radar data processed by the PSInSAR technique for detecting the cyclic thermal deformations of the Dniester HPP concrete dam. The dataset includes 185 ascending and 184 descending Sentinel-1A SAR images (2019–2025, 12-day repeat cycle). PSInSAR processing was performed using StaMPS, with validation through comparison of InSAR-derived vertical displacements and GNSS data from the stationary monitoring system of the dam. The GNSS and InSAR time series have revealed consistent seasonal patterns and a common long-term trend. Harmonic components with amplitudes of 4–5 mm, peaking in late summer and declining in winter, confirm the dominant influence of thermal processes. In order to reduce noise, Fourier-based filtering and approximation were applied, thus ensuring balance between accuracy and data retention. The combined use of GNSS, robotic total station, and InSAR has increased the density of reliable control points and improved the thermal deformation model. Maximum vertical displacements of 6–13 mm were observed on the horizontal sections most exposed to solar radiation. Full article

A series of machine learning models have been proposed in the past decades, but it remains undetermined which is optimal for specific applications. Establishing mathematical prediction models for dam deformation and structural health monitoring based on environmental factors is crucial to dam safety assessment. This paper takes Zhexi Dam, a concrete gravity-type dam in China, as an example to conduct a comparative study on the performance of deformation prediction models. The physical factors that cause dam deformation include the air temperature, reservoir water temperature, reservoir water level, and dam aging. The correlations between environmental factors and dam deformation are evaluated by maximum information coefficient (MIC) and Pearson, Kendall, and Spearman correlation coefficients. The monitoring data reveal that the deformation has a high correlation with environmental factors. A number of the most representative monitoring points from hundreds of monitoring points are selected for modeling. For comparison, seven modeling methods, i.e., multiple linear regression (MLR), gradient boosting decision tree (GBDT), random forest (RF), support vector machine (SVM), and long short-term memory network (LSTM), weighted average model (WAM) of the above five algorithms, and Transformer-based neural network, are introduced to establish dam deformation prediction models. The experimental results indicate that both the weighted average model and the Transformer-based neural network achieve consistently high accuracy, showing strong agreement with the monitoring data generally. However, in scenarios involving small sample sizes, the SVM model demonstrates relatively superior predictive performance compared to the other models. Full article

Fiber-Optic Sensors (FOSs) offer unprecedented performance for Structural Health Monitoring (SHM) of concrete dams, addressing the critical need for robust instrumentation. This study evaluates the capabilities of Raman-type Distributed Fiber-Optic Sensors (DFOSs) and Bragg grating-type Localized Fiber-Optic Sensors (LFOSs) for concrete temperature monitoring in a case study. Raman-type DFOSs offer superior spatial resolution and comprehensive thermal mapping, enabling the detection of detailed thermal phenomena, such as the cooling effects of dam galleries and significant thermal gradients, that conventional technologies cannot capture. They are also easier and faster to install, as they do not require trench construction. However, monitoring data acquisition can be more expensive with Raman-type DFOSs. Bragg-type LFOSs offer reliable localized measurements analogous to conventional thermometers. A key benefit is their multiplexing capability, which significantly reduces the total number of cables needed, making a complete LFOS-based monitoring system easier and potentially cheaper to install than an equivalent conventional system, even though individual LFOS installation still requires trenches. Overall, both FOS technologies are effective and reliable for concrete dam temperature monitoring, providing data quality comparable to conventional sensors and representing a significant advancement for SHM systems. Full article