Search Results (61)

Heterocapsa bohaiensis is an emerging harmful dinoflagellate increasingly reported from coastal regions of the Pacific. However, an available molecular assay offering rapid and sensitive detection is still lacking. This study developed a SYBR Green real-time quantitative PCR (qPCR) assay for the identification and quantification of H. bohaiensis. Species-specific primers (F: 5′-CCATCGAACCAGAACTCCGT-3′; R: 5′-AGTGTAGTGCACCGCATGTC-3′) were designed and the assay was optimized and evaluated using laboratory cultures for specificity, sensitivity, and quantitative performance. Primer screening and melt-curve analysis confirmed that the selected primer pair produced a single, specific amplification peak for H. bohaiensis, with no cross-reactivity observed in non-target species (Chlorella pyrenoidosa, Phaeocystis globosa, Skeletonema costatum, Alexandrium tamarense) or mixed algal communities. The standard curve displayed strong linearity (R² = 0.9868) and a high amplification efficiency (102.5%). The limit of detection (LOD) was approximately 2–3 cells per reaction, as determined from 24 replicates of 5-cell equivalents and verified at ~2.7-cell equivalents. This sensitivity was comparable to or exceeded that reported for assays targeting other HABs forming dinoflagellates. Quantitative results derived from the qPCR assay closely matched microscopic cell counts, with a relative error of 10.79%, falling within the acceptable threshold for phytoplankton surveys. In summary, this study established and validates a species-specific qPCR assay for H. bohaiensis under controlled laboratory conditions. The method shows strong potential for incorporation into HAB monitoring programs, early-warning systems, and future ecological investigations of this emerging species. Full article

(1) Background: This study presents an adaptive, contactless, and privacy-preserving respiratory-rate monitoring system based on thermal imaging, designed for real-time operation on embedded edge hardware. The system continuously processes temperature data from a compact thermal camera without external computation, enabling practical deployment for home or clinical vital-sign monitoring. (2) Methods: Thermal frames are captured using a $256\times 192$ TOPDON TC001 camera and processed entirely on an NVIDIA Jetson Orin Nano. A YOLO-based detector localizes the nostril region in every even frame (stride = 2) to reduce the computation load, while a Kalman filter predicts the ROI position on skipped frames to maintain spatial continuity and suppress motion jitter. From the stabilized ROI, a temperature-based breathing signal is extracted and analyzed through an adaptive median–MAD hysteresis algorithm that dynamically adjusts to signal amplitude and noise variations for breathing phase detection. Respiratory rate ($RR$) is computed from inter-breath intervals (IBI) validated within physiological constraints. (3) Results: Ten healthy subjects participated in six experimental conditions including resting, paced breathing, speech, off-axis yaw, posture (supine), and distance variations up to 2.0 m. Across these conditions, the system attained a MAE of $0.57\pm 0.36$ BPM and an RMSE of $0.64\pm 0.42$ BPM, demonstrating stable accuracy under motion and thermal drift. Compared with peak-based and FFT spectral baselines, the proposed method reduced errors by a large margin across all conditions. (4) Conclusions: The findings confirm that accurate and robust respiratory-rate estimation can be achieved using a low-resolution thermal sensor running entirely on an embedded edge device. The combination of YOLO-based nostril detector, Kalman ROI prediction, and adaptive MAD–hysteresis phase that self-adjusts to signal variability provides a compact, efficient, and privacy-preserving solution for non-invasive vital-sign monitoring in real-world environments. Full article

This study analyzes the relationship between fires and climate extremes in the Caatinga biome from 2012 to 2023 by integrating Fire Radiative Power (FRP) from VIIRS (S-NPP and NOAA-20), Vapor Pressure Deficit (VPD) and air temperature from ERA5, drought indices (SPI-1 and SPI-6), and heatwave events from the Xavier database. Daily percentiles of maximum (CTX90pct) and minimum (CTN90pct) temperatures were used to characterize heatwaves. Spatial and temporal dynamics of fire patterns were identified using the HDBSCAN algorithm, an unsupervised Machine Learning clustering method applied in three-dimensional space (latitude, longitude, and time). A marked seasonality was observed, with fire activity peaking from August to November, especially in October, when FRP reached ~1000 MW/h. The years 2015, 2019, 2021, and 2023 exhibited the highest fire intensities. A statistically significant upward trend in cluster frequency was detected (+1094.96 events/year; p < 0.001). Cross-correlations revealed that precipitation deficits (SPI) preceded FRP peaks by about four months, while VPD and air temperature exerted immediate positive effects. FRP correlated positively with heatwave frequency (r = 0.62) and negatively with SPI (r = −0.69). These findings highlight the high vulnerability of the Caatinga to compound drought and heat events, indicating that fire management strategies should account for both antecedent drought conditions, monitored through SPI, and real-time atmospheric dryness, measured by VPD, to effectively mitigate fire risks. Full article

Pedicle screw malpositioning remains a frequent complication, with reported rates from 2% to 15%, often leading to revision surgeries. Analyzing mechanical resistance and torque encountered during screw insertion has been implicated as a promising approach for real-time detection. Five fresh-frozen human thoracolumbar spine specimens were utilized in this study. Using 3D-printed templates, correct trajectories were systematically compared against four defined malpositions (medial, lateral, superior, superolateral), with offsets ranging from 2.0 mm to 3.5 mm. Drilling, tapping, and insertion phases were conducted at a constant speed and defined feed force. Contrary to the anticipated behavior, malpositioned trajectories showed no statistically significant difference in peak torque compared to correct trajectories across all phases (e.g., tapping $p=0.944$, $r=0.01$; insertion $p=0.693$, $r=0.05$). Regional stratification between thoracic and lumbar spine also failed to yield significant differences. The only statistically significant difference was observed between the correct trajectory and the superolateral malposition during drilling ($p=0.038$). Under the tested standardized conditions, torque-based mechanical resistance during pedicle screw placement is generally not a reliable and consistent real-time indicator of malposition. Full article

A compact and high-sensitivity carbon monoxide (CO) detection system based on multi-pass cell enhanced light-induced thermoelastic spectroscopy (MPC-LITES) was developed for real-time monitoring. A 2.3 μm distributed feedback (DFB) diode laser targeting the CO absorption line at 4300.699 cm⁻¹ was employed, offering strong line intensity and minimal interference from H₂O, CO₂, NO₂, and SO₂. The optimal modulation depth of 0.76 cm⁻¹ produced the maximum second harmonic (2f) signal. Experimental results demonstrated excellent linearity (R² = 0.998) and a minimum detection limit of 230 ppb at 1 s, further reduced to 47 ppb at 367 s by Allan deviation analysis. Application tests were carried out for real-time monitoring of cigarette smoke in a 20 m² indoor environment. Under closed conditions, the CO concentration rapidly increased to approximately 165 ppm, while in ventilated conditions, it peaked at 45 ppm and decayed quickly due to air exchange. The results confirm that the proposed MPC-LITES sensor enables accurate, real-time detection of transient CO variations, demonstrating strong potential for indoor air quality evaluation, environmental safety, and public health protection. Full article

Pediatric thrombosis (PT) represents a rare condition that can manifest from neonatal life to adolescence, encompassing life-threatening complications. Its pathogenesis is attributed to immature hemostasis in conjunction with environmental and genetic factors, predominantly including those resulting in increased levels of plasminogen activator inhibitor 1 (PAI-1), the principal inhibitor of fibrinolysis, which is subject to upstream regulation by angiotensin-converting enzyme (ACE). Although the implication of microRNAs (miRNAs), epigenetic modulators of gene expression, has been demonstrated in adult thrombosis, evidence is lacking in the pediatric setting. Here, we investigated the involvement of two miRNA regulators of PAI-1 (SERPINE1 gene) in PT, in relation to clinical and genetic parameters that induce PAI-1 fluctuations. Following bioinformatic target-prediction, miRNA expression was assessed by quantitative real-time PCR in serum-samples of 19 pediatric patients with thrombosis (1–18 months post-incident), and 19 healthy controls. Patients were genotyped for the SERPINE1-4G/5G and ACE-I/D polymorphisms by PCR-based assays. Genotypic and thrombosis-related clinical data were analyzed in relation to miRNA-expression. Two miRNAs (miR-145-5p, miR-34a-5p) were identified to target SERPINE1 mRNA, with miR-34a additionally targeting the mRNA of ACE. The expression of miR-34a was significantly decreased in patients compared to controls (p = 0.029), while no difference was observed in miR-145 expression. Within patients, miR-34a expression demonstrated a peak 1–3 months post-thrombosis and was diminished upon treatment completion (p = 0.031), followed by a slight long-term increase. MiR-34a levels differed significantly by thrombosis site (p = 0.019), while a significant synergistic interaction between site and onset type (provoked/unprovoked) was detected (p = 0.016). Finally, an epistatic modification was observed in cerebral cases, since double homozygosity (4G/4G + D/D) led to a miR-34 decrease, with D/D carriership reversing the 4G/4G-induced upregulation of miR-34a (p = 0.006). Our findings suggest that in pediatric thrombosis, downregulation of miR-34a is indicative of impaired fibrinolytic capacity, attributed to deficient regulation of the inhibitory ACE/PAI-1 axis. Full article

Accurate short-term forecasting of urban water demand is a persistent challenge for utilities seeking to optimize operations, reduce energy costs, and enhance resilience in smart distribution systems. This study presents a multi-scale Artificial Neural Network (ANN) modeling approach that integrates temporal lag optimization to predict daily and hourly water consumption across heterogeneous user profiles. Using high-resolution smart metering data from the SunRise Smart City Project in Lille, France, four demand nodes were analyzed: a District Metered Area (DMA), a student residence, a university restaurant, and an engineering school. Results demonstrate that incorporating lagged consumption variables substantially improves prediction accuracy, with daily R² values increasing from 0.490 to 0.827 at the DMA and from 0.420 to 0.806 at the student residence. At the hourly scale, the 1-h lag model consistently outperformed other configurations, achieving R² up to 0.944 at the DMA, thus capturing both peak and off-peak consumption dynamics. The findings confirm that short-term autocorrelation is a dominant driver of demand variability, and that ANN-based forecasting enhanced by temporal lag features provides a robust, computationally efficient tool for real-time water network management. Beyond improving forecasting performance, the proposed methodology supports operational applications such as leakage detection, anomaly identification, and demand-responsive planning, contributing to more sustainable and resilient urban water systems. Full article

The article presents Modified Scaled Adaptive Daqrouq Wavelet (MSADW) as an autonomous wavelet framework to overcome the analysis obstacles of traditional wavelets (Morlet and Daubechies) for signals with non-stationary characteristics. MSADW adjusts its waveform shape and frequency in real time based on the specific characteristics of the signal, allowing it to outperform conventional wavelet methods. The system reaches adaptability through three core methods featuring gradient-dependent scale adjustments for fast transient detection and smooth regions, and instantaneous frequency monitoring achieved by a combination of STFT and Hilbert transforms and an iterative error reduction process using gradient descent and genetic algorithms. Continuous Wavelet Transform (CWT) combined with Discrete Wavelet Transform (DWT) extracts features from ECG and speech signals. Throughout this process, MSADW maintains great time precision to detect transients as well as maintain sensitivity for the audio’s base stability. Testing MSADW in practical use reveals its superior performance because it detects R-peaks accurately within 0.01 s through zero-crossing methods, which combine P/T-wave detection with effective ECG signal segmentation and noise-free reconstructed speech ($\mathit{MSE:} 1.17\times {10}^{-31}$). The localized parameterization framework of MSADW, enabled by feedback refinement, fulfills missing aspects in biomedical signal evaluation and creates space for low-cost real-time evaluation methods for medical devices and arrhythmia and ischemic detection platforms. The theoretical backbone for MSADW establishes itself because this work shows how wavelet analysis can transition toward managing non-stationary and noise-prone domains. Full article

This study aimed to identify bacterial isolates associated with mortality events in Salmo trutta rearing farms in Spain and to assess their antibiotic resistance profiles. The analysis covered five fish farms: two with a recent history of antibiotic use and three without any antibiotic application in the six months prior to sampling. Tissue samples were collected from moribund fish displaying clinical signs such as erratic swimming, ocular hemorrhages, fin hemorrhages, and skin lesions during disease outbreaks in 2022 and 2023. The samples were analyzed using real-time PCR, amplification and sequencing of the 16S rRNA gene and the ITS-1 intergenic spacer, and MALDI-TOF mass spectrometry. A total of 19 bacterial isolates were identified, with Gram-negative bacteria, particularly Aeromonas spp., being the most prevalent. Other identified taxa included Plesiomonas sp., Hafnia alvei, Pseudomonas fulva, and Kluyvera intermedia, as well as Gram-positive species such as Carnobacterium maltaromaticum, Lactococcus sp., and Enterococcus faecium. Notably, resistant strains were found in four of the five farms, even in those that had not administered antibiotics, suggesting that environmental contamination and anthropogenic factors may significantly contribute to the spread of resistance. Environmental stressors—such as sudden increases in water temperature and high turbidity caused by suspended organic matter—appeared to precede mortality peaks. The findings highlight the role of Aeromonas spp. as a key bacteria associated with mortality events in S. trutta and underscore the multifactorial nature of antibiotic resistance in aquaculture. No florfenicol-resistant isolates were detected in the farms where it is routinely used, indicating that florfenicol remains an effective antibiotic in aquaculture. However, the continuous and systematic monitoring of its use remains essential. The detection of bacteria not traditionally associated with fish pathology in samples from diseased animals suggests the need for further studies into their pathogenic potential. Overall, this descriptive study emphasizes the importance of preventive health strategies, prudent antibiotic use, and environmental monitoring to mitigate bacterial diseases and limit the spread of antimicrobial resistance in brown trout farming. These findings align with a One Health perspective, linking aquaculture practices, ecosystem integrity, and public health. Full article

This study presents a novel cyber-resilient, data-driven optimisation framework for real-time energy management in electric vehicle (EV)-integrated smart grids. The proposed framework integrates a hybrid optimisation engine—combining genetic algorithms and reinforcement learning—with a real-time analytics module to enable adaptive scheduling under uncertainty. It accounts for dynamic electricity pricing, EV mobility patterns, and grid load fluctuations, dynamically reallocating charging demand in response to evolving grid conditions. Unlike existing GA/RL schedulers, this framework uniquely integrates adaptive optimisation with resilient forecasting under incomplete data and lightweight blockchain-inspired cyber-defence, thereby addressing efficiency, accuracy, and security simultaneously. To ensure secure and trustworthy EV–grid communication, a lightweight blockchain-inspired protocol is incorporated, supported by an intrusion detection system (IDS) for cyber-attack mitigation. Empirical evaluation using European smart grid datasets demonstrates a daily peak demand reduction of 9.6% (from 33 kWh to 29.8 kWh), with a 27% decrease in energy delivered at the original peak hour and a redistribution of demand that increases delivery at 19:00 h by nearly 25%. Station utilisation became more balanced, with weekly peak normalised utilisation falling from 1.0 to 0.7. The forecasting module achieved a mean absolute error (MAE) of 0.25 kWh and a mean absolute percentage error (MAPE) below 20% even with up to 25% missing data. Among tested models, CatBoost outperformed LightGBM and XGBoost with an RMSE of 0.853 kWh and $R^2$ of 0.416. The IDS achieved 94.1% accuracy, an AUC of 0.97, and detected attacks within 50–300 ms, maintaining over 74% detection accuracy under 50% novel attack scenarios. The optimisation runtime remained below 0.4 s even at five times the nominal dataset scale. Additionally, the study outlines a conceptual extension to support location-based planning of charging infrastructure. This proposes the alignment of infrastructure roll-out with forecasted demand to enhance spatial deployment efficiency. While not implemented in the current framework, this forward-looking integration highlights opportunities for synchronising infrastructure development with dynamic usage patterns. Collectively, the findings confirm that the proposed approach is technically robust, operationally feasible, and adaptable to the evolving demands of intelligent EV–smart grid systems. Full article

The increasingly severe issue of marine debris presents a critical threat to the sustainable development of marine ecosystems. Real-time detection is essential for timely intervention and cleanup. Furthermore, the density of marine debris exhibits significant depth-dependent variation, resulting in degraded detection accuracy. Based on 9625 publicly available underwater images spanning various depths, this study proposes UTNet, a lightweight neural model, to improve the effectiveness of real-time intelligent identification of marine debris through multidimensional optimization. Compared to Faster R-CNN, SSD, and YOLOv5/v8/v11/v12, the UTNet model demonstrates enhanced performance in random image detection, achieving maximum improvements of 3.5% in mAP50 and 9.3% in mAP50-95, while maintaining reduced parameter count and low computational complexity. The UTNet model is further evaluated on underwater videos for real-time debris recognition at varying depths to validate its capability. Results show that the UTNet model exhibits a consistently increasing trend in confidence levels across different depths as detection distance decreases, with peak values of 0.901 at the surface and 0.764 at deep-sea levels. In contrast, the other six models display greater performance fluctuations and fail to maintain detection stability, particularly at intermediate and deep depths, with evident false positives and missed detections. In summary, the lightweight UTNet model developed in this study achieves high detection accuracy and computational efficiency, enabling real-time, high-precision detection of marine debris at varying depths and ultimately benefiting mitigation and cleanup efforts. Full article

Background: Colorectal cancer (CRC) is strongly influenced by miRNAs as well as the circadian system. Methods: High-throughput sequencing of miRNAs expressed in the rat colon during 24 h light (L)/dark (D) cycle was performed to identify rhythmically expressed miRNAs. The role of miR-150-5p in CRC progression was analyzed in DLD1 cell line and human CRC tissues. Results: Nearly 10% of mature miRNAs showed a daily rhythm in expression. A peak of miRNAs’ levels was in most cases observed during the first half of the D phase of the LD cycle. The highest amplitude was detected in expression of miR-150-5p and miR-142-3p. In the L phase of the LD cycle, the maximum in miR-30d-5p expression was detected. Gene ontology enrichment analysis revealed that genes interfering with miRNAs with peak expression during the D phase influence apoptosis, angiogenesis, the immune system, and EGF and TGF-beta signaling. Rhythm in miR-150-5p, miR-142-3p, and miR-30d-5p expression was confirmed by real-time PCR. Oncogenes bcl2 and myb and clock gene cry1 were identified as miR-150-5p targets. miR-150-5p administration promoted camptothecin-induced apoptosis. Expression of myb showed a rhythmic profile in DLD1 cells with inverted acrophase with respect to miR-150-5p. miR-150-5p was decreased in cancer compared to adjacent tissue in CRC patients. Decrease in miR-150-5p was age dependent. Older patients with lower expression of miR-150-5p and higher expression of cry1 showed worse survival in comparison with younger patients. Conclusions: miRNA signaling differs between the L and D phases of the LD cycle. miR-150-5p, targeting myb, bcl2, and cry1, can influence CRC progression in a phase-dependent manner. Full article

Downhole kick is one of the most severe safety hazards in deep and ultra-deep well drilling operations. Traditional monitoring methods, which rely on surface flow rate and fluid level changes, are limited by their delayed response and insufficient sensitivity, making them inadequate for early warning. This study proposes a real-time monitoring technique for gas content in drilling fluid based on the attenuation principle of Ba-133 γ-rays. By integrating laboratory static/dynamic experiments and Geant4-11.2 Monte Carlo simulations, the influence mechanism of gas–liquid two-phase media on γ-ray transmission characteristics is systematically elucidated. Firstly, through a comparative analysis of radioactive source parameters such as Am-241 and Cs-137, Ba-133 (main peak at 356 keV, half-life of 10.6 years) is identified as the optimal downhole nuclear measurement source based on a comparative analysis of penetration capability, detection efficiency, and regulatory compliance. Compared to alternative sources, Ba-133 provides an optimal energy range for detecting drilling fluid density variations, while also meeting exemption activity limits (1 × 10⁶ Bq) for field deployment. Subsequently, an experimental setup with drilling fluids of varying densities (1.2–1.8 g/cm³) is constructed to quantify the inverse square attenuation relationship between source-to-detector distance and counting rate, and to acquire counting data over the full gas content range (0–100%). The Monte Carlo simulation results exhibit a mean relative error of 5.01% compared to the experimental data, validating the physical correctness of the model. On this basis, a nonlinear inversion model coupling a first-order density term with a cubic gas content term is proposed, achieving a mean absolute percentage error of 2.3% across the full range and R² = 0.999. Geant4-based simulation validation demonstrates that this technique can achieve a measurement accuracy of ±2.5% for gas content within the range of 0–100% (at a 95% confidence interval). The anticipated field accuracy of ±5% is estimated by accounting for additional uncertainties due to temperature effects, vibration, and mud composition variations under downhole conditions, significantly outperforming current surface monitoring methods. This enables the high-frequency, high-precision early detection of kick events during the shut-in period. The present study provides both theoretical and technical support for the engineering application of nuclear measurement techniques in well control safety. Full article

Barcodes are ubiquitous in manufacturing and logistics, but defects can reduce decoding efficiency and disrupt the supply chain. Existing studies primarily focus on a single barcode type or rely on small-scale datasets, limiting generalizability. We propose Y8-LiBAR Net, a lightweight two-stage framework for multi-type barcode defect detection. In stage 1, a YOLOv8n backbone localizes 1D and 2D barcodes in real time. In stage 2, a dual-branch network integrating ResNet50 and ViT-B/16 via hierarchical attention performs three-class classification on cropped regions of interest (ROIs): intact, defective, and non-barcode. Experiments conducted on the public BarBeR dataset, covering planar/non-planar surfaces, varying illumination, and sensor noise, show that Y8-LiBARNet achieves a detection-stage mAP@0.5 = 0.984 (1D: 0.992; 2D: 0.977) with a peak F1 score of 0.970. Subsequent defect classification attains 0.925 accuracy, 0.925 recall, and a 0.919 F1 score. Compared with single-branch baselines, our framework improves overall accuracy by 1.8–3.4% and enhances defective barcode recall by 8.9%. A Cohen’s kappa of 0.920 indicates strong label consistency and model robustness. These results demonstrate that Y8-LiBARNet delivers high-precision real-time performance, providing a practical solution for industrial barcode quality inspection. Full article

Determining the soil’s nitrogen supply accurately and quickly is essential for effective agricultural management. This study explores the use of near-infrared (NIR) spectroscopy combined with spectral pre-processing techniques (such as Savitzky–Golay filtering) and partial least squares regression (PLSR) to assess soil nitrogen levels. Six soil types of varying compositions, treated with different levels of Urea-N fertilizer, were examined. Nitrogen-specific NIR peaks were identified, and regression models were consequently developed. Through a comparison of the performance of the models, the most effective model for nitrogen detection was selected. In calibration, the models performed well, with high R² (over 0.9) and low root mean square error (RMSE) values. The second derivative-based (SD) model slightly outperformed the first derivative-based (FD) model in terms of accuracy. Both models showed minimal bias, indicating reliable performance. During validation, the FD model outperformed the SD model in terms of R², root mean square error of prediction (RMSEP), and residual prediction deviation (RPD). Thus, the FD model demonstrated good predictive ability (R² = 0.77, RPD = 2.06), while the SD model was less effective (R² = 0.65, RPD = 1.77). Compared to previous studies, this study uniquely combines real-time online detection capability with low computational cost, unlike most prior offline approaches, and includes model validation across various soil types. Overall, NIR spectroscopy coupled with multivariate models proves to be a promising tool for the detection of nitrogen levels in various soils. Full article