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The l 2 regularization is usually used to deal with the problems of under-determinacy and measurement noise for the conventional sensitivity-based model updating damage detection methods. However, the l 2 regularization technique often provides overly smooth solutions and thus cannot exhibit the sparsity of the structural damage due to the promotion of the 2-norm term on smoothness. In the study, a structural damage detection method is proposed based on an improved modal flexibility sensitivity function and an iterative reweighted l p (IR l p) regularization. Specifically, the sensitivity function is established by introducing changes in the mode shapes into the derivative of eigenvalue and can be applied to identify the localized damage more accurately. Additionally, IR l p regularization is proposed to deal with the ill-posed problem of damage detection in a noisy environment. The proposed IR l p regularization is compared with the l 1 and l 2 regularizations through a numerical and an experimental examples. The numerical and experimental results indicate that the IR l p regularization can more accurately locate and quantify the single and multiple damages under the noise situation. The maximum identification errors are only 5.16% and 5.67%, respectively. Moreover, compared to the basic modal flexibility sensitivity function, the improved function is more sensitive to the damage. The maximum identification error of the improved function is less than 6%, while the relative errors are significantly larger in the basic function. [ABSTRACT FROM AUTHOR]

Previous vibration-based damage detection studies mostly focus on developing a more sensitive optimization function to promote the effectiveness of damage identification. However, a few studies have conducted comparative analyses on the detection performance of different optimization functions. In the study, changes in the frequency and mode shape are applied as the inputs to different optimization functions for damage identification. Three optimization functions are established using the frequency residuals, the combinations of frequency and mode shape residuals, and the modal flexibility residuals, respectively. Considering the sparsity of damage element distribution, an iterative reweighted l1/2 (IRl1/2) regularization is added as a norm penalty to the optimization function. A numerical model and an experimental example are applied to assess the performance of distinct optimization functions. The results show that the increase in modal data number cannot significantly improve the detection accuracy when the number meets the basic requirements for identifying damage. The detection error of the optimization function established by combining the frequency and mode shape residuals is 6.65% and 5.18% using the first four and fourteen-order noisy modal data, respectively. Furthermore, the optimization function constructed using the modal flexibility residuals requires more less modal data to identify damage than the other two functions. [ABSTRACT FROM AUTHOR]

Protoporphyrin IX (PPIX) serves as a pivotal precursor in the biosynthesis of porphyrins, playing a crucial role in both biological metabolism and biogeochemical cycling. This study investigates the spatial dynamics and co-occurrence patterns of PPIX within the microbial community of marine sediment. The concentrations of PPIX ranged from 7.38 - 91.33 ng/g. Clear patterns in spatiotemporal dynamics in the PPIX were observed, with a more pronounced spatial rather than seasonal variation. Notably, the PPIX and its derivatives in brackish sediment samples exhibited distinct characteristics from those found in saltwater samples. The Spearman test found that PPIX has a positive relationship with chlorophyll-a and pheophytin-a but not with chlorophyll-b or pheophytin-b. Significant positive correlations were identified between PPIX and nitrogen nutrients. Network and variation partitioning analyses highlighted the potential interactions between PPIX and microbial lineages. These findings indicate that PPIX is necessary for microorganisms and widely distributed in sedimentary environments. Additionally, this study underscores the influence of organic matter on microbial community composition and its responsiveness to environmental variations. These insights contribute to a comprehensive understanding of the potential ecological roles of PPIX within natural ecosystems. Protoporphyrin IX is necessary for microorganisms and widely distributed in sedimentary environments. PPIX showed a more pronounced spatial rather than seasonal variation. Porphyrins found in brackish sediment samples exhibited distinct characteristics compared with those present in saltwater samples. Environmental factors can further influence the distribution of porphyrins by affecting microorganisms and their interactions. [ABSTRACT FROM AUTHOR]

Vibrio vulnificus is an important bacterial pathogen that causes serious infections in fish and is also highly pathogenic to humans. Many effective detection methods targeting this pathogen have previously been designed, but many of these methods are time‐consuming, complicated and expensive. Thus, these approaches cannot be widely used by small aqacultural concerns. Although DNA aptamers have been used to detect pathogenic bacteria, these have not been applied to marine bacteria, including V. vulnificus. Therefore, we developed a highly specific DNA aptamer for V. vulnificus detection using systematic evolution of ligands by exponential enrichment (SELEX), coupled with asymmetric PCR. After 13 rounds of cross‐selection, we identified a novel DNA aptamer (Vapt2). We evaluated the affinity, specificity and limit of detection (LOD) of this aptamer for V. vulnificus. We found that Vapt2 had a high affinity for V. vulnificus (Kd = 26.8 ± 5.3 nM) and detected this pathogen at a wide range of concentrations (8–2.0 × 108 cfu/ml). Vapt2 bound to V. vulnificus with high selectivity in the presence of other pathogenic bacteria. Our study increases our knowledge of the possible applications of aptamers with respect to marine bacteria. Moreover, our work might provide a framework for the rapid detection of pathogenic bacteria and water pollution. [ABSTRACT FROM AUTHOR]

To solve the challenges of low recognition accuracy, slow speed, and weak generalization ability inherent in traditional methods for multi‐damage recognition of concrete bridges, this paper proposed an efficient lightweight damage recognition model, constructed by improving the you only look once v4 (YOLOv4) with MobileNetv3 and fused inverted residual blocks, named YOLOMF. First, a novel lightweight network named MobileNetv3 with fused inverted residual (MobileNetv3‐FusedIR) is constructed as the backbone network for YOLOMF. This is achieved by integrating the fused mobile inverted bottleneck convolution (Fused‐MBConv) into the shallow layers of MobileNetv3. Second, the standard convolution in YOLOv4 is replaced with the depthwise separable convolution, resulting in a reduction in the number of parameters and complexity of the model. Third, the effects of different activation functions on the damage recognition performance of YOLOMF are thoroughly investigated. Finally, to verify the effectiveness of the proposed method in complex environments, a data enhancement library named Imgaug is used to simulate concrete bridge damage images under challenging conditions such as motion blur, fog, rain, snow, noise, and color variations. The results indicate that the YOLOMF shows excellent multi‐damage recognition proficiency for concrete bridges across varying field‐of‐view sizes as well as complex environmental conditions. The detection speed of YOLOMF reaches 85f/s, facilitating effective real‐time multi‐damage detection for concrete bridges under complex environments. [ABSTRACT FROM AUTHOR]

Objective: To explore the significance of multimodal monitoring in the monitoring and treatment of neurocritical care patients., Methods: 104 neurocritical care patients admitted to the department of Critical Care Medicine of Fujian Provincial Hospital from March 2019 to January 2020 were enrolled. Patients were randomly assigned into two groups, with 52 in each group. In the routine monitoring treatment group, heart rate, blood pressure, respiratory rate and the changes in consciousness and pupils were monitored after operation. The patients were treated with routine medicine to reduce intracranial pressure (ICP), maintain proper cerebral perfusion pressure (CPP), balance fluid intake and output, and maintain the airway clear. Patients in the multimodal monitoring treatment group were treated with invasive ICP monitoring, ultrasound to assess brain structure, ultrasound to measure optic nerve sheath diameter (ONSD), transcranial color doppler (TCCD), internal jugular venous blood oxygen saturation monitoring, near-infrared spectroscopy (NIRS), non-invasive cerebral blood oxygen saturation monitoring and quantitative electroencephalogram monitoring. According to the monitoring results, the patients were given targeted treatment with the goal of controlling ICP and improving brain metabolism. The length of intensive care unit (ICU) stay, the incidences of neurological complications (secondary cerebral infarction, cerebral hemorrhage, high intracranial pressure, etc.), and the incidences of poor prognosis [6 months after the onset of Glasgow outcome score (GOS) 1 to 3] were compared between the two groups. Spearman rank correlation analysis of the correlation between invasive ICP and the ICP value which was calculated by TCCD. The receiver operating characteristic (ROC) curve of invasive ICP and pulsatility index of middle cerebral artery (PI MCA ) were used to predict poor prognosis., Results: The length of ICU stay in the multimodal monitoring treatment group was significantly shorter than that of the routine monitoring treatment group (days: 6.27±3.81 vs. 9.61±5.09, P < 0.01), and the incidence of neurological complications was significantly lower than that in the routine monitoring treatment group (9.62% vs. 25.00%, P < 0.05). In the multimodal monitoring treatment group, 37 cases had a good prognosis and 15 cases had a poor prognosis, while the routine monitoring treatment group had a good prognosis in 27 cases and a poor prognosis in 25 cases. The incidence of poor prognosis in the multimodal monitoring treatment group was lower than that of the routine monitoring treatment group (28.85% vs. 48.08%, P < 0.05). In the multimodal monitoring treatment group, the invasive ICP and PI MCA of patients with good prognosis were significantly lower than those of patients with poor prognosis [invasive ICP (mmHg, 1 mmHg = 0.133 kPa): 16 (12, 17) vs. 22 (20, 24), PI MCA : 0.90±0.33 vs. 1.39±0.58, both P < 0.01]. There was no significant difference in resistance index of the middle cerebral artery (RI MCA ) between the good prognosis group and the poor prognosis group (0.63±0.12 vs. 0.66±0.15, P > 0.05). There was a positive correlation between the invasive ICP and the ICP value which was calculated by TCCD (r = 0.767, P < 0.001). ROC curve analysis showed that the area under ROC curve (AUC) of invasive ICP for poor prognosis prediction was 0.906, the best cut-off value was ≥ 18 mmHg, the sensitivity was 86.49%, and the specificity was 86.67%. The AUC of PIMCA for poor prognosis prediction was 0.759, the best cut-off value was ≥ 1.12, the sensitivity was 81.08%, and the specificity was 60.00%. The AUC of invasive ICP was greater than PI MCA (Z = 2.279, P = 0.023)., Conclusions: Comprehensive analysis of multimodal monitoring indicators for neurocritical care patients to guide clinical treatment can reduce the length of hospital stay, and reduce the risk of neurosurgery complications and disability; invasive ICP can predict poor prognosis of neurocritical care patients.