Crack is an important indicator for evaluating the damage level of concrete structures. However, traditional crack detection algorithms have complex implementation and weak generalization. The ...existing crack detection algorithms based on deep learning are mostly window-level algorithms with low pixel precision. In this article, the CrackUnet model based on deep learning is proposed to solve the above problems. First, crack images collected from the lab, earthquake sites, and the Internet are resized, labeled manually, and augmented to make a dataset (1200 subimages with 256 × 256 × 3 resolutions in total). Then, an improved Unet-based method called CrackUnet is proposed for automated pixel-level crack detection. A new loss function named generalized dice loss is adopted to detect cracks more accurately. How the size of the dataset and the depth of the model affect the training time, detecting accuracy, and speed is researched. The proposed methods are evaluated on the test dataset and a previously published dataset. The highest results can reach 91.45%, 88.67%, and 90.04% on test dataset and 98.72%, 92.84%, and 95.44% on CrackForest Dataset for precision, recall, and F1 score, respectively. By comparing the detecting accuracy, the training time, and the information of datasets, CrackUnet model outperform than other methods. Furthermore, six images with complicated noise are used to investigate the robustness and generalization of CrackUnet models.
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NUK, OILJ, SAZU, UKNU, UL, UM, UPUK
Damage detection is a key procedure in maintenance throughout structures’ life cycles and post-disaster loss assessment. Due to the complex types of structural damages and the low efficiency and ...safety of manual detection, detecting damages with high efficiency and accuracy is the most popular research direction in civil engineering. Computer vision (CV) technology and deep learning (DL) algorithms are considered as promising tools to address the aforementioned challenges. The paper aims to systematically summarized the research and applications of DL-based CV technology in the field of damage detection in recent years. The basic concepts of DL-based CV technology are introduced first. The implementation steps of creating a damage detection dataset and some typical datasets are reviewed. CV-based structural damage detection algorithms are divided into three categories, namely, image classification-based (IC-based) algorithms, object detection-based (OD-based) algorithms, and semantic segmentation-based (SS-based) algorithms. Finally, the problems to be solved and future research directions are discussed. The foundation for promoting the deep integration of DL-based CV technology in structural damage detection and structural seismic damage identification has been laid.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The Rayleigh-Plesset equation is the fundamental model of bubble dynamics and is widely used in the study of cavitation mechanisms. Since cavitation bubble often occurs at the micro-scale, the effect ...of surface tension will have an important influence on the bubble motion. Based on the Sundman transformation and Weierstrass elliptic function theory, this paper studies the Rayleigh-Plesset equation considering surface tension, and establishes the parametric theoretical solutions of vapor bubble and gas bubble respectively. The results show that the vapor-bubble and gas-bubble dynamic equations can be solved theoretically. Further, based on the theoretical solutions, the effects of the surface tension on the motion of vapor bubbles and gas bubbles are studied in detail. For the two types of bubbles, the influence of the surface tension on the bubble motion will increase gradually as the scale of the bubble gets smaller. Especially when the bubble scale is reduced to 10 µm, the effect of the surface tension becomes too significant to be negligible.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Acknowledging individuals in research articles is known to be a personal and private expression of appreciation compared to other types of acknowledgment, such as financial support. Early studies ...have demonstrated the significant relationship between acknowledgement, coauthor, and citation. Little did we know to what extent of these relationships and which prompt what to some degree among them. We adopt a series of multivariate analyses, Bayes’ theorem, statistical analysis, and “before and after” matched-group studies to illustrate the acknowledgement patterns in 6323 research articles of 196 Nobel Prize laureates (NPL) from 2008 to 2018. Acknowledgment is consistently proved to significantly relate to co-authorship and citation where co-authorship and citing have an approximately 10% increasing effect on acknowledgement behavior. Our study is the first to state the order of such triangle: acknowledgement is significantly ahead of co-authorship and arguably occurs before citing behavior. Moreover, acknowledgement strengthens more than half of NPL on their co-authorship for 11% and citation for 72% after they acknowledge others. We verify the substantive possibility of co-authorship and citing behavior from acknowledgement and introduce a formation of a new norm of scholarly communication. This will greatly contribute to the matter of evaluation metrics and social network detection.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
When dealing with the oscillations of fixed-base structures or machines induced by external forces, suppressing the vibrational impact on the adjacent structures and the environment helps to maintain ...the structural durability and ensure the users’ comfort level. This study proposed an inerter-based optimal solution to suppress the vibrational forces and energy transmitted to the supporting ground by utilizing the great potential of the inerter. For the external force, which contains various frequency bands, the stochastic response and an energy balance analysis are conducted to evaluate the force transmissibility, structural displacement, and vibration power flow. Given the benefits of the inerter, a transmitted-force-based optimal design framework is proposed for inerter systems, of which the effectiveness is validated by numerical examples. The obtained results show that inerter systems are capable of providing significant reductions in the structural displacement and the force transmitted to the supporting ground. Particularly, the closed-form power equation indicated that a grounded inerter can suppress the force transmission and vibrational energy, thus leading to a less negative impact on the ground and environment. Revealing the working mechanism and optimal design strategy of the inerter can help solve the force-transmission control problem experienced by some practical structures.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
The Internet of Vehicles (IoV) as a promising application of Internet of Things (IoT) has played a significant role in autonomous driving, by connecting intelligent vehicles. Autonomous driving needs ...to process the mass environmental sensing data in coordination with surrounding vehicles, and makes an accurate driving judgment accordingly. Since the vehicles always have limited computing resources, processing these data in parallel with efficient task scheduling is one of the most important topics. Most current work focuses on formulating special scenarios and service requirements as optimization problems. However, the complicated and dynamic environment of vehicular computing is hard to model, predict and control, making those previous methods unscalable and unable to reflect the real scenario. In this paper, a Multi-task Deep reinforcement learning approach for scalable parallel Task Scheduling (MDTS) is firstly devised. For avoiding the curse of dimensionality when coping with complex parallel computing environments and jobs with diverse properties, we extend the action selection in Deep Reinforcement Learning (DRL) to a multi-task decision, where the output branches of multi- task learning are fine-matched to parallel scheduling tasks. Child tasks of a job are accordingly assigned to distributed nodes without any human knowledge while the resource competition among parallel tasks is leveraged through shared neural network layers. Moreover, we design an appropriate reward function to optimize multiple metrics simultaneously, with emphasis on specific scenarios. Extensive experiments show that the MDTS significantly increases the overall reward compared with least- connection scheduling and particle swarm optimization algorithm from -16.71, -0.67 to 2.93, respectively.
Selective serotonin reuptake inhibitors (SSRIs) are standard of care for major depressive disorder (MDD) pharmacotherapy, but only approximately half of these patients remit on SSRI therapy. Our ...previous genome-wide association study identified a single-nucleotide polymorphism (SNP) signal across the glutamate-rich 3 (ERICH3) gene that was nearly genome-wide significantly associated with plasma serotonin (5-HT) concentrations, which were themselves associated with SSRI response for MDD patients enrolled in the Mayo Clinic PGRN-AMPS SSRI trial. In this study, we performed a meta-analysis which demonstrated that those SNPs were significantly associated with SSRI treatment outcomes in four independent MDD trials. However, the function of ERICH3 and molecular mechanism(s) by which it might be associated with plasma 5-HT concentrations and SSRI clinical response remained unclear. Therefore, we characterized the human ERICH3 gene functionally and identified ERICH3 mRNA transcripts and protein isoforms that are highly expressed in central nervous system cells. Coimmunoprecipitation identified a series of ERICH3 interacting proteins including clathrin heavy chain which are known to play a role in vesicular function. Immunofluorescence showed ERICH3 colocalization with 5-HT in vesicle-like structures, and ERICH3 knock-out dramatically decreased 5-HT staining in SK-N-SH cells as well as 5-HT concentrations in the culture media and cell lysates without changing the expression of 5-HT synthesizing or metabolizing enzymes. Finally, immunofluorescence also showed ERICH3 colocalization with dopamine in human iPSC-derived neurons. These results suggest that ERICH3 may play a significant role in vesicular function in serotonergic and other neuronal cell types, which might help explain its association with antidepressant treatment response.
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EMUNI, FIS, FZAB, GEOZS, GIS, IJS, IMTLJ, KILJ, KISLJ, MFDPS, NLZOH, NUK, OILJ, PNG, SAZU, SBCE, SBJE, SBMB, SBNM, UKNU, UL, UM, UPUK, VKSCE, ZAGLJ
Carbon dots (C-dots) are a class of novel fluorescent nanomaterials, which have drawn great attention for their potential applications in bio-nanotechnology. Multicolor C-dots have been synthesized ...by chemical nitric acid oxidation using the reproducible plant soot as raw material. TEM analysis reveals that the prepared C-dots have an average size of 3.1nm. The C-dots are well dispersed in aqueous solution and are strongly fluorescent under the irradiation of ultra-violet light. X-ray photoelectron spectroscopy characterization demonstrates that the O/C atomic ratio for C-dots change to from 0.207 to 0.436 due to the chemical oxidation process. The photo bleaching experiment reveals that the C-dots show excellent photostability as compared with the conventional organic dyes, fluorescein and rhodamine B. The fluorescence intensity of the C-dots did not change significantly in the pH range of 3–10. To further enhance the fluorescence quantum yield, the C-dots were surface modified with four types of passivation ligands, 4,7,10-trioxa-1,13-tridecanediamine (TTDDA), poly-L-lysine (PLL), cysteine and chitosan and the fluorescence quantum yields of the TTDDA, PLL, cysteine and chitosan passivated C-dots were improved 1.53-, 5.94-, 2.00- and 3.68-fold, respectively. Fourier-transform infrared (FTIR) spectra were employed to characterize the surface groups of the C-dots. The bio-application of the C-dots as fluorescent bio-probes was evaluated in cell imaging and ex vivo fish imaging, which suggests that the C-dots may have potential applications in biolabeling and bioimaging.
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•Using plant soot as the carbon source, multicolor carbon dots were prepared via a facile and effective one-pot chemical oxidation method.•Enhanced photoluminescence was observed when these C-dots were surface modified using various surface passivation agents.•These C-dots have been successfully used for cellular and fish imaging, which may have potential applications in bio-technology.
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GEOZS, IJS, IMTLJ, KILJ, KISLJ, NUK, OILJ, PNG, SAZU, SBCE, SBJE, UL, UM, UPCLJ, UPUK, ZRSKP
Breast adenoid cystic carcinomas (AdCCs) can pose diagnostic difficulty due to their rarity, particularly on limited biopsy material. Given that these tumors are triple-negative breast cancers with ...favorable prognosis, accurate diagnosis is critical for clinical management. A total of 12 cases of breast AdCCs were studied; 17 age-matched salivary gland AdCCs and 5 metastatic AdCCs (1 breast and 4 salivary gland primaries) were also examined. Immunohistochemical stains for SOX10, Ki-67, c-KIT, β-catenin, epithelial membrane antigen (EMA), p63, cytokeratin 7 (CK7), cytokeratin 5/6 (CK5/6), and androgen receptor (AR) were performed. All breast (100%) and metastatic (100%) AdCCs and all but 2 salivary gland AdCCs showed diffuse nuclear staining (>50% of cells) for SOX10. Epithelial membrane antigen showed lowest expression in breast AdCCs and the highest expression in metastatic AdCCs (P < .01). Except one case of salivary gland AdCC that showed loss of β-catenin expression and developed subsequent metastasis, all AdCCs showed strong and diffuse membranous β-catenin expression. There were no significant differences in expression of CK7, p63, CK5/6, AR, Ki-67, and c-KIT (P > .05) among breast, salivary gland, and metastatic AdCCs. We investigated the immunophenotypic features of breast AdCCs in comparison with salivary gland and metastatic AdCCs. Despite the contrast in prognosis, these tumors are immunophenotypically similar. SOX10 is a sensitive diagnostic marker in all AdCCs, which could potentially aid in diagnosis of these tumors on limited material.
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IZUM, KILJ, NUK, PILJ, PNG, SAZU, UL, UM, UPUK, VSZLJ
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