Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Lignin and Cellulose Extraction from Vietnam’s Rice Straw Using Ultrasound-Assisted Alkaline Treatment Method

The process of cellulose and lignin extraction from Vietnam’s rice straw without paraffin pretreatment was proposed to improve economic efficiency and reduce environmental pollution. Treatment of the rice straw with ultrasonic irradiation for 30 min increased yields of lignin separation from 72.8% to 84.7%. In addition, the extraction time was reduced from 2.5 h to 1.5 h when combined with ultrasonic irradiation for the same extraction yields. Results from modern analytical methods of FT-IR, SEM, EDX, TG-DTA, and GC-MS indicated that lignin obtained by ultrasound-assisted alkaline treatment method had a high purity and showed a higher molecular weight than that of lignin extracted from rice straw without ultrasonic irradiation. The lignin and cellulose which were extracted from rice straw showed higher thermal stability with 5% degradation at a temperature of over 230°C. The ultrasonic-assisted alkaline extraction method was recommended for lignin and cellulose extraction from Vietnam’s rice straw

Identifying undamaged-beam status based on singular spectrum analysis and wavelet neural networks

In this paper, the identifying undamaged-beam status  based on singular spectrum analysis (SSA) and wavelet neural networks (WNN)  is presented. First, a database is built from measured sets and SSA which  works as a frequency-based filter. A WNN model is then designed which consists of the wavelet frame building, wavelet structure designing and  establishing a solution for training the WNN. Surveys via an experimental  apparatus for estimating the method are carried out. In this work, a  beam-typed iron frame, Micro-Electro-Mechanical (MEM) sensors and a  vibration-signal processing and measuring system named LAM_BRIDGE are all  used

Damage assessment in beam-like structures by correlation of spectrum using machine learning

Damage assessment in the actual operating process of the structure is a modern and exciting problem of construction engineering due to several practical knowledge about the current condition of the inspected structures. However, the problem faced is the difficulty in controlling the excitation in structures. Therefore, the output-based structural damage identification method is becoming attractive because of its potential to be applied to an actual application without being constrained by the collection of the information excitation source. An approach of damage assessment based on supervised Machine Learning is introduced in this study by using the correlation of spectral signal as an input feature for artificial neural network (ANN) and decision tree. The output of machine learning algorithms consists of the appearance of new cuts, the level of cutting and the cutting position. A supported beam model was constructed as an experiment to determine if the method is reasonable for engineering structures. Two machine learning algorithms have been applied to check the relevance of the proposed feature from vibration data. This study contributes a standard in the damage identification problem based on spectral correlation

Immunohistochemical expression of anaplastic lymphoma kinase in neuroblastoma and its relations with some clinical and histopathological features

Background Anaplastic lymphoma kinase (ALK) mutations have been identified as a prominent cause of some familial and sporadic neuroblastoma (NB). ALK expression in NB and its relationship with clinical and histopathological features remains controversial. This study investigated ALK expression and its potential relations with these features in NB. Methods Ninety cases of NB at the Department of Pathology, University of Medicine and Pharmacy at Ho Chi Minh City, Viet Nam from 01/01/2018 to 12/31/2021, were immunohistochemically stained with ALK (D5F3) antibody. The ALK expression and its relations with some clinical and histopathological features were investigated. Results The rate of ALK expression in NB was 91.1%. High ALK expression (over 50% of tumor cells were positive with moderate-strong intensity) accounted for 65.6%, and low ALK expression accounted for 34.4%. All the MYCN-amplified NB patients had ALK immunohistochemistry positivity, most cases had high ALK protein expression. The undifferentiated subtype of NB had a lower ALK-positive rate than the poorly differentiated and differentiated subtype. The percentages of ALK positivity were significantly higher in more differentiated histological types of NB (p = .024). There was no relation between ALK expression and: age group, sex, primary tumor location, tumor stage, MYCN status, clinical risk, Mitotic-Karyorrhectic Index, prognostic group, necrosis, and calcification. Conclusions ALK was highly expressed in NB. ALK expression was not related to several clinical and histopathological features. More studies are needed to elucidate the association between ALK expression and ALK gene status and to investigate disease progression, especially the oncogenesis of ALK-positive NB

Depletion of Foxp3(+) regulatory T cells is accompanied by an increase in the relative abundance of Firmicutes in the murine gut microbiome

A reciprocal interaction exists between the gut microbiota and the immune system. Regulatory T (Treg) cells are important for controlling immune responses and for maintaining the intestinal homeostasis but their precise influence on the gut microbiota is unclear. We studied the effects of Treg cell depletion on inflammation of the intestinal mucosa and analysed the gut microbiota before and after depletion of Treg cells using the DEpletion of REGulatory T cells (DEREG) mouse model. DNA was extracted from stool samples of DEREG mice and wild‐type littermates at different time‐points before and after diphtheria toxin application to deplete Treg cells in DEREG mice. The V3/V4 region of the 16S rRNA gene was used for studying the gut microbiota with Illumina MiSeq paired ends sequencing. Multidimensional scaling separated the majority of gut microbiota samples from late time‐points after Treg cell depletion in DEREG mice from samples of early time‐points before Treg cell depletion in these mice and from gut microbiota samples of wild‐type mice. Treg cell depletion in DEREG mice was accompanied by an increase in the relative abundance of the phylum Firmicutes and by intestinal inflammation in DEREG mice 20 days after Treg cell depletion, indicating that Treg cells influence the gut microbiota composition. In addition, the variables cage, breeding and experiment number were associated with differences in the gut microbiota composition and these variables should be respected in murine studies

Interleukin-33 signaling exacerbates experimental infectious colitis by enhancing gut permeability and inhibiting protective Th17 immunity.

A wide range of microbial pathogens is capable of entering the gastrointestinal tract, causing infectious diarrhea and colitis. A finely tuned balance between different cytokines is necessary to eradicate the microbial threat and to avoid infection complications. The current study identified IL-33 as a critical regulator of the immune response to the enteric pathogen Citrobacter rodentium. We observed that deficiency of the IL-33 signaling pathway attenuates bacterial-induced colitis. Conversely, boosting this pathway strongly aggravates the inflammatory response and makes the mice prone to systemic infection. Mechanistically, IL-33 mediates its detrimental effect by enhancing gut permeability and by limiting the induction of protective T helper 17 cells at the site of infection, thus impairing host defense mechanisms against the enteric pathogen. Importantly, IL-33-treated infected mice supplemented with IL-17A are able to resist the otherwise strong systemic spreading of the pathogen. These findings reveal a novel IL-33/IL-17A crosstalk that controls the pathogenesis of Citrobacter rodentium-driven infectious colitis. Manipulating the dynamics of cytokines may offer new therapeutic strategies to treat specific intestinal infections