Detection of Spoilage in Canned Pasteurized Milk Using the Radiographic Imaging Technique

After packed into sterilized containers with a closed and rigorous process, pasteurized milk has been ensured for its hygiene and safety factors. However, distortions can occur during storage and transportation, causing the container to open, allowing harmful microorganisms to enter and damage the product. This research proposed a radiographic imaging technique to detect and evaluate the spoilage of canned pasteurized milk. The X-ray images show that the milk cans, which were left open for three days at 300 K, indicated regions with abnormal density with the smallest detectable size from 100 µm or larger. Density heterogeneity would be clearer in the following days and depending on the sample. An algorithm was developed to identify spoilage products automatically with an accuracy of up to 100 % and a speed of 0.0057 s/product. This approach may be suitable for industrial scale to control the quality of dairy products

A metric learning-based method for biomedical entity linking

Biomedical entity linking task is the task of mapping mention(s) that occur in a particular textual context to a unique concept or entity in a knowledge base, e.g., the Unified Medical Language System (UMLS). One of the most challenging aspects of the entity linking task is the ambiguity of mentions, i.e., (1) mentions whose surface forms are very similar, but which map to different entities in different contexts, and (2) entities that can be expressed using diverse types of mentions. Recent studies have used BERT-based encoders to encode mentions and entities into distinguishable representations such that their similarity can be measured using distance metrics. However, most real-world biomedical datasets suffer from severe imbalance, i.e., some classes have many instances while others appear only once or are completely absent from the training data. A common way to address this issue is to down-sample the dataset, i.e., to reduce the number instances of the majority classes to make the dataset more balanced. In the context of entity linking, down-sampling reduces the ability of the model to comprehensively learn the representations of mentions in different contexts, which is very important. To tackle this issue, we propose a metric-based learning method that treats a given entity and its mentions as a whole, regardless of the number of mentions in the training set. Specifically, our method uses a triplet loss-based function in conjunction with a clustering technique to learn the representation of mentions and entities. Through evaluations on two challenging biomedical datasets, i.e., MedMentions and BC5CDR, we show that our proposed method is able to address the issue of imbalanced data and to perform competitively with other state-of-the-art models. Moreover, our method significantly reduces computational cost in both training and inference steps. Our source code is publicly available here

Mass transfer properties of Acacia mangium plantation wood

This study investigated the mass transfer properties (permeability and mass diffusivity) in the longitudinal, radial and tangential directions of plantation-grown Acacia mangium in VinhPhuc province,northeast, Vietnam. These properties will be used to complement a conventional drying model in the future. Measurements of gas and liquid permeability were performed using a Porometer (POROLUXTM1000). Mass diffusivity was determined in a constant humidity and temperature chamber using PVC-CHA vaporimeters. Results showed the gas permeability was significant higher than liquid with the descending order of longitudinal, radial, and tangential directions. The permeability anisotropy ratios from the longitudinal to transverse directions of Acacia mangium were much lower than other published species. However, the obvious anisotropy ratios from radial to tangential for both permeability and diffusivity, is one of concerns as they can exacerbate defects during drying. Besides, the high permeability and diffusivity of Acaciamangium compared to some other species reported compounds its relatively fast drying rate

Data-driven structural health monitoring using feature fusion and hybrid deep learning

Smart structural health monitoring (SHM) for large-scale infrastructures is an intriguing subject for engineering communities thanks to its significant advantages such as timely damage detection, optimal maintenance strategy, and reduced resource requirement. Yet, it is a challenging topic as it requires handling a large amount of collected sensors data continuously, which is inevitably contaminated by random noises. Therefore, this study developed a practical end-to-end framework that makes use of physical features embedded in raw data and an elaborated hybrid deep learning model, namely 1DCNN-LSTM, featuring two algorithms - Convolutional Neural Network (CNN) and Long-Short Term Memory (LSTM). In order to extract relevant features from sensory data, the method combines various signal processing techniques such as the autoregressive model, discrete wavelet transform, and empirical mode decomposition. The hybrid deep learning 1DCNN-LSTM is designed based on the CNN’s capacity of capturing local information and the LSTM network’s prominent ability to learn long-term dependencies. Through three case studies involving both experimental and synthetic datasets, it is demonstrated that the proposed approach achieves highly accurate damage detection, as accurate as the powerful two-dimensional CNN, but with a lower time and memory complexity, making it suitable for real-time SHM

Mass transfer properties of Acacia mangium plantation wood

This study investigated the mass transfer properties (permeability and mass diffusivity) in the longitudinal, radial and tangential directions of plantation-grown Acacia mangium in VinhPhuc province,northeast, Vietnam. These properties will be used to complement a conventional drying model in the future. Measurements of gas and liquid permeability were performed using a Porometer (POROLUXTM1000). Mass diffusivity was determined in a constant humidity and temperature chamber using PVC-CHA vaporimeters. Results showed the gas permeability was significant higher than liquid with the descending order of longitudinal, radial, and tangential directions. The permeability anisotropy ratios from the longitudinal to transverse directions of Acacia mangium were much lower than other published species. However, the obvious anisotropy ratios from radial to tangential for both permeability and diffusivity, is one of concerns as they can exacerbate defects during drying. Besides, the high permeability and diffusivity of Acaciamangium compared to some other species reported compounds its relatively fast drying rate

Contributions of rotation, expansion and line broadening to the morpho-kinematics of the inner CSE of oxygen-rich AGB star R Hya

We use archival ALMA observations of the CO(2-1) and SiO(5-4) molecular line emissions of AGB star R Hya to illustrate the relative contributions of rotation, expansion and line broadening to the morpho-kinematics of the circumstellar envelope (CSE) within some ~0.5 arcsec from the centre of the star. We give evidence for rotation and important line broadening to dominate the inner region, within ~100 mas from the centre of the star. The former is about an axis that projects a few degrees west of north and has a projected rotation velocity of a few km/s. The latter occurs within some 50-100 mas from the centre of the star, the line width reaching two to three times its value outside this region. We suggest that it is caused by shocks induced by stellar pulsations and convective cell ejections. We show the importance of properly accounting for the observed line broadening when discussing rotation and evaluating the radial dependence of the rotation velocity.Comment: 8 pages, 8 figure

The impact of cataract surgey on vision-related quality of life for bilateral cataract patients in Ho Chi Minh City, Vietnam: a prospective study

BACKGROUND: To determine the impact of cataract surgery on vision-related quality of life (VRQOL) and examine the association between objective visual measures and change in VRQOL after surgery among bilateral cataract patients in Ho Chi Minh City, Vietnam. METHODS: A cohort of older patients with bilateral cataract was assessed one week before and one to three months after first eye or both eye cataract surgery. Visual measures including visual acuity, contrast sensitivity and stereopsis were obtained. Vision-related quality of life was assessed using the NEI VFQ-25. Descriptive analyses and a generalized linear estimating equation (GEE) analysis were undertaken to measure change in VRQOL after surgery. RESULTS: Four hundred and thirteen patients were assessed before cataract surgery and 247 completed the follow-up assessment one to three months after first or both eye cataract surgery. Overall, VRQOL significantly improved after cataract surgery (p < 0.001) particularly after both eye surgeries. Binocular contrast sensitivity (p < 0.001) and stereopsis (p < 0.001) were also associated with change in VRQOL after cataract surgery. Visual acuity was not associated with VRQOL. CONCLUSIONS: Cataract surgery significantly improved VRQOL among bilateral cataract patients in Vietnam. Contrast sensitivity as well as stereopsis, rather than visual acuity significantly affected VRQOL after cataract surgery

Cathelicidin suppresses lipid accumulation and hepatic steatosis by inhibition of the CD36 receptor.

Background and objectivesObesity is a global epidemic which increases the risk of the metabolic syndrome. Cathelicidin (LL-37 and mCRAMP) is an antimicrobial peptide with an unknown role in obesity. We hypothesize that cathelicidin expression correlates with obesity and modulates fat mass and hepatic steatosis.Materials and methodsMale C57BL/6 J mice were fed a high-fat diet. Streptozotocin was injected into mice to induce diabetes. Experimental groups were injected with cathelicidin and CD36 overexpressing lentiviruses. Human mesenteric fat adipocytes, mouse 3T3-L1 differentiated adipocytes and human HepG2 hepatocytes were used in the in vitro experiments. Cathelicidin levels in non-diabetic, prediabetic and type II diabetic patients were measured by enzyme-linked immunosorbent assay.ResultsLentiviral cathelicidin overexpression reduced hepatic steatosis and decreased the fat mass of high-fat diet-treated diabetic mice. Cathelicidin overexpression reduced mesenteric fat and hepatic fatty acid translocase (CD36) expression that was reversed by lentiviral CD36 overexpression. Exposure of adipocytes and hepatocytes to cathelicidin significantly inhibited CD36 expression and reduced lipid accumulation. Serum cathelicidin protein levels were significantly increased in non-diabetic and prediabetic patients with obesity, compared with non-diabetic patients with normal body mass index (BMI) values. Prediabetic patients had lower serum cathelicidin protein levels than non-diabetic subjects.ConclusionsCathelicidin inhibits the CD36 fat receptor and lipid accumulation in adipocytes and hepatocytes, leading to a reduction of fat mass and hepatic steatosis in vivo. Circulating cathelicidin levels are associated with increased BMI. Our results demonstrate that cathelicidin modulates the development of obesity