• Nutrition Research and Practice
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• v.15 no.2
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• pp.187-202
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• 2021

BACKGROUND/OBJECTIVES: The exposure to sucrose in rats has mimic abnormalities attributed to metabolic syndrome (MetS). The effects of honey bee and "free" glucose and fructose, have not been explored in this context. The aim was to expose Wistar rodents to sucrose solution (SS), honey solution (HS) and fructose/glucose solution (GFS) at 30% to assess their effects. SUBJECTS/METHODS: HS (n = 10), SS (n = 10) and GFS (n = 10) groups were formed. Solutions were ad libitum along 14-weeks. RESULTS: Between solutions consumptions, honey was significantly 42% higher (P = 0.000), while similar consumption was observed among GFS and SS. The feeding pattern of HS consumption was irregular along experiment; while the food intake pattern showed the similar trend among groups along time. Non statistical differences were obtained in any biochemical and anthropometric measure, however, a higher concentration of leptin (721 ± 507 pg/mL), lower concentration of total cholesterol (TC; 48.87 ± 2.41 mg/100 mL), very low density lipoprotein (VLDL; 16.47 ± 6.55 mg/100 mL) and triglycerides (82.37 ± 32.77 mg/100 mL) was obtained in SS group. For anthropometric values, HS showed less total adipose tissue (AT; average 26 vs. 31-33 g) and adiposity index (average 6.11 vs. 7.6). Due to sugar-sweetened beverages consumption increases the risk for the development of chronic diseases; correlations between fluid intake and anthropometric and biochemical parameters were assessed. A moderate correlation was obtained in groups with the weight of total AT and solution intake; for the weight gain in GFS group and for triglycerides in HS and GFS. The highest hepatic tissue damage was observed in SS group with multiple intracytoplasmic vacuoles, atypia changes, moderate pleomorphism and hepatocellular necrosis. CONCLUSIONS: In spite of the significantly higher consumption of HS, biochemical, anthropometrical and histological effects were not remarkably different in comparision to other sweeteners.

• Journal of Physiology & Pathology in Korean Medicine
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• v.34 no.4
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• pp.159-169
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• 2020

Shanghanlun (Treatise on Cold Damage Diseases) is the oldest document in the literature on clinical records of Traditional Asian medicine (TAM), on which TAM theories about symptoms-herbs relationships are based. In this study, we aim to quantitatively explore the relationships between symptoms and herbs in Shanghanlun. The text in Shanghanlun was converted into structured data. Using the structured data, Term Frequency - Inverse Document Frequency (TF-IDF) scores of symptoms and herbs were calculated from each chapter to derive the major symptoms and herbs in each chapter. To understand the structure of the entire document, principal component analysis (PCA) was performed for the 6-dimensional chapter space. Bipartite network analysis was conducted focusing on Jaccard scores between symptoms and herbs and eigenvector centralities of nodes. TF-IDF scores showed the characteristics of each chapter through major symptoms and herbs. Principal components drawn by PCA suggested the entire structure of Shanghanlun. The network analysis revealed a 'multi herbs - multi symptoms' relationship. Common symptoms and herbs were drawn from high eigenvector centralities of their nodes, while specific symptoms and herbs were drawn from low centralities. Symptoms expected to be treated by herbs were derived, respectively. Using measurable metrics, we conducted a computational study on patterns of Shanghanlun. Quantitative researches on TAM theories will contribute to improving the clarity of TAM theories.

• Journal of the Korean Society of Manufacturing Process Engineers
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• v.20 no.3
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• pp.60-67
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• 2021

Recently, research attention has been focused on vibration-free vehicles to transport small numbers of expensive electronic products. Vibration-free vehicles can be used to transport expensive test equipment or semiconductors, mainly produced in the domestic IT industry, and can serve as a readily available transportation system for short driving distances due to the increased efficiency on narrow national highways. This study was aimed at developing a Z-Spring to minimize the vibration by installing an air spring instead of the plate spring applied to conventional freight cars and to prevent the damage of the loaded cargo from the shock occurring during movement. The mechanical properties (elastic modulus, tensile strength, and shear strength) of carbon fiber (CF) and glass fiber (GF) prepreg were derived, and ANSYS ACP PrepPost analyses were performed. It was observed that in the case of hybrid composites, the total deformation and equivalent stress are higher than that of CFRP; however, in terms of the unit cost, the hybrid Z-Spring is more inexpensive and durable compared to the GF.

• Journal of Korean Neurosurgical Society
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• v.65 no.5
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• pp.652-664
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• 2022

The Kernohan-Woltman notch phenomenon (KWNP) refers to an intracranial lesion causing massive side-to-side mass effect which leads to compression of the contralateral cerebral peduncle against the free edge of the cerebellar tentorium. Diagnosis is based on "paradoxical" motor deficit ipsilateral to the lesion associated with radiologic evidence of damage to the contralateral cerebral peduncle. To date, there is scarce evidence regarding KWNP associated neuroimaging patterns and motor function prognostic factors. A systematic review was conducted on Medline database from inception to July 2021 looking for English-language articles concerning KWNP, in accordance with the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) guidelines. The research yielded 45 articles for a total of 51 patients. The mean age was 40.7 years-old and the male/female sex ratio was 2/1. 63% of the patients (32/51) suffered from head trauma with a majority of acute subdural hematomas (57%, 29/51). 57% (29/51) of the patients were in the coma upon admission and 47% (24/51) presented pupil anomalies. KWNP presented the neuroimaging features of compression ischemic stroke located in the contralateral cerebral peduncle, with edema in the surrounding structures and sometimes compression stroke of the cerebral arteries passing nearby. 45% of the patients (23/51) presented a good motor functional outcome; nevertheless, no predisposing factor was identified. A Glasgow coma scale (GCS) of more than 3 showed a trend (p=0.1065) toward a better motor functional outcome. The KWNP is a regional compression syndrome oftentimes caused by sudden and massive uncal herniation and leading to contralateral cerebral peduncle ischemia. Even though patients suffering from KWNP usually present a good overall recovery, patients with a GCS of 3 may present a worse motor functional outcome. In order to better understand this syndrome, future studies will have to focus on more personalized criteria such as individual variation of tentorial notch width.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.32 no.3
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• pp.555-564
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• 2022

Recently, cyberattacks are using hacking techniques utilizing intelligent and advanced malicious codes for non-face-to-face environments such as telecommuting, telemedicine, and automatic industrial facilities, and the damage is increasing. Traditional information protection systems, such as anti-virus, are a method of detecting known malicious URLs based on signature patterns, so unknown malicious URLs cannot be detected. In addition, the conventional static analysis-based malicious URL detection method is vulnerable to dynamic loading and cryptographic attacks. This study proposes a technique for efficiently detecting malicious URLs by dynamically learning malicious URL data. In the proposed detection technique, malicious codes are classified using machine learning-based feature selection algorithms, and the accuracy is improved by removing obfuscation elements after preprocessing using Weighted Euclidean Distance(WED). According to the experimental results, the proposed machine learning-based malicious URL detection technique shows an accuracy of 89.17%, which is improved by 2.82% compared to the conventional method.

• Journal of Animal Science and Technology
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• v.64 no.1
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• pp.123-134
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• 2022

Toll-like receptors (TLRs), as a part of innate immunity, plays an important role in detecting pathogenic molecular patterns (PAMPs) which are structural components or product of pathogens and initiate host defense systems or innate immunity. Precise negative feedback regulations of TLR signaling are important in maintaining homeostasis to prevent tissue damage by uncontrolled inflammation during innate immune responses. In this study, we identified and characterized the function of the pancreatic progenitor cell differentiation and proliferation factor (PPDPF) as a negative regulator for TLR signal-mediated inflammation in chicken. Bioinformatics analysis showed that the structure of chicken PPDPF evolutionarily conserved amino acid sequences with domains, i.e., SH3 binding sites and CDC-like kinase 2 (CLK2) binding sites, suggesting that relevant signaling pathways might contribute to suppression of inflammation. Our results showed that stimulation with polyinosinic:polycytidylic acids (Poly [I:C]), a synthetic agonist for TLR3 signaling, increased the mRNA expression of PPDPF in chicken fibroblasts DF-1 but not in chicken macrophage-like cells HD11. In addition, the expression of pro-inflammatory genes stimulated by Poly(I:C) were reduced in DF-1 cells which overexpress PPDPF. Future studies warrant to reveal the molecular mechanisms responsible for the anti-inflammatory capacity of PPDPF in chicken as well as a potential target for controlling viral resistance.

• Smart Structures and Systems
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• v.29 no.3
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• pp.445-455
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• 2022

Steel anchor bolts are installed in concrete using a variety of methods. One of the most common methods of anchor bolt installation is using epoxy resin as an infill material injected into the drilled hole to act as a bonding material between the steel bolt and the surrounding concrete. Typical design standards assume uniform stress distribution along the length of the anchor bolt accompanied with single crack leading to pull-out failure. Experimental evidence has shown that the steel anchor bolts fail owing to the multiple failure patterns, hence these design assumptions are not realistic. In this regard, the presented research work details the analytical model that takes into consideration multiple micro cracks in the infill material induced via impact loading. The impact loading from the Schmidt hammer is used to evaluate the bond condition bond condition of anchor bolt and the epoxy material. The added advantage of the presented analytical model is that it is able to take into account the various type of end conditions of the anchor bolts such as bent or U-shaped anchors. Through sensitivity analysis the optimum stiffness and shear strength properties of the epoxy infill material is achieved, which have shown to achieve lower displacement coupled with reduced damage to the surrounding concrete. The accuracy of the presented model is confirmed by comparing the simulated deformational responses with the experimental evidence. From the comparison it was found that the model was successful in simulating the experimental results. The proposed model can be adopted by professionals interested in predicting and controlling the deformational response of anchor bolts.

• Smart Structures and Systems
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• v.29 no.1
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• pp.181-193
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• 2022

Data-driven structural health monitoring (SHM) of civil infrastructure can be used to continuously assess the state of a structure, allowing preemptive safety measures to be carried out. Long-term monitoring of large-scale civil infrastructure often involves data-collection using a network of numerous sensors of various types. Malfunctioning sensors in the network are common, which can disrupt the condition assessment and even lead to false-negative indications of damage. The overwhelming size of the data collected renders manual approaches to ensure data quality intractable. The task of detecting and classifying an anomaly in the raw data is non-trivial. We propose an approach to automate this task, improving upon the previously developed technique of image-based pre-processing on one-dimensional (1D) data by enriching the features of the neural network input data with multiple channels. In particular, feature engineering is employed to convert the measured time histories into a 3-channel image comprised of (i) the time history, (ii) the spectrogram, and (iii) the probability density function representation of the signal. To demonstrate this approach, a CNN model is designed and trained on a dataset consisting of acceleration records of sensors installed on a long-span bridge, with the goal of fault detection and classification. The effect of imbalance in anomaly patterns observed is studied to better account for unseen test cases. The proposed framework achieves high overall accuracy and recall even when tested on an unseen dataset that is much larger than the samples used for training, offering a viable solution for implementation on full-scale structures where limited labeled-training data is available.

• Smart Structures and Systems
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• v.29 no.1
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• pp.129-140
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• 2022

The effectiveness of system identification, damage detection, condition assessment and other structural analyses relies heavily on the accuracy and reliability of the measured data in structural health monitoring (SHM) systems. However, data anomalies often occur in SHM systems, leading to inaccurate and untrustworthy analysis results. Therefore, anomalies in the raw data should be detected and cleansed before further analysis. Previous studies on data anomaly detection mainly focused on just single type of data anomaly for denoising or removing outliers, meanwhile, the existing methods of detecting multiple data anomalies are usually time consuming. For these reasons, recognising multiple anomaly patterns for real-time alarm and analysis in field monitoring remains a challenge. Aiming to achieve an efficient and accurate detection for multi-type data anomalies for field SHM, this study proposes a pattern-recognition-based data anomaly detection method that mainly consists of three steps: the feature extraction from the long time-series data samples, the training of a pattern recognition neural network (PRNN) using the features and finally the detection of data anomalies. The feature extraction step remarkably reduces the time cost of the network training, making the detection process very fast. The performance of the proposed method is verified on the basis of the SHM data of two practical long-span bridges. Results indicate that the proposed method recognises multiple data anomalies with very high accuracy and low calculation cost, demonstrating its applicability in field monitoring.

• Conservation Science in Museum
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• v.28
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• pp.1-16
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• 2022

Treatment was performed to conserve the Underwood Typewriter (Hangi 6863) from the collection of the National Hangeul Museum. Since the typewriter was presumably manufactured in the modern or contemporary era and bore patterns of damage such as component loss, corrosion, and paint loss, as well as being made of multiple materials, a condition survey and a scientific analysis were conducted ahead of the conservation treatment in order to carry out appropriate treatments for each material. The analysis confirmed that the typewriter was made of various materials including metal, paint, and rubber, and the conservation treatment was performed in the sequence of removal of contamination, reinforcement, and restoration under conditions where each material was stable. Conservation treatment was completed in a stable state by strengthening the layer of damaged paint and restoring the lost leg. These processes have enabled a better understanding of the materials and characteristics of typewriters manufactured in the early modern era, which is expected to provide basic data for typewriter conservation research to be conducted in the future.