• Korean Journal of Construction Engineering and Management
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• v.21 no.4
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• pp.30-37
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• 2020

The purpose of this study is to compare and contrast the amount of overseas construction orders of South Korea and China by using various time series models that measure the overseas construction orders. Based on the analysis we propose better specification (model selection) with much more predictive power and prove the universality of the model developed by applying our findings with respect to the prediction power of overseas construction orders from other countries viewpoints (verification of generalization). The input variables include Dubai crude oil and exchange rates by country from 1981 to 2019. The VAR model is proposed based on the prediction power test, with respect to MAPE, RMSE, and MAE between the estimates and actual measurements from 2016 to 2019. We also conclude the results of the prediction of overseas construction orders time series of China are again consistent with the actual numbers. These analyses suggest the possibility of developing a comprehensive model that predict the potential construction orders of other countries.

• Nuclear Engineering and Technology
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• v.52 no.5
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• pp.925-936
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• 2020

To ascertain the characteristics of pool sloshing behavior that might be encountered during a core disruptive accident of sodium-cooled fast reactors, in our earlier work several series of experiments were conducted under various scenarios including the condition with mono-sized solid particles. It is found that under the particle-bed condition, three typical flow regimes (namely the bubble-impulsion dominant regime, the transitional regime and the bed-inertia dominant regime) could be identified and a flow-regime model (base model) has been even successfully established to estimate the regime transition. In this study, aimed to further understand this behavior at more realistic particle-bed conditions, a series of simulated experiments is newly carried out using mixed-size particles. Through analyses, it is verified that for present scenario, by applying the area mean diameter, our previously-developed base model can provide the most appropriate predictive results among the various effective diameters. To predict the regime transition with a form of extension scheme, a correction factor which is based on the volume-mean diameter and the degree of convergence in particle-size distribution is suggested and validated. The conducted analyses in this work also indicate that under certain conditions, the potential separation between different particle components might exist during the sloshing process.

• The Korea Journal of Herbology
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• v.36 no.3
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• pp.39-46
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• 2021

Objectives : Mangifera indica leaves are well known for having a variety of benefits, including anti-inflammatory, anti-tumor, diabetic retinopathy and diabetic vasculosis. However, the effects of Mangifera indica leaves on hair loss inhibition have not been studied. In this study, we investigated to find out the activity of Mangifera indica leaves on hair loss. Methods : 2,2'-azino-bis-3-ethylbenzothiazoline-6-sulphonic acid(ABTS) analysis was performed to confirm the antioxidant efficacy of the water extract of Mangifera indica leaves (WEML). To examine the effect of WEML on cell viability in dermal papillar (DP) cells, 3-(4,5-dimethylthiazol-2-yl)-5-(3-carboxymethoxyphenyl)-2-(4-sulfophenyl)-2H-tetra Zolium (MTS) analysis was performed. The changes in the mRNA expression level of the hair loss and hair growth-related genes in dermal papilla cells by WEML treatment were confirmed by quantitative RT-PCR. Results : In dermal papilla (DP) cells, ABTS analysis and MTS analysis of WEML showed antioxidant efficacy and low cytotoxicity. As a result of gene expression analysis through Quantitative RT-PCR, no changes in hair growth-related genes BMP6 and CTNNB1 was confirmed. but inhibitory activity of WEML on hair loss-related genes EGR1, SGK, DKK1, SRD5A1 and SRD5A2 was confirmed. Conclusion : We confirmed that WEML has excellent antioxidant efficacy and a inhibitory activity of hair loss-related genes including 5α-reductase genes. These results suggest that Mangifera indica leaves have a potential activity as a hair loss treatment for hair loss and hair growth. Biochemical or molecular biological research on hair loss is needed.

• Asian Spine Journal
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• v.12 no.6
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• pp.1100-1105
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• 2018

Study Design: Prospective observational study. Purpose: To determine the incidence of postoperative urinary retention (POUR) in patients undergoing elective posterior lumbar spine surgery and identify the risk factors associated with the development of POUR. Overview of Literature: POUR following surgery can lead to detrusor dysfunction, urinary tract infections, prolonged hospital stay, and a higher treatment cost; however, the risk factors for POUR in spine surgery remain unclear. Methods: A prospective, consecutive analysis was conducted on patients undergoing elective posterior lumbar surgery in the form of lumbar discectomy, lumbar decompression, and single-level lumbar fusions during a 6-month period. Patients with spine trauma, preoperative neurological deficit, previous urinary disturbance/symptoms, multiple-level fusion, and preoperative catheterization were excluded from the study. Potential patient- and surgery-dependent risk factors for the development of POUR were assessed. Univariate analysis and a multiple logistical regression analysis were performed. Results: A total of 687 patients underwent posterior lumbar spine surgery during the study period; among these, 370 patients were included in the final analysis. Sixty-one patients developed POUR, with an incidence of 16.48%. Significant risk factors for POUR were older age, higher body mass index (BMI), surgery duration, intraoperative fluid administration, lumbar fusion versus discectomy/decompression, and higher postoperative pain scores (p<0.05 for all). Sex, diabetes, and the type of inhalational agent used during anesthesia were not significantly associated with POUR. Multiple logistical regression analysis, including age, BMI, surgery duration, intraoperative fluid administration, fusion surgery, and postoperative pain scores demonstrated a predictive value of 92% for the study population and 97% for the POUR group. Conclusions: POUR was associated with older age, higher BMI, longer surgery duration, a larger volume of intraoperative fluid administration, and higher postoperative pain scores. The contribution of postoperative pain scores in the multiple regression analysis was a significant predictor of POUR.

• Nutrition Research and Practice
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• v.13 no.3
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• pp.247-255
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• 2019

BACKGROUND/OBJECTIVES: Accurate, early identification of acutely malnourished children has the potential to reduce related child morbidity and mortality. The current World Health Organisation (WHO) guidelines classify non-oedematous acute malnutrition among children under five using Mid-Upper Arm Circumference (MUAC) or Weight-for-Height Z-score (WHZ). However, there is ongoing debate regarding the use of current MUAC cut-offs. This study investigates the diagnostic performance of MUAC to identify children aged 6-24 months with global (GAM) or severe acute malnutrition (SAM). SUBJECTS/METHODS: Cross-sectional, secondary data from a community sample of children aged 6-24 months in Niger were used for this study. Children with complete weight, height and MUAC data and without clinical oedema were included. Using WHO guidelines for GAM (WHZ < -2, MUAC < 12.5 cm) and SAM (WHZ < -3, MUAC < 11.5 cm), the sensitivity (Se), specificity (Sp), predictive values, Youden Index and Receiver Operating Characteristic (ROC) curves were calculated for MUAC when compared with the WHZ reference criterion. RESULTS: Of 1161 children, 23.3% were diagnosed with GAM using WHZ, and 4.4% with SAM. Using current WHO cut-offs, the Se of MUAC to identify GAM was greater than for SAM (79 vs. 57%), yet the Sp was lower (84 vs. 97%). From inspection of the ROC curve and Youden Index, Se and Sp were maximised for MUAC < 12.5 cm to identify GAM (Se 79%, Sp 84%), and MUAC < 12.0 cm to identify SAM (Se 88%, Sp 81%). CONCLUSIONS: The current MUAC cut-off to identify GAM should continue to be used, but when screening for SAM, a higher cut-off could improve case identification. Community screening for SAM could use MUAC < 12.0 cm followed by appropriate treatment based on either MUAC < 11.5 cm or WHZ < -3, as in current practice. While the practicalities of implementation must be considered, the higher SAM MUAC cut-off would maximise early case-finding of high-risk acutely malnourished children.

• Tuberculosis and Respiratory Diseases
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• v.84 no.2
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• pp.115-124
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• 2021

Background: This study aimed to determine the parameters for worsening oxygenation in non-severe coronavirus disease 2019 (COVID-19) pneumonia. Methods: This retrospective cohort study included cases of confirmed COVID-19 pneumonia in a public hospital in South Korea. The worsening oxygenation group was defined as that with SpO₂ ≤94% or received oxygen or mechanical ventilation (MV) throughout the clinical course versus the non-worsening oxygenation group that did not experience any respiratory event. Parameters were compared, and the extent of viral pneumonia from an initial chest computed tomography (CT) was calculated using artificial intelligence (AI) and measured visually by a radiologist. Results: We included 136 patients, with 32 (23.5%) patients in the worsening oxygenation group; of whom, two needed MV and one died. Initial vital signs and duration of symptoms showed no difference between the two groups; however, univariate logistic regression analysis revealed that a variety of parameters on admission were associated with an increased risk of a desaturation event. A subset of patients was studied to eliminate potential bias, that ferritin ≥280 ㎍/L (p=0.029), lactate dehydrogenase ≥240 U/L (p=0.029), pneumonia volume (p=0.021), and extent (p=0.030) by AI, and visual severity scores (p=0.042) were the predictive parameters for worsening oxygenation in a sex-, age-, and comorbid illness-matched case-control study using propensity score (n=52). Conclusion: Our study suggests that initial CT evaluated by AI or visual severity scoring as well as serum markers of inflammation on admission are significantly associated with worsening oxygenation in this COVID-19 pneumonia cohort.

• Genomics & Informatics
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• v.20 no.3
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• pp.27.1-27.13
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• 2022

Oral squamous cell carcinoma (OSCC) is the most prevalent head and neck malignancy, with frequent cervical lymph-node metastasis, leading to a poor prognosis in OSCC patients. The present study aimed to identify potential markers, including microRNAs (miRNAs) and genes, significantly involved in the etiology of early-stage OSCC. Additionally, the main OSCC's dysregulated Gene Ontology annotations and significant signaling pathways were identified. The dataset GSE45238 underwent multivariate statistical analysis in order to distinguish primary OSCC tissues from healthy oral epithelium. Differentially expressed miRNAs (DEMs) with the criteria of p-value < 0.001 and |Log2 fold change| > 1.585 were identified in the two groups, and subsequently, validated targets of DEMs were identified. A protein interaction map was constructed, hub genes were identified, significant modules within the network were illustrated, and significant pathways and biological processes associated with the clusters were demonstrated. Using the GEPI2 database, the hub genes' predictive function was assessed. Compared to the healthy controls, main OSCC had a total of 23 DEMs. In patients with head and neck squamous cell carcinoma (HNSCC), upregulation of CALM1, CYCS, THBS1, MYC, GATA6, and SPRED3 was strongly associated with a poor prognosis. In HNSCC patients, overexpression of PIK3R3, GIGYF1, and BCL2L11 was substantially correlated with a good prognosis. Besides, "proteoglycans in cancer" was the most significant pathway enriched in the primary OSCC. The present study results revealed more possible mechanisms mediating primary OSCC and may be useful in the prognosis of the patients with early-stage OSCC.

• Maxillofacial Plastic and Reconstructive Surgery
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• v.42
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• pp.37.1-37.13
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• 2020

Background: The human mandible is variable in shape, size and position and any deviation from normal can affect the facial appearance and dental occlusion. Objectives: The objectives of this study were to determine whether the Sassouni cephalometric analysis could help predict two-dimensional mandibular shape in humans using cephalometric planes and landmarks. Materials and methods: A retrospective computerised analysis of 100 lateral cephalometric radiographs taken at Kingston Hospital Orthodontic Department was carried out. Results: Results showed that the Euclidean straight-line mean difference between the estimated position of gonion and traced position of gonion was 7.89 mm and the Euclidean straight-line mean difference between the estimated position of pogonion and the traced position of pogonion was 11.15 mm. The length of the anterior cranial base as measured by sella-nasion was positively correlated with the length of the mandibular body gonion-menton, r = 0.381 and regression analysis showed the length of the anterior cranial base sella-nasion could be predictive of the length of the mandibular body gonion-menton by the equation 22.65 + 0.5426x, where x = length of the anterior cranial base (SN). There was a significant association with convex shaped palates and oblique shaped mandibles, p = 0.0004. Conclusions: The method described in this study can be used to help estimate the position of cephalometric points gonion and pogonion and thereby sagittal mandibular length. This method is more accurate in skeletal class I cases and therefore has potential applications in craniofacial anthropology and the 'missing mandible' problem in forensic and archaeological reconstruction.

• Smart Structures and Systems
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• v.29 no.1
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• pp.251-266
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• 2022

Structural Health Monitoring (SHM) of critical infrastructure comprises a major pillar of maintenance management, shielding public safety and economic sustainability. Although SHM is usually associated with data-driven metrics and thresholds, expert judgement is essential, especially in cases where erroneous predictions can bear casualties or substantial economic loss. Considering that visual inspections are time consuming and potentially subjective, artificial-intelligence tools may be leveraged in order to minimize the inspection effort and provide objective outcomes. In this context, timely detection of sensor malfunctioning is crucial in preventing inaccurate assessment and false alarms. The present work introduces a sensor-fault detection and interpretation framework, based on the well-established support-vector machine scheme for anomaly detection, combined with a coalitional game-theory approach. The proposed framework is implemented in two datasets, provided along the 1st International Project Competition for Structural Health Monitoring (IPC-SHM 2020), comprising acceleration and cable-load measurements from two real cable-stayed bridges. The results demonstrate good predictive performance and highlight the potential for seamless adaption of the algorithm to intrinsically different data domains. For the first time, the term "decision trajectories", originating from the field of cognitive sciences, is introduced and applied in the context of SHM. This provides an intuitive and comprehensive illustration of the impact of individual features, along with an elaboration on feature dependencies that drive individual model predictions. Overall, the proposed framework provides an easy-to-train, application-agnostic and interpretable anomaly detector, which can be integrated into the preprocessing part of various SHM and condition-monitoring applications, offering a first screening of the sensor health prior to further analysis.

• International Journal of Computer Science & Network Security
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• v.22 no.12
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• pp.185-196
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• 2022

The Internet of Things (IoT) has become more and more widespread in recent years, thus attackers are placing greater emphasis on IoT environments. The IoT connects a large number of smart devices via wired and wireless networks that incorporate sensors or actuators in order to produce and share meaningful information. Attackers employed IoT devices as bots to assault the target server; however, because of their resource limitations, these devices are easily infected with IoT malware. The Distributed Denial of Service (DDoS) is one of the many security problems that might arise in an IoT context. DDOS attempt involves flooding a target server with irrelevant requests in an effort to disrupt it fully or partially. This worst practice blocks the legitimate user requests from being processed. We explored an intelligent intrusion detection system (IIDS) using a particular sort of machine learning, such as Artificial Neural Networks, (ANN) in order to handle and mitigate this type of cyber-attacks. In this research paper Feed-Forward Neural Network (FNN) is tested for detecting the DDOS attacks using a modified version of the KDD Cup 99 dataset. The aim of this paper is to determine the performance of the most effective and efficient Back-propagation algorithms among several algorithms and check the potential capability of ANN- based network model as a classifier to counteract the cyber-attacks in IoT environments. We have found that except Gradient Descent with Momentum Algorithm, the success rate obtained by the other three optimized and effective Back- Propagation algorithms is above 99.00%. The experimental findings showed that the accuracy rate of the proposed method using ANN is satisfactory.