• Tunnel and Underground Space
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• v.33 no.6
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• pp.495-507
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• 2023

The roughness of discontinuity significantly influences the mechanical characteristics of rock masses and extensively affects thermal and hydraulic behaviors. In this study, we utilized photogrammetry to generate 3D point cloud data for discontinuity and applied this data to characterize the roughness of discontinuity. The discontinuity profiles, reconstructed from the 3D point cloud data, were compared with those manually measured using a profile gauge. This comparison served to validate the accuracy and reliability of the acquired point cloud data in replicating the actual configurations of rock surfaces. Subsequent to this validation, influence of the number of profiles for representative JRC assessment was further investigated followed by suggestion of roughness anisotropy evaluation method with application of it to actual rock discontinuity surfaces.

• Structural Engineering and Mechanics
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• v.88 no.1
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• pp.53-65
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• 2023

With the gradual implementation of long-span suspension bridges into high-speed railway operations, the main beam's bending stiffness contribution to the live load response permanently grows. Since another critical control parameter of railway suspension bridges is the beam-end rotation angle, it should not be ignored by treating the main beam deflection as the only deformation response. To this end, the current study refines the existing method of the main cable shape and simply supported beam bending moment analogy. The bending stiffness of the main beam is considered, and the main beam's analytical expressions of deflection and rotation angle in the whole span are obtained using the cable-beam deformation coordination relationship. Taking a railway suspension bridge as an example, the effectiveness and accuracy of the proposed analytical method are verified by the finite element method (FEM). Comparison of the results by FEM and the analytical method ignoring the main beam stiffness revealed that the bending stiffness of the main beam strongly contributed to the live load response. Under the same live load, as the main beam stiffness increases, the overall deformation of the structure decreases, and the reduction is particularly noticeable at locations with original larger deformations. When the main beam stiffness is increased to a certain extent, the stiffening effect is no longer pronounced.

• Journal of the Korea Institute of Building Construction
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• v.23 no.5
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• pp.629-638
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• 2023

This study introduces an innovative approach to monitor the whereabouts of workers within indoor construction settings. While traditional modalities such as GPS and NTRIP have demonstrated efficacy for outdoor localizations, their precision dwindles in indoor environments. In response, this research advocates for the adoption of Aruco markers. Leveraging computer vision technology, these markers facilitate the quantification of the distance between a worker and the marker, subsequently pinpointing the worker's instantaneous location with heightened accuracy. The methodology's efficacy was rigorously evaluated in a real-world construction scenario. Parameters including system stability, the influence of lighting conditions, the extremity of measurable distances, and the breadth of recognition angles were methodically appraised. System stability was ascertained by maneuvering the camera at a uniform velocity, gauging its marker recognition prowess. The impact of varying luminosity on marker discernibility was scrutinized by modulating the ambient lighting. Furthermore, the camera's spatial movement ascertained both the upper threshold of distance until marker recognition waned and the maximal angle at which markers remained discernible.

• Structural Engineering and Mechanics
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• v.90 no.2
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• pp.159-175
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• 2024

In order to solve the problem of calculating the reasonable completed bridge state of a self-anchored hybrid cable-stayed suspension bridge (SA-HCSB), this paper proposes an analytical method. This method simplifies the main beam into a continuous beam with multi-point rigid supports and solves the support reaction forces. According to the segmented catenary theory, it simultaneously solves the horizontal forces of the main span main cables and the stay cables and iteratively calculates the equilibrium force system on the main beam in the collaborative system bridge state while completing the shape finding of the main span main cable and stay cables. Then, the horizontal forces of the side span main cables and stay cables are obtained based on the balance of horizontal forces on the bridge towers, and the shape finding of the side spans are completed according to the segmented catenary theory. Next, the difference between the support reaction forces of the continuous beam with multiple rigid supports obtained from the initial and final iterations is used to calculate the load of ballast on the side span main beam. Finally, the axial forces and strains of each segment of the main beam and bridge tower are obtained based on the loads applied by the main cable and stay cables on the main beam and bridge tower, thereby obtaining analytical data for the bridge in the reasonable completed state. In this paper, the rationality and effectiveness of this analytical method are verified through a case study of a SA-HCSB with a main span of 720m in finite element analysis. At the same time, it is also verified that the equilibrium force of the main beam under the reasonably completed bridge state can be obtained through iterative calculation. The analytical algorithm in this paper has clear physical significance, strong applicability, and high accuracy of calculation results, enriching the shape-finding method of this bridge type.

• Analytical Science and Technology
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• v.37 no.2
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• pp.98-113
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• 2024

The current investigation entails the characterization of five degradation products (DPs) formed under different stress conditions of loteprednol using liquid chromatography-tandem mass spectrometry (LC-MS/MS). In addition, this study developed a stable high-performance liquid chromatography (HPLC) method for evaluating loteprednol along with impurities. The method conditions were meticulously fine-tuned which involved the exploration of the appropriate solvent, pH, flow of the mobile phase, columns, and wavelength. The method conditions were carefully chosen to successfully resolve the impurities of loteprednol and were employed in subsequent validation procedures. The stability profile of loteprednol was exposed to stress degradation experiments conducted under five conditions, and DPs were structurally characterized by employing LC-MS/MS. The chromatographic resolution of loteprednol and its impurities along with DPs was effectively achieved using a Phenomenex Luna 250 mm C18 column using 0.1 % phosphoric acid, methanol, and acetonitrile in 45:25:30 (v/v) pumped isocratically at 0.8 mL/min with 243 nm wavelength. The method produces an accurate fit calibration curve in 50-300 ㎍/mL for loteprednol and LOQ (0.05 ㎍/mL) - 0.30 ㎍/mL for its impurities with acceptable precision, accuracy, and recovery. The stress-induced degradation study revealed the degradation of loteprednol under basic, acidic, and photolytic conditions, resulting in the formation of seven distinct DPs. The efficacy of this method was validated through LC-MS/MS, which allowed for the verification of the chemical structures of the newly generated DPs of loteprednol. This method was appropriate for assessing the impurities of loteprednol and can also be appropriate for structural and quantitative assessment of its degradation products.

• Korean Journal of Radiology
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• v.22 no.4
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• pp.513-524
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• 2021

Objective: To assess the noncontrast two-dimensional single-shot balanced turbo-field-echo magnetic resonance angiography (b-TFE MRA) features of the abdominal aortic aneurysm (AAA) status following endovascular aneurysm repair (EVAR) and evaluate to detect endoleaks (ELs). Materials and Methods: We examined four aortic stent-grafts in a phantom study to assess the degree of metallic artifacts. We enrolled 46 EVAR-treated patients with AAA and/or common iliac artery aneurysm who underwent both computed tomography angiography (CTA) and b-TFE MRA after EVAR. Vascular measurements on CTA and b-TFE MRA were compared, and signal intensity ratios (SIRs) of the aneurysmal sac were correlated with the size changes in the AAA after EVAR (AAA prognoses). Furthermore, we examined six feasible b-TFE MRA features for the assessment of ELs. Results: There were robust intermodality (r = 0.92-0.99) correlations and interobserver (intraclass correlation coefficient = 0.97-0.99) agreement. No significant differences were noted between SIRs and aneurysm prognoses. Moreover, "mottled high-intensity" and "creeping high-intensity with the low-band rim" were recognized as significant imaging findings suspicious for the presence of ELs (p < 0.001), whereas "no signal black spot" and "layered high-intensity area" were determined as significant for the absence of ELs (p < 0.03). Based on the two positive features, sensitivity, specificity, and accuracy for the detection of ELs were 77.3%, 91.7%, and 84.8%, respectively. Furthermore, the k values (0.40-0.88) displayed moderate-to-almost perfect agreement. Conclusion: Noncontrast MRA could be a promising imaging modality for ascertaining patient follow-up after EVAR.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.158-168
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• 2024

There is hardly any person in modern times who has not taken soft drinks instead of drinking water. The rate of people taking soft drinks being surprisingly high, researchers around the world have cautioned from time to time that these drinks lead to weight gain, raise the risk of non-communicable diseases and so on. Therefore, in this work an image-based tool is developed to monitor the nutritional information of soft drinks by using deep convolutional neural network with transfer learning. At first, visual saliency, mean shift segmentation, thresholding and noise reduction technique, collectively known as 'pre-processing' are adopted to extract the location of drinks region. After removing backgrounds and segment out only the desired area from image, we impose Discrete Wavelength Transform (DWT) based resolution enhancement technique is applied to improve the quality of image. After that, transfer learning model is employed for the classification of drinks. Finally, nutrition value of each drink is estimated using Bag-of-Feature (BoF) based classification and Euclidean distance-based ratio calculation technique. To achieve this, a dataset is built with ten most consumed soft drinks in Bangladesh. These images were collected from imageNet dataset as well as internet and proposed method confirms that it has the ability to detect and recognize different types of drinks with an accuracy of 98.51%.

• Journal of Practical Engineering Education
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• v.16 no.1_spc
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• pp.21-31
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• 2024

This study focused on the development of an AI essay tool for assessing writing-communication competence using ChatGPT. During the development process, the interaction between content expert and technical expert was emphasized to explore the fusion of IT and humanities and social sciences. Through close communication and interaction between the content and technical experts, they incorporated scoring criteria for writing-communication competence and developed an AI essay test tool that provides scores and feedback in the appropriateness of content, effectiveness of organization, and accuracy of grammar. This process revealed how content and technology combine and presented considerations for future fusion researchers, including technical experts in generative AI assessment tools.

• Journal of Chest Surgery
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• v.57 no.2
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• pp.205-212
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• 2024

Background: Postoperative atrial fibrillation (A-fib) is a serious complication of cardiac surgery that is associated with increased mortality and morbidity. Traditional 24-hour Holter monitors have limitations, which have prompted the development of innovative wearable electrocardiogram (ECG) monitoring devices. This study assessed a patch-type wearable ECG device (MobiCARE-MC100) for monitoring A-fib in patients undergoing cardiac surgery and compared it with 24-hour Holter ECG monitoring. Methods: This was a single-center, prospective, investigator-initiated cohort study that included 39 patients who underwent cardiac surgery between July 2021 and June 2022. Patients underwent simultaneous monitoring with both conventional Holter and patchtype ECG devices for 24 hours. The Holter device was then removed, and patch-type monitoring continued for an additional 48 hours, to determine whether extended monitoring provided benefits in the detection of A-fib. Results: This 72-hour ECG monitoring study included 39 patients, with an average age of 62.2 years, comprising 29 men (74.4%) and 10 women (25.6%). In the initial 24 hours, both monitoring techniques identified the same number of paroxysmal A-fib in 7 out of 39 patients. After 24 hours of monitoring, during the additional 48-hour assessment using the patch-type ECG device, an increase in A-fib burden (9%→38%) was observed in 1 patient. Most patients reported no significant discomfort while using the MobiCARE device. Conclusion: In patients who underwent cardiac surgery, the mobiCARE device demonstrated diagnostic accuracy comparable to that of the conventional Holter monitoring system.

• Korean Journal of Agricultural Science
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• v.50 no.4
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• pp.675-696
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• 2023

Terpenoids, also referred to as terpenes, are a large family of naturally occurring chemical compounds present in the essential oils extracted from medicinal plants. In this study, a nondestructive methodology was created by combining ATR-FT-IR (attenuated total reflectance-Fourier transform infrared), and Raman spectroscopy for the terpenoids assessment in medicinal plants essential oils from ten different geographical locations. Partial least squares regression (PLSR) and support vector regression (SVR) were used as machine learning methodologies. However, a deep learning based model called as one-dimensional convolutional neural network (1D CNN) were also developed for models comparison. With a correlation coefficient (R²) of 0.999 and a lowest RMSEP (root mean squared error of prediction) of 0.006% for the prediction datasets, the SVR model created for FT-IR spectral data outperformed both the PLSR and 1 D CNN models. On the other hand, for the classification of essential oils derived from plants collected from various geographical regions, the created SVM (support vector machine) classification model for Raman spectroscopic data obtained an overall classification accuracy of 0.997% which was superior than the FT-IR (0.986%) data. Based on the results we propose that FT-IR spectroscopy, when coupled with the SVR model, has a significant potential for the non-destructive identification of terpenoids in essential oils compared with destructive chemical analysis methods.