• Journal of English Language & Literature
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• v.57 no.3
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• pp.515-527
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• 2011

Miss Pettigrew Lives for a Day, Winifred Watson's novel of 1938, is a fairytale in novel form. Set in London of 1938, the story revolves around a one-day adventure of an ill-starred but truthful governess who is granted a second chance. This light-hearted comedy of manners was turned into a film by director Bharat Nalluri in 2008. An Anglo-American collaboration, co-scripted by Simon Beaufoy and David McGee, the film converts Watson's quaint novel into an edged heritage piece that encapsulates the 1930s, the problematic decade between the two World Wars. The film, while sustaining the narrative core of Watson's Cinderella story, attempts to place it firmly within a wider current of the novel's setting or London in 1938, tapping into the major concerns of the interwar years that engage with characters in one way or another. Stylistically, the film presents Art Deco as a main visual idiom to convey the prevailing mood of nihilism and decadence of the day. The setting here takes on significance in that it offers a telling counterpoint to the giddy superficial world of the novel. The 1930s was a highly charged decade under the threat of fascism and the Great Depression, fraught with economic and socio-political tensions and apprehensions. The film makes an explicit reference to the dismal context which is suppressed in the original text. The thirties is, therefore, portrayed as a decade of contradiction. It features gay buoyant festivity, rampant consumerism, and shifting morals and attitudes towards love, marriage and sexuality. Yet lurking beneath the surface glamour are the symptoms of crises and the deep-seated anxieties on the eve of World War II. In this way, Watson's novel of manners has been recreated into a defining film on the 1930s with its period feel propped by the atmospheric lighting, the exuberant Jazz score, and the splendid Art Deco costume and production design.

• Journal of the Computational Structural Engineering Institute of Korea
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• v.36 no.5
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• pp.315-321
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• 2023

Long-span bridges are flexible structures with low natural frequencies and damping ratios, making them susceptible to vibrational serviceability problems. However, the current design guideline of South Korea assumes a uniform threshold of wind speed or vibrational amplitude to assess the occurrence of harmful vibrations, potentially overlooking the complex vibrational patterns observed in long-span bridges. In this study, we propose a pointwise vortex-induced vibration (VIV) detection method using a deep-learning-based signalsegmentation model. Departing from conventional supervised methods of data acquisition and manual labeling, we synthesize training data by generating sinusoidal waves with an envelope to accurately represent VIV. A Fourier synchrosqueezed transform is leveraged to extract time-frequency features, which serve as input data for training a bidirectional long short-term memory model. The effectiveness of the model trained on synthetic VIV data is demonstrated through a comparison with its counterpart trained on manually labeled real datasets from an actual cable-supported bridge.

• Advances in nano research
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• v.15 no.5
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• pp.467-484
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• 2023

Despite the significant features of fiber-reinforced cementitious composites (FRCCs), including better mechanical, fractural, and durability performance, their high content of cement has restricted their use in the construction industry. Although ground granulated blast furnace slag (GGBFS) is considered the main supplementary cementitious material, its slow pozzolanic reaction stands against its application. The addition of nano-sized mineral modifiers, including nano-silica (NS), is an alternative to address the drawbacks of using GGBFS. The main object of this empirical and numerical research is to examine the effect of NS on the strain-hardening behavior of cementitious composites; ten mixes were designed, and five levels of NS were considered. This study proposes a new method, using a four-point bending test to assess the use of nano-silica (NS) on the flexural behavior, first cracking strength, fracture energy, and micromechanical parameters including interfacial friction bond strength and maximum bridging stress. Digital image correlation (DIC) was used for monitoring the initiation and propagation of the cracks. In addition, to attain a deep comprehension of fiber/matrix interaction, scanning electron microscope (SEM) analysis was used. It was discovered that using nano-silica (NS) in cementitious materials results in an enhancement in the matrix toughness, which prevents multiple cracking and, therefore, strain-hardening. In addition, adding NS enhanced the interfacial transition zone between matrix and fiber, leading to a higher interfacial friction bond strength, which helps multiple cracking in the composite due to the hydrophobic nature of polypropylene (PP) fibers. The findings of this research provide insight into finding the optimum percent of NS in which both ductility and high tensile strength of the composites would be satisfied. As a concluding remark, a new criterion is proposed, showing that the optimum value of nano-silica is 2%. The findings and proposed method of this study can facilitate the design and utilization of green cementitious composites in structures.

• Advances in nano research
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• v.15 no.5
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• pp.451-466
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• 2023

Gastrointestinal cancer (GC) is a prevalent malignant tumor of the digestive system that poses a severe health risk to humans. Due to the specific organ structure of the gastrointestinal system, both endoscopic and MRI diagnoses of GIC have limited sensitivity. The primary factors influencing curative efficacy in GIC patients are drug inefficacy and high recurrence rates in surgical and pharmacological therapy. Due to its unique optical features, good biocompatibility, surface effects, and small size effects, nanotechnology is a developing and advanced area of study for the detection and treatment of cancer. Because of its deep location and complex surgery, diagnosing and treating gastrointestinal cancer is very difficult. The early diagnosis and urgent treatment of gastrointestinal illness are enabled by nanotechnology. As diagnostic and therapeutic tools, nanoparticles directly target tumor cells, allowing their detection and removal. XGBoost was used as a classification method known for achieving numerous winning solutions in data analysis competitions, to capture nonlinear relations among many input variables and outcomes using the boosting approach to machine learning. The research sample included 300 GC patients, comprising 190 males (72.2% of the sample) and 110 women (27.8%). Using convolutional neural networks (CNN) and artificial neural networks (ANN)-EXtreme Gradient Boosting (XGBoost), the patients mean± SD age was 50.42 ± 13.06. High-risk behaviors (P = 0.070), age at diagnosis (P = 0.037), distant metastasis (P = 0.004), and tumor stage (P = 0.015) were shown to have a statistically significant link with GC patient survival. AUC was 0.92, sensitivity was 81.5%, specificity was 90.5%, and accuracy was 84.7 when analyzing stomach picture.

• Journal of Veterinary Clinics
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• v.41 no.2
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• pp.79-87
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• 2024

The tarsus in dogs has a complex structure that makes its evaluation relatively challenging. Because an accurate diagnosis of the tarsus is difficult through basic examinations alone, imaging tests are essential. Previous studies have explored the anatomical and radiological features of the canine tarsus using several imaging modalities. However, the imaging utility of the tarsus across different modalities has not been thoroughly evaluated. This study aimed to visualize the tarsal structures using magnetic resonance imaging (MRI) and ultrasonography, compare their utility, and propose suitable imaging modalities and conditions for evaluating specific tarsal structures. Magnetic resonance imaging and ultrasound scans of the tarsus of four healthy dogs were performed, and two observers rated the utility of each image on a five-point scale. Although MRI is more beneficial for assessing the tarsal structures than ultrasound, ultrasound also appears clinically useful for evaluating the cranial tibialis muscle, deep digital flexor tendon, subcutaneous fat, joint space, and superficial digital flexor tendon. In addition, each structure of interest can be evaluated for optimal visibility using specific ultrasound sections, MRI sequences, and planes. In veterinary clinical practice, an initial assessment using ultrasound imaging with optimal visibility is required and if further evaluation is necessary, MRI examinations with optimal MRI sequences and planes can be performed.

• International Journal of Computer Science & Network Security
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• v.24 no.2
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• pp.101-112
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• 2024

Telugu language is considered as fourth most used language in India especially in the regions of Andhra Pradesh, Telangana, Karnataka etc. In international recognized countries also, Telugu is widely growing spoken language. This language comprises of different dependent and independent vowels, consonants and digits. In this aspect, the enhancement of Telugu Handwritten Character Recognition (HCR) has not been propagated. HCR is a neural network technique of converting a documented image to edited text one which can be used for many other applications. This reduces time and effort without starting over from the beginning every time. In this work, a Unicode based Handwritten Character Recognition(U-HCR) is developed for translating the handwritten Telugu characters into English language. With the use of Centre of Gravity (CG) in our model we can easily divide a compound character into individual character with the help of Unicode values. For training this model, we have used both online and offline Telugu character datasets. To extract the features in the scanned image we used convolutional neural network along with Machine Learning classifiers like Random Forest and Support Vector Machine. Stochastic Gradient Descent (SGD), Root Mean Square Propagation (RMS-P) and Adaptative Moment Estimation (ADAM)optimizers are used in this work to enhance the performance of U-HCR and to reduce the loss function value. This loss value reduction can be possible with optimizers by using CNN. In both online and offline datasets, proposed model showed promising results by maintaining the accuracies with 90.28% for SGD, 96.97% for RMS-P and 93.57% for ADAM respectively.

• Journal of the Korea Society of Computer and Information
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• v.29 no.1
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• pp.77-84
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• 2024

In this paper, we propose a method for accurately and rapidly detecting defects in wire harnesses by utilizing computer vision to calculate six crucial measurement values: the length of crimped terminals, the dimensions (width) of terminal ends, and the width of crimped sections (wire and core portions). We employ Harris corner detection to locate object positions from two types of data. Additionally, we generate reference points for extracting measurement values by utilizing features specific to each measurement area and exploiting the contrast in shading between the background and objects, thus reflecting the slope of each sample. Subsequently, we introduce a method using the Euclidean distance and correction coefficients to predict values, allowing for the prediction of measurements regardless of changes in the wire's position. We achieve high accuracy for each measurement type, 99.1%, 98.7%, 92.6%, 92.5%, 99.9%, and 99.7%, achieving outstanding overall average accuracy of 97% across all measurements. This inspection method not only addresses the limitations of conventional visual inspections but also yields excellent results with a small amount of data. Moreover, relying solely on image processing, it is expected to be more cost-effective and applicable with less data compared to deep learning methods.

• Journal of Korean Society of Disaster and Security
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• v.16 no.4
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• pp.129-136
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• 2023

A special offshore bridge with a high pylon has special structural features.Special offshore bridges have inspection blind spots that are difficult to visually inspect. To solve this problem, safety inspection methods using drones are being studied. In this study, image data of the pylon of a special offshore bridge was acquired using a drone. In addition, an artificial intelligence algorithm was developed to detect damage to the pylon surface. The AI algorithm utilized a deep learning network with different structures. The algorithm applied the stacking ensemble learning method to build a model that formed the ensemble and collect the results.

• Journal of Digital Policy
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• v.3 no.1
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• pp.27-32
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• 2024

In this paper, proposes a super resolution method that enhances the quality of results by refining texture features, contrasting each, and utilizing the results as weights. For the improvement of quality, a precise and clear restoration result in details such as boundary areas is crucial in super resolution, along with minimizing unnecessary artifacts like noise. The proposed method constructs a residual block structure with multiple paths and skip-connections for feature estimation in conventional Convolutional Neural Network (CNN)-based super resolution methods to enhance quality. Additional learning is performed for sharpened and blurred image results for further texture analysis. By contrasting each super resolution result and allocating weights through this process, the proposed method achieves improved quality in detailed and smoothed areas of the image. The experimental results of the proposed method, evaluated using the PSNR and SSIM values as quality metrics, show higher results compared to existing algorithms, confirming the enhancement in quality.

• Journal of the Korea Institute of Information Security & Cryptology
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• v.34 no.1
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• pp.103-114
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• 2024

In a situation where drone-related crimes continue to rise, research in drone forensics becomes crucial for preventing and responding to incidents involving drones. Conducting forensic analysis on flight record files stored internally is essential for investigating illegal activities. However, analyzing flight record files generated through the exclusive DUML protocol requires a deep understanding of the protocol's structure and characteristics. Additionally, a forensic analysis tool capable of handling cryptographic payloads and analyzing various drone models is imperative. Therefore, this study presents the methods and characteristics of flight record files generated by drones. It also explains the structure of the flight record file and the features of the DUML packet. Ultimately, we conduct forensic analysis based on the presented structure of the DUML packet and propose an extension forensic analysis system that operates more universally than existing tools, performing expanded syntactic analysis.