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In order to reduce the weight of parts in the automobile and electronic industries, various research on dissimilar welding techniques of aluminum and steel is being carried out. Since dissimilar materials have different physical and electrochemical characteristics, joining through conventional fusion welding is challenging, and there is a high probability of a decrease in strength of the welded joints. To solve this problem, a mechanical fastening method is mainly applied to join dissimilar parts with different material properties, but this process has disadvantages in terms of productivity improvement and cost reduction because additional consumables, such as rivets, are required. In this research, we investigated the optimization of the weld bonding conditions of joints using epoxy-based adhesive bonding and DeltaSpot welding for Al/Fe dissimilar materials. For each experimental condition, the corrosion resistance and tensile shear strength of the welded joints were evaluated according to salt spray test times of 0 h, 640 h, 1280 h, and 1920 h. As a whole, as the salt spray test time increased, the tensile shear strength of the welded joints decreased. It was confirmed that weld bonding, after manual polishing of the aluminum side, resulted in the highest average tensile shear strength of 5.88 kN at 1920 h, which was an increase compared with other conditions.

Imaging plays an important role in assessing severity of COVID-19 pneumonia. The recent COVID-19 researches indicate that in many cases, disease progress propagates from the bottom of the lungs to the top. However, semantic interpretation of chest radiography (CXR) findings do not provide a quantitative description of radiographic opacities, and the existing AI-assisted CXR image analysis frameworks do not quantify the severity regionally. To address this issue, we propose a deep learning-based four-region lung segmentation method to assist accurate quantification of COVID-19 pneumonia. Specifically, a segmentation model to separate left and right lung is firstly applied, and then a carina and left hilum detection network is used to separate the upper and lower lungs. To improve the segmentation performance of COVID-19 images, ensemble strategy with five models is exploited. For each region, we evaluated the clinical relevance of the proposed method compared with the Radiographic Assessment of the Quality of Lung Edema (RALE). The proposed ensemble strategy showed dice score of 0.900, which outperforms the conventional methods. Mean intensities of segmented four regions indicate positive correlation to the extent and density scores of pulmonary opacities based on the RALE framework. Therefore, the proposed method can accurately segment four-regions of the lungs and quantify regional pulmonary opacities of COVID-19 pneumonia patients.

Degraded electrodes in a resistance spot welding system should be replaced to ensure that weld quality is maintained. Welding electrodes are subjected to different environmental and operational loading conditions during use. When they are replaced with a fixed interval, replacement may occur too early (raising maintenance costs) or too late (leading to quality issues). This motivates condition monitoring strategies for resistance spot welding electrode tips. Thus, this paper proposes a modified recurrence plot (RP) for robust condition monitoring of welding electrode tips in resistance spot welding systems. The overall procedure for the proposed condition monitoring approach consists of three steps: (1) transformation of a one-dimensional signal to a two-dimensional image, (2) unsupervised feature extraction with LeNet architecture-based convolutional neural networks, and (3) health indicator calculation. RP methods convert dynamic resistance waveforms to RPs. The original RP method provides an image with binary-colored pixels (i.e., black or white) that makes this method insensitive to the change of the waveform signal. The proposed RP method is devised to be sensitive to the change of the waveform signal, while enhancing robustness to external noise. The performance of the proposed RP method is evaluated by examining simulated aperiodic waveform signals with and without external noise. A case study is presented to examine the proposed method&rsquo, s ability to monitor the condition of resistance spot welding electrodes. The results show that the proposed method outperforms handcrafted, feature-based condition monitoring methods. This study can be used to accurately determine the lifetime of welding electrodes in real time during the spot welding process.

The behavior of nanogels in suspension can be dramatically affected by the grafting of a canopy of end-tethered polymer chains. The architecture of the interfacial layer, defined by the grafting density and length of the polymer chains, is a crucial parameter in defining the conformation and influencing the dynamics of the grafted chains. However, the influence of this architecture when the core substrate is itself soft and mobile is complex, the dynamics of the core influences the dynamics of the tethered chains, and, conversely, the dynamics of the tethered chains can influence the dynamics of the core. Here, poly(styrene) (PS) particles were functionalized with poly(methyl acrylate) (PMA) chains and swollen in a common solvent. NMR relaxation reveals that the confinement influences the mobility of the grafted chain more prominently for densely grafted short chains. The correlation time associated with the relaxation of the PMA increased by more than 20% when the grafting density increased for short chains, but for less than 10% for long chains. This phenomenon is likely due to the steric hindrance created by the close proximity to the rigid core and of the neighboring chains. More interestingly, a thick layer of a densely grafted PMA canopy efficiently increases the local mobility of the PS cores, with a reduction of the correlation time of more than 30%. These results suggest an interplay between the dynamics of the core and the dynamics of the canopy.

Purpose Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin-stained frozen tissue sections of SLNs in breast cancer patients. Materials and methods A total of 297 digital slides were obtained from frozen SLN sections, which include post-neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). Results The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. Conclusion In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.

Despite the wide application of next-generation sequencing, Sanger sequencing still plays a necessary role in clinical laboratories. However, recent developments in the field of bioinformatics have focused mostly on next-generation sequencing, while tools for Sanger sequencing have shown little progress. In this study, SnackVar (https://github.com/Young-gonKim/SnackVar, last accessed June 22, 2020), a novel graphical user interface-based software for Sanger sequencing, was developed. All types of variants, including heterozygous insertion/deletion variants, can be identified by SnackVar with minimal user effort. The featured reference sequences of all of the genes are prestored in SnackVar, allowing for detected variants to be precisely described based on coding DNA references according to the nomenclature of the Human Genome Variation Society. Among 88 previously reported variants from four insertion/deletion-rich genes (BRCA1, APC, CALR, and CEBPA), the result of SnackVar agreed with reported results in 87 variants [98.9% (93.0%; 99.9%)]. The cause of one incorrect variant calling was proven to be erroneous base callings from poor-quality trace files. Compared with commercial software, SnackVar required less than one-half of the time taken for the analysis of a selected set of test cases. We expect SnackVar to be a cost-effective option for clinical laboratories performing Sanger sequencing.

The quality of cephalometric analysis depends on the accuracy of the delineating landmarks in orthodontic and maxillofacial surgery. Due to the extensive number of landmarks, each analysis costs orthodontists considerable time per patient, leading to fatigue and inter- and intra-observer variabilities. Therefore, we proposed a fully automated cephalometry analysis with a cascade convolutional neural net (CNN). One thousand cephalometric x-ray images (2 k × 3 k) pixel were used. The dataset was split into training, validation, and test sets as 8:1:1. The 43 landmarks from each image were identified by an expert orthodontist. To evaluate intra-observer variabilities, 28 images from the dataset were randomly selected and measured again by the same orthodontist. To improve accuracy, a cascade CNN consisting of two steps was used for transfer learning. In the first step, the regions of interest (ROIs) were predicted by RetinaNet. In the second step, U-Net detected the precise landmarks in the ROIs. The average error of ROI detection alone was 1.55 ± 2.17 mm. The model with the cascade CNN showed an average error of 0.79 ± 0.91 mm (paired t-test, p = 0.0015). The orthodontist’s average error of reproducibility was 0.80 ± 0.79 mm. An accurate and fully automated cephalometric analysis was successfully developed and evaluated.

Background We aimed to investigate the efficacy of the lifestyle intervention (LSI) program in controlling blood glucose regulation and health promotion in type 2 diabetic (T2D) patients. Methods Thirty adults with a diagnosed with diabetes were randomly assigned to LSI and control groups. The LSI group maintained their daily routines after participating twice in the LSI program, while control group maintained 4 weeks of daily life without participating in an intervention. Results HbA1c levels in the LSI group decreased significantly after participation (p = 0.025) compared with levels before the study, but there was no significant difference between the groups. The weight and body mass index (BMI) of the LSI group tended to decrease significantly compared with the control group (p = 0.054 and p = 0.055, respectively), and the waist circumference (WC) of the LSI group decreased significantly compared with that of the control group (p = 0.048). In the effects of the LSI program according to the polymorphism of GCKR genes, changes in glycated albumin (GA) (%), HbA1c, WC, BMI, and weight showed a significant decrease in the non-risk (TT genotype) GCKR group compared with the risk group (CC and TC genotype). Conclusion Application of the four-week LSI program to diabetics revealed positive effects on blood-glucose control and improvement in obesity indicators. In particular, the risk group with variations in the GCKR gene was associated with more genetic effects on indicators such as blood glucose and obesity than was the non-risk group.

Grafting polymer chains on the surface of nanoparticles (NPs) is a strategy used to control the interaction between the NPs and their environment. The fate of the resulting particles in a given environment is strongly influenced by the solvent-polymer interaction. The solvent quality affects the behavior, conformation, and dynamics of the grafted polymer chains. However, when this polymer grafting strategy is used to functionalized polymer particles, the influence of solvent quality becomes even more complex; when the grafted polymer chains and the polymer nanoparticles are tethered together, the effect of the solvent quality on the behavior and dynamics of the system depends on the solvent interaction with both polymer components. To explore the relationship between the solvent quality and the dynamics of polymer-functionalized soft polymer NPs, we designed a system based on cross-linked polystyrene (PS) NPs grafted with a canopy of poly(methyl acrylate) (PMA). PS and PMA, two immiscible polymers, can be selectively solvated by using binary mixtures of solvents. NMR spectroscopy was used to address the effect of those selective solvents on the local mobility of the PS-PMA core-canopy NPs and revealed an interplay between the local mobility of the core and the local mobility of the canopy. A selective reduction of the solvent quality for the PMA canopy resulted in the expected reduction of the local mobility of the PMA chains, but also in the slower dynamics of the PS core. Similarly, a selective reduction of the solvent quality for the PS core resulted in a slower dynamics for both the PS core and the PMA canopy.

Assessing the status of metastasis in sentinel lymph nodes (SLNs) by pathologists is an essential task for the accurate staging of breast cancer. However, histopathological evaluation of sentinel lymph nodes by a pathologist is not easy and is a tedious and time-consuming task. The purpose of this study is to review a challenge competition (HeLP 2018) to develop automated solutions for the classification of metastases in hematoxylin and eosin–stained frozen tissue sections of SLNs in breast cancer patients. A total of 297 digital slides were obtained from frozen SLN sections, which include post–neoadjuvant cases (n = 144, 48.5%) in Asan Medical Center, South Korea. The slides were divided into training, development, and validation sets. All of the imaging datasets have been manually segmented by expert pathologists. A total of 10 participants were allowed to use the Kakao challenge platform for six weeks with two P40 GPUs. The algorithms were assessed in terms of the AUC (area under receiver operating characteristic curve). The top three teams showed 0.986, 0.985, and 0.945 AUCs for the development set and 0.805, 0.776, and 0.765 AUCs for the validation set. Micrometastatic tumors, neoadjuvant systemic therapy, invasive lobular carcinoma, and histologic grade 3 were associated with lower diagnostic accuracy. In a challenge competition, accurate deep learning algorithms have been developed, which can be helpful in making frozen diagnosis of intraoperative sentinel lymph node biopsy. Whether this approach has clinical utility will require evaluation in a clinical setting.

The design of surface-modified functional nanoparticles (NPs) is used to control the properties of the NPs and the NP/environment interactions. The efficient control of the final behavior of the NPs demands a comprehensive understanding of the resulting system. This is particularly challenging for systems with an architecture of the type polymer core–polymer canopy. In such systems, one of the key parameters influencing the behavior of the NPs is the local dynamics of the polymer canopy. However, because the grafting points of the canopy are experiencing their own local dynamics, predicting the final behavior of such systems is difficult. To get a deeper understanding of NPs made of a soft and swollen polymer core and a swollen polymer canopy, we prepared a library of hairy NPs made of a polystyrene (PS) core and a canopy of grafted poly(methyl acrylate) (PMA) chains. The softness of the PS core and the thickness of the PMA canopy were controlled, and the behavior and dynamics of the soft and hairy PS–PMA NPs in suspension were measured by 1H NMR relaxation and dynamic light scattering. It was observed that the rigid PS core slowed down the subsegmental dynamics of the PMA chains, while thick PMA canopies accelerated the relaxation of the PS core. The dynamics of the NPs in suspension was the result of the interplay between the PS core and the PMA canopy.

Background Complete blood count (CBC) results play an important role in peripheral blood smear (PBS) examinations. Many descriptions in PBS reports may simply be translated from CBC parameters. We developed a computer program that automatically generates a PBS draft report based on CBC parameters and age- and sex-matched reference ranges. Methods The Java programming language was used to develop a computer program that supports a graphical user interface. Four hematology analyzers from three different laboratories were tested: Sysmex XE-5000 (Sysmex, Kobe, Japan), Sysmex XN-9000 (Sysmex), DxH800 (Beckman Coulter, Brea, CA, USA), and ADVIA 2120i (Siemens Healthcare Diagnostics, Eschborn, Germany). Input data files containing 862 CBC results were generated from hematology analyzers, middlewares, or laboratory information systems. The draft reports were compared with the content of input data files. Results We developed a computer program that reads CBC results from a data file and automatically writes a draft PBS report. Age- and sex-matched reference ranges can be automatically applied. After examining PBS, users can modify the draft report based on microscopic findings. Recommendations such as suggestions for further evaluations are also provided based on morphological findings, and they can be modified by users. The program was compatible with all four hematology analyzers tested. Conclusions Our program is expected to reduce the time required to manually incorporate CBC results into PBS reports. Systematic inclusion of CBC results could help improve the reliability and sensitivity of PBS examinations.

The thermally insulating, fire-retardant, smokeless and flexible polymer nanofiber is an incredibly important material for mankind. This polymer nanofiber is expected to breed technological innovations in applications such as thermally insulated clothing and building materials. Here, we propose an electrospinning process to fabricate silica aerogel-filled polymer nanofibers by modifying the typical sol–gel chemistry process for the synthesis of silica aerogel. Silica aerogel-filled polyvinylidene fluoride (PVdF) nanofiber webs, fabricated using the proposed process, were fire-retardant, smokeless and flexible; they also exhibited a low thermal conductivity of 0.028 W·m−1·K−1, which was 26.3% lower than that of pure PVdF nanofiber webs. To study the unique thermal characteristics of nanofibers theoretically, a micromechanics-based, two-scale multiphase model was proposed, with consideration of fiber structure including nano and micro pores. These developments make an important contribution to bringing silica aerogel products to everyday life.

The modified crucible in the top seeded solution growth (TSSG) method was proposed to get the stable growth condition and the enhanced growth rate. The temperature gradient and the distribution of dissolved carbon in the solvent were obtained by numerical approaches. Compared simulation results and experiment, we investigated the region where is the most of dissolved carbon and it is supplied to the seed through what path. The cross-sectional samples of crucibles and grown SiC crystals were systematically analyzed to investigate the effect of the crucible modification on SiC crystal growth.

The crucible design having a stepped wall was introduced for increasing the growth rate of SiC crystal without metal addition in top-seeded solution growth (TSSG) method. The numerical simulation confirmed that new crucible design to increase the solvent-crucible interface could definitely change the temperature distribution and increase the carbon concentration. The simulation result, SiC single crystals were grown with Si solvent at 1900°C using a normal crucible and a stepped crucible to investigate the effect of crucible design having a stepped wall. Grown SiC layers were analyzed using Optical microscopy and Raman spectra. The growth rate in the stepped crucible was finally 66um/h observed.

6H-SiC single crystal was grown with simple modification in PVT process to investigate the aspect of resistivity change in crystal. The modified process consisted of a new initial step to get rid of impurities in the growth cell before the main growth of SiC crystal. The new step in the modified process was designed to consist of higher temperature than the growth temperature for sublimation of impurities. SiC crystal grown with using 2 times of impurities sublimation process (ISP) step exhibited lower variation of resistivity value on whole wafer than SiC grown using with 1 time. With implementation of new modified step in growth process, SiC single crystal with resistivity value above 103Ωcm could be obtained by simple PVT process and conventional low-purity SiC source material.

The demand of crack tip opening displacement (CTOD) test which evaluates fracture toughness of a cracked material is very important to ensure the stability of structure under severe service environment. The validity of the CTOD test result is judged using several criterions of the specification standards. One of them is the artificially generated fatigue pre-crack length inside the specimen. For acceptable CTOD test results, fatigue pre-crack must have a reasonable sharp crack front. The propagation of fatigue crack started from the tip of the machined notch, which might have propagated irregularly due to residual stress field. To overcome this problem, test codes suggest local compression method, reversed bending method and stepwise high-R ratio method to reduce the disparity of residual stress distribution inside the specimen. In this paper, the relation between the degree of local compression and distribution of welding residual stress has been analyzed by finite element analyses in order to determine the amount of effective local compression of the test piece. Analysis results show that initial welding residual stress is dramatically varied three-dimensionally while cutting, notch machining and local compressing due to the change of internal restraint force. From the simulation result, the authors find that there is an optimum amount of local compression to modify regularly for generating fatigue pre-crack propagation. In the case of 0.5% compressions of the model width is the most effective for uniforming residual stress distribution.

Structural-adhesive-assisted DeltaSpot welding was used to improve the weldability and mechanical properties of dissimilar joints between 6061 aluminum alloy and galvannealed HSLA steel. Evaluation of the spot-weld-bonded surfaces from lap shear tests after long-term exposure to chloride and a humid atmosphere (5% NaCl, 35 °C) indicated that the long-term mechanical reliability of the dissimilar weld in a corrosive environment depends strongly on the adhesive–Al6061 alloy bond strength. Corrosive electrolyte infiltrated the epoxy-based adhesive/Al alloy interface, disrupting the chemical interactions and decreasing the adhesion via anodic undercutting of the Al alloy. Due to localized electrochemical galvanic reactions, the surrounding nugget matrix suffered accelerated anodic dissolution, resulting in an Al6061-T6 alloy plate with degraded adhesive strength and mechanical properties. KrF excimer laser irradiation of the Al alloy before adhesive bonding removed the weakly bonded native oxidic overlayers and altered the substrate topography. This afforded a low electrolyte permeability and prevented adhesive delamination, thereby enhancing the long-term stability of the chemical interactions between the adhesive and Al alloy substrate. The results demonstrate the application of excimer laser irradiation as a simple and environmentally friendly processing technology for robust adhesion and reliable bonding between 6061 aluminum alloy and galvannealed steel.

The Internet of Things (IoT) technology is rapidly being applied to real life, but the application of a corresponding secure and convenient authentication method is still in significant challenge. So far, pattern, password and fingerprint authentication are the most used methods, but it is important to address various security vulnerabilities and limitations of these approaches. In the case of fingerprint recognition, additional hardware such as a fingerprint scanner is required, which causes cost issues and could be vulnerable to fingerprint theft. To solve this problem, this paper proposes a model that uses both biometric and behavioral authentication at the same time. This method exploits the biometric authentication that measures the length of the contact region that occurs when three fingers are placed side by side on the touch screen or pad. In addition, it utilizes the behavioral authentication itself using three-finger L-shape touch, as well as secure geometric information generated by smart watch such as acceleration sensors. Therefore, this proposed model will be useful to implement more secure, rapid and user-friendly way of authentication in many practical busy and buzzling field where deal with sensitive private information.

In this study, welding-induced out-of-plane distortion of stainless steel sheets is examined in the case of applying air-atomized mist jet on the rear side of the weld pool. An internally mixed type air atomizing spray nozzle is attached to the welding torch and both are moved at the same speed and direction onto the welding centerline. The test specimens are 2 mm thick grade 304 stainless steel plates. Bead-on-plate welds are made on the plates using a DC Gas tungsten arc welding (GTAW) process. The measured welding distortion and residual stresses are compared with those of a conventional GTAW welding process. The experimental results show that the welds performed with a trailing heat sink using air-atomized mist have almost zero out-of-plane distortion. In addition, a slight welding residual stress reduction is achieved.

Recently, Car makers have required a light weight of vehicles in relation to fuel efficiency. In that respect, dissimilar welding method is very important in the car body assembly process. In this study, resistance spot welding was performed to evaluate the welding quality and mechanical properties for dissimilar materials of the cold rolled DP590 and Al 5052. The welding experiment was carried out changing with the welding current and the other process parameters such as electrode force, weld time, squeezing time and holding time were fixed with the basic welding condition of the ISO18278-2 standard. Especially, the shear tensile strength was evaluated at each condition and the cross-sectional observation for weld zone such as nugget diameter was performed. As a result, the thickness of IMC (Intermetallic Compound) layer at 11.5 kA was thinner than those of 9.5 kA and 10.5 kA conditions. On the other hand, Delta-spot welding experiment was performed using various process tapes at welding current of 11.5 kA. The other conditions were same with that of the conventional spot welding. Sound welds was formed by applying to the optimum welding conditions in the Delta-spot process due to the thermal balance of dissimilar materials.

Nasopharyngeal swabs (NPSs) are being widely used as specimens for multiplex real-time reverse transcription (RT)-PCR for respiratory virus detection. However, it remains unclear whether NPS specimens are optimal for all viruses targeted by multiplex RT-PCR. In addition, the procedure to obtain NPS specimens causes coughing in most patients, which possibly increases the risk of nosocomial spread of viruses. In this study, paired NPS and saliva specimens were collected from 236 adult male patients with suspected acute respiratory illnesses. Specimens were tested for 16 respiratory viruses by multiplex real-time RT-PCR. Among the specimens collected from the 236 patients, at least 1 respiratory virus was detected in 183 NPS specimens (77.5%) and 180 saliva specimens (76.3%). The rates of detection of respiratory viruses were comparable for NPS and saliva specimens ( P = 0.766). Nine virus species and 349 viruses were isolated, 256 from NPS specimens and 273 from saliva specimens ( P = 0.1574). Adenovirus was detected more frequently in saliva samples ( P < 0.0001), whereas influenza virus type A and human rhinovirus were detected more frequently in NPS specimens ( P = 0.0001 and P = 0.0289, respectively). The possibility of false-positive adenovirus detection from saliva samples was excluded by direct sequencing. In conclusion, neither of the sampling methods was consistently more sensitive than the other. We suggest that these cost-effective methods for detecting respiratory viruses in mixed NPS-saliva specimens might be valuable for future studies.

To investigate the reproducibility of computer-aided detection (CAD) for detection of pulmonary nodules and masses for consecutive chest radiographies (CXRs) of the same patient within a short-term period. A total of 944 CXRs (Chest PA) with nodules and masses, recorded between January 2010 and November 2016 at the Asan Medical Center, were obtained. In all, 1092 regions of interest for the nodules and mass were delineated using an in-house software. All CXRs were randomly split into 6:2:2 sets for training, development, and validation. Furthermore, paired follow-up CXRs (n = 121) acquired within one week in the validation set, in which expert thoracic radiologists confirmed no changes, were used to evaluate the reproducibility of CAD by two radiologists (R1 and R2). The reproducibility comparison of four different convolutional neural net algorithms and two chest radiologists (with 13- and 14-years’ experience) was conducted. Model performances were evaluated by figure-of-merit (FOM) analysis of the jackknife free-response receiver operating curve and reproducibility rates were evaluated in terms of percent positive agreement (PPA) and Chamberlain’s percent positive agreement (CPPA). Reproducibility analysis of the four CADs and R1 and R2 showed variations in the PPA and CPPA. Model performance of YOLO (You Only Look Once) v2 based eDenseYOLO showed a higher FOM (0.89; 0.85–0.93) than RetinaNet (0.89; 0.85–0.93) and atrous spatial pyramid pooling U-Net (0.85; 0.80–0.89). eDenseYOLO showed higher PPAs (97.87%) and CPPAs (95.80%) than Mask R-CNN, RetinaNet, ASSP U-Net, R1, and R2 (PPA: 96.52%, 94.23%, 95.04%, 96.55%, and 94.98%; CPPA: 93.18%, 89.09%, 90.57%, 93.33%, and 90.43%). There were moderate variations in the reproducibility of CAD with different algorithms, which likely indicates that measurement of reproducibility is necessary for evaluating CAD performance in actual clinical environments.

This paper reports the interconnection design and analysis of the embedded IC package. Instead of real chips, we used a dummy chip made of silicon. Dummy chip is GCPW structure and chip's lower ground is not in the chip back but below 20 μm polymer. The thickness of dummy IC is 100 μm and the depth of the cavity is 110 μm. An organic lamination process fills the gap between the inserted chip and the cavity and forms a flat insulating layer. The interconnection for the two inserted dummy ICs was realized using the patterning process, with a very short length of 400 μm. By forming sufficient via holes, the parasitic resonance caused by the cavity is minimized. As a result, the loss of the interconnection is less than 0.25 dB and return loss is more than 30 dB at all measured frequency.

Objective Neuraminidase (sialidase) inhibitors are considered to delay platelet clearance through the inhibition of platelet desialylation. A novel neuraminidase inhibitor, peramivir, was recently approved for intravenous administration by the US FDA. We aimed to compare the effects of peramivir and oseltamivir on patient platelet count. Materials and methods Consecutive patients who were treated with peramivir or tested positive for influenza between January 2015 and December 2017 were analyzed. The analysis included 461 patients with platelet counts available; the patients were divided into three groups: patients with proven influenza treated with peramivir (n = 305); those treated with peramivir without proven influenza (n = 83), and those with proven influenza treated with oseltamivir (n = 73). Results Patients treated with peramivir did not show an increase in platelet count from the baseline count, regardless of proven influenza (from 263.4 × 109/L to 267.4 × 109/L; 9 = 0.410) or not (from 257.1 × 109/L to 255.4 × 109/L; p = 0.873); wheeras for patients treated with oseltamivir, a significant increase above the baseline was found (from 223.3 × 109/L to 249.9 × 109/L; p = 0.016), although it was transient. Conclusion Peramivir and oseltamivir appear to have different effects on patient platelet count when administered at the recommended doses. .

Background Recent advances in robotics and deep learning can be used in endoscopic surgeries and can provide numerous advantages by freeing one of the surgeon's hands. This study aims to automatically detect the tip of the instrument, localize a point, and evaluate the detection accuracy in biportal endoscopic spine surgery (BESS). The tip detection could serve as a preliminary study for the development of vision intelligence in robotic endoscopy. Methods The dataset contains 2310 frames from 9 BESS videos with x and y coordinates of the tip annotated by an expert. We trained two state-of-the-art detectors, RetinaNet and YOLOv2, with bounding boxes centered around the tip annotations with specific margin sizes to determine the optimal margin size for detecting the tip of the instrument and localizing the point. We calculated the recall, precision, and F1-score with a fixed box size for both ground truth tip coordinates and predicted midpoints to compare the performance of the models trained with different margin size bounding boxes. Results For RetinaNet, a margin size of 150 pixels was optimal with a recall of 1.000, precision of 0.733, and F1-score of 0.846. For YOLOv2, a margin size of 150 pixels was optimal with a recall of 0.864, precision of 0.808, F1-score of 0.835. Also, the optimal margin size of 150 pixels of RetinaNet was used to cross-validate its overall robustness. The resulting mean recall, precision, and F1-score were 1.000 ± 0.000, 0.767 ± 0.033, and 0.868 ± 0.022, respectively. Conclusions In this study, we evaluated an automatic tip detection method for surgical instruments in endoscopic surgery, compared two state-of-the-art detection algorithms, RetinaNet and YOLOv2, and validated the robustness with cross-validation. This method can be applied in different types of endoscopy tip detection.

The automotive industry has a target goal to improve fuel consumption due to restricted exhaust gas regulation. For this reason, the applicability of lightweight material, Al alloys, Mg alloys is also being expanded. In this concept, high strength steel, DP590 and light alloy, AL5052 are joined in the right place of the car body. However, it is difficult to join to steel and aluminum by conventional fusion welding. Generally, in respect to dissimilar metal joining by fusion welding, intermetallic compound layer is formed at the joint interface, hot cracking is generated. In this study, the effect of the current waveform on the mechanical characteristics and microstructure in Delta spot welding process of dissimilar metal was investigated. As results, Intermetallic compound (IMC) layer was reduced from 2.355 μm to 1.09 μm by using Delta spot welding process; also the welding current range improved by 50% in the delta spot welding, higher than in the inverter resistance welding. To conclude, the delta spot welding process adopting the process tapes contributes to improving the welding quality for dissimilar metals (Al5052 and DP590) due to a decrease in IMC layer.

As the development of recent science and technology, high-performance computing resources is needed to solve complex problems. To reach these requirements, it has been actively studied about grid computing that consist of a huge system which bind a heterogeneous high performance computing resources into on which are geographically dispersed. However, The current research situation which are the process to obtain the best results in the limited resources and the scheduling policy to accurately predict the total execution time of the real-time task are very poor. In this paper, in order to overcome these problems, we suggested a grid computing-based risk management system which derived from the system structure and the process for improving the efficiency of the system, grid computing-based working methodology, risk policy module which can manage efficiently the problem of the work of resources(Agent), scheduling technique and allocation method which can re-allocate the resource allocation and the resources in problem, and monitoring which can manage resources(Agent).

In this paper, we consider the discussion of the breakdown phenomenon of korean language that is displayed in the language of the Internet media, as a technical improvement approach to this, and to provide an automatic translation technology of standard language. The collected through real life surveys and experience and damage case regarding language breakdown phenomena in the FGI (Focussed Group Interview), and presents the results of the analysis, based on the analysis result data, we defined the index of language destroyed (degree of Korean destruction). The limit of the national language break or automatic translation technology standard language marked with translation errors that have to be minimized, the efficiency of the automatic translation technology standard language which may be utilized as a measure to get the maximum, and presented through the Internet media texts 2,480 pieces of analysis. Automatic translation technology of the standard language and was also measured the proposed language destroyed in this paper, we implemented in the form of a REST API using the Java language, was confirmed to work on the Web browser.

Fog Computing is an emerging paradigm that distributes computing and networking services closer to where user data is generated. The Fog presents a new architecture that “takes processing to the data”. The fog will resolve in the Internet of Things such as bandwidth and cost constraints and performing data fusion, etc. Considering the “distributed” property, this paper proposes to exploit the multi agent system technology for the development of the fog system. To demonstrate its feasibility, we adopt an open source platform for a multi agent system, VOLTTRON. Then, four types of fog agents are developed that collaborate to conduct building energy management tasks in an autonomous manner. The agents can reduce the energy waste in a building by managing the brightness of lights, power status of the HVAC systems, and various energy loads. They also receive weather data and price data from Korea Meteorological Administration and Korea Power Exchange and present the data on the web. This helps user to recognize the information easier.

This paper presents a study on the thermal efficiency improvement of the user equipment room in the district heating system based on reinforcement learning [1], and suggests a general method of constructing a learning network(DQN)[2] using deep Q learning[3], which is a reinforcement learning algorithm that does not specify a model. In addition, we introduce the big data platform system and the integrated heat management system for the energy field in the massive data processing from the IoT sensor installed in large number of thermal energy control facilities.

The construction process and safety management are an important means of the total projects for the decision-making. As an important management tool of cost, resources and risks of the project, the analysis and evaluations are recorded as an important asset of company. Also, it has become a safety device that could reduce risk factors repeatedly by the decision-making methods for the future similar projects. Component-based software, by reusing the useful part, which was prepared without newly developing all that, by building a new software product, the long-term continuous/steady through the provision of a component-based software, by securing a sales base, it should be the development and supply a wide range of applications. In this paper, we propose the construction process control systems for increasing modifiability of process creation and modification for business efficiency in accordance with the diverse trends in the construction process as a low-cost niche through a component-based software supplier to solve these problems.

In this paper, in order to increase the production efficiency of the industrial plant, and predicts the resources of the manufacturing process, we have proposed a decision-making system for resource implementing the risk management effectively forecasting and risk management. A variety of information that occurs at each step efficiently difficult the creation of detailed process steps in the scenario you want to manage, is a frequent condition change of manufacturing facilities for the production of various products even within the same process. The data that is not contiguous products production cycle also not constant occurs, there is a problem that needs to check the variation in the small amount of data. In order to solve these problems, data centralized manufacturing processes, process resource prediction, risk prediction, through a process current status monitoring, must allow action immediately when a problem occurs. In this paper, the range of change in the design drawing, resource prediction, a process completion date using a regression algorithm to derive the formula, classification tree technique was proposed decision system in three stages through the boundary value analysis.