• KSII Transactions on Internet and Information Systems (TIIS)
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• v.17 no.2
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• pp.504-519
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• 2023

Heart disease is becoming the top reason of death all around the world. Diagnosing cardiac illness is a difficult endeavor that necessitates both expertise and extensive knowledge. Machine learning (ML) is becoming gradually more important in the medical field. Most of the works have concentrated on the prediction of cardiac disease, however the precision of the results is minimal, and data integrity is uncertain. To solve these difficulties, this research creates an Integrated Accurate-Secure Heart Disease Prediction (IAS) Model based on Deep Convolutional Neural Networks. Heart-related medical data is collected and pre-processed. Secondly, feature extraction is processed with two factors, from signals and acquired data, which are further trained for classification. The Deep Convolutional Neural Networks (DCNN) is used to categorize received sensor data as normal or abnormal. Furthermore, the results are safeguarded by implementing an integrity validation mechanism based on the hash algorithm. The system's performance is evaluated by comparing the proposed to existing models. The results explain that the proposed model-based cardiac disease diagnosis model surpasses previous techniques. The proposed method demonstrates that it attains accuracy of 98.5 % for the maximum amount of records, which is higher than available classifiers.

• Journal of Microbiology and Biotechnology
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• v.33 no.2
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• pp.228-234
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• 2023

In this study, the performance of a gold biosensor combined with light microscope imaging system (GB-LMIS) was comparatively evaluated against enzyme-linked immunosorbent assay (ELISA) for detecting Salmonella under simulated chilling condition. The optimum concentration of antiSalmonella polyclonal antibodies (pAbs) was determined to be 12.5 and 100 ㎍/ml for ELISA and GBLMIS, respectively. GB-LMIS exhibited a sufficient and competitive specificity toward three tested Salmonella among only. To mimic a real-world situation, chicken was inoculated with Salmonella cocktail and stored under chilling condition for 48 h. The overall growth of Salmonella under chilling condition was significantly lower than that under non-exposure to the chilling condition (p < 0.05). No significant differences in bacterial growth were observed between brain heart infusion and brilliant green broth during the enrichment period (p > 0.05). Finally, both GB-LMIS and ELISA were employed to detect Salmonella at every 2-h interval. GB-LMIS detected Salmonella with a competitive specificity by the direct observation of bacteria on the sensor using a charge-coupled device camera within a detection time of ~2.5 h. GB-LMIS is a feasible, novel, and rapid method for detecting Salmonella in poultry facilities.

• Nuclear Engineering and Technology
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• v.54 no.11
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• pp.3985-3995
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• 2022

The emergence of new nanoscale technologies has imposed significant challenges to designing reliable electronic systems in radiation environments. A few types of radiation like Total Ionizing Dose (TID) can cause permanent damages on such nanoscale electronic devices, and current state-of-the-art technologies to tackle TID make use of expensive radiation-hardened devices. This paper focuses on a novel and different approach: using machine learning algorithms on consumer electronic level Field Programmable Gate Arrays (FPGAs) to tackle TID effects and monitor them to replace before they stop working. This condition has a research challenge to anticipate when the board results in a total failure due to TID effects. We observed internal measurements of FPGA boards under gamma radiation and used three different anomaly detection machine learning (ML) algorithms to detect anomalies in the sensor measurements in a gamma-radiated environment. The statistical results show a highly significant relationship between the gamma radiation exposure levels and the board measurements. Moreover, our anomaly detection results have shown that a One-Class SVM with Radial Basis Function Kernel has an average recall score of 0.95. Also, all anomalies can be detected before the boards are entirely inoperative, i.e. voltages drop to zero and confirmed with a sanity check.

• Journal of Microbiology and Biotechnology
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• v.34 no.3
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• pp.681-688
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• 2024

The accurate and rapid detection of methicillin-resistance of Staphylococcus aureus (SA) holds significant clinical importance. However, the methicillin-resistance detection strategies commonly require complicated cell lysis and gene extraction. Herein, we devised a novel colorimetric approach for the sensitive and accurate identification of methicillin-resistance of SA by combining allosteric probe-based target recognition with self-primer elongation-based target recycling. The PBP2a aptamer in the allosteric probe successfully identified the target MRSA, leading to the initiation of self-primer elongation based-cascade signal amplification. The peroxidase-like hemin/G-quadruplex undergo an isothermal autonomous process that effectively catalyzes the oxidation of ABTS2- and produces a distinct blue color, enabling the visual identification of MRSA at low concentrations. The method offers a shorter duration for bacteria cultivation compared to traditional susceptibility testing methods, as well as simplified manual procedures for gene analysis. The overall amplification time for this test is 60 min, and it has a detection limit of 3 CFU/ml. In addition, the approach has exceptional selectivity and reproducibility, demonstrating commendable performance when tested with real samples. Due to its advantages, this colorimetric assay exhibits considerable potential for integration into a sensor kit, thereby offering a viable and convenient alternative for the prompt and on-site detection of MRSA in patients with skin and soft tissue infections.

• Journal of Platform Technology
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• v.11 no.5
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• pp.110-125
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• 2023

In this paper, we utilize time-series aircraft location data measured based on 2019 domestic airports to analyze Go-Around and UOC_D situations during the approach phase of domestic airports. Various clustering-based machine learning techniques are applied to determine the most appropriate analysis method for domestic aviation data through experimentation. The ADS-B sensor is solely employed to measure aircraft positions. We designed a model using clustering algorithms such as K-Means, GMM, and DBSCAN to classify abnormal situations. Among them, the RF model showed the best performance overseas, but through experiments, it was confirmed that the GMM showed the highest classification performance for domestic aviation data by reflecting the aspects specialized in domestic terrain.

• KSBB Journal
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• v.21 no.5
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• pp.325-330
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• 2006

This study presents the characterization of an integrated portable microfluidic electrical detection system for fast and low volume immunoassay using polystyrene microbead, which are used as immobilization surfaces. In our chip, a filtration method using the microbead was adopted for sample immobilization and immunogold silver staining(IGSS) was used to increase the electrical signal. The chip is composed of an inexpensive and biocompatible Polydimethylsiloxane(PDMS) layer and Pyrex glass substrate. Platinum microelectrodes for electric signal detection were fabricated on the substrate and microchannel and pillar-type microfilters were formed in the PDMS layer. With a fabricated chip, we reacted antigen and antibody according to the procedures. Then, silver enhancer was injected to increase the size of nanogold particles tagged with the second antibody. As a result, microbeads were connected to each other and formed an electrical bridge between microelectrodes. Resistance measured through the electrodes showed a difference of two orders of magnitude between specific and nonspecific immuno-reactions. The detection limit was 10 ng/ml. The developed immunoassay chip reduced the total analysis time from 3 hours to 50 min. Fast and low-volume biochemical analysis has been successfully achieved with the developed microfilter and immuno-sensor chip, which is integrated to the microfluidic system.

• Korean Journal of Materials Research
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• v.21 no.3
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• pp.133-137
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• 2011

Gold have been used as an electrode materials having a good mechanical flexibility as well as electrical conductivity, however the stretchability of the gold on a flexible substrate is poor because of its small elastic modulus. To overcome this mechanical inferiority, the reinforcing gold is necessary for the stretchable electronics. Among the reinforcing materials having a large elastic modulus, carbon nanotube (CNT) is the best candidate due to its good electrical conductivity and nanoscale diameter. Therefore, similarly to ferroconcrete technology, here we demonstrated gold electrodes mechanically reinforced by inserting fabrics of CNTs into their bodies. Flexibility and stretchability of the electrodes were determined for various densities of CNT fabrics. The roles of CNTs in resisting electrical disconnection of gold electrodes from the mechanical stress were confirmed using field emission scanning electron microscope and optical microscope. The best mechanical stability was achieved at a density of CNT fabrics manufactured by 1.5 ml spraying. The concept of the mechanical reinforced metal electrode by CNT is the first trial for the high stretchable conductive materials, and can be applied as electrodes materials in various flexible and stretchable electronic devices such as transistor, diode, sensor and solar cell and so on.

• Smart Structures and Systems
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• v.8 no.1
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• pp.17-37
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• 2011

The microcantilever (MCL) sensor is one of the most promising platforms for next-generation label-free biosensing applications. It outperforms conventional label-free detection methods in terms of portability and parallelization. In this paper, an overview of recent advances in our understanding of the coupling between biomolecular interactions and MCL responses is given. A dual compact optical MCL sensing platform was built to enable biosensing experiments both in gas-phase environments and in solutions. The thermal bimorph effect was found to be an effective nanomanipulator for the MCL platform calibration. The study of the alkanethiol self-assembly monolayer (SAM) chain length effect revealed that 1-octanethiol ($C_8H_{17}SH$) induced a larger deflection than that from 1-dodecanethiol ($C_{12}H_{25}SH$) in solutions. Using the clinically relevant biomarker C-reactive protein (CRP), we revealed that the analytical sensitivity of the MCL reached a diagnostic level of $1{\sim}500{\mu}g/ml$ within a 7% coefficient of variation. Using grazing incident x-ray diffractometer (GIXRD) analysis, we found that the gold surface was dominated by the (111) crystalline plane. Moreover, using X-ray photoelectron spectroscopy (XPS) analysis, we confirmed that the Au-S covalent bonds occurred in SAM adsorption whereas CRP molecular bindings occurred in protein analysis. First principles density functional theory (DFT) simulations were also used to examine biomolecular adsorption mechanisms. Multiscale modeling was then developed to connect the interactions at the molecular level with the MCL mechanical response. The alkanethiol SAM chain length effect in air was successfully predicted using the multiscale scheme.

• Journal of information and communication convergence engineering
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• v.7 no.4
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• pp.580-585
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• 2009

Harmful Algal Blooms (HABs), caused by Cochlodinium polykrikoides that causative fishery mortality, impact on aquaculture and economic loss appear particularly in summer and fall seasons in the Korean seas. It was studied on characteristics of HABs in the South Sea of Korea by using satellite and in-situ data. The in-situ data encompassed oceanic and meteorological data from July to October 2002-2008 and satellite data from July to October 2002-2006. Chlorophyll concentrations were calculated using Seaviewing Wide Field-of-view Sensor images by an Ocean Color (OC4) algorithm, and HABs were estimated using the Red tide index Chlorophyll Algorithm (RCA). The HAB occurrences were dominant when water temperature was $22.6-28^{\circ}C$ in August. The frequency of the individual numbers during 2002-2008, the HABs more than 1000 cells/ml (alert condition), were 73.57 %. In meteorological data from July to September during 2002-2008, the average precipitation, the mean air temperature, the mean wind speed and direction, and the sunshine were 9.31 mm/day, $24.07^{\circ}C$, 2.34 m/s and easterly, and 1-11 h, respectively. Our results suggest that the upwelling is caused by southwesterly wind in summer season and the Tsushima Warm Current which have influenced on the dispersion and moving of HAB (chlorophyll). In addition, the fresh water from Nakdong River, as the source of nutrients, also influences the occurrence of HABs.

• Journal of Microbiology and Biotechnology
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• v.18 no.1
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• pp.43-47
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• 2008

The nickel and cobalt resistance of Cupriavidus metallidurans CH34 is mediated by the CnrCBA efflux pump encoded by the cnrYHXCBAT metal resistance determinant. The products of the three genes cnrYXH transcriptionally regulate expression of cnr. CnrY and CnrX are membrane-bound proteins, probably functioning as anti-sigma factors, whereas CnrH is a cnr-specific extracytoplasmic functions (ECF) sigma factor. The periplasmic domain of CnrX (residues 29-148) was cloned as a N-terminal His-tagged protein, expressed in Escherichia coli, and purified using affinity chromatography and gel filtration. The molecular mass was estimated to be about 13.6kDa by size exclusion chromatography, corresponding to a monomer. The tetragonal bipyramid crystals were obtained by mixing an equal volume of protein in 50mM Tris-HCl, pH 7.5, 1% glycerol, 100mM NaCl, 1mM DTT, and the reservoir solution of 15% w/v PEG 2000, 100mM lithium chloride at 277K in 2-4 days using hanging drop vapor diffusion. The protein concentration was 24mg/ml. The crystal that diffracted to $2.42{\AA}$ resolution belongs to space group $P4_1\;or\;P4_3$ with unit cell parameters of $a=b=32.14{\AA},\;c=195.31{\AA},\;{\alpha}={\beta}={\gamma}=90^{\circ}$, with one molecule of CnrX in the asymmetric unit.