• 제목/요약/키워드: cross-gradient

검색결과 294건 처리시간 0.03초

A Fluorescent Recombinase Aided Amplification Assay for Detection of Babesia microti

  • Lin, Hong;Zhao, Song;Ye, Yuying;Shao, Lei;Jiang, Nizhen;Yang, Kun
    • Parasites, Hosts and Diseases
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    • 제60권3호
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    • pp.201-205
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    • 2022
  • Babesia microti is one of the most common causative agents of babesiosis. A sensitive and rapid detection is necessary for screening potentially infected individuals. In this study, B. microti cytochrome c oxidase subunit I (cox1) was selected as the target gene, multiple primers were designed, and optimized by a recombinase-aided amplification (RAA) assay. The optimal primers and probe were labeled with fluorescein. The sensitivity of fluorescent RAA (fRAA) was evaluated using gradient diluents of the cox1 recombinant plasmid and genomic DNA extracted from whole blood of B. microti infected mice. The specificity of fRAA was assessed by other transfusion transmitted parasites. The analytical sensitivity of the fRAA assay was 10 copies of recombinant plasmid per reaction and 10 fg/µl B. microti genomic DNA. No cross-reaction with any other blood-transmitted parasites was observed. Our results demonstrated that the fRAA assay would be rapid, sensitive, and specific for the detection of B. microti.

Form-finding of lifting self-forming GFRP elastic gridshells based on machine learning interpretability methods

  • Soheila, Kookalani;Sandy, Nyunn;Sheng, Xiang
    • Structural Engineering and Mechanics
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    • 제84권5호
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    • pp.605-618
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    • 2022
  • Glass fiber reinforced polymer (GFRP) elastic gridshells consist of long continuous GFRP tubes that form elastic deformations. In this paper, a method for the form-finding of gridshell structures is presented based on the interpretable machine learning (ML) approaches. A comparative study is conducted on several ML algorithms, including support vector regression (SVR), K-nearest neighbors (KNN), decision tree (DT), random forest (RF), AdaBoost, XGBoost, category boosting (CatBoost), and light gradient boosting machine (LightGBM). A numerical example is presented using a standard double-hump gridshell considering two characteristics of deformation as objective functions. The combination of the grid search approach and k-fold cross-validation (CV) is implemented for fine-tuning the parameters of ML models. The results of the comparative study indicate that the LightGBM model presents the highest prediction accuracy. Finally, interpretable ML approaches, including Shapely additive explanations (SHAP), partial dependence plot (PDP), and accumulated local effects (ALE), are applied to explain the predictions of the ML model since it is essential to understand the effect of various values of input parameters on objective functions. As a result of interpretability approaches, an optimum gridshell structure is obtained and new opportunities are verified for form-finding investigation of GFRP elastic gridshells during lifting construction.

경년열화를 고려한 전단벽 구조물의 기계학습 기반 지진응답 예측모델 개발 (Development of Machine Learning Based Seismic Response Prediction Model for Shear Wall Structure considering Aging Deteriorations)

  • 김현수;김유경;이소연;장준수
    • 한국공간구조학회논문집
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    • 제24권2호
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    • pp.83-90
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    • 2024
  • Machine learning is widely applied to various engineering fields. In structural engineering area, machine learning is generally used to predict structural responses of building structures. The aging deterioration of reinforced concrete structure affects its structural behavior. Therefore, the aging deterioration of R.C. structure should be consider to exactly predict seismic responses of the structure. In this study, the machine learning based seismic response prediction model was developed. To this end, four machine learning algorithms were employed and prediction performance of each algorithm was compared. A 3-story coupled shear wall structure was selected as an example structure for numerical simulation. Artificial ground motions were generated based on domestic site characteristics. Elastic modulus, damping ratio and density were changed to considering concrete degradation due to chloride penetration and carbonation, etc. Various intensity measures were used input parameters of the training database. Performance evaluation was performed using metrics like root mean square error, mean square error, mean absolute error, and coefficient of determination. The optimization of hyperparameters was achieved through k-fold cross-validation and grid search techniques. The analysis results show that neural networks and extreme gradient boosting algorithms present good prediction performance.

Validation on the Analytical Method of Ginsenosides in Red Ginseng

  • Cho B. G.;Nho K. B.;Shon H. J.;Choi K. J.;Lee S. K.;Kim S. C;Ko S. R.;Xie P. S.;Yan Y. Z.;Yang J. W.
    • 고려인삼학회:학술대회논문집
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    • 고려인삼학회 2002년도 학술대회지
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    • pp.491-501
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    • 2002
  • A cross-examination between KT&G Central Research Institute and Guangzhou Institute for Drug Control was carried out in order to select optimum conditions for extraction, separation and determination of ginsenosides in red ginseng and to propose a better method for the quantitative analysis of ginsenosides. The optimum extraction conditions of ginsenosides from red ginseng were as follows: the extraction solvent, $70\%$ methanol; the extraction temperature, $100^{\circ}C;$ the extraction time, 1 hour for once; and the repetition of extraction, twice. The optimum separation conditions of ginsenosides on the SepPak $C_{18}$ cartridge were as follows: the loaded amount, 0.4 g of methanol extract; the washing solvents, distilled water of 25 ml at first and then $30\%$ methanol of 25 ml; the elution solvent, $90\%$ methanol of 5 ml. The optimum HPLC conditions for the determination of ginsenosides were as follows: column, Lichrosorb $NH_2(25{\times}0.4cm,$ 5${\mu}m$, Merck Co.); mobile phase, a mixture of acetonitrile/water/isopropanol (80/5/15) and acetonitrile/water/isopropanol (80/20/15) with gradient system; and the detector, ELSD. On the basis of the optimum conditions a method for the quantitative analysis of ginsenosides were proposed and another cross-examination was carried out for the validation of the selected analytical method conditions. The coefficient of variances (CVs) on the contents of ginsenoside-$Rg_{1}$, -Re and $-Rb_1$ were lower than $3\%$ and the recovery rates of ginsenosides were $89.4\~95.7\%,$ which suggests that the above extraction and separation conditions may be reproducible and reasonable. For the selected HPLC/ELSD conditions, the CVs on the detector responses of ginsenoside-Rg, -Re and $-Rb_1$) were also lower than $3\%$, the regression coefficients for the calibration curves of ginsenosides were higher than 0.99 and two adjacent ginsenoside peaks were well separated, which suggests that the above HPLC/ELSD conditions may be good enough for the determination of ginsenosides.

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인체 뇌 대사물질에서의 In vitro 2D-COSY와 2D-NOESY 스펙트럼 분석 평가 (Evaluations of Spectral Analysis of in vitro 2D-COSY and 2D-NOESY on Human Brain Metabolites)

  • 최보영;우동철;김상영;최치봉;이성임;김은희;홍관수;전영호;정재준;김상수;임향숙
    • Investigative Magnetic Resonance Imaging
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    • 제12권1호
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    • pp.8-19
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    • 2008
  • 목적 : 본 연구에서는 인체 뇌 대사물질에 대하여 2D MR 기술인 correlation spectroscopy (COSY) 와 nuclear Overhauser effect/enhancement spectroscopy (NOESY)를 직접 적용하고, 데이터를 획득하여 인체 뇌대사물질들간의 스칼라 짝지움 (coupling)과 쌍극자 (dipolar) 상호작용- NOE에 대한 분석을 통하여 결합연결관계 및 공간연결관계에 대한 정보를 획득하고자 하였다. 대상 및 방법 : 모든 2-D (dimensional) MR 실험 (즉, COSY와 NOESY)은 z축 능동차폐 펄스경사자장, 삼중공명 동결탐침기가 장착된 Bruker Avance 500 (11.8 T) 장비에서 298 K에 수행되었다. MRS상에서 뇌 대사물질과 유사하도록만든 희석액을 만들었고, 최종 MRS 샘플은 10% $D_2O$를 이용하였다. 2-D 스펙트라는 2048 복합 (complex) 데이터 포인트로서 총 320개 의 free induction decay (FID)를 평균화 (averaging)하였고, $H_2O$에서 얻어진 스펙트라는 8012 Hz 였다. 반복지연 (repetition delay) 시간은 2초, 각각의 FID는 4개의 평균화를 선택하였다. 얻어진 2D-COSY, 2D-NOESY 데이터는 Top Spin 2.0 소프트웨어에서 후 처리기법 (post-processing)에 의해 분석되었다. 분석 대상 대사물질은 N-acetyl aspartate (NAA), creatine (Cr), choline (Cho), glutamine (Gln), glutamate (Glu), myo-inositol (Ins), lactate (Lac)로서 총 7 가지 화학물로서 주요 목표 피크로 정했다. 결과 : 인체 뇌 대사물질에 대한 대칭형태의 2D-COSY와 2D-NOESY 스펙트럼을 획득하였고, COSY 스펙트럼상에서는 오직 1.0-4.5 ppm 사이에서만 교차피크들이 생성된 반면 NOESY 스펙트럼상에서는 1.0-4.5 ppm 외에도 7.9 ppm에서 공명 교차피크를 발견할 수 있었다. COSY 스펙트럼을 통하여 lactate에서 메틸 프로톤과 CH 프로톤의 COSY 크로스피크가 발견되었고, NAA에서 메틸렌 프로톤들간과 메틸렌 프로톤과 NH프로톤의 크로스피크가 발견되었고, Ins에서 CH 프로톤 들간의 크로스피크가 발견되었다. NOESY 스펙트럼을 통하여 NAA 분자내 NH 프로톤과 메틸 (-CH3) 프로톤과의 NOESY 크로스피크가 발견되었고, lactate에서 메틸 프로톤과 CH 프로톤과의 크로스피크가 발견되었고, Cr에서 메틸 프로톤과 메틸렌 프로톤과의 크로스피크가 발견되었고, Glu에서 메틸렌 프로톤 들간과 또한 메틸렌 프로톤과 CH 프로톤과의 크로스피크가 발견되었고, Gln에서 메틸렌 프로톤과 CH 프로톤과의 크로스피크가 발견되었고, Ins에서 CH 프로톤 들간의 크로스피크가 발견되었다. 결론 : 본 연구에서는 in vitro 상태의 인체 뇌 대사물질에 2D-COSY와 2D-NOESY 기술을 직접 적용하고, 결합연결관계 및 공간연결관계에 대한 정보를 성공적으로 획득하여 분석하여 보았다. 본 연구 결과물은 향후 인체 내 in vivo 2D-COSY를 이용한 뇌 대사물질 연구에 매우 유용하리라 사료된다.

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경기만 염하수로에서의 잔차류 및 수송량의 대조-소조 변동과 단면 특성 (The Cross-Sectional Characteristic and Spring-Neap Variation of Residual Current and Net Volume Transport at the Yeomha Channel)

  • 이동환;윤병일;우승범
    • 한국해안·해양공학회논문집
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    • 제29권5호
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    • pp.217-227
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    • 2017
  • 경기만 염하수로에서 시 공간적으로 변화하는 총수송량과 잔차류를 산정하고자 소조기와 대조기에 염하수로의 하류(정선-1), 염하수로 중간 지점(정선-2)에서 13시간 동안 단면 유속을 관측하였다. 총수송량은 Eulerian flux와 Stokes drift의 합인 Lagrange flux로 계산하였고, 잔차류는 최소자승법을 이용하여 구하였다. 총수송량과 잔차류의 계산은 관측 시간별, 수평 수직 sigma 좌표계로 변환하여 수행하였다. 변환된 sigma 좌표체계는 z-level 좌표 체계와 비교하였을 때 주 방향 유속 오차가 3~5% 내외로 자료 분석에 무리가 없는 것으로 판단되었다. 분석결과 단면 잔차류는 정선-1에서는 대조기에 주 수로 방향에서 북향, 수로 양 끝 단에서 남향하였으며, 소조기에는 수직적으로 표층에서는 창조, 저층에서는 낙조하는 이층흐름 구조를 보였다. 반면 정선-2에서는 대조, 소조 모두 남향(낙조)하였다. 한편 총수송량은 정선-1에서는 대조 시와 소조 시에 각각 $359m^3s^{-1}$, $248m^3s^{-1}$로 북향(창조), 정선-2에서 대조 시와 소조 시에 각각 $576m^3s^{-1}$, $67m^3s^{-1}$로 남향(낙조)하였다. 정선 별 공간 수송량 차이로 영종도와 강화도 사이의 조간대 지역의 순 유출량을 추정하였으며, 크기는 대조기와 소조기에 각각 $935m^3s^{-1}$, $315m^3s^{-1}$로 나타났다. 이처럼 대 소조기와 공간적 특성에 따라 잔차류와 순 수송량이 변화되는 주된 요인은 순압력구배와 Stokes drift가 복합적으로 작용한 결과이다.

Enhanced Recovery of Gravity Fields from Dense Altimeter Data

  • Kim, Jeong-Hee
    • 한국측량학회지
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    • 제14권2호
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    • pp.127-139
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    • 1996
  • 본 연구에서는 고밀도 해면고도 자료를 이용하여 모든 스펙트럼에 걸쳐 정밀도가 향상된 해면고도와 중력이상을 계산하였다. 평형 위성궤도상의 해면고도 자료에 주파수 영역에서의 상관계수를 이용한 필터를 적용하여 표층 지각구조에 의한 중력 시그날을 추출하였다. 교점오차를 보정하여 편향 위성궤도 오차를 수정하였으며 그 결과로 상향및 하향궤도별로 격자상의 중력 시그날을 계산하였다. 또한 방향성 제거 필터를 사용하여 잔재하는 방향성 궤도오차를 수정하였다. 그 결과에 dynamic sea surface topography를 제거하면 지오이드가 되는데 이 지오이드에 gradient 필터를 적용하여 고분해능 중력이상값을 계산하였다. 이 방법들을 약 $900km\;\times{1,200}\;km$지역의 Geosat Geodetic Mission 자료에 적용하여 약 10 km의 분해능을 갖는 지오이드와 중력이상을 계산하였다, 정확한 선상중력 자료와 least squares collection에 의한 중력이상, 그리고 NOAA의 중력이상값들과 비교, 검토하였다 약 1,600 km에 걸쳐 150 mgal의 중력변화가 있는 선상중력 측선에서 , 본 연구 결과의 중력이상값과 고정밀도 선상중력값을 비교한 결과, 차이의 평균은 0.1 mgal, RMS는 3.5 mgal, 그리고 최대차이는 10.2 mgal과 -18.6 mgal이고, 상관계수는 0.993이었다.

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Sodium Dependent Taurine Transport into the Choroid Plexus, the Blood-Cerebrospinal Fluid Barrier

  • Chung, Suk-Jae;Ramanathan, Vikram;Brett, Claire M.;Giacomini, Kathleen M.
    • Journal of Pharmaceutical Investigation
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    • 제25권3호spc1호
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    • pp.7-20
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    • 1995
  • Taurine, a ${\beta}-amino$ acid, plays an important role as a neuromodulator and is necessary for the normal development of the brain. Since de novo synthesis of taurine in the brain is minimal and in vivo studies suggest that taurine dose not cross the blood-brain barrier, we examined whether the choroid plexus, the blood-cerebrospinal fluid (CSF) barrier, plays a role in taurine transport in the central nervous system. The uptake of $[^3H]-taurine$ into ATP depleted choroid plexus from rabbit was substantially greater in the presence of an inwardly directed $Na^+$ gradient taurine accumulation was negligible. A transient in side-negative potential gradient enhanced the $Na^+-driven$ uptake of taurine into the tissue slices, suggesting that the transport process is electrogenic, $Na^+-driven$ taurine uptake was saturable with an estimated $V_{max}$ of $111\;{\pm}\;20.2\;nmole/g/15\;min$ and a $K_M\;of\;99.8{\pm}29.9\;{\mu}M$. The estimated coupling ratio of $Na^+$ and taurine was $1.80\;{\pm}\;0.122.$ $Na^+-dependent$ taurine uptake was significantly inhibited by ${\beta}-amino$ acids, but not by ${\alpha}-amino$ acids, indicating that the transporter is selective for ${\beta}-amino$ acids. Since it is known that the physiological concentration of taurine in the CSF is lower than that in the plasma, the active transport system we characterized may face the brush border (i.e., CSF facing) side of the choroid plexus and actively transport taurine out of the CSF. Therefore, we examined in vivo elimination of taurine from the CSF in the rat to determine whether elimination kinetics of taurine from the CSF is consistent with the in vitro study. Using a stereotaxic device, cannulaes were placed into the lateral ventricle and the cisterna magna of the rat. Radio-labelled taurine and inulin (a marker of CSF flow) were injected into the lateral ventricle, and the concentrations of the labelled compounds in the CSF were monitored for upto 3 hrs in the cisterna magna. The apparent clearance of taurine from CSF was greater than the estimated CSF flow (p<0.005) indicating that there is a clearance process in addition to the CSF flow. Taurine distribution into the choroid plexus was at least 10 fold higher than that found in other brain areas (e. g., cerebellum, olfactory bulb and cortex). When unlabelled taurine was co-administered with radio-labelled taurine, the apparent clearance of taurine was reduced (p<0.0l), suggesting a saturable disposition of taurine from CSF. Distribution of taurine into the choroid plexus, cerebellum, olfactory bulb and cortex was similarly diminished, indicating that the saturable uptake of taurine into these tissues is responsible for the non-linear disposition. A pharmacokinetic model involving first order elimination and saturable distribution described these data adequately. The Michaelis-Menten rate constant estimated from in vivo elimination study is similar to that obtained in the in vitro uptake experiment. Collectively, our results demonstrate that taurine is transported in the choroid plexus via a $Na^+-dependent,saturable$ and apparently ${\beta}-amino$ acid selective mechanism. This process may be functionally relevant to taurine homeostasis in the brain.

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터널의 기하학적 형태 및 캐노피 설치가 터널 환기 및 화재 확산에 미치는 영향 분석 (The Effects of Tunnel Geometrical Characteristics and Canopy Installation on the Ventilation and Fire Propagation)

  • 이창우;서기윤;김정욱
    • 한국터널지하공간학회 논문집
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    • 제8권4호
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    • pp.325-334
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    • 2006
  • 시계조절이나 설해방지 목적으로 설치되는 캐노피 구간 내에서의 기류유동특성의 이해는 정상환기 뿐만 아니라 비상시 대처방안 강구를 위한 요건이다. 또한 터널 방재시스템 설계를 위하여서는 종단구배, 평면선형, 단면크기 및 형태 등과 같은 터널의 다양한 특성이 화재확산에 미치는 영향에 대한 정량적인 이해가 필요하다. 본 연구에서는 국내도로터널의 전형적인 특성을 적용한 터널에 캐노피가 된 경우와 종단 및 선형구배, 단면적 및 형태, 곡선구간이 환기 및 화재확산에 미치는 영향을 CFD분석함을 목적으로 하였다. 분석결과 145m길이의 캐노피인 경우 50%정도의 개구율이 기류유동 패턴 및 환기효과면에서 가장 바람직하였다. 1.8km 터널내에서 20MW 화재발생시 종단구배는 풍속분포와 화재연 확산에 큰 영향을 미치며 제트팬$({\varnothing}1250)$ 4대를 가동한 경우 화재발생 후 5분 경과시 하류 40m지점 부근에서의 화재연 농도는 +2% 구배에서는 13% 감소, -2% 구배의 경우에는 20%정도 증가하며 또한 backlayering거리가 45m정도에 달한다. 직사각형 단면터널의 경우, 화재연 농도 및 풍속분포는 말굽형 터널과 비교하여 현저한 차이가 관찰되지 않는다. 3차선 터널에서는 이들 변수는 모두 감소하며 100초 경과시 50m 정도의 backlayering을 보이며 이후 서서히 감소한다. 곡선터널인 경우는 화재연의 확산이 느리며 100초 경과시 50m에 달하던 backlayering현상은 급격히 사라진다.

Calibration of Portable Particulate Mattere-Monitoring Device using Web Query and Machine Learning

  • Loh, Byoung Gook;Choi, Gi Heung
    • Safety and Health at Work
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    • 제10권4호
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    • pp.452-460
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    • 2019
  • Background: Monitoring and control of PM2.5 are being recognized as key to address health issues attributed to PM2.5. Availability of low-cost PM2.5 sensors made it possible to introduce a number of portable PM2.5 monitors based on light scattering to the consumer market at an affordable price. Accuracy of light scatteringe-based PM2.5 monitors significantly depends on the method of calibration. Static calibration curve is used as the most popular calibration method for low-cost PM2.5 sensors particularly because of ease of application. Drawback in this approach is, however, the lack of accuracy. Methods: This study discussed the calibration of a low-cost PM2.5-monitoring device (PMD) to improve the accuracy and reliability for practical use. The proposed method is based on construction of the PM2.5 sensor network using Message Queuing Telemetry Transport (MQTT) protocol and web query of reference measurement data available at government-authorized PM monitoring station (GAMS) in the republic of Korea. Four machine learning (ML) algorithms such as support vector machine, k-nearest neighbors, random forest, and extreme gradient boosting were used as regression models to calibrate the PMD measurements of PM2.5. Performance of each ML algorithm was evaluated using stratified K-fold cross-validation, and a linear regression model was used as a reference. Results: Based on the performance of ML algorithms used, regression of the output of the PMD to PM2.5 concentrations data available from the GAMS through web query was effective. The extreme gradient boosting algorithm showed the best performance with a mean coefficient of determination (R2) of 0.78 and standard error of 5.0 ㎍/㎥, corresponding to 8% increase in R2 and 12% decrease in root mean square error in comparison with the linear regression model. Minimum 100 hours of calibration period was found required to calibrate the PMD to its full capacity. Calibration method proposed poses a limitation on the location of the PMD being in the vicinity of the GAMS. As the number of the PMD participating in the sensor network increases, however, calibrated PMDs can be used as reference devices to nearby PMDs that require calibration, forming a calibration chain through MQTT protocol. Conclusions: Calibration of a low-cost PMD, which is based on construction of PM2.5 sensor network using MQTT protocol and web query of reference measurement data available at a GAMS, significantly improves the accuracy and reliability of a PMD, thereby making practical use of the low-cost PMD possible.