Displaying publications 41 - 60 of 390 in total

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  1. Teng KH, Kot P, Muradov M, Shaw A, Hashim K, Gkantou M, et al.
    Sensors (Basel), 2019 Jan 28;19(3).
    PMID: 30696110 DOI: 10.3390/s19030547
    : Concrete failure will lead to serious safety concerns in the performance of a building structure. It is one of the biggest challenges for engineers to inspect and maintain the quality of concrete throughout the service years in order to prevent structural deterioration. To date, a lot of research is ongoing to develop different instruments to inspect concrete quality. Detection of moisture ingress is important in the structural monitoring of concrete. This paper presents a novel sensing technique using a smart antenna for the non-destructive evaluation of moisture content and deterioration inspection in concrete blocks. Two different standard concrete samples (United Kingdom and Malaysia) were investigated in this research. An electromagnetic (EM) sensor was designed and embedded inside the concrete to detect the moisture content within the structure. In addition, CST microwave studio was used to validate the theoretical model of the EM sensor against the test data. The results demonstrated that the EM sensor at 2.45 GHz is capable of detecting the moisture content in the concrete with linear regression of R² = 0.9752. Furthermore, identification of different mix ratios of concrete were successfully demonstrated in this paper. In conclusion, the EM sensor is capable of detecting moisture content non-destructively and could be a potential technique for maintenance and quality control of the building performance.
    Matched MeSH terms: Linear Models
  2. Yong CY, Sudirman R, Chew KM
    Sains Malaysiana, 2015;44(12):1661-1669.
    A scalable tracking human model was proposed for recognizing human jogging and walking activities. The model aims to detect and track a particular subject by using wearable sensor. Data collected are in accelerometer readings in three axes and gyroscope readings in three axes. The development of proposed human model is based on the moderating effects on human movements. Two moderators were proposed as the moderating factors of human motion and they are angular velocity and elevation angle. Linear regression is used to investigate the relationship among inputs, moderators and outputs of the model. The result of this study showed that the angular velocity and elevation angle moderators are affecting the relation of research output. Acceleration in x-axis (Ax) and angular velocity in y-axis (Gy) are the two main components in directing
    a motion. Classification between jogging and walking motions was done by measuring the magnitude of angular velocity and elevation angle. Jogging motion was classified and identified with larger angular velocity and elevation angle. The two proposed hypotheses were supported and proved by research output. The result is expected to be beneficial and able to assist researcher in investigating human motions.
    Matched MeSH terms: Linear Models
  3. Mohidem NA, Osman M, Muharam FM, Elias SM, Shaharudin R, Hashim Z
    Int J Mycobacteriol, 2021 12 18;10(4):442-456.
    PMID: 34916466 DOI: 10.4103/ijmy.ijmy_182_21
    Background: Early prediction of tuberculosis (TB) cases is very crucial for its prevention and control. This study aims to predict the number of TB cases in Gombak based on sociodemographic and environmental factors.

    Methods: The sociodemographic data of 3325 TB cases from January 2013 to December 2017 in Gombak district were collected from the MyTB web and TB Information System database. Environmental data were obtained from the Department of Environment, Malaysia; Department of Irrigation and Drainage, Malaysia; and Malaysian Metrological Department from July 2012 to December 2017. Multiple linear regression (MLR) and artificial neural network (ANN) were used to develop the prediction model of TB cases. The models that used sociodemographic variables as the input datasets were referred as MLR1 and ANN1, whereas environmental variables were represented as MLR2 and ANN2 and both sociodemographic and environmental variables together were indicated as MLR3 and ANN3.

    Results: The ANN was found to be superior to MLR with higher adjusted coefficient of determination (R2) values in predicting TB cases; the ranges were from 0.35 to 0.47 compared to 0.07 to 0.14, respectively. The best TB prediction model, that is, ANN3 was derived from nationality, residency, income status, CO, NO2, SO2, PM10, rainfall, temperature, and atmospheric pressure, with the highest adjusted R2 value of 0.47, errors below 6, and accuracies above 96%.

    Conclusions: It is envisaged that the application of the ANN algorithm based on both sociodemographic and environmental factors may enable a more accurate modeling for predicting TB cases.

    Matched MeSH terms: Linear Models
  4. Hedzlin Zainuddin, Maisarah Ismail, Nurul Hidayah Bostamam, Muhamad Mukhzani Muhamad Hanifah, Mohamad Fariz Mohamad Taib, Mohamad Zhafran Hussin
    Science Letter, 2016;10(2):23-25.
    MyJurnal
    The study is conducted to evaluate the significance of solar irradiance, ambient temperature and relative humidity as predictors and to quantify the relative contribution of these ambient parameters as predictors for photovoltaic module temperature model. The module temperature model was developed from experimental data of mono-crystalline and poly-crystalline PV modules retrofitted on metal roof in Klang Valley. The model was developed and analyzed using Multiple Linear Regressions (MLR) and Principle Component Analysis (PCA) Techniques. Solar irradiance, ambient temperature and relative humidity have been proven to be the significant predictors for module temperature. For poly-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 64.28 %, 17.45 % and 12.64 % respectively. For mono-crystalline PV module, the relative contribution of solar irradiance, ambient temperature and relative humidity are 66.12 %, 17.46 % and 12.48 % respectively. Thus, there is no significant difference in terms of relative contribution of these ambient parameters towards photovoltaic module temperature between poly-crystalline and mono-crystalline PV module technologies.
    Matched MeSH terms: Linear Models
  5. Hakimi M, Omar MB, Ibrahim R
    Sensors (Basel), 2023 Jan 16;23(2).
    PMID: 36679816 DOI: 10.3390/s23021020
    The gas sweetening process removes hydrogen sulfide (H2S) in an acid gas removal unit (AGRU) to meet the gas sales' specification, known as sweet gas. Monitoring the concentration of H2S in sweet gas is crucial to avoid operational and environmental issues. This study shows the capability of artificial neural networks (ANN) to predict the concentration of H2S in sweet gas. The concentration of N-methyldiethanolamine (MDEA) and Piperazine (PZ), temperature and pressure as inputs, and the concentration of H2S in sweet gas as outputs have been used to create the ANN network. Two distinct backpropagation techniques with various transfer functions and numbers of neurons were used to train the ANN models. Multiple linear regression (MLR) was used to compare the outcomes of the ANN models. The models' performance was assessed using the mean absolute error (MAE), root mean square error (RMSE), and coefficient of determination (R2). The findings demonstrate that ANN trained by the Levenberg-Marquardt technique, equipped with a logistic sigmoid (logsig) transfer function with three neurons achieved the highest R2 (0.966) and the lowest MAE (0.066) and RMSE (0.122) values. The findings suggested that ANN can be a reliable and accurate prediction method in predicting the concentration of H2S in sweet gas.
    Matched MeSH terms: Linear Models
  6. Che Azemin MZ, Ab Hamid F, Aminuddin A, Wang JJ, Kawasaki R, Kumar DK
    Exp Eye Res, 2013 Nov;116:355-358.
    PMID: 24512773 DOI: 10.1016/j.exer.2013.10.010
    The fractal dimension is a global measure of complexity and is useful for quantifying anatomical structures, including the retinal vascular network. A previous study found a linear declining trend with aging on the retinal vascular fractal dimension (DF); however, it was limited to the older population (49 years and older). This study aimed to investigate the possible models of the fractal dimension changes from young to old subjects (10-73 years). A total of 215 right-eye retinal samples, including those of 119 (55%) women and 96 (45%) men, were selected. The retinal vessels were segmented using computer-assisted software, and non-vessel fragments were deleted. The fractal dimension was measured based on the log-log plot of the number of grids versus the size. The retinal vascular DF was analyzed to determine changes with increasing age. Finally, the data were fitted to three polynomial models. All three models are statistically significant (Linear: R2 = 0.1270, 213 d.f., p linear regression (p linear and cubic models in a sample with a broader age spectrum.
    Matched MeSH terms: Linear Models
  7. Gazzaz NM, Yusoff MK, Juahir H, Ramli MF, Aris AZ
    Water Environ Res, 2013 Aug;85(8):751-66.
    PMID: 24003601
    This study investigated relationships of a water quality index (WQI) with multiple water quality variables (WQVs), explored variability in water quality over time and space, and established linear and non-linear models predictive of WQI from raw WQVs. Data were processed using Spearman's rank correlation analysis, multiple linear regression, and artificial neural network modeling. Correlation analysis indicated that from a temporal perspective, the WQI, temperature, and zinc, arsenic, chemical oxygen demand, sodium, and dissolved oxygen concentrations increased, whereas turbidity and suspended solids, total solids, nitrate nitrogen (NO3-N), and biochemical oxygen demand concentrations decreased with year. From a spatial perspective, an increase with distance of the sampling station from the headwater was exhibited by 10 WQVs: magnesium, calcium, dissolved solids, electrical conductivity, temperature, NO3-N, arsenic, chloride, potassium, and sodium. At the same time, the WQI; Escherichia coli bacteria counts; and suspended solids, total solids, and dissolved oxygen concentrations decreased with distance from the headwater. Lastly, regression and artificial neural network models with high prediction powers (81.2% and 91.4%, respectively) were developed and are discussed.
    Matched MeSH terms: Linear Models
  8. Omar AF, MatJafri MZ
    Sensors (Basel), 2013;13(4):4876-83.
    PMID: 23584118 DOI: 10.3390/s130404876
    This study presents a novel application of near infrared (NIR) spectral linearisation for measuring the soluble solids content (SSC) of carambola fruits. NIR spectra were measured using reflectance and interactance methods. In this study, only the interactance measurement technique successfully generated a reliable measurement result with a coefficient of determination of (R2) = 0.724 and a root mean square error of prediction for (RMSEP) = 0.461° Brix. The results from this technique produced a highly accurate and stable prediction model compared with multiple linear regression techniques.
    Matched MeSH terms: Linear Models
  9. Al-Mansoob MAK, Al-Mazzah MM
    Med J Malaysia, 2005 Aug;60(3):349-57.
    PMID: 16379191
    The aim of study was to investigate the role of climate on the Malaria Incidence Rates (MIR) in some regions in of Yemen. For such purpose, the monthly (MIR) were calculated from the records of the hospitals' laboratories and centers of the Malaria Rollback centers in the main cities of the governorates Hudeidah, Taiz, Sana'a and Hadramout for the period 1989-1998. The readings of the climatic factors (CF) particularly the average monthly temperature (T), relative humidity (RH), volume of rain fall (RF) and wind speed (WS) for the same period of time were also collected from different weather and climatic information resources. Descriptive statistics, simple linear regression and multiple linear regression techniques were used to analyse the relationship between MIR and CF. The analysis shows highly significant relationship between MIR and the CF in these regions of Yemen (p-value 0.001).
    Matched MeSH terms: Linear Models
  10. Harnen S, Umar RS, Wong SV, Wan Hashim WI
    Traffic Inj Prev, 2003 Dec;4(4):363-9.
    PMID: 14630586
    In conjunction with a nationwide motorcycle safety program, the provision of exclusive motorcycle lanes has been implemented to overcome link-motorcycle accidents along trunk roads in Malaysia. However, not much work has been done to address accidents at junctions involving motorcycles. This article presents the development of predictive model for motorcycle accidents at three-legged major-minor priority junctions of urban roads in Malaysia. The generalized linear modeling technique was used to develop the model. The final model reveals that motorcycle accidents are proportional to the power of traffic flow. An increase in nonmotorcycle and motorcycle flows entering the junctions is associated with an increase in motorcycle accidents. Nonmotorcycle flow on major roads had the highest effect on the probability of motorcycle accidents. Approach speed, lane width, number of lanes, shoulder width, and land use were found to be significant in explaining motorcycle accidents at the three-legged major-minor priority junctions. These findings should enable traffic engineers to specifically design appropriate junction treatment criteria for nonexclusive motorcycle lane facilities.
    Matched MeSH terms: Linear Models
  11. Intan Syafinaz Mat Shafie, Yuslina Liza Mohammad Yunus, Nur Izzah Jamil, Aini Hayati Musa
    MyJurnal
    Television (TV) advertisements have become one of the most powerful marketing tools in attracting their audience to become potential customers. They are widely used among food industry locally and also internationally. This study is conducted to identify the factors that contribute to an effective TV advertisement in food industry among centennials using predictive analysis. The aims of this study are to; 1) Identify significant factors; Attractive Visual, Persuasive Message and Repetition of Advertisement to the Effectiveness of TV advertisements in food industry. 2) Identify the most contribute significant factors; Attractive Visual, Persuasive Message and Repetition of Advertisement to the Effectiveness of TV advertisements in food industry. Thus, it was tested to 300 respondents in Shah Alam area using convenient sampling technique. Preliminary analysis included reliability analysis, checking for the correlation, multiple linear regression requirement and R-Square score. Correlation analysis shows that there was a significant positive linear relationship between attractive visuals, persuasive message and repetition of advertisements towards Effectiveness of TV advertisements in food industry. Among independent variables entered into the model, persuasive message (t=7.474, p-value=0.000
    Matched MeSH terms: Linear Models
  12. Qudsiah Suliman, Salmiah Md. Said, Lim Poh Ying, Nor Afiah Mohd. Zukefli, Tan Kit-Aun, Alif Ramli, et al.
    MyJurnal
    Introduction: Evidently, stigma has potentially prompted the negative outcome in Tuberculosis (TB) control through delayed diagnosis and poor adherence to treatment. Amidst accelerating treatment interruption in Selangor, little attention is paid to the quantitative assessment of stigma, thus warrant further characterisation of TB stigma in ur-ban districts, Selangor. This study aimed to determine the predictors of internalised stigma among newly diagnosed PTB smear positive in urban districts, Selangor. Methods: A multi-centric longitudinal study recruited 345 newly diagnosed PTB smear positive patients who started TB treatment from November 2018 until June 2019. Baseline assessments utilised pre-tested self-administered questionnaire and standardised data collection form. Using IBM SPSS version 25.0, multiple linear regression was computed to determine the predictors. Results: The response rate was 84.7% with most of respondents were married and attained educational level up to secondary school. Other than low mean score of social support [mean (SD)=33.39(5.86)], the prominent findings were lacking knowledge of anti-TB side effect and wrongly perceived damaging effect of anti-TB drug to internal organ. The mean internalised stigma score was 24.88 (SD=4.70), which predicted by age, educational level (no formal education), employment status (retiree), alternative medicine practice, baseline symptoms score, perceived barrier, and social support, with entire group of variables significantly predicted TB stigma (F [9, 331] =21.476, p
    Matched MeSH terms: Linear Models
  13. Mohamad Syamim Hilm, Sofianita Mutalib, Sarifah Radiah Shari, Siti Nur Kamaliah Kamarudin
    ESTEEM Academic Journal, 2020;16(2):31-40.
    MyJurnal
    Electricity is one of the most important resources and fundamental infrastructure for every nation. Its milestone shows a significant contribution to world development that brought forth new technological breakthroughs throughout the centuries. Electricity demand constantly fluctuates, which affects the supply. Suppliers need to generate more electrical energy when demand is high, and less when demand is low. It is a common practice in power markets to have a reserve margin for unexpected fluctuation of demand. This research paper investigates regression techniques: multiple linear regression (MLR) and vector autoregression (VAR) to forecast demand with predictors of economic growth, population growth, and climate change as well as the demand itself. Auto-Regressive Integrated Moving Average (Auto-ARIMA) was used in benchmarking the forecasting. The results from MLR and VAR (lag-values=20) and Auto-ARIMA are monitored for five months from June to October of 2019. Using the root mean square error (RMSE) as an indicator for accuracy, Auto-ARIMA has the lowest RMSE for four months except in June 2019. VAR (lag-values=20) shows good forecasting capabilities for all five months, considering it uses the same lag values (20) for each month. Three different techniques have been successfully examined in order to find the best model for the prediction of the demand.
    Matched MeSH terms: Linear Models
  14. Gilbert Ringgit, Shafiquzzaman Siddiquee, Suryani Saallah, Mohammad Tamrin Mohamad Lal
    MyJurnal
    In this work, an electrochemical method for detection of trace amount of aluminium (Al3+), a heavy metal ion, based on a bare gold electrode (AuE) was developed. Current responses of the AuE under various type of electrolytes, redox indicators, pH, scan rate and accumulation time were investigated using cyclic voltammetry (CV) method to obtain the optimum conditions for Al3+ detection. The sensing properties of the AuE towards the target ion with different concentrations were investigated using differential pulse voltammetry (DPV) method. From the CV results, the optimal conditions for the detection of Al3+ were Tris-HCl buffer (0.1 M, pH 2) supported by 5 mM Prussian blue with scan rate and accumulation time respectively of 100 mVs−1 and 15 s. Under the optimum conditions, the DPV method was detected with different concentrations of aluminium ion ranging from 0.2 to 1.0 ppm resulted in a good linear regression r² = 0.9806. This result suggests that the optimisation of the basic parameters in electrochemical detection using AuE is crucial before further modification of the Au-electrode to improve the sensitivity and selectivity especially for the low concentration of ion detection. The developed method has a great potential for rapid detection of heavy metal ion (Al3+) in drinking water samples.
    Matched MeSH terms: Linear Models
  15. Tao H, Rahman MA, Jing W, Li Y, Li J, Al-Saffar A, et al.
    Work, 2021;68(3):903-912.
    PMID: 33720867 DOI: 10.3233/WOR-203424
    BACKGROUND: Human-robot interaction (HRI) is becoming a current research field for providing granular real-time applications and services through physical observation. Robotic systems are designed to handle the roles of humans and assist them through intrinsic sensing and commutative interactions. These systems handle inputs from multiple sources, process them, and deliver reliable responses to the users without delay. Input analysis and processing is the prime concern for the robotic systems to understand and resolve the queries of the users.

    OBJECTIVES: In this manuscript, the Interaction Modeling and Classification Scheme (IMCS) is introduced to improve the accuracy of HRI. This scheme consists of two phases, namely error classification and input mapping. In the error classification process, the input is analyzed for its events and conditional discrepancies to assign appropriate responses in the input mapping phase. The joint process is aided by a linear learning model to analyze the different conditions in the event and input detection.

    RESULTS: The performance of the proposed scheme shows that it is capable of improving the interaction accuracy by reducing the ratio of errors and interaction response by leveraging the information extraction from the discrete and successive human inputs.

    CONCLUSION: The fetched data are analyzed by classifying the errors at the initial stage to achieve reliable responses.

    Matched MeSH terms: Linear Models
  16. Noor Artika Hassan, Hashim JH, Wan Puteh SE, Wan Mahiyuddin WR, Faisal MS
    MyJurnal
    Introduction: Altered weather patterns and changes in precipitation, temperature and humidity resulting
    from climate change could affect the distribution and incidence of cholera. This study is to quantify climateinduced increase in morbidity rates of cholera. Material and Methods: Monthly cholera cases and monthly
    temperature, precipitation, and relative humidity data from 2004 to 2014 were obtained from the Malaysian
    Ministry of Health and Malaysian Meteorological Department, respectively. Poisson generalized linear models
    were developed to quantify the relationship between meteorological parameters and the number of reported
    cholera cases. Results: The findings revealed that the total number of cholera cases in Malaysia during the 11
    year study period was 3841 cases with 32 deaths. Out of these, 45.1% of the cases were among children below
    12 years old and 75% of the cases were from Sabah. Temperature and precipitation gave significant impact on
    the cholera cases in Sabah, (p
    Matched MeSH terms: Linear Models
  17. Samsudin MS, Azid A, Khalit SI, Sani MSA, Lananan F
    Mar Pollut Bull, 2019 Apr;141:472-481.
    PMID: 30955758 DOI: 10.1016/j.marpolbul.2019.02.045
    The prediction models of MWQI in mangrove and estuarine zones were constructed. The 2011-2015 data employed in this study entailed 13 parameters from six monitoring stations in West Malaysia. Spatial discriminant analysis (SDA) had recommended seven significant parameters to develop the MWQI which were DO, TSS, O&G, PO4, Cd, Cr and Zn. These selected parameters were then used to develop prediction models for the MWQI using artificial neural network (ANN) and multiple linear regressions (MLR). The SDA-ANN model had higher R2 value for training (0.9044) and validation (0.7113) results than SDA-MLR model and was chosen as the best model in mangrove estuarine zone. The SDA-ANN model had also demonstrated lower RMSE (5.224) than the SDA-MLR (12.7755). In summary, this work suggested that ANN was an effective tool to compute the MWQ in mangrove estuarine zone and a powerful alternative prediction model as compared to the other modelling methods.
    Matched MeSH terms: Linear Models
  18. Mahmud MA, Hazrin M, Muhammad EN, Mohd Hisyam MF, Awaludin SM, Abdul Razak MA, et al.
    Geriatr Gerontol Int, 2020 Dec;20 Suppl 2:63-67.
    PMID: 33370852 DOI: 10.1111/ggi.14033
    AIM: This study aimed to determine the factors that influence perceived social support among older adults in Malaysia.

    METHODS: We used the 11-item Duke Social Support Index to assess perceived social support through a face-to-face interview. Higher scores indicate better social support. Linear regression analysis was carried out to determine the factors that influence perceived social support by adapting the conceptual model of social support determinants and its impact on health.

    RESULTS: A total of 3959 respondents aged ≥60 years completed the Duke Social Support Index. The estimated mean Duke Social Support Index score was 27.65 (95% CI 27.36-27.95). Adjusted for confounders, the factors found to be significantly associated with social support among older adults were monthly income below RM1000 (-0.8502, 95% CI -1.3523, -0.3481), being single (-0.5360, 95% CI -0.8430, -0.2290), no depression/normal (2.2801, 95% CI 1.6666-2.8937), absence of activities of daily living (0.9854, 95% CI 0.5599-1.4109) and dependency in instrumental activities of daily living (-0.3655, 95% CI -0.9811, -0.3259).

    CONCLUSION: This study found that low income, being single, no depression, absence of activities of daily living and dependency in instrumental activities of daily living were important factors related to perceived social support among Malaysian older adults. Geriatr Gerontol Int 2020; 20: 63-67.

    Matched MeSH terms: Linear Models
  19. Gopinath SCB, Ismail ZH, Shapiai MI, Yasin MNM
    PMID: 34009645 DOI: 10.1002/bab.2196
    Current developments in sensors and actuators are heralding a new era to facilitate things to happen effortlessly and efficiently with proper communication. On the other hand, Internet of Things (IoT) has been boomed up with er potential and occupies a wide range of disciplines. This study has choreographed to design of an algorithm and a smart data-processing scheme to implement the obtained data from the sensing system to transmit to the receivers. Technically, it is called "telediagnosis" and "remote digital monitoring," a revolution in the field of medicine and artificial intelligence. For the proof of concept, an algorithmic approach has been implemented for telediagnosis with one of the degenerative diseases, that is, Parkinson's disease. Using the data acquired from an improved interdigitated electrode, sensing surface was evaluated with the attained sensitivity of 100 fM (n = 3), and the limit of detection was calculated with the linear regression value coefficient. By the designed algorithm and data processing with the assistance of IoT, further validation was performed and attested the coordination. This proven concept can be ideally used with all sensing strategies for immediate telemedicine by end-to-end communications.
    Matched MeSH terms: Linear Models
  20. Muhammad Nur Arsyad Azman, Ng, Choy Peng, Faridah Hanim Khairuddin, Neza Ismail, Wan Mohamed Syafuan Wan Sabri
    MyJurnal
    Road surface condition of a pavement is one of the most important features as it affect driving comfort and safety. A good road surface condition could reduce the risk of traffic accidents and injuries. Pavement Condition Index (PCI) is one of the important tools to measure the pavement performance. By conducting pavement evaluation, civil engineers could prioritize the maintenance and rehabilitation which usually incurred a huge cost. In University Pertahanan Nasional Malaysia (UPNM), there was no proper maintenance and rehabilitation scheduled for the roads as no performance evaluation tool available to measure the pavement condition. Thus, the objective of this study was to develop a Composite Pavement Performance Index (CPPI) to monitor the pavement condition and to rank the roads in UPNM. To develop the CPPI, road defects data were collected from 6 internal roads in UPNM. From the data collected, 4 major distresses were identified: longitudinal cracking, crocodile cracking, potholes and ravelling were found more likely to affect the pavement’s condition in UPNM. By measuring the growth of the distresses over a period of 6 months, modelling was conducted using simple linear regression. The growth of the distresses were compared, and odds ratios were computed to calculate the weightage of each distress for the determination of the CPPI value. The CPPI value developed could be used to rank the roads in UPNM. This study demonstrated that the road connecting to the library building in UPNM experienced the worst pavement deterioration with a PCI of 24 or a CPPI value of 1.1915. The level of severity was classified as “SERIOUS” in accordance to ASTM D6433. This road was recommended for reconstruction to increase the comfort and safety for road users
    Matched MeSH terms: Linear Models
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