High Correlation Values Research Articles

A hybrid machine learning model for predicting continuous cooling transformation diagrams in welding heat-affected zone of low alloy steels

Continuous cooling transformation diagrams in synthetic weld heat-affected zone (SH-CCT diagrams) show the phase transition temperature and hardness at different cooling rates, which is an important basis for formulating the welding process or predicting the performance of welding heat-affected zone. However, the experimental determination of SH-CCT diagrams is a time-consuming and costly process, which does not conform to the development trend of new materials. In addition, the prediction of SH-CCT diagrams using metallurgical models remains a challenge due to the complexity of alloying elements and welding processes. So, in this study, a hybrid machine learning model consisting of multilayer perceptron classifier, k-Nearest Neighbors and random forest is established to predict the phase transformation temperature and hardness of low alloy steel using chemical composition and cooling rate. Then the SH-CCT diagrams of 6 kinds of steels are calculated by the hybrid machine learning model. The results show that the accuracy of the classification model is up to 100%, the predicted values of the regression models are in good agreement with the experimental results, with high correlation coefficient and low error value. Moreover, the mathematical expressions of hardness in welding heat-affected zone of low alloy steel are calculated by symbolic regression, which can quantitatively express the relationship between alloy composition, cooling time and hardness. This study demonstrates the great potential of the material informatics in the field of welding technology.

Comparing between permeability prediction by using classical and FZI methods/Tertiary Reservoir in Khabaz Oil Field / Northern Iraq

This study are developed to predict the permeability of Tertiary reservoir in Khabaz oil field northern of Iraq. The Tertiary reservoir include many geological formations (Aaliji, Jaddala, Oligocene system, Jeribe, Fatha, Muqdadia formations). Actually Seven wells (Kz-1, Kz-2, Kz-3, Kz-4, Kz-9, Kz-14, Kz-15) used for this study in Khabaz oil field. Two methods are used, which are classical methods depending on core test and well log interpretations and FZI methods, the results show that the permeability estimated by core and well log interpretation method which depending on just the porosity are not close enough for the real values because it’s not toke in the consideration all the parameters that effect the values of permeability. The FZI method are show more real values and provides best correlation coefficient in comparing with the classical methods. The must conclusions of the result refer that the FZI method results are more accurate because of the high correlation coefficient (R2) values.

Modelling and investigating the impacts of climatic variables on ozone concentration in Malaysia using correlation analysis with random forest, decision tree regression, linear regression, and support vector regression

Climate change is generally known to impact ozone concentration globally. However, the intensity varies across regions and countries. Therefore, local studies are essential to accurately assess the correlation of climate change and ozone concentration in different countries. This study investigates the effects of climatic variables on ozone concentration in Malaysia in order to understand the nexus between climate change and ozone concentration. The selected data was obtained from ten (10) air monitoring stations strategically mounted in urban-industrial and residential areas with significant emissions of pollutants. Correlation analysis and four machine learning algorithms (random forest, decision tree regression, linear regression, and support vector regression) were used to analyze ozone and meteorological dataset in the study area. The analysis was carried out during the southwest monsoon due to the rise of ozone in the dry season. The results show a very strong correlation between temperature and ozone. Wind speed also exhibits a moderate to strong correlation with ozone, while relative humidity is negatively correlated. The highest correlation values were obtained at Bukit Rambai, Nilai, Jaya II Perai, Ipoh, Klang and Petaling Jaya. These locations have high industries and are well urbanized. The four machine learning algorithms exhibit high predictive performances, generally ascertaining the predictive accuracy of the climatic variables. The random forest outperformed other algorithms with a very high R2 of 0.970, low RMSE of 2.737 and MAE of 1.824, followed by linear regression, support vector regression and decision tree regression, respectively. This study's outcome indicates a linkage between temperature and wind speed with ozone concentration in the study area. An increase of these variables will likely increase the ozone concentration posing threats to lives and the environment. Therefore, this study provides data-driven insights for decision-makers and other stakeholders in ensuring good air quality for sustainable cities and communities. It also serves as a guide for the government for necessary climate actions to reduce the effect of climate change on air pollution and enabling sustainable cities in accordance with the UN's SDGs 13 and 11, respectively.

Application of soft computing approaches to predict gabion weir oxygen aeration efficiency

ABSTRACT Aeration is a physical process through which air is brought into contact with water, and thereby both times of contact and contact area between water and air are enhanced. Gabion weirs are eco-friendly alternative structures having the least negative environmental impact. The paper examines the performance of the neuro-fuzzy, neural network, and Gaussian process regression (GPR) models in estimating the gabion weir oxygen aeration efficiency using the test data sets with regards to the evaluation indicators. The model utilizes non-dimensional inputs: Froude number, Reynolds number, the ratio of mean size to weir length, and porosity of gabion weir particles. The GPR with normalized poly kernel model having the highest value of correlation coefficient (CC), 0.9132, and value of root mean square error (RMSE), 0.392, outperforms all applied models and is followed by the neural network model with CC 0.8952 and RMSE 0.398. The neuro-fuzzy triangular shape with CC 0.8865 and RMSE 0.07576 is an equally performing model; nevertheless, all applied AI-based models are mostly performing well, but all mathematical models are exceptionally performing poorly except the nonlinear model with CC 0.8612 and RMSE 0.0029. To assess the relative relevance of input parameters on the output results, sensitivity and parametric analysis are also performed.

Photocatalytic remediation of methylene blue and antibacterial activity study using Schiff base-Cu complexes.

This work describes the design of novel Cu(II) complexes and their application in the photocatalytic degradation of methylene blue (MB). The same photocatalyst exhibits antibacterial activity against Escherichia coli (gram-negative) and Bacillus circulans (gram-positive). The characterisation of the photocatalysts has been done by several up-to-date physical methods. The rationale behind the photocatalysts' beneficial intervention is discussed in this study. Statistical analysis of the degradation of MB is done using a one-way ANOVA, and the significance of means is determined by a multiple comparison test using Turkey HSD. Also, the degradation of MB follows pseudo first-order kinetics with high correlation coefficient values (R2 > 0.95), making them useful as simple and low-cost organic dye degradation agents.

Establishment of models for reliability evaluation of 3CLpro ligand-receptor complexes with different binding sites.

Aim: Recent research shows that 3CLpro enzyme of SARS-CoV-2 is a significant targetagainst COVID-19. Drug modeling allows the design of inhibitors of 3CLpro, butthe accuracy of those methods remains unclear. Therefore, it is important to determine the trustworthiness of the designedligand-receptorcomplexes. Method & materials: The authors built models for the reliability evaluation of 3CLpro complexes with ligands using anin-house developed AlteQ approach and complementarity principles. The models were based on 145 experimentally found 3CLpro complexes with ligands for five different binding sites. Result & conclusion: The obtained models correspond to linear regression with high values of correlation coefficients and can be successfully used to determine the reliability of the docked 3CLpro complexes with ligands.

Determining sustainability in contract farming: An evidence of melon farmers from Klotok Village, Plumpang District, Tuban Regency, Indonesia

The main problems of small farmers who plant melons in open and intensive agricultural land are capital, technology, and markets. One way to overcome the problem is by establishing contract farming with a sponsor. Relationships with sponsors do not always benefit economically. For this purpose, this study was carried out on the indicators that determine the sustainability of contract farming between melon farmers and sponsors in Klotok Village, Plumpang District, Tuban Regency, Indonesia. The study was conducted using a combination of qualitative and quantitative approaches. The results showed that of the three contract farming indicators, equity has the highest correlation value (0.302). It illustrates that the equality of farmers and sponsors is a crucial aspect for the sustainability of farming contracts. Even the dimensions of mutual benefit have low correlation values (0.296). It was shown by some farmers who suffered losses but did not break relations with the sponsors. This study concludes that the presence of sponsors plays an important role in the activities of melon farmers. The sustainability of melon farmers can drive rural economic activities and can further improve the welfare of the rural population.

Modulus of Elasticity of Elastic Tubes Used in Neuromuscular Rehabilitation Programs.

The elastic tubes have been used for clinical rehabilitation programs in which exercises are performed with submaximal intensities due to the difficulty in the measure the applied force. The authors aimed to quantify the elastic constant of elastic tubes used in neuromuscular rehabilitation programs predicting the force related to elastic tube elongation. A force test was performed by stretching the elastic tubes to determine the relationship between force and elongation. Eight elastic tubes with progressive levels of resistance represented by colors (yellow, red, blue, gray, black, grape, purple, and gold-low to higher resistance) were used. Experimental. The test and retest were compared using the paired t test. The agreement and reliability between the test versus retest of pooled means colors were analyzed by plotting the Bland-Altman graph and intraclass correlation coefficient and the coefficient of variation. Pearson correlation was used to verify the validity between measurements. The force values generated from the elastic tube elongation increase according to the color and thickness of elastic tubes with a strong and significant association between them (P < .0001). The elastic constant measurements were similar and presented high intraclass correlation coefficient values, low coefficient of variation values, and were reproducible (P < .0001). The force could be quantified according to elastic tube length variation by the linear regression equation with reproducibility. It gives greater measurement precision and better training load control when using elastic tubes in strength training programs.

Adsorption of Cd and Cr ions from industrial wastewater using Ca doped Ni–Zn nanoferrites: Synthesis, characterization and isotherm analysis

Removal of heavy metals (Cd and Cr) from industrial wastewater by adsorption onto a series of Ca substituted nickel zinc nanoferrites (CaxNi0.4Zn0.6-xFe2O4 (x = 0.1, 0.2, 0.3, 0.4, 0.5 and 0.6)) (CNZNFs) was studied in detail. The effect of calcium doping and contact time over adsorption of cadmium and chromium ions was investigated and maximum uptake of Cd (98.25%) and Cr (51%) ions were marked by prepared nanoadsorbents. Structural studies confirmed the formation of spinel structured nanoparticles with crystallite size ranging from 24 to 38 nm while porosity was observed to vary from 52% to 58% with calcium concentration. Pseudo second order (PSO) kinetic model for adsorption of both heavy metals (cadmium and chromium) was observed to be better fitted with adsorption data. Maximum adsorption efficiencies for Cd (128.20 mg/g) and Cr (23.54 mg/g) were found by the data fit of Langmuir isotherm model. Additionally, the adsorption data was found to obey Langmuir isotherm model for both heavy metals due to higher values of correlation coefficients (R2 = 0.94131 for cadmium and 0.91091 for chromium) than Freundlich isotherm model (R2 = 0.5865 for cadmium and 0.4599 for chromium).

Hubungan Jiwa Kewirausahaan dengan Keberhasilan Usaha Hidroponik di Kota Kendari

This study aims to determine the relationship between the entrepreneurial spirit and the success of hydroponic business in Kendari City. This research was conducted in June-July 2021. The population in this study were 24 hydroponic business actors, using the saturated sampling method so that the research sample was the same as the total population. Using descriptive statistical analysis through a Likert scale measuring instrument and Spearman Rank correlation using SPSS. The results showed that the Spearman Rank correlation analysis of all entrepreneurial spirits was significantly related to moderate to strong levels of hydroponic business actors in Kendari City. Each variable is positively related at the 0.01 level of significance. The highest correlation value is owned by the relationship between daring to take risks and business growth with a score of 0.818 and is included in the very strong category.

KORELASI UKURAN TUBUH TERHADAP BOBOT BADAN KAMBING SABURAI DI UPTD PEMBIBITAN PETERNAKAN DI KABUPATEN PESAWARAN

ABSTRACT The research was conducted with the aim of knowing the correlation between body size and bodyweight of Saburai goats. The study was carried out using secondary data in the form of recordings of 49 saburai goats which were divided into 5 age groups, namely group 1 (birth), group 2 (age 3 months), group 3 (age 6 months), group 4 (age 9 months) and group 5 (12 months old). The results showed that the correlation between chest circumference and body weight for age at birth, 3 months, 6 months, 9 months, 12 months was (0.78; 0.81; 0.81; 0.80; 0.70), then body length to bodyweight for each age group at birth, 3 months, 6 months, 9 months, 12 months, namely (0.55; 0.80-0; 74; 0.68; 0.66) and gumba height to birth weight , 3 months, 6 months, 9 months, 12 months ie (0.62; 0.77; 0.77; 0.73; 0.48) in saburai goats. The conclusion that can be drawn based on the results of the study is that the chest circumference of the Saburai goat has the highest correlation value to body weight, the second highest is body length to body weight and body height has the lowest correlation to body weight. Keywords : Age, Body size, body weight

Multi-Scale Assessment of SMAP Level 3 and Level 4 Soil Moisture Products over the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin, China

The Soil Moisture Active Passive (SMAP) mission with high-precision soil moisture (SM) retrieval products provides global daily composites of SM at 3, 9, and 36 km earth grids measured by L-band active and passive microwave sensors. The capability of passive microwave remote sensing has been recognized for the estimation of SM variations. The purpose of this work was to establish an interaction between the highly variable SM spatial distribution on the ground and the SMAP’s coarse resolution radiometer-based SM retrievals. In this work, SMAP Level 3 (L3) and Level 4 (L4) SM products are validated with in situ datasets observed from the different locations of the Soil Moisture Network within the ShanDian River (SMN-SDR) Basin over the period of January 2018 to December 2019. The values of the unbiased root mean square error (ubRMSE) for L3 (SPL3SMP_E) SM retrievals are close to the standard SMAP mission SM accuracy requirement of 0.04 m3/m3 at the 9-km scale, with an averaged ubRMSE value of 0.041 m3/m3 (0.050 m3/m3) for descending (ascending) SM with the correlation (R) values of 0.62 (0.42) against the sparse network sites. The L4 (SPL4SMGP) Surface and Root-zone SM (RZSM) estimates show less error (ubRMSE &lt; 0.04) and high correlation (R &gt; 0.60) values, and are consistent with the previous SMAP-based SM estimations. The SMAP L4 SM products (SPL4SMGP) performed well compared to the L3 SM retrieval products (SPL3SMP_E). In vegetated land, the variability and compatibility of the SMAP SM estimates with the evaluation metrics for both products (L3 and L4) showed a good performance in the grassland, then in the farmland, and worst in the woodlands. Finally, SMAP algorithm parameters sensitivity analysis of the satellite products was conducted to produce time-series and highly precise SM datasets in China.

Detecting cortical responses to continuous running speech using EEG data from only one channel

Objective To explore the detection of cortical responses to continuous speech using a single EEG channel. Particularly, to compare detection rates and times using a cross-correlation approach and parameters extracted from the temporal response function (TRF). Design EEG from 32-channels were recorded whilst presenting 25-min continuous English speech. Detection parameters were cross-correlation between speech and EEG (XCOR), peak value and power of the TRF filter (TRF-peak and TRF-power), and correlation between predicted TRF and true EEG (TRF-COR). A bootstrap analysis was used to determine response statistical significance. Different electrode configurations were compared: Using single channels Cz or Fz, or selecting channels with the highest correlation value. Study sample Seventeen native English-speaking subjects with mild-to-moderate hearing loss. Results Significant cortical responses were detected from all subjects at Fz channel with XCOR and TRF-COR. Lower detection time was seen for XCOR (mean = 4.8 min) over TRF parameters (best TRF-COR, mean = 6.4 min), with significant time differences from XCOR to TRF-peak and TRF-power. Analysing multiple EEG channels and testing channels with the highest correlation between envelope and EEG reduced detection sensitivity compared to Fz alone. Conclusions Cortical responses to continuous speech can be detected from a single channel with recording times that may be suitable for clinical application.

Kinetics observations of bacterial cellulose thickness formation using image processing approach during the fermentation process

Bacterial cellulose is an exopolysaccharide that has a higher level of purity compared to cellulose from plants. Bacterial Cellulose (BC) is widely used for various uses so that it requires certain initial conditions, one of which is thickness. During the fermentation process, cellulose will be secreted into the medium to form BC sheets and visually visible over the time period. The aim of this research was to study the relationship between variables that influence during the fermentation and to fit the kinetic model of the BC thickness formation using image processing approach during the fermentation process. A USB camera was placed in front of the fermenter to capture the formation of BC thickness. Python programming language was used to process the image and calculated the thickness of the BC sheet from the beginning to the end of the fermentation process. Several supporting parameters were observed by placing the turbidity, pH, and medium temperature sensors. Observations were made in real time with a range of data collection every 15 mins during fermentation. The highest correlation value was obtained from the relationship between time and thickness. The fermentation process is divided into 2 clusters, a change in cluster occurs at the 61st hour. The model that describes the relationship between time and thickness was the Gompertz model.

Geo-Statistical Estimation and Temporal Distribution of Wind Speed Data of Karachi Airport

Karachi city is a coastal city near the Arabian sea. Due to its location, wind speed may provide a sustainable small scale wind energy system as well as reduction in power shortage in the city. In this study, wind speed data of Karachi Airport station at 10m height is used to estimate wind speed in the surrounding area with reference to measured wind speed data of the station. This estimated wind speed will then be helpful to assess small scale wind power generation at the unsampled locations. Geo-Statics tool in ArcGIS version 10.1 software was utilised to estimate measured wind speed using different interpolation methods. Descriptive statistics were used to analyse and compare measured and estimated wind speed. The analysis summarises the effectiveness of the estimated wind speed. Time series variations of the wind speed data was also analysed. Temporal mapping showing seasonal variations of the wind speed. The descriptive statistics illustrated a high value of correlation coefficient ‘r’, coefficient of determination R2 which is 99.8% for Ordinary and Universal kriging interpolation methods, while it was calculated 99.6% for Simple kriging. A slightly higher coefficient of variation resulted in Ordinary and Universal kriging methods than the Simple kriging method. The results indicated that all three kriging methods performed better and are more effective to estimate wind speed and wind power in the surrounding area and for Temporal display.

Evaluation by ultrasound of the velocity time integral in carotid stenosis

Abstract Funding Acknowledgements Type of funding sources: None. Introduction Atherosclerotic disease is one of the main causes of cerebrovascular accident. Doppler ultrasound is one of the most important tools in the diagnosis and quantification of such issues. The classics variables to assess through this method are peak systolic velocity (PSV) at the highest stenosis carotid level, diastolic velocity at such level and the PSV rate between the PSV at the stenosis level of the internal carotid artery (ICA) respect the PSV at the common carotid artery (CCA). Objectives Compare the correlation of the velocity time integral (VTI) and the ratio between VTI of ICA and CCA to identify carotid stenosis by carotid ultrasound. Material and methods We included patients that assist at the institution for a carotid ultrasound to identify a carotid stenosis, between 2017 and 2020. Correlation tests between continuous variables were applied according to the type of distribution. Pearson"s test for continuous variables with normal distribution and Spearman"s for those with abnormal distribution.Fig. 1 Results Three hundred and forty two patients were included, 78% male, mean age 67 ± 8. According to its distribution, the VTI ACI / CCA ratio obtained as the median is 5 cm (1.27-8). By dividing the VTI ratio by quartiles, we observe that the percentage of individuals with a higher degree of carotid obstruction, increase significantly as the quartile increase: 0%, 36.5%, 94.2%, 98.8 %. p: 0.0001. Likewise, a high correlation value between the classical quantitative values and VTI and ratio of VTI at different grade of carotid stenosis was seen by carotid ultrasound (Table 1). Conclusions VTI and VTI ratio are parameters that show good correlation with the classical variables to assess a carotid stenosis by carotid ultrasound. In future investigations, the evaluation of these parameters could lead the estimation of the stenosis area, considering the principles of the continuity equation. Abstract Figure. Fig 1 Abstract Table. 1

Comparative studies on age and growth patterns of cultivated and wild Catla catla (Hamilton)

Catla catla (Hamilton) is one of the fastest growing Indian major carps (IMCs) with increasing market demand. Consistent demand and exploitation invites the attention of ichthyologists for its conservation strategies. Age and growth studies play a pivotal role for managing the fishery stocks in different water bodies. Here, we studied the age and growth patterns using opercula of both farm grown as well as the wild grown C. catla adopting standard methodologies. Wild specimens were caught from Harike wetland (Ramsar site) and the cultivated one from a farm at Dhudike in Punjab. High value of correlation coefficient ‘r’ 0.976 (wetland) and 0.983 (farm) reveals the strong relationship in total length and operculum radius of the fish. C. catla achieved average total length from wetland and farm, respectively at 1st (273.44 mm) 2nd (427.44 mm) 3rd (525.49 mm) 4th (624.52 mm) 5th (744.98 mm) 6th (813.62 mm) and 1st (282.1 mm) 2nd (463 mm) 3rd (601.7 mm) year of age of opercular bones study. The growth parameters such as, index of species average size (φh) 135.6 (wetland) and 200.6 (farm), growth constant (Clt) showed two growth phases from both localities i.e. sexual immaturity up to 2 years and sexual maturity afterwards. Whereas, growth characteristic (Cth) has revealed irregular growth pattern at wetland in comparison to growth of fish from farm. The results of this study concluded that the fish experienced more growth from the farm due to better and controlled conditions but, in wetland surrounding conditions were observed to be unfavourable for survival of the fish C. catla.

Hyperspectral Imagery Detects Water Deficit and Salinity Effects on Photosynthesis and Antioxidant Enzyme Activity of Three Greek Olive Varieties

The olive tree (Olea europaea L.) is one of the main crops of the Mediterranean region which suffers from drought and soil salinization. We assessed the photosynthetic rate, leaf water content and antioxidative enzyme activity (APX, GPX, SOD and CAT) of three Greek olive cultivars (‘Amfisis’, ‘Mastoidis’ and ‘Lefkolia Serron’) subjected to drought and salinity stresses. Hyperspectral reflectance data were acquired using an analytical spectral device (ASD) FieldSpec® 3 spectroradiometer, while principal component regression, partial least squares regression and linear discriminant analysis were used to estimate the relationship between spectral and physiological measurements. The photosynthetic rate and water content of stressed plants decreased, while enzyme activity had an increasing tendency. ‘Amfisis’ was more resistant to drought and salinity stress than ‘Mastoidis’ and ‘Lefkolia Serron’. The NDVI appeared to have the highest correlation with the photosynthetic rate, followed by the PRI. APX enzyme activity was the most highly correlated with the 1150–1370 nm range, with an additional peak at 1840 nm. CAT enzyme activity resulted in the highest correlation with the visible part of the spectrum with two peaks at 1480 nm and 1950 nm, while GPX enzyme activity appeared to have a strong correlation within all the available spectral ranges except for 670–1180 nm. Finally, SOD activity showed high correlation values within 1190–1850 nm. This is the first time the correlation of hyperspectral imagery with photosynthetic rate and antioxidant enzyme activities was determined, providing the background for high-throughput plant phenotyping through a drone with a hyperspectral camera. This progress would provide the possibility of early stress detection in large olive groves and assist farmers in decision making and optimizing crop management, health and productivity.

J. Young's Early Maladaptive Schemas and Symptoms of Male Depression.

Aim: There are more non-specific, hence harder to diagnose, symptoms in the picture of male depression. These symptoms are strongly linked to social norms and roles traditionally assigned to men. The aim of this study was to assess the interrelationship of early maladaptive schemas that affect the formation of self-image as a man with indicators of male depression. Materials and methods: The Gender-Sensitive Depression Screening (GSDS-26) by A.M. Möller-Leimkühler and the Early Maladaptive Schema Questionnaire by J. Young (YSQ-S3-PL) were used. A group of 75 men (aged 18 to 50) were qualified to take part in the research. Results: The total score of the GSDS-26 scale and individual indicators of male depression are strongly positively correlated with the severity of all five domains of the YSQ-S3-PL questionnaire. The highest correlation coefficient value was obtained in the following areas: “Disconnection and rejection” (0.741), “Other-directedness” (0.711), and “Overvigilance and inhibition” (0.711). In case of the GSDS-26 total score and the following indicators—Elevated stress, Aggressiveness, Emotional control, Risky behavior, and Classic symptoms of depression—positive statistically significant associations were confirmed with each of the 18 schemas from the YSQ-S3-PL questionnaire. Multiple regression results revealed that the following domains were significant for symptoms typical of male depression: “Disconnection and rejection” and “Impaired autonomy and performance”. The “Impaired limits” area was found to be statistically significant only for symptoms of classic depression. Conclusions: (1) The GSDS-26 scale scores show positive associations with each domain of the YSQ-S3-PL questionnaire. (2) The following areas seem to be more important for atypical depressive symptoms in men: “Disconnection and rejection” and “Impaired autonomy and performance”, while for classic depression: “Impaired limits” was more important. (3) In therapeutic work with male depressive symptoms, it is useful to focus on dominant maladaptive schemas alongside beliefs about stereotypical male roles.

Parental Income, Socio-Cultural, and Living Environment as Factors Causing Children to Drop Out of Middle School in Rengat Barat District

The analyzing whether parental income, socio-culture, and living environment are factors that cause children to drop out of school at SMP Negeri Rengat Barat District were investigated in this study. The study used a quantitative approach because the research data was in the form of numbers and the analysis uses statistics to test hypotheses. The types of data used in this study were primary data and secondary data. The data analysis technique used to solve the problem in this study was a quantitative analysis technique using the KMO test (Kaiser Meyer Olkin Measure of Sampling Adequacy) and Bartleets test of sphericity. The results showed that parents' income, socio-cultural and living environment were factors that caused children to drop out of school at the State Junior High School in West Rengat District. However, the correlation value of the living environment variable is greater than the socio-cultural variables and parents' income. Thus it can be interpreted that the dominant variable affecting children dropping out of school is the environment where they live because it has the highest correlation value. Then followed by socio-cultural and finally the income of parents.