Pearson Correlation Value Research Articles

Hubungan Antara Kompetensi Pedagogik Mahasiswa Kampus Mengajar dengan Hasil Belajar Praktik Mengajar TP FIP UNP

This research was motivated by students who participated in the Teaching Campus (KM) who had not been able to master and apply pedagogic competencies properly while participating in the KM program. This study aims to determine the relationship between the pedagogic competence of teaching campus students and the learning outcomes of TP FIP UNP teaching practice. This research was conducted using a quantitative research method in the form of correlation. Where there are 31 sample students who took part in KM. The data collection technique is the learning outcomes of student teaching practice. The data analysis techniques are normality test and linearity test with product moment correlation test mode. Based on the person correlation test of variable X of 0.043, this value is between 0.00-0.19 which means that the level of closeness of the variable X with Y is very weak, the Pearson Correlation value in a negative form indicates that if the variable X increases, the Y variable decreases and indicates that the Pearson correlation relationship is not unidirectional. Therefore, it can be concluded that there is a significant relationship between pedagogic competence and the learning outcomes of KM students' teaching practice.

Pengaruh Pengelolaan Kelas Terhadap Efektivitas Belajar Siswa Pada Mata Pelajaran IPS Terpadu di MTS Negeri 2 Parigi Moutong.

This research aims to determine the influence of classroom management on the effectiveness of student learning in integrated social studies subjects at Mts Negeri 2 Parigi, Central Sulawesi Province. The data source for this research is primary data. Data collection techniques through distributing questionnaires. The population in this study was 121 students. Meanwhile, the sample in this research was 30 students. The results of the research show that Class Management has a positive effect on the Effectiveness of Student Learning in Integrated Social Sciences Subjects at Mts Negeri 2 Parigi, Central Sulawesi Province. In the results of the correlation coefficient test, the Pearson correlation coefficient value was 0.521. This shows that there is a fairly strong relationship between Class Management (X) and Student Learning Effectiveness (Y). The coefficient of determination test results obtained an R-Square value of 0.27.1. This value means that 27.1% of the variability regarding Student Learning Effectiveness in Integrated Social Sciences Subjects at MTs Negeri 2 Parigi Moutong, Central Sulawesi Province can be explained by Class Management, while the remaining 72.9% is influenced by other variables not examined in this research.

Pengaruh Perencanaan Program Kesiswaan Terhadap Pengembangan Karakter Siswa di Sekolah Menengah Pertama

The student program, which includes extracurricular activities, student organizations, and social activities, plays an important role in supporting the character development of students. This research aims to analyze the influence of student participation in extracurricular programs on character development at SMPN Sijunjung. This study employs a quantitative method with a survey approach and cross-sectional design. The research sample consists of 200 students randomly selected from several middle schools in Sijunjung. Data were collected using a Likert scale questionnaire to measure students' perceptions of their participation in student programs and character development. Data analysis was conducted using descriptive and inferential statistical tests, specifically Pearson correlation, using SPSS software. The research results indicate that students' participation in student programs is positively and significantly related to their character development, with a Pearson correlation value of r = 0.65. Sports extracurricular programs have the strongest influence on character development, followed by student organizations. More frequent participation in student activities is also associated with a higher level of character development. This research suggests the need for an improvement in the quality of student programs and stronger support from teachers, parents, and school authorities to maximize the positive impact on students' character

The Correlation Between Students’ Anxiety and Their Speaking Ability in EFL Classroom

The objectives of this study were to identify students' anxiety levels and to investigate the relationship between students' speaking anxiety and their speaking ability in EFL classes. This study is quantitative research in the form of correlation research. The participants were 30 eleventh-grade students at one of senior high school in Yogyakarta. The data were collected using a questionnaire and a speaking test. The questionnaire was employed to assess the student's level of anxiety, while the speaking test was utilized to gauge the extent of their speaking proficiency in the EFL lesson. In addition, the researcher employed SPSS 27 to examine the correlation between students' anxiety levels and speaking proficiency.The results of the questionnaire showed that most students have a high level of anxiety when speaking in the EFL Classroom. Some factors that cause students' anxiety when speaking English in EFL classes are lack of confidence, shyness, fear of speaking inaccurately and fear of negative evaluation. Furthermore, the result of the hypothesis test showed that the significant value is 0.025. It showed that the significance value is lower than 0.05. It can be said that Ha is accepted where there is a relationship between students' anxiety level and their speaking ability. The relationship between the two variables is a negative relationship with a Pearson Correlation coefficient value of -409. It indicated that when students have a high speaking score, their anxiety level will be low. Conversely, if students have a low speaking score, it shows that they have a high level of anxiety. Moreover, the strength of the relationship between variable x and variable y is a moderate correlation because it has a coefficient value of 0.409, which is between 0.40 - 0.59.

Improved ADHD Diagnosis Using EEG Connectivity and Deep Learning through Combining Pearson Correlation Coefficient and Phase-Locking Value.

Attention Deficit Hyperactivity Disorder (ADHD) is a widespread neurobehavioral disorder affecting children and adolescents, requiring early detection for effective treatment. EEG connectivity measures can reveal the interdependencies between EEG recordings, highlighting brain network patterns and functional behavior that improve diagnostic accuracy. This study introduces a novel ADHD diagnostic method by combining linear and nonlinear brain connectivity maps with an attention-based convolutional neural network (Att-CNN). Pearson Correlation Coefficient (PCC) and Phase-Locking Value (PLV) are used to create fused connectivity maps (FCMs) from various EEG frequency subbands, which are then inputted into the Att-CNN. The attention module is strategically placed after the latest convolutional layer in the CNN. The performance of different optimizers (Adam and SGD) and learning rates are assessed. The suggested model obtained 98.88%, 98.41%, 98.19%, and 98.30% for accuracy, precision, recall, and F1 Score, respectively, using the SGD optimizer in the FCM of the theta band with a learning rate of 1e-1. With the use of FCM, Att-CNN, and advanced optimizers, the proposed technique has the potential to produce trustworthy instruments for the early diagnosis of ADHD, greatly enhancing both patient outcomes and diagnostic accuracy.

Ensembling methods for protein-ligand binding affinity prediction

Protein-ligand binding affinity prediction is a key element of computer-aided drug discovery. Most of the existing deep learning methods for protein-ligand binding affinity prediction utilize single models and suffer from low accuracy and generalization capability. In this paper, we train 13 deep learning models from combinations of 5 input features. Then, we explore all possible ensembles of the trained models to find the best ensembles. Our deep learning models use cross-attention and self-attention layers to extract short and long-range interactions. Our method is named Ensemble Binding Affinity (EBA). EBA extracts information from various models using different combinations of input features, such as simple 1D sequential and structural features of the protein-ligand complexes rather than 3D complex features. EBA is implemented to accurately predict the binding affinity of a protein-ligand complex. One of our ensembles achieves the highest Pearson correlation coefficient (R) value of 0.914 and the lowest root mean square error (RMSE) value of 0.957 on the well-known benchmark test set CASF2016. Our ensembles show significant improvements of more than 15% in R-value and 19% in RMSE on both well-known benchmark CSAR-HiQ test sets over the second-best predictor named CAPLA. Furthermore, the superior performance of the ensembles across all metrics compared to existing state-of-the-art protein-ligand binding affinity prediction methods on all five benchmark test datasets demonstrates the effectiveness and robustness of our approach. Therefore, our approach to improving binding affinity prediction between proteins and ligands can contribute to improving the success rate of potential drugs and accelerate the drug development process.

Zynq FPGA for hardware co-simulation of Takagi-Sugeno neuro-fuzzy for MPPT algorithm incorporating sensorless wind speed estimation in grid-connected wind system

This paper presents a hardware implementation on the Field Programmable Gate Array (FPGA) Zed-Board of a Maximum Power Point Tracking (MPPT) incorporating pitch angle control system and sensorless wind speed estimation using the Takagi-Sugeno (TS) Adaptive Neuro-Fuzzy Inference System (ANFIS). The described approach is applied specifically to a grid-connected Permanent Magnet Synchronous Generator-based Variable Speed Wind Turbine (VSWT). Firstly, the paper proposes an estimator-based TS-ANFIS model for real-time wind speed estimation, addressing challenges with conventional anemometers, such as precision, and susceptibility to adverse weather conditions. The estimated wind speed guides the calculation of an optimized mechanical speed for MPPT control. Secondly, an MPPT-based TS-ANFIS controller is introduced to achieve the maximum power point, integrating pitch angle control to prevent turbine failures in high wind speeds. Finally, the paper emphasizes the hardware implementation on the FPGA Zed-Board, leveraging its parallel processing capabilities to enhance control system quality by reducing sampling periods and loop delays. Validation includes simulations in Matlab/Simulink using the Xilinx system generator and hardware co-simulation on the FPGA Zed-Board. A comparative analysis highlights the contributions and advancements of the proposed models and controllers compared to recent schemes in the field. Indeed, the proposed MPPT-based TS-ANFIS approach effectively maximizes the power extracted from the VSWT, achieving an estimated average efficiency of 99.84 %. In contrast, PID methods show average efficiencies of 92.63 %. Additionally, compared to other published works, the proposed MPPT-based TS-ANFIS method demonstrates a rapid response time of 0.001 s and lower static error at 0.02 %. Furthermore, the proposed WSE-based TS-ANFIS model exhibits superior performance and effectiveness, yielding a root mean squared error (RMSE) of 0.0085381, a determination coefficient (R2) value of 0.99985, and a Pearson correlation coefficient (r) value of 0.99996. Moreover, the proposed hardware implementation of these approaches maintains lower power usage, operating at a high frequency of 80.38 MHz and achieving a high throughput of 2572.16 Mbps.

A novel combined model based on advanced optimization algorithm, and deep learning model for abnormal wind speed identification and reconstruction

When the state of the sensors of the wind turbine, especially the anemometer, is abnormal, it will affect the correctness of other parameters of the whole power system. To recognize and reconstruct the abnormal data accurately and efficiently, this study proposes an improved complete ensemble empirical mode decomposition with adaptive noise (ICEEMDAN), extreme learning machine (ELM), crazy improve butterfly optimization algorithm (CIBOA), and bi-directional long short-term memory (BILSTM). In this system, the ICEEMDAN is utilized to decompose the original wind speed sequence, and then the ELM is employed as the initial prediction engine to extract the features of each sub-sequence to achieve the preliminary prediction outcomes. The CIBOA is employed to optimize the BILSTM model parameters. To further explore the unsteady features in the wind speed series, the residual results of the preliminary prediction are modeled using BILSTM, and the predicted residuals and preliminary results are integrated to obtain the final reconstructed values. In addition, the combined model is discussed in detail employing six assessment indicators, improvements of the reconstruction model, Diebold-Mariano test, operation time, and sensitivity analysis. The results indicate that the Pearson correlation coefficient (PCC) values of the proposed model are 0.9986, 0.9978, and 0.9979, respectively. It is concluded that the proposed hybrid reconstruction model accurately identifies and reconstructs abnormal wind speed, which provides a new technique for the reasonable utilization of wind energy.

Spiritual Growth and Stress Management of Physical Education Sport and Health Students

The purpose of this research was to identify the spiritual growth and stress management of Physical Education, Health and Recreation students. The method of this research was qualitative. The respondents to this study were students of the Physical Education, Health and Recreation study program at the Faculty of Sports and Health Sciences, Universitas Negeri Surabaya. The sampling technique used was quota sampling, which was determined by researchers to include as many as 170 students. The data collection technique was a survey with a questionnaire filled out online. The research instrument used was the Health Promoting Lifestyle Profile II instrument, which was adjusted to the characteristics of the activities of Physical Education, Health and Recreation students in the sub-questions about spiritual growth and stress management. The research data were processed descriptively and correlatively using Pearson Correlation analysis. The results of the Pearson Correlation test showed a significance value of. 000 < 0.05, so it was stated that the two variables were correlated. Spiritual growth and stress management, with a Pearson Correlation value of.728, have a degree of relationship with a strong correlation. The significance value of.728 is positive, indicating that the relationship between spiritual growth and stress management increases with the assumption that the higher the spiritual growth, the higher the stress management. The conclusion of this study is that the spiritual growth of Physical Education, Health and Recreation students has a positive correlation with stress management. This study contributes to providing recommendations for conducting stress management activities for students.

The Development of a Yolov8-Based Model for the Measurement of Critical Shoulder Angle (CSA), Lateral Acromion Angle (LAA), and Acromion Index (AI) from Shoulder X-ray Images.

Background: The accurate and effective evaluation of parameters such as critical shoulder angle, lateral acromion angle, and acromion index from shoulder X-ray images is crucial for identifying pathological changes and assessing disease risk in the shoulder joint. Methods: In this study, a YOLOv8-based model was developed to automatically measure these three parameters together, contributing to the existing literature. Initially, YOLOv8 was used to segment the acromion, glenoid, and humerus regions, after which the CSA, LAA angles, and AI between these regions were calculated. The MURA dataset was employed in this study. Results: Segmentation performance was evaluated with the Dice and Jaccard similarity indices, both exceeding 0.9. Statistical analyses of the measurement performance, including Pearson correlation coefficient, RMSE, and ICC values demonstrated that the proposed model exhibits high consistency and similarity with manual measurements. Conclusions: The results indicate that automatic measurement methods align with manual measurements with high accuracy and offer an effective alternative for clinical applications. This study provides valuable insights for the early diagnosis and management of shoulder diseases and makes a significant contribution to existing measurement methods.

An Artificial Neural Network approach to assess road roughness using smartphone-based crowdsourcing data

Monitoring road surface conditions is a crucial task for road authorities to develop effective infrastructure maintenance programs. Despite smartphones have been introduced as cost-effective and real-time solution for this purpose, several challenges must be addressed before their real-world application. This study investigates the utilization of smartphone-based crowdsourcing data and Artificial Neural Networks (ANN) to enhance the precision of road surface condition estimation. Initially, data are collected from four different smartphone models mounted in various vehicles, including vertical acceleration, geographic location, and speed. The root mean square of the vertical acceleration data, along with vehicle speed, is then employed as input features for the ANN, while the true International Roughness Index (IRI) values serve as the corresponding output features. Comparative analysis between ANN and regression models based on statistical metrics such as Mean Squared Error (MSE) and Pearson correlation revealed that ANN outperforms regression models. The obtained MSE and Pearson correlation values for ANN (0.56 and 0.91) surpass those of regression models (0.72 and 0.88). Moreover, results indicated that utilizing crowdsourcing smartphone data yielded superior outcomes compared to using a single smartphone for this purpose.

Single vs. multi-slice assessments of in vivo placental T2∗ measurements

IntroductionPlacental health is vital for maternal and fetal well-being, and placental T2∗ has been suggested to identify in vivo placental dysfunction prior to delivery. However, ideal regions of interest to best inform functional assessments of the placenta remain unknown. The aim of this study is to compare global and slice-wise measures of in-vivo placental T2∗ assessments. MethodsThis prospective study recruited pregnant people with singleton pregnancies between December 2017 and February 2022.3D multi-echo RF-spoiled gradient echo sequences were acquired, and placental T2∗ values were derived from global and slice-wise approaches. Statistical analyses included Pearson correlation coefficients, concordance correlation coefficients (CCC), intraclass correlation coefficients (ICC), and Bland-Altman analyses. ResultsOf 115 participants (mean gestational age, 29.25 ± 5.05 weeks), 68 were healthy controls, and 47 were high-risk pregnancies. Global and slice-wise placental T2∗ assessments for the entire cohort showed no significant difference nor for individual subgroups (healthy controls or high-risk). Pearson correlation values ranged between 0.88 and 0.99 for mean global and slice-wise placental T2∗. CCC analyses ranged from 0.88 to 0.99 for mean T2∗, and ICC analyses ranged between 0.88 and 0.99 for mean T2∗, showing a strong agreement between measurements. Bland-Altman analyses depicted T2∗ differences across coverage methods, and groups resided within the 95 % limits of agreement. DiscussionSingle-slice placental assessments offer robust, comparable T2∗ values to global assessments, with the added benefit of reducing post-processing time and SAR exposure. This supports slice-wise approaches as valid alternatives for assessing placental health in various pregnancies.

Improving GNSS-IR Sea Surface Height Accuracy Based on a New Ionospheric Stratified Elevation Angle Correction Model

Approximately 71% of the Earth’s surface is covered by vast oceans. With the exacerbation of global climate change, high-precision monitoring of sea surface height variations is of vital importance for constructing global ocean gravity fields and preventing natural disasters in the marine system. Global Navigation Satellite System Interferometry Reflectometry (GNSS-IR) sea surface altimetry is a method of inferring sea surface height based on the signal-to-noise ratio of satellite signals. It enables the retrieval of sea surface height variations with high precision. However, navigation satellite signals are influenced by the ionosphere during propagation, leading to deviations in the measured values of satellite elevation angles from their true values, which significantly affects the accuracy of GNSS-IR sea surface altimetry. Based on this, the contents of this paper are as follows: Firstly, a new ionospheric stratified elevation angle correction model (ISEACM) was developed by integrating the International Reference Ionosphere Model (IRI) and ray tracing methods. This model aims to improve the accuracy of GNSS-IR sea surface altimetry by correcting the ionospheric refraction effects on satellite elevation angles. Secondly, four GNSS stations (TAR0, PTLD, GOM1, and TPW2) were selected globally, and the corrected sea surface height values obtained using ISEACM were compared with observed values from tide gauge stations. The calculated average Root Mean Square Error (RMSE) and Pearson Correlation Coefficient (PCC) were 0.20 m and 0.83, respectively, indicating the effectiveness of ISEACM in sea surface height retrieval. Thirdly, a comparative analysis was conducted between sea surface height retrieval before and after correction using ISEACM. The optimal RMSE and PCC values with tide gauge station observations were 0.15 m and 0.90, respectively, representing a 20.00% improvement in RMSE and a 4.00% improvement in correlation coefficient compared to traditional GNSS-IR retrieval heights. These experimental results demonstrate that correction with ISEACM can effectively enhance the precision of GNSS-IR sea surface altimetry, which is crucial for accurate sea surface height measurements.

Socio-Economic Impact of Ethnobotanical Appraisal of Traded Medicinal Plants on Local Communities of District Astore (Gilgit Baltistan)

Medicinal plants are the only possible solution to treat various complications of human beings. The local community of district Astore, Gilgit Baltistan are dependent on medicinal plants for their primary healthcare. The present study was conducted to identify and document the medicinal uses of traded medicinal plants and to assess their socio-economic impact and current status in the respective area. A total of 26 traded medicinal plants were explored from the various villages as informed by the experienced respondents. The collected plants were pasted on herbarium sheets for identification purpose. Ethno medicinal uses were also documented and the collected data was analyzed by using statistical indices i.e. Use value, Relative frequency citation, Fidelity level and Pearson correlation coefficient. Questionnaire survey was conducted and 80 respondents were interviewed to assess the socio-economic impact and current status of traded medicinal plants. The most frequent used part of medicinal plant was leaves followed by roots and seeds. The RFC (0.88) and UV (0.98) were recorded highest for Thymus linearis. The Pearson correlation coefficient value between RFC and UV was 0.944, indicating a strong relationship between RFC and UV. Survey results showed that the traded medicinal plants played a pivotal role by enhancing socio-economic condition of local communities. Findings also indicated that the traded medicinal plants are gradually decreasing due to anthropogenic activities like improper method of collection, illegal trade, unsustainable use and lack of protection and conservation measures.

A fault diagnosis method for analog circuits based on EEMD-PSO-SVM

Analog circuit is an crucial component of electronic equipment, and the ability to diagnose its fault state quickly and accurately is essential for ensuring the safety and reliability of these electronic equipment. This paper addresses the problems of low diagnostic accuracy and the difficulties associated with model parameter selection in traditional fault diagnosis methods, particularly when dealing with nonlinear and non-stationary fault signals. A fault diagnosis method for analog circuits is proposed, which utilizes Ensemble Empirical Mode Decomposition (EEMD) for features extraction, the Maximum Information Coefficient algorithm (MIC) for features selection, and Particle Swarm Optimization (PSO) for optimizing Support Vector Machine (SVM) classification. Firstly, EEMD is used to adaptively decompose the fault signals in the circuit to extract multi-scale fault features. Secondly, the extracted features are quantitatively evaluated using the Pearson correlation coefficient and energy value analysis, leading to the construction of a fault feature vector is constructed. On this basis, the MIC feature selection algorithm is applied to further optimize the feature vector. Finally, an efficient fault classification model is developed by optimizing the hyperparameters of the SVM model using PSO. The simulation results show that the proposed method effectively overcome the problems of model complexity and low classification accuracy caused by improper selection of wavelet basis function. The accuracy of fault diagnosis and the efficiency of model training are significantly superior to those of traditional methods.

The Indian Geriatrics Quality of Life Inventory Scale (IGQOLI): Development and Validation of a Scale to Evaluate the Quality of Life among Geriatric Population of India.

Quality of life (QOL) refers to an individual's perception of their overall life and well-being. As people age, their QOL often deteriorates. Although various outcome measures exist to assess QOL, most are limited in scope and not specific to the Indian ethnicity of geriatrics. Therefore, a new outcome measure was developed and validated to more accurately evaluate the QOL for the geriatric population in India. The outcome measure was developed in three stages, followed by validation. Data was collected using multistage cluster sampling from 13 subdivisions of Jaipur district. A total of 423 participants were interviewed face-to-face. The collected data was analyzed using SPSS version 20, and the reliability and validity of the outcome measure were assessed. The Indian Geriatrics Quality of Life Inventory (IGQOL) scale demonstrated excellent reliability, with a Cronbach's alpha (α) of 0.95 and an intraclass correlation coefficient of 0.93. The measure also exhibited excellent face and content validity, with a K value of 1. Correlation analysis revealed a significant relationship with other outcome measures, with a Pearson correlation coefficient value of > 7 and a p-value of < 0.05. The study concludes that the IGQOLI scale is a reliable and valid outcome measure for evaluating the QOL in the geriatric population in India.

Self-attention transformer model for pan evaporation prediction: a case study in Australia

ABSTRACT In drought-prone regions like Australia, accurately assessing evaporation rates is essential for effectively managing and maximising the use of precious water resources and reservoirs. Current estimates show that evaporation reduces Australia's open water lake capacity by about 40% annually. With climate change, this water loss is expected to become an even greater concern. This study investigates a transformer-based neural network (TNN) to estimate monthly evaporation in three Australian locations. The models were trained and tested using monthly weather data spanning from 2009 to 2022. Input parameters were chosen based on Pearson's correlation coefficient values to identify the most impactful combinations. The developed TNN model was compared with two widely used empirical methods, namely Thornthwaite and Stephens and Stewart. The TNN model's impressive accuracy in evaporation prediction, attributed to its unique self-attention mechanism, suggests its promising potential for future use in evaporation forecasting. Additionally, the study revealed an intriguing result: Despite using the same input datasets, the TNN model surpassed traditional methods, achieving an average improvement of 18% in prediction accuracy. The TNN prediction model accurately predicts water loss (average R² = 0.970), supports irrigation management and agricultural planning and offers financial benefits to farming and related industries.

Machine Learning Approaches for Short-Term Photovoltaic Power Forecasting

A photovoltaic (PV) power forecasting prediction is a crucial stage to utilize the stability, quality, and management of a hybrid power grid due to its dependency on weather conditions. In this paper, a short-term PV forecasting prediction model based on actual operational data collected from the PV experimental prototype installed at the engineering college of Misan University in Iraq is designed using various machine learning techniques. The collected data are initially classified into three diverse groups of atmosphere conditions—sunny, cloudy, and rainy meteorological cases—for various seasons. The data are taken for 3 min intervals to monitor the swift variations in PV power generation caused by atmospheric changes such as cloud movement or sudden changes in sunlight intensity. Then, an artificial neural network (ANN) technique is used based on the gray wolf optimization (GWO) and genetic algorithm (GA) as learning methods to enhance the prediction of PV energy by optimizing the number of hidden layers and neurons of the ANN model. The Python approach is used to design the forecasting prediction models based on four fitness functions: R2, MAE, RMSE, and MSE. The results suggest that the ANN model based on the GA algorithm accommodates the most accurate PV generation pattern in three different climatic condition tests, outperforming the conventional ANN and GWO-ANN forecasting models, as evidenced by the highest Pearson correlation coefficient values of 0.9574, 0.9347, and 0.8965 under sunny, cloudy, and rainy conditions, respectively.

Near-surface soil hydrothermal response feedbacks landslide activity and mechanism

Surface moisture has recently been reported to be used in regional-scale landslide early warning. Nevertheless, near-surface multi-depth hydrothermal measurements as a hillslope scale are often less concerned and rarely linked to landslide kinematics. In this paper, we selected two neighboring landslides with different deformation mechanisms as case studies. Using in-situ multi-source sensors, we monitored real-time soil temperature and moisture at specific depths within approximately 1.5 m. The measurements span two complete monsoon seasons, representing concurrent dry and wet hydrological extremes. Statistical Pearson correlation analysis was employed to quantify the relationships between landslide activity and environmental variables such as soil temperature and moisture content. The results indicate that the near-surface soil temperatures and moisture contents contribute to a better understanding of the factors controlling landslide activity, in which variations synergistically reflect hydrothermal interaction and potential deformation mechanisms. These soil temperatures and moisture contents at certain depths (specifically at 20, 50, and even 100 cm) show moderate to strong correlations (with Pearson correlation coefficient values ranging from 0.4 to 0.8) with landslide deformation. In cases where discrete daily rainfall data exhibited unsatisfactory correlations due to their data attributes, soil temperature and moisture effectively served as alternative indicators for rainfall inputs, aiding in the analysis. Overall, this work emphasizes the critical influence of soil moisture and temperature on landslide dynamics. This study also highlights the need for comprehensive monitoring and forecasting strategies that consider a wide range of environmental factors to mitigate landslide risks associated with climate change, particularly in the context of intensified extreme weather events.

The Relationship of Self-Regulation and Resilience in Parents of Attention Deficit Hyperactivity Disorder (ADHD) at Special School-C Karya Tulus Tuntungan II

&lt;strong&gt;&lt;strong&gt;&lt;/strong&gt;&lt;/strong&gt;&lt;p&gt;Attention deficit hyperactivity disorder is a mental development disorder that becomes a problem for parents if it is not treated immediately. &lt;em&gt;So that parents can increase their resilience and bounce back from difficulties wisely. &lt;/em&gt;The type of research is descriptive quantitative with a cross-sectional approach. &lt;em&gt;This research aims to analyze the relationship between self-regulation and the resilience of parents with children with ADHD at &lt;/em&gt;Special School-C &lt;em&gt;Karya Tulus Tuntungan II. The type of research is descriptive quantitative with a cross-sectional approach. Technique. Sampling is purposive sampling. This research's respondents were parents with children with ADHD at Special School-C Karya Tulus Tuntungan II, totalling 50 respondents. &lt;/em&gt;This research was carried out from March 27 to May 5 2024.&lt;em&gt; The instruments used were the Connor-Davidson Resilience Scale (CD-RISC) and the self-regulation questionnaire (SRQ). Data were analyzed using the Spearman rank statistical test. The results of the study showed that the majority of respondents (74.0% had self-regulation in the capable category, there was a strong positive relationship between self-regulation and the resilience of parents who have children with ADHD with a Pearson correlation value of +0.450, p-value -value=0.001; p&amp;gt;0.05). The conclusion is that there is a relationship between self-regulation and the resilience of parents who have children with ADHD at Special School -C Karya Tulus Tuntungan II, which is quite strong and in the same direction. It is recommended as a nursing intervention that can be developed to explore self-regulation and resilience in patients and families, especially in parents who have children with ADHD.&lt;/em&gt;&lt;/p&gt;&lt;p&gt;&lt;strong&gt;&lt;em&gt;Keywords: ADHD, parent, Self-regulation, Resilience&lt;/em&gt;&lt;/strong&gt;&lt;/p&gt;