Correlation Coefficient Research Articles

Analyzing meteorological parameters using Pearson correlation coefficient and implementing machine learning models for solar energy prediction in Kuching, Sarawak

Solar energy is one of the clean renewable energy sources that can offset the rising consumption of fossil fuels. However, the meteorological parameters, such as solar irradiance, ambient and solar module temperatures, relative humidity, etc., constantly change, and so does the solar power generation. Such variations cause instability in the power grid operation due to injecting an unpredicted amount of power. Hence, solar energy prediction models capable of learning from past weather data and predicting future energy generation are highly desired for grid operation and planning. The objective of this study is to determine the suitable meteorological parameters for the solar energy prediction model based on the Pearson correlation coefficient and to implement them in different machine learning models. It is found in this study that five meteorological parameters, namely Air temperature, cloud opacity, global tilted irradiance, relative humidity, and zenith angle, correlate highly with solar energy generation. Later, based on the correlations, four machine-learning models were implemented to predict the solar power for Kuching, Sarawak. The accuracy of the models is measured through standard matrices such as root mean square error, mean square error, mean absolute error, and R-squared value.

Financial deepening on income inequality: A quantitative meta-analysis study

AbstractThere exists a vast empirical literature on Financial Sector Development (FSD) and the income inequality nexus; however, it lacks consensus. To study this, 24 studies with 87 regression estimates on financial institution depth and income inequality were collected. This paper used the most common method of economic meta-analysis, the Partial Correlation Coefficient (PCC), to answer the question: What is the magnitude and impact, if any, of financial institution depth on income inequality? In addition, a multivariate meta-regression model was used to find moderator variables that produced mixed results in the literature. The results show that the global average comovement of financial institution depth (domestic credit) on income inequality is very small but positive; suggesting that growth in domestic credit may widen income inequality. The positive correlation between domestic credit and income inequality highlights how financial institutions use household income and collateral as a signal when deciding on credit applications. Finally, the multivariate regression results suggest that the present heterogeneity within the literature stems from different methodologies and control variables included in the econometric models, and panel studies that mix countries with heterogeneous characteristics. These suggest that different components of FSD may impact income inequality differently.

Mapping Opium Poppy Cultivation: Socioeconomic Insights from Satellite Imagery

Over 30 million people globally consume illicit opiates. In recent decades, Afghanistan has accounted for 70–90% of the world’s illicit supply of opium. This production provides livelihoods to millions of Afghans, while also funneling hundreds of millions of dollars to insurgent groups every year, exacerbating corruption and insecurity, and impeding development. Remote sensing and field surveys are currently used in official estimates of total poppy cultivation area. These aggregate estimates are not suited to study the local socioeconomic conditions surrounding cultivation. Few avenues exist to generate comprehensive, fine-grained data under poor security conditions, without the use of costly surveys or data collection efforts. Here, we develop and test a new unsupervised approach to mapping cultivation using only freely available satellite imagery. For districts accounting for over 90% of total cultivation, our aggregate estimates track official statistics closely (correlation coefficient of 0.76 to 0.81). We combine these predictions with other grid-level data sources, finding that areas with poppy cultivation have poorer outcomes such as infant mortality and education, compared to areas with exclusively other agriculture. Surprisingly, poppy-growing areas have better healthcare accessibility. We discuss these findings, the limitations of mapping opium poppy cultivation, and associated ethical concerns.

Evaluation of the effects of the application of Ti−C:H DLC coatings obtained by PVD techniques in the kinematic pairs of internal combustion engines and powertrain systems.

The article attempts to analyze the possible effects of using Ti−C:H DLC carbon coatings produced by pulsed magnetron sputtering (PVD) to reduce friction coefficient and wear in kinematic pairs found in internal combustion engines and powertrain systems used in automotive vehicles. The aim of such action is primarily to reduce internal losses in the aforementioned units. The coatings were deposited on heat-treated bearing steel 100Cr6, examined using a scanning electron microscope FEI Quanta 200 Mark II with the chemical analyzer EDS EDAX Genesis XM 2i, tribotester T−01M examining the friction coefficient in the ball-disc correlation and Hommel Werke T8000 profilometers, additionally, in order to check the coating thickness, studies were carried out using the Calotest method. The results obtained indicate that both the friction coefficient and wear are drastically reduced in relation to samples on which no DLC coatings were applied.

Feasibility of Remote Blood Pressure Estimation via Narrow-band Multi-wavelength Pulse Transit Time

Contact-free sensing gained much traction in the past decade. While remote monitoring of some parameters (heart rate) is approaching clinical levels of precision, others remain challenging (blood pressure). We investigated the feasibility of estimating blood pressure (BP) via pulse transit time (PTT) in a novel remote single-site manner, using a modified RGB camera. A narrow-band triple band-pass filter allowed us to measure the PTT between different skin layers, harvesting information from green and near-infrared wavelengths. The filter minimizes the inter-channel influence and band overlap, however, some overlap remains within the filter bands. We further resolve this using a color-channel model and a novel channel-separation method. Using the proposed setup and algorithm, we obtained multi-wavelength (MW) PTTs in an experiment inducing BP changes to 9 subjects. The results showed good absolute Pearson’s correlation coefficient between both MW PTT and systolic BP (R = 0.61, p = 0.08) as well as diastolic BP (R = 0.54, p = 0.05), pointing to feasibility of the proposed novel remote MW BP estimation via PTT. This was further confirmed in a leave-one-subject-out experiment, where a simple Random Forest regression model achieved mean absolute errors of 3.59 and 2.63 mmHg for systolic and diastolic BP respectively.

Translation, cross-cultural adaptation and psychometric properties of the Urdu version of the back pain functional scale in low back pain patients.

Translation and validation of important scales in other languages reduce bias in reporting the functional status of the patient. To translate the Back Pain Functional Scale into Urdu (BPFSu), adapt it for use in other cultures, and evaluate its psychometric properties. According to Beaton guidelines, translation and cultural adaption was carried out. On 100 Urdu-literate men and women aged 18-60years with lower back pain, the final BPFSu was assessed for psychometric qualities. First at baseline and again after 7days, participants completed the BPFSu, the Functional Rating Index (FRI), and the Numeric Pain Rating Scale (NPRS). Internal consistency of the BPFSu was excellent (Cronbach's alpha: 0.937). No floor and ceiling effects were found Excellent test-retest reliability (ICC = 0.882, CI 95%; 0.830-0.919) was achieved. Spearman correlation coefficient showed criterion validity with the NPRS (rho =â⁢⁢0.701, p⩽ 0.001) and Pearson correlation coefficient showed construct validity with the FRI (r=â⁢⁢0.740, p⩽ 0.001). The minimum detectable changes were 9.96, while the standard error of measurement was 3.6. The BPFSu is a valid and reliable instrument for assessing physical function in individuals experiencing low back discomfort.

Exact detection thresholds and minimax optimality of Chatterjee’s correlation coefficient

Exact detection thresholds and minimax optimality of Chatterjee’s correlation coefficient

Determination of hexogen recovered from scrapped 2-chlorobenzalmalononitrile mixed explosives by gas chromatography

This study focuses on the extraction of Hexogen (RDX) from decommissioned CS mixed explosives using the Soxhlet extraction method. Recrystallization using a solution of rtnanol is utilized to purify the extract and remove impurities, such as CS. Qualitative analysis is conducted using Fourier transform infrared spectroscopy and a melting point tester. Additionally, gas chromatography is employed for qualitative and quantitative analysis of the recovered RDX. The experimental results demonstrate that the recovered RDX exhibits a strong linear relationship at concentrations ranging from 500 mg/L to 6000 mg/L, with a correlation coefficient of r = 0.9999 and a relative standard deviation (RDS) of ≤5% (n = 6). Furthermore, the purity of the recovered RDX can reach 100% through secondary recrystallization using ethanol. The quantitative analysis of RDX by gas chromatography offers the advantages of operating cylinder, high accuracy and high sensitivity.

Graph-Driven Simultaneous and Proportional Estimation of Wrist Angle and Grasp Force via High-Density EMG.

Myoelectric prostheses are generally unable to accurately control the position and force simultaneously, prohibiting natural and intuitive human-machine interaction. This issue is attributed to the limitations of myoelectric interfaces in effectively decoding multi-degree-of-freedom (multi-DoF) kinematic and kinetic information. We thus propose a novel multi-task, spatial-temporal model driven by graphical high-density electromyography (HD-EMG) for simultaneous and proportional control of wrist angle and grasp force. Twelve subjects were recruited to perform three multi-DoF movements, including wrist pronation/supination, wrist flexion/extension, and wrist abduction/adduction while varying grasp force. Experimental results demonstrated that the proposed model outperformed five baseline models, with the normalized root mean square error of 13.2% and 9.7% and the correlation coefficient of 89.6% and 91.9% for wrist angle and grasp force estimation, respectively. In addition, the proposed model still maintained comparable accuracy even with a significant reduction in the number of HD-EMG electrodes. To the best of our knowledge, this is the first study to achieve simultaneous and proportional wrist angle and grasp force control via HD-EMG and has the potential to empower prostheses users to perform a broader range of tasks with greater precision and control, ultimately enhancing their independence and quality of life.

Comparison of magnetic data products from Solar Orbiter SO/PHI-FDT and SDO/HMI

Context. The Polarimetric and Helioseismic Imager (SO/PHI), on board the Solar Orbiter mission, is the first photospheric magnetograph and tachograph to observe the Sun from outside the Sun-Earth line. The Full Disc Telescope (FDT) of SO/PHI, images the whole solar disk with a spatial resolution that varies with the distance between the Sun and the spacecraft. Aims. We check for consistency between the magnetic field strength (B), the field inclination (γ), the line-of-sight (LoS) magnetic component (BLoS) and the field azimuth (ϕ), inferred by SO/PHI-FDT and the Helioseismic and Magnetic Imager (HMI), on board Solar Dynamics Observatory (SDO), and obtain linear correlation coefficients among them. Methods. We use data from both instruments obtained on 8 March 2022, when the angle between SDO and Solar Orbiter was 3.4° and the solar disk showed four developed active regions. Before comparing the magnetic field products of both instruments we perform a precise alignment of the data, including a matching of the plate scale. Further, in order to improve the homogeneity of the compared data products, the SDO/HMI data were convolved with the SO/PHI-FDT point spread function (PSF). The linear correlation coefficients are obtained through a linear regression of SDO/HMI to SO/PHI-FDT. Results. The two instruments yield comparable magnetic field data products. The slope coefficients for a linear fit are 1.37 for B, 1.11 for γ, 1.35 for BLoS and 1 for the azimuth. The corresponding fit offsets are −94 G, −9.8°, 5.2 G and 0.1°, respectively. The agreement between both instruments is significantly better when we take into account the different spatial resolution of both instruments. The fitting results vary slightly depending on the analyzed active region except for one of the four active regions, which shows larger differences and has been excluded from the comparison. The comparison of the LoS magnetic field products from SDO/HMI at 45 s and 720 s with SO/PHI-FDT shows a slope value of 1.17, with the offset less than 6 G, in both cases.

Sociotechnical feasibility of natural language processing-driven tools in clinical trial eligibility prescreening for Alzheimer's disease and related dementias.

Alzheimer's disease and related dementias (ADRD) affect over 55 million globally. Current clinical trials suffer from low recruitment rates, a challenge potentially addressable via natural language processing (NLP) technologies for researchers to effectively identify eligible clinical trial participants. This study investigates the sociotechnical feasibility of NLP-driven tools for ADRD research prescreening and analyzes the tools' cognitive complexity's effect on usability to identify cognitive support strategies. A randomized experiment was conducted with 60 clinical research staff using three prescreening tools (Criteria2Query, Informatics for Integrating Biology and the Bedside [i2b2], and Leaf). Cognitive task analysis was employed to analyze the usability of each tool using the Health Information Technology Usability Evaluation Scale. Data analysis involved calculating descriptive statistics, interrater agreement via intraclass correlation coefficient, cognitive complexity, and Generalized Estimating Equations models. Leaf scored highest for usability followed by Criteria2Query and i2b2. Cognitive complexity was found to be affected by age, computer literacy, and number of criteria, but was not significantly associated with usability. Adopting NLP for ADRD prescreening demands careful task delegation, comprehensive training, precise translation of eligibility criteria, and increased research accessibility. The study highlights the relevance of these factors in enhancing NLP-driven tools' usability and efficacy in clinical research prescreening. User-modifiable NLP-driven prescreening tools were favorably received, with system type, evaluation sequence, and user's computer literacy influencing usability more than cognitive complexity. The study emphasizes NLP's potential in improving recruitment for clinical trials, endorsing a mixed-methods approach for future system evaluation and enhancements.

Research on probabilistic language multi-attribute group decision-making method based on correlation coefficient and improved entropy

To address the issue of unknown expert and attribute weights in the comprehensive assessment of hospitals, as well as the potential challenges posed by distance measures, this paper presents a probabilistic language multi-attribute group decision-making (MAGDM) approach that utilizes correlation coefficients and improved entropy. First, the correlation function, called the probabilistic linguistic correlation coefficient, is introduced into the probabilistic linguistic term set(PLTS) to measure the consistency among experts, so as to obtain the weights of experts. Next, based on Shannon entropy, an improved probabilistic linguistic entropy is proposed to measure the uncertainty of PLTS considering the number of alternatives and information quantity. Then, based on the correlation coefficient and improved entropy, the attribute weights are obtained. In addition, in order to overcome the counter-intuitive problem of existing distance measurement, this paper proposes a probabilistic language distance measurement method based on the Bray-Curtis distance to measure the differences between PLTSs. On this basis, by applying the technique for order preference by similarity to ideal solution (TOPSIS) method and using PLTSs to construct the MAGDM method, the ranking of alternative schemes is generated. Finally, the improved MAGDM method is applied to an example of the comprehensive evaluation of the smart medical hospitals. The results show that compared with the existing methods, this method can determine the weight information more reasonably, and the decision-making results are not counter-intuitive, so it can evaluate the hospital more objectively.

Optimizing bearing health condition monitoring: exploring correlation feature selection algorithm

Vibration signals are a critical source of information for detecting and diagnosing bearing faults, making this research particularly relevant to the condition monitoring of industrial machinery, particularly bearings using vibration signals. This study delves into how feature selection can be done using Pearson’s Correlation Co-efficient within the context of monitoring bearing health conditions, utilizing two distinct approaches. Approach-1 involves feature selection without considering labels, while Approach-2 incorporates labels for feature selection. Comparative analysis is conducted against outcomes obtained when all features are selected. The research scrutinizes the impact of feature selection on classifier performance, accuracy, and execution times, utilizing various machine learning algorithms such as Decision Tree (DT), K Nearest Neighbor (KNN), Support Vector Machine (SVM), and Naïve Bayes (NB). The findings underscore that feature selection significantly enhances classifier accuracy while reducing execution times. Specifically, only DT and KNN with 50 neighbors achieved 100% accuracy when all features were considered. However, with feature selection using Approach-1 (without labels), DT, KNN, SVM (excluding 100 neighbors), and NB (with Normal/Gaussian kernel) attained 100% accuracy. Employing Approach-2 (with labeled features), DT with 0.7 and 0.9 thresholds, SVM-G with all thresholds (0.6, 0.7, and 0.9), KNN with all thresholds (except 100 neighbors), and NB-n (with all thresholds) achieved 100% accuracy. The study emphasizes the pivotal role of feature selection using Pearson’s Correlation Coefficient in enhancing machine learning classifier performance, offering promising avenues for future research and practical applications across diverse domains.

Multi-attribute three-way decision making based on interval-valued intuitionistic fuzzy sets

In the interval-valued intuitionistic fuzzy environment, a new multi-attribute three-way decision-making model is proposed to address the problems that the relative loss function in the existing multi-attribute three-way decision-making model does not consider the degree of hesitancy, and the alternative conditional probabilities are given subjectively by the authors, which lacks objectivity. First, three types of ideal solutions are introduced, and the correlation coefficients between the evaluated values and ideal solutions are utilized to construct alternative relative loss functions. Second, the ELECTRE-I method is generalized to the interval-valued intuitionistic fuzzy environment to establish the outranking relation and a method for estimating the conditional probability of alternatives is given. Finally, the model is used to experimentally analyze examples to illustrate the effectiveness and rationality of the model.

Analysing lexical semantic changes in chinese educational texts by integrating machine learning methods

Analyzing Lexical Semantic Changes (LSC) in Educational Texts (ET) refers to examining how the meanings of words, terms, or phrases used in ET have evolved. It involves learning shifts in the semantic content, connotations, and language associations within educational resources such as textbooks, research articles, and instructional content. The analysis can reveal how educational models, pedagogical methods, and terminology have transformed in response to technological innovations, societal changes, and pedagogical developments. This analysis provides visions into the dynamic nature of educational discourse, helping researchers, educators, and policymakers understand how language has adapted to reflect changes in educational paradigms and the broader educational context. This research investigates the semantic analysis and classification performance within ET, employing the innovative Decision Tree + Feed Forward Neural Networks (DT + FFNNs) framework. This research shows the dynamic semantic relationships inherent in educational terminology by diverse semantic similarity measures and contextualized embeddings. It looks at how educational language changes to reflect changes in society, technology, and pedagogy. The study uses a DT + FFNN framework for semantic analysis and classification. The study uses several embeddings and semantic similarity metrics, and Spearman’s Correlation Coefficient (SCC) is employed to evaluate their effectiveness. This study highlights the DT + FFNN framework’s capacity to capture complex semantics in an educational setting and offers insights into the adaptive nature of educational discourse. SCC serves as a guiding metric, offering insights into the efficiency of several embeddings and measures. The findings show the pivotal role of fine-tuning in significantly enhancing the accuracy of DT + FFNNs across measures, revealing its remarkable potential in capturing semantics within an educational context.

Method for wind power forecasting based on support vector machines optimized and weighted composite gray relational analysis

This study proposes a weighted composite approach for grey relational analysis (GRA) that utilizes a numerical weather prediction (NWP) and support vector machine (SVM). The approach is optimized using an improved grey wolf optimization (IGWO) algorithm. Initially, the dimension of NWP data is decreased by t-distributed stochastic neighbor embedding (t-SNE), then the weight of sample coefficients is calculated by entropy-weight method (EWM), and the weighted grey relational of data points is calculated for different weather numerical time series data. At the same time, a new weighted composite grey relational degree is formed by combining the weighted cosine similarity of NWP values of the historical day and to be measured day. The SVM’s regression power prediction model is constructed by the time series data. To improve the accuracy of the system’s predictions, the grey relational time series data is chosen as the input variable for the SVM, and the influence parameters of the ideal SVM are discovered using the IGWO technique. According to the simulated prediction and analysis based on NWP, it can be observed that the proposed method in this study significantly improves the prediction accuracy of the data. Specifically, evaluation metrics such as root mean squared error (RMSE), regression correlation coefficient (r2), mean absolute error (MAE) and mean absolute percent error (MAPE) all show corresponding enhancements, while the computational burden remains relatively low.

CORRELATION RATIO OF CHRONIC ENDOMETRITIS WITH ANAMNESTIC FACTORS AND ULTRASOUND CHARACTERISTICS OF THE ENDOMETRIUM IN WOMEN OF REPRODUCTIVE AGE

The diagnosis of chronic endometritis is problematic because of its asymptomatic course and the need for invasive uterineprocedures to confi rm the diagnosis histologically. Therefore, it is important to identify factors that indicate a high risk of this condition. Aim: The aim of this study is to identify factors that have a statistically signifi cant correlation with the presence of CE in women of reproductive age and to validate them by histologic examination of the endometrium.Material and Methods. A retrospective analysis of 400 histories of women of reproductive age who underwent hysteroscopyand endometrial biopsy for histologic examination was performed. Histologically, 154 women (38.5 %) were found to havechronic endometritis (the group of women with chronic endometritis), while 246 women (61.5 %) were found to have otherconditions unrelated to chronic endometritis (the group of women without chronic endometritis). Analysis of anamnestic factors and ultrasound criteria associated with an increased likelihood of chronic endometritis was performed when comparing data from women in these groups. The strength of the relationship between these factors on the one hand and the development of chronic endometritis on the other hand was assessed by calculating the Kendall’s τ (tau) rank correlation coeffi cient. The strength of the relationship between the mentioned factor and CE was considered strong (+++) with a τ coeffi cient of 0.5 or higher, moderate (++) with a τ coeffi cient in the range of 0.2 to 0.499, weak (+) with a τ coeffi cient in the range of 0.1 to 0.199. A τ-coeffi cient value between 0 and 0.099 indicated no correlation between the factor and chronic endometritis.The study was conducted in accordance with the principles of patient- centered care, the requirements of the Tokyo Declaration of the World Medical Association, the international recommendations of the Helsinki Declaration of Human Rights, the Convention of the Council of Europe on Human Rights and Biomedicine, the laws of Ukraine, and the Code of Ethics of the Ukrainian Physician.The paper is an excerpt from the initiative scientifi c research project of the Department of Obstetrics and Gynecology No.2 at Poltava State Medical University, entitled «Optimization of approaches to the management of pregnancy in women at high risk of obstetric and perinatal pathology» (State registration number 0122U201228, duration: 10.2022-09.2027) in collaboration with the Department of Pathological Physiology at Poltava State Medical University.Results and Discussion. The determination of the correlation coeffi cient allowed us to identify the factors with the strongestassociation with chronic endometritis. Among them are 5 infectious factors: cervicitis in the past medical history (τ coeffi cient 0.625245229; p<0.0000000001); chronic infl ammation of the uterine appendages (τ coeffi cient 0.522536031; p<0.0000000001); sexually transmitted diseases in the past medical history (τ coeffi cient 0.547218916; p<0.0000000001); chronic urogenital infl ammatory diseases in the sexual partner (τ coeffi cient 0. 529314979; p<0.0000000001); previous multiple use of intrauterine contraception (τ-coeffi cient 0.502383401; p<0.0000000001); 4 symptomatic consequential factors: infertility in past medical history (τ-coeffi cient 0.683492482; p<0.0000000001); missed miscarriages in past medical history (τ-coeffi cient 0. 644489429; p<0.0000000001); recurrent early pregnancy loss (τ-coeffi cient 0.625942138; p<0.0000000001); abnormal uterine bleeding (τ-coeffi cient 0.650850348; p<0.0000000001); and 4 ultrasound criteria, such as the presence of local endometrial thickening in the form of a polyp (τ-coeffi cient 0.641820318; p<0. 0000000001); increased echogenicity of the endometrium (τ-coeffi cient 0.665249637; p<0.0000000001); heterogeneity of the endometrial echo structure with areas of increased and decreased echogenicity (τ-coeffi cient 0.693152163; p<0.0000000001); hyperechogenic structures in the basal layer of the endometrium (τ-coeffi cient 0.521658745; p<0.0000000001). In our opinion, the combination of an infectious or symptomatic factor with an ultrasound criterion indicates a high probability of chronic endometritis. In the absence of ultrasound signs, the combination of 1 infectious and 1 symptomatic- consequential factor indicates a high risk of chronic endometritis. To confirm the representativeness of this method, we analyzed endometrial biopsies obtained by curettage in 100 women with this combination of factors. The diagnosis was confi rmed histologically in 87 women. Conclusion. Our proposed assessment of high-risk criteria for chronic endometritis, based on the combination of infectious,symptomatic- consequential, and ultrasound criteria, allows predicting the presence of this pathology in 87 % of patients withoutтthe need for invasive interventions. This helps to improve patient selection for hysteroscopy and avoid unnecessary uterine interventions in the diagnosis of chronic endometritis in women planning pregnancy.

Cross-Cultural Adaptation and Psychometric Properties of the Persian Version of the Life Skills Scale for Physical Education

Purpose: The purpose of this research was to develop and assess the psychometric properties of the Persian version of the Life Skills Scale for Physical Education (P-LSSPE). Method: During Study 1, which included four translators, eight physical education experts, and 45 physical education students, the LSSPE was translated and adapted into Persian, and its content validity was assessed. Study 2 assessed evidence for the factorial validity and reliability of the scale with a sample of 1,004 students. Study 3, which included 462 students, assessed nomological validity evidence. Results: In Study 1, the content validity analyses indicated that the P-LSSPE items and their dimensions were clear in language, practical in application, and represented the life skills in question. In Study 2, a bifactor confirmatory factor analysis model was the best representation of the data and provided evidence for the construct validity of the scale. In Study 3, evidence for the nomological validity of the P-LSSPE was provided, with the correlation coefficients indicating that teacher autonomy support was associated with students’ development of all eight life skills and total life skills. Discussion/Conclusion: Overall, the findings of this research suggest that the P-LSSPE can be used to accurately measure the life skills development of Iranian physical education students.

A New Visible Spectrophotometric Approach for Determination of Methyldopa in Pharmaceuticals

الهدف من البحث هو تطوير طريقة طيفية جديدة لتقدير عقار الميثيل دوبا بشكليه النقي والصيدلاني تعتمد الطريقة المقترحة على التفاعل بين المثيل دوبا و كاشف الانسدين بوجود نيتروبروسيد البوتاسيوم وهيدروكسيد الصوديوم لتكوين ناتج ملون تم دراسة تأثير العديد من العوامل المؤثرة على ناتج التفاعل تتضمن تركيز الكاشف و زمن التفاعل و استقرارية الناتج الملون مع ضبط هذه العوامل. متابعة التفاعل تمت بقياس الامتصاص عند 597 نانومتر للناتج الملون. في مدى من التركيز من (0.50 إلى 80.0) ميكروغرام.مل -1 كانت حساسية ساندل 0.0218 مايكروغرام.سم-1 و حد الكشف 0.0353 مايكروغرام.مل-1 و الحد الكمي 0.2691 مايكروغرام.مل-1. تم تطبيق الطريقة المقترحة بنجاح لتقدير المثيل دوبا في المستحضرات الصيدلانية.

The Effect of the Number of Rating Categories of the Likert Scale on the Correlation Coefficients

The Effect of the Number of Rating Categories of the Likert Scale on the Correlation Coefficients