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.

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.

New empirical equations to assess energy efficiency of flow-dissipating vortex dropshaft

Vortex structures are generally considered the common energy-dissipating infrastructures in order to convey urban surface waters. During transferring wastewater in such urban networks, the major proportion of water energy is dissipated and additionally, the hydraulic performance of these infrastructures is evaluated on the basis of the dissipation of energy efficiency in the vortex dropshaft. Over the last decade, the literature review of vortex structures demonstrated that there is no reliable mathematical expression to predict energy efficiency although various experimental investigations were done to evaluate the hydraulics of vortex structures. In this research, robust numerical models based on conceptions of Artificial Intelligence (AI) methodologies (i.e., classification, non-parametric analysis, and evolutionary computing) were developed by using 144 experimental data extracted from a physical model of vortex dropshaft. Through analysis of experiments, three main factors, called flow Froude number (Fr), the ratio of sump height (Hs) to shaft diameter (D), and the ratio of drop total height (L) to shaft diameter (D) were determined to estimate the efficiency of flow energy dissipation. The numerical models were calibrated/trained by different algorithms such as multi/single-objective optimization strategies (e.g., Particle Swarm Optimization [PSO] and Multi-Objective Genetic Algorithm [MOGA]). Various statistical tests, known as quantitative assessments for training and testing stages of AI models, demonstrated that Evolutionary Polynomial Regression (EPR) had the best performance (Coefficient of Correlation [R] = 0.9736, Root Mean Square Error [RMSE] = 0.5923, Mean Absolute Percentage Error [MAPE] = 0.5035) and followed by MT (R = 0.9665, RMSE = 0.7039, MAPE = 0.6225), GEP (R = 0.9113, RMSE = 1.0803, MAPE = 0.9535), and MARS (R = 0.8337, RMSE = 1.6473, MAPE = 1.1010). Additionally, expressions given by AI models provided more accurate predictions of energy dissipation efficiency when compared with a regression-based equation (R = 0.9113, RMSE = 0.7039, MAPE = 0.6225) from the literature. Additionally, variations of three effective factors versus the predicted responses (values of energy dissipation efficiency given by AI models) were in coincidence with experimental observations. Overall, this study proved that the usability of mathematical expressions could provide new insights for experts to assess the hydraulic evaluation of vortex structures before their physical and prototype models are constructed.

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

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

Psychometric Properties of the Persian Dizziness Handicap Inventory – Screening Version

ObjectivesThe Dizziness Handicap Inventory (DHI) is the most popular and widely used questionnaire in evaluating patients with vertigo. This questionnaire has a screening version with a high correlation with DHI. This study aimed to assess the psychometric properties of the Persian DHI – Screening version (DHI-S). MethodsThis cross-sectional and methodological study was conducted on 300 patients at the central vestibular clinic in Mashhad, Iran, from 2020 to 2021. First, the DHI-S was translated into Persian using the backward-forward translation method. Subsequently, the following types of validity and reliability were examined: content validity based on content validity index and content validity ratio, face validity based on impact score, construct validity based on confirmatory factor analysis, internal consistency based on Cronbach’s α and Ω, and test-retest stability based on intraclass correlation coefficient and smallest detectable change. ResultsThe DHI-S had high content and face validity. Score 8 was selected for the cutoff point between patients and the normal group with a sensitivity of 63.67% and specificity of 96.08%. The construct validity indicated that the questionnaire is one-dimensional. The Cronbach’s α and Ω for internal consistency were 0.855 and 0.851, respectively. The intraclass correlation coefficient was 0.981 and the smallest detectable change was 5.521. ConclusionsThe DHI-S in the Persian language has high and acceptable psychometric properties. This questionnaire can be used in research and clinical settings.

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.

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.

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

An optimistic-pessimistic game cross-efficiency method based on a Gibbs entropy model for ranking decision making units

The game cross-efficiency method, a commonly utilized approach for ranking decision-making units in tie-breaking scenarios, is based on secondary goals. However, in certain data envelopment analysis ranking problems, the classical game cross-efficiency method may fail to differentiate all decision-making units effectively. To address this limitation, it is prudent to explore the development of a new method that can enhance the ranking performance of the classical game cross-efficiency approach. In this study, we propose a novel Gibbs entropy linear programming model that integrates both optimistic and pessimistic perspectives of the classical game cross-efficiency method for data envelopment analysis ranking problems. To validate the reliability and utility of our proposed method, we present three examples: the six nursing homes problem, numerical example 2, and an application involving twenty Thai provinces with cash crop data. The reliability of the proposed method is assessed using Spearman’s correlation coefficient (rs) on the numerical examples. The results demonstrate that the rs values for both the proposed method and the classical game crossefficiency method, specifically for the six nursing homes problem, numerical example 2, and the application involving twenty Thai provinces, are determined to be rs=0.998, 0.998, and 0.986 respectively.

Developing a smart system for infant incubators using the internet of things and artificial intelligence

This research is developing an incubator system that integrates the internet of things and artificial intelligence to improve care for premature babies. The system workflow starts with sensors that collect data from the incubator. Then, the data is sent in real-time to the internet of things (IoT) broker eclipse mosquito using the message queue telemetry transport (MQTT) protocol version 5.0. After that, the data is stored in a database for analysis using the long short-term memory network (LSTM) method and displayed in a web application using an application programming interface (API) service. Furthermore, the experimental results produce as many as 2,880 rows of data stored in the database. The correlation coefficient between the target attribute and other attributes ranges from 0.23 to 0.48. Next, several experiments were conducted to evaluate the model-predicted value on the test data. The best results are obtained using a two-layer LSTM configuration model, each with 60 neurons and a lookback setting 6. This model produces an R<sup>2</sup> value of 0.934, with a root mean square error (RMSE) value of 0.015 and a mean absolute error (MAE) of 0.008. In addition, the R<sup>2</sup> value was also evaluated for each attribute used as input, with a result of values between 0.590 and 0.845.

Predictive modeling for breast cancer based on machine learning algorithms and features selection methods

Breast cancer is one of the leading causes of death among women worldwide. However, early prediction of breast cancer plays a crucial role. Therefore, strong needs exist for automatic accurate early prediction of breast cancer. In this paper, machine learning (ML) classifiers combined with features selection methods are used to build an intelligent tool for breast cancer prediction. The Wisconsin diagnostic breast cancer (WDBC) dataset is used to train and test the model. Classification algorithms, including support vector machine (SVM), light gradient boosting machine (LightGBM), random forest (RF), logistic regression (LR), k-nearest neighbors (k-NN), and naïve Bayes, were employed. Performance measures for each of them were obtained, namely: accuracy, precision, recall, F-score, Kappa, Matthews correlation coefficient (MCC), and time. The results indicate that without feature selection, LightGBM achieves the highest accuracy at 95%. With minimum redundancy maximum relevance (mRMR) feature selection (15 features), LightGBM outperforms other classifiers, achieving an accuracy of 98%. For Pearson correlation coefficient feature selection (15 features), LightGBM also excels with a 95% accuracy rate. Lasso feature selection (5 features) produces varied results across classifiers, with logistic regression achieving the highest accuracy at 96%. These findings underscore the importance of feature selection in refining model performance and in improving detection for breast cancer.

Electrocardiogram signal processing algorithm on microcontroller using wavelet transform method

The electrocardiogram (ECG) is an important parameter for analyzing the cardiac system. It serves as the primary diagnostic tool for patients with suspected heart disease, guiding appropriate cardiac investigations according to the disease or condition suspected. However, ECG measurements may generate noise, leading to false diagnoses. The wavelet transform is an effective and widely-used technique for eliminating noise. Typically, analysis and generation algorithms are developed on computer and using software built in. This paper presents a noise elimination algorithm based on the wavelet transform method, designed to operate on resource-limited Node microcontroller unit (MCU). An efficiency study was conducted to determine the optimum mother wavelet implementation of the algorithm, and the results showed that, when considering synthetic ECG signals, db4 was the most suitable for eliminating interference by achieving the highest signal to noise ratio (SNR) and correlation coefficient. In addition, this algorithm prototype can analyze ECG signals using the wavelet transform method processed in a microcontroller and is accurate compared to reliable programs. It has the potential to be further developed into a low-cost portable ECG signal measurement tool for use in remote medicine, healthcare facilities in resource-limited areas, education and training, as well as home monitoring for chronic patients.

Accuracy of prenatal and postnatal biomarkers for estimating gestational age: a systematic review and meta-analysis

Knowledge of gestational age (GA) is key in clinical management of individual obstetric patients, and critical to be able to calculate rates of preterm birth and small for GA at a population level. Currently, the gold standard for pregnancy dating is measurement of the fetal crown rump length at 11-14 weeks of gestation. However, this is not possible for women first presenting in later pregnancy, or in settings where routine ultrasound is not available. A reliable, cheap and easy to measure GA-dependent biomarker would provide an important breakthrough in estimating the age of pregnancy. Therefore, the aim of this study was to determine the accuracy of prenatal and postnatal biomarkers for estimating gestational age (GA). Systematic review prospectively registered with PROSPERO (CRD42020167727) and reported in accordance with the PRISMA-DTA. Medline, Embase, CINAHL, LILACS, and other databases were searched from inception until September 2023 for cohort or cross-sectional studies that reported on the accuracy of prenatal and postnatal biomarkers for estimating GA. In addition, we searched Google Scholar and screened proceedings of relevant conferences and reference lists of identified studies and relevant reviews. There were no language or date restrictions. Pooled coefficients of correlation and root mean square error (RMSE, average deviation in weeks between the GA estimated by the biomarker and that estimated by the gold standard method) were calculated. The risk of bias in each included study was also assessed. Thirty-nine studies fulfilled the inclusion criteria: 20 studies (2,050 women) assessed prenatal biomarkers (placental hormones, metabolomic profiles, proteomics, cell-free RNA transcripts, and exon-level gene expression), and 19 (1,738,652 newborns) assessed postnatal biomarkers (metabolomic profiles, DNA methylation profiles, and fetal haematological components). Among the prenatal biomarkers assessed, human chorionic gonadotrophin measured in maternal serum between 4 and 9 weeks of gestation showed the highest correlation with the reference standard GA, with a pooled coefficient of correlation of 0.88. Among the postnatal biomarkers assessed, metabolomic profiling from newborn blood spots provided the most accurate estimate of GA, with a pooled RMSE of 1.03 weeks across all GAs. It performed best for term infants with a slightly reduced accuracy for preterm or small for GA infants. The pooled RMSEs for metabolomic profiling and DNA methylation profile from cord blood samples were 1.57 and 1.60 weeks, respectively. We identified no antenatal biomarkers that accurately predict GA over a wide window of pregnancy. Postnatally, metabolomic profiling from newborn blood spot provides an accurate estimate of GA, however, as this is known only after birth it is not useful to guide antenatal care. Further prenatal studies are needed to identify biomarkers that can be used in isolation, as part of a biomarker panel, or in combination with other clinical methods to narrow prediction intervals of GA estimation. The research was funded by the Bill and Melinda Gates Foundation (INV-000368). ATP is supported by the Oxford Partnership Comprehensive Biomedical Research Centre with funding from the NIHR Biomedical Research Centre funding scheme. The views expressed are those of the authors and not necessarily those of the UK National Health Service, the NIHR, the Department of Health, or the Department of Biotechnology. The funders of this study had no role in study design, data collection, analysis or interpretation of the data, in writing the paper or the decision to submit for publication.

Structural and functional abnormalities in the medial prefrontal cortex were associated with pain and depressive symptoms in patients with adhesive capsulitis.

Chronic pain and depression have been shown to coexist in patients with adhesive capsulitis (AC). Recent studies identified the shared brain plasticity between pain and depression; however, how such neuroplasticity contributes to AC remains unclear. Here, we employed a combination of psychophysics, structural MRI, and functional MRI techniques to examine the brain's structural and functional changes in AC. Fifty-two patients with AC and 52 healthy controls (HCs) were included in our study. Voxelwise comparisons were performed to reveal the differences in grey matter volume (GMV) and regional homogeneity (ReHo) between AC and HCs. Furthermore, region of interest to whole brain functional connectivity (FC) was calculated and compared between the groups. Finally, Pearson correlation coefficients were computed to reveal the association between clinical data and brain alterations. Mediation analyses were performed to investigate the path association among brain alterations and clinical measures. Three main findings were observed: (1) patients with AC exhibited a higher depression subscale of hospital anxiety and depression scale (HADS-D) score correlating with the GMV within the right medial prefrontal cortices (mPFC) compared with HCs; (2) relative to HCs, patients with AC exhibited lower ReHo within the right mPFC, which largely overlapped with the structural abnormalities; (3) the impact of pain duration on HADS-D score was mediated by ventral part of medial prefrontal cortices (vmPFC) GMV in patients with AC. In summary, our current findings suggest that vmPFC alterations correlate with both the pain duration and the emotional comorbidities experienced by patients with AC. Our research provides an enhanced comprehension of the underlying mechanisms of AC, thereby facilitating the development of more effective treatment approaches for AC.

363 Anatomic Study of Cervical Neural Foraminal Dimensions, Interpedicular Distance, and Disc Space Height From C2-T1 Using Computed Tomography of 1,000 Patients

INTRODUCTION: The normative relationships among cervical neuroforaminal dimensions (NFD), interpedicular distance (IPD), and disc space height (DSH) remain unknown. METHODS: We reviewed CT imaging of 1,000 patients between 18 and 35 years of age without spinal pathology to measure cervical NFD, defined as follows: foraminal height, sagittal anterior-to-posterior width, and area. Anterior, middle, and posterior DSH, and IPD were also measured. Interobserver reliability was assessed via the intraclass correlation coefficient (ICC) two-way mixed model on absolute agreement. RESULTS: Bilateral NFD followed bimodal distribution patterns moving caudally from C2-T1, whereas DSH measurements exhibited multimodal distribution patterns and IPD demonstrated a unimodal distribution clustered at the lower cervical spine. Significant differences were observed in NFD, DSH, and IPD measurements with respect to patient sex and race taking into consideration patient demographic and anthropometric characteristics. The African American cohort demonstrated the smallest NFD, DSH, and IPD measurements, followed by the Hispanic cohort, followed by the Caucasian cohort. Weak-to-moderate correlations were observed among NFD, DSH, and IPD measurements across all levels. CONCLUSIONS: This study describes 70,800 DSH, NFD, and IPD measurements from C2-T1 using CT imaging of young patients without spinal pathology. Findings from this study may provide normative guidelines necessary to establish the foundation for diagnosis of cervical neuroforaminal stenosis. Surprisingly, this study found no strong, or even moderate, correlations between normative NFD, DSH, and IPD measurements across all levels from C2-T1. Patient sex, ethnicity, and height are associated with NFD, DSH, and IPD, though patient weight is not.

DNA based phenotype optimization of oryza sativa using machine learning and MolCNN

The prediction of phenotype from the genotype of oryza sativa (rice) is very crucial for optimizing the crop management. utilizing molecular convolutional neural networks (MolCNNs) and machine learning for crop management in oryza sativa provides a data-driven method for phenotype prediction based on DNA data, improving farming techniques. Data gathering, preparation, and integration of phenotypic and DNA data are all part of this process. Meaningful DNA features are extracted by MolCNN, and phenotypic qualities are predicted by a machine learning algorithm. Making educated decisions is ensured by assessing the model’s effectiveness, applying it to crop management, and updating it frequently. Choosing crop varieties, planting schedules, and management techniques are guided by molecular insights, which support sustainable agriculture and increase yields and quality. In the proposed research we are calculating pearson correlation coefficients between anticipated and actual trait values and the model’s performance on a test set. Additionally, it determines the (PCC) for every characteristic in the model and we have received a binary accuracy of 0.9998 in 139 seconds.

Inflammatory Type Focal Cerebral Arteriopathy of the Posterior Circulation in Children: A Comparative Cohort Study.

Inflammatory type focal cerebral arteriopathy (FCA-i) in the anterior circulation (AC) is well characterized, and the focal cerebral arteriopathy severity score (FCASS) reflects the severity of the disease. We identified cases of FCA-i in the posterior circulation (PC) and adapted the FCASS to describe these cases. In this comparative cohort study, patients from the Swiss NeuroPaediatric Stroke Registry with ischemic stroke due to FCA-i between January 2000 and December 2018 were analyzed. A comparison between PC and AC cases regarding pediatric National Institutes of Health Stroke Scale score and pediatric stroke outcome measure and FCASS was performed. We estimated infarct size by the modified pediatric Alberta Stroke Program Early Computed Tomography Score in children with AC stroke and the adapted Bernese posterior diffusion-weighted imaging score in the PC. Thirty-five children with a median age of 6.3 (interquartile range, 2.7-8.2 [95% CI, 0.9-15.6]; 20 male; 57.1%) years with FCA-i were identified. The total incidence rate was 0.15/100 000/year (95% CI, 0.11-0.21). Six had PC-FCA-i. Time to final FCASS was longer in the PC compared with AC; the evolution of FCASS did not differ. Initial pediatric National Institutes of Health Stroke Scale score was higher in children with FCA-i in the PC with a median of 10.0 (interquartile range, 5.75-21.0) compared with 4.5 (interquartile range, 2.0-8.0) in those with AC-FCA-i. Different from the anterior cases, PC infarct volume did not correlate with higher discharge, maximum, or final FCASS scores (Pearson correlation coefficient [r], 0.25, 0.35, and 0.54). FCA-i also affects the PC. These cases should be included in future investigations into FCA-i. Although it did not correlate with clinical outcomes in our cohort, the modified FCASS may well serve as a marker for the evolution of the arteriopathy in posterior FCA-i.