Percentage Of Classification Research Articles

Relación entre consumo de riesgo de alcohol y síntomas depresivos en estudiantes universitarios de México

Introduction: alcohol is a legal drug in most parts of the world, it is mostly accepted among different cultures. Therefore, alcohol consumption typically does not carry the same stigma associated with the use of other drugs, such as marijuana or methamphetamines. Previous research in Mexico, the United States, Ecuador, and England has shown a positive and significant association between alcohol consumption and depressive symptoms among university students. Objective: to determine the magnitude of the relationship between alcohol consumption and depressive symptoms, and whether this relationship is independent of sex, age, and socioeconomic status in first-year university students. Method: the sample consisted of 2,377 students aged 18 to 25 years (M = 18.82, SD = 1.30), 66.5% of whom were women. Results: multiple logistic regression analyses demonstrated a relationship between risk alcohol consumption and depressive symptoms, independent of sex, age, and socioeconomic status (adjusted OR = 1.88 [1.14, 3.10] with a percentage of correct classification of the 67.2% and a R2 Nagelkerke = .07). Discussion and Conclusions: the evidence of the relationship between alcohol consumption and depressive symptoms emphasizes that limiting or reducing alcohol consumption among young people could prevent the onset of depressive symptoms and their consequences.

Validation of an App-based Food Frequency Questionnaire: Comparison With 24-hour Diet Recalls Among Women in two Rural Areas of Kenya

A new mobile application with a new semi-quantitative Food Frequency Questionnaire (FFQ) was developed as a quick dietary assessment tool. This study aimed to test the validation of food and nutrient intake measurements from an app-based FFQ, which included 49 dishes and food items, using non-consecutive two-day 24-hour diet recalls as the standard. A total of 179 women aged 19–70 years (mean (SD) = 33.7 (9.9)) from two rural areas of Kenya, Vihiga and Kitui counties, were included. Dietary assessment by the two dietary survey methods was conducted from November to December 2021. The intake of macro- and micronutrients, and food groups between the two methods were compared by Spearman's rank correlation coefficients, cross-classification agreement, and Bland-Altman plots for assessing the validation of the app-based FFQ. The median correlation coefficients between energy-adjusted intakes estimated by the app-based FFQ and the 24-h diet recall were 0.19 (0.10 (protein)–0.26 (fat)) for macronutrients, 0.24 (0.11 (sodium)–0.46 (vitamin B12)) for micronutrients, and 0.26 (0.07 (seasonings)–0.45 (pulses)) for 17-food groups. The median percentage of classification into the same quartiles between the two methods was 31% for macronutrient, 32% for micronutrient and 39% for food group. While 9% for macronutrient, 8% for micronutrient and 10% for the food group were misclassified into the opposite categories. Bland-Altman Plots showed acceptable agreement for macronutrients intake between the two methods. The app-based FFQ was shown to be able to rank or categorize individuals according to their intake of most of the nutrients and food groups we examined. This dietary assessment tool can be used for quantitative dietary data collection. Further research is needed to improve the validation of the app-based FFQ, as it has been shown that intakes of staple foods and sugar are underestimated and others are overestimated, and that estimating intakes of some specific nutrients, such as protein, is challenging. Key words: Diet assessment, food frequency questionnaire, 24-hour diet recall, validation, Kenya

Detection of Diffusion Interlayers in Dissimilar Welded Joints in Processing Pipelines by Acoustic Emission Method

The paper considers the neural network application to detect microstructure defects in dissimilar welded joints using the acoustic emission (AE) method. The peculiarity of the proposed approach is that defect detection is carried out taking into account a priori information about the properties of the AE source and the acoustic waveguide parameters of the testing structure. Industrial process pipelines with dissimilar welded joints were studied as the testing object, and diffusion interlayers formed in fusion zones of welded joints were considered microstructure defects. The simulation of AE signals was carried out using a hybrid method: the signal waveform was determined based on a finite element model, while the amplitudes of AE hits were determined based on a physical experiment on mechanical testing of dissimilar welded joints. Measurement data from industrial process pipelines were used as noise realizations. As a result, a data sample was formed that considered the parameters of the AE source and the parameters of the acoustic waveguide with realistic noise parameters and a signal-to-noise ratio. The proposed method allows for a more accurate determination of the waveform, spectrum, and amplitude parameters of the AE signal. Greater certainty in the useful signal parameters allows for achieving a more accurate and reliable classification result. When using a backpropagation neural network, a percentage of correct classification of more than 90% was obtained for a data set in which the signal-to-noise ratio was less than (−5 dB) in 90% of cases.

The Gastroscopy RAte of Cleanliness Evaluation (GRACE) Scale: an international reliability and validation study.

Mucosal visualization during upper gastrointestinal (UGI) endoscopy can be impaired by the presence of foam, bubbles, and mucus. Some UGI endoscopy visibility scales have been proposed but have not undergone multicenter validation. This study aimed to develop and validate the Gastroscopy RAte of Cleanliness Evaluation (GRACE) scale. A multicenter, international, cross-sectional study was conducted. The GRACE scale is based on a score from 0 (worst) to 3 (excellent) for esophagus, stomach, and duodenum, for a total ranging from 0 to 9. In phase 1, four expert endoscopists evaluated 60 images twice, with a 2-week interval between rounds; in phase 2, the same 60 images were scored twice by one expert and one nonexpert endoscopist from 27 endoscopy departments worldwide. For reproducibility assessment and real-time validation, the scale was applied to consecutive patients undergoing gastroscopy at each center. On internal validation, interobserver agreement was 0.81 (95 %CI 0.73-0.87) and 0.80 (95 %CI 0.72-0.86), with reliability of 0.73 (95 %CI 0.63-0.82) and 0.72 (95 %CI 0.63-0.81), in the two rounds, respectively. On external validation, overall interobserver agreement was 0.85 (95 %CI 0.82-0.88) and reliability was 0.79 (95 %CI 0.73-0.84). In real-time evaluation, the overall proportion of correct classifications was 0.80 (95 %CI 0.77-0.82). The GRACE scale showed good interobserver agreement, reliability, and validity. The widespread use of this scale could enhance quality and standardize the assessment of mucosal cleanliness during UGI endoscopy, pushing endoscopists to strive for excellent visibility and reducing the risk of missed lesions.

Evaluation of Hand-Crafted Feature Extraction for Fault Diagnosis in Rotating Machinery: A Survey.

This article presents a comprehensive collection of formulas and calculations for hand-crafted feature extraction of condition monitoring signals. The documented features include 123 for the time domain and 46 for the frequency domain. Furthermore, a machine learning-based methodology is presented to evaluate the performance of features in fault classification tasks using seven data sets of different rotating machines. The evaluation methodology involves using seven ranking methods to select the best ten hand-crafted features per method for each database, to be subsequently evaluated by three types of classifiers. This process is applied exhaustively by evaluation groups, combining our databases with an external benchmark. A summary table of the performance results of the classifiers is also presented, including the percentage of classification and the number of features required to achieve that value. Through graphic resources, it has been possible to show the prevalence of certain features over others, how they are associated with the database, and the order of importance assigned by the ranking methods. In the same way, finding which features have the highest appearance percentages for each database in all experiments has been possible. The results suggest that hand-crafted feature extraction is an effective technique with low computational cost and high interpretability for fault identification and diagnosis.

Characteristics of pelvic obliquity in dysplastic hip osteoarthritis.

Factors affecting direction of pelvic obliquity (PO) in dysplastic hip osteoarthritis (DHOA) remains unclear. This retrospective cohort study evaluates morphological characteristics, spinal alignment, and hip function in patients with unilateral DHOA. Between 2018 and 2022, 104 patients with unilateral DHA were enrolled. Patients were categorized into flat PO (F-PO group; PO < 2°), affected side PO (A-PO group; PO downward by ≥ 2°), and unaffected side PO (U-PO group; PO upward by ≥ 2°). Demographics, radiographic hip and lower limb parameters, spinal parameters, and functional scores were compared between the groups. There were 39, 44, and 21 patients in the F-PO, A-PO, and U-PO group, respectively. The subluxation percentage of Crowe classification showed a significant difference among the three groups. The femoral head lateralization distance was significantly greater in the U-PO group than in the F-PO and A-PO groups. Furthermore, the hip adduction angle was significantly lower in the A-PO group than in the F-PO and U-PO groups. The lumbar scoliosis angle was significantly different between the groups. In multivariate analysis, hip adduction angle was extracted as an independent factor associated with the A-PO. Age, subluxation percentage, and hip adduction angle were identified as independent factors associated with the U-PO. Harris hip score was significantly poorer in U-PO group than in F-PO group. Hip adduction angle influenced A-PO, while age, subluxation percentage, and hip adduction angle influenced U-PO; lumbar scoliosis angle was associated with PO direction. U-PO patients had poorer functional scores, indicating the impact of hip contracture and subluxation on PO direction in DHOA.

Use of whistles for acoustic classification of delphinids (odontoceti: Delphinidae) in the Western South Atlantic Ocean.

This study focuses on the acoustic classification of delphinid species at the southern continental slope of Brazil. Recordings were collected between 2013 and 2015 using towed arrays and were processed using a classifier to identify the species in the recordings. Using Raven Pro 1.6 software (Cornell Laboratory of Ornithology, Ithaca, NY), we analyzed whistles for species identification. The random forest algorithm in R facilitates classification analysis based on acoustic parameters, including low, high, delta, center, beginning, and ending frequencies, and duration. Evaluation metrics, such as correct and incorrect classification percentages, global accuracy, balanced accuracy, and p-values, were employed. Receiver operating characteristic curves and area-under-the-curve (AUC) values demonstrated well-fitting models (AUC ≥ 0.7) for species definition. Duration and delta frequency emerged as crucial parameters for classification, as indicated by the decrease in mean accuracy. Multivariate dispersion plots visualized the proximity between acoustic and visual match data and exclusively acoustic encounter (EAE) data. The EAE results classified as Delphinus delphis (n = 6), Stenella frontalis (n = 3), and Stenella longirostris (n = 2) provide valuable insights into the presence of these species between approximately 23° and 34° S in Brazil. This study demonstrates the effectiveness of acousting classification in discriminating delphinids through whistle parameters.

Intuitionistic fuzzy divergence for evaluating the mechanical stress state of steel plates subject to bi-axial loads

The uncertainty that characterizes the external mechanical loads to which any connection plate in steel structures is subjected determines the non-uniqueness of the isochoric deformation distributions. Since the eddy currents induced on the plates produce magnetic field maps with a high fuzziness content, similar to those of the isochoric deformations, their use can be exploited to evaluate the extent of the external load that determines a specific induced current map. Starting from an approach known in the literature, according to which the map-external load association is operated through fuzzy similarity computations, in this paper, we generalize this method by reformulating it in terms of intuitionistic fuzzy logic by proposing a classification based on divergence computations. Our approach, acting adaptively on the fuzzification of the maps, results in a better classification percentage, besides significantly reducing the presence of doubtful cases due to the uncertainty of each applied load. Furthermore, a FEM software tool was developed, which turned out to be, to a certain extent, a substitute for the experimental procedure, notoriously more expensive. Even if the procedure was applied on plates subjected to bi-axial loads, it could be used for other types of loads since the classification operator processes the eddy current maps exclusively, regardless of their cause.

Discriminant analysis for valuing voluntary work among university youth in the United Arab Emirates

This study aimed at identifying the extent to which university youth value voluntary work by using discriminant analysis to compare two groups of people who value and who don't value voluntary work according to a number of variables, referred to as discrimination variables. The study sample consisted of 250 male and female students from social departments in some universities in the United Arab Emirates, where an electronic questionnaire was distributed to the sample individuals. The results revealed that most male and female university students generally value voluntary work, and that there was a significant difference between the study groups in most independent variables, except for the variable of monthly income of the youth or their families. Discrimination variables accounted for 44.6% of variance between the two groups, with Wilks' Lambda (λ) =0.554. The percentage of the right classification of those who value and who don't value voluntary work was 88%. The results of (Tau) value revealed that the respondents with positive attitudes towards voluntary work who have a good social and health status, good educational level, a younger age, higher affiliation and citizenship, good economic status, and belong to larger families agree with the opinion stating that the university youth in the United Arab Emirates value voluntary work with a percentage of 76%. The study concluded with a number of recommendations.

Is Type and Grade of Emphysema Important for Bone Mineral Density and Aortic Calcifications?

Background: Chronic obstructive pulmonary disease has extrapulmonary manifestations, such as cardiovascular diseases and osteoporosis. The purpose of this research was to determine the relationship between the type and extent of emphysema with thoracic aorta calcification (TAC) and bone mineral density (BMD) at Th4, Th8, and L1 vertebrae. Methods: Emphysema was described by computed tomography parameters (both Fleischner classification and low attenuation value percentage, LAV%) and the clinical FEV1/FVC ratio (Tiffeneau-Pinelli index, TI, TI < 0.7; TI > 0.7). Results: Of 200 included patients (median age 64, 33% women), signs of clinical obstruction (TI) were observed in 104 patients, which had significantly lower BMD and more heavy TAC. BMD correlated negatively with LAV%, Rho = -0.16 to -0.23, while a positive correlation of aortic calcification with LAV% was observed, Rho = 0.30 to 0.33. Multiple linear regression showed that age and TI < 0.7 were independent predictors of BMD, β = -0.20 to -0.40, and β = -0.21 to -0.25; age and hypercholesterolemia were independent predictors of TCA, β = 0.61 and β = 0.19. Conclusions: Clinical TI and morphological LAV% parameters correlated with BMD and TAC, in contrast to Fleischer-graded emphysema, which showed no correlation. However, only TI was an independent predictor of BMD, while the morphologically described type and extent of emphysema could not independently predict any extrapulmonary manifestation.

Comparative Study of Statistical Techniques for Preliminary Diagnosis of Cancer Risk

The purpose of the is to elaborate models for preliminary diagnosis using statistical techniques. We compare two models for the estimation of cervical cancer risks.This article aims to compare predictive models for cervical cancer using machine learning techniques. We set up classification tables to compare the overall correct prediction rates.The data used comes from 30 cases of cervical cancer. We fitted a Logistic Regression (LR) model and trained Artificial Neural Networks (ANNs). The multicollinearity problem, usually present in modeling with numerous predictive variables, was addressed with factor analysis and Pearson Correlations.The LR model and ANN model were evaluated based on their percentage of correct classifications. The LR model achieved an accuracy of 33.33%, while the ANN model achieved an accuracy of 16.67%.Based on the percentage of correct classification, the Logistic Regression model was superior to the Neural Networks for the cervical cancer dataset. This highlights the need for further exploration of different machine learning approaches and data preprocessing techniques to improve predictive performance for cervical cancer risks.

A Framework to Identify Non-Achievers in eLearning Business Informatics Lab Courses

Learning Management Systems store valuable data in their repositories. Analyzing such data could contribute to identifying non-achievers in eLearning courses. This study presents an integrated framework (encompassing stages from a risk management process) to predict non-achievers in eLearning Business Informatics Lab Courses through a discriminant function analysis of student engagement data. In detail, data regarding with student interaction with the learning activities were elicited from the Moodle LMS log files. The paper also presents a specific eLearning Business Informatics Lab Course, designed upon Business Informatics competencies, tailored to a Business Informatics Curriculum for undergraduate Accounting Students. A discriminant function analysis was used to develop a competent prediction model. Linear discriminant functions were generated for achievers and non-achievers respectively. Students were classified into non-achievers or achievers according to the maximum score of the discriminant functions. The high classification percentage of our model indicates that our framework could be used to identify non-achievers in any eLearning Business Informatics Lab course sharing the same structure. A linear discriminant analysis (LDA) was also employed to indicate the training potential of our model. The evaluation metrics of our trained model indicate that our model could potentially be used to develop an alert system.

Feature Selection Information Gain pada Klasifikasi Pasien Penyakit Jantung (Heart Disease)

Heart disease, also known as cardiovascular disease, is a condition where there is a blockage or narrowing of blood vessels that can lead to heart attack, chest pain, or stroke. It needs appropriate medical treatment because this disease can be the cause of death. Data mining methods are helpful in diagnosing and treating heart disease. Data mining methods can play a major role in the process of improving the quality of care for heart disease patients, providing valuable information for informed decision-making regarding prevention and treatment. The data analysis process uses classification algorithms, namely Decision Tree (C4.5), Naive Bayes (NB), Support Vector Machine (SVM), and Random Forest (RF) combined with feature selection information gain method. The results show that data mining methods are very useful in diagnosing and treating heart disease. The highest percentage of correct classifications for both models before and after the implementation of feature selection information gain was obtained by the RF algorithm, which amounted to 95.71%. However, the implementation of the feature selection information gain method in this study did not contribute significantly to improving the classification quality of each algorithm used.

Comparison of the predictive validity of the Braden, Munro and 3S scales in surgical patients

Aim of the studyThis research is a descriptive and cross-sectional study aimed at examining the use of three different scales to assess the risk of pressure injury (PI) in surgical patients. Materials and methodsThis study was conducted between February 1 and July 1, 2022. The study included patients who had planned surgery in general surgery clinic. The sample size was 388 patients. Patients who agreed to participate in the study were asked to fill out the " Introductory Information Form " and perform a risk assessment using the Braden, Munro and 3S Scales. The statistics program IBM SPSS Statistics 25.0 packaged software was used in the analyses of data. ResultsIt is evident that the relationship between the scales and the diagnosis of PI, as well as the strength of this relationship, are statistically significant (p < 0.05). The Munro Scale exhibited the highest value of Phi value and validity coefficient c, indicating a stronger association with the diagnosis of PI, thus suggesting its greater effectiveness in discrimination. According to the validity coefficients found, it can be stated that the correct classification percentages for Munro, Braden, and 3S Scales were 91 %, 71 %, and 66 %, respectively. ConclusionThe findings of this study indicate that both the Braden, Munro, and 3S Scales can be used for PI risk assessment in surgical patients, but the Munro Scale exhibits superior predictive validity compared to the Braden and 3S Scales in terms of overall sensitivity and specificity.

Influence of soil type and ripening degree on the intensity and sensory dominance of Mexican habanero chili (Capsicum chinense Jacq.)

AbstractThe objective of this research was to determine the intensity and sensory dominance of habanero peppers grown in different types of soils and with different degrees of maturation. Chili samples grown in red soil (Mayan name: K'ankab lu'um) and black soil (Mayan name: Box lu'um) were evaluated in an immature state (green coloration) and intermediate ripening (green‐orange coloration) using descriptive analysis (DA) and temporal dominance of sensations (TDS). Sensory representation and classification percentage of chili peppers were evaluated using principal component analysis and discriminant factor analysis. The sensory results demonstrated that intermediate ripening chili peppers presented higher color intensities associated with pungency. Immature chili peppers were characterized by having high odor intensities. The TDS results showed that the dominant attributes were burn feeling, chili flavor, heat, and numbness. Immature chili peppers grown in black soil tend to be burn feeling dominant and chili peppers grown in red soil were characterized as chili flavor. The ripe habanero peppers were chili flavor burn feeling and heat. The correct classification percentage was 89.81%.Practical ApplicationsThis research exposes the sensory differences of habanero peppers with different degrees of maturation and grown in different types of soils. The findings can serve as a reference for future research on the sensory characterization of pungent and for the industry dedicated to the production of habanero chili and for the gastronomy area in charge of the preparation of different dishes that use habanero chili as an ingredient.

Machine learning-aided algorithm design for prediction of severity from clinical, demographic, biochemical and immunological parameters: Our COVID-19 experience from the pandemic.

The severity of laboratory and imaging finding was found to be inconsistent with clinical symptoms in COVID-19 patients, thereby increasing casualties. As compared to conventional biomarkers, machine learning algorithms can learn nonlinear and complex interactions and thus improve prediction accuracy. This study aimed at evaluating role of biochemical and immunological parameters-based machine learning algorithms for severity indexing in COVID-19. Laboratory biochemical results of 5715 COVID-19 patients were mined from electronic records including 509 admitted in COVID-19 ICU. Random Forest Classifier (RFC), Support Vector Machine (SVM), Naive Bayesian Classifier (NBC) and K-Nearest Neighbours (KNN) classifier models were used. Lasso regression helped in identifying the most influential parameter. A decision tree was made for subdivided data set, based on randomization. Accuracy of SVM was highest with 94.18% and RFC with 94.04%. SVM had highest PPV (1.00), and NBC had highest NPV (0.95). QUEST modelling ignored age, urea and total protein, and only C-reactive protein and lactate dehydrogenase were considered to be a part of decision-tree algorithm. The overall percentage of correct classification was 78.31% in the overall algorithm with a sensitivity of 87.95% and an AUC of 0.747. C-reactive protein and lactate dehydrogenase being routinely performed tests in clinical laboratories in peripheral setups, this algorithm could be an effective predictive tool. SVM and RFC models showed significant accuracy in predicting COVID-19 severity and could be useful for future pandemics.

Position of Fovea Palatinae relative to the vibrating line in various soft palate classifications among Jordanian edentulous population

This study aims to examine the relationship between the locations of Fovea Palatinae and the posterior vibrating line in different classes of soft palate angulation (House Classification), accordingly determine its reliability as a landmark and a tool for determining the posterior limit of the maxillary complete denture. 280 completely edentulous patients with normal healthy mucosa from both genders were randomly selected. The House classification of the soft palate angulation was identified and recorded as Class I, II, or III. Phonation was used to determine the position of the vibrating line. The Fovea Palatinae was then marked. Then, the distance between the Fovea Palatinae and the vibrating line was measured and recorded. Finally, the relative position of the Fovea Palatinae to the vibrating line was recorded as being anterior, posterior, or on the vibrating line. The Chi Square test, the effect size measures (Eta and Cramer’s V tests), The Spearman’s Rho rank correlation test, and multinominal logistic regression analysis were utilized to analyse the data. House classification percentages were measured among people whose Fovea Palatinae was detectable; Class II palate was the most prevalent (47.14%), followed by Class I (43.93%), and then Class III (8.93%). Based on vibrating line position, 129 (58%) had a vibrating line anterior to Fovea Palatinae, 57 (26%) on the Fovea Palatinae, 36 (16%) posterior to Fovea Palatinae, and in 58 (21%) Fovea Palatinae were not detected. The mean distance between the vibrating line and Fovea Palatinae was 3.66 ± 1.6 mm anteriorly and 2.97 ± 1.36 mm posteriorly. No significant differences were found between males and females in regard to House classification and vibrating line position. The odds of having the fovea posterior to the vibrating line would increase by 5% for each year increase in the age (P = 0.035, odds ratio = 1.050). Class II House classification of the soft palate was found to be the most prevalent among the study participants. Also, the vibrating line was anterior to the Fovea Palatinae in the majority of cases. The odds of having the fovea posterior to the vibrating line would increase by age. The Fovea Palatinae could be considered a useful guide for locating the vibrating line.

Predicting the development of vulvar lichen sclerosus

Relevance. The issue of timely diagnosis and treatment of vulvar lichen sclerosus has become especially acute in recent years due to the “rejuvenation” of the disease and the risk of its malignancy. In this regard, it is urgent to search for effective methods for predicting and early detection of the disease. The aim of the study - to develop amodel for predicting vulvar lichen sclerosus based on established clinical and anamnestic risk factors. Materials and Methods. The prospective case-control study included 404 women aged 20 to 70 years, of which 344 were patients with vulvar lichen sclerosus and 60 were women without vulvar diseases. At the first stage, acomparative statistical correlation analysis of the clinical and anamnestic data of the subjects was carried out using the Spearman correlation coefficient (R0.15), Chi-square tests, Phi and Cramer statistics, the Mann-­Whitney U test and the Student t test (p 0.05). The data obtained were used to develop aneural network model for predicting vulvar lichen sclerosus in the second stage of the study. Results and Discussion. Based on established reliably significant (p0.05) obstetric-­gynecological, somatic, infectious, hygienic and household factors influencing the risk of developing vulvar lichen sclerosus (Rindicator - from 0.16 to 0.38 confirms the statistical significance of correlations), aneural network model for predicting vulvar lichen sclerosus was developed (the percentage of correct classification on the test sample is the maximum possible value - 100%) and acomputer program was written that automates the procedure for predicting the disease. Conclusion. The neural network model for predicting the disease, developed on the basis of reliably (p0.05) significant risk factors for vulvar lichen sclerosus, has high prognostic properties, and acomputer program written on its basis allows the doctor in amatter of minutes to identify the patient at risk for the development of vulvar lichen sclerosus and give she needs preventive recommendations aimed at preventing or early detection of the disease.

Computer simulation study of cementless cup placement for dysplastic hip osteoarthritis using subluxation percentage of Crowe classification.

It is often difficult to achieve adequate bone coverage of the cup in total hip arthroplasty in cases of severe developmental dysplasia of the hip (DDH). This study aimed to evaluate the relationship between subluxation percentage of Crowe classification and cup center-edge (cup-CE) angle to investigate whether subluxation percentage according to the Crowe classification is a useful indicator for cementless cup placement. Cementless cup placement was simulated in 77 women with DDH in 91 hips (Crowe I, 35 hips; Crowe II, 35 hips; and Crowe III, 21 hips) using computed tomography-based computer simulation software. The cups were placed at the anatomic hip center (AHC) and 10-mm high hip center (HHC). The relationship between the subluxation percentage and cup-CE angle was evaluated using Spearman's rank correlation coefficient. In addition, the cutoff values for the subluxation percentage that satisfied a cup-CE angle ≥0° were determined using the receiving operating characteristic curve. The cup-CE angle was negatively correlated with the subluxation percentage in both AHC and 10-mm HHC (correlation coefficient ρ = -0.542 [p < 0.01] and -0.704 [p < 0.01], respectively). The cutoff values for subluxation percentage that satisfied a cup-CE angle ≥0° were 56.1% and 73.6% for AHC and 10-mm HHC, respectively. Cementless cup placement in AHC is difficult in cases with the subluxation percentage ≥56.1%, and HHC reconstruction or femoral structural autograft technique should be considered as an alternative. Moreover, placement at 10 mm above AHC is difficult in cases with subluxation percentage ≥73.6%.

Assessment of Groundwater Availability for Rice Farming in Tuban Regency, East Java in 2018-2022

A study has been carried out on deficit, surplus, percentage of groundwater availability and Oldeman climate type classification in Tuban Regency, East Java in 2018-2022 with the aim of increasing the productivity of rice plants in the area. This process begins with data collection, including rainfall, air temperature, coordinates and height of rain posts, as well as physical soil data which is used to calculate rice ETC (Evapotranspirasi Tanaman/Plant Evapotranspiration), deficit, surplus and ATS (Air Tanah Tersedia/Groundwater Available). The method used in this calculation is the Thornthwaite and Mather method. From data analysis, it was found that the smallest deficit of 0.1 mm occurred in Medalem in June, while the largest deficit occurred in Ngimbang in November amounting to 279.6 mm. The smallest surplus of 0.9 mm was recorded in Sumurgung in April, while the largest surplus of 313 mm occurred in Jenu in January. Groundwater availability reaching 100% generally occurs in November-April, while 0% ATS occurs in June-September. and the Oldeman climate patterns in Tuban Regency are C3, D3, D4, and E3 respectively.