Predictive Criteria Research Articles

Post-bariatric contour deformity correction: an endeavour to establish objective criteria nationally.

A marked increase in bariatric surgery has led to higher numbers of patients with contour deformities after massive weight loss seeking plastic surgical correction. Insurance coverage for these post-bariatric interventions is highly subjective and a common set of objective criteria has not yet been established. The aim was to evaluate the factors influencing insurance coverage for post-bariatric surgery, focusing on finding objective, reproducible criteria. This was a retrospective single centre chart review of all post-bariatric patients with redundant skin requesting body contouring surgery from 2013 to 2018. Demographic, bariatric and surgical, as well as insurance information were collected. A logistic regression model was used to identify predictors of successful insurance coverage. 116 Patients were included in the study. Insurance approval for post-bariatric body contouring surgery was obtained for only 47 patients (41%). Mentioning the term "medical indication" in the application letter was associated with a 15.2 times higher rate of receiving a positive answer (p <0.001), whereas mentioning "mental suffering" was associated with 82.3% lower chance of getting a positive response (p <0.001). A high body mass index (BMI) (p <0.009) before the bariatric operation as well as a high BMI reduction (p <0.021) were associated with a higher approval rate by insurance companies . An additional application letter to the insurance company (p <0.024) as well as mentioning mechanical restriction (p <0.022) were associated with a positive response from the insurance companies. We were able to establish certain objective predictive criteria for insurance coverage of post-bariatric surgery. However, it appears that the decisions of insurance companies for this condition are still rather randomly taken. Therefore, the establishment of objective criteria for insurance coverage may allow fairer treatment for this growing patient population.

Prediction of Increased Mobility of Yogyakarta Residents in Controlling the Spread of COVID-19 Cases Using the Neural Network Algorithm

High mobility in D.I Yogyakarta arises from tourist visits and educational activities as well as community activities which are quite dense. High mobility and density will affect the spread of COVID-19 in D.I Yogyakarta. Early preparation is needed to predict displacement in D.I Yogyakarta so that policies can be implemented as early as possible to prevent the emergence of a new wave of spread of COVID-19 in D.I Yogyakarta. In this study, the Neural Network Algorithm with Backpropagation model is used to predict the mobility of Yogyakarta D.I data from September 2021 to January 2022. To form the Neural Network Algorithm model using Rapidminer software with prediction criteria of accuracy and kappa. The prediction accuracy value obtained is 95.49% and the kappa value is 0.908. The results of this study indicate that the predictive value of Yogyakarta D.I mobility tends to be high.

Abstract PD11-06: Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer

Abstract Background and Purpose: There is currently lack of recognized imaging criteria for prediction of treatment response to NAST in breast cancer patients. And early identification of treatment response to neoadjuvant systemic therapy (NAST) in Triple Negative Breast Cancer (TNBC) patients is important for appropriate treatment selection and response monitoring. A novel MRI sequence, Magnetic Resonance Image Compilation (MagIC) is capable of simultaneous quantitation of several tissue water properties including longitudinal (T1), transverse (T2) relaxation times, and proton density (PD). In this study we evaluated the ability of a radiomic model extracted from a novel MagIC sequence acquired early during NAST to predict pathologic complete response to NAST in TNBC. Materials and Methods: This IRB approved prospective ARTEMIS trial (NCT02276443) included 184 women (122 training dataset, 62 testing dataset) diagnosed with stage I-III TNBC. All patients were scanned with MagIC on a 3T MRI scanner at baseline (184 patients), and after 4 cycles (156 Patients) of NAST. T1, T2 and PD maps were generated from the source images using SyMRI (SyntheticMR, Linkoping, Sweden). Histopathology at surgery was used to determine pathologic complete response (pCR) which was defined as absence of the invasive cancer in the breast and axillary lymph nodes. 3D contouring of the tumors was performed using an in-house toolbox. 310 (10 first-order, 300 GLCM) textural features were extracted from each map, with total of 930 features/patient. Radiomic features were compared between pCR and non-pCR using Wilcoxon Rank Sum test and Fisher’s exact test. To build a multivariate, predictive model, logistic regression with elastic net regularization was performed for texture feature selection. The tuning parameter was optimized using 5-fold cross-validation based on the average area under curve (AUC) of each fold of a cross-validation using training data. Then the testing data were used to compare model’s performance by AUC. Results: Univariate analysis found 23 PD, 17 T1 and 10 T2 radiomic features at C4 time point to be able to predict pCR status with AUC &amp;gt;70% in both training and testing cohort. The top performing radiomic features were Entropy, Variance, Homogeneity and Energy (Tables1-2). Multivariate radiomics models from C4-PD, and C4-T1 maps showed best performance during both cross validation and independent testing. The radiomic signature of C4-T1 map that included 27features had best performance, with an AUC of 0.77, 0.70 (95% CI: 0.571-0.868) in training and testing cohort respectively. C4-PD map radiomic signature that included 6features was able to predict the pCR status with AUC of 0.73, 0.72 (95% CI: 0.571-0.868) in training and testing cohort respectively. Conclusion: Our data found that MagIC-based radiomics signature could potentially predict pathologic complete response in TNBC early during NAST. This data shows the potential application of MagIC radiomic model for improvement of response assessment in TNBC. Table 1.Best performing radiomic features from PD map after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valuePD-mapAngular Variance of Sum entropy1060.73820.6437-0.8328500.73240.5895-0.8752&amp;lt;0.001Range of Sum entropy1060.73930.6446-0.834500.72120.5753-0.867&amp;lt;0.001Angular Variance of Sum entropy1060.75960.6662-0.853500.70190.5538-0.8501&amp;lt;0.001Average of Sum entropy1060.73470.6367-0.8327500.70990.5613-0.8585&amp;lt;0.001Angular Variance of Sum variance1060.70160.602-0.8011500.70190.5543-0.8495&amp;lt;0.001Range of Sum variance1060.70050.6001-0.8009500.700.5499-0.8476&amp;lt;0.001 Table 2.Best performing radiomic features from T1-T2 maps after 4 cycles of NAST in TNBC patients.FeatureTraining CohortTraining CohortTraining CohortTesting CohortTesting CohortTesting CohortNAUC95% CINAUC95% CIP-valueT1-mapAngular Variance of Sum entropy1060.76530.6762-0.8544500.70510.5524-0.8579&amp;lt;0.001Range of Sum entropy1060.76530.6759-0.8547500.70350.5503-0.8567&amp;lt;0.001Average of Entropy1060.75250.6568-0.8482500.71630.572-0.8607&amp;lt;0.001Average of Sum entropy1060.750.6552-0.8448500.70190.555-0.8488&amp;lt;0.001Angular Variance of Energy1060.7450.6493-0.8407500.73080.59-0.8715&amp;lt;0.001Range of Energy1060.74290.6466-0.8392500.72920.5885-0.8699&amp;lt;0.001Average of Energy1060.74110.6438-0.8384500.7260.5852-0.8667&amp;lt;0.001Average of Entropy1060.73360.635-0.8322500.74040.602-0.8787&amp;lt;0.001Average of Maximum probability1060.70760.6054-0.8098500.71630.5704-0.8623&amp;lt;0.001Range of Maximum probability1060.70550.6018-0.8092500.75640.6195-0.8933&amp;lt;0.001T2-mapAngular Variance of Energy1060.74820.6531-0.8433500.70990.5644-0.8555&amp;lt;0.001Range of Energy1060.7450.6495-0.8405500.70350.5569-0.8501&amp;lt;0.001Average of Entropy1060.74070.6416-0.8399500.72920.585-0.8733&amp;lt;0.001Average of Sum entropy1060.73860.6405-0.8367500.72440.5797-0.869&amp;lt;0.001Average of Energy1060.73180.6309-0.8327500.72120.5743-0.86&amp;lt;0.001Angular Variance of Sum entropy1060.7290.631-0.827500.72760.5857-0.8695&amp;lt;0.001Range of Sum entropy1060.72760.6295-0.8257500.72280.5796-0.8659&amp;lt;0.001Average of Information measure of correlation 11060.71580.6147-0.8169500.70990.5638-0.8561&amp;lt;0.001Average of Entropy1060.700.5903-0.8028500.74360.6014-0.8858&amp;lt;0.001 Citation Format: Nabil Elshafeey, Ken-Pin Hwang, Beatriz Elena Adrada, Rosalind Pitpitan Candelaria, Medine Boge, Rania M Mahmoud, Huiqin Chen, Jia Sun, Wei Yang, Aikaterini Kotrotsou, Benjamin C Musall, Jong Bum Son, Gary J Whitman, Jessica Leung, Huong Le-Petross, Lumarie Santiago, Deanna Lynn Lane, Marion Elizabeth Scoggins, David Allen Spak, Mary Saber Guirguis, Miral Mahesh Patel, Frances Perez, Abeer H Abdelhafez, Jason B White, Lei Huo, Elizabeth Ravenberg, Wei Peng, Alastair Thompson, Senthil Damodaran, Debu Tripathy, Stacey L Moulder, Clinton Yam, Mark David Pagel, Jingfei Ma, Gaiane Margishvili Rauch. Radiomics model based on magnetic resonance image compilation (MagIC) as early predictor of pathologic complete response to neoadjuvant systemic therapy in triple-negative breast cancer [abstract]. In: Proceedings of the 2021 San Antonio Breast Cancer Symposium; 2021 Dec 7-10; San Antonio, TX. Philadelphia (PA): AACR; Cancer Res 2022;82(4 Suppl):Abstract nr PD11-06.

A joint fairness model with applications to risk predictions for underrepresented populations.

In data collection for predictive modeling, underrepresentation of certain groups, based on gender, race/ethnicity, or age, may yield less accurate predictions for these groups. Recently, this issue of fairness in predictions has attracted significant attention, as data-driven models are increasingly utilized to perform crucial decision-making tasks. Existing methods to achieve fairness in the machine learning literature typically build a single prediction model in a manner that encourages fair prediction performance for all groups. These approaches have two major limitations: (i) fairness is often achieved by compromising accuracy for some groups; (ii) the underlying relationship between dependent and independent variables may not be the same across groups. We propose a joint fairness model (JFM) approach for logistic regression models for binary outcomes that estimates group-specific classifiers using a joint modeling objective function that incorporates fairness criteria for prediction. We introduce an accelerated smoothing proximal gradient algorithm to solve the convex objective function, and present the key asymptotic properties of the JFM estimates. Through simulations, we demonstrate the efficacy of the JFM in achieving good prediction performance and across-group parity, in comparison with the single fairness model, group-separate model, and group-ignorant model, especially when the minority group's sample size is small. Finally, we demonstrate the utility of the JFM method in a real-world example to obtain fair risk predictions for underrepresented older patients diagnosed with coronavirus disease 2019 (COVID-19).

The load criteria for ship mechanical noise prediction in low frequencies and experimental validation

The loading method of the external excitations generated by the equipment directly affects the predicted result of the mechanical noise which should be the same under different excitation forms for the given equipment. In this paper, general load criteria are proposed to define forces/moments as the standard form and convert other forms of loads in the low-frequency domain. As the most typical form to characterize equipment excitation, acceleration load loading methods for different conditions are investigated. The equivalent formula between ideal accelerations and generalized forces establishes the first load criterion. The second load criterion is proposed to address the issue of an average acceleration loading, in which the phase and amplitude distribution are both absent, and cannot apply to the load identification. The upper and lower limits of the mechanical noise can be determined by the vibroacoustic transfer function of the three load models, and the energy-averaged value is used to represent the mechanical noise. Furthermore, the third criterion is used to handle the case where the acceleration load is given by the results of a bench test. According to the equipment source descriptor invariance, the conversion method is achieved between the bench test and the real ship based on the transfer function of a load model, and the mechanical noise is predicted by an equivalent energy method. Finally, a three-parameter method to quantitatively evaluate the well-fitting of experimental and numerical results, and the load criteria are well validated by underwater acoustic experiments of an experimental model.

Water Level Prediction through Hybrid SARIMA and ANN Models Based on Time Series Analysis: Red Hills Reservoir Case Study

Reservoir water level (RWL) prediction has become a challenging task due to spatio-temporal changes in climatic conditions and complicated physical process. The Red Hills Reservoir (RHR) is an important source of drinking and irrigation water supply in Thiruvallur district, Tamil Nadu, India, also expected to be converted into the other productive services in the future. However, climate change in the region is expected to have consequences over the RHR’s future prospects. As a result, accurate and reliable prediction of the RWL is crucial to develop an appropriate water release mechanism of RHR to satisfy the population’s water demand. In the current study, time series modelling technique was adopted for the RWL prediction in RHR using Box–Jenkins autoregressive seasonal autoregressive integrated moving average (SARIMA) and artificial neural network (ANN) hybrid models. In this research, the SARIMA model was obtained as SARIMA (0, 0, 1) (0, 3, 2)12 but the residual of the SARIMA model could not meet the autocorrelation requirement of the modelling approach. In order to overcome this weakness of the SARIMA model, a new SARIMA–ANN hybrid time series model was developed and demonstrated in this study. The average monthly RWL data from January 2004 to November 2020 was used for developing and testing the models. Several model assessment criteria were used to evaluate the performance of each model. The findings showed that the SARIMA–ANN hybrid model outperformed the remaining models considering all performance criteria for reservoir RWL prediction. Thus, this study conclusively proves that the SARIMA–ANN hybrid model could be a viable option for the accurate prediction of reservoir water level.

Predictive factors of clinical assays on hydroxychloroquine for COVID-19 mortality during the first year of the pandemic: A meta-synthesis

Background: The COVID-19 pandemic led to a violent debate about the efficacy of a repurposed drug hydroxychloroquine (HCQ) and a new broad-spectrum antiviral (remdesivir) and about randomized controlled trials (RCTs) and observational studies. To understand conflicting results in the literature, we performed a meta-synthesis to determine whether intrinsic qualitative criteria within studies may predict apparent efficacy or ineffectiveness of HCQ and remdesivir. Methodology: Predictive criteria were identified through critical review of studies assessing HCQ and remdesivir for COVID-19 mortality from March to November 2020. Multiple correspondence analysis, comparative meta-analysis, and predictive value were used to explore and identify criteria associated with study outcomes. Results: Among the 61 included studies, potential conflict of interest, detailed therapeutic protocol, toxic treatment (overdose or use in contraindicated patients), known centers and doctors, and private data computing company were the most predictive criteria of the direction of effect of the studies. All 18 observational studies evaluating HCQ and reporting detailed therapeutic protocol without conflict of interest were Pro. Potential conflict of interest was a perfect predictor for remdesivir efficacy. RCTs were associated with HCQ inefficacy and potential conflict of interest. The most predictive criteria were validated and allowed perfect classification of 10 additional studies.Conclusion: In therapeutic trials on COVID-19, the major biases predicting the conclusions are not methodology nor data analysis, but conflict of interest and absence of medical expertise. The thorough search for declared or undeclared and direct or indirect conflict of interest, and medical expertise should be included in the quality criteria for the evaluation of future therapeutic studies in COVID-19 and beyond. A new checklist evaluating not only methodology but also conflict of interest and medical expertise is proposed.

Application of C4.5 Algorithm to Prediction Sales at PT. Sumber Sayur Segar

Fresh vegetables, fruits and fresh meat are one of the basic needs for human life. The need for fresh vegetables, fruits and meat is one of the most important factors for buyers before making a purchase transaction. Likewise with the needs of fresh vegetables, fruit and meat needed by restaurants, cafes, hospitals, hotels and so on. With the increasing number of requests from customers for the needs of fresh vegetables, fruit and meat, companies engaged in the supply and sale of these necessities need to record sales transactions so that there are no stock vacancies and excess stock of goods. Therefore, companies must be more careful in providing fresh vegetables, fruits and meat which are in great demand, so it needs a data processing in the form of data mining using the C4.5 algorithm. In this study, the predicted sales transactions are the last three months of January, February and March 2021. Then for the sales prediction criteria used are in the form of price, type of goods, type of unit and month of sale so that from these criteria can be obtained sales transactions that are selling or not selling. Data mining is a process of mining important information from a very large data. While the C4.5 algorithm is a data classification that has numeric and categorical attributes, where the results of the classification process in the form of rules can be used to predict the value of discrete type attributes from new records. The system was built using the PHP programming language and MySQL as the database. This study obtained predictive results which were implemented in the form of a decision tree, namely the category of types of vegetables belonging to the best-selling sales transactions.

Machine Learning Approaches for Predicting Difficult Airway and First-Pass Success in the Emergency Department: Multicenter Prospective Observational Study.

BackgroundThere is still room for improvement in the modified LEMON (look, evaluate, Mallampati, obstruction, neck mobility) criteria for difficult airway prediction and no prediction tool for first-pass success in the emergency department (ED).ObjectiveWe applied modern machine learning approaches to predict difficult airways and first-pass success.MethodsIn a multicenter prospective study that enrolled consecutive patients who underwent tracheal intubation in 13 EDs, we developed 7 machine learning models (eg, random forest model) using routinely collected data (eg, demographics, initial airway assessment). The outcomes were difficult airway and first-pass success. Model performance was evaluated using c-statistics, calibration slopes, and association measures (eg, sensitivity) in the test set (randomly selected 20% of the data). Their performance was compared with the modified LEMON criteria for difficult airway success and a logistic regression model for first-pass success.ResultsOf 10,741 patients who underwent intubation, 543 patients (5.1%) had a difficult airway, and 7690 patients (71.6%) had first-pass success. In predicting a difficult airway, machine learning models—except for k-point nearest neighbor and multilayer perceptron—had higher discrimination ability than the modified LEMON criteria (all, P≤.001). For example, the ensemble method had the highest c-statistic (0.74 vs 0.62 with the modified LEMON criteria; P<.001). Machine learning models—except k-point nearest neighbor and random forest models—had higher discrimination ability for first-pass success. In particular, the ensemble model had the highest c-statistic (0.81 vs 0.76 with the reference regression; P<.001).ConclusionsMachine learning models demonstrated greater ability for predicting difficult airway and first-pass success in the ED.

The development of nano-QSPR models for viscosity of nanofluids using the index of ideality of correlation and the correlation intensity index

Utilizing nanofluids as a suspension containing nanoparticles in an ordinary liquid is a relatively new field, which received great attention in recent years. The main object of this investigation is focused on modeling the effective viscosity of nanofluids using the nano-quantitative structure-property relationship (nano-QSPR) paradigm. Two distinct data sets were considered containing four types of nanoparticles (Al2O3, CuO, SiO2, and ZnO) dispersed in water (as the most common base fluid) at volume fraction ranges of 1–5% and various shapes (blades, bricks, cylindrical, spherical, and platelets). Simplified Molecular Input-Line Entry System (SMILES) is a tool to represent the molecular structure. Quasi-SMILES is a sequence of symbols that represents all available data e.g. molecular structure together with physicochemical conditions. Taking into account the capability of quasi-SMILES molecular representation to define the eclectic data such as size and shape of nanoparticles, this notation was chosen to exemplify nanofluids structure. It is remarkable to point out that the proposed attitude to generate nano-QSPR models introduced a comparison between two specific predictive potential criteria using by Monte Carlo technique. It was concluded that the development of models based on Correlation intensity index (CII) is statistically more reliable than model generation based on the Index of ideality of correlation (IIC).

Three-dimensional progressive damage and failure analysis of double-lap composite bolted joints under quasi-static tensile loading

Bolted joints are the dominant connection method in assembling composite structures. To investigate the failure mechanism of composite bolted joints under tensile loading, a three-dimensional progressive damage model for composite bolted joints was developed and implemented using the subroutine UMAT in Abaqus/Standard. This model considered several significant damage phenomena, such as the matrix crack orientation, the closure effect of matrix crack, and the longitudinal compressive responses of failed material under transversal constraints in the crush zone. The model utilized Hashin criterion for fiber fracture and Mohr–Coulomb based criterion for matrix cracking prediction separately. For validation of the model, a composite double-lap single-bolt joint configuration was adopted. The simulation results show high accuracy and precision compared with experimental results from the literature concerning inflection load, failure load, load–displacement response, and failure modes. Such a high-fidelity progressive damage model can overcome the inherent limitations of the experimental method. The bearing damage onset and propagation in the distinct plies and interfaces were investigated in detail at different load levels. It was found that matrix cracking initiates the joint damage onset and triggers fiber fracture. The initiation of interface delamination was identified as the mechanism by which the load–displacement response becomes nonlinear. The study revealed that matrix cracking is the dominant failure mode and induces the final rupture of the joints.

Comprehensive comparison of the applicability of internally ribbed and microfin tubes for TORC systems

TORC (Trans-critical Organic Rankine Cycle) systems are a promising thermal conversion technology. However, the heat transfer deterioration with the supercritical organic working fluid can affect the system safety. Heat transfer enhanced tubes (HTET) increase the heat transfer coefficient which limits the heat transfer deterioration and improves TORC system safety and economy. However, the heat transfer enhancement in the various types of tubes has not been well quantified for equivalent conditions with contradictory conclusions affecting existing heat transfer enhancement prediction criteria. Therefore, the present study presents experimental comparisons of the heat transfer of a supercritical organic working fluid (R134a) in two different types of enhanced tubes (an internally ribbed tube and a micro-finned tube) to provide reliable experimental data to solve this contradiction. A total of 14,100 experimental data points were obtained. The results compare the heat transfer enhancement, the restriction of the buoyancy induced heat transfer deterioration and the pressure loss factor of the tubes. The experimental results are then used to evaluate the accuracy and limitations of the existing criteria. Then, a more general tube selection criterion is presented for TORC system enhanced tube selection.

Usage of the Ranson criteria and neutrophil-lymphocyte ratio measured on presentation to the emergency department in the prediction of mortality in acute pancreatitis

The aim of this study is to examine the usefulness of the Ranson criteria and neutrophil-to-lymphocyte ratio (NLR) on admission to the emergency department (ED) in regard to the prediction of mortality for patients diagnosed with acute pancreatitis (AP) and compare the predictive ability of these two parameters. All patients information aged over 18 years who presented to ED and were diagnosed with AP in the three-year study period were analyzed retrospectively. The data were provided from the electronic-based hospital information system. To assess the cut-off value of each variable in discriminating between the survivor and non-survivor groups, the receiver operating characteristic (ROC) analysis and the area under the curve (AUC) were used. This study included 318 adult patients (47.5% male) with a mean age of 58.6 ± 17.6 years. As a result of the ROC analysis of the Ranson criteria for mortality prediction, the AUC value was calculated as 0.65 (95% confidence interval (CI): 0.57–0.70), the Youden index as 0.28, and the p value as 0.002, while the ROC analysis of NLR revealed an AUC value of 0.58 (95% CI: 0.52–0.64), Youden index of 0.29, and p value of 0.08. The Ranson criteria can provide a clinical guide in the prediction of the prognosis of AP patients. However, according to the results of this study, NLR recorded at the time of admission was found to be ineffective on predicting the prognosis of AP patients.

Secular Trend of Body Dimensions in Highly Qualified Wrestlers

The purpose of the paper was to study the physique of highly qualified martial art athletes engaged in different types of wrestling, and to perform a retrospective analysis of the morphological characteristics of wrestlers examined since the early 1920s. The materials of the anthropometric survey of 48 athletes engaged in different types of wrestling with qualification from the candidate for master of sports and above were used for this purpose and compared to the "control group" of young men (N = 97) who were not engaged in sports (similar to the group of athletes by age, ethnicity and percentage of individuals with different weight categories). A set of morphological traits was established that contributed to the successful achievements in sports. When comparing the physique of modern athletes with that of the wrestlers surveyed in the early and mid-20th century (the 1920s and 1960s), it has been shown that the secular trend towards increase in height typical for modern population, was expressed in athletes to a much lesser extent. Striking similarities were revealed for absolute and relative dimensions characterizing the skeletal body proportions of the wrestlers, which pointed to the secular stability of this sports morphotype. The obtained results can be used as additional morphological criteria for sports selection, professional orientation and prediction of competitive success.

Ten-year outcomes of patients who developed persistent azoospermia following chemotherapy associated with different oncological diagnoses: A retrospective cohort study from a different perspective.

This study evaluated the treatment procedures for chemotherapy (CT)-induced persistent azoospermia and their outcomes from a different perspective. In 63 patients (mean age: 30.16 ± 4.91 years) who had undergone CT 11 ± 5 years earlier, the semen volume, gonadotropins level, FSH level, genetics, micro-testicular sperm extraction (m-TESE) result, sperm DNA fragmentation index (SDFI), semen reactive oxidative stress (ROS) rate, duration of embryonic development, and pregnancy and baby take-home rates were examined. The correlations between the ROS rates and the SDFIs, m-TESE results, sperm motility, pathology scores, time-lapses, and baby take-home rates were evaluated. The semen volumes were 3.5 ± 1.1/ml. The FSH level following CT was 17.87 ± 5.80 mIU/ml. A sperm rate of 34.9% was found from the m-TESE result. The mean SDFI and ROS rate were 4 (<15-30>) and 1.29 ± 0.51, respectively. The time-lapse was calculated as 5h. Pregnancy and live birth were achieved at 20.63% and 12.7%, respectively. In the patients with a low ROS (≤1.42) and SDFI (≤15), the m-TESE success rate was high, the FSH value was low, the pathological score and fertilization rate were elevated, the embryonic cleavage period was normal, and the pregnancy and baby take-home rates were high. The sperms may be detected using m-TESE in patients who develop persistent azoospermia associated with CT due to different oncological diagnoses. Our study revealed that a low FSH value and normal ejaculatory ROS rates are positive predictive factors of sperm detection before m-TESE. The motility of the sperms detected after m-TESE and normal SDFI rates were found to be positive predictive criteria of high fertilization, good embryonic cleavage, pregnancy, and live birth.

New EU Enlargement Strategy Towards the Western Balkans and Its Impact on Rule of Law

The rule of law is incorporated in the EU Founding Treaties and case-law of the Court of Justice of the EU and was included as a key requirement already in 1993 Copenhagen accession criteria. The EU enlargement is not only territorial increase, but also transposition of EU acquis to third countries. Since 1993, the monitoring mechanism of the rule of law reform in the EU accession countries was enhanced, including two specific negotiation chapters, Chapter 23 – judiciary and fundamental rights and Chapter 24 – justice, freedom and security. Over the last two decades, the EU was struggling to develop an adequate mechanism in this area, from mechanism for coordination and verification, to action plans for Chapter 23, to more specific tools like perception and experience surveys of the judiciary and functional reviews. Due to the challenges to measure progress and track record in the rule of law, in February 2020 the European Commission presented the new approach to EU Enlargement that aims to push reforms forward. The intention is to make the accession negotiations more credible, predictable and dynamic and criteria for assessing reforms in the accession countries will be based on the clearer criteria and more concise EU requirements. The article examines how EU enlargement policies influenced the rule of law reforms in Western Balkan countries over the years and what could be expected from the new approach. The research hypothesis is based on the correlation between Enlargement strategy towards the Western Balkans and its impact on rule of law in countries of the mentioned region. The methodological approach applied in the assessment is based on analysis of Enlargement strategy and other EU and national documents, as well as results of the work of judicial institutions in order to provide insight into the bottlenecks of the state rule of law in Western Balkan countries and enable identification of recommendations for improvement. The authors concluded that the new methodology would improve the measurability of the achieved results in the rule of law area, however, the approach might slow down the accession process of Serbia and Montenegro as a frontrunners in the process.

The Performance of Artificial Neural Network Using Heterogeneous Transfer Functions

Neural networks have been very important models across computer vision, natural language processing, speech and image recognition, aircraft safety and many more. It uses a variety of architectures that centres on the Multi-Layer Perceptron (MLP) which is the most commonly used type of Artificial Neural Network. MLP has been found to be good in terms of model precision in the usage of Homogenous Transfer/activation Functions (HTFs), especially with large data set. Based on the preliminary investigations of ranking of transfer functions by error variance (Udomboso, 2014), three HTFs are considered to perform better than other HTFs in prediction. These HTFs are the Hyperbolic Tangent Transfer functions (TANH), Hyperbolic Tangent Sigmoid Transfer function (TANSIG) and the Symmetric Saturating Linear Transfer Function (SSLTF). In this work, the performance of two Heterogeneous Transfer Functions (HETFs), which came as a result of the convolution of the three best HTFs, were compared with the performance of the three above listed HTFs. The hidden neurons used are 2, 5 and 10, while the sample sizes include 50, 100, 200, 500 and 1000. The data were divided into training sets of 90, 80 and 70 respectively.&#x0D; The results showed that the HETFs performed better in terms of the forecast using Mean Square Error (MSE), Mean Absolute Error (MAE) and Test Error as the forecast prediction criteria.

Evaluation of a robust regression method (RoBoost-PLSR) to predict biochemical variables for agronomic applications: Case study of grape berry maturity monitoring

Visible and near infrared spectroscopy (VIS-NIR) is increasingly being transferred from laboratory to industry for in-line and portable applications in various domains. By intensively using VIS-NIR spectroscopy, some abnormal observations may certainly arise. It is then important to properly handle outliers to elaborate effective prediction models. The objective of this study is to investigate the potential of using a robust method called Roboost-PLSR to improve prediction model performances for a viticulture application. This work focuses on a case study to predict sugar content in grape berries of three different grape varieties of Vitis Vinifera in a maturity monitoring context. Hyperspectral images were acquired of grape berries of Syrah, Fer-Servadou and Mauzac varieties. Reference measurements of sugar levels were made in the laboratory by densimetric baths. Performances of RoBoost-PLSR models were compared to performances of reference models using Partial Least Square Regression (PLSR). Reference prediction criteria using PLSR were obtained for all varieties with these following values: Syrah (Rp2 ​= ​0.971; RMSEp ​= ​5.36 ​g/L), Fer-servadou (Rp2 ​= ​0.788; RMSEp ​= ​11.69 ​g/L) and Mauzac (Rp2 ​= ​0.690; RMSEp ​= ​15.61 ​g/L). Prediction qualities are improved with RoBoost-PLSR: Syrah (Rp2 ​= ​0.990; RMSEp ​= ​3.14 ​g/L), Fer-Servadou (Rp2 ​= ​0.848; RMSEp ​= ​10.20 ​g/L) and Mauzac (Rp2 ​= ​0.927; RMSEp ​= ​7.58 ​g/L). Results confirm that Roboost-PLSR method allows a better consideration of outliers within the calibration set.

Эпохальный ретроспективный анализ показателей телосложения у спортсменов-единоборцев высокой квалификации

Introduction. The purpose of the paper was to study the physique of highly qualified martial art athletes engaged in different types of wrestling, and to perform a retrospective analysis of the morphological fea-tures of wrestlers examined since the early 1920s. Materials and methods. Anthropometric data of 48 male athletes who were engaged in martial arts (different types of wrestling) for many years and reached high qualifications (from a candidate for master of sports and above) were used in this study. Athletes were 18-31 years old (mean age – 20.62±0.43), almost all of Russian ethnicity, permanent residents of Moscow. For comparative analysis, the materials of the anthro-pometric survey of young men – students of different Moscow universities were taken (N = 97). The stu-dents were not involved in sports, were of the same age (20.19±0.29), of Russian ethnicity and represented with different weight categories. They were used as a "control group". Results. On the materials of the anthropometric survey of athletes engaged in different types of wrestling, in comparison with the so-called "control group", a set of morphological features was established contrib-uting to the successful achievement in sports. When comparing the physique of modern athletes and wres-tlers surveyed in the early and middle of the last century (1920s and 1960s), it is shown that the trend of the secular increase in height typical for modern population, is expressed in athletes to a much lesser extent. Striking similarities are revealed for absolute and relative dimensions characterizing the skeletal body pro-portions of the wrestlers, which indicates the secular stability of this sports morphotype. Conclusion. The obtained results demonstrate the patterns of formation of morphological features and secular stability of the morphotype of highly qualified athletes involved in different types of wrestling. Re-vealed complex of somatic features can be used as additional morphological criteria for sports selection, professional orientation and prediction of competitive success.

Estimation of lactation curves of Gyr cattle and some associated production parameters in the Colombian low tropic

Background: The Gyr breed is widely used in Colombian low tropic dairy production systems. During the last 10 years, the Asociación Colombiana de Criadores de Ganado Cebú† - ASOCEBU, has been leading a dairy milk control program which led to the creation of a dataset that permits to carry out the first analysis of milk yield in Gyr cattle in the country using records from several herds. Objectives: To study milk production dynamics of Gyr cattle in the Colombian low tropic through the estimation of lactation curves and four derived production parameters: total milk yield between 5 and 305 days (TMY305), peak milk yield (PMY), days at peak (DP) and persistency (P). Methods: 13,798 daily milk yield records from 1,510 cows performing in 103 herds were used; the total number of lactations was 2,480. Four models were considered: Wood, Wiltmink, Papajcsik &amp; Bordero, and a second-degree polynomial. Mean square error, mean absolute error, mean square error of prediction, Akaike and Bayesian information criteria were used to select the model better describing each lactation using the majority rule, that is, the model selected by most criteria was the chosen one. The shape of each fitted lactation curve was checked using basic results from calculus which permitted the classification of the estimated curves into two groups: typical and atypical; only typical functions were used to compute the four aforementioned production parameters. Results: The second-order polynomial was the model most frequently selected, while the Papajcsik &amp; Bordero model had the lowest frequency. Average TMY305, PMY, DP and P were 3,489.86 kg, 17.28 kg, 57.17 days, and 0.83, respectively, with coefficients of variation: 0.27, 0.21, 0.41, and 0.16. Conclusions: This study permitted to identify individuals with outstanding phenotypic performance. To the best of our knowledge, this is the first study of this kind involving thousands of lactations from Gyr cows performing in several regions of Colombian low tropic.