Predictive Criteria Research Articles

Classification of deposits of the Dnipro-Donetsk oil and gas region by the content of metals in oils


 
 
 The issues considered in the paper bear a direct relation to the disputable geo- chemical problems of the origin of oil – one of the main sources of modern energy gen- eration. Features of metal distribution in oils have not been studied yet, and natural classifications of oil fields of the Dnipro-Donets depression according to their contents have not been developed before. That stipulates the scientific novelty of the obtained results. Topicality and practical implications of the carried out studies are mainly in the fact the determined features of metal contents in oils and the classifications of deposits developed on their basis will favor the elaboration of a set of predictive criteria for industrial accumulations of hydrocarbons and scientific sub- stantiation of geological-economic, technological, and environmental estimation of their use. The objects of long-term studies include 36 oil fields: Bakhmatske; Prylutske; Krasnozaiarske; Kachalivske; Kremenivske; Karaikozovske; Korobochkynske; Kulychykhinske; Lipovodolynnske; Monastyrishchynske; Matlakhivske; Malosorochynske; Novomykolayivske; Perekopivske; Prokopenkivske; Radchenkovske; Raspashnovske; Sofiyivske; Sukhodolivske; Solontsivske; Solokhivske; Talalayivske; Trostianetske; Turutynske;Kharkovtsivske; Shchurynske; Yuryivske; Yaroshivske; Khukhrianske; Sahaidatske No 1; Sahaidatske No 13; Kybytsivske No 5; Ky- bytsivske No 51; Kybytsivske No 52; Kybytsivske No 56; and Kybytsivske No 1. They are located within the large tectonic structure of Eastern-European platform – Dnipro-Donets depression. The fields form the Dnipro-Donets oil and gas area of Ukraine. The paper is based on the results of studies of not less than 30 oil samples from each of the fields aimed at identification of metal content with the help of X-ray fluorescence analysis using energy-dispersion spectrometer «Sprut» SEF 01. Natural classification of oil fields in terms of content of each of the considered element have been developed basing on the results of cluster analyses implemented in terms of professional software platforms of versions STATISTICA 13.3 and IBM SPSS Statistics 22. The main scientific value of the obtained results is represented by the following: the development of natural classifications of oil fields of the Dnipro-Donets depression accord- ing to the results of cluster analysis of the content of metals, sulfur, and V/Ni ratio and substantiation of 5 new geochemical criteria for the division of fields formed by oils generated mostly by abiogenic or biogenic processes.
 
 

Nonlinear Soliton-like Oscillations and Waves during Geomaterial Destruction Based on Electromagnetic Radiation Signals

The work is devoted to the results of processing electromagnetic radiation signals obtained during laboratory loading of marble and diabase samples using a technique for determining the parameters of microcracks, developed and published by the author earlier. As a result of such processing, certain patterns were found in the nature of the evolution of the oscillatory process ensemble of microcracks. For example, solitary non-linear waves almost always preceded a sequence of High Frequency traces. Equations for straight lines approximating High Frequency traces in logarithmic coordinates, close to the equation of the Gutenberg–Richter law. Due to the similarity of seismic processes at different scale levels, the results of modeling at the microscale level can be used to describe seismic processes at the macroscale level, for example, to study the processes occurring immediately before destruction and at the time of destruction in order to search for repeatability and regularities. The regularities obtained can be used in the development of a predictive criterion that makes it possible to predict the time of one or another geophysical (seismic) event.

The Role of Knee Flexors Hypertonia in the Decision-Making of Hamstring Lengthening Surgery for Individuals with Cerebral Palsy

Popliteal angle (PA) and the knee position at the end of the swing phase during walking (Kts) are considered criteria for treatment selection and outcome prediction of hamstring lengthening surgery in individuals with cerebral palsy (CP). However, the relationships among Kts, PA, and hypertonia are not clear. This study aimed to determine whether hamstrings hypertonia affects the values of PA and Kts, and how it may affect the hamstring lengthening decision for CP. One hundred and twenty-six subjects with CP (male = 72, female = 54; age = 11.1 ± 3.9 years) underwent gait analysis and examination of hamstrings hypertonia (Tardieu scale) and length (PA). We found that Kts increased with PA (Kts = 5.00 + 0.31 × PA (r = 0.39; p < 0.001)). Every 10° change of PA leads to 3° improvement of knee position in walking. Kts were larger (p < 0.001) in the limbs with (20.40 ± 11.27°) than without (15.60 ± 9.99°) knee flexors hypertonia (Tardieu slow); and were larger (p < 0.001) in the limbs with (20.39 ± 11.01°) than without (14.85 ± 9.89°) knee flexors hypertonia (Tardieu fast). PAs were larger (p < 0.05) in the limbs with (42.81 ± 12.66°) than without (38.96 ± 14.38°) hypertonia (Tardieu fast). Kts = 13.93° and PA = 30° were cutoff values of the presence of hypertonia with sensitivities of 75.0% and 89.1%, respectively. Kts increased with the PA in ambulatory CP. It is estimated that pathological increase of Kts occurs at PA ≥ 40°. The hypertonia of knee flexors affected Kts and PA. The presence of knee flexors hypertonia should be considered in the decision-making of hamstring lengthening for individuals with CP if Kts ≥ 13.93° and PA ≥ 30°.

Evaluation of Disability Progression in Multiple Sclerosis via Magnetic-Resonance-Based Deep Learning Techniques.

Short-term disability progression was predicted from a baseline evaluation in patients with multiple sclerosis (MS) using their three-dimensional T1-weighted (3DT1) magnetic resonance images (MRI). One-hundred-and-eighty-one subjects diagnosed with MS underwent 3T-MRI and were followed up for two to six years at two sites, with disability progression defined according to the expanded-disability-status-scale (EDSS) increment at the follow-up. The patients’ 3DT1 images were bias-corrected, brain-extracted, registered onto MNI space, and divided into slices along coronal, sagittal, and axial projections. Deep learning image classification models were applied on slices and devised as ResNet50 fine-tuned adaptations at first on a large independent dataset and secondly on the study sample. The final classifiers’ performance was evaluated via the area under the curve (AUC) of the false versus true positive diagram. Each model was also tested against its null model, obtained by reshuffling patients’ labels in the training set. Informative areas were found by intersecting slices corresponding to models fulfilling the disability progression prediction criteria. At follow-up, 34% of patients had disability progression. Five coronal and five sagittal slices had one classifier surviving the AUC evaluation and null test and predicted disability progression (AUC > 0.72 and AUC > 0.81, respectively). Likewise, fifteen combinations of classifiers and axial slices predicted disability progression in patients (AUC > 0.69). Informative areas were the frontal areas, mainly within the grey matter. Briefly, 3DT1 images may give hints on disability progression in MS patients, exploiting the information hidden in the MRI of specific areas of the brain.

Validity of the counter-app method in the assessment of intrusions

Background and objectivesThe counter-app method is often used to assess the frequency of intrusions. The method requires the participants to press a button on a smartphone whenever an intrusion occurs during a predefined time period. We evaluated the convergent, discriminant, and predictive validity of the counter-app method in two studies. MethodsIn the first study, we assessed the frequency of intrusions with a counter-app method, thought-sampling method, and a retrospective measure in N = 77 students. Additionally, we assessed retrospectively-estimated duration, percentage of time, intensity, and intrusiveness of intrusions. The second study (N = 65) was identical to the first except the thinking-aloud method replaced the thought-sampling method, and additionally we assessed behavioral neutralizing. ResultsThe counter-app frequency was positively correlated with the convergent (thought-sampling and thinking-aloud frequencies, and retrospectively-estimated frequencies of intrusions) and predictive (behavioral neutralizing) validity criteria. The correlations between counter-app frequency and discriminant validity criteria (retrospectively-estimated duration, percentage of time, intensity, and intrusiveness of intrusions) were not smaller than the correlations between counter-app frequency and convergent validity criteria. LimitationsWe evaluated the predictive validity of the counter-app method using a criterion typical of obsessive-compulsive disorder research. Thus, the result for predictive validity might not transfer to other areas. ConclusionsOur findings support convergent and predictive, but not discriminant, validity of the counter-app method. The counter-app method can validly assess the frequency of intrusions but is not appropriate if the study requires the differentiation of frequency from other constructs such as duration.

Ultrasonography predicts difficult airway management: A prospective double blinded study

PurposeWe evaluate ultrasonographic measurements and clinical criteria of difficult airway management to establish a new predictive score for difficult intubation. MethodsWe conducted a prospective observational double-blinded and monocentric study involving patients proposed for surgery under general anesthesia with orotracheal intubation. After collecting demographic and clinical data of difficult intubation, we evaluated 8 ultrasound measurements of airway assessment using an abdominal probe for the dimensions of the tongue and the hyomental distance. Then using a linear probe, we evaluated the thickness of the anterior neck soft tissues at three levels (hyoid bone, epiglottis, and trachea). After intubation, the sample was separated into 2 groups: easy laryngoscopy (EL) and difficult laryngoscopy (DL).Univariate comparisons between variables were performed using t student test, Mann-Whitney test, and Pearson's Chi-square test. Statistical significance was defined as p < 0.05. Logistic regression was then performed to test correlations between sonographic measurements, clinical criteria, and difficult laryngoscopy.These independent predictive criteria were the score parameters defined by values corresponding to their respective odd ratios. Then we established the cutoff value of the score from its ROC curve. ResultsDifficult laryngoscopy and difficult intubation were found in 20.5% and 14.5% of patients respectively. In univariate analysis, the coronal diameter of the tongue and the thickness of anterior neck soft tissues at 3 levels (hyoid bone, epiglottis, and trachea) had areas under the ROC curves 0.70, 0.87, 0.89, and 0.83 respectively with cut-off values of 4.57 cm, 1.54 cm, 2.69 cm, and 1.15 cm. In multivariable analysis, the clinical and sonographic criteria independently associated with DL were Mallampati score >2 (OR = 3), a TANST at the epiglottis >2.69 cm (OR = 23) and a TANST at the trachea> 1.15 cm (OR = 8). We determined a predictive score of difficult laryngoscopy with a sensitivity = 88%, a specificity = 88%, and an AUC = 0,9298. A score strictly superior to 10 multiplied the risk of a difficult laryngoscopy by 53. ConclusionA combination of clinical criteria and ultrasound measurements increases the ability to predict difficult airway management. Clinical trial registration numberThis study was registered in the Pan African Clinical Trials Registry (PACTR) under the reference (PACTR202109682609409)

ZELLNER’S G-PRIORS IN BAYESIAN MODEL AVERAGING OF LARGE MODEL SPACE USING MARKOV CHAIN MONTE CARLO MODEL COMPOSITION APPLICABLE UNDER BAYESIAN MODEL SAMPLING

Applied researchers are frequently faced with the issue of model uncertainty in situations where many possible models exist. For large model space in regression analysis, the challenge has always been how to select a single model among competing large model space when making inferences. Bayesian Model Averaging (BMA) is a technique designed to help account for the uncertainty inherent in the model selection process. Informative prior distributions related to a natural conjugate prior specification are investigated under a limited choice of a single scalar hyper parameter called g-prior which corresponds to the degree of prior uncertainty on regression coefficients. This study focuses on situations with extremely large model space made up of large set of regressors generated by a small number of observations, when estimating model parameters. A set of g-prior structures in literature are considered with a view to identify an improved g-prior specification for regression coefficients in Bayesian Model Averaging. The study demonstrates the sensitivity of posterior results to the choice of g-prior on simulated dataand real-life data. Markov Chain Monte Carlo (MCMC) are used to generate a process which moves through large model space to adequately identify the high posterior probability models using the Markov Chain Monte Carlo Model Composition (MC3), a method applicable under Bayesian Model Sampling (BMS). To assess the sensitivity and predictive ability of the g-priors,predictive criteria like Log Predictive Score (LPS) and Log Marginal Likelihood (LML) are employed. The results reveal a g-prior structure that exhibited equally competitive and consistent predictive ability among considered g-prior structures in literature.

Neural ordinary differential gray algorithm to forecasting nonlinear systems

Due to the feasibility of the gray model for predicting time series with small samples, the gray theory is well investigated since it is presented and is currently evolved in an important manner for forecasting small samples. This study proposes a new gray prediction criterion based on the Neural Ordinary Differential Equation (NODE), which is named the NODGM (Neural Based Ordinary Differential gray Mode). This mode permits the forecasting approximation to be learned by a training process which contains a new whitening equation. It is needed to prepare the structure and time series, compared with other models, according to the regularity of actual specimens in advance, therefore this model of NODGM can provide comprehensive applications as well as learning the properties of distinct data specimens. In order to acquire a better model which has highly predictive efficiency, afterwards, this study trains the model by NODGM meanwhile using the Runge-Kutta method to obtain the prediction sequence and solve the model. The controller establishes an advantageous theoretical foundation in adapting to novel wheels and comprehensive spreads the utilize extent of Mechanical Elastic Vehicle Wheel (MEVW).

Substrate geometry effect on the strength of repaired plates: Combined XFEM and CZM approach

Aluminum alloys are commonly used in aeronautical applications because of their specific strength and improved corrosion resistance. These structures, during their service, are exposed to various loading cycles, eventually leading to failure at the loci of geometric discontinuities. Repairing by metal or composite patch bonding is widespread to extend the structures’ life by limiting stress concentrations and delaying crack initiation. This work consists of a numerical study, validated by experimental test data, to assess the effect of a central circular notch in an aluminum plate, either reinforced or not by an adhesively bonded composite patch, on the global tensile response of the structure. The constitutive law of the aluminum and adhesive is assumed nonlinear and follows Von Mises equivalent stress flow theory with a hardening variable in incremental form. Damage initiation in the aluminum alloy is modeled by the XFEM (eXtended Finite Element Method), using the maximum principal stress criterion (MAXPS) for damage initiation prediction. Damage evolution is based on the energy approach. The adhesive layer was modeled by CZM (Cohesive Zone Model). A good agreement was found between the experimental results of the tensile curves of the repaired and unrepaired plates with those resulting from numerical modeling. Once the numerical model was validated, several parameters werenumerically studied, namely the shape of the composite patch, the size of the notch, the nature of the adhesive and repair mode by single and double patch, to reduce maximum stress of the damaged area and provide maximum repair efficiency.

Computer-aided clinical image analysis as a predictor of sentinel lymph node positivity in cutaneous melanoma.

BACKGROUNDDelays in sentinel lymph node (SLN) biopsy may affect the positivity of non-SLNs. For these reasons, effort is being directed at obtaining reliable information regarding SLN positivity prior to surgical excision. However, the existing tools, e.g., dermoscopy, do not recognize statistically significant predictive criteria for SLN positivity in melanomas.AIMTo investigate the possible association of computer-assisted objectively obtained color, color texture, sharpness and geometry variables with SLN positivity.METHODSWe retrospectively reviewed and analyzed the computerized medical records of all patients diagnosed with cutaneous melanoma in a tertiary hospital in Germany during a 3-year period. The study included patients with histologically confirmed melanomas with Breslow > 0.75 mm who underwent lesion excision and SLN biopsy during the study period and who had clinical images shot with a digital camera and a handheld ruler aligned beside the lesion.RESULTSNinety-nine patients with an equal number of lesions met the inclusion criteria and were included in the analysis. Overall mean (± standard deviation) age was 66 (15) years. The study group consisted of 20 patients with tumor-positive SLN (SLN+) biopsy, who were compared to 79 patients with tumor-negative SLN biopsy specimen (control group). The two groups differed significantly in terms of age (61 years vs 68 years) and histological subtype, with the SLN+ patients being younger and presenting more often with nodular or secondary nodular tumors (P < 0.05). The study group patients showed significantly higher eccentricity (i.e. distance between color and geometrical midpoint) as well as higher sharpness (i.e. these lesions were more discrete from the surrounding normal skin, P < 0.05). Regarding color variables, SLN+ patients demonstrated higher range in all four color intensities (gray, red, green, blue) and significantly higher skewness in three color intensities (gray, red, blue), P < 0.05. Color texture variables, i.e. lacunarity, were comparable in both groups.CONCLUSIONSLN+ patients demonstrated significantly higher eccentricity, higher sharpness, higher range in all four color intensities (gray, red, green, blue) and significantly higher skewness in three color intensities (gray, red, blue). Further prospective studies are needed to better understand the effectiveness of clinical image processing in SLN+ melanoma patients.

Adaptive Residual Life Prediction for Small Samples of Mechanical Products Based on Feature Matching Preprocessor-LSTM

In order to solve the problem of predicting the residual life of mechanical products accurately based on small-sample data, this paper proposes a small-sample adaptive residual life prediction model of mechanical products based on feature matching preprocessor-LSTM. First, aiming at the problem of low accuracy of remaining life prediction for small samples of mechanical products caused by multiple time scales and multiple fault states, the failure time data and performance degradation data are fused, and the failure rate and standard deviation are used as the remaining life prediction criteria to intuitively reflect The possibility of failure of a component or system at a certain point in time. Considering the demand of adaptive small-sample residual life prediction data, this paper establishes the adaptive matching pre-processor model of life characteristics. On this basis, the LSTM neural network is used to establish a small-sample adaptive residual life prediction model. Then, the XJTU-SY bearing life data set and the test data of the small-sample life characteristics measured by the RV reducer are used as the research objects, and a small amount of the data set is randomly selected. The remaining life expectancy is predicted from the sample data and compared with its standard remaining life, respectively. The comparison results show that the overall prediction error is small. This study shows that the remaining life prediction model established can better predict the remaining life of mechanical product sub-sample data and provides a feasible method for predicting the remaining life of mechanical product sub-samples.

An Overview on Phenomics Applications in Different Agriculture Disciplines

In the past ten years, ground and aerial platforms with several sensors have been rapidly adopted for phenotyping diverse biotic, abiotic stressors and other characters during the crop plant's growth stages. An increase in yield achieved from traditional breeding programs worldwide are no longer sufficient to meet the projected demand for the three major cereals like Rice, Wheat and Maize. Thus the phenomics application which includes high throughput phenotyping (HTP) i.e artificial intelligence based techniques should be bought over traditional phenotyping in order to meet the needy demands of traditional ones. Also Artificial intelligence (AI)-based data analysis techniques can increase the reliability of diagnoses and, as a result, be included into instruments for effective treatment. These methods find pertinent data for use in plant breeding and pathology activities by using feature extraction, identification, classification, and prediction criteria as well as for precision breeding. This approach has various applications in various agriculture disciplines. The main steps under such techniques includes: Image acquisition, Preprocessing of images, segmentation of images, Image Representation and Description and Image recognition. The use of these helps to speed up genetic progress and lessen the phenotyping drawbacks.

Risk assessment indicators and brachial-ankle pulse wave velocity to predict atherosclerotic cardiovascular disease.

Brachial-ankle pulse wave velocity (baPWV) is used for predicting the severity of vascular damage and prognosis of atherosclerotic cardiovascular disease (ASCVD) in people with hypertension and diabetes mellitus. This correlation study aimed to compare the baPWV with other risk indicators for identification of subclinical vascular disease for primary prevention and to determine the clinical utility of baPWV-guided therapy in improving prognosis in high-risk subjects.We included 4881 subjects who underwent voluntary health examination at Mackay Memorial Hospital, Taiwan between 2014 and 2019. Participants were categorized into the low-risk (<5%), borderline-risk (5%–7.4%), intermediate-risk (7.5%–19.9%), and high-risk (≥20%) groups based on the 10-year risk for ASCVD. The predictive risk criteria, that is, the metabolic syndrome score, Framingham Risk Score, estimated glomerular filtration rate, and baPWV were compared among these groups. The chief cause of induced responses and the relationships between parameters were identified using principal component analysis. The participants’ ages, body mass index, systolic, diastolic blood pressure, triglycerides, fasting glucose, hemoglobin A1c, creatinine, neutrophil-to-lymphocyte ratio, monocyte-to-lymphocyte ratio, metabolic syndrome, Framingham Risk Score, and age-related arterial stiffness (vascular age) increased significantly from the low-risk to high-risk groups (P < .001). The mean estimated glomerular filtration rate decreased significantly from the low- to high-risk groups (P < .001). The predicted vascular age and actual age differed significantly between the intermediate- and high-risk groups (P < .001). High-density lipoprotein levels plummeted significantly among the 4 groups (P < .001). The right and left baPWV and ankle brachial index differed significantly among the 4 groups (all P < .001) and increased from the low-risk to high-risk groups (P < .001). Carotid Doppler ultrasonography revealed a significant increase in plaque formation (23.5%, 35.4%, 46.3%, and 61.5% for the low-, borderline-, intermediate, and high-risk groups, respectively). The total explanatory variation was 61.9% for 2 principal variation factors (baPWV, 36.8% and creatinine, 25.1%). The vascular age predicted using baPWV greatly exceeded the chronological age. Plaque formation was significant even in the low-risk group, and its frequency increased with the predicted ASCVD risk. Risk indicators and baPWV are useful predictors of ASCVD, which in conjunction with conventional pharmacotherapy could be useful for primary prevention of plaque formation in subjects with cardiovascular comorbidities.

Artificial intelligence-enabled prediction model of student academic performance in online engineering education

Online education has been facing difficulty in predicting the academic performance of students due to the lack of usage of learning process, summative data and a precise prediction of quantitative relations between variables and achievements. To address these two obstacles, this study develops an artificial intelligence-enabled prediction model for student academic performance based on students’ learning process and summative data. The prediction criteria are first predefined to characterize and convert the learning data in an online engineering course. An evolutionary computation technique is then used to explore the best prediction model for the student academic performance. The model is validated using another online course that applies the same pedagogy and technology. Satisfactory agreements are obtained between the course outputs and model prediction results. The main findings indicate that the dominant variables in academic performance are the knowledge acquisition, the participation in class and the summative performance. The prerequisite knowledge tends not to play a key role in academic performance. Based on the results, pedagogical and analytical implications are provided. The proposed evolutionary computation-enabled prediction method is found to be a viable tool to evaluate the learning performance of students in online courses. Furthermore, the reported genetic programming model provides an acceptable prediction performance compared to other powerful artificial intelligence methods.

On the volatility of cryptocurrencies

We perform a large-scale analysis to evaluate the performance of traditional and Markov-switching GARCH models for the volatility of 292 cryptocurrencies. For each cryptocurrency, we estimate a total of 27 alternative GARCH specifications. We consider models that allow up to three different regimes. First, the models are compared in terms of goodness-of-fit using the Deviance Information Criterion and the Bayesian Predictive Information Criterion. Next, we evaluate the ability of the models in forecasting one-day ahead conditional volatility and Value-at-Risk. The results indicate that for a wide range of cryptocurrencies, time-varying models outperform traditional ones.

Fretting fatigue performance and life prediction of 1120 aluminum alloy wires of overhead conductors

Aluminum alloy (AA) 1120 wires have increasingly been used in overhead conductors due to their high tensile and creep resistance and electrical conductivity compared to other aluminum alloys. However, few studies have been conducted to characterize the fatigue strength of this material, specially under fretting conditions. In this work, fretting fatigue tests are performed on AA1120 wires of an AAAC 823 MCM conductor. A tension test, and fatigue tests on smooth and circumferentially V-notched wires were also carried out. The experimental results are compared to test data from AA1350 and AA6201 wires. Under tension, the AA1120 displayed an intermediate ultimate tensile strength between the ones from the AA1350 and AA6201. For the same stress amplitudes, the AA1120 wires used in the axial fatigue tests had longer lives than the AA1350 wires but considerably shorter lives than the AA6201 wires. However, under fretting fatigue, both AA1120 and AA6201 wires had similar fatigue strengths. A nonlocal life prediction criterion was evaluated using the fretting fatigue data. Most of the life estimates were within a factor of 3 of the observed lives when the input stress field was obtained from an elastic-plastic contact model.

Chronic clomipramine treatment reverses depressogenic-like effects of a chronic treatment with dexamethasone in rats.

Corticosteroids are widely used in medicine, for their anti-inflammatory and immunosuppressive actions, but can lead to troubling psychiatric side-effects. In fact, corticosteroids can induce many symptoms and syndromes, for example, mood disorders, anxiety and panic disorder, suicidal thinking and behavior. Furthermore, chronic stress and the administration of exogenous glucocorticoids are reported to induce affective changes in humans and rodents that relate to depressive state. Animal models are highly useful tools for studying the depression etiology. Face validity, construct validity, and predictive validity are the main criteria to evaluate animal depression models. The present study aimed to investigate the behavioral, cognitive, and biochemical effects of a chronic administration of DEX on Wistar rats. Wistar rats were administered daily with DEX (1.5 mg/kg, i.p., 21 days) or saline, the clomipramine treatment (2 mg/kg, i.p.) was realized just after the DEX injections for 21 days. DEX induced changes were evaluated by: forced swimming, novelty suppressed feeding, saccharin preference, open field, Morris water maze, and oxidative stress state in the brain. Results showed that chronic DEX administration conduct to a range of depression-related behavioral traits, including anhedonia, despair, weight loss, anxiety-like behavior, and cognitive impairments, which fill the face validity criterion. The DEX induced behavioral changes may result from the massive production of oxidative stress agents. This sustains the etiological hypothesis claiming that hyper-circulating glucocorticoid resulting from HPA dysfunction induces damage in certain neural structures related to depressive disorder, essentially the hippocampus. The antidepressant treatment has restored the behavioral state of rats which fills the predictive validity criterion.

Prospecting models of primary diamond deposits of the north of the East European Platform

As a result of a comprehensive study of the geological structure and diamond presense of the northern part of the East European Platform, generalization of the data accumulated by various organizations in the USSR, the Russian Federation, and other states, three main prospecting models of primary diamond deposits have been identified and characterized: Karelian, Finnish, and Arkhangelsk. Geological, structural, mineralogical, and petrographic criteria of local prediction, as well as the features of the response of kimberlite and lamproite bodies in dispersion haloes and geophysical fields, are considered using known examples, including data on the developed M.V.Lomonosov and V.P.Grib mines. It is shown that the most complicated prospecting environments occur in the covered areas of the Russian Plate, where, in some cases, the primary diamond-bearing rocks are similar in their petrophysical properties to the host formations. The buried dispersion haloes of kimberlite minerals in the continental Carboniferous and Quaternary deposits are traced at a short distance from the sources. Differences in the prospecting features of magnesian (Lomonosov mine) and ferromagnesian (Grib mine) kimberlites are also shown. Conclusions about the diamond potential of the model objects of various types are given in this paper.

Comparison of runaway criteria for prediction of different thermal behaviors in the acetic anhydride hydrolysis reaction performing in batch or semi-batch reactors

In this paper various runaway criteria for batch reactors (BRs) and semi-batch reactors (SBRs) have been used and compared to study the boundaries of different thermal behaviors for the acetic anhydride hydrolysis reaction with the solvent of acetic acid carried out in BRs and SBRs. It is shown that the majority of criteria for BRs could give relatively precise results, but the remaining minority are less accurate due to they all have some specific assumptions. Besides, the simulated results indicated that all the criteria for SBRs could accurately identify the transition point from TR to QFS behaviors in spite of that they give slight different results of transition point corresponding to NI and TR behaviors. The results can give an effective instruction for selection and usage of runaway criteria, especially in terms of effectiveness and applicability, to easily identify thermal behaviors in chemical industry.

Predicting Crude Oil Price in Nigeria with Machine Learning Models

Background: The rise in crude oil prices yields serious consequences for both oil-producing and non-oil-producing countries. The increase in global commodity prices contributes to the financial income and foreign exchange reserves of oil-exporting countries. However, countries such as Nigeria that sell crude oil and purchase refined fuel confront more complicated situations. To this end, there exists a need to obtain a robust prediction model for the crude oil price of Nigeria. Objective: This study is to determine the best model among the machine-learning time series models considered to predict crude oil prices in Nigeria. Methods: The alternative models were the auto-regressive integrated moving average model, Naive Bayes, Holtwinter trend model, exponential smoothing model, and neural network autoregressive (NNETAR) model. The prediction criteria adopted for model screening were the root mean square error (RMSE), mean absolute error (MAE) and mean absolute percentage error (MAPE). Daily crude oil prices in dollars obtained from the Central Bank of Nigeria were used for analysis spanning from October 1, 2009 to March 22, 2022 with 2836 data points. Results: The NNETAR model showed the minimum RMSE, MAE, and MAPE for cross-validation sets considered. Conclusion: The NNETAR model was recommended for the prediction of crude oil prices in Nigeria.