Miracle-dz/UniFAR-FTUs huggingface dataset:
Quantitative description of the microstructure of duplex stainless steels using selective etching

: The properties of duplex stainless steels (DSSs) depend on the ferrite–austenite ratio and on the contents of secondary phases. Therefore, it is necessary to control the volume fractions, morphologies, and distribution patterns of all phases. The phases in the samples were identified using thermodynamic modeling and scanning electron microscopy. Investigated specimens were obtained after different heat treatments, such as solution annealing and quenching from 1050 to 1250 °C to obtain different amounts of ferrite and annealing at 850 °C to precipitate the σ -phase. There-fore, a metallographic technique for assessing the phases in DSSs based on selective etching and subsequent analysis according to ASTM E 1245 was developed. It was shown that the developed method of quantitative analysis based on selective etching and metallographic assessment according to ASTM E 1245 allows obtaining much more accurate results compared to the proposed ASTM E 562 method, which correlates well with the XRD quantitative phase analysis.
Human resource management in the context of organic farming development

The article describes the main elements of the human resource management system in the context of the development of organic farming. The features of personnel processes in the conditions of transformation of traditional technology of cultivation of agricultural crops to the organic type are characterized. Attention is paid to the influence of external and internal factors on human resource management.
Estimation of shear‐wave velocities in unconventional shale reservoirs

Analysis of compressional and shear‐wave sonic logs in seven organic‐rich shale reservoirs exhibiting a wide range of velocities, total organic content, thermal maturity, fluid compressibility and mineral composition indicates that compressional‐wave velocity is a strong predictor of shear‐wave velocity as is the case for conventional reservoirs. Excluding data with high water saturations, a simple linear relationship is found between the compressional and shear‐wave velocities in shale reservoirs in accordance with laboratory measurements. The relationship can be further refined for prediction purposes by local calibration or with a linear correction for total organic content. Correcting for compositional variation and fluid effects requires more complex treatment. An empirical rock physics model that explicitly incorporates these effects results in high accuracy (0.2% average mean error) in shear‐wave velocity estimation for the entire data set. The model also exhibits good precision with mean absolute error of 2%. This is significantly better than what is achieved by rock physics models that do not explicitly consider fluid properties. These results are obtained without local calibration and without requiring correction for thermal maturity, degree of lithification or the shape or distribution of the solid organic matter. Explicit consideration of mineral composition, fluid substitution effects and amount of solid organic matter was necessary in this formulation to achieve this high average prediction accuracy with near zero bias as well as good precision across all formations and without local calibration. The successful use of Gassmann's equations in this algorithm suggests that errors in shear‐wave velocity prediction in shale reservoirs due to violation of the assumptions underlying these equations are either small or self‐compensating.
Linking microbial community structure to pollution: biolog-substrate utilization in and near a landfill leachate plume.

Previously, we observed that microbial community structure and functional diversity in aquifers might be enhanced by landfill leachate infiltration. To study this hypothesis, groundwater samples were taken near the Banisveld landfill, The Netherlands. Based on hydrochemical parameters, the samples clustered into two groups. One group corresponded to polluted samples from the plume of landfill leachate and the second group to clean samples from outside the plume. Most Probable Number-Biolog was used to select Eco Biolog plates with similar inoculum densities. Analysis of substrate utilization profiles of these plates revealed that anaerobic microbial communities in polluted samples clustered separately from those in clean samples. Especially substrates containing an aromatic nucleus were more utilized by microbial communities in the leachate plume. Both substrate richness and functional diversity were significantly enhanced in the plume of pollution. This study shows that community-level physiological profiling is a useful and simple tool to distinguish between anaerobic microbial communities in and near a landfill leachate plume.
Weight Gain and the Risk of Ovarian Cancer in BRCA1 and BRCA2 Mutation Carriers

Background: Weight gain and other anthropometric measures on the risk of ovarian cancer for women with BRCA mutations are not known. We conducted a prospective analysis of weight change since age 18, height, body mass index (BMI) at age 18, and current BMI and the risk of developing ovarian cancer among BRCA1 and BRCA2 mutation carriers. Methods: In this prospective cohort study, height, weight, and weight at age 18 were collected at study enrollment. Weight was updated biennially. Cox proportional hazards models were used to estimate the hazard ratio (HR) and 95% confidence intervals (CI) for ovarian cancer. Results: This study followed 4,340 women prospectively. There were 121 incident cases of ovarian cancer. Weight gain of more than 20 kg since age 18 was associated with a 2-fold increased risk of ovarian cancer, compared with women who maintained a stable weight (HR, 2.00; 95% CI, 1.13–3.54; P = 0.02). Current BMI of 26.5 kg/m2 or greater was associated with an increased risk of ovarian cancer in BRCA1 mutation carriers, compared with those with a BMI less than 20.8 kg/m2 (Q4 vs. Q1 HR, 2.13; 95% CI, 1.04–4.36; P = 0.04). There were no significant associations between height or BMI at age 18 and risk of ovarian cancer. Conclusions: Adult weight gain is a risk factor for ovarian cancer in women with a BRCA1 or BRCA2 mutation. Impact: These findings emphasize the importance of maintaining a healthy body weight throughout adulthood in women at high risk for ovarian cancer.
THE EFFECT OF WORK DISCIPLINE, MOTIVATION, AND COMPENSATION ON TEACHER PERFORMANCE SMK YUPPENTEK 1 TANGERANG

The research aims to analyze the effect of work Discipline, motivation, and compensation on teacher performance. The data analysis is quantitative with the purpose of answering the predetermined research hypotheses. The sampling technique that used in this research is nonprobability technique, with the research subjects totaling 45 permanent teachers who work in the organization of SMK Yuppentek 1 Tangerang. The results obtained, show that: (1) Work discipline partially has a positive and significant effect on the teacher performance, (2) Motivation partially has a positive and significant effect on the teacher performance, (3) Compensation has no significant effect on the performance of teachers at SMK Yuppentek 1 Tangerang.
Impact of operating conditions on thermal hydrolysis pre-treated digestion return liquor

Return liquor from thermal hydrolysis process (THP) can significantly add to the nitrogen load of a wastewater treatment plant (WWTP) and introduce UV quenching substances to the wastewater stream when recycled. While there are mature technologies in place to handle the inorganic nitrogen produced due to the thermal pretreatment, organic nitrogen remains a parameter of concern for utilities employing THP pretreatment. The impact of operating conditions of the THP on dissolved organic nitrogen (DON) and UV absorbance in return liquor was investigated. Operating conditions studied were (1) operating temperature (2) solids retention time (SRT) in the anaerobic digester (3) THP flash pressure (4) the effect of co-digestion of sewage sludge with food waste and, (5) polymer conditioning. Operating temperature and polymer dose had the most significant impact on DON and UV quenching. It was found that an increase in operating temperature resulted in an increase in DON, which was primarily contributed by the hydrophilic fraction. An increase in temperature also resulted in increased UV254 absorbance. However, this trend was not linear and the increase was more pronounced when the temperature was increased from 150°C to 170°C. Increasing flash pressure from 25 psi to 45 psi did not have a significant impact on the return liquor. However, increasing the flash pressure to 75 psi increased the DON and UV254 absorbing compounds. Co-digesting the sludge with food waste resulted in a slight increase in DON and a decrease in dissolved organic carbon (DOC) and UV quenching compounds. Increasing the SRT from 10 days to 15 days resulted in a slight decrease in DON but did
Effect of Co-administration Rotenone and Crocin on Biochemical and Histopathological Changes in Rat᾽s Kidney

Background: Rotenone is one of the most widely used pesticides in agriculture and may affect public health through the food chain. A possible mechanism of rotenone toxicity is oxidative stress. This study, the supportive effect of crocin (a substance derived from saffron) as an antioxidant on the renal toxicity of rotenone in an experimental animal model was investigated. Methods: First, male rats were divided into 4 groups (6 in each group) and received saline, crocin, rotenone and rotenone-crocin combination intraperitoneally for 10 days, respectively. Blood and kidney tissue samples were then isolated and biochemical factors including creatinine, nitrogen blood urea (BUN), sodium, potassium, Malondialdehyde and catalase were measured. The kidneys were examined histopathologically with a light microscope. Results: The results showed that rotenone caused damage to kidney tissue, which was observed in the form of vacuole degeneration, hyperemia and influx of inflammatory cells. Crocin has also been shown to reduce the severity of rotenone-induced kidney damage. Also results showed that biochemical changes were not statistically significant between the groups. Conclusions: the crocin has protective influence on histopathological changes in rat ̓s kidney following rotenone administration.
Fundamentals of Mass Transfer in Gas Carburizing

Gas carburizing is an important heat treatment process used for steel surface hardening of automotive and aerospace components. The quality of the carburized parts is determined by the hardness and the case depth required for a particular application. Despite its worldwide application, the current carburizing process performance faces some challenges in process control and variability. Case depth variability if often encountered in the carburized parts and may present problems with i) manufacturing quality rejections when tight tolerances are imposed or ii) insufficient mechanical properties and increased failure rate in service. The industrial approach to these problems often involves trial and error methods and empirical analysis, both of which are expensive, time consuming and, most importantly, rarely yield optimal solutions. The objective for this work was to develop a fundamental understanding of the mass transfer during gas carburizing process and to develop a strategy for the process control and optimization. The research methodology was based on both experimental work and theoretical developments, and included modeling the thermodynamics of the carburizing atmosphere with various enriching gasses, kinetics of mass transfer at the gas-steel interface and carbon diffusion in steel. The models accurately predict: 1) the atmosphere gas composition during the enriching stage of carburizing, 2) the kinetics of carbon transfer at the gas-steel surfaces, and 3) the carbon diffusion coefficient in steel for various process conditions and steel alloying. The above models and investigations were further combined to accurately predict the surface carbon concentration and the carbon concentration profile in the steel during the heat treatment process. Finally, these models were used to develop a methodology for the process optimization to minimize case depth variation, carburizing cycle time and total cycle cost. Application of this optimization technique provides a tradeoff between minimizing the case depth variation and total cycle cost and results in significant energy reduction by shortening cycle time and thereby enhancing carburizing furnace capacity.
Deep transfer learning based on magnetic resonance imaging can improve the diagnosis of lymph node metastasis in patients with rectal cancer.

Background Lymph node (LN) metastasis is the main prognostic factor for local recurrence and overall survival of patients with rectal cancer. The accurate evaluation of LN status in rectal cancer patients is associated with improved treatment and prognosis. This study aimed to apply deep transfer learning to classify LN status in patients with rectal cancer to improve N staging accuracy. Methods The study included 129 patients with 325 rectal cancer screenshots of LN T2-weighted (T2W) images from April 2018 to March 2019. Deep learning was applied through a pre-trained model, Inception-v3, for recognition and detection of LN status. The results were compared to manual identification by experienced radiologists. Two radiologists reviewed images and independently identified their status using various criteria with or without short axial (SA) diameter measurements. The accuracy, positive predictive value (PPV), negative predictive value (NPV), sensitivity, specificity, and the area under the receiver operating characteristic (ROC) curve (AUC) were calculated. Results When the same radiologist performed the analysis, the AUC was not significantly different in the presence or absence of LN diameter measurements (P>0.05). In the deep transfer learning method, the PPV, NPV, sensitivity, and specificity were 95.2%, 95.3%, 95.3%, and 95.2%, respectively, and the AUC and accuracy were 0.994 and 95.7%, respectively. These results were all higher than that achieved with manual diagnosis by the radiologists. Conclusions The internal details of LNs should be used as the main criteria for positive diagnosis when using MRI. Deep transfer learning can improve the MRI diagnosis of positive LN metastasis in patients with rectal cancer.
Experimental and Modelling Study of Catalytic Diesel Soot Oxidation

The objective of this Ph.D. thesis is the development of kinetic models for platinum-ceria-zirconia (Pt/CexZr1−xO2)-catalysed diesel particle filter (DPF) regeneration. Such models have a practical purpose in that they can be used to calculate source terms in 3D flow models, and they also have an intrinsic interest for the understanding of the mechanisms of catalysed soot oxidation. The core of this kinetic analysis consists in estimating Arrhenius parameters of a number of detailed reaction steps by fitting calculated curves to experimental ones. In doing so, the aim is not only to understand the roles of the different reactants, but also to investigate the efficacy and limitations of the modelling procedure for gas-solid heterogeneous reactions. Principal among the examined aspects is the feasibility of attributing physically meaningful values to individual parameters in large reaction mechanisms. The problem is approached by fitting one "subset"of reaction steps at a time. This procedure is illustrated by means of the cases of soot oxidation by NO, NO2 and/or O2, NO oxidation catalysed by ceria-based catalysts and soot oxidation catalysed by Pt/CexZr1−xO2. Interesting insights are gained into the mechanisms of these reactions, since the kinetics had often not been analysed thoroughly in the relevant literature. The fitting approach is shown to be effective as it allows for reproduction of several key species profiles in each of a number of experiments. Experiments used for these kinetic studies are temperature programmed tests in a fixed bed reactor, many of which were performed during the course of the study and data concerning others taken from the literature. In order to extract kinetic parameters from the temperature programmed data, a reactor model describing gas flow through a bed of particles and a mean field model of soot and catalyst surface chemistry are developed. The role of soot structure, ceria-zirconia composition/structure and soot/catalyst ratio on reaction are considered. Parameters used to deal with their effects are estimated.
Depth variation of the Conrad discontinuity in the Qaidam Basin, northwestern China, and its crustal dynamic implications

We use broadband records from a dense seismic network deployed in and around the Qaidam Basin in northwestern China to analyze the crustal phases and investigate the depth of the Conrad and Moho discontinuities as well as the P‐wave velocity. Waveform cross‐correlation is used to assist in the identification of the crustal phases and in determining their arrival times. Depth of the Conrad discontinuity is determined by fitting the travel times of Conrad‐diffracted P‐waves using a two‐layer model. The depth of the Conrad discontinuity under the eastern part of the basin is shallower than the western part, which can be attributed to different crustal shortening mechanisms. The upper crust shortening in the western part of the basin leads to thickening of the upper crust, while multiple thrust faults result in the rise of the Conrad discontinuity in the east. These two different mechanisms determine the depth change of the Conrad discontinuity in the basin from the west to the east, which is supported by the results in this study.
Cytogenetic evaluation of α, β-amirina obtained in resin of Protium heptaphylum (Aubl.) Marchand in polychromatics erythrocytes of mice swiss.

The Amazon Rainforest has a great variety of medicinal plants, among them we can highlight the “Almecegueira” or “Breu Branco” (Protium heptaphylum) in Portuguese, the producer of a greenish-white resin that hardens when it touches the air, known by its gastroprotective and anti-inflammatory effects. These effects are attributed to a triterpene mixture of α and β amirine, predominant in the resin. The purpose of the study is to obtain a cytogenetic profile to the α, β-amirine mixture obtained in the resin of P. heptaphylum. For this, the micronucleus test was used in peripheral blood and bone marrow; administering solution in Swiss mice with the dosages of 1mg/Kg, 3mg/Kg, and 10mg/Kg, diluted in 5% DMSO, the effects were observed in 24h and 48h after the treatment. For the test in peripheral blood the mice’s caudal vein was punctured, while for the bone marrow test, the femurs of the animals were obtained from which bone marrow samples were taken. It was found that in peripheral blood, the administration of the compounds did not cause genotoxicity in 24h and 48h, in contrast, antigenotoxicity was, for concentrations 1; 3 and 10mg/kg, respectively 10%; 12%; 67% in 24h and 9%; 15%; 73% in 48h. In the bone marrow, no genotoxicity was observed, as for antigenotoxicity was observed that for concentrations 1; 3 e 10mg/kg the percentage of reduction was respectively: 11%, 15%, and 30% in 24h and 13% 16% 33% in 48h. It is concluded that the studied compound can be an alternative for treatments in the future since it presents low toxicity and high antigenotoxic potential.
Basic Statistical Estimation Outperforms Machine Learning in Monthly Prediction of Seasonal Climatic Parameters

Machine learning (ML) has been utilized to predict climatic parameters, and many successes have been reported in the literature. In this paper, we scrutinize the effectiveness of five widely used ML algorithms in the monthly prediction of seasonal climatic parameters using monthly image data. Specifically, we quantify the predictive performance of these algorithms applied to five climatic parameters using various combinations of features. We compare the predictive accuracy of the resulting trained ML models to that of basic statistical estimators that are computed directly from the training data. Our results show that ML never significantly outperforms the statistical baseline, and underperforms for most feature sets. Unlike previous similar studies, we provide error bars for the relative performance of different predictors based on jackknife estimates applied to differences in predictive error magnitudes. We also show that the practice of shuffling data sequences which was employed in some previous references leads to data leakage, resulting in over-estimated performance. Ultimately, the paper demonstrates the importance of using well-grounded statistical techniques when producing and analyzing the results of ML predictive models.
What is Known about the Intervention with Gender Abusers? A Meta-analysis on Intervention Effectiveness

In this research, a meta-analytic study is proposed with the aim of determining the state of the art of the efficacy of treatment programs for gender aggressors across different variables. Doing so is relevant in view of the inconsistency in the results of previous meta-analyses and the proliferation of new intervention programs for gender aggressors. The results of general meta-analysis show a positive, though not significant, effect of the intervention. The analysis of moderating variables shows a positive, significant, and close-to-moderate effect size for interventions lasting fewer than 16 weeks/sessions ( δ w = .39) and follow-up periods greater than or equal to 12 months ( δ w = .38), and a positive, significant and moderate size for interventions using cognitive-behavioral therapy ( δ w = .57). However, along the same lines, as previous meta-analyses, no definite conclusions can be drawn about the effectiveness of this type of program.
ECG Denoising Using Artificial Neural Networks and Complete Ensemble Empirical Mode Decomposition

Electrocardiogram (ECG) is a documentation of the electrical activities of the heart. It is used to identify a number of cardiac faults such as arrhythmias, AF etc. Quite often the ECG gets corrupted by various kinds of artifacts, thus in order to gain correct information from them, they must first be denoised. This paper presents a novel approach for the filtering of low frequency artifacts of ECG signals by using Complete Ensemble Empirical Mode Decomposition (CEED) and Neural Networks, which removes most of the constituent noise while assuring no loss of information in terms of the morphology of the ECG signal. The contribution of the method lies in the fact that it combines the advantages of both EEMD and ANN. The use of CEEMD ensures that the Neural Network does not get over fitted. It also significantly helps in building better predictors at individual frequency levels. The proposed method is compared with other state-of-the-art methods in terms of Mean Square Error (MSE), Signal to Noise Ratio (SNR) and Correlation Coefficient. The results show that the proposed method has better performance as compared to other state-of-the-art methods for low frequency artifacts removal from EEG.