Tolerance Classes Research Articles

Perceived parental prejudice and a tolerant class context in ethnic bullying: The buffering role of teachers.

Despite recent efforts to understand the possible impact of contextual factors on adolescents' involvement in ethnic bullying, most existing studies have focused on the effects of one context at a time. As adolescents are simultaneously exposed to the influence of multiple socialization agents, the aim of this study was to investigate whether teachers' and classmates' tolerance towards ethnic minorities could buffer the effect of perceived parental prejudice on adolescents' involvement in ethnic bullying. Data were collected between January and February 2020 from 9th grade adolescents (N = 582; Mage = 15.23; SD = 0.65; 50.9% female; 30.7% with an immigrant background), and their teachers (N = 72; aged between 27 and 65 years; 79% female), belonging to 37 classrooms located in Italy. A cross-sectional multilevel analysis showed that teachers' tolerance moderated the effect of perceived parental prejudice on adolescents' involvement in ethnic bullying. Specifically, we found that in classrooms with low levels of teachers' tolerance, perceived parental prejudice was significantly associated with students' involvement in ethnic bullying. Conversely, in classrooms with high levels of teachers' tolerance, parental prejudice was no longer associated with ethnic bullying. Furthermore, classmates' tolerance was not significantly associated with students' involvement in ethnic bullying and did not moderate the association between perceived parental prejudice and adolescents' engagement in ethnic bullying. Findings are discussed highlighting the important role of school as a context to promote positive multicultural relations and the unique role played by teachers in affecting adolescents' behaviors.

Assessing Heat Stress Tolerance of Wheat Genotypes through Integrated Molecular and Physio-Biochemical Analyses

Heat as an abiotic stress significantly impairs the sustainable productivity of wheat (Triticum aestivum L.). To determine the tolerance of genotypes to heat stress, a comprehensive approach should be used that integrates simultaneous phenotyping and genotyping analyses. The aim of this study is to identify local heat-tolerant genotypes using simple sequence repeat (SSR) markers and evaluate the selected genotypes under field conditions for their tolerance to heat stress. Of the 12 SSR markers that showed polymorphism, eight were associated with six important traits. The use of hierarchical cluster analysis (HC) based on SSR markers led to the identification of 13 genotypes that showed varying results and were grouped into three distinct heat tolerance classes: tolerant (T), moderately tolerant (MT), and sensitive (S). The results showed that heat stress had a significant effect on 19 traits under this study, with significant variation in tolerance to heat stress between genotypes. The tolerant genotypes exhibited a range of average thousand-kernel weight (TKW) values between 40.56 and 44.85, while the sensitive genotype (Yecora Rojo) had an average TKW of 35.45. Furthermore, the tolerant genotypes showed two to three times higher levels of antioxidants compared to the sensitive genotypes when exposed to heat stress. Among the traits analyzed, six showed a favorable combination of high heritability (>60%) and genetic gain (>20%). Through the integration of principal component analysis and stepwise multiple linear regression, it was determined that six traits (grain yield, 1000-kernel weight, plant height, intercellular carbon dioxide, flag leaf area, and grain filling duration) revealed differences between the 13 genotypes. HC analysis of the six traits resulted in the same division of genotypes into three main categories as observed in an HC analysis based on SSR markers. It is worth noting that Saudi wheat, including KSU106, KSU105, and KSU115 as local genotypes, in addition to the 16HTWYT-22 genotype, showed higher heat tolerance compared to the other genotypes tested, indicating its potential suitability for agriculture in Saudi Arabia. These results contribute to breeding programs focused on developing heat-tolerant wheat varieties and accelerate progress in wheat productivity improvement programs.

A Novel generalization of sequential decision-theoretic rough set model and its application

This paper introduces a refined and broadened version of decision-theoretic rough sets (DTRSs) named Generalized Sequential Decision-Theoretic Rough Set (GSeq-DTRS), which integrates the three-way decision (3WD) methodology. Operating through multiple levels, this iterative approach aims to comprehensively explore the boundary region. It introduces the concept of generalized granulation, departing from conventional equivalence-relation-based structures to incorporate similarity/tolerance relations. GSeq-DTRS addresses the limitations encountered by its predecessor, Seq-DTRS, particularly in managing sequential procedures and integrating new attributes. A notable advancement is its seamless handling of continuous-scale datasets through a defined Generalized Granular Structure (GGS), enabling classification across multiple levels without necessitating reduction of attributes. A refined version of conditional probability (CP), aligned with tolerance classes, enhances the approach, supported by a meticulously designed algorithm. Extensive experimental analysis conducted on two datasets sourced from https://www.kaggle.com demonstrates the efficacy of the procedure for both continuous and discrete datasets, effectively classifying probable elements into POS or NEG regions based on their membership. Unlike traditional Seq-DTRS, it does not require reduction of attributes at each new level. Additionally, the algorithm exhibits lower sensitivity to parametric values and yields results in fewer iterations. Thus, its potential impact on decision-making processes is readily apparent.

A novel decision support system for proactive risk management in healthcare based on fuzzy inference, neural network and support vector machine

BackgroundThe nature of activities practiced in healthcare organizations makes risk management the most crucial issue for decision-makers, especially in developing countries. New technologies provide effective solutions to support engineers in managing risks. PurposeThis study aims to develop a Decision Support System (DSS) adapted to the healthcare constraints of developing countries that enables the provision of decisions about risk tolerance classes and prioritizations of risk treatment. MethodsFailure Modes and Effects Analysis (FMEA) is a popular method for risk assessment and quality improvement. Fuzzy logic theory is combined with this method to provide a robust tool for risk evaluation. The fuzzy FMEA provides fuzzy Risk Priority Number (RPN) values. The artificial neural network is a powerful algorithm used in this study to classify identified risk tolerances. The risk treatment process is taken into consideration in this study by improving FMEA. A new factor is added to evaluate the feasibility of correcting the intolerable risks, named the control factor, to prioritize these risks and start with the easiest. The new factor is combined with the fuzzy RPN to obtain intolerable risk prioritization. This prioritization is classified using the support vector machine. FindingsResults prove that our DSS is effective according to these reasons: (1) The fuzzy-FMEA surmounts classical FMEA drawbacks. (2) The accuracy of the risk tolerance classification is higher than 98%. (3) The second fuzzy inference system developed (the control factor for intolerable risks with the fuzzy RPN) is useful because of the imprecise situation. (4) The accuracy of the fuzzy-priority results is 74% (mean of testing and training data). ConclusionsDespite the advantages, our DSS also has limitations: There is a need to generalize this support to other healthcare departments rather than one case study (the sterilization unit) in order to confirm its applicability and efficiency in developing countries.

A probabilistic identification of the buckling resistance of imperfect cylindrical shells in axial compression

The buckling resistance of imperfect circular cylindrical shells of elastic and isotropic material subject to axial compression is studied focusing on initial geometric imperfections. Considering the imperfections as a random field, the suggested probabilistic representation of the buckling resistance parallels the deterministic dependence of buckling loads on the size and shape of imperfections. It derives from the probability density function of an imperfection size measure and the conditional probability density functions of the resistance confined to the realizations of imperfections given their size levels. The separated representation of the buckling resistance allows: (1) supplementary checking the accuracy of the random field representation, (2) classifying measured and simulated imperfections by their size and shape effects on buckling loads, (3) prospective delimiting the range of working imperfections for fabrication tolerance classes, and (4) defining the characteristic imperfection for the semi-probabilistic design. Introductory calculations illustrate the dependence of the normalized buckling loads of a shell on the shape and size of imperfections.

Comparative analysis of the ISO Tolerance class of 3D-printed spur cylindrical gears produced with Material Extrusion (MEX) and Powder Bed fusion (PBF) techniques

Comparative analysis of the ISO Tolerance class of 3D-printed spur cylindrical gears produced with Material Extrusion (MEX) and Powder Bed fusion (PBF) techniques

Comparative analysis of the ISO tolerance class of 3D-printed spur cylindrical gears produced with material extrusion and powder bed fusion techniques

Comparative analysis of the ISO tolerance class of 3D-printed spur cylindrical gears produced with material extrusion and powder bed fusion techniques

Incremental feature selection for dynamic incomplete data using sub-tolerance relations

Tolerance Rough Set (TRS) theory is commonly employed for feature selection with incomplete data. However, TRS has limitations such as ignoring uncertainty, which often leads to the inclusion of redundant features and diminished classification accuracy. To address these limitations, we propose an extension called Subrelation Tolerance Class (STC). STC decomposes the tolerance relation into two subrelations, enabling a two-stage certainty measurement. This approach progressively filters out certain regions, thereby reducing computational space requirements, and introduces a new significance measure that considers both certain and uncertain information. Leveraging STC and our proposed measure, we develop an incremental feature selection algorithm capable of handling incomplete streaming data. We conduct experiments on real-world datasets and compare the performance with existing algorithms to validate the superiority of our method. The experimental results show that our algorithm reduces the execution time by over 89.78% compared to the baselines while maintaining the classification accuracy.

Satu Saudara: Pelaksanaan Pendidikan Multikultural di TK X Kabupaten Jember

Indonesia is a country rich in diversity, encompassing various ethnicities, religions, races, and languages, which necessitates its citizens to cultivate a spirit of tolerance towards one another. One effective means of fostering an understanding of diversity is through multicultural education. Multicultural education is especially important when applied to children from a young age, as it equips them to approach differences with acceptance and a sense of tolerance in the future. The aim of this research is to describe the implementation of multicultural education in one of the preschools in Jember Regency, where students exhibit different religious characteristics. The research methodology employed in this study is qualitative, involving data collection through methods such as observation, interviews, and documentation. The findings of this research indicate that, in response to the religious diversity present within the school, the institution packages multicultural education in the form of a flagship program called "One Brotherhood," held annually. In its implementation, teachers divide students according to their respective religions, which include Christians, Catholics, Muslims, Buddhists, and Hindus. Furthermore, the "One Brotherhood" program includes a tolerance class that serves as a bridge to reinforce religious tolerance among young children.

Outlier detection for incomplete real-valued data via rough set theory and granular computing

Outlier detection is an important topic in data mining. An information system (IS) is a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. People often encounter missing values during data processing. A RVIS with the miss values is an incomplete real-valued information system (IRVIS). Due to the presence of the missing values, the distance between two information values is difficult to determine, so the existing outlier detection rarely considered an IS with the miss values. This paper investigates outlier detection for an IRVIS via rough set theory and granular computing. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relation on the object set is defined according to the distance, and the tolerance class is obtained, which is regarded as an information granule. After then, λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the outlier factor of every object in an IRVIS is presented. Finally, outlier detection method for IRVIS via rough set theory and granular computing is proposed, and the corresponding algorithms is designed. Through the experiments, the proposed method is compared with other methods. The experimental results show that the designed algorithm is more effective than some existing algorithms in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the proposed method.

Spatial error modeling and accuracy distribution of line laser gear measuring center

The Line laser gear measuring center (LLGMC) is an innovative gear measurement equipment that offers high efficiency but low accuracy. One crucial factor that influences its measurement accuracy is the presence of geometric errors. In this study, we conducted a thorough analysis of these geometric errors and proposed a method for modeling spatial errors. Instead of directly considering the geometric errors, we replaced them with the installation errors of the gear and line laser probe. This approach simplifies and improves the error transmission relationship. Subsequently, the installation errors are converted into a unified representation of the height error of the incident light from the line laser. A spatial error model that considers nine installation errors is then further established. By numerically calculating the sensitivity of different error sources, we effectively identified the errors that have a significant impact on the accuracy of LLGMC. Moreover, accuracy distribution is carried out to ensure that LLGMC can meet the measurement accuracy requirements for gears with a tolerance class of 6. This article provides a theoretical foundation for the structural design and accuracy assurance of LLGMC during the research and development phase.

Project Assessment of the Reliability of the Joint of a Circulation-Loaded Ring of a Rolling Bearing with a Shaft of Tolerance Class js6

Project Assessment of the Reliability of the Joint of a Circulation-Loaded Ring of a Rolling Bearing with a Shaft of Tolerance Class js6

Distinguishing the Effects of Stress Intensity and Stress Duration in Plant Responses to Salinity.

Species-specific variation in response to stress is a key driver of ecological patterns. As climate change alters stress regimes, coastal plants are experiencing intensifying salinity stress due to sea-level rise and more intense storms. This study investigates the variation in species' responses to presses and pulses of salinity stress in five glycophytic and five halophytic species to determine whether salinity intensity, duration, or their interaction best explain patterns of survival and performance. In salinity stress exposure experiments, we manipulated the intensity and duration of salinity exposure to challenge species' expected salinity tolerances. Salinity intensity best explained patterns of survival in glycophytic species, while the interaction between intensity and duration was a better predictor of survival in halophytic species. The interaction between intensity and duration also best explained biomass and chlorophyll production for all tested species. There was interspecific variability in the magnitude of the interactive effect of salinity intensity and duration, with some glycophytic species (Persicaria maculosa, Sorghum bicolor, and Glycine max) having a more pronounced, negative biomass response. For the majority of species, prolonged stress duration exacerbated the negative effect of salinity intensity on biomass. We also observed an unexpected, compensatory response in chlorophyll production in two species, Phragmites australis and Kosteletzkya virginica, for which the effect of salinity intensity on chlorophyll became more positive with increasing duration. We found the regression coefficient of salinity intensity versus biomass at the highest stress duration, i.e., as a press stressor, to be a useful indicator of salinity tolerance, for which species' salinity-tolerance levels matched those in the literature. In conclusion, by measuring species-specific responses to stress exposure, we were able to visualize the independent and interactive effects of two components of a salinity stress regime, intensity, and duration, to reveal how species' responses vary in magnitude and by tolerance class.

Project Assessment of the Reliability of the Joint of a Circulation-Loaded Ring of a Rolling Bearing with a Shaft of Tolerance Class js6

Study and assessment of the reliability of a joint of a circulation-loaded ring of a rolling bearing 50217 and a flange of the Yaroslavl Engine Plant gearbox demultiplier have been carried out. It has been found that, upon adding the dispersion of the sizes of the analyzed holes and shafts, a dispersion of gaps and interferences is formed, in which the probability of occurrence of interference fits (93%) prevails. After pressing, due to the collapsing of the roughness of the hole and the shaft, the probability of interference with a gap increases (51.7%). In addition, upon operating under the action of a load, the possibility arises that joints with an actual interference less than the smallest functional one (8.1 µm) can turn. It has been proven that, when shaft tolerance class js6 is assigned, interference with a gap will prevail in the joint, which will lead to rotation of the circulation-loaded bearing ring in more than 80% of the joints, significantly reducing the reliability of the demultiplier bearing assembly and the gearbox of the Yaroslavl Engine Plant engine as a whole.

Dynamic Scheduling in a Partially Fluid, Partially Lossy Queueing System

We consider a single server queueing system with two classes of jobs: eager jobs with small sizes that require service to begin almost immediately upon arrival, and tolerant jobs with larger sizes that can wait for service. While blocking probability is the relevant performance metric for the eager class, the tolerant class seeks to minimize its mean sojourn time. In this article, we analyse the performance of each class under dynamic scheduling policies, where the scheduling of both classes depends on the instantaneous state of the system. This analysis is carried out under a certain fluid limit, where the arrival rate and service rate of the eager class are scaled to infinity, holding the offered load constant. Our performance characterizations reveal a (dynamic) pseudo-conservation law that ties the performance of both classes to the standalone blocking probabilities associated with the scheduling policies for the eager class. Furthermore, the performance is robust to other specifics of the scheduling policies. We also characterize the Pareto frontier of the achievable region of performance vectors under the same fluid limit, and identify a (two-parameter) class of Pareto-complete scheduling policies.

Designing face resemblance technique using near set theory under varying facial features.

Near sets (also called Descriptively Near Sets) classify nonempty sets of objects based on object feature values. The Near Set Theory provides a framework for measuring the similarity of objects based on features that describe them in much the same way humans perceive the similarity of objects. This paper presents a novel approach for face recognition using Near Set Theory that takes into account variations in facial features due to varying facial expressions, and facial plastic surgery. In the proposed work, we demonstrate two-fold usage of Near set theory; firstly, Near Set Theory as a feature selector to select the plastic surgery facial features with the help of tolerance classes, and secondly, Near Set Theory as a recognizer that uses selected prominent intrinsic facial features which are automatically extracted through the deep learning model. Extensive experimentation was performed on various facial datasets such as YALE, PSD, and ASPS. Experimentation demonstrates 93% of accuracy on the YALE face dataset, 98% of accuracy on the PSD dataset, and 98% of accuracy on the ASPS dataset. A detailed comparative analysis of the proposed work of facial resemblance with other state-of-the-art algorithms is presented in this paper. The experimentation results effectively classify face resemblance using Near Set Theory, which has outperformed several state-of-the-art classification approaches.

Design of optical transmission line of ITER poloidal polarimeter

ITER Poloidal Polarimeter is laser-aided diagnostics that measures the change of polarization of injected far-infrared laser beam in order to identify plasma current profile. PoPola consists of thirteen measurement chords, and each chord is comprised of a set of optical components. This study shows the design of optics in the ITER vacuum vessel and those in high radiation area outside the vacuum vessel. The design takes into account zero-offset configuration, the minimization of the neutron streaming, remote alignment, accessibility, manufacturability and radiation hardness. The laser power loss due to vignetting loss and absorption is evaluated, and the signal-to-noise ratio at a detector is estimated at 50,000. This study evaluates the acceptable errors of manufacturing and assembly of the optical transmission line on the condition that the demonstrated alignment method using the two steering mirrors outside the vacuum vessel is applied. The evaluation results show that the medium tolerance class of ISO 2768 is basically enough for manufacturing ITER poloidal polarimeter. Only for assembling the in-vessel mirrors to the mirror module, the mirror angles need to be carefully adjusted with accuracy of 0.1° Finally, radiation hardness of a steering mirror is presented. The steering mirror consists of an angle sensor and a piezo actuator. The angle sensors are developed by the authors and free from semiconductor and soft resin. The prototype test results show that angle sensor can measure the mirror angle in the range of ±2.3° with accuracy of 0.37%. As for the piezo actuator, the irradiation test was carried out. The test results show that the piezo actuator is able to operate after the neutron fluence of 3.5 × 1015 n/cm2 and the gamma dose of 0.99 MGy.

Drought tolerance classification of grapevine rootstock by machine learning for the São Francisco Valley

Machine Learning (ML) algorithms are increasingly being used in several areas of agricultural studies, such as plant breeding. ML can assist in the recognition of relevant patterns or groups, or even in the prediction of the outcome under new settings, thus accelerating experiments and interpretating their results. The identification and selection of drought-tolerant grapevine rootstock (Vitis spp.) have become more relevant in late years, motivated mostly by global climate change scenarios. However, the grapevine is a perennial species, with polygenic characteristics and a complex traits inheritance by offspring, thus making it very challenging to discover new, drought tolerant cultivars. For this reason, this study's main objective was to compare the performance of six machine learning models on the prediction of drought tolerance levels of grapevine rootstock cultivars. A data set with forty-five distinct cultivars was used to evaluate the methods, and the best performing model (AUC 0.9857) was used to predict the drought tolerance class of three cultivars (IAC 313, IAC 572, and IAC 766) whose drought tolerance level was still unknown. The results predicted a high drought tolerance for IAC 313 and IAC 766 cultivars, and a low tolerance for IAC 572.

Outlier detection for incomplete real-valued data based on inner boundary

Outlier detection is a process to find out the objects that have the abnormal behavior. It can be applied in many aspects, such as public security, finance and medical care. An information system (IS) as a database that shows relationships between objects and attributes. A real-valued information system (RVIS) is an IS whose information values are real numbers. A RVIS with missing values is an incomplete real-valued information system (IRVIS). The notion of inner boundary comes from the boundary region in rough set theory (RST). This paper conducts experiments directly in an IRVIS and investigates outlier detection in an IRVIS based on inner boundary. Firstly, the distance between two information values on each attribute of an IRVIS is introduced, and the parameter λ to control the distance is given. Then, the tolerance relations on the object set are defined according to the distance, by the way, the tolerance classes, the λ-lower and λ-upper approximations in an IRVIS are put forward. Next, the inner boundary under each conditional attribute in an IRVIS is presented. The more inner boundaries an object belongs to, the more likely it is to be an outlier. Finally, an outlier detection method in an IRVIS based on inner boundary is proposed, and the corresponding algorithm (DE) is designed, where DE means degree of exceptionality. Through the experiments base on UCI Machine Learning Repository data sets, the DE algorithm is compared with other five algorithms. Experimental results show that DE algorithm has the better outlier detection effect in an IRVIS. It is worth mentioning that for comprehensive comparison, ROC curve and AUC value are used to illustrate the advantages of the DE algorithm.

Filtering effects on involute gear form measurement

Traditional gear measurement results are used to classify a gear tolerance grade in accordance with ISO 1328-1:2013 but as more stringent requirements are placed on gear performance it is important to also characterise performance related features properly on the gear flank surface. However, the measurement uncertainty of the measurement machines is not improving at the same rate as the tolerance requirements, and it is not uncommon to have less than 10 μm tolerances to be measured with a 1–3 μm measurement uncertainty capability. This breaks the ‘rule of thumb’ that the uncertainty should be smaller than one tenth of the value you are trying to measure. It is therefore important to review the measurement and evaluation process to identify if there are any areas for improvement within our current methods. Additionally, the measurements are being used to refine gear tooth contact analysis performance models and the development of optical and high speed tactile scanning measurement methods is allowing the full 3D gear flank surface measurement a feasible option on the shop floor. Greater understanding of the involute flank beyond the standard evaluation limits is thus required. These additional requirements have prompted this review of the effect that the Gaussian filters specified in ISO 1328-1:2013 have and that their characteristics are fully understood by those who must perform and interpret the measurement results. This paper examines the effect that these filters have on measurements where micro geometry corrections, such as tip relief, or tooth tip and end face chamfers are present. Methods of minimising these effects are reviewed with specific reference to strategies defined in ISO 16610-28:2016. Recommendations are offered to minimise the effects when evaluating the gear tolerance class and quantifying gear flank micro geometry.