Minority Ones Research Articles

Luminescence and Faraday rotation properties of Tb2O3 and Tb:Y2O3 single crystals

Heavily-doped and fully concentrated 2.78 % Tb:Y2O3 and Tb2O3 single crystals with high optical quality and very low levels of impurities have been grown and studied for their luminescence and Faraday rotation properties. Absorption, emission and fluorescence decay measurements performed vs excitation wavelength and temperature and their confrontation with Judd-Ofelt and crystal-field calculations show the contributions of two types of luminescent centers: dominant ones with a 5D4 emission lifetime of 23 μs corresponding to coupled near-neighbor Tb3+ ions, all in C2 symmetry sites, and minority ones with a 5D4 emission lifetime of about 2 ms corresponding to coupled Tb3+ ions in C2 and C3i near-neighbor symmetry sites. Faraday rotation measurements confirm Tb2O3 as the Tb-based Faraday crystalline material with the largest ever measured Verdet constant, at all temperatures and from the visible to the near-infrared. They also show that the dominant luminescent centers contribute more particularly to this large Verdet constant thanks to a favorable crystal-field splitting of their 7F6 ground multiplet and also to the contributions of both types of spin-allowed and spin-forbidden 4f-5d absorption bands.

Engine option in aircraft purchase: Company strategies and policy implications

Airlines select an aircraft's engine simultaneously while purchasing the aircraft. Usually, an aircraft model has multiple engine options, but recently the number of engine options has been decreasing. We develop a three-echelon supply chain model including engine manufacturers, aircraft manufacturers, and airlines to analyze the engine options in airlines' new aircraft purchases. We show that given an airline's existing fleet composition, larger fleet size and higher aircraft production efficiency would make it more likely to choose multiple engine options. In contrast, higher production costs and engine substitutability would lead to a shift to a single engine or hinder the airline's fleet expansion. In the model, we highlight the impact of MRO (Maintenance, Repair, Overhaul) costs with economies of scale on airline engine option decisions as well. In particular, our theoretical findings suggest that the airline would buy more aircraft that are the majority types in their existing fleets, thus crowding out the minority ones. Our findings provide a reasonable explanation for the homogeneity of engine options and provide policy implications regarding airline expansion and aviation supply chain development.

KNN-SVM Classifiers in Complex Diagnosis

In many applications, classification plays an indispensable role due to its powerful detection and diagnosis function. Especially in real data on disease, the detection of important factors and the diagnosis of the result usually bring huge contributions to patients. Simultaneously, complex problems in real data such as imbalanced data and missing data also lead to more challenges and difficulties. The ignorance of missing data will undermine study efficiency, and sometimes introduce substantial bias. Imbalanced data tends to be overwhelmed by the majority classes and ignoring the minority ones. The paper develops new support vector machine classifiers using k-nearest neighbors’ information (KNN-SVM), to impute missing data by calculating k-nearest neighbors’ statistical characteristic values and to interpolate some new samples between k-nearest minority class examples. As comparisons, the paper uses different kernel functions in KNN-SVM classifiers to show the different performances in disease diagnosis accuracy.

Deep Insight on Land Use/Land Cover Geospatial Assessment through Internet-Based Validation Tool in Upper Karkheh River Basin (KRB), South-West Iran

Recently, the demand for high-quality land use/land cover (LULC) information for near-real-time crop type mapping, in particular for multi-relief landscapes, has increased. While the LULC classes are inherently imbalanced, the statistics generally overestimate the majority classes and underestimate the minority ones. Therefore, the aim of this study was to assess the classes of the 10 m European Satellite Agency (ESA) WorldCover 2020 land use/land cover product with the support of the Google Earth Engine (GEE) in the Honam sub-basin, south-west Iran, using the LACOVAL (validation tool for regional-scale land cover and land cover change) online platform. The effect of imbalanced ground truth has also been explored. Four sampling schemes were employed on a total of 720 collected ground truth points over approximately 14,100 ha. The grassland and cropland totally canopied 94% of the study area, while barren land, shrubland, trees and built-up covered the rest. The results of the validation accuracy showed that the equalized sampling scheme was more realistically successful than the others in terms of roughly the same overall accuracy (91.6%), mean user’s accuracy (91.6%), mean producers’ accuracy (91.9%), mean partial portmanteau (91.9%) and kappa (0.9). The product was statistically improved to 93.5% ± 0.04 by the assembling approach and segmented with the help of supplementary datasets and visual interpretation. The findings confirmed that, in mapping LULC, data of classes should be balanced before accuracy assessment. It is concluded that the product is a reliable dataset for environmental modeling at the regional scale but needs some modifications for barren land and grassland classes in mountainous semi-arid regions of the globe.

Skew Class-Balanced Re-Weighting for Unbiased Scene Graph Generation

An unbiased scene graph generation (SGG) algorithm referred to as Skew Class-Balanced Re-Weighting (SCR) is proposed for considering the unbiased predicate prediction caused by the long-tailed distribution. The prior works focus mainly on alleviating the deteriorating performances of the minority predicate predictions, showing drastic dropping recall scores, i.e., losing the majority predicate performances. It has not yet correctly analyzed the trade-off between majority and minority predicate performances in the limited SGG datasets. In this paper, to alleviate the issue, the Skew Class-Balanced Re-Weighting (SCR) loss function is considered for the unbiased SGG models. Leveraged by the skewness of biased predicate predictions, the SCR estimates the target predicate weight coefficient and then re-weights more to the biased predicates for better trading-off between the majority predicates and the minority ones. Extensive experiments conducted on the standard Visual Genome dataset and Open Image V4 and V6 show the performances and generality of the SCR with the traditional SGG models.

О некоторых проблемах применен ия принципа справедливости во внутренней деятельности хозяйственных обществ

Achieving farness in the internal activities of business corporation requires a combination of both organizational and contractual mechanisms, which is due to the dualistic (statutory and contractual) nature of the business corporation. Effectively applied for asset allocation the principle of proportionality cannot fully guarantee fairness in the implementation of management activities, since consolidation of corporate power is required for managerial decision-making. The exercise of power functions by majority shareholders may lead to infringement of the investment interests of minority ones. The solution to the problem is the redemption of the share by the business corporation at the request of the stockholder upon the occurrence (non-occurrence) of certain circumstances, which can be considered as the redemption of an unspecified rent in advance. Despite the existence of special rules in corporate legislation governing the repurchase of shares at the request of a shareholder in order to achieve fairness it is advisable to fix the general rule on the right of a shareholder of a business corporation, taking into account the conditions provided for by law and the charter, to demand the repurchase by a business corporation of his shares in case of violation by the corporation of his reasonable investment expectations.

Predicting Soil Textural Classes Using Random Forest Models: Learning from Imbalanced Dataset

Soil provides a key interface between the atmosphere and the lithosphere and plays an important role in food production, ecosystem services, and biodiversity. Recently, demands for applying machine learning (ML) methods to improve the knowledge and understanding of soil behavior have increased. While real-world datasets are inherently imbalanced, ML models overestimate the majority classes and underestimate the minority ones. The aim of this study was to investigate the effects of imbalance in training data on the performance of a random forest model (RF). The original dataset (imbalanced) included 6100 soil texture data from the surface layer of agricultural fields in northern Iran. A synthetic resampling approach using the synthetic minority oversampling technique (SMOTE) was employed to make a balanced dataset from the original data. Bioclimatic and remotely sensed data, distance, and terrain attributes were used as environmental covariates to model and map soil textural classes. Results showed that based on mean minimal depth (MMD), when imbalanced data was used, distance and annual mean precipitation were important, but when balanced data were employed, terrain attributes and remotely sensed data played a key role in predicting soil texture. Balanced data also improved the accuracies from 44% to 59% and 0.30 to 0.52 with regard to the overall accuracy and kappa values, respectively. Similar increasing trends were observed for the recall and F-scores. It is concluded that, in modeling soil texture classes using RF models through a digital soil mapping approach, data should be balanced before modeling.

Adaptive cost-sensitive stance classification model for rumor detection in social networks

As online social networks are experiencing extreme popularity growth, determining the veracity of online statements denoted by rumors automatically as earliest as possible is essential to prevent the harmful effects of propagating misinformation. Early detection of rumors is facilitated by considering the wisdom of the crowd through analyzing different attitudes expressed toward a rumor (i.e., users’ stances). Stance detection is an imbalanced problem as the querying and denying stances against a given rumor are significantly less than supportive and commenting stances. However, the success of stance-based rumor detection significantly depends on the efficient detection of “query” and “deny” classes. The imbalance problem has led the previous stance classifier models to bias toward the majority classes and ignore the minority ones. Consequently, the stance and subsequently rumor classifiers have been faced with the problem of low performance. This paper proposes a novel adaptive cost-sensitive loss function for learning imbalanced stance data using deep neural networks, which improves the performance of stance classifiers in rare classes. The proposed loss function is a cost-sensitive form of cross-entropy loss. In contrast to most of the existing cost-sensitive deep neural network models, the utilized cost matrix is not manually set but adaptively tuned during the learning process. Hence, the contributions of the proposed method are both in the formulation of the loss function and the algorithm for calculating adaptive costs. The experimental results of applying the proposed algorithm to stance classification of real Twitter and Reddit data demonstrate its capability in detecting rare classes while improving the overall performance. The proposed method improves the mean F-score of rare classes by about 13% in RumorEval 2017 dataset and about 20% in RumorEval 2019 dataset.

Patched patterns and emergence of chaotic interfaces in arrays of nonlocally coupled excitable systems.

We disclose a new class of patterns, called patched patterns, in arrays of non-locally coupled excitable units with attractive and repulsive interactions. The self-organization process involves the formation of two types of patches, majority and minority ones, characterized by uniform average spiking frequencies. Patched patterns may be temporally periodic, quasiperiodic, or chaotic, whereby chaotic patterns may further develop interfaces comprised of units with average frequencies in between those of majority and minority patches. Using chaos and bifurcation theory, we demonstrate that chaos typically emerges via a torus breakup and identify the secondary bifurcation that gives rise to chaotic interfaces. It is shown that the maximal Lyapunov exponent of chaotic patched patterns does not decay, but rather converges to a finite value with system size. Patched patterns with a smaller wavenumber may exhibit diffusive motion of chaotic interfaces, similar to that of the incoherent part of chimeras.

A Weakly Supervised Learning-Based Oversampling Framework for Class-Imbalanced Fault Diagnosis

With the lack of failure data, class imbalance has become a common challenge in the fault diagnosis of industrial systems. The oversampling methods can tackle the class-imbalanced problem by generating the minority samples to balance the training set. However, one of the main challenges of the existing oversampling methods is how to generate high-quality minority samples. Traditional oversampling methods regard all synthetic samples as minority ones to be added to the training set without filtering. The low-quality synthetic samples would distort the distribution of the dataset and worsen the classification performance. In this article, we propose a weakly supervised oversampling method that treats all synthetic samples as unlabeled samples and develops a graph semisupervised learning algorithm to select high-quality synthetic samples, adding into the final training set as minority samples. To improve the quality of synthetic samples, we propose a cost-sensitive neighborhood component analysis dimensionality reduction method to enhance domain information validity in high-dimensional datasets. Finally, combining a boosting-based ensemble framework, we propose a new imbalanced learning framework suitable for high dimensionality and highly imbalanced fault diagnosis in industrial systems. The experimental validation is performed on five real-world wind turbine blade cracking failure datasets and compared to 15 benchmark methods. The experimental results show that average performances and robustness of the proposed framework are significantly better than those of the benchmark methods.

Tyranny of the majority: Problem to Nigerian democracy

Tyranny of the majority is one of the most pressing issues of today’s democracy and Nigeria especially. It arises when the dominance of a group in a democratic government’s impedes the benefits of the minority ones; or a system where there are no checks on the majority’s ability to dominate the minority. In context, democracy is a system of government made of popular rule, especially in promoting fundamental human rights, freedom, and common good. In fact, the revere is the case in Nigeria. Citizens in most time have demonstrated publicly in demand for their trampled right. Democracy is no longer the government of the people but for the elected, corrupt polithiefcians, and the rich majority which sole aim is to oppress the poor minority. This consequently pose a great problem to growth of democracy and democratic practice in Nigeria and a threat to the freedom and human dignity of the minority members of the society. However, the focus of this paper is to note how tyranny of majority has bedevilled our contemporary democracy; because the opinions of political minorities have been silenced and disregarded in the society. It also indicates what means could be used to control the tyranny of the majority and strengthen democracy in our society.

Importance and influence of parliamentary committees in the conditions of minority governments’ functioning: theorizing and findings in the countries of Central and Eastern Europe.

The article analyzes the importance and influence of parliamentary committees in the conditions of minority governments at the theoretical level and empirically, in particular in the countries of Central and Eastern Europe. The author assumes that the legitimate mechanism of people’s representation in countries of this type is the parliamentary method of government formation and responsibility. Therefore, governmental cabinets, including minority ones, depend on party composition of parliaments in their formation, functioning and responsibility. It was suggested that the frequency of minority governments’ formation is additionally determined by the specialization of parliaments, in particular through the prism of parliamentary committees as an arena of government-opposition relations, where the opposition almost always prevails in the case of minority governments. However, it was analytically proved that the committees themselves, in particular their various attributes and “power”, do not or almost do not affect the frequency of minority governments’ formation in the countries of Central and Eastern Europe. At the same time, the strengthening of committees can serve to strengthen parliamentary opposition, which contributes to the increase in frequency of minority governments’ formation.

Diagnosing glaucoma on imbalanced data with self-ensemble dual-curriculum learning.

Glaucoma diagnosis often suffers from two types of data imbalances: 1) class imbalance, i.e.,the non-glaucoma majority cases occupy most of the data; 2) rare cases, i.e.,few cases present the uncommon retinopathy e.g.,bayoneting or physiologic cupping. This dual-imbalances make glaucoma diagnosis model easy to be dominated by the majority cases but cannot correctly classify the minority and/or rare ones. In this paper, we propose an adaptive re-balancing strategy in the feature space, Self-Ensemble Dual-Curriculum learning (SEDC), to improve the glaucoma diagnosis on imbalanced data by augmenting feature distribution with feature distilling and feature re-weighting. Firstly, the self-ensembling (SEL) is developed to reinforce the discriminative ability of feature representations for the minority class and rare cases by distilling the features learned from the abundant majority cases. Secondly, the dual-curriculum (DCL) is designed to adaptively re-weight the imbalanced data in the feature space to learn a balanced decision function for accurate glaucoma diagnosis. Benefiting from feature distilling and re-weighting, the proposed SEDC fairly represents fundus images, regardless of the majority or rare cases, by augmenting the feature distribution to obtains the optimal decision boundary for accurate glaucoma diagnosis on the imbalanced dataset. Experimental results on three challenging glaucoma datasets show that our SEDC successfully delivers accurate glaucoma diagnosis by the adaptive re-balancing strategy, with the average mean value of Accuracy 0.9712, Sensitivity 0.9520, Specificity 0.9816, AUC 0.9928, F2-score 0.9547. Ablation and comparison studies demonstrate that our method outperforms state-of-the-art methods and traditional re-balancing strategies. The experiment also shows that the adaptive re-balancing strategy proposed in our method provides a more effective training approach with optimal convergence performance. It endows our SEDC a great advantage to handle the disease diagnosis on imbalanced data distribution.

Linguistic evolution driven by network heterogeneity and the Turing mechanism

Given the rapidly evolving landscape of linguistic prevalence, whereby a majority of the world's existing languages are dying out in favor of the adoption of a comparatively fewer set of languages, the factors behind this phenomenon has been the subject of vigorous research. The majority of approaches investigate the temporal evolution of two competing languages in the form of differential equations describing their behavior at large scale. In contrast, relatively few consider the spatial dimension of the problem. Furthermore while much attention has focused on the phenomena of language shift---the adoption of majority languages in lieu of minority ones---relatively less light has been shed on linguistic coexistence, where two or more languages persist in a geographically contiguous region. Here, we study the geographical component of language spread on a discrete medium to monitor the dispersal of language species at a microscopic level. Language dynamics is modeled through a reaction-diffusion system that occurs on a heterogeneous network of contacts based on population flows between urban centers. We show that our framework accurately reproduces empirical linguistic trends driven by a combination of the Turing instability, a mechanism for spontaneous pattern-formation applicable to many natural systems, the heterogeneity of the contact network, and the asymmetries in how people perceive the status of a language. We demonstrate the robustness of our formulation on two datasets corresponding to linguistic coexistence in northern Spain and southern Austria.

Class Imbalanced Fault Diagnosis via Combining K-Means Clustering Algorithm with Generative Adversarial Networks

Recent advancements in machine learning and communication technologies have enabled new approaches to automated fault diagnosis and detection in industrial systems. Given wide variation in occurrence frequencies of different classes of faults, the class distribution of real-world industrial fault data is usually imbalanced. However, most prior machine learning-based classification methods do not take this imbalance into consideration, and thus tend to be biased toward recognizing the majority classes and result in poor accuracy for minority ones. To solve such problems, we propose a k-means clustering generative adversarial network (KM-GAN)-based fault diagnosis approach able to reduce imbalance in fault data and improve diagnostic accuracy for minority classes. First, we design a new k-means clustering algorithm and GAN-based oversampling method to generate diverse minority-class samples obeying the similar distribution to the original minority data. The k-means clustering algorithm is adopted to divide minority-class samples into k clusters, while a GAN is applied to learn the data distribution of the resulting clusters and generate a given number of minority-class samples as a supplement to the original dataset. Then, we construct a deep neural network (DNN) and deep belief network (DBN)-based heterogeneous ensemble model as a fault classifier to improve generalization, in which DNN and DBN models are trained separately on the resulting dataset, and then the outputs from both are averaged as the final diagnostic result. A series of comparative experiments are conducted to verify the effectiveness of our proposed method, and the experimental results show that our method can improve diagnostic accuracy for minority-class samples.

Corporate governance: Theories, mechanisms and the case of oil and gas exporting developing countries

This paper reviews the literature on the quality of corporate governance practices in the oil and gas exporting developing countries (Russia, Venezuela, Nigeria, the MENA, and the GCC countries). We investigate if the internal and external governance mechanisms function efficiently in these countries. The findings of the reviewed literature show that the quality of corporate governance practices in the countries of our focus is not efficient at internal and external levels. Regarding the internal mechanisms, weak governance mechanisms originate from low transparency levels and give rise to poor voluntary disclosure in the firms. However, some internal mechanisms are more efficient in some of these countries as presented in the conclusion section. Regarding the inefficiency of external mechanisms, all the studied countries share common characteristics with respect to weak legal systems, inefficient law enforcement infrastructures, and low levels of protection for properties, investors, and shareholders especially the minority ones

Identification and semi-relative quantification of intact glycoforms by nano-LC-(Orbitrap)MS: application to the α-subunit of human chorionic gonadotropin and follicle-stimulating hormone.

Human chorionic gonadotropin (hCG) and follicle-stimulating hormone (FSH) belong to the family of glycoprotein polypeptide hormones called gonadotropins. They are heterodimers sharing the α-subunit structure that has 2 N-glycosylation sites. A method based on nano-reversed-phase liquid chromatography coupled to high-resolution mass spectrometry with an Orbitrap analyzer was developed for the first time to characterize the glycosylation state of the α-subunit at the intact level. A recombinant hCG-based drug, Ovitrelle®, was analyzed. This method combined with an appropriate data treatment allowed the detection of not only the major isoforms but also the minority ones with a high mass accuracy. More than 30 hCGα glycoforms were detected without overlapping of the isotopic patterns. The figures of merit of the method were assessed. The relative standard deviations (RSDs) of the retention time ranged between 0.1 and 6.08% (n = 3), with an average of 0.4%. The RSDs of the peak area measured on the extracted ion chromatogram of each glycoform are below 38% (n = 3), with an average of 16%, thus allowing semi-relative quantification. The ability to accurately profile glycosylated variants of hCGα was next demonstrated by comparing qualitatively and semi-quantitatively 3 batches of Ovitrelle®. The method was also used to analyze 3 batches of a recombinant FSH-based drug, Puregon®, and 30 FSHα glycoforms were detected and semi-quantified. This demonstrates the high potential of this method for fast quality control or comparison of the glycosylation of glycoprotein-based pharmaceutical preparations. Graphical abstract.

English Language, Globalisation and the Future of the Nigerian Indigenous Languages

In the present globalised world, languages have come to be ranked based on their roles in the world arena, the status assigned to them in the environment inwhich they are spoken as well as the number of their respective speakers. As such, some languages, including English, is considered a world language. English language wields great power in Nigeria, as an official and national language. Its ever-increasing roles have detrimental effects on the survival of the minority languages, with most of them playing just peripheral roles. Most of the minority languages are further threatened by the major ones spoken in different geopolitical zones. As the functional loads of these minority languages continue to reduce gradually and consistently, their level of vitality equally reduces. A minority language whose functional domains are overtaken by a dominant one will eventually disappear as their users shift to the latter and therefore, would no longer use them for any linguistic function. This paper discusses the current precarious situation of the Nigerian indigenous languages, especially the minority ones in the face of globalisation, the domineering effect of English language and what can be done to make these languages withstand the test of time.

The significance of mixed family backgrounds for mainstream integration in Canada

ABSTRACT Recent research has shown that, in the U.S., individuals from mixed majority-minority families generally occupy positions in between whites and minorities but ‘lean’ white in the sense that they behave in ways more similar in key respects to white Americans than to minority ones; those who are partly black are an exception. Using the data from the 2002 Ethnic Diversity Survey, we examine here whether the same pattern appears in Canada. We employ multiple indicators of integration – including self-classification as white rather than a visible minority, the perception of encountering discrimination, the choice of a marital partner, and household income – and find that, overall, Canadians from mixed majority-minority backgrounds are also in-between but lean white in some respects. The degree of this lean depends on the minority origin involved and tends to be strongest when the minority parent has an Arab background and weakest when that parent is black; individuals of white and Asian ancestry are intermediate. However, the distance of white–black Canadians from the others is not as extreme as is the case for white–black individuals in the U.S.

An examination of different perspectives on philanthropic corporate social responsibility

PurposeThe purpose of this study is to identify different perspectives on philanthropic corporate social responsibility (CSR) activities by using the mixed-method approach and Q methodology. In addition, this study examines why people consider the revealed perspectives to be important factors.Design/methodology/approachIn this study, the authors used a structured sampling of 30 CSR-related statements (a Q sample) and had participants rank-order the statements from “most important” (+4) to “most unimportant” (−4).FindingsThe results, from the factor analysis and correlation using the Q method software program PQMETHOD, revealed two distinctive perspectives. The first group emphasized the importance of supporting environmental-related activities. The second group ranked supporting arts-related activities as the most important factor of philanthropic CSR activities.Research limitations/implicationsThe goal of Q methodology is not the generalizability of the results but to identify the different views that exist regarding a topic, even minority ones. The findings are discussed in terms of practical implications for how this framework can be used to understand the subjective papers of different types of stakeholders.Originality/valueThis study identifies the different perspectives that exist regarding philanthropic CSR activities by using Q methodology, which is a good alternative to other research methods that provides a unique way of uncovering subjectivity from the participants’ point of view.