Majority Ones Research Articles

In‐Depth Investigation of the Reaction Parameters Tuning the Ethyl Levulinate Synthesis from Fructose and Inulin

AbstractAlkyl levulinates (ALs) are strategic compounds for the development of sustainable energy transition. In this regard, the direct alcoholysis of fructose and inulin for the selective ethyl levulinate (EL) production was investigated with a One‐Factor‐At‐a‐Time (OFAT) approach employing diluted H2SO4 as catalyst to clarify the role of the main reaction parameters (substrate and acid loadings, temperature, reaction time). The OFAT investigation on fructose ethanolysis allowed to reach the EL yield of 91.5 mol%. The inulin ethanolysis was then optimized adopting the multivariate approach based on the Response Surface Methodology (RSM), which highlighted the interplay of the reaction parameters on the selective EL production. This allowed to identify the optimal conditions to reach the highest EL yield (up to 89.3 mol%) and also those which ensured the highest EL concentration, adopting a substrate loading (14 wt%) higher than the majority ones reported in the literature according to the high gravity approach, and the lowest diethyl ether (DEE) by‐product yield. The DEE formation is scarcely investigated in the literature, but it can negatively influence the alcoholysis process, thus it was considered in this work. Moreover, the humin solid residue was deeply characterized to envisage its possible applications, under a circular economy perspective.

Imbalanced complemented subspace representation with adaptive weight learning

Class imbalance problems pose significant challenges in the field of data mining. The skewed distribution of classes in imbalanced datasets often leads conventional classification methods to neglect the importance of minority classes, favoring the majority ones. In this paper, we propose an imbalanced complemented subspace representation model with adaptive weight learning to address imbalanced classification challenges. This approach incorporates a novel regularization technique and an adaptive weighting mechanism. The regularization technique integrates a complemented subspace term based on space theory into the efficient collaborative representation-based classification (CRC) framework, enhancing the overall accuracy of existing CRC-based methods. Furthermore, it takes into account both intra-class and inter-class density information to assign greater weights to minority classes. This highlights the contribution of minority classes and mitigates bias towards majority classes. Additionally, it is demonstrated that the proposed model can be efficiently solved with a closed-form solution. Extensive experiments conducted on diverse imbalanced datasets showcase the superiority of our method over most state-of-the-art imbalanced classification algorithms.

ВИКОРИСТАННЯ ПРОСТОРОВИХ ВАГОВИХ МАСОК З НЕЗБАЛАНСОВАНИМИ ДАТАСЕТАМИ ДЛЯ ЗАДАЧІ СЕГМЕНТАЦІЇ СУПУТНИКОВИХ ЗНІМКІВ

This study addresses the issue of imbalanced datasets in satellite image segmentation tasks, where machine learning models often neglect minority classes in favor of majority ones. We propose using spatial weight masks for the loss function computation to take into account the reliability score of individual pixels. This approach enhances segmentation quality, significantly improving metrics for minority classes. Additionally, a dataset augmentation method using generative adversarial networks (GANs) is explored, showing slight improvements in recognizing less represented crop types in the dataset, and it is compared with the proposed method. The simultaneous usage of weighted masks and generative networks is investigated. Keywords: dataset quality assessment, imbalanced datasets, classification, segmentation, generative adversarial networks, training data generation.

Fault Diagnosis of Rolling Bearings in Primary Mine Fans under Sample Imbalance Conditions.

Rolling bearings are crucial parts of primary mine fans. In order to guarantee the safety of coal mine production, primary mine fans commonly work during regular operation and are immediately shut down for repair in case of failure. This causes the sample imbalance phenomenon in fault diagnosis (FD), i.e., there are many more normal state samples than faulty ones, seriously affecting the precision of FD. Therefore, the current study presents an FD approach for the rolling bearings of primary mine fans under sample imbalance conditions via symmetrized dot pattern (SDP) images, denoising diffusion probabilistic models (DDPMs), the image generation method, and a convolutional neural network (CNN). First, the 1D bearing vibration signal was transformed into an SDP image with significant characteristics, and the DDPM was employed to create a generated image with similar feature distributions to the real fault image of the minority class. Then, the generated images were supplemented into the imbalanced dataset for data augmentation to balance the minority class samples with the majority ones. Finally, a CNN was utilized as a fault diagnosis model to identify and detect the rolling bearings' operating conditions. In order to assess the efficiency of the presented method, experiments were performed using the regular rolling bearing dataset and primary mine fan rolling bearing data under actual operating situations. The experimental results indicate that the presented method can more efficiently fit the real image samples' feature distribution and generate image samples with higher similarity than other commonly used methods. Moreover, the diagnostic precision of the FD model can be effectively enhanced by gradually expanding and enhancing the unbalanced dataset.

Linear-exponential loss incorporated deep learning for imbalanced classification

The class imbalance issue is a pretty common and enduring topic all the time. When encountering unbalanced data distribution, conventional methods are prone to classify minority samples as majority ones, which may cause severe consequences in reality. It is crucial yet challenging to cope with such problems. In this paper, inspired by our previous work, we borrow the linear-exponential (LINEX) loss function in statistics into deep learning for the first time and extend it into a multi-class form, denoted as DLINEX. Compared with existing loss functions in class imbalance learning (e.g., the weighted cross entropy-loss and the focal loss), DLINEX has an asymmetric geometry interpretation, which can adaptively focus more on the minority and hard-to-classify samples by solely adjusting one parameter. Besides, it simultaneously achieves between and within class diversities via caring about the inherent properties of each instance. As a result, DLINEX achieves 42.08% G-means on the CIFAR-10 dataset at the imbalance ratio of 200, 79.06% G-means on the HAM10000 dataset, 82.74% F1 on the DRIVE dataset, 83.93% F1 on the CHASEDB1 dataset and 79.55% F1 on the STARE dataset The quantitative and qualitative experiments convincingly demonstrate that DLINEX can work favorably in imbalanced classifications, either at the image-level or the pixel-level.

Direct hydrogenation of CO2 into valuable aromatics over K/Fe-Cu-Al @HZSM-5 tandem catalysts: Effects of zeolite surface acidity on aromatics formation

Direct hydrogenation of carbon dioxide (CO2) to value-added aromatics can not only provide a sustainable aromatics synthesis route but also realize CO2 mitigation. However, it remains a great challenge to identify the optimal surface acidity and regulate the selective formation of aromatics during CO2 hydrogenation. Herein, we designed and prepared a series of K/Fe-Cu-Al@HZSM-5 tandem catalysts, and deeply investigated the effects of zeolite surface acidity on the catalytic performance, by changing the SiO2/Al2O3 ratios of HZSM-5 from 25 to 400. It was found that the surface acidity, especially for the Brønsted acidity, plays a crucial role in the aromatics formation. With an increasing Brønsted acidity from 0 to 290 μmol/g, the CO2 conversion keeps relatively stable (around 44%), however, the aromatics yield monotonously increases from 0.9 to 12.8%, indicating a distinct correlation with the Brønsted acidity. Among the obtained aromatics, the majority ones are C6–8 light aromatics, accounting for c.a. 30–60% within time on stream (TOS) of 24 h, but their proportion tends to gradually decrease with TOS, probably due to the catalyst deactivation by coking. Moreover, the possible reaction pathways for aromatics formation over the prepared K/Fe-Cu-Al@HZSM-5 tandem catalysts were also proposed.

CLEANSE – Cluster-based Undersampling Method

Class imbalance is a common problem with datasets relating to various areas of life. It causes many traditional machine learning algorithms to tend to misclassify minority samples as majority ones. Despite various studies, the class imbalance still remains a relevant problem for which no one-size-fits-all solution has been found.In this paper, an undersampling method based on clustering is presented. In the proposed approach K-means algorithm is used to cluster data. In homogenous "majority" clusters, i.e., clusters containing objects of only the majority class, objects within the specified distance from the center are removed. In the case of non-homogeneous clusters, objects located at the class decision boundary are removed using the KNN algorithm.As tests have shown, the clustering-based solution can improve classification quality. The results of experiments show that in many cases the proposed solution outperformed other undersampling techniques described in the literature.

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.

Semi-Supervised Ensemble Learning Framework for Accelerating Power System Transient Stability Knowledge Base Generation

Machine learning approaches have exhibited high potential in power system transient stability assessment (TSA), yet their initial preparation stages of stability knowledge base generation (SKBG) based on time-domain simulations often undergo high computational costs. In fact, how to ease the heavy computational burden of SKBG without sacrificing the reliability is still a significant challenge. To address this problem, this paper develops a semi-supervised ensemble learning (SSEL) framework for reliable SKBG acceleration, without additional need for computing hardware upgrading. In particular, it performs detailed simulations for a minority of cases and fast simulations for the majority ones to reduce the total computation time. Considering the absence of stability status information of those fast simulated cases, an SSEL scheme is systematically designed to reliably label their stability status. Before implementing SSEL, two concise feature descriptors are first introduced to efficiently extract transient features from multiplex system trajectories. Then, all the cases are characterized in a unified feature space, whereby a series of semi-supervised support vector machines are trained in randomly formed subspaces. Afterward, these single machines are systematically combined to derive an enhanced SSEL model, which is able to make reliable and robust labeling decisions. Further, a backtrace strategy is carefully devised for SSEL, so as to maintain the high reliability of SKBG. Test results on the IEEE 39-bus system and the realistic GD Power Grid in South China illustrate the excellent performances of the proposed framework on SKBG acceleration.

Polarization of GRB prompt emission and its application to POLAR’s data

Synchrotron emission polarization is very sensitive to the magnetic field configuration. Recently, polarization of synchrotron emission with a mixed (SM) magnetic field in the gamma-ray burst (GRB) afterglow phase has been developed. Here, we apply these SM models to the GRB prompt phase and compare their polarization properties with that of synchrotron emission in purely ordered (SO) magnetic field. We find that the polarization properties in an SM model are very similar to these in a corresponding SO model (e.g., synchrotron emission in a mixed magnetic field with an aligned ordered part (SMA) and synchrotron emission with a purely ordered aligned magnetic field (SOA)), only with a lower polarization degree (PD). We also discuss the statistical properties of the models. We find PDs of the simulated bursts are concentrated around 25% for both SOA and synchrotron emission in a purely ordered toroidal magnetic field (SOT), while they can range from 0% to 25% for SMA and synchrotron emission in a mixed magnetic field with a toroidal ordered part (SMT), depending on ξB value, i.e., the ratio of magnetic reduction of the ordered magnetic field over that of random magnetic field. From statistics, if PDs of majority GRBs are non-zero, then it favors SO and SM models. Further, if there are some bright GRBs with prominently lower PDs than that of the majority GRBs, it favors SOT (SMT) models; if all the bright GRBs have comparable PDs with the majority ones, it favors SOA (SMA) models. Finally, we apply our results to POLAR’s data and find that ∼ 10% time-integrated PDs of the observed bursts favor SMA and SMT models, and the ξB parameter of these bursts is constrained to be around 1.135.

Stability of minority governments and the role of support agreements

How does the minority status of a government influence cabinet duration? This article analyzes the influence of minority status on cabinet duration by differentiating between different types of minority governments. Minority governments are not in general less stable than majority ones but different types of minority governments have different effects on cabinet duration. The main theoretical argument is that substantive minority governments without support agreements are less stable than majority governments, while contract minority governments that rely on written support agreements with non-cabinet parties which secure majority support in parliament are as stable as majority ones. Drawing on survival analysis, the effect of different minority cabinet types on cabinet stability is tested for 471 cabinets in 30 countries from 1977 until 2019. The results show that only minority governments without support partnerships have a substantially higher risk of early government termination than majority cabinets. The findings of this analysis have important implications for the coalition theory and the evaluation of minority governments.

Small in Size but Powerful in Parliament? The Legislative Performance of Minority Governments

Minority cabinets do not control a legislative majority and are thus vulnerable to parliamentary defeat. Consequently, the legislative performance of minority cabinets has been often brought into question. In this article, I argue that minority cabinets do not perform generally worse than majority cabinets. I hypothesize that only substantive minority cabinets which do not enjoy formal majority support by noncabinet parties work significantly worse than majority ones. The empirical analysis is based on a large new dataset that combines original information on minority‐cabinet attributes and legislative data for 197 governments in 21 parliamentary democracies since the 1980s. The results of a fractional logit analysis support my theoretical expectations and show that not all types of minority cabinets perform worse than majority governments. My findings provide a comparative insight into the functioning of minority governments and have important implications for our understanding of legislative politics, party competition, and policymaking.

Decolonising Terrorism Journals

Decolonisation of knowledge over the past few years has gained much traction among scholars and students in many countries. This situation has led to calls for the decolonisation of knowledge, academia, the university, and university curricula. That said, the knowledge production side of the terrorism industry, which sits inside academia, so far has escaped calls to decolonise. This situation is somewhat surprising because the terrorism industry has had a tremendous impact on many countries, especially Muslim majority ones. The 9/11 terrorist attacks have resulted in a tremendous amount of knowledge being produced and published on terrorism and counterterrorism. However, little is known about “who is publishing on terrorism and where they are based”. To this end, this paper adopts a decolonial approach and addresses the questions of “who is publishing on terrorism and where they are based” by analysing seven terrorism journals. It argues that most of the publications and knowledge on terrorism in the seven terrorism journals are produced by scholars with Western heritage and are based at Western institutions, which is connected to the coloniality of knowledge.

Bayesian networks for imbalance data to investigate the contributing factors to fatal injury crashes on the Ghanaian highways

The crash data are often predominantly imbalanced, among which the fatal injury (or minority) crashes are significantly underrepresented relative to the non-fatal injury (or majority) ones. This unbalanced phenomenon poses a huge challenge to most of the statistical learning methods and needs to be addressed in the data preprocessing. To this end, we comparatively apply three data balance methods, i.e., the Synthetic Minority Oversampling Technique (SMOTE), the Borderline SMOTE (BL-SMOTE), and the Majority Weighted Minority Oversampling (MWMOTE). Then, we examine different Bayesian networks (BNs) to explore the contributing factors of fatal injury crashes. The 2016 highway crash data of Ghana are retrieved for the case study. The results show that the accuracy of the injury severity classification is improved by using the preprocessed data. Highest improvement is observed on the data preprocessed by the MWMOTE technique. Statistical verification is done by the Wilcoxon signed-rank test. The inference results of the best BNs show the significant factors of fatal crashes which include off-peak time, non-intersection area, pedestrian involved collisions, rural road environment, good tarred road, roads without shoulders, and multiple vehicles involved crash.

Polymerization Kinetics of Propylene with the MgCl2-Supported Ziegler-Natta Catalysts—Active Centers with Different Tacticity and Fragmentation of the Catalyst

The catalytic activity and stereospecificity of olefin polymerization by using heterogeneous TiCl4/MgCl2 Ziegler-Natta (Z-N) catalysts are determined by the structure and nature of active centers, which are mysterious and fairly controversial. In this work, the propylene polymerization kinetics under different polymerization temperatures by using Z-N catalysts were investigated through monitoring the concentration of active centers [C*] with different tacticity. SEM was applied to characterize the catalyst morphologies and growing polypropylene (PP) particles. The lamellar thickness and crystallizability of PP obtained under different polymerization conditions were analyzed by DSC and SAXS. The PP fractions and active centers with different tacticity were obtained with solvent extraction fractionation method. The catalytic activity, active centers with different tacticity and propagation rate constant kp, fragmentation of the catalyst, crystalline structure of PP are correlated with temperature and time for propylene polymerizations. The polymerization temperature and time show complex influences on the propylene polymerization. The higher polymerization temperature (60 °C) resulted higher activity, kp and lower [C*], and the isotactic active centers Ci* as the majority ones producing the highest isotactic polypropylene (iPP) components showed much higher kp when compared with the active centers with lower stereoselectivity. Appropriate polymerization time provided full fragmentation of the catalyst and minimum diffusion limitation. This work aims to elucidate the formation and evolution of active centers with different tacticity under different polymerization temperature and time and its relations with the fragmentation of the PP/catalyst particles, and provide the solutions to the improvement of catalyst activity and isotacticity of PP.

A novel riboswitch classification based on imbalanced sequences achieved by machine learning.

Riboswitch, a part of regulatory mRNA (50–250nt in length), has two main classes: aptamer and expression platform. One of the main challenges raised during the classification of riboswitch is imbalanced data. That is a circumstance in which the records of a sequences of one group are very small compared to the others. Such circumstances lead classifier to ignore minority group and emphasize on majority ones, which results in a skewed classification. We considered sixteen riboswitch families, to be in accord with recent riboswitch classification work, that contain imbalanced sequences. The sequences were split into training and test set using a newly developed pipeline. From 5460 k-mers (k value 1 to 6) produced, 156 features were calculated based on CfsSubsetEval and BestFirst function found in WEKA 3.8. Statistically tested result was significantly difference between balanced and imbalanced sequences (p < 0.05). Besides, each algorithm also showed a significant difference in sensitivity, specificity, accuracy, and macro F-score when used in both groups (p < 0.05). Several k-mers clustered from heat map were discovered to have biological functions and motifs at the different positions like interior loops, terminal loops and helices. They were validated to have a biological function and some are riboswitch motifs. The analysis has discovered the importance of solving the challenges of majority bias analysis and overfitting. Presented results were generalized evaluation of both balanced and imbalanced models, which implies their ability of classifying, to classify novel riboswitches. The Python source code is available at https://github.com/Seasonsling/riboswitch.

Reducing ethnic discrimination in resume-screening: a test of two training interventions

ABSTRACT Resume-screening by human raters is vulnerable to hiring discrimination but recruiter training as a way to overcome biased resume-screening is under-researched. The present study addresses this gap. Building on key cognitive processes that steer discriminatory decision-making in resume-screening and insights from diversity literature, we investigated the effectiveness of two cognitive training interventions (i.e., a culture-general assimilator and a structured free recall intervention) for reducing hiring discrimination against ethnic minority job applicants in the resume-screening stage. A pre-test, repeated post-test experimental study showed initial hiring discrimination (i.e., less positive evaluations of minority job applicants than majority ones), which was reduced shortly after both training interventions. Hiring discrimination, however, resurfaced 3 months later for both interventions. The culture-general assimilator also positively affected participants’ perceived ability to suppress stereotypes, both short-term and long-term. Findings are considered in the light of a comparison of these training interventions, their programme features, and their compatibility with the resume-screening task. Implications for prejudice reduction initiatives, their potential differential effects, and further research are also discussed.

Hybrid Classifier Ensemble for Imbalanced Data.

The class imbalance problem has become a leading challenge. Although conventional imbalance learning methods are proposed to tackle this problem, they have some limitations: 1) undersampling methods suffer from losing important information and 2) cost-sensitive methods are sensitive to outliers and noise. To address these issues, we propose a hybrid optimal ensemble classifier framework that combines density-based undersampling and cost-effective methods through exploring state-of-the-art solutions using multi-objective optimization algorithm. Specifically, we first develop a density-based undersampling method to select informative samples from the original training data with probability-based data transformation, which enables to obtain multiple subsets following a balanced distribution across classes. Second, we exploit the cost-sensitive classification method to address the incompleteness of information problem via modifying weights of misclassified minority samples rather than the majority ones. Finally, we introduce a multi-objective optimization procedure and utilize connections between samples to self-modify the classification result using an ensemble classifier framework. Extensive comparative experiments conducted on real-world data sets demonstrate that our method outperforms the majority of imbalance and ensemble classification approaches.

ELECTORAL SYSTEMS – A SPECIAL REVIEW OF THE LOCAL-SELF GOVERNMENT IN SERBIA

The electoral system is part of the broader election law, which also includes an electoral form, constituencies, electoral competition, voting, methods of converting votes into mandates, as well as election threshold. Most often, the choice of the model by which the members of the representative bodies will be elected has a major effect on the entire political system of the state.Regarding the electoral form, the electoral systems at both the national and local level are divided into majority and proportional ones, but there is also a large number of "mixed" electoral systems. When it comes to the Republic of Serbia, as well as many other countries of Central and Eastern Europe, the initial dominance of the majority electoral system in almost all the countries in the 1990s was gradually replaced by a proportional electoral system.Local self-government represents a democratic framework for citizen participation in managing the affairs of their immediate interest,where citizens, directly and by secret ballot, elect the municipal assembly members, as the highest representative body of the municipality. In the case of local self-government, the D'Hondt election method and the 5% threshold are applied.When it comes to the electoral systems and the parties of national minorities, the laws of the Republic of Serbia provide significant measures for the participation of this type of political organization in the distribution of mandates, subject to special conditions, even when they receive less than 5% of the total number of votes.

Responsibility attribution for corruption scandals

ABSTRACTThis paper studies the effect of the type of government on responsibility attribution for corruption scandals. Most studies on corruption voting have assumed that voters punish all kinds of governments. This article challenges this assumption by distinguishing between two types of governments: single-party majority governments and minority/coalition governments. This hypothesis is tested using data from the 2011 Spanish local elections. Our findings reveal that the effect of coalition governments on corruption voting is not statistically different than majority ones. However, when we differentiate between welfare-enhancing and welfare decreasing corruption, we find that welfare-decreasing corruption has a clear negative effect on the electoral performance of the mayor’s party when it impacts single-party majority governments, but not when the mayor needs the support of other parties (minority or coalition governments).