Popular Class Research Articles

On the upward book thickness problem: Combinatorial and complexity results

Among the vast literature concerning graph drawing and graph theory, linear layouts of graphs have been the subject of intense research over the years, both from a combinatorial and from an algorithmic perspective. In particular, upward book embeddings of directed acyclic graphs (DAGs) form a popular class of linear layouts with notable applications, and the upward book thickness of a DAG is the minimum number of pages required by any of its upward book embeddings.A long-standing conjecture by Heath, Pemmaraju, and Trenk (1999) states that the upward book thickness of outerplanar DAGs is bounded above by a constant. In this paper, we show that the conjecture holds for subfamilies of upward outerplanar graphs, namely those whose underlying graph is an internally-triangulated outerpath or a cactus, and those whose biconnected components are st-outerplanar graphs.On the complexity side, it is known that deciding whether a graph has upward book thickness k is NP-hard for any fixed k≥3. We show that the problem, for any k≥5, remains NP-hard for graphs whose domination number is O(k), but it is fixed-parameter tractable (FPT) in the vertex cover number.

Determining factors and impact of household income on dairy cattle insurance in Nepal

Nepalese agriculture insurance market is dominated by livestock sector and cattle are the most popular class of animal in livestock production system. This study was conducted to identify the determining factors and impact of household income on dairy cattle insurance. This study was based on the field survey conducted in Chitwan and Nawalparasi districts of Nepal. A total of 160 households were sampled using a stratified simple random sampling technique. Data were collected using a pre-tested interview schedule and focus group discussion (FGD) in 2017. The result of t-test showed annual household income, income from milk sell, and average cattle holding significantly higher in cattle insurer farmers than that of non-insurers. A probit model was used to assess the factors affecting the decision for the adoption of cattle insurance. The probit model revealed that cattle breed, access to loan, income from livestock, and number of cattle had a positive and significant relationship with the adoption of cattle insurance whereas, household size and district dummy had a negative and significant relationship with the adoption of cattle insurance. Income regression function revealed that the adoption of insurance, the number of cattle, and the district of the respondents had a positive and significant relation with income from livestock. The instrumental variable model adopted to combat endogeneity bias showed that adoption of insurance and district of the respondents had a positive and significant relation with income from livestock. Although the results are farmers specific, findings have implications for policymakers regarding promotion of improved breeds and facilitation of farm credit.

Nonparametric scanning tests of homogeneity for hierarchical models with continuous covariates.

In many applications of hierarchical models, there is often interest in evaluating the inherent heterogeneity in view of observed data. When the underlying hypothesis involves parameters resting on the boundary of their support space such as variances and mixture proportions, it is a usual practice to entertain testing procedures that rely on common heterogeneity assumptions. Such procedures, albeit omnibus for general alternatives, may entail a substantial loss of power for specific alternatives such as heterogeneity varying with covariates. We introduce a novel and flexible approach that uses covariate information to improve the power to detect heterogeneity, without imposing unnecessary restrictions. With continuous covariates, the approach does not impose a regression model relating heterogeneity parameters to covariates or rely on arbitrary discretizations. Instead, a scanning approach requiring continuous dichotomizations of the covariates is proposed. Empirical processes resulting from these dichotomizations are then used to construct the test statistics, with limiting null distributions shown to be functionals of tight random processes. We illustrate our proposals and results on a popular class of two-component mixture models, followed by simulation studies and applications to two real datasets in cancer and cariesresearch.

Difference rewards policy gradients

AbstractPolicy gradient methods have become one of the most popular classes of algorithms for multi-agent reinforcement learning. A key challenge, however, that is not addressed by many of these methods is multi-agent credit assignment: assessing an agent’s contribution to the overall performance, which is crucial for learning good policies. We propose a novel algorithm called Dr.Reinforce that explicitly tackles this by combining difference rewards with policy gradients to allow for learning decentralized policies when the reward function is known. By differencing the reward function directly, Dr.Reinforce avoids difficulties associated with learning the Q-function as done by counterfactual multi-agent policy gradients (COMA), a state-of-the-art difference rewards method. For applications where the reward function is unknown, we show the effectiveness of a version of Dr.Reinforce that learns an additional reward network that is used to estimate the difference rewards.

Computational Insights on Periodicity in Bonding and Lewis Acidity and Basicity of the p‐Block Trispyrazolylborate Complexes

AbstractHydrotris(pyrazolyl)borate ligands are a popular class of ligands to stabilize both Lewis acidic and Lewis basic main group compounds. In this work we analyze the low‐valent group 13–15 compounds of hydrotris(3,5‐dimethyl‐pyrazolyl)borate ligand, [Tp*E]x, 1–15 (E=group 13 element, x=0; E=group 14 element, x=1+ ; E=group 15 element, x=2+) based on density functional theory. A periodic increment in intrinsic Lewis acidity is observed down the group due to increased E−N bond polarization towards nitrogen, resulting in lower energy secondary binding sites. However, global Lewis acidity measures in terms of FIA and IIA indicate the dependency on the choice of the nucleophile. In general, this study underscores the potential of the Tp* ligand in stabilizing the reactive low‐valent p‐block elements.

Selective Catalytic Reduction Catalyst Modeling for Control Purposes

In markets with strict emission legislations Selective Catalytic Reduction (SCR) has become the industry standard for NOx abatement in heavy-duty vehicles, and therefore modeling and control of these systems are vital. Many SCR catalyst models are available in the literature and in this paper different models are discussed and classified into groups. Two models, based on the two most popular classes for control-oriented models, are implemented and compared with each other, one based on the continuously stirred-tank reactor approximation, and the other on a quasi-static behavior of the gas phase. The results show that assuming a quasi-static behavior of the gas phase in the catalyst gives better results in terms of accuracy and simulation time, especially when it comes to predictions of ammonia slip.

Caudillos y plebeyos: La construcción social del estado en América del Sur (Argentina, Perú, Chile 1830–1860)

Caudillos y plebeyos: La construcción social del estado en América del Sur (Argentina, Perú, Chile 1830–1860)

Understanding the abnormal effects of ultrasonic vibration on tool wear and surface generation in Zr-based bulk metallic glass cutting

Bulk metallic glasses (BMGs), which represent an emerging popular class of alloys, are regarded as difficult-to-cut materials. Ultrasonic vibration-assisted cutting (UVAC) has been reported as an effective method to improve the machinability of such materials. In UVAC, it is widely accepted that, the larger the ultrasonic amplitude, the better the processing outcome, i.e., lower cutting force, lower surface roughness and longer tool life. However, this study demonstrates that the cutting performance of BMGs can be enhanced only if the ultrasonic amplitude is within a carefully-determined window. Due to the high hardness, high elastic limit, and inhomogeneous deformation of BMGs, an excessive ultrasonic amplitude would violate the cutting performance of UVAC. On the one hand, the increase of ultrasonic amplitude can increase the cutting-in speed and actual cutting distance of the tool, which, due to the extreme hardness and elastic limit of BMGs, might lead to severe tool wear in the form of abrupt fracture and rapid flank abrasion, respectively. On the other hand, the increase of ultrasonic amplitude can increase the instantaneous maximum cutting speed of the tool, which, due to the inhomogeneous deformation of BMGs, might lead to local melting of the BMG. When the instantaneous cutting speed reaches a critical value, local material melting takes place on the newly generated surface, which will induce the deterioration of surface quality due to the formation of dimples and periodic micro-pools during UVAC. Based on the theoretical and experimental results, the process window of UVAC of BMGs has been identified. A process optimization strategy is proposed by synergistically considering the surface quality, tool life, and machining efficiency. Additional confirmatory experiments reveal that UVAC can effectively reduce the cutting force and surface roughness of BMGs if the process parameters are well-matched within the identified window. • UVAC improves the machinability of BMGs, unless an excessive vibration amplitude. • An analytical model of UVAC tool failure was established and verified by experiments. • The unique properties of BMGs induce the abnormal failure of UVAC tool. • Feathered dimples and micro-pools were found, the mechanism is revealed. • the process window and optimization strategy of UVAC of BMGs were proposed.

Robust Inference and Modeling of Mean and Dispersion for Generalized Linear Models

Generalized Linear Models (GLMs) are a popular class of regression models when the responses follow a distribution in the exponential family. In real data the variability often deviates from the relation imposed by the exponential family distribution, which results in over- or underdispersion. Dispersion effects may even vary in the data. Such datasets do not follow the traditional GLM distributional assumptions, leading to unreliable inference. Therefore, the family of double exponential distributions has been proposed, which models both the mean and the dispersion as a function of covariates in the GLM framework. Since standard maximum likelihood inference is highly susceptible to the possible presence of outliers, we propose the robust double exponential (RDE) estimator. Asymptotic properties and robustness of the RDE estimator are discussed. A generalized robust quasi-deviance measure is introduced which constitutes the basis for a stable robust test. Simulations for binomial and Poisson models show the excellent performance of the RDE estimator and corresponding robust tests. Penalized versions of the RDE estimator are developed for sparse estimation with high-dimensional data and for flexible estimation via generalized additive models (GAMs). Real data applications illustrate the relevance of robust inference for dispersion effects in GLMs and GAMs. Supplementary materials for this article are available online.

Scalable training of graph convolutional neural networks for fast and accurate predictions of HOMO-LUMO gap in molecules

Graph Convolutional Neural Network (GCNN) is a popular class of deep learning (DL) models in material science to predict material properties from the graph representation of molecular structures. Training an accurate and comprehensive GCNN surrogate for molecular design requires large-scale graph datasets and is usually a time-consuming process. Recent advances in GPUs and distributed computing open a path to reduce the computational cost for GCNN training effectively. However, efficient utilization of high performance computing (HPC) resources for training requires simultaneously optimizing large-scale data management and scalable stochastic batched optimization techniques. In this work, we focus on building GCNN models on HPC systems to predict material properties of millions of molecules. We use HydraGNN, our in-house library for large-scale GCNN training, leveraging distributed data parallelism in PyTorch. We use ADIOS, a high-performance data management framework for efficient storage and reading of large molecular graph data. We perform parallel training on two open-source large-scale graph datasets to build a GCNN predictor for an important quantum property known as the HOMO-LUMO gap. We measure the scalability, accuracy, and convergence of our approach on two DOE supercomputers: the Summit supercomputer at the Oak Ridge Leadership Computing Facility (OLCF) and the Perlmutter system at the National Energy Research Scientific Computing Center (NERSC). We present our experimental results with HydraGNN showing (i) reduction of data loading time up to 4.2 times compared with a conventional method and (ii) linear scaling performance for training up to 1024 GPUs on both Summit and Perlmutter.

Music teacher attitudes toward popular music education

ABSTRACT The purpose of this study was to examine the attitudes, values, and beliefs of New York music teachers towards the inclusion of popular music in the classroom. This research was guided by the following questions: 1) To what extent are K-12 teachers interested in teaching a variety of popular music instruments and styles? 2) What attitudes and beliefs do music teachers express about popular music education? 3) What popular music approaches are used most frequently by music teachers? and 4) What are the opportunities or barriers associated with popular music classes and ensembles in school settings? Survey respondents (N = 120), all of whom were members of the New York State School Music Association in the United States, showed strong support for the inclusion of popular music education in school programmes, with approximately 75 per cent of respondents agreeing or strongly agreeing that popular music instruments and repertoire should be included in K-12 schools.

History at the Roundabout

Abstract The Gilets jaunes brought attention to the existence and demands of the popular classes as other than the deplorables of the far right. This essay examines the ideas of an innovative group of historians who interpret the Gilets jaunes as evoking and enacting the unresolved issues of the revolutions of 1789, 1848, and 1871 on the nature and practice of democracy in France.

Some Heterocyclic Hydrazone Compounds: Synthesis, Spectral Characterization and Anticancer Activity Study

Cancer is currently ongoing to be a significant health problem threatening human health. Hydrazone compounds constitute a popular class of organic compounds used in novel drug discovery studies in therapy of cancer. In the current study, the preparation and structural characterization of some heterocyclic hydrazone compounds (7-12) and their anticancer capacities against HeLa cervical cancer and MCF-7 breast cancer cell lines were reported. The target compounds were characterized by some spectroscopic techniques (1H NMR, 13C NMR and FT-IR). The in vitro cytotoxic potentials of the target molecules were assessed by using MTT assay against two cancer cell lines. L929 mouse fibroblast cell lines were employed as normal cell. The results displayed that some of the tested molecules had varying anticancer activities. Among the tested compounds, compound 8 indicated anticancer activity against HeLa cells with IC50 value of 34.38 µM. On the other hand, of these tested compounds, compound 11 (IC50 = 26.84 µM) displayed anticancer activity against MCF-7 cells.

Real-world usage of sulphonylureas in Asian patients with type 2 diabetes using the Joint Asia Diabetes Evaluation (JADE) register.

To explore the patterns of use of oral glucose-lowering drugs (OGLDs) in Asian patients with type 2 diabetes (T2D), focusing on sulphonylureas (SUs), and to describe patient profiles according to treatment regimen. We conducted a cross-sectional analysis of data from adults with T2D from 11 Asian countries/regions with structured assessment enrolled in the prospective Joint Asia Diabetes Evaluation (JADE) register between November 2007 and December 2019. Patients receiving insulin and/or injectable glucagon-like peptide-1 receptor agonists were excluded. Amongst 62 512 patients (mean ± standard deviation age: 57.3 ± 11.8 years; 53.6% men), 54 783 (87.6%) were treated with OGLDs at enrolment. Most received one (37.5%) or two (44.2%) OGLDs. In the entire cohort, 59.4% of treated patients received SU-based therapy with variations amongst countries/regions. Overall, 79.5% of SU regimens were based on SUs plus metformin, and 22.1% on SUs plus dipeptidyl peptidase-4 inhibitors. Among SU users, gliclazide was most commonly prescribed (46.7%), followed by glimepiride (40.0%) and glibenclamide (8.1%). More gliclazide users entered the cohort with glycated haemoglobin levels <53 mmol/mol (7%) than non-gliclazide SU users (odds ratio [OR] 1.09, 95% CI 1.02-1.17), with less frequent self-reported hypoglycaemia in the 3 months before registration (OR 0.81, 95% CI 0.72-0.92; adjusted for sociodemographic factors, cardiometabolic risk factors, complications, use of other OGLDs, country/region and year of registration). In Asia, SUs are a popular OGLD class, often combined with metformin. Good glycaemic control and safety profiles associated with the use of SUs, including gliclazide, support their position as a key treatment option in patients with T2D.

Micro-mesoporous graphitized carbon fiber as hydrophobic adsorbent that removes volatile organic compounds from air

Activated carbon fibers (ACFs) are a popular class of adsorbents for volatile organic compounds (VOCs), however, their abundant hydrophilic functional groups greatly limit their selective adsorption capacity for organics under humid conditions. In this work, porous graphitized carbon fibers (PGCFs) with enhanced hydrophobicity were prepared by KOH-promoted catalytic graphitization of viscose-based ACF, and their adsorption capacity for representative VOCs was investigated. Characterizations showed that the PGCFs had reduced O/C ratios, high specific surface areas exceeding 2200 m2/g, and micro-mesoporous structure. Under dry conditions, the adsorption capacity of a typical PGCF for toluene, cyclohexane and ethanol reached 7.2, 4.6 and 2.0 mmol/g, respectively. At 80 % relative humidity, the adsorption capacity for toluene and cyclohexane retained 92 % and 85 %, respectively, while that for ethanol even increased by 54 %. The abundant π electrons and high polarizability of the PGCF strengthened the π-π interaction and dispersion force with non-polar organics, thereby enhancing their removal. The capillary condensed water in mesopores could act as absorbate for the uptake of polar ethanol under humid conditions. The toluene-adsorbed PGCF was effectively regenerated at a low temperature of 80 °C. Benefiting from their excellent adsorption capacity and moisture-resistance, PGCFs may be promising adsorbents to remove VOCs from air.

Charge-Transfer Luminescence in a Molecular Donor-Acceptor Complex: Computational Insights.

Donor-acceptor molecular complexes are a popular class of materials utilizing charge-transfer states for practical applications. A recent class of donor-acceptor dyads based on the fluorescent BODIPY functionalized with triphenylamine (TPA) shows the peculiar property of dual fluorescence. It is hypothesized that instead of the sensitized charge-transfer state being optically dark, it provides an additional bright radiative pathway. Here we use time-dependent density functional theory to characterize the energetic alignment of excitonic and charge-transfer states in a BODIPY-TPA molecular complex. We observe that using a long-range exchange corrected functional in combination with state-specific solvation scheme gives a qualitatively correct alignment of the exciton and charge-transfer states and an enhancement in oscillator strength for the equilibrium solvated charge-transfer state, in agreement with experiment. This work provides rationalization of charge-transfer state emission and provides a foundation to explore charge-transfer using ab initio excited-state nonadiabatic dynamics.

Toward a unifying framework for the modeling and identification of motor primitives.

A large body of evidence suggests that human and animal movements, despite their apparent complexity and flexibility, are remarkably structured. Quantitative analyses of various classes of motor behaviors consistently identify spatial and temporal features that are invariant across movements. Such invariant features have been observed at different levels of organization in the motor system, including the electromyographic, kinematic, and kinetic levels, and are thought to reflect fixed modules—named motor primitives—that the brain uses to simplify the construction of movement. However, motor primitives across space, time, and organization levels are often described with ad-hoc mathematical models that tend to be domain-specific. This, in turn, generates the need to use model-specific algorithms for the identification of both the motor primitives and additional model parameters. The lack of a comprehensive framework complicates the comparison and interpretation of the results obtained across different domains and studies. In this work, we take the first steps toward addressing these issues, by introducing a unifying framework for the modeling and identification of qualitatively different classes of motor primitives. Specifically, we show that a single model, the anechoic mixture model, subsumes many popular classes of motor primitive models. Moreover, we exploit the flexibility of the anechoic mixture model to develop a new class of identification algorithms based on the Fourier-based Anechoic Demixing Algorithm (FADA). We validate our framework by identifying eight qualitatively different classes of motor primitives from both simulated and experimental data. We show that, compared to established model-specific algorithms for the identification of motor primitives, our flexible framework reaches overall comparable and sometimes superior reconstruction performance. The identification framework is publicly released as a MATLAB toolbox (FADA-T, https://tinyurl.com/compsens) to facilitate the identification and comparison of different motor primitive models.

Systematic replication enables normalization of high-throughput imaging assays.

High-throughput fluorescent microscopy is a popular class of techniques for studying tissues and cells through automated imaging and feature extraction of hundreds to thousands of samples. Like other high-throughput assays, these approaches can suffer from unwanted noise and technical artifacts that obscure the biological signal. In this work, we consider how an experimental design incorporating multiple levels of replication enables the removal of technical artifacts from such image-based platforms. We develop a general approach to remove technical artifacts from high-throughput image data that leverages an experimental design with multiple levels of replication. To illustrate the methods, we consider microenvironment microarrays (MEMAs), a high-throughput platform designed to study cellular responses to microenvironmental perturbations. In application to MEMAs, our approach removes unwanted spatial artifacts and thereby enhances the biological signal. This approach has broad applicability to diverse biological assays. Raw data are on synapse (syn2862345), analysis code is on github: gjhunt/mema_norm, a reproducible Docker image is available on dockerhub: gjhunt/mema_norm. Supplementary data are available at Bioinformatics online.

Seeking an intersectional “convergence des luttes” in the occupied theaters of Marseille

In recent culture wars in France, critics claim that intersectionality “fractures everything” (Emmanuel Macron, Le Point). Stéphane Beaud and Gérard Noiriel argue that “identitarian” discourses—and an emphasis on race in particular—have contributed to divisions in popular classes and divided progressive activists (Le Monde Diplomatique). At issue in these debates is whether explicit references to modes of gendered and racialized oppression will be recognized as legitimately universalist in accordance with the French republican tradition, a question explicitly addressed by recent scholarship proposing a rethinking of universalism—more “concrete” for Lépinard and Mazouz (2021) and more “inclusive” for Hourya Bentouhami (2020). This article examines the occupation of two theaters in Marseille as a setting where universalist ideals about citizenship were expressed and put into practice. In March 2021, more than 100 theaters were occupied across France, some by the CGT (Confédération Générale du Travail) union, and some by theater students. Whereas unions prioritized economic issues in the arts sector, students framed the occupations as a broader struggle against racism, sexism, police violence, and environmental degradation, arguing that these must be considered together. Drawing on interviews and occupation documents, this article examines the differences and points of alignment between students and unions. This analysis highlights points of collaboration and exchange, and also shows how the intersectional priorities of students extended the scope of the occupations to include other marginalized populations. Here, an intersectional approach fostered collaboration not only across two currents of social justice activism, but also with other partners across the urban territory of Marseille, thereby showing intersectionality’s value and compatibility within contemporary French social justice activism.

PREPARATION, CHARACTERIZATION, AND OPTIMIZATION OF MEBENDAZOLE SPHERICAL AGGLOMERATES USING MODIFIED EVAPORATIVE PRECIPITATION IN AQUEOUS SOLUTION (EPAS)

Objective: Mebendazole is a popular benzimidazole class anthelmintic drug useful in the treatment of main infections of threadworms as well as other less common worm infections like whipworm, roundworm, and hookworm in adults and children over 2 y of age. It is poorly soluble in water resulting in poor absorption from the intestinal tract leading to a decrease in bioavailability. Moreover, Mebendazole has poor flowability due to the needle-shaped crystals. This work was carried out with the aim of increasing the flowability and solubility of Mebendazole. Methods: A 32 full factorial design was used to investigate the effect of the concentration of Mebendazole and the quantity of water as an external phase using evaporative precipitation into an aqueous solution. The prepared agglomerates were characterized for particle size distribution, shape, Hausner ratio, Carr’s index and % dissolved in 60 min (C60). Results: The prepared agglomerates were found to be monodispersed. They also showed a decrease in the Hausner ration and Carr’s index, indicating improved flowability. Increase in C60 indicated that the agglomerates were found to have increased water solubility. Conclusion: Scanning Electron Microscopy showed that the agglomerates were spherical in shape. Fourier Transformed Infra-Red studies showed no chemical change in the prepared spherical agglomerates. Differential Scanning Calorimetry and X-ray diffraction studies showed an increase in amorphous characteristics of prepared spherical agglomerates. This method may be used for drugs with similar characteristics as Mebendazole.