Original Data Research Articles

Partisanship and the Pandemic: How and Why Americans Followed Party Cues on COVID-19.

The United States underperformed its potential in responding to the COVID-19 pandemic. Using original survey data from April 2020 to March 2022, we show that political partisanship may have contributed to this inconsistent response by distinguishing elites and citizens who took the crisis seriously from those who did not. This division was not inevitable; when the crisis began, Democrats and Republicans differed little in their viewpoints and actions. However, partisans increasingly diverged when their preferred political leaders provided them with opposing cues. We outline developments in party politics over the last half-century that contributed to partisan division on COVID-19, most centrally an anti-expertise bias among Republicans. Accordingly, Republicans' support for mitigation measures, perception of severity of COVID-19, and support for vaccines gradually decreased after the initial outbreak. Partisan differences also showed up at the state level; Trump's vote share in 2016 was negatively associated with mask use and positively associated with COVID-19 infections. Diverging elite cues provided fertile ground for the partisan pandemic, underscoring the importance of political accountability, even in an era of polarization.

Causal relationship between hypertension and risk of constipation: A 2-way 2-sample Mendelian randomization study.

Patients with hypertension have a higher risk of having constipation and vice versa. The causal association between these 2 variables is not proven. We performed a retrospective Mendelian randomization analysis to determine the causal association between constipation and hypertension. Two-sample 2-way Mendelian randomization analysis was used. Genetic variants for constipation were derived from genome-wide association study data of European origin (15,902 cases and 395,721 controls). Corresponding genetic associations for hypertension were derived from European ancestry GWAS data (54,358 cases and 408,652 controls). Genetic susceptibility to hypertension was associated with an increased risk of constipation (OR: 3.459, 95% CI: 1.820-6.573, P < .001). In an inverse Mendelian randomization analysis, no causal effect of constipation on hypertension was found (OR: 0.999, 95% CI: 0.987-1.011, P = .834). In sensitivity analyses, these associations persisted and no multiple effects were found. This study suggests that there is a causal relationship between hypertension and constipation and that hypertension may increase the risk of developing constipation.

Fast Attack Detection for Cyber-Physical Systems Using Dynamic Data Encryption.

To defend the cyber-physical system (CPSs) from cyber-attacks, this work proposes an unified intrusion detection mechanism which is capable to fast hunt various types of attacks. Focusing on securing the data transmission, a novel dynamic data encryption scheme is developed and historical system data is used to dynamically update a secret key involved in the encryption. The core idea of the dynamic data encryption scheme is to establish a dynamic relationship between original data, secret key, ciphertext and its decrypted value, and in particular, this dynamic relationship will be destroyed once an attack occurs, which can be used to detect attacks. Then, based on dynamic data encryption, a unified fast attack detection method is proposed to detect different attacks, including replay, false data injection (FDI), zero-dynamics, and setpoint attacks. Extensive comparison studies are conducted by using the power system and flight control system. It is verified that the proposed method can immediately trigger the alarm as soon as attacks are launched while the conventional χ2 detection could only capture the attacks after the estimation residual goes over the predetermined threshold. Furthermore, the proposed method does not degrade the system performance. Last but not the least, the proposed dynamic encryption scheme turns to normal operation mode as the attacks stop.

Time Efficient Ultrasound Localization Microscopy Based on A Novel Radial Basis Function 2D Interpolation.

Ultrasound localization microscopy (ULM) allows for the generation of super-resolved (SR) images of the vasculature by precisely localizing intravenously injected microbubbles. Although SR images may be useful for diagnosing and treating patients, their use in the clinical context is limited by the need for prolonged acquisition times and high frame rates. The primary goal of our study is to relax the requirement of high frame rates to obtain SR images. To this end, we propose a new time-efficient ULM (TEULM) pipeline built on a cutting-edge interpolation method. More specifically, we suggest employing Radial Basis Functions (RBFs) as interpolators to estimate the missing values in the 2-dimensional (2D) spatio-temporal structures. To evaluate this strategy, we first mimic the data acquisition at a reduced frame rate by applying a down-sampling (DS = 2, 4, 8, and 10) factor to high frame rate ULM data. Then, we up-sample the data to the original frame rate using the suggested interpolation to reconstruct the missing frames. Finally, using both the original high frame rate data and the interpolated one, we reconstruct SR images using the ULM framework steps. We evaluate the proposed TEULM using four in vivo datasets, a Rat brain (dataset A), a Rat kidney (dataset B), a Rat tumor (dataset C) and a Rat brain bolus (dataset D), interpolating at the in-phase and quadrature (IQ) level. Results demonstrate the effectiveness of TEULM in recovering vascular structures, even at a DS rate of 10 (corresponding to a frame rate of sub-100Hz). In conclusion, the proposed technique is successful in reconstructing accurate SR images while requiring frame rates of one order of magnitude lower than standard ULM.

Comprehensive Multiview Representation Learning via Deep Autoencoder-Like Nonnegative Matrix Factorization.

Learning a comprehensive representation from multiview data is crucial in many real-world applications. Multiview representation learning (MRL) based on nonnegative matrix factorization (NMF) has been widely adopted by projecting high-dimensional space into a lower order dimensional space with great interpretability. However, most prior NMF-based MRL techniques are shallow models that ignore hierarchical information. Although deep matrix factorization (DMF)-based methods have been proposed recently, most of them only focus on the consistency of multiple views and have cumbersome clustering steps. To address the above issues, in this article, we propose a novel model termed deep autoencoder-like NMF for MRL (DANMF-MRL), which obtains the representation matrix through the deep encoding stage and decodes it back to the original data. In this way, through a DANMF-based framework, we can simultaneously consider the multiview consistency and complementarity, allowing for a more comprehensive representation. We further propose a one-step DANMF-MRL, which learns the latent representation and final clustering labels matrix in a unified framework. In this approach, the two steps can negotiate with each other to fully exploit the latent clustering structure, avoid previous tedious clustering steps, and achieve optimal clustering performance. Furthermore, two efficient iterative optimization algorithms are developed to solve the proposed models both with theoretical convergence analysis. Extensive experiments on five benchmark datasets demonstrate the superiority of our approaches against other state-of-the-art MRL methods.

Manifold Peaks Nonnegative Matrix Factorization.

Nonnegative matrix factorization (NMF) has attracted increasing interest for its high interpretability in recent years. It is shown that the NMF is closely related to fuzzy k -means clustering, where the basis matrix represents the cluster centroids. However, most of the existing NMF-based clustering algorithms often have their decomposed centroids deviate away from the data manifold, which potentially undermines the clustering results, especially when the datasets lie on complicated geometric structures. In this article, we present a manifold peaks NMF (MPNMF) for data clustering. The proposed approach has the following advantages: 1) it selects a number of MPs to characterize the backbone of the data manifold; 2) it enforces the centroids to lie on the original data manifold, by restricting each centroid to be a conic combination of a small number of nearby MPs; 3) it generalizes the graph smoothness regularization to guide the local graph construction; and 4) it solves a general problem of quadratic regularized nonnegative least squares (NNLSs) with group l0 -norm constraint and further develops an efficient optimization algorithm to solve the objective function of the MPNMF. Extensive experiments on both synthetic and real-world datasets demonstrate the effectiveness of the proposed approach.

Diagnosis-Guided Deep Subspace Clustering Association Study for Pathogenetic Markers Identification of Alzheimer's Disease Based on Comparative Atlases.

The roles of brain region activities and genotypic functions in the pathogenesis of Alzheimer's disease (AD) remain unclear. Meanwhile, current imaging genetics methods are difficult to identify potential pathogenetic markers by correlation analysis between brain network and genetic variation. To discover disease-related brain connectome from the specific brain structure and the fine-grained level, based on the Automated Anatomical Labeling (AAL) and human Brainnetome atlases, the functional brain network is first constructed for each subject. Specifically, the upper triangle elements of the functional connectivity matrix are extracted as connectivity features. The clustering coefficient and the average weighted node degree are developed to assess the significance of every brain area. Since the constructed brain network and genetic data are characterized by non-linearity, high-dimensionality, and few subjects, the deep subspace clustering algorithm is proposed to reconstruct the original data. Our multilayer neural network helps capture the non-linear manifolds, and subspace clustering learns pairwise affinities between samples. Moreover, most approaches in neuroimaging genetics are unsupervised learning, neglecting the diagnostic information related to diseases. We presented a label constraint with diagnostic status to instruct the imaging genetics correlation analysis. To this end, a diagnosis-guided deep subspace clustering association (DDSCA) method is developed to discover brain connectome and risk genetic factors by integrating genotypes with functional network phenotypes. Extensive experiments prove that DDSCA achieves superior performance to most association methods and effectively selects disease-relevant genetic markers and brain connectome at the coarse-grained and fine-grained levels.

A Unified Approach to Coreset Learning.

Coreset of a given dataset and loss function is usually a small weighed set that approximates this loss for every query from a given set of queries. Coresets have shown to be very useful in many applications. However, coresets' construction is done in a problem-dependent manner and it could take years to design and prove the correctness of a coreset for a specific family of queries. This could limit coresets' use in practical applications. Moreover, small coresets provably do not exist for many problems. To address these limitations, we propose a generic, learning-based algorithm for construction of coresets. Our approach offers a new definition of coreset, which is a natural relaxation of the standard definition and aims at approximating the average loss of the original data over the queries. This allows us to use a learning paradigm to compute a small coreset of a given set of inputs with respect to a given loss function using a training set of queries. We derive formal guarantees for the proposed approach. Experimental evaluation on deep networks and classic machine learning problems show that our learned coresets yield comparable or even better results than the existing algorithms with worst case theoretical guarantees (that may be too pessimistic in practice). Furthermore, our approach applied to deep network pruning provides the first coreset for a full deep network, i.e., compresses all the networks at once, and not layer by layer or similar divide-and-conquer methods.

Lean Six Sigma and Productivity Improvement in a Metalworking Company’s Mining Equipment Repair Service

El artículo aborda el estudio de una propuesta de mejora de la productividad de una empresa metalmecánica mediante la implementación de la metodología Lean Six Sigma (LSS). El objetivo es reducir los defectos de reparación de los componentes de maquinaria pesada para la actividad minera. El estudio describe el proceso de simulación a partir de los datos originales con la finalidad de plantear un límite de nivel de defectos, metas de eficacia y eficiencia para lograr un mejor nivel de productividad. Se aplicaron las etapas del sistema DMAIC para definir el problema de productividad en la reparación de los componentes pertenecientes a la categoría V. A partir de los datos originales, se obtuvo un Cp de 0.91 y un Cpk de −0.64. En la simulación se utilizaron las herramientas Kanban y Heinjunka, lo que dio como resultado un Cp de 1.09 y un Cpk de 0.95. El estudio concluye que la metodología LSS es viable y beneficiosa para las empresas metalmecánicas.

SFL-MDrone: Synchronous federated learning enabled multi drones

UAVs or Drones can be used to support wireless communication by acting as flying or mobile Base Stations for the accumulation of the different types of data to train the models. However, in traditional or DL-based UAVs, the raw data is sent from the devices to the centralized server, which causes problems in terms of the privacy of the devices and the UAVs’ communication resources or limited processing. Therefore, the issue with DL-based UAVs is that sending the original data to the centralized body raises questions about security and privacy. The transmission of distributed, unprocessed data from the drones to the cloud, including interactive media information data types, requires a significant amount of network bandwidth and more energy, which has an enormous effect on several trade-offs, including communication rates and computation latencies. Data packet loss caused by asynchronous transmission, which doesn’t prevent peer-to-peer communication, is a concern with AFL-based UAVs. Therefore, in order to address the aforementioned issues, we have introduced SFL-based UAVs that focus on creating algorithms in which the models simultaneously update the server as they wait for all of the chosen devices to communicate. The proposed framework enables a variety of devices, including mobile and UAV devices, to train or learn their algorithms for machine learning before updating the models and parameters simultaneously to servers or manned aerial data centers for model buildup without transferring their original private information. This decreases packet loss and privacy threats while also enhancing round effectiveness as well as model accuracy. The comparative analysis of AFL and SFL techniques in terms of accuracy, global rounds, and communication rounds are offered. Simulation findings suggest that the proposed methodology improves in terms of global rounds and accuracy.

Keeping up appearances: US domestic extremist organizations and the effects of membership on domestic terrorism perpetrators

ABSTRACT Within the broader discussion of US domestic terrorism, the impact of formal extremist groups on the behaviour of perpetrators of ideologically motivated crime stands as a key albeit under examined issue. Radicalization and terrorism have long been understood as group-driven processes, yet the degree and rate of group affiliation among US extremist offenders is highly variable, especially in the context of rising instances of lone actor attacks. This raises the question of the role that group affiliation plays in relation to perpetrators of extremist violence. For while discourse surrounding domestic terrorism has largely classified these organizations as facilitators or enablers of radicalization and extremist violence, patterns in recent years suggest these groups may in fact do the opposite and dampen the violent impulses of their members to preserve their organizational interests. This paper presents an analysis of data adapted from the Profiles of Individual Radicalization in the United States (PIRUS) dataset as well as original data and finds that membership in formal extremist groups does appear to create a moderating effect on perpetrators of ideologically motivated crime, reducing the probability of group members in engaging in violence.

The role of the new institutional sociology in implementing the environmental agenda

The new institutional Sociology declares that organizations, whether public or private, are influenced by norms, customs and beliefs institutionalized in their structure. Therefore, it is possible to infer that these factors can also influence organizations' actions regarding the environment and sustainability. Based on this understanding, we sought to investigate the role of the new institutional Sociology in the implementation of the environmental agenda. Given the purpose of this work, it is possible to classify it as bibliographic and exploratory in nature. Scientific articles, books, reports and other secondary data of national and international origin were used as theoretical support for the research. In the context of the environmental agenda, institutions can materialize in the form of laws, regulations, public policies, positioning of the professional class, behavior of successful organizations, consumer preferences and many other factors. It was noticed that institutions pressure organizations to develop sustainable practices and these, in turn, enable environmental conservation without necessarily impeding economic development. It was also possible to identify that the pressures felt by organizations to adopt similar environmental practices are explained through the phenomenon understood as institutional isomorphism. This occurs in three ways: coercive; mimetic and normative. Furthermore, this study allowed us to understand the levels of implementation of an environmental agenda. However, it was observed that agendas can be implemented at global, national and local levels. And, the adoption of environmentally sustainable practices in organizations is motivated, above all, by the need to legitimize organizations.

Improving ventilation classification in under-actuated zones: a k-nearest neighbor and data preprocessing approach

This study investigates the use of k-nearest neighbors (k-NN) for classifying occupant positions in under-actuated zones, aiming to enhance ventilation control. The focus is on evaluating different data preprocessing techniques, particularly cumulative moving average (CMA), Kalman filtering (KF), and their combination, to boost the k-NN model's reliability and accuracy. The research uses received signal strength indicator (RSSI) data in a controlled setting. The methodology involves dividing the dataset into training and testing subsets and using root mean squared error (RMSE) to determine the best k value for model validation. The study performs a comparative analysis of the k-NN model's performance with both original and preprocessed RSSI data, focusing on metrics such as accuracy, precision, recall, F1-score, and RMSE. The findings emphasize the significant impact of the combined CMA-KF preprocessing technique in improving the model's accuracy and reliability. Specifically, this approach achieved an accuracy of 98.58%. The RMSE values are particularly noteworthy, exhibiting a perfect fit (RMSE of 0) for training data and a remarkably low RMSE of 0.119 for testing data, confirming the model's high accuracy and predictive capability.

Religious Leaders and Resistance to Repression: The Bishops Opposed to Argentina’s Dirty War

Can religious leaders who oppose state violence reduce its use? Communal elites, such as religious leaders, may oppose human rights violations. This article argues that these leaders, part of institutions embedded in local communities and with influence based on traditional power, reduce repression when they oppose dictatorships. The argument’s main implication is tested in Argentina during the Dirty War of its 1976???1983 military dictatorship, using original archival data on the country’s Catholic bishops. Opposed bishops are associated with reduced disappearances and killings. A variety of evidence is consistent with opposed bishops taking two types of actions to resist repression: assisting likeminded local agents and participating in human rights advocacy campaigns. The findings point to the importance of influential civil society actors in reducing state violence.

Reverse Graph Learning for Graph Neural Network.

Graph neural networks (GNNs) conduct feature learning by taking into account the local structure preservation of the data to produce discriminative features, but need to address the following issues, i.e., 1) the initial graph containing faulty and missing edges often affect feature learning and 2) most GNN methods suffer from the issue of out-of-example since their training processes do not directly generate a prediction model to predict unseen data points. In this work, we propose a reverse GNN model to learn the graph from the intrinsic space of the original data points as well as to investigate a new out-of-sample extension method. As a result, the proposed method can output a high-quality graph to improve the quality of feature learning, while the new method of out-of-sample extension makes our reverse GNN method available for conducting supervised learning and semi-supervised learning. Experimental results on real-world datasets show that our method outputs competitive classification performance, compared to state-of-the-art methods, in terms of semi-supervised node classification, out-of-sample extension, random edge attack, link prediction, and image retrieval.

LiFoL: An Efficient Framework for Financial Distress Prediction in High-Dimensional Unbalanced Scenario

Corporate financial distress will significantly damage the company’s and its stakeholders’ interests and even lead to a global financial crisis. Therefore, finding an efficient method for financial distress prediction (FDP) to avoid greater losses is essential. Although there is a lot of research and progress in this field, the existing methods rarely consider the problems of high dimensionality and class imbalance, which will largely limit the models to achieve satisfactory performance. To alleviate these problems, this article first proposes a novel Lightspace-SMOTE upsampling method, which can reduce the feature dimensionality and increase the signal-to-noise ratio (SNR) of the original data and then upsample it to increase the number of minor class samples. In addition, this article proposes an efficient ensemble framework (LiFoL) that combines Lightspace-SMOTE, focal loss (FL), and LightGBM, which can not only focus more on minor class and the hard-to-class samples but also obtain better performance. At the same time, the feature importance provided by the model can provide strong support for model interpretability. Experimental results show that the Lightspace-SMOTE upsampling method can help the model achieve higher scores in area under ROC curve (AUC) and recall, especially in the case of longer prediction periods. Compared with current methods, LiFoL can achieve more than 10% improvement in AUC and more than 20% in recall.

Linguistic diversity in media and state language proficiencies: evidence from Hungarian minority regions in central-Eastern Europe

ABSTRACT Can linguistic diversity in the media impede state language proficiency? In this paper, we test the extent to which the availability of minority languages in the media affects state language proficiency for ethnic minorities. We take advantage of an exogeneous development: The rapid expansion of Budapest-originating media outside Hungary in the mid-2000s transformed the media landscape in neighbouring states. Using an original survey data of ethnic Hungarians in minority regions in four Central-Eastern European states over two waves, we find (1) the availability of minority language media means minorities are consuming less media in the state language, which in turn induces low levels of state language proficiency – i.e. through an exogeneous exposure mechanism; and (2) minorities with weaker state language proficiency choose to consume minority language media – i.e. through an endogenous selection mechanism. The results suggest a strong relationship between the language of media consumption and linguistic proficiency.

Revitalising smallholder agriculture: the impact of technical training in rural Lebanon

AbstractThis paper provides new evidence about the impact of an agricultural development initiative focused on the cherry sector and implemented in rural areas of the Bekaa Valley (Lebanon). The initiative aims to enhance economic opportunities of smallholder farmers by strengthening technical skills, fostering sustainable productions and developing market linkages. Using original micro data from a sample of 118 smallholder cherry farmers, we explore, through a Difference-in-Differences approach, whether the development initiative, based on the provision of extension services and accompaniment through technical training, impacts on a broad set of alternative agricultural outcomes—namely, total cherry production, average market price, management competency and the adoption of improved agricultural practices. The results show that beneficiary small-scale farmers achieve better performances in three outcomes out of the four considered, with the adoption of improved and sustainable agricultural practices as the most remarkable result. Conversely, the management of the agricultural economic activity does not experience any statistically significant variation connected to the initiative implementation. The analysis of a limited source of treatment heterogeneity discloses the primary role of technical training, rather than other kinds of material support, to explain the main results.

Publication of pharmacy resident research projects: A systematic review and meta-analysis.

In an effort to expedite the publication of articles, AJHP is posting manuscripts online as soon as possible after acceptance. Accepted manuscripts have been peer-reviewed and copyedited, but are posted online before technical formatting and author proofing. These manuscripts are not the final version of record and will be replaced with the final article (formatted per AJHP style and proofed by the authors) at a later time. Pharmacy residents often aspire to develop research skills through conducting a research project. Project publication rates among pharmacy residents are variable and at times low; however, previous studies have been limited to specific geographic regions and timeframes. This study sought to conduct a systematic review and meta-analysis to determine the proportion of pharmacy resident research projects published in the peer-reviewed literature. A systematic review of PubMed MEDLINE, Embase, and the Web of Science Core Collection was performed from database inception to May 25, 2023. Articles were included if they were full-text, peer-reviewed manuscripts of original research presenting observational data regarding pharmacy resident research project publication rates. Data extraction and assessment of risk of bias were conducted by 2 independent reviewers. A proportional meta-analysis using a random effects model of the included studies was conducted to generate a pooled, overall proportion. The search yielded 5,225 records and 12 articles that met the inclusion criteria. All studies were retrospective and observational. Risk of selection and cohort identification biases was "high," whereas that of detection and timeframe biases was "low." The included studies represented 6,990 resident research projects, 777 of which were published in the peer-reviewed literature. Publication rates across individual studies ranged from 1.8% to 36.2%. The pooled proportion (scale of 0 to 1) of projects published was 0.13 (0.09-0.19). Pharmacy resident research project publication rates are low at 13%. Furthermore, studies reporting project publication rates over time suggest a neutral or negative trend in publication rates despite an exponential increase in the number of pharmacy residents.

The prevalence of gambling problems in prison populations: A systematic review and meta-analysis.

The overall prevalence of gambling problems across prison populations is currently unknown. The objective of the present study was therefore to quantitatively synthetize prevalence estimates of gambling problems in prison populations using a random effects meta-analytic model and to investigate if the estimates were moderated by time frame, cut-off levels, and sample size. To be included the studies had to report original data on the prevalence of gambling problems in a prison sample and to be written in a European language, whereas data based on abstracts or qualitative reports were excluded. The search ended on December 1, 2023 and were conducted in Web of Science, PubMed, Cinahl, PsycINFO, Embase, Google Scholar, Grey Literature Report, and GreyNet. Risk of bias was assessed with a standardized 10-item measure for epidemiological studies. A total of 26 studies comprising 9,491 participants were included. The vast majority of the participants were males. The most commonly used instrument for assessment of gambling problems was the South Oaks Gambling Screen. The pooled random-effects gambling problems prevalence estimate was 30.8% (95% CI = 25.1-37.3). The meta-regression analysis showed that none of the three moderator variables (criteria, timeframe, sample size) were related to the gambling problems prevalence. Common limitations of the included studies entailed not being representative nationally or for the target population, lack of randomization, and low response rate. The meta-analysis was restricted to studies published in a European language. Overall, the studies show that 1 in 3 prisoners has gambling problems and suggests that more emphasis on relevant prevention and treatment is warranted for this population. The study was funded by the Norwegian Competence Center for Gambling and Gaming Research and pre-registered at PROSPERO (CRD42023390552).