Electromagnetic Response of Clustered Charged Particles

Electromagnetic response of clustered charged particles is the foundation of electromagnetic wave interaction with various natural phenomena, such as sandstorm, cloud, and volcano eruption. Previous studies usually employed assumption of independent charged particles, without considering the coupling between them. Here, we build up a general numerical model considering the multiple scattering effect, and test it with a charged two- and four-particle system. The numerical results show that independence assumption fails, while the number density of clustered charged particles is getting larger. This work may pave the way for deeper understanding on the electromagnetic interaction of clustered charged particles.

Alleviating the independence assumptions of averaged one-dependence estimators by model weighting

Of numerous proposals to refine naive Bayes by weakening its attribute independence assumption, averaged one-dependence estimators (AODE) has been shown to be able to achieve significantly higher classification accuracy at a moderate cost in classification efficiency. However, all one-dependence estimators (ODEs) in AODE have the same weights and are treated equally. To address this issue, model weighting, which assigns discriminate weights to ODEs and then linearly combine their probability estimates, has been proved to be an efficient and effective approach. Most information-theoretic weighting metrics, including mutual information, Kullback-Leibler measure and the information gain, place more emphasis on the correlation between root attribute (value) and class variable. We argue that the topology of each ODE can be divided into a set of local directed acyclic graphs (DAGs) based on the independence assumption, and multivariate mutual information is introduced to measure the extent to which the DAGs fit data. Based on this premise, in this study we propose a novel weighted AODE algorithm, called AWODE, that adaptively selects weights to alleviate the independence assumption and make the learned probability distribution fit the instance. The proposed approach is validated on 40 benchmark datasets from UCI machine learning repository. The experimental results reveal that, AWODE achieves bias-variance trade-off and is a competitive alternative to single-model Bayesian learners (such as TAN and KDB) and other weighted AODEs (such as WAODE).

Investigating the transparency of reporting in two-sample summary data Mendelian randomization studies

Background: Two-sample Mendelian randomization (2SMR) is an increasingly popular epidemiological method that uses genetic variants as instruments for making causal inferences. Clear reporting of methods employed in such studies is important for evaluating their underlying quality. However, the quality of methodological reporting of 2SMR studies is currently unclear. Objectives: We aimed to assess the reporting quality of studies that used MR-Base, one of the most popular platforms for implementing 2SMR analysis. Methods: We searched Web of Science Core Collection, PsycInfo, MEDLINE, EMBASE and citations listed in Google Scholar of the MR-Base descriptor paper for any published MR study that used MR-Base during any component of the MR analysis. Studies were screened by two independent reviewers. We created a bespoke reporting checklist to evaluate reporting quality of 2SMR studies. Information was extracted by at least two independent reviewers. Results: 87 studies were included in the primary analysis, of which 14 had at least 10 phenotypes. Reporting quality was generally poor with a mean of 53% (SD = 14%) of items reported in each study. Many items required for evaluating the validity of key assumptions made in MR were poorly reported: only 44% of studies provided sufficient details for assessing if the variant associates with the exposure ('relevance' assumption), 31% for the assessing if there are any variant-outcome confounders ('independence' assumption), 89% for the assessing if the variant causes the outcome independently of the exposure ('exclusion restriction' assumption), and 32% for assumptions of falsification tests. We found no evidence of a change in reporting over time and findings were similar in a random sample of MR studies that did not use the MR-Base platform. Discussion: The quality of reporting of two-sample Mendelian randomization studies in our sample was generally poor. Journals and researchers should implement the STROBE-MR guidelines to improve reporting quality. Other: Funding: ESRC, Regression: This study pre-registered on the OSF, and the protocol can be found at DOI 10.17605/OSF.IO/NFM27

Effect of Experimental Blocking on the Suppression of Spatial Dependence Potentially Attributable to Physicochemical Properties of Soils

One of the basic principles of experimental design is blocking, which is an important factor in the treatment of the systematic spatial variability that can be found in the edaphic properties where agricultural experiments are conducted. Blocking has a mitigating or suppressing effect on the spatial dependence in the residuals of a model, something desirable in standard linear modeling, specifically in design models. Some computer programs yield a p value associated with the blocking effect in the analysis of variance table that in many cases has been incorrectly used to discard it, and although it may improve some properties of the analysis, it may affect the independence assumption required in several models. Therefore, the present research recommends the use of the H statistic associated with the corrected blocking efficiency to show the role of blocking in modeling with the incorporation of an additional advantage rarely considered related to the suppression or mitigation of spatial dependence. With the use of the Moran index, the spatial dependence of the residuals was studied in a simple factorial design in a completely randomized and blocking field layout, which evidenced the advantages of blocking in the mitigation or suppression of the spatial dependence despite the apparently little or no importance it seems to show in the analysis of variance table.

Accounting for preference dependency in target costing – a note

Target costing is a well-established strategic cost management tool in theory and practice. The original target costing model implies independence of customer preferences resulting in additive utility functions for the customer-oriented optimization of cost structures. We argue that this independence of preferences is not given until a minimum variant of a product is reached that provides its inherent functionality. This is reasonable since one cannot assign customer utility to a product that does not function in its most basic way. Our modified model accounts for the dependency of customer preferences and differentiates between the costs necessary to produce a minimum variant and those related to product features beyond this minimum variant. The customer-oriented optimization of the cost structure is then conducted only for those costs that exceed the costs of the minimum variant. This modification justifies the preference independence assumption in target costing and allows for a more reasonable assignment of required adjustments in costs per product component.