Correction to: Cluster identification, selection, and description in Cluster randomized crossover trials: the PREP-IT trials

An amendment to this paper has been published and can be accessed via the original article

A New Virtual Backbone forWireless Ad-Hoc Sensor Networks with Connected Dominating Set

http://citi.insa-lyon.fr/wons2006/index.htmlA wireless Ad-hoc sensor network consists of a number of sensors spread across a geographical area as a collection of sensors that form an ad-hoc wireless network. Sensors are very tiny devices that their primary function is to sense the target, convert the signal into a suitable data format, and pass on the data to a command node. These sensor nodes are very heavily constrained in processing power, and have a limited energy supply. Since energy is such a scarce resource, several algorithms have been developed at the routing and MAC layers to utilize energy efficiently and extend the lifetime of the network. First layer of the sensor networks is the infrastructure layer and there is no backbone for these networks. In this paper, we propose a virtual backbone for these networks and we measure the network lifetime and survivability as the performance evaluation metrics of the proposed model

Multi-Trait Genomic Prediction of Yield-Related Traits in US Soft Wheat under Variable Water Regimes

The performance of genomic prediction (GP) on genetically correlated traits can be improved through an interdependence multi-trait model under a multi-environment context. In this study, a panel of 237 soft facultative wheat (Triticum aestivum L.) lines was evaluated to compare single- and multi-trait models for predicting grain yield (GY), harvest index (HI), spike fertility (SF), and thousand grain weight (TGW). The panel was phenotyped in two locations and two years in Florida under drought and moderately drought stress conditions, while the genotyping was performed using 27,957 genotyping-by-sequencing (GBS) single nucleotide polymorphism (SNP) makers. Five predictive models including Multi-environment Genomic Best Linear Unbiased Predictor (MGBLUP), Bayesian Multi-trait Multi-environment (BMTME), Bayesian Multi-output Regressor Stacking (BMORS), Single-trait Multi-environment Deep Learning (SMDL), and Multi-trait Multi-environment Deep Learning (MMDL) were compared. Across environments, the multi-trait statistical model (BMTME) was superior to the multi-trait DL model for prediction accuracy in most scenarios, but the DL models were comparable to the statistical models for response to selection. The multi-trait model also showed 5 to 22% more genetic gain compared to the single-trait model across environment reflected by the response to selection. Overall, these results suggest that multi-trait genomic prediction can be an efficient strategy for economically important yield component related traits in soft wheat

Religious secularity

Despite its promises, the Islamic state of Iran has systematically prioritized political considerations over religious precepts, inadvertently generating a reformist religious discourse that challenges the very foundations of the Islamic state. This article conceptualizes the religious secularity discourse and the paradoxes ingrained in the Islamic state. The religious secularity discourse rejects the notion that Islamic holy texts offer a blueprint for governance and calls for the secular democratic state to realize the core principle of Islam: justice [Adl]. Towards this end, inclusive secular democratic principles champion a socio-political polity conducive to the cultivation of genuine religiosity, whereby faith and religious practices are free of coercion by fellow citizens or state institutions. By blurring the boundary between the religious and the secular and challenging the religious secular dichotomy, religious secularity highlights the relevance and possibility of harmonizing Islamic and inclusive secular democratic principles