Explore the vast range of titles available.

Aspiring to Fullness in a Secular Age, whose title is inspired by Charles Taylor's magisterial A Secular Age, offers a host of expert analyses of the religious and theological threads running throughout Taylor's oeuvre, illuminating further his approaches to morality, politics, history, and philosophy. Although the scope of Taylor's insight into modern secularity has been widely recognized by his fellow social theorists and philosophers, Aspiring to Fullness focuses on Taylor's insights regarding questions of religious experience. It is with a view to such experience that the volume's contributors consider and assess Taylor's broad analysis of the limits and potentialities of the present age in regard to human fullness or fulfillment. The essays in this volume address crucial questions about the function and significance of religious accounts of transcendence in Taylor's overall philosophical project; the critical purchase and limitations of Taylor's assessment of the centrality of codes and institutions in modern political ethics; the possibilities inherent in Taylor's brand of post-Nietzschean theism; the significance and meaning of Taylor's ambivalence about modern destiny; the possibility of a practical application of his insights within particular contemporary religious communities; and the overall implications of Taylor's thought for theology and philosophy of religion. Although some commentators have referred to a recent religious \"turn\" in Taylor's work, the contributors to Aspiring to Fullness in a Secular Age examine the ways in which transcendence functions, both explicitly and implicitly, in Taylor's philosophical project as a whole.

Causal effects of biodiversity on ecosystem functions can be estimated using experimental or observational designs — designs that pose a tradeoff between drawing credible causal inferences from correlations and drawing generalizable inferences. Here, we develop a design that reduces this tradeoff and revisits the question of how plant species diversity affects productivity. Our design leverages longitudinal data from 43 grasslands in 11 countries and approaches borrowed from fields outside of ecology to draw causal inferences from observational data. Contrary to many prior studies, we estimate that increases in plot-level species richness caused productivity to decline: a 10% increase in richness decreased productivity by 2.4%, 95% CI [−4.1, −0.74]. This contradiction stems from two sources. First, prior observational studies incompletely control for confounding factors. Second, most experiments plant fewer rare and non-native species than exist in nature. Although increases in native, dominant species increased productivity, increases in rare and non-native species decreased productivity, making the average effect negative in our study. By reducing the tradeoff between experimental and observational designs, our study demonstrates how observational studies can complement prior ecological experiments and inform future ones.

This paper presents a high-throughput method for Above Ground Estimation of Biomass (AGBE) in rice using multispectral near-infrared (NIR) imagery captured at different scales of the crop. By developing an integrated aerial crop monitoring solution using an Unmanned Aerial Vehicle (UAV), our approach calculates 7 vegetation indices that are combined in the form of multivariable regressions depending on the stage of rice growth: vegetative, reproductive or ripening. We model the relationship of these vegetation indices to estimate the biomass of a certain crop area. The methods are calibrated by using a minimum sampling area of 1 linear meter of the crop. Comprehensive experimental tests have been carried out over two different rice varieties under upland and lowland rice production systems. Results show that the proposed approach is able to estimate the biomass of large areas of the crop with an average correlation of 0.76 compared with the traditional manual destructive method. To our knowledge, this is the first work that uses a small sampling area of 1 linear meter to calibrate and validate NIR image-based estimations of biomass in rice crops.

This study investigates the differences in mechanical properties between acrylonitrile butadiene styrene (ABS) samples produced using fused deposition modeling (FDM) and stereolithography (SLA) using ABS filaments and ABS-like resin, respectively. The central question is to determine how these distinct printing techniques affect the properties of ABS and ABS-like resin and which method delivers superior performance for specific applications, particularly in dental treatments. The evaluation methods used in this study included Shore D hardness, accelerated aging, tensile testing, Izod impact testing, flexural resistance measured by a 3-point bending test, and compression testing. Poisson’s ratio was also assessed, along with microstructure characterization, density measurement, confocal microscopy, dilatometry, wettability, Fourier-transform infrared spectroscopy (FTIR), and nanoindentation. It was concluded that ABS has the same hardness in both manufacturing methods; however, the FDM process results in significantly superior mechanical properties compared to SLA. Microscopy demonstrates a more accurate sample geometry when fabricated with SLA. It is also concluded that printable ABS is suitable for applications in dentistry to fabricate models and surgical guides using the SLA and FDM methods, as well as facial protectors for sports using the FDM method.

Traditional methods to measure spatio-temporal variations in above-ground biomass dynamics (AGBD) predominantly rely on the extraction of several vegetation-index features highly associated with AGBD variations through the phenological crop cycle. This work presents a comprehensive comparison between two different approaches for feature extraction for non-destructive biomass estimation using aerial multispectral imagery. The first method is called GFKuts, an approach that optimally labels the plot canopy based on a Gaussian mixture model, a Montecarlo-based K-means, and a guided image filtering for the extraction of canopy vegetation indices associated with biomass yield. The second method is based on a Graph-Based Data Fusion (GBF) approach that does not depend on calculating vegetation-index image reflectances. Both methods are experimentally tested and compared through rice growth stages: vegetative, reproductive, and ripening. Biomass estimation correlations are calculated and compared against an assembled ground-truth biomass measurements taken by destructive sampling. The proposed GBF-Sm-Bs approach outperformed competing methods by obtaining biomass estimation correlation of 0.995 with R2=0.991 and RMSE=45.358 g. This result increases the precision in the biomass estimation by around 62.43% compared to previous works.

The OMICAS alliance is part of the Colombian government’s Scientific Ecosystem, established between 2017-2018 to promote world-class research, technological advancement and improved competency of higher education across the nation. Since the program’s kick-off, OMICAS has focused on consolidating and validating a multi-scale, multi-institutional, multi-disciplinary strategy and infrastructure to advance discoveries in plant science and the development of new technological solutions for improving agricultural productivity and sustainability. The strategy and methods described in this article, involve the characterization of different crop models, using high-throughput, real-time phenotyping technologies as well as experimental tissue characterization at different levels of the omics hierarchy and under contrasting conditions, to elucidate epigenome-, genome-, proteome- and metabolome-phenome relationships. The massive data sets are used to derive in-silico models, methods and tools to discover complex underlying structure-function associations, which are then carried over to the production of new germplasm with improved agricultural traits. Here, we describe OMICAS’ R&D trans-disciplinary multi-project architecture, explain the overall strategy and methods for crop-breeding, recent progress and results, and the overarching challenges that lay ahead in the field.

Biosolids generated as waste from a Wastewater Treatment Plant (WTP) are a pollution problem by the provision of large volumes in landfills and the waste of their potential as an agricultural input.  The research conducted trials to analyze the agricultural use of biosolids in a food company's WTP, their effects on the germination and development of the vegetal plant species Coriandrum sativum were assessed through trials that mixed different amounts of biosolids, land soil and commercial fertilizer, and took into account: planting site characteristics, biosolid and Coriandrum sativum. A random block design was made to compare treatments understudy and resulted in the combination of 50% biosolids with 50% land soil was the best test by germination, height, mass and length of the roots of the plant studied.  In the evaluation of results, the behavior of dependent variables was analyzed: germination, height, mass and length with respect to the four test types with their respective repetitions using ANOVA and Fisher's significant minimum difference (LSD) to determine the effect the biosolid had on the plant and to know the optimal dose for its development.  The germination rate (GR) was also determined in the trials, and 98.3% was found for the best treatment indicating that the substrate does not contain phytotoxic elements.