Showing papers by "Iowa State University published in 2021"
Daniel J. Klionsky1, Amal Kamal Abdel-Aziz2, Sara Abdelfatah3, Mahmoud Abdellatif4 +2980 more•Institutions (777)
TL;DR: In this article, the authors present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes.
Abstract: In 2008, we published the first set of guidelines for standardizing research in autophagy. Since then, this topic has received increasing attention, and many scientists have entered the field. Our knowledge base and relevant new technologies have also been expanding. Thus, it is important to formulate on a regular basis updated guidelines for monitoring autophagy in different organisms. Despite numerous reviews, there continues to be confusion regarding acceptable methods to evaluate autophagy, especially in multicellular eukaryotes. Here, we present a set of guidelines for investigators to select and interpret methods to examine autophagy and related processes, and for reviewers to provide realistic and reasonable critiques of reports that are focused on these processes. These guidelines are not meant to be a dogmatic set of rules, because the appropriateness of any assay largely depends on the question being asked and the system being used. Moreover, no individual assay is perfect for every situation, calling for the use of multiple techniques to properly monitor autophagy in each experimental setting. Finally, several core components of the autophagy machinery have been implicated in distinct autophagic processes (canonical and noncanonical autophagy), implying that genetic approaches to block autophagy should rely on targeting two or more autophagy-related genes that ideally participate in distinct steps of the pathway. Along similar lines, because multiple proteins involved in autophagy also regulate other cellular pathways including apoptosis, not all of them can be used as a specific marker for bona fide autophagic responses. Here, we critically discuss current methods of assessing autophagy and the information they can, or cannot, provide. Our ultimate goal is to encourage intellectual and technical innovation in the field.
1,129 citations
TL;DR: This paper presents a comprehensive review of the general architecture and principals of 1D CNNs along with their major engineering applications, especially focused on the recent progress in this field.
659 citations
TL;DR: This paper aims to fulfill the gap by presenting the highlights of the traditional methods and provide a comprehensive review of the most recent applications of ML and DL algorithms utilized for vibration-based structural damage detection in civil structures.
440 citations
National Technical University of Athens1, University of Manchester2, University of Chile3, Iowa State University4, University of Waterloo5, University of Duisburg-Essen6, University of Hong Kong7, University of Michigan8, Hydro-Québec9, Imperial College London10, Tufts University11, Arizona State University12
TL;DR: This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.
Abstract: Since the publication of the original paper on power system stability definitions in 2004, the dynamic behavior of power systems has gradually changed due to the increasing penetration of converter interfaced generation technologies, loads, and transmission devices. In recognition of this change, a Task Force was established in 2016 to re-examine and extend, where appropriate, the classic definitions and classifications of the basic stability terms to incorporate the effects of fast-response power electronic devices. This paper based on an IEEE PES report summarizes the major results of the work of the Task Force and presents extended definitions and classification of power system stability.
345 citations
01 Feb 2021
TL;DR: In this paper, the role of heat and electricity storage systems on the rapid rise of renewable energy resources and the steady fall of fossil fuels is discussed, which is attributed to sustainable energy systems, energy transition, climate change, and clean energy initiatives.
Abstract: Funding information Higher Education Commission, Pakistan, Grant/Award Number: TDF02-086 Abstract We present the role of heat and electricity storage systems on the rapid rise of renewable energy resources and the steady fall of fossil fuels. The upsurge in renewable resources and slump in fossil fuel consumptions is attributed to sustainable energy systems, energy transition, climate change, and clean energy initiatives. The fast growth of renewables brings new design and operational challenges to transition towards 100% renewable energy goal. Energy storage systems can help ride-through energy transition from hydrocarbon fuels to renewable sources. Nuclear fusion and artificial photosynthesis are the ultimate Holy Grails for permanent clean energy solutions. Plants harvest light and store it in chemical energy to regulate the food supply chain that may be a guideline for an energy transition from fossil fuels to renewables. Heat and electricity storage devices can account for the periodic nature of solar and wind energy sources. Solar thermal systems for water and space heating are also a viable solution for subzero temperature areas. This study presents the transition of world's energy prospect from fossil fuels to renewables and new advances in energy storage systems.
209 citations
TL;DR: Chen et al. as discussed by the authors built an extended time series (2000-2018) of NPP-VIIRS-like NTL data through a new cross-sensor calibration from DMSP-OLS-NTL data (2000, 2004, 2006, and 2010).
Abstract: . The nighttime light (NTL) satellite data have been widely
used to investigate the urbanization process. The Defense Meteorological
Satellite Program Operational Linescan System (DMSP-OLS) stable nighttime
light data and Suomi National Polar-orbiting Partnership Visible Infrared
Imaging Radiometer Suite (NPP-VIIRS) nighttime light data are two widely
used NTL datasets. However, the difference in their spatial resolutions and
sensor design requires a cross-sensor calibration of these two datasets for
analyzing a long-term urbanization process. Different from the traditional
cross-sensor calibration of NTL data by converting NPP-VIIRS to
DMSP-OLS-like NTL data, this study built an extended time series (2000–2018)
of NPP-VIIRS-like NTL data through a new cross-sensor calibration from
DMSP-OLS NTL data (2000–2012) and a composition of monthly NPP-VIIRS NTL
data (2013–2018). The proposed cross-sensor calibration is unique due to the
image enhancement by using a vegetation index and an auto-encoder model.
Compared with the annual composited NPP-VIIRS NTL data in 2012, our product
of extended NPP-VIIRS-like NTL data shows a good consistency at the pixel
and city levels with R2 of 0.87 and 0.95, respectively. We also found
that our product has great accuracy by comparing it with DMSP-OLS radiance-calibrated NTL (RNTL) data in 2000, 2004, 2006, and 2010. Generally, our
extended NPP-VIIRS-like NTL data (2000–2018) have an excellent spatial
pattern and temporal consistency which are similar to the composited
NPP-VIIRS NTL data. In addition, the resulting product could be easily
updated and provide a useful proxy to monitor the dynamics of demographic
and socioeconomic activities for a longer time period compared to existing
products. The extended time series (2000–2018) of nighttime light data is
freely accessible at https://doi.org/10.7910/DVN/YGIVCD (Chen et
al., 2020).
203 citations
TL;DR: Kappler et al. as discussed by the authors highlighted recent advances in understanding of the biogeochemical iron cycle, exploring the great complexity of the processes involved and novel mechanistic insights that have been gained.
Abstract: Biogeochemical cycling of iron is crucial to many environmental processes, such as ocean productivity, carbon storage, greenhouse gas emissions and the fate of nutrients, toxic metals and metalloids. Knowledge of the underlying processes involved in iron cycling has accelerated in recent years along with appreciation of the complex network of biotic and abiotic reactions dictating the speciation, mobility and reactivity of iron in the environment. Recent studies have provided insights into novel processes in the biogeochemical iron cycle such as microbial ammonium oxidation and methane oxidation coupled to Fe(iii) reduction. They have also revealed that processes in the biogeochemical iron cycle spatially overlap and may compete with each other, and that oxidation and reduction of iron occur cyclically or simultaneously in many environments. This Review discusses these advances with particular focus on their environmental consequences, including the formation of greenhouse gases and the fate of nutrients and contaminants. In this Review, Kappler and colleagues highlight recent advances in our understanding of the biogeochemical iron cycle, exploring the great complexity of the processes involved and novel mechanistic insights that have been gained.
184 citations
TL;DR: In this article, de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population were reported.
Abstract: We report de novo genome assemblies, transcriptomes, annotations, and methylomes for the 26 inbreds that serve as the founders for the maize nested association mapping population. The number of pan-genes in these diverse genomes exceeds 103,000, with approximately a third found across all genotypes. The results demonstrate that the ancient tetraploid character of maize continues to degrade by fractionation to the present day. Excellent contiguity over repeat arrays and complete annotation of centromeres revealed additional variation in major cytological landmarks. We show that combining structural variation with single-nucleotide polymorphisms can improve the power of quantitative mapping studies. We also document variation at the level of DNA methylation and demonstrate that unmethylated regions are enriched for cis-regulatory elements that contribute to phenotypic variation.
183 citations
TL;DR: In this article, the authors discuss the PEGylation methods of nanoparticles and related techniques that have been used to detect the coverage density and thickness on the surface of the nanoparticles in recent years.
Abstract: The rapid development of drug nanocarriers has benefited from the surface hydrophilic polymers of particles, which has improved the pharmacokinetics of the drugs. Polyethylene glycol (PEG) is a kind of polymeric material with unique hydrophilicity and electrical neutrality. PEG coating is a crucial factor to improve the biophysical and chemical properties of nanoparticles and is widely studied. Protein adherence and macrophage removal are effectively relieved due to the existence of PEG on the particles. This review discusses the PEGylation methods of nanoparticles and related techniques that have been used to detect the PEG coverage density and thickness on the surface of the nanoparticles in recent years. The molecular weight (MW) and coverage density of the PEG coating on the surface of nanoparticles are then described to explain the effects on the biophysical and chemical properties of nanoparticles.
171 citations
University of Maryland, College Park1, Ford Motor Company2, University of Colorado Boulder3, Lawrence Berkeley National Laboratory4, Oak Ridge National Laboratory5, Argonne National Laboratory6, University of California, Los Angeles7, Applied Materials8, Michigan Technological University9, Pacific Northwest National Laboratory10, Iowa State University11, Georgia Institute of Technology12, University of California, San Diego13, University of Michigan14, General Motors15
TL;DR: In this paper, Albertus, P; Anandan, V; Ban, C; Balsara, N; Belharouak, I; Buettner-Garrett, J; Chen, Z; Daniel, C, Doeff, M; Dudney, NJ; Dunn, B; Harris, SJ; Herle, S; Herbert, E; Kalnaus, S, Libera, JA; Lu, D; Martin, S., McCloskey, BD; McDowell, MT; Meng, YS; Nanda, J, Sak
Abstract: Author(s): Albertus, P; Anandan, V; Ban, C; Balsara, N; Belharouak, I; Buettner-Garrett, J; Chen, Z; Daniel, C; Doeff, M; Dudney, NJ; Dunn, B; Harris, SJ; Herle, S; Herbert, E; Kalnaus, S; Libera, JA; Lu, D; Martin, S; McCloskey, BD; McDowell, MT; Meng, YS; Nanda, J; Sakamoto, J; Self, EC; Tepavcevic, S; Wachsman, E; Wang, C; Westover, AS; Xiao, J; Yersak, T
170 citations
TL;DR: An expanding number of methodological resources, reviews, and commentaries both highlight endogeneity as a threat to causal claims in management research and note that practices for addressing endo... as mentioned in this paper.
TL;DR: In this article, a suite of new approaches that fast-track targeted manipulation of allelic variation for creating novel diversity and facilitate their rapid and efficient incorporation in crop improvement programs is presented.
TL;DR: In this article, a series of polyamide desalination membranes were synthesized in an industrial-scale manufacturing line and varied in processing conditions but retained similar chemical compositions, showing increasing water permeability and active layer thickness with constant sodium chloride selectivity.
Abstract: Biological membranes can achieve remarkably high permeabilities, while maintaining ideal selectivities, by relying on well-defined internal nanoscale structures in the form of membrane proteins. Here, we apply such design strategies to desalination membranes. A series of polyamide desalination membranes—which were synthesized in an industrial-scale manufacturing line and varied in processing conditions but retained similar chemical compositions—show increasing water permeability and active layer thickness with constant sodium chloride selectivity. Transmission electron microscopy measurements enabled us to determine nanoscale three-dimensional polyamide density maps and predict water permeability with zero adjustable parameters. Density fluctuations are detrimental to water transport, which makes systematic control over nanoscale polyamide inhomogeneity a key route to maximizing water permeability without sacrificing salt selectivity in desalination membranes.
TL;DR: In this paper, a pan-genome-scale genomic resources including a graph-based genome, providing access to rice genomic variations, were developed to facilitate rice breeding as well as plant functional genomics and evolutionary biology research.
01 Jan 2021
TL;DR: This work proposes FLTrust, a new federated learning method in which the service provider itself bootstraps trust and normalization limits the impact of malicious local model updates with large magnitudes.
Abstract: Byzantine-robust federated learning aims to enable a service provider to learn an accurate global model when a bounded number of clients are malicious. The key idea of existing Byzantine-robust federated learning methods is that the service provider performs statistical analysis among the clients' local model updates and removes suspicious ones, before aggregating them to update the global model. However, malicious clients can still corrupt the global models in these methods via sending carefully crafted local model updates to the service provider. The fundamental reason is that there is no root of trust in existing federated learning methods.
In this work, we bridge the gap via proposing FLTrust, a new federated learning method in which the service provider itself bootstraps trust. In particular, the service provider itself collects a clean small training dataset (called root dataset) for the learning task and the service provider maintains a model (called server model) based on it to bootstrap trust. In each iteration, the service provider first assigns a trust score to each local model update from the clients, where a local model update has a lower trust score if its direction deviates more from the direction of the server model update. Then, the service provider normalizes the magnitudes of the local model updates such that they lie in the same hyper-sphere as the server model update in the vector space. Our normalization limits the impact of malicious local model updates with large magnitudes. Finally, the service provider computes the average of the normalized local model updates weighted by their trust scores as a global model update, which is used to update the global model. Our extensive evaluations on six datasets from different domains show that our FLTrust is secure against both existing attacks and strong adaptive attacks.
TL;DR: Wang et al. as discussed by the authors investigated the morphological and social evolution of rural communities from the perspective of touristification and analyzed their drivers, finding that from 1988 to 2016, the selected sample case (Jinshitan scenic area, a tourist location situated in the Liaodong Peninsula in China) experienced continuous increases in the average weighted building height, building volume and floor area ratio; the proportion of non-agricultural employment increased by 99.57%.
TL;DR: In this article, the ATLAS particle-flow reconstruction method is used to reconstruct the topo-clusters of the proton-proton collision data with a center-of-mass energy of 13$ TeV collected by the LHC.
Abstract: Jet energy scale and resolution measurements with their associated uncertainties are reported for jets using 36-81 fb$^{-1}$ of proton-proton collision data with a centre-of-mass energy of $\sqrt{s}=13$ TeV collected by the ATLAS detector at the LHC. Jets are reconstructed using two different input types: topo-clusters formed from energy deposits in calorimeter cells, as well as an algorithmic combination of charged-particle tracks with those topo-clusters, referred to as the ATLAS particle-flow reconstruction method. The anti-$k_t$ jet algorithm with radius parameter $R=0.4$ is the primary jet definition used for both jet types. Jets are initially calibrated using a sequence of simulation-based corrections. Next, several $\textit{in situ}$ techniques are employed to correct for differences between data and simulation and to measure the resolution of jets. The systematic uncertainties in the jet energy scale for central jets ($|\eta| 2.5$ TeV). The relative jet energy resolution is measured and ranges from ($24 \pm 1.5$)% at 20 GeV to ($6 \pm 0.5$)% at 300 GeV.
TL;DR: More than one in six UK adults increased their alcohol consumption during lockdown and a higher proportion of these were younger adults compared to older adults as discussed by the authors, indicating that increased alcohol consumption was associated with poor overall mental health, increased depressive symptoms and lower mental wellbeing.
TL;DR: The first empirical study in the management realm on the impacts of COVID-19 on restaurant industry and positive impacts of three aspects of operational characteristics and brand effects as uncertainty minimizing factors amid distinctive business shutdowns and restrictions are highlighted.
Universidade Federal do Rio Grande do Sul1, State University of New York System2, Universidade São Francisco3, Iowa State University4, University of Newcastle5, Zayed University6, ISCTE – University Institute of Lisbon7, Washington State University8, University of Manitoba9, Ondokuz Mayıs University10
TL;DR: Overall, these analyses indicate that the DASS-21 would best be used as a general score of distress rather than three separate factors of depression, anxiety, and stress, in the countries studied.
Abstract: This study evaluated the dimensionality, invariance, and reliability of the Depression, Anxiety, and Stress Scale-21 (DASS-21) within and across Brazil, Canada, Hong Kong, Romania, Taiwan, Turkey, United Arab Emirates, and the United States (N = 2,580) in college student samples. We used confirmatory factor analyses to compare the fit of four different factor structures of the DASS-21: a unidimensional model, a three-correlated-factors model, a higher order model, and a bifactor model. The bifactor model, with three specific factors (depression, anxiety, and stress) and one general factor (general distress), presented the best fit within each country. We also calculated ancillary bifactor indices of model-based dimensionality of the DASS-21 and model-based reliability to further examine the validity of the composite total and subscale scores and the use of unidimensional modeling. Results suggested the DASS-21 can be used as a unidimensional scale. Finally, measurement invariance of the best fitting model was tested across countries indicating configural invariance. The traditional three-correlated-factors model presented scalar invariance across Canada, Hong Kong, Romania, Taiwan, and the United States. Overall, these analyses indicate that the DASS-21 would best be used as a general score of distress rather than three separate factors of depression, anxiety, and stress, in the countries studied.
TL;DR: These perovskite-type (ABO3) binary and ternary nanocrystal superlattices, created via the shape-directed co-assembly of steric-stabilized, highly luminescent cubic CsPbBr3 nanocrystals, exhibit superfluorescence-a collective emission that results in a burst of photons with ultrafast radiative decay that could be tailored for use in ultrabright (quantum) light sources.
Abstract: Atomically defined assemblies of dye molecules (such as H and J aggregates) have been of interest for more than 80 years because of the emergence of collective phenomena in their optical spectra1–3, their coherent long-range energy transport, their conceptual similarity to natural light-harvesting complexes4,5, and their potential use as light sources and in photovoltaics. Another way of creating versatile and controlled aggregates that exhibit collective phenomena involves the organization of colloidal semiconductor nanocrystals into long-range-ordered superlattices6. Caesium lead halide perovskite nanocrystals7–9 are promising building blocks for such superlattices, owing to the high oscillator strength of bright triplet excitons10, slow dephasing (coherence times of up to 80 picoseconds) and minimal inhomogeneous broadening of emission lines11,12. So far, only single-component superlattices with simple cubic packing have been devised from these nanocrystals13. Here we present perovskite-type (ABO3) binary and ternary nanocrystal superlattices, created via the shape-directed co-assembly of steric-stabilized, highly luminescent cubic CsPbBr3 nanocrystals (which occupy the B and/or O lattice sites), spherical Fe3O4 or NaGdF4 nanocrystals (A sites) and truncated-cuboid PbS nanocrystals (B sites). These ABO3 superlattices, as well as the binary NaCl and AlB2 superlattice structures that we demonstrate, exhibit a high degree of orientational ordering of the CsPbBr3 nanocubes. They also exhibit superfluorescence—a collective emission that results in a burst of photons with ultrafast radiative decay (22 picoseconds) that could be tailored for use in ultrabright (quantum) light sources. Our work paves the way for further exploration of complex, ordered and functionally useful perovskite mesostructures. Through precise structural engineering, perovskite nanocrystals are co-assembled with other nanocrystal materials to form a range of binary and ternary perovskite-type superlattices that exhibit superfluorescence.
National University of Río Negro1, Iowa State University2, Spanish National Research Council3, Chinese Academy of Sciences4, National University of San Luis5, American Institute for Economic Research6, University of British Columbia7, Xishuangbanna Tropical Botanical Garden8, Balseiro Institute9, University of Canberra10, Utrecht University11, National University of Cordoba12, University of Cambridge13, University of East Anglia14, University of Maryland, Baltimore County15
TL;DR: In this article, Benedetti and Astafurova provided important assistance in searching for legislation in support of NWL, and two anonymous referees critically reviewed previous versions of the manuscript.
Abstract: G. Benedetti, A. Peralta, A. Zarate, C. Evequoz, M. Muller, M. Piotti, and Y. Astafurova provided important assistance in searching for legislation in support of NWL. D. Caceres, C. Levers, and two anonymous referees critically reviewed previous versions of the manuscript. We appreciate funding from Agencia Nacional de Promocion Cientifica y Tecnologica (PICT 2015–2333, PICT‐2018‐00941), Natural Environment Research Council (Grant code: NE/N014472/1), Universidad Nacional de Rio Negro (PI 40‐B‐567), and the 2017–2018 Belmont Forum and BiodivERsA joint call for research proposals (under the BiodivScen ERA‐Net COFUND programme and with the funding organizations AEI, NWO, ECCyT and NSF).
TL;DR: Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits as discussed by the authors, enabling the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes.
Abstract: Genome-wide association studies (GWAS) have developed into a powerful and ubiquitous tool for the investigation of complex traits. In large part, this was fueled by advances in genomic technology, enabling us to examine genome-wide genetic variants across diverse genetic materials. The development of the mixed model framework for GWAS dramatically reduced the number of false positives compared with naive methods. Building on this foundation, many methods have since been developed to increase computational speed or improve statistical power in GWAS. These methods have allowed the detection of genomic variants associated with either traditional agronomic phenotypes or biochemical and molecular phenotypes. In turn, these associations enable applications in gene cloning and in accelerated crop breeding through marker assisted selection or genetic engineering. Current topics of investigation include rare-variant analysis, synthetic associations, optimizing the choice of GWAS model, and utilizing GWAS results to advance knowledge of biological processes. Ongoing research in these areas will facilitate further advances in GWAS methods and their applications.
TL;DR: In this paper, a ternary system comprising of a prion-like polypeptide (PLP), arginine-rich polypeptic (RRP), and RNA was used to demix the two types of biomolecular condensates into stable coexisting phases.
Abstract: Multivalent protein-protein and protein-RNA interactions are the drivers of biological phase separation Biomolecular condensates typically contain a dense network of multiple proteins and RNAs, and their competing molecular interactions play key roles in regulating the condensate composition and structure Employing a ternary system comprising of a prion-like polypeptide (PLP), arginine-rich polypeptide (RRP), and RNA, we show that competition between the PLP and RNA for a single shared partner, the RRP, leads to RNA-induced demixing of PLP-RRP condensates into stable coexisting phases—homotypic PLP condensates and heterotypic RRP-RNA condensates The morphology of these biphasic condensates (non-engulfing/ partial engulfing/ complete engulfing) is determined by the RNA-to-RRP stoichiometry and the hierarchy of intermolecular interactions, providing a glimpse of the broad range of multiphasic patterns that are accessible to these condensates Our findings provide a minimal set of physical rules that govern the composition and spatial organization of multicomponent and multiphasic biomolecular condensates Liquid ribonucleoprotein condensates typically involve a dense network of multiple proteins and RNAs Here, the authors employ a minimal system composed of Prion-like polypeptides (PLP), Arg-rich polypeptides (RRP), and RNA to form biphasic condensates with diverse morphologies tunable via mixture stoichiometry and hierarchy of intermolecular interactions
TL;DR: Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.
Abstract: The Deep Underground Neutrino Experiment (DUNE) will be a powerful tool for a variety of physics topics. The high-intensity proton beams provide a large neutrino flux, sampled by a near detector system consisting of a combination of capable precision detectors, and by the massive far detector system located deep underground. This configuration sets up DUNE as a machine for discovery, as it enables opportunities not only to perform precision neutrino measurements that may uncover deviations from the present three-flavor mixing paradigm, but also to discover new particles and unveil new interactions and symmetries beyond those predicted in the Standard Model (SM). Of the many potential beyond the Standard Model (BSM) topics DUNE will probe, this paper presents a selection of studies quantifying DUNE’s sensitivities to sterile neutrino mixing, heavy neutral leptons, non-standard interactions, CPT symmetry violation, Lorentz invariance violation, neutrino trident production, dark matter from both beam induced and cosmogenic sources, baryon number violation, and other new physics topics that complement those at high-energy colliders and significantly extend the present reach.
TL;DR: In this article, the authors investigated whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt and found that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%.
Abstract: This study investigates whether coupling crop modeling and machine learning (ML) improves corn yield predictions in the US Corn Belt. The main objectives are to explore whether a hybrid approach (crop modeling + ML) would result in better predictions, investigate which combinations of hybrid models provide the most accurate predictions, and determine the features from the crop modeling that are most effective to be integrated with ML for corn yield prediction. Five ML models (linear regression, LASSO, LightGBM, random forest, and XGBoost) and six ensemble models have been designed to address the research question. The results suggest that adding simulation crop model variables (APSIM) as input features to ML models can decrease yield prediction root mean squared error (RMSE) from 7 to 20%. Furthermore, we investigated partial inclusion of APSIM features in the ML prediction models and we found soil moisture related APSIM variables are most influential on the ML predictions followed by crop-related and phenology-related variables. Finally, based on feature importance measure, it has been observed that simulated APSIM average drought stress and average water table depth during the growing season are the most important APSIM inputs to ML. This result indicates that weather information alone is not sufficient and ML models need more hydrological inputs to make improved yield predictions.
TL;DR: In this article, the authors review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.
Abstract: Crop genomics has seen dramatic advances in recent years due to improvements in sequencing technology, assembly methods, and computational resources. These advances have led to the development of new tools to facilitate crop improvement. The study of structural variation within species and the characterization of the pan-genome has revealed extensive genome content variation among individuals within a species that is paradigm shifting to crop genomics and improvement. Here, we review advances in crop genomics and how utilization of these tools is shifting in light of pan-genomes that are becoming available for many crop species.
TL;DR: This work critically review and update the application of goal-setting theory in physical activity promotion, by examining core developments in this theory since 1990 and discussing implications for policy, research, and practice.
Abstract: Goal-setting is a widely used and accepted strategy for promoting physical activity. Locke and Latham's goal-setting theory is the primary theoretical framework for setting goals in psychology and plays a prominent role in physical activity promotion. Recently, however, there have been calls to reconsider current goal-setting practice in this field. Therefore, we aimed to critically review and update the application of goal-setting theory in physical activity promotion, by examining core developments in this theory since 1990. Current practice relies on setting specific 'performance' goals as a means of increasing physical activity (e.g., 10,000 steps; national physical activity guidelines). This approach was initially consistent with key tenets of goal-setting theory. However, since 1990 this theory has evolved to differentiate between performance and learning goals. Both goal types are context-dependent and it is now recognised that, in some cases, performance goals can even be detrimental to the achievement of desired outcomes. Consequently, current practice may be theoretically appropriate for physically active individuals but a different approach (e.g., learning goals) may be preferable for inactive individuals who are new to physical activity (i.e., most of the population). We conclude by discussing implications for policy, research, and practice in goal-setting for physical activity promotion.
TL;DR: In this paper, the authors proposed a theory-based model for interorganizational adoption of blockchain technology (BCT) in supply chains and analyzed a unique in-depth revelatory case study.
Abstract: D espite the anticipated benefits and the numerous announcements of pilot cases, we have seen very few successful implementations of blockchain technology (BCT) solutions in supply chains. Little is empirically known about the obstacles to blockchain adoption, particularly in a supply chain’s interorganizational setting. In supply chains, blockchains’ benefits, for example, BCT-based tracking and tracing, are dependent on a critical mass of supply chain actors adopting the technology. While previous research has mainly been conceptual and has lacked both theory and empirical data, we propose a theory-based model for interorganizational adoption of BCT. We use the proposed model to analyze a unique in-depth revelatory case study. Our case study confirms previous conceptual work and reveals a paradox as well as several tensions between drivers for and against (positive and negative determining factors, respectively) of BCT adoption that must be managed in an interorganizational setting. In this vertical context, the adoption and integration decision of one supply chain actor recursively affects the adoption and integration decisions of the other supply chain actors. This paper contributes midrange theory on BCT in supply chain management (SCM), future research directions, and managerial insights on BCT adoption in supply chains.