A Study on the Change Path of Public Administration Mode in the Era of Big Data

The advent of the big data era presents social public administration with a host of novel opportunities and challenges. Harnessing the capabilities and guiding influence of big data in a scientific manner is essential for enhancing the current standards of social and public administration. This paper delves into the critical issues confronting the existing public administration model within the big data context, including information security concerns, challenges in information integration, and the lack of information awareness among staff. It proposes targeted reform measures aimed at fostering the evolution of the public administration model to better adapt to and thrive in the new era

Parallel Randomized Tucker Decomposition Algorithms

The Tucker tensor decomposition is a natural extension of the singular value decomposition (SVD) to multiway data. We propose to accelerate Tucker tensor decomposition algorithms by using randomization and parallelization. We present two algorithms that scale to large data and many processors, significantly reduce both computation and communication cost compared to previous deterministic and randomized approaches, and obtain nearly the same approximation errors. The key idea in our algorithms is to perform randomized sketches with Kronecker-structured random matrices, which reduces computation compared to unstructured matrices and can be implemented using a fundamental tensor computational kernel. We provide probabilistic error analysis of our algorithms and implement a new parallel algorithm for the structured randomized sketch. Our experimental results demonstrate that our combination of randomization and parallelization achieves accurate Tucker decompositions much faster than alternative approaches. We observe up to a 16X speedup over the fastest deterministic parallel implementation on 3D simulation data

Bayesian regularized regression methods for quantitative genetics with focus on longitudinal data

Quantitative trait loci (QTL) /association mapping aims to identify the genomic loci associated with the complex traits. From a statistical perspective, multiple linear regression is often used to model, estimate and test the effects of molecular markers on a trait. With genotype data derived from contemporary genomics techniques, however, the number of markers typically exceed the number of individuals, and it is therefore necessary to perform some sort of variable selection or parameter regularization to provide reliable estimates of model parameters. In addition, many quantitative traits are changing during their development process of life. Accordingly, a longitudinal study that jointly maps the repeated measurements of the phenotype over time may increase the statistical power to identify QTLs, compared with the single trait analysis. In this thesis, a series of Bayesian variable selection/regularization linear methods were developed and applied for analyzing quantitative traits measured at either single or multiple time points. The first work provided an overview of the principal frequentist regularization methods for analyzing single traits. The second work also focused on single trait analysis, where a variational Bayesian (VB) algorithm was derived for estimating parameters in several Bayesian regularization methods. The VB methods can be quickly implemented on large data sets in contrast to the classical Markov Chain Monte Carlo methods. In the third work, the Bayesian regularization method was extended to a non-parametric varying coefficient model to analyze longitudinal traits. Particularly, an efficient VB stepwise algorithm was used for variable selection, so that the method can be quickly implemented even on data sets with a large number of time points and/or a large number of markers. The fourth work is an application of variable selection methods on forest genetics data collected from Northern Sweden. From several conifer wood properties traits with multiple time points, four QTLs located at genes were identified, which are promising targets for future research in wood molecular biology and breeding.Eri organismeilla pituus ja paino ovat tyypillisiä ilmiasuja eli fenotyyppejä, joita voidaan mitata luonnossa. Lisäksi molekyylibiologia tarjoaa menetelmät yksilön genotyypin määrittämiseen DNA sekvenssistä. Yleisesti uskotaan että perinnölliset tekijät vaikuttavat monien ominaisuuksien fenotyyppeihin. Siksi yhteyden löytäminen perinnöllisten tekijöiden ja tietyn fenotyypin välille on kiinnostava tieteellinen kysymys. Esimerkiksi kasvi- ja eläintieteessä, tällaista tutkimusta käytetään ruuan tuotannon parantamiseen. Yksinkertaisimmassa tapauksessa, yksittäinen geenikohta (tietty pätkä DNA:ta) voi täysin määrätä kaksiarvoisen fenotyypin tilan. Tämä voidaan helposti osoittaa käyttämällä yksinkertaisia todennäköisyyssääntöjä. Monet jatkuvat ominaisuudet ovat kuitenkin monitekijäisiä siten että niiden taustalla on useita geenejä ja ympäristöllisiä tekijöitä. Monitekijäisten ominaisuuksien analysointiin on siksi käytettävä edistyneempiä tilastollisia menetelmiä kuten lineaarista regressiota. Tässä väitöskirjassa on kehitetty useita moderneja lineaarimalleihin pohjautuvia tekniikoita geenikohtien paikantamiseksi. Lisäksi työssä analysoitiin ruotsalainen metsätieteeseen liittyvä geneettinen aineisto jossa löydettiin neljä uutta geenikohtaa joidenka toimintaa voidaan myöhemmin tutkia tarkemmin

A deep dive into worker psychological well-being in the construction industry: A systematic review and conceptual framework

The construction industry is stressful and concerns for workers' psychological well-being (PWB) are on the rise with the high prevalence of mental health problems. However, there is currently no clear framework or system in the mainstream construction literature to guide management practices such as allocating resources, optimizing work systems, and supporting worker well-being. In this study, a state-of-the-art review was conducted on PWB constructs and the associated theoretical perspectives. This review of theories and dimensions aims to provide a more complete account of the factors associated with PWB and provide more systemic guidance for organizations. Drawing on a three-dimensional taxonomy of PWB in social science literature, this study identified five themes of PWB antecedents in the construction community: motivational, relational, working environment, personal attributes, and social cognitive. Findings in this study could contribute to both PWB theory development and management practices. Theoretically, this review introduced more clarity to PWB theories in the construction literature, linking different dimensions of PWB constructs with their antecedents. This also allows for identifying future research avenues to expand the boundaries of the existing body of knowledge. Practically, management practices are offered to support management, policy makers, and decision makers to optimize and improve health and well-being strategies in the construction industry

Age-dependent genetic architecture across ontogeny of body size in sticklebacks

Heritable variation in traits under natural selection is a prerequisite for evolutionary response. While it is recognized that trait heritability may vary spatially and temporally depending on which environmental conditions traits are expressed under, less is known about the possibility that genetic variance contributing to the expected selection response in a given trait may vary at different stages of ontogeny. Specifically, whether different loci underlie the expression of a trait throughout development and thus providing an additional source of variation for selection to act on in the wild, is unclear. Here we show that body size, an important life-history trait, is heritable throughout ontogeny in the nine-spined stickleback (Pungitius pungitius). Nevertheless, both analyses of quantitative trait loci and genetic correlations across ages show that different chromosomes/loci contribute to this heritability in different ontogenic time-points. This suggests that body size can respond to selection at different stages of ontogeny but that this response is determined by different loci at different points of development. Hence, our study provides important results regarding our understanding of the genetics of ontogeny and opens an interesting avenue of research for studying age-specific genetic architecture as a source of non-parallel evolution.Peer reviewe

Direct single-molecule dynamic detection of chemical reactions.

Single-molecule detection can reveal time trajectories and reaction pathways of individual intermediates/transition states in chemical reactions and biological processes, which is of fundamental importance to elucidate their intrinsic mechanisms. We present a reliable, label-free single-molecule approach that allows us to directly explore the dynamic process of basic chemical reactions at the single-event level by using stable graphene-molecule single-molecule junctions. These junctions are constructed by covalently connecting a single molecule with a 9-fluorenone center to nanogapped graphene electrodes. For the first time, real-time single-molecule electrical measurements unambiguously show reproducible large-amplitude two-level fluctuations that are highly dependent on solvent environments in a nucleophilic addition reaction of hydroxylamine to a carbonyl group. Both theoretical simulations and ensemble experiments prove that this observation originates from the reversible transition between the reactant and a new intermediate state within a time scale of a few microseconds. These investigations open up a new route that is able to be immediately applied to probe fast single-molecule physics or biophysics with high time resolution, making an important contribution to broad fields beyond reaction chemistry