WebServices and GIS-based Management System for QingShuihai Reservoir

The development of a modern integrated water quality model of Qingshuihai Reservoir and its watershed would permit decision-makers and water quality managers to address mechanisms underlying observed trends in water quality within Qingshuihai Reservoir, to assess the potential benefits of reductions in point source, non-point source inputs of nutrients, and to provide the Authority with a user-friendly management software for basin management and drinking water security system

Self-Regulated Learning: A Study of Feedback Seeking By Integrating Self-Motives and Social Influences in an Online Context

To have an effective online communication, individuals need to be self-regulated and self-initiate online conversations when needed. Feedback seeking is a key strategy of self-regulated learning through which individuals can gain more knowledge and become more adapted. Existing studies on feedback seeking mainly focus on personal motivation rather than social factors. Drawing on the theory of planned behaviour, this study examines how both self-motives and social influence affect individuals’ feedback-seeking behaviour. Moreover, based on the relational communication theory, we also investigate how the perceptions of informational and relational value mediate the relationships between self-motives, social influences and feedback-seeking behaviour. As learning styles can affect individuals’ learning motivation and learning effectiveness, individuals’ learning styles may interact with self-motives and social influence to affect their value perceptions toward feedback. We further examine whether learning styles moderate the effects of personal and social factors on value perceptions. A survey will be undertaken to collect the data and test the proposed hypotheses. This study is expected to inspire researchers and practitioners to pay equal attention to personal and social factors in online learning. The findings also attempt to shed light on the necessity of considering informational and relational value simultaneously in studying feedback seeking behaviour

Customization Mode, Decision Outcome, and Task Enjoyment: the Role of Regulatory Focus

This research investigates the interaction effects between customization mode and regulatory focus on consumers' decision outcome and task enjoyment in customization service. Concerned with decision outcome, we posit that in subtractive customization preventionfocused consumers retain more options in the final customized offering than promotion-focused consumers, whereas such an effect is reduced in additive customization. Concerned with task enjoyment, we propose that promotion-focused consumers more enjoy additive customization than prevention-focused consumers, whereas prevention-focused consumers more enjoy subtractive customization than promotion-focused consumers. The current research both contributes to multi-option screening literature and extends regulatory fit theory into a customization service context. [to cite]

Analysis of High Frequency Noise of Inverter Rotary Compressor

The inverter compressor driven by the inverter will cause high frequency noise, which will have adverse influence on total noise value and sound quality. In order to improve this problem, an existing compact rotary inverter compressor is studied in this paper. The influence law of inverter carrier wave of space vector pulse width modulation(SVPWM) technique on motor vibration and noise of compressor is analyzed and summarized. Combining order analysis and motor modal analysis, the results show that the high harmonic current induced by inverter carrier wave will produce high frequency electromagnetic force which excites the stator resonance, and finally results in high frequency noise of the compressor. Through optimization of the motor structure, the high frequency noise is reduced by more than 5dB(A), the sound quality is improved as well

HLA-DRB1 May Be Antagonistically Regulated by the Coordinately Evolved Promoter and 3′-UTR under Stabilizing Selection

HLA-DRB1 is the most polymorphic MHC (major histocompatibility complex) class II gene in human, and plays a crucial role in the development and function of the immune system. Extensive polymorphisms exist in the promoter and 3′-UTR of HLA-DRB1, especially a LTR (Long terminal repeat) element in the promoter, which may be involved in the expression regulation. However, it remains unknown how the polymorphisms in the whole promoter region and 3′-UTR to regulate the gene expression. In this study, we investigated the extensive polymorphisms in the HLA-DRB1 promoter and 3′-UTR, and how these polymorphisms affect the gene expression in both independent and jointly manners. It was observed that most of the haplotypes in the DRB1 promoter and 3′-UTR were clustered into 4 conserved lineages (H1, H2, H3 and H4), and showed high linkage disequilibrium. Compared with H1 and H2 lineage, a LTR element in the promoter of H3 and H4 lineage significantly suppressed the promoter activity, whereas the activity of the linked 3′-UTR increased, leading to no apparent difference in the final expression product between H1/H2 and H3/H4 lineage. Nevertheless, compared with the plasmid with a promoter and 3′-UTR from the same lineage, the recombinant plasmid with a promoter from H2 and a 3′-UTR from H3 showed about double fold increased luciferase activity, Conversely, the recombinant plasmid with a promoter from H3 and a 3′-UTR from H2 resulted in about 2-fold decreased luciferase activity. These results indicate that the promoter and 3′-UTR of HLA-DRB1 may antagonistically regulate the gene expression, which may be subjected to stabilizing selection. These findings may provide a novel insight into the mechanisms of the diseases associated with HLA-DRB1 genes

Aberrant hippocampal subregion networks associated with the classifications of aMCI subjects: a longitudinal resting-state study

Background: Altered hippocampal structure and function is a valuable indicator of possible conversion from amnestic type mild cognitive impairment (aMCI) to Alzheimer’s disease (AD). However, little is known about the disrupted functional connectivity of hippocampus subregional networks in aMCI subjects. Methodology/Principal Findings: aMCI group-1 (n = 26) and controls group-1 (n = 18) underwent baseline and after approximately 20 months follow up resting-state fMRI scans. Integrity of distributed functional connectivity networks incorporating six hippocampal subregions (i.e. cornu ammonis, dentate gyrus and subicular complex, bilaterally) was then explored over time and comparisons made between groups. The ability of these extent longitudinal changes to separate unrelated groups of 30 subjects (aMCI-converters, n = 6; aMCI group-2, n = 12; controls group-2, n = 12) were further assessed. Six longitudinal hippocampus subregional functional connectivity networks showed similar changes in aMCI subjects over time, which were mainly associated with medial frontal gyrus, lateral temporal cortex, insula, posterior cingulate cortex (PCC) and cerebellum. However, the disconnection of hippocampal subregions and PCC may be a key factor of impaired episodic memory in aMCI, and the functional index of these longitudinal changes allowed well classifying independent samples of aMCI converters from non-converters (sensitivity was 83.3%, specificity was 83.3%) and controls (sensitivity was 83.3%, specificity was 91.7%). Conclusions/Significance: It demonstrated that the functional changes in resting-state hippocampus subregional networks could be an important and early indicator for dysfunction that may be particularly relevant to early stage changes and progression of aMCI subjects

Conversion of lignocellulose into biochar and furfural through boron complexation and esterification reactions

The aim of this work was to study the conversion of lignocellulose into biochar and furfural through boron complexation and esterification reaction. Boric acid was used to modify lignocellulose to obtain a high biochar yield boron-lignocellulosic material through complexation and esterification reactions. Furthermore, clean furfural was obtained as the gas products of boron-lignocellulosic materials pyrolysis. The structures of the boron-lignocellulosic materials were characterized, and their compound principle was revealed. Boric acid treatments increased the initial thermal degradation temperature of lignocellulose and promoted the formation of biochar and furfural. The biochar yield rate increased by 135.7% from 18.6 to 42.9% at 600 ℃ after 5% boric acid solution treatment. Compared with pure lignocellulose, cleaner and higher quantities of furfural were obtained from boron-lignocellulose pyrolysis. Finally, the possible chemical decomposition pathways of boron-lignocellulosic materials were identified. This study provides a new perspective on the thermochemical conversion of lignocellulose to furfural and biochar

Identification and discovery of imaging genetic patterns using fusion self-expressive network in major depressive disorder

IntroductionMajor depressive disorder (MDD) is a prevalent mental illness, with severe symptoms that can significantly impair daily routines, social interactions, and professional pursuits. Recently, imaging genetics has received considerable attention for understanding the pathogenesis of human brain disorders. However, identifying and discovering the imaging genetic patterns between genetic variations, such as single nucleotide polymorphisms (SNPs), and brain imaging data still present an arduous challenge. Most of the existing MDD research focuses on single-modality brain imaging data and neglects the complex structure of brain imaging data.MethodsIn this study, we present a novel association analysis model based on a self-expressive network to identify and discover imaging genetics patterns between SNPs and multi-modality imaging data. Specifically, we first build the multi-modality phenotype network, which comprises voxel node features and connectivity edge features from structural magnetic resonance imaging (sMRI) and resting-state functional magnetic resonance imaging (rs-fMRI), respectively. Then, we apply intra-class similarity information to construct self-expressive networks of multi-modality phenotype features via sparse representation. Subsequently, we design a fusion method guided by diagnosis information, which iteratively fuses the self-expressive networks of multi-modality phenotype features into a single new network. Finally, we propose an association analysis between MDD risk SNPs and the multi-modality phenotype network based on a fusion self-expressive network.ResultsExperimental results show that our method not only enhances the association between MDD risk SNP rs1799913 and the multi-modality phenotype network but also identifies some consistent and stable regions of interest (ROIs) multi-modality biological markers to guide the interpretation of MDD pathogenesis. Moreover, 15 new potential risk SNPs highly associated with MDD are discovered, which can further help interpret the MDD genetic mechanism.DiscussionIn this study, we discussed the discriminant and convergence performance of the fusion self-expressive network, parameters, and atlas selection