Integrated spatial decision support system for precision agriculture

Excessive application of plant nutrients and pesticides on agricultural land has resulted in both environmental degradation and economic loss to the farming community. Agricultural non-point source pollution was cited as the primary source of the water quality problems in many areas of the United States. Environmental concerns resulting from agricultural non-point source pollution has placed demands on farmers and ranchers to implement the best management practices (BMPs) to reduce the delivery of pollutants to streams and aquifers. Precision agriculture, a relatively recent crop production and agricultural management strategy holds great promise to minimize environmental pollution while to maximize economic productivity and profitability. It has benefited from rapidly evolving geospatial information technologies, such as global positing systems (GPS), geographic information systems (GIS), remote sensing (RS), and electronic sensors and intelligent controllers. However, the complexity of making routine, coherent, and cost-effective farm management decisions presents a formidable challenge to farmers. What is lacking in precision agriculture is an analytical tool that integrates these component technologies with biophysical and economic models for tactical, strategic, and policy-level decision make. In this dissertation, a decision support system called IDSSPA is developed to include modules for evaluating crop yield and chemical losses in response to site-specific management of agricultural inputs. Using this system, not only can users store, visualize, manipulate, and analyze spatial/non-spatial field experiment data, but they also can do various simulations through the easy-operated biophysical models, which take field spatial variability into account. In the system, the functionalities of the traditional models and analysis methods have been enhanced by coupling them with each other and with ArcView GIS. Uniquely designed GIS-based interfaces enable the lumped biophysical models to incorporate and represent field spatial variability. Statistical and data mining tools are also included in the system to improve analysis of field measured data and to further enhance interpretation of model simulation results. Other components incorporated into the system are as follows: The CERES-Maize plant growth model seamlessly integrated with RZWQM to provide an alternative phonologically based model for predicting growth and yield of maize (corn), and several tools for evaluating economic and ecologic risks of precision agriculture implementation. The application examples indicated that IDSSPA is a useful research and decision make tool for precision agriculture at field and watershed scales

Temporal Variations of Streamflow in a Mid-Latitude Eurasian Steppe Watershed in the Past Half Century

Previous studies either did not identify abrupt change or identified such change but did not exclude it from the detection of trend in streamflow. As a result, an overall downward trend might be erroneously detected as an upward trend because of abrupt increase, while an overall upward trend could be faked as a downward trend due to abrupt decrease. The objectives of this study were to: (1) present a methodology to analyze trend in streamflow in the presence of abrupt change; and (2) use this methodology to detect trend and extreme occurrence of streamflow in the Upper Balagaer River watershed, a mid-latitude nearly pristine precipitation-fed Eurasian steppe watershed in north China. The results indicate that streamflow abruptly decreased around 1994 and exhibited no significant trend from 1960 to 1993 but a significant decrease trend since 1994 (in particular after 1999). In addition, the occurrence of days with a low streamflow was greater after 1994, whereas the occurrence of days with a high streamflow was smaller. Further, the inclusion of the abrupt change in the analysis could compound the detection of the pre-1994 trends but had minimal influences on the detection of the post-1994 trends. These results can be representative across the Eurasian steppe region beyond the study watershed

The Effects of Transformational Leadership on Employee’s Pro-social Rule Breaking

The construct of pro-social rule breaking occupies an important, but largely neglected position within exi-sting frameworks of organizational deviance Pro-social rule breaking (PSRB) is a form of constructive de-viance characterized by volitional rule breaking in the interest of the organization or its stakeholders.Usi-ng survey data collected from 252 employees in different organizations in China,the researchers empirically examines the relationship between transformational leadership and employee’s pro-social rule breaking and the mediating role of job autonomy. Results indicate that transformational leadership is positively rel-ated to pro-social rule breaking,job autonomy fully mediates the relationships between transformational le-adership and employee’s pro-social rule breaking. Theoretical and practical implications are discussed. A set of future research directions are offered

Next-Generation Rainfall IDF Curves for the Virginian Drainage Area of Chesapeake Bay

Probability-based intensity-duration-frequency IDF curves are needed but currently lacking for Department of Defense DoD to construct and manage its infrastructure in changing climate. The objectives of this project were to 1 develop an innovative approach for considering rainfall non-stationarity in developing such IDF curves and 2 apply this approach to the state of Virginia. In this regard, the observed data on 15-min rainfall at 57 gauges and the precipitations projected by twelve pairs of Regional Climate Model RCM and Global Circulation Model GCM were used. For a given gauge or watershed, in terms of fitting the empirical exceedance probabilities, a best statistical distribution was chosen and then used to create the existing, projected historic, and projected future IDF curves. For a given return period, the projected historic IDF curves were compared with the existing ones to determine the lower and upper limits of the future IDF curve. The most-probable future IDF curve was determined as the average of the twelve curves responding to the GCM-RCM models. In addition, for a given duration and return period, the responding rainfall intensities were used to create a probability-based IDF curve. Further, the areal precipitations for each of the 53 watersheds were used to create the watershed-level future IDF curves. The project results are expected to be a useful and usable tool in guarding against over- or under committing resources

Transformational Leadership and Digital Creativity: The Mediating Roles of Creative Self-Efficacy and Ambidextrous Learning

Drawing insights from social cognitive theory and organizational learning theory, this study aims to uncover themediating mechanisms between direct manager’s transformational leadership behaviors and employees’ digital creativity in the context of digital technology. We conducted a field survey in China and collected data from 234 employees who utilized digital technologies to support daily work. Structural equation modelling analysis results showed that employees’ creative self-efficacy and two learning activities (exploitation vs. exploration) effectively transmitted the influence of transformational leadership ondigital creativity. Our study not only contributes to the understanding on effective use of digital technologies, but also provides practical insights for managers in the big data era

Rethinking Batch Sample Relationships for Data Representation: A Batch-Graph Transformer based Approach

Exploring sample relationships within each mini-batch has shown great potential for learning image representations. Existing works generally adopt the regular Transformer to model the visual content relationships, ignoring the cues of semantic/label correlations between samples. Also, they generally adopt the "full" self-attention mechanism which are obviously redundant and also sensitive to the noisy samples. To overcome these issues, in this paper, we design a simple yet flexible Batch-Graph Transformer (BGFormer) for mini-batch sample representations by deeply capturing the relationships of image samples from both visual and semantic perspectives. BGFormer has three main aspects. (1) It employs a flexible graph model, termed Batch Graph to jointly encode the visual and semantic relationships of samples within each mini-batch. (2) It explores the neighborhood relationships of samples by borrowing the idea of sparse graph representation which thus performs robustly, w.r.t., noisy samples. (3) It devises a novel Transformer architecture that mainly adopts dual structure-constrained self-attention (SSA), together with graph normalization, FFN, etc, to carefully exploit the batch graph information for sample tokens (nodes) representations. As an application, we apply BGFormer to the metric learning tasks. Extensive experiments on four popular datasets demonstrate the effectiveness of the proposed model

Human Umbilical Cord Mesenchymal Stem Cells and their Extracellular Vesicles Modulate Pro- and Anti-inflammatory Cytokines in Ligature-induced Periodontitis

Background: Periodontitis is a chronic inflammatory condition that affects the tissues supporting the teeth, ultimately leading to tooth loss. Mesenchymal stem cells (MSCs) and their extracellular vesicles (EVs) play a crucial role in periodontitis by modulating the activities of gum cells and the immune system.Objective: To investigate the therapeutic potential of human umbilical cord mesenchymal stem cells (hUCSCs) and EVs in regulating the inflammatory response associated with periodontitis.Methods: hUCSCs were isolated, subjected to flow cytometry analysis of surface markers, and differentiated into adipocyte and osteocyte. hUCSC-EVs were isolated and characterized using flow cytometry and electron microscopy. A periodontitis animal model was established in 30 female C57Bl/6 mice. Experimental groups received hUCSCs or hUCSCs-EVs, or vehicles intravenously. Animals were monitored for 4 weeks, and the periodontal tissues were used to assess the effects of hUCSCs and hUCSCs-EVs on the expression of pro- (TNF-α, IFN-γ, and IL-17a) and anti-inflammatory cytokines (TGF-β, IL-10, and IL-4). The secretion of these cytokines by splenocytes was also evaluated using ELISA.Results: The levels of IL-17a, IFN-γ, and TNFα significantly reduced, while TGF-β and IL-10 significantly increased in the periodontal tissues of the hUCSC and hUCSCEVs-treated mice. The expression of TNF-α, IFN-γ, and IL-17a significantly decreased, while the production of IL-10 and TGF-β significantly increased in splenocytes from the hUCSC and EVs-treated mice.Conclusion: hUCSCs and their EVs have the potential to attenuate the inflammatory response associated with periodontitis, possibly by downregulating pro-inflammatory cytokines and upregulating anti-inflammatory ones