7 research outputs found
Burra tied by house
No full textBurra tied by house; photo by Sebastian Rodriguezhttps://digitalcommons.acu.edu/coc_missions_photos/1602/thumbnail.jp
The Influence of Leadership Style and Motivation on Employee Performance
No full textHuman resources are the main element that quality must be maintained in a company, good human resources are assessed based on the results of the performance of its employees. Leadership style and motivation are part of the factors that can affect the level of employee performance. This study aims to determine how much influence of leadership style and motivation on employee performance in the management services division of PT. Adyawinsa telecommunication & electrical Bandung either partially or simultaneously. This research is a descriptive quantitative method using primary and secondary data sources in the form of a questionnaire with 25 employees as respondents. The type of analysis used is path analysis, and to perform data processing, the author uses the help of the SPSS version 23 program. not good, and also the employee's performance is in a bad category. And then the results of this study indicate that partially and simultaneously leadership style has a direct effect on employee performance, then motivation has a direct effect on employee performance and leadership style and motivation affect employee performance
-Independent Antiviral Role of NK Cells Revealed in Murine Cytomegalovirus-Infected New Zealand White Mice
No full textPRODUCCION Y CALIDAD DE FORRAJE VERDE HIDROPONICO DE TRES VARIEDADES DE ALFALFA (Medicago sativa)./
No full textMeta-Analysis of Satellite Observations for United Nations Sustainable Development Goals: Exploring the Potential of Machine Learning for Water Quality Monitoring
No full textThis review paper adopts bibliometric and meta-analysis approaches to explore the application of supervised machine learning regression models in satellite-based water quality monitoring. The consistent pattern observed across peer-reviewed research papers shows an increasing interest in the use of satellites as an innovative approach for monitoring water quality, a critical step towards addressing the challenges posed by rising anthropogenic water pollution. Traditional methods of monitoring water quality have limitations, but satellite sensors provide a potential solution to that by lowering costs and expanding temporal and spatial coverage. However, conventional statistical methods are limited when faced with the formidable challenge of conducting pattern recognition analysis for satellite geospatial big data because they are characterized by high volume and complexity. As a compelling alternative, the application of machine and deep learning techniques has emerged as an indispensable tool, with the remarkable capability to discern intricate patterns in the data that might otherwise remain elusive to traditional statistics. The study employed a targeted search strategy, utilizing specific criteria and the titles of 332 peer-reviewed journal articles indexed in Scopus, resulting in the inclusion of 165 articles for the meta-analysis. Our comprehensive bibliometric analysis provides insights into the trends, research productivity, and impact of satellite-based water quality monitoring. It highlights key journals and publishers in this domain while examining the relationship between the first author’s presentation, publication year, citation count, and journal impact factor. The major review findings highlight the widespread use of satellite sensors in water quality monitoring including the MultiSpectral Instrument (MSI), Ocean and Land Color Instrument (OLCI), Operational Land Imager (OLI), Moderate Resolution Imaging Spectroradiometer (MODIS), Thematic Mapper (TM), Enhanced Thematic Mapper Plus (ETM+), and the practice of multi-sensor data fusion. Deep neural networks are identified as popular and high-performing algorithms, with significant competition from extreme gradient boosting (XGBoost), even though XGBoost is relatively newer in the field of machine learning. Chlorophyll-a and water clarity indicators receive special attention, and geo-location had a relationship with optical water classes. This paper contributes significantly by providing extensive examples and in-depth discussions of papers with code, as well as highlighting the critical cyber infrastructure used in this research. Advances in high-performance computing, large-scale data processing capabilities, and the availability of open-source software are facilitating the growing prominence of machine and deep learning applications in geospatial artificial intelligence for water quality monitoring, and this is positively contributing towards monitoring water pollution
A Cmv2 QTL on chromosome X affects MCMV resistance in New Zealand male mice.
No full textNK cell-mediated resistance to viruses is subject to genetic control in humans and mice. Here we used classical and quantitative genetic strategies to examine NK-mediated murine cytomegalovirus (MCMV) control in genealogically related New Zealand white (NZW) and black (NZB) mice. NZW mice display NK cell-dependent MCMV resistance while NZB NK cells fail to limit viral replication after infection. Unlike Ly49H(+) NK resistance in C57BL/6 mice, NZW NK-mediated MCMV control was Ly49H-independent. Instead, MCMV resistance in NZW (Cmv2) involves multiple genetic factors. To establish the genetic basis of Cmv2 resistance, we further characterized a major chromosome X-linked resistance locus (DXMit216) responsible for innate MCMV control in NZW x NZB crosses. We found that the DXMit216 locus affects early MCMV control in New Zealand F(2) crosses and demonstrate that the NZB-derived DXMit216 allele enhances viral resistance in F(2) males. The evolutionary conservation of the DXMit216 region in mice and humans suggests that a Cmv2-related mechanism may affect human antiviral responses
