scholarly journals Establishment of a prognostic model of four genes in gastric cancer based on multiple data sets

2021 ◽  
Author(s):  
Liqiang Zhou ◽  
Shi H. Li ◽  
You Wu ◽  
Lin Xin
Keyword(s):  
Gastric Cancer ◽  
Prognostic Model ◽  
Data Sets ◽  
Multiple Data ◽  
Multiple Data Sets

scholarly journals Constructing a new prognostic signature of gastric cancer based on multiple data sets

Bioengineered ◽  
2021 ◽  
Vol 12 (1) ◽  
pp. 2820-2835
Author(s):  
Liqiang Zhou ◽  
Hao Lu ◽  
Fei Zeng ◽  
Qi Zhou ◽  
Shihao Li ◽  
...  
Keyword(s):  
Gastric Cancer ◽  
Data Sets ◽  
Prognostic Signature ◽  
Multiple Data ◽  
Multiple Data Sets

The development of nurses’ foundational values

2021 ◽  
pp. 096973302110032
Author(s):  
Sastrawan Sastrawan ◽  
Jennifer Weller-Newton ◽  
Gabrielle Brand ◽  
Gulzar Malik
Keyword(s):  
Professional Training ◽  
Reference Points ◽  
Constructivist Grounded Theory ◽  
Professional Values ◽  
Data Sets ◽  
Value System ◽  
Institutional Values ◽  
Multiple Data ◽  
Human Ethics ◽  
Multiple Data Sets

Background: In the ever-changing and complex healthcare environment, nurses encounter challenging situations that may involve a clash between their personal and professional values resulting in a profound impact on their practice. Nevertheless, there is a dearth of literature on how nurses develop their personal–professional values. Aim: The aim of this study was to understand how nurses develop their foundational values as the base for their value system. Research design: A constructivist grounded theory methodology was employed to collect multiple data sets, including face-to-face focus group and individual interviews, along with anecdote and reflective stories. Participants and research context: Fifty-four nurses working across various nursing settings in Indonesia were recruited to participate. Ethical considerations: Ethics approval was obtained from the Monash University Human Ethics Committee, project approval number 1553. Findings: Foundational values acquisition was achieved through family upbringing, professional nurse education and organisational/institutional values reinforcement. These values are framed through three reference points: religious lens, humanity perspective and professionalism. This framing results in a unique combination of personal–professional values that comprise nurses’ values system. Values are transferred to other nurses either in a formal or informal way as part of one’s professional responsibility and customary social interaction via telling and sharing in person or through social media. Discussion: Values and ethics are inherently interweaved during nursing practice. Ethical and moral values are part of professional training, but other values are often buried in a hidden curriculum, and attained and activated through interactions during nurses’ training. Conclusion: Developing a value system is a complex undertaking that involves basic social processes of attaining, enacting and socialising values. These processes encompass several intertwined entities such as the sources of values, the pool of foundational values, value perspectives and framings, initial value structures, and methods of value transference.

scholarly journals Evaluation of daily precipitation statistics and monsoon onset/retreat over western Sahel in multiple data sets

2014 ◽  
Vol 45 (5-6) ◽  
pp. 1325-1354 ◽  
Author(s):  
Emilia Paula Diaconescu ◽  
Philippe Gachon ◽  
John Scinocca ◽  
René Laprise
Keyword(s):  
Daily Precipitation ◽  
Monsoon Onset ◽  
Data Sets ◽  
Multiple Data ◽  
Western Sahel ◽  
Multiple Data Sets

Can earning much lead to doing good? Firm performance implications for corporate social responsibility

2021 ◽  
Vol ahead-of-print (ahead-of-print) ◽  
Author(s):  
Siyu Hou ◽  
Zhaoyang Guo ◽  
Chuangneng Cai ◽  
Xiaobo Jiao
Keyword(s):  
Corporate Social Responsibility ◽  
Firm Performance ◽  
Social Responsibility ◽  
State Ownership ◽  
Data Sets ◽  
Content Type ◽  
Multiple Data ◽  
Instrumental Variable Regression ◽  
Multiple Data Sets ◽  
Corporate Social

Purpose The purpose of this study is to examine the influence of firm performance on corporate social responsibility (CSR) and its possible moderating effect. Despite the significance of CSR, there remains an extensive debate about how it is affected by firm performance. Design/methodology/approach The conceptual model is mainly built on goal-setting theory. Based on archival data from multiple data sets on 1,650 companies, collected from 2010 to 2017, the hypotheses are tested using the two-stage instrumental variable regression method. Findings There is an inverted U-shaped relationship between firm performance and CSR that first increases and then decreases. In addition, considering the boundary conditions, state ownership makes the inverted U-shaped curve steeper, while high executive wage concentration makes the inverted U-shaped curve flatter. Research limitations/implications This study harmonizes the traditional contradictory findings of the influence of firm performance on CSR, that is, it supports a positive, negative or neutral relationship between the two. Originality/value This research provides a necessary structure for the CSR literature. By delving deeply into the relationship between firm performance and CSR, it enables scholars to better address the critical management question of whether earning more will lead to doing good.

Assimilation of multiple data sets with the ensemble Kalman filter to improve forecasts of forest carbon dynamics

2011 ◽  
Vol 21 (5) ◽  
pp. 1461-1473 ◽  
Author(s):  
Chao Gao ◽  
Han Wang ◽  
Ensheng Weng ◽  
S. Lakshmivarahan ◽  
Yanfen Zhang ◽  
...  
Keyword(s):  
Kalman Filter ◽  
Ensemble Kalman Filter ◽  
Carbon Dynamics ◽  
Forest Carbon ◽  
Data Sets ◽  
Multiple Data ◽  
Multiple Data Sets

scholarly journals Approaches to improve the robustness of the Comparison Model Method for the inverse problem of groundwater hydrology

2021 ◽  
Author(s):  
Alessandro Comunian ◽  
Mauro Giudici
Keyword(s):  
Geometric Mean ◽  
Optimization Methods ◽  
Forward Problem ◽  
Data Sets ◽  
Direct Inversion ◽  
Model Method ◽  
Comparison Model ◽  
Multiple Data ◽  
Hydraulic Gradients ◽  
Multiple Data Sets

<p>Indirect inversion approaches are widely used in Geosciences, and in particular also for the identification of the hydraulic properties of aquifers. Nevertheless, their application requires a substantial number of model evaluation (forward problem) runs, a task that for complex problems can be computationally intensive. Reducing this computational burden is an active research topic, and many solutions, including the use of hybrid optimization methods, the use of physical proxies or again machine-learning tools <span>allow to avoid</span> considering the full physics of the problem when running a numerical implementation of the forward problem.</p><p>Direct inversion approaches represent computationally frugal alternatives to indirect approaches, because in general they require a smaller number of runs of the forward problem. The classical drawbacks of these methods can be alleviated by some implementation approaches and in particular by using multiple sets of data, when available.</p><p>This work is an effort to improve the robustness of the Comparison Model Method (CMM), a direct inversion approach aimed at the identification of the hydraulic transmissivity of a confined aquifer. The robustness of the CMM is here ameliorated by (i) improving the parameterization required to handle small hydraulic gradients; (ii) investigating the role of different criteria aimed at merging multiple data-sets corresponding to different flow conditions.</p><p>On a synthetic case study, it is demonstrated that correcting a small percentage of the small hydraulic gradients (about 10%) allows to obtain reliable results, and that a criteria based on the geometric mean is adequate to merge the results coming from multiple data-sets. In addition, the use of multiple-data sets allows to noticeably improve the robustness of the CMM when the input data are affected by noise.</p><p>All the tests are performed by using open source and widely <span>used</span> tools like the USGS Modflow6 and its Python interface flopy to foster the application of the <span>CMM. The scripts and corresponding package</span>, named <em>cmmpy</em>, is available on the Python Package Index (PyPI) and on bitbucket at the following address: https://bitbucket.org/alecomunian/cmmpy.</p>

Multiple Data Set ILI for Mechanical Damage Assessment

2012 ◽  
Author(s):  
Chris Goller ◽  
James Simek ◽  
Jed Ludlow
Keyword(s):  
Damage Assessment ◽  
Public Awareness ◽  
Mechanical Damage ◽  
Data Sets ◽  
Data Set ◽  
Multiple Data ◽  
Pipe Joints ◽  
Multiple Data Sets ◽  
Multiple Field ◽  
Flux Leakage

The purpose of this paper is to present a non-traditional pipeline mechanical damage ranking system using multiple-data-set in-line inspection (ILI) tools. Mechanical damage continues to be a major factor in reportable incidents for hazardous liquid and gas pipelines. While several ongoing programs seek to limit damage incidents through public awareness, encroachment monitoring, and one-call systems, others have focused efforts on the quantification of mechanical damage severity through modeling, the use of ILI tools, and subsequent feature assessment at locations selected for excavation. Current generation ILI tools capable of acquiring multiple-data-sets in a single survey may provide an improved assessment of the severity of damaged zones using methods developed in earlier research programs as well as currently reported information. For magnetic flux leakage (MFL) type tools, using multiple field levels, varied field directions, and high accuracy deformation sensors enables detection and provides the data necessary for enhanced severity assessments. This paper will provide a review of multiple-data-set ILI results from several pipe joints with simulated mechanical damage locations created mimicing right-of-way encroachment events in addition to field results from ILI surveys using multiple-data-set tools.

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