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REVIEWING THE IMPLICATIONS OF TRAINING FOR ACADEMIC ADMINISTRATION STAFF AT CENTRAL MICHIGAN UNIVERSITY
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Training has an effect on employees’ performances. Accordingly, the person who is responsible for employees’ development must figure out the most effective way to train and develop employees. Central Michigan University (CMU) has recognized the importance of providing appropriate training for employees who have a duty in advising students. The reason is that these employees have a significant impact on students’ educational performances. Thus, special attention to this category of employees is needed to improve advising quality. This research attempted to explore the impact of training on academic advising at CMU. Face-to-face interviews and online surveys were used as data collection tools for this study. The study scope consisted of professional and administrative employees who provide academic advising for students and two employees who provide academic advising for students in the MSA department. The primary question asked, what are the impacts of training on academic advisors at CMU? From the data collection, we found out that training had a major role in increasing the performance of professional and administrative employees when they served in an academic advising capacity. However, there were obstacles that the employees believed should be addressed, including unequal chances to receive training, inconsistent training, a focus on certain skills while others were ignored, a lack of trainers, problems in scheduling enough training time, lack of resources for maintaining policy awareness. Thus, this study provided recommendations to eliminate the negative outcomes of those obstacles. we suggested applying practices such as performing needs assessment for employees, evaluating training outcomes, presenting equal opportunities for receiving training, and providing available resources for employees to stay updated on the policy.  

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Publication Date
Fri Oct 07 2016
Journal Name
Fourth International Conference On Wireless And Optical Communications, 2016, Beijing, China
ESAM: Endocrine inspired Sensor Activation Mechanism for multi-target tracking in WSNs
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Target tracking is a significant application of wireless sensor networks (WSNs) in which deployment of self-organizing and energy efficient algorithms is required. The tracking accuracy increases as more sensor nodes are activated around the target but more energy is consumed. Thus, in this study, we focus on limiting the number of sensors by forming an ad-hoc network that operates autonomously. This will reduce the energy consumption and prolong the sensor network lifetime. In this paper, we propose a fully distributed algorithm, an Endocrine inspired Sensor Activation Mechanism for multi target-tracking (ESAM) which reflecting the properties of real life sensor activation system based on the information circulating principle in the endocr

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Publication Date
Sat Jan 01 2022
Journal Name
Journal Of The Mechanical Behavior Of Materials
A spherical fuzzy AHP model for contractor assessment during project life cycle
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Abstract<p>Measurement of construction performance is essential to a clear image of the present situation. This monitoring by the management team is necessary to identify locations where performance is exceptionally excellent or poor and to identify the primary reasons so that the lessons gained may be exported to the firm and its progress strengthened. This research attempts to construct an integrated mathematical model utilizing one of the recent methodologies for dealing with the fuzzy representation of experts’ knowledge and judgment considering hesitancy called spherical fuzzy analytic hierarchy process (SFAHP) method to assess the contractor’s performance per the project performance pa</p> ... Show More
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Publication Date
Sun Apr 01 2018
Journal Name
Journal Of Construction Engineering And Management
Developing a Decision-Making Framework to Select Safety Technologies for Highway Construction
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Publication Date
Wed Jan 01 2020
Journal Name
International Journal Of Computational Intelligence Systems
Evolutionary Feature Optimization for Plant Leaf Disease Detection by Deep Neural Networks
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Publication Date
Wed Jan 01 2014
Journal Name
Siam Journal On Control And Optimization
A Duality Approach for Solving Control-Constrained Linear-Quadratic Optimal Control Problems
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Publication Date
Fri Jan 01 2021
Journal Name
Agrosystems, Geosciences &amp; Environment
Cover crop influence on soil water dynamics for a corn–soybean rotation
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Abstract<p>Crop production is reduced by insufficient and/or excess soil water, which can significantly decrease plant growth and development. Therefore, conservation management practices such as cover crops (CCs) are used to optimize soil water dynamics, since CCs can conserve soil water. The objective of this study was to determine the effects of CCs on soil water dynamics on a corn (<italic>Zea mays</italic> L.)–soybean [<italic>Glycine max</italic> (L.) Merr.] rotation at three soil depths over 3 yr. The study was conducted at the Chariton County Cover Crop Soil Health Research and Demonstration Farm (CCSH) in Missouri. Initial CC establishment occurred in 2012. Volumetric soil water </p> ... Show More
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Publication Date
Wed Sep 01 2021
Journal Name
Expert Systems With Applications
A long short-term recurrent spatial-temporal fusion for myoelectric pattern recognition
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Publication Date
Sun Jul 01 2012
Journal Name
Applied Soft Computing
A new evolutionary based routing protocol for clustered heterogeneous wireless sensor networks
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Publication Date
Tue Aug 31 2021
Journal Name
International Journal Of Intelligent Engineering And Systems
FDPHI: Fast Deep Packet Header Inspection for Data Traffic Classification and Management
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Traffic classification is referred to as the task of categorizing traffic flows into application-aware classes such as chats, streaming, VoIP, etc. Most systems of network traffic identification are based on features. These features may be static signatures, port numbers, statistical characteristics, and so on. Current methods of data flow classification are effective, they still lack new inventive approaches to meet the needs of vital points such as real-time traffic classification, low power consumption, ), Central Processing Unit (CPU) utilization, etc. Our novel Fast Deep Packet Header Inspection (FDPHI) traffic classification proposal employs 1 Dimension Convolution Neural Network (1D-CNN) to automatically learn more representational c

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Publication Date
Sun Jul 01 2018
Journal Name
Ieee Transactions On Intelligent Transportation Systems
Real-Time Intersection-Based Segment Aware Routing Algorithm for Urban Vehicular Networks
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High vehicular mobility causes frequent changes in the density of vehicles, discontinuity in inter-vehicle communication, and constraints for routing protocols in vehicular ad hoc networks (VANETs). The routing must avoid forwarding packets through segments with low network density and high scale of network disconnections that may result in packet loss, delays, and increased communication overhead in route recovery. Therefore, both traffic and segment status must be considered. This paper presents real-time intersection-based segment aware routing (RTISAR), an intersection-based segment aware algorithm for geographic routing in VANETs. This routing algorithm provides an optimal route for forwarding the data packets toward their destination

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