| 1. |
- Mousavi, Arash, et al.
(author)
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A method for Human Resource Risk Management in Mobile Workforce Brokering Systems
- 2011
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In: American Journal of Applied Sciences. - : Science Publications. - 1546-9239 .- 1554-3641. ; 8:12, s. 1287-1294
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Journal article (peer-reviewed)abstract
- Problem statement: Human Resource Risk Management is one of the crucial issues in Mobile Workforce Management Systems (MWM) in general, and in Mobile Workforce Brokering Systems (MWBS) in Particular. It is important because, if not properly managed, it will cause reduction in accuracy of the automated MWBS, which in turn necessitates more human involvement in the task allocation process. Thus, no reliable planning and scheduling schema can be made or achieved. Approach: However, a proven approach to tackle this problem is via contingency planning. In this study, we examined a specific type of HR risk called Unexpected Absence of mobile workforces in the context of an ontology-driven and multiagent-based MWBS. Our contingency plan that mainly consists of a statistical method is incorporated into the body of a coordination medium represented in OWL ontology format. Results: The proposed statistical method evaluates the past history and the current claims of an MW in order to find out a realistic plan for the next period of the system's run. Conclusion: Finally, via a case study we have illustrated that this method increases the accuracy and reliability of a periodical plan, made for MWBS in its initialization phase.
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| 2. |
- Mousavi, Arash, et al.
(author)
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Providing fairness to mobile workforces in an automated task allocation process : A semantic multi-agent approach
- 2012
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In: American Journal of Applied Sciences. - : Science Publications. - 1546-9239 .- 1554-3641. ; 9:7, s. 1055-1062
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Journal article (peer-reviewed)abstract
- Problem statement: Mobile Workforces (MW) unlike computational resources of an automated system are active but not passive entities. Therefore, an automated resource allocation system that deals with MWs should assign tasks to them fairly and in a comparatively equal manner. An unfair task allocation in a group will cause dissatisfaction, which in turn demotivates MWs who are supposed to work as a team. Approach: In an automated Mobile Workforce Brokering System (MWBS) tasks are automatically assigned to MWs at Run-Time phase of the system's run. However, the environmental risks specifically risk of disconnection disrupts the task allocation process. Disconnection causes unfair task allocation when an MW must carry the next upcoming task according to a rotator work schedule, but he is disconnected. In this situation another MW has to perform the task in order to satisfy a pre-planned daily workload. Results: In this study we explore through the Run-Time phase of MWBS and explain how its underpinning ontology-driven coordination model tackles the risk of disconnection and improves the fairness in the task allocation process. Conclusion: Moreover, fairness rates in task allocation processes are compared between an existing system and MWBS and improvement in fairness rate is shown and analyzed for 4 consecutive periods (months) of the system's run
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| 3. |
- Sabri, Muhammad Haziq Mohamad, et al.
(author)
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Environmental Analysis of Quasi-Static Electric Field Changes of Tropical Lightning Flashes
- 2019
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In: Ekoloji. - 1300-1361. ; 28:107, s. 373-378
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Journal article (peer-reviewed)abstract
- The environmental conditions leading to the bouncing-wave discharge and the subsequent electron beam remain to be investigated in more detailed future studies. The analysis of quasi-static initial electric field changes (IECs) were found at the beginning of all 24 lightning flashes detected within reversal distance (22 Negative Cloud-to-Ground (–CG) and 2 normal Intra-Cloud (IC) flashes) in a tropical storm on June 15th, 2017 close to our station in Malacca, Malaysia (2.314077° N, 102.318282° E). The IECs durations averaged 4.28 ms for –CG flashes (range 1.48 to 9.45 ms) and averaged 11.30 ms for normal ICs flashes (range 7.24 to 15.35 ms). In comparison to Florida storms, the duration of IECs for –CG and IC flashes were 0.18 ms (range 0.08 to 0.33 ms) and 1.53 ms (range 0.18 to 5.70 ms), respectively. Moreover, the magnitudes of E-change for tropical thunderstorm were 0.13 V/m (range 0.03 to 0.44 V/m) for –CG flashes and -0.20 V/m (range -0.13 to -0.27 V/m) for IC flashes. The E-change magnitudes of tropical flashes are significantly larger than Florida flashes.
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| 4. |
- Yusop, Norbayah, et al.
(author)
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Seasonal Analysis of Cloud-To-Ground Lightning Flash Activity in the Western Antarctica
- 2019
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In: Atmosphere. - : MDPI. - 2073-4433. ; 10:12
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Journal article (peer-reviewed)abstract
- This paper presents a seasonal analysis of cloud-to-ground (CG) lightning flash activity in the Western Antarctica using a lightning detector sensor installed at the Carlini Base station. Data obtained from the detection system between February and December 2017 were analyzed. Three common locations and areas of composite active thunderstorms (labelled storm regions A, B, and C) were detected by the sensor within a 1000 km radius from the station. Storm region A was located to the northwest (N/W) of the station and covered the Amundsen/Bellingshausen Sea (ABS), whereas storm region C was located on the southeastern (S/E) side of the station over the Weddell Sea (WS), with distances ranging from 500 to 800 km and bearings of 270 degrees to 360 degrees and 90 degrees to 180, respectively. Storm region B was located around 100 km from the station with the bearings of stroke taken from 0 degrees to 360 degrees. A total of 2,019,923 flashes were detected, of which 43.01% were positive CG and 56.99% were negative CG flashes. The analysis revealed that more than 96% of the CG flashes (both positive CG and negative CG) were produced during the summer and fall seasons as compared with less than 4% during the winter and spring seasons. Most detected lightning strokes (>85%) were located in the central area around the station produced by storm region B and less than 15% were produced by storm region A and storm region C, located in the ocean areas over the Amundsen/Bellingshausen Sea and the Weddell Sea.
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