Minimal acceptable handling time intervals for lifting and lowering tasks

A laboratory study was conducted to determine the minimal acceptable handling time interval (MAHTI) for 4 h of endurance manual materials handling tasks using a psychophysical approach. Nine experienced male subjects were recruited to perform manual materials handling tasks with three load weights (10, 15, and 20 kg) and six ranges (floor to knuckle, FK; knuckle to floor, KF; knuckle to shoulder, KS; shoulder to knuckle, SK; floor to shoulder, FS; shoulder to floor, SF). Subjects were asked to adjust the handling time interval to the minimum that they could perform without becoming strained, uncomfortable, tired, weakened, overheated, or out-of-breath for 4 h of the endurance handling period. The results showed that the heavier load produced a larger MAHTI, and the overall descending sequence of MAHTI values for the six ranges was FS, KS, SF, FK, KF, and SK. The MAHTI for each lifting task was significantly higher than that of its corresponding lowering task (p<0.05). All MAHTI data were further verified by tests run on an additional nine subjects; this showed that the psychophysically determined MAHTIs were appropriate with no apparent underestimation.     

An effective and robust two-phase resource allocation scheme for interdependent tasks in mobile ad hoc computational Grids

This paper addresses the problem of resource allocation to interdependent tasks in mobile ad hoc computational Grids. Dependencies between tasks imply that there can be heavy communication induced by data transfers between tasks executed on separate nodes. The communication in mobile ad hoc Grids is always expensive and unreliable, and therefore plays a critical role in application performance. There are several factors that contribute to communication cost. Unreliable and short-term connectivity can increase communication cost due to frequent failure and activation of links, and ineffective resource allocation can increase communication cost due to multi hop communication between dependent tasks. To reduce communication cost, an effective and robust resource allocation scheme is required. However, the design of such a scheme for mobile ad hoc computational Grids exhibits numerous difficulties due to the constrained communication environment, node mobility, and lack of pre-existing network infrastructure.     

Selection of character/background colour combinations for onscreen searching tasks: An eye movement,subjective and performance approach

The aim of the study was to investigate the effect of various character and background colour combinations on cognitive performance during onscreen searching tasks and to identify the best combinations with a multimodal approach of physiological (eye movement recording), subjective and performance data collection. In the absence of proper character and background colour combination the optimum performance for a cognitive task is greatly affected which in turn affects the productivity of the individual worker or communications among the operators working under the same network through information sharing. This study was designed by selecting six colours, i.e., white, black, yellow, red, blue, and green and subsequently combining them to 16 character and background colour combinations for a searching task. Right and left headed arrows were used as the character for the searching maneuver. Forty-four (N = 44) volunteers participated in the experiments. Various eye movement variables, legibility rating scale, NASA-TLX questionnaire, searching time and percentage of error were recorded. Subjects performed better wherever a good contrast was there because of a high legibility. A poorly contrasted display affected the physiological variables as well as subjective responses to negative directions. Among the combinations of dark character/light background, blue and red character on white background is highly recommended; and while that for light character/dark background, white is found to be the best character on blue and green backgrounds.     

An execution time and energy model for an energy-aware execution of a conjugate gradient method with CPU/GPU collaboration

The parallel preconditioned conjugate gradient method (CGM) is used in many applications of scientific computing and often has a critical impact on their performance and energy consumption. This article investigates the energy-aware execution of the CGM on multi-core CPUs and GPUs used in an adaptive FEM. Based on experiments, an application-specific execution time and energy model is developed. The model considers the execution speed of the CPU and the GPU, their electrical power, voltage and frequency scaling, the energy consumption of the memory as well as the time and energy needed for transferring the data between main memory and GPU memory. The model makes it possible to predict how to distribute the data to the processing units for achieving the most energy efficient execution: the execution might deploy the CPU only, the GPU only or both simultaneously using a dynamic and adaptive collaboration scheme. The dynamic collaboration enables an execution minimising the execution time. By measuring execution times for every FEM iteration, the data distribution is adapted automatically to changing properties, e.g. the data sizes.