Dynamic partner identification in mobile agent-based distributed job workflow execution

This paper is concerned with the design, implementation, and evaluation of algorithms for communication partner identification in mobile agent-based distributed job workflow execution. We first describe a framework for distributed job workflow execution over the Grid: the Mobile Code Collaboration Framework (MCCF). Based on the study of agent communications during a job workflow execution on MCCF, we identify the unnecessary agent communications that degrade the system performance. Then, we design a novel subjob grouping algorithm for preprocessing the job workflow's static specification in MCCF. The obtained information is used in both static and dynamic algorithms to identify partners for agent communication. The mobile agent dynamic location and communication based on this approach is expected to reduce the agent communication overhead by removing unnecessary communication partners during the dynamic job workflow execution. The proof of the dynamic algorithm's correctness and effectiveness are elaborated. Finally, the algorithms are evaluated through a comparison study using simulated job workflows executed on a prototype implementation of the MCCF on a LAN environment and an emulated WAN setup. The results show the scalability and efficiency of the algorithms as well as the advantages of the dynamic algorithm over the static one.     

A new generic approach to model reduction for complex physiologically based drug models

This paper explores the identification problem when dealing with physiological models relating to anaesthetic drugs such as fentanyl. The Mapleson model for drug concentration, which will be the focus of this study, is described by algebraic equations, which are derived from the laws of physics and chemistry, and there are some limitations in its system's analysis, i.e. in the study of its relevant dynamics, and its exploitation from a control design viewpoint. Hence, we propose to represent this model via dynamic differential equations with a reduced number of variables using MATLAB–SIMULINK. Using Mapleson's approach for modelling, the input–output data for each organ can be obtained under a particular drug regimen which in turn can be used to obtain a continuous time-transfer function fit for each of these organs.     

最小化碳排放的可持续机器调度问题

针对单机器生产系统、允许在生产间歇开关机器的可持续调度问题,以最小化碳排放为目标建立数学规划模型,同时对机器的开关机和产品的生产计划进行决策.利用动态规划算法对模型进行求解分析,提出阶段决策最优性条件,并给出精确算法.通过模拟算例和企业案例的计算分析表明,利用所提出的可持续调度方法可以显著减少生产过程中的碳排放.     

The influence of individual low back health status on workplace trunk kinematics and risk of low back disorder

A case-control study was conducted to determine whether or not kinematic-based low back disorder risk measurement (Marras et al. ) of the job was significantly different for those workers suffering from recent low back injuries compared to asymptomatic controls. Two hundred low back injured workers returning to full duty work and 200 asymptomatic controls were evaluated while performing the same job. There were no statistically significant differences between the two groups on any trunk motion measures or workplace measures. Therefore, job design is dictating the kinematic motions of the torso and not the worker's low back health. In addition, there was not a significant difference in job risk estimates using the lumbar motion monitor risk model. The mean risk (and standard deviation) for the low back injured group and the asymptomatic controls was 0.502 (0.178) and 0.501 (0.193), respectively. This study suggests that trunk kinematics and subsequent risk estimates are dictated primarily by job design and not influenced by the low back health status of the worker.     

Implementation of Office Automation and Its Effects on Job Characteristics and Strain in a District Court

Using a longitudinal case study design, the implementation of office automation was examined at a district court office during a 4-year period. The aims of the study were to examine the effects of the implementation of office automation on perceived job characteristics and on the personnel's short-term mental strain, long-term mental strain, and job satisfaction. All occupational groups at the district court, that is, the office workers, the judges, and the court clerks, participated in the study. After the implementation, the office workers perceived their job as slightly more interesting than before the implementation. After the implementation, their short-term mental strain was slightly lower than before it. However, this effect seemed to diminish in the long run. On the other hand, a slight increase in the office workers' long-term strain was found. Moreover, the judges' job characteristics changed slightly. Using a longitudinal research design, the development of job stress among the personnel at the district court could be followed up, and the changes in job stress could be compared according to the phases of the implementation of office automation.     

Using IDS fitted Q to develop a real-time adaptive controller for dynamic resource provisioning in Cloud's virtualized environment

Reinforcement learning (RL) is a powerful solution to adaptive control when no explicit model exists for the system being controlled. To handle uncertainty along with the lack of explicit model for the Cloud's resource management systems, this paper utilizes continuous RL in order to provide an intelligent control scheme for dynamic resource provisioning in the spot market of the Cloud's computational resources. On the other hand, the spot market of computational resources inside Cloud is a real-time environment in which, from the RL point of view, the control task of dynamic resource provisioning requires defining continuous domains for (state, action) pairs. Commonly, function approximation is used in RL controllers to overcome continuous requirements of (state, action) pair remembrance and to provide estimates for unseen statuses. However, due to the computational complexities of approximation techniques like neural networks, RL is almost impractical for real-time applications. Thus, in this paper, Ink Drop Spread (IDS) modeling method, which is a solution to system modeling without dealing with heavy computational complexities, is used as the basis to develop an adaptive controller for dynamic resource provisioning in Cloud's virtualized environment. The performance of the proposed control mechanism is evaluated through measurement of job rejection rate and capacity waste. The results show that at the end of the training episodes, in 90 days, the controller learns to reduce job rejection rate down to 0% while capacity waste is optimized down to 11.9%.     

Psychosocial aspects of working with video display terminals (VDTs) and employee physical and mental health

Psychosocial aspects of using video display terminals (VDTs) have been recognized as contributors to employees' mental and physical health problems for more than 15 years. Yet, little has been done by employers to change work organization conditions to improve the psychosocial work environment of VDT users. Thus, psychosocial aspects of work are emerging as one of the biggest problems for VDT users in the late 1990s. This paper explores how psychosocial aspects of VDT work are related to job stress, and their consequences for mental and physical health. Using the research literature, it defines various aspects of work organization and job design that have been shown to be related to VDT users' ill-health. Some of the important work design aspects uncovered include a lack of employee skill use, monotonous tasks, high job demands and work pressure, a lack of control over the job, poor supervisory relations, fear of job loss, and unreliable technology. These are the same job stressors that have been defined as problematic for a variety of blue collar jobs in previous research. Work organization improvements for healthier VDT jobs are proposed. These include organizational support, employee participation, improved task content, increased job control, reasonable production standards, career development, enhanced peer socialization, and improved workstation ergonomics. These organizational improvements are derived from a more detailed organizational strategy for job stress reduction. A model of job redesign through proper ‘balancing’ of work organization features is discussed.     

Multi-objective bionics design method of passive suspension parameters based on hybrid behavior game

In this paper, a linear passive vehicle suspension model with eight DOF, including six vertical and two rotation motions is given. Considering road surface damage, and ride comfort, taking stiffness and damping of seat and suspension system as design variables; taking seat acceleration RMS, tire’s relative dynamic load and suspension’s maximum dynamic stroke as objective functions, it established a tri-objective optimization model; and it also gave a multi-objective method based on game theory, takes three design objectives as three players, by calculating the affecting factors of the design variables to objective functions and fuzzy clustering, the design variables are divided into different strategic spaces owned by each player. Bionic research on three kinds of male side-blotched lizard’s behavior and survival of multiply mechanism, it defines three lizards as opportunism, egoism and collectivism; sets the three behaviors as corresponding player, and takes self-adaptive function as objective as optimal mono-objective its own strategic spaces and obtains the best strategy to deal with the others. All the best strategies are combined as a game strategy set. By multi-gaming, the final solution meeting given convergence of criterion is obtained. Calculation results show the effectiveness and practicability of the proposed method.     

Determinants of Innovative Work Behaviour: Development and Test of an Integrated Model

In this study, we developed and tested a causal model to predict innovative work behaviour (IWB) integrating the literatures on psychological contract, job design and organizational justice. Two hundred and four employees from Irish manufacturing organizations participated in the study, and we collected data using a survey questionnaire. The psychological contract variable of perceived obligation to innovate, job autonomy and pay showed direct effects on IWB. In addition, pay and job autonomy also had indirect effects on IWB through the mediating variable of psychological contract – perceived obligation to innovate. The organizational process of meritocracy, equity perceptions and procedural justice perceptions influenced IWB through the mediating variables of psychological contract, although none of these variables influenced IWB directly. Overall, the results indicated good support for the integrative model and provided support for the crucial role played by psychological contract in influencing IWB. Implications are discussed.     

Dynamic Bayesian networks and variable length genetic algorithm for designing cue-based model for dialogue act recognition

The automatic recognition of dialogue act is a task of crucial importance for the processing of natural language dialogue at discourse level. It is also one of the most challenging problems as most often the dialogue act is not expressed directly in speaker’s utterance. In this paper, a new cue-based model for dialogue act recognition is presented. The model is, essentially, a dynamic Bayesian network induced from manually annotated dialogue corpus via dynamic Bayesian machine learning algorithms. Furthermore, the dynamic Bayesian network’s random variables are constituted from sets of lexical cues selected automatically by means of a variable length genetic algorithm, developed specifically for this purpose. To evaluate the proposed approaches of design, three stages of experiments have been conducted. In the initial stage, the dynamic Bayesian network model is constructed using sets of lexical cues selected manually from the dialogue corpus. The model is evaluated against two previously proposed models and the results confirm the potentiality of dynamic Bayesian networks for dialogue act recognition. In the second stage, the developed variable length genetic algorithm is used to select different sets of lexical cues to constitute the dynamic Bayesian networks’ random variables. The developed approach is evaluated against some of the previously used ranking approaches and the results provide experimental evidences on its ability to avoid the drawbacks of the ranking approaches. In the third stage, the dynamic Bayesian networks model is constructed using random variables constituted from the sets of lexical cues generated in the second stage and the results confirm the effectiveness of the proposed approaches for designing dialogue act recognition model.     

Does job insecurity hinder innovative work behaviour? A threat rigidity perspective

Current work life is characterized by globalization, technological changes and the aftermath of the economic recession, thereby increasing the need for organizations to be innovative to maintain their competitive position. At the same time, this turbulent organizational landscape gave rise to perceptions of job insecurity (JI), that is, the subjectively perceived likelihood of involuntary job loss. The present study investigates whether job insecurity relates to innovative work behaviour (IWB) and introduces threat rigidity theory as an explanatory framework for this relationship. Based on this theory, we propose a serial mediation model, in which job insecurity relates to an increase in irritation, which subsequently relates to a decrease in concentration, resulting in a decrease in both dimensions of IWB, namely idea generation and idea implementation. By means of survey data from 394 Dutch‐speaking Belgian employees, we used structural equation modelling to compute our mediation analyses (bootstrapping method). Our findings are in line with threat rigidity theory, as the results demonstrate that the threat of job loss impairs employees' innovativeness through increased irritation and decreased concentration. This study contributes to job insecurity as well as IWB research, by introducing a process model that sheds light on job insecurity outcomes and antecedents of IWB.     

Quad‐Rotor Modeling and Attitude Control Using State‐Dependent ARX Type Model

A state‐dependent autoregressive with exogenous variables (SD‐ARX) model whose functional coefficients are approximated by sets of radial basis function (RBF) networks is proposed to describe the dynamic behavior of a quad‐rotor in this paper. This model is identified offline and used as an internal predictor of a receding horizon predictive controller to address the quad‐rotor's attitude control issue. In addition, the physical constraints of the system have been also taken into account during the controller design process. The results of real‐time control on a quad‐rotor aircraft illustrate satisfactory modeling accuracy in a large operating range and good performance of control approach proposed in this paper.     

A genetic algorithm and the Monte Carlo method for stochastic job-shop scheduling

This paper proposes a method for solving stochastic job‐shop scheduling problems using a hybrid of a genetic algorithm in uncertain environments and the Monte Carlo method. First, the genetic algorithm in uncertain environments is applied to stochastic job‐shop scheduling problems where the processing times are treated as stochastic variables. The Roulette strategy is adopted for selecting the optimum solution having the minimum expected value for makespan. Applying crossover based on Giffler and Thompson's algorithm results in two offspring inheriting the ancestor's characteristics as the operation completion times averaged up to the parent's generation. Individuals having very high frequency through all generations are selected as the good solutions. Second, the Monte Carlo method is effectively used for finding out the approximately optimum solution among these good solutions.     

The validity of visual observation to assess posture in a laboratory-simulated,manual material handling task

A wide range of methods to evaluate posture in work situations relies on simple, unaided visual observation. In the present study the validity of visual observation to assess posture in a dynamic job was evaluated. Postural aspects were observed and recorded during a manual materials handling job simulated in a laboratory. The results from these observations, concerning gross body posture, torso flexion, arms and legs position, and load to be handled, were compared to the results obtained by direct opto-electronic recording. The agreement on a sample-to-sample basis (expressed by Cohen's k) was poor for the variables torso flexion (mean value for tc = 0.38), position of arms (tc = 0.43) and legs (tc = 0.46) and load to be handled (tc — 0.50) and acceptable only for the gross body posture (k = 0.79). Moreover, for each variable except gross body posture, the crude distributions of all observations and recordings across categories (irrespective of time) were significantly different. The results demonstrate that the observations are not valid. It is concluded mat dynamic work situations require less simple, more time consuming methods (e.g., analyzing film or video records of the job) than a posture registration method based on direct visual observation.     

Population evolution analysis in collective intelligence design ecosystem

The Collective Intelligent Design Ecosystem is a dynamic ecosystem founded on an online design platform that leverages collective intelligence to support the creation of novel products. The system's primary components are its users and designers. Maintaining the system's sustainability requires expanding the scale of the designer and user populations as it evolves to stabilize. However, the unity of ecological interactions between various populations is fragmented in contemporary studies of population-scale evolution, and the parameterization of evolutionary models is illogical. To overcome this gap, this research provides a population evolution model of collective intelligent design incorporating participants' intra- and interspecific ecological connections. The model's validity is verified by the evolutionary simulation of 110 designers and 5990 users of China's largest collective intelligence design platform, the Zhubajie platform, and illuminating conclusions are in turn drawn from this simulation. First, the designer's influence on the user is greater than the user's impact on the designer. Second, keeping current members engaged is more crucial to the system's viability than luring in new ones. Third, fostering collaboration among designers while retaining user competitiveness can promote system growth. Fourth, decreasing the reliance between particular designers and users might hasten the system's evolution.     

How job satisfaction can influence air traffic controllers' backup‐providing behavior in parallel runway operation

Team members can request and provide backup from one to other members, which can help teams with high workload achieve dynamic workload optimization at the group level. However, studies on this issue are limited, especially on real‐world high‐risk operations. This study explored how to approach air traffic controllers make backup decisions in the dependent parallel approach operation. In this study, we focused on how individual difference factors can influence the controller's backup willingness and decision. Forty licensed controllers performed 32 simulated scenarios varied in their own task load, the backup recipients' task load, and shorter taxing possibility. We also measured their job satisfaction and work experience. Multilevel regression analyses showed that controllers with a higher level of job satisfaction were more likely to provide help to their colleagues. Moreover, when their job satisfaction was high, they were less sensitive to the seemingly inappropriate request, but they only used their additional resources to help others. The findings were discussed in the literature of teamwork and aviation safety.     

MGP-INTACTSKY: Multitree Genetic Programming-based learning of INTerpretable and ACcurate TSK sYstems for dynamic portfolio trading

In this paper, a Multitree Genetic Programming-based method is developed to learn an INTerpretable and ACcurate Takagi-Sugeno-Kang (TSK) fuzzy rule based sYstem (MGP-INTACTSKY) for dynamic portfolio trading. The MGP-INTACTSKY utilizes a TSK model with a new structure to develop a more interpretable and accurate system for dynamic portfolio trading. In the new structure of TSK, disjunctive normal form rules with variable structured consequent parts are developed in which the absence of some input variables is allowed. Input variables are the most influential technical indices which are selected by stepwise regression analysis. The technical indices are computed using wavelet transformed stock price series to eliminate the noise. The proposed system directly induces the preferred portfolio weights from the stock's technical indices through time. Here, genetic programming with the multitree structure is applied to learn the TSK fuzzy rule bases with the Pittsburgh approach. With this approach, the correlation of different stocks is properly considered during the evolutionary process. To evaluate the performance of the MGP-INTACTSKY for portfolio trading, the proposed model is implemented on the Tehran Stock Exchange as an emerging market as well as Toronto and Frankfurt Stock Exchanges as two mature markets. The experimental results show that the proposed model outperforms other methods such as the momentum strategy, the multitree genetic programming-based crisp system, the genetic algorithm-based first order TSK system, the buy and hold approach and the market's main index in terms of accuracy and interpretability.     

When carrots and sticks meet: A mixed-methods study on internet taxi drivers’ job engagement in the IT-driven sharing economy

Information technology (IT)-enabled taxi services facilitate people's lives. However, little is known about how Internet taxi employees are motivated and how motivation relates to their job engagement. Using a mixed-methods design, this research explores intrinsic motivators and the effects of external regulations on intrinsic motivation and job engagement. The qualitative study identified three context-specific intrinsic motivators: stress reduction, self-efficacy, and job autonomy. A follow-up quantitative research revealed the significance of external regulations in determining intrinsic motivation and job engagement. The study contributes to knowledge by emphasizing the role of intrinsic motivators and the significance of both monetary incentives and punishment.     

The relationship between recruiter characteristics and applicant assessment on social media

Given the growing practice of social recruiting, this study is an investigation of how job applicant assessment on social media differs based on recruiter characteristics. In particular, this study touches upon how the assessment of both non-professional and professional categories of content on job applicants' social media differs by recruiters' gender and national culture, and whether these characteristics and how recruiters view non-professional content on social media are associated with the recruiters' perceived tendency to exclude applicants from the recruitment process. The theoretical foundations of this study were based on the selectivity model and the existing literature on national culture. The analysis of data collected from 256 Italian and Dutch recruiters using ANCOVA and logistic regression indicated that the assessment of job candidates' non-professional content differed by recruiters' culture but not by their gender, whereas the assessment of job candidates' professional content on social media differed by recruiters' gender but not by their culture. Factors related to the recruiters' perceived tendency to exclude candidates from the recruitment process were also identified. The findings of this study provide practical implications for recruiters and job seekers, and present new suggestions for future research.     

Dynamic appearance model for particle filter based visual tracking

This paper made major research on the target representation problem, which plays a significant role in visual tracking, but has received little attention in most researches. In order to fulfill the requirements of tracking robustness and effectiveness in practical conditions, a dynamic appearance model is constructed. Due to particle filter's excellent characteristics, it is employed in this paper not only to estimate target's state, but also to construct the dynamic observation model integrated by multiple cues. In the proposed method, a dynamic multi-cue integration model is constructed for particle filter framework. And a systematic study is done on evaluating cue's weight. Specially, a particle filter based weight tracker is designed to update multi-cue's integrating manner online, so as to adapt the observation model to target's appearance changes. In such a way, a double-particle-filter based tracking framework is formed, and it is field tested on a variety of videos in different tracking conditions. In the experiments and comparisons, the applicable conditions of the proposed dynamic model are discussed, and its robustness and effectiveness are demonstrated.