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Existing research on how professionals manage after-hours connectivity to work has been dominated by studies on the strategies/practices individuals develop. In these studies, mobile technology is perceived as a tool or an enabler that supports otherwise human-centric connectivity decisions. This view sees technology as separate or external to the organisation, missing out on its nuanced role in shaping connectivity decisions. Our study aims to bring technology back into the sociomaterially imbricated context of connectivity and to unpack its parameters.

Drawing on data collected from documents and semi-structured interviews, we adopt the framework of “sociomaterial imbrications” (Leonardi, 2011) to understand the social and material parameters that influence connectivity management practices at two different academic institutions in Saudi Arabia.

The study identifies a set of social and material parameters (organisational, individual, technological and situational) that imbricate to shape, collectively and not individually, professionals’ connectivity management practices. Connectivity decisions to change practice (such as decisions of where, when or why to connect) or technology (how to connect) are not as distinct as they appear but originate from, and are founded on, imbricated sociomaterial parameters. Our study further suggests that connectivity decisions are shaped by individuals’ perceptions of sociomaterial imbrications, but decisions are not solely idiosyncratic. The context within which connectivity decisions are taken influences the type of decisions made.

Connectivity management emerged from sociomaterial imbrications within a context constitutive of four interacting parameters: organisational, technological, situational and individual. Decisions around the “how” and the “what” of connectivity – i.e. the practice of connectivity and its underpinning technology – originate from how people perceive sociomaterial imbrications as enabling or constraining within a context. Individual perceptions account for changes in practice and in technology, but the context they find themselves in is also important. For instance, we show that professionals may perceive a certain technology as affording, but eventually they may use another technology for communications due to social norms.

This paper aims to propose a novel control scheme and offer a control parameter optimizer to achieve better automatic carrier landing. Carrier landing is a challenging work because of the severe sea conditions, high demand for accuracy and non-linearity and maneuvering coupling of the aircraft. Consequently, the automatic carrier landing system raises the need for a control scheme that combines high robustness, rapidity and accuracy. In addition, to exploit the capability of the proposed control scheme and alleviate the difficulty of manual parameter tuning, a control parameter optimizer is constructed.

A novel reference model is constructed by considering the desired state and the actual state as constrained generalized relative motion, which works as a virtual terminal spring-damper system. An improved particle swarm optimization algorithm with dynamic boundary adjustment and Pareto set analysis is introduced to optimize the control parameters.

The control parameter optimizer makes it efficient and effective to obtain well-tuned control parameters. Furthermore, the proposed control scheme with the optimized parameters can achieve safe carrier landings under various severe sea conditions.

The proposed control scheme shows stronger robustness, accuracy and rapidity than sliding-mode control and Proportion-integration-differentiation (PID). Also, the small number and efficiency of control parameters make this paper realize the first simultaneous optimization of all control parameters in the field of flight control.

This study aims to analyze the two-dimensional ferrofluid flow in porous media. The effects of changes in parameters such as permeability parameter, buoyancy parameter, Reynolds and Prandtl numbers, radiation parameter, velocity slip parameter, energy dissipation parameter and viscosity parameter on the velocity and temperature profile are displayed numerically and graphically.

By using simplification, nonlinear differential equations are converted into ordinary nonlinear equations. Modeling is done in the Cartesian coordinate system. The finite element method (FEM) and the Akbari-Ganji method (AGM) are used to solve the present problem. The finite element model determines each parameter’s effect on the fluid’s velocity and temperature.

The results show that if the viscosity parameter increases, the temperature of the fluid increases, but the velocity of the fluid decreases. As can be seen in the figures, by increasing the permeability parameter, a reduction in velocity and an enhancement in fluid temperature are observed. When the Reynolds number increases, an increase in fluid velocity and temperature is observed. If the speed slip parameter increases, the speed decreases, and as the energy dissipation parameter increases, the temperature also increases.

When considering factors like thermal conductivity and variable viscosity in this context, they can significantly impact velocity slippage conditions. The primary objective of the present study is to assess the influence of thermal conductivity parameters and variable viscosity within a porous medium on ferrofluid behavior. This particular flow configuration is chosen due to the essential role of ferrofluids and their extensive use in engineering, industry and medicine.

Traction applications, e.g. the IMs are mainly operated by field-oriented control (FOC). This control technique requires an accurate knowledge of the machine’s parameters, such as the main inductance, the leakage inductances and the stator and rotor resistance. The accuracy of the parameters influences the precision of the calculated rotor flux and the rotor flux angle and the decoupling of the machine’s equations into the direct and quadrature coordinate system (dq-components). Furthermore, the parameters are used to configure the controllers of the FOC system and therefore influence the dynamic behavior and stability of the control.

In this paper, three different methods to calculate the machine’s parameters, in an automated and rapid procedure with minimal measuring expenditure, are analyzed and compared. Moreover, a method to configure a control that reduces the overall Ohmic losses of the machine in every torque speed operation point is presented. The machine control is configured only with the identified machine parameter.

Simulations and test bench measurements show that the evolutionary strategy is able to identify the electrical parameters of the machine in less time and with low error. Moreover, the controller is able to control the torque of the machine with a deviation of less than 2 per cent.

The most significant contribution of the research is the potential to identify the machine parameter of an induction motor and to configure an accurate control with these parameters.

The purpose of the paper is to propose a novel approach, based on grey clustering decision, to fill in an omission of quantitative monitoring parameter selection methods.

The basic monitoring parameter selection criteria and the corresponding calculation methods are presented. Then, the grey clustering decision model for monitoring parameter optimization selection is constructed, and an integrated weight determination method based on analytic hierarchy process (AHP) and information entropy is provided.

Basic principle for monitoring parameter selection is proposed and quantitative description is carried out for selection principle in engineering application. Grey clustering decision‐making model for monitoring parameter optimization selection is established. Comprehensive weight ascertainment method based on AHP and information entropy is provided to make the index weight more scientific.

At system design stage, it is of significance to carry out selection and optimization of monitoring parameters. After the optimization of monitoring parameters is confirmed, measurability analysis and design in parallel are carried out for convenience of timely information feedback and system design revision. Therefore, the system integration efficiency is improved and the cost of research and manufacturing is reduced.

Monitoring parameter optimization selection process based on grey clustering decision‐making model is described and the analysis result shows that the proposed method has certain degree of effectiveness, rationality and universality.

High precision assembly processes using industrial robots require the process parameters to be tuned to achieve desired performance such as cycle time and first time through rate. Some researchers proposed methods such as design-of-experiments (DOE) to obtain optimal parameters. However, these methods only discuss how to find the optimal parameters if the part and/or workpiece location errors are in a certain range. In real assembly processes, the part and/or workpiece location errors could be different from batch to batch. Therefore, the existing methods have some limitations. This paper aims to improve the process parameter optimization method for complex robotic assembly process.

In this paper, the parameter optimization process based on DOE with different part and/or workpiece location errors is investigated. An online parameter optimization method is also proposed.

Experimental results demonstrate that the optimal parameters for different initial conditions are different and larger initial part and/or workpiece location errors will cause longer cycle time. Therefore, to improve the assembly process performance, the initial part and/or workpiece location errors should be compensated first, and the optimal parameters in production should be changed once the initial tool position is compensated. Experimental results show that the proposed method is very promising in reducing the cycle time in assembly processes.

The proposed method is practical without any limitation.

The proposed technique is implemented and tested using a real industrial application, a valve body assembly process. Hence, the developed method can be directly implemented in production.

This paper provides a technique to improve the assembly efficiency by compensating the initial part location errors. An online parameter optimization method is also proposed to automatically perform the parameter optimization process without human intervention. Compared with the results using other methods, the proposed technology can greatly reduce the assembly cycle time.

The purpose of this paper is to propose the grey target decision method based on three-parameter interval grey number for dealing with multi-attribute decision-making problems under uncertain environment.

First, the kernel and ranking method of three-parameter interval grey number are defined, which is the basis of determining the positive and negative bull’s-eye. Next, a new distance measure of three-parameter interval grey number is defined in view of the importance of the “center of gravity” point. Furthermore, a new comprehensive bull’s-eye distance is proposed based on the kernel which integrates the distance between different attributes to the positive and negative bull’s-eye. Then attribute weights are obtained by comprehensive bull’s-eye distance minimum and grey entropy maximization.

The paper provides a grey target decision method based on three-parameter interval grey number and example analysis shows that the method proposed in this paper is more reasonable and effective.

If we have a better understanding of the distribution characteristics of three-parameter interval grey number, it is possible to have a more reasonable measure of the distance of three-parameter interval grey number.

The paper provides a grey target decision method, which can help decision maker deal with multi-attribute decision-making problems under uncertain environment.

This paper proposed the kernel and ranking method of three-parameter interval grey number, and defined a new distance measure of three-parameter interval grey number and proposed a new comprehensive bull’s-eye distance, Furthermore, this paper structured a grey target decision method based on three-parameter interval grey number.

The purpose of this paper is to demonstrate application of the Taguchi method to determine the optimum level of three important parameters (factors) related to the use of a mobile phone for the text message entering task, namely illumination level, noise level and mobile angle that maximizes the performance of mobile phone users.

Three levels of each parameter as available in the literature, except for the mobile angle, were considered. The design of the experiment, as proposed by Genichi Taguchi, was used to conduct nine experiments. A total of 30 male subjects participated in the experimental study. The text message entry task, in the form of Arabic text, was presented to the participating subjects and their performance, measured in terms of mean number of characters entered per minute, was recorded. The signal‐to‐noise (S/N) ratio and the analysis of variance (ANOVA) were employed to investigate the users' performance. Finally, a confirmation test was conducted to verify the validity of the results.

Results showed that, at the illumination level of 475 lux, noise level of 45 dB(A), and mobile angle of 70 degrees, the subjects were quite comfortable, efficient and entered the maximum number of characters in the mobile phone per minute. The noise was found to be the dominant parameter with a contribution of 95.53 percent towards the laid down objective followed by mobile angle, 3.25 percent and illumination level, 0.66 percent.

To the best of the authors' knowledge no study has been conducted in the past to investigate the effect of these parameters on the performance of the mobile phone users. In addition, no attempt has yet been made to find the optimal level of these parameters from a text‐entering viewpoint. The paper represents original research and in the authors' opinion carries significantly important values as it provides new information for those involved in the design of the mobile phone environment.

The purpose of this paper is to evaluate and analyze lube oil performance. The plant experience shows that the oil, even after completion of a number of hours of recommended operation, has some residual life. It is desirable to maximize its use to conserve this scarce resource but avoid failures. At present, continuation or change of the oil is decided, based on the manufacturer's recommendation and experience in the plant. The suggested oil change period is conservative and results in non‐efficient usage of oil. This practice needs refinement to include all possible properties/attributes of oil and use of appropriate procedure to assess its realistic performance.

Parameter profile approach (PPA) is applied to evaluate and analyze the lubricating oil performance parameters.

Physical and chemical properties related to degradation of the lube oil are considered as lubricating oil performance parameters. The value of these performance parameters from time to time, obtained by analyzing samples drawn from the system, are analyzed through PPA. In this approach, mean, median, performance parameter index and time performance index are defined and evaluated at different time intervals. The suggested procedure is illustrated by means of an example.

The suggested procedure will be helpful for the maintenance personnel in planned oil replacement, and in identifying the weak oil properties in respect of the identified lube oil performance parameters.

Emitter parameter estimation via signal sorting is crucial for communication, electronic reconnaissance and radar intelligence analysis. However, due to problems of transmitter circuit, environmental noises and certain unknown interference sources, the estimated emitter parameter measurements are still inaccurate and biased. As a result, it is indispensable to further refine the parameter values. Though the benchmark clustering algorithms are assumed to be capable of inferring the true parameter values by discovering cluster centers, the high computational and communication cost makes them difficult to adapt for distributed learning on massive measurement data. The paper aims to discuss these issues.

In this work, the author brings forward a distributed emitter parameter refinement method based on maximum likelihood. The author’s method is able to infer the underlying true parameter values from the huge measurement data efficiently in a distributed working mode.

Experimental results on a series of synthetic data indicate the effectiveness and efficiency of the author’s method when compared against the benchmark clustering methods.

With the refined parameter values, the complex stochastic parameter patterns could be discovered and the emitters could be identified by merging observations of consistent parameter values together. Actually, the author is in the process of applying her distributed parameter refinement method for PRI parameter pattern discovery and emitter identification. The superior performance ensures its wide application in both civil and military fields.