Task Dimensions Research Articles

The digital transformation in the psychology of workplace spirituality

PurposeThere is evidence that spirituality at the workplace has positive effects on work outcomes, and there are different models conceptualizing the construct. To date, there is no discussion highlighting how digitalization is affecting workplace spirituality and vice versa. The present review tries to close this gap by discussing the psychological dynamics in light of digitalization and spirituality in the context of work.Design/methodology/approachThis is a conceptual discussion based on an extensive narrative review. The conceptual design is further tested with a real-life case study.FindingsThe result is a model that may guide future research, which consists of the four highly interdependent domains, namely psychology (with the dimensions of emotion, cognition and behavior), digitalization (with the dimensions of platforms, data and algorithms), spirituality (with the dimensions of meaning, self-transcendation and belonging), as well as the workplace (with the dimensions of work tasks, location, community and culture and values). The discussion includes implications for the future of work, suggestions for management decisions and potential future research directions.Originality/valueTo date, there are many discussions about digital transformation and a limited amount of them have invested in analyzing psychological dimensions. The application to spirituality and the workplace – especially when the two are combined – is almost wholly absent, which makes the present discussion both innovative and original.

Multitasking evolutionary algorithm based on adaptive seed transfer for combinatorial problem

Evolutionary computing (EC) is widely used in dealing with combinatorial optimization problems (COP). Traditional EC methods can only solve a single task in a single run, while real-life scenarios often need to solve multiple COPs simultaneously. In recent years, evolutionary multitasking optimization (EMTO) has become an emerging topic in the EC community. And many methods have been designed to deal with multiple COPs concurrently through exchanging knowledge. However, many-task optimization, cross-domain knowledge transfer, and negative transfer are still significant challenges in this field. A new evolutionary multitasking algorithm based on adaptive seed transfer (MTEA-AST) is developed for multitasking COPs in this work. First, a dimension unification strategy is proposed to unify the dimensions of different tasks. And then, an adaptive task selection strategy is designed to capture the similarity between the target task and other online optimization tasks. The calculated similarity is exploited to select suitable source tasks for the target one and determine the transfer strength. Next, a task transfer strategy is established to select seeds from source tasks and correct unsuitable knowledge in seeds to suppress negative transfer. Finally, the experimental results indicate that MTEA-AST can adaptively transfer knowledge in both same-domain and cross-domain many-task environments. And the proposed method shows competitive performance compared to other state-of-the-art EMTOs in experiments consisting of four COPs.

Multitask Particle Swarm Optimization With Dynamic Transformation

Multitask optimization (MTO) mainly utilizes knowledge transfer among tasks to address multiple optimization problems in parallel. However, the decision space dimensions of different tasks often differ, which leads to the failure of knowledge transfer. Therefore, it is a challenging problem to transfer knowledge among tasks with different dimensions to achieve parallel optimization of multiple tasks. To address this problem, multitask particle swarm optimization with a dynamic transformation strategy (MTPSO-DTS) is proposed to improve the performance of MTO. First, an intertask similarity index based on gradient information and location information is designed to dynamically assess the degree of similarity among different tasks. Then, the intertask similarity is determined to assist the transformation of dimensions. Second, a dynamic transformation strategy based on intertask similarity is developed to achieve a uniform representation of different dimension knowledge, including dimension supplementation and dimension reduction. Then, the MTPSO-DTS algorithm, which takes advantage of the search characteristics of particle swarm optimization, can facilitate knowledge transfer among tasks with different dimensions. Third, the convergence of the MTPSO-DTS algorithm is analyzed to verify the validity theoretically. Finally, numerical comparison experiments on benchmark problems and a practical application are carried out to demonstrate the effectiveness of the MTPSO-DTS algorithm. The results indicate that the proposed MTPSO-DTS algorithm can facilitate knowledge transfer among tasks of different dimensions to promote parallel optimization of multiple tasks.

STUDENT-GENERATED CONNECTIONS IN LEARNING ABOUT COMPOUND PROBABILITY AND THEIR EMERGENCE DURING INSTRUCTION

In this report, we analyze students’ learning of compound probability by describing connections they generated during individual interviews and group lessons. Several of their connections were compatible with the development of expertise, such as recognizing the need to determine sample spaces across a variety of situations and noting structural similarities among tasks, even when their task solutions were incomplete from a normative standpoint. Students reasoned about dimensions of context, variation, mathematical structure, sample space, and probability quantification. We describe the extent to which they coordinated these dimensions. We also describe teaching moves, such as posing idealized situations and shifting to structurally similar tasks, which prompted students to attend to multiple relevant task dimensions.

Незначимые параметры в обратных задачах фотограмметрии

The third article in this series of publications deals with solving inverse problems of photometry. The matter of reducing the dimension of the original inverse task with excluding insignificant parameters from the equation is considered. The main known ways of identifying those ones having the greatest and least influence over the main characteristics of the equalization results are noted. Their disadvantages are indicated at solving poorly conditioned issues and problems with initial measurements, the actual accuracy of which is unknown. The characteristics of the results include indicators of precision and statistical quality of the equalization. The main indicators its exactness are the average square deviations of the indirect measurements’ residuals, primarily the coordinates of the reference and connecting points. Those of the statistical quality are estimates of the direct and indirect measurements statistical quality equalization, as well as evaluating the mean square error of the weight unit after that. An algorithm was developed to refine the initial weights of direct measurements, identify and first eliminate insignificant parameters based on the analysis of intermediate data of the task being solved. An addition to the algorithm for adjusting the weights of direct measurements during equalization was formulated; it provides identification and secondary screening of insignificant parameters. Experimental approbation of the identified ways is carried out. The necessity of adjusting the weights of indirect measurements was established.

Algorithms and Systems for Manipulating Multiple Objects

Robot manipulation of multiple objects is an important topic for applications including warehouse automation, service robots performing cleaning, and large-scale object sorting. Although problems can range in complexity from a few objects to large disordered piles, autonomy remains a significant technical challenge due to the high-dimensional joint configuration space of the robot and all objects, the complex dynamics of object interaction, and the ambiguity and occlusion caused by clutter. This article surveys a broad range of classical and state-of-the-art research in multiobject manipulation and categorizes them along the dimensions of tasks, perception, predictive models, and decision-making algorithms. It also covers emerging trends and open problems faced in the ongoing effort to realize robust multiobject manipulation systems in practice.

Dynamics of flexible work and digital platforms: Task and spatial flexibility in the platform economy

Flexibility is recognized as one of the major shifts in the future of work. As emerging forms and structures of work challenge us to rethink how work is organized, specifically through the digitization of work by digital platforms, we are witnessing shifts in workplaces that challenge common models of full-time employment. To date, most frameworks looking to understand flexibility and flexible work arrangements are rooted in traditional employment settings. Building on existing concepts of flexibility and flexible work arrangements, this study examines how platform workers make sense of flexibility. Our findings conceptualize two central dimensions of worker flexibility, task and spatial flexibility, which are driven by the different attributes of the platform work. Our findings also demonstrate that different platform types, and the ways in which they mediate flexibility, leads to important differences in the ways workers experience their work.

Estilo de gestão na auditoria interna: influência entre fatores pessoais e conflito de papéis

ABSTRACT This article analyzes the leader's management style effect on the relationship between personal factors and role conflict in the internal audit function. It sheds light on the factors per se regarding the presence of role conflict in internal audit, addressing the effects that various management styles of the audit leader can cause in the relationship previously addressed in the literature between personal characteristics and role conflict. Evidence shows that the skills and expertise of leading internal audit executives impact the role played by internal auditors, becoming major insights to be analyzed and understood when delimiting the internal audit function. This article contributes to grasping the impact that aspects at a personal, professional, and interaction level in the management hierarchy in companies can have on the internal audit function. A survey was used involving 218 internal auditors certified by the Institute of Internal Auditors of Brazil (Instituto dos Auditores Internos do Brasil [IIA Brasil]), working in the internal audit area and with active profiles on the professional networking platform LinkedIn. Use of multiple linear regression for data analysis. Personal factors encompass the independence and expertise of internal auditors. Role conflict comprises three dimensions: demand from the organization vs. the auditor profession; auditing vs. consulting; and personal values vs. professional expectations. Management style measures the behavior of immediate superiors in the dimensions of task, relationship, and situational context. As results and contributions, it was evidenced that internal auditor’s commitment to independence and expertise have a negative influence on role conflict. A management style with behaviors aimed at relationships between individuals, carrying out tasks, or adapting to situations, on the part of immediate superiors, contributes to reducing the level of role conflict in the internal audit function.

Robust Hierarchical Tracking Control of Vehicle-Manipulator Systems

For vehicle-manipulator systems (VMSs) to perform precise operations, a robust tracking control framework is required. Moreover, to exploit the platform's redundancy, it is desirable that the framework allows several tasks to be completed simultaneously. This work presents a robust hierarchical tracking controller for floating-base robots. In addition to providing uniform global asymptotic stability (UGAS) in free motion and allowing a total task dimension greater than the number of degrees of freedom (DoFs), the proposed controller includes a sliding mode effect and is proved to achieve UGAS even in the presence of bounded disturbances. Moreover, it is proved that uniform global ultimate boundedness (UGUB) is obtained if a continuous approximation of the sliding mode term is used. The stability results are proved mathematically, and are validated through simulations of an articulated intervention autonomous underwater vehicle (AIAUV).

Quality of the therapeutic working alliance as a factor in intensive residential treatment of obsessive-compulsive disorder

Objective: Intensive residential treatment (IRT) for obsessive-compulsive disorder (OCD) includes frequent meetings with a cognitive–behavioral therapist. We examined whether this therapeutic working alliance relates to IRT outcomes. Method: Data came from a naturalistic sample of patients with OCD (n = 124) who received IRT at a specialty OCD clinic. Patients completed measures of OCD severity and well-being at admission and discharge. Both the patient and treating psychologist completed the Working Alliance Inventory-Short Form (WAI-SF). Alliance ratings were tested as predictors in models predicting outcomes (discharge scores adjusting for baseline and treatment duration) as well as logistic regression predicting treatment response (≥35% symptom reduction in OCD symptoms). Results: Patient and clinician ratings of the quality of the alliance were weakly yet significantly correlated. Patient ratings of the alliance predicted outcomes, while therapist ratings did not. Moreover, greater discrepancy between patient and client ratings predicted worse outcomes. Patient ratings of the task dimension of the alliance uniquely related to responder status. Conclusions: Patient perceptions of the working alliance, particularly as pertaining to agreement on therapeutic tasks, related to success with IRT for OCD. Further study is needed test interventions to improve task alliance as a strategy to enhance treatment.

Human-machine symbiosis: A multivariate perspective for physically coupled human-machine systems

The notion of symbiosis has been increasingly mentioned in research on physically coupled human-machine systems. Yet, a uniform specification on which aspects constitute human-machine symbiosis is missing. By combining the expertise of different disciplines, we elaborate on a multivariate perspective of symbiosis as the highest form of interaction in physically coupled human-machine systems, characterized by a oneness of the human and the machine. Four dimensions are considered: Task, interaction, performance, and experience. First, human and machine accomplish a common objective by completing tasks conceptualized on a decomposition, a decision and an action level (task dimension). Second, each partner possesses an internal representation of the oneness they form, including the partner’s inner states (e.g. experiences) and their joint influence on the environment. This representation constitutes the “symbiotic understanding” between both partners, being the basis of a joint and highly coordinated action (interaction dimension). Third, the symbiotic interaction leads to synergetic effects regarding the complementary strengths of the partners, resulting in a higher overall performance (performance dimension). Fourth, symbiotic systems specifically change the user’s experiences, like flow, acceptance, sense of agency, and embodiment (experience dimension). Our multivariate perspective allows a clear description of symbiotic human-machine systems and helps to bridge barriers between different disciplines.

From QoS to QoE plus QoT in Beyond 5G Networks

In the Beyond 5G (B5G) era, a paradigm shift from technical Quality of Service (QoS) oriented networks to user-centric Quality of Experience (QoE) centred network architectures is expected to occur. With this development, the infusion of QoE user requirements into B5G networks will be critical to the emergence of ultra-reliable and ultra-low latency haptic-enabled Internet applications of the future. One such application which will signify the emergence of a tele-haptic Internet will be the mission-critical use case of remote robotic surgical task performance, precipitating a transition from content-based to skillset delivery networks for an augmented user experience. In extending network QoS to user focused QoE and with it, Quality of Task (QoT) dimensions, human users in a global control loop (such as robotic surgeons) will be capable of true-to-life immersive remote task performance through the manipulation of objects in real-time and across large geographical distances. In this paper, we discuss the linkages between network and user-centric QoS and QoE (with QoT) perspectives. Further, we explain the emergence of a future B5G network and haptic-enabled Internet of Skills (IoS), and draw on the example of an architecture applied to the task-sensitive mission-critical use case of tele-haptic surgery. In doing so, we conceptualise and present Task-Technology Fit (TTF) theoretical and predictive modelling that can be empirically applied to this futuristic use case for a novel QoE/QoT perspective of future B5G communication networks.

The Easier-Is-Better Heuristic: The False Allure of Easy Work

People are highly sensitive to effort expenditure and have an evolved goal to minimize it. As such, people tend to favor tasks that are easier over more difficult tasks. However, there are several downsides to reducing task demand, such as experiencing boredom, that make easier tasks less enjoyable. Across six studies, we examine people’s beliefs about the relationship between task demand and task enjoyment. We find that people inaccurately anticipate that making a task less demanding will increase its enjoyability (Study 1). We extend our findings to a more objective conceptualization of task demand (Study 2) and find that the effect persists even when tasks are familiar (Study 3). We test our proposed mechanism that people fail to consider other task dimensions that impact task enjoyment because effort avoidance is a highly salient goal (Study 4). In Study 5 we find that people accurately anticipate that challenging, yet attainable, tasks will be more enjoyable than tasks which exceed their abilities. Finally, in Study 6 we surveyed working professionals to test the implication of the effect on job satisfaction and retention. Taken together, these results suggest that people operate on an “easier-is- better” heuristic, meaning they believe that any decrease in task demand will lead to an increase in task enjoyment. These beliefs matter because they likely guide people’s decisions about what work to pursue and what work to avoid.

A data cube modeling method for longitudinal cohort study

Longitudinal cohort study is an effective way to probe into the risk factors of disease and evaluate intervention measures. It has gradually become the mainstream research method in precision medicine, chronic disease management and evidence-based education, and has been deployed in many National Science and Technology Major Projects, which established its specialized cohorts for natural populations, chronic diseases, specialized diseases. The quality of data is a make-or-break factor of longitudinal cohort study. The subjects and test tasks in longitudinal cohort study have dynamic changes over time, and the data generated involves multiple modalities and scales. Therefore, exploring how to model business-oriented longitudinal cohort data will contribute to a unified understanding and governance of longitudinal cohort data, and ultimately improve data quality. On the one hand, because different modal data in longitudinal cohort study have different dimensional indicators, it is difficult to carry out data modeling based on unified dimensional indicators through simple dimensional splicing; on the other hand, the needs of the longitudinal cohort management scenario determine the calculations should be focused on the granularity of individual subjects and data modal types. Considering the above, the traditional multi-dimensional data modeling method based on data dimension indicators and their measurements as basic elements couldn’t be fully adapted to the counting and statistical requirements under the longitudinal cohort scenarios. This paper proposes a data cube model based on MOLAP named SubTaP, which take multimodal data objects as basic granularity. This model constructs a cube structure with three dimensions of Subject, Task and Phase. It can be applied to meet the visualization requirements of longitudinal cohort management scenario and guide the construction of a data information platform for cohort study. At the same time, it helps to build a unified understanding of longitudinal cohort study data among data generators, cohort maintainers, and data users.

Multi-objective Mathematical Modeling for Scheduling Machines in Parallel with Batch Processors

In this paper, the problem of scheduling the production of unrelated parallel machines to simultaneously minimize the goals of production time span, early and late fines, and the cost of purchasing machines is investigated, and a two-objective mathematical model is considered considering clearance and preparation times and limit capacity. Due to faster and cheaper operations with batch processors and increasing the efficiency of operating systems, all machines are batch processors. Initially, the model is coded and executed using the exact method in GAMS software. Due to the hard-NP nature and complex nature of the problem, a multi-objective meta-heuristic algorithm based on the coordination search method is proposed and designed. Then Taguchi method is used to find the best level for the algorithm parameters, and two examples of problems in different dimensions of tasks and machines are presented and solved by this proposed algorithm. The results of the calculations show the efficiency of this algorithm to generate more solutions at a much lower solution time.

Effects of context and discrepancy when reading multiple documents

On a daily basis, most people read about issues of interest from a diversity of sources. Moreover, the information they encounter frequently encompass discrepancies, ranging from minor inconsistencies to straight contradictions. Readers may construct coherent representations from discrepant contents by linking contents to their respective sources and connecting the sources with agree-disagree or other types of connectives. Across research studies, however, college-level readers' attention to sources has been found to vary according to individual, text and task dimensions. The present study tested the assumption that readers' strategies depend both on the discrepancy of the information and on the context in which the task is framed. Moreover, beliefs about science were included as potential moderator of context effects. One hundred and sixty university students were tasked to read about a series of social-scientific issues. The task was framed in either a university context or a personal context scenario. For each topic, the participants read two short texts which provided either consistent or discrepant information, and then they wrote a short overview essay. The university context had a significant impact on indicators related to a documents model representation (e.g., text switches, number of adversative connectors in the essay) and standards for presentation (e.g., time on the essay/task page, formal features of the essay). The data support a context-dependent view of reading comprehension, whereby both reading behavior and outcomes are primarily a function of the standards and goals set by the reader.

Teacher strategies in implementing English medium instruction

AbstractWith the expanding role of English medium instruction (EMI) worldwide there has been an increasing use of English to teach content subjects at tertiary level. In implementing EMI a common assumption has been that the primary issue affecting successful EMI is the English proficiency level of the EMI instructor. This paper reviews this assumption by exploring three dimensions of EMI—the cognitive, the genre, and the academic task dimensions of teaching in a discipline—and the role that English plays in developing disciplinary literacy and competence. From a review of EMI teachers’ accounts of EMI in Hong Kong as well as other EMI contexts, information is presented to demonstrate the ways in which EMI teachers adapt their teaching in order to prepare for and manage EMI. Goals are suggested for the professional development of EMI teachers.

Sharpening, focusing, and developing: A study of change in nonsymbolic number comparison skills and math achievement in 1st grade.

Children's ability to discriminate nonsymbolic number (e.g., the number of items in a set) is a commonly studied predictor of later math skills. Number discrimination improves throughout development, but what drives this improvement is unclear. Competing theories suggest that it may be due to a sharpening numerical representation or an improved ability to pay attention to number and filter out non-numerical information. We investigate this issue by studying change in children's performance (N=65) on a nonsymbolic number comparison task, where children decide which of two dot arrays has more dots, from the middle to the end of 1st grade (mean age at time 1=6.85 years old). In this task, visual properties of the dot arrays such as surface area are either congruent (the more numerous array has more surface area) or incongruent. Children rely more on executive functions during incongruent trials, so improvements in each congruency condition provide information about the underlying cognitive mechanisms. We found that accuracy rates increased similarly for both conditions, indicating a sharpening sense of numerical magnitude, not simply improved attention to the numerical task dimension. Symbolic number skills predicted change in congruent trials, but executive function did not predict change in either condition. No factor predicted change in math achievement. Together, these findings suggest that nonsymbolic number processing undergoes development related to existing symbolic number skills, development that appears not to be driving math gains during this period. Children's ability to discriminate nonsymbolic number improves throughout development. Competing theories suggest improvement due to sharpening magnitude representations or changes in attention and inhibition. The current study investigates change in nonsymbolic number comparison performance during first grade and whether symbolic number skills, math skills, or executive function predict change. Children's performance increased across visual control conditions (i.e., congruent or incongruent with number) suggesting an overall sharpening of number processing. Symbolic number skills predicted change in nonsymbolic number comparison performance.

Stable Strategy Formation for Mobile Users in Crowdsensing Using Co-Evolutionary Model

In crowdsensing, the diversity of the sensing tasks and an enhancement of the smart devices enable mobile users to accept multiple types of tasks simultaneously. In this study, we propose a new practical framework for dealing with the challenges of task assignment and user incentives posed by complex heterogeneous task scenarios in a crowdsensing market full of competition. First, based on the non-cooperative game property of mobile users, the problem is formulated into a Nash equilibrium problem. Then, to provide an efficient solution, a judgment method based on constraints (sensing time and sensing task dimension) is designed to decompose the problems into different situations according to the complexity. We propose a genetic-algorithm-based approach to find the combination of tasks that maximizes the utility of users and adopts a co-evolutionary model to formulate a stable sensing strategy that maintains the maximum utility of all users. Furthermore, we reveal the impact of competition between users and tasks on user strategies and use a cooperative weight to reflect it mathematically. Based on this, an infeasible solution repair method is designed in the genetic algorithm to reduce the search space, thus effectively accelerating the convergence speed. Extensive simulations demonstrate the effectiveness of the proposed method.

The Influence of Work Life Balance and Job Characteristics to Individual Work Performance: The mediating effect of Work Engagement

The focus of work performance measurement is only on task performance, however this concept has been developed to be a multidimensional construct and it is still needed to explore in order to get comprehensive information for employee developing program. The purpose of this study is to to elucidate the mediating role of work engagement (WE) in the complex relationship of multidimentional work performace within measures of work life balance and job characteristics. Data were collected from civil servants in Jakarta (N=300), using four scales to measure work performance; work engagement; work life balance; job characteristic and the hypotheses were tested using Structural Equation Model. The result showed that the structural model is supported by empirical data; The influence of work life balance to work performance and also from job characteristic to work performance, both are proven to be mediated by WE. With regarding the multidimensional construct, WE acts as a full mediator in the dimensions of task and contextual performances and as a partial mediator in the dimension of counter productive performance. Keywords: work engagement, mediator, work life balance, job characteristics and multidimensional work performance.