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Based on data from the International Federation of Robotics (IFR) and Chinese A-Share Listed Companies 2011–2019, this paper evaluates how the penetration of industrial robots in China affects the labor income share from both theoretical and empirical perspectives. We first develop a theoretical framework considering robots in the task model, consider different tasks matching different types of labor, construct a labor income share model including robots based on the task model, and perform a theoretical analysis finding that industrial robots can improve labor income. Then, we explore the mechanism of labor price distortion as an intermediate variable that can reduce the labor income share, finding that robots are able to effectively reduce the negative impact of labor price distortions on labor income share. After correcting for potential endogeneity problems, the results confirm a positive, significant, and lasting impact of industrial robot penetration on labor income share, confirming that labor price distortion is the underlying mechanisms through which the robots can effectively reduce the negative impact of labor price distortions on labor income share. At the same time, the impact of robots on firms' labor income shares varies significantly across different regions, external financial dependence, and skill premiums. Our findings can help the government to provide a decision-making basis for how industrial robots can better serve people in the new century, ensuring that people can share in the achievements of economic development. [ABSTRACT FROM AUTHOR]

Utilizing a robot in a new application requires the robot to be programmed at each time. To reduce such programmings efforts, we have been developing “Learning-from-observation (LfO)” that automatically generates robot programs by observing human demonstrations. So far, our previous research has been in the industrial domain. From now on, we want to expand the application field to the household-service domain. One of the main issues with introducing this LfO system into the domain is the cluttered environments, which makes it difficult to discern which movements of the human body parts and their relationships with environment objects are crucial for task execution when observing demonstrations. To overcome this issue, it is necessary for the system to have task common-sense shared with the human demonstrator to focus on the demonstrator's specific movements. Here, task common-sense is defined as the movements humans take almost unconsciously to streamline or optimize the execution of a series of tasks. In this paper, we extract and define three types of task common-sense (semi-conscious movements) that should be focused on when observing demonstrations of household tasks and propose representations to describe them. Specifically, the paper proposes to use labanotation to describe the whole-body movements with respect to the environment, contact-webs to describe the hand-finger movements with respect to the tool for grasping, and physical and semantic constraints to describe the movements of the hand with the tool with respect to the environment. Based on these representations, the paper formulates task models, machine-independent robot programs, that indicate what-to-do and where-to-do. In this design process, the necessary and sufficient set of task models to be prepared in the task-model library are determined on the following criteria: for grasping tasks, according to the classification of contact-webs along the purpose of the grasping, and for manipulation tasks, corresponding to possible transitions between states defined by either physical constraints and semantic constraints. The skill-agent library is also prepared to collect skill-agents corresponding to tasks. The skill-agents in the library are pre-trained using reinforcement learning with the reward functions designed based on the physical and semantic constraints to execute the task when specific parameters are provided. Third, the paper explains the task encoder to obtain task models and task decoder to execute the task models on the robot hardware. The task encoder understands what-to-do from the verbal input and retrieves the corresponding task model in the library. Next, based on the knowledge of each task, the system focuses on specific parts of the demonstration to collect where-to-do parameters for executing the task. The decoder constructs a sequence of skill-agents retrieving from the skill-agnet library corresponding and inserts those parameters obtained from the demonstration into these skill-agents, allowing the robot to perform task sequences with following the Labanotation postures. Finally, this paper presents how the system actually works through several example scenes.

Introduction: Educating residents on laparoscopic operations requires direct teaching and deliberate practice. Attending surgeons are often systematic when performing surgery, which creates a challenge when instructing surgery residents. The aim of this study was to use cognitive task analysis to expand laparoscopic cholecystectomy into microsteps reflecting expert surgeon cognition (perceptions, assessments, decisions, etc.) throughout the operation such that these could be better formalized and conveyed to residents in educational materials or assessments and to attending surgeons as teaching scripts. Materials and methods: One surgeon, a surgical resident, and a human factors specialist conducted cognitive task analyses with three expert general surgeons and one hepatobiliary surgeon using semi-structured interviews. These interviews expanded an existing task model of laparoscopic cholecystectomy to specifically add patient safety aspects including injury prevention, risk management, and complication detection for each step. Interview analysis resulted in an expanded task diagram. Results: Cognitive task analysis expanded the current laparoscopic cholecystectomy task model from 19 to 97 microsteps. In addition to microsteps, an additional major step was identified, the planning step or step zero. Steps with the greatest number of microsteps included dissection with 15 microsteps and intraoperative cholangiogram with 10 microsteps. Discussion: Laparoscopic cholecystectomies are complex operations with multiple microsteps. Identification of these steps can lead to explicit strategies that can improve training of surgeons, with an end towards efficacy and safety. The identification of a planning step prior to beginning the operation is a significant finding that should arguably be included in all future cognitive task analyses regardless of operation or procedure, to emphasize to trainees what senior surgeons have learned through experience. These findings inform the development of interventions for surgical training and evaluation of competency. [ABSTRACT FROM AUTHOR]

This paper analyses the impact of the use of industrial robots on labor income shares at both the theoretical and empirical levels. On the theoretical side, the role of induced technological progress, the creation of new tasks, and the penetration of industrial robots on labor income shares are systematically explored by incorporating industrial robots into task models and endogenizing the task induced technological progress. Empirically, industrial robot penetration at the regional level in China is constructed from the industrial robots data released by the International Federation of Robotics (IFR), which is matched with Chinese A-share listed companies in 2011–2019, and the causal strategy of Bartik-style instrumental variables is used to analyze the impact of the penetration of industrial robots and task induced technological progress on the share of labor income. The study shows that the penetration of industrial robots significantly increases the share of labor income, but the task induced technological progress reduces the share of labor income, and that the impact of the penetration of industrial robots varies significantly external financing dependence, ownership, and regions. From the perspective of the impact mechanism, robot penetration increase the levels of inputs complementarity and hence reduce the elasticity of substitution between robots and labor. Meanwhile, robots can effectively increase the wage level of the labor force, thus increasing the labor income share. The findings of this paper provide a decision-making reference for how industrial robots can better serve human beings in the new era so that workers can share the fruits of economic development. • It presents a theoretical model integrating robots into profit-maximizing tasks to assess their impact on labor income share. • Robot penetration affects labor income share across external financing dependence, ownership, and regions. • Increased robot penetration raises the labor share through effects on the elasticity of substitution and wages. [ABSTRACT FROM AUTHOR]

This article has been retracted. [ABSTRACT FROM AUTHOR]

Vocabulary Guru (VG) is a custom-made mobile application (app) developed based on the task model and agile methodology via qualitative needs analysis. This investigation intended to assess the impact of VG on EFL learners’vocabulary recognition. First, 84 academic vocabularies were selected, based on their frequencies defined by the Corpus of Contemporary American English (COCA) (Davies, 2017) and the Academic Core Vocabulary Website (Davies & Gardner, 2019). From 141 university students, 60 intermediate female students were selected who were randomly assigned to an experimental group and a control group. The experimental group practiced the target vocabularies via VG, whereas the control group learned them via the conventional method. A vocabulary recognition test was developed and administered twice to both groups. Finally, two paired samples t-tests and two independent samples t-tests were utilized to estimate within and between-groups differences. The findings revealed that the experimental group outperformed the control group significantly.

BCI games are widely familiar and accessible for brain stimulation; however, scientific research is insufficient to justify their effectiveness. Practical BCI game implementations are desperately required in concrete situations. Neurofeedback practice proposes a task modeling of a sports event in the Augmentative and Alternative Communication (TMSE-AAC) framework. People with autism will face new intellectual, engine, compartmental, and focus difficulties, which current techniques for other people cannot overcome. The game aims to keep children's focus beyond a level to monitor a rider looking for missing farms. To monitor the viewer's attention, TMSE-AAC uses BCI headphones. Via a 4-week implementation study of 12 autism students, TMSE-accessibility AAC's and user interface were assessed. Our statistical findings indicate that TMSE-AAC is superior to a consumer BCI game used only to train guidance. TMSE-AAC can help children with autism effectively engage in guidance testing sessions by increasing their concentration and decreasing anxiety. Researchers focus closely on our architecture aspects and explore future job strategies. [ABSTRACT FROM AUTHOR]

Vocabulary Guru (VG) is a custom-made mobile application (app) developed based on the task model and agile methodology via qualitative needs analysis. This investigation intended to assess the impact of VG on EFL learners' vocabulary recognition. First, 84 academic vocabularies were selected, based on their frequencies defined by the Corpus of Contemporary American English (COCA) (Davies, 2017) and the Academic Core Vocabulary Website (Davies & Gardner, 2019). From 141 university students, 60 intermediate female students were selected who were randomly assigned to an experimental group and a control group. The experimental group practiced the target vocabularies via VG, whereas the control group learned them via the conventional method. A vocabulary recognition test was developed and administered twice to both groups. Finally, two paired samples t-tests and two independent samples t-tests were utilized to estimate within and between-groups differences. The findings revealed that the experimental group outperformed the control group significantly. [ABSTRACT FROM AUTHOR]

In this work, a user friendly cost analysis software for solar photovoltaic (PV) and diesel power systems was developed using Java programming language and task model approach. The software was used to perform benefit to cost ratio (BCR) analysis and life cycle cost analysis (LCCA) for Trina solar company (a solar PV module manufacturing company) and Generac Holdings Inc. (a diesel generator manufacturing company) taking into account the costs and benefits applicable to these companies. The purpose of the analysis was to determine the best option for investment and most cost effective option for consumers use. Performing BCR and LCCA involves complex mathematical calculations which are laborious for most power system users. Also, errors such as under sizing and over sizing of systems usually occur during sizing of power generating systems which results to inaccurate analysis and results. This software application was developed to address the errors stated above. The BCR result of the analysis carried out with the software application developed shows that both power options are beneficial for investment. However, solar PV is more beneficial with BCR value of 3.7 while Diesel generator is less beneficial with BCR value of 1.27. In addition, LCCA result of a household in Nsukka Enugu State, Nigeria shows that solar PV power option is more cost effective with value of N45577197.51 (45.6 million Naira) while diesel generator has value of N130651093.96 (130.7 million Naira) for a period of 25 years.

Background: Drawing on theories of purposeful reading, this study explored task conditions that support fifth graders' selective reading of documents in order to answer questions. More specifically, we investigated whether promoting students' elaboration of their task model increases task performance. Methods: Participants had to answer series of questions by searching documents for the relevant information. Half the participants received a task model prompt, and half received a control prompt. Participants' eye movements were recorded during text search, in order to investigate more precisely the effect of an enhanced task model. Results: Participants provided more correct answers in the task model condition than in the control condition, but there was no difference in response time. Unexpectedly, eye movement analysis did not reveal any differences between conditions concerning the number of visits and visit time to relevant and irrelevant areas of the document. Conclusions: A better representation of the information to be searched facilitated students' identification of relevant information. Unexpectedly, however, the task conditions did not result in any significant difference in students' patterns of document search. Highlights: What is already know about this topicExperienced readers adapt their behaviour to task demands.Purposeful reading is challenging for children, but little is known about how to support them. What this paper addsThis study highlights the importance of constructing a coherent task model in order to answer specific questions from textThis study reports eye movement data about the selective reading patterns of a population seldom studied in this domain (i.e., children). Implications for theory, policy or practiceThis study offers a means to enhance purposeful reading that is easily applicable in a classroom situation. [ABSTRACT FROM AUTHOR]

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Despite Cognitive Translation Studies' (CTS) interest in didactic applications, the actual impact of research on training programs has been modest in comparison with the advances made in terms of methodology, theoretical sophistication and expansion of the object of study. It is argued that the modest impact of CTS on training originates, on the one hand, in an epistemological problem – one of mild incommensurability – and, on the other, on the difficulty of developing realistic cognitive task models. Adopting constructs and models in CTS and didactics that share an embodied, extended view on cognition may contribute to fitting empirical data and describing skill development. To that end, a sketch of a template to develop translation task models is presented for discussion. [ABSTRACT FROM AUTHOR]

Abstract The motion of the robot to realize assembly work includes the part where the reuse of the motion adjusted for the real objects is effective and the part where automatic generation in the simulator is suitable. In order to smoothly teach such assembly work, teaching software that enables to combine previously used motions and perform overall adjustment of the workflow and integrated environment representation in the simulator is expected. Some teaching tools focus on the function of making robot motion in detail, and it assumes that the adjustment of the whole workflow including system layout using the real work environment. For this reason, the environmental expression is not sufficient for the above purpose. Although offline teaching tools and motion planning tools are rich in the representation of the environment, there are not many studies on a systematic reuse mechanism of motions adjusted in a real environment, including environment representations. In this paper, we present software design to solve this problem and the implementation of it as a plugin for Choreonoid. By an experiment, we confirmed that we can describe a comparatively complicated assembly work with the proposed software.

Computer aided detection and diagnosis system (CAD) is designed to improve the efficiency and quality of medical diagnosis. The existing CAD system tries to realize a diagnosis process in an automatic way to replace the doctor's diagnosis work completely, but it is difficult to capture the doctor's detection and diagnosis needs and goals accurately due to lack of doctor's experience. Thus, the accuracy is not up to the doctor's manual diagnosis results, and it is a tedious and cumbersome job to use the system because it requires manual intervention, which leads to the fact that CAD system was difficult to be widely used. This paper analyzes the characteristics of doctors' diagnosis tasks deeply, and makes the reasonable allocation of tasks between the doctor and computer. Then the perceptual control theory is introduced into the task model, a PTT task model is proposed to make it express uncertainty of the doctor's diagnosis tasks, and it can be used as a reference for future uncertainty task analysis. Therefore, an interactive CAD tasks framework is put forward with the assistant decision support provided for doctors by means of Bayesian network model and conceptual graph semantic matching method to diagnose tasks. In addition, effective support is provided for interface development staffs to design interactive systems that are suitable for the physicians' diagnostic tasks. The experimental results show that the method not only improves the accuracy and efficiency of the medical diagnosis, but also improves the usability of the system design.

The use of computers in the e-Health domain is becoming increasingly common, since technology is present in most aspects of our lives. In the rehabilitation field in particular, some additional issues requiring the use of computer-assisted therapies arise. On the one hand, there is a scarce availability of rehabilitation specialists and centers to satisfy the growing demand of their services. This problem gets even magnified because of the ageing population. On the other hand, the huge opportunities that the new interaction devices can bring to rehabilitation smooth the path towards novel therapies. Nevertheless, even if a proper rehabilitation therapy is prescribed, it can fail because of the patient´s lack of motivation There are assorted motivation theories available in the literature to address this demotivation of patients. Unfortunately, there is no model or guide to put those theories into practice in computer-assisted rehabilitation. This paper is aimed at filling this gap by providing a model, namely Influence Awareness, to support the specification of motivation aspects in those applications used in computer-assisted rehabilitation. Furthermore, some guidelines are also provided, so that the designer can get some extra guidance on some heuristics about how to design motivation. The integration of motivation design into a model-based development process is presented by showing how this motivation model is integrated into a task model. Finally, to better illustrate our approach a case study based on a collaborative e-Health system is also included. [ABSTRACT FROM AUTHOR]

Telerobotic systems have attracted growing attention because of their superiority in the dangerous or unknown interaction tasks. It is very challenging to exploit such systems to implement complex tasks in an autonomous way. In this paper, we propose a task learning framework to represent the manipulation skill demonstrated by a remotely controlled robot. Gaussian mixture model is utilized to encode and parametrize the smooth task trajectory according to the observations from the demonstrations. After encoding the demonstrated trajectory, a new task trajectory is generated based on the variability information of the learned model. Experimental results have demonstrated the feasibility of the proposed method. [ABSTRACT FROM AUTHOR]

Clinical judgment has become an increasingly important aspect of modern health service professionals. To ensure public safety, licensure exams must go beyond assessing only knowledge and skills when evaluating entry-level professions to evaluating clinical judgment. This importance necessitates licensure and certification examinations in these professions to evaluate the extent to which this domain can be measured. This study will provide background of a large-scale licensure examination's process for measuring clinical judgment in entry-level nursing. As clinical judgment is seen to be a higherorder, more complex construct than measuring basic knowledge, an outline of the process that was used to develop the construct and build items to a specific task model will be highlighted. In accordance with existing adult learning theories and contemporary nurse program delivery, the process of specifying the requisite cognitive theories guiding nursing practice will be elucidated and interwoven with the item development discussion. The item writing and review process will be described in detail and would be generalizable to professions outside of nursing. [ABSTRACT FROM AUTHOR]