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Learning as the way in which labor acquire new knowledge and skills has important strategic implications for the competitive advantage of an organization. The purpose of this paper is to present an agent-based modeling (ABM) approach to investigate the learning behavior of workers. The effect of interactions among different workers as well as the factors affecting the workers’ learning behavior is assessed using the proposed ABM approach.

For this purpose, the processes through which the competency value of worker is changed are understood and the workers’ learning behavior is modeled, taking account of various influencing factors such as knowledge flow, social ability to teach and forgetting factor.

The proposed model is implemented on a real steel structure project to evaluate its applicability and performance. The variation in the competency value of different workers involved in the project is simulated over time taking account of all the influencing factors using the proposed ABM approach.

In order to assess the effect of interactions among welders as well as the welders’ characteristics on their learning behavior, the competence value of different welders is evaluated.

This research presents an ABM approach to investigate the workers’ learning behavior. To evaluate the performance of the proposed ABM approach, it was implemented on a real steel structure project. The learning behavior of different welders (agents) was simulated taking account of their interactions as well as the factors affecting the welders’ learning behavior. The project involved the welding of a 240-ton steel structure. The initial project duration was estimated as 100 days. In this project, it has been planned to execute the welding process using three different welders namely welder A, B and C.

As the demand for human–robot collaboration in manufacturing applications grows, the necessity for collision detection functions in robots becomes increasingly paramount for safety. Hence, this paper aims to improve the existing method to achieve efficient, accurate and sensitive robot collision detection.

The external torque is estimated by momentum observers based on the robot dynamics model. Because the state of the joints is more accessible to distinguish under the action of the suppression operator proposed in this paper, the mutated external torque caused by joint reversal can be accurately attenuated. Finally, time series analysis (TSA) methods can continuously generate dynamic thresholds based on external torques.

Compared with the collision detection method based only on TSA, the invalid time of the proposed method is less during joint reversal. Although the soft-collision detection accuracy of this method is lower than that of the symmetric threshold method, it is superior in terms of detection delay and has a higher hard-collision detection accuracy.

Owing to the mutated external torque caused by joint reversal, which seriously affects the stability of time series models, the collision detection method based only on TSA cannot detect continuously. The consequences are disastrous if the robot collides with people or the environment during joint reversal. After multiple experimental verifications, the proposed method still exhibits detection capabilities during joint reversal and can implement real-time collision detection. Therefore, it is suitable for various engineering applications.

Following the Agenda 2030 for Sustainable Development, the main challenge for the agrofood sector is to innovate food production, offering sustainable, smart and safe solutions. The future of food production will be oriented more and more towards sustainable industries with high technological content to guarantee food safety and food security. It implies that a change not only in the way food is conceived, but also in the way it is produced, processed and consumed is needed. The aim of the present study is to investigate the role of innovation, sustainability, smartness and health within the agrofood industry.

A literature review was conducted using 596 academic documents written in English language and published in peer-reviewed scientific journals as well as in conference proceedings. The relevant articles were analyzed using both a bibliometric and a systematic approach.

The results confirm the role of innovation and sustainability as key drivers in the food industry. The main findings concern the benefits deriving from the adoption of digital technologies, the ever-increasing involvement of consumers in health and environmental issues and the introduction of the open innovation concept in the agrofood industry.

This study jointly considers the dimensions of innovation, sustainability, smartness and health in the agrofood sector, demonstrating how they are strongly interdependent.

Wireless sensor networks (WSNs) are periodically collecting data through randomly dispersed sensors (motes), which typically consume high energy in radio communication that mainly leans on data transmission within the network. Furthermore, dissemination mode in WSN usually produces noisy values, incorrect measurements or missing information that affect the behaviour of WSN. In this article, a Distributed Data Predictive Model (DDPM) was proposed to extend the network lifetime by decreasing the consumption in the energy of sensor nodes. It was built upon a distributive clustering model for predicting dissemination-faults in WSN. The proposed model was developed using Recursive least squares (RLS) adaptive filter integrated with a Finite Impulse Response (FIR) filter, for removing unwanted reflections and noise accompanying of the transferred signals among the sensors, aiming to minimize the size of transferred data for providing energy efficient. The experimental results demonstrated that DDPM reduced the rate of data transmission to ∼20%. Also, it decreased the energy consumption to 95% throughout the dataset sample and upgraded the performance of the sensory network by about 19.5%. Thus, it prolonged the lifetime of the network.

The purpose of this study is to examine the propagation of consumer and business confidence in the euro area with a particular focus on the global financial crisis (GFC), the European sovereign debt crisis (ESDC) and the COVID-19-induced Great Lockdown.

The authors apply Diebold and Yilmaz’s connectedness framework and the improved method based on the time-varying parameter vector autoregressive model.

The authors find that although the evolution of business confidence marked the GFC and the ESDC the role of consumer confidence (mainly in those countries with stricter containment and closure measures) increased in the COVID-19-induced crisis.

The findings are related to the different origins of the examined crisis periods, and the analysis of their interrelationship is a very relevant topic for future research.

The purpose of the current study was to examine the role of distance learning in enhancing introverted students’ lack of communication and social interaction to improve their performance in translation class. Cain (2013) and Kuzeljevich (2017) agreed that identifying “introverted” and “extroverted” students is important for meeting their learning needs. While extroverted students have strong social skills that allow them to interact comfortably in different learning environments, introverted students tend to be more shy, quiet, and silent, thus, requiring more careful planning in classroom settings. Therefore, educators need to support introverted students in reaching their full academic and social potential.

The present case study adopted a qualitative research method to explore the role of online/distance learning during the COVID-19 pandemic in enhancing introverted students’ performance and communication abilities in translation classes. The researcher of the current study spent a considerable time observing and set herself as part of the group (i.e. translation students of level 6 class) to understand the phenomenon, events and the new situation of having translation students interact in online settings. Data collection was based on this observation, interviews with the participants and archival documents. To enhance the validity and credibility of this research, the researcher employed the method of triangulation.

The results (see Appendixes A, B and C) revealed the level of students interactions in translation classes and their attitudes toward online learning. Based on the observations made by the instructor, the researcher found that the involvement of the introverted students during online translation learning was remarkable, as they provided their translation outputs in the chat window of Microsoft Teams with no hesitation. Consequently, 65% of the students were providing their translation output through the chat window, which indicates that they are more introverted and preferred not to speak. Comparing this result to face-to-face translation class, the researcher found that 25% of the students provided their translation outputs through oral participation.

This study contributes to the field of translation and education. Previous studies have not sufficiently examined the role of distance learning in enhancing the performance and communication of introverted students in translation classes. The current study is also expected to provide insight into the field of technical translation in remote teaching and learning settings.