“Robot Emotions Are Not Real!”: Future Factory Workers’ Perceptions, Attitudes, and Experience of Collaborative Robots, Conversational AIs, and AI-Empowered, Voice-Enabled Collaborative Robots

As collaborative robots begin to appear on factory floors, there is a need to consider how these robots can best help their human partners. In this paper, we propose an optimization framework that generates task assignments and schedules for a human-robot team with the goal of improving both time and ergonomics and demonstrate its use in six real-world manufacturing processes that are currently performed manually. Using the strain index method to quantify human physical stress, we create a set of solutions with assigned priorities on each goal. The resulting schedules provide engineers with insight into selecting the appropriate level of compromise and integrating the robot in a way that best fits the needs of an individual process.

The Industry 5.0 paradigm focuses on industrial operator well-being and sustainable manufacturing practices, where humans play a central role, not only during the repetitive and collaborative tasks of the manufacturing process, but also in the management of the factory floor assets. Human factors, such as ergonomics, safety, and well-being, push the human-centric smart factory to efficiently adopt novel technologies while minimizing environmental and social impact. As operations at the factory floor increasingly rely on collaborative robots (CoBots) and flexible manufacturing systems, there is a growing demand for redundant safety mechanisms (i.e., automatic human detection in the proximity of machinery that is under operation). Fostering enhanced process safety for human proximity detection allows for the protection against possible incidents or accidents with the deployed industrial devices and machinery. This paper introduces the design and implementation of a cost-effective thermal imaging Safety Sensor that can be used in the scope of Industry 5.0 to trigger distinct safe mode states in manufacturing processes that rely on collaborative robotics. The proposed Safety Sensor uses a hybrid detection approach and has been evaluated under controlled environmental conditions. The obtained results show a 97% accuracy at low computational cost when using the developed hybrid method to detect the presence of humans in thermal images.

Boosting productivity with tech

Intelligence (AI) has emerged as a transformative force in modern financial reporting, promising to revolutionize accuracy and efficiency across various industries. This study delves into the effects of AI on financial reporting accuracy, addressing critical questions surrounding its implementation, challenges, and best practices. Through a comprehensive investigation, the research aims to provide valuable insights to guide organizations in leveraging AI effectively while maintaining the integrity of their financial reporting practices. The main objective of this study is to explore the effects of artificial intelligence on the accuracy of financial reporting, examining both the benefits and challenges associated with its integration into organizational processes. Specific Objectives; To analyze how various AI technologies influence the accuracy of financial data and reporting in organizations, to explore the challenges and limitations faced by organizations when integrating AI into their financial reporting systems, to assess the importance of human oversight in ensuring the accuracy of AI-generated financial reports, to develop best practices for organizations to enhance the accuracy of financial reporting when using AI technologies. This study employed a mixed-methods approach, combining qualitative and quantitative data collection techniques to comprehensively explore the effects of AI on financial reporting accuracy. Quantitative data was gathered through surveys distributed to accountants, finance professionals, auditors, and personnel from manufacturing and tourism sectors across various industries. The surveys focused on assessing perceptions of AI's impact on financial reporting accuracy and included Likert-scale questions to gauge agreement levels. Qualitative data was obtained through in-depth interviews with selected participants to gain deeper insights into their experiences and perspectives regarding AI technologies in financial reporting. Thematic analysis was applied to interview transcripts to identify recurring themes and patterns related to AI's effects on accuracy. Participants were informed about the study's purpose and their rights, including confidentiality and anonymity. Informed consent was obtained prior to data collection, ensuring ethical standards were adhered to throughout the research process. Survey responses indicated a generally positive perception of AI's impact on financial reporting accuracy, with a majority of respondents acknowledging improvements in efficiency and error reduction. However, challenges such as data security concerns and the need for skilled personnel were highlighted as significant barriers to AI integration. Human oversight emerged as a crucial factor in validating AI-generated outputs, emphasizing the complementary role of human judgment alongside technological advancements. The findings underscored AI's potential to enhance financial reporting accuracy through advanced data analytics and automation. Key recommendations include investing in comprehensive training programs for staff, integrating AI with human expertise, implementing robust data governance frameworks, conducting regular audits of AI systems, and engaging stakeholders throughout the integration process. In conclusion, this study provided valuable insights into how AI technologies can improve the accuracy of financial reporting while addressing challenges and emphasizing the importance of human oversight. By adopting recommended best practices, organizations can maximize the benefits of AI in financial reporting, paving the way for more reliable and informed decision-making in the digital age. This research contributes to the growing body of knowledge on AI's impact on financial practices, offering practical recommendations for organizations aiming to leverage technology effectively in their financial reporting processes.

Smart machine companions such as artificial intelligence (AI) assistants and collaborative robots are rapidly populating the factory floor. Future factory floor workers will work in teams that include both human co-workers and smart machine actors. The visions of Industry 5.0 describe sustainable, resilient, and human-centered future factories that will require smart and resilient capabilities both from next-generation manufacturing systems and human operators. What kinds of approaches can help design these kinds of resilient human–machine teams and collaborations within them? In this paper, we analyze this design challenge, and we propose basing the design on the joint cognitive systems approach. The established joint cognitive systems approach can be complemented with approaches that support human centricity in the early phases of design, as well as in the development of continuously co-evolving human–machine teams. We propose approaches to observing and analyzing the collaboration in human–machine teams, developing the concept of operations with relevant stakeholders, and including ethical aspects in the design and development. We base our work on the joint cognitive systems approach and propose complementary approaches and methods, namely: actor–network theory, the concept of operations and ethically aware design. We identify their possibilities and challenges in designing and developing smooth human–machine teams for Industry 5.0 manufacturing systems.

Stimulation of medial amygdala GABA neurons with kinetically different channelrhodopsins yields opposite behavioral outcomes.

ARIAC is a robotic simulation competition promoted by NIST annually since 2017, aiming to present competitors’ with contemporary industry problems to be solved using agile robotics. For the 2023 competition, ARIAC competitors must perform assembly and kitting tasks by controlling four autonomous ground vehicles (AGVs), one floor-based robot, and one ceiling-based (Gantry) robot in an attempt to overcome a range of agility challenges in the supplied simulated environment, itself based on the Robot Operating System (ROS 2) and Gazebo. The 2023 competition also included a “human” agility challenge, comprising a (simulated) human operator working among robots on the factory floor. This development was motivated by the fact that, while robots and automation play an increasingly significant role in modern manufacturing, there still remains a close relationship between machines and humans. They should complement each other’s strengths and cover each other’s limitations while also observing any required safety rules. For example, the ISO standard “Robots and Robotic Devices – Collaborative robots” (ISO 15066:2016) prescribes the distances required between humans and robots. Within the ARIAC simulation environment, each human operator is controlled using autonomous Belief-Desire-Intention (BDI) agents. At the same time, competitors can monitor the position of each human operator at any time by subscribing to the relevant ROS topic. In this article, we analyse the effects of this (simulated) human presence in the 2023 ARIAC competition and perform a detailed analysis of how the three different human personalities that were implemented affect the assembly tasks undertaken at the four different locations of the assembly stations. Given how the system is currently implemented, it appears that the influence of each encoded personality on the competitors is not as predictable as anticipated. We expand on why this may be a problem when addressing real collaborative spaces involving humans and industrial robots and the improvements that can be undertaken to mitigate the ensuing problems.

Refining levels of collaboration to support the design and evaluation of human-robot interaction in the manufacturing industry

Various factors controlling health care in any country, but still there is a weakness in the infrastructure of health care systems in some developing countries, including many Arab countries, due to the shortage of medical specialists personnel, global economic crisis, and wars and disasters that struck many countries. A crying need for a tele-medical care becomes a necessity, and in order to overcoming this weakness we have proposed a multi-agent intelligent Telemedicine system in this research. The proposed system can help solve the shortage of specialists urgently and at lower costs. Furthermore, it can save the efforts of the patients' expensive travel for treatment abroad. Moreover, it can avoid infections from social closeness in times of disasters and pandemics.
 Nowadays Telemedicine systems have gradually become a popular medical model which have always attracted much attention due to the continuous development of the multi-agents' structure and capabilities of the artificial intelligence systems. So we will focus in the prototype design of our system on the algorithms, techniques and devices that support the multi-agents' structure and AI technologies. The Multi-agent approach happens to be a suitable structure for the development of Telemedicine systems. Worth mentioning it can solve problems that are difficult or impossible for an individual agent or a monolithic system, and the AI capabilities will minimize human errors and improve health care outcomes.
 We will start by a state-of-the-art literature survey as conducted on the E-health system architecture, AI capabilities, and multi-agent platform, and then we will illustrate prototype design of the suggested Multi-agent intelligent system for Telemedicine which will contribute in the development of the health care systems in the developing countries.
 Keywords: Healthcare system; Medical System; Artificial Intelligence; Multi-Agent, Telemedicine; Telehealth.

Collaborative robots (in short: cobots) have the potential to assist workers with physically or cognitive demanding tasks. However, it is crucial to recognize that such assistance can have both positive and negative effects on job quality. A key aspect of human-robot collaboration is the interdependence between human and robotic tasks. This interdependence influences the autonomy of the operator and can impact the work pace, potentially leading to a situation where the human's work pace becomes reliant on that of the robot. Given that autonomy and work pace are essential determinants of job quality, design decisions concerning these factors can greatly influence the overall success of a robot implementation. The impact of autonomy and work pace was systematically examined through an experimental study conducted in an industrial assembly task. 20 participants engaged in collaborative work with a robot under three conditions: human lead (HL), fast-paced robot lead (FRL), and slow-paced robot lead (SRL). Perceived workload was used as a proxy for job quality. To assess the perceived workload associated with each condition was assessed with the NASA Task Load Index (TLX). Specifically, the study aimed to evaluate the role of human autonomy by comparing the perceived workload between HL and FRL conditions, as well as the influence of robot pace by comparing SRL and FRL conditions. The findings revealed a significant correlation between a higher level of human autonomy and a lower perceived workload. Furthermore, a decrease in robot pace was observed to result in a reduction of two specific factors measuring perceived workload, namely cognitive and temporal demand. These results suggest that interventions aimed at increasing human autonomy and appropriately adjusting the robot's work pace can serve as effective measures for optimizing the perceived workload in collaborative scenarios.

<p><strong>Context and relevance.</strong> This article presents the results of the study conducted among mathematics teachers – the category of teachers particularly inclined toward critical thinking and evidence-based application of innovations in education. <strong>Objective.</strong> The objective of this study is to identify the awareness of math teachers about the AI capabilities and potential in teaching as well as the practice of their application in the educational process. <strong>Methods and materials.</strong> To achieve this objective, a questionnaire was developed, comprising three main sections: awareness, readiness, and practical application. The survey was conducted online using Yandex Forms. A total of 122 mathematics teachers from 44 regions of the Russian Federation, varying in age and teaching experience, participated in the study. <strong>Results.</strong> The results showed that approximately 70% of the respondents express a willingness to use AI in their teaching process. The directions in which math teachers are most and least inclined to trust AI have been identified. The proportion of teachers currently using AI technologies and specific software products based on AI ranges from 13% to 40%. <strong>Conclusions. </strong>A significant part of teachers is generally aware of AI's potential. However, their knowledge is fragmentary, covering only certain aspects and lacking systematic understanding. Promising directions for further research include examining the issues surrounding the use of AI technologies in the educational process while taking into account their specific characteristics. Special attention is recommended to improving teaching methodologies based on AI technologies and identifying effective ways to apply them for the development of students’ cognitive abilities.</p>

Students nowadays are reluctant to go to the mosque to learn Quran. Their characters are influenced by technology such as television, the internet, and video game which then make parents and teachers worried and anxious if they will act negatively. This thesis uses research design of mixed-method with the objective to complete the image of research result about the phenomenon and to strengthen the analysis. The research strategy is done by combining the data from observation, interviews, and questionnaires in order to get qualitative and quantitative data. The data analysis is continuous qualitative-quantitative analysis, it is done by analyzing qualitative data and followed by analyzing the data through quantitative one. The hypothesis testing is done by using simple linear regression analysis with SPSS 18 program.
 The result of qualitative data is the students’ affection on Quran in Islamic Elementary School through Quran memorizing a program. Students’ social behavior is positive, whether toward friends and teachers in school. The result of quantitative data shows that students affection on Quran is 44% categorized as high, 40% as fair, and 16% as low. Students’ social behaviour shows that 74% as high, 10% as fair and 16% as low. The hypothesis testing uses t-test and the result of tcount is 6.356 with the significance of 0.026. The tcount ? ttable (6.356 ? 2.010) or sig. t ? 5% (0.026 ? 0.05), it can be concluded that the variable of students’ affection on Quran has significant influence toward the variable of students’ social behaviour. The students’ social behavior is influenced by 55% of a variable of students’ affection on Quran while 45% is influenced by other variables outside this research

The widespread use of radioactive equipment in hospitals necessitates adequate knowledge and tools among healthcare workers to prevent and monitor radiation exposure. The study investigated the effectiveness of radiation protection in the detection of exposures among healthcare workers in Nyeri County, Kenya, focusing on radiation exposure levels, level of awareness, and control measures. Using a cross-sectional design, the study targeted 1121 healthcare workers, with a sample of 294. Data was collected through semi-structured questionnaires and a checklist, generating both quantitative and qualitative data. Dosimeter read-outs were conducted for one month and a radiation safety assessment survey in the Radiology department was also conducted using a radiation detector meter. Quantitative data were analyzed using SPSS version 27, employing descriptive and inferential statistics, while qualitative data were analyzed thematically. Findings revealed that about half of the healthcare workers had not received training on radiation hazards, and less than half were aware of the maximum permissible dose limit for adults. Approximately half of the workers knew that the eyes, thyroid glands, ovaries, and testis are susceptible to radiation hazards. The study recommended comprehensive and regular training programs for all healthcare workers, emphasizing the correct handling of lead aprons and the consistent use of personal protective devices such as lead aprons, lead glasses, portable lead shields, automatic interlock devices, and thyroid shields.

As the prevalence of Alzheimer's disease and related dementias (ADRD) continues to rise worldwide, so does the demand for home care workers who provide essential personal care that enables individuals living with ADRD to age in place. However, there is limited knowledge about dementia-specific training programs for home care workers. This scoping review aims to examine existing dementia training programs available for home care workers and evaluate their outcomes. We searched five databases, including PubMed, Web of Science, CINAHL, Sociological Abstracts, and Scopus. We used the PRISMA Extension for Scoping Reviews (PRISMA-ScR) and Arksey and O'Malley's five-step scoping review framework. Eligibility criteria included relevant study population (paid home care workers), dementia education or training programs, and original evaluations and published in English. Of the 903 articles identified through the five databases, 17 articles met eligibility criteria and 12 were included in the final analytic sample. The results are presented in three sections: (1) training details, (2) methods and measures, and (3) training outcomes. This scoping review has implications for three groups of stakeholders, including researchers, governments and policymakers, and home care workers. This work underscores the importance of further implementation and evaluation of dementia training programs for home care workers.

The trend of automation in industrial production has led to massive use of autonomous robots. In classical approaches, safety is usually guaranteed by isolating robots from humans. Collaborative robots, i.e., humans and robots working together, are expected to increase both productivity and performance. However, removing fences and putting the robot working in collaboration with humans causes new hazardous situations. Therefore, proper risk assessment should be performed to avoid those hazardous situations without compromising the productivity. We present an automated warehouse where autonomous robots load trucks with products while sharing the same environment with human workers. In this position paper we propose a safety strategy that is modeled based on dynamic safety fields around the robot, which is consistent with important guidelines in collaborative robotics (i.e., ISO15066). We propose three different safety levels of dynamic fields: red (critical), yellow (warning) and green (clear). Instead of completely stopping the robot in the presence of humans it can keep performing its operations with some enforced constrains for safety reasons. We also propose a risk assessment of hazardous situations based on proprioceptive and exteroceptive data. This evaluation generates different warnings or actions to be performed based on those safety levels and is responsible for changing the size of the dynamic fields.