Human Work Research Articles

Rethinking Dignity and Exploitation in Human Trafficking and Sex Workers’ Rights Cases

As forced migration increases dramatically due to such factors as climate change, rising conflict, and authoritarianism, more legal cases on human trafficking and sex work are sure to arise. To date, very few cases on these issues have been decided in international human rights tribunals, and they have been subject to extensive criticism, especially for their conflation of slavery, human trafficking, forced prostitution, and consensual sex work. This article analyzes recent jurisprudence from Europe and Africa to address this conceptual confusion and argue that tribunals must interrogate their use of the terms dignity and exploitation or risk further marginalizing already marginalized people.

RANCANGAN MESIN SIKAT KARPET DENGAN MODIFIKASI TABUNG CAIRAN DETERJEN

Now, washing carpets no longer requires a lot of human power. Just one person can wash carpets on a large scale at the same time as three human workers. By using this carpet brush machine, the job of cleaning carpets will be easier and faster. The aim of designing this machine is a form of innovation and creativity in a carpet brush machine with an electric motor drive. The machine is made with the addition of a detergent channel tube to make work easier and increase efficiency. The working principle of this carpet brushing machine is that when the motor is turned on, a pulley with a size of Ø 76.2 mm will rotate with a rotation of 1400 rpm, followed by a belt with a size of 24 inches to rotate the pulley on the shaft with a rotation of 703.5 rpm with a pulley size of Ø 152 mm. The rotation will be continued by the shaft, and the brush will rotate to clean up dirt or stains on the carpet. In making this machine, we obtained a motor power of ½ HP and a rotation of 1400 rpm. This machine has dimensions of 580x380x980 mm and uses a belt and pulley transmission with brush components. This carpet washing machine is used for MSME businesses (micro, small and medium businesses)

Current State Analysis of Croatian Manufacturing Industry with Regard to Industry 4.0/5.0

It has been more than a decade since the introduction of the Industry 4.0 paradigm. Since then, many issues have been raised in the world: the COVID-19 pandemic, sustainable development goals, and recent dramatic changes in global politics. The global value chains were broken during the pandemic, and the importance of humans as the most important element of the production system was highlighted. It caused rethinking about current industrial paradigms, including the brand new paradigm of Industry 4.0. More focus has been put on human workers, sustainability, and the resilience of the value chain, so the Industry 4.0 update was presented as Industry 5.0. A specific methodology to evaluate the maturity level of the manufacturing industry with regard to Industry 4.0/5.0 is presented and tested in the Croatian manufacturing industry. The developed methodology is unique since it puts Industry 5.0 in the right context with Industry 4.0. Therefore, the Industry 4.0 index remains the main indicator; however, alignment with three Industry 5.0 aims (human-centricity, sustainability, and resilience) represents three additional indicators. The results of the current state analysis are presented as a case study with a discussion about the results and methodology itself.

Testing scenarios for using telepresence robots in healthcare settings

The ageing global population puts heavy pressure on healthcare systems everywhere. Addressing ageing-related chronic conditions requires employment of novel innovative solutions. Telehealth technologies, including telepresence robots (TPRs), are being rapidly developed to provide healthcare services efficiently wherever needed. This article explores the role of TPRs in addressing the challenges of providing healthcare to an ageing population, emphasizing their potential advantages and drawbacks. Employing an exploratory research approach with qualitative data collection techniques, we tested three TPR usage scenarios in simulated healthcare settings: anamnesis, measurements, and falls and frailty. The study employed a non-random purposive sample comprising 25 participants, and was conducted at a medical facility in June 2023. The findings suggest that TPRs offer promising solutions for healthcare professionals and patients, especially in scenarios when physical presence is impossible or physical isolation is required to prevent contagion. However, the technology is not yet ready to substitute fully human medical workers, potentially causing patient reluctance and emphasizing the need for patient-centered approaches to technology adoption. In addition, more studies are needed to address ethical, privacy, and scalability concerns.

Toward Optimal Robot Machining Considering the Workpiece Surface Geometry in a Task-Oriented Approach

Robot workpiece machining is interesting in industry as it offers some advantages, such as higher flexibility in comparison with the conventional approach based on CNC technology. However, in recent years, we have been facing a strong progressive shift to custom-based manufacturing and low-volume/high-mix production, which require a novel approach to automation via the employment of collaborative robotics. However, collaborative robots feature only limited motion capability to provide safety in cooperation with human workers. Thus, it is highly necessary to perform more detailed robot task planning to ensure its feasibility and optimal performance. In this paper, we deal with the problem of studying kinematic robot performance in the case of such manufacturing tasks, where the robot tool is constrained to follow the machining path embedded on the workpiece surface at a prescribed orientation. The presented approach is based on the well-known concept of manipulability, although the latter suffers from physical inconsistency due to mixing different units of linear and angular velocity in a general 6 DOF task case. Therefore, we introduce the workpiece surface constraint in the robot kinematic analysis, which enables an evaluation of its available velocity capability in a reduced dimension space. Such constrained robot kinematics transform the robot’s task space to a two-dimensional surface tangent plane, and the manipulability analysis may be limited to the space of linear velocity only. Thus, the problem of physical inconsistency is avoided effectively. We show the theoretical derivation of the proposed method, which was verified by numerical experiments.

Peningkatan Kualitas Emping Melinjo melalui Proses Pengepresan dan Penurunan Kadar Purin di UKM Emping Jago

<em><em><strong>Quality Improvement of Emping Melinjo through Pressing Process and Reduction of Purin Level at SME Emping Jago.</strong> </em>Emping melinjo is one of the popular foods among Indonesians. Kartasura is one of the districts in Sukoharjo that has potential in the production of emping melinjo. This study aimed to improve the quality of emping melinjo by using a mechanical pressing machine and reducing the purin level. The method used was a literature review based on scientific journals, books, and others. The results were to substitute the pressing process using mechanical machinery and by soaking treatment to reduce the purin levels. The improvement on pressing process were performed as an innovation so the process will not depend on human workers. Then, the soaking in a 4% acetic acid solution (vinegar) varied for 0; 10; 20; and 40 minutes.The hypoxanthine and adenine levels (in micrograms per gram), according to the soaking duration, showed 46,43; 41,50; 41,05; 36,71; and 124,74; 110,90; 109,83; 108,82 respectively. <em>The 40 minutes soaking process could decrease the purin level by 20%.</em></em>

An audio‐based risky flight detection framework for quadrotors

AbstractDrones have increasingly collaborated with human workers in some workspaces, such as warehouses. The failure of a drone flight may bring potential risks to human beings' life safety during some aerial tasks. One of the most common flight failures is triggered by damaged propellers. To quickly detect physical damage to propellers, recognise risky flights, and provide early warnings to surrounding human workers, a new and comprehensive fault diagnosis framework is presented that uses only the audio caused by propeller rotation without accessing any flight data. The diagnosis framework includes three components: leverage convolutional neural networks, transfer learning, and Bayesian optimisation. Particularly, the audio signal from an actual flight is collected and transferred into time–frequency spectrograms. First, a convolutional neural network‐based diagnosis model that utilises these spectrograms is developed to identify whether there is any broken propeller involved in a specific drone flight. Additionally, the authors employ Monte Carlo dropout sampling to obtain the inconsistency of diagnostic results and compute the mean probability score vector's entropy (uncertainty) as another factor to diagnose the drone flight. Next, to reduce data dependence on different drone types, the convolutional neural network‐based diagnosis model is further augmented by transfer learning. That is, the knowledge of a well‐trained diagnosis model is refined by using a small set of data from a different drone. The modified diagnosis model has the ability to detect the broken propeller of the second drone. Thirdly, to reduce the hyperparameters' tuning efforts and reinforce the robustness of the network, Bayesian optimisation takes advantage of the observed diagnosis model performances to construct a Gaussian process model that allows the acquisition function to choose the optimal network hyperparameters. The proposed diagnosis framework is validated via real experimental flight tests and has a reasonably high diagnosis accuracy.

A Continuously Variable Transmission-Based Variable Stiffness Actuator for pHRI: Design Optimization and Performance Verification

Abstract Physical human–robot interfaces (pHRIs) enabled the robots to work alongside the human workers complying with the regulations set for physical human–robot interaction systems. A variety of actuation systems named variable stiffness/impedance actuators (VSAs) are configured to be used in these systems’ design. Recently, we introduced a new continuously variable transmission (CVT) mechanism as an alternative solution in configuring VSAs for pHRI. The optimization of this CVT has significant importance to enhance its application area and to detect the limitations of the system. Thus, in this paper, we present a design optimization approach (an adjustment strategy) for this system based on the design goals, desired force, and minimization of the size of the system. To implement such design goals, the static force analysis of the CVT is performed and validated. Furthermore, the fabrication of the optimized prototype is presented, and the experimental verification is performed considering the requirements of VSAs: independent position and stiffness variation, and shock absorbing. Finally, the system is calibrated to display 6 N continuous output force throughout its transmission variation range.

The impact of community resilience, well-being, and community attachment on human service workers’ burnout

Summary Burnout has become an increasingly prevalent condition, especially affecting professionals in direct contact with clients. Human service workers (HSWs) have quite emotionally taxing jobs supporting vulnerable groups. In developing a close relationship with the communities they work with, it could be possible that community-related variables have an impact on HSWs’ burnout. We aim to analyze the prevalence of burnout in a sample of HSWs from Portugal and the role of community resilience as a predictor of their burnout, as well as explore if their subjective well-being and community attachment could explain the relationship between community resilience and burnout, during the COVID-19 pandemic. Data were collected through an online questionnaire between August 2020 and January 2021 ( N = 598). Findings Results indicate that HSWs presented medium to low levels of burnout, although 8.9% presented high levels. Those who reported lower levels of burnout perceived higher community resilience and had much higher well-being and community attachment. Findings point to community resilience having a significant direct effect on burnout and also an indirect effect, which was explained by subjective well-being and community attachment. Applications Results seem to suggest that this sample's low-medium levels of burnout could be linked to the benefit of experiencing high community attachment and working in communities with high resilience, which consequently impacted their well-being and burnout. This highlights the crucial role of the work context in professionals’ mental health, showing that a broader context needs to be considered in professional mental health promotion programs.

Identification of mature Nutmeg using Colour Space segmentation Algorithm

Automation in Agriculture is essential to achieve a better quality harvest and alleviating the dependency on human workers. As the southern part of India is rich in spices cultivation, the work emphases on one of the spice which has got more medicinal value and commercial viability. The spice considered in this is nutmeg which is mostly cultivated in the mountain ranges of Kerala and Kanyakumari District. The identification of mature nutmeg in a large group is a bit time consuming task. The current fruit-picking methods takes a long time in fixing the clamp on the right fruit from the cluster of fruits, these leads to the cervical spondylitis problem. Recent developments in image processing and the extensive usage of autonomous platforms have provided the opportunity for fast and automatic harvesting machines. This paper proposes an image segmentation algorithm to identify the mature nutmeg. The datasets used are KAU Kochukudy, IISR Keralashree, Punnathanam and local clone. The matured fruit is identified by it’s boundaries using the boundary edge detection algorithm. The color detection method and colour space method were considered. Boundary Edge Detection algorithm is focused on identifying the edges or boundaries present in an image. Color Detection Method relies on identifying objects based on their color characteristics.Colour Space method involves converting the image from RGB color space (Red, Green, Blue) to a different color space, which may be more effective for certain types of analysis. Colour space methods outperforms image segmentation algorithm in terms of identification of matured nutmeg images with 96% accuracy. The average elapsed time was 1.150 secs. Also, the processing time to identify the right matured fruit requires much lesser time than other methods.

Transformative Potential of AI in Healthcare: Definitions, Applications, and Navigating the Ethical Landscape and Public Perspectives.

Artificial intelligence (AI) has emerged as a crucial tool in healthcare with the primary aim of improving patient outcomes and optimizing healthcare delivery. By harnessing machine learning algorithms, natural language processing, and computer vision, AI enables the analysis of complex medical data. The integration of AI into healthcare systems aims to support clinicians, personalize patient care, and enhance population health, all while addressing the challenges posed by rising costs and limited resources. As a subdivision of computer science, AI focuses on the development of advanced algorithms capable of performing complex tasks that were once reliant on human intelligence. The ultimate goal is to achieve human-level performance with improved efficiency and accuracy in problem-solving and task execution, thereby reducing the need for human intervention. Various industries, including engineering, media/entertainment, finance, and education, have already reaped significant benefits by incorporating AI systems into their operations. Notably, the healthcare sector has witnessed rapid growth in the utilization of AI technology. Nevertheless, there remains untapped potential for AI to truly revolutionize the industry. It is important to note that despite concerns about job displacement, AI in healthcare should not be viewed as a threat to human workers. Instead, AI systems are designed to augment and support healthcare professionals, freeing up their time to focus on more complex and critical tasks. By automating routine and repetitive tasks, AI can alleviate the burden on healthcare professionals, allowing them to dedicate more attention to patient care and meaningful interactions. However, legal and ethical challenges must be addressed when embracing AI technology in medicine, alongside comprehensive public education to ensure widespread acceptance.

Monitoring progress on Antimicrobial Resistance (AMR) response in the World Health Organization African region: Insights from the Tracking AMR Country Self-Assessment Survey (TrACSS) 2021 results for the human health sector.

Antimicrobial resistance (AMR) is a major 21st century global health challenge. The WHO African Region Member States committed to develop and implement multisectoral national action plans (NAPs) that address AMR, in line with the Global Action Plan on AMR (GAP). The aim of this paper is to present the progress of AMR response in the WHO African Region based on the annual Tracking AMR Country Self-Assessment Survey (TrACSS), with a focus on human health indicators. This was a secondary data analysis of responses from forty-one countries that participated in the 2021 TrACSS. Of the 41 countries that responded to the 2021 TrACSS, 35(85%) have developed NAPs. Fifteen 15(37%) of countries have functional AMR multisector working groups. 55% (21/41) of countries are collating data nationally on AMR surveillance. Forty nine percent of countries conducted small-scale AMR awareness campaigns and 53% (21/41) covered AMR in some pre- and in-service training for human health workers. While 83% of countries reported having laws and regulations on the prescription and sale of antimicrobials, only 32% (13/41) have national systems for monitoring antimicrobial use. Twenty-three (58%, 23/41) reported having Infection Prevention and Control (IPC) programs at select health facilities. Countries have developed and are implementing AMR NAPs. Gaps still exist across key indicators monitored through TrACSS. Effective AMR response requires established functional multisectoral governance mechanisms in the One Health approach; political commitment, sustainable funding, and clear monitoring and reporting is critical.

How to Model Human–Robot Collaborative Logistics Systems: Systematic Literature Review and Future Perspectives

Thanks to rapid technological developments in robotics, various automation technologies have been introduced into different logistics systems. A human–robot collaborative logistics system can contain different technologies. Until now, most studies have focused on technologies that could only partially automate the logistics process. In contrast, some market-ready products exist with which the entire logistics process could be automated. An example is the hybrid order picking system, which utilizes autonomous mobile robots to conduct picking and transporting tasks and can be integrated into manual warehouses. In this case, human workers and robots can be modeled as heterogeneous agents with strengths and weaknesses. To date, the issue of how to model these logistics systems has remained largely unaddressed. Inspired by this system, this work intends to contribute to the discussion of possible (analytical) modeling approaches to leverage the strengths of both humans and robots to achieve better outcomes. To this end, a systematic literature review was conducted to find usable approaches in adjacent disciplines. We identified modelling approaches in the field of last-mile delivery, in-house transportation, assembly, and robotics, which considered a (human-robot) heterogenous team in the task allocation. Efforts are then made to construct a framework for possible design variables for such logistics systems to offer promising insights and perspectives for future research.

Mathematical Model for Multi-Objective Optimization of Facility Allocation in Flexible Manufacturing Systems with Collaborative Robots

The model proposed in this study addresses the complex task of job-machine assignment in flexible manufacturing systems (FMS) enhanced with collaborative robots (cobots). It aims to optimize three critical performance metrics simultaneously: makespan, energy consumption, and resource utilization. The model formulates these objectives as a multi-objective optimization problem, with each objective weighted to reflect its importance in the manufacturing process. The primary objective is to minimize makespan, representing the total time required to complete all jobs, while also minimizing energy consumption and maximizing resource utilization. By considering these objectives comprehensively, the model provides a valuable framework for manufacturers to make informed decisions about job allocation, leveraging the capabilities of both traditional machines and cobots. This approach enhances the efficiency and sustainability of FMS, aligning with the trend of automation and collaboration between human workers and robotic counterparts. The proposed model contributes to the optimization of manufacturing processes in an era of increasing automation and technological advancements.

Design, modeling, and experimental analysis of the Crawler Unit for inspection in constrained space

Inspections of industrial and civil infrastructures prevent unexpected failures that may lead to loss of life. Although inspection robotics is gaining momentum, most of field operations are still performed by human workers. For inspection robots, the main limiting factors are the low versatility and reliability in dynamic, non-structured and highly complex environments. To tackle these issues, we have designed a modular and self-reconfigurable hybrid platform, which consists of three units: the mobile Main Base and two Crawler Units with docking interfaces. The Crawler Unit operates in constrained environments and narrow spaces, while the Main Base will inspect wide areas and deploy/recover the Crawler Units near/from inspection sites, as in marsupial robots. Docking interfaces will allow the Crawler Units to reconfigure into a snake robot or mobile manipulators. In particular, the Crawler Units consist of four modules connected by three kinematic chains for nine active joints in total. Each module is equipped with half active, half passive tracks for moving. This paper discusses in detail the dynamic model of the Crawler Unit, especially focusing on the definition of effective constraint equations, which closely model the system features avoiding common simplifications. Numerical simulations and physical experiments validate the proposed dynamic model of the Crawler Unit.

Task Allocation in Human-Robot Collaboration: A Simulation-based approach to optimize Operator's Productivity and Ergonomics

This paper delves into the transformative realm of Industry 4.0, emphasizing the pivotal role of Human-Robot Collaboration (HRC) in redefining manufacturing landscapes. Central to this revolution is the integration of cyber-physical systems, automation, and the Internet of Things (IoT), culminating in the advent of smart manufacturing. We explore how HRC fosters a synergistic work environment, blurring the traditional boundaries between human workers and robotic counterparts. This research emphasizes the importance of intelligent task allocation in optimizing operational efficiency while safeguarding the ergonomic well-being of human operators. A simulation-based approach is presented, focusing on task allocation within HRC contexts to enhance both human operators' productivity and fatigue levels. The study provides an analysis of fatigue dynamics and its repercussions on industrial settings. The paper outlines various collaborative scenarios, including sole human operation, robot support, a Flexible Workstation (F-WS) support, and combined robot-FWS support, evaluated through a case study of an assembly station. The results underscore the tangible benefits of integrating robotic and F-WS support, demonstrating a balanced improvement in processing speed, operator efficiency, and a significant reduction in fatigue accumulation. In conclusion, this paper advocates for a balanced, human-centric approach to Industry 4.0, where the integration of HRC and ergonomic considerations pave the way for a sustainable, efficient, and health-preserving industrial future.

Challenges in Developing Digital Twins for Labor-intensive Manufacturing Systems: A Step towards Human-centricity

Many existing manufacturing systems still rely heavily on human workers as the backbone of their production processes. Such systems are commonly termed labor-intensive. Developing Digital Twins for labor-intensive manufacturing lines is a complex and challenging task as human involvement adds another level of uncertainty. While Digital Twins offer numerous benefits, such as improved efficiency, reduced downtime, and enhanced decision-making, they also come with unique challenges when they need to be developed for labor-intensive manufacturing systems. In this paper, we discuss the main challenges and their implications that arise from existing research. Considering these challenges, we propose a framework for developing data-driven Digital Twins of labor-intensive manufacturing systems as an initial step towards addressing these challenges. We illustrate the challenges associated with Digital Twins of labor-intensive manufacturing systems through a practical case study derived from our collaboration with two companies. In the case study, we make necessary preparations for developing Digital Twins for decision support in job scheduling in a hybrid machine-worker environment while considering the well-being of workers.

Optimal Task and Motion Planning and Execution for Multiagent Systems in Dynamic Environments.

Combining symbolic and geometric reasoning in multiagent systems is a challenging task that involves planning, scheduling, and synchronization problems. Existing works overlooked the variability of task duration and geometric feasibility intrinsic to these systems because of the interaction between agents and the environment. We propose a combined task and motion planning approach to optimize the sequencing, assignment, and execution of tasks under temporal and spatial variability. The framework relies on decoupling tasks and actions, where an action is one possible geometric realization of a symbolic task. At the task level, timeline-based planning deals with temporal constraints, duration variability, and synergic assignment of tasks. At the action level, online motion planning plans for the actual movements dealing with environmental changes. We demonstrate the approach's effectiveness in a collaborative manufacturing scenario, in which a robotic arm and a human worker shall assemble a mosaic in the shortest time possible. Compared with existing works, our approach applies to a broader range of applications and reduces the execution time of the process.

How does the application of augmented reality affect the mental workload of human workers? A collection of preliminary results

Digitalisation and the introduction of smart manufacturing technologies are changing the shape of industrial shop floors. For manufacturing companies, it becomes imperative to assess the operational and economic advantages derived from these technologies. Simultaneously, in alignment with the Industry 5.0 paradigm, understanding the influence of digital technologies on human workers, especially those directly interacting with these technologies, is equally critical. This study focuses on augmented reality (AR) as a specific technology, conducting an experimental campaign to explore its effects on learning curves and cognitive workload. Voluntary participants engaged in learning manual tasks of varying durations and required dexterity. This study evaluates the impact of AR on the mental workload of operators, utilising two main eye-related measures: fixation duration and pupil diameter. Preliminary results indicate that augmented reality increases the cognitive workload of workers undergoing training.

Hybrid Intelligence in Production Systems and Its Effects on Human Work: Insights from Four Use-Cases

Industry 4.0 has initiated a data-driven transformation of production systems. With AI applications capitalizing on the surge of data availability, their introduction is reshaping workplaces and altering work tasks and profiles around the world. As AI-driven automation of work proliferates, so does the potential for the substitution of human labor. Rather than replacing human work, the concept of hybrid intelligence seeks to combine human and artificial intelligence with the effect of increasing productivity of the overall work system. As such, the concept may prove useful to support human-friendly automation of work, i.e., automation that supports human well-being and empowerment. This requires a deeper understanding of the projected effects of different automation solutions on human workers. In this context, this paper examines possible effects of AI applications in production systems based on four use-cases of coating and machining processes, thereby focussing on the changes from the perspective of workers and the resulting human-AI interactions. The potential challenges and opportunities of the workplace transformation are discussed, specifically highlighting possible implications for the workforce.