Manual Work Research Articles

Evaluating visual encoding quality of a mixed reality user interface for human–machine co-assembly in complex operational terrain

During human–machine collaboration in manufacturing activities, it is important to provide real-time annotations in the three-dimensional workspace for local workers who may lack relevant experience and knowledge. For example, in MR assembly, workers need to be alerted to avoid entering hazardous areas when manually replacing components. Recently, many researchers have explored various visual cues for expressing physical task progress information in the MR interface of intelligent systems. However, the relationship between the implantation of visual cues and the balance of interface cognition has not been well revealed, especially in tasks that require annotating hazardous areas in complex operational terrains. In this study, we developed a novel MR interface for an intelligent assembly system that supports local scene sharing based on dynamic 3D reconstruction, remote expert behavior intention recognition based on deep learning, and local personnel operational behavior visual feedback based on external bounding box. We compared the encoding results of the proposed MR interface with 3D annotations combined with 3D sketch cues (3DS), which combines 3D spatial cues (3DSC) and 3DS combined with adaptive cues (AVC), through a case study. We found that for physical tasks that require specific area annotations, 3D annotations with context (3DAC) can better improve the quality of manual work and regulate the cognitive load distribution of the MR interface more reasonably.

Comprehensive Review of the Impact of Advanced Technology Adoption on Work and Continuous Improvement

Technology advances are changing how companies and their employees do their respective jobs. The intervention of technology has revolutionized the way jobs are conceptualized, discussed, performed, and delivered. This analysis aims to deliver a summary of the impact of the adoption of advanced technology on job characteristics and the impact on job demand and continuous improvement, which can act as a reference for theorists and practitioners to map, research, and analyze the effect of technology on work systems and productivity. By presenting a systematic review of the literature along with several avenues for future research, we hope that this research will contribute to job demand and ongoing research. A total of 30 articles were reviewed from a total generated article database of 335, which were systematically selected from different academic databases between 2001 and 2021. The review signifies the role of technology in influencing work complexities, privacy, workload, workflow interruptions, manual work, role expectations, and developmental opportunities. This study is pivotal in substantiating the influence of technology in work systems, besides furnishing variables and themes for further studies in the area. Doi: 10.28991/HIJ-2023-04-03-014 Full Text: PDF

‘The Northern chemist’—Truth behind the stereotype in the British scientific elite?

In a prosopographical study of the British scientific elite, defined as Fellows of the Royal Society born since 1900, chemists were found to be distinctive in their social origins and schooling, being more likely than Fellows in other fields to come from relatively disadvantaged class backgrounds and to have attended state rather than private secondary schools. In thinking of possible explanations, we called to mind the student stereotype of ‘the Northern chemist’. Could this give some indications of how it should come about that those chemists who enter the scientific elite—a small minority—tend to differ from other elite members in the ways in question? Our more detailed analyses of the biographies of elite chemists, comparing those of different class origins, point to the following conclusions. The Northern chemist was a male stereotype, and chemists prove to be more predominantly male than other members of the scientific elite. Young people, mainly male, often growing up in industrial areas of the North of England (or in Wales) and in families whose male members were in manual work, were particularly likely to develop an interest in chemistry rather than in other sciences, and it was in chemistry that state education gave them their greatest comparative advantage over those privately educated. Generalising from these analyses, we suggest that a larger pool was created in chemistry than in other scientific fields of people who were of relatively disadvantaged social origins and state educated, and that this difference was then maintained through into the social composition of the small number of chemists who eventually gained elite status.

New Pathways into the Profession: How Organisations Have Realised the Value of Apprenticeships in Library, Information, Knowledge, Records and Archives

AbstractFor many years apprenticeships were associated with heavy industry and skilled manual work, but in more recent times the focus has shifted towards other fields and professions, including librarianship. In 2017 CILIP – the Chartered Institute of Library and Information Professionals – began working with employers across sectors designing apprenticeships specific to library, information, knowledge and archives work. The Level 3 Library, Information and Archive Services Assistant Apprenticeship was the first to be developed and came online in 2019 and to date over 100 apprentices have been through the apprenticeship, while other apprenticeships to do with the profession are now up and running. Here Claire Laybats, Professional Services Development Manager for CILIP, gives LIM the full story.

HGNAS++: Efficient Architecture Search for Heterogeneous Graph Neural Networks

Heterogeneous graphs are commonly used to describe networked data with multiple types of nodes and edges. Heterogeneous Graph Neural Networks (HGNNs) are powerful tools for analyzing heterogeneous graphs. However, designing neural architectures of HGNNs requires extensive domain knowledge and time-consuming manual work. Recently, neural architecture search algorithms have become popular in automatically designing neural architectures for homogeneous graph neural networks. In this paper, we present a Heterogeneous Graph Neural Architecture Search algorithm (HGNAS for short) which allows the automatic design of heterogeneous graph neural architectures. Specifically, HGNAS first designs a new search space based on existing popular HGNNs. Then, HGNAS uses a policy network as the controller to sample and find the best neural architecture from the designed search space by maximizing the expected accuracy of the selected architectures on a given validation dataset. Moreover, we design a new method HGNAS++ to improve the efficiency of HGNAS by training the RNN controller within a generative adversarial learning framework. The basic idea of HGNAS++ is to embed a pairwise ranker into the reinforcement learning based architecture search algorithm. The pairwise ranker can be taken as a discriminator which selects more accurate architectures between pairs of candidate architectures. Then, the RNN controller can be updated more efficiently by only using a relatively small number of candidate architectures selected by the pairwise ranker. Experiments on real-world heterogeneous graph datasets show that HGNAS is capable of designing novel HGNNs that beat the best human-invented HGNNs. On the benchmark datasets, HGNAS++ improves HGNAS in terms of evaluation cost, with a reduction of 50% of the evaluated candidate architectures and a decrease of 24% in search time on average. As a byproduct, HGNAS++ can find sparse yet powerful neural architectures for HGNNs.

Automatic Creation of 3D Documentation in CAD/BIM Based on Topology

This paper deals with reverse engineering, namely the as-built documentation of actual construction. The input is data measured in the field using geodetic methods. In addition to modern methods of surveying 3D objects, such as laser scanning, it is still necessary to use classic surveying using a total station. The bottleneck of the process is the creation of documentation of the construction, which is still created manually in the appropriate CAD/BIM software. The goal of this research was to find a method that would reduce the amount of manual work when drawing documentation in CAD/BIM to a minimum. The core of the solution is the use of a topology that interconnects the points measured in-field. The entire procedure has two parts: (1) creating a topological drawing template in a suitable CAD/GIS software (digital sketch) and (2) adding geometry to this sketch and creating a drawing using topological codes. This method was verified in practice by applying it to several specific buildings in the Czech Republic. The practical application of the method demonstrated 30% time savings and a reduction in work and error rate in the entire process.

Occupational factors and low back pain: a Mendelian randomization study.

Low back pain (LBP) is a common condition and a leading cause of health function loss worldwide. This study assessed the impact of occupational factors on LBP using Mendelian Randomization (MR) method, controlling for confounding variables. Based on publicly available genome-wide association studies (GWAS), two-sample univariate and multivariate MR analyses were performed to assess the causal effect of occupational factors on LBP. We used the inverse variance weighted (IVW) method and sensitivity analyses to generate the total results for the univariate MR analysis. Furthermore, we performed multivariate MR analysis to assess the direct causal association between occupational factors and LBP after accounting for potential confounding variables. The total causal effect of genetically predicted job involves heavy manual or physical work on LBP was found to be significant (IVW OR, 2.117; 95% CI, 1,288-3.479; p = 0.003). Upon adjusting for potential confounding variables, the direct effect of job involves heavy manual or physical work on LBP remained statistically significant. Similarly, the total causal effect of genetically predicted job involves mainly walking or standing on LBP was also found to be significant (IVW OR, 1.429; 95% CI, 1,035-1.975; p = 0.030). However, upon adjusting for potential confounding variables, the direct effect of job involves mainly walking or standing on LBP became insignificant. In contrast, the findings from the MR analysis indicated a lack of association between work/job satisfaction and LBP. Sensitivity analysis consistently supported these trends. Our results supported a causal link between job involves heavy manual or physical work and increased risk of LBP, while finding no significant associations between prolonged walking/standing at work, job satisfaction, and LBP, providing valuable insights for the development of targeted prevention and intervention strategies for LBP.

Use of digital tools for preclinical training in complete denture: A pilot study.

Advances in CAD-CAM complete dentures open up opportunities in preclinical laboratory education. A cross-over study was conducted to assess the benefit of digital training in custom tray fabrication. Hypotheses were that digital training improved conceptual skill acquisition and that assessment of digital work helps in the discrimination of students' difficulty. Third-year students were allocated either into group A and took the manual practical classes before the digital ones, or into group B (N = 154). Prior to the sessions, a motor skill test was conducted. The influence on the learning process was evaluated by comparing the groups' results to the manual lab work with a Student's t-test. The effectiveness of the assessment in discriminating manual aptitude and conceptual skill was studied through Spearman's rank coefficient between digital and manual scores in conceptual skill and with a subgroup analysis according to the results of the motor skill test. The level of significance was set up at .05. Students' satisfaction was also assessed with a questionnaire. Students in group B achieved better scores. Spearman's rank coefficient test showed a weak correlation, R = .16. For manual lab work, the most manually skilled students did significantly better. In contrast, for the digital session, the medium manually skilled students did better. More than half of the students (55%) found the manual and digital sessions complementary, without identifying any difference depending on the learning sequence. Digital training improved the students' results in the conventional practical exercise. Assessments of the digital and conventional custom trays were complementary in order to distinguish conceptual and motor skills.

Prevalence and Determinants of Intimate Partner Violence Against Women in India

Intimate partner violence against women is a global concern. Over the years, the occurrence of such violence has recorded a significant increase, especially in developing countries, including India. The present study aims to investigate the prevalence of IPV in India. The study also seeks to determine the demographic and socio-economic factors associated with IPV. The study utilizes the fourth-round data of the National Family Health Survey 2015–2016. Chi-square (χ2) test and binary logistic regression model were used to determine IPV-related factors. Of 62,716 married women surveyed, 30.59% were found to have suffered from IPV. The prevalence of physical, sexual, and emotional violence against married women was 26.98%, 6.45%, and 12.07%, respectively. Women belonging to the following categories were found to have a higher likelihood of experiencing IPV: (i) women with higher educational attainment than their husbands; (ii) women involved in manual work, (iii) women having more than two children, and (iv) women belonging to the Muslim community. In addition, women earning more than their partners, women practicing independent decision-making, and women with partners addicted to alcohol consumption were also at higher risk of experiencing IPV. The alarming rate of IPV in India needs urgent attention. The need of the hour is to organize context-specific and community-based IPV awareness programs. In this regard, collaboration with various stakeholders and non-government organizations may help minimize the incidence of IPV in the country.

Examine the Efficacy of the Strategies Used to Spread Knowledge About NREGA in the Different Domains of Kashmir Valley

The Mahatma Gandhi National Rural Employment Guarantee Act was notified in India on September 2005 with a mandate to provide at least hundred days of guaranteed wage employment in a financial year to every rural household whose members above the age of eighteen years volunteer to do unskilled manual work. The main focus of the act is to facilitate the social protection and to provide livelihood security for the people living in rural India by providing employment opportunities and therefore contributing towards the overall development of the local people. The present research paper is attempted to examine the efficiency of the strategies used to spread knowledge about NREGA in different domains of Kashmir Valley. The paper focuses on understanding the efficacy of strategies employed to spread knowledge about MGNREGA and how it has impacted the lives of beneficiaries. This was studied through in-depth interviews and analysis of Geographic, Socio-economic, and Politico-cultural aspects in the regions of Gutyar, Jalsheri, Tulmulla B, and Drend.

Perancangan Sistem Persewaan Ruang Bisnis dengan Metode Prototype

Automation helps humans do manual work easily and the information they get is up-to-date. One of the automation implementations is room rental. Room rentals are currently still not widely done online and reach all over Indonesia. Some room providers only use automation for their own space rentals. Ordering and marketing allow only limited automation within the scope of the provider. The purpose of this research is to design a website-based business space rental system using the prototype method. Research shows that business space reservation information systems can manage business space rental data owned by the owner. The system is also capable of automating orders, so that the owner and tenant do not need to communicate directly or go to the rental location. This research can be used as a reference for the industry in designing a space rental information system

A Bayesian inferential sensor for predicting the reactant concentration in an exothermic chemical process

In many chemical reactors, concentration measurements are conducted off-line in a laboratory, which involve manual work and can therefore be conducted only infrequently. We propose a Bayesian inferential sensor to predict the reactant concentration in the inlet stream of an exothermic chemical process. The inferential sensor is based on the Bayesian inverse approach and the autoregressive integrated moving average (ARIMA) model. It enables the prediction of the reactant concentration at the frequency of automated on-line measurements, which is typically much higher than that of laboratory measurements. We demonstrate the method on real industrial process data from catalytic hydrogenation of aromatic compounds. The predicted aromatics concentration in the inlet stream, generated based on the latest on-line measurements and two-week-old laboratory data, has a coefficient of determination of 0.936 and a root mean square error of 0.654 mass-%.

Image recognition model of pipeline magnetic flux leakage detection based on deep learning

Abstract Deep learning algorithm has a wide range of applications and excellent performance in the field of engineering image recognition. At present, the detection and recognition of buried metal pipeline defects still mainly rely on manual work, which is inefficient. In order to realize the intelligent and efficient recognition of pipeline magnetic flux leakage (MFL) inspection images, based on the actual demand of MFL inspection, this paper proposes a new object detection framework based on YOLOv5 and CNN models in deep learning. The framework first uses object detection to classify the targets in MFL images and then inputs the features containing defects into a regression model based on CNN according to the classification results. The framework integrates object detection and image regression model to realize the target classification of MFL pseudo color map and the synchronous recognition of metal loss depth. The results show that the target recognition ability of the model is good, its precision reaches 0.96, and the mean absolute error of the metal loss depth recognition result is 1.14. The framework has more efficient identification ability and adaptability and makes up for the quantification of damage depth, which can be used for further monitoring and maintenance strategies.

Designing and Developing a Weed Detection Model for California Thistle

With a great percentage of farms in New Zealand as pastures, they are mainly important in contributing to the milk and meat industries. Pasture quality is highly affected by weeds. Weeds grow fast and invade pastures by seed pollination. They consume the nutrients, water, and other minerals, and once they are bitter, cattle do not eat them. Therefore, dairy farmers have to allocate a significant portion of their budget and time to monitor and clean weeds. Unfortunately, most weed management tasks are manual with no consistent technology. Thus, the motivation behind this article was to design an object detection model for weed monitoring and control in pastures. The model was designed and tested on California thistle, a dominant and widespread weed on New Zealand pastures. Our study is one of the major model designs for identifying weeds in an in-pasture environment, one of the most complicated environments for any object detection model. A synthetic methodology was used to create three types of datasets: plant-based, leaf-based, and mixed. The trained model based on the leaf-based dataset is one of the major contributions of our work and has not been conducted by any other weed detection models. After models had been trained, tuning experimentation was undertaken to improve the model’s performance. This involved studying the model’s hyperparameters in various ranges and then recording their values at the optimum points. The improved model showed a 93% mAP accuracy in the detection of training images and over 95% accuracy for testing images. The experimentation showed that the leaf-based model was slightly better than other models. The model can automate highly any weed management system. The use of this model will save farmers time and money and help them reduce the errors of manual work.

Steel Surface Defect Detection Based on SSAM-YOLO

The defect inspection of the steel surface is crucial to modern manufacturing and highly depends on inefficient manual work. The emergence of deep learning has prompted the development of automated defect detection methods, but the current methods perform badly in the detection of the crazing and rolled-in scale-two types of defects on steel surfaces. The difficulty in the detection of crazing and rolled-in scale is mainly due to the similarity between object regions and background regions. Based on this, the authors propose a supervised spatial-attention module (SSAM). It introduces a priori knowledge compared to the traditional spatial attention mechanism, which can enhance the supervision of relevant parameters in the attention mechanism module during network training. Finally, they introduced the SSAM to the YOLOv5 and got the SSAM-YOLO. The test result on the NEU-DET dataset shows that the proposed method has better detection accuracy, achieving improvements of 7.3% and 3.02% on the AP@0.5 for the crazing and rolled-in scale. The method also outperforms the comparative main stream algorithms for steel surface defect detection, verifying the effectiveness of our algorithm.

Design and Implementation of a Low-Cost Torque Sensor for Manipulators

Humanoid and collaborative robots find their application in numerous sectors, such as automotive, electrical and mechanical engineering, not excluding the field of bioengineering. They replace repetitive and often monotonous human activity. As the trend nowadays is for continuous optimization of production processes, their advantage is easy relocation and operative application to new tasks, which allows the automation of practically all manual work. A common feature of manipulators is the control of the positioning of the actuators, primarily by adjusting the parameters of the drive units. Feedback is often implemented through various sensors that provide real-time information. However, most of the sensors in use do not provide information that would allow obtaining data on the history of the operating load in order to assess the further safe and reliable operation of the mechanical parts. This paper presents a low-cost torque sensor that was proposed by modifying the design of an existing part. The torque sensor was developed on the principle of strain gauge measurement. The results of strain–stress analysis obtained by numerical modelling were experimentally validated under static and dynamic loading. Practical application is mainly the development and long-term testing of prototypes of various types of manipulators and collaborative robots, where high accuracy and repeatability of positioning are essential.

Scaphoid nonunion: a novel modification of Matti-Russe technique with enhanced recovery and full clinical and radiographic union.

A scaphoid fracture is a common injury affecting the wrist joint. A fracture of the carpus scaphoid can heal uneventfully or be complicated by non-union. Scaphoid non-union can result in persistent wrist pain, and with functional difficulties affecting all activities of daily living of the patients, this disability is expected to be significant since most of these patients are young active adults. Extensive removal of the bone from the scaphoid, with the application of a large amount of cancellous bone graft and fixation with two to three wires, could lead to a high union rate and a good functional outcome. Eighteen patients with scaphoid fracture non-union were recruited during their visit to the upper limb clinic at our institute. Demographic data were collected, and data regarding comorbidities, smoking, manual work, and others were recorded. Data regarding the interval between injury and surgery, time to radiographic union, and functional wrist scores were reported as well. A cohort of 18 patients was included. The mean age of patients was 30 years; most of our patients were healthy (83.3%), and more than two-thirds were smokers (72.2%). The mean follow-up time was 18 months (1.5 years), 15 patients (83.3%) achieved radiographic unions by 2-3 months, and the remaining 3 patients (16.7%) achieved radiographic unions by (4-5) months, i.e., all patients achieved successful radiographic unions by 5 months at maximum. The mean Mayo score for our series was 83.6 (± 12.4), with 5 patients (27%) achieved ≥ 95% which indicates a significantly high functioning wrist in our cohort. Our modified technique with enhanced stability from using three k-wires can achieve full clinical and radiographic unions and result in enhanced recovery postoperatively with cast immobilization limited to 6 weeks total. IV Case series study.

Analysis of Employment Generation through Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) in Uttar Pradesh Pre and Post Covid-19 Pandemic Issues and Challenges

This study analyses the employment generation trends through the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) in the state of Uttar Pradesh. The study is primarily based on secondary data to examine the performance and effectiveness of the Mahatma Gandhi National Rural Employment Guarantee Scheme (MGNREGS) in providing employment to rural people who are willing to do unskilled manual work. The study examines the categorizes-wise employment demanded by households and employment offered to household from 2014-15 to 2022-23. The study shows that MGNREGS provided the highest employment during covid 19. Approximately 12.05 crore people got the job, that has been declined to 77.43 lakh in 2022-23. It also examines the SCs and STs employment status and shows that 29.10 lakh SCs population and 91.72 thousand STs got the job in 2020-21. This study also examines employment provided to disabled persons and shows that 17.43 thousand people got the job in 2020-21 and 14.06 thousand people got the job in 2021-22, and 14.03 thousand people got the job in 2022-23.

Reducing the Delayed Lead Time of Purchase Requisition to Purchase Orders for Standard Part Commodity in the Procurement Department of the Avionics Sector

The efficiency and effectiveness of procurement processes perform a critical role in the smooth operation of organizations, particularly in industries with intricate supply chains, such as the aviation sector. This thesis focuses on the delayed lead time of purchase requisitions to purchase orders for standard part commodities inside Procurement Department. Through a comprehensive analysis of the procurement workflow, data collection, and interviews with key stakeholders, this study aims to identify the root causes and consequences of the delayed lead time in the procurement and also determine the best strategy supply chain for the procurement department. The root cause reveals several factors that contribute to the delayed lead time, such as ineffective and inefficient processes, manual working procedures, particularly in the negotiation and evaluation process between buyer and Supplier, bureaucracy in the document, poor in managing and updating the data of requirements supplier, no back up for new prospect supplier, and a lack of monitoring and communication between internal and external departments. Furthermore, the consequences of this delay include delays in following processes in other departments, increasing the potential of product delivery delays to clients, and affecting customer satisfaction. Based on the findings of these studies, several business solutions have been proposed to address these challenges. The conclusion of the entire analysis process is the implementation of e-procurement with a vendor management system. By implementing these suggestions, the Procurement Department significantly reduced the lead time by 65%. The benefits obtained lead to increased operational efficiency, reduced possibility of product delays to consumers, and increased customer satisfaction.

Control Method of Wire Repairing Robot Based on Semantic Segmentation and Current Detection

The traditional repair of conductors in power systems, using manual work at height, carries a high level of risk. To ensure personnel safety and improve operational efficiency. Based on the development of a conductor repair robot using pre-stranded wires, the focus is on a semantic segmentation-based adaptive conductor arc sag control method, which enables the robot to adapt to conductors with different arc sags by segmentation identification of the conductor, offset calculation, and proportional-integral controller design to realize the robot hanging on the conductor. Subsequently, the winding gap is monitored according to the change of motor current, and a fuzzy controller is used to realize the robot winding operation. The robot prototype tests show that the robot is capable of adapting to the wire pitch and completing soft repair work.