Operator Performance Research Articles

Time Monitoring System for Assembly Line Operators Using RFID

This project discussed monitoring system in an assembly line to monitor and track the performance of the operators in industries. Industry is an important workplace around the world including research and development, design, process, production, quality assurance and so on. A large number of jobs involve humans, and there are also parts that require humans to work using machines, especially operational workers or operators. There are many systems developed in the industry that involve the operator, some of them are to monitor the machining process, and some are to monitor and control the machine. Assembly line operator performance is a critical aspect that severely affects the overall production performance in any production nature. Non-productive time contributes to inequality in task distribution that often results in bottleneck points on a production line. Therefore, the non-productive time is considered as a waste of resources that has a relative impact on the production yield of an industry. In this project, Radio Frequency Identification (RFID) is used on the production line as a monitoring system so that it can help organizations to increase the performance of the working environment among all the employees as well as to achieve better production yield by reducing the non-productive time of assembly line operator.

Pathophysiological markers of reduced resistance of healthy people to moderate hypoxic hypoxia

BACKGROUND: Despite the abundance of research in the field of high-altitude physiology and medicine, the problem of resistance to moderate hypoxia has not been practically studied. This is partly due to methodological difficulties: the studies were conducted on healthy people and climbers who tolerated the effects of moderate hypoxia well. Such studies have become necessary in connection with the development of hypoxic therapy.
 AIM: To search for indicators of the functional state of a person that are highly sensitive to the effects of moderate hypobaric hypoxia.
 MATERIAL AND METHODS: The examined group consisted of 65 practically healthy men aged 1925 years who were selected for work in high mountains. The main indicators characterizing the state of the central nervous system, cardiovascular and respiratory systems were studied in a state of relative rest and when performing a dosed two-stage bicycle ergometric test, in normoxia conditions (altitude 0) and during a pressure chamber rise in the TABAY climate complex (Japan) to altitudes of 3500 and 4500 m Statistically analyzed were the characteristics of the dynamics of indicators during the transition from normoxia to states of moderate hypoxia, correlations between indicators, their sensitivity to hypoxic effects, the significance of reactivity to hypoxic effects and mobilization of the bodys functional reserves in resistance to hypoxia.
 RESULTS: It has been shown that the key pathophysiological markers of reduced body resistance to moderate hypoxic exposure are a rapid decrease in the level of peripheral blood saturation, high reactivity of the cardiovascular and respiratory systems to hypoxic exposure, as well as excessive mobilization of the bodys functional reserves in response to additional physical activity.
 CONCLUSION: The presented materials confirm that the functional state of a healthy person under conditions of moderate hypoxia at rest is determined by the level of saturation of his peripheral blood. Maintaining saturation at a higher level ensures the maintenance of an optimal level of functional state, physical and operator performance, a lower degree of stress on the respiratory and cardiovascular systems.

The Effect of Guild Training Courses on the Health Knowledge and Performance of Operators of Food Preparation and Distribution Centers in Zarand in 2021

Background: The present study aimed to investigate the effect of guild training courses on the health knowledge and performance of food operators in food preparation and distribution centers in Zarand, Kerman, Iran in 2021. Methods: This quasi-experimental (uncontrolled) study was conducted on 120 food operators in food preparation and distribution centers in Zarand. The data were collected using the Health Knowledge and Performance Questionnaire. The questionnaire was completed at the time of enrollment and in the final evaluation phase. The food operators’ performance was evaluated two weeks before and one month after the completion of the training course in the food operators’ workplace through questions and checklists. The knowledge and performance scores for the participants ranged from 0 to 100. The collected data were analyzed with SPSS-23 software using paired and independent samples t tests, analysis of variance (ANOVA), and Pearson correlation test. Results: The participants’ mean age was 36.73±8.92 years. The mean scores of health knowledge were 65.70±21.60 and 89.62±12.12 before and after the training course, respectively, showing a statistically significant increase in the post-training phase (P<0.001). Moreover, the mean scores of health performance were 78.70±7.50 and 82.42±6.65 before and after the training course, respectively, indicating a statistically significant increase in the participants’ health performance in the post-training phase (P<0.001). Conclusion: The findings revealed that guild training courses are effective in improving the health knowledge and performance of food operators in food preparation and distribution centers. Thus, the potential of guild training courses can be used to raise food operators’ and workers’ awareness of health-related problems and provide effective solutions.

Research on the influences of information presentation on information capture and performance in digital control systems

ABSTRACT This study investigates the effects of information presentation on operator performance in digital control systems at different levels of task difficulty in order to resolve the conflict between ‘large amounts of information’ and ‘limited display’. By using software to present figures of varying difficulty as target information, the operator’s behaviour such as page switching and target clicking is simulated to obtain data. Simultaneously, eye-tracking technique was used to analyse how the way information was presented at different levels of difficulty influenced the subjects’ search performance. The results show that subjects’ performance gradually decreases as the difficulty of the numbers grows for both presentations, and it is found that the complexity of the content of the navigation bar may have influenced the subjects to some extent. In addition, the different position of the navigation bar where the target is located will result in a change in the search pattern. It is noticeable that dividing the single page into double pages can effectively alleviate the contradiction of ‘large amount of information’ and ‘limited display’ in designing the system interface. Therefore, this study provides certain guideline value for the interface design of digital control systems.

Shaping Human Movement via Bimanually-Dependent Haptic Force Feedback.

Haptic feedback can enhance training and performance of human operators; however, the design of haptic feedback for bimanual coordination in robot-assisted tasks (e.g., control of surgical robots) remains an open problem. In this study, we present four bimanually-dependent haptic force feedback conditions aimed at shaping bimanual movement according to geometric characteristics: the number of targets, direction, and symmetry. Haptic conditions include a virtual spring, damper, combination spring-damper, and dual springs placed between the hands. We evaluate the effects of these haptic conditions on trajectory shape, smoothness, and speed. We hypothesized that for subjects who perform worse with no haptic feedback (1) a spring will improve the shape of parallel trajectories, (2) a damper will improve the shape of point symmetric trajectories, (3) dual springs will improve the shape of trajectories with one target, and (4) a damper will improve smoothness for all trajectories. Hypotheses (1) and (2) were statistically supported at the p < 0.001 level, but hypotheses (3) and (4) were not supported. Moreover, bimanually-dependent haptic feedback tended to improve shape accuracy for movements that subjects performed worse on under no haptic condition. Thus, bimanual haptic feedback based on geometric trajectory characteristics shows promise to improve performance in robot-assisted motor tasks.

Evaluation of the effectiveness of radiation detection systems in measurement of equivalent dose rate for nuclear reactor workers

This paper introduces a practical study to evaluate and analyze the effectiveness of radiation detection systems used to measure the equivalent dose of the workers involved in Gemstones (Topaz) colour treatment process at Egypt Second Research Reactor (ETRR-2). Typically, thermoluminescence dosimeters (TLDs) are used to record the accumulated radiation dose equivalent to the periodic assessment of the health and safety of reactor workers. The accuracy of using TLDs has been evaluated in the International Atomic Energy Agency's Radiological Dosimetry Laboratory as they can be used in various nuclear environments such as nuclear reactors. This paper proposes the use of a gas-filled radiation detector, a Geiger-Muller (GM) tube, to assess the intervention of workers in handling Topaz irradiation boxes and preparing them for regular irradiation at the reactor. A comparison was made between the TLDs and the GM tube detectors and the difference in their readings and their dependence on the radiation dose rate were assessed. The difference in sub-linearity phenomena of both detectors and its effect on the GM tube values and the effect on the operator's performance to complete the filling and preparing Topaz boxes for irradiation were investigated. The difference in recording dose for both the TLD and the GM tube was taken into account based on the equation shown.

Impact of Work Skills, Performance, and Maintenance on Productivity: A Study on Head Truck Operators in PT. Terminal Petikemas Surabaya

&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D; This scientific article investigates the relationship between work skills, performance, maintenance, and productivity in the context of head truck operations at PT. Terminal Petikemas Surabaya. The study, conducted through a descriptive research approach, involved a sample of 72 head truck operators. Multiple linear regression analysis was employed to examine the data. The results revealed a significant positive impact of work skills, performance, and maintenance on productivity. The regression model exhibited a high predictive capability, indicating that the variables of interest could be utilized to forecast productivity. The findings support the well-established understanding that individuals' work skills directly influence their level of productivity. Moreover, the study confirms the widely accepted notion that improved performance among head truck operators leads to enhanced output. Additionally, it highlights the significance of routine maintenance in increasing production levels. The implications of this research underscore the importance of investing in skill development, fostering optimal performance, and implementing effective maintenance practices to enhance productivity in head truck operations.&#x0D; Highlight:&#x0D; &#x0D; Work skills significantly influence productivity: The study reveals that the level of work skills directly impacts the productivity of head truck operators in PT. Terminal Petikemas Surabaya.&#x0D; Performance drives positive output: The findings confirm that the performance of head truck operators has a beneficial influence on their overall output.&#x0D; Routine maintenance enhances production: The research highlights the significance of regular maintenance of head trucks in contributing to increased productivity levels.&#x0D; &#x0D; Keyword:&#x0D; PT. Terminal Petikemas Surabaya, Head Truck Operators, Work Skills, Performance, Maintenance, Productivity&#x0D; &#x0D; &#x0D; &#x0D; &#x0D; &#x0D;

Field pilot testing and reservoir simulation to evaluate processes controlling CO2 injection and associated in-situ fluid migration in deep coal

A key strategy for reducing global CO2 emissions is geologic sequestration of CO2. Deep, unmineable coal seams have been proposed as possible targets for CO2 injection and storage because of the global distribution of coal resources, and relative security of CO2 storage associated with CO2 adsorption. However, the current paradigm is that near-injector permeability loss caused by pure CO2 adsorption-induced coal swelling limits the effectiveness of this process making coal a less than an ideal target for CO2 injection/sequestration.In order to test the viability of CO2 injection and storage in deep coal seams, and evaluate processes controlling CO2 injection, migration and storage therein, a field pilot was implemented in the Mannville Coal (∼1500 m depth) of Alberta, Canada. An additional objective of the field pilot was to quantify the migration of water and natural gas away from the CO2 injection well, an important mechanism used to improve the performance of the pilot operator's enhanced hydrogen recovery (EHR™) process. To achieve these multiple objectives, a vertical injection well and closely-spaced offsetting vertical observation well were drilled, cored, and completed in the Mannville coal seams, and instrumented with pressure sensors to monitor both the coal and bounding zones during injection testing. Periodic fluid sampling at the observation well allowed for quantification of CO2 plume and fluid migration. Pre-CO2 injection/falloff testing was performed with water at variable injection rates/pressures to assess coal permeability/porosity changes without the complication of CO2-coal interaction. CO2 injection/falloff tests were then performed at increasing CO2 injection rates to assess the combined impact of injection pressure/CO2 rate and CO2-coal interaction (e.g., adsorption) on injectivity. All tests were history-matched with a numerical reservoir simulator; the calibrated model was used to study the key controls on CO2 storage, CO2 plume migration, in-situ fluid displacement, and injectivity.A key finding from simulation of the pilot results is that, for the Mannville coals in the study area, permeability increases caused by coal natural fracture dilation during injection outweigh permeability decreases caused by CO2 adsorption-induced coal swelling, resulting in an injectivity index increase with CO2 injection rate. In addition, the coals exhibit strong anisotropy in permeability and geomechanical properties; ignoring coal anisotropy in the simulation model causes coal swelling effects to be overestimated, resulting in an underestimate in CO2 plume extent.From the pilot results and analysis, it can be concluded that the deep Mannville coal could be a viable target for CO2 storage.

Lightweight Transformer for Multi-Modal Object Detection (Student Abstract)

It has become a common practice for many perceptual systems to integrate information from multiple sensors to improve the accuracy of object detection. For example, autonomous vehicles use visible light, and infrared (IR) information to ensure that the car can cope with complex weather conditions. However, the accuracy of the algorithm is usually a trade-off between the computational complexity and memory consumption. In this study, we evaluate the performance and complexity of different fusion operators in multi-modal object detection tasks. On top of that, a Poolformer-based fusion operator (PoolFuser) is proposed to enhance the accuracy of detecting targets without compromising the efficiency of the detection framework.

Virtual reality for improving cyber situational awareness in security operations centers

Security operations centers (SOCs) are the 911 centers of many organizational networks, except they not only respond, but also monitor. SOC operators are charged with detection, response, and mitigation. This is a tall task when one considers the volume, velocity, and variety of both internal and external organizational network and system data. SOC operations are truly a big data problem. Security orchestration, incident event management, data fusion, and anomaly detection systems help, but more is needed. This study examines the impact virtual reality (VR) can have on SOC operator performance and perceived task load. We developed a VR based network monitoring tool and assigned human subjects to one of three conditions – VR only, traditional tool only, or both. Our results, though small in scale, provide very promising indication that VR based technology may be beneficial for improving cyber situational awareness (SA), particularly with overall data perception involving novice SOC operators. The results are promising, but the sample size is small, so future research should validate this pilot study. Given the workforce challenges in the cybersecurity space, and the need to perceive large quantities of data, VR may be a very good addition to SOCs.

A review of fit to work assessment program on the work performance of heavy equipment operators during the pandemic covid-19 at Indonesia

Introduction: Fit-to-work assessment is one of the health examination efforts to assess the physical function and occupational health of workers before starting work. This fit-to-work assessment is very important during the current pandemic because the workplace is a place that is at risk of transmitting the COVID-19 virus, especially a workplace that continues to operate during the pandemic. Thus, this study aims to analyze the influence of fit-to-work assessment on the work performance of Heavy Equipment Operators at a port service terminal in Indonesia during the COVID-19 pandemic. Methods: The type of research used descriptive analytics with the cross-sectional study. The sample was collected with a simple random sampling technique. The population in this study is heavy equipment operators in one of the sub-holding of PT. Pelindo Terminal Petikemas Surabaya- Indonesia with a sample of 54 operators who can represent each work shift. Each participant was given a questionnaire and needed to fulfill it. The data will be analyzed using SPSS 25. The data analyzed was descriptive only. Results: According to the study of the characteristics, mostly the ages were 31-40 (46.30%), with a high prevalence of high school (76%). The work shift schedule was classified into three parts. Shift two from 3.30 pm until 11.40. The length of work was also more than five years or greater than others (59.25%), with the work performance from 54 respondents, we found that they had a high work performance (72%), and all of the respondents were mostly in a reasonable condition (94.04%). Conclusion: Fit-to-work assessment is carried out before work to determine whether the worker is fit or unfit to work according to the level of health required. The work performance we found that mostly they have a high work performance. It was also in line with the fit-to-work assessment results that most respondents were in a reasonable condition.

A human operator model for simulation-based resilience assessment of power grid restoration operations

Human operations play a vital role for the resilience of power grids. While past research concentrated on performance in cascading failures, this work proposes a system model that focuses on assessing the impact of the performance of operators in grid operations and restorations. The operator model developed for this purpose addresses human performance and variability by accounting for stochastic durations, potential errors and alternative responses, and the probabilities of these outcomes. Discrete event simulation is the assessment framework, with the model implemented in MATLAB Simulink in conjunction with a dynamic power system model. A re-energization cell provided by a Swiss generation system operator is used for a case study demonstration of the simulation model, which produces distributions of the total restoration duration and restoration success rates while identifying the parts of restoration plans that could be modified to enhance restoration performance. The overall outcomes of the case study suggest that the human element can be treated practically in grid simulations in order to produce findings that will increase grid resilience.

Using Cloud Functions as Accelerator for Elastic Data Analytics

Cloud function (CF) services, such as AWS Lambda, have been applied as the new computing infrastructure in implementing analytical query engines. For bursty and sparse workloads, CF-based query engine is more elastic than the traditional query engines running in servers, i.e., virtual machines (VMs), and might provide a higher performance/price ratio. However, it is still controversial whether CF services are good suites for general analytical workloads, in respect of the limitations of CFs in storage, network, and lifetime, as well as the much higher resource unit prices than VMs. In this paper, we first present micro-benchmark evaluations of the features of CF and VM. We reveal that for query processing, though CF is more elastic than VM, it is less scalable and is more expensive for continuous workloads. Then, to get the best of both worlds, we propose Pixels-Turbo - a hybrid query engine that processes queries in a scalable VM cluster by default and invokes CFs to accelerate the processing of unpredictable workload spikes. In the query engine, we propose several optimizations to improve the performance and scalability of the CF-based operators and a cost-based optimizer to select the appropriate algorithm and parallelism for the physical query plan. Evaluations on TPC-H and real-world workload show that our query engine has a 1-2 orders of magnitude higher performance/price ratio than state-of-the-art serverless query engines for sustained workloads while not compromising the elasticity for workload spikes.

The Effect of Training and Work Motivation on Operator Performance During the COVID-19 Pandemic

This research was conducted on a quay container crane operator at KSO TPK Koja. The quantitative research method was collecting data on 28 questionnaires and using SPSS version 21 applications to analyze data. The study used the validity, reliability, normality and hypothesis test. This study aimed to identify the influence of training and work motivation on operator performance during the COVID-19 pandemic at KSO TPK Koja. The result of this study showed that training and work motivation have a positive influence on operator performance, but training had no significant effect on operator performance. In addition, work motivation had a significant influence on operator performance.&#x0D; Keywords: Training, Work Motivation, Operator Performance, COVID-19

A Scoping Review of Simulation-Based Training Paradigms for Carotid Artery Endarterectomy and Carotid Artery Stenting

Surgical simulation has come to the forefront to enhance the training of residents. The aim of our scoping review is to analyze the available simulation-based carotid revascularization techniques, including carotid endarterectomy (CEA) and carotid artery stenting (CAS) and suggest critical steps for evaluating competency in a standardized fashion. A scoping review of all reports on simulation-based carotid revascularization techniques including CEA and CAS was performed in PubMed/MEDLINE, Scopus, Embase, Cochrane, Science Citation Index Expanded, Emerging Sources Citation Index, and Epistemonikos databases. Data were collected according to the Preferred Reporting Items for Systematic Reviews and Meta-analysis guidelines. The English language literature was searched from January 1, 2000 to January 9, 2022. The outcomes evaluated included measures of assessment of operator performance. Five CEA and 11 CAS manuscripts were included in this review. The methods of assessments employed by these studies to judge performance were comparable. The 5 CEA studies sought to validate and demonstrate improved performance with training or distinguish surgeons by their experience level, either through assessing operative performance or end-product results. The 11 CAS studies used 1 of 2 types of commercial simulators and focused on determining the efficacy of simulators as teaching tools. By examining the steps of the procedure associated with preventable perioperative complications, it provides a reasonable framework for determining which elements of the procedure should be emphasized most. Furthermore, using potential errors as a basis for assessment of competency could reliably distinguish operators based on level of experience. Competency-based simulation training is becoming more relevant as our surgical training paradigm shifts with the increased scrutiny within training programs of work-hour regulations and the need to develop a curriculum to assess our trainees' ability to perform specific operations competently during their stipulated training period. Our review has given us an insight into the current efforts in this space regarding 2 specific procedures that are key for all vascular surgeons to master. Although many competency-based modules are available, there is a lack of standardization in the grading/rating system of what surgeons consider vital steps of each procedure to assess these simulation-based modules. Therefore, the next steps of curriculum development should be based on standardization efforts for the different protocols available.

Applications of physics informed neural operators

We present a critical analysis of physics-informed neural operators (PINOs) to solve partial differential equations (PDEs) that are ubiquitous in the study and modeling of physics phenomena using carefully curated datasets. Further, we provide a benchmarking suite which can be used to evaluate PINOs in solving such problems. We first demonstrate that our methods reproduce the accuracy and performance of other neural operators published elsewhere in the literature to learn the 1D wave equation and the 1D Burgers equation. Thereafter, we apply our PINOs to learn new types of equations, including the 2D Burgers equation in the scalar, inviscid and vector types. Finally, we show that our approach is also applicable to learn the physics of the 2D linear and nonlinear shallow water equations, which involve three coupled PDEs. We release our artificial intelligence surrogates and scientific software to produce initial data and boundary conditions to study a broad range of physically motivated scenarios. We provide the source code, an interactive website to visualize the predictions of our PINOs, and a tutorial for their use at the Data and Learning Hub for Science.

Technology and managerial performance of farm operators by age in Ghana

Technology and managerial performance of farm operators by age in Ghana

Mental workload assessments of aerial photography missions performed by novice unmanned aerial vehicle operators.

Mental workload is one of the important variables in understanding human performance in drone operation. To test the effects of gender, age group, flight route, and altitude on the flight performance and mental workload of the novice drone operators. Ten male and ten female participants without prior drone operating experience joined. They were split into two age groups. After attending a training, the participants operated a drone to perform photo taking missions under flight route and altitude conditions. The weighted NASA Task Load Index (TLX), Modified Cooper-Harper (MCH) scale, heart rate, and interbeat interval were measured to assess the mental workload of the participants. Flight time to complete the mission was adopted to indicate flight performance. The effect of age group was significant (p < 0.05) on flight time, weighted TLX score, and MCH score. Flight route and altitude were not significant on the two subjective ratings and two cardiac measures. The flight performance of younger participants was significantly better than that of their older counterpart. The effects of both the flight route and altitude on the perceived mental workload of the drone operators were insignificant. Both the weighted NASA TLX and MCH scales were appropriate in measuring the mental workload of the novice drone operators.

Modeling the optimal sizing problem of the biogas-based electrical generator in a livestock farm considering a gas storage tank and the anaerobic digester process under the uncertainty of cow dung

Cow dung is the main source of producing methane, as one of the greenhouse gases. To decrease this pollution, cow dung can be transformed into biogas, and consequently, the produced biogas can be used as a fuel for electrical generators. This problem is addressed in the so far researches without proposing the appropriate mathematical models. In this paper, the optimal sizing problem of the biogas-based generators (BGs) is formulated for a livestock farm by modeling the anaerobic digester process and the gas storage tank. To protect the system operator's decisions against the uncertainty of the cow dung, the proposed model is reformulated as a risk-based model using the information gap decision theory. The obtained capacity of the BG and the storage tank for a livestock farm with 2000 cows are 433.24 kW and 628.056 m3, respectively. Also, the designed system prevents releasing approximately 522-ton pollution. The results show the appropriate performance of the system operator to use the produced gas to be directly sent to the BG and/or to charge the storage tank. The results show that with increasing the risk-aversion parameter, the capacity of the BG and the storage tank decrease which leads to decreasing the total profit.

Intelligent neuro-computing to analyze the awareness programs of fractional epidemic system outbreaks

A new model of fractional for the influence of consciousness initiatives on epidemic incidences (ICIEI) is described in this research. The fractional Caputo operator (FCO) is used to expand this system to the typical awareness program model (ICIEI-FCO). Analytically and quantitatively, the properties of the new system are investigated. This article presents an artificial neural network-based fractional model (ANN-FM) for estimating the efficacy of epidemic outbreak awareness initiatives. The reliability, resolution, durability, and robustness of the proposed model are examined using the suggested ANN-BLM approach for five distinct situations. The population of the model is created by utilizing the power of the explicit Runge-Kutta numerical approach, and it is represented by a system of nonlinear ordinary differential equations. The Grunwald–Letnikov (GL) technique is used to numerically evaluate the modeled differential system of the physical issue for multiple scenarios to anticipate numerical data, and these results are utilized as a reference dataset of the networks. The data needed to answer the fractional model's questions on how awareness campaigns affect epidemic outbreaks is broken down as follows: training takes up 80% of the time, testing 10%, and authorization 10%. There are two aspects to the strategy: First, the fundamental ANN-BLM operator performances are displayed. In the meantime, the ANN-BLM execution approach is used to address the fractional-order problem. The GL-mathematical system was used to compare the numerical findings. The TSs, regression, correlation, EHs, and MSE are used to demonstrate the dependability and competency of ANN-BLM as well as their numerical performances in the presented numerical findings, which were developed using ANN-BLM to lower the MSE. The Levenberg-Marquart training (LMT) algorithm is used to optimize network results in terms of mean-square errors, training states graphs, errors of the histogram, recession analysis, auto-correlation and time series responses, which demonstrates the system's accurate and proficient trend acknowledgment. The mean-square error fitness analysis in the ranges of 10-6 to 10-11 validates the authenticity and effectiveness of the designed solver. The suggested AI-based study is expected to pave the way for new, creative approaches to fractional order modeling and analysis of naturally unpredictable dynamic systems.