Risk Of Human Error Research Articles

Sistem Informasi Monitoring Pengiriman Kargo DAMRI Logistik Lintas Jawa - Sumatera Berbasis Web

Cargo delivery is an integral part of logistics operations, where monitoring plays a crucial role. Manual approaches in monitoring increase the risk of human error and prolong the process duration. Major issues frequently encountered in cargo delivery, such as damages, losses, and bus travel delays, can be addressed through the implementation of monitoring systems. Damages and losses during cargo transportation, coupled with significant bus travel delays, stem from finalizing cargo delivery data after passenger check-in. This research aims to identify the root causes of these three issues and offer solution recommendations to enhance the reliability of the cargo delivery process. The research methodology includes analysis of historical delivery data, interviews with operational crews, and the development of a web-based information system for more structured monitoring processes. Research findings indicate that discrepancies between delivery schedules and inconsistent monitoring are primary factors contributing to damages, losses, and delays. Recommended solutions involve implementing real-time tracking systems and providing training to enhance awareness of swift and accurate delivery procedures. In conclusion, understanding the challenges encountered in cargo delivery can contribute to developing better service solutions, thereby improving overall logistics efficiency within DAMRI logistics.

The Effects of an Order-Assist Mobile Application on Pediatric Anesthesia Safety: An Observational Study.

Pediatric anesthesia requires the rapid creation, communication, and execution of anesthesia orders, and there is a risk of human error. The authors developed an order-assisted mobile application (app) to reduce human error during pediatric anesthesia preparation. The authors conducted an observational study that compared the effects of the application by comparing anesthesiologists' errors, nurses' errors, nurses leaving the operating room, and delays in surgery, between the Conventional group (n = 101) and the App group (n = 101). The app was associated with reduced human error by anesthesiologists and nurses, and it lowered the frequency and duration of nurses leaving the operating room during anesthesia. In addition, the authors surveyed anesthesia nurses regarding the effectiveness of the app. The nurses confirmed that the app was convenient and reduced human error. This study revealed that the order-assisted mobile app developed by a pediatric anesthesiologist could reduce human errors by anesthesiologists and nurses during pediatric anesthesia preparation.

The Relations Between Digitalization and Employee Satisfaction in Aviation

Digital transformation and Industry 4.0 concepts constitute an important interaction in professional life. Recently academic studies have been focusing on this area. The aim of this research is to examine the positive or negative effects of digital transformation in the aviation sector on employee satisfaction with a survey. The sample of the study consists of 245 employees in the aviation sector. Digitalization can have both positive and negative effects on employee satisfaction in aviation. On the positive side, digital technologies can improve the efficiency and safety of operations, leading to increased job satisfaction and a sense of accomplishment among employees. Automation can reduce the workload of employees, freeing up their time for more rewarding tasks, and reducing the risk of human error. Additionally, digital technologies can provide employees with access to real-time data and information, enabling them to make better decisions and be more productive. It has been demonstrated that digital transformation in the aviation industry has a positive relationship with employee satisfaction. It is recommended that the study be implemented in other sectors and then a holistic approach should be developed.

Applications of automated Petrel workflows in 3D reservoir geologic modelling – A case study

AbstractMature fields have been playing a significant role in the oil and gas realm recently, and redevelopment and optimization efforts are being made globally to prolong the lifetime of these resources. The aim of this study is to showcase the benefits of hydrocarbon reservoir modelling, with a special focus on various aspects of Petrel workflows.This article is a direct continuation of Nemes et al. (2021), which described the Phase 1 geomodel of the same field described in this study. The Phase 2 geomodel – the scope of the current article – is based on a significantly more complete, more detailed, and fundamentally rebuilt dataset compared to Phase 1. The seismic and petrophysical interpretations were updated, and additional data sources were incorporated into the analysis.The geomodel was created in Schlumberger's Petrel software, and during the building of it, a comprehensive 800-plus-step, full-cycle, automated workflow was outlined. The created workflow makes the model update faster by a minimum of five times, makes it more transparent and decreases the risk of human error.The created workflow describes the entire geomodelling process from data loading, via surface adjustments, structural modelling, and property modelling, to a closing of the loop with volumetric calculation. The whole workflow can be rerun easily, and beside the updates made to the geomodel, a full range of quality-check supporting calculations and visualizations were created in order to provide the user with full control.The geomodel showcased here is a key building block of the ongoing and planned development and redevelopment activities in the field, serves as a tool for well and workover planning, water injection system adjustments and a direct input to dynamic simulation, and also provides direct inputs to the documentation of an updated field development plan.

Development of a Multilingual Web-Based Food Frequency Questionnaire for Adults in Switzerland.

Dietary assessment is a major challenge in epidemiological research and is associated with a high time and financial burden. Automated food frequency questionnaires (FFQs) have the potential to rapidly collect dietary intake data in large studies while reducing human error risk during data processing. We developed a semiquantitative, multilingual, electronic FFQ for real-time dietary intake assessment in the Swiss adult population, called "Swiss eFFQ". The iterative development process involved stages of content identification, construction, pretesting, translation, and adaptation of the FFQ. Using 24 h dietary recalls from 2085 participants aged 18-75 years from a nationally representative survey, we conducted a stepwise regression analysis to identify foods contributing to >90% of the variance in intakes of energy and six nutrients. All 118 foods identified in the overall cohort or in any of the Swiss linguistic regions were selected and standardized to define the comprehensive 83-item food list, covering >90% of the intake of key nutrients in the entire study population. Once validated, the Swiss eFFQ can be used to classify individuals based on their habitual diets. The methodology described in this paper enhances the transparency of the Swiss eFFQ and may help researchers to develop multilingual dietary assessment tools for other populations.

Success rate of rapid DNA technology in kinship analysis for forensic purpose.

Reducing the time required for DNA analysis in forensic genetics can yield significant benefits, both in determining genealogical relationships for legal proceedings and in criminal cases. Swift identification of individuals plays a pivotal role in solving crimes and apprehending perpetrators. Additionally, in situations like mass disasters, prompt victim identification holds utmost importance. The Rapid DNA technology, introduced in 2012 to expedite DNA analysis, has evolved to streamline the process into a single step. This advancement not only minimizes the risk of human error and contamination, but also boasts a remarkable time advantage, delivering results in as little as 90min. In this study, DNA profiles of 30 families (consisting of mothers, fathers, and children) were analyzed using the RapidHITTM ID System. The system automatically calculated maternity-paternity probabilities to assess the suitability of Rapid DNA technology for kinship analysis. For validation, DNA profiles were also generated using the 3500 GA method. The study revealed that 9 out of 30 families exhibited discrepancies in DNA profiling, leading to inaccuracies in automatic kinship analysis. Consequently, while the method offers rapid and user-friendly advantages for forensic sciences, the software underlying the system requires re-evaluation. Issues such as maternal-paternal exclusion in kinship analyses, arising from challenges like un-called alleles, warrant further attention.

Tdsa: An R package for time‐dependent sensitivity analysis

Abstract Sensitivity analysis of ecological and epidemiological models is often used to identify the best targets of opportunity for control or management, for example, which subpopulations should be prioritised for vaccination or protection. However, effectiveness of a management action depends not just on which system component is targeted but also on when an action is taken. Traditional methods of model sensitivity analysis apply to time‐invariant parameter perturbations, which limits their ability to address questions about the timing of management interventions. A semianalytic method for performing time‐dependent sensitivity analysis (TDSA) has been recently introduced to address this need (Ng et al., in press). However, some of the steps typically require substantial time and effort. We have developed an R package, tdsa, that automates all steps required for TDSA by using numerical rather than analytic differentiation of model terms to evaluate the sensitivity equations. By avoiding analytic differentiation, the package substantially reduces user effort and risk of human error and increases the generality of TDSA by making it possible to analyse nonparametric or equation‐free models. The results of TDSA may be sensitive to assumptions of the system model. By reducing the effort needed to perform TDSA, the tdsa package facilitates comparison of results under different model assumptions, hence allowing for more robust conclusions. We illustrate this using an example involving the transmission of Morogoro virus in the Natal multimammate mouse, under different assumptions about how the contact rate depends on mouse population density. By automating most of the mathematical and programming steps of the semianalytical approach, the tdsa package makes TDSA accessible to a wide range of users with varying quantitative expertise.

B-018 Compact all in one, Fully Automated Molecular Diagnostic System “ExiStation™ FA 96” in 100 minutes

Abstract Background Traditional PCR method’s instruments are discrete the processes of sample collection, nucleic acid(NA) extraction, and NA amplification. As human labor is required between the instruments, there is a risk of human error, infection and contamination. Biosafety facilities such as biosafety cabinet, level D protective clothing, and negative pressure facilities are required for ensuring safe testing. To improve the testing environment, the molecular diagnostic market has developed and started using automated instruments, though, which take up larger space and take longer time to test. As a result, it is necessary to develop instrument that takes up less space and reduces testing time to a minimum, so, space and time can be saved. Methods In this study, we developed an automated systematic genetic diagnostic instrument. In order to carry out the entire process within one integrated instrument, automatic mechanism of de-capping and re-capping of sample tube, sample aspiration, and sample transfer were developed. The system also includes integrated key steps of NA extraction and amplification (qPCR). We developed a primer/probe design technology that can accurately detect viruses with high accuracy in performing PCR tests using extracted genes, as well as precision temperature control algorithm and optimized real-time nucleic acid amplification using reverse transcription enzymes and gene amplification enzymes. In order to carry out all these functions within one device, we maximized user convenience by developing software for instrument operation, result analysis, and a Graphic User Interface for automation Results ExiStation™ FA 96 is largely composed of sample processing, gene extraction, and gene amplification (qPCR). (Photo to be in poster) Sample Processing automatically opens a cap of sample tube, then transfer a required quantity of sample to cartridge. While opening the cap, the barcode attached to sample tube is automatically identified and the information required for a test is entered. After sampling, the cap is automatically re-capped and placed back in the sample tube rack. When collecting samples, it measures the sample level in the tube and indirectly check the viscosity by detecting the pressure inside the tip, minimizing aspiration errors. The Extraction step performs the function of extracting genes. It consists of three modules of 32 wells, and the number of samples can even be adjusted in eight units, so the instrument can be operated flexibly upon the number of samples requested. Then, the qPCR step follows. The ExiStation™ FA 96 setup allows simultaneous testing of up to 20 targets for 94 samples in approximately 100 min. The instrument is equipped with a HEPA filter and UV lamp to maintain clean, and since the sample tubes are inserted directly on the instrument, it is no of necessity or save the costs such as of negative pressure rooms, biosafety cabinet, and level D protective clothing. Conclusion ExiStation™ FA 96 is a fully automated molecular diagnostic test instrument that can quickly and accurately process large amounts of samples, while greatly reducing user risks and shortening molecular diagnostic test time.

Enhancing crack detection with convolutional neural networks and oriented non-maximal suppression

Detecting of cracks and damages, especially in multi storied buildings is a crucial aspect of infrastructure and building maintenance, as it ensures safety and reliability. An enhanced framework for the crack detection is proposed to identify the fine cracks which are present at greater heights and not captured to the human vision from the ground. The cracks are identified and classified by the deep convolutional neural network model. The Oriented Non-Maximal Suppression module reduces the false positives to improve the classification accuracy and reliability. The proposed method O-CNN(CNN with ONMS)can be used in real-world for the infrastructure inspection and potential applications in civil engineering construction. The ability to input different types of data, including images and videos, makes the proposed system user-friendly and easy to use. Furthermore the system reduces the risk of human error and prevents the huge damages caused to the building. Also, it prevents the major loss which may be caused to the lives. Overall, the proposed system contributes to the field of deep learning and computer vision by providing an effective and better solution for crack detection in real-world scenarios.

Contemporary Role and Applications of Artificial Intelligence in Dentistry.

Artificial Intelligence (AI) technologies play a significant role and significantly impact various sectors, including healthcare, engineering, sciences, and smart cities. AI has the potential to improve the quality of patient care and treatment outcomes while minimizing the risk of human error. Artificial Intelligence (AI) is transforming the dental industry, just like it is revolutionizing other sectors. It is used in dentistry to diagnose dental diseases and provide treatment recommendations. Dental professionals are increasingly relying on AI technology to assist in diagnosis, clinical decision-making, treatment planning, and prognosis prediction across ten dental specialties. One of the most significant advantages of AI in dentistry is its ability to analyze vast amounts of data quickly and accurately, providing dental professionals with valuable insights to enhance their decision-making processes. The purpose of this paper is to identify the advancement of artificial intelligence algorithms that have been frequently used in dentistry and assess how well they perform in terms of diagnosis, clinical decision-making, treatment, and prognosis prediction in ten dental specialties; dental public health, endodontics, oral and maxillofacial surgery, oral medicine and pathology, oral & maxillofacial radiology, orthodontics and dentofacial orthopedics, pediatric dentistry, periodontics, prosthodontics, and digital dentistry in general. We will also show the pros and cons of using AI in all dental specialties in different ways. Finally, we will present the limitations of using AI in dentistry, which made it incapable of replacing dental personnel, and dentists, who should consider AI a complimentary benefit and not a threat.

GUI-Based Data Visualisation Tool for Option C IPMVP using Jupyter Notebook

IPMVP is an acronym for International Performance Measurement and Verification Protocol, used for calculating energy savings by taking some external factors into account to give an equal base for a linear comparison between two periods. However, determining the significant independent variable for routine adjustment causes inconvenient repeated action of regression analysis modeling, which leads to the exhaustive calculation and human error risk. These exasperating actions led to the creation of this project, which aims to provide a user-friendly, insightful, and accurate energy-saving calculation program tool. Thus, the main objective of this project is to develop an energy-saving computational tool for option C IPMVP using web-based data analytic tools to improve efficiency and accuracy in each project. This paper used a web-based mathematical computation, Jupyter Notebook, to determine the significant independent variable for routine adjustment of the adjusted baseline. The program tool was validated using V-model software verification and validation methodology based on IEEE 1012 standards. The study can be concluded that the exhaustive calculation to determine the energy saving based on IPMVP standards can be significantly reduced with precision values at 50%, 80%, 90%, and 95% confidence levels using the proposed program which is equipped with insightful graphs and a user-friendly interface.

Autonomous Vehicle via Raspberry PI

The Project pursuits to construct a monocular imaginative and prescient independent vehicle prototype the use of Raspberry Pi as a processing chip. A Raspberry Pi camera is employed to provide crucial real-time information from the physical world to the vehicle. This enables the vehicle to efficiently and intelligently reach its designated destination, thereby mitigating the risk of human errors. By integrating advanced algorithms such as lane detection and object detection, this technology holds the potential to bring substantial benefits to the automotive industry. It has the capacity to reduce the cognitive load on drivers, alleviating the mental strain while also reducing accidents resulting from reckless or non-compliant driving behaviors

Four-DOF Robotic Arm System for Goods Transport

Paper presents the design, development, and implementation of a Four-Degree-of-Freedom (Four-DOF) Robotic Arm System tailored for the purpose of efficient goods transport. The increasing demand for automation in logistics and industrial sectors has prompted the need for versatile robotic solutions capable of manipulating objects in various environments. The proposed robotic arm system addresses this need by offering four independent degrees of freedom, enabling it to perform intricate tasks involved in goods transport. The system's mechanical design incorporates four joints, each contributing to a specific movement: lifting, lowering, rotating, and extending. These movements provide the necessary flexibility to manoeuvre in complex spaces, making it suitable for applications within warehouses, factories, and distribution canters. The design process involved careful consideration of structural integrity, weight distribution, and motion optimization to ensure safe and precise operation. In conjunction with the mechanical design, the robotic arm is equipped with advanced sensing and control mechanisms. Sensor arrays, including proximity sensors and cameras, enable the arm to perceive its environment and detect objects, ensuring accurate object detection and collision avoidance. The control system, driven by a combination of microcontrollers and software algorithms, orchestrates the movement of each joint to achieve seamless coordination and efficient goods transport. Real-world implementation of the Four-DOF Robotic Arm System showcases its ability to streamline goods transport operations. By automating tasks that previously required manual intervention, the system enhances operational efficiency, reduces labour costs, and minimizes the risk of human error. Furthermore, its adaptability to diverse environments underscores its potential to revolutionize the logistics industry.

Virtual-Simulation-Based Multi-Objective Optimization of an Assembly Station in a Battery Production Factory

The planning and design process of manufacturing factory layouts is commonly performed using digital tools, enabling engineers to define and test proposals in virtual environments before implementing them physically. However, this approach often relies on the experience of the engineers involved and input from various cross-disciplinary functions, leading to a time-consuming and subjective process with a high risk of human error. To address these challenges, new tools and methods are needed. The Industry 5.0 initiative aims to further automate and assist human tasks, reinforcing the human-centric perspective when making decisions that influence production environments and working conditions. This includes improving the layout planning process by making it more objective, efficient, and capable of considering multiple objectives simultaneously. This research presents a demonstrator solution for layout planning using digital support, incorporating a virtual multi-objective optimization approach to consider safety regulations, area boundaries, workers’ well-being, and walking distance. The demonstrator provides a cross-disciplinary and transparent approach to layout planning for an assembly station in the context of battery production. The demonstrator solution illustrates how layout planning can become a cross-disciplinary and transparent activity while being automated to a higher degree, providing results that support decision-making and balance cross-disciplinary requirements.

KLASIFIKASI KONDISI BAN KENDARAAN MENGGUNAKAN ARSITEKTUR VGG16

Tyres are the main component that a vehicle needs to work with reducing vibration due to uneven road surfaces, protecting the wheels from wear to provide stability between the vehicle and the ground helping to improve acceleration to facilitate travel while driving. Wear ensures stability between the vehicle and the ground helps improve acceleration for easy movement and driving. Caused including components that are often used, tires can experience damage such as the appearance of cracks in the tires. Cracks in tires can be triggered by factors such as age or the cause of the road that has been exceeded. Detection of tire cracks at this time is still carried out conventionally, where users see directly the state of the tire whether the tire is in good condition or cracked. Conventional methods are important because they maintain tire quality and rider safety. The Conventional Method certainly has weaknesses because vehicle users must have good vision and the ability to distinguish normal tires or cracked tires, but this method is considered less effective because it still uses human labor, causing the risk of human error (human negligence) which can hinder the process of identifying tire cracks. Based on this problem, this study will develop a deep learning model that can classify cracked tires using the VGG16 architecture. In this study, the model was created using 8 scenarios by changing the value of epochs, to get the best parameters in making the model. The results of the 8 scenarios carried out in this study are the best scenario obtained in scenarios 1,3,4 which get 98% accuracy in model testing

Increasing Quality and Efficiency of the Radiotherapy Treatment Planning Process by Constructing and Implementing a Workflow-Monitoring Application.

To demonstrate how the efficiency of the treatment planning processes of a university radiation oncology department (2,500 new patients/year) could be improved by constructing and implementing a workflow-monitoring application. A web-based application was developed in house, which enhanced the process management tools of the clinic's oncology information system. The application calculates the days left for the next task in the treatment planning process and visualizes the information on a browser-based whiteboard. Workflow monitoring considers tumor types (breast, prostate, lung, etc) and treatment techniques and is backward planned from the planned start of treatment. The effect of introducing this application was analyzed over four phases: (1) baseline data without the workflow-monitoring application, (2) after introducing workflow visualization via a browser-based whiteboard, (3) after upgrading the whiteboard and introducing backend rules, and (4) after updating these rules on the basis of data from the previous phase. Implementing the workflow-monitoring application and the introduced measures significantly reduced delays and, consequently, stress and a negative working atmosphere in the treatment planning process. Most notably, the amount of last-minute physics checks (on the day of the treatment start) could be reduced by 50%. The study showed what measures can help organize and prioritize the treatment planning workflow. The increased efficiency is believed to improve the quality and reduce the risk of human error.

Pengembangan Sistem Informasi Laboratorium Untuk Meningkatkan Efisiensi dan Akurasi

The development of laboratory information systems has become a primary focus in efforts to enhance efficiency and accuracy in laboratory operations. This article discusses the need for system development and evaluates the existing systems, which may be inefficient or inaccurate. Through analysis, it is found that repetitive manual tasks, such as data entry, inventory tracking, and report generation, result in time wastage and increased human errors. Furthermore, fragmented and non-integrated systems in laboratories impede smooth information flow, reduce operational efficiency, and increase the risk of errors. Reporting is also limited and time-consuming. Based on this background, this journal aims to develop an efficient and accurate laboratory information system. The development methodology includes user requirement gathering, needs analysis, system design, development, and implementation. In the development process, tools and technologies such as RFID are employed to improve operational efficiency and data accuracy. Integration among system components is also enhanced to facilitate smoother information flow. The evaluation results demonstrate significant benefits of the laboratory information system development. Manual tasks can be reduced, operational efficiency is enhanced, and the risk of human errors is decreased. Tracking and monitoring become easier, while reporting becomes faster and more accurate. With the adoption of an integrated laboratory information system, laboratories can improve their efficiency and accuracy in operations. In conclusion, the development of a laboratory information system is a crucial step in enhancing efficiency and accuracy. Through appropriate approaches and the implementation of information technology, laboratories can reduce reliance on manual work, improve data accuracy, and facilitate smooth information flow. The implementation of an efficient laboratory information system will have a positive impact on productivity and service quality in laboratories.

Performance Shaping Factor Dependency Assessment Based on International Civil Aviation Accident Report Data

The quantitative assessment of human error risk in computerized aircraft cockpit need a comprehensive understanding of performance shaping factor (PSF) effects on interaction tasks and the dependency between PSFs. This study employed stepwise logistic regression analysis and mediating effect analysis to explore PSF dependencies based on 872 air accident report data. Among the 14 PSFs of the established PSF taxonomy, five PSFs have significant influences on crew error. Further dependency analysis shows that a significant mediating effect exists in 16 PSF combinations, however, only 8 combinations should be considered in the actual human reliability analysis (HRA) process (indirect effect exceeds 20%). The study proposes an empirical method for identifying and assessing PSF dependencies. The results can be used to develop new or modify existing HRA methods according to the civil aviation context, as well as provide guidance to human error risk prevention and mitigation from broader perspectives in addition to interface facet.

Web-Based Price Estimation and Data Entry Application in Pawnshops

This study introduces a web-based application that has been developed to offer price estimation services and facilitate data input within pawnshop establishments. The primary objective of this program is to optimize the effectiveness of collateral evaluation procedures and streamline data entry for pawnshop personnel. The application's development methodology integrates web technology and advanced data management systems to enhance the accuracy and efficiency of price evaluation. The application's users will be able to access it via the web-based platform, where they may input pertinent collateral information. Subsequently, they will receive prompt and precise price estimates. The experimental findings suggest that this application can enhance productivity and mitigate the risk of human errors in the pricing calculation procedure. In addition, the web interface's user-friendly design enhances the application's ease of adoption among pawnshop officers. This application is anticipated to streamline customer care procedures and improve overall customer satisfaction within the pawnshop establishment.

Telerobotic Transcatheter Delivery System for Mitral Valve Implant.

Mitral regurgitation (MR) is the most common type of valvular heart disease, affecting over 2% of the world population, and the gold-standard treatment is surgical mitral valve repair/replacement. Compared to open-heart surgeries, minimally invasive surgeries (MIS) using transcatheter approaches have become popular because of their notable benefits such as less postoperative pain, shorter hospital stay, and faster recovery time. However, commercially available catheters are manually actuated, causing over-exposure of clinical staff to radiation and increased risk of human error during medical interventions. To tackle this problem, in this letter, we propose a telerobotic transcatheter delivery system, which consists of a robotic catheter (5.7 mm OD), a reinforced guide tube (1.11m length), and an actuation system. We present the robotic system design, fabrication of key components, and static model of reinforced quadlumen tube. The robot interface design enables the user to intuitively control the robot. We demonstrate the effectiveness of the telerobotic transcatheter delivery system and reinforced quadlumen tube in a realistic human cardiovascular phantom for preclinical evaluation.