Automatic Solution Research Articles

A qualitative, multi-centre approach to the current state of digitalisation and automation of surveillance in infection prevention and control in German hospitals

BackgroundHealthcare associated infections (HAI) pose a major threat to healthcare systems resulting in an increased burden of disease. Surveillance plays a key role in rapidly identifying these infections and preventing further transmissions. Alas, in German hospitals, the majority of surveillance efforts have been heavily relying on labour intensive processes like manual chart review. In order to be able to identify further starting points for future digital tools and interventions to aid the surveillance of HAI we aimed to gain an understanding of the current state of digitalisation in the context of the general surveillance organisation in German clinics across all care-levels. The end user perspective of infection prevention and control (IPC) professionals was chosen to identify digital interventions that have the biggest impact on the daily surveillance work routines of IPC professionals. Perceived impediments in the advancement of surveillance digitalisation should be explored.MethodsFollowing the development of an interview guideline, eight IPC professionals from seven German hospitals of different care levels were questioned in semi- structured interviews between December 2022 and January 2023. These included questions about general surveillance organisation, access to digital data sources, software to aid the surveillance process as well as current issues in the surveillance process and implementation of software systems. Subsequently, after full transcription, the interview sections were categorized in code categories (first deductive then inductive coding) and analysed qualitatively.ResultsResults were characterised by high heterogeneity in terms of general surveillance organisation and access to digital data sources. Software configuration of hospital and laboratory information systems (HIS/LIS) as well as patient data management systems (PDMS) varied not only between hospitals of different care levels but also between hospitals of the same care level. Outside research projects, neither fully automatic software nor solutions utilising artificial intelligence have currently been implemented in clinical routine in any of the hospitals.ConclusionsAccess to digital data sources and software is increasingly available to aid surveillance of HAI. Nevertheless, surveillance processes in hospitals analysed in this study still heavily rely on manual processes. In the analysed hospitals, there is an implementation and funding gap of (semi-) automatic surveillance solutions in clinical practice, especially in healthcare facilities of lower care levels.

Improving processing efficiency through workflow process reengineering, simulation and value stream mapping: a case study of business process reengineering

PurposeBusiness Process Reengineering (BPR) eliminates non-value-added (NVA) and essential non-value-added (ENVA) waste through radical process redesign to improve organizational operations. Comprehensive research integrating BPR tools is needed to understand their benefits for manufacturing firms. This research presents an integrated BPR-simulation framework tailored to the manufacturing sector to maximize process improvements and operational excellence.Design/methodology/approachThe BPR design methodology adopts a systematic, multi-stage approach. The first phase involves identifying a specific improvement process aligned with BPR's core objectives. This phase analyses and redesigns workflows to optimize task sequences, roles, and stakeholder interactions while eliminating redundancies and inefficiencies via Workflow Process Reengineering. Visual process mapping tools, including VSM and simulation, pinpoint areas of waste, delay, and potential enhancement. The second phase follows the workflow analysis and aims to improve efficiency and effectiveness by redefining roles, rearranging tasks, and integrating automation and technology solutions. The redesigned process undergoes evaluation against key performance indicators to ensure measurable improvements are achieved. The final phase validates the proposed changes through simulation models, assesses the impact on key performance metrics, and establishes the necessary infrastructure for successful implementation. The proposed model is empirically validated through a case study of a leading apparel company in Vietnam, confirming its effectiveness.FindingsThe findings reveal that NVA activities are being eliminated, and ENVA activities in key departments are significantly reduced. This yielded a substantial improvement, reducing 25 out of 186 combined ENVA and NVA operations in the sewing facility, involving a decrease of 15 ENVA operations and the removal of 10 NVA operations. Consequently, this led to an 8.5% reduction in the proportion of ENVA operations, accompanied by a complete 100% elimination of NVA activities.Research limitations/implicationsThe single case study limits generalizability; thus, expanded implementation across diverse manufacturing sub-sectors is required to establish validity and broader applicability of the integrated framework.Originality/valueThe experimental results highlight the proposed model's effectiveness in optimizing resource utilization and its practical implementation potential. This structured BPR methodology enables organizations to validate, evaluate, and establish proposed process changes to enhance operational performance and productivity.

Automating pouring process in precision casting

Standing at the intersection of industry 4.0, most traditional manufacturers, especially those produce non-standard parts, are still facing the challenges from multiple aspects on the implementation of automations, that indicates a significant and necessary step towards their upgrading. The potential performance improvement that could be brought by the automation may be continuingly squeezed as the increasement of complexity when dealing with the various targets. This article is extended by a general concept of implementing automation on the metal pouring process of precision casting, aims to explore an efficient and robust automation solution with the integration of human-robots collaboration and the adoption of computer science techniques. The implementation emphasizes the reduction of unnecessary complexities from each working step, the applied algorithms, such as Object Bounding, Greedy Strategy and Last-In-First-Out, have been correspondingly tailored based on the characteristics of its engaged working steps and illustrated by the flowcharts. Both the adaptability and practicability of the automation are expected to be enhanced with the principles of constructing easy-interactive frames, allowing a certain degree of human intervention, and proactively utilizing the matured algorithms.

SSO-CCNN: A Correlation-Based Optimized Deep CNN for Brain Tumor Classification Using Sampled PGGAN

Recently, new advancements in technologies have promoted the classification of brain tumors at the early stages to reduce mortality and disease severity. Hence, there is a need for an automatic classification model to automatically segment and classify the tumor regions, which supports researchers and medical practitioners without the need for any expert knowledge. Thus, this research proposes a novel framework called the scatter sharp optimization-based correlation-driven deep CNN model (SSO-CCNN) for classifying brain tumors. The implication of this research is based on the growth of the optimized correlation-enabled deep model, which classifies the tumors using the optimized segments acquired through the developed sampled progressively growing generative adversarial networks (sampled PGGANs). The hyperparameter training is initiated through the designed SSO optimization that is developed by combining the features of the global and local searching phase of flower pollination optimization as well as the adaptive automatic solution convergence of sunflower optimization for precise consequences. The recorded accuracy, sensitivity, and specificity of the SSO-CCNN classification scheme are 97.41%, 97.89%, and 96.93%, respectively, using the brain tumor dataset. In addition, the execution latency was found to be 1.6 s. Thus, the proposed framework can be beneficial to medical experts in tracking and assessing symptoms of brain tumors reliably.

Automatic Search for a Solution in the Information Management System to Monitor the State of the Production Environment

The article presents a method for monitoring and managing the state of a production environment based on an information management system with an automatic solution search module using a hybrid neural network. The object of the study is the state of the production environment. Monitoring and management are carried out on the basis of predicting changes within the environment state, taking into account the interdependence of the measured parameters. In addition to the task of regulating the value of the controlled parameter, there is an additional task of automatically compiling a control program depending on external parametersprovided by this method of monitoring and control. To do this, an automatic solution search device with software based on neural networks is installed in a closed control loop. This device analyzes many parameters of the production environment and supplies the control device with the required value of the controlled parameter. To analyze dependencies between parameters and predict the state of the production environment, a neural network is used, the weights and biases of which are calculated using the harmonic search algorithm; training was carried out with the gradient descent algorithm. To assess the accuracy and stability of the automatic search for solutions in the information management system for monitoring the state of the production environment, an experiment was conducted, the results of which made it possible to obtain the parameters of the hybrid model. Further study of the obtained parameters showed that its use in the control loop of the information system for monitoring the state of the production environment makes it possible to provide a high-quality and stable forecast of changes in the state of the production environment. The proposed method made it possible to obtain stable operation of the information and control system for monitoring the state of the production environment and maintaining controlled parameters within the acceptable range.

Robust deep learning based shrimp counting in an industrial farm setting

Shrimp production is one of the fastest growing sectors in the aquaculture industry. Despite extensive research in recent years, stocking densities in shrimp systems still depend on manual sampling which is neither time nor cost efficient and additionally challenges shrimp welfare. This paper compares the performance of automatic shrimp counting solutions for commercial Recirculating Aquaculture System (RAS) based farming systems, using eight Deep Learning based methods. The entire dataset includes 1379 images of shrimps in RAS farming tanks, taken at a distance using an iPhone 11 mini. These were manually annotated, with bounding boxes for every clearly visible shrimp. The dataset was partitioned into training (60 %, 828 samples), validation (20 %, 276 samples) and test (20 %, 275 samples) splits for purposes of training and evaluating the models. The present work demonstrates that state-of-the-art object detection models outperform manual counting and achieve high performance across the entire production range and at various circumstances known to be challenging for object detection (dim light, overlapping and small animals, various acquisition devices and image resolutions and camera distance to object). Highest counting performance was obtained with models based on YOLOv5m6 and Faster R–CNN (as opposed to neural network autoencoder architecture to estimate a density map). The best model generalizes well on an independent test set and even shows promising performance when tested with different taxa. The model performs best at densities below 200 shrimps per image with an overall error of 5.97 %. It is assumed that this performance can be improved by increasing the dataset size, especially with images at high shrimp stocking density, and it is strongly believed that a performance below the 5 % error threshold is close to being achieved, which will allow for deployment of the model in an industrial setting.

O-239 Automation in the embryology laboratory

Abstract Most processes in an IVF laboratory are performed manually by technicians and embryologists, such as tracking and prepare dishes, pipetting of gametes and embryos from one dish to another, loading freezing straws, and operating micromanipulators for ICSI or biopsy. In most centres doctors manage hormonal stimulation based on their own learned experience or at most of their team. This results in significant variability in pregnancy outcomes from centre to centre and even operator to operator. For example, centres can be compared based on egg donor cycles, which are all young and fertile patients selected in similar ways in most centres. But even In egg donor cycles, embryo aneuploidy rates vary 20% to 60% (1) while pregnancy rates fluctuate from 10% to 80% depending on the clinic (2). Furthermore, although large amounts of data are generated from >2M procedures of IVF a year worldwide, most of it is siloed in each centre in a myriad of EMRs or worse, spreadsheets, impossible to query globally. The lack of a massive dataset to query and the absence of a constant baseline in pregnancy outcomes makes introducing new innovations harder to determine if they work or not. Automation should allow constant results with similar results clinic to clinic, especially desirable for networks to offer a consistent service across centres. By reducing the reliance on highly skilled manual labour, which is facing a global scarcity, automation has the potential to offer more people affordable treatment. Moreover, the utilization of automation generates vast amounts of data, with machines sharing information to inform medical decisions based on millions of standardized treatments, surpassing the insights of individual physicians. This data-driven approach also establishes a stable foundation for innovation by minimizing human variables. Advancements in Affordable Robotics, coupled with high precision and miniaturization, alongside the evolution of software, are transforming robots into versatile tools akin to smartphones. This transformation encompasses various fields, including microfluidics, miniaturized imaging systems, Artificial Intelligence, and the Internet of Things (IoT). Some automation has already occurred, such time-lapse incubators with imaging allowing for AI classification of embryos, or witness systems. Although historical lack of government funding and current trends of consolidation of clinics into networks, may slow innovation, numerous startup companies are actively developing automation solutions for IVF laboratory operations and patient management. These solutions range from tools for remote patient monitoring to automating processes such as egg denudation, sperm analysis, oocyte and embryo vitrification and warming, as well as procedures like automated ICSI and in vitro fertilization. Furthermore, artificial intelligence is being harnessed for tasks like embryo selection and hormonal stimulation, with groundbreaking concepts like artificial uterus also under exploration. Nonetheless, the objective of automation should be to enhance the abilities of the embryologist, not to reduce their need. Once the process has been standardized, it should pave the way for the creation of novel and more interesting tasks. This empowers the embryologist to engage in higher complexity or more high touch tasks, rather than merely replacing labor.

Advanced Parallel Laser Line-Camera System for Automatic and Precise Crack Width Measurement

Accurate quantification of cracks is crucial in evaluating building conditions. However, current non-contact measurement technologies encounter challenges in measuring crack widths, particularly in hard-to-reach areas. This paper presents a novel portable parallel laser line-camera system that is specifically designed for automatic and precise measurement of crack widths from different angles and over long distances. The system is composed of a digital camera, parallel line laser diodes, and rigid positioning arms, forming a triangulation-based configuration. A newly developed image processing algorithm is at the core of this innovation, allowing for the extraction of accurate pixel scale information from laser strips, thus overcoming the challenge of pixel distortion in non-perpendicular photography. The U-Net algorithm is utilized to efficiently identify crack regions, followed by the Canny algorithm for meticulous extraction of crack features. By utilizing the pixel scale data obtained from the laser strips, the actual width of cracks can be accurately determined. The system's efficacy and precision have been validated through laboratory and field tests. This automatic, precise, and portable solution significantly advances the field of on-site crack measurement, offering substantial potential for practical applications in building condition assessments.

The Future of Aerospace

Automation specialist FANUC UK will showcase a suite of next-generation automation solutions at Farnborough International Airshow from 22-26 July 2024

SGAM – Smart Geotechnical Asset Management

SGAM (Smart Geotechnical Asset Management) is a service provided through a web-platform running the cloud-based Software as a Service (SaaS) or via API to connect external systems with our service. It represents an innovative and semi-automatic decision-making supporting system for asset management and predictive maintenance purposes to improve the financial resilience of assets, taking advantages of innovative data-fusion algorithms and satellite Earth Observation (EO) technologies (e.g., A-DInSAR and PhotomonitoringTM). SGAM focuses on the risks related to structures and infrastructures, analysing their interaction with the local geological and environmental context, by automatically evaluating the direct or potential future interferences with geohazards (e.g., landslides, floods, fires). The service aims to support Decision Makers in defining and prioritizing the maintenance operations based on the asset vulnerability and loss scenarios. This goal is reached through the integration of the big amount of archived and newly acquired EO data, the available worldwide and regional geodatabases, the information collected by field surveys or other ground-based Structural Health Monitoring and Geotechnical/Geomorphological Monitoring systems, and the proven scientific know-how and experience of the proponent team on geohazards effects in the civil engineering field. SGAM is also designed to be able to implement satellite products from new future space missions, ensuring a vision oriented to the maximum exploitation of the most recent technologies. These components are managed and analysed by specific Process Automation solutions, based on Artificial Intelligence, allowing for a redundant first-level risk-based priority action assessment for cost-effective and optimized maintenance investments.

Analisis Kemampuan Pemecahan Masalah Matematis Siswa Kelas VIII Pada Materi Notasi Ilmiah

In the 21st century, the skills demanded are not only the ability to memorize, but are demanded more on critical thinking skills, creativity, and the ability to solve problems in everyday life. One of the abilities that must be developed by students is mathematical problem solving ability. Without this ability, students cannot solve daily life problems related to mathematics or problems that do not have automatic solutions properly. The problem solving ability steps using the Polya's indicators, namely: 1) understand the problem, 2) make a solution plan, 3) solve the problems, and 4) re-examine. The purpose of this research is to analyze the types of errors that are still made by students in solving story problems. The method used is qualitative research with 32 subjects of 8th grade students. The data analysis technique uses indicators of mathematical problem solving ability by using student answers from the problems given, then assessed based on scoring guidelines. The results obtained obtained information that students' mathematical problem solving skills at the stage of understanding the problem and solving the problem are good, but on the indicators of compiling a solution plan and checking back most students have not done it.

Radiologic text correction for better machine understanding

AbstractRadiologic reports often contain misspellings that compromise report quality and pose challenges for machine understanding methods, which require syntactical correctness. General automatic misspell correction solutions are less effective in specialized documents, such as spinal radiologic reports, particularly in morphologically rich languages like Hungarian. Issues arise from complex conjugations and the modification of Latin terms per the rules of the native language. This study introduces a method for the automatic correction of these misspellings, utilizing the Hunspell software and field‐specific dictionaries. This approach, enhanced by linguistic analysis and statistical data, improves information retrieval, as demonstrated in machine‐learning‐based classification and rule‐based identification tasks. Notably, our method identified over 30% more valid errors than human annotators, highlighting its efficiency. We offer a primarily dictionary‐based solution for correcting highly specialized texts and explore the impact of nonword correction on machine understanding. This work underscores the significance of tailored spelling correction in enhancing text processing algorithms' accuracy.

Application of the Multi-Threading Method and Python Script for the Automate of Network

In recent times, there has been a noticeable surge in the inclination towards the implementation of network automation solutions. This trend is driven by the objective of optimizing network availability within the context of hybrid data center networks, which are becoming increasingly prevalent. Reliability, performance, scalability, and minimal resource overhead are crucial solution design characteristics that significantly impact the decision- making process regarding adoption. Recent research has shown that 90% of network outages happen because of human factors and 69% of respondents manage their network with manual method. That high human error of up to 90%, occurs when configuring network devices using manual method. These problems had a negative impact on the functioning of the organization and were harmful to the user. In this research, we investigate solutions to reduce network outage that caused human error using network automation using the Python programming language and multi-threading method. This network automation will reduce configuration time, eliminate human error, and significantly increase efficiency. The aim of this research is to investigate the efficacy of network automation using Python and multi-threading to reduce network outages.The method used in this research is a parallel or multi-tread execution process method using GNS3 as network simulator. Based on experimental results, the multi- thread automation approach is significantly faster than both the serial automation method (67 seconds) and the manual method (248 seconds), this method requiring only 41 seconds to configure all Cisco router devices. If you're looking for speed, look no farther than the multi- thread automation approach, which is 6 times quicker than the manual method and 3.7 times quicker than the serial automation approach. The findings of this study have substantial and immediate implications for the ability of network engineers to speed up the configuration of networks and lower the rate of human error in doing so.

Effort in manual operation of reproductive health cryostorage facilities

Research questionWhat areas of manual IVF cryostorage operations are common to the safe operation of IVF cryostorage facilities and require effort from embryologists? DesignObservational time and motion data were collected by two observers equipped with the digital cameras over 2 weeks at four well-characterized US IVF centres (sites α, β, γ and δ) from 12 participants performing cryostorage tasks. To understand the work processes of the different sites and assist in the data analysis, informal interviews were conducted with the study participants and laboratory directors. Data were analysed to identify work processes that might be eliminated or diminished by automation and software improvements. ResultsOn average, it took 3.4 data record queries per retrieval from cryostorage to identify a cane, while the canister was lifted an average of 1.5 times per retrieval, with a mean 11.8 ± 9.2 s per lift. Of the total time spent working with cryostorage equipment, 47.25% was of a fatiguing nature. Sites α, β and γ utilized one person to fill the liquid nitrogen storage Dewars, while site δ had two technicians working in tandem to move and fill the Dewars, with different frequencies and determination factors for refills and efficiencies. ConclusionsThis time and motion study demonstrated significant time investment, task redundancy and fatiguing working conditions among embryologists using manual cryostorage processes. There was a disparity of processes and space capacity across different laboratories. Some of these issues may be addressed by the integration of automation and technology solutions.

The automation of the processes of students’ reporting documentation standard control: The software solution concept

Reporting documentation is one of the assessment tools used to verify the students’ mastery of several universal, general professional, and vocational competencies, reflecting the ability to search, critically analyze, and synthesize information, as well as the ability to design methodological materials and develop technical documents related to professional activities. Mastering the relevant competencies can be assessed by the results of analyzing the report text and controlling the compliance of documents with requirements to the design, accompanied by a set of standard checks. Such standard control does not imply any variability in the assessment and therefore has prospects for automation. The introduction of a software solution aimed at automating the processes of standard control will reduce the time spent on checking the reporting documentation and the formation of recommendations for students to correct the detected inconsistencies.The article analyzes the most common errors in the design of students’ reporting documentation on the results of internships, compiles a classification of errors, and suggests methods for detecting each type of non-compliance with the requirements to the design of documents. The concept of the software solution for automation of standard reporting documentation control, which offers a realization of the proposed methods and creates interfaces for interaction with users, is created using the principle of modularity. The results of the research presented in the article demonstrate the potential of applying automation methods to the tasks of checking students’ reporting documents.

Digital foundry production: modeling systems, personnel training, digitization stages.

The article describes modern digital solutions for production automation, simulation of production processes in order to optimize technology, eliminate defects, improve manufacturability of products, reduce production costs, as well as the company's services in the implementation of calculation projects and training, including reverse engineering of products, opportunities for advanced training. As an example of the implemented calculation is presented the work on optimization of the design of mold used for casting copper ingots in order to improve the durability of equipment; simulation of cyclic thermal heating of mold taking into account stress-strain state and fatigue criterion was performed in the software ProCAST. An example of automatic optimization of the design of feeding system for cast iron casting “pump body” using ProCAST and pSeven packages is also presented. The optimization package performed the rebuilding of 3D model in CAD-system with the change of height and diameter of feeder, calculated the case of filling the mold and solidification of the casting taking into account the changed geometry in the calculation CAE package and evaluated the obtained values of porosity in the casting and yield to find the optimal size of feeder

Automation of 3D digital rolling circle amplification using a 3D-printed liquid handler

Automation of liquid handling is indispensable to improve throughput and reproducibility in biochemical assays. However, the incorporation of automated systems into laboratory workflows is often hindered by the high cost and complexity associated with building robotic liquid handlers. Here, we report a 3D-printed liquid handler based on a fluidic manifold, thereby obviating the need for complex robotic mechanisms. The fluidic manifold, termed a dispensing and aspirating (DA) device, comprises parallelized multi-pipette structures connected by distribution and aspiration channels, enabling the precise supply and removal of reagents, respectively. Leveraging the versatility of 3D printing, the DA device can be custom-designed and printed to fit specific applications. As a proof-of-principle, we engineered a 3D-printed liquid handler dedicated for 3D digital rolling circle amplification (4DRCA), an advanced biochemical assay involving multiple sample preparation steps such as antibody incubation, cell fixation, nucleic acid amplification, probe hybridization, and extensive washing. We demonstrate the efficacy of the 3D-printed liquid handler to automate the preparation of clinical samples for the simultaneous, in situ analysis of oncogenic protein and transcript markers in B-cell acute lymphoblastic leukemia cells using 4DRCA. This approach provides an effective and accessible solution for liquid handling automation, offering high throughput and reproducibility in biochemical assays.

Docimological Quality Analysis of LLM-Generated Multiple Choice Questions in Computer Science and Medicine

Assessment is an essential part of education, both for teachers who assess their students as well as learners who may evaluate themselves. Multiple-choice questions (MCQ) are one of the most popular types of knowledge assessment, e.g., in medical education, as they can be automatically graded and can cover a wide range of learning items. However, the creation of high-quality MCQ items is a time-consuming task. The recent advent of Large Language Models (LLM), such as Generative Pre-trained Transformer (GPT), caused a new momentum for automatic question generation solutions. Still, evaluating generated questions according to the best practices for MCQ item writing is needed to ensure docimological quality. In this article, we propose an analysis of the quality of LLM-generated MCQs. We employ zero-shot approaches in two domains, namely computer science and medicine. In the former, we make use of 3 GPT-based services to generate MCQs. In the latter, we developed a plugin for the Moodle learning management system that generates MCQs based on learning material. We compare the generated MCQs against common multiple-choice item writing guidelines. Among the major challenges, we determined that while LLMs are certainly useful in generating MCQs more efficiently, they sometimes create broad items with ambiguous keys or implausible distractors. Human oversight is also necessary to ensure instructional alignment between generated items and course contents. Finally, we propose solutions for AQG developers.

Antarstick: Extracting Snow Height From Time‐Lapse Photography

AbstractThe evolution and accumulation of snow cover are among the most important characteristics influencing Antarctica's climate and biotopes. The changes in Antarctica are also substantially impacting global climate change. Therefore, detailed monitoring of snow evolution is key to understanding such changes. One way to conduct this monitoring is by installing trail cameras in a particular region and then processing the captured information. This option is affordable, but has some drawbacks, such as the fully automatic solution for the extraction of snow height from these images is not feasible. Therefore, it still requires human intervention, manually correcting the inaccurately extracted information. In this paper, we present Antarstick, a tool for visual guidance of the user to potentially wrong values extracted from poor‐quality images and support for their interactive correction. This tool allows for much quicker and semi‐automated processing of snow height from time‐lapse photography.

An automated two-stage approach to kidney and tumor segmentation in CT imaging.

The incidence of kidney tumors is progressively increasing each year. The precision of segmentation for kidney tumors is crucial for diagnosis and treatment. To enhance accuracy and reduce manual involvement, propose a deep learning-based method for the automatic segmentation of kidneys and kidney tumors in CT images. The proposed method comprises two parts: object detection and segmentation. We first use a model to detect the position of the kidney, then narrow the segmentation range, and finally use an attentional recurrent residual convolutional network for segmentation. Our model achieved a kidney dice score of 0.951 and a tumor dice score of 0.895 on the KiTS19 dataset. Experimental results show that our model significantly improves the accuracy of kidney and kidney tumor segmentation and outperforms other advanced methods. The proposed method provides an efficient and automatic solution for accurately segmenting kidneys and renal tumors on CT images. Additionally, this study can assist radiologists in assessing patients' conditions and making informed treatment decisions.