Physics Workshop Research Articles

Research progress and remarks on helicon plasma: a report on the Second Helicon Plasma Physics and Applications Workshop

Research progress and remarks on helicon plasma: a report on the Second Helicon Plasma Physics and Applications Workshop

Knowledge-based planning, multicriteria optimization, and plan scorecards: A winning combination

Background and purposeThe ESTRO 2023 Physics Workshop hosted the Fully-Automated Radiotherapy Treatment Planning (Auto-RTP) Challenge, where participants were provided with CT images from 16 prostate cancer patients (6 prostate only, 6 prostate + nodes, and 4 prostate bed + nodes) across 3 challenge phases with the goal of automatically generating treatment plans with minimal user intervention. Here, we present our team’s winning approach developed to swiftly adapt to both different contouring guidelines and treatment prescriptions than those used in our clinic. Materials and methodsOur planning pipeline comprises two main components: 1) auto-contouring and 2) auto-planning engines, both internally developed and activated via DICOM operations. The auto-contouring engine employs 3D U-Net models trained on a dataset of 600 prostate cancer patients for normal tissues, 253 cases for pelvic lymph node, and 32 cases for prostate bed. The auto-planning engine, utilizing the Eclipse Scripting Application Programming Interface, automates target volume definition, field geometry, planning parameters, optimization, and dose calculation. RapidPlan models, combined with multicriteria optimization and scorecards defined on challenge scoring criteria, were employed to ensure plans met challenge objectives. We report leaderboard scores (0–100, where 100 is a perfect score) which combine organ-at-risk and target dose-metrics on the provided cases. ResultsOur team secured 1st place across all three challenge phases, achieving leaderboard scores of 79.9, 77.3, and 78.5 outperforming 2nd place scores by margins of 6.4, 0.4, and 2.9 points for each phase, respectively. Highest plan scores were for prostate only cases, with an average score exceeding 90. Upon challenge completion, a “Plan Only” phase was opened where organizers provided contours for planning. Our current score of 90.0 places us at the top of the “Plan Only” leaderboard. ConclusionsOur automated pipeline demonstrates adaptability to diverse guidelines, indicating progress towards fully automated radiotherapy planning. Future studies are needed to assess the clinical acceptability and integration of automatically generated plans.

Quantifying and visualising uncertainty in deep learning-based segmentation for radiation therapy treatment planning: What do radiation oncologists and therapists want?

Background and purposeDuring the ESTRO 2023 physics workshop on “AI for the fully automated radiotherapy treatment chain”, the topic of deep learning (DL) segmentation was discussed. Despite its widespread use in radiotherapy, the time needed to evaluate and correct DL segmentations remains burdensome. While segmentation uncertainty could be beneficial for clinicians, there is a lack of understanding on what information should be presented to ease their task. This study aimed to gather insights from clinicians on uncertainty visualisation options. Materials and methodsTwo sessions of structured interviews were conducted across four institutions already using DL segmentation clinically. The first session focused on the main problems hindering the clinical use of DL. In the second session, ten visualisation options displaying uncertainty information at different levels (structure, slice, or voxel) with binary or continuous values were presented. Dosimetric information was also present in some visualisations. For each case, sixteen clinicians (radiation oncologists and radiation therapists) were asked to grade an overall score, the usability, the training required, and the expected time gain. ResultsParticipants preferred the binary voxel-level uncertainty visualisation, followed by binary structure-level uncertainty visualisation. Combining structure-level and voxel-level visualisation methods has been proposed as a promising approach. The benefits of dosimetric information were perceived diversely among participants since it complexifies the display but could be useful for the online adaptive workflow. ConclusionPreferences for uncertainty visualisation methods were assessed within a multi-institutional experienced group of clinicians. Further refinement of preferences may help in selecting the best options for clinical implementation.

Construction of digital twin workshop integrated with edge computing and deep learning

Abstract This study addresses the challenges of real-time data synchronization and big data processing in the construction of digital twin workshops under the background of intelligent manufacturing. A solution that integrates edge computing with deep learning technology is proposed. Through a multi-layered, modular framework structure, this solution achieves real-time synchronous mapping and interaction between physical workshops and virtual workshops, effectively improving production efficiency, reducing maintenance costs, and ensuring product quality. The edge computing layer in the research is responsible for preprocessing heterogeneous data from multiple sources and making rapid response decisions. Simultaneously, deep learning algorithms are employed to conduct in-depth analysis, forecasting, and optimization of the production process. Case studies demonstrate that this solution can significantly improve equipment utilization and optimize workpiece scheduling, validating its application value in actual manufacturing environments.

Impact of Restricted Public Transport Since COVID-19 on Women in Rural India: A Qualitative Study

PurposeRailways are crucial as public transport in rural India in connecting the villages with cities. Since COVID-19, most of the trains in rural India have not stopped at small railway stations, which has impacted the mobility of the villagers. This study is conducted in Piprai, a village in North India, to explore the impact of non-halting trains since COVID-19 on young girls in rural India. Design/methodology/approachThe study adopts a qualitative research design to explore the impact of non-halting trains since COVID-19 on young girls in rural India. Qualitative data was collected from three sources. Two physical participatory workshops were conducted in Piprai in March 2021 and October 2022 with 10 and 12 participants, respectively. A WhatsApp group was formed with six girls for continuous feedback and discussions. A social activist from the same area was the point of contact and was interviewed to understand the overall condition of women in the village. FindingsThe results provide valuable insights into how the lack of public transportation since COVID-19 has affected young girls in rural India. Women's mobility has been limited, which has led to a halt in higher studies, being unable to visit hospitals regularly and meeting relatives, especially parents in different cities. Furthermore, the above reasons have led to anxiety and mental disturbance. Originality/valueThis study is among the first to explore the impact of lack of transportation since COVID-19 on women in rural India. This is the first time any such study has been conducted in the remote area of Bundelkhand, UP, India.

3446: Overview and report from the 2021 ESTRO Physics workshop topic on DIR commissioning and QA

3446: Overview and report from the 2021 ESTRO Physics workshop topic on DIR commissioning and QA

Multi-Agent Reinforcement Learning for Job Shop Scheduling in Dynamic Environments

In response to the challenges of dynamic adaptability, real-time interactivity, and dynamic optimization posed by the application of existing deep reinforcement learning algorithms in solving complex scheduling problems, this study proposes a novel approach using graph neural networks and deep reinforcement learning to complete the task of job shop scheduling. A distributed multi-agent scheduling architecture (DMASA) is constructed to maximize global rewards, modeling the intelligent manufacturing job shop scheduling problem as a sequential decision problem represented by graphs and using a Graph Embedding–Heterogeneous Graph Neural Network (GE-HetGNN) to encode state nodes and map them to the optimal scheduling strategy, including machine matching and process selection strategies. Finally, an actor–critic architecture-based multi-agent proximal policy optimization algorithm is employed to train the network and optimize the decision-making process. Experimental results demonstrate that the proposed framework exhibits generalizability, outperforms commonly used scheduling rules and RL-based scheduling methods on benchmarks, shows better stability than single-agent scheduling architectures, and breaks through the instance-size constraint, making it suitable for large-scale problems. We verified the feasibility of our proposed method in a specific experimental environment. The experimental results demonstrate that our research can achieve formal modeling and mapping with specific physical processing workshops, which aligns more closely with real-world green scheduling issues and makes it easier for subsequent researchers to integrate algorithms with actual environments.

An Expandable and Generalized Method for Equipment Information Reflection in Digital Twin Workshop Systems

A production workshop is equipped with diverse equipment and machinery, and manufacturing data generated by different equipment show certain differences. Therefore, developing a method with an information presentation capability for a digital twin workshop system (DTWS) can be challenging due to the numerous information sources and types. The level of detail provided for a DTWS is directly related to the management comprehension of the production status of a physical workshop. Thus, arranging and presenting different data effectively represents a significant challenge. To address this challenge, this study proposes an equipment information self-reflection method with expandability for DTWSs. First, a generic equipment information model with an expandability is developed. This model encompasses all relevant data types in a workshop. The analysis of these data is conducted from two perspectives: workshop equipment categorization and equipment internal composition. The analysis results are used for the generation of extensible styles for property elements and function components. For the DTWSs, these two perspectives offer a standardized specification for equipment information presentation. In addition, a self-reflection method is introduced to address the demand for generating diverse information types ranging from equipment-based to extensible types. The system framework and operational flow of this method are explained in detail. To verify the proposed method, this paper conducts a practical case study using the DTWorks software development kit to simulate a real workshop. The proposed self-reflection method is implemented into the DTWorks. The disparities in equipment information types are analyzed for both pre- and post-implementation of the proposed method. The obtained results demonstrate the efficiency and advantages of the proposed self-reflection method. Finally, the results show that the proposed method exhibits advantageous performance regarding intricate business contexts.

A dual-robot cooperative arc welding path planning algorithm based on multi-objective cross-entropy optimization

In this paper a novel discrete multi-objective cross-entropy optimization (CrMOCEO) algorithm is proposed to solve the path planning problem of dual-robot cooperative arc welding. We strive to find a low-cost, fast and more efficient solution for robotic welding of large complex components. Firstly, an optimization model of dual-robot welding path planning is established by considering various variables and constraints in the actual welding process. Then, three strategies are introduced to improve the multi-objective cross-entropy optimization (MOCEO) algorithm to better solve the discrete path planning problem. Finally, in order to verify feasibility and effectiveness of the proposed algorithm, it is used to solve the 2-, 3-, 5- and 7-objective WFG2–9 problems and plan some typical welding seams of a large complex component, the MOCEO, NSGA-Ⅱ, MOPSO and MOGWO are used for comparison. The simulation demonstrates that the CrMOCEO can obtain better solutions for multiple objectives than the other four algorithms, and the path solved by the CrMOCEO is tested in the Gazebo physical model and workshop site, the results further verified the effectiveness of the CrMOCEO algorithm. Particularly, a series of experiments provide more solutions for actual production.

Correlations in strong-field-emitted ultrashort electron pulses from metal needle tips

When two electrons are emitted from a metal needle tip with the help of femtosecond laser pulses, they show a strong anticorrelation signal in the energy domain. Depending on the wavelength and intensity of the driving laser pulses, the electron emission process can be either in a perturbative regime, like single- or multi-photon photoemission, or in the strong-field regime, where emission is dominated by the instantaneous electric field of the laser pulse, or in the intermediate regime. Here, we report on the two-electron anticorrelation signal and how it evolves from the multiphoton toward the strong-field emission regime. We show that in both cases, the resulting anticorrelation signal can be well explained by semi-classical simulations using a point-particle model, thus the dynamics is dominated by the center-of-mass dynamics of the individual electrons. However, the actual emission process of multiple interacting electrons requires improved quantum mechanical models and therefore remains the subject of future work.This paper is part of the Special Topic Collection: papers from the 31th Annual International Laser Physics Workshop 2023 (LPHYS 2023).

Hearing Care, as part of basic care in a senior home.

Background: World wide, 1 in 6 people is hearing impaired. Age-related hearing loss is the most frequent cause. The number of deaf and hearing impaired persons increases as the population is getting older.
 Unfortunately, the impact on quality of life is underestimated. 
 Hearing is an important part of communication. Several studies of the World Health Organization show the consequences of bad hearing s.a. isolation, frustration, loneliness and even acceleration of the dementia process. A hearing disability is invisible and problems are often not recognized.
 A great part of the elderly people lives in a senior home. The prevalence of hearing problems there is estimated around 80% as average age is about 87 years. The ringing of the telephone, a knock on the door, the radio, the television, the alarm signals are difficult to hear. This absence of familiar sounds produces a feeling of confusion and anxiety instead of safety and recognition. Communication is often done ‘on the job’ ignoring some special needs of the hearing impaired. Facemasks may make things even more difficult. The elderly hearing impaired, their family and caretakers have often little or no knowledge about the risks of untreated hearing loss, about innovative technologies in hearing aids and other hearing devices and about some interactional adaptations to improve their quality of life.
 Aim: Installing high-quality hearing care in senior homes in order to stimulate the inclusion and participation of hearing impaired elderly.
 Method: A team of AHOSA vzw, a Flemish interest group for persons with a hearing loss who communicate in spoken language, developed a project to put into the spotlight the importance of hearing care in senior homes.
 Basic relevant information is given to all the persons who are involved in the care process, but also to the residents themselves and to their family. 
 Physical and digital workshops include a theoretical approach as well as practical information s.a. how wearing a hearing aid, changing a battery, seeing that the person didn’t understand the message… Tips and tricks are given to improve communication.
 The workshops use tools such as a PowerPointpresentation, an instruction film and leaflets.
 We started with a pilot study: a partnership between the association AHOSA VZW and the Senior Home Sint-Jozef, Assenede Belgium, with support of the Artevelde Highschool Ghent.
 During this trial period, the workshops and tools are adjusted by means of feedback of all participants, and the effects are measured by pre- and post questionnaires.
 Result: This participatory process with co-creation leads to a workable model of hearing care for all senior homes in Flanders, and can be a national or an international policy recommendation in a later stage.
 The scoop can be more durable by also aiming at integration of this model in the education programs of schools and departments that prepare students to work with elderly people.
 Conclusion: A well-developed Hearing Care model as part of basic care in a senior home leads to better quality of life.
 
 

Future MEDIC: Medical Student–Led Healthcare Career Exploration Program for Underserved Middle School Students

Future Medical Education and Development Introductory Course (Future MEDIC) is a medical student–led school program designed to inspire primary school students in underserved communities by sharing a passion for medicine and healthcare careers. Through hands-on activities—including physical examination workshops, an on-site airport introduction to flight medicine, and interactive demonstrations with a patient simulator—26 eighth-grade students were empowered to actively explore the world of healthcare. To assess the impact of Future MEDIC on students’ knowledge and interest in healthcare careers, we administered pre- and post-surveys, informal exit interviews, and participation prizes. Survey responses were analyzed with a Mann Whitney U test for significance. There was a significant increase (p = 0.01) from pre- to post-survey about “I enjoy learning about how the body works.” Yet there was also a significant decrease (p = 0.03) from pre- to post-survey about “In the future, I am interested in working in healthcare or medicine.” Despite enjoying the activities and content of the Future MEDIC program, students did not have an increased interest in entering the healthcare field. Although interest in entering the healthcare field was not increased, Future MEDIC was a rewarding and fun experience for students to partake in, and an opportunity to create a multidimensional view of the healthcare field.

A framework of cloud-edge collaborated digital twin for flexible job shop scheduling with conflict-free routing

Synchronous scheduling of transportation resources such as automated guided vehicles (AGVs) and production resources can effectively improve the production efficiency of an automated flexible job shop, however limited transportation resources and persistent abnormal disturbances compromise the accuracy, reliability and practicability of such scheduling. In order to address the flexible job shop scheduling problem with limited transportation resources (FJSP-T) under abnormal disturbances, here we propose a cloud-edge combined digital twin (DT) flexible job shop scheduling (CE-DTFJSS) framework to realize real-time scheduling. Firstly, a time-space network based zero-one programming (TSN-ZOP) model is established to optimize makespan and energy consumption of flexible job shop scheduling with conflict-free routing. Subsequently, to achieve real-time interaction between scheduling system and physical workshop, a cloud-edge architecture is introduced to allocate computing resources for scheduling-supporting tasks such as simulation, state monitoring and distributed control in the DT system. Additionally, we design a cloud edge collaboration rescheduling strategy based on a rolling window, to update the parameters’ values in the improved time window Dijkstra nested multi-layer coding genetic algorithm we proposed, thus real-time scheduling can be achieved. The effectiveness of CE-DTFJSS are verified by a numeric simulation based on a real-world case of a digitalized automobile welding workshop. The experimental results indicate that compared with the scheduling scheme without considering AGVs routing problem, the CE-DTFJSS framework can reduce makespan and energy consumption by 8.30% and 3.73%, respectively, and real-time responses to abnormal disturbances can be rendered within 0.1 s.

Towards discrete manufacturing workshop-oriented digital twin model: Modeling, verification and evolution

Digital twin (DT) is one of key enabling technologies of intelligent transformation in discrete manufacturing workshop (DMW). The construction of digital twin system in DMW has greatly expanded the connotation and extension of smart manufacturing. Although previous studies have shown the success in DT, there is still a lack of clear and systematic methods of DT in DMW. To bridge this gap, this article focuses on how to systematically and effectively construct DT model methods for DMW. Taking the modeling, verification, and evolution of DT model as the main line, three key technologies related to digital twin in DMW are proposed, including model migration based matching modeling technology, graph convolutional network and temporal convolutional network based DT model verification and Adaboost based synchronous evolution of DT model, which will provide a systematic theory and method for the construction of DT model for DMW. The experiment demonstrates that the proposed methods have good performance for physical workshop in industrial environment and provide a holistic understanding of DT model modeling in DMW.

Recommendations from Diabetes UK's 2022 diabetes and physical activity workshop.

To describe the process and outputs of a workshop convened to identify key priorities for future research in the area of diabetes and physical activity and provide recommendations to researchers and research funders on how best to address them. A 1-day research workshop was conducted, bringing together researchers, people living with diabetes, healthcare professionals, and members of staff from Diabetes UK to identify and prioritise recommendations for future research into physical activity and diabetes. Workshop attendees prioritised four key themes for further research: (i) better understanding of the physiology of exercise in all groups of people: in particular, what patient metabolic characteristics influence or predict the physiological response to physical activity, and the potential role of physical activity in beta cell preservation; (ii) designing physical activity interventions for maximum impact; (iii) promoting sustained physical activity across the life course; (iv) designing physical activity studies for groups with multiple long-term conditions. This paper outlines recommendations to address the current gaps in knowledge related to diabetes and physical activity and calls on the research community to develop applications in these areas and funders to consider how to stimulate research in these areas.

An Adaptive Job Shop Scheduling Mechanism for Disturbances by Running Reinforcement Learning in Digital Twin Environment

Abstract Practical manufacturing system operates in highly dynamic and uncertain environments, where stochastic disturbances disrupt the execution of the production schedule as originally developed. Previous dynamic scheduling mainly focuses on the constructing predictive models for machine unavailability, with little studies on the adaptive and self-learning capacities for changing scheduling environments. Therefore, a digital twin (DT) driven scheduling with a dynamic feedback mechanism is proposed, in which a reinforcement learning (RL) based adaptive scheduling is developed in DT to make corrective decisions for the disturbances during production runs. In the proposed architecture, the happening disturbance is first detected in the virtual layer by the status continuously updating in accordance with the physical workshop. Furthermore, the reschedule triggering condition is determined in real-time through the calculation of the progress deviations resulting from disturbances. For the scheduling approach, the distributed RL (DRL) based adaptive scheduling method is built to perceive the dynamic production status from virtual environment and implement corrective strategies to hedge against the occurred disturbances. Finally, the proposed method is verified by a practical job shop case and the corresponding DT system is developed to show the effectiveness and advantages after a practical implementation.

LPHYS’22

The thirtieth annual International Laser Physics Workshop (LPHYS’22) was held in an exclusively virtual format on Zoom from July 18 to July 22, 2022.This year the Workshop was carried out in an exclusively virtual format on Zoom. The LPHYS’22 Steering and Advisory&Program committees and the LPHYS’22 Management Team have worked hard to make this event possible.We kept the conference virtual Zoom format as closely as possible to the standard in-person format of the previous years, albeit without popular extravagant welcome parties, sumptuous gala dinners, and many other in-person activities and friendly gatherings. However, the cordial atmosphere that had always ruled our annual meetings was kept on.List of the Proceedings Editors, Plenary speakers were world-renowned scientists, The Workshop is being organized by, Sponsors are available in this pdf.

SP-1018 Report from 2022 ESTRO Physics Workshop: How to progress from the concept to clinical reality?

SP-1018 Report from 2022 ESTRO Physics Workshop: How to progress from the concept to clinical reality?

Digital twin driven production progress prediction for discrete manufacturing workshop

• This article focuses on a novel synchronous update method for digital twin workshop model with integrating learning approach. • The representation model is established to describe the performance degradation rules of discrete manufacturing workshop. • An Adaboost-DNN-LSTM based synchronous update method is proposed to guarantee the consistency between DTM and physical workshop. • A competitive election mechanism is designed to select base learner among DNN and LSTM between historical and dynamic sample. In make-to-order manufacturing enterprises, accurate production progress (PP) prediction is an important basis for dynamic production process optimization and on-time delivery of orders. Digital twin technology offers an enabling tool for PP analysis. Although the production process can be observed, analyzed, and controlled in real-time by digital twin model (DTM), there exist some uncertain events, degradation of manufacturing elements, and abnormal disturbance in physical workshop (PW), which would cause the deviation between DTM and PW performance and affect the prediction accuracy of PP. Synchronous evolution of DTM for precision holding to ensure the consistency between DTM and the performance of PW, and guarantee the accuracy of DTM is still a challenging issue, especially when dealing with new dynamic samples for complex production environment of discrete manufacturing workshop (DMW). This article focuses on how to effectively construct DTM synchronous update methods based on dynamic sample data for DMW. This study proposes a representation model of performance degradation and an Adaboost-DNN-LSTM based synchronous update model with competitive election mechanism to enhance the accuracy of PP prediction with time in industrial environment. The experiment is conducted in the realistic production dataset, which demonstrates that the proposed synchronous evolution model has good performance for realizing the synchronization of the performance of physical workshop in industrial environment, and can greatly improve the prediction ability for PP.

Multi-Robot Collaborative Flexible Manufacturing and Digital Twin System Design of Circuit Breakers

Circuit breakers (CBs) are mainly designed to interrupt current flow when faults are detected and have been widely used in industrial applications. The existing CBs manufacturing method is semi-automatic and requires a lot of labor. To realize flexible manufacturing, a multi-robot cooperative CBs flexible manufacturing system (CBFMS) is presented in this study. Aiming at the efficiency of the multi-robot cooperative CBFMS key units, a two-arm cooperation robot approach is proposed. The reinforcement learning algorithm is developed to optimize the manufacturing trajectory of the two-arm cooperation robot. To build and optimize the multi-robot cooperative CBFMS, a digital twin (DT) system describing all physical properties of the physical manufacturing plant is constructed for simulation. In the developed DT system, a kinematic control model of the collaboration robot is established. A real-time display of the robot’s trajectory, manufacturing status, and process manufacturing is provided by the data interaction with the physical cell flow between the units. Following this design, a synchronous mapping between the flexible manufacturing DT system of the CBs and the physical workshop is realized, which enables real-time monitoring and management of the physical production line. The experiments’ results show that the manufacturing efficiency, compared with traditional CBs production, is improved by 22%. Moreover, the multi-robot cooperative CBFMS can make process changes according to the production requirements, which can improve the stability of production.