As technology has improved in recent years, it has become possible to create new valuable functions by combining various devices and sensors in a network. This concept is referred to as the Internet of Things (IoT), and predictive maintenance is a new valuable function associated with the IoT. In large-scale experimental facilities with many researchers, it is not desirable that experiments cannot be performed due to sudden failure of equipment. For this reason, it is important to predict the failure in advance based on the measurement results of sensors and to perform repairs in a planned manner. On the Q-shu University experiment with steady-state spherical tokamak (QUEST) large experimental device, it is necessary to drive a large current of 50 kA, and the diagnosis of its power line deterioration is well performed as predictive maintenance through the evaluation of its contact resistances of several micro ohms order on the network. In addition, as an example of the IoT, mechanisms to assist safe operation, such as a sound alarm system and an entrance management system, are built by sharing experimental information between devices via the network. Computer Science; Electrical Engineering; Safety engineering; Internet of Things; System Diagnostics; Electric Power Transmission; System Fault Detection; Computer-Aided Engineering; Predictive maintenance; IoT; safety operation; FPGA; QUEST Using QThe reminder of the present paper is organized as follows. Section 2The network used for the experiment involving QUEST is composed of two networks: an experimental network and a control network. The experimental network is used by many researchers, and various devices such as personal computers and measuring instruments are connected to this network. In this network, although some devices are registered to have static Internet Protocol (IP) addresses, other devices can be connected easily by dynamic IP addresses provided by a Dynamic Host Configuration Protocol (DHCP) server. On the other hand, in the control network, all of the devices are registered to have static IP addresses, and the table of Media Access Control (MAC) addresses and IP addresses is managed strictly to avoid wasting of network resources. On the boundary between the experimental network and the control network, a network router configured by a Linux server is installed. This server provides appropriate services, such as a common storage service to both networks, and controls the communication between these networks via a firewall, for example.The overall control configuration for experiments is shown in In the control network, communication between devices is performed using User Datagram Protocol (UDP). Simple text data that contains linefeed code and tab code as delimiters to separate data types and data values are sent to other devices via UDP in the control network. By adopting simple text-based communication with UDP, numerous types of devices can communicate with each other, even though their programming languages are different. Another advantage of using UDP is the ability to transfer data by broadcasting or multicasting. Thus, information concerning the sequence from the CCS is sent to other devices by broadcasting.The PCS is also composed of a PXI Express (PXIe) system manufactured by National Instruments Corporation. During plasma discharge, the PCS acquires data, such as coil currents, magnetic signals, and visible lights of plasma, and analyzes the status of the plasma in real time. Then, the PCS sends control signals to other subsystems, such as coil power supplies, high-frequency oscillators, and gas fueling devices, for the real-time control of plasma. When the PCS detects the abnormal state of a device, the PCS terminates its plasma discharge and informs the CCS of the abnormal state by means of UDP.3For the plasma production and its confinement in a vacuum vessel, large currents must be conducted in the coils in order to produce magnetic fields, and cooling water must be circulated in order to remove the heat of the coils generated by the large currents. In QUEST, a power supply of 50 kA with a steady state is connected to a fundamental coil, referred to as a toroidal field (TF) coil, which generates a primary magnetic field of 0.25 T in order to confine the plasma .Figure 3Since the ionization tendency of aluminum is higher than that of copper or iron, electric erosion tends to occur on the surface, where the cooling water flows, because of the ionization of aluminum. In particular, when the electric erosion occurs at the contacting surface, the erosion causes not only an increase in the electrical contact resistance but also leakage of cooling water, and a serious failure involving other devices may occur. Thus, the periodic maintenance (polishing) of the contacting surface must be performed to make it flat. However, until now, there was no indication as to how often maintenance should be performed because the deterioration of the contacting surface was unknown and unpredictable. Therefore, in order to obtain this information, the measurements of contact resistances are considered because erosion of the conducting surface appears to cause an increase in contact resistance. The overall resistance of the power transmission line is designed and manufactured to be less than 100μΩ in order to reduce the power loss of the transmission line. Since there are 16 connection points on the power transmission line, each contact resistance will be several micro-ohms, values too low to be measured by commonly used multimeters. On the other hand, the maximum current flowing in this connection point is 50 kA, and the potential difference at this current and resistance is considered to become approximately several tens of millivolts. Thus, the contact resistances can be evaluated by measuring the potential differences during the usual usage of the TF coil power supply.The potential differences are measured using a TF coil voltage monitor (TFvm), which uses a CompactRIO (cRIO) controller  including a field programmable gate array (FPGA). A FPGA is an integrated circuit, and the term “field programmable” means that a user or a customer can configure the integrated circuit after manufacturing using a hardware description language (HDL) . The meaIn a typical discharge sequence of QUEST, because the plasma discharge starts after t = 0 s, the TF coil current starts from approximately -10 s, and is increased up to the target current, taking several seconds before t = 0 s. The current is thereafter typically kept constant during the plasma discharge, and the current is finally decreased to zero, taking several seconds. Thus, the time scales of the voltages measured by the TFvm should be several seconds.An actual raw waveform of the voltage at one connection point is shown in Since the sampling period In actuality, the condition of by wires , and canThe time history of the resistance of 16 connection points is shown in 4The experimental hall of QUEST is a restricted area during plasma discharge in order to avoid damaging human health due to X-ray emission or the emission of electromagnetic waves. However, researchers often enter the experimental hall during the interval of plasma discharges to adjust their measurement instruments. Thus, it is necessary to carefully perform plasma discharges after confirming that no human is in the experimental hall. In order to support this confirmation, two mechanisms are implemented as examples of the IoT to create new valuable functions by linking devices: the entrance management system (EMS) or the sound alarm system (SAS).The EMS includes the CCS and other peripherals, and peripherals are installed near each entrance of the experimental hall. One peripheral is composed of a touch display and a mini PC having dimension of 113 mm × 38 mm × 12 mm, which can communicate with the CCS via Wi-Fi. The runtime environment of LabVIEW is installed on the mini PC, and a user interface of buttons with the names of researchers created using LabVIEW is displayed on the touch display . ResearcIn order to facilitate maintenance of the EMS, the executable program LabVIEW is stored on common storage, and each peripheral executes LabVIEW on its terminals. The name list of researchers is also stored in common storage, and each peripheral reads that list at the initialization of the program. As a result, when the program or the name list is modified, changes need only be made at one place. Although an executable program is commonly used, this program can also be operated differently at each peripheral by judging the IP addresses of the mini PCs. For example, each peripheral sends its information to a different receiving UDP port of the CCS.The SAS also includes the CCS and other terminals that can produce sound with their speakers. Since researchers enter the experimental hall to make adjustments of their measurement instruments during the interval of plasma discharges, researchers have to continuously monitor the status of the sequence in order to exit the experimental hall before the plasma discharges start. The SAS helps researchers to know the status of the sequence through several kinds of sounds at terminals. Receiving the status of the sequence with UDP from the CCS, the terminals play music 2 min before plasma discharge and sound an alarm and a chime 1 min and 10 s, respectively, before plasma discharge. These terminals also sound warning alarms notifying researchers of abnormal status from the CCS. Since general desktop PCs or laptop PCs can be used as terminals, the installation location and number of installations can be changed freely. In QUEST, these terminals are installed in the control room and the experimental hall in order to enable researchers to call for attentions. Thus, although the EMS and the SAS are composed of simple touch panels, mini PCs, and general PCs, new valuable functions, such as entry management and acoustic alarms, can be produced by connecting simple devices.5Diagnostics for the deterioration of the TF power transmission line have been developed as a method of predictive maintenance. These diagnostics are very effective and will help to reduce operational costs by reducing the risk of sudden failures. This is one example of the IoT, which can create new benefits by network communication between various devices and sensors. An entry management system and a sound alarm system have also been developed as examples of IoT systems. These systems, which are composed of simple equipment, are very useful and assist in the safe operation of QUEST. In the future, the data of many measurement instruments, such as vacuum gauges, flowmeters, and temperature gauges, installed at various locations will be shared via network communication at QUEST, and new benefits to assist in predictive maintenance and safe operations will be created more frequently.Makoto Hasegawa: Conceived and designed the experiments; Performed the experiments; Analyzed and interpreted the data; Contributed reagents, materials, analysis tools or data; Wrote the paper.Kazuaki Hanada, Hiroshi Idei, Ryuya Ikezoe, Kengoh Kuroda & Aki Higashijima: Conceived and designed the experiments.Shoji Kawasaki, Takahiro Nagata & Takumi Onchi: Conceived and designed the experiments; Performed the experiments; Contributed reagents, materials, analysis tools or data.NIFS Collaboration Research Program (NIFS17KUTR122).This work was supported by a grant from the The authors declare no conflict of interest.No additional information is available for this paper.


This review aims to assess the current modelling and experimental achievements in the design for additive manufacturing of bonded joints, providing a summary of the current state of the art. To limit its scope, the document is focused only on polymeric additive manufacturing processes. As a result, this review paper contains a structured collection of the tailoring methods adopted for additively manufactured adherends and adhesives with the aim of maximizing bonded joint performance. The intent is, setting the state of the art, to produce an overview useful to identify the new opportunities provided by recent progresses in the design for additive manufacturing, additive manufacturing processes and materials’ developments. The industrial interest in additive manufacturing (AM) has been a driving factor behind the development of modern 3D printer machines  and theiAs the knowledge in DfAM increases, so too does the potential of AM. Although still limited by a relatively modest production rate, if compared to traditional manufacturing processes, AM finds practical use in the production of highly optimized components of limited size to be assembled in larger structures . MoreoveOne of the earliest works on joining AM components was carried out by Espalin et al. , in whicSubsequent studies explored joint performance improvements using AM as a joining tool, the AddJoining process ,13 with After the introduction, the existing literature is sorted according to the design strategy used by authors and details are given for each of these techniques. In the conclusion section, the advantages and disadvantages of the proposed techniques are summarized, and some general remarks are drawn.The process of optimizing the design of bonded joints can be briefly described as maximizing the load carrying capacity of assembled components in order to withstand higher service loads. Initially, joint design strategies were based on selecting materials with better material properties and on maximizing the bonding area. Later, with the growing need for developing lightweight structures, the investigations on the physics of the adhesion and the advances in modelling techniques enabled the development of alternative joint design strategies . These dThe first design strategy discussed is the use of AM to locally change the material properties in the joint. This approach can be implemented by simultaneous deposition of different materials, for example with MMAM  in orderIn the following sub sections, the works concerning AM tailored materials and joints shown in Kumar et al. ,24 and KIn a preliminary study, Khan et al.  implemencv of low-stiffness material of the total material in the bondline as a design parameter. Taking advantage of the photopolymerization process resolution, the authors were able to obtain two digital materials (DMs) with hybrid characteristics in respect to the bulk materials EMIN and Ubaid et al.  focused This subsection presents works that use, as design strategies, the introduction of tailored porosities, macro voids or cavities in the adherends to locally modify their mechanical properties. This allows us, for example, to locally vary the material density  and thusA first feasibility study on crack trapping using AM for bonded joints was made by Alfano et al. . In thisThese first promising results led to further investigation carried out by Morano et al. . At firsThe work by Afferrante et al.  can be cAM enables control over the material at voxel resolution and this, if coupled to MMAM, can be used to create joints with tailored physical and mechanical adhesive properties. One of the main engineering challenges in tailoring adhesives is to obtain a seamless variation in the material properties in order to obtain a functionally graded adhesive (FGA) behavior . FGA canRedmann et al. , using AThe work of Dahmen et al.  was baseThe T-joint solution  is desigThe joining process was performed using three different processes to manufacture the deltoid: adhesive bonding with AM, a manual process with adhesive bonding and a manual process with pre impregnated carbon fiber . At firsThe work by Niese et al.  assessedThe work by Schmidt et al.  completeSekiguchi et al.  applied To control the mixing ratio of agents during the adhesive deposition at the overlap, a special apparatus was purposely developed , making While tailoring the adhesive properties along the overlap was already proven to be an effective solution to improve the performance of bonded joints , AM procThis section presents and summarizes works which aim to improve the performance of adhesive bonded joints performance with an optimized geometrical design of the adherend in the overlap area. The approaches presented here were often originally proposed for laminate materials ,47, whicThe AM design strategies to increase the bonding area or to modify surface properties, presented in this section and those that follow it, are shown in Spaggiari et al.  exploredCavalcanti et al.  expandedGarcia-Guzman et al.  investigGarcia et al.  investigBürenhaus et al.  investigData on the effects of overlap tailoring provided by the previously described works have been completed by the investigations carried out by Fieger et al.  on tailoThe use of mechanical interlocking in AM bonded joints is still an open topic of research and few works are available on this specific subject, even if it seems to have a mostly positive effect on joint performance. This is probably due to the limitations on buildable geometries and the geometrical tolerances achievable with current AM processes. However, recent studies on the effect of the positioning and AM process developments are now enabling these novel design strategies. An example of mechanical interlocking applied to AM bonded components can be found in the work of Jilich et al. and Frascio et al. ,58, wherThis section reports works that focus on surface modifications in AM joints, carried out in order to obtain performance improvements. According to Packham , adhesioAM offers the possibility to implement integrated surface modifications. This can be done, for example, by mounting an atmospheric plasma torch on the nozzle or to affect the surface properties through the printing set up.An AM integrated approach was first presented in the work of E. Dugbenoo et al. , where tBy setting a partial infill at the overlap interface, while compiling the G-code programming language, it is possible to obtain optimal surface morphology avoiding post processing. Thus, it is clear how this approach could be advantageous for a reduction of production costs. By setting an offset of ~120 µm in the material deposition pattern, the authors obtained a 50% infill density at the adherend–adhesive interface that led to the increase of the bonding surface area by ~150% and enhanced the interlocking effect. To assess the effect of this design on actual joints AM SLJs were manufactured with printed adherends composed of long fiber, carbon, Kevlar and glass, reinforced PA matrix supplied by Markforged. SLJs were manufactured using a two-component epoxy adhesive  to join mechanically abraded and air gap tailored adherends. Experimental data identified a joint strength increase of up to ~145% and a joint toughness increase of up to ~820% for tailored joints, values which are dependent on the nature of the material being used as reinforcement.Fieger et al.  experimeLeicht et al.  investigBürenhaus et al.  investigRa) while the use of APP led to almost no changes. SLJs were fabricated and tested according to ASTM D5868 [Li et al.  investigTM D5868  using soYap et al.  investigKovan et al. ,72 used Kariz et al.  investigIt is worth noting that vacuum-ultraviolet (V-UV) surface treatment at date of writing was not investigated for AM adherends.Recent developments in additive manufacturing processes and in innovative materials have expanded the use of additively manufactured components to many new engineering fields, leading to an increased interest in the use of this technology. In particular, the joining of additively manufactured components has been a key area of study, as this technology is essential for manufacturing structures of increased complexity.Different design strategies have been investigated to take advantage of the beneficial effect of voxel size control provided by additive manufacturing on the geometry and material properties.Most of works focused on additively manufactured adherend tailoring. Kumar et al. ,24 and KOther authors focused on the so called geometrical-complexity-for-free provided by additive manufacturing processes and used tailoring of adherends at the bonding interface and in the thickness. The first approach is used to create a desired pattern able to provide mechanical interlocking and increase the area at the interface between adherend and adhesive. Several authors ,50,52,61The second approach draws inspiration from organisms which show high adhesion and toughness. Using a fracture mechanics-based approach, several authors ,34,35,37The results of these works provided the first quantification of the effects of tailored additively manufactured adherends and highlighted the advantages and disadvantages of this design approach. Major limitations are due to the minimum size of the printable features, inherent to a given additive manufacturing process specifications, and the effect of the tailoring on the adherend stiffness. Indeed, an unsuccessful tailoring procedure can increase the susceptibility to bending, leading to increased peel stress in the bondline and causing failure at loads lower than that of a non-tailored joint configuration.New developments in additive manufacturing processes, such as the multi material additive manufacturing and the use of new materials, are enabling different design solutions, such as the creation of functionally graded adherends or embedded sensors, although they urgently require the development of new testing methods and standards to address the resultant complex material response. Available results indicate that this approach can improve joints’ performance, although it is still common for joints manufactured using additive manufacturing technology to fail predominantly by adhesive failure, pointing out that further performance improvements can be achieved by improving the quality of surface treatments and material compatibility.A possible solution for these limitations could be the combination of several different design approaches for the manufacture of the adherends or the use of surface modifications. Some of the studies applied surface modifications which are already in industrial use ,62,65,68Some authors explored the feasibility of using additive manufacturing to tailor the bondline by controlling the adhesive composition or shape ,41,42,43Finally, tailoring additively manufactured adherends and adhesives, following a design for additive manufacturing approach, has proven to be an extremely useful, yet relatively unexplored, solution to improve the performance of adhesively bonded joint. While many researchers have laid the foundations for more extensive use of additive manufacturing in conjunction with adhesive bonding, additional research is still required to enhance manufacturing reliability and repeatability. Another important conclusion of this work is that aspects related to the degradation and long-term durability of polymeric additively manufactured bonded joints are relatively unstudied. This represents a topic of great interest for industrial applications and must certainly be an important subject of research in the coming years.


Evidence-based practice refers to building clinical decisions on credible research evidence, professional experience, and patient preferences. However, there is a growing concern that evidence in the context of electronic health  is not sufficiently used when forming policies and practice of health care. In this context, using evaluation and research evidence in clinical or policy decisions dominates the discourse. However, the use of additional types of evidence, such as professional experience, is underexplored. Moreover, there might be other ways of using evidence than in clinical or policy decisions.This study aimed to analyze how different types of evidence  obtained within the development and evaluation of an eHealth trial are used by diverse stakeholders. An additional aim was to identify barriers to the use of evidence and ways to support its use.This study was built on a case of an eHealth trial funded by the European Union. The project included 4 care centers, 2 research and development companies that provided the web-based physical exercise program and an activity monitoring device, and 2 science institutions. The qualitative data collection included 9 semistructured interviews conducted 8 months after the evaluation was concluded. The data analysis concerned (1) activities and decisions that were made based on evidence after the project ended, (2) evidence used for those activities and decisions, (3) in what way the evidence was used, and (4) barriers to the use of evidence.Evidence generated from eHealth trials can be used by various stakeholders for decisions regarding clinical integration of eHealth solutions, policy making, scientific publishing, research funding applications, eHealth technology, and teaching. Evaluation evidence has less value than professional experiences to local decision making regarding eHealth integration into clinical practice. Professional experiences constitute the evidence that is valuable to the highest variety of activities and decisions in relation to eHealth trials. When using existing scientific evidence related to eHealth trials, it is important to consider contextual relevance, such as location or disease. To support the use of evidence, it is suggested to create possibilities for health care professionals to gain experience, assess a few rather than a large number of variables, and design for shorter iterative cycles of evaluation.Initiatives to support and standardize evidence-based practice in the context of eHealth should consider the complexities in how the evidence is used in order to achieve better uptake of evidence in practice. However, one should be aware that the assumption of fact-based decision making in organizations is misleading. In order to create better chances that the evidence produced would be used, this should be addressed through the design of eHealth trials. Evidence-based medicine has taken a central role in health care, aiming to increase the quality of clinical practice. In the medical domain, it is conceptualized as building clinical decisions on credible research evidence, professional experience and judgement, and patient preferences -3. This Evidence produced by evaluations dominates the discourse related to the evidence-based practice of eHealth implementations ,12,13. TWhen the expectation is to use the evaluation evidence in making decisions regarding clinical implementations of eHealth , it refeFurthermore, the considered users of evidence in the context of eHealth are usually limited to policy makers or health care professionals. However, eHealth is a multi-disciplinary field , and theThe purpose of this study was to analyze how different types of evidence  are used by diverse stakeholders. An additional aim was to identify barriers to the use of evidence and ways to support its use.This study was built on a case of a multinational and interdisciplinary European Union–funded project . It was a nonpharmacological eHealth trial aimed at improving quality of life and increasing the independence of elderly with mild cognitive impairment and mild dementia. The Alpha trial introduced a case manager role and an ICT platform that consisted of web-based physical and cognitive exercise programs and an activity monitoring device to be used at home. As such, innovations were introduced on both the service model and technological levels.The Alpha approach was implemented and tested in 4 countries. The project involved 8 partners: 4 care centers (in the aforementioned countries), 2 research and development companies that provided the eHealth solutions, and 2 science institutions. The trial lasted for 6 months. The evaluation included a number of variables, such as clinical efficacy, quality of life, patient adherence to technology, patient and health care professional’s satisfaction, process effectiveness, and a cost-benefit analysis. Clinical professionals were asked to collect patient data using a number of standardized and custom-made questionnaires, as well as to register some data in the registries in Excel files. The evaluation was performed at the end of the project and was finalized in June 2018. During the evaluation, several project partners were charged analyzing the different variables. As it frequently happens in multinational projects, the evaluation had to overcome several practical circumstances such as ethical approvals and systems integration issues that delayed patient recruitment and the start of the trial. This created a situation in which the trial time had to be shortened for some patients, resulting in a smaller dataset than planned.The evidence considered in this study included evaluation results (including patient preferences) and professional experiences from the Alpha trial as well as existing scientific evidence from other research. Data were collected through 9 semistructured interviews with all the partners involved in the Alpha project  that were conducted 8 months after the evaluation was concluded. The stakeholders were delimited to the partners of the Alpha project, since they had deep knowledge and experience from the trial and they were the primary candidates to consider using the evidence developed. However, the funding institution did not require the project partners to use evidence from the Alpha project.The interviewee selection followed the principles of purposive sampling  and invoThe interviews followed a guide structured around the components of evidence use ,19,25, sFor every stakeholder, we analyzed the following: 1) the types of activities and decisions that were made after the Alpha project ended; (2) the types of evidence  that were used for those activities and decisions, based on the definitions of evidence  the type,17-19,22The findings were grouped by the types of activities and decisions made by the stakeholders using evidence from Alpha. For each activity or decision, the use of evidence is discussed.At 8 months after the Alpha project ended, partners in the project (stakeholders) used evidence in the following decisions and activities: (1) integrating or abandoning the Alpha approach in clinical practice, (2) publishing results from the Alpha study, (3) applying for new research funding, (4) supporting regional policymaking, (5) improving technology, and (6) teaching students and health care professionals. Next, the ways that evidence were used by the stakeholders in every decision and activity are explained.At the time of this study, 2 of the 4 care centers had decided to integrate the Alpha approach into clinical practice (care centers 1 and 2). In these care centers, the decision was mainly informed by the evidence from professional experiences. The health care professionals decided to adopt the Alpha approach based on their experiences with usability and adherence to the technology, as well as on patient satisfaction with the service. At the time of the decision, the evaluation results were not available yet. However, the professionals relied on their experience and the existing scientific evidence from other research that demonstrated that technologies and care models similar to Alpha can be beneficial for the patients targeted. The existing research was quite explicit on the benefits of physical and cognitive exercise (with and without the help of technology) for patients with cognitive impairments.Specific clinical data are not a reason to not try to implement this model. Existing research can provide us such information. <…> Data related to adherence are enough to be interpreted as a useful model for these patients.Care center 1, clinical neuropsychologistThe decisions to adopt the Alpha approach in care centers 1 and 2 were facilitated by the fact that resources for implementation were available from regional policies supporting and financing care models like Alpha. Care centers 1 and 2 planned to perform deeper statistical analyses on the clinical outcomes, cost, and savings. If a positive effect was found, the results would be disseminated, which would support their decision to integrate the Alpha approach in practice. Hence, in the case of care centers 1 and 2, professional experiences and existing scientific evidence from other research were used instrumentally (directly in decision making and action), while evaluation evidence was used symbolically .When the decision was made, we didn’t have any results yet. <...> Our experience and preliminary data showed that this model is quite good. <…> Managers trusted our previous evaluations of similar models and thought that it will be the same. <…> We have to redo the economic evaluation to see how much it actually costs and how much we save.Care center 2, quality directorCare center 3 planned to use the evaluation results to make a decision regarding adopting the Alpha approach in its clinical practice. The organization planned to present the evaluation results to the board and express a need for an eHealth solution like that tested in the Alpha project. Once approval from the board is obtained, the technology can be purchased and integrated in clinical practice. In this case, using the evaluation results in decision making for practice improvement indicates instrumental use.Once we demonstrate that the results are OK, we are in a position to escalate it to decision makers, and we are able to incorporate it in our organization.Care center 3, Head of eHealth researchCare center 4 decided to abandon the Alpha approach. Professional experiences were the primary influence on this decision. The concept was abandoned when the staff realized, over the course of the trial, how many resources the new model requires when applying it within the context of care center 4. It was deemed not the right time for the concept to be adopted in the organization. After the project finished, staff’s experiences in the care process of Alpha were presented to management. In the case of care center 4, professional experiences were used instrumentally (directly in decision making and action).We didn’t pay so much attention to the results of the evaluation. We looked at what does it mean to work with patients in a situation like that. <…> For management, the descriptive conclusions were more interesting than the analysis.Care center 4, project managerPublishing the results from the Alpha project in scientific outlets was initiated by almost all the partners of Alpha (except care center 4). The care centers used the clinical outcomes, quality of life, patient and employee satisfaction, and cost data in the scientific publications. The research and development companies used the adherence data and the feedback from patients and health care professionals related to their specific technology. Publishing the evaluation results helped these companies strengthen their image by demonstrating a case of the technology application in a real setting. In addition to the evaluation results, the partners used the existing scientific evidence from other research to build a case for research.Since scientific publishing did not directly relate to decision making in practice, such use of evaluation evidence and existing evidence from other research was deemed conceptual.Most of the partners  used Alpha evaluation results and professional experiences when applying for further research funding. Evaluation outcomes, experiences, and lessons learned in Alpha provided the basis for the case and allowed ideas to be built that could be applied in the next project. In such a case, Alpha evaluation results and professional experiences did not change local practices but increased the knowledge and understanding of the partners that subsequently helped to develop better research ideas and improve the design of the future studies. Hence, such use of evaluation evidence and professional experiences was deemed conceptual.Science institutions 1 and 2 and care centers 1, 2, and 3 presented Alpha results in regional policymaking activities as a concrete local example of an eHealth-supported care model within their regions. For this purpose, science institution 2 used the managerial and economic evidence from the evaluation of Alpha to demonstrate the possible impact of the eHealth solution on the local care facility. In addition, science institutions 1 and 2 used the health care professionals’ feedback and perceptions on working with Alpha in their local contexts to demonstrate local applicability. Since the Alpha case served as an example and was not meant to make decisions based on its results, such use of evaluation evidence and professional experiences in policymaking was deemed conceptual.When you discuss a real case here in <region>, these messages are stronger than to discuss cases in <another country> or to say that literature says these things are useful.Science institution 2, scientific coordinatorThe evaluation results of Alpha helped research and development company 2 in making decisions to improve its technology. The company focused on the patients’ and health care professionals’ feedback and preferences in relation to its technology and initiated actions to improve it. Since the evaluation evidence was used directly for decisions and action, we classified such use of evidence as instrumental.Professional experiences with Alpha were used by science institution 1 in teaching students and health care professionals. Science institution 1 relied on the professionals’ feedback and perceptions on working with Alpha in their local contexts, as it demonstrates local applicability. Since the Alpha case served as an example and decisions were not meant to be made based on its results, such use of professional experiences in teaching was deemed conceptual.We used the experience with the care models as an example in our courses. <…> We use it for practitioners as a subject to discuss and reflect upon.Science institution 1, director of the research centerThe first barrier to the use of evidence was related to the number of variables included in the Alpha evaluation. In most of the project locations, the scope of evaluation was deemed too extensive . The interviewees indicated that the time needed to collect this amount of data for every patient was too long. The clinicians had to fit the data collection into their routine work during meetings with the patients. Consequently, the clinicians were making choices about which data to collect at a particular time. Such trade-offs between data collection for the project and time spent with a patient affected the completeness of data collected and consequently the quality of evidence produced in the evaluation.When you want to monitor a lot of variables, it is directly related to the time you need to spend with the patients. <…> The target should be to optimize how we collect the variables and information using not that much time.Care center 3, Head of eHealth researchThe second barrier to the use of evidence was related to the Alpha evaluation design when comparing before-after situations. Some of the interviewees with a clinical background disagreed that eHealth integration decisions can be purely based on hard facts and not on evidence from the evaluation when making such a decision. According to these respondents, novel eHealth-supported care models tested during trials are complex dynamic systems within the local context that vary, have differences in culture, and are affected by social interaction. Since these care models cannot be assumed as stable controlled systems, the assumption of stability in the traditional before-after measurement design of an eHealth trial provides less valuable information for eHealth integration decisions to improve practice. Additionally, such an evaluation design comparing before-after situations does not maximize the potential to enhance local learning. The interviewees suggested that people’s experiences with an eHealth solution and process measures, both captured continuously, could lead to iterative adaptation and adjustment between the eHealth solution and the context. Such iterative evaluation would provide higher value in these eHealth integration decisions to improve practice.If we own the evaluation, we would take repeated measurements for improvement efforts and enhanced learning, rather than traditional approaches.Care center 2, quality directorIn this study, we analyzed how different types of evidence, generated through the development and evaluation of an eHealth trial, are used by diverse stakeholders. This work demonstrated that evidence from eHealth trials is used in more ways than decision making regarding clinical integration or policymaking. In addition, different stakeholders can use the evidence for scientific publishing and dissemination, eHealth technology improvement, research funding applications, and teaching.We found that professional experiences seem to have more influence over decisions regarding eHealth integration into clinical practice than formal evaluations and research. Learning whether and how an eHealth solution could fit within the local context provides key information for local decision making. If the design of an eHealth trial fails to create conditions for professionals to gain experiences, it might prevent learning and obtaining evidence that are crucial to increase the success rate of eHealth trials in their post-pilot phase and reduce “pilotism” ,20. MoreTo support the use of evaluation evidence, one could consider assessing a few, rather than a large number of, variables during an evaluation. It can help ensure the quality of data collected, preventing from making trade-offs between the time required and quality of the data. Additionally, shorter iterative cycles of evaluation could create better possibilities for health care professionals to gain more experience and use it as evidence in decision making regarding integration of eHealth solutions. Professional experiences could enhance evidence when relevant professionals are included and accumulate experience during eHealth trials. Such an approach could increase the degree of learning and chances that the eHealth solution would be integrated into practice and reach sustainability. Existing scientific evidence collected from previously conducted projects or initiatives in the same location as an eHealth solution is implemented can support decisions regarding eHealth integrations better than scientific evidence obtained from other locations. Research evidence produced in other contexts can be problematic for direct translation into making such decisions. However, it has value for scientific dissemination.The findings of this study were based on a specific innovation research project funded by the European Union. The interviewee sample was delimited to the partners of the project, since they had deep knowledge of the project and its results and were in the favorable position to use the evidence obtained. However, perspectives of the funding agency, industry, or governments could be a valuable avenue for further research. Similarly, evidence produced in other settings and study designs could provide a different view of the use of evidence. Furthermore, the study captured the situation 8 months after the Alpha project was finished. However, the use of evidence might be more extensive in later stages due to the so-called “gestation period” .Previous research on evidence-based practice usually described clinical implementations and policymaking ,7 as eviOur study shows that the typical discourse on the instrumental use (in making decisions) of evidence (and lack of it)  does notOur study indicates that experiences in eHealth trials matter more than facts when it comes to how evidence is used for eHealth integration. The reason for this could be that “hard facts” from evaluation are difficult to straightforwardly implement in a complex health care reality ,27. AlthWe identified a number of strategies to support the use of evidence in practice in the context of eHealth trials. First, we suggest focusing on a smaller number of variables during evaluation, to ensure the quality of collected data. Such a strategy is contradictory to the ever-expanding evaluation frameworks that include a growing number of variables eg, ,32) and  and 32])We conclude that various stakeholders  benefit from evidence differently. Therefore, the delimited focus of research on decision making in clinical settings or policymaking does not capture the actual beneficiaries and realities of evidence use. In addition, when making decisions regarding improving practice, stakeholders do not necessarily rely on the factual evidence produced by evaluations. We conclude that, in the context of eHealth, professional experiences seem to have more influence over decisions than formal evaluations and research. Hence, we suggest that scientific and practical discussions around evidence-based practice in the context of eHealth should include all sorts of evidence . Additionally, it could be beneficial to have an in-depth view on how the evidence is used. This could help expand the conventional focus on clinical settings or policymaking and the direct use of evidence produced by research or evaluation when making decisions. Initiatives to support and standardize evidence-based practice in the context of eHealth should take these complexities into consideration to achieve better uptake of evidence in practice.


In most occupational settings, several chemical agents are commonly found, and the associated exposure risk for workers must be assessed. For this purpose, air samples can be collected and analyzed. AltrexChimie is a web application that helps industrial hygienists in the organization of the air sampling strategy and in the subsequent phases of data management, analysis, and communication. AltrexChimie contains a database of more than 550 chemical substances and their respective French Occupational Exposure Limit Values (OELV): Custom OELVs can also be defined by the user. AltrexChimie helps with the definition of key features of the sampling strategy, in particular by promoting a methodology for the design of Similar Exposure Groups (SEGs). Once measurement data are entered, they can be analyzed to obtain exposure diagnostics. Data management features allow for the easy storage and retrieval of measurements, and comprehensive dashboards help industrial hygienists (IHs) in the communication of results. Finally, with AltrexChimie it is also possible to assess exposure to multiple chemical substances and their additive effects. While most free software applications for the assessment of chemical exposure focus on the statistical computation of specific indicators, AltrexChimie offers several tools to assist IHs in the exposure assessment workflow. In most occupational settings, several chemical agents are commonly found and the associated exposure risk for workers must be assessed. Exposure assessment is a multi-step procedure which consists in making an inventory of the chemical products used at the workplace, of their hazards and their use conditions followed by a chemical risk assessment to prioritize those work situations where risk is higher. In Europe, these rules aiming at assessing and managing risks are laid down in several European Directives ,2,3. TheThe chemical substances targeted by air measurements are related either to possible long-term health effects, like carcinogenic substances with a dose–effect relationship, or to short-term effects, like irritant or neurotoxic substances. Conversely, poorly volatile substances with acute toxicity, sensitizers, or carcinogens without a dose–effect relationship are less likely to be targeted by measurements.A sampling strategy defines the organization required to perform representative measurements of exposure by inhalation to chemical agents in the workplace. The concept was first introduced by US National Institute for Occupational Safety and Health in the 1970s  and subsSEGs are the basic unit for establishing a sampling strategy. A SEG is a group of workers sharing a similar profile of exposure, meaning an exposure to the same chemicals, with the same intensity and variability. Industrial hygienists (IHs) use their expertise and their own perception of the work performed in the enterprise to define the SEGs. For each SEG in the enterprise, measurements are collected during a certain period of time to take into account possible variations related to operational conditions, as well as to other factors such as temperature or humidity.Often, the aim of exposure measurements is to assess compliance with occupational exposure limit values (OELVs). Statistical inference is used to compute indicators allowing IHs to decide whether a work situation is compliant or not. In general, exposure to agents with chronic health effects is considered acceptable when less than 5% of all exposure measurements are above the OELV ,7,8. OneThe computation of statistical indicators of exposure is made easier by the availability of several software tools. SAND and STAND are two software libraries for the R programming language  which inhttps://altrex.inrs.fr) was initially developed as a standalone program in 1993 and recently received a major update and migration to a web application. It computes statistical indicators and it manages exposure measurements at enterprise level. AltrexChimie is also linked to other tools for prior risk assessment such as Seirich [Finally, AltrexChimie , and the list of substances correctly or poorly controlled. An overview of the computed diagnostics is presented in Two additional views are available in the “Dashboards” tab: The first one introduces the substances and the SEGs and the second one presents the multiple exposure diagnostics.These management functionalities are a core characteristic of AltrexChimie and they are not found in similar free software applications, which are primarily designed for the computation of statistical indicators from a data series.An exhaustive exposure assessment of the entire worker population would require measurements carried out each day, on all workers, during all working shifts. Such a sampling strategy is clearly impossible to put into place. Therefore, the population’s features are inferred from the characteristics of the SEGs: Measurements from a SEG should be representative of both exposure levels and their random variability, which is made up of both a spatial and a temporal component.Spatial variability is taken into account by using a map of the company, that is by identifying those areas within the enterprise where chemical substances are used. The map should be precise enough to identify work units, workplaces, and tasks performed at the workplace. Additional information related to the spatial variability of exposures can also be obtained, for example the differences in gestures and postures among workers.Define a hierarchical map of the enterprise that describes the different work units. The workstations are included in the work units. Each workstation has its own type of general ventilation and its own set of performed tasks. Each task has its type of emitting process and its own local exhaust ventilation (LEV);List the chemical substances to be measured for each task;List the jobs in the enterprise;Define the SEG as different associations of work units/tasks/jobs;Complete the definition of the SEG with additional specific information.The following five-step procedure is recommended for designing a SEG that considers spatial variability:The temporal component of the variability accounts for the variation of exposure due to environmental conditions: e.g., high temperatures can increase the volatilization of substances, or workers may open doors and windows, increasing natural ventilation. Temporal variability also includes the random variations observed from one day to another. Therefore, measurements for each SEG have to be spread over a year, for all possible work shifts.The number of measurements to be performed for each SEG is directly related to occupational health regulations, to standardization requirements, and to the internal procedures of the enterprise. On the other hand, the type of statistical analysis that can be applied to data is dependent on the number of measurements performed . Thus, tIn AltrexChimie, the necessary steps for SEG design are implemented in three tab screens: “Map”, “SEG”, and “Determinants of exposure”. The “Map” tab implements steps 1 and 2 of the methodology . The “SEG” tab implements steps 3 and 4: jobs and linking of the map with the jobs . The creIn other free software applications, the definition of the SEG is omitted most of the time, or it has to be a description not directly connected to the data series . In some tools, like Expostats or BWStats, it is possible to include determinants of exposure in the analysis and also to identify the workers linked with each measurement of the data series.The exposure assessment diagnostic in AltrexChimie relies on the assumption that results follow a lognormal distribution ,17,18, tSome additional hypotheses must be verified on the data series. The variability of measurements must be low enough to ensure that the SEG is indeed homogeneous. The Geometric Standard Deviation (GSD) is the preferred statistical indicator for this control: As a rule of thumb, a GSD lower than 3 is generally considered a good indicator of the SEG homogeneity . The exiAmong data, some values can be lower than the analytical limit of detection (LOD) or the limit of quantification (LOQ). These so-called left-censored data require specific statistical analysis which depends on their proportion ,20,21. WUnder a lognormal model, computed statistical indicators provide insights on the probability of measurements being in exceedance of the OELV. For example, if the 95th percentile of the probability distribution of concentration values is inferior to the OELV, then the expected proportion of measurements in exceedance of the OELV is lower than 5% . The excIn France, because of uncertainties inherent to the lognormal model, a 70% confidence interval on the exceedance fraction is also computed to assess compliance with regulatory OELVs ,8,27. ThThe complete online report provided by AltrexChimie contains some additional graphs: The probability density, the log-log graph, and the box and whiskers plot. Data provided in the report include: The number of measurements and their type (short-term/long-term), descriptive statistics , the Shapiro–Wilk statistic and its p-value , Spearman’s test and its p-value , an ANOVA for each determinant of exposure and the exposure diagnostic .When multiple substances having the same toxic effects are measured for a SEG on the same day, a sum of the measurement/OELV ratios calculated for each substance can be computed. The result of this computation is a multiple exposure index, which is considered indicative of the possible combined toxic effects of the mixture of substances to which workers could be exposed in the described situation, assuming additive effects . In pracIf the result of the exposure diagnostic is non-compliance with the OELV, then corrective actions must be performed in order to suppress or reduce exposure. If the result is compliance with the OELV, then periodical controls must be regularly performed, in order to ensure that the process is well controlled. The lognormal model can be used to provide indications on the optimal frequency of these controls. The higher the exceedance fraction, the more frequent the controls. In AltrexChimie, the value of the multiple exposure index is used like an exposure measurement value, thus allowing its statistical treatment.No other free software provides explicit multiple exposure assessment functionalities. Meanwhile, all of them can be used with multiple exposure indexes instead of measurements. The statistical computations offered by AltrexChimie are among the simpler ones, like the ones proposed by BWStat, IHStats, or the R libraries SAND and STAND. Hyginist offers a greater number of statistical indicators, allowing for a comprehensive assessment for experts. Expostats and IHDA offer Bayesian-based computations, which facilitate the communication of results under a probabilistic perspective; IHDA also allows prior expert judgment to be included in the computation.To better illustrate the different functionalities offered by AltrexChimie, two examples of application are proposed: They concern (1) the manufacture of swimming pools and (2) rotogravure.The manufacture of swimming pools using composite materials is a major activity in France: The related industrial sector, “Plastic products manufacture for construction” includes more than 1000 enterprises . ManufacIn the following example, four task-based SEGs are designed, considering the manufacturing process and the map of the establishment: Gel coating, stratification, reinforcement, and finishing a. For eaThe printing industry represents around 3000 companies and employs almost 40,000 workers in France. The inks in use are composed of mixtures of pigments and solvents. The printing machines are composed of engraved rotating cylinders, which apply the ink on a large coil of paper at high speed. Once the ink has dried, the coil is cut and folded to produce different types of publications, including newspapers, booklets, or catalogues. When the production changes, the printing cylinders are replaced in the machine and cleaning and maintenance operations are performed, during specific work shifts. During these operations, workers are exposed to some solvents. In this example, the solvents are ethanol, methyl ethyl ketone (MEK) and 2-butoxyethanol. Only one SEG is defined (“Printing Operators”) and its collective protection equipment are shown on With AltrexChimie, it is possible to compute several multiple exposure diagnostics for a SEG, depending on the different combinations of substances. In When using AltrexChimie to compute multiple exposure diagnostics, the appropriate combination of substances has to be carefully defined: The toxicological effects of the substances must be considered additive on selected target organs. Depending on their toxicological properties, all the measured substances may not be assessed at the same time. Groups may be formed by toxicological classes and a substance may contribute to several classes. For more information, refer to MiXie ,30. To support IHs, AltrexChimie provides a list of 567 substances, complete with OELVs, EU’s regulatory hazard statements  and INRS’s toxicological and technical fact sheets. Users can also import or create their custom list of substances, complete with alternative limit values for non-regulatory assessments.The regulatory context in France does not take into account important parts of exposure assessment. In particular, assessment relies on the exceedance fraction computed on the lognormal hypothesis, but the regulation does not make it mandatory to verify this hypothesis. AltrexChimie compensates this gap by automatizing this verification. However, as a rule, results of the exposure assessment should always be subject to critical evaluation and the (in)adequacy of the diagnostic with prior risk assessment should be verified and analyzed.The requirements of the concept of threshold versus the concept of an estimate interval, such as the result of a Bayesian analysis ,32,33 shBecause of the variability in exposure levels in the workplace, the odds of observing a measurement with a concentration greater than the OEL increases with the number of measurements. Thus, when a high number of measurements is available, exposures above the OEL are generally observed. In this case, an enterprise complying to the EN 689 would be required to improve the process or the risk mitigation equipment, even if the exceedance fraction is low and its value reliable, because of the high number of measurements. This is an intrinsic contradiction of the proposed procedure that clearly highlights the lack of guidance when a substantial number of measurements is available. This explains the computational choices made in AltrexChimie, which are different to IHDA or Expostats.One major issue during the development of the new version of AltrexChimie as a web application was the protection of user data. While an online tool is less limited by operating systems and updates, an offline tool is more reassuring to the user because important data are not transmitted over the network. To guarantee data confidentiality, innovative software functionalities were implemented, so that users can keep their data on their own computers, and no data is stored on the INRS servers. Other online tools such as Expostats and BWStats V3 do not provide a fully structured data storage system: Only screenshots and copy/pasting are allowed. The possibility of publishing an open-source software library derived from AltrexChimie was also discussed during the development phase. Some companies already have measurement management tools, either purpose-designed or included in laboratory information management software and they may therefore be interested in upgrading by adding only AltrexChimie’s statistical computation engine. This possibility was rapidly excluded, because such a library would not provide significant advantages over already existing packages such as STAND, which can be used instead of AltrexChimie in this particular context.Finally, some important limitations of AltrexChimie are related to the choices made for the multiple exposure assessment. First of all, the toxicological information of the effects of substances on target organs is not included in AltrexChimie, and the effects are only considered additive. More importantly, the statistical procedure used to compute a multiple exposure diagnostic is the same as for a single substance. This hypothesis does not take into account important issues both in toxicological terms like the toxicokinetics of substances in the body and in statistical terms like the possibility of multimodal distributions of exposure measurements. This feature requires more research work to be fully reliable but it is a first step towards a statistical assessment of multiple exposure measurement data.Measurement-based exposure assessment is the final step in chemical risk assessment, which is applicable when regulations make it mandatory  or when other simpler and cheaper methods fail to conclude on the risk posed by a work situation. Exposure assessments must be carefully planned, organized, and carried out in order to obtain reliable results to support conclusions on the risk of workers. Thus, the adopted assessment methodology is of paramount importance. Because this methodology relies on complex expert statements and hypotheses, it is important to have suitable tools to put it into practice.In this paper, we presented the methodology implemented in AltrexChimie for the collection and interpretation of chemical substances measurement data, obtained from air sampling in occupational settings. AltrexChimie implements the statistical models required for the computation of an exposure diagnostic with regards to a limit value. The software provides assistance for the design of similar exposure groups (SEGs), a set of workers whose exposure to chemical substances is assumed to be similar. It also provides all the functionalities for the management of the SEGs, the measurements, and the diagnostics. Industrial hygienists must use complex methods to assess exposure, but they also have to communicate the results in a relevant manner. Indeed, they must convince decision-makers to engage in the required preventive actions. AltrexChimie offers comprehensive dashboards, which can be copy/pasted. Finally, AltrexChimie allows assessment of exposure related to a mixture of substances by hypothesizing that the effects on target organs are additive. AltrexChimie can be accessed from altrex.inrs.fr; it does not require users to log in, and user data is stored on the user’s computer only.Several improvements of the exposure assessment methodology and the associated tools can be envisioned. Among these, a simple procedure for handling left-censored data (measurements below the LOD); guidelines for analyzing SEGs with large measurement datasets, using more specific models; recommendations on the use of the procedure for biometrology data. Finally, a shift towards the Bayesian methodology with its underlying assessment based on uncertainty would certainly allow both more realistic (yet less accurate) assessments and provide a greater use of the knowledge and expert judgment of industrial hygienists in the overall assessment, as opposed to simply applying statistical routines to analyze measurement data.


Several physicians have been nominated for the Nobel Prize in literature, but so far none of them have received it. Because physicians as women and men of letters have been a major topic of feuilletons, seminars and books for many years, questions arise to what extent medicine was a topic in the proposals for the Nobel Prize and in the Nobel jury evaluations: how were the nominees enacted (or not) as physicians, and why were none of them awarded? Drawing on nomination letters and evaluations by the Nobel committee for literature collected in the archive of the Swedish Academy in Stockholm, this article offers a first overview of nominated physician-author candidates. The focus is on the Austrian historian of medicine Max Neuburger (1868–1955), the German novelist Hans Carossa (1878–1956), and the German poet Gottfried Benn (1886–1956), but it also briefly takes further physician-author nominees into account such as Sigmund Freud (1856–1939) and William Somerset Maugham (1874–1965). The article is part of an interdisciplinary medical humanities project that analyses nominations and committee reports for physicians and natural scientists nominated for the Nobel Prize from 1901 to 1970. Physician-authors have long attracted the interest of scholars in the field of medical humanities. Why do they write? The reasons for the decision to pursue a literary career parallel to their medical work are of course multifaceted. As Anne Hunsaker Hawkins notes "the act of writing in some way seems to facilitate recovery: the healing of the whole person" , for patThe International Federation of Societies of Physician-Writers (FISEM), in 1973 renamed World Union of Physician Writers (UMEM). A closer look at these lists reveals that no physician-author to date has received the Nobel Prize in Literature so far. Does this matter? At least it does in terms of reputation for single authors but also for the group of physician-authors.Extensive lists of well-known and less famous physician-authors exist, made available by associations founded especially by and for physician-authors, like The Economy of Prestige: Prizes, Awards, and the Circulation of Cultural Value, the decisions by the Nobel committee not only means a commercial boom for the selected authors and their publishers, they also shape the literary canon globally  or works like Das Verbrechen als Krankheit (1927) [The Crime as Disease] or Über Eugenik und Euthanasie im Licht der nationalsozialistischen Ethik (1934) [About eugenics and euthanasia in the light of National Socialist ethics], was signed by twelve scholars in 1926.Language, length and style vary: Most of the nominations are written in English, German, French or one of the Scandinavian languages. Some contain only a few sentences, but in other cases nominators write 50–100 hundred pages in order to try to convince the jury. The latter is not seldom the result of campaigns where a comprehensive nomination is followed by numerous endorsement letters. One example is the nomination for the nowadays largely forgotten German author and physician Georg Bonne (1859–1945). The first nomination of Bonne, author of decidedly anti-Semitic books like The method of selecting a Nobel laureate is quite similar in all of the five different prize committees , but there is at least one important difference relevant for this study. In contrast to the Nobel science and medicine prizes, for which only former laureates as well as professors from a number of invited universities each year are eligible to nominate, all university professors of literature and philology as well as presidents of national literary societies may nominate candidates for the Nobel Prize in Literature. Once the nominations have been submitted, the proposals are discussed in the Nobel committee, and the most promising candidates are examined in detail. The Nobel committee evaluations were during the examined period of time written in Swedish.Nobelprize.org), we found thirteen nominated physician-authors from 1901 to 1969. Afterwards, the application (by the first author NH) to study the original documents at the Swedish Academy in Stockholm was accepted.Among the large pool of nominees (often 50–100 candidates per year), we find world-renowned candidates, but also–even in their time–completely unknown authors. After a systematical review of the Nobel nomination database for the Nobel Prize in literature , Mikhail Bulgakov (1891–1940), Arthur Schnitzler (1862–1931), Louis-Ferdinand Céline (1894–1961), or William Carlos Williams (1883–1963) were never proposed. However, this sample of physician-authors reflects the wider picture of the "Nobel population" (both Nobel nominees and Nobel laureates) in the sense that men from the West (in this case only Europeans) are overrepresented. We found no female physicians among the nominees in the Nobel committee archive for literature. The only writer who had studied medicine (but never graduated) and subsequently received the Nobel Prize in Literature was Johannes V. Jensen in 1944 with the prize motivation: "for the rare strength and fertility of his poetic imagination with which is combined an intellectual curiosity of wide scope and a bold, freshly creative style."In the following, we will in a chronological order briefly discuss the nominations and evaluations of Max Neuburger, Gottfried Benn, and Hans Carossa to show how their background and experiences in medicine colored the discussions in three different ways.Josephinum in Vienna. In 1938, he was forced to leave Austria because of his Jewish descent  created waves that had hit the literature”, and that Gottfried Benn was a central character in this context. He had according to Malmberg succeeded in transforming his experiences as young doctor to compelling poems, novels and essays.Much is written about how Benn’s experiences in hospitals colored his writings, apparent already in his first book entitled In spite of the strong support, the nominators did not convince the Nobel jury. Benn was in the end not viewed as prize-worthy. What Benn himself thought about the prize is unclear, however, even before his first nomination he wrote in February 1949 to the Bremen merchant Friedrich Wilhelm Oelze (1891–1978), who had mentioned him in a previous letter as a possible candidate: "Nobel Prize. No jokes, please! I know where I belong and where not" .Even after his death in 1956, Benn was mentioned in the Nobel files. In 1961, the Stuttgart professor of Literature Fritz Martini (1909–1991) wrote in a nomination that Benn along with Bertolt Brecht (1898–1956) would have been obvious candidates, but since they both had passed away and posthumous prizes were not allowed, he put forward Heinrich Böll (1917–1986), who was to receive the award in 1972.Hans Carossa is one of the most well-known German physician-authors of the 20 Century . SeveralGesammelte Gedichte were published. He received several awards and honors for his books, including the Gottfried Keller Prize (1931), the Goethe Prize (1938), and the Grand Cross of Merit of the Federal Republic of Germany (1953). On his 70th birthday, he was awarded a doctor honoris causa of the University of Munich.Carossa studied medicine in Munich and Wurzburg and earned the title Dr. med. at Leipzig university in 1903. One year later, he took over the medical practice of his father in the city of Passau in southern Germany close to the Czech border. In 1910, his first poems From 1942 to 1956, Carossa was nominated at least six times . In 1952That year, Carossa was evaluated by Per Hallström, who judged him as prizeworthy, but the committee preferred other candidates. As was the case with Gottfried Benn, Carossa was repeatedly discussed in the prize committee and viewed to be a strong nominee. In 1955, one year before he passed away, the committee stated that with the book "Der Tag des jungen Arztes" he has continued his very "captivating autobiography" that " most certainly will become one of the classical novels in the literary canon of modern Germany". Nevertheless, the committee concluded that it could not yet give him a full recommendation .The result of this overview is not clear-cut. Some of the nomination letters did barely contain any links to medicine at all. In the proposals for William Somerset Maugham, for instance, it was only with the abbreviations M.R.C.S. and L.R.C.P. indicated that he was a member of the Royal College of Surgeons (England) and a Licentiate of the Royal College of Physicians of England. Medicine was not a focus in the nominations for the Dutch physician Simon Vestdijk either . Other nliterature and what as medicine in a Nobel Prize context. The Nobel committee seems not to have had a clear definition, and the boundaries have changed over time. The topic was repeatedly discussed in the committee, e.g. after nominations of physicians who had been nominated for a Nobel Prize in more than one category. When Sigmund Freud was proposed for the literature prize, for instance, the committee members stated that he should better have been nominated for the prize in physiology or medicine [The nomination for Neuburger, in particular, raises the question what is to be considered as medicine . (In facNobel Prize nominations and evaluations give new insights into the reputation of some physician-authors of the 20th century, particularly regarding the nominees who never received the award, or to paraphrase the Anna Karenina-principle: "All Nobel laureates are alike; each unhappy nominee is unhappy in its own way". Most of the above-mentioned doctors  gave up


Sir,We have read with interest the concerns from Dr. We were aware of the error in the recommendation regarding progesterone for luteal phase support prior to the correspondence with Dr. Piette was received, and steps were already taken to correct this. The error was introduced by the publisher during typesetting. The error has been corrected and the paper has been republished with an accompanying erratum.https://www.eshre.eu/Guidelines-and-Legal/Guidelines/Guideline-development-process), the guideline group performed a formal literature search on efficacy and safety of dydrogesterone for luteal phase support. Studies reporting on safety of dydrogesterone for luteal support in IVF .As part of the guideline development protocol, the guideline draft was open for review for 6 weeks, between 12 February and 26 March 2019. All reviewers, their comments and the reply of the guideline development group are summarized in the review report, which is published on the ESHRE website as supporting documentation to the guideline (The opportunity for people outside the guideline group to formulate comments and concerns, and the transparency in which these are either addressed or refuted, is an important process in guideline development ensuring trustworthy and independent guidelines. However, adapting recommendations based on comments formulated outside the stakeholder process may jeopardize the objectivity of the guideline.Of course, if new evidence is published, recommendations may need to be revised. As such, we have performed an update of the literature published on dydrogesterone for luteal support in IVF which confirmed the lack of new data published in this context. The meta-analysis by In conclusion, and although we value any feedback to the ESHRE Guidelines, the guideline group has no arguments to consider an adaptation of its current recommendation regarding dydrogesterone for luteal support in IVF/ICSI.


Electrocardiogram (ECG) signals are time series data that are acquired by time change. A problem with these signals is that comparison data that have the same size as the registration data must be acquired every time. A network model of an auxiliary classifier based generative adversarial neural network that is capable of generating synthetic ECG signals is proposed to resolve the data size inconsistency problem. After constructing comparison data with various combinations of the real and generated synthetic ECG signal cycles, a user recognition experiment was performed by applying them to an ensemble network of parallel structure. Recognition performance of 98.5% was demonstrated when five cycles of real ECG signals were used. Moreover, 98.7% and 97% accuracies were provided when the first cycle of synthetic ECG signals and the fourth cycle of real ECG signals were repetitively used as the last cycle, respectively, in addition to the four cycles of real ECG. When two cycles of synthetic ECG signals were used with three cycles of real ECG signals, 97.2% accuracy was shown. When the last third cycle was repeatedly used with the three cycles of real ECG signals, the accuracy was 96%, which was 1.2% lower than the performance obtained while using the synthetic ECG. Therefore, even if the size of the registration data and that of the comparison data are not consistent, the generated synthetic ECG signals can be applied to a real life environment, because a high recognition performance is demonstrated when they are applied to an ensemble network of parallel structure. Among the biometric signals investigated in studies of user recognition methods, electrocardiogram (ECG) signals are bio-signals that are produced autonomously and show unique characteristics of individuals according to such factors as the heart’s position, size, and structure, as well as age and gender . HoweverTo solve this problem, when conventional data normalization methods such as data copy and interpolation method are applied to the ECG signal and converted into data of the same size, important characteristic information such as time information, amplitude, and interval of P, QRS, and T waves is lost. In addition, the heart rate and waveform of the ECG signal changes due to the individual’s physical activity, measurement time period, or mental influence, and accordingly, a section in which the waveform changes significantly and a section with relatively little change occurs.In fact, as shown in To solve this problem, studies have been conducted for various data schemes using neural networks. A generative adversarial network (GAN), which is the algorithm attracting the most attention in recent studies, is a model that generates data through adversarial learning of a generator and discriminator composed of two mutually different multilayer perceptrons. With a GAN, generation of data is simple, but it has the inherent problem of learning instability, because its approach in the learning process is a minimization problem .In this study, synthetic data were generated through an auxiliary classifier GAN (ACGAN) using the class information as an auxiliary classifier. The structure of the ACGAN used in this study was designed to have mutually different convolution neural network (CNN) models for the generator and the discriminator. For the generation of one-cycle ECG signals, the input data used for the generator were the class information and the noise that had the same size as the cycle to be generated ,8. BecauThis work is organized as follows: In The construction of large-scale data using personal biometric information is difficult in the field of medicine because of constraints, such as private information protection and acquisition cost; as a result, the problem of imbalanced big data arises. Imbalanced big data consist of majority classes containing larger data than other classes and minority classes containing smaller data than other classes. If a machine-learning model is learned using imbalanced big data, it is biased toward the majority classes, and, consequently, a problem occurs: low recognition performance is shown for the minority classes, or the possibility of recognizing newly input data as minority classes decreases.In recent years, wearable device products like those shown in Hartmann et al.  used a WTomer et al.  proposedA variety of studies have been performed on personal recognition using ECG signals based on deep learning. As a conventional method of applying the ECG signal to deep learning, Jun projected a one-dimensional ECG signal into a two-dimensional space to solve the problem of extracting various sampling rates and amplitudes depending on the acquisition equipment and to increase the limited ECG signal. The result of 99.05% recognition is shown by applying the 2D transformed data to the CNN. Compared to general 2D images, 2D ECG images showed simple patterns and showed high recognition performance without deep network design.Zhai et al.  transforKiranyaz et al.  applied Liu et al.  proposedThe ACGAN is a model that generates data by using input class information, and it discriminates the classes of generated data through the auxiliary classifier. While the conventional discriminator performs learning to discriminate whether data are real data or generated synthetic data, the ACGAN performs learning to classify the data classes as well. The generator generates data not to deceive the discriminator, but to produce accurate classification results for the generated data. The data generated through such a learning process can obtain similar results to the real data. Furthermore, data of a certain class can be generated using the class information.The structure of the ACGAN used in this study was designed to ensure that the generator and the discriminator have mutually different CNN models, as shown in Therefore, the generator model was designed with nine convolutional layers, two pooling layers, and a fully connected layer. The learning is performed for the discriminator model in a direction of classifying the signals and classes of the real ECG and the synthetic ECG input in the discriminator model. The structure of the discriminator model is designed with a CNN model that repeats an operation of convolution and has a shallower structure than the generator model, because it classifies the real ECG signals and class information and the synthetic ECG signals and class information. Accordingly, the generator and discriminator models generated synthetic ECG signals similar to the real ECG data through the repeated learnings.An ensemble network structure of parallel structure was designed using acquired ECG signals by user status change, as shown in The average pooling produces an effect of reducing the features of robust properties output from the convolution layer  when “0” is output many times for the operation result by ReLU, an activation function. Therefore, max-pooling, in which the maximum value is selected in the window, is used. The ensemble method improves the performance through combinations of mutually different models, and each 1D single network is composed with different parameters to detect different features. The learning is set to perform 500 and 750 times repeatedly, and the batch size, which is the number of data used in the learning each time, is set to 256 and 512. The dropout that reduces the computation time and amount by omitting a part of the network is set to between 50~70%. For the learning rate, 0.001 is applied, which is often used in general.Next, the ECG signals of output results from each network are used as registration data for retraining by combining them into one database. However, when the ECG signals output from each network are all used as registration data, even the results of low recognition rates are used as registration data because of the parameters and incorrect network design; consequently, there is a problem that the recognition performance declines. Therefore, not all output data are used, and by combining the result data of the top-three networks showing excellent performance, the registration data are composed. Finally, user recognition is performed for the recomposed registration data by relearning the time-independent comparison data in the single CNN.The type of noise in the ECG signal is a power line interference arising from the device used for acquiring the ECG signal, motion artifacts caused by subjects’ movement, muscle contractions by irregular muscle activity, and baseline drift by breathing . This noECG signals were acquired from 89 adults of various age groups from their 20s to 50s to analyze the ECG signal changes and apply them to user recognition. The equipment used for measurement was an MP160 model  and lead-1 ECG signals were acquired by using wet electrodes. Measurements were taken over the course of one year, in order to acquire time independent ECG signals, and the ECG signals were measured by defining the following four environment as ones that could change ECG signals in real environment as show in Lying down: acquiring signals for 1-min in the lying posture after rest stateStanding: acquiring signals for 1-min in the standing posture after rest stateBefore exercise: acquiring signals for 1-min in the sitting position before exercise after rest stateAfter exercise: acquiring signals for 1-min while maintaining the heart rate above 120 through the stepper exercise equipment after exerciseThe similarity between the acquired ECG signals and the generated synthetic ECG signals was measured and checked using the cosine similarity and cross correlation ,27. FirsNext, the similarity results were checked using the cross correlation quantified through the correlation analysis to investigate how similar two different signals are in the signal-processing area. After overlapping the cross-correlation waveforms of the current ECG signal cycle and the synthetically generated ECG signal cycle on the cross-correlation waveforms output from the current ECG signal cycle and the next ECG signal cycle, the similarity between the two signals was expressed numerically by using the Euclidean distance. As the Euclidean distance of two cross correlations approaches 0, it indicates the generation of synthetic ECG signals similar to the real ECG signals of the same experimentee; as it moves away from 0, it indicates a cross-correlation result of real ECG signals and synthetic signals between different experimentees.Because ECG signals of certain cycles are difficult to acquire in a real-life environment, the comparison data were constructed with various combinations of the real ECG signal cycles and the generated synthetic ECG signal cycles, as shown in In the experimental results, 98.5% recognition performance was shown when the five cycles of real ECG signals were used, as shown in In addition, the performance of the latest technology was compared using the MIT-BIH database, an open database. As shown in In conventional user recognition studies using ECG signals, experiments are performed by constructing comparison data with the same size as the registration data in the initial experimental environment. However, when the size is not identical to that of the registration data because of a lack of comparison data acquisition time, they cannot be applied to user recognition because of the data size inconsistency problem. To resolve this problem, an ACGAN network model was proposed. In this study, the similarity was measured using the cosine similarity and the cross correlation to evaluate the generated synthetic ECG signals. Furthermore, after constructing comparison data with various combinations of the real ECG signal cycles and the generated synthetic ECG signal cycles, the user recognition experiment was conducted by applying them to an ensemble network of parallel structure.In the experimental results, the proposed ACGAN confirmed the data generation of various features, and even when the size was not consistent between the registration and comparison data, the data size inconsistency problem was solved through generation of synthetic data. Furthermore, because ECG signals of certain cycles are difficult to acquire in a real-life environment, the comparison data were constructed with various combinations of the generated synthetic ECG signal cycles and the real ECG signal cycles, in which heart rate and waveform changes occurred in real life according to the user status. Then, the user recognition experiment was conducted by applying them to the ensemble network of parallel structure.In the experimental results, the recognition performance obtained when the synthetic ECG signals were used was higher than that obtained when the real ECG signals were repeatedly used. In other words, high recognition performance was shown when the generated synthetic ECG signals were applied to an ensemble network of parallel structure according to the user status changes, even if the size was not consistent between the registration and comparison data. Therefore, the applicability of the proposed model in real-life environments was confirmed. The study was conducted focusing on generation of synthetic ECG signals similar to real ECG signals. At present, synthetic ECG signals similar to real ECG signals can be generated only if the learning process is undergone for the generation of synthetic ECG signals. Therefore, a study for real-time generation of ECG signals using user information learned in advance has been planned.