The STEAM education involves children’s ability to integrate and apply their knowledge of science, technology, engineering, arts, and mathematics. The application and transfer of interdisciplinary knowledge in practical activities is the structure of STEAM education. This study assesses children’s practical drawing skills and transferable skills based on the global features of their realistic figure drawing. The drawings incorporate the visual information and the multidisciplinary knowledge that children acquire. The assessment variables of the global features are observation perspectives, baseline, and comparison. The results showed that most children present their works through the front view. The children of different age groups show differences in express baseline and comparison features. Boys and girls show some variances in baseline features. Moreover, children are relatively unskilled at applying interdisciplinary knowledge in their drawings. The education in the school fine arts curriculum is discipline-based art education (DBAE) . The DBAThe drawings present appearance information about the height, width, volume, texture, and color of objects . The appThe role of the fine arts in interdisciplinary STEAM education is to guide children in acquiring knowledge of science, technology, engineering, and mathematics in arts-themed activities. The arts-themed activities require children to sketch their pieces, learn engineering knowledge, prepare the materials for their creation, finish the artwork and present the achievements to classmates . These cSTEAM education aims to achieve children’s academic skills through practical activities . The ChiThis study assesses children’s knowledge transferable skills through the observation perspectives, baseline feature and comparison feature of their drawings. The experimental component of the study consisted of a drawing creation task. The drawing task requires asking children to draw a piece of work contain persons and a real-life scene. There is no limitation on the gender and age of the people in the drawing. Before creating drawings, children need to learn the characteristics of the structure, color, volume, and texture of objects associated with life scene theme. Objects from life scenes are familiar and accessible to children. Children can draw regarding the actual appearance of objects. These observations and drawing processes also ensure that the children’s work is closer to the requirements of the test task. Realistic drawing is a record of people’s visual experience. In drawing creation, children need to show the observed features of shape, height, texture, texture, and location relationship of objects in their works. Therefore, children’s knowledge transferable skills in this study is assessed by the composition, structure, and location relationship features between objects in their drawings.The total number of children who participated in the test was 1,000, including 526 girls and 474 boys. These children’s intelligence levels are within the normal range. The cultural environment, educational level, and economic development level affect the academic skills of children . To avoiBefore the drawing test, children and their teachers have been told the content and purpose of the drawing test. Children were required to complete the drawing test independently within 40 min. They were not given any hints about drawing techniques, material application, or creative ideas during the test. This step ensures that their drawings more accurately reflect their drawing abilities.Children utilize the A4 (210 mm × 297 mm) paper during the drawing task. Using A4 size paper can help children to better complete the drawing. The smaller paper, such as A5 and B6, has a limited drawing area. This condition will disturb children from drawing the details of figures or scenes. Oversized drawing paper  has too much blank space. This condition may cause children to be unable to complete drawing within the time specified. Children’s drawings may show incomplete scene content. As for drawing tools, children can choose pens according to their preference during the test. Different types of paintbrushes hold correspond to lines and color features. For instance, watercolor pens present thicker lines. Pencils, colored pencils, pens, and ballpoint pens deliver fine lines. On the one hand, children may choose brightly colored watercolor pens to highlight the main content of the drawing. For example, emphasize the contour lines of the figures and objects in the drawing. On the other hand, some children may prefer to use pencils, colored pencils, pens, and ballpoint pens to show the detailed features of the objects in the drawing. Children may use these drawing tools to express the details of the plant’s leaf veins, flower stamens, and the figure’s eyebrows and hair. Watercolor pens present a higher brightness of color than colored pencils, pencils, pens, and ballpoint pens. Some children may tend to create brightly colored or light-toned works. This study assesses the shape and position of objects according to children’s viewing perspective. The color and texture of the drawing materials do not determine the positional and shape characteristics of the objects in drawings. Also, the ability to apply material is not the evaluation indicator for this study. Therefore the color effects presented by different painting materials will not affect the results of this study. If the children’s drawing tools are specified in advance, it may change their original creative drawing habits. Limiting drawing tools may affect the expression of textural features of lines and detailed features in children’s work. Drawing tools familiar to children will reduce the time it takes for children to adjust new drawing materials. Allowing children to choose drawing materials according to their habits may lead them to participate more rapidly in creating paintings.Drawings are result from the integration of information about the appearance features of children’s observation, such as the observer’s angle of viewing the object and the position relationship between the observer and the observed object. The data evaluation in this study relied on assessing the observation perspective, the position of the baseline, and the comparison relationship between the objects and the figures in children’s drawings .The types of observation are classified according to the person’s observation perspective of objects in space . The obsThe role of baselines in children’s drawings is to create and divide different painting spaces . BaselinThe position and proportional relationship between different objects constitute the visual–spatial effect of the drawing. The position and proportion of different objects are related to the spatial depth of the drawing . For exaMost of the children represent the figures, objects, and scene environment of the drawing based on the front view . Only 0.The role of the baseline in children’s drawings is to divide the work into different visual areas. Children use baselines to divide the area of ground and sky. The ground element in the drawing is the supporting area to make the objects placed in a stable condition. The ground element is also the ground plane of work. The sky element represents the facade of the work. The ground plane and the facade compose the three-dimensional visual space of the work. The comparison features of objects in works that accurately represent the baseline are more evident than in works of others baseline types .. Althouχ2 = 7.236, p = 0.027 < 0.05) showed a significant difference in the baseline performance of different genders of children in the works  showed that children of different age have differences in performing baseline features (The results (features . Comparifeatures . Most chThe number of children aged 9–10 years old who did not express baseline features was less than the number of children aged 7–8 years old. This result suggests that children aged 9–10 years old have better at performing baseline than children aged 7–8 years old. There is a clear increasing trend in the number of children who do not draw a baseline during children between ages 11 and 12 years . This paχ2 = 36.991，p = 0.000 < 0.01) in the performance of comparison relationship between objects in drawings  and position and position changes in features . Fine arts teachers also need to guide children to observe the changes in the shape and spatial from different viewing perspectives. In addition, teachers need to guide children not only limited in identifying different viewing perspectives of objects but also in analyzing changes in the contour features of objects from different viewing perspectives.The appearance of baseline features shows that more than half of the children between the ages of 7 and 12 have the sense to show baseline features. This result also indicates that children have cognition of dividing the visual space area of the drawing and expressing the ground plane. Although more than half of the children were conscious of the baseline characteristics, children drew objects on the baseline. Therefore, children’s ability to accurately perform baseline performance still needs to be trained. The performance of baseline correlated with children’s ability to represent visuospatial regions. Therefore, the premise of baseline feature performance is to guide children to use the location features of different objects to paint the regional elements of the sky and the ground. For example, the location features of elements such as clouds, sun, moon, and birds represent the regional features of the sky. Use the location elements such as plants, houses, and people to express the regional characteristics of the ground. At the same time, fine arts teachers can guide children to identify the position of the apparent horizon and horizon in real-life scenes. The horizon is the mark that divides the sky and ground areas in the drawing. The position of the apparent horizon determines the viewing angle of a person. If children display the position features of apparent horizon and horizon, they are likely to accurately characterize areas of the ground and sky in their drawings. Therefore, the position training of observing the horizon and eye level is an effective method to improve the characteristics of different visual areas of children’s performance works.Children’s ability to draw distant comparison features remains to be improved. The proportional features between objects are related to the length and width features of the objects. It indicates that children have well able to express the length and width features of objects. The comparison of distant features in the drawing relates to expressing the spatial depth of the volumetric features. The children aged 7–12 can display the visual effects of objects in a flat. However, the ability of these children to express the three-dimensional visual effect of works still needs further training. The key to improving three-dimensional visual space ability is to establish children’s cognition of the volume of objects and the cognition of position. When guiding children’s drawing practice, fine arts teachers need to provide children with the methods of visualizing objects’ contour and volume features. For instance, fine arts teachers can assist children in achieving their ability to visualize contour features by observing the length and width features of objects. Children’s ability to represent volumetric features can be facilitated by directing their attention to the height and depth of the object in space. Meanwhile, teachers need to instruct children to observe the changes in the contours and volume features of the objects with different position relationships. Therefore, the development of the ability to express comparison features achieves by training children’s ability to draw the volumetric and positional features of objects in their drawings.The above results indicate that children’s awareness of applying interdisciplinary knowledge in practical drawing creation activities is weak. Children have only grasped the estimation methods in the mathematics curriculum related to estimating the features of length, width, and volume of objects. However, children’s ability to represent the geometric features from visual assessment in their drawings still needs to be improved. Teachers need to guide children to observe the features of objects in real-life situations. Some teachers may only use images to teach children to estimate the geometric characteristics of objects. As a result, children will be unable to accurately understand the width, height, and volume of objects in real-life scenes. Therefore instructing children to observe the geometric and spatial features of objects in real-life situations needs to be actually integrated into the fine arts curriculum.The raw data supporting the conclusions of this article will be made available by the authors, without undue reservation.The studies involving human participants were reviewed and approved by Beijing Normal University Ethics Committee. The patients/participants provided their written informed consent to participate in this study.Written informed consent was obtained from the individual(s) for the publication of any potentially identifiable images or data included in this article.LY contributed to the conceptualization, methodology, formal analysis, investigation, data curation, and draft writing. YL is responsible for reviewing and editing the article. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


Decarbonisation of the automotive sector will require increased amounts of raw materials such as lithium, cobalt, nickel and rare earth elements. Consequently, it is crucial to assess whether supply will be able to meet forecast demand within the required timescale. The automotive sector relies on complex global supply chains comprising four tiers. We have developed an integrated timeline from tier 4  through to tier 1, the production of electric vehicles (EVs). Numerous factors, mainly economic, political, social and environmental, influence the duration of tier 4 leading to considerable variation between projects. However, our analysis demonstrates that it commonly takes more than 30 years from initial exploration to EV production. Tier 4, which is often neglected by the automotive industry, may account for 20 years of that period. This suggests that raw material supply is unlikely to match the projected demand from electrification of the automotive sector up to 2030. Reducing the duration of tier 4 will be difficult, although governments and industry can mitigate supply risks in various ways. These include multi-disciplinary international research across the supply chain and the transformation of research findings into policy and best practice. Supply chain convergence, with businesses across the supply chain working to develop long-term plans for secure and sustainable supply, will also be beneficial. In addition, global stakeholders should work together to resolve ESG challenges to supply. All these measures depend on the availability of researchers and industry personnel with appropriate skills and knowledge. The major industrial metals, such as iron, aluminium and copper, are already produced in huge quantities (tens to hundreds of millions of tonnes per annum) for myriad applications, including automotive, from mines located in many countries. In contrast, the ‘technology metals’, such as cobalt, lithium, rare earths and platinum group metals, are generally produced in much smaller amounts (hundreds to thousands of tonnes) from a small number of mines worldwide. We will require a massive and rapid increase in the production of technology metals, essential to the function and performance of electric vehicles (EVs), if we are to meet the targets of governments and the car industry.Other materials will also be required for generating energy to charge the batteries used in EVs. The decarbonisation of automotive transport will not deliver reduced carbon emissions if the fuel used for charging is not ‘green’. Renewable energy technologies, such as photovoltaics, wind turbines and water electrolysis, rely heavily on technology metals such as rare earths, tellurium, gallium and platinum.The demand projections for many of the minerals and metals required for decarbonisation show exponential growth up to 2050 and beyond . Tier 1 suppliers are companies that supply parts or systems tailored to the OEMs, while tier 2 suppliers make specialised products for OEMs across a range of sectors. For example, companies such as Intel, which produces computer chips, supply products to tier 1 automotive suppliers, but also to electrical and electronic equipment manufacturers, telecommunication industries and many more. Tier 3 suppliers are those that supply materials, such as metals, composites and glass, to a wide market rather than a single sector. The automotive sector is an important client for many tier 3 suppliers so many of their products, such as alloys and composites, are produced to comply with specific vehicle design requirements , which is widely used in the automotive sector, provides substantial cost savings to OEMs by eliminating the need for large stock, warehouses and long inventories. However, it is heavily reliant on the supply chain to deliver the products needed at the right time. The timely and secure supply of materials and components is therefore fundamental to the automotive sector. Factors potentially disrupting raw material supply, such as commodity price volatility, regulatory changes affecting mine permitting and international trade policy, may have serious negative impacts on the entire production process . Similar radical revision will take place as the complex transmission systems utilised in ICE vehicles are replaced by simpler electronic drive systems used by EVs. Some suppliers, such as engine manufacturers, will have to adapt to the new technologies or face elimination. The supply chain will need to be modified to accommodate completely new products, notably EV batteries. Without the timely installation of adequate capacity for the production of motors and batteries, the electrification of the automotive sector will be delayed.This transformation will inevitably involve changes in the location of different parts of the supply chain. The vertical integration seen in China, from raw material production to battery and EV manufacture, as well as the increase in battery production capacity in central Europe, the USA and south-east Asia, are already leading to significant reconfiguration. Many factors may influence how and where these transformations take place and the time taken to effect such changes. These include, for example, distribution networks, trade rules, the political stability of new participants and their ability to access materials and maintain appropriate environmental standards.However, the transformation required in tier 4 is likely to be the most problematic one. The market for battery metals has been expanding fast and, based on demand projections, the growth rate is likely to peak between 2025 and 2030. After that time, the market is projected to grow at a steady rate  are identified and then converted to mineral reserves . Without exploration, there can be no reserves and consequently no mining.Exploration is carried out in a series of stages which serve to identify a minable deposit. Each successive stage requires increasingly detailed and expensive investigations. Throughout exploration and mine development technical, funding and market risks persist to varying degrees, while continuing political risk can, in an instant, undermine the viability of a project. This risk varies greatly between countries and is largely dependent on the prevailing political stability and governance standards within the country in which the deposit is located Eggert .The first stage of the exploration cycle comprises the selection of an area that is considered favourable for the occurrence of a particular type of deposit which may contain the target metal or mineral in potentially economic amounts (stage 1). This selection is based on an evaluation of the available geoscience data in the light of accumulated knowledge and experience of seeking similar deposits elsewhere. Initial reconnaissance exploration, typically over large areas of hundreds or thousands of square kilometres, seeks to identify the ‘fingerprint’ of a potential deposit and thus locate promising targets. The initial area selection is based on many factors, such as the presence of known mineral occurrences and deposits, the geological setting, the age and type of rocks present, and the presence of certain geochemical and/or geophysical characteristics. Thereafter field investigations, commonly including geological mapping and geochemical and geophysical surveys, are undertaken to assess the mineral potential of the chosen area.Our ability to explore successfully depends fundamentally on the availability of reliable deposit models that can guide where and how exploration should be carried out. For the major metals, such as iron, aluminium and copper, which have been widely used by industry for many decades, we have well-constrained models that define the deposit fingerprint that may indicate the presence of a deposit. In contrast, many technology metals have only recently been of commercial interest and the markets are very small. Consequently, for these materials, our deposit models are poorly constrained and do not provide reliable guidance on where resources might be found. In order to improve exploration targeting and effectiveness, it is, therefore, important that we improve our knowledge of how and where these materials are concentrated in the Earth’s crust.When we have a reasonable understanding of how a deposit is formed, we can look for evidence of these processes in available datasets, such as geological maps and other regional survey data. However, there is considerable variation in the availability of this fundamental data which underpins mineral exploration. For example, a wide range of data and materials specific to critical minerals is available in Australia  is carried out over the most prospective zones that are typically a few kmIf the results of the preliminary target appraisal are positive then, given the availability of funding, the next stage involves more detailed deposit evaluation to determine the extent and quality of the mineralisation below the ground surface. This is accomplished through drilling to provide a three-dimensional picture of the depth, form, extent, grade and continuity of the mineralised body. Some preliminary metallurgical testing is often undertaken at this stage to evaluate if the target metal can be effectively extracted and separated from its host rock.It may take many years from initial conceptual development and area selection to completion of the reconnaissance and detailed exploration stages. The actual timescale is dependent on numerous variables such as the target commodity, the type and location of the deposit, market conditions and the availability of funds, the host government’s policies and permitting procedures, and the relationship with local communities. Few exploration projects actually proceed beyond the stage of detailed exploration. The reasons for this are many and varied, commonly involving some combination of economic, technical, environmental, social or legal issues. Some projects may be completely abandoned, while others may be temporarily halted for periods that may last from a few months up to many years, until, for example, market conditions improve, a new technology for mining or ore processing is developed or the host government changes a policy which had previously been a barrier to re-opening. What is clear is that in the majority of cases, providing new metal supply is quite unlike turning on a tap; bringing a new mine into production or re-opening an old one is seldom straightforward and rarely accomplished without delay at some stage.There are numerous examples from every continent that serve to illustrate the time taken from deposit discovery to the opening of a new mine Schodde . A recenIn a few cases, given favourable economic, political and social conditions, projects are taken to the next stage which is a full bankable feasibility study (stage 3). This involves more comprehensive technical investigations to confirm the size and grade of the resource, to determine how the ore can be mined and processed and the valuable metal or mineral extracted. The construction of a pilot plant to optimise the mineral separation and recovery of the target material is sometimes included at this stage. At the same time, a full financial analysis is undertaken to calculate development and operational costs and to confirm economic viability. This feasibility study, which must conform to an agreed international standard, forms the basis for attracting further investment to fund mine development, either through a public listing on a stock exchange or from private investors. It is also required in most countries in order to obtain the permission of the host government to mine.Although every case is different, it is possible to provide some general estimates of the time taken for each exploration stage. These are summarised in Fig. The mine development stage starts with the acquisition of a mining licence (stage 4), which involves a commitment by the project owner to undertake work in compliance with a raft of technical, environmental, fiscal and administrative legislation. Following payment of the appropriate fees, the host government grants permission for the specified activities to be undertaken over an agreed timeframe.Stage 5 begins with design of the mine, which is followed by a range of engineering, procurement and construction processes. This is followed by procurement of the necessary equipment and materials and securing the services of contractors to undertake the construction. The work begins with preparation of the site for erecting mine plant and for installation of the required infrastructure needed for the mine operations. For an open-pit mine, preparatory work includes clearing vegetation, pre-stripping, removal of overburden material and stockpiling of any excavated ore for later processing when a plant is in place. In the case of an underground mine, access to the ore involves the excavation of shafts and ramps and the preparation of the orebody for first production  and the prevailing commodity prices. The acquisition of a mining licence typically takes 2 to 3 years depending on the location of the deposit and the permitting procedures of the host country (stage 4)  project in southern Sweden provides another pertinent example. This deposit, discovered in 2009, contains major resources of the much sought-after heavy rare earth elements  was discovered in 1977, but its low grade meant that it was not economic to mine at that time , involves the use of various technologies that may be used on their own or in combination to separate the metals from their host ores and to make the products required for particular applications. Most extractive metallurgical processes used today belong to three major classes: pyrometallurgy, also referred to as smelting, which relies on reactions at high temperatures between gases and solids, or gases and molten materials; hydrometallurgy, which uses aqueous solutions to extract metals from ores; and electrometallurgy, which utilises electrochemical processes, such as electrowinning. In certain cases, for example for lithium recovery from brines, the extractive metallurgy includes chemical processes taking place through solar evaporation that enrich the brine in lithium.It is important to note two key points regarding the extractive metallurgy routes which are utilised. First, each metal extraction flowsheet is unique to a particular deposit or ore type, and, secondly, that no two metals are recovered using exactly the same sequence of extraction steps. Numerous factors influence the extractive metallurgy process routes that are adopted. These include variables such as the mineralogy of the feedstock , the physical and chemical characteristics of the ore body and associations with other constituents, the thermodynamic reactivity of the target metals and their melting and boiling points Rankin . AnotherThe increasing demand for metals such as lithium, cobalt, nickel and manganese used in batteries, but also rare earths for electric powertrains, will require major scaling up of not just mine production, but also of smelting and refining. Although some refining capacity is already in place, this is not considered sufficient to satisfy the projected demand or the requirements for specific intermediate compounds . The OEMs are, therefore, currently developing the capacity to deliver their plans for the early 2030s. The EV design and development stages (stage 8), which have already started, take between 3 and 4 years with mass production of electric vehicles following afterwards  in the USA Kavanagh  and AustJoint actions by government and industry can also expedite the EV transition, ensuring supply chain resilience and reducing the timescale. China has been particularly successful in this regard by developing industrial strategies that account for raw material requirements and promote the exploitation of domestic resources, the development of conversion capacity and integrated supply chains. These have all been facilitated by public–private partnerships and direct investment by China overseas. For example, the budget for policies related to new energy vehicle (NEV) development in China has amounted to hundreds of billions of euros. An analysis of NEV by the European Commission estimates that between 2015 and 2020, China spent USD 60 billion (EUR 51 billion) on subsidies alone for the development of an EV industry and the uptake of NEV by the public Pelkonen . ResearcAs highlighted in the American Mineral Security Act  challenges. Such initiatives should provide financial support, promote improved data collection, capacity building and knowledge transfer and undertake systemic research to address key ESG issues.development of the skilled workforce required throughout the supply chain so that raw materials can be sourced, processed, used and recycled in an efficient, safe and sustainable manner.conduct of applied research to provide new data on all aspects of raw material demand and supply and to develop holistic methods to provide sustainable solutions to technical and non-technical challenges.Priority actions to mitigate raw material supply barriers include:


Clinical workplace learning takes place in a dynamic and complex learning environment that is designated as a site for patient care and education. Challenges in clinical training can be overcome by implementing blended learning, as it offers flexible learning programs suitable for student-centered learning, web-based collaboration, and peer learning.The aim of this study is to evaluate the Small Private Online Course (SPOC) by interns’ first impressions and satisfaction measures (N=20) on using the SPOC. This study describes the design process of a SPOC from a theoretical and practical perspective and how it has been integrated into a clinical internship in internal medicine.The design of the SPOC was based on general theoretical principles that learning should be constructive, contextual, collaborative, and self-regulated, and the self-determination theory to stimulate intrinsic motivation. Interns’ impressions and level of satisfaction were evaluated with a web-based questionnaire and group interview.Interns thought the web-based learning environment to be a useful and accessible alternative to improve knowledge and skills. Peer learning and web-based collaboration through peer interaction was perceived as less effective, as student feedback was felt inferior to teacher feedback. The interns would prefer more flexibility within the course, which could improve self-regulated learning and autonomy.The evaluation shows that the SPOC is a useful and accessible addition to the clinical learning environment, providing an alternative opportunity to improve knowledge and skills. Further research is needed to improve web-based collaboration and interaction in our course. Clinical workplace learning (WPL) mostly takes place during normal daily collaboration and patient care activities, or organized formal learning activities ,2. It hain control, and may better fit the needs of different learning styles that students might have [Blended learning can be used to remedy several of these problems. Blended learning refers to a deliberate blending of face-to-face and web-based learning, with the goal of stimulating and supporting learning . When thght have -13. Overght have ,14-17.The web-based component of blended learning permits flexible education at a time, place, and pace convenient for the learner . It can A Small Private Online Course (SPOC) is one possible instrument to blend web-based learning with clinical WPL. The SPOC concept was first introduced in 2013, and it has been progressively implemented in higher education thereafter. This type of course is often used locally with on-campus students and has a limited number of students that can enroll in the course ,21-23. IThe development of a dedicated SPOC instead of using publicly available course materials has the advantage that several secondary conditions such as contents definition, availability to interns, the alignment of goals, desired teaching modes, and assessments can be addressed by design. This avoids many of the current challenges with using open web-based education from others as described by de Jong et al  and HendIn 2017, we developed a SPOC for the Internal Medicine internship at Leiden University Medical Center (LUMC) in the Netherlands. The theoretical framework of the course is based on the self-determination theory (SDT)  and the In the Netherlands, medicine students enter medical school at the bachelor level, which is followed by internships at the master level. At LUMC, each month approximately 20 interns start their clinical internships. In the first 4 weeks, the interns attend a joint program at the university. The so-called introductory internship (2 weeks) prepares them for the internships in general and is being followed by a specific 2-week theoretical course as preparation for the Internal Medicine internship. Thereafter, they start their clinical internship in Internal Medicine (12 weeks), in which they work in different affiliated hospitals in the region and cannot meet each other physically. A major aim during the internship is to obtain clinical reasoning skills for a broad range of clinical scenarios. However, the interns only have limited exposure to new patients who have not yet been diagnosed by other physicians, and the clinical scenarios that interns face in practice do not cover all the clinical scenarios that they need to know and understand. Collaboration between peers is limited because the group of interns is split up to have their internship in different hospitals.To overcome several of the limitations mentioned, a SPOC has been developed. The course was designed using a design-based research approach in which practical and theoretical aspects are integrated in the educational design ,31. AtteFor the development of the course, a group of stakeholders has been identified including clinical interns, clinical teachers, educational experts, technical experts, and a graphic designer. During a dedicated learning experience design (LED) session the stakeholders set a framework for the SPOC, including the team and course’s goals , students’ needs , learning goals , and the aimed look and feel .The outcomes of the LED session were used to define the exact content and learning activities centered around authentic clinical problems that are typically encountered in internal medicine. NEO Learning Management System was used as a platform for the SPOC. Authentic cases in Dutch were developed to train clinical reasoning skills and medical knowledge. The SPOC has a modular design that involves preparing for the internships (2 weeks), internal medicine (2 weeks), and several inpatient, outpatient, and emergency room cases (12 weeks). Every week, the interns can study 1 new activity. A course overview of the 12 clinical weeks is shown in On the basis of the SDT and the general learning principles that learning should be constructive, contextual, collaborative, and self-regulated, authentic patient cases that are centered around virtual reality patients with complaints of diabetes, electrolyte disorders, infectious diseases, oncological diseases, and tiredness in a setting of the inpatient clinic, outpatient clinic, or emergency room have been developed. A 3D virtual reality patient ward has been developed, giving the interns a realistic impression of the inpatient setting. It aims to introduce interns to the ward and ward rounds and train professional standards such as hygiene regulations. Constructive learning activities such as doing rounds on 3D virtual reality patients with increasing complexity are used to gather knowledge and to improve learning effectiveness in the workplace.Relatedness is promoted in the SPOC, as the interns  meet up on the web on discussion forums, during peer feedback sessions and group assignments. This stimulates collaboration and relatedness among interns who would normally not meet each other during the internship. Collaborative forum assignments are included for peer learning and direct feedback by both peers and dedicated clinical teachers. Peer feedback is either given through open peer discussion on a forum or using a rubric format. Peer discussion, in this case on the web, might help interns to develop their critical thinking and clinical reasoning skills. Interns are prepared on their role as assessors by a web-based peer feedback training.Besides obligatory content, the SPOC contains several optional assignments and resources that interns can choose from. The roster during the internship is different for each intern. An intern can spend 2 weeks in the cardiology department, whereas another intern works in the emergency room. Therefore, the interns can choose several sequential assignments for more extensive learning, depending on the training they need at that specific moment. By stimulating autonomy and attention to precourse goal setting and tracking and ranking course activity, it is aimed that interns can control their own learning process.Training and assessment of competencies occur through peer feedback and self-assessment. Interns that have self-assessed their competencies and know that they are on the right track feel more confident while carrying out their new skills on real patients. In this blended program, skills that are trained on the web can be directly transferred to clinical practice. Practical examples of the integration of theory in the SPOC are shown in Participants were a first group of 20 interns who worked with the SPOC during a pilot period. They enrolled during their introductory internship and remained in the SPOC until completion of their Internal Medicine internship. The participants were in the first year of their clinical phase. In this phase, interns are aged approximately 21 to 25 years, and, on average, 60% to 70% are women. As the aim is to evaluate the impressions of the first group, no sample size calculation has been performed.The evaluation existed of a web-based questionnaire  and an iNederlandse Gedragscode Wetenschapsbeoefening of the Universiteiten van Nederland (Association of Universities The Netherlands). Institutional educational review board approval was obtained under reference OEC/ERRB/20220208/1. All interns provided written informed consent to participate in the study. In the information letter, the full study procedure was explained, as well as the option for the interns to opt out of the study at any moment without any reason.As the study did not involve patients and no health intervention has been administered to participants, the study was not subjected to the Dutch WMO . The study has been conducted in compliance with the European Union General Data Protection Regulation 2016/679, and data have been anonymized and stored according to the snowball method: the participants first individually recalled their own experiences with the course, and then 2 participants paired up to discuss their experiences and wrote down their most important findings, which was repeated in groups of 4 participants. All the experiences were shared with the group by oral presentation. The interviews were audio recorded and converted into a transcript by the first author (ECH).The students completed the weekly assignments and remained in the SPOC until they had completed their Internal Medicine internship. Subsequently, they were asked to fill out the web-based questionnaire. A group interview was led by an independent interviewer who had no intern-teaching relationship with the interns. The interview used the Means and SDs were calculated for the Likert-scale questions in the web-based questionnaire. The answers to the 5 open-ended questions were summarized.The first and third authors discussed the transcript coding template, which was based on the different items in the theoretical framework, until consensus was obtained. The outcome was a template consisting of six predefined, overarching themes: (1) contextual learning, (2) collaboration and relatedness, (3) constructive learning, (4) self-regulated learning or autonomy, (5) competence, and (6) other. For each category, the same authors agreed on a definition for each theme. The principal investigator (ECH) analyzed the interview data and clustered the answers in the template using a Microsoft Word.The aim of this study is to investigate the perceptions of interns concerning the use of the SPOC using a questionnaire and interview. Of the 20 interns eligible to enroll in the SPOC, 19 (95%) actually enrolled in the course, 10 (50%) finished the whole course, and 1 (5%) never started. Ten interns filled out the web-based questionnaire. Only questionnaires that were fully completed were included in the analysis. All 20 interns attended the group interview.The results are shown in solve a patient case by finding the diagnosis. Interns liked the graphical layout of the course. The SPOC supported training clinical reasoning skills, although some interns felt that those skills are better trained in practice or when the specific SPOC cases were also experienced in clinical practice. Concerning peer feedback, the interaction during the patient cases was thought to be the most useful, as were the feedback training and having insight in the answers of peers. Some interns indicated that the technology to give feedback did not always work well or that interaction in clinical practice was more useful. The information about deadlines, the instruction for how to give peer feedback, the quality of the feedback received from peers, and the fixed order of the assignments were mentioned as limitations of the course. Owing to time constraints, some interns indicated they had difficulties finalizing the mandatory contents within the lessons. They would have preferred optional content only.The interns highlighted the patient cases as a positive aspect of the course. They particularly valued their connection with clinical practice and the elaboration and variety of relevant cases. Furthermore, the interns appreciated the group assignment in which they The interns were interviewed in their last week of the internship. The interns experienced the SPOC to be accessible and adequately designed for them as a target group. The participants felt the patient cases and SPOC content were useful and informative. However, although authenticity was integrated in the design of the SPOC, the participants felt that the contents did not match the real world. It seemed unclear to them how the SPOC should complement clinical WPL. They would prefer assignments that matched the clinical problems they encountered at that time instead of fixed weekly assignments.Development of critical thinking skills by peer discussion was also integrated in the SPOC’s design; however, the interns indicated that they preferred model answers over peer discussion. The participants also felt it was not really useful to receive feedback from peers instead of a teacher, because in their opinion peers know as much as they do themselves. In general, feedback was perceived to be very short.The interns indicated they had enough time to finish the assignments during their normal day shifts, although not everyone agreed. They also appreciated the facultative character of the SPOC. They felt that more facultative assignments would be helpful, although they addressed the possibility that nobody would mind finishing them. They also indicated that they needed more flexibility and choice in the course, and the fixed order of the assignments did not work for everyone. Some participants felt they had “finally reached the clinical phase of their internship” and therefore did not appreciate completing web-based assignments in this stage of the curriculum. They also preferred a complete overview of a certain clinical presentation instead of looking up information themselves through links in the SPOC.The interns particularly valued the competence training received through completion of the patient cases, e-learning, and quizzes. The virtual reality patient cases and videos were rated as useful and likable. The videos concerning observation of patient interviews were enjoyable and led to useful discussion between peers. However, others felt that observation in clinical practice is more informative than watching web-based videos. Although the virtual reality patient cases can be an alternative for practicing clinical scenarios that have not been encountered in live patient contact, the interns indicated that the SPOC did not completely fill the gaps owing to the limited number of virtual reality patient cases addressed in the SPOC.In general, the interns appreciated the accessibility of the SPOC and its learning environment, and the videos were enjoyable. They were less content about the deadlines that sometimes appeared to be unclear and the planning of SPOC activities that were directly patient related, which was challenging because of fast patient turnover rates. Completion of some of the assignments was difficult because of technical shortcomings or insufficient support.In this study, a SPOC has been developed and implemented into an internship curriculum of Internal Medicine at LUMC. Course development was based on general learning principles and SDT, aiming to promote learner motivation and optimize the course’s quality and integration in the clinical curriculum. The SPOC was evaluated by measuring the satisfaction of the interns with the course. Interns thought the SPOC to be a useful and accessible addition to the clinical learning environment. Peer learning and web-based collaboration through peer interaction were perceived as less effective, as student feedback was felt inferior to teacher feedback. The interns preferred more flexibility within the course, which could improve self-regulated learning and autonomy. Overall, the interns felt that the web-based learning environment provided an alternative opportunity to improve knowledge and skills.Our SPOC was integrated into clinical practice by making it part of the internship program, instead of using it as a stand-alone web-based training. The advantage of this is that learners can directly apply their new knowledge in clinical practice and go back to the web-based resources for further exploration of a topic. In our opinion, using design sessions and a theoretical framework can facilitate integration of such a course into an existing curriculum and can be an effective solution for some of the complexities faced in clinical training.From the results, we learned that the SPOC meets the expectations for some of the categories fairly well, although the overall student satisfaction seems to be modest. In general, completing the SPOC assignments during the internships seemed feasible but not for all interns. Concerning learning in the clinical context, interns appreciated the authenticity and usefulness of patient cases as an additional opportunity to improve their clinical knowledge and skills. Regarding the clinical skills development, the SPOC therefore meets our hypothesis; however, this is not the case for stimulation of the social cohesion of the interns through working in the SPOC. The quantitative data show a modest to very low outcome on several topics .It appeared that the interns were not satisfied with the collaboration and relatedness aspects of the SPOC, in particular the web-based interaction and peer feedback. Other studies confirm that students prefer teacher feedback over peer feedback -34. WhenLearning activities in a SPOC are on the web and asynchronous, with only written interaction and no visual interaction and body language . This maAlthough flexibility was integrated in the design of the course based on previous literature , internsRegarding the feelings of being a physician making decisions and contextual learning, it seemed that the SPOC somehow failed in this aspect. In the LED session it was predefined to make authentic simulated patient cases to enhance this feeling and the feeling of increased patient exposure; however, this was not experienced by the interns. Although studies have shown that virtual reality patients can improve clinical reasoning skills and knowledge, it should be recognized that patient simulations are not equivalent to real patients and cannot replace traditional clinical WPL -52.Reflecting on our study, this may imply some improvements in our SPOC’s design. First of all, we should critically review the technical shortcomings, deadlines, and obligatory components of the SPOC and improve the flexibility of the learning environment. For instance, replacing the weekly assignments by a more variable set of assignments on a monthly basis could be considered. We are currently investigating the feasibility of such adaptations and the expansion of the number of optional lessons. In addition, the SPOC will soon be transferred to another internet platform, which is technically supported by our University’s Information Technology department.Second, it may be helpful to incorporate training of self-regulated learning skills before using our hypermedia environment and more support and augmentation of learners’ self-regulated learning within the learning system ,48,53. FThird, several other conditions may be met to increase student satisfaction. Student satisfaction in clinical internships is enhanced by supervisor support, perceived social value , dedicatWhen critically looking at the quantitative data, it is apparent that the variance is modest and particularly low for the questions concerning the interaction with peers and increasing difficulty. An explanation might be that the interns know each other fairly well and their answers may not be independent or even biased by information, sharing leading to a group opinion. Limitations of the study were the small sample size and use of an unvalidated questionnaire. As the study was designed just to evaluate the first group of interns enrolled in the SPOC, no formal sample size calculation has been performed. However, we did include qualitative data to supplement these limited quantitative outcomes. We therefore think that the impressions still provide valuable insights in the SPOC’s strengths and weaknesses that we can use for further adaptation.From our study, we have learned that interns perceive several learning opportunities after adding the SPOC to their clinical learning environment, mainly in skills and knowledge acquisition. However, particularly web-based collaboration and perception of relatedness among the interns within the course need further improvement. In the future, interviews with the interns may be beneficial for a deeper investigation of this issue and others, their context, and which additional adaptations might be needed to our SPOC. Future research is also needed to further investigate how learning principles can be optimally integrated in web-based courses.


Social prescribing can promote the creation of new relationships, which may then promote the building of social capital in communities. One example of a social prescribing tool in Japan is Osekkai conferences, which increase social participation and mitigate the degree of loneliness in rural communities. A clarification of the changes in social interaction and intensity of connections among people through Osekkai conferences could contribute to better social prescribing in rural communities. This social network study was conducted among people who have participated in an Osekkai conference. The primary outcomes of degrees and centrality were measured as the degree of social capital. The primary outcomes were compared between April and September 2021 and between October 2021 and March 2022. The continuous performance of Osekkai conferences as social prescribing tools led to an increase in conference participation, mainly by middle-aged women in the communities. Based on a social network analysis, the average direct connection with each person did not increase; the network density decreased gradually; the network diameter decreased from 6 to 5. Regarding the node-level statistics, harmonic closeness centrality and eccentricity decreased, and modularity increased. Social prescribing initiatives should focus on improving social capital in communities, which may improve the number and meaningfulness of the collaborations among organizations and indigenous communities. Within community settings, social prescribing can promote the creation of new relationships, potentially building up social capital. Social capital refers to the networks of relationships among people who live and work in a particular society, enabling that society to function effectively and leading to better conditions for individuals and communities ,2. SociaSocial prescribing refers to a means by which healthcare professionals seek to address the nonmedical causes of ill health with nonmedical interventions ,13. ThroSocial prescribing is performed worldwide, although the strategies vary by the context and culture of countries. There are various indigenous activities related to helping other people that are rooted in various Asian cultures ,13. One To the best of our knowledge, no study thus far has analyzed the changes in social interactions and the intensity of the connection among people in communities promoted through Osekkai conferences. Thus, we posit the following research question: “Do Osekkai conferences, as social prescribing tools, increase social connections in communities?” The increase in connections promoted by Osekkai conferences may facilitate the creation of new networks and opportunities to establish social capital ,20. In sThis study used SNA to assess the changes in the degree of community interaction through participation in Osekkai conferences . This stUnnan City, in the Shimane Prefecture, is a rural Japanese area. In 2020, the total population of Unnan was 37,638 , with 39% being over 65 years old; this proportion is expected to reach 50% by 2050 . There aVarious rural individuals participate in Osekkai conferences, and some of the participants have different professional backgrounds . Partakers work collaboratively to solve their community’s problems by sharing their own experiences and suggesting solutions, generating an Osekkai conference plan. This plan is then carried out in their community. The results of the plan are shared in the following Osekkai conference. Through continuous discussions, Osekkai conference plans can be revised and the quality of care in the community can be improved . SubsequThe participants of this study were people who were interested and participated in Osekkai conferences or who facilitated and performed Osekkai conferences. The information regarding an Osekkai conference is provided at the Unnan City Hall via social media and the city’s local newspapers. People interested in the conference send a mail or make a call to the office responsible for the conference to register as participants. Regarding participants’ activities related to an Osekkai conference, two roles were focused on in this study: those implementing an Osekkai conference and those participating in an Osekkai conference. The citizens who hoped to participate in these conferences went to the community center, where the conference was held on a previously established day.This study used an SNA to analyze the change in social connections among participants of an Osekkai conference. An SNA is a structured process for investigating social structures using concepts established from network analyses and graph theory . Using tThe participants’ names, gender, age, place of residence, and their role in the conference and provision of Osekkai conferences were measured at the beginning of each conference and the provision of Osekkai. The SNA dataset was created from the Osekkai conference participants’ list. The network was described using the level of each Osekkai conference and participants by using Gephi software. In graphing, the participants who organized the Osekkai conference and provision of Osekkai were considered a source of connection, and participants of the Osekkai conference and provision of Osekkai as a target of the connection. Each participant was coded anonymously before the analysis.p-value < 0.05.We reported the descriptive statistics of the number of participants in the Osekkai conference, of Osekkai, and participants’ demographics. Second, the SNA was used for a relational matrix representing the network. These are presented as a sociogram, with the periods of the conferences as a source of connection and the links between the participants in the network. We used the SNA software Gephi to generate sociograms and network-wide node (participant-levels) statistics/metrics and visually depict the network structure. To demonstrate how the connections among participants evolved during the study period, we explored the connections of all participants (nodes) at two times, developing a visual representation for the end of September 2021  and the The average degree describes how connected a typical participant is within the network. It is calculated by taking the average number of connections for each participant. Regarding the direction of the connection, the degree is differentiated into indegree and outdegree. The network density describes the number of connections between participants. In the context of this study, a denser network would mean participants are more directly connected to each other, while a less dense network would mean fewer connections. Network density is calculated as the proportion of actual connections to all possible connections (range: 0–1). The network diameter is the length of the longest of all the computed shortest paths between all pairs of nodes in the network.Eigenvector centrality measures a node’s importance while considering the importance of its neighbors. It assigns relative scores to all nodes in the network based on the concept that connections to high-scoring nodes contribute more to the score of the node in question than equal connections to low-scoring nodes.Closeness centrality measures how close a node is to all the other nodes in a network and whether the node is located on the shortest path between other nodes. A high closeness centrality means that there is a large average distance to other nodes in the network.Harmonic closeness centrality is a variant of closeness centrality that was developed to solve the problem the original formula had when dealing with unconnected graphs. The closeness centrality is calculated, including unconnected graphs.Betweenness centrality is measured based on the number of shortest paths between any two nodes passing through a particular node. Nodes around the edge of the network would typically have a low betweenness centrality, and a high betweenness centrality might suggest that the individual is connecting various parts of the network together.Measuring eccentricity captures the distance between a node and the furthest node from it. A high eccentricity means that the furthest node in the network is a long way away, and a low eccentricity means that the furthest node is quite close.Measuring modularity captures the structure of networks or graphs based on each node connection. It was designed to measure the strength of the division of a network into modules . Networks with high modularity have dense connections between the nodes within modules but sparse connections between nodes in different modules.The participants’ anonymity and confidentiality were ensured throughout the study. All participants provided written informed consent before participating in the conferences and answering the questionnaire. All procedures in this study were performed in compliance with the Declaration of Helsinki and its subsequent amendments. The Unnan City Hospital Clinical Ethics Committee approved the study protocol (no. 20220021).Throughout the duration of this study, the number of participants increased from 567 to 1098. The rate of female participants increased from 55.9% to 67.9%. Most participants were between 30–40 years old and from Unnan City. Between the periods, the rate of female participants increased by a statistically significant extent. There were no significant differences in age, performing Osekkai, or living places .In the network-level statistic, average degree, indegree, and outdegree did not statistically change over the study period. The network density decreased gradually, and the network diameter decreased from 6–5. Regarding the node-level statistic, Eigenvector centrality, closeness centrality, and between centrality did not change statistically. In contrast, harmonic closeness centrality and eccentricity decreased, and modularity increased statistically .The findings from the SNA demonstrated that continuously performing Osekkai conferences  and Osekkai plans was associated with an increase in the number of conference participants, mainly young to middle-aged women, in the communities. Based on the SNA, each person’s average number of direct connections did not increase, but the network density decreased gradually, and the network diameter decreased from 6 to 5. This demonstrates an increase in indirect connections among the participants. Regarding the node-level statistics, harmonic closeness centrality and eccentricity decreased, and modularity increased, indicating that each participant could increase their social capital, regarding the strength of their connection with others, in their communities.This study shows that more women participated in the conferences throughout the duration of the study, mainly those aged between 20–40 years. Hence, social prescribing tools such as Osekkai conferences could drive the participation of women based on their demographic characteristics. This trend could be affected by rural female characteristics . Two priBy participating in an Osekkai conference, the participants can become connected with others intentionally and find it easier to connect to others in their communities. However, the degree of direct connections that each participant in our study did not change, so the participants might not notice that they acquired various new relationships through an Osekkai conference. In contrast, the network density and diameter both decreased, meaning that the participants could connect to others in the community more easily and quickly. This phenomenon occurred while the number of participants gradually increased. Previous studies have demonstrated that network density can exhibit the community’s strength and nurturing empowerment, which in turn can drive effective community activities ,26. FurtThis study also demonstrates that harmonic closeness centrality and eccentricity decreased, and modularity increased, indicating that each participant could increase their social capital via the strength of the connection with others in their communities. Direct and indirect connections can affect individuals’ lives in communities ,31, as pBased on our results, we believe that the application of Osekkai conferences as social prescribing tools could be broadly expanded by three measures: ensuring their continuity; finding ways to collaborate with other community activities and resources; encouraging the transmission of good health practices using the existing positive connections within the community. First, for the continuity of social prescribing, conference organizers should collaborate with various private and public organizations to acquire funding, information, and resources to perform the conferences ,36. In tSecond, other community resources should be used to provide social prescribing. In communities, indigenous community activities that connect people ,39 can bFinally, the transmission of good health practices should be encouraged based on the existing positive connections within the community. Indeed, research shows that social connections can transmit not only information but also health behaviors. For example, the obesity and nutrition conditions of an individual could affect their surroundings, then which could increase obesity and the number of unhealthy community members ,43,44. FThis study has several limitations. It focused on rural Japanese communities, so the study’s setting may represent only rural communities with a lack of medical resources, an aging society, and high numbers of isolated older people. Future studies should focus on a broader range of communities and cultures. Another limitation pertains to the sampling methods. This study focused on participants’ connections within the conferences as judged by whether they participated in a specific conference; that is, we did not assess their specific collaboration to the conference and provision of Osekkai conferences using variables and methods that allowed for the direct assessment of these variables. Future studies should investigate the relationships and interactions in Osekkai conferences that affect the participants’ behaviors related to Osekkai. Furthermore, the study was a single-arm interventional study with no comparison group, affecting internal validity, and the Osekkai conferences  were provided based on previously published studies ,17,18. TThe continuous performance of Osekkai conferences in rural communities may serve as social prescribing tools, and this continuity may lead to an increase in the number of conference participants. Based on an SNA, the provision of Osekkai conferences was related to a gradual decrease in network density and diameter. The participants could then increase their social capital using the strength of the connection with others in their communities. Accordingly, social prescribing interventions should focus on improving social capital, which may improve the number and meaningfulness of the collaborations among organizations and indigenous communities as well as help ensure enhanced connections and social capital in the community.


Aggregatibacter actinomycetemcomitans as well as by newly dominant species like Filifactor alocis. These two oral pathogens prevail and grow within the periodontal pocket which highlights their ability to evade the host immune response. This review focuses on the virulence factors and potential pathogenicity of both oral pathogens in periodontitis, accentuating the recent description of F. alocis virulence factors, including the presence of an exotoxin, and comparing them with the defined factors associated with A. actinomycetemcomitans. In the disease setting, possible synergistic and/or mutualistic interactions among both oral pathogens might contribute to disease progression.Periodontitis is a dysbiotic disease caused by the interplay between the microbial ecosystem present in the disease with the dysregulated host immune response. The disease-associated microbial community is formed by the presence of established oral pathogens like  Porphyromonas gingivalis, Tannerella forsythia, Treponema denticola, and Aggregatibacter actinomycetemcomitans are now well-established “periodontal pathogens”, with evidenced involvement in disease initiation and progression. In the last decade, the advance in high throughput technology allowed us to obtain an in-depth characterization of the complexity of the oral microbiome both in health and in disease , and by grants from Insamlingsstiftelsen, Medical Faculty, Umeå University (JO and AJ).The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.All claims expressed in this article are solely those of the authors and do not necessarily represent those of their affiliated organizations, or those of the publisher, the editors and the reviewers. Any product that may be evaluated in this article, or claim that may be made by its manufacturer, is not guaranteed or endorsed by the publisher.


The experimental results show promising results in a hybrid solution combining the defense algorithms and the multiclass discriminator in an effort to revitalize the attacked base models and robustify the DNN classifiers. The proposed architecture is ratified in the context of a real manufacturing environment utilizing datasets stemming from the actual production lines.Adversarial machine learning (AML) is a class of data manipulation techniques that cause alterations in the behavior of artificial intelligence (AI) systems while going unnoticed by humans. These alterations can cause serious vulnerabilities to mission-critical AI-enabled applications. This work introduces an AI architecture augmented with adversarial examples and defense algorithms to safeguard, secure, and make more reliable AI systems. This can be conducted by robustifying deep neural network (DNN) classifiers and explicitly focusing on the specific case of convolutional neural networks (CNNs) used in non-trivial manufacturing environments prone to noise, vibrations, and errors when capturing and transferring data. The proposed architecture enables the imitation of the interplay between the attacker and a defender based on the deployment and cross-evaluation of adversarial and defense strategies. The AI architecture enables (i) the creation and usage of  Image classification algorithms fueled by artificial intelligence (AI) and realized via deep neural network (DNN) architectures are commonly used into several application domains for image recognition and classification , object Among others, Industry 4.0, which will be the focal point of the evaluation of this work, capitalizes on AI methods to enable flexible production lines and support innovative functionalities such as mass customization, predictive maintenance, zero-defect manufacturing, and digital twins. Large-scale AI deployments in manufacturing involve many interactions between the AI systems and other elements of the surrounding environment, including hardware, software, and physical systems. Thus, in order to ensure the success of this blend of technologies, the design of trusted and reliable AI systems positioned in the manufacturing floors is of the utmost importance.On the downside, the emergence of adversarial machine learning (AML) has become a major concern towards the adoption of AI technologies for critical applications and has already been identified as a barrier in multiple application domains. AML is a class of data manipulation techniques that cause changes in the behavior of AI algorithms while, usually, going unnoticed by humans. An ever-increasing problem is that AML can create slight modifications over the mathematical representations and, therefore, over the model, which may remain imperceptible to the human eye while changing the output of an AI system. Suspicious object misclassification in airport control systems , computeThe challenges posed by AML have recently attracted the attention of the research community, the Industry 4.0, and the manufacturing domains ,11 as poIn this reality, defenders are not just passive spectators. On the other side of the same coin, AML defense algorithms have made significant progress in uncovering the space of AML, providing solutions that act both on the digital domain  and even on more recent cases which are applicable to the physical dimensions  considerIn this context, there is a pressing need for robustifying image classifiers against diverse perils of adversarial attacks against AI systems. Research needs to be intensified and drill into the details and the specificities of existing AI systems, investigate their vulnerabilities, and introduce solutions that make DNNs more resilient against adversarial inputs, attempts of poisoning, and evasion attacks.multiclass discriminator, as a post-processing module, generates alerts and reports on the detection of evasion attempts. The proposed architecture and the individual attacks and defenses are evaluated in the context of a real manufacturing environment, utilizing real industrial datasets stemming from the DNN-based Visual Quality Inspection system deployed in Philips’ factory. Our evaluation results advocate that the proposed approach offers advanced resilience and recovery to the quality inspection system by augmenting the training dataset with adversarial examples and by training the multiclass discriminator in order to distinguish between non-attacked and attacked classes of the datasets. In addition, the designed evaluation testbed gives the opportunity to investigate the behavior of attack-defense pairs in order to conclude to the most appropriate defense which can deliver a more robust AI system based on DNN models, which, in this specific case, are built on convolutional neural network (CNN) classifiers.In this paper, we introduce a robust AI architecture empowered with multiple AML attacks and defense algorithms using the open-source Adversarial Robustness Toolbox (ART) . The tooWe introduce a reproducible AI architecture for robustifying CNN image classifiers that has been evaluated in the context of a real manufacturing environment which is prone to noise, vibrations, and errors when capturing and transferring data.We present a thorough evaluation of attack-defense pairs for uncovering the peculiarities of different adversarial techniques and investigating the applicability and effectiveness of the defense strategies under real conditions.preprocessing module) and techniques to robustify and guide the deployment of AML attacks and defense algorithms  for revitalizing CNN classifiers’ accuracy and detection .We deliver a modular approach for the recruitment of different modules . Current production lines are often tailored for the mass production of one product or product series in the most efficient way. However, the manufacturing landscape is changing. Due to global shortages, manufacturing assets and components are becoming scarcer, and a shift in market demand requires the production of smaller batches more often. To adhere to these changes, production flexibility, re-use of assets, and a reduction of reconfiguration times are becoming more important for the cost-efficient production of consumer goods. In this context, one of the topics currently investigated within Philips is the painless setting up of the automated AI-based quality inspections that aim to make the reconfiguring of quality control systems faster and easier.The setup used to inspect the quality of the printed logo on the Philips products can be seen in The DNN-based quality inspection is based on a classic machine learning model setup. Its training takes place on the servers for external control, under human supervision. The historical datasets reside on the file system of the external server and have been manually gathered and stored. The human has the necessary ML domain and application knowledge to carry out the experimentation and the classification model training. The resulting model is then placed manually in the file system of the industrial server, and the CNN-based QI app puts the model in the inspection workflow.Evasion Attacks: In this work, we consider adversaries, under the notion of ML model evasion attacks [ attacks , who canML Attack Staging: In addition, we consider an adversary who may attempt to poison the target model and craft adversarial data [ial data  to feed In this context, the attacks and defenses generated and evaluated in adversarial training) while training an AI model to boost its resilience is a common defense strategy to let the latter classify and predict more enhanced features. While the adversarial examples are useful for the purposes of augmenting benign datasets, there is an imminent need for frameworks that enable the exploration of realistic adversarial attacks with varying threat models. For instance, in our case, we define realistic attacks for image classification tasks involved in an AI-based quality inspection solution in a shop-floor. This enables early decision-making before the product is released to the next step of the production line, i.e., right after the label printing. Attacks may come in at different stages. For example, digital attacks such as the projected gradient descent (PGD) are injected at the recognition stage. In The generation and augmentation of adversarial examples  in a shop-floor production line. A preprocessing module, which is responsible for images curation, cleaning and performs resizing and filtering;Training and evaluation of the base CNN model, which serves as the baseline for the evaluation of the model’s accuracy before and after the attacks and defenses;Adversarial imagery example generation and adversarial CNN model training, which takes as input the preprocessed images, passes them through different ART attacks to modify them to generate adversarial imagery examples, and then trains an adversarial CNN model;A multiclass discriminator, which combines the preprocessed images and their labels/categories along with the adversarial imagery examples with their labels/categories. We build a multiclass classifier which lets the model distinguish images between attacked and their category vs. non-attacked and their category;Defense algorithms, which enforce methods enabling the recovery of the attacked images and raise the accuracy of the base CNN model.The logical modules of the architecture are the following:For this study, we analyzed and processed datasets coming from a shop-floor production line at the level of quality inspection. This step of quality inspection is critical to the business of manufacturing because it determines if the product will be propagated to the finalization stage, packaging, and route to market or will be withdrawn.preprocessing module is necessary to curate the images in order to let more features be extracted during the training of the models. The module has been implemented using Keras as the preprocessing library [The  library . After dDataset_1 is natively challenging to distinguish the differences in two out of three categories because it has good and flow lines, rendering the samples not trivial to process and extract features due to their close similarity. Dataset_2 is easier to handle for feature extraction, as presented in the following paragraphs.Dataset_1, we took the following steps. The images were loaded from the local database and transformed to arrays by using the Keras library. Next, we applied some filtering methods to clean and make the dataset more visible. More specifically, we used exposure filters and the histogram of oriented gradients (HOG) technique [Dataset_1 to extract more features is depicted in More specifically, for echnique . HOG is Dataset_2 were loaded from the local database and transformed into arrays using the Keras library. In addition, to ensure that all the features are visible and can be extracted to train the models, we applied Otsu’s threshold [Dataset_2 to extract more features is depicted in The images of hreshold , which hbase CNN model facilities to build a model which serves as the baseline to investigate its robustness measured by means of accuracy under two concrete conditions: (i) after applying different attack methods by using the ART tool [base CNN model can recover from the attacks. adversarial imagery examples. To build the base CNN model, we used different Keras layers, including Sequential, Dense, Flatten, Conv2D, MaxPooling2D, Activation, and Dropout layers. The activation functions were accordingly different based on the multiclass or the binary classification task that we supported. The output model was evaluated by means of accuracy using confusion matrices and classification reports. The model evaluation was performed using an independent test set that had not been used for the training.The training and evaluation of the ART tool ; and  attack, DeepFool, NewtonFool, projected gradient descent (PGD), BasicIterative using PGD, SpatialTransformation, SquareAttack, CarliniLIn Method, CarliniL2 Method, and UniversalPerturbation using EAD-elastic-net attacks. The different attack algorithms were used to investigate how effective the attack was and at which level by means of decreasing the model’s accuracy. Decreased accuracy has a direct effect on the model’s robustness, with each attack resulting in adding noise to the images and misclassifying their category.The binary output of the ART tool  were useDataset_1. We have selected two representative examples of non-attacked vs. attacked images. Although in the first example, according to the visual inspection, the effect of the attack is slight, the model’s accuracy is greatly decreased.At the same time, in the second example, the image cannot be easily distinguished via visual inspection; however, the model’s accuracy is slightly decreased.Dataset_2 example of non-attacked  and attacked images by the fast gradient descent (FGD) and the projected gradient descent (PGD) are depicted in A base CNN model compared with the adversarial CNN model. A detailed experimental study is presented in base CNN model misclassified the adversarial imagery examples, and its accuracy was dramatically decreased. Given this model’s accuracy decrease, we devised a multiclass discriminator as a countermeasure to distinguish between original and attacked images and report on each category, as presented in the following section.We performed prediction tasks over the adversarial imagery examples to assess the accuracy of the multiclass discriminator is a model that combines the preprocessed images and the adversarial imagery examples with their labels and is trained to distinguish the non-attacked from the attacked data, i.e., data that an attack algorithm has generated. multiclass discriminator. The scenario of the multiclass discriminator labels attacked and original images in different categories. The latter results in multiplying by two (2) categories of the original and the attacked images. In this case, the adversarial imagery examples are labeled with a new label to let the model be trained and predict more accurately if an image lies within an adversarial or an original image category.The Defense algorithms are used as another countermeasure to recover and increase the accuracy of a model. We used specific defense algorithms from the ART tool and evaluated the model’s accuracy before and after the recovery from an attack to quantify its score. We used the FeatureSqueezing, JpegCompression, SpatialSmoothing, and TotalVarMin algorithms as defense. We used the test set from the adversarial examples to evaluate how the defense algorithms facilitate recovery from the attacks. We also visualized the results to investigate how the defense algorithms recovered the adversarial examples, as can be seen in This section presents experimental results on the presented datasets over different attack methods and defense algorithms. Furthermore, we present the experimental results by means of accuracy for the multiclass discriminator. The experimental evaluation is based on the current version of the Adversarial Robustness Toolbox (ART version 1.11.0) , Keras  and attacked = 1 .The preprocessing module takes care to set the labels of the images. We execute separate experiments for multiclass and binary classification. As far as the multiclass classification is concerned, the images receive the original labels they have, e.g., normal = 1, flow lines = 2 and marks = 3 with respect to base CNN model’s accuracy. More than half of the attacks are unable to poison or evade ML algorithms such as SVMs, decision trees or DNNs. Additionally, some attack algorithms are designed and implemented to support only a specific ML/DNN model, library, algorithm and application. In our experiments, we executed different ART attacks to generate the adversarial examples and various defense algorithms to recover the Our motivation is to compare and assess the robustness of a DNN model, i.e., the base CNN model, considering its architecture’s specificities, as well as gradient-based attacks, which are more infectious to the backpropagation method used for DNN model training. With these considerations, the reason behind our choice is twofold. On the one hand, our purpose was to be aligned with the existing model used in the actual visual inspection setup described in Dataset_1, normal = 1, flow lines = 2, and marks = 3. Furthermore, we have used the K-fold cross validation method in all the experiments. This method enables us to measure the model’s accuracy in different data samples. Using this method, we also manage to avoid overfitting the data over the model since, before the model training task, the dataset is split into different training and test sets. Specifically, in the experiments conducted, we used K-fold to split the dataset into buckets containing all the image categories for both Dataset_1 and Dataset_2.All the following figures visualize the comparison of the model’s accuracy before any attack, on the attacked data, and on the recovered data by the defense. The discriminator’s accuracy has been evaluated on the original and adversarial examples by assessing its predictive capabilities to efficiently identify the correct data category, e.g., for Dataset_1.In the binary classification task, the data were separated into two categories/classes, i.e., usual = 0 and malicious = 1. This task was evaluated towards the accuracy of the adversarial CNN model and the recovery rate/percentage that the defense algorithms achieved. The algorithms used for attacks are the FastGradient, DeepFool, NewtonFool, PGD, BasicIterative, SpatialTransformation, SquareAttack, CarliniLInf, CarliniL2, and UniversalPerturbation algorithms. We observe in At the same time, according to Dataset_2, we observe in Dataset_1, the attack algorithms have a bigger impact on the model’s accuracy than on Dataset_2. This proves once again the importance of the specificity of the data.Regarding defense algorithms, the following figures concentrate the outcomes of the TotalVarMin, FeatureSqueezing, JpegCompression, and SpatialSmoothin algorithms. As far as Dataset_1 is concerned, in Regarding the results of Dataset_2, Regarding Dataset_1 and Dataset_2), we observe that training the base CNN model with adversarial examples results in making it more robust and recovering its accuracy compared to the defense algorithms.Overall, for binary classification tasks in both datasets  attacks on deep neural networks (DNNs), and more specifically, convolutional neural networks (CNNs). In a nutshell, CNNs are prone to adversarial attacks, which present a challenge to safety-critical domains where calibrated, robust, and efficient measures of data uncertainty are crucial.We introduced a reproducible AI architecture for making CNN image classifiers more robust, which has been evaluated in non-trivial manufacturing environments prone to noise, vibrations, and errors when capturing and transferring data. The proposed architecture’s reproducibility is guaranteed through open-source tools and adversarial and defense algorithms, synthesized and engineered under a systematic approach to guarantee the security and operational assurance of the native systems used in the production lines. In addition, the architecture is composed of modules that robustify and guide the deployment of AML attacks and defense algorithms for recovering the CNN classifiers’ accuracy. That is, new modules can be added to easily extend the architecture’s functionalities and the protection coverage of classifiers used in the underlined systems.We presented a qualitative study showcasing how the different attacks and defense methods affect the resolution and clarity of the images. This study was further enriched by quantitative experiments measuring the base CNN model’s accuracy in various attack-defense contexts. Overall, it is evident that binary classification results achieve higher accuracy categorizing as usual vs. malicious data, enabling fewer opportunities to uniquely detect from which class the image was originally from. At the same time, despite multiclass classification results achieving lower accuracy, defense algorithms still achieve high recovery rates, enabling the unique prediction of the class of new images.We may conclude that each case and dataset may require a different defense strategy to be deployed in order to safeguard a baseline model. Different adversarial approaches can be addressed by different defenses, but there is no clear indication that there is a defense strategy that can cover the wide range of attack techniques in an adequate manner. As expected, between the adversarial training and the tested ART defenses, the former seems to be the most effective way to recover the overall model robustness. According to the results of the plots that demonstrate an average accuracy increase after the adversarial training technique, one can observe that it has better performance than the evaluated off-the-shelf defenses offered by ART. Of course, this does not imply that these defenses should not be considered in future experiments, but there is evidence suggesting that, at least for the evaluated datasets, they cannot adequately limit the impact of an adversarial technique. Again, the challenging nature of the problem is that, as advocated by the results, each dataset and each different attack imply the need for the placement of a different defense strategy.The high success rates of the adversarial attacks against a real-world case, utilizing actual data stemming from a real manufacturing environment, make evident that today’s AI-enabled manufacturing system can become the low-hanging fruits of intelligent attackers. That is, research and innovation need to be fostered in order to design robust architectures for sanitizing the data pipelines of manufacturing environments, filtering out malicious instances and detecting the injection of adversarial examples in the process.In the near future, we plan to assess the efficacy of the data-centric AI architecture in other application contexts and extend it to other imagery categories with variable features and resolution characteristics.