Predicting the variations in climate for the coming 1–10 years is of great interest for decision makers, as this time horizon coincides with the strategic planning of stakeholders from climate-vulnerable sectors such as agriculture. This study attempts to illustrate the potential value of decadal predictions in the development of climate services by establishing interactions and collaboration with stakeholders concerned with food production and security. Building on our experience from interacting with users and the increased understanding of their needs gathered over the years through our participation in various European activities and initiatives, we developed a decadal forecast product that provides tailored and user-friendly information about multi-year dry conditions for the coming five years over global wheat harvesting regions. This study revealed that the coproduction approach, where the interaction between the user and climate service provider is established at an early stage of forecast product development, is a fundamental step to successfully provide useful and ultimately actionable information to the interested stakeholders. The study also provides insights that shed light on the reasons for the delayed entry of decadal predictions in the climate services discourse and practice, obtained from surveying climate scientists and discussing with decadal prediction experts. Finally, it shows the key challenges that this new source of climate information still faces. Practical implications:The coproduction process is essential for developing useful forecast products and promoting the uptake of recently developed decadal climate information. In the context of climate services, coproduction starts with informing the relevant stakeholders about the potential role of climate information through different communication channels. It further involves knowledge exchange between scientists and users, as well as direct collaboration and co-design of new climate information, applying tools such as the development of case studies and user decision support systems. In this context, the present study strives to coproduce a climate service based on decadal predictions through the development of a tailored decadal forecast product in close collaboration with users from the agriculture sector, to demonstrate the potential applicability of such climate information.Providing skillful and robust climate information on a multi-annual to decadal timescale holds the potential of being of great value for a broad range of users from different socio-economic sectors that are influenced by the varying climate conditions. In this study, through the interaction with stakeholders from the agriculture sector, we present some user needs and key decision areas that could be improved with decadal climate information. For instance, this type of climate information is useful for supporting planning decisions that require several years to be implemented, such as decisions in terms of equipment purchase (irrigation plants), planning of supply chain at international level and studying the use of new crop varieties. In addition, such information can have an impact on developing strategic policies related to agriculture, from regional to the EU common agricultural policy, as the policy changes within the agricultural sector usually happen at this timescale.1Decadal climate predictions represent a source of near-term, understood to span from the following few years to a couple of decades into the future, climate change and variability information that has the potential to improve climate-related decisions in a wide range of socio-economic sectors, which are heavily influenced by climate variability and change . The firDespite the scientific progress achieved in this field , only liWhile research to showcase the application of decadal predictions is rapidly evolving, the climate services community, which is working on enhancing the usability and facilitating the penetration of climate information in socio-economic sectors, is still barely using these predictions. Further efforts are needed to establish transdisciplinary partnerships between scientific and sectoral communities that would coproduce climate services based on decadal predictions and communicate the resulting climate information to a broader stakeholder community. This study aims to showcase an attempt made to coproduce decadal prediction-based climate services with users from the agriculture sector. At the same time, we wanted to shed light on the reasons behind the delayed arrival of decadal climate information into the climate services arena and the key challenges faced in this field. To do so, we opened a dialogue with the users to understand their perspective on the usability of decadal predictions, and a discussion with climate scientists to understand why it took longer for decadal predictions than for other sources of climate information to enter the climate services discourse and practice and what is novel about decadal predictions.2Over the past few decades, the agriculture user community working on food production and security  has been commonly using long-term climate change projections as an important source of climate change information . Only reClimate models, which are a mathematical representation of the Earth’s climate and built using the basic laws of classical physics and thermodynamics, are used to run both long-term climate change simulations and decadal predictions. Climate change simulations are represented in Decadal predictions , by using a number of different models and/or by perturbing the parameters of a given climate model . An indiIt is fundamental to determine how well the climate model incorporates all the relevant components of the Earth’s climate system. For this reason, the climate community has traditionally compared the predictions of the past climate (‘hindcasts’) with the recorded observations. The forecasts are considered to be skillful if these hindcasts .In an attempt to illustrate the potential value of decadal predictions in the development of climate services for the agriculture sector, we designed a prototype decadal forecast product that provides tailored, useful and user-friendly information for stakeholders in agriculture, in particular those from the wheat sector. In designing this prototype, we built on our experience interacting with users from the agriculture sector and the increased understanding of their needs gathered over the years through our participation in various EU-funded projects and initiatives . The designed prototype was further developed under the framework of the Copernicus Climate Change Services  was selected to study the evolution of multi-year droughts. SPEI6 is the standardised estimate of climate water balance  accumulated over the six-month period prior to the wheat harvesting months over wheat harvesting areas. This prototype forecast product consists of a two-page document that presents a map of the most likely tercile category , normal and above-normal (or wet) conditions estimated from the climatological distribution) of predicted SPEI6 for the period 2020–2024, as well as a time series of the historical values of predicted SPEI6 over a particular spatial grid point. It is followed by a brief text with the interpretation of the results, providing the users with an overview of the expected variation in the wet and dry conditions for the coming five years.The prototype forecast product uses decadal forecasts  produced by four European institutions which update their forecast annually: 10 members from EC-Earth3 ; 10 membThe document also provides the background information for obtaining the product along with a summary of the decadal forecast quality assessment. We use Ranked Probability Skill Score RPSS;  and RelaThe initial prototype forecast product is shown in 4Given that the co-development of climate services based on decadal predictions is still in its early stage, this study aimed to better understand the current needs of stakeholders in the agriculture sector at this time scale and, at the same time, gather feedback on the user perception of the climate information provided in the forecast product introduced in For this purpose, we conducted an online workshop with stakeholders interested in multi-year drought predictions on 18th and 19th of February 2021. This workshop was organised as part of a C3S stakeholder event, whose main aim was to introduce general aspects of decadal predictions, challenges and opportunities to a wide range of stakeholders  and in food security. The prototype forecast product was sent to the participants ahead of the workshop for them to familiarise themselves with the forecast information. During the workshop, we facilitated the discussion by raising questions related to five different aspects: (i) opinion: whether decadal prediction would be useful for their institution and/or sector; (ii) clarity of content: whether the key concepts presented were easy to understand; (iii) clarity of format: whether the figures and presentation of information were clear; (iv) usefulness: whether the forecast information could be useful for their work and the work of others in their sector, and if modifications were needed; and (v) future applications: whether they saw potential future applications of decadal forecast product for both them and their sector.After the workshop, the feedback provided by the participants on the forecast product was compiled and evaluated. For this, we used the coding technique supported by the qualitative analysis software MAXQDA, which organises the information by common topics and themes . The forIn parallel to the evaluation of the decadal forecast product and its subsequent modifications driven by stakeholders’ feedback, this paper looks into the reasons, from a scientific perspective, as to why the use of decadal climate predictions were a latecomer in the climate services arena in comparison with other climate timescales. We conducted a short online survey to gain insight on the key challenges faced by the decadal prediction community, as well as to discuss the added value of using decadal predictions. The survey consisted of six questions and was completed by 13 climate scientists, among which 15% were senior scientists, 54% were mid-career researchers, 8% early-career researchers and 23% were anonymous. The participants had diverse experience in fundamental climate research such as model initialisation, ensemble generation, understanding the physical processes at the origin of predictability, bias adjustment, model evaluation and predicting extremes. In addition, they have contributed to the development of state-of-the-art climate forecast systems that predict climate at different timescales, ranging from a few weeks to a few decades into the future .The climate researchers were asked the following questions: (i) how they would explain decadal predictions to someone who is not a scientist; (ii) what makes decadal predictions different from climate information for other time scales; (iii) why research in decadal prediction has only started recently ; (iv) what are the key challenges in making real-time decadal predictions; (v) whether there is an added value in using decadal predictions compared to climate projections; and (vi) for which sectors or applications decadal predictions could be useful.The survey results were then further discussed in a roundtable discussion with decadal prediction experts, to find agreements and complement the expertise and experience of the authors. The answers obtained in the survey and the roundtable discussion were also analysed using the coding technique provided with the MAXQDA software.55.1This section presents the outcomes of the workshop with stakeholders from the agriculture sector. The 11 workshop participants came from different agricultural sectors. This includes participants from: a family-owned wine company holding over 1500 hectares of vineyards; a retail firm that designs, manufactures and distributes clothing and sport goods; a company providing hi-tech solutions for sustainable management of crops such as wheat; and two public institutions directing its research capabilities to improve food security, as well as to help achieve climate and environmental goals. The discussion in the workshop addressed the participants’ perspective on the usability of decadal predictions in the agriculture sector and, more generally, identified a few key strengths and weaknesses of the developed decadal forecast product . All these decisions can be well informed with both decadal predictions or long-term climate projections. Furthermore, decadal prediction could be a tool for improving the resilience of retail operations and the supply chain at the international level by predicting the climate extremes/shocks that could disrupt the global market. “We see the possibility of these predictions to help in developing business strategies at local and global levels by taking into account crop yield output scenarios of natural fibers under climate change conditions” (stakeholder from the retail sector).Another example was provided by a participant working on food security research, who stated that multi-annual predictions can support their decadal outlook exercise, where they would potentially present the evolution of the global agriculture market in the coming ten years. In addition, “the policy changes within the agricultural sector usually happen at this timescale. For instance, the EU’s common agricultural policy (CAP) is acting on a 10-year timescale. Such an outlook, in terms of the economic component of the agriculture sector, is always looking at what could happen in the coming five to ten years by assessing the complex interaction between the biophysical, economic and climate components together, to understand what are the risks for the agriculture sector” (stakeholder working on food security research). Therefore, providing tailored information for the next five to ten years could provide robust information for policy makers from regional to national level, to better support the choices and the political agenda for the agriculture sector.5.1.2During the workshop, a two-page prototype forecast product, described in The participants agreed that the concept and structure (format of the product sheet) was well drafted, but stated that the product sheet was targeting advanced stakeholders with technical experience as it contained climate-specific terms used by the forecasting community that might have different meanings within different stakeholder communities. For instance, a participant took notice of the usage of “normal”, “below-normal” and “above-normal” categories without clear definition in the prototype forecast sheet. “For forecasters, normal is a 30-year or multi-decadal average of data. On the contrary, for the common person, normal is something that is not unusual, which has a totally different meaning.” (stakeholder from the wine sector).In addition, the participants found some of the information presented in the figures displayed in the prototype forecast product, such as the time series plot a, hard tAccording to feedback received, The decadal predictions discriminate between wet  and dry  events relatively well, and the prediction of most likely category matched the observation for 29 years  out of 55 years. The relative operating characteristic (ROC) skill score metric was used to measure the ability of the forecast system to discriminate/predict a particular categorical event. The decadal predictions are found to have skill in predicting the above and below normal categories over the selected area, which is indicated by positive values of ROC skill score in Additionally, we have made an attempt to further simplify the global forecast map shown in in white b. The fiA workshop participant stated that it was unclear if the positive skill . The same participant advocated for the use of stock images, e.g. of wheat fields, to improve visual attractiveness of the product. To address the food security concerns more generally, a participant suggested simultaneously showing forecasts for other major crops, such as maize, cotton and grape.These key suggestions for further improvement will be addressed by continuing our collaboration with the participants in the future and tailoring the climate information based on their needs. Altogether, this particular decadal forecast product was perceived as useful for researchers and policy makers alike, in particular for the new European adaptation strategy and the CAP: “The information provided could serve to better foresee food security issues arising in the following 5 years, affecting market trading mechanisms, as well as allowing better preparation for grain yield redistribution.” (stakeholder working on food security research). Finally, the participation in the workshop and evaluation questionnaire was positively appraised by all the participants.5.2In this section, we present results received from the survey sent to climate scientists, and a roundtable discussion with decadal prediction experts. The discussion on the perception about the position and late arrival of decadal predictions in the climate services arena was carried-out only with the climate scientists, as the aim of the survey was to understand the research challenges bound to this type of climate information.According to the results from these interactions, climate scientists consider the novel aspect of decadal climate prediction to be that it takes into account the impact of changes in atmospheric composition and other external forcings, as well as the naturally generated internal climate variability. In particular, decadal predictions include both current or recent information about the climate state to initialise the climate model. Until the use of decadal predictions became more widespread, the time scale of 1–10 years was practically not covered in the production of climate information , since sEvaluating the quality of the predictions is considered a fundamental step in climate prediction because it assesses whether the prediction system can be trusted to forecast certain events. This so-called verification process is typically based on validating extensive sets of hindcasts or retrospective predictions against observational references . It takeAnother aspect that postponed the development of decadal predictions, according to the roundtable and survey participants, is that there are few sources of decadal predictability in comparison to other timescales. Most of these sources of predictability, such as Atlantic Multi-decadal Variability AMV;  and PaciThe roundtable discussion also considered the fact that decadal predictions had to learn both from the climate projection and seasonal prediction “schools of thought”, which could have limited advancements in this field. Finally, answering to the question why decadal predictions appeared as a latecomer in the climate information world, the climate scientists participating in the survey listed a few other factors, such the lack of an established “protocol” for developing and comparing decadal predictions and the lack of application .As the main persisting challenge in making real-time decadal predictions, climate scientists perceived the lack of long homogenous past data series . As explEven though experimenting at the decadal timescale is limited due to high computational costs, investigating predictability at this time horizon brought several interesting insights to scientists: they learnt about the behaviour of some patterns that were not obvious before, for e.g. AMV has been linked to the observed slowdown of global warming over 1998–2012 through its impact on the tropical Pacific . In addiEvaluating decadal prediction has also allowed scientists to better understand the issues and errors present in climate models. “A prominent example is the signal-to-noise paradox  x 10 forecast years x 10 ensemble members  = 6,100 simulated years. For the same ensemble size, such an experiment is more than twice as expensive as a typical historical and future scenario CMIP6 experiment, which covers the period 1850–2100, as pointed out during the roundtable discussion conducted in our study. Producing decadal predictions at higher resolution could be one to two orders of magnitude more expensive than running the prediction provided by the current state-of-the-art decadal forecast systems.The SPEI forecast product delivered in the prototype was presented using terciles. This format, co-developed with users, aimed to showcase multi-year dry, normal, and wet conditions. However, users are not only interested in multi-annual prediction of the most likely tercile category of SPEI6 , Fig. 3,dictions  and climdictions , much ledictions . These shttps://s2s4e-dst.bsc.es/).During the interaction with stakeholders we also found that users in the agriculture sector traditionally based their decisions on deterministic information and that probabilistic forecasts were relatively new to them. This could be a reason why climate scientists find it challenging to communicate probabilistic information to users. Presenting technical concepts, such as the skill of probability forecasts in an intuitive way can favour the uptake of decadal climate information. Efforts to better link forecast quality and risks involved in making decisions based on probabilistic forecasts include tools and applications like the Weather Roulette app  or the SPotential users of climate information are looking for the actual benefit that the skill information brings during their decision making process. While a number of studies have shown that decadal prediction can skillfully predict essential climate variables  and userUsers demand simple and intuitive visualisations, especially if the objective is that anyone  can understand and use the prediction . Also, dThis fine balance between simple and sufficiently informative could only be achieved in the process of co-exploring and co-designing climate information and decadal products between scientists and users. The coproduction approach applied in the current study confirmed what has been argued in the literature e.g., , i.e., tThe collaboration with the 11 users from the various domains of the agriculture sector included an intensive interaction with climate service providers at the stakeholder workshop. The users had received the product draft prior to the workshop to familiarise themselves with the provided prototype forecast product. This setting helped the interaction between scientists (service providers) and the workshop participants  and allowed for building relationships. Ideally this type of collaboration could involve even more participants, to cover the array of user needs and expectations. However, keeping in mind that participants volunteer their time in these types of interactions , we coulhttps://climate.copernicus.eu/sectoral-applications-decadal-predictions). Although the research about climate services on decadal timescale has just started to emerge and further research is still needed, this study provides evidence on the potential value that decadal predictions hold in supporting decision making and strengthening the resilience of climate-vulnerable sectors.From the interaction with stakeholders, it is evident that there is an increasing interest in the decadal prediction. As well, there is an increasing interest within the climate services community in operationalization of climate prediction to support the uptake of such climate information by the user community. In this regard, there are already a number of initiatives being developed. As an example, the Grand Challenge on Near-Term Climate Prediction (GC-NTCP) aims to facilitate the development of decadal prediction towards its operational use. A primary goal of the GC-NTCP is to produce annually updated climate outlooks for the forthcoming years based on real-time forecasts produced by a number of institutions gathering both meteorological services and research institutes as part of an informal exchange . While t7The present study evaluated the coproduction of a prototype climate service based on decadal predictions, which took place through interactions and collaboration between climate service providers and stakeholders from the agriculture sector participating in a number of European activities such as the MEDGOLD and EUCP projects, as well as C3S. As a part of the coproduction process, we developed a decadal forecast product that presents the multi-year forecast of SPEI6 for the coming five years over global wheat harvesting regions. The study summarises the lessons learnt from the interactions with the user community about their perspective on the usability of decadal predictions, and their feedback on the developed forecast product. The stakeholders expressed interest in uptaking decadal climate information as it presents potential in supporting planning decisions that take a number of years to develop or implement. For instance, the stakeholders interested in decadal prediction pointed out that this type of climate information could help to choose crop varieties or future crop plantation sites, to develop national policies such as the EU common agricultural policy and to develop business strategies at an international level.The paper also presents the results received from the survey sent to climate scientists, and a roundtable discussion with decadal prediction experts. These interactions aimed to explore the perception of climate scientists on the reasons behind the late arrival of decadal predictions to the climate services arena and the key challenges faced by this research line. The participants considered that the key factors contributing to the delay in the development of decadal predictions are the following: the high computational demand for producing decadal hindcasts, the requirement of long homogeneous observational dataset for initialising the climate model and fewer sources of decadal predictability in comparison to other timescales. Nevertheless, since decadal predictions were first made available, the research line has been rapidly evolving. For instance, research has already started to generate seamless climate data sources from the near-term to longer timescales. The H2020 project EUCP is currently exploring the added value of blending together initialised decadal climate predictions with climate projections over the time period of 1 to 30 years , taking advantage of both approaches to provide a seamless multi-decadal climate information . In addiWhile such advances in research aims to further enhance the predictive skill and reliability of available climate information, the development of climate services based on decadal predictions is still in its early stages. Tailoring near-term climate information to different socio-economic sectors, through building partnerships between the climate service providers and the user community, is an important first step for developing successful climate services. Therefore, identifying and engaging with potential users, and communicating the value of using near-time climate information during their decision-making processes should be considered as the utmost priority for ensuring the uptake and usage of this new source of climate information.Balakrishnan Solaraju-Murali: Conceptualization, Formal analysis, Writing - original draft, Visualization. Dragana Bojovic: Conceptualization, Formal analysis, Writing - review & editing, Supervision. Nube Gonzalez-Reviriego: Conceptualization, Writing - review & editing, Supervision, Funding acquisition. Andria Nicodemou: Conceptualization, Writing - review & editing. Marta Terrado: Conceptualization, Writing - review & editing. Louis-Philippe Caron: Writing - review & editing, Supervision. Francisco J. Doblas-Reyes: Writing - review & editing, Funding acquisition.The authors declare that they have no known competing financial interests or personal relationships that could have appeared to influence the work reported in this paper.


Interprofessional collaboration is key to improving the health of individuals and communities. It is supported by provision of Interprofessional education (IPE) which has recently emerged in the Middle East region. This study investigated changes in healthcare students’ attitudes towards interprofessional collaboration after undertaking the Interprofessional Education and Collaboration (IPEC) course.A paper-based anonymous survey using the Interprofessional Attitude Scale (IPAS) was administered to a sample of 346 health students  pre/post undertaking the IPEC course. Less than half of the students provided a post response, with pre/post survey results of 111 pairs subsequently matched and analyzed.p = 0.003**).Results showed elevated pre-course scores, an improvement in students’ attitudes towards the interprofessional biases domain of the IPAS, and a slight decline in their scores in the remaining 4 domains . These changes were not statistically significant, except for the patient centeredness domain (The study provided important results about attitudes towards interprofessional collaboration. These findings are essential because our institution is one of few in Lebanon that provides this mandatory course to a large group of health professionals. Future studies should investigate these changes in attitude scores in a larger sample size, and how these attitudes would influence collaboration post-graduation. It is increasingly recognized that interprofessional collaboration and effective team building are needed to improve the health of individuals and populations. This interprofessional collaboration must begin in educational institutions. According to World Health Organization, interprofessional education (IPE) occurs when “students from two or more professions learn about, from, and with each other to enable effective collaboration and improve health outcomes” . The LanThe core of IPE lies in training synergy among health workers and equipping them with collaborative skills needed for today’s complex environment . HoweverThe aim of this study was to evaluate the attitudes of students at the American University of Beirut (AUB) towards interprofessional collaboration and to assess whether the recently introduced IPEC course impacted that attitude.At AUB, an IPE task force from the Faculty of Medicine, the School of Nursing, and the Faculty of Public Health was created in 2013 to introduce IPE and collaboration (IPEC) course into the three curricula. The impetus for the introduction of the IPEC was the hosting of the Lancet commission on health professionals’ education launch, on which a former Dean of the Faculty of Health Science had been represented.Specifically, the IPEC committee introduced a new, required IPE course in 2016–2017 that combined students from the 3 mentioned specialties , and introduced them to the IPEC through case-based group discussions utilizing collaborative learning. The course is based on small groups working together on hypothetical cases requiring the input and role of all three specialties. The cases were designed by interprofessional teams of faculty members and focus both on individual and population health perspectives on specific health topics. Collaborative learning was adapted as an approach given its dual focus both on content and on building social skills . While students in each area are expected to have learned about the topic in their curriculum from the perspective of their own fields, the course necessitates collaboration among the fields. Essential skills such as understanding each other’s roles and responsibilities, teamwork, communication, and ethics are the focus of this course, as they are the cornerstones of competency-based IPE . The comThe course typically involved 170 students, 90 from the Medical School, 40 from the Nursing School, and 40 from the faculty of Public Health. They were divided into groups of nine . Due to logistical reasons, the course was given twice a year, in the fall and spring semesters.The course was a novel experience in many ways at the three concerned faculties. It was the first interprofessional course. It was also the first collaborative educational offering based on a small case-based discussion necessitating the input of the whole group.Because of this novelty, multiple workshops were performed to train the faculty involved in developing the course and on the cases and running a collaborative learning experience. Faculty members from the three faculties who signed up for the course were also trained as group facilitators.The implementation of the course faced many challenges ranging from curricular change of the three faculties involved, to securing adequate resources to train and organize the implementation of such a resource intensive course. The course involved 40 faculty members per year to facilitate the case-based discussions. The diversity of student knowledge background, experience in teamwork, expectations from the course, and their perspective of healthcare added to the level of difficulty in implementing the course. Five main topics were used to explicate Interprofessional Education and Collaboration during the course, and they included: smoking cessation, HIV testing, cardiac care, substance abuse, and care of the elderly. These were chosen as health topics that require interprofessional collaboration, are prominent across different stages of the life cycle, and for which there was faculty expertise.We chose to utilize a quantitative survey-based study to assess the students’ attitudes toward the core competencies of interprofessional practice before and after the implementation of the newly developed IPEC course at AUB the first time it was taught.The study included students in the three AUB faculties involved in the implementation of IPEC course in the academic years of 2016–2017 and 2017–2018. During these two academic years, 180 s year medical students, 80 fourth year nursing students and 86 Master of Public Health Students took the course. Students taking the IPEC course were asked to complete the survey at the beginning of the course and three months later at the completion of the course. The study was approved by the institutional review board (IRB) at the American University of Beirut.A self-administered paper-based anonymous survey was created in English, the language of instruction. The first part of the survey contained a user-generated anonymous code to track each student’s responses pre and post IPEC course and allow us to do paired analysis of the pre and post survey. The second part included a validated survey instrument, the Interprofessional Attitude Scale (IPAS) with the permission of the authors. The IPAS is a 27-item instrument that assesses the five subdomains of teamwork: roles and responsibilities (nine questions), patient centeredness (five questions), interprofessional biases (three questions), diversity and ethics (four questions), and community centeredness (six questions). These five domains were inspired from the four competency domains in the 2011 IPEC competencies report . these aThe Likert scale was scored as follows: 1, strongly disagree; 2, disagree; 3, somewhat disagree; 4, neither agree nor disagree; 5, somewhat agree; 6, agree; and 7, strongly agree. According to the survey instructions, one question was reverse scored. The total score and sub-domain averages were calculated.At the beginning of the first session and at the end of the last session of the IPEC course, paper-based surveys were provided to the student participants by the respective group facilitator. The first page included an informed consent explaining the purpose of the study, that participation is voluntary, and as such informed consent of participation was confirmed by completing the questionnaire. Although the first part of the survey contained a user-generated anonymous code to track pre and post responses of students for paired statistical analysis, the response and the code were in no way traceable back to the individual. The study results are reported in aggregate form.p-value of 0.05 was considered statistically significant.SPSS version 24 was used for the statistical analysis. The answers to the IPAS instrument were not analyzed individually but at the domain level, for the five domains covered. Categorical variables were presented using frequencies and percentages. Continuous variables were presented using means. The differences between students pre and post test scores was calculated using paired analysis. A Table Table The aim of the current study was to assess students’ attitudes towards interprofessional collaboration, before and after completing the IPEC course.The baseline results with the highest mean for patient centeredness and lowest mean for interprofessional biases are comparable to those obtained from health sciences students  in Oklahoma university, where the lowest mean score was reported for interprofessional bias (4.8 ± 1.1 vs. 4.12 in our study) . SimilarWhen comparing the pre and post IPE course survey results, the scores remained almost the same, with a slight decline post IPE course in four out of the five domains. A minor improvement was seen in the interprofessional biases domain. The survey scores were already elevated before the students took the IPE course. A study conducted on third year students from various health professions  in Dublin, Ireland showed similarly that the participants’ readiness and attitudes towards IPE were good pre workshop, and these continued to improve after the workshop . The sigOnly 111 students completed the questionnaires before and after the course, therefore paired analyses were limited to less than 50% of students who took the IPEC course. Four domains of IPE  declined after the course, although the results failed to reach statistical significance, except for the patient-centered care domain. This could be attributed to the sample size or to the fact that the cases were theoretical. The students did not interact with patients in a real-life setting but rather discussed hypothetical cases.We utilized the IPAS which is a validated instrument, with good factor structure and internal consistency . The novThis study investigated baseline score and the impact of one IPE course on the attitude of students from different professions towards IPE. The results have shown elevated baseline scores, an improvement, although non-significant, in students’ attitudes towards the interprofessional biases domain, and no improvement in other domains after taking the course as compared to the baseline scores. These results suggests that further studies are needed to understand how IPE impacts students’ attitudes from different professions. As this study was carried out in one setting, we recommend that future studies are carried out on a larger sample size across different institutions, as well as to study the impact of such courses on the practices of health professionals.


Although most list-ranking frameworks are based on multilayer perceptrons (MLP), they still face limitations within the method itself in the field of recommender systems in two respects: (1) MLP suffer from overfitting when dealing with sparse vectors. At the same time, the model itself tends to learn in-depth features of user–item interaction behavior but ignores some low-rank and shallow information present in the matrix. (2) Existing ranking methods cannot effectively deal with the problem of ranking between items with the same rating value and the problem of inconsistent independence in reality. We propose a list ranking framework based on linear and non-linear fusion for recommendation from implicit feedback, named RBLF. First, the model uses dense vectors to represent users and items through one-hot encoding and embedding. Second, to jointly learn shallow and deep user–item interaction, we use the interaction grabbing layer to capture the user–item interaction behavior through dense vectors of users and items. Finally, RBLF uses the Bayesian collaborative ranking to better fit the characteristics of implicit feedback. Eventually, the experiments show that the performance of RBLF obtains a significant improvement. Many experiments have shown that deep neural networks (DNNs) are used in several fields because of their ability to capture complex and deeper information, including image segmentation , naturalAlthough all of these methods have achieved good results, they have different problems in user preference modeling and in terms of rating prediction. In terms of user preference modeling models, they either focus only on shallow information or only on in-depth information. Meanwhile, models based on MLP alone can easily fall into the overfitting problem and lose plenty of low-rank features. In terms of rating, there are many 0/1 ratings in implicit feedback, and the model has difficulty specifying the ranking order of these items with the same rating, thus limiting the model performance.To solve these two problems, we propose a column label ordering architecture called RBLF that combines linear-nonlinear features with Bayesian coordination ranking for the first time. We set the potential features of user and item to different size dimensions to make the model more realistic. We explicitly fused user-embedding vectors and item-embedding vectors to learn the shallow linear interaction, feeding the obtained results into MLP to enhance the non-linearity of the model. The model fits the user–item feature relationship more comprehensively to guide the prediction method to better learn the user’s true preference attributes, thus giving more accurate rating prediction results. In the list ranking, we used Bayesian collaborative ranking, called deep-setrank , to bettRBLF focuses on predicting the exact ranking of each item rather than the accuracy of specific scores. In practice, users mostly try to click the music ranked first in the list. Therefore, the ultimate goal of RBLF is list ranking. Furthermore, since there are far more implicit data (indirectly reflecting user preferences) in the real world than explicit feedback, this results in low cost of data collection. Our main contributions are as follows:We propose a novel architecture to accommodate linear and non-linear interaction feature vector processes and design a neural network-based list-learning RBLF framework.We use the deep-setrank list ranking and two other traditional list ranking algorithms. We compare these three ranking algorithms and conclude that our ranking algorithm is the best.We explore the impact of the shallow and deep interaction behavior on feature grabbing layers.We conduct many experiments on three datasets and show that RBLF greatly outperforms other recent algorithms.Many collaborative filtering technologies have been used in recommendation systems ,20, and However, high-quality prediction accuracy and high-quality sorting recommendations are not strongly correlated ,27; thusThere is little user–item interaction information because of the sparse rating matrix, and the performance of MF, which can be hindered by simply choosing the interaction function (inner product) , is poorIn recent years, DNNs, which can theoretically model any function, have been used in recommendation systems. Bai, et al.  created Explicit feedback refers to numerical feedback with explicit rating criteria, such as the five-star rating system for MovieLens movies. Implicit feedback contains only positive and unobserved samples. Common implicit feedback includes click history, purchase history, like history, etc.Given a recommendation problem, we suppose there is a series of users Here, In daily life, most users feedback is implicit, and the implicit feedback data will have a large number of identical items; this is a challenge for the list sorting model based on implicit feedback.MLP is essentially a neural network. It aims to solve the nonlinear question that the single-layer perceptron cannot solve. The MLP model is defined as follows:(1)The hidden layer is added, which can have multiple layers to enhance the model’s expressive ability. However, it also increases the complexity of the model.(2)To extend the activation function, although the activation function of the perceptron is simple, the processing capacity is limited. Thus, other activation functions are generally used in the neural network. By using different activation functions, the expression ability of the neural network is further enhanced.The structure of MLP is improved in two aspects from the single-layer perceptron structure:For the activation function, sigmoid has limited the performance and caused a overfitting problem. Although tahn is applied more, it does not solve the above issues. The relu function is simple, has a fast-fitting speed, and will not cause oversaturation problems. We used the relu function.The input layer transforms the sparse user–item vector into one-hot encoding and feeds the result into the embedding layer.The function of the embedding layer in RBLF is to transform the one-hot encoding of users and items into a low-dimensional space and represent them using a dense vector.The embedding layer is defined as follows:In reality, users and items are independent of each other, and they own a different number of latent features. Conversely, user preferences change over time, which leads to changing latent features. Still, items have relatively fixed attributes from the beginning of their creation; thus, the latent features of items do not change drastically . We set We want to capture shallow linear user–item interactions and deep non-linear interactions in this layer.First, the shallow interaction grabbing layer model carries the shallow user–item interaction behavior through explicitly fusing  feature . We placThe goal of the deep interaction grabbing layer is to learn deep and non-linear interaction behavior, and a standard MLP (multilayer perceptron) is used to learn the interaction latent features. Therefore, we can give the model nonlinear modeling capability rather than simply using the inner product multiplied element-by-element as MF  to describe the potential interaction characteristics. Since multilayer perceptron can simulate any function, we hope that this layer perceptron can better simulate the implicit preferences between user items. The model connects element-wise in series with multilayer perceptrons, which can be defined as follows:After we obtain the result of the interaction grabbing layer, we need the interaction grabbing layer to map the result to the probability The pairwise RBLF is a special case of the listwise RBLF method. When the list is 2 (K = 2), the list-wise becomes the pairwise RBLF. The pairwise method models the relative ranking of double items to make predictions. Therefore, the pairwise method constructs the relationship between positive and negative examples. Then, the loss function we set through cross entropy is as follows:Compared with listwise RBLF, the pairwise RBLF is used to determine the relative order of two products and to integrate the results to obtain the final recommendation list, which emphasizes the short running time of the code.Finally, we use regularization methods to avoid overfitting, and the regularization is defined as:As in method pairwise RBLF, the listwise RBLF also needs the interaction-grabbing layer to map the result to the probability Considering that each user’s rating process can be approximated as independent of each other and not influenced by other users, we first assume that each user’s rating results are independent. Then, for each user, its preference for the positive sample can be considered higher than that of the unobserved sample. Thus, we can compare each positive sample of users with the set of unobserved samples and assume that the probability of a user liking a positive sample is greater than the probability of liking the set of unobserved samples. We can then maximize the likelihood probability values of these comparisons to solve the problem. Compared to the pairwise assumption, the setrank assumption avoids the problem of inconsistent independence by relaxing the independence requirement. The Bayesian posterior probability of the setrank preference structure can be given as:As shown in Our method only cares about the sorting probability when a positive sample is ranked first; thus, introducing the listwise method’s list order-based probability modeling formula to calculate the probability can be simplified as:The complete probabilistic modeling form of the setrank method as:Since we do not sort the set of unobserved items or the set of positive sample items internally, we use the sigmoid function to map the results of the interaction-grabbing layer into probabilities:By means of maximizing the posterior probabilities, the final optimization objective function can be given as:In this section, we introduce the dataset, the experimental evaluation index and the algorithm to compare with our experiment. Our experiment aims to answer four key questionsRQ1: How does RBLF perform compared to the currently popular list-sorting algorithms?RQ2: What are the effects of shallow and deep interaction methods in the feature capture module on RBLF?RQ3: What are the effects of different list ranking methods in the predictive layer on RBLF?RQ4: How does it impact the effect of different hyperparameters of the model?As shown in In addition, we used two popular accuracy metrics, the HR@N and the NDCG@N (N denotes that the RBLF generate the number of the top-n items).The larger the value of HR and NDCG, the better the model’s performance. The HR@N score is defined as:NDCG@N is defined as follows:We used the compared methods as shown below:The ItemKNN model considers the evaluation bias after the calculation is completed and obtains the k most similar items.BPR  is a widListRank-MF  uses a lNeural cooperative filtering (NCF)  directlyThe DeepCF  model isThe DeepRank  model isThe model is compared with the benchmark in We compared the performance of RBLF and the adaptability of the model when facing different datasets in As seen in On the sparsest dataset Yahoo! Movie, RBLF is also superior to other methods, indicating that the idea of combining RBLF in our model simulates the invisible preferences of users, ensuring high performance and high flexibility of the model. Since BPR and ListRank-MF are simply linear interactions, they perform relatively poorly on all datasets, although they also model invisible preferences. The DeepRank model uses the MLP model, which omits some low-rank information and some simple user characteristics in the user–item matrix. As a result, although their performance is high, they are still inferior to RBLF. Although the NCF model uses MLP and MF, our model is a cascade fusion, which allows the model to better integrate these two algorithms, and thus, our model performance is even better. The DeepCF method is concerned with the point-by-point method and ignores the paired ranking information. Our model captures the user’s characteristics from the user’s paired item. This result in our model is more powerful than theirs in predicting the performance of personalized rankings.As shown in The results for different list ranking methods are shown in The reason Deep-setrank performance is substantially better than pairwise ranking is that pairwise methods typically model the preference structure in implicit feedback based on an item pair consisting of a positive feedback item and an unobserved item. This approach is prone to the problem of inconsistent independence in assumptions and implementation. Pairwise ranking attempts to maximize the probability of pairwise comparisons between positive and unobserved simples. This work requires the strict assumption that two items have independent pairwise preferences as the basis for constructing the loss function. Therefore, the independence between preference pairs cannot be guaranteed, which affects the optimization results of the pairwise loss function. Only the order of the two documents is considered, and the position of the documents in the search list is not, resulting in a less-than-optimal final ranking.The reason Deep-setrank outperforms listwise ranking is that the list method is implemented by defining a probabilistic relationship between the preference sizes on the list of items. For the list method, items with the same rating value cannot be handled efficiently, especially because there is no explicit graded rating in the implicit feedback but 0/1 rating, which can lead to a large number of items with the same rating. In contrast, Deep-setrank does not sort the set of unobserved items or the set of positive sample items internally but only ensures that each positive sample is larger than the set of unobserved samples. Therefore, Deep-setrank models the implicit feedback data more realistically than listwise ranking.The effect of the length of the list is shown in The embedding size is important for the representation of the project. We conducted experiments to determine the impact of embeddings of different dimensional sizes on the performance of the model. We set the user and item embedding dimension size After conducting many experiments, we can conclude the following: First, when the user–item embedding dimensions are different, the performance is better than that of the user and the item with the same embedding dimension. Second, if you want to achieve better results, neither user embedding nor item embedding can take 32 because excessive dimensions.Third, the dimension of user embedding is preferably smaller than the dimension of item embedding. Finally, we conclude that when In the new experiment, we set the size of MLP to [8], , ,  and , and the results are shown in In the beginning, as the number of layers of MLP increases, the performance of the model also improves because the deep neural network is similar to the structure of the shallow neural network when the number of layers is too small, and the model does not have sufficient fitting capabilities. Therefore, as L increases, the fitting ability of the deep neural network also increases, which drives the improvement of the model’s performance. After L = 4, the performance decreases instead because the model adds a shallow interaction-grabbing layer, limiting the MLP model’s depth.In this section, we compare the performance and time cost of three list ranking methods. The time cost of the pairwise-ranking model is far superior to that of those models, although the performance of pairwise-ranking is the worst. As for listwise-ranking and deep-setrank, they take about the same amount of time, but the performance of deep-setrank is better than the listwise-ranking. Thus, we suggest that if you care about the time cost, you should choose pairwise-ranking, and if you prefer higher performance, you should choose deep-setrank.This paper proposes a list-ranking framework based on linear and non-linear fusion for recommendation (RBLF). The model addresses the problem in that current list-sorting recommender systems always fail to capture the full range of user–item interaction information by adding a shallow–deep interaction-grabbing layer, thus improving the model performance.In the future, we want to optimize the way to obtain the potential vectors of users and items. As the theory of graph neural networks matures, the potential vectors obtained by graph neural networks are much richer than one-hot coding and embedding. In addition, multimodal auxiliary information can make the features more adequate, such as using the item’s image and the user’s comment to optimize the feature vector.


In light of the problems of a single vibration feature containing limited information on the degradation of rolling bearings, the redundant information in high-dimensional feature sets inaccurately reflecting the reliability of rolling bearings in service, and assessments of the degradation performance being disturbed by outliers and false fluctuations in the signal, this study proposes a method of assessing rolling bearings’ performance in terms of degradation using adaptive sensitive feature selection and multi-strategy optimized support vector data description (SVDD). First, a high-dimensional feature set of vibration signals from rolling bearings was extracted. Second, a method combining the Technique for Order Preference by Similarity to an Ideal Solution (TOPSIS) and K-medoids was used to comprehensively evaluate the features with multiple evaluation indicators and to adaptively select better degradation features to construct the sensitive feature set. Next, multi-strategy optimization of the SVDD model was carried out by introducing the autocorrelation kernel regression (AAKR) and a multi-kernel function to improve the ability of the evaluation model to overcome outliers and false fluctuations. Through validation, it could be seen that the method in this study uses samples of rolling bearings in the healthy early stage to establish the evaluation model, which can adaptively determine the starting point of the bearing’s degradation. The stability and accuracy of the model were effectively improved. The failure of rolling bearings, as one of the key components of rotating machinery, leads to the breakdown of the whole mechanical system ,2. DurinAssessment of the degradation in the performance of rolling bearings mainly includes three steps: acquisition of the rolling bearings’ monitoring data, feature extraction, and establishment of the model for assessing the degradation. The degradation mechanism of rolling bearings is complex, and the vibration signals of rolling bearings are nonlinear and nonstationary. A single feature contains less information about bearings degradation and has poor anti-interference ability, so it cannot accurately characterize the whole process of degradation during performance. Constructing a high-dimensional feature set can comprehensively reflect the information on the bearings’ degradation and benefit from the complementarity of the differences among features, but some features are unrelated to degradation. Irrelevant features can be eliminated using feature selection methods, which can be classified into two categories based on whether the methods are independent of subsequent learning algorithms, namely filters and wrappers . The wraIn general, data-driven fault diagnosis techniques use machine learning algorithms to identify fault status to train predictive models based on the condition data collected under normal and different faulty states . The traIn summary, extracting effective feature sets is a prerequisite for accurately assessing the performance of bearings, and improving the ability of the model to overcome outliers and false fluctuations is one of the critical tasks in assessing degradation. Accordingly, a rolling bearing performance degradation assessment method with the combination of adaptive sensitive feature selection and multi-strategy optimized SVDD was proposed in this paper. The specific contributions of this study are described below. TOPSIS-Kmedoids, an adaptive sensitive feature selection method, was proposed, which could determine the adaptive sensitive feature set without prior knowledge. In addition, SVDD was optimized using a multi-strategy, in which AAKR was introduced to correct the errors in monitoring data, and a multi-kernel function was constructed to improve the learning ability and generalization ability of the model. Lastly, the effectiveness of the proposed method was verified using the XJTU-SY dataset for the full life cycle of rolling bearings from Xi’an Jiaotong University, the PHM2012 Data Challenge dataset for the full life cycle of rolling bearings, and a set of data from a self-made bench test of accelerated fatigue in rolling bearing.F1–F7 are the frequency domain features, n is the feature length and m is the number of features.In the field of prognostics and health management (PHM) of rolling bearings, the vibration signal is one of the most commonly used means because it contains much information on degradation. Feature extraction can reveal information on the performance of sensor data. Twenty-four commonly used statistical features of vibration were extracted from the time domain and the frequency domain of vibration signals, as shown in (1)Monotonicity: The degradation of rolling bearings’ performance is an irreversible process, so the features should be able to monotonically characterize the process of rolling bearings from operation to failure. Because the time vector  feature . In prac feature , so this feature  as:The quality of features significantly affects the results when assessing the degradation. Good features should correlate strongly with the bearings’ degradation process with monotonic increasing or decreasing characteristics and robustness to outliers . Most ofn is the feature length.(2)Robustness: During the use of rolling bearings, the signal acquisition process is inevitably disturbed by the environment, changes in the working condition, and noise. The robustness index l values . Equatiol values , as follith feature =fit+fir .(3)Correlation: The correlation index bearings . The equbearings , as:ith feature All the indexes above are positive indicators; that is, the higher the evaluation score, the better the feature’s quality. A single metric can barely make a comprehensive and accurate evaluation of the degradation features. Sensitive features should be selected using integration of the evaluation indicators mentioned in M = 48 in this study), and N is the number of evaluation metrics (N = 3 in this study). The evaluation matrix is normalized using Equation (4):M, 1 ≤ N) represents the elements in the evaluation matrix jth evaluation metric for the ith feature.Step 1: Construction and normalization of the evaluation matrix. The feature evaluation matrix Step 2: Calculation of the TOPSIS score. The maximum and minimum values of each evaluation index are obtained and defined as the superior solutions tion (5) :(5)Si=∑iJ is calculated as follows:jth cluster, jth medoid, and Step 3: Feature clustering. The K-medoids algorithm was improved from the K-means method. K-medoids is a clustering algorithm with good robustness. To achieve adaptive classification of the features, K-medoids is used to divide the data into class clusters according to certain rules, so that samples of the class cluster are similar. In order to adaptively determine the sensitive features set, the feature selection process is transformed into the K-medoids clustering problem with the TOPSIS score. The core idea of the K-medoids algorithm is to divide the feature scores, as obtained in Step 2, into clusters under the condition that the sum of dissimilarities between the cluster’s elements and the cluster’s center is minimized . The higaccuracy . TherefoL is the length of the sensitive features, ith sensitive feature.Step 4: Adaptive sensitive feature set. The medoid is adjusted using iteration according to Equation (6) until the center point no longer changes. The cluster with a large TOPSIS value of M in the center of the cluster is determined as the adaptive sensitive feature set SVDD is an effective one-class classification algorithm proposed by Tax et al.  in 1999.SVDD is a semi-supervised model in which only one type of target sample is required for the model’s training, that is, the model can be trained with samples from normal stages. The labeled samples are the training set of SVDD, which needs to construct the minimum radius hypersphere. The objective function can be described using Equation (8):R and the number of samples falling outside the sphere.The objective function must also satisfy the constraints Equation (8) is a quadratic convex optimization problem with univariate variables. By introducing Lagrangian multipliers, constraints are fused into the objective functions to form dual forms, and the following results can be obtained:To cope with the nonlinearity problem, the kernel function is introduced to replace the inner product. The kernel function maps the data to the high-dimensional feature space, which makes the nonlinear data easier to linearly separate in the high-dimensional feature space. According to the Karush–Kuhn–Tucher condition , it is kThe equation for calculating the distance between the new sample Single-kernel functions have limitations in dealing with outliers and false fluctuations. Different kernel functions have different levels of efficacy, and multiple kernel functions combine different types of kernel functions, which can combine good learning ability and generalization. Using multi-kernel functions can make the results of SVDD more robust ,24. The The single-kernel functions include Gaussian, Sigmoid, and Laplace kernel functions, and the kernel functions are calculated using:The monitoring data of rolling bearings cannot avoid outliers and false fluctuations due to the interference of noise, working conditions, and environmental changes. These may greatly impact the performance of the model . Using aThe core idea of AAKR is to map the characteristic matrix at the current moment to estimate the characteristic matrix of rolling bearings in the healthy state. AAKR maps the characteristic matrix at the current moment The parameters mization ,28, and D between the test samples and the center of the hypersphere was calculated as the health indicator (HI) according to Equation (11). When To carry out a PDA of rolling bearings, the radius of the hypersphere was determined by carrying out multi-strategy optimized SVDD training with some of the previous normal samples. In the testing stage, the distance The process used for a PDA of rolling bearings with an adaptive sensitive feature set and multi-strategy optimized SVDD is shown in The main steps are as follows:Step 1: Data acquisition. Obtain vibration signals of the rolling bearing degradation test platform. X is determined with the TOPSIS–K-medoids method described in Step 2: Construction of the adaptive sensitive feature set. The multi-domain high-dimensional feature set Step 3: Optimize SVDD with multiple strategies. AAKR is introduced to correct the errors in the monitoring data, and a multi-kernel function is constructed to improve the learning ability and generalization ability of the model.R and center O.Step 4: Complete the training of the model. The samples in the early normal state of the sensitive feature set are taken as the training data and are determined to be the historical observation matrix. Training of the SVDD hypersphere using multi-strategy optimization is completed to obtain the hypersphere’s radius D outputted by the model. Meanwhile, R is set as the adaptive alarm threshold. When Step 5: Assessment of the degradation in performance. The test samples are inputted into the completed model, and the performance is evaluated according to the distance value The XJTU-SY rolling bearing dataset was used for verification. The subset Bearing 1–3 was used for the experiment and analysis, which collected 158 samples. D from each sample to the center of the sphere was recorded. The PDA of rolling bearings was carried out according to the technical scheme in To verify the effectiveness of the proposed method, commonly used degradation assessment methods were selected for comparison, including (a) constructing performance metrics by fusing the sensitive features with a PCA, (b) using root mean square (RMS) feature metrics, (c) combining a continuous hidden Markov model (CHMM) with the sensitivity to build assessment metrics, and (d) using the original SVDD combined with sensitive features to build assessment metrics. Method (d) and the proposed method could determine the warning threshold of degradation adaptively, and the rest of the methods determined the threshold using the three principles of the international engineering standard ISO-10816 . When thIn order to quantitatively evaluate the pros and cons of HIs constructed using different methods, multiple evaluation indicators are used to evaluate the results in this study. In addition to the monotonicity index (Mon), the robustness index (Rob), and the correlation index (Cor) constructed according to Equations 1)–(3), this paper also introduces the separability index (Sep) to quantitatively evaluate the HIs. The Sep was used to measure the ability of the assessment results to discriminate the degradation stage and the ability to warn of the early failure of rolling bearings [–(3), thiThe evaluation index and FPT determined using each method are displayed in The local magnification of each evaluation method showed that the HI of the proposed method was the smoothest in the healthy phase, while the HI of other methods fluctuates greatly. In addition, the HI of the proposed method had the highest robustness and comprehensive score. Moreover, the envelope spectrum analysis shows that the proposed method accurately determines the FPT, which provides an adequate warning for equipment maintenance. Therefore, it can be seen that the proposed method is sensitive to the performance degradation of rolling bearings in the whole life cycle and can better tolerate outliers and false fluctuations.The IEEE PHM2012 Data Challenge dataset provides the full-life vibration signals of rolling bearings in both the horizontal and vertical directions . Figure Verification was carried out using the “Bearing 1.1” subset of the data, which contained 2803 samples. The results obtained with the methods used for comparison and the proposed methods are shown in The indexes used for evaluating the health indicators of the previous methods for this subset of data are shown in Furthermore, the local magnification of each evaluation method shows that the HIs of this study’s method were the smoothest in the healthy phase, and the HIs of this study’s method had the highest robustness score, which shows that this method could better overcome the outliers. In addition, the HIs constructed using the proposed method had the highest comprehensive score, and it determines the FPT earlier, which shows that the proposed method could appropriately reflect the degradation of rolling bearings across their entire life cycle.In order to further verify the effectiveness of the method, experimental verification was carried out with a home-made experimental rig for testing accelerated fatigue in rolling bearings. The test bench is displayed in To maintain the consistency of the experimental conditions, the test bearings were run to failure under a constant load and constant speed. The vibration acceleration data collected for the bearings’ entire life cycle from the healthy state to severe degradation are shown in The results obtained using the proposed method and the methods used for comparison are presented in For these data, the evaluation indicators of the results of the methods mentioned above are listed in The proposed method had the best comprehensive score, and the FPT was determined earlier than other methods. Meanwhile, local magnification showed that the HI of this method had no false alarm values and had minimal fluctuations, and the HI had the highest robustness score, indicating that the model could better overcome the influence of outliers. Therefore, this showed a good agreement between the results of the degradation assessment and the degree of failure, accurately reflecting the health status of the bearing.(1)The TOPSIS–K-medoids method was proposed for adaptive determination of the sensitive feature set. This method determines the adaptive sensitive feature set by using the monotonicity, correlation, and robustness indexes for evaluation, and the process does not need to rely on a priori knowledge to subjectively determine parameters such as the weights and thresholds, which improves the quality of the input data used for the PDA model.(2)The multi-strategy optimized SVDD strategy trained the model using only the early samples of the healthy phase, adaptively determined the FPT, overcame the interference of outliers and false fluctuations, and better characterized the bearings’ degree of failure. The HI showed better consistency with the development trend of faults.(3)After verification with the XJTU-SY bearing data, the IEEE PHM2012 Data Challenge dataset for bearings, and data obtained with a self-made test bench of accelerated fatigue in rolling bearings, the multi-strategy optimized SVDD model proposed in this paper demonstrated better performance compared to multiple mainstream methods according to a comparison of multiple evaluation indexes, such as monotonicity, correlation, robustness, and separability.Determining the sensitive features set relies heavily on the prior knowledge of experts and degradation models having low-tolerance outliers and false fluctuations, and a method for evaluating the degradation of rolling bearings using adaptive sensitive feature selection and multi-strategy optimized SVDD was proposed. The effectiveness of the method was proved using experiments, leading to the following conclusions:In summary, a rolling bearing performance degradation assessment method with the combination of adaptive sensitive feature selection and multi-strategy optimized SVDD was proposed in this paper. The proposed feature selection method determines the adaptive sensitive feature set with multiple feature evaluation indexes instead of prior knowledge; the multi-strategy optimized SVDD only uses the early samples in the healthy stage to train the model and adaptively determines the FPT while better overcoming the interference of outliers and false fluctuations. The proposed model could accurately reflect the degradation status of rolling bearings verified using experiments, which has a positive effect on the early detection of potential failure of rolling bearings and their maintenance.