Structural health monitoring (SHM) systems are used to analyze the health of infrastructures such as bridges, using data from various types of sensors. While SHM systems consist of various stages, feature extraction and pattern recognition steps are the most important. Consequently, signal processing techniques in the feature extraction stage and machine learning algorithms in the pattern recognition stage play an effective role in analyzing the health of bridges. In other words, there exists a plethora of signal processing techniques and machine learning algorithms, and the selection of the appropriate technique/algorithm is guided by the limitations of each technique/algorithm. The selection also depends on the requirements of SHM in terms of damage identification level and operating conditions. This has provided the motivation to conduct a Systematic literature review (SLR) of feature extraction techniques and pattern recognition algorithms for the structural health monitoring of bridges. The existing literature reviews describe the current trends in the field with different focus aspects. However, a systematic literature review that presents an in-depth comparative study of different applications of machine learning algorithms in the field of SHM of bridges does not exist. Furthermore, there is a lack of analytical studies that investigate the SHM systems in terms of several design considerations including feature extraction techniques, analytical approaches (classification/ regression), operational functionality levels (diagnosis/prognosis) and system implementation techniques (data-driven/model-based). Consequently, this paper identifies 45 recent research practices (during 2016–2023), pertaining to feature extraction techniques and pattern recognition algorithms in SHM for bridges through an SLR process. First, the identified research studies are classified into three different categories: supervised learning algorithms, neural networks and a combination of both. Subsequently, an in-depth analysis of various machine learning algorithms is performed in each category. Moreover, the analysis of selected research studies  in terms of feature extraction techniques is made, and 25 different techniques are identified. Furthermore, this article also explores other design considerations like analytical approaches in the pattern recognition process, operational functionality and system implementation. It is expected that the outcomes of this research may facilitate the researchers and practitioners of the domain during the selection of appropriate feature extraction techniques, machine learning algorithms and other design considerations according to the SHM system requirements. The construction of cities, villages and related infrastructure are among the unavoidable necessities due to the acceleration of population growth in recent decades. To conduct social, welfare, political and economic activities, a city, nation, or region uses a variety of systems, equipment and services together referred to as infrastructure ,2,3,4,5.The first two levels in a typical damage identification process for bridges are damage identification and damage localization. As the name implies, the damage identification level detects the flaws while the damage localization level determines the position of the flaw in bridges. These two levels fall under the category of diagnosis . The nexHistorically, the damage identification process has been performed manually to monitor the health of the bridges. However, some major limitations in a manual process are the lack of regular monitoring, inspection-dependent monitoring, the delay in flaw detection and the inability to determine the flaw growth stages . As an aThe SHM systems were originally constructed with wired sensor networks; however, the great trustworthiness and inexpensive installation and maintenance costs of wireless sensor networks (WSNs) introduce them as a viable option platform ,17,18,19The SHM process begins with the “excitation” step that arouses the existing structure looking for a sign of deterioration . The nexAmong other stages in the SHM system, the feature extraction and pattern recognition stages are critical. The feature extraction stage is a method of transforming the gathered data into a form that can be easily recognized and analyzed by any machine learning algorithm in the pattern recognition stage. The feature extraction can be implemented by signal processing techniques to identify, locate and predict structural health in bridges ,32,33,34As stated earlier that the most crucial stages in the process of SHM are the pattern recognition process and the associated feature extraction technique. Various design techniques have been deployed to integrate machine learning algorithms in the pattern recognition process. These techniques employ different types of learning algorithms  for different SHM schemes for bridges. Each scheme requires a different analytical approach . For example, supervised learning algorithms are deployed for classification and regression problems, whereas unsupervised learning algorithms are deployed for clustering problems. Moreover, signal processing techniques are becoming popular in the feature extraction stage of the SHM process, especially with the advancement in the areas of the Internet-of-things (IoT) and big data. Consequently, a systematic study is required to classify and describe the existing design implementation. Furthermore, a systematic study can reveal the need for future studies to overcome the challenges of the existing systems.In order to address the research gap identified in Research question 1: What important research has been reported from 2016 to 2023 where machine learning algorithms have been utilized in the pattern recognition process for SHM in bridges?Research question 2: Which of the machine learning algorithms and the analytical approaches are more frequently utilized in the pattern recognition process for SHM in bridges during the 2016–2023 research? Research question 3: Which of the signal processing techniques are more frequently utilized in the feature extraction process for SHM in bridges during the 2016–2023 research? Research question 4: Which of the system implementation techniques and operational functionality approaches are more frequently utilized in the process for SHM in bridges during the 2016–2023 research?In order to find responses to the constructed questions (Q1–Q4), the actual layout/framework of the used SLR approach in this article is shown in Using three scientific databases , the research studies were carefully selected through some inclusion and exclusion rules. The details of the employed research methodology for the selection of research studies are provided in The systematic literature review process described in  was usedWe defined three categories to organize the selected research studies. This categorization significantly improves the accuracy of the answers to our research questions. The details of the categories are given below.Supervised algorithms utilize a labeled dataset. These algorithms are used for classification and regression analytical processes. Consequently, the “Supervised learning algorithms “category includes all those research articles in which one or more than one supervised learning algorithms  are used for the pattern recognition process of the SHM system for bridges. Neural network algorithms such as the Artificial Neural Network (ANN) and Convolutional Neural Network (CNN) are designed to perform supervised or unsupervised learning algorithms . UnsuperThe research articles in which a combination of supervised and neural network learning algorithms is used for the pattern recognition process are included in the combined category.Once the categories are defined, we developed a review protocol for our research on the basis of predefined SLR standards . The devWe defined a concrete criterion for the selection and rejection of research works. Six parameters were defined to ensure the correctness of the answers to our research questions. The research work is selected based on these parameters as given below:Subject relevant: Select the research work only if it is relevant to the research context. It must support the answers to our research questions and must be relevant to one of the three predefined categories The data appraisal of the research is based on concrete facts and theoretical perspectives without any vague statements.(2)The validation of research has been performed through proper validation methods .(3)The research provides information about the implementation of the SHM systems.(4)As we intend to investigate the latest machine learning algorithms and trends, the objective is to include the most recent research as much as possible. Therefore, 78% of research articles are from 2020 to 2023. Moreover, 91% of the research articles included are from 2018 to 2023 as shown in We developed quality criteria to understand the important outcomes of selected research studies. The developed criteria also define the credibility of each selected research and its decisive findings: (5)The originality of the research is another important factor. Therefore, we only included articles that are published in at least one of the three renowned and globally accepted scientific databases, i.e., IEEE, SPRINGER and ELSEVIER. In addition, we excluded conference papers from our scope.Data extraction and synthesis, as shown in This section first classifies the selected studies ,101,102 Pattern recognition is the last step of the SHM process in which the machine learning algorithms are deployed to automatically identify patterns and regularities in data. In this stage, the decision is taken on the state of the structure. The scope of this SLR is limited to the supervised learning algorithms  and the neural network learning algorithms (ANN and CNN) as they are the most used algorithms in the given research context. Another important aspect of pattern recognition is the selection of analytical methods applied to recognized patterns. The most analytical classes used in SHM of bridges are classification and regression. Regression techniques are used to predict the output numeric values based on the input characteristics found in the dataset. In contrast, the classification technique produces a class rather than a numeric value. It can be observed from As can be seen in As mentioned in It can be observed from From an implementation point of view, SHM systems can be model-driven or data-driven . It can In the model-based category, the undamaged condition model of the building is created using finite element analysis (FEA) . Model cIn the first category , 16 studies are selected, which cover the following algorithms DT, RF, SVM and KNN. The 16 selected studies for supervised learning algorithms are further categorized as: 2 studies for the RF category, 2 studies for the DT category, 7 studies the for SVM category, 3 studies for the KNN category and 2 studies for the mixed category in which three algorithms are applied in the same study  ,73. Decision trees are one of the most widely used algorithms in data mining. The decision tree-based predictive models can be applied to both stratified and regression models . MoreoveAs can be seen in For example, Entezami et al.  propose The random forest algorithm is a nonparametric tree-based ensemble technique. It was first proposed by Breiman . Random As shown in One of the most efficient supervised machine learning algorithms is the support vector machine, which was proposed by Cortes and Vapnik . One of As shown in The K-Nearest Neighbors method (KNN) algorithm is a supervised learning algorithm that is considered one of the simplest algorithms in terms of application complexity. The most important parameter that affects the operation of the KNN algorithms is the number of neighbors, which is highly influenced by the noise in the dataset. Furthermore, the size of the dataset needed for training the algorithm is directly proportional to the dimension of the feature under investigation, which, in turn, increases the computational cost of the system ,115. TabAs can be seen in Svendsen et al.  introducWang et al.  present In the second category , 23 studies were selected, which cover the following algorithms ANN and CNN algorithms. The 23 selected studies for NN learning algorithms are further categorized as: 12 studies for the ANN category and 13 studies for the CNN category.The operation of the artificial neural network (ANN) depends on isolating the input into several levels of abstraction. This network can be trained using datasets to recognize patterns in images or sounds. The optimal behavior of the network can be achieved by the methodology of connections between different components in the network and the weight of those components. An automated process is normally utilized to modify the component’s weight during the training process ,117. TabAs can be seen in The convolutional neural network (CNN) is a feed-forward neural network, which is the most commonly used deep learning approach in the area of image and object classification. CNN consists of many layers including a convolutional layer, pooling layer, ReLU correction layer and fully connected layers. It is widely used for 2D image classification . The XAIAs shown in In the third category , four studies have been selected. Two of them have combined CNN and SVM algorithms ,102. SimThe proposed systems in the combined category utilize different machine learning algorithms for different purposes. For example, the ANN, SVM and DT algorithms have been used in  as a tooParisi et al.  propose Samir et al.  deploy tResearch question 1: What important research has been reported from 2016 to 2023 where machine learning algorithms have been utilized in the pattern recognition process for SHM in bridges?Answer: 45 important research studies published from 2016 to 2023 have been recognized as per the selection and rejection criterion  and unsupervised learning (clustering) contexts. Consequently, they are the most used algorithms in the pattern recognition process for SHM in bridges. Further details are available in Research question 3: Which of the signal processing techniques are more frequently utilized in the feature extraction process for SHM in bridges during the 2016–2023 research?Answer: on the basis of this SLR, the signal processing techniques most deployed in the feature extraction process for SHM in bridges are FRF, PCA and FFT methods. These methods enhance the system’s efficiency by reducing the processing time required for damage recognition. Further details are available in Research question 4: Which of the system implementation techniques and operational functionality approaches are more frequently utilized in the process of SHM in bridges during the 2016–2023 research?Answer: From the selected studies, it can be seen that the diagnosis process is the most technique used in the SHM for bridges (26 studies out of 45) as shown in Discussion on learning algorithms for pattern recognition process: In this SLR, we focused on the applications of machine learning algorithms in the pattern recognition process of SHM systems for bridges. In addition, we restricted our focus to the supervised and neural network learning algorithms as unsupervised learning algorithms are less likely to be used and their applications are limited to clustering problems only. The supervised learning algorithms, discussed in this SLR, include RF, DT, SVM and KNN. It is important to note that there are some other supervised learning algorithms such as Bayesian [Bayesian ,122,123 Bayesian  that havDiscussion on analytical approaches: The supervised machine learning algorithms are capable of two main analytical approaches: classification and regression. The classification approach can be used in SHM systems to cover the first four levels of damage identification including identification, localization, classification and assessment. On the other hand, the regression approach is limited to prediction studies only. It can be argued that the classification approach is the most analytical approach implemented in the SHM system for bridges as can be seen in Discussion on feature extraction techniques: The feature extraction process is an important stage in the SHM procedure. Here, the datasets from different sensors are processed to extract the relevant feature from the design perspective. In other words, feature extraction is implemented by signal processing techniques such as wavelet-based techniques, frequency–response functions (FRF), soft computing-based techniques and Kalman filter-based techniques. As can be seen in Discussion on operational functionality: The damage identification process is classified into five levels including identification, localization, classification., assessment and lifetime prediction. The first two levels are used for the diagnosis process of the structural health of the bridge, whereas the latter three are considered prognosis levels to predict the impact of the damage on the life-time of the structure. As can be seen in 2), respectively. Discussion on system implementation: The physical implementation of the SHM system for bridges can be classified into two main categories: data-driven and model-based approaches. The operation of model-based systems has many computational limitations, therefore, a data-driven system that incorporates different sensing capabilities has recently been deployed more often in SHM systems for bridges. As can be seen in Limitations of research: Even though, we have strictly followed the guidelines of SLR presented by Kitchenham [We utilized the relevant search terms and systematically scanned the search outcomes. Nevertheless, a few search terms obtained thousands of outcomes that we could not scan comprehensively. In addition, we excluded several studies on the basis of their titles in accordance with the search process. Therefore, there is a possibility that the scope of the article is not appropriately clear in the title. Subsequently, we do not claim the comprehensiveness of our research in this SLR.We used three prestigious scientific databases, i.e., IEEE, ELSEIVER and SPRINGER, which contain a huge number of journal and conference publications. Nevertheless, there exist other databases that provide a lot of publications. Consequently, there is a fair possibility that we could have excluded recent research from other databases. However, we firmly believe that the final results of this SLR are not considerably affected because high-quality recent research is available in the selected scientific databases.tchenham  and compThis research presents the applications of machine learning algorithms used for the pattern recognition process for SHM in bridges. To accomplish this objective, SLR has been performed to recognize 45 research articles. On the basis of different machine learning techniques, selected research studies are classified into three different categories, and six learning algorithms are discussed. Consequently, a comprehensive analysis is performed on the identified algorithms by considering various important parameters according to the type of the algorithm. Furthermore, the selected studies have been analyzed in terms of different design considerations including the feature selection approaches, the operational functionality, the analytical approaches and system implementation. Thus, the latest applications of machine learning algorithms in the process of pattern recognition for SHM in bridges are presented and analyzed under this SLR, which is rarely available to the best of our knowledge. This will facilitate the selection of the appropriate machine learning algorithm according to the SHM system requirements.On the basis of this SLR, it can be claimed that the classification method and neural network learning algorithms including the ANN and CNN are the most used techniques and algorithms in the pattern recognition process for SHM in bridges in recent studies. Furthermore, the most signal processing techniques deployed in the feature extraction process for SHM in bridges are the FRF, PCA and FFT methods. In addition, the diagnostic techniques and the data-driven approach are the most widely used operational functionality and system implementation techniques for SHM in bridges, respectively.The learning algorithms and signal processing techniques considered in this SLR exhibit limitations including the computational complexity, memory requirement and time consumption in the pattern recognition and feature extraction processes, respectively. Accordingly, further investigations of novel combined algorithms and techniques to overcome these limitations should be considered in future research studies. Furthermore, most of the recent studies in the field focus only on the investigations of the damage diagnosis levels including damage identification and localization. Consequently, future research studies should further investigate the implementation of prognosis levels to classify, assess and predict the lifetime of the bridge. This will increase the efficiency of the SHM system in damage detection and increase the lifetime of the bridge.


This article was migrated. The article was marked as recommended.No abstract. AMEE MedEdPublish span the breadth of the diverse enterprise aimed at enhancing the capabilities of health professional educators globally. They include the full range of methodologies, from narrative accounts of program implementation to formal qualitative, mixed methods, quantitative and ‘learning analytic’ studies, as well as a broad array of topic areas from skills development in veterinary practice to the promotion of professionalism in a medical school. What they all have in common is a focus on developing and supporting the academics and clinicians who educate health care workers to enhance their capabilities and improve their pedagogical effectiveness.The eight original contributions to this themed issue ofIn a paper from Australia,In another paper fromOn a very different tack,Finally,AMEE MedEdPublish underline the breadth and complexity of the processes undertaken globally in order to develop and support the capabilities that educators, across the full range of health professions, require to undertake their vital work effectively. Collectively, they argue for a focus of attention and resources on a thoughtful and concerted approach to this important area. Effective programs should encompass scholarly attention educators’ development needs, careful design and execution of faculty development programs informed by evidence and theory, then meticulous evaluation of these innovations, utilising the full range of investigative strategies and subsequent modification of the learning activities based on the findings.The diverse papers included in this themed edition ofGary D. Rogers MBBS, MGPPsych, PhD, FAMEE, FANZAHPE, PFHEA is Professor of Medical Education Deputy Head (Learning & Teaching) of the School of Medicine, Griffith University, Queensland, Australia, as well as a role as Program Lead for Interprofessional and Simulation-Based Learning in the Griffith Health Institute for the Development of Education and Scholarship . This review has been migrated. The reviewer awarded 5 stars out of 5 An excellent paper that draws together several papers related to a very diverse topic.Resources and faculty development stand out from the papers, both very important but still require quite a lot of work to be done in their area. I would happily suggest that not only this paper but the papers in the theme should be read by all involved in curriculum development.Reviewer Expertise:NANo decision status is available This review has been migrated. The reviewer awarded 5 stars out of 5 I thoroughly enjoyed reading this commentary. It is good to see the concluding commentary on the development of the health professional educators. I joined the AMEE MedEdpublsih forum in the middle of this theme. But this commentary helped me with the articles that were published on this theme. It is very important to see the faculty development activities for the health professional educators during the process of developing healthcare providers to be the effective teachers. I liked the article by Crichton and Jones (2018) based on a study conducted in Scotland. Clinicians perceived negatively regarding the clinical educators' program even though they recognized the benefits of the program. The other article that got my attention is Hassanien and Abou-Kamer (2018) in Saudi Arabia and Egypt. They clearly explained the role of youtube videos in faculty development activities and how short videos are more effective. The other paper that I was excited is the paper from Australia, Keir, Saad & Davin (2018). This paper is based on how faculty development activities for problem-based learning facilitators improved their diagnostic skills. Thank you for a wonderful commentary. These themed articles on faculty development are very useful for the faculty members and the deans and administrators who are monitoring the faculty development activities.Reviewer Expertise:NANo decision status is available


Depression and anxious symptoms are prevalent in the general population, and their onset and persistence may be linked to biological and psychosocial factors, many of which are lifestyle-related. The way we manage our care, physical and emotional health and/or discomfort is highly influenced by our own abilities, skills and attitudes despite life’s circumstances. The main aim of this protocol to analyze the relationship between psychological constructs , and the presence of affective-emotional problems  and addictions in primary health care.This is a protocol of a prospective longitudinal cohort study including people of 35–74 years old of Aragon primary health care centers (Spain). Three evaluations will be conducted: baseline evaluation, and follow-up assessments five and ten years after recruitment. The primary outcomes will be severity of depression, severity of anxiety, and addictive behaviors. A detailed set of secondary outcomes will be assessed across all three assessments. This will include psychosocial or personal factors on health behavior, social support, lifestyle patterns, quality of life, the use of health and social resources, and chronic comorbid pathology.The analysis of the impact of psychological constructs and lifestyles on the mental health of people and communities will provide evidence that will make it possible to better address and prevent these prevalent problems and address their improvement from a more global and holistic perspective. The evaluation of psychological constructs should be incorporated into health services to improve people’s ability their self-care, the level of knowledge of managing their disease and their physical, mental and social health.https://www.isrctn.com/, identifier ISRCTN12820058. Affective disorders are a relevant and growing health problem, with a significant level of morbidity worldwide . In 2017According to the International Classification of Diseases 11th, depression, anxiety, and addictive behavior disorders are mental, behavioral, or neurodevelopmental disorders . These sThe way we manage our care, physical and emotional health and/or discomfort is highly influenced by our own abilities, skills and attitudes regardless of life circumstances , 14. SomFurthermore, the onset and persistence of affective disorders has been associated with specific lifestyle characteristics  –43. TherMental health disorders generate a great economic burden in the use of health system services and represent a significant proportion of health-seeking contacts in primary care , 58. In Promoting the participation of the population in coping with diseases and their self-care and self-management of health is a key element in the health of the population in general and especially of the population with affective disorders . It is nOur main objective in this study is to analyze the relationship between psychological constructs , affective-emotional problems  and addiction in primary health care. Therefore, our main hypothesis is that subjects with lower levels of the psychological constructs  will have more severe depressive, anxious, and addictive behavior symptoms than those with higher values of the psychological constructs.One of the secondary objectives of this study is to analyze whether these psychological constructs act as mediating or moderating factors in the relationship between the diagnosis of comorbidities or healthy lifestyles and depression, anxiety, and addictive disorders. Therefore, our secondary hypothesis is that the relationship between the diagnosis of comorbidities or healthy lifestyles and depression, anxiety, or addictive disorders will change according to the value of the different moderators, such as self-efficacy, activation, health literacy, resilience, personality traits, sense of coherence, and self-esteem.Moreover, a final secondary objective is to create a cohort for an analysis of the relationship between these psychological constructs and lifestyles and the incidence rate of diagnoses of depression and/or anxiety to be revisited after 5–10 years. Consequently, our last secondary hypothesis is that people with lower levels of the psychological constructs and less healthy lifestyle habits will be more likely to experience increased incidence of diagnoses of depression and anxiety or worsening symptoms in the following 5 to 10 years than will those with higher values of the psychological constructs of healthy lifestyles.This study is a protocol of a prospective longitudinal cohort study. This study will be the first baseline measurement for creating a cohort with expected follow-up at 5–10 years. According to current evidence from prospective studies in the community population , and theThe study will be carried out within the framework of primary health care in the Spanish region of Aragon. Aragon is an Autonomous Community located in the northeast of Spain. It is the fourth Spanish Autonomous Community by territory but ranks 11 out of 17 in terms of population. It has a population of 1.3 million inhabitants: 60% live in urban areas  and semi-urban areas and 40% of the remaining population lives in towns with fewer than 5,000 inhabitants. Aragon has an aging population, with 23.5% of people over 64 years of age. The average socioeconomic level of Aragon is medium, and the unemployment rate is lower than the national average. Public health provides assistance for nearly the entire population [around 70–85% of the population opts for public primary health care over private ]. AragonThe inclusion criteria are (A) participants of 35–74 years old; (B) ability to speak and write in Spanish; (C) ability to understand the study and provide consent to participate in the study voluntarily. According to national clinical data from Primary Care, the most frequent stage of onset of depression and anxiety is the decade between 30 and 40 years of age. At this age, these disorders are more prevalent in women than in men  . The preThe exclusion criteria are (A) suffering from a terminal illness; (B) being institutionalized at the time of the appointment; (C) difficulty participating due to cognitive dysfunction, dementia, or any serious disease that may seriously interfere with the patient’s participation in the study; (D) persons who plan to move out of Spain within five years after the initiation of the study.The sample size was computed using the formula for caclulating the population estimates of the most prevalent affective disorders in primary care (anxiety and depression) . To calcFor selecting potential participants, stratified selection will be made by age, sex and population distribution in urban and rural areas with respect to the National Institute of Statistics data from the census of Aragon 2021 . This stThe research team will perform an information session about the study at the selected health centers. In these sessions, the research team will explain the project to be carried out and its methodology to all primary care professionals  who work in the selected health centers. With these informative sessions for PHC professionals and a good line of contact established with the research team, the aim is to ensure and improve the recruitment rate since PHC professionals maintain good and trustworthy contact with the population. They will also be provided with the telephone number of the research unit for the initial appointment if any patient that these professionals receive in their daily work is interested in participating in the study.The research team will send an information letter to the participants obtained from the stratified sampling. This letter will include information on the study procedure and contact details of the research unit, so participants who are interested can contact the research team. All members of the multidisciplinary research group come from primary health care and public health.In addition, broadcasting will be carried out through general media, posters, and press releases, which will raise awareness and obtain greater data at the beginning of the study. Patients who will be interested in the study will arrange a meeting with the research team at their health center to explain the study in more detail, verify that they meet the inclusion and stratification criteria and sign the informed consent if they wish . All resAn evaluation will be carried out at baseline with further assessments five and ten years following the end of the baseline assessment. In this study, we will use valid instruments whose psychometric properties have been tested on the Spanish population to collect data; the validity and reliability of the questionnaires are reported in The main variables will be severity of depression, severity of anxiety, and addictive behaviors.Depression will be assessed by Patient Health Questionnaire-9 and Beck Depression Inventory. The Patient Health Questionnaire-9  will be used to measure the degree of depression in the past 2 weeks . The sevThe Generalized Anxiety Disorder-7  will be used to measure anxiety level. Each item describes one of the typical symptoms of generalized anxiety disorders experienced over the last 2 weeks . The sevThe Alcohol Use Disorders Identification Test Form  will be used to screen for excessive alcohol consumption. It will act as a support in the evaluation and identification of excessive alcohol consumption as a cause of disease, dependence and consequences of harmful consumption . AlcoholThe Pathological Gambling Brief Questionnaire (PGBQ) will be used to assess the psychometric behavior with respect to gambling addictions in the general population . A scoreThe MULTICAGE-ICT Questionnaire will be used to Assess problems related to the use of the Internet, mobile phones, video games, instant messaging, and social networks . Composead hoc questionnaire prepared by the research team. Health-related characteristics will include physical and psychological comorbidities. These data will be collected from the electronic medical record and will be included in the database following the definitions of diseases according to the International Classification of Primary Care (3rd edition). In addition, the physical examination will obtain anthropometric measurements . The analytical determination will complement the diagnosis of comorbidities with chronic diseases. In all patients, the concentrations of glucose (mg/dL), cholesterol, triglycerides, glycosylated hemoglobin (%), Thyroid-Stimulating Hormone (μIU/mL), complete blood count, iron metabolism, and calcifediol will be obtained.Sociodemographic information, including age, sex, marital status, education, socioeconomic status, and family sustainability, will also be obtained through an The General Self Efficacy Scale  will be used to evaluate perceived global self-efficacy as feeling confident in one’s abilities to adequately handle certain stressors in life . The finThe Connor–Davidson Resilience Scale  will be used to evaluate resilience as the positive adaptation to circumstances of significant adversity such as misfortunes and tragic situations in life . The finThe Sense of Coherence Scale  will be used to measure sense of coherence in terms of the personal disposition toward the values of vital experiences. It also measures understandability, manageability and meaning . Higher The Rosenberg Self-Esteem Scale  will be used to assess self-esteem during the past 2 weeks . Self-esThe Patient Activation Questionnaire  will be used to measure activation as the capacity and ability to manage one’s personal condition of his/her health and how competent he/she feels in taking on this role . The finThe Health Literacy Survey European Questionnaire  will be used to measure the level of health literacy as knowledge of the population, motivation, and individual abilities to understand and make decisions related to the promotion and management of participants’ own health . The scoThe Big Five Inventory-10  will be used to examine personality characteristics . The queThe European Quality of Life-5 Dimensions Questionnaire  will be used to measure health-related quality of life. It will be used to calculate the quality-adjusted life year (QALY) during the monitoring period by adjusting the length of time affected by the health result in relation to the utility value . It contr = 0.52 and k = 0.43 for the 14-item version and r = 0.61 and k = 0.41 for the 17-item version) (The Mediterranean Diet Adherence Screener (MEDAS) was developed within the Mediterranean diet (PREDIMED) study group. We will also evaluate the adherence to the Mediterranean diet, assessed with the MEDAS (14 items)  and MEDAversion) , 88.Pittsburgh Sleep Quality Index  will be used to measure quality and patterns of sleep over the past month . It diffInternational Physical Activity Questionnaire-Short Form  will be used to measure levels of habitual physical activity over the last 7 days . It has ad hoc questionnaire prepared by the research team measured with the 4-question scale adapted from the WHO MONICA study  and univariate analysis  to evaluate the differences between the patients who have some of the most prevalent affective-emotional problems in primary care  compared with those who do not. Statistical analyses will be chosen based on the size of the sample (parametric or non-parametric tests). To compare the two groups, missing data will be assessed and the need to use an intention-to-treat (ITT) analysis and a multiple imputation (MI) technique to handle missing data will be assessed.To answer the main objective, we will use a multiple linear regression model. To do this, each main variable, the PHQ-9 score, BDI-II, GAD-7, PGBQ, AUDIT, and MULTICAGE-ICT, will be used as a continuous variable. Multiple linear regression will be performed using a stepwise method to obtain a better fit result to the statistical analysis. This stepwise regression simply will repeat the multiple regression, each time removing the least correlated variable. Only the significant variables obtained in the bivariate analysis will be entered into the regression model.Finally, mediation/moderation analyses will be carried out to analyze whether psychological constructs participate as a mediator or as a moderator in the association of lifestyles (exposure) and anxiety, depression and/or addictions (outcome). The PROCESS analytical tool developed by Hayes will be used to assess mediation/moderation. These analyses were based on multiple linear regression path analysis . BootstrAffective disorders are a serious and expanding public health issue, with a high morbidity rate worldwide , 58. AccThe onset and persistence of affective disorders have been associated with psychological constructs  and lifestyles  . These pThere is evidence from longitudinal studies that evaluate personal factors in the adult population and their relationship with the development of depressive and anxious symptoms. In several cohort studies in older patients, advancing age, alcohol use, sleep problems, severe pain, and multiple disease burden are shown to be risk factors for predicting the development of depression and anxiety in participants who did not have these mental health problems at baseline. Protective factors were income, social support, higher self-efficacy, resilience, and well-being at baseline , 103. InPrimary health care is the ideal setting for a psychosocial conflict and mental illness prevention approach for three reasons . First, The strengths of this study include the design and the wide range of outcome measures. Another strength is that it involves recruiting a general adult population from a Spanish region whose sociodemographic characteristics  are very similar to those of the rest of the European population and other regions of the world . These cOur study will provide a wealth of information on the interaction between depression, anxiety, addictive behaviors, personal health behavioral factors, and lifestyles. In addition, no longitudinal studies carried out in our population have been identified that analyze in depth all these psychological constructs together while considering their impact on health. Therefore, this study will allow us to provide new evidence on the relationship between psychological constructs and lifestyles in the onset and development of depression and anxiety.This study includes self-report and hetero-administered questionnaires, as well as a physical and laboratory examination. Among the limitations of the study will be the difficulty of the questionnaires used. This difficulty will be minimized by prior training and quality control of the data collection process to ensure that accurate results comparable to other studies are obtained.The most important difficulties of the study would be a possible low participation rate and the possible withdrawal of participants due to refusal to complete the follow-up . HoweverThe evaluation of psychological constructs should be incorporated into health services to improve people’s ability to improve their self-care, as well as their level of knowledge of managing their disease and their physical, mental and social health. Mental health is a social and health challenge.This study was approved by the Clinical Research Ethics Committee of Aragon N° PI20/302. This study was developed in accordance with the Declaration of Helsinki. Since the project involves the collection and processing of personal data, including personal information; the collection, treatment, communication, and transfer of personal data of all participating subjects must comply with the provisions of the General Data Protection Regulation (EU) (GDPR 2016/679) and the applicable national legislation, Organic Law 3/2018, of December 5, on the Protection of Personal Data. Informed consent was obtained from all participants. All subjects allowed their data to be anonymized and used only for the purposes and publication of the results of this study.RM-B and BO-B conceived the quantitative part of the study. FM-L and RM-B led the drafting of this manuscript. RM-B obtained the ethical approval from each institution and contributed to design the training program on the questionnaires and measurements. CB-M, MD-G, and IR advised and contributed to the study design. FM-L, CB-M, and BO-B developed the statistical analysis plan. All authors reviewed the manuscript content and approved the final version for submission addressed.


Technological innovation and preoccupation with new markets through technological innovation have become critical factors in achieving success in the global market. Currently, companies cannot develop and commercialize all technologies. Therefore, the importance of technology transfers is rapidly increasing. Technology transfer is a crucial strategy adopted by organizations to remain innovative and competitive. However, Korea’s technology transfer rate is only 37.9%. In particular, the technology transfer rate from universities to companies is lower than that from government-funded research institutes in Korea. Although the fundamental approach for resolving barriers to technology transfer have been studied, previous research has been conducted from a narrow definition of technology transfer. Furthermore, previous research has focused on analyzing the success factors of technology transfer, presenting technology transfer processes, or conducting case studies. Therefore, it is necessary to develop a technology donor diagnosis framework based on CSFs  to eliminate obstacles to technology transfers. To lower the barriers to technology transfers, it is necessary to develop a strategy for a successful technology transfer based on the diagnosis of technology donors. This study develops a diagnosis framework for universities from the perspective of technology donors, implements and tests the framework using case studies, and proposes strategies for each stage of technology transfer growth. The framework is able to assess multidimensional perspectives, because CSFs and PMs were extracted based on BSC. Furthermore, by comparing the perspectives score of technology donors in different universities, technology donors can identify the areas in which each university is lacking in its current situation. Multidimensional diagnosis and aggregation score of technology donors offer to extract optimal CSFs for technology transfer activation for each growth stage. The effective acquisition and utilization of new technologies are essential aspects of corporate success and critical determinants of corporate competitiveness . The radNot only the Korean government but also government of many countries have been continuously increasing their government R&D budgets. The Ministry of Industry and Energy in Korea is formulating and promoting various policies for technology transfer and commercialization. The technology transfer rate from public research institute to company in the United States is 42.4% in 2020. However, Korea’s technology transfer rate is only 37.9%. In particular, the technology transfer rate from universities to companies is lower than that from government-funded research institutes in Korea. The commercialization and business expansion of technology produced through university R&D activities should be transferred to companies for visible results. However, in Korea, technology transfer through university R&D is hindered by barriers and is not being activated. The main reason for the hindered technology transfer activation is the existence of technology transfer barriers . VariousMost research related to technology transfer do not diagnose or evaluate technology donors but instead focuses on developing common key factors or cases to achieve success in technology transfers. Although diagnosing technology donors based on the framework of technology transfer activation is the key to a successful technology transfer , researcTherefore, it is necessary to develop a technology donor diagnosis framework based on CSFs  to eliminate obstacles to technology transfers. To lower the barriers to technology transfers, it is necessary to develop a strategy for a successful technology transfer based on the diagnosis of technology donors. The objectives of this study are as follows.■ The first objective is to develop a technology donor diagnosis framework to reduce barriers of technology transfer. The framework aims to diagnose universities, the technology donors, from various perspectives, and derive CSFs that are linked to the strategies. CSFs are derived using the process of building a BSC .■ The second objective is to develop a framework for deriving the normalized score of CSF. The PMs (Performance Measures) that make up CSF have different relative importance and measurement units. To diagnose the technology donors and derive strategies based on the diagnosis, it is essential to calculate the aggregation Score for each CSF. Therefore, in this study, we aim to derive the relative importance of PMs based on AHP  and normalize the scores of each PMs based on the normal distribution.■ This study aims to derive strategies based on diagnosis, rather than just evaluation. To do this, the technology donors are classified according to their growth stages, and the scores of the technology donors are compared to those of others in the same growth stage to identify the CSFs that are lacking. By comparing technology donors in the same growth stage, optimized strategies for improving technology transfer will be derived.This study develops a diagnosis framework for universities from the perspective of technology donors, implements and tests the framework using case studies, and proposes strategies for each stage of technology transfer growth. The research was conducted according to the following structure to achieve the research purpose. Section 2 presents a literature review on how technology transfers were evaluated from previous research and technology transfer perspectives. This helps determine the subsequent research methodology and achieve a basic understanding. Section 3 provides detailed research on the methods used and explains the diagnostic framework used step by step in this study. Section 4 examines the technology donor’s diagnostic framework presented in the previous chapter using a case study and summarizes the results. Section 5 compares the present study with existing research and highlights the specifics and strengths of the current investigation. Finally, Section 6 summarizes the research conclusions, provides further suggestions, details the technology donor’s diagnosis, and evaluates the research limitations.Section 2 analyzes the literature on technology transfers. The section is divided into three sub-topics. The first lays the theoretical foundations for research by collecting and consolidating previous research on technology transfer activation. The second summarizes previous research on technology transfer evaluation. The third summarizes prior research on university technology transfers.The research related to technology transfer activation can be classified into activation strategy research and technology transfer models. Research that focuses on activation strategies can be classified into research that analyzes the establishment of institutions or policy derivation for the activation of technology transfers and research that derives and verifies success and failure factors. Moreover, the technology transfer model can be classified into presenting the technology transfer process through theoretical analysis and demonstrating the proposed model through case research. The There have been studies not only on the aforementioned research, but also on criteria for selecting potential technology transfer partners –37, 41. There have also been studies on determining potential markets related to technology transfer , 47–50. There are no general theories, models, structures, or explanatory theories on technology transfer . MoreoveLastly, to assess the risks associated with a technology transfer, a risk management plan should be developed that includes identifying potential risks, analyzing their impact, and developing mitigation strategies. Measures to mitigate risk may include conducting due diligence, implementing contracts and agreements, establishing quality control measures, developing contingency plans, and providing training and support to recipients. The effectiveness of risk mitigation strategies should be regularly monitored and reviewed to ensure that risks are effectively managed throughout the technology transfer process –39.Technology transfers can be classified into technology and knowledge according to the scope of the evaluation target. Research that evaluates technology can be classified into research that evaluates technology value according to research methods, research that assesses the effectiveness and efficiency of technology transfer, or research that analyzes the pathways of technology transfer. Research evaluating technology has focused only on technology valuation, and there has been little research that diagnoses technology donors or suggests strategies. However, it is necessary to comprehensively assess technology-related knowledge and expertise regarding technology transfers. Furthermore, most studies have only developed technological evaluation and diagnostic methodologies. The research related to the assessment of technology transfer is detailed in Most research has used different technology valuation models, with each model having its own scope, level of implementation, and methodology. For example, refer to the redevelopment of models based on previous research , 62, 63,Modern entrepreneurial universities play a role in changing today’s competitive society . Higher Recently, universities have added new dimensions to their core research and training goals, with the additional purpose of commercializing their research-based technology. This dimension is subject to a broad field of research called technology transfers. It is a process by which universities use their dynamic capabilities to recognize and respond to changing opportunities and challenges . UniversThe Many types of research are related to university technology transfers. The latest research trends include university research examining the efficiency of technology transfers to companies, research on the implementation of activities between countries and universities, and research investigating the transfer of nonprofit technology for the benefit of society. Furthermore, the implementation and role of the technology transfer department within the university , 24, staUniversity-specific factors have been examined in the transfer of technological, human, and financial resources for research and technology licensing –108, andThe research methodology is illustrated in the following The first phase comprises the PMs (Performance Measures) extraction method and a cause-and-effect diagram to reveal the causal relationship between CSFs . Most previous research related to technology transfers between technology donors or universities measured single aspects of technology transfers, such as organizational or engineering aspects. To evaluate the technology donor to be linked to the process rather than a simple measurement, the PM should be drawn from the technology transfer strategy and the CSF should be connected . TherefoAs the PMs derived from Phase 1 have different measurement units, a normalization method should be used. Because the weights of each PM are also different and the PM has a causal relationship, an Analytic Network Process (ANP) should be used. It is possible to calculate the normalized score by point of view through the normalization and aggregation methods. Thus, it is possible to establish a strategy for each university (technology donor).Technology transfers have been described in various ways in research and academic studies . In initThe Korean government’s R&D budget has continuously increased over the last several years. The Korean government and major science and technology countries are constantly expanding their R&D budgets. The Ministry of Trade, Industry, and Energy has established and promoted various technology transfer and commercialization policies. However, the technology transfer rates of the significant countries in science and technology are 35.9% in the United States and 46.7% in Europe, whereas in South Korea, they are only 24.2% .Despite the increasing outcomes of university technology commercialization, evidence shows that TTOs (Technology Transfer Organizations) need to enhance their efficiency. For instance, although licensing revenue rose from 48,320 million Korean won in 2011  to 68,489 million Korean won in 2015, TTOs and universities paid 57,119 million won in registration and maintenance fees in 2015. Furthermore, the licensing revenue of universities still indicates a low leverage of government R&D spending in South Korea at only 1.41% in 2018. The average contribution of licensing revenue to the overall university revenue was approximately 0.9% in 2016. Considering this situation, researchers, the government, and university administrators in South Korea have begun questioning the role of TTOs in developing innovation and knowledge-based economies , 130.The results are only tangible when the technology produced through the R&D activities of the university is commercialized through transfer to the company. However, in Korea, technology developed through university R&D is blocked by technology transfer barriers, which prevent the activation of technology transfer . There aIn attempts to determine the pillars of technology transfers, researchers have identified many important factors that determine commercial success, such as designer involvement –140, proRockart  defined The literature introduces these factors as CSFs in technology transfers . ParmentThe BSC has been hailed as a widely used, high-impact management tool , and it Most previous research on technology transfer performance focused on the internal factors of an organization. According to Carlsson and Fridh , the traPrevious research on technology transfer performance has focused on the role of the government as a source of research funding, ignoring the information channel , 176. ToAt a university, performance measurement is a structured process in which the technology transfer office identifies, measures, and monitors essential programs, systems, and procedures . The tecThere are limitations to assessing the technology transfer of technology donors using the PMs used in specific studies. Therefore, key performance indicators for measuring CSF were derived based on a literature review. Two to three PMs were extracted to measure the CSF based on a cause-and-effect relationship analysis.PMs were expressed in different quantities. Therefore, it was necessary to use normalization and aggregation methods. Based on the following formula, the PMs can be normalized:ijp: Value of j th PM of i th perspectiveijσ: Standard deviation of j th PM of i th perspectiveijμ: Mean of j th PM of i th perspectiveThe normalized value of the ith perspective is expressed as follows and aggregated using the following equation:ijW: Relative importance weight of i th perspectiveThe relative importance weight was derived using the Analytical Hierarchy Process (AHP). The existing technology transfer evaluation-related studies have been conducted from a specific perspective. Because the scope for evaluating technology transfers is vast, there is a limit to the extent to which the focus of research can be scattered when considering all factors. Therefore, it is needed to extract and test the elements that influence the success of technology transfers. In this study, the CSF that directly affects the success of technology transfer was derived from the cause-and-effect diagram of the BSC. Subjective influence was identified by deriving the relative importance of CSF through the AHP. Moreover, an aggregation method was developed and a method for quantitative evaluation is presented. Multi-criteria decision-making (MCDM) is a popular decision-making method. It is a branch of operational research that addresses the multi-criteria decision-making process . AHP hasThe growth stages of technology donors are classified as follows:■ Infant stage: Technology donors who scored low in all perspectives■ Growth stage: Technology donors who scored high only in institutional or organizational perspectives■ Expansion stage: Technology donors with high scores in terms of institution and organizations, and high scores in terms of human or knowledge perspectives■ Maturity stage: Technology donors who scored high in all perspectives except for value perspectiveBased on the technology donor’s diagnostic system, a strategy was proposed for each growth stage. The strategy of technology transfers, according to a technology donor’s growth stage, substantially affects the success of technology transfer. It is possible to develop a strategy based on accurate evaluation and diagnosis of technology donors. Moreover, the growth stages of technology donors were classified into the Infant, Growth, Expansion, and Maturity stages. The stage to which the technology donor belongs and the key success factors lacking among the CSFs of technology transfer are expected to lay the foundation for successful technology transfers. Therefore, we intend to develop a technology-donor diagnostic framework that includes this series of processes.This research redefines the technology transfer process by comparing and summarizing the theories of 35 research papers related to technology transfers published between 2015 and 2022.As shown in Based on the technology transfer barriers mentioned in previous studies, the perspectives on the technology transfer process can be divided into four main categories. Human factors include professionals, participants, intermediaries, and engagement in technology transfers, which are categorized into a single group. Knowledge factors are grouped according to knowledge, information, and species regarding the technology being transferred. Institutional factors that indicate involvement in the intermediary institutional or technology transfer process, are in another group. Finally, organizational factors, which refer to the receiver or transmitter of the technology, are in one group. This section illustrates these factors more clearly based on previous research.As shown in These five perspectives with 20 CSFs were sorted from the literature review, and the cause-effect diagrams between the identified perspectives and CSFs were illustrated. In the cause-and-effect relationship diagram, the CSFs of technology transfer are categorized as leading and lagging factors, and the relationships are presented in ascending order. The cause-and-effect relationship between the leading and lagging CSFs is depicted in Human and knowledge perspectives are directly related to the efficiency of technology transfer and are influenced by organizational and institutional perspectives. In the cause-and-effect relationship diagram, the CSFs of technology transfer are categorized as leading factors, and the lagging factors and relationships are illustrated in ascending order. For example, the CSFs in a human perspective, CSFs provide training, which is a form of continuous education that builds human resources for research and development. Expertise and experience in R&D are, in turn, used to create rewards and motivational systems. These cause-and-effect diagrams were meticulously crafted based on an extensive review of existing literature , 208–239In this step, candidate PMs were developed to measure CSF and technology donors by designating indices that are highly relevant to CSF performance among the candidate PMs as core PMs. The With previous research establishing a foundation, technology transfer strategies were studied, the CSFs influencing them were identified, and PMs were extracted based on CSFs.All PMs were measured according to standardization and normalization formulas. The relative importance weights derived through the AHP and the statistics of the PMs are shown in the The following To develop a strategic proposal for each stage of the growth of technology transfer providers based on the diagnostic diagram of technology transfers, the previous phase determined which technology transfer provider belonged to which growth stage of technology transfer. Based on this, the In the case of universities in the infancy stage, most universities showed low scores in institutional perspectives on technology transfer. Therefore, new technology transfer universities should pursue strategies to accelerate technology transfers and move towards the next stage of growth by involving intermediaries, experts, and technology transfer offices. In the case of universities in their growth stage, most showed low scores from the knowledge perspective of technology transfers. Therefore, growing technology transfer universities should pursue strategies to develop technology transfers and move to the next growth stage by increasing their R&D, academic research, technology, and knowledge. In the case of universities in the expansion stage, most showed low scores in the value and organizational perspectives of technology transfers. Therefore, sustainable technology transfer universities should pursue a strategy to evaluate technology transfer efficiency and move to the next growth stage by calculating human and financial resources and developing organizational establishment and culture. In the case of universities in the maturity stage, most showed low scores in the value perspectives of technology transfer. Therefore, advanced technology transfer universities should pursue strategies to evaluate technology transfer efficiency and move to the next growth stage by evaluating technology transfer efficiency and conducting research to further develop technology transfers.To activate technology transfer, various studies are being conducted. Studies related to technology transfer activation can be classified into those that provide activation strategies and those that present technology transfer models. Studies that provide activation strategies can be further classified into those that analyze the establishment of institutions or policies for technology transfer activation, and those that derive and verify success and failure factors. Research examining the dependence between the characteristics of technology donors and the success of technology transfer had been actively being conducted . The resThe growth stage of a technology donor affects the type of technology transfer and the technology transfer strategy. Some researchers have classified the growth stages of technology donors from a technology transfer perspective and suggested appropriate technology transfer types according to the growth stages . HoweverThe distinctiveness of this study lies in its multidimensional diagnosis and strategy formulation based on the diagnosis. It is able to assess multidimensional perspectives, because CSFs and PMs were extracted based on BSC. Furthermore, by comparing the perspectives score of technology donors in different universities, technology donors can identify the areas in which each university is lacking in its current situation. Multidimensional diagnosis and aggregation score of technology donors offer to extract optimal CSFs for technology transfer activation for each growth stage. Therefore, we compared this study with previous technology transfer evaluation studies based on the criteria of multidimensional diagnosis and strategy formulation. The comparison results are shown in the following The rapid development of technology had created limitations for all companies to develop technology and as a result, the demand for technology transfer was gradually increasing. In Korea, technology transfer is carried out from public research institutes to companies. While technology transfer from government-funded research institutes to companies was relatively active, technology transfer from universities to companies was not insufficient. Many studies had been conducted to remove barriers to technology transfer from universities to companies. However, for successful technology transfer, strategies for selecting technology donors based on the evaluation is necessary. In addition, in order to activate technology transfer from universities, it is necessary for universities to identify their weaknesses as technology donors and to develop strategies for maximizing resource utilization. Therefore, a diagnosis framework for activating technology transfer from the viewpoint of the technology donors rather than the technology users who received the technology was developed. Moreover, the framework was applied to Korean universities.To provide an accurate diagnosis of technology donors, the study proposed two stages of framework: the first stage is to derive PMs based on the BSC and the second stage is to provide normalized score for each perspective based on normalization and aggregation method. To derive PMs, barriers to technology transfer were identified and strategies to reduce them were developed. CSFs were then derived based on the strategies and verified through a cause and effect relationship. Finally, PMs were derived to measure CSFs. A relative importance weight was extracted using the AHP to derive score for each perspective of technology donors. The normalization and aggregation methods were used to provide a score for each perspective. Finally, universities were classified by growth stage and through relative comparison between universities belonging to each growth stage, lacking perspectives was identified for each university. As technology donors, universities can increase the efficiency of technology transfer by selecting and implementing strategies and CSFs to raise the insufficient perspective score.To verify the applicability of the research framework presented in this study, we applied the framework to universities in Korea that were playing the role of technology donors. As a result, universities in the maturity stage were found to have high scores in all perspectives. However, Univ.11 had a relatively lower score in the value perspective compared to other universities in the maturity stage. While Univ.11 had a high level of infrastructure, institutional organization, human resource support, and knowledge support, the efficiency and effectiveness of technology transfer were still insufficient. As technology transfer donor, Univ11 needs publicity so that technology transfer can take place. The perspective scores of the 65 universities in the expansion and growth stage showed a significant difference. Among the 65 universities, there were many engineering-oriented universities. Univ.24, Univ.64, Univ.68, and Univ.69, which were engineering-oriented universities, did not have well-established systems and organizations, but technology transfer was relatively active. These universities are expected to have more active technology transfer if they improve their institutions and organizations. The 37 universities in the infant stage had relatively low scores in all perspectives. Therefore, it is necessary to strengthen the TTO for technology transfer by reorganizing the infrastructure and replenishing manpower.The implications obtained through this study are as follows. First, technology transfer barriers, which are the main factors hindering technology transfer, were redefined. Previous studies that defined technology barriers were unable to comprehensively present technology barriers by only deriving technology barriers from a specific perspective. In this study, barriers to technology transfer reflecting various perspectives were extracted through extensive literature research. Second, most studies related to technology transfer had been focused on technology users, but in this study, it has been focused on technology donors. It is important to select technology donors from the viewpoint of a technology user. However, it is also important to increase the efficiency of technology transfer through various knowledge and support systems of technology donor. By presenting the diagnosis framework based on evaluation of technology donor, it is expected that it will be possible to activate technology transfer through the development of technology donors. Third, CSFs and PMs directly linked to the success of technology transfer were derived. CSFs that were directly related to the success of technology transfer were derived based on the BSC. Through the CSFs, universities can develop strategies to activate technology transfer. Fourth, the aggregation and normalization method that derived the score for each perspective of the university was presented and applied. Since what can be measured can be improved, the score allows universities to identify where they are lacking as technology transfer donors.This study has some limitations. First, it only focused on domestic universities and conducted evaluations on some of them. There are 143 universities and 138 public research institutes in Korea that provide technology transfer, but due to time and physical limitations, this study only evaluated some domestic universities using the framework derived from this study. A comprehensive survey will be needed to evaluate all technology donors. Second, although evaluations were conducted on technology donors, it was not possible to evaluate whether technology donors who received high scores have actually successfully transferred their technology. Since evaluating technology transfer cannot be done in a short period of time, this study did not carry out such an assessment of technology transfer, but it would be worthwhile to confirm it in the long run in future research. Third, although there are various forms, such as university-company, university-university, and intra-university technology transfer, only technology transfer between universities and corporations was able to diagnosed based on framework of this study. Therefore, the subject of this research was limited solely to university-company technology transfers. Finally, a mathematical method is needed to validate the CSFs of technology transfer. In previous studies, some CSFs had been validated. The effect of individual CSFs on technology transfer had been validated based on statistical method. However, studies on how various CSFs interact with each other to affect technology transfer are lacking. As this study focused on performance evaluation, it was not statistically tested. However, if the CSFs presented in this study is mathematically verified, it will be possible to increase the reliability of the influencing CSFs affecting technology transfer.


Hypertension is a severe and highly prevalent disease. It is considered a leading contributor to mortality worldwide. Diagnosis guidelines for hypertension use systolic and diastolic blood pressure (BP) together. Mean arterial pressure (MAP), which refers to the average of the arterial blood pressure through a single cardiac cycle, can be an alternative index that may capture the overall exposure of the person to a heightened pressure. A clinical hypothesis, however, suggests that in patients over 50 years old in age, systolic BP may be more predictive of adverse events, while in patients under 50 years old, diastolic BP may be slightly more predictive. In this study, we investigated the correlation between cerebrovascular changes, (impacted by hypertension), and MAP, systolic BP, and diastolic BP separately. Several experiments were conducted using real and synthetic magnetic resonance angiography (MRA) data, along with corresponding BP measurements. Each experiment employs the following methodology: First, MRA data were processed to remove noise, bias, or inhomogeneity. Second, the cerebrovasculature was delineated for MRA subjects using a 3D adaptive region growing connected components algorithm. Third, vascular features (changes in blood vessel’s diameters and tortuosity) that describe cerebrovascular alterations that occur prior to and during the development of hypertension were extracted. Finally, feature vectors were constructed, and data were classified using different classifiers, such as SVM, KNN, linear discriminant, and logistic regression, into either normotensives or hypertensives according to the cerebral vascular alterations and the BP measurements. The initial results showed that MAP would be more beneficial and accurate in identifying the cerebrovascular impact of hypertension (accuracy up to  Hypertension is a disease that contributes to the mortality rate of millions of people in the world . The 201Whereas both systolic and diastolic measurements remain essential for the diagnosis and the treatment, clinical research suggests that in patients above 50 years old in age, systolic BP may be a more important predictor of adverse events, whereas in patients under 50 years old, diastolic BP may be slightly more predictive . High syPerhaps, in part to the variable importance assigned to systolic and diastolic BP in the literature, Kundu et al.  suggesteOne important seeming consequence of hypertension is the alterations in the cerebrovascular that include the loss of vessels as well as changes in their morphology . Such chIn the current research, we focus on measures of the cerebrovasculature and ask whether MAP shows a stronger association with these measures than either systolic BP or diastolic BP taken separately. Our study presented extensive experimentation that emphasized the pathophysiological significance of MAP and supported prior views that recommended using MAP as a superior index for the definition of hypertension and related research.Methodology started with classifying data into three different datasets. One dataset was classified into normotensive and hypertensive subjects based on systolic BP. The second dataset was classified into normotensive and hypertensive subjects based on diastolic BP. Finally, the third dataset was classified based on MAP values of the subjects. The following step was studying the correlation between each dataset and the vascular changes that predict and diagnose hypertension to see which dataset  would give higher prediction accuracy. To find the vascular changes, the following procedure was used. First, magnetic resonance angiography (MRA) data were prepared and preprocessed to eliminate noise and bias effects and to improve the quality of data. Second, clean data were segmented to extract the cerebrovasculature from each subject using a 3D adaptive region growing connected components algorithm. After that, cerebrovascular features which describe the changes in cerebral vasculature were extracted. These features are able to predict the potentiality of hypertension development. Feature vectors were then constructed to be fed into different classifiers. Finally, data were classified into either hypertensive or normotensive according to alterations of the cerebrovascular features. More details of each step are presented in the following subsections. A data set of 342 subjects of MRA was used in the experimentation. Data were acquired by the University of Pittsburgh and approved by IRB according to the relevant guidelines and regulations . ParticiBlood pressure readings were taken for participants 4 times during 3 visits within 2 weeks. Among other procedures, visits 1 and 2 included BP readings. In visit 3, participants were asked to sit down and relax while supporting their back and arms for at least 5 min before trained assistants took their BP readings twice, separated by 1 min. The auscultatory technique was used to measure BP of participants with cuff size fitted according to arms circumferences. All four readings were averaged and used as the final resting BP measurement for each individual. This study followed the 2017 guidelines . The procedure repeats with the new random weights for all features of the selected k-nearest neighbors. A new synthetic sample is then estimated as the accumulation of multiplications between generated random weights and feature vectors. This technique guarantees that the training process is balanced enough to build an unbiased classification model. That model uses the synthetically generated data that have the common characteristics of the original data without being a linear combination of any subset of the samples in the minority class.The current study uses the synthetic minority over-sampling technique (SMOTE)  to generDifferent measurements were applied to categorize data into hypertensive and normotensive classes. In this research, we had two stages of experimentation . In the The first experiment was performed using the original dataset, which was imbalanced. The systolic sub-experiment was performed using a data set of 116 subjects, of which 68 were normotensive and 48 were hypertensive, whereas the diastolic sub-experiment was conducted using a total of 226 subjects, of which 83 were normotensive and 143 were hypertensive.The other two experiments were performed using both the original data and the new synthetic data samples. The synthetic samples were generated to enlarge the original data set and to ensure it was balanced. In the second experiment (systolic sub-Experiment), 20 synthetic hypertensive samples were generated, making a total of 136 subjects in the balanced dataset, of which 68 were hypertensive and 68 were normotensive. Similarly, in the diastolic sub-experiment, 60 normotensive samples were generated, making a total of 286 subjects, of which 143 were hypertensive and 143 were normotensive. For the third experiment, more synthetic data samples were generated to increase the number of subjects tested in both sub-experiments to 600 subjects .In the second stage, we also conducted three experiments similar to the experiments in stage 1, but this time based on MAP categorization . The firData are classified using various classifiers (linear and non-linear) with various parameters and scenarios of validation. Classification accuracy is computed as follows:Several classifiers were used in this study, such as SVM, decision trees, linear discriminant, logistic regression, and KNN. Support vector machine (SVM)  can be uThe MATLAB R2017a built-in classification learner was used in the study experimentation to allow for using different classifiers with various parameters and validation scenarios. The partitioning of data between training, testing, and validation is done randomly between different patients. In the first stage experiments, the accuracy of classification recorded in the first experiment was unpromising, neither in the systolic sub-experiment nor in the diastolic sub-experiment. The best achieved results for the various classifiers recorded an accuracy of On the other hand, the classification accuracy achieved in the second experiment was more promising as given in The diastolic sub-experiment, however, recorded an accuracy of on setup .For the second stage experiments, the best achieved accuracy in the first experiment (161 subjects) was on setup . This waIn this manuscript, we included accuracy values that are According to the 2017 guidelines for hypertension ,7,8,9,10In this study, we investigated the importance of the systolic component and diastolic component separately in hypertension diagnosis. The aim was to find out whether one component would be of more value in hypertension detection than the other. Additionally, we studied the usefulness of MAP in hypertension diagnosis as a measurement that benefits from both components (the systolic and the diastolic) rather than taking only one component into consideration as explained in the previous few lines. The data set used in the study experimentation consisted of individuals with a wide range of ages (35–62 years) to test whether the age would have a role in the importance of component over the other in hypertension diagnosis. Preliminary results showed that using the systolic BP component for people aged 35–62 years can be more predictive than using diastolic BP see . AdditioExperiments conducted in the study were designed to perform supervised classification tasks. Labeled data were introduced to various classifiers to measure the accuracy of differentiating between normal and hypertensive individuals based on both blood pressure measurement and cerebral vascular changes. The cerebral vascular changes data were incorporated in the classification tasks to study the correlation between hypertension-related cerebrovascular changes and systolic BP, diastolic BP, and MAP. The results showed that these cerebral changes affect systolic and diastolic components to quite different degrees. Generally, as seen in To the best of our knowledge, this is the first study to analyze the role of systolic BP, diastolic BP, and MAP independently and their correlations to changes in the cerebral vasculature. One limitation in our study is that it is very hard and expensive to obtain MRA scans for individuals over long periods of time so that we can track the cerebral changes easier. That was the main reason that the dataset is not big enough and that we needed synthetic data samples to enlarge the dataset. However, we will work on recruiting more individuals in the future to solve this issue. Additionally, MRA scans are expensive. Thus, we recommend to currently limit their usage in hypertension prediction for people with high potential of developing hypertension due to family history, for example. Another limitation is that the conclusion of this study cannot be followed if the underlying study is targeting studying people who suffer from systolic-only hypertension or diastolic-only hypertension because in such cases, we need to study only one component (either systolic BP or diastolic BP) and not both. So, MAP, which incorporates information from both components, is not a good measure in this case.This study investigated the significance of systolic BP versus diastolic BP in defining cerebrovascular sequelae of hypertension and compared that to the significance of using MAP. Preliminary results indicated that systolic BP might be more predictive of hypertensive association with cerebrovascular indices than diastolic blood pressure. However, using MAP values that incorporate information from both systolic and diastolic BP recorded the highest predictability in detecting hypertension-related vascular alteration than using systolic BP or diastolic BP separately. This result emphasizes the pathophysiological significance of MAP and supports prior views that this simple measure may be a superior index for the definition of hypertension and research on hypertension. Future plans include collecting more data over longer periods of time to allow for tracking the changes of the cerebral vascular and their impact on developing hypertension and to test the proposed methodology with more original data to enhance the accuracy and reliability of the results.


Pharmacy and pharmaceutical sciences embrace a series of different disciplines. Pharmacy practice has been defined as “the scientific discipline that studies the different aspects of the practice of pharmacy and its impact on health care systems, medicine use, and patient care”. Thus, pharmacy practice studies embrace both clinical pharmacy and social pharmacy elements. Like any other scientific discipline, clinical and social pharmacy practice disseminates research findings using scientific journals. Clinical pharmacy and social pharmacy journal editors have a role in promoting the discipline by enhancing the quality of the articles published. As has occurred in other health care areas , a group of clinical and social pharmacy practice journal editors gathered in Granada, Spain to discuss how journals could contribute to strengthening pharmacy practice as a discipline. The result of that meeting was compiled in these Granada Statements, which comprise 18 recommendations gathered into six topics: the appropriate use of terminology, impactful abstracts, the required peer reviews, journal scattering, more effective and wiser use of journal and article performance metrics, and authors’ selection of the most appropriate pharmacy practice journal to submit their work. Disciplines are shaped by and in turn help to shape human behavior . SeveralA lack of consistency and consensus attenuates a discipline’s progress and has a deleterious impact on its constituent scholars. Some of the findings from previous research indicate that scholars in low-consensus fields have a more difficult time publishing, tend to persist at “re-creating the wheel”, are less successful with acquisition of extramural grants, and have a poorer outlook on research and scholarship . This trClinical and social pharmacy practice are important research areas within Pharmaceutical Sciences that have undergone (and are still undergoing) substantial changes , 10. As The scope of pharmacy practice has expanded over the past decades to encompass clinical, behavioral, economic, and humanistic implications of the practice of pharmacy, as well as the implementation of innovations in practice , which are often provided in collaboration with other health care professionals  , 15. ThuPublication patterns and practices are one of these differential characteristics of a scientific discipline. Publishing refereed work is a hallmark of science, primarily aiming at disseminating new, advanced, and high-quality research knowledge and findings as widely as possible in a timely and efficient manner. Regardless of the scientific publishing mechanisms—which have significantly evolved over the years especially in response to technological progress , 16—thishttps://www.icmje.org/), which is now one of the most used standards in scholarly publishing. A similar initiative was created approximately 30 years ago for nursing with the International Academy of Nursing Editors (INANE—https://nursingeditors.com/).With the aim to identify the elements that may reinforce clinical and social pharmacy practice as a scientific discipline by consolidating common publication patterns, a group of pharmacy practice journal editors met in June 2022 in Granada, Spain. As a consequence of this meeting, a series of recommendations to improve publication patterns in pharmacy practice was created, i.e., these “Granada Statements”. This type of initiative is not unprecedented. In 1978, a group of medical journal editors gathered in Vancouver, Canada to create the Uniform Requirements to submit a paper to a medical journal. Years later, this group became the International Committee of Medical Journal Editors  and the International Union of Basic and Clinical Pharmacology (https://www.guidetopharmacology.org/) are good examples of this procedure.One of the differential characteristics of disciplines with a high degree of consensus is the consistent use of precise terms to refer to each concept. Several areas have created task forces to maintain glossaries. The International Union of Pure and Applied Chemistry  were created to classify articles and are especially important when authors do not use standardized terminology. MeSH terms have been known in pharmacy since their inception . UnfortuIn addition to the reduced number of MeSH terms defining clinical and social pharmacy practice elements, a poor allocation of existing MeSH to pharmacy practice articles has been reported , 28.AlsoIn the recent past, allocation of MeSH terms to articles indexed in MEDLINE was a responsibility of NLM catalogers. Since the NLM announcement of the complete implementation of the Medical Text Indexer First Line indexing (MTIFL) that will select the MeSH, authors, reviewers and journal editors should take responsibility for the appropriate allocation of MeSH terms to the articles.MTIFL is an automated natural language processing system which identifies the appropriate MeSH terms from the MeSH thesaurus using only the text in article title and abstract. As stated by the NLM, after mid-2022, all articles indexed in MEDLINE will have MeSH terms allocated by MTIFL, more mechanistically rather than through human judgment/intervention. This modification of the process increases even more the relevance of the title and abstract, that in the past had a role only in summarizing the content of the article and helping potential readers to decide proceeding to the full text article.https://www.ncbi.nlm.nih.gov/mesh/). Thus, if an article’s title or abstract includes the exact wording of any of these descriptors or entry terms, the system will allocate the given MeSH to that article  is one of several journals in comportment with the Granada Statements publishing high-quality, peer-reviewed content in health services research specifically as it relates to some aspect of the medication use process. The medication use process includes but is not limited to the prescribing, preparation, dispensing, administration, adherence to, evaluation, monitoring, and outcomes associated with legend or with over-the-counter medications, incorporating the concept of clinical pharmacy which aims to optimize utilization of medicines to achieve person-centered and public health goals. The medication use process includes attitudes, perspectives, knowledge, and behaviors of any actor in this process, including prescribers, pharmacists, pharmacy personnel, other health practitioners, patients, and caregivers. As such, the Granada Group journals often refer to “pharmacy” in their title or description, as these persons are central to medication use process; however, research articles reviews, and commentaries can refer to any person involved in this process, as well as any evaluation  of the drug products themselves or systems employed to optimize the use process.The Granada Group journals share certain commonalities and also goals to improve the medication use process and the outcomes emanating from this endeavor; however, each journal has an established niche and optimally suited for certain types of manuscripts. Further description of the aims and scopes of [this journal] follows below:The journals comprising the Granada Group producing these Statements stand in unison in their endeavor to promote the quality and status of research in clinical and social pharmacy practice, as well as to advance the scientific paradigm of the discipline and broaden the impact of our respective journals to an international audience within and outside of pharmacy. The journals recognize that they are part of a larger phenomenon in health services research having much in common with journals outside of pharmacy practice, per se, yet focusing on some aspect of the medication use process. In light of the Statements offered here and in recognition of the need for the journals to recognize their commonality, assist authors with selecting the most appropriate venue to publish their work, and unite in their mission to promote all journals in the area, the Granada Group journals have agreed to a common introductory description among all. The shared description among all the Granada Group journals will then be followed by specific descriptions that then help to establish the unique niches and processes associated with each of them. The common introductory description used for all Granada Group journals is as follows.The Granada Statements were created with the strong conviction that pharmacy practice is a scientific discipline that deserves reaching the high-consensus discipline category. The recommendations in these Statements aim to contribute to increase the quality of the articles that pharmacy practice researchers try to publish to disseminate their scientific contributions. At the end of the day, a scientific area and the profession behind it will benefit from the advancements published in these articles. The advancement of pharmacy practice is a conjoint responsibility between pharmacy practice researchers, peer reviewers, editors, and publishers, where scientific articles should be seen as the means to disseminate new knowledge that will improve practice.