In recent years, the interest in the relationship between urban green space and residents’ mental health has gradually risen. A number of researchers have investigated the causal relationship and possible mediators between the two, although few have summarized these mediators. For this reason, we searched for relevant studies and filtered them by criteria and quality score, and analyzed the mediators and paths of the impact of urban green space on residents’ mental health. The mediators can be divided into environmental factors, outdoor activity, and social cohesion. From the perspective of heterogeneity, both individual characteristics  and group characteristics  of residents are considered to be the cause of various mediating effects. Types of urban green space tend to affect residents’ mental health through different paths. Furthermore, this review discusses the details of each part under the influence paths. Finally, the policy implications for urban green space planning from three mediator levels are put forward based on an analysis of the situation in different countries. In recent years, the built environment and human mental health have attracted extensive attention from the international community. The World Health Organization has pointed out that the Healthy Cities movement has become a pioneer in urban development and transformation, providing an impetus to the creation of a healthier and friendlier urban environment as well as maintaining human mental health and well-being ,2. As anIn general, certain theoretical achievements have been made in the research on the correlation between urban green space and residents’ mental health. A series of studies have confirmed that urban green space is closely related to the mental health of residents. Lee et al.  pointed Based on the benefits of urban green space on mental health, it is of great significance to clarify the mechanism of urban green space on mental health. The main challenges that still need to be addressed in this research field are the causes and mediators of green space’s beneficial effects ,8. HowevThe structure of the remainder of this article is as follows: First, we summarize the possible mediating factors and their influencing paths between urban green space and residents’ mental health by searching for relevant studies around the world. Literature search and selection are carried out in In order to carefully evaluate the existing literature, this review referred to the selection process and filter criteria of other review articles, and conducted quality analysis on the selected articles. We listed the search strategy and filter criteria and then screened the literature by specification .Additionally, there are many different definitions of urban green space and mental health . In ordeWe searched Web of Science and Scopus in December 2020. For the purposes of this review, the search terms for urban green space included “green space” OR “open space” OR “urban space” OR “urban forest” OR “forest therapy”. The search terms for mental health included “mental health” OR “psychological relaxation” OR “psychological health”. In order to avoid missing the articles that illustrate the relationship between mental health and urban green space from the perspective of human well-being, the combination term, restore OR restoration AND “human well-being”, was also considered. All these terms were searched in titles, key words, and abstracts. Combinations of the search terms were also run in these databases.We included (a) empirical studies and research designs from around the world and made no restrictions relating to gender, age, nationality, region, or race, and (b) both descriptive and observational studies were included. Among them, (c) either cross-sectional or longitudinal designs, randomized controlled trials or intervention trials were acceptable. The cross-sectional study is good at identifying and measuring the strength of the relationship between green space and mental health . The lonIn addition to following the above criteria, there are several points to be clarified during the screening process. After filtering the two databases, we first eliminated duplicate articles. For linguistic reasons, (g) we excluded the studies if they were not written in English.At the stage of title and abstract filtering, we mainly considered the correlation between the search results and the search terms, as well as whether the research focused on urban green space and health. (h) Some studies were only related to either urban green space or health. These studies were excluded. In addition, (i) we also excluded studies if they described the general benefits of nature. Since our research subjects were urban residents, (j) we excluded articles that were non-urban or non-human studies . In our research, we sought to draw conclusions regarding the path of the impact of green space on mental health, according to the research process. Therefore, (k) we excluded studies if they were reviews, meta-analyses, or qualitative or planning articles. Furthermore, we have selected articles that included heterogeneity analysis, that is, exclude those articles that did not analyze the differences caused by population characteristics or carry out descriptive statistical analysis of the research objects.In the full-text screening stage, in order to prevent the review from producing biased results, (l) we did not screen the results of the study or exclude those articles showing insignificant mediating variables or negative effects. (m) For articles with similar methods and conclusions, we included only one of them, which was always the most comprehensive or most recently published one. In addition, (n) generalized or mixed studies of green space  were excluded, because it is hard to judge the contribution of different spaces on resident’s mental health.Key data from each selected article was collected and extracted into a complete data collection form in Microsoft Excel. The selection of attributes referred to previous reviews on green space and mental health. ,16,19. TTo date, numerous studies have shown that there is a correlation between green space and mental health, but such a correlation cannot represent causality ,21. AfteThe possible mediators that exist between the two were analyzed, and the possible influencing path was deduced . Figure Urban green space affects residents’ mental health. However, how does this effect come about? That is why we are reviewing and considering mediating variables and influencing pathways. The impact of mediators on urban green space and mental health should be a factor, but is one that is not included in the concept of urban green space or mental health. For example, the quality of green space and the time spent in green spaces are measures of green space, and the perception of loneliness and stress are indicators of mental health, which cannot be regarded as the mediators we are discussing. Mediators can be divided into direct and indirect mediators ,73. A diAccording to the definition of green space, it is obvious that the addition of green plants can improve the environment. The environmental factors discussed here are mainly considered from the perspective of human beings. From an objective point of view, some of the characteristics of green plants themselves directly affect the residents. On the one hand, green space can reduce physical damage to the residents ,76. On tFirst, green space can absorb pollutants from the air and improve air quality. Green space has a significant impact on pollution exposure . Yin andSecond, green space also absorb noise from the environment, thus reducing the stress of living and promoting mental health of residents. Several studies have been conducted to explore this link. The presence of vegetation can also weaken the negative perception of noise, to a certain extent . Plants Furthermore, green space can allow for visual stimulation, which can make people’s minds more relaxed. Horiuchi et al.  suggesteOutdoor activities not only include sports but also leisure activities such as walking, social contact, and interaction with residents. It is an indirect mediator, which is a spontaneous activity of residents. It is not entirely dependent on green space, which means that residents can engage in outdoor activities without green space. Green space simply indicates an outdoor space with more vegetation; that is, residents prefer to go to green spaces for outdoor activities . BecauseGreen space can provide spaces for outdoor sporting activities and opportunities for physical activity. The duration of time spent engaging in green space has an effect in reducing stress levels ,81. A stHowever, some studies have suggested that the relationship between green space and physical activity is not significant. While physical activity is higher in greener neighborhoods, this does not fully explain the relationship between green space and mental health . RichardIn addition to physical activities, social activities are also a part of outdoor activities. Social activities can further enhance contact and interaction between residents. More specifically, Dadvand et al.  showed tSocial cohesion has been defined in many ways. Unlike social activity in outdoor activity, social cohesion starts from human perception. In this review, we see social cohesion as residents’ senses of belonging and security, and their neighborhood satisfaction ,43,74,75Green space affects adults’ perceptions of loneliness, security, and happiness. Research by Maas et al.  showed tGreen space can improve residents’ environmental satisfaction. Empirical research shows that residents’ subjective perception of neighborhood characteristics has an impact on residents’ neighborhood satisfaction . By studFurthermore, residents’ perceived neighborhood satisfaction links environmental characteristics with self-assessed mental health, that is, neighborhood satisfaction is the link between green neighborhood space and happiness . Leslie It is worth noting that such experimental studies can only observe a significant correlation between the environmental characteristics of a specific green space and social cohesion through the joint significance test , that isThe impact of green space has a heterogeneous effect, i.e., green space affects people in different ways. It has been suggested that certain residents, such as children, the elderly, and women, may benefit more from the presence of neighborhood green space than others . The reaEnvironmental factors refer to the impact of green space on residents’ mental health by reducing physical damage and increasing visual benefits. From the perspective of age, this effect is particularly pronounced for children with cognitive immaturity. Dadvand et al.  showed tFor people who are sedentary or have limited mobility, the environmental factor of green space can be achieved by appreciated green areas through the window. Furthermore, it is easier to make small repairs through the window . This viFor the elderly, time spent in parks is associated with mental health, and physical activity also helps promote mental health. Older people’s own physical conditions can also affect their tendency to go outdoors. Elderly people with cardiovascular disease are more likely to go to a park, while those with hypertension are less likely . In consIn terms of subgroups, green space encourages pregnant women to engage in outdoor leisure activities to a certain extent . This isResidents in cities with high levels of urbanization spend more time sitting and less time on sightseeing and outdoor activities, thus weakening the mediating effect of outdoor activities . In highGreen space enhances children’s interaction with nature and affects their cross-cultural communication and growth ,102. FraGreen space affects adults’ perceptions of loneliness, security, and happiness. Research by Maas et al.  showed tThrough an analysis of a series of previous studies on green space and mental health, it is not difficult to find that studies of the same population often draw different conclusions, and the significance of each mediator is not the same in different studies. A green space is a geographical system with rich functions and a complex structure. Each country has different characteristics in terms of the climate, status of development, and living conditions. For example, in countries with poor sanitation, living in green spaces may be detrimental to mental health because such areas have a higher risk of infectious diseases . On the Based on Part IV of The problem is that the factors identified in this way are not necessarily mediations, which, in many literatures, overlap with the concepts of green space and mental health. For example, the concept of loneliness may be included in the measurement of mental health . It is aFrom our point of view, the impact of mediators on urban green space and mental health should be a factor that is not included in the concept of urban green space and mental health. Yet, the study of mediators is not over. It needs more researchers to pay attention to this problem and more evidence to further support and improve relevant theories.There are many aspects of heterogeneity. Various influencing factors, such as individual characteristics and social characteristics of residents, should be considered comprehensively to reduce random errors to the greatest extent, so as to clarify the mediator of green space on mental health. In the future, the correlation between urban green space and residents’ mental health should be demonstrated in a broader space–time scope. Researchers should try to avoid the existence of confounding factors in sample screening, and long-term follow-up observations should be conducted on participants’ mental health to improve the effectiveness of the results. Therefore, we need to fully consider the individual characteristics and social characteristics of the residents in the research process to ensure the accurate analysis of how the mediator works. This corresponds to Part IV of The individual characteristics of the residents need to be included in the category of research variable control. In most studies, the analysis’ object is an individual. Only in a few “time–activity” detection studies has a specific area been taken as the research object . TherefoThe social characteristics of the sample population need to be included in the category of research variable control. Some studies have found that the health benefits of green spaces can be modified by variables such as education level and socioeconomic status ,98. For Stratified analysis can be conducted according to social class, education level, age, and gender. These factors may change the direction and extent of the impact of green space on mental health, which means different mediators and influencing mechanisms.Urban green space includes neighborhood green space, urban forests, and parks, which corresponds to Part IV of The quality of green space should not be discussed in general in the research on green space and mental health. Instead, the different dimensions of green space quality should be explored according to the different tendencies and emphases of different mediators. The green space index (GSI) has been used by Occidental countries in recent years to quantitatively evaluate green infrastructure in designated sites. By superimposing the different weights of green space types, which have different ecological benefits, and by comparing them with the minimum value of the set index, a quantitative reference space can be obtained. Among them, ecological service, infrastructure spatial allocation, and maintenance of green space correspond to environmental factor, outdoor activity, and social cohesion, respectively.Nowadays, cities around the world that have implemented the GSI have received favorable feedback from city managers, project builders, and the general public. Taking the Berlin habitat index as an example, relevant surveys have shown that, since its implementation, urban green infrastructure has achieved remarkable results in terms of regulating the urban ecological environment, improving the environmental quality of residents, and promoting the health of residents .In a word, measuring the type and quality of green space from the perspective of mediators is more conducive to exerting its benefits, thus promoting the mental health of residents. The practice of urban green space-related policies has been widely carried out across the world, and the psychological health benefits of residents are relatively significant. There is an urgent need to fully consider mediators to distinguish types of urban green space and measure green space quality and to study the positive effects of urban green space on residents’ mental health from the perspective of type and quality of urban green space. The purpose is to ensure the comparability of related researches on urban green space.Based on the measurement methods of mental health, empirical research was conducted on urban green space and the mental health of residents. With the help of specific measurement tools, the relationship between these psychological factors and environmental factor, outdoor activity, and social cohesion was further analyzed. Such methods are more diverse, focusing on the use of observation methods and interviews, while preferences and other behavioral social survey methods are based on scales. The measures of mental health in the selected literature are shown in Environmental factors as a mediator mainly affect mental health from the level of recovery, such as improving air quality, reducing ambient noise, and increasing visual stimulation. Therefore, the mental health under this mediator is mainly measured by restoration. It refers to the relief of stress and psychological relaxation. The restorative outcome scale (ROS) is used to assess human recovery of forest environments . The perOutdoor sports often bring about interaction between residents and people or the environment. The effects on mental health tend to be direct in mood. Positive emotions are part of mental health. The most commonly used are the Center for Epidemiological Studies Depression Scale (CES-D), the Profile of Mood States (POMS), and the Depression Anxiety Stress Scale-21 (DASS-21). The Positive and Negative Affect Schedule (PANAS) is also used to evaluate the positive and negative feelings of participants, and it has already been applied in many studies ,49,82.Social cohesion refers more to the residents’ subjective feelings about their living environment, so it often corresponds directly to the mental state scale. Some studies use the General Health Questionnaire (GHQ) ,111 to mOf course, this correspondence is not absolute. Some studies measure the impact of environmental factors and outdoor activities on mental health directly through mood. By recording the electroencephalography (EEG) signals of participants, Olszewska-Guizzo et al.  found thGreen space plays an increasingly significant role in residents’ life. More and more urban policy makers are including green space in urban planning and considering the coordination between green space and building in order to maximize the health benefits of green space. The planning and design of urban green infrastructure is the mainstream policy practice in urban green space construction. According to David Ross, an American Landscape Architecture Planning scholar, green infrastructure is an internally connected green space ecological network which is formed by combining the natural environment and artificial environment . This neThe mediating effect of green spaces’ environmental factors is the direct harm reduction and the physical gain to residents. Green space, such as the plants in the streets and office buildings, works by reducing air pollution and environmental noise and increasing green visual stimulation. Therefore, urban planning needs to ensure that there is enough green space between buildings and roads.To be specific, the construction of green and gray infrastructure should be coordinated. Grey infrastructure is the traditional municipal public infrastructure which has a single function, such as roads and bridges. Green infrastructure is a green space system, which should be connected with grey infrastructure. The accelerated development of urbanization leads to green infrastructure not being able to play its role in promoting health independently, which creates the necessity for networked support of gray infrastructure. It is necessary for densely urbanized regions to apply a more environment and ecosystem friendly planning approach and system . Urban aThe mediating effect of outdoor activities is that green space strengthens residents’ behavior. In order to make it work, we must first ensure that residents have green space, want to have green space, and have access to green space. This involves three aspects of availability, security, and accessibility. In addition to the guarantee of the quantity of green space, we should also consider the improvement of quality. It is important to add facilities that enhance the quality of sports or social activities.On the one hand, full consideration should be given to special groups, such as the disabled, the elderly, children, etc. Combined with heterogeneity, the different demands for green space should be fully considered from the perspective of all age groups, so that the green space can be reasonably allocated to all residents. For example, for the elderly and children, special activity areas can be set up, and corresponding entertainment facilities, fitness facilities, and rest seats can be added. For the disabled, sloping passageways can be designed to ensure green space accessibility. In addition, the open vision of green space should be fully considered in order to improve the sense of security of residents’ while they undertake activities in the green space.On the other hand, regarding the quality of the green space, we should not only consider whether the green rate is up to the standard, but also fully consider the accessibility and convenience in terms of actual use. First of all, community green space is the most frequently used activity space for community residents. In order to ensure its use, it is necessary to make public green space more attractive to residents as much as possible. Therefore, the design of community public green space should be more beautiful and interesting in order to increase the frequency of residents using it. Secondly, the greening design should fully integrate the spatial structure of the community and the behavioral habits of residents in order to ensure the availability and accessibility of the green space and, thus, minimize any negative effects on the convenience of residents’ life.The mediating effect of community cohesion is to increase residents’ sense of belonging and satisfaction with the surrounding environment. Policy makers need to organically combine the urban production environment and green space. Meanwhile, as a kind of environmental resource, monitoring and maintenance of green space also needs to be considered.The connection between green infrastructure and the local environment is deep. In the planning and design of green space, we should pay attention to the regional concept, preserve the natural landscape, and reduce the damage to the original ecological landscape. The transition between the buildings and the surrounding green space should be considered at the same time. For example, with the help of the tributaries of urban rivers, the continuity principle was adopted to build an ecological park in Louisville, Kentucky, USA. The project of Queen’s Square Park in the United States involved the use of a large number of green plants to green the dangerous intersection, which is integrated with the characteristics of the natural area. The project not only ensured road safety, but also improved the health and well-being of residents. In Maryland, USA, rain gardens provided extended green spaces for urban residents. Because of the low costs of construction and maintenance and high health benefits, these gardens were widely adopted around the world.In addition, community green space should be constantly transformed and maintained. Managers should combine practical experience and pay continuous attention to the green space of the community. According to the change in residents’ demands, the green space and related facilities should be restored and updated regularly. On the basis of reasonable planning, the management of community green space should be strengthened. For example, private occupation of public green space and malicious damage to the green environment by some residents should be prevented.At present, fruitful achievements have been made in the research on green space and mental health. Through the above review, the path of the impact of green space on residents’ mental health is fully discussed and analyzed. We summarize the current mediators and identify the impact paths of different mediators. Furthermore, this article specifically analyzes the heterogeneous effects of the above-mentioned influences, considering not only different types of green spaces, but also residents with different socioeconomic characteristics. From the perspective of direct contact, the environmental factor is considered as the main mediator, which includes improving air quality, absorbing noise, and visual stimulation. It mainly works through the neighborhood green space. It can be helpful for adults to reduce stress and improve sleep quality, which in turn improves their mental health. From the perspective of passive attraction, this can be divided into outdoor activity and social cohesion. Parks and urban forests provide venues for residents to engage in outdoor activities and communication, and make them mentally healthier. Children and the elderly often benefit from this impact pathway. A greener environment can improve residents’ sense of social satisfaction and happiness in life. These results are more consistent in dense cities. These findings should make an important contribution to the field of causality analysis between urban green space and residents’ mental health, as well as demand-oriented urban green space planning and management.In addition, there are limitations to the study. When analyzing the heterogeneity, we cannot guarantee that every mediator contains all the dimensions of heterogeneity due to the limited search results. In future studies on green space, it will be necessary to fully consider the impact of heterogeneity, including not only the individual, but also the social characteristics of samples, and to adopt a relatively uniform standard to measure green space. Detailed and comprehensive research should also be carried out on the mechanism between green space and mental health, including a study of the mixed effects of the mediators.


A large number of studies, including single case and case series studies, have shown that patients with different types of frontotemporal dementia (FTD) are characterized by the emergence of artistic abilities. This led to the hypothesis of enhanced creative thinking skills as a function of these pathological conditions. However, in the last years, it has been argued that these brain pathologies lead only to an augmented “drive to produce” rather than to the emergence of creativity. Moreover, only a few studies analyzed specific creative skills, such as divergent thinking (DT), by standardized tests. This Mini-Review aimed to examine the extent to which DT abilities are preserved in patients affected by FTD. Results showed that DT abilities  are altered in different ways according to the specific anatomical and functional changes associated with the diverse forms of FTD. On the one hand, patients affected by the behavioral form of FTD can produce many ideas because of unimpaired access to memory stores , but are not able to recombine flexibly the information to produce original ideas because of damages in the pre-frontal cortex. On the other hand, patients affected by the semantic variant are impaired also in terms of fluency because of the degradation of their semantic memory store. Potential implications, limitations, and future research directions are discussed. Frontotemporal lobar degeneration (FTLD) represents a group of neurodegenerative brain diseases characterized by a fairly focal onset of degeneration of the frontal and/or anterior temporal lobes. One of the clinical entities associated with FTLD is frontotemporal dementia (FTD), a neurodegenerative disorder that expresses itself with different clinical presentations  can be expressed by some exceptional cases of patients affected by neurodegenerative diseases  associated with DT through in-depth assessments of FTD patients could also have some important practical implications. In particular, this could help researchers to implement cognitive stimulation programs through DT tasks to improve cognitive functioning in both healthy older adults (see, e.g., Fusi et al., GF wrote the article. MC, LC, and MP were involved in the literature search, in the discussion of theoretical and methodological issues, and supported the writing procedures. AA, LR, and MR supervised all the writing process and the hypotheses advanced. All authors contributed to the article and approved the submitted version.The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.


Controlling bovine tuberculosis (bTB) disease in cattle farms in England is seen as a challenge for farmers, animal health, environment and policy-makers. The difficulty in diagnosis and controlling bTB comes from a variety of factors: the lack of an accurate diagnostic test which is higher in specificity than the currently available skin test; isolation periods for purchased cattle; and the density of active badgers, especially in high-risk areas. In this paper, to enable the complex evaluation of bTB disease, a dynamic Bayesian network (DBN) is designed with the help of domain experts and available historical data. A significant advantage of this approach is that it represents bTB as a dynamic process that evolves periodically, capturing the actual experience of testing and infection over time. Moreover, the model demonstrates the influence of particular risk factors upon the risk of bTB breakdown in cattle farms. The bTB is an incessant bacterial disease of cattle caused by M. bovis (Mycobacterium bovis) infection. The disease is a persistent economic and veterinary problem in cattle herds in the UK despite the policy of cattle herd testing and slaughter . bTB affIn England, the majority of bTB cases are reported in southwestern England and Wales which are considered to be high-risk areas. To control and annihilate bTB in these areas, a control program has been in place since 1947 consisting primarily of routine and targeted surveillance of cattle herds, culling of positively tested for bTB animals and movement restrictions on infected herds [Surveillance is based on the administration of a single intradermal comparative cervical tuberculin test (skin test) involving two separate injections of sterile purified mixtures of Mycobacterium avium and M. bovis antigens (tuberculins) in the deep layer of the skin of the neck, followed by examination of the skin for localised allergic reactions after 72 h ,6.For negative or non-reactor skin test results, the reaction to the M. bovis tuberculin injection is reported less than or equal to that to the M. avium tuberculin injection, while a positive skin test result (known as a reactor) is declared when the reaction to M. bovis tuberculin exceeds that of M. avium tuberculin by more than 4 mm, according to the standard international interpretation . In all Animals with a positive or two inconclusive skin test results in sequence are immediately slaughtered and tested at the slaughterhouse for noticeable lesions of bTB in their organs. When the result of the postmortem examination is positive because of the presence of lesions, positive M. bovis culture, or both, then the herd’s status from suspended downgrades to withdrawn . InfectiAlthough lots of efforts and investments have been made to prevent NHI and herd breakdowns in England, they constantly increased between the mid-1980s and 2012 . HoweverThe bTB epidemiology, in England, is complicated, and it is difficult to define the relationship between evidence, uncertainty and risks ,10. MoreDespite the non-husbandry risk factors which are related to the environmental factors such as badger sett density and habitat composition, the husbandry and farm management-related risk factors also contribute to the heightened risk of herd breakdown ,15. The Thus, the identification of variables which are statistically significantly affecting bTB status in England is essential and important to control bTB . For insIn the last few years, Bayesian Network (BN) has rapidly been adopted across different areas of science ,26,27,28For example, Smith et al.  exploredMoreover, most of the methods in policy and evaluation studies are usually based on statistical tests which are performed on functional or structural analysis across or within subjects. Such methods are definite in nature and they have challenges when there is a lack of good-quality data. In this paper, we propose a DBN approach combining the data and expert knowledge and other reliable information to evaluate the consequences of the existing bTB prevention mechanisms in the England’s cattle farms. Our application of such procedure describes nonlinear multivariate relationships between the England’s cattle farms husbandry structure, Defra’s test and slaughtering policy and other measurements of interest.This manuscript is organised as follows. In general, probabilistic models are more complicated than deterministic ones. It means that they need more knowledge and attention to the structure and sensible choices of data or input distributions are very important. When the available data are not appropriate, more challenges will arise in terms of uncertainty . A BN isDeeper understanding of dependencies and relationships between variables can be achieved through more investigation in Bayesian statistics .To construct a BN, three steps are required. First, determine the Directed Acyclic Graph . Next, aStatic BN (SBN): All variables are discrete.Dynamic BN (DBN): It consists of a limited number of BNs, each of which corresponds to a particular time interval. The connections between adjacent BNs represent how the states of the system evolve over time. In DBN is a powerful algorithm that can be applied to various applications with the temporal dependencies in sequential data. Moreover, inference and learning with DBNs including temporal structural learning and prediction have been used routinely in several fields where the filtering and smoothing are performed in Kalman filter models .Hybrid dynamic BN (HDBN): Random variables can be discrete such as someone’s gender or continuous such as someone’s age. A BN which contains both discrete and continuous variables is called an HDBN.There are different types of BNs:Unlike Naïve BN, which only considers conditionally dependent relationships among the random variables by a direct link between those variables, BNs can capture both the conditionally dependent relationships (direct cause and effect relationships) and conditionally independent relationships (the variables which are not connected by a direct link). The probability of variables which are conditionally dependent are obtained where the probability of their parents are known and the probability of the conditionally independent variables are independent of each other when another “separating” variable is known .We use a DBN to identify risk factors in England’s cattle farms in spreading bTB . We explFailed test: At least one animal tests positive during any herd-level test .Breakdown: A failed test occurs on an officially bTB free herd, not currently subject to movement restriction.bTB is considered an ongoing problem in cattle farms in England despite current policy including cattle herd testing and slaughter. Besides badgers acting as an important wildlife reservoir, cattle husbandry and farm management practices play a role in heightening the risk of spreading the disease. The following definitions are essential for this report:DBNs are used generally to explore and display causal relationships, between key factors and outcomes in a complex system, in a straightforward and understandable manner. Moreover, DBNs can also be used to calculate the effectiveness of interventions, such as alternative management decisions or policies where the associated uncertainties with these causal relationships can also be explored at the same time.Farm food: There are different types of food and storage methods and storage periods. However, routes of transmission from badgers that are attracted to the cattle food are considered important. Studies show that farms that feed maize silage, grass silage or molasses to cattle are more attractive to badgers. In addition, studies show that 100% of farms with persistent breakdown fed grass silage to cattle, whereas feeding hay was effective against persistent breakdown.Badgers: Active badger sett density showed significant associations with herd incidents. In our model, active sett density was entered as a categorical variable as zero, medium (≤0–3 setts/km) or high (>3 setts/km). Based on available studies, a relatively high active sett density is associated with persistent breakdown, which shows that badgers play a part in spreading the disease, especially in hotspot areas.Age of the cattle: Based on available information, age of purchased animals was categorised as:–Calves: Stores or replacement breeding animal less than 12 months–Yearlings: Stores or replacement breeding animal between 12 and 24 months–Cows: Female breeding animals over 24 monthsBased on available information, farms with more cattle older than 24 months are more likely to find bTB becomes persistent compared with farms with younger cattle. In other words, bTB prevalence tends to increase with age.Stock density: Based on available data, farms with relatively low stocking densities (≤3 head of cattle/ha) were more protected from breakdown versus the farms with high densities (>3 head of cattle/ha).Purchased cattle: In control farms, the main reason of increasing the risk of infection is cattle movement onto farms from market or other farms. Since purchased cattle is responsible for the majority of newly infected farms, buying animals accelerates bTB incidents. Studies demonstrate that farms with the purchase of larger number of cattle (>50 heads compared to ≤50 heads) are at more risk of breakdown (short period is more likely than persistent).Manure storage: Based on available literature, manure storage for over six months is one of the significant factors for breakdown in the farms.There are several risk factors related to the bTB disease: (i) environmental biosecurity such as farm waste and management and foodstuff storage; (ii) herd characteristics such as herd size, farm enterprise and animal movements; (iii) contact between cattle and badgers; and (iv) animal management including stocking densities, isolation, feeding and grazing regimes. Among all of these key factors, the following are considered in our model:Data are available publicly as secondary data in Quarterly TB in cattle in Great Britain statistical notice (data to March 2018). Data are available for Great Britain, England, high-risk, edge-risk and low-risk areas of England, Wales and Scotland. Data are mostly presented as monthly data or yearly data from 1996 to 2016. Some of the data are available at herd level such as total test on herds, NHI, not officially TB-free herds and herds under movement restriction. Some data are presented at a number of animal levels such as total number of slaughtered animals, total number of animals reported to the slaughter house and the number of confirmed slaughtered. Our model is a herd-level model that does not distinguish between individual cattle on a farm, thus we include herd-level data but not individual cattle data.Our DBN model allows analysts to make probability judgments consistent with direction of causality . Since DIn our model, nodes are elicited from the literature and available documents regarding the bTB policy published by the government. The list of all nodes and their states are presented in The prior probability of an uncertain quantity is the probability distribution that would express one’s belief about this quantity before some evidence is taken into account . In our This project was supported by the Centre for the Evaluation of Complexity Across the Nexus (CECAN), which comprises social scientists, policy makers, policy analysts and other academic researchers who have collaborated in expert elicitation. In this research, the main source of information was retrieved from a comprehensive literature review, which was used to train the BN structure and estimate the conditional probabilities. The learned BN was then revised by the domain experts, including experts in the fields of sociology and complex systems to avoid the cognitive factors and biases that could affect the aggregated elicited judgments .Dynamic networks are merely static networks straddling time periods or time slices. The future state of some nodes can be influenced by their prior state (feedback nodes). Some nodes can also effect other nodes in other time slices. The prior probabilities of these nodes are also assigned before evidence is added to the model. In The final step in the model is to run the DBN at monthly intervals. For each time slice, any new information is added to the model to update current priors. All updated probabilities from the first to the last time slice are presented as time dependent figures.A combination of the data-driven and knowledge-based approaches was utilised in our study for the construction and evaluation of the proposed DBN model (the DBN structure and its prior probabilities) to identify the main risk factors and the severity rate of NHI of cattle farms in high-risk areas of England. Then, the prior probability distribution of the variables is updated when a new observation of the interested variables is made to show different scenarios. The updated or posterior distributions of all the other variables are then computed by using the inference algorithms. The emerging risk factors will be detected using the bottom-up inference in the proposed DBN .These probabilities explore the relationships between different risk factors and their roles individually in spreading bTB, particularly to identify those which have the highest impact. We then use the top-to-bottom inference to examine the effect of current management practices in cattle farms upon new herd bTB incidents and provide a measure of the severity of the new herd bTB incident or the breakdowns as a set of probabilities.Outcomes are then given to show the applicability of this method to diagnose the high-impact risk-causes in the England’s cattle farms with the current Defra policy on bTB.After the DBN graph was constructed and the prior probabilities were elicited from the available literature and data, the DBN updated the prior probabilities every time new information (evidence) was added to the network.Each node has upper, estimated and lower bounds. Thus, the proposed DBN was run several times to compute the posterior probabilities of the interested variables for all three bounds of each node and different node combinations (different scenarios). This computation allows us to handle the uncertainty due to the lack of information in selecting our priors see .The DBN ran 66 times for each risk area  to define the sensitivity sets for each area. The sensitivity sets are the risk factors which have the greatest influence on NHI.Finally, the DBN ran for different combinations of sensitivity sets to find out which risk factors may have grater effect if they are combined together.The following steps were performed in order to achieve the presented results:The following figures demonstrate the effectiveness of our DBN to identify the effect of each risk factor and their combinations on NHI.The probability of silage being the cause of OTUC decreased.The probability of the purchased cattle over 50 heads being the cause of OTUC decreased.Stock density and manure storage over six months had a greater chance to be the causes of OUTC.Moreover, Silage had a greater chance to be the cause of OTS (in comparison with OTW and OTUC).The effect of purchased cattle over 50 heads was decreasing, although its effect was greater than the age of cattle.Stock density and manure storage had the greater chance to be the causes of OTS with some level of uncertainty by the end of the simulation.At this point, we used the bottom-up inference outcomes to identify our sensitivity set. As shown in Moreover, −2, the effect of all these factors would still be less than the combination of the silage fed and badgers.However, Based on the presented simulations in this paper, the most important risk factors for NHI in the high-risk areas of England are identified as age, badger densities and silage. Similarly, the most important risk factors for the edge-risk areas are silage and stock densities and for the low-risk areas is the purchased cattle. Thus, a better surveillance of wildlife, especially badgers, in the high-risk areas of England is required to reduce the risk for transmission to cattle, in England and also the whole GB since wildlife epidemio-surveillance is poorly implemented.The main aim of the current policy is to slow down and stop the spread of bTB into new areas or low-risk areas and achieve a sustained and steady reduction in the disease in high-risk areas. However, bTB remains of great concern in England since policy interventions are not improving the incidence of the disease. Key known risk factors at herd and region levels are reviewed in this paper. Some of the risk factors may have a major impact in some areas of the England but may not be significant elsewhere, due to the different control programs applied and specific epidemiological situations. For illustration, the routine surveillance test intervals in high-risk areas are every year, in edge-risk areas are every two years and in low-risk areas are repeated every four years. Moreover, the wildlife which plays a great role in the spreading of the bTB infection is also dissimilar in each risk areas. However, in all risk areas, farm management was identified as a risk factor but also purchase, wildlife and environment.The sensitivity sets for different risk areas are different.The effect of one risk factor when it is integrated with other risk factors may change significantly. As the results show, in the high-risk areas, the age of cattle is not very concerning on its own; however, when it is associated with the types of feeding , their joint probability increases the chance of NHI significantly. Furthermore, since the trade of live cattle between different areas is constantly increasing, the risk of moving infected cattle from an OTW region into an OTF area exists. The risk related to the movement of live animals must be emphasised mostly in the low-risk areas. It is thus necessary to remind the people involved in cattle trading of the importance of testing at purchase, especially if the animal introduced in an OTF area comes from a non-OTF-region. Besides, pre-movement testing is important. Thus, limiting movement of cattle onto the farm or purchasing from high-risk areas, along with follow up tests and longer isolation periods (more than 30 days) could reduce the risk of breakdowns. Based on available data, there are five categories from which farmers decide to isolate their newly purchased animals or milking equipment:–7 day–8–15 days–16–21 days–22–30 days–More than 30 daysMost farmers adopt 15 days of isolation because of lack of space. The level of infection in the environment is predicted to decay with a half-life of 34 days . Although decay is rapid, infection may arise months after infectious cattle are removed. Farmers who choose a longer period of isolation have less breakdown incidents.The results are presented graphically and are easy to understand which is one of the strengths of BN.The presented results are not based on one dataset analysis. In fact, the model considered monthly data from 2008 to 2015. Therefore, the model is dynamic, rather than static and, anytime that new evidence is added to the model, the results are updated accordingly. Hence, it is possible to track the effect of risk factors on NHI for the whole period of study.Our proposed method is able to identify, not only the probability of the emerging factors which increase the risk of NHI in different risk areas in England individually, but also the strength of the different combinations of those risk factors. In this study, for each risk area, the risk factors with the highest probabilities on NHI were identified as the sensitivity set. Then, the different combination of those risk factors were studied. The results demonstrate that:Moreover, improved farm biosecurity is also required in order to reduce bTB incidence at the herd level: e.g., secure feed storage, secure feeding habits and correct hygiene, as well as a grazing system minimising the cattle/wildlife interface. Thus, for our further work, we should target several points or particular risk factors discussed in this paper but also focus on the role of the environment on bTB transmission as well as determining the exact role of badgers potentially implicated in the transmission of bTB. Further studies are needed to better understand the transmission mechanisms between probable wild maintenance hosts and cattle. The role of environmental persistence of M. bovis needs further investigation in order to assess its role in the epidemiology and transmission of bTB to cattle. Genetic resistance in cattle and wildlife species are also areas that could be targeted for the further research.Initial beliefs about the values of each variable (prior probabilities) can be updated in the face of new evidence via Bayes’ theorem.A BN can perform three types of inference: deductive , diagnostic  and intercausal . This is useful since the same BNs can be used for both the assessment and the evaluation.Analysts can make probability judgments consistent with the direction of causality.Evidence can be entered into the model, and the effect on the other variables (nodes) can be observed .BNs are able to work with data of different types and sources: they handle a mix of subjective and objective data and, hence, supplement traditional experimental and statistical methods.BNs, in addition to their simple causal graphical structure, have some other appealing properties:Various assumptions are made in the DBN model which are a consequence of either lack of complete knowledge on how bTB infection really works and/or poor data . The assumptions made are listed below.Assumption 1.Risk factors are orthogonal, meaning there are no interactions between risk factors.Assumption 2.The effect of the risk factors on infection rates is additive.Assumption 3.The model is a reduced order model, meaning it is parsimonious and that variables were removed.Assumption 4.Spatial factors are not relevant . If we want to track the movement of badgers, we need empirical data about their patterns of movement and we would use a different modelling method.Assumption 5.The three risk areas are discrete/separate, i.e., there are no ’grey’ areas in between. However, in the real world, there may or may not be real borders  between the areas.The model could be developed to address some of the above assumptions. This is likely to result in longer computational times and more resources to collect data and theories on infection risk mechanisms. The current model represents a satisfying position that replicates bTB infection experience and evaluates extant policy.® environment (versions In this research, the probabilistic data-driven models were implemented and simulated in BNT version The model presented in this paper is able to replicate corresponding new herd incidents from 2008 to 2015. The model demonstrates the influence of particular risk factors upon the risk of breakdown in cattle farms. The model shows the importance of risk factors already considered under current management recommendations which are part of bTB policy aimed at reducing the number of breakdowns and NHI in England’s cattle farms. Our results for high-risk and edge-risk areas suggest that biosecurity is a key risk factor that requires improved control. This is because the mechanisms of disease transmission from badgers to cattle are likely to involve cattle foodstuffs and/or environmental contamination from M. bovis in urine, faeces or sputum. Controls could involve consideration of the type and length of storage of food, as well as limiting contact between cattle and infected badgers sources by fencing, building maintenance and design. Regarding low-risk areas, acquired cattle are the source of the majority of breakdowns in herds, previously free of disease. Pre-movement testing, which was recently introduced to prevent the spread of bTB infection from purchased cattle, can reduce the risk of breakdowns in the low-risk areas.Finally, it is concluded that applying the DBN modelling approach decreases endogenous uncertainty, reduces investment and policy risks associated with cattle farms husbandry interventions within three risk areas of England and can act as a decision support tool for some of the private, public and community sector stakeholders operating in this field, especially key decision makers and policymakers.


Laryngoscopes are widely used in the clinical diagnosis of laryngeal lesions, but such diagnosis relies heavily on the physician's subjective experience. The purpose of this study was to develop a computer-aided diagnostic system for the detection of laryngeal lesions based on objective criteria. This study used the distinct features of the image contour to find the clearest image in the laryngoscopic video. First to reduce the illumination problem caused by the laryngoscope lens, which could not fix the position of the light source, this study proposed image compensation to provide the image with a consistent brightness range for better performance. Second, we also proposed a method to automatically screen clear images from laryngoscopic film. Third, we used ACM to segment automatically them based on structural features of the pharynx and larynx, using hue and geometric analysis in the vocal cords and other zones. Finally, the support vector machine was used to classify laryngeal lesions based on a decision tree. This study evaluated the performance of the proposed system by assessing the laryngeal images of 284 patients. The accuracy of the detection for vocal cord polyps, cysts, leukoplakia, tumors, and healthy vocal cords were 93.15%, 95.16%, 100%, 96.42%, and 100%, respectively. The cross-validation accuracy for the five classes were 93.1%, 94.95%, 99.4%, 96.01% and 100%, respectively, and the average test accuracy for the laryngeal lesions was 93.33%. Our results showed that it was feasible to take the hue and geometric features of the larynx as signs to identify laryngeal lesions and that they could effectively assist physicians in diagnosing laryngeal lesions. Laryngoscopic examination may result in disagreement in diagnosis among providers2. To assist physicians in the correct diagnosis and appropriate treatment for patients, computer analysis of endoscopic images has been used in many studies to objectively diagnose laryngeal lesions. Menon et al.3 proposed using the glottic contour as the edge of the vocal cords to carry out optical flow analysis for statistical motion vectors on the left and right vocal cords respectively, and proved that the normal left and right vocal cords had similar motion vector statistics, which could effectively distinguish vocal cord paralysis and normal vocal cords. However, the analysis of vocal cord-based patterns without assessing other information can be applied only to neurogenic or functional voice disorders. Mendez et al.4 used image segmentation technique i.e. active contour method (ACM) to detect the morphological pathologies by using color images in RGB format, and then used connected component analysis to select the region of interest (ROI) as central component of foreground class for features measurement. The sizes of the polyps or cysts and the glottal spaces and locations were collected into a database to provide complete pathology information that would help doctors diagnose vocal cord disease. However, the literature does not mention how to correct the lens position, and the distance between the endoscopic lens and vocal cords is not consistent, thereby making it difficult to objectively compare the features of polyps and cysts. Verikas et al.5 used a computer-aided diagnosis system to automatically analyze vocal cord images for disease classification. Based on texture, hue, and geometric features, the pattern classifier was applied to categorize images into healthy, diffuse corditis, and nodules, with an accuracy of 87%. However, this system still has its limits in detecting multiple lesions simultaneously.Structural abnormalities in the larynx or inconsistencies in neuromuscular coordination may cause an abnormal sound. About 90% of such abnormal sounds are caused by structural voice disorderPrevious literatures have used static images for an automated analysis to categorize diseases of vocal cords. In this study, for the first time we take image automatically from endoscope video for the analysis. The clearest image was selected automatically for segmentation. By using hue and geometric analysis for the laryngeal region, automatic pathological change detection and classification were proposed for healthy vocal cords and laryngeal lesions, including vocal cord polyps, cysts, leukoplakia, and tumors. Image processing technology was applied to quantify the changes in the larynx caused by specific lesions. So, the doctor could support his diagnosis with our system results.Laryngoscopic images were provided by a tertiary referral center. Our clinical database included a Total of 284 patients. Among them, 38 samples of healthy vocal cords and 246 samples of vocal cord lesions were collected, including 76 vocal polyps, 62 vocal cysts, 52 vocal leukoplakia and 56 vocal tumors. Where training samples were 200, validation samples were 74 and testing samples were 10. In the validation samples healthy, vocal cord polyps, cysts, leukoplakia, and tumors were 14, 18, 16, 12 and 14, respectively. Patients were excluded from this study if they had anemia, smoking habit, asthma, and those who had previously received radiotherapy or surgery in head and neck region.8. First, adaptive histogram equalization (AHE) with good local contrast was used to divide the images into different grid regions, which limited the histogram to a small region so as to perform operations via different regions. The grayscale values of the upper and lower limits were expanded from 0 to 255 by contrast enhancement of the maximum and minimum gray values of the region; if the region was large, the contrast would be reduced, and vice versa. Later, CLAHE was used to effectively solve the additional problem of noise amplification due to the contrast adjustment. To smooth and remove the noise resulting from swaying, swallowing, or saliva reflecting light from laryngoscopic images, Gauss smoothing was used to remove noise by calculating weighted average of each pixel’s neighborhood where neighborhood pixel’s value becomes or lower closer to the central pixel, improve the areas where the gray scale values changed dramatically, and reduce the segmentation errors in subsequent steps.Problems like uneven brightness distribution and low contrast in laryngoscopic images could lead to inaccurate segmentation and inconsistent results. In this study, contrast-limited adaptive histogram equalization (CLAHE) was used to reduce these problems9, which is less computational, was used to overcome the disadvantage of the traditional Otsu method. By calculating the average grayscale value of the entire image (icpm), images above the average grayscale value were regarded as the foreground (icpf), and vice versa as the background (icpb). The search area was divided into 0 ~ icpb, icpb ~ icpm, icpm ~ icpf and icpf ~ 255. The inter-group variances of the four regions were calculated separately, and the largest inter-group variance was regarded as the possible optimal threshold. For the subsequent vocal cord segmentation, we used the ACM10 as the image segmentation method. The vocal cords and the surrounding tissue boundaries were blurred. The algorithm defined the initial parameterized curve, and made the curve move to the target boundary by minimizing the energy function, i.e. the reconciliation of the internal and external forces of the image. The initial parameterized curve was mutually pulled by the internal and external forces of the energy function, and finally the balance of the forces, that is, the minimized energy function, was achieved, and the final contour boundary was obtained. Details on CLAHE, the fast Otsu method and ACM processing, and the parameters of the Gaussian template are provided in the The grayscale value was used to automatically segment the glottis. In order to highlight the difference in image brightness and retain features details of ROI pixel’s during segmentation, and avoid the result of over-segmentation, the fast Otsu method12]. Its functional design is to maximize boundaries between the classes and its main purpose is to maximize the hyper-plane. The SVM solves the quadratic problems repeatedly during the training. After maximize the boundaries and the hyper-plane, it can effectively classify classes. The purpose of this study was to identify healthy vocal folds and the different types of laryngeal lesions, including vocal cord polyps, cysts, leukoplakia, and tumors. The decision tree method was used to extend the SVM from binary classification to multivariate classification required for this study, and the SVM was used to classify data points with unclear decision boundaries. The decision tree was implemented to perform the non-linearly separable data points to quickly classify laryngeal lesions, which can reduce the training time of the test set.In this study, the extracted histogram of the segmented image were classified using a support vector machine (SVM), which was able to find the optimal demarcation hyperplane in limited samples[The clearest images from the dynamic throat images were searched automatically for following segmentation and feature analysis. The algorithm performs the noise identification and used the Peak Signal to Noise Ratio (PSNR) to calculate error between each frame in the video. Therefore, if error is very low than it can automatically accept the frame or the clearest image. The automatic segmentation area included the glottis, left vocal cords and right vocal cords. The hue and geometric features of each part were analyzed. The detailed process is as shown in supplementary Fig. (1). Centric position: The vocal cords can be observed only when the glottis is at the center of the image, so the areas with high and low glottis are excluded. In this study, the Y axis in the centroid position and if the pixel values are below 50 or above 240 was filtered out.(2). Aspect ratio: The shape of the glottis is an inverted triangle. The aspect ratio method can be used to filter out the area towards the east–west direction. In this study, the area with an aspect ratio greater than 0.85 was filtered out.(3). Area size: The area refers to the sum of the pixels in the area. This study excluded blocks with an area of less than 900 pixels. The purpose was to remove the excess area other than the glottis.For subsequent image segmentation, it was necessary to select laryngeal images with a consistent standard from the laryngeal endoscopic video. The number of glottic block pixels of all laryngeal images with glottis was counted in this study. Finally, the largest glottal block in each image was retained as the laryngeal image for subsequent segmentation, because the vocal cord image could be clearly identified, as shown in Fig. x, y is the image coordinate position, I s the grayscale value of the image after translation, O is the grayscale value of the current image, S is the set value , and Om is the average value of the current image.When taking pictures of the larynx, the physiological reaction of the patient often made the laryngoscopic images unable to be fixed at the shooting position, resulting in inconsistent illumination. For example, when the lens position was far away, the illumination was darker, and vice versa. To ensure the images have similar color ranges, this study used the histogram translation method to compensate for the difference in the current average value for each pixel, making each laryngeal area of the image reach a similar gray scale value. The gray scale characteristics of each region of the image were clearly demarcated, so as to make sure the subsequent image segmentation and feature analysis had better results and credibility. Figure The glottic area was segmented in the previous step, and then the left and right vocal cords were segmented. The segmented areas were removed from the original image to avoid subsequent segmentation errors. In this study, the vocal cords were segmented and its edge image was extracted. The glottis was transected along the glottic centroid, and then the lowest point of the glottis was searched to vertically cut the lowest point. The image left was the seed image of the vocal cords, as shown in supplementary Fig. Since the size of the vocal cord in the image varies greatly because of the lens distance, with close distances producing a magnified effect, the number of iterations using the ACM cannot be segmented by fixed values. To solve this problem, this study proposed an adaptive way to determine the number of iterations, that is, the gap between the vocal cords and pseudo-vocal cords can make the growth range of the active contour method relax gradually, so it can be used as the basis to distinguish the two. Therefore, for each iteration, the entropy of the growing range was calculated and subtracted from the entropy of the growing range in the previous iteration to find the iteration number with minimum entropy difference, which gave the optimal number of iterations of the vocal cords. In this study, the minimum number of iterations given by the ACM was 13 and the maximum was 41. The entropy of the growth range of each iteration was calculated, and the difference in entropies between iterations was calculated to find the minimum difference, as shown in Fig. Vocal cord polyps and cysts are convex objects on the vocal cords. Geometric features are used to identify whether the vocal cords have abnormal appearance. In this study, vocal cord geometry was used to identify whether there were convex objects on the vocal cords. A straight line was drawn along the vocal fold. If an object had vocal cord line beyond protruding lines, the geometric feature was judged as abnormal, as shown in Fig. The research protocol (No: 1-108-05-132) was reviewed and approved by the Institutional Review Board of the Tri-Service General Hospital, Taipei, Taiwan. All methods were performed in accordance with the relevant guidelines and regulations. All patients provided written informed consents prior to participation.Consent to publish has been obtained from all participants.In this study, 10 samples were randomly selected and taken as testing purposes as well as for comparing segmentation accuracy. Others were used to training and rest if they were used for training and validation. Manual segmentation by three physicians was compared with the method proposed in this study, and the comparison results are shown in Table By analyzing the features of the laryngeal image using an image processing technology, we were able to discriminate healthy vocal cords from laryngeal lesions, including vocal cord polyps, leukoplakia, cysts, and tumors, in real time. Firstly, training samples were used to construct the SVM model, and then the test samples were classified based on the trained SVM model, as shown in Fig. In this study, 10 hierarchical cross-validation verifications were used to evaluate the classification results. The overall recognition rate was 97.56%, and the identification results are shown in Table 5 used vocal cord images to analyze hue, texture, and geometric features, and classified the images, according to their patterns, into three categories: healthy, nodular, and diffuse. However, it is difficult to detect multiple lesions simultaneously by pattern classification. The current study used the decision tree-based support vector machine to detect vocal cord lesions, improving the classification speed of the SVM in the test phase. Based on the test results presented in Table To diagnose laryngeal diseases, physicians use laryngoscopy to evaluate the color, shape, geometry, irregularity, and roughness of vocal cords. The results of the examination are subjective and depend upon the physicians’ own experience. Verikas et al.15; however, the literature rarely mentions how laryngoscopic images standardize light sources and how to obtain clear images. Based on the characteristic of the clear image contour, to remove blurred images during image screening, it has been proposed to find the clearest laryngeal image in the laryngeal endoscopic film using the largest area of the glottis. In this study, the proposed image compensation method used the brightness translation method to give different laryngoscopic images a consistent brightness range, and pulled the average brightness of all samples to the grayscale value of 125, to ensure that the images would not be over-compensated. Despite the use of different endoscopes or light sources, effective and standard analysis can be performed according to this method, and the results were valid, consistent, and reproducible.A common problem in the field of laryngoscopic image recognition is that it is difficult to capture clear images, and the inability to fix the lens in position results in inconsistent illumination. Many studies have used video recording to analyze glottic motion in order to detect laryngeal lesions16. In this study, adaptive vocal cord segmentation and automatic searching of the optimal number of vocal cord iterations were proposed to solve the problems caused by the lens position and provide good segmentation accuracy. Segmentation will not be accurate if the sizes of the left and right vocal cords are inconsistent. This study verified that the error values of the left vocal cord, right vocal cord, and glottis segmentation were 4.35%, 5.73%, and 3.08%, respectively.In this study, the three parts used for the automatic segmentation of the larynx were the glottic space and the left and right vocal cords. The purpose of the glottis segmentation was to screen images, and for this segmentation to act as seed points for the vocal cord segmentation, therefore no follow-up feature analysis was needed for the glottic region. Because a laryngoscopic image cannot fix the shooting distance, the vocal cord size will vary with the lens distance. For example, a vocal cord image will be larger when the lens is closer but smaller when the lens is far, resulting in numerous iterations of the ACM without a fixed value. Some studies have demonstrated that color and texture analysis may be used to classify images in patients with reflux laryngitis. The brightness and saturation could also provide additional inputs for analysis, but these parameters are dependent on the lighting and positioning of the laryngoscopeIn this study, image processing technology was used to realize the hue and geometric quantitative data of laryngoscopic images, which differed from the subjective clinical observations of physicians and was helpful for the objective analysis of laryngeal lesions. The methods of laryngeal endoscopic film automatic screening of clear images and image compensation proposed in this study could automatically segment areas and analyze their hue and geometric features using laryngeal endoscopic film or a single laryngoscopic image. The proposed method takes only 0.1 s for performing the segmentation and classification of each CT image. In this study, laryngeal lesion identification had high classification accuracy.As it is difficult to take clear images by laryngoscopy, this study proposed searching for the clearest of the images taken by a laryngoscope, so as to solve the problem of difficulty of capturing clear images. To eliminate the light source problem caused by inconsistent lens positions of the laryngoscope, the histogram translation method was used to give all samples a consistent grayscale range and subsequently facilitate the part segmentation and feature analysis. Automatic laryngeal segmentation reduced the subjective difference found in manual segmentation and saved time. The proposed decision tree-based support vector machine algorithm had the advantages of fast classification and high classification accuracy. Using three distinctive features of laryngeal lesions with the SVM, the detection results for laryngeal lesions had high accuracy and could assist with relevant medical decision-making.Supplementary Information.


Existing railway line (ERL) construction safety has received significant attention during the past decades due to the high accident rate and the difficulty of progress development under the limited synthesis construction time schedule (SCTS). However, the previous literature is dominated by the construction safety of new railway lines, while research on construction safety of ERLs is limited. This paper analyzed the interactions and causal relationships between construction safety risk (CSR) and multiple factors and classified feedback loops. Hence, a system dynamics model was developed, and a series of tests were conducted to simulate the evolution of CSR under different group environments. The results indicated that (1) the CSR considering ERLs is significantly relevant to the implementation degree of SCTS. For situations where there are more delays and more schedule pressure, construction safety accidents tend to have a higher level. (2) Work efficiency is negatively related to construction safety accidents probability. The increase of work intensity could reduce schedule pressure in the short term but could increase construction safety risk in a long time. Applying both appropriate work efficiency and work intensity may achieve an acceptable result. This paper adds to the knowledge of construction safety risk management in terms of implementation and offers lessons and references for future construction safety management considering ERLs. With the great improvement in train speed and operating conditions, some new railway projects will inevitably be built close to the existing railway lines (ERLs) . Under tEach country has different management modes for the construction and maintenance of ERLs. In Japan, for the traffic density of Shinkansen during the daytime, except for necessary patrol inspection, all construction and maintenance work will be arranged within the SCTS of 12:00∼6:00 am, and all trains in the SCTS will be suspended. 12:00∼3:00 am is the time for construction or maintenance, and 3:00∼6:00 am is the time of inspection and acceptance . High-spSafety accidents during construction have been and continue to be a global problem , and theSystem dynamics is a system simulation method for analyzing production management and inventory management created by Jay Forrester and originally named Industrial Dynamics . It has The paper is organized as follows. Construction schedule: if the schedule of the railway construction was out of control, economic and social losses would be inevitable . It is iCost: this paper does not consider the total production cost during the construction. It only analyzes the safety investment within the boundary range of the model. Safety investments generally refer to funds spent on workplace injury prevention measures or activities that aim to protect workers' health and physical integrity , 41. In Safety subjects: the research on accident causation theory was pioneered by Heinrich , who anaThis study is based on unsafe behaviors and unsafe conditions in those two perspectives and only considers the construction progress, cost, and safety factors. Other relevant factors were not considered. This study identifies and refines the factors affecting construction safety and analyzes the relationship between the various factors to establish a causal model. It can visually show the influence of each factor on construction safety and make the relationship among each factor clear. This paper made use of a large amount of literature involving railway construction safety in recent years to summarize while forming a preliminary understanding of the identification of railway construction safety impact factors considering ERLs. Literature was based on Google Scholar, Elsevier, Web of Science, and Scopus, searched by entering keywords such as railway construction safety. Descriptions of construction safety influence factors considering ERLs in the literature were carefully extracted. According to the literature, the driving factors of construction safety can be categorized as follows:In order to understand the causal relationships of construction safety considering ERLs, the causal loop diagram is proposed, as shown in System dynamics modeling is a typical simulation method, focusing on the interaction between factors of complex systems . It predThe SCTS is the most important element in the construction of ERLs; the cost that construction enterprises pay special attention to is taken as the reference elements, while other elements such as quality and environment are not taken as the analysis object. According to the causal model of construction safety, safety accidents (SA), safety training (ST), safety investment (SI), safety supervision (SS), safety protection (SP), SCTS, schedule pressure, fatigue, unsafe conditions (UC), unsafe behaviors (UB), construction schedule (CS), and safety consciousness (SC) are defined as main systematic variables. This study focuses only on the relationships and interactions among the eleven variables.Based on the causal model of construction safety under the context of ERLs, we further defined the stock, flow, and feedback loop in the dynamic system. A stock is the value or level of the core variables accumulated in a dynamic system that reflects the system's changing state. According to the dynamic regulation system of construction safety considering ERLs, the system stocks are the UB level of people and the UC level of physical or mechanical. A flow is an activity that changes the stock. In this study, the flows are the decrease and increase of UB of people as well as the decrease and increase of UC of physical or mechanical. According to the causal model, the reduction and improvement of UB are related to dynamic variables such as ST, SC, SS, fatigue, and schedule pressure. The improvement and reduction of UC are associated with dynamic variables such as SP, working platform restriction, and invasion barriers of equipment and materials. In addition, the auxiliary variables, such as working platform restriction and invasion barriers of equipment and materials which reflect the UC, are closely related to the dynamic regulation of SA and need to be defined separately. In conclusion, the structure of the dynamics model of construction safety considering ERLs is shown in Construction safety considering ERLs may be interfered with by many factors. It is hard to enumerate and quantify all the interactions between variables. The rational assumptions of the system dynamics model allow itself to focus on the vital variables and their influence paths while ignoring the interference of other unimportant and small probability events, thereby significantly reducing the complexity of the algorithm. In order to standardize the study, five hypotheses are proposed: (1) Only consider the relationship between unsafe behaviors, unsafe conditions, and safety accidents in the construction stage under the background of ERLs. (2) It does not consider the total production cost during the construction and only analyzes the safety investment. (3) The variables, such as total quantity, remaining quantities planned, daily actual quantities completed, daily planned quantities completed, actual quantities completed, planned quantities completed, and construction schedule, should be uniformly treated according to the progress unit. For example, the total quantity is 12,000 m, and the constructions schedule is 50 m/d. (4) Delay is set as a random function, and it is assumed that the delay occurs randomly. (5) Different lines have different actual situations. The values of relevant variables in this paper are derived from the production data of the case. The assigned values of correlation coefficients are the average values obtained after statistical analysis after collecting actual data.Based on the above model assumptions, some mathematical functions are needed to describe the relationship between the key variables. Planned quantities completed (PQC) is the accumulation of daily planned quantities completed (DPQC); DPQC is related to planned quantities (PQ) and construction period (CP), which is an IF THEN ELSE function, and the specific description is shown in Similarly, the mathematical function of the calculation related to the actual quantities completed (AQC) can be obtained as formula , where dIn the above formula, WT is actually determined by planned SCTS and delay per day. The detailed description is shown in α1−5 is the influence weight of each variable.The increment of unsafe behaviors (UBI) is related to the following two variables: fatigue stands for the count of those unsafe behaviors caused by workers' fatigue, SP stands for the count of those unsafe behaviors caused by the schedule pressure of workers. Then, the decrement of unsafe behaviors (UBD) is related to ST, SC, and SS, which stand for the conversion of the safety investment to safety training, safety consciousness, and safety supervision, respectively. So the mathematical expression of the level of unsafe behaviors (UBL) is shown in formula , and theβ1−3 is the influence weight of each variable.Correspondingly, the increment of unsafe conditions (UCI) is related to the following three variables: MBI stands for the materials beyond the boundaries set for transportation safety, EBI stands for the equipment beyond the boundaries set for transportation safety, and WPR stands for the restrictions on work platforms set to ensure transportation safety. In this study, the decrement of unsafe conditions (UCD) is related to the SP, which refers to the conversion of the safety investment to safety protection. Therefore, the mathematical expression of the level of unsafe conditions (UCL) is shown in formula , and theγs is set as a constant standing for the base probability of an accident. Therefore, SA is calculated as follows:The unsafe behaviors and unsafe conditions actually determine safety accidents (SA), but the former two are necessary not sufficient conditions for the latter. All the previous formulas are the main mathematical functions in this study; more descriptions of variables and constants are shown in The validity tests of the model include mechanical error tests, dimensional consistency tests, and extreme condition tests. The model was built with Vensim in this study, which has passed the mechanical error tests, dimensional consistency tests, and extreme condition tests. The system model in this study included 35 variables and 10 constants. The main variables and function relationships in the model are described in Workers in the real world are faced with different situations every day and therefore make decisions accordingly. The conditions of the workplace vary day to day. As time goes on, the decision of workers and conditions of the workplace may be changed. To simulate this, the model sets some events , which are triggered every day. According to the functions we set above, unsafe behaviors were performed, and unsafe conditions were accumulated. Consequently, the time step of the model was set to 200 days to ensure that a long enough observation time was available to show all possible trends. The initial value of safety accidents probability was set as 0. Vensim 8.2 was used to simulate the system dynamics in this study. And the input parameters of a railway cutting project close to the ERLs were designed. The main input parameters are shown in The construction risk degree and grade standards of existing lines were set by referring to “Technical Regulations for Risk Management of Railway Construction Engineering and Relevant Regulations,” as shown in The influence of SCTS on safety accidents is essentially reflected in the work time. Although the time of SCTS is planned, which means the work time is fixed, delay on ERLs is inevitable, resulting in frequent changes of work time. To observe the correlation between SCTS and safety incidents, different assignments of delay are shown in From Using the three major components categorized by existing researches , 65–67 aThe result from the simulation demonstrates the effectiveness of safety investment on construction safety risk. From Improving work efficiency and increasing work intensity are common means to reduce schedule pressure. To observe the influence of those two factors on the whole model, 5 modes were set, respectively, as shown in 8According to the comparative analysis of Figures 8Compared with other modes, the safety accidents probability, schedule pressure, and loss of construction period of modes 3 and 5 all maintain low values, which can be a satisfactory solution. The safety accidents probability of mode 5 is relatively lower, and mode 3 performs better in terms of schedule pressure. However, the progress pressure of mode 4 in the first week is slightly lower than that of mode 3 and mode 5 and then increases significantly; the safety accidents probability and loss of construction period are also at a high level. It indicates that the strategy based on increasing work intensity can only accelerate the progress in the short term. When the fatigue degree of personnel reaches a certain value, the safety accidents probability will be increased, which will interfere with the progress and cause economic losses of accidents and rework.The results from the simulations demonstrate that the schedule pressure after stabilization and loss of construction period are proportional to the implementation degree of SCTS. With the decrease of implementation degree of SCTS, the schedule pressure, loss of construction period, and safety accidents probability all increase to different degrees; especially, the safety accidents probability changes significantly. It can be seen that the evolution of the implementation degree of SCTS has a significant influence on the safety accident probability. To effectively enhance and improve the construction safety management of ERLs, the transportation organization should be optimized, and the implementation degree of SCTS should be improved.Different safety investments have different effects on the safety accident probability, and safety investments have a positive effect on accident prevention. Good safety training not only raises the level of risk awareness but also persuades individuals to be less tolerant of risks. The findings of this study further showed that the interventions that combine good safety supervision with safety protection are more likely to reduce safety accident probability.When the schedule pressure is low, it can be preferred to ensure safety. Increasing a certain amount of work efficiency can accelerate the schedule by reasonably arranging the working platform and optimizing equipment and processes. When the schedule pressure is high, increasing work intensity and work efficiency simultaneously is better. Under the condition of ensuring the low safety accidents probability, parallel construction can be organized as far as possible to expand the working platform. At the same time, construction organization and safety protection should be optimized to avoid interference between working platforms and construction procedures. When the construction period is nearing the end, appropriate consideration can be made to increase the work intensity, which can avoid the safety accidents and loss caused by fatigue accumulation in the later period and complete the planned project faster.Based on the causal model of construction safety considering ERLs, a system dynamics model is proposed to simulate the construction safety evolution process, and a series of simulations are performed under different conditions. The results confirmed that the construction safety considering ERLs is significantly relevant to the implementation degree of SCTS. The incidence of construction safety incidents tends to be higher because the greater the possibility of construction delays, the greater the schedule pressure. However, even in each level of construction safety accidents probability, the evolution curve reached the maximum at about one month. As the countermeasure took effect, the evolution curve declined after reaching the maximum value and finally stabilized. Work efficiency has a negative impact on construction safety accidents probability. The increase of work intensity could reduce schedule pressure in the short term. Still, due to psychological and physiological factors, unsafe behaviors increased in a certain range. Interestingly, there is a marginal cost effect on the effect of work intensity on schedule pressure. The improvement in schedule pressure is not significant when work intensity increases to a certain level.The main contribution of this study is that it is a major step forward in integrating system dynamics and safety in the examination of construction safety management considering the ERLs. Theoretically, rather than studying construction safety considering ERLs in a static manner, this study examined the evolution as it unfolds over time. This study treats construction safety management considering the ERLs as a dynamic problem caused by “system structure” and a complex phenomenon. In this system, unsafety behaviors and unsafety conditions have significant impacts on construction safety risk considering ERLs. In contrast, almost all existing studies focus on construction safety risks relevant to new railway projects, with little research on construction safety management considering ERLs. Methodologically, a system dynamics approach can provide a rich dynamic perspective to visually explain the causes of construction safety risk changes. In general, this paper complements the body of construction safety management considering the ERLs in terms of theoretical foundations and implementation and offers references and lessons for the design and operation of construction safety management in the railway construction industry in the future.The limitation of this study is the simplification of the interactions involved in construction safety. In fact, the mutual impacts are very complex, far more than those proposed in the hypothesis of this study. In fact, the vast majority of construction safety accidents are caused by people's unsafe behaviors. Furthermore, workers' hazard perception does play an essential role in the construction safety management considering ERLs. In the future, “how does the worker's hazard perception impact construction safety considering ERLs dynamically” should be studied further.