The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to th...The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.展开更多
Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physic...Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physicians’diagnostic processes in terms of speed and accuracy.Using data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms.In this work,we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’information and exposing them to cyber and network attacks.Solving the same problem with a different methodology,the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms.Moreover,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.展开更多
The Kou watershed, situated in the Southwestern part of Burkina Faso, has succumbed since a couple of decades in a typical theater play of anarchistic water management. With its 1800 km2, this small watershed holds th...The Kou watershed, situated in the Southwestern part of Burkina Faso, has succumbed since a couple of decades in a typical theater play of anarchistic water management. With its 1800 km2, this small watershed holds the second largest city of Burkina Faso (Bobo-Dioulasso), a former State ran irrigated rice scheme and several informal agricultural zones. Despite the abundance on water resources, most water users find themselves regularly facing to water shortages due to an increase in population and low irrigation efficiencies. Local stakeholders are hence in need of easy-to-use and low-cost decision support tools for the monitoring and exploitation of the water resources at different spatial and user levels. A top-to-bottom string of adapted water management tools has been successfully installed to tackle the problems: from watershed (top) to field level (bottom), passing by the 1200 ha irrigation scheme. Land use maps have been derived from time-series of free satellite images. Combined with data from a network of hydrologic gauging stations, regional water use maps were established. SIMIS was put in place for the public-private management of the regions irrigated rice scheme. Day to day water use on irrigated plots was followed by soil humidity and crop canopy measurements. A simple field-cropwater balance model Aqua Crop was used by extension workers to draft optimal irrigation charts. Each tool was applied independently, requiring only limited data;but their combined results contributed to an improved integrated water management.展开更多
Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship....Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration behavior.From the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the upstream, midstream and downstream areas. Furthermore, establishing mechanisms for water resource transfer and trade among different industries can be instrumental in maximizing the benefits derived from water resources.Finally, we envisage a future of agriculture characterized by the integration of digital, robotic and biological farming techniques.This vision aims to incorporate small tasks, big models, and deep intelligence into the regular ecological practices of intelligent agriculture.展开更多
With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipul...With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.展开更多
Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new genera...Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.展开更多
Recent research carried out in the public sector shows that outsourcing can indisputably bring many benefits to the organizations which master the art of devising, deploying and maintaining outsourcing relationships. ...Recent research carried out in the public sector shows that outsourcing can indisputably bring many benefits to the organizations which master the art of devising, deploying and maintaining outsourcing relationships. However, for many organizations, these benefits remain elusive, while outsourcing projects are usually accompanied by unexpected and often negative effects. The paper focuses on in-depth analysis of the current situation concerning outsourcing of information technology projects （IT-projects） in Slovenian public sector. Presented research initially analyses substantive, procedural and other relevant aspects of outsourcing and provides a set of applicable decision making criteria. Stemming from the analysis results, this paper eventually presents the development of a multi criteria decision-support model based on Analytic Hierarchy Process （AHP） which could facilitate enhanced evaluation, selection and implementation of IT outsourcing projects in the public sector.展开更多
In this study, strategic electricity market scenarios are considered in a grid of Scandinavia. This multiple-objective decision environment includes the allocation of a number of renewable forest fuel procurement chai...In this study, strategic electricity market scenarios are considered in a grid of Scandinavia. This multiple-objective decision environment includes the allocation of a number of renewable forest fuel procurement chains to a combined heat and power plant in Finland. The decision environment includes also electricity procurement from Sweden and Russia. The environment is further complicated by sequence-dependent operations of the local procurement chains during different periods. Due to the complex nature of the environment, multiple-objective methods cannot be directly used to solve the electricity production problem in a manner that is techno-economically relevant to the forest energy industry. Therefore, local and time-varying parameters were measured in local wood procurement conditions to improve the solution method. Using these measurements the smart decision-support system automatically adjusted the multiple-objective methodology to better describe the combinatorial complexity of the production sector. The properties of this methodology are discussed and three scenarios of how the system works based on local real-world data and optional feed-in tariff of green electricity are presented. The Finnish electricity market is subject to policy decisions regarding green energy production regulations. These decisions should be made on the basis of local techno-economic analysis presented in this study accounting for the effects of forest operations on the electricity production and import.展开更多
Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital ...Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Therefore, there are several well known risk assessment tools, unfortunately they present some limitations.This work addresses this problem with two different methodologies:1) combination of risk assessment tools based on fusion of Bayesian classifiers complemented with genetic algorithm optimization;2) personalization of risk assessment through the creation of groups of patients that maximize the performance of each risk assessment tool. This last approach is implemented based on subtractive clustering applied to a reduced-dimension space.Both methodologies were developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva-tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies:1) Santa Cruz Hospital, Portugal, N=460 patients;2)LeiriaPombal Hospital Centre, Portugal, N=99 patients.This work improved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%, 81.5%. Besides this enhancement, the proposed methodologies allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification) should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters derived from heart rate variability (HRV), is introduced in this work. HRV is a strong and independent predictor of mortality in patients following acute myocardial infarction. The impact of HRV parameters in the characterization of coronary artery disease (CAD) patients will be conducted during hospitalization of these patients in the Leiria-Pombal Hospital Centre (LPHC).展开更多
Background: Around 20% of birthing women report high levels of childbirth fear. Fear potentially impacts women’s emotional health, preparation for birth, and birth outcomes. Evidence suggests that personal and extern...Background: Around 20% of birthing women report high levels of childbirth fear. Fear potentially impacts women’s emotional health, preparation for birth, and birth outcomes. Evidence suggests that personal and external factors contribute to childbirth fear, however results vary. Aim: To identify pyscho-social factors associated with childbirth fear and possible antenatal predictors of childbirth fear according to women’s parity. Method: 1410 women in second trimester and attending one of three public hospitals in south-east Queensland were screened for childbirth fear using the Wijma Delivery Expectancy/Experience Questionnaire (W-DEQ). Other measures included the Edinburgh Depression Scale (EPDS), Decisional Conflict Scale (DCS) and items from the EuroQol (EQ-5D) targeting Anxiety/Depression and Pain/Discomfort. In addition items measuring a previous mental health condition, social support and knowledge were used. Preferred mode of birth was also collected. Psycho-social factors were analysed to determine associations with childbirth fear. Multivariate analysis was used to determine predictors of fear. Results: Thirty-one percent (n = 190/604) of nulliparous and 18% (n = 143/782) of multiparous women reported high fear levels. Having a mental health history, desiring a caesarean section, reporting moderate to high pain during pregnancy, having a non-supportive partner and perceiving less childbirth knowledge than peers, were associated with childbirth fear. Standard multiple regression analyses by parity determined that depression, decisional conflict, low social support and less perceived knowledge predicted levels of childbirth fear. The model explained 32.4% of variance in childbirth fear for nulliparous and 29.4% for multiparous women. Conclusion: Psychosocial factors are significantly associated with childbirth fear. The identification of predictive psychosocial factors for childbirth fear indicates the importance of observing, assessing, and developing support strategies for women. Such strategies are required to decrease anxiety and depression for women during pregnancy, promote normal birth, and build social support to improve women’s feelings and positive expectations of birth.展开更多
In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators...In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.展开更多
文摘The limitations of traditional approaches to selection problems are examined. A problemsolving strategy is presented in which decision-support and knowledge-based techniques play complementary roles. An approach to the representation of knowledge to support the problem-solving strategy is presented which avoids commitment to a specific programming language or implementation environment. The problem of choosing a home is used to illustrate the representation of knowledge in a specific problem domain. Techniques for implementation of the problem-solving strategy are described. Knowledge elicitation techniques and their implementation in a development shell for application of the problem-solving strategy to any selection problem are also described.
文摘Clinical decision-support systems are technology-based tools that help healthcare providers enhance the quality of their services to satisfy their patients and earn their trust.These systems are used to improve physicians’diagnostic processes in terms of speed and accuracy.Using data-mining techniques,a clinical decision support system builds a classification model from hospital’s dataset for diagnosing new patients using their symptoms.In this work,we propose a privacy-preserving clinical decision-support system that uses a privacy-preserving random forest algorithm to diagnose new symptoms without disclosing patients’information and exposing them to cyber and network attacks.Solving the same problem with a different methodology,the simulation results show that the proposed algorithm outperforms previous work by removing unnecessary attributes and avoiding cryptography algorithms.Moreover,our model is validated against the privacy requirements of the hospitals’datasets and votes,and patients’diagnosed symptoms.
文摘The Kou watershed, situated in the Southwestern part of Burkina Faso, has succumbed since a couple of decades in a typical theater play of anarchistic water management. With its 1800 km2, this small watershed holds the second largest city of Burkina Faso (Bobo-Dioulasso), a former State ran irrigated rice scheme and several informal agricultural zones. Despite the abundance on water resources, most water users find themselves regularly facing to water shortages due to an increase in population and low irrigation efficiencies. Local stakeholders are hence in need of easy-to-use and low-cost decision support tools for the monitoring and exploitation of the water resources at different spatial and user levels. A top-to-bottom string of adapted water management tools has been successfully installed to tackle the problems: from watershed (top) to field level (bottom), passing by the 1200 ha irrigation scheme. Land use maps have been derived from time-series of free satellite images. Combined with data from a network of hydrologic gauging stations, regional water use maps were established. SIMIS was put in place for the public-private management of the regions irrigated rice scheme. Day to day water use on irrigated plots was followed by soil humidity and crop canopy measurements. A simple field-cropwater balance model Aqua Crop was used by extension workers to draft optimal irrigation charts. Each tool was applied independently, requiring only limited data;but their combined results contributed to an improved integrated water management.
基金supported in part by the National Key Research and Development Program of China (2021ZD0113704)the National Natural Science Foundation of China (62076239, 42041005,62103411)+1 种基金the Science and Technology Development FundMacao SAR(0050/2020/A1)。
文摘Plants sequester carbon through photosynthesis and provide primary productivity for the ecosystem. However, they also simultaneously consume water through transpiration, leading to a carbon-water balance relationship. Agricultural production can be regarded as a form of carbon sequestration behavior.From the perspective of the natural-social-economic complex ecosystem, excessive water usage in food production will aggravate regional water pressure for both domestic and industrial purposes. Hence, achieving a harmonious equilibrium between carbon and water resources during the food production process is a key scientific challenge for ensuring food security and sustainability. Digital intelligence(DI) and cyber-physical-social systems(CPSS) are emerging as the new research paradigms that are causing a substantial shift in the conventional thinking and methodologies across various scientific fields, including ecological science and sustainability studies. This paper outlines our recent efforts in using advanced technologies such as big data, artificial intelligence(AI), digital twins, metaverses, and parallel intelligence to model, analyze, and manage the intricate dynamics and equilibrium among plants, carbon, and water in arid and semiarid ecosystems. It introduces the concept of the carbon-water balance and explores its management at three levels: the individual plant level, the community level, and the natural-social-economic complex ecosystem level. Additionally, we elucidate the significance of agricultural foundation models as fundamental technologies within this context. A case analysis of water usage shows that, given the limited availability of water resources in the context of the carbon-water balance, regional collaboration and optimized allocation have the potential to enhance the utilization efficiency of water resources in the river basin. A suggested approach is to consider the river basin as a unified entity and coordinate the relationship between the upstream, midstream and downstream areas. Furthermore, establishing mechanisms for water resource transfer and trade among different industries can be instrumental in maximizing the benefits derived from water resources.Finally, we envisage a future of agriculture characterized by the integration of digital, robotic and biological farming techniques.This vision aims to incorporate small tasks, big models, and deep intelligence into the regular ecological practices of intelligent agriculture.
基金funded by the Deanship of Scientific Research at Umm Al-Qura University,Makkah,Kingdom of Saudi Arabia.Under Grant Code:22UQU4281755DSR05.
文摘With the rapid growth in the availability of digital health-related data,there is a great demand for the utilization of intelligent information systems within the healthcare sector.These systems can manage and manipulate this massive amount of health-related data and encourage different decision-making tasks.They can also provide various sustainable health services such as medical error reduction,diagnosis acceleration,and clinical services quality improvement.The intensive care unit(ICU)is one of the most important hospital units.However,there are limited rooms and resources in most hospitals.During times of seasonal diseases and pandemics,ICUs face high admission demand.In line with this increasing number of admissions,determining health risk levels has become an essential and imperative task.It creates a heightened demand for the implementation of an expert decision support system,enabling doctors to accurately and swiftly determine the risk level of patients.Therefore,this study proposes a fuzzy logic inference system built on domain-specific knowledge graphs,as a proof-of-concept,for tackling this healthcare-related issue.The system employs a combination of two sets of fuzzy input parameters to classify health risk levels of new admissions to hospitals.The proposed system implemented utilizes MATLAB Fuzzy Logic Toolbox via several experiments showing the validity of the proposed system.
文摘Since the early 1990, significant progress in database technology has provided new platform for emerging new dimensions of data engineering. New models were introduced to utilize the data sets stored in the new generations of databases. These models have a deep impact on evolving decision-support systems. But they suffer a variety of practical problems while accessing real-world data sources. Specifically a type of data storage model based on data distribution theory has been increasingly used in recent years by large-scale enterprises, while it is not compatible with existing decision-support models. This data storage model stores the data in different geographical sites where they are more regularly accessed. This leads to considerably less inter-site data transfer that can reduce data security issues in some circumstances and also significantly improve data manipulation transactions speed. The aim of this paper is to propose a new approach for supporting proactive decision-making that utilizes a workable data source management methodology. The new model can effectively organize and use complex data sources, even when they are distributed in different sites in a fragmented form. At the same time, the new model provides a very high level of intellectual management decision-support by intelligent use of the data collections through utilizing new smart methods in synthesizing useful knowledge. The results of an empirical study to evaluate the model are provided.
文摘Recent research carried out in the public sector shows that outsourcing can indisputably bring many benefits to the organizations which master the art of devising, deploying and maintaining outsourcing relationships. However, for many organizations, these benefits remain elusive, while outsourcing projects are usually accompanied by unexpected and often negative effects. The paper focuses on in-depth analysis of the current situation concerning outsourcing of information technology projects （IT-projects） in Slovenian public sector. Presented research initially analyses substantive, procedural and other relevant aspects of outsourcing and provides a set of applicable decision making criteria. Stemming from the analysis results, this paper eventually presents the development of a multi criteria decision-support model based on Analytic Hierarchy Process （AHP） which could facilitate enhanced evaluation, selection and implementation of IT outsourcing projects in the public sector.
文摘In this study, strategic electricity market scenarios are considered in a grid of Scandinavia. This multiple-objective decision environment includes the allocation of a number of renewable forest fuel procurement chains to a combined heat and power plant in Finland. The decision environment includes also electricity procurement from Sweden and Russia. The environment is further complicated by sequence-dependent operations of the local procurement chains during different periods. Due to the complex nature of the environment, multiple-objective methods cannot be directly used to solve the electricity production problem in a manner that is techno-economically relevant to the forest energy industry. Therefore, local and time-varying parameters were measured in local wood procurement conditions to improve the solution method. Using these measurements the smart decision-support system automatically adjusted the multiple-objective methodology to better describe the combinatorial complexity of the production sector. The properties of this methodology are discussed and three scenarios of how the system works based on local real-world data and optional feed-in tariff of green electricity are presented. The Finnish electricity market is subject to policy decisions regarding green energy production regulations. These decisions should be made on the basis of local techno-economic analysis presented in this study accounting for the effects of forest operations on the electricity production and import.
文摘Cardiovascular disease (CVD) risk assessment is an important instrument to enhance the clinical decision in the daily practice as well as to improve the preventive health care promoting the transfer from the hospital to patient’s home. Due to its importance, clinical guidelines recommend the use of risk scores to predict the risk of a cardiovascular disease event. Therefore, there are several well known risk assessment tools, unfortunately they present some limitations.This work addresses this problem with two different methodologies:1) combination of risk assessment tools based on fusion of Bayesian classifiers complemented with genetic algorithm optimization;2) personalization of risk assessment through the creation of groups of patients that maximize the performance of each risk assessment tool. This last approach is implemented based on subtractive clustering applied to a reduced-dimension space.Both methodologies were developed to short-term CVD risk prediction for patients with Acute Coronary Syndromes without ST segment eleva-tion (ACS-NSTEMI). Two different real patients’ datasets were considered to validate the developed strategies:1) Santa Cruz Hospital, Portugal, N=460 patients;2)LeiriaPombal Hospital Centre, Portugal, N=99 patients.This work improved the performance in relation to current risk assessment tools reaching maximum values of sensitivity, specificity and geometric mean of, respectively, 80.0%, 82.9%, 81.5%. Besides this enhancement, the proposed methodologies allow the incorporation of new risk factors, deal with missing risk factors and avoid the selection of a single tool to be applied in the daily clinical practice. In spite of these achievements, the CVD risk assessment (patient stratification) should be improved. The incorporation of new risk factors recognized as clinically significant, namely parameters derived from heart rate variability (HRV), is introduced in this work. HRV is a strong and independent predictor of mortality in patients following acute myocardial infarction. The impact of HRV parameters in the characterization of coronary artery disease (CAD) patients will be conducted during hospitalization of these patients in the Leiria-Pombal Hospital Centre (LPHC).
文摘Background: Around 20% of birthing women report high levels of childbirth fear. Fear potentially impacts women’s emotional health, preparation for birth, and birth outcomes. Evidence suggests that personal and external factors contribute to childbirth fear, however results vary. Aim: To identify pyscho-social factors associated with childbirth fear and possible antenatal predictors of childbirth fear according to women’s parity. Method: 1410 women in second trimester and attending one of three public hospitals in south-east Queensland were screened for childbirth fear using the Wijma Delivery Expectancy/Experience Questionnaire (W-DEQ). Other measures included the Edinburgh Depression Scale (EPDS), Decisional Conflict Scale (DCS) and items from the EuroQol (EQ-5D) targeting Anxiety/Depression and Pain/Discomfort. In addition items measuring a previous mental health condition, social support and knowledge were used. Preferred mode of birth was also collected. Psycho-social factors were analysed to determine associations with childbirth fear. Multivariate analysis was used to determine predictors of fear. Results: Thirty-one percent (n = 190/604) of nulliparous and 18% (n = 143/782) of multiparous women reported high fear levels. Having a mental health history, desiring a caesarean section, reporting moderate to high pain during pregnancy, having a non-supportive partner and perceiving less childbirth knowledge than peers, were associated with childbirth fear. Standard multiple regression analyses by parity determined that depression, decisional conflict, low social support and less perceived knowledge predicted levels of childbirth fear. The model explained 32.4% of variance in childbirth fear for nulliparous and 29.4% for multiparous women. Conclusion: Psychosocial factors are significantly associated with childbirth fear. The identification of predictive psychosocial factors for childbirth fear indicates the importance of observing, assessing, and developing support strategies for women. Such strategies are required to decrease anxiety and depression for women during pregnancy, promote normal birth, and build social support to improve women’s feelings and positive expectations of birth.
基金The Center for Risk-Based Community Resilience Planning is a NIST-funded Center of Excellencethe Center is funded through a cooperative agreement between the U.S.National Institute of Standards and Tech-nology and Colorado State University(NIST Financial Assistance Award Numbers:70NANB15H044 and 70NANB20H008)。
文摘In 2015,the U.S National Institute of Standards and Technology(NIST)funded the Center of Excellence for Risk-Based Community Resilience Planning(CoE),a fourteen university-based consortium of almost 100 col-laborators,including faculty,students,post-doctoral scholars,and NIST researchers.This paper highlights the scientific theory behind the state-of-the-art cloud platform being developed by the CoE-the Interdisciplinary Networked Community Resilience Modeling Environment(IN-CORE).IN-CORE enables communities,consul-tants,and researchers to set up complex interdependent models of an entire community consisting of people,businesses,social institutions,buildings,transportation networks,water networks,and electric power networks and to predict their performance and recovery to hazard scenario events,including uncertainty propagation through the chained models.The modeling environment includes a detailed building inventory,hazard scenario models,building and infrastructure damage(fragility)and recovery functions,social science data-driven house-hold and business models,and computable general equilibrium(CGE)models of local economies.An important aspect of IN-CORE is the characterization of uncertainty and its propagation throughout the chained models of the platform.Three illustrative examples of community testbeds are presented that look at hazard impacts and recovery on population,economics,physical services,and social services.An overview of the IN-CORE technology and scientific implementation is described with a focus on four key community stability areas(CSA)that encompass an array of community resilience metrics(CRM)and support community resilience informed decision-making.Each testbed within IN-CORE has been developed by a team of engineers,social scientists,urban planners,and economists.Community models,begin with a community description,i.e.,people,businesses,buildings,infras-tructure,and progresses to the damage and loss of functions caused by a hazard scenario,i.e.,a flood,tornado,hurricane,or earthquake.This process is accomplished through chaining of modular algorithms,as described.The baseline community characteristics and the hazard-induced damage sets are the initial conditions for the recovery models,which have been the least studied area of community resilience but arguably one of the most important.Communities can then test the effect of mitigation and/or policies and compare the effects of“what if”scenarios on physical,social,and economic metrics with the only requirement being that the change much be able to be numerically modeled in IN-CORE.
