Integration of Decision-Support and Knowledge-Based Techniques in a Problem-Solving Strategy for Selection Problems

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.

A Privacy-Preserving Algorithm for Clinical Decision-Support Systems Using Random Forest

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.

A framework for the use of decision-support tools at various spatial scales for the management of irrigated agriculture in West-Africa

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.

主动脉夹层手术决策辅助工具的构建与应用研究

目的构建主动脉夹层手术决策辅助工具,提高主动脉夹层患方决策者的决策质量。方法通过文献分析、专家函询、用户调适等形成主动脉夹层决策辅助工具,于2022年8-10月将心脏大血管外科主动脉夹层手术的患方决策者按病区分为对照组和干预组各35例。对照组行常规健康宣教及术前谈话,干预组在此基础上使用决策辅助工具。比较两组出院前决策冲突、决策准备度、决策满意度。结果主动脉夹层决策辅助工具包含4项一级指标,9项二级指标,31项三级指标。干预组决策冲突得分显著低于对照组,决策准备度及满意度得分显著高于对照组(均P<0.05)。结论主动脉夹层手术决策辅助工具具有科学性和实用性,可以减轻患方决策者决策冲突,提高决策质量,实现医患共享决策。

Can Digital Intelligence and Cyber-Physical-Social Systems Achieve Global Food Security and Sustainability?

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.

Fuzzy Logic Inference System for Managing Intensive Care Unit Resources Based on Knowledge Graph

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.

全程救援指挥决策系统中多网络无线通信保障策略研究

目的:研究多种无线通信技术在动静态医学救援单元与指挥平台通信中的应用,满足"全程救治链"中应急救援指挥决策系统的信息传输需求。方法:分析现有多种无线通信技术特点及救治链系统通信需求,设计不同应用环境的多网络无线通信保障策略。结果:提出了基于GPRS、3G移动公网及卫星通信等动态可切换无线通信和数据可靠传输的保障策略,研制了多网络无线通信链路终端一体机。结论:该无线通信保障策略和多网络无线通信链路终端一体机有利于保持各救援单元与指挥平台通信链路的持续畅通,保证了指挥决策系统在应急医学救援行动中的稳定可靠。

身份证IC卡识别在全程连续救治指挥决策系统中的应用研究

目的:研究一种身份证IC卡识别技术在医学应急救援指挥决策系统中的应用。方法:采用对比分析现有识别模式的方法,遴选数据来源广泛的身份证IC卡模式,并与计算机系统连接使用。结果:解决了医学应急救援中医护人员身份的高效验证及伤病员基本信息的快速采集问题。结论:采用身份证IC卡作为验证医护人员身份及采集伤病员基本信息的方法简便易行,原始数据提取快捷,具有良好的唯一性、可靠性和易操作性。

A New Approach for Knowledge Discovery in Distributed Databases Using Fragmented Data Storage Model

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.

Selection of IT Sourcing Alternatives in Public Sector Using the Analytic Hierarchy Process

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.

中青年乳腺癌患者手术方式决策困境及影响因素研究

目的了解中青年乳腺癌患者手术方式决策困境现状及其影响因素,为针对性干预提供参考。方法对198例住院拟行手术治疗的中青年乳腺癌患者,采用一般资料调查表、中文版决策困境量表和领悟社会支持量表进行问卷调查。结果患者决策困境总分(43.59±15.68)分,多元逐步回归分析结果显示,领悟社会支持量表中的家庭支持和其他支持、家庭人均月收入、年龄、文化程度和对疾病知识了解程度是患者决策困境的主要影响因素(调整R 2=0.783,P<0.05,P<0.01)。结论中青年乳腺癌患者手术方式决策困境程度较严重,宜从患者家庭及工作关系入手提高其社会支持水平,应特别重视年轻、文化程度较低、疾病知识不足等患者针对性决策信息的提供,从而降低决策困境。

心房颤动患者抗凝治疗决策冲突现状及影响因素研究

目的调查房颤患者抗凝治疗决策冲突现状,并分析其影响因素。方法采用一般资料调查表、心房颤动知识问卷及决策冲突量表对219例房颤患者进行调查。结果房颤患者抗凝治疗决策冲突总分为(41.47±17.54)分,其中有33例(15.07%)患者不存在决策冲突,90例(41.10%)患者决策冲突处于中等水平,96例(43.83%)患者决策冲突水平较高,存在决策延迟情况。多元线性回归分析显示,患者的年龄、性别、婚姻状况、文化程度、合并症种类、对疾病了解程度是抗凝治疗决策冲突的影响因素(P<0.05,P<0.01)。结论房颤患者抗凝治疗决策冲突水平较高,不容忽视。决策冲突受多种因素影响,医护人员应针对影响因素制定个性化的治疗决策辅助方案,以降低患者决策冲突水平,改善医患关系,提高治疗满意度。

Challenges of Renewable Forest Fuels for Green Electricity Market

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.

Heart Rate Variability Applied to Short-Term Cardiovascular Event Risk Assessment

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).

222名颅内肿瘤患者家属决策冲突现状及影响因素分析

目的调查颅内肿瘤患者家属决策冲突现状,分析其影响因素。方法采用便利抽样法,于2021年9月—2022年2月选取乌鲁木齐市3所三级甲等医院符合纳入标准的222名颅内肿瘤患者家属作为研究对象。采用一般资料调查表、决策冲突量表、领悟社会支持量表和自我效能量表进行调查。采用多重线性回归分析颅内肿瘤患者家属决策冲突的影响因素。结果本组颅内肿瘤患者家属决策冲突总分为(34.74±14.38)分,多重线性回归分析结果显示,患者肿瘤类型及患者家属经济压力、与医护讨论患者病情频率、领悟社会支持和自我效能进入回归方程(P<0.05),共解释总变异的33.5%。结论本组颅内肿瘤患者家属决策冲突处于中等偏下水平,患者肿瘤类型及患者家属经济压力、与医护讨论患者病情频率、领悟社会支持和自我效能是决策冲突的影响因素。医护人员应借助个性化、针对性的医疗决策辅助工具,改善患者家属的决策信心,降低其决策冲突水平。

乳腺癌术后乳房重建患者决策辅助工具的构建及效果评价

目的构建乳腺癌重建患者决策辅助工具,并评价其临床应用效果。方法在渥太华决策支持理论框架指导下,通过文献分析法形成国内首个乳腺癌重建患者决策辅助草案,通过专家会议法,初步修订决策辅助内容,再通过半结构式访谈利益相关人员,形成决策辅助工具最终版。根据纳入标准,选取2019年5—8月接受乳腺癌术后乳房重建手术的52例患者作为对照组,选取2019年9—12月接受乳腺癌术后乳房重建手术的54例患者作为干预组。对照组采用常规护理,干预组在常规护理的基础上使用决策辅助工具。两组患者在术前分别使用决策冲突量表,Breast-Q术前模块和期望模块进行基线调查,并在术后一个月进行决策冲突量表和决策后悔量表调查,在术后的3个月进行决策冲突量表、决策后悔量表和Breast-Q术后模块调查。结果对照组和干预组中94.5%的乳腺癌患者希望得到更多的乳房重建信息,80.8%的患者希望能够一定程度地参与到乳房重建决策中。干预组和对照组在决策冲突的比较中存在显著的时间效应、分组效应和交互效应(P<0.05)。干预组和对照组的决策后悔得分在术后1个月时差异无统计学意义,但在术后3个月时存在显著差异(P<0.05),干预组的决策后悔得分显著低于对照组(P<0.05)。两组Breast-Q术后医护人员满意度模块存在显著差异(P<0.05),干预组对外科医师及其他工作人员的满意度显著高于对照组。结论本研究构建的决策辅助工具有科学性、可靠性和可行性,乳腺癌患者对重建期望值较高,同时也希望能够参与到重建决策中;决策辅助工具的临床应用能够减少患者的重建决策冲突和远期决策遗憾,提高患者对医护人员的满意度。

Psycho-Social Predictors of Childbirth Fear in Pregnant Women: An Australian Study

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.

颅内动脉瘤患者治疗决策困境的影响因素分析

目的研究颅内动脉瘤患者治疗决策困境的影响因素。方法选取2022年1-12月在该院住院的210例颅内动脉瘤患者作为研究对象,采用一般资料调查表、决策困境量表(DCS)、领悟社会支持量表(PSSS)和医院焦虑抑郁量表(HADS)对患者进行问卷调查。结果颅内动脉瘤患者决策困境得分为(38.08±14.05)分,领悟社会支持得分为(50.59±3.38)分,焦虑得分为(11.12±3.46)分,抑郁得分为(9.56±2.11)分。多元线性回归分析显示,年龄、家庭人均月收入、动脉瘤直径、社会支持和焦虑是颅内动脉瘤患者治疗决策困境的主要影响因素(P<0.05)。结论颅内动脉瘤患者决策困境处于中等水平,医护人员应根据患者年龄、经济状况、动脉瘤大小、患者社会支持及焦虑情况等因素制订有针对性的决策支持措施。

The interdependent networked community resilience modeling environment(IN-CORE)

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.

Developing China's roadmap for air quality improvement:A review on technology development and future prospects

Air pollution control policies in China have been experiencing profound changes,highlighting a strategic transformation from total pollutant emission control to air quality improvement,along with the shifting targets starting from acid rain and NO_(x)emissions to PM_(2.5)pollution,and then the emerging O_(3)challenges.The marvelous achievements have been made with the dramatic decrease of SO_(2)emission and fundamental improvement of PM_(2.5)concentration.Despite these achievements,China has proposed Beautiful China target through 2035 and the goal of 2030 carbon peak and 2060 carbon neutrality,which impose stricter requirements on air quality and synergistic mitigation with Greenhouse Gas(GHG)emissions.Against this background,an integrated multi-objective and multi-benefit roadmap is required to provide decision support for China’s long-term air quality improvement strategy.This paper systematically reviews the technical system for developing the air quality improvement roadmap,which was integrated from the research output of China’s National Key R&D Program for Research on Atmospheric Pollution Factors and Control Technologies(hereafter Special NKP),covering mid-and long-term air quality target setting techniques,quantitative analysis techniques for emission reduction targets corresponding to air quality targets,and pathway optimization techniques for realizing reduction targets.The experience and lessons derived from the reviews have implications for the reformation of China’s air quality improvement roadmap in facing challenges of synergistic mitigation of PM_(2.5)and O_(3),and the coupling with climate change mitigation.