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.

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.

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.

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。

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

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.

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。

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).

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.

Evaluation of conflict-resolution policies on controller taskload

In an effort to maintain safety while satisfying growing air traffic demand,air navigation service providers are considering the inclusion of advisory systems to identify potential conflicts and propose resolution commands for the air traffic controller to verify and issue to aircraft.To understand the potential workload implications of introducing advisory conflict-detection and resolution tools,this paper examines a metric of controller taskload:how many resolution commands an air traffic controller issues under the guidance of an advisory system.Through a simulation study,the research presented here evaluates how the underlying protocol of a conflict-resolution tool affects the controller taskload(system demands)associated with the conflict-resolution process,and implicitly the controller workload(physical and psychological demands).Ultimately,evidence indicates that there is significant flexibility in the design of conflict-resolution algorithms supporting an advisory system.