This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By e...This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.展开更多
In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,sp...In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.展开更多
Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now en...Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.展开更多
Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of ca...Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.展开更多
Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefit...Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.展开更多
BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT of...BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT offer adaptability,domain-specific systems(e.g.,DeepSeek)may better align with clinical guidelines.However,their comparative efficacy in oncology remains underexplored.This study hypothesizes that domain-specific AI will outperform general-purpose models in technical accuracy,while the latter will excel in patient-centered communication.AIMS To compare ChatGPT and DeepSeek in oncology decision support for diagnosis,treatment,and patient communication.METHODS A retrospective analysis was conducted using 1200 anonymized oncology cases(2018–2023)from The Cancer Genome Atlas and institutional databases,covering six cancer types.Each case included histopathology,imaging,genomic profiles,and treatment histories.Both models generated diagnostic interpretations,staging assessments,and therapy recommendations.Performance was evaluated against NCCN/ESMO guidelines and expert oncologist panels using F1-scores,Cohen'sκ,Likert-scale ratings,and readability metrics.Statistical significance was assessed via analysis of variance and post-hoc Tukey tests.RESULTS DeepSeek demonstrated superior performance in diagnostic accuracy(F1-score:89.2%vs ChatGPT's 76.5%,P<0.001)and treatment alignment with guidelines(κ=0.82 vs 0.67,P=0.003).ChatGPT exhibited strengths in patient communi-cation,generating layman-friendly explanations(readability score:8.2/10 vs DeepSeek's 6.5/10,P=0.012).Both models showed limitations in rare cancer subtypes(e.g.,cholangiocarcinoma),with accuracy dropping below 60%.Clinicians rated DeepSeek's outputs as more actionable(4.3/5 vs 3.7/5,P=0.021)but highlighted ChatGPT's utility in palliative care discussions.CONCLUSION Domain-specific AI(DeepSeek)excels in technical precision,while general-purpose models(ChatGPT)enhance patient engagement.A hybrid system integrating both approaches may optimize oncology workflows,contingent on expanded training for rare cancers and real-time guideline updates.展开更多
We examined the relationship between social support and career adaptability,as well as the mediating roles of proactive personality and career decision-making self-efficacy in this process.A total of 1354 Chinese coll...We examined the relationship between social support and career adaptability,as well as the mediating roles of proactive personality and career decision-making self-efficacy in this process.A total of 1354 Chinese college students(female=964;mean age=19.53 years,SD=1.33 years)completed an online questionnaire.Path analysis indicated that social support was positively associated with higher levels of career adaptability.Both proactive personality and career decision-making self-efficacy served as parallel mediators,strengthening the relationship between social support and career adaptability.The complete chain mediation analysis revealed that social support influences career adaptability primarily through proactive personality,which in turn enhances career decision-making self-efficacy,further contributing to increased career adaptability.These findings extend career capital theory by demonstrating that social and psychological resources jointly facilitate career adaptability.展开更多
Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic...Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.展开更多
Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic...Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.展开更多
Critical infrastructures（CI） are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependenc...Critical infrastructures（CI） are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependency of critical infrastructures failure of one infrastructure during a natural disaster such as earthquake or flood may cause failure of another and so on through a cascade or escalating effect. Quantification of these types of interdependencies between critical infrastructures is essential for effective response and management of resources for rescue, recovery, and restoration during times of crises. This paper proposes a new mathematical framework based on an asymmetric relation matrix constructed in a bottom-up approach for modeling and analyzing interdependencies of critical infrastructures. Asymmetric dependency matrices can be constructed using the asymmetric incidence coefficient based on node-level relationships defined between nodes for measuring the strength of interdependency between node and node, node and network, and networks and networks. These asymmetric matrices are further analyzed for ranking infrastructures in terms of their relative importance and for identifying nodes and infrastructure networks that play a critical role in chain effects among infrastructures involved in geo-disaster events such as flooding. Examples of interdependency analysis for the identification of vulnerabilities among fifteen national defense-related infrastructure sectors by the Australian government and a simulated example using the newly developed GIS-based network simulator Geo PN are used to validate and demonstrate the implementation and effectiveness of interdependency analysis methods in analyzing infrastructure interdependency during a flooding event.展开更多
This paper presents a Spatial Decision Support System for local governments of developing countries.It allows municipality government,enterprises,scientific community and civil society to address decision problems usi...This paper presents a Spatial Decision Support System for local governments of developing countries.It allows municipality government,enterprises,scientific community and civil society to address decision problems using GIS.The framework is supported by four modules of information technologies:Environmental Decision Support Database,Data Manipulation,Decision Support,and Mapping.A case study is presented covering the implementation of this framework in one municipality of Cuba.An example of land suitability planning for coconut crops is used to evaluate the system performance and usability.Results show local municipalities are able to use this framework to solve local decision problems using state of the art decision making even with low infrastructure development.展开更多
The paper proposes an ontology-based multicriteria spatial decision support system(MC-SDSS)for the house selection problem.The house selection ontology serves as a foundation for spatial multicriteria decision analysi...The paper proposes an ontology-based multicriteria spatial decision support system(MC-SDSS)for the house selection problem.The house selection ontology serves as a foundation for spatial multicriteria decision analysis(MCDA)in the house selection domain.It is built using the Web Ontology Language(OWL).The ontology represents the spatial MCDA knowledge associated with house selection using semantic machine-interpretable concepts and relationships in such a way that they can be used by machines not just for display purposes,but also for processing,automation,integration,and reuse across applications.It contains concepts(or classes)including quantitative and qualitative criteria(objectives and attributes),decision alternatives(houses for sale),criterion weights,and location attributes of the decision alternatives.The concepts are organized into a hierarchical classification structure using the Analytic Hierarchy Process.To evaluate the decision alternatives,a set of rules is implemented within the OWL knowledge base with the Semantic Web Rule Language.The rules are expressed as combinations of the OWL concepts and their properties.The paper illustrates an implementation of the proposed ontology-based MC-SDSS architecture using a case study of house selection in the City of Tehran,Iran.展开更多
The basic mathematic models,such as the statistic model,the time-serial model,the spatial dynamic model etc.,and some typical analysis methods based on 3DCM are proposed and discussed.A few typical spatial decision ma...The basic mathematic models,such as the statistic model,the time-serial model,the spatial dynamic model etc.,and some typical analysis methods based on 3DCM are proposed and discussed.A few typical spatial decision making methods integrating the spatial analysis and the basic mathematical models are also introduced,e.g.visual impact assessment,dispersion of noise immissions,base station plan for wireless communication.In addition,a new idea of expectation of further applications and add-in-value service of 3DCM is promoted.As an example,the sunshine analysis is studied and some helpful conclusions are drawn.展开更多
Coastal zones are very dynamic and fragile environments, constituting a landscape ever more heterogeneous, fragmented and with increasing levels of complexity due to the changing relationship between man and nature. I...Coastal zones are very dynamic and fragile environments, constituting a landscape ever more heterogeneous, fragmented and with increasing levels of complexity due to the changing relationship between man and nature. Integrated coastal zone management therefore requires detailed knowledge of the system and its components, based—to a large extent—on technical and scientific information. However, the information generated must be in line with the political requirements necessary for decision-making and planning. Thus the use of indicators to give a simplified view of the many components of the territory, and at the same time to provide important information about patterns or trends, becomes a tool of the utmost importance. These indicators can be understood as measurable characteristics of the environment, which facilitate comprehension of the processes occurring at different scales and serve as a reference to inform the population and support decision-making. The aim of the present note is to demonstrate briefly the need to develop geographical-environmental and natural risk indicators to facilitate comprehension of the dynamic of spatial and temporal landscape patterns, particularly in coastal environments. This approach offers an historical summary of the natural, socio-economic and political processes which currently make up the territory, and which without doubt will continue to influence it in the future. At the same time, it is proposed that information should be integrated on the basis of this framework with a view to generating spatial decision support systems in a context of planning and integrated management of the coastal zones of Chile.展开更多
In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes ...In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.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
In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, an...In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.展开更多
In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatia...In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.展开更多
Based on platform of GIS software ArcView and theory of management information system(MIS), a decision support system on urban landscape planning was designed via GIS technology, module design technique and object-ori...Based on platform of GIS software ArcView and theory of management information system(MIS), a decision support system on urban landscape planning was designed via GIS technology, module design technique and object-oriented programming technique. The function of this system is realized by its two subsystems—one is for height limit model of city and another is for landscape belt planning, which can help administors in landscape planning.展开更多
文摘This study presents an AI-driven Spatial Decision Support System (SDSS) aimed at transforming groundwater suitability assessments for domestic and irrigation uses in Visakhapatnam District, Andhra Pradesh, India. By employing advanced remote sensing, GIS, and machine learning techniques, groundwater quality data from 50 monitoring wells, sourced from the Central Ground Water Board (CGWB), was meticulously analysed. Key parameters, including pH, electrical conductivity, total dissolved solids, and major ion concentrations, were evaluated against World Health Organization (WHO) standards to determine domestic suitability. For irrigation, advanced metrics such as Sodium Adsorption Ratio (SAR), Kelly’s Ratio, Residual Sodium Carbonate (RSC), and percentage sodium (% Na) were utilized to assess water quality. The integration of GIS for spatial mapping and AI models for predictive analytics allows for a comprehensive visualization of groundwater quality distribution across the district. Additionally, the irrigation water quality was evaluated using the USA Salinity Laboratory diagram, providing essential insights for effective agricultural water management. This innovative SDSS framework promises to significantly enhance groundwater resource management, fostering sustainable practices for both domestic use and agriculture in the region.
基金Supported by National"863"High-tech Project(2006AA10A309)Jilin Technology Gallery Key Project(20060213)~~
文摘In order to solve the problem of the maze precision fertilizer,soil fertility evaluation,soil fertility classify and yield projections,the geographic information system with spatial information processing functions,spatial data mining techniques with spatial information analysis capabilities,expert system technology in the field of artificial intelligence,traditional information management systems and decision support system were effectively integrated in this study,and the statistical analysis method of GIS and data visualization were combined to design and implement the maize precise intelligent space decision-making system.This system had greatly improved the decision-making ability in agricultural production carried out by agricultural management.
基金supported by the National Key Research and Development Program (No.2023YFC3502604)the National Natural Science Foundation of China (Nos.U23B2062, 82274352,82174533, 82374302, 82204941)+3 种基金the Noncommunicable Chronic Diseases-National Science and Technology Major Project (No.2023ZD0505700)the Beijing-Tianjin-Hebei Basic Research Cooperation Project (No.22JCZXJC00070)the State Key Laboratory on Technologies for Chinese Medicine Pharmaceutical Process Control and Intelligent Manufacture (No.SKL2024Z0102)Key R&D project of Ningxia Autonomous Region (No.2022BEG02036).
文摘Traditional Chinese medicine(TCM)represents a paradigmatic approach to personalized medicine,developed through the systematic accumulation and refinement of clinical empirical data over more than 2000 years,and now encompasses large-scale electronic medical records(EMR)and experimental molecular data.Artificial intelligence(AI)has demonstrated its utility in medicine through the development of various expert systems(e.g.,MYCIN)since the 1970s.With the emergence of deep learning and large language models(LLMs),AI’s potential in medicine shows considerable promise.Consequently,the integration of AI and TCM from both clinical and scientific perspectives presents a fundamental and promising research direction.This survey provides an insightful overview of TCM AI research,summarizing related research tasks from three perspectives:systems-level biological mechanism elucidation,real-world clinical evidence inference,and personalized clinical decision support.The review highlights representative AI methodologies alongside their applications in both TCM scientific inquiry and clinical practice.To critically assess the current state of the field,this work identifies major challenges and opportunities that constrain the development of robust research capabilities—particularly in the mechanistic understanding of TCM syndromes and herbal formulations,novel drug discovery,and the delivery of high-quality,patient-centered clinical care.The findings underscore that future advancements in AI-driven TCM research will rely on the development of high-quality,large-scale data repositories;the construction of comprehensive and domain-specific knowledge graphs(KGs);deeper insights into the biological mechanisms underpinning clinical efficacy;rigorous causal inference frameworks;and intelligent,personalized decision support systems.
文摘Background: Clinical decision support tools provide suggestions to support healthcare providers and clinicians, as they attend to patients. Clinicians use these tools to rapidly consult the evidence at the point of care, a practice which has been found to reduce the time patients spend in hospitals, promote the quality of care and improve healthcare outcomes. Such tools include Medscape, VisualDx, Clinical Key, DynaMed, BMJ Best Practice and UpToDate. However, use of such tools has not yet been fully embraced in low-resource settings such as Uganda. Objective: This paper intends to collate data on the use and uptake of one such tool, UpToDate, which was provided at no cost to five medical schools in Uganda. Methods: Free access to UpToDate was granted through the IP addresses of five medical schools in Uganda in collaboration with Better Evidence at The Global Health Delivery Project at Harvard and Brigham and Women’s Hospital and Wolters Kluwer Health. Following the donation, medical librarians in the respective institutions conducted training sessions and created awareness of the tool. Usage data was aggregated, based on logins and content views, presented and analyzed using Excel tables and graphs. Results: The data shows similar trends in increased usage over the period of August 2022 to August 2023 across the five medical schools. The most common topics viewed, mode of access (using either the computer or the mobile app), total usage by institution, ratio of uses to eligible users by institution and ratio of uses to students by institution are shared. Conclusion: The study revealed that the tool was used by various user categories across the institutions with similar steady improved usage over the year. These results can inform the librarians as they encourage their respective institutions to continue using the tool to support uptake of point-of-care tools in clinical practice.
基金supported by the National Research Council of Sri Lanka(Grant No.NRC TO 16-07).
文摘Decision Support Tool(DST)enables farmers to make site-specific crop management decisions;however,comprehensive calibration can be both costly and time-consuming.This study assessed the production and economic benefits of two calibrations of the Nutrient Expert(NE)tool for rice in Sri Lanka’s Alfisols:the basic calibration(Nutrient Expert Sri Lanka 1,NESL1)and the comprehensive calibration(Nutrient Expert Sri Lanka 2,NESL2).NESL1 was developed by adapting the South Indian version of NE to local conditions,while NESL2 was an updated version,using three years of data from 71 farmer fields.
文摘BACKGROUND Cancer care faces challenges due to tumor heterogeneity and rapidly evolving therapies,necessitating artificial intelligence(AI)-driven clinical decision support.While general-purpose models like ChatGPT offer adaptability,domain-specific systems(e.g.,DeepSeek)may better align with clinical guidelines.However,their comparative efficacy in oncology remains underexplored.This study hypothesizes that domain-specific AI will outperform general-purpose models in technical accuracy,while the latter will excel in patient-centered communication.AIMS To compare ChatGPT and DeepSeek in oncology decision support for diagnosis,treatment,and patient communication.METHODS A retrospective analysis was conducted using 1200 anonymized oncology cases(2018–2023)from The Cancer Genome Atlas and institutional databases,covering six cancer types.Each case included histopathology,imaging,genomic profiles,and treatment histories.Both models generated diagnostic interpretations,staging assessments,and therapy recommendations.Performance was evaluated against NCCN/ESMO guidelines and expert oncologist panels using F1-scores,Cohen'sκ,Likert-scale ratings,and readability metrics.Statistical significance was assessed via analysis of variance and post-hoc Tukey tests.RESULTS DeepSeek demonstrated superior performance in diagnostic accuracy(F1-score:89.2%vs ChatGPT's 76.5%,P<0.001)and treatment alignment with guidelines(κ=0.82 vs 0.67,P=0.003).ChatGPT exhibited strengths in patient communi-cation,generating layman-friendly explanations(readability score:8.2/10 vs DeepSeek's 6.5/10,P=0.012).Both models showed limitations in rare cancer subtypes(e.g.,cholangiocarcinoma),with accuracy dropping below 60%.Clinicians rated DeepSeek's outputs as more actionable(4.3/5 vs 3.7/5,P=0.021)but highlighted ChatGPT's utility in palliative care discussions.CONCLUSION Domain-specific AI(DeepSeek)excels in technical precision,while general-purpose models(ChatGPT)enhance patient engagement.A hybrid system integrating both approaches may optimize oncology workflows,contingent on expanded training for rare cancers and real-time guideline updates.
基金supported by“Planning Subject for the 14th Five Year Plan of National Education Sciences of China(DBA210296)”.
文摘We examined the relationship between social support and career adaptability,as well as the mediating roles of proactive personality and career decision-making self-efficacy in this process.A total of 1354 Chinese college students(female=964;mean age=19.53 years,SD=1.33 years)completed an online questionnaire.Path analysis indicated that social support was positively associated with higher levels of career adaptability.Both proactive personality and career decision-making self-efficacy served as parallel mediators,strengthening the relationship between social support and career adaptability.The complete chain mediation analysis revealed that social support influences career adaptability primarily through proactive personality,which in turn enhances career decision-making self-efficacy,further contributing to increased career adaptability.These findings extend career capital theory by demonstrating that social and psychological resources jointly facilitate career adaptability.
文摘Artificial intelligence(AI)is rapidly transforming radiology and computed tomography(CT)imaging by enabling automated image analysis,improved diagnostic accuracy,and clinical decision-support.We performed a systematic review of peerreviewed studies published between January 1,2010 and March 31,2025 to quantify reported gains in diagnostic performance and workflow efficiency,to evaluate clinical decision-support benefits and risks,and to identify integration priorities.We searched PubMed,IEEE Xplore,Scopus,ScienceDirect,and Google Scholar and screened 128 records;26 studies met the inclusion criteria.Extracted data included study design,AI architecture,sample size,and quantitative performance metrics;study quality was assessed using Newcastle-Ottawa Scales(NOS),Cochrane RoB 2,or AMSTAR 2 as appropriate.Across included studies,AI applications in CT showed consistent improvements in sensitivity,specificity,and time-to-diagnosis in specific tasks(notably lung-nodule detection and intracranial hemorrhage triage),with reported detection-rate increases up to~20%and reduced turnaround times in several real-world implementations.Barriers include dataset bias,limited external validation,interpretability(“black-box”)concerns,workflow integration challenges,and evolving regulatory issues.Economic analyses suggest potentially favorable return on investment(ROI)in high-volume settings but are sensitive to licensing and infrastructure costs.To realize AI's benefits in CT imaging,rigorous multi-center validation,transparent reporting,humancentered workflow design,and post-deployment surveillance are essential.
文摘Decision support systems(DSS)based on physically based numerical models are standard tools used by water services and utilities.However,few DSS based on holistic approaches combining distributed hydrological,hydraulic,and hydrogeological models are operationally exploited.This holistic approach was adopted for the development of the AquaVar DSS,used for water resource management in the French Mediterranean Var watershed.The year 2019 marked the initial use of the DSS in its operational environment.Over the next 5 years,multiple hydrological events allowed to test the performance of the DSS.The results show that the tool is capable of simulating peak flows associated with two extreme rainfall events(storms Alex and Aline).For a moderate flood,the real-time functionality was able to simulate forecast discharges 26 h before the flood peak,with a maximum local error of 30%.Finally,simulations for the drought period 2022-2023 highlighted the essential need for DSS to evolve in line with changing climatic conditions,which give rise to unprecedented hydrological processes.The lessons learned from these first 5 years of AquaVar use under operational conditions are synthesized,addressing various topics such as DSS modularity,evolution,data positioning,technology,and governance.
基金finically supported by a project “Modeling Infrastructure Interdependency for Emergency Management Using a Network-Centric Spatial Decision Support System Approach” awarded jointly by the Natural Science and Engineering Research Council of Canada (NSERC)the Public Safety and Emergency Preparedness Canada (PSEPC) (No.JIIRP 312733-04)
文摘Critical infrastructures（CI） are designated sectors that if incapacitated or destroyed by natural disasters would have a serious impact on national security and economic and social welfare. Due to the interdependency of critical infrastructures failure of one infrastructure during a natural disaster such as earthquake or flood may cause failure of another and so on through a cascade or escalating effect. Quantification of these types of interdependencies between critical infrastructures is essential for effective response and management of resources for rescue, recovery, and restoration during times of crises. This paper proposes a new mathematical framework based on an asymmetric relation matrix constructed in a bottom-up approach for modeling and analyzing interdependencies of critical infrastructures. Asymmetric dependency matrices can be constructed using the asymmetric incidence coefficient based on node-level relationships defined between nodes for measuring the strength of interdependency between node and node, node and network, and networks and networks. These asymmetric matrices are further analyzed for ranking infrastructures in terms of their relative importance and for identifying nodes and infrastructure networks that play a critical role in chain effects among infrastructures involved in geo-disaster events such as flooding. Examples of interdependency analysis for the identification of vulnerabilities among fifteen national defense-related infrastructure sectors by the Australian government and a simulated example using the newly developed GIS-based network simulator Geo PN are used to validate and demonstrate the implementation and effectiveness of interdependency analysis methods in analyzing infrastructure interdependency during a flooding event.
基金This paper has been supported by the project 2009DFA13000 funded by the Ministry of Science and Technology of the People’s Republic of China.The authors want to thank the researchers from Instituto de Investigaciones en Fruticultura Tropical,Republic of Cuba,in special Dr Jorge Cuetothe staff of Nipe-Sagua-Baracoa mountain office,and the government of Baracoa for their kind support and advice.
文摘This paper presents a Spatial Decision Support System for local governments of developing countries.It allows municipality government,enterprises,scientific community and civil society to address decision problems using GIS.The framework is supported by four modules of information technologies:Environmental Decision Support Database,Data Manipulation,Decision Support,and Mapping.A case study is presented covering the implementation of this framework in one municipality of Cuba.An example of land suitability planning for coconut crops is used to evaluate the system performance and usability.Results show local municipalities are able to use this framework to solve local decision problems using state of the art decision making even with low infrastructure development.
文摘The paper proposes an ontology-based multicriteria spatial decision support system(MC-SDSS)for the house selection problem.The house selection ontology serves as a foundation for spatial multicriteria decision analysis(MCDA)in the house selection domain.It is built using the Web Ontology Language(OWL).The ontology represents the spatial MCDA knowledge associated with house selection using semantic machine-interpretable concepts and relationships in such a way that they can be used by machines not just for display purposes,but also for processing,automation,integration,and reuse across applications.It contains concepts(or classes)including quantitative and qualitative criteria(objectives and attributes),decision alternatives(houses for sale),criterion weights,and location attributes of the decision alternatives.The concepts are organized into a hierarchical classification structure using the Analytic Hierarchy Process.To evaluate the decision alternatives,a set of rules is implemented within the OWL knowledge base with the Semantic Web Rule Language.The rules are expressed as combinations of the OWL concepts and their properties.The paper illustrates an implementation of the proposed ontology-based MC-SDSS architecture using a case study of house selection in the City of Tehran,Iran.
基金Funded by the National Seientific Foundation of China(No.40001017)the Fok Ying Tung Education Foundation(No.71017)the LIESM ARS Foun-dation(W KL(02)0301)and the Chinese PostdoctoraI Foundation(No.2003033454).
文摘The basic mathematic models,such as the statistic model,the time-serial model,the spatial dynamic model etc.,and some typical analysis methods based on 3DCM are proposed and discussed.A few typical spatial decision making methods integrating the spatial analysis and the basic mathematical models are also introduced,e.g.visual impact assessment,dispersion of noise immissions,base station plan for wireless communication.In addition,a new idea of expectation of further applications and add-in-value service of 3DCM is promoted.As an example,the sunshine analysis is studied and some helpful conclusions are drawn.
基金support provided by Co-mision Nacional de Investigacion Cientifica y Tecnologica(CONICYT),through FONDECYT project 1110 798:“Determinacion de indicadores geograficoambien-tales y de riesgo natural en el paisaje de La Araucania y Los Rios:Herramientas de soporte decisional para la planificacion y gestion territorial en sistemas costeros”.
文摘Coastal zones are very dynamic and fragile environments, constituting a landscape ever more heterogeneous, fragmented and with increasing levels of complexity due to the changing relationship between man and nature. Integrated coastal zone management therefore requires detailed knowledge of the system and its components, based—to a large extent—on technical and scientific information. However, the information generated must be in line with the political requirements necessary for decision-making and planning. Thus the use of indicators to give a simplified view of the many components of the territory, and at the same time to provide important information about patterns or trends, becomes a tool of the utmost importance. These indicators can be understood as measurable characteristics of the environment, which facilitate comprehension of the processes occurring at different scales and serve as a reference to inform the population and support decision-making. The aim of the present note is to demonstrate briefly the need to develop geographical-environmental and natural risk indicators to facilitate comprehension of the dynamic of spatial and temporal landscape patterns, particularly in coastal environments. This approach offers an historical summary of the natural, socio-economic and political processes which currently make up the territory, and which without doubt will continue to influence it in the future. At the same time, it is proposed that information should be integrated on the basis of this framework with a view to generating spatial decision support systems in a context of planning and integrated management of the coastal zones of Chile.
基金This research is supported by the MIC ( Ministry of Information and Communication) , Korea ,underthe ITRC(Information Technology Research Center) support program supervised by the IITA(Institute of Information Tech-nology Assessment)
文摘In decision support system for spatial site selection, the fuzzy synthetic evaluation is a useful way. However, the method can’t pay attention to the randomness in factors. To remedy the problem, this paper proposes a clouded-base fuzzy approach which combines advantages of cloud transform and fuzzy synthetic evaluation. The cloud transform considers the randomness in the factors and product the higher concept layer for data mining. At the same time, the check mechanism controls the quality of partitions in factors. Then the fuzzy approach was used to get final evaluation value with randomness and fuzziness. It make the final result is optimization. Finally, performance evaluations show that this approach spent less runtime and got more accuracy than the fuzzy synthetic. The experiments prove that the proposed method is faster and more accuracy than the original method.
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.
文摘In order to solve existing problems about the method of establishing traditional system structure of decision support system(DSS), O S chart is applied to describe object oriented system structure of general DSS, and a new method of eight specific steps is proposed to establish object oriented system structure of DSS by using the method of O S chart, which is applied successfully to the development of the DSS for the energy system ecology engineering research of the Wangheqiu country. Supplying many scientific effective computing models, decision support ways and a lot of accurate reliable decision data, the DSS plays a critical part in helping engineering researchers to make correct decisions. Because the period for developing the DSS is relatively shorter, the new way improves the efficiency of establishing DSS greatly. It also makes the DSS of system structure more flexible and easy to expand.
文摘In this article, our research aims to set up a geo-decisional system, more precisely we are particularly interested in the spatial analysis system of agricultural production in Madagascar. For this, we used the spatial data warehouse technique based on the SOLAP spatial analysis tool. After having defined the concepts underlying these systems, we propose to address the research issues related to them from four points of view: needs study of the Malagasy Ministry of Agriculture, modeling of a multidimensional conceptual model according to the MultiDim model and the implementation of the system studied using GeoKettle, PostGIS, GeoServer, SPAGO BI and Géomondrian technologies. This new system helps improve the decision-making process for agricultural production in Madagascar.
文摘Based on platform of GIS software ArcView and theory of management information system(MIS), a decision support system on urban landscape planning was designed via GIS technology, module design technique and object-oriented programming technique. The function of this system is realized by its two subsystems—one is for height limit model of city and another is for landscape belt planning, which can help administors in landscape planning.
