This work presents a numerical methodology for modeling the Winkler supports and nonlinear conditions by proposing new boundary conditions. For the boundary conditions of Winkler support model, the surface tractions a...This work presents a numerical methodology for modeling the Winkler supports and nonlinear conditions by proposing new boundary conditions. For the boundary conditions of Winkler support model, the surface tractions and the displacements normal to the surface of the solid are unknown, but their relationship is known by means of the ballast coefficient, whereas for nonlinear boundary conditions, the displacements normal to the boundary of the solid are zero in the positive direction but are allowed in the negative direction. In those zones, detachments of nodes might appear, leading to a nonlinearity, because the number of nodes that remain fixed or of the detached ones (under tensile tractions) is unknown. The proposed methodology is applied to the 3D elastic receding contact problem using the boundary element method. The surface t r actions and the displacements of the common int erface bet ween the two solids in contac t under the influence of different supports are calculated as well as the boundary zone of the solid where the new boundary conditions are applied. The problem is solved by a double-iterative met hod, so in the final solut ion, t here are no t r act ions or pene trations between the two solids or at the boundary of the solid where the nonlinear boundary conditions are Simula ted. The effectiveness of the proposed method is verified by examples.展开更多
With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there i...With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.展开更多
This work aims to investigate the manufacturing equipment support model for the purpose of improving the efficiency and quality of manufacturing. First, the concept of manufacturing capacity is defined, and the re- la...This work aims to investigate the manufacturing equipment support model for the purpose of improving the efficiency and quality of manufacturing. First, the concept of manufacturing capacity is defined, and the re- lationship between practical and expected manufacturing capacity is described. Then the concept of role is intro- duced and the manufacturing equipment role is defined in detail. Based on the analysis of manufacturing capacity and manufacturing equipment role, the three-stage manufacturing equipment support model is proposed. With this model, the manufacturing task can be decomposed into several manufacturing equipment roles, and the ex- pended manufacturing capacity involved in the manufacturing equipment role can be matched with the practical manufacturing capacity of the enterprise. The measures are discussed depending on different matching degrees.展开更多
This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical ...This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.展开更多
In order to estimate the readiness, sustainability and support capability of the operational unit, an support simulation concept model of the military equipment is given as viewed from the system engineering modeling ...In order to estimate the readiness, sustainability and support capability of the operational unit, an support simulation concept model of the military equipment is given as viewed from the system engineering modeling and simulation. Simulation test of military aircraft is analyzed in detail, it is composed of the operational mission, function maintenance process and resource modeling.展开更多
Purpose: In super-aging societies, prosthodontists will have a growing role and will need to improve their nutrition knowledge. This study aimed to evaluate the effectiveness of a workshop-based model for increasing d...Purpose: In super-aging societies, prosthodontists will have a growing role and will need to improve their nutrition knowledge. This study aimed to evaluate the effectiveness of a workshop-based model for increasing dysphagia diet awareness among prosthodontists working with head and neck cancer patients. Methods: The study had a post-intervention design and included 10 maxillofacial prosthetic educators from eight countries who participated in a 120-minute workshop focused on theoretical and practical training in nutrition support for patients with dysphagia. Sessions were held in a specialized restaurant in Tokyo and included lectures, observation of Japanese cooking techniques, hands-on preparation of dysphagia-friendly foods, and cross-cultural comparisons. Knowledge, confidence, and practical application were assessed using a post-workshop questionnaire. Descriptive statistics and thematic analysis were used to evaluate outcomes. Results: Seven of the 10 prosthodontists completed the post-intervention questionnaire. All respondents reported overall satisfaction with the workshop. Session content was regarded as easy to understand by 57.14%, appropriate by 28.57%, and easy by 14.29%. Most respondents (85.71%) were “very satisfied” with the instructors’ explanations, and 100% were “very satisfied” with the workshop’s length and structure;71.42% felt they could apply the knowledge in clinical practice, while 28.58% anticipated challenges. The respondents appreciated the workshop’s focus on dysphagia, particularly in elderly patients, and valued the insights into Japanese dysphagia diets and culture. Conclusions: Workshops on nutrition provide an interactive platform for prosthodontists to enhance their knowledge and improve comprehensive patient care, highlighting the importance for prosthodontists to stay updated on developments in nutrition, particularly in dysphagia.展开更多
Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument...Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.展开更多
Objective: To explore nutritional support under the Neuman systems model in treating dysphagia in stroke patients. Methods: In this retrospective study, we enrolled 97 patients with dysphagia after stroke admitted to ...Objective: To explore nutritional support under the Neuman systems model in treating dysphagia in stroke patients. Methods: In this retrospective study, we enrolled 97 patients with dysphagia after stroke admitted to our hospital, and randomly divided them into the Neuman group (n = 51) given nursing intervention based on Neuman systems model and a control group (n = 46) given routine nursing intervention. Both groups received nutritional support for 3 months. Nutritional indexes (serum total protein, plasma albumin, serum albumin, hemoglobin and transferrin levels) and immune indexes (immunoglobulin (Ig) A, IgG, IgM and total lymphocyte count (TLC) in both groups were recorded and compared. Pulmonary function recovery, video fluoroscopic swallowing study score, water swallowing test score, complication rate, and health knowledge mastery level were also compared between the two groups. Results: After the intervention, the Neuman group showed less decrease in the nutritional and immune index scores (serum total protein, plasma albumin, hemoglobin, serum albumin;IgA, IgG, IgM, and TLC;all P Conclusion: For patients with stroke and dysphagia, comprehensive nursing intervention (e.g., nutritional support) under theNeuman systems model can promote the recovery of immune, swallowing, and pulmonary function, reduce complication incidence and facilitate comprehensive rehabilitation, ensuring adequate nutritional intake.展开更多
With the beginning of the information systems’ spreading, people started thinking about using them for making business decisions. Computer technology solutions, such as the Decision Support System, make the decision-...With the beginning of the information systems’ spreading, people started thinking about using them for making business decisions. Computer technology solutions, such as the Decision Support System, make the decision-making process less complex and simpler for problem-solving. In order to make a high-quality business decision, managers need to have a great deal of appropriate information. Nonetheless, this complicates the process of making appropriate decisions. In a situation like that, the possibility of using DSS is quite logical. The aim of this paper is to find out the intended use of DSS for medium and large business organizations in USA by applying the Technology Acceptance Model (TAM). Different models were developed in order to understand and predict the use of information systems, but the information systems community mostly used TAM to ensure this issue. The purpose of the research model is to determine the elements of analysis that contribute to these results. The sample for the research consisted of the target group that was supposed to have completed an online questionnaire about the manager’s use of DSS in medium and large American companies. The information obtained from the questionnaires was analyzed through the SPSS statistical software. The research has indicated that, this is primarily used due to a significant level of Perceived usefulness and For the Perceived ease of use.展开更多
The aim of this article is to develop a structural equation model to assess key factors of residents' support for hosting mega event based on previous literature.The model consisted of five latent constructs and e...The aim of this article is to develop a structural equation model to assess key factors of residents' support for hosting mega event based on previous literature.The model consisted of five latent constructs and eight path hypotheses.A survey was conducted in Shanghai before 2010 World Expo.It was found that the support for mega events is affected directly and/or indirectly by four determinants factors:perceived benefits,perceived costs,personal benefits and community attachment,and support relies heavily on perceived benefits rather than costs.This study contributes to the existing body of knowledge in an attempt to understand local residents' support for a mega event in different economic and cultural settings.展开更多
Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for ...Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(PI-12,PI-12-2,and PI-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56%(n=18)and 6.06%(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95%confidence interval[CI]:0.71–0.96)for PI-24.PI-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.PI-24 performed well in the outer validation cohort with an AUC value of 0.82(95%CI:0.65–0.98),specificity of 0.97(95%CI:0.91–1.00),and PPV of 0.67(95%CI:0.00–1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.展开更多
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 this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the...In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.展开更多
In transonic wind tunnel tests,the pulsating airflow is prone to induce the first order resonance of the sting support system.The resonance limits the wind tunnel test envelope,makes the test data inaccurate,and bring...In transonic wind tunnel tests,the pulsating airflow is prone to induce the first order resonance of the sting support system.The resonance limits the wind tunnel test envelope,makes the test data inaccurate,and brings potential security risks.In this paper,a model support sting with constrained layer damping(CLD)treatment is proposed to reduce the first order resonance response.The CLD treatment mainly consists of material selection and geometric optimization processes.The damping performance of the optimized CLD sting is compared with an AISI 1045 steel sting with the identical diameter in laboratory.The frequency response curves of the CLD sting support system and the AISI 1045 steel sting support system are obtained by sine sweep tests.The test results show that the first order resonance response of the CLD sting support system is 37.3%of that of the AISI 1045 steel sting support system.The first order damping ratios are calculated from the frequency response curves by half power point method.It is found that the first order damping ratio of the CLD sting support system is approximately 2.6 times that of the AISI 1045 steel sting support system.展开更多
Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measur...Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.展开更多
Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new s...Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.展开更多
This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used ...This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.展开更多
Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning fo...Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.展开更多
In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression ...In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.展开更多
文摘This work presents a numerical methodology for modeling the Winkler supports and nonlinear conditions by proposing new boundary conditions. For the boundary conditions of Winkler support model, the surface tractions and the displacements normal to the surface of the solid are unknown, but their relationship is known by means of the ballast coefficient, whereas for nonlinear boundary conditions, the displacements normal to the boundary of the solid are zero in the positive direction but are allowed in the negative direction. In those zones, detachments of nodes might appear, leading to a nonlinearity, because the number of nodes that remain fixed or of the detached ones (under tensile tractions) is unknown. The proposed methodology is applied to the 3D elastic receding contact problem using the boundary element method. The surface t r actions and the displacements of the common int erface bet ween the two solids in contac t under the influence of different supports are calculated as well as the boundary zone of the solid where the new boundary conditions are applied. The problem is solved by a double-iterative met hod, so in the final solut ion, t here are no t r act ions or pene trations between the two solids or at the boundary of the solid where the nonlinear boundary conditions are Simula ted. The effectiveness of the proposed method is verified by examples.
文摘With the expansion of the office building area,the energy consumption of office buildings is growing.High⁃performance building design contributes to energy saving and the development of green buildings.However,there is a lack of high⁃performance building tools and the workflow is often time⁃consuming.The building performance simulation,multiple objective optimizations,and the decision support model are the new approaches of high⁃performance building design.This paper proposes a newly developed decision support model,a high⁃performance building decision model named HPBuildingDSM,which integrates the building performance simulation,building performance multiple objective optimizations,building performance sampling,and parameter sensitivity analysis to design high⁃performance office buildings.In this research,the HPBuildingDSM was operated to search for the desirable office building design results with low⁃energy and high⁃quality daylighting performances.The simulated results had better daylighting performance and lower energy consumption,whose UDI100-2000 was 37.94%and annual energy consumption performance was 76.28 kWh/(m2·a),indicating a better building performance than the optimized results in the previous case study.
基金supported by the Special Fund for Basic Scientific Research of Central Colleges under Grant No.CHD2011JC092the Special Fund Project of Basic Research Support Plan o f Chang'an Universitythe Ministry of Education Foundation of Humanities and Social Science under Grant No.12YJC630201
文摘This work aims to investigate the manufacturing equipment support model for the purpose of improving the efficiency and quality of manufacturing. First, the concept of manufacturing capacity is defined, and the re- lationship between practical and expected manufacturing capacity is described. Then the concept of role is intro- duced and the manufacturing equipment role is defined in detail. Based on the analysis of manufacturing capacity and manufacturing equipment role, the three-stage manufacturing equipment support model is proposed. With this model, the manufacturing task can be decomposed into several manufacturing equipment roles, and the ex- pended manufacturing capacity involved in the manufacturing equipment role can be matched with the practical manufacturing capacity of the enterprise. The measures are discussed depending on different matching degrees.
文摘This study aimed to develop a clinical Decision Support Model (DSM) which is software that provides physicians and other healthcare stakeholders with patient-specific assessments and recommendation in aiding clinical decision-making while discharging Breast cancer patient since the diagnostics and discharge problem is often overwhelming for a clinician to process at the point of care or in urgent situations. The model incorporates Breast cancer patient-specific data that are well-structured having been attained from a prestudy’s administered questionnaires and current evidence-based guidelines. Obtained dataset of the prestudy’s questionnaires is processed via data mining techniques to generate an optimal clinical decision tree classifier model which serves physicians in enhancing their decision-making process while discharging a breast cancer patient on basic cognitive processes involved in medical thinking hence new, better-formed, and superior outcomes. The model also improves the quality of assessments by constructing predictive discharging models from code attributes enabling timely detection of deterioration in the quality of health of a breast cancer patient upon discharge. The outcome of implementing this study is a decision support model that bridges the gap occasioned by less informed clinical Breast cancer discharge that is based merely on experts’ opinions which is insufficiently reinforced for better treatment outcomes. The reinforced discharge decision for better treatment outcomes is through timely deployment of the decision support model to work hand in hand with the expertise in deriving an integrative discharge decision and has been an agreed strategy to eliminate the foreseeable deteriorating quality of health for a discharged breast cancer patients and surging rates of mortality blamed on mistrusted discharge decisions. In this paper, we will discuss breast cancer clinical knowledge, data mining techniques, the classifying model accuracy, and the Python web-based decision support model that predicts avoidable re-hospitalization of a breast cancer patient through an informed clinical discharging support model.
文摘In order to estimate the readiness, sustainability and support capability of the operational unit, an support simulation concept model of the military equipment is given as viewed from the system engineering modeling and simulation. Simulation test of military aircraft is analyzed in detail, it is composed of the operational mission, function maintenance process and resource modeling.
文摘Purpose: In super-aging societies, prosthodontists will have a growing role and will need to improve their nutrition knowledge. This study aimed to evaluate the effectiveness of a workshop-based model for increasing dysphagia diet awareness among prosthodontists working with head and neck cancer patients. Methods: The study had a post-intervention design and included 10 maxillofacial prosthetic educators from eight countries who participated in a 120-minute workshop focused on theoretical and practical training in nutrition support for patients with dysphagia. Sessions were held in a specialized restaurant in Tokyo and included lectures, observation of Japanese cooking techniques, hands-on preparation of dysphagia-friendly foods, and cross-cultural comparisons. Knowledge, confidence, and practical application were assessed using a post-workshop questionnaire. Descriptive statistics and thematic analysis were used to evaluate outcomes. Results: Seven of the 10 prosthodontists completed the post-intervention questionnaire. All respondents reported overall satisfaction with the workshop. Session content was regarded as easy to understand by 57.14%, appropriate by 28.57%, and easy by 14.29%. Most respondents (85.71%) were “very satisfied” with the instructors’ explanations, and 100% were “very satisfied” with the workshop’s length and structure;71.42% felt they could apply the knowledge in clinical practice, while 28.58% anticipated challenges. The respondents appreciated the workshop’s focus on dysphagia, particularly in elderly patients, and valued the insights into Japanese dysphagia diets and culture. Conclusions: Workshops on nutrition provide an interactive platform for prosthodontists to enhance their knowledge and improve comprehensive patient care, highlighting the importance for prosthodontists to stay updated on developments in nutrition, particularly in dysphagia.
基金supported by the National Key R&D Program of China(Grant No.2022YFC3005401)the Fundamental Research Funds for the Central Universities(Grant No.B230201013)+2 种基金the National Natural Science Foundation of China(Grants No.52309152,U2243223,and U23B20150)the Natural Science Foundation of Jiangsu Province(Grant No.BK20220978)the Open Fund of National Dam Safety Research Center(Grant No.CX2023B03).
文摘Deformation monitoring is a critical measure for intuitively reflecting the operational behavior of a dam.However,the deformation monitoring data are often incomplete due to environmental changes,monitoring instrument faults,and human operational errors,thereby often hindering the accurate assessment of actual deformation patterns.This study proposed a method for quantifying deformation similarity between measurement points by recognizing the spatiotemporal characteristics of concrete dam deformation monitoring data.It introduces a spatiotemporal clustering analysis of the concrete dam deformation behavior and employs the support vector machine model to address the missing data in concrete dam deformation monitoring.The proposed method was validated in a concrete dam project,with the model error maintaining within 5%,demonstrating its effectiveness in processing missing deformation data.This approach enhances the capability of early-warning systems and contributes to enhanced dam safety management.
文摘Objective: To explore nutritional support under the Neuman systems model in treating dysphagia in stroke patients. Methods: In this retrospective study, we enrolled 97 patients with dysphagia after stroke admitted to our hospital, and randomly divided them into the Neuman group (n = 51) given nursing intervention based on Neuman systems model and a control group (n = 46) given routine nursing intervention. Both groups received nutritional support for 3 months. Nutritional indexes (serum total protein, plasma albumin, serum albumin, hemoglobin and transferrin levels) and immune indexes (immunoglobulin (Ig) A, IgG, IgM and total lymphocyte count (TLC) in both groups were recorded and compared. Pulmonary function recovery, video fluoroscopic swallowing study score, water swallowing test score, complication rate, and health knowledge mastery level were also compared between the two groups. Results: After the intervention, the Neuman group showed less decrease in the nutritional and immune index scores (serum total protein, plasma albumin, hemoglobin, serum albumin;IgA, IgG, IgM, and TLC;all P Conclusion: For patients with stroke and dysphagia, comprehensive nursing intervention (e.g., nutritional support) under theNeuman systems model can promote the recovery of immune, swallowing, and pulmonary function, reduce complication incidence and facilitate comprehensive rehabilitation, ensuring adequate nutritional intake.
文摘With the beginning of the information systems’ spreading, people started thinking about using them for making business decisions. Computer technology solutions, such as the Decision Support System, make the decision-making process less complex and simpler for problem-solving. In order to make a high-quality business decision, managers need to have a great deal of appropriate information. Nonetheless, this complicates the process of making appropriate decisions. In a situation like that, the possibility of using DSS is quite logical. The aim of this paper is to find out the intended use of DSS for medium and large business organizations in USA by applying the Technology Acceptance Model (TAM). Different models were developed in order to understand and predict the use of information systems, but the information systems community mostly used TAM to ensure this issue. The purpose of the research model is to determine the elements of analysis that contribute to these results. The sample for the research consisted of the target group that was supposed to have completed an online questionnaire about the manager’s use of DSS in medium and large American companies. The information obtained from the questionnaires was analyzed through the SPSS statistical software. The research has indicated that, this is primarily used due to a significant level of Perceived usefulness and For the Perceived ease of use.
基金supported by Shanghai Municipal Education Commission (Grant no.egd08025)
文摘The aim of this article is to develop a structural equation model to assess key factors of residents' support for hosting mega event based on previous literature.The model consisted of five latent constructs and eight path hypotheses.A survey was conducted in Shanghai before 2010 World Expo.It was found that the support for mega events is affected directly and/or indirectly by four determinants factors:perceived benefits,perceived costs,personal benefits and community attachment,and support relies heavily on perceived benefits rather than costs.This study contributes to the existing body of knowledge in an attempt to understand local residents' support for a mega event in different economic and cultural settings.
基金supported by the National Natural Science Fund(no.82072200,82200169).
文摘Background:The recognition of pancreatic injury in blunt abdominal trauma is often severely delayed in clinical practice.The aim of this study was to develop a machine learning model to support clinical diagnosis for early detection of abdominal trauma.Methods:We retrospectively analyzed of a large intensive care unit database(Medical Information Mart for Intensive Care[MIMIC]-IV)for model development and internal validation of the model,and performed outer validation based on a cross-national data set.Logistic regres-sion was used to develop three models(PI-12,PI-12-2,and PI-24).Univariate and multivariate analyses were used to determine variables in each model.The primary outcome was early detection of a pancreatic injury of any grade in patients with blunt abdominal trauma in the first 24 hours after hospitalization.Results:The incidence of pancreatic injuries was 5.56%(n=18)and 6.06%(n=6)in the development(n=324)and internal validation(n=99)cohorts,respectively.Internal validation cohort showed good discrimination with an area under the receiver operator characteristic curve(AUC)value of 0.84(95%confidence interval[CI]:0.71–0.96)for PI-24.PI-24 had the best AUC,specificity,and positive predictive value(PPV)of all models,and thus it was chosen as the final model to support clinical diagnosis.PI-24 performed well in the outer validation cohort with an AUC value of 0.82(95%CI:0.65–0.98),specificity of 0.97(95%CI:0.91–1.00),and PPV of 0.67(95%CI:0.00–1.00).Conclusion:A novel machine learning-based model was developed to support clinical diagnosis to detect pancreatic injuries in patients with blunt abdominal trauma at an early stage.
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Project supported by the National Natural Science Foundation of China (Grant No 60573065)the Natural Science Foundation of Shandong Province,China (Grant No Y2007G33)the Key Subject Research Foundation of Shandong Province,China(Grant No XTD0708)
文摘In this paper we apply the nonlinear time series analysis method to small-time scale traffic measurement data. The prediction-based method is used to determine the embedding dimension of the traffic data. Based on the reconstructed phase space, the local support vector machine prediction method is used to predict the traffic measurement data, and the BIC-based neighbouring point selection method is used to choose the number of the nearest neighbouring points for the local support vector machine regression model. The experimental results show that the local support vector machine prediction method whose neighbouring points are optimized can effectively predict the small-time scale traffic measurement data and can reproduce the statistical features of real traffic measurements.
基金supported by Fenglei Youth Innovation Fund of China Aerodynamics Research&Development Center(PJD20180189)Shandong Provincial Natural Science Foundation of China(2019JMRH0307)supported by grants from Shandong University and Taishan Scholar Foundation。
文摘In transonic wind tunnel tests,the pulsating airflow is prone to induce the first order resonance of the sting support system.The resonance limits the wind tunnel test envelope,makes the test data inaccurate,and brings potential security risks.In this paper,a model support sting with constrained layer damping(CLD)treatment is proposed to reduce the first order resonance response.The CLD treatment mainly consists of material selection and geometric optimization processes.The damping performance of the optimized CLD sting is compared with an AISI 1045 steel sting with the identical diameter in laboratory.The frequency response curves of the CLD sting support system and the AISI 1045 steel sting support system are obtained by sine sweep tests.The test results show that the first order resonance response of the CLD sting support system is 37.3%of that of the AISI 1045 steel sting support system.The first order damping ratios are calculated from the frequency response curves by half power point method.It is found that the first order damping ratio of the CLD sting support system is approximately 2.6 times that of the AISI 1045 steel sting support system.
基金This project is supported by National Natural Science Foundation of China(No.50375153).
文摘Aiming at solving the problems of machine-learning in fault diagnosis, a diagnosis approach is proposed based on hidden Markov model （HMM） and support vector machine （SVM）. HMM usually describes intra-class measure well and is good at dealing with continuous dynamic signals. SVM expresses inter-class difference effectively and has perfect classify ability. This approach is built on the merit of HMM and SVM. Then, the experiment is made in the transmission system of a helicopter. With the features extracted from vibration signals in gearbox, this HMM-SVM based diagnostic approach is trained and used to monitor and diagnose the gearbox＇s faults. The result shows that this method is better than HMM-based and SVM-based diagnosing methods in higher diagnostic accuracy with small training samples.
基金This project is supported by Special Foundation for Major State Basic Research of China (No.G1998030415).
文摘Soft sensor is widely used in industrial process control. It plays animportant role to improve the quality of product and assure safety in production. The core of softsensor is to construct soft sensing model. A new soft sensing modeling method based on supportvector machine (SVM) is proposed. SVM is a new machine learning method based on statistical learningtheory and is powerful for the problem characterized by small sample, nonlinearity, high dimensionand local minima. The proposed methods are applied to the estimation of frozen point of light dieseloil in distillation column. The estimated outputs of soft sensing model based on SVM match the realvalues of frozen point and follow varying trend of frozen point very well. Experiment results showthat SVM provides a new effective method for soft sensing modeling and has promising application inindustrial process applications.
基金Supported by the National Natural Science Foundation of China(21076179)the National Basic Research Program of China(2012CB720500)
文摘This paper presents a nonlinear model predictive control(NMPC) approach based on support vector machine(SVM) and genetic algorithm(GA) for multiple-input multiple-output(MIMO) nonlinear systems.Individual SVM is used to approximate each output of the controlled plant Then the model is used in MPC control scheme to predict the outputs of the controlled plant.The optimal control sequence is calculated using GA with elite preserve strategy.Simulation results of a typical MIMO nonlinear system show that this method has a good ability of set points tracking and disturbance rejection.
基金Supported by the National Natural Science Foundation of China(11072035)
文摘Aiming at reducing the deficiency of the traditional fire pre-warning algorithms and the intelligent fire pre-warning algorithms such as artificial neural network,and then to improve the accuracy of fire prewarning for high-rise buildings,a composite fire pre-warning controller is designed according to the characteristic（ nonlinear,less historical data,many influence factors）,also a high-rise building fire pre-warning model is set up based on the support vector regression（ SV R）. Then the wood fire standard history data is applied to make empirical analysis. The research results can provide a reliable decision support framework for high-rise building fire pre-warning.
基金the 973 Program of China (No.2002CB312200)the National Science Foundation of China (No.60574019)
文摘In this paper, a support vector machine-based multi-model predictive control is proposed, in which SVM classification combines well with SVM regression. At first, each working environment is modeled by SVM regression and the support vector machine network-based model predictive control （SVMN-MPC） algorithm corresponding to each environment is developed, and then a multi-class SVM model is established to recognize multiple operating conditions. As for control, the current environment is identified by the multi-class SVM model and then the corresponding SVMN-MPC controller is activated at each sampling instant. The proposed modeling, switching and controller design is demonstrated in simulation results.
