检索结果-内蒙古大学图书馆

Predictability of real temporal networks

National Science Review 2020年第5期7卷 929-937页

作者： Disheng Tang Wenbo Du Louis Shekhtman Yijie Wang Shlomo Havlin Xianbin Cao Gang Yan School of Electronic and Information Engineering Beihang University Shanghai Institute of Intelligence Science and Technology Tongji University Network Science Institute Northeastern University Department of Physics Bar Ilan University CAS Center for Excellence in Brain Science and Intelligence Technology Chinese Academy of Sciences National Engineering Laboratory of Big Data Application Technologies of Comprehensive Transportation School of Physics Science and Engineering Tongji University

Links in most real networks often change over time. Such temporality of links encodes the ordering and causality of interactions between nodes and has a profound effect on network dynamics and *** evidence has shown that the temporal nature of links in many real-world networks is not ***, it is challenging to predict temporal link patterns while considering the entanglement between topological and temporal link patterns. Here, we propose an entropy-rate-based framework, based on combined topological–temporal regularities, for quantifying the predictability of any temporal network. We apply our framework on various model networks, demonstrating that it indeed captures the intrinsic topological–temporal regularities whereas previous methods considered only temporal aspects. We also apply our framework on 18 real networks of different types and determine their predictability. Interestingly,we find that, for most real temporal networks, despite the greater complexity of predictability brought by the increase in dimension, the combined topological–temporal predictability is higher than the temporal predictability. Our results demonstrate the necessity for incorporating both temporal and topological aspects of networks in order to improve predictions of dynamical processes.

关键词： temporal network predictability network entropy predictive algorithm

来源：评论

学校读者我要写书评

暂无评论

Cooperative Vehicle Following based on predictive Control with Communication Delay Compensation 24

Cooperative Vehicle Following based on Predictive Control wi...

引用

24th International Conference on System Theory, Control and Computing (ICSTCC)

作者： Tiganasu, Alexandru Lazar, Corneliu Caruntu, Constantin Florin Gheorghe Asachi Tech Univ Iasi Dept Automat Control & Appl Informat Iasi Romania

ISBN: (纸本)9781728198095

In recent years, the problem of crowded public roads became more and more stringent. Some solutions for this problem may be represented by the individual advanced driver assistance systems (ADAS) that can make a vehicle more or less autonomous through a set of sensors, actuators and control algorithms. Depending on the level of automation, the driver needs to intervene or not in some traffic situations. Most of the ADAS systems treat two cases given by the movement direction of the vehicle: longitudinal or lateral. For the longitudinal direction the variable that must be controlled is the vehicle's velocity. In this paper, a cooperative vehicle following strategy is proposed based on predictive control. The movement of a leader and a follower together as a single entity is in focus for this case study. The leader is designed using a Cruise Control (CC) algorithm based on a generalized predictive controller (GPC). For the follower, a cooperative predictive adaptive controller (CPAC) with delay compensation is considered. Besides the feedback part, CPAC contains a feedforward element to counteract the negative effect of measurable disturbances that are represented by the velocities received from the leader. For this group of two vehicles implemented with the above-mentioned algorithms, a MATLAB/Simulink simulator was developed to analyze the obtained performance.

关键词： vehicle following cooperative control delay compensation predictive algorithm spacing/velocity adaptation inter-vehicle communications

来源：评论

学校读者我要写书评

暂无评论

Friction Coefficient Analysis of Nano-crystalline TiO2-Added Alumina Ceramics

Friction Coefficient Analysis of Nano-crystalline TiO2-Added...

引用

International Conference on Thermal Engineering and Management Advances, ICTEMA 2020

作者： Haldar, Partha Bhattacharya, Tapas Kumar Modak, Nipu Department of Mechanical Engineering Government College of Engineering and Ceramic Technology Kolkata India Department of Ceramic Technology Government College of Engineering and Ceramic Technology Kolkata Kolkata India Department of Mechanical Engineering Jadavpur University Kolkata India

ISBN: (纸本)9789811623462

The friction coefficient of sintered alumina with nano-crystalline titania (up to 4 wt.%) is investigated in relation with sliding frequency and normal load by computational method. The proposed work studies the variation of friction coefficient with change in sliding frequency up to 60 Hz and normal load up to 1 kgf independently, and the data is used to develop an algorithm following the best fit topology. Among various curve fitting techniques like polynomial fit of degree two, exponential fit of degree one and two, smoothing spline and Weibull fit, with minimum root mean square error, cubic spline fit method is adopted to form smooth curves in MATLAB. The algorithm is validated to predict the addition level of titania in alumina for given friction coefficient and sliding frequency or normal load. The result lies within a maximum error of 4.43% for cubic spline fit. © 2021, The Author(s), under exclusive license to Springer Nature Singapore Pte Ltd.

关键词： Coefficient of friction Cubic spline fit predictive algorithm RMSE TiO2-Alumina system

来源：评论

学校读者我要写书评

暂无评论

A review on machine learning algorithms to predict daylighting inside buildings

引用

SOLAR ENERGY 2020年 202卷 249-275页

作者： Ayoub, Mohammed Arab Acad Sci Technol & Maritime Transport Architectural Engn & Environm Design Dept Alexandria Egypt

Steep increases in air temperatures and CO2 emissions have been associated with the global demand for energy. This is coupled with population growth and improved living standards that encourages the reliance on mechanical acclimatization. Lighting energy alone is responsible for a large portion of total energy consumption in office buildings;and the demand for artificial light is expected to grow in the next years. One of sustainable approaches to enhance energy-efficiency is to incorporate daylighting strategies, which entail the controlled use of daylight inside buildings. Daylight simulation is an active area of research that offers accurate estimations, yet requires a complex set of inputs. Even with today's computers, simulations are computationally expensive and time-consuming, hindering to acquire accelerated preliminary approximations in acceptable timeframes, especially for the iterative design alternatives. Alternatively, predictive models that build on machine learning algorithms have granted much interest from the building design community due to their ability to handle such complex non-linear problems, acting as proxies to heavy simulations. This research presents a review on the growing directions that exploit machine learning to rapidly predict daylighting performance inside buildings, putting a particular focus on scopes of prediction, used algorithms, data sources and sizes, besides evaluation metrics. This work should improve architects decision-making and increase the applicability to predict daylighting. Another implication is to point towards knowledge gaps and missing opportunities in the related research domain, revealing future trends that allow for such innovative approaches to be exploited more commonly in Architectural practice.

关键词： Daylighting Illuminance Prediction Estimation Machine learning predictive algorithm

来源：评论

学校读者我要写书评

暂无评论

Optimal Operation of Microgrid Employing predictive Control 15

Optimal Operation of Microgrid Employing Predictive Control

引用

15th International Conference on Development and Application Systems (DAS)

作者： Butaru, Florinel Carstea, Ciprian Lascu, Cristian Ancuti, Mihaela-Codruta Univ Politehn Timisoara Elect Engn Dept Timisoara Romania

ISBN: (纸本)9781728168708

The purpose of the present paper is to build a predictive algorithm based on the output voltage of the LC filter in order to achieve the AC Microgrids operation in the maximum power point tracking of the captured solar energy. Methods of regulating the microgrids input voltage are conventionally achieved by linear control loops. This type of control presents limitations like high sensitivity to parameter variations and slow transient response. The operation of the microgrids at the maximum power point is analyzed. The inverter's the optimal power values are determined based on the simulated data. The simulations were carried out in the MATLAB/Simulink simulation environment. Based on the vector control, the voltage drops on the inverter transistors are calculated in advanced for the next step, based on the phase diagram. A predictive algorithm is designed to ensure a stable operation in the maximum power point area, MPP, (i.e. the optimal area from the energy point of view).

关键词： predictive algorithm solar power plant energy efficiency maximum power point variable solar power

来源：评论

学校读者我要写书评

暂无评论

The Development and Validation of Simplified Machine Learning algorithms to Predict Prognosis of Hospitalized Patients With COVID-19: Multicenter, Retrospective Study

引用

JOURNAL OF MEDICAL INTERNET RESEARCH 2022年第1期24卷 e31549页

作者： He, Fang Page, John H. Weinberg, Kerry R. Mishra, Anirban Amgen Inc Ctr Observat Res 1120 Vet Blvd San Francisco CA 94080 USA Amgen Inc Digital Hlth & Innovat Thousand Oaks CA 91320 USA Amgen Inc Ctr Observat Res Thousand Oaks CA 91320 USA

Background: The current COVID-19 pandemic is unprecedented;under resource-constrained settings, predictive algorithms can help to stratify disease severity, alerting physicians of high-risk patients;however, there are only few risk scores derived from a substantially large electronic health record (EHR) data set, using simplified predictors as input. Objective: The objectives of this study were to develop and validate simplified machine learning algorithms that predict COVID-19 adverse outcomes;to evaluate the area under the receiver operating characteristic curve (AUC), sensitivity, specificity, and calibration of the algorithms;and to derive clinically meaningful thresholds. Methods: We performed machine learning model development and validation via a cohort study using multicenter, patient-level, longitudinal EHRs from the Optum COVID-19 database that provides anonymized, longitudinal EHR from across the United States. The models were developed based on clinical characteristics to predict 28-day in-hospital mortality, intensive care unit (ICU) admission, respiratory failure, and mechanical ventilator usages at inpatient setting. Data from patients who were admitted from February 1, 2020, to September 7, 2020, were randomly sampled into development, validation, and test data sets;data collected from September 7, 2020, to November 15, 2020, were reserved as the postdevelopment prospective test data set. Results: Of the 3.7 million patients in the analysis, 585,867 patients were diagnosed or tested positive for SARS-CoV-2, and 50,703 adult patients were hospitalized with COVID-19 between February 1 and November 15, 2020. Among the study cohort (n=50,703), there were 6204 deaths, 9564 ICU admissions, 6478 mechanically ventilated or EMCO patients, and 25,169 patients developed acute respiratory distress syndrome or respiratory failure within 28 days since hospital admission. The algorithms demonstrated high accuracy (AUC 0.89, 95% CI 0.89-0.89 on the test data set [n=

关键词： COVID-19 predictive algorithm prognostic model machine learning

来源：评论

学校读者我要写书评

暂无评论

Using real-world data to dynamically predict flares during tapering of biological DMARDs in rheumatoid arthritis: development, validation, and potential impact of prediction-aided decisions

引用

ARTHRITIS RESEARCH & THERAPY 2022年第1期24卷 74-74页

作者： van der Leeuw, Matthijs S. Messelink, Marianne A. Tekstra, Janneke Medina, Ojay van Laar, Jaap M. Haitjema, Saskia Lafeber, Floris Veris-van Dieren, Josien J. van der Goes, Marlies C. den Broeder, Alfons A. Welsing, Paco M. J. Univ Utrecht Univ Med Ctr Utrecht Dept Rheumatol & Clin Immunol Heidelberglaan 100 NL-3584 CX Utrecht Netherlands Univ Utrecht Univ Med Ctr Utrecht Dept Digital Hlth Heidelberglaan 100 NL-3584 CX Utrecht Netherlands Univ Utrecht Univ Med Ctr Utrecht Cent Diagnost Lab Heidelberglaan 100 NL-3584 CX Utrecht Netherlands Reumazorg Zuid West Nederland Van Hertumweg 17-19 NL-4462 EV Goes Netherlands Meander Med Ctr Dept Rheumatol Maatweg 3 NL-3813 TZ Amersfoort Netherlands Sint Maartensklin Dept Rheumatol Hengstdal 3 NL-6574 NA Ubbergen Netherlands

Background Biological disease-modifying antirheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis. However, as bDMARDs may also lead to adverse events and are expensive, tapering them is of great clinical interest. Tapering according to disease activity-guided dose optimization (DGDO) does not seem to affect long term remission rates, but flares are frequent during this process. Our objective was to develop a model for the prediction of flares during bDMARD tapering using data from routine care and to evaluate its potential clinical impact. Methods We used a joint latent class model to repeatedly predict the probability of a flare occurring within the next 3 months. The model was developed using longitudinal data on disease activity (DAS28) and other routine care data from two clinics. predictive accuracy was assessed in cross-validation and external validation was performed with data from the DRESS (Dose REduction Strategy of Subcutaneous tumor necrosis factor inhibitors) trial. Additionally, we simulated the reduction in number of flares and bDMARD dose when implementing the model as a decision aid during bDMARD tapering in the DRESS trial. Results Data from 279 bDMARD courses were used for model development. The final model included two latent DAS28-trajectories, bDMARD type and dose, disease duration, and seropositivity. The area under the curve of the final model was 0.76 (0.69-0.83) in cross-validation and 0.68 (0.62-0.73) in external validation. In simulation of prediction-aided decisions, the mean number of flares over 18 months decreased from 1.21 (0.99-1.43) to 0.75 (0.54-0.96). The reduction in he bDMARD dose was mostly maintained, increasing from 54 to 64% of full dose. Conclusions We developed a dynamic flare prediction model, exclusively based on data typically available in routine care. Our results show that using this model to aid decisions during bDMARD tapering may significantly reduce the number of flares while maintaini

关键词： Rheumatoid arthritis predictive algorithm Tapering bDMARD therapy Applied data analytics in medicine Biologicals

来源：评论

学校读者我要写书评

暂无评论

Predicting misdiagnosed adult-onset type 1 diabetes using machine learning

引用

DIABETES RESEARCH AND CLINICAL PRACTICE 2022年 191卷 110029-110029页

作者： Cheheltani, Rabee King, Nicholas Lee, Suyin North, Benjamin Kovarik, Danny -Molina, Carmella Evans Leavitt, Nadejda Dutta, Sanjoy IQVIA Real World Solut Predict Analyt Wayne PA USA JDRF New York NY USA Indiana Univ Sch Med Ctr Diabet & Metab Dis Indianapolis IN USA 200 Vesey St 28th Floor New York NY 10281 USA

Aims: It is now understood that almost half of newly diagnosed cases of type 1 diabetes are adult-onset. However, type 1 and type 2 diabetes are difficult to initially distinguish clinically in adults, potentially leading to inef-fective care. In this study a machine learning model was developed to identify type 1 diabetes patients mis-diagnosed as type 2 ***: In this retrospective study, a machine learning model was developed to identify misdiagnosed type 1 diabetes patients from a population of patients with a prior type 2 diabetes diagnosis. Using Ambulatory Elec-tronic Medical Records (AEMR), features capturing relevant information on age, demographics, risk factors, symptoms, treatments, procedures, vitals, or lab results were extracted from patients' medical ***: The model identified age, BMI/weight, therapy history, and HbA1c/blood glucose values among top predictors of misdiagnosis. Model precision at low levels of recall (10 %) was 17 %, compared to <1 % incidence rate of misdiagnosis at the time of the first type 2 diabetes encounter in ***: This algorithm shows potential for being translated into screening guidelines or a clinical decision support tool embedded directly in an EMR system to reduce misdiagnosis of adult-onset type 1 diabetes and implement effective care at the outset.

关键词： Adult -onset type 1 diabetes Machine learning Misdiagnosis Clinical decision support tool predictive algorithm AI

来源：评论

学校读者我要写书评

暂无评论

Prediction Aided Tapering In rheumatoid arthritis patients treated with biOlogicals (PATIO): protocol for a randomized controlled trial

引用

TRIALS 2022年第1期23卷 494-494页

作者： Messelink, Marianne A. van der Leeuw, Matthijs S. den Broeder, Alfons A. Tekstra, Janneke van der Goes, Marlies C. Heijstek, Marloes W. Lafeber, Floris Welsing, Paco M. J. Univ Utrecht Univ Med Ctr Utrecht Dept Rheumatol & Clin Immunol Heidelberglaan 100 NL-3508 GA Utrecht Netherlands Sint Maartensklin Dept Rheumatol Hengstdal 3 NL-3508 GA Ubbergen Netherlands Meander Med Ctr Dept Rheumatol Maatweg 3 NL-3813 TZ Amersfoort Netherlands

Background: Biological disease-modifying anti-rheumatic drugs (bDMARDs) are effective in the treatment of rheumatoid arthritis (RA) but are expensive and increase the risk of infection. Therefore, in patients with a stable low level of disease activity or remission, tapering bDMARDs should be considered. Although tapering does not seem to affect long-term disease control, (short-lived) flares are frequent during the tapering process. We have previously developed and externally validated a dynamic flare prediction model for use as a decision aid during stepwise tapering of bDMARDs to reduce the risk of a flare during this process. Methods: In this investigator-initiated, multicenter, open-label, randomized (1:1) controlled trial, we will assess the effect of incorporating flare risk predictions into a bDMARD tapering strategy. One hundred sixty RA patients treated with a bDMARD with stable low disease activity will be recruited. In the control group, the bDMARD will be tapered according to "disease activity guided dose optimization" (DGDO). In the intervention group, the bDMARD will be tapered according to a strategy that combines DGDO with the dynamic flare prediction model, where the next bDMARD tapering step is not taken in case of a high risk of flare. Patients will be randomized 1:1 to the control or intervention group. The primary outcome is the number of flares per patient (DAS28-CRP increase > 1.2, or DAS28-CRP increase > 0.6 with a current DAS28-CRP >= 2.9) during the 18-month follow-up period. Secondary outcomes include the number of patients with a major flare (flare duration >= 12 weeks), bDMARD dose reduction, adverse events, disease activity (DAS28-CRP) and patient-reported outcomes such as quality of life and functional disability. Health Care Utilization and Work Productivity will also be assessed. Discussion: This will be the first clinical trial to evaluate the benefit of applying a dynamic flare prediction model as a decision aid during bDMARD tape

关键词： Rheumatoid arthritis Dose reduction Tapering bDMARD Biological Clinical decision aid Prediction model Randomized controlled trial Study protocol predictive algorithm

来源：评论

学校读者我要写书评

暂无评论

The application of a neural network to predict hypotension and vasopressor requirements non-invasively in obstetric patients having spinal anesthesia for elective cesarean section (C/S)

引用

BMC ANESTHESIOLOGY 2020年第1期20卷 98-98页

作者： Gratz, Irwin Baruch, Martin Takla, Magdy Seaman, Julia Allen, Isabel McEniry, Brian Deal, Edward Cooper Univ Hosp 1 Cooper Plaza Camden NJ 08103 USA Caretaker Med Charlottesville VA USA Quahog Res Grp Oakland CA USA Univ Calif San Francisco San Francisco CA 94143 USA

Background Neural networks are increasingly used to assess physiological processes or pathologies, as well as to predict the increased likelihood of an impending medical crisis, such as hypotension. Method We compared the capabilities of a single hidden layer neural network of 12 nodes to those of a discrete-feature discrimination approach with the goal being to predict the likelihood of a given patient developing significant hypotension under spinal anesthesia when undergoing a Cesarean section (C/S). Physiological input information was derived from a non-invasive blood pressure device (Caretaker [CT]) that utilizes a finger cuff to measure blood pressure and other hemodynamic parameters via pulse contour analysis. Receiver-operator-curve/area-under-curve analyses were used to compare performance. Results The results presented here suggest that a neural network approach (Area Under Curve [AUC] = 0.89 [p < 0.001]), at least at the implementation level of a clinically relevant prediction algorithm, may be superior to a discrete feature quantification approach (AUC = 0.87 [p < 0.001]), providing implicit access to a plurality of features and combinations thereof. In addition, the expansion of the approach to include the submission of other physiological data signals, such as heart rate variability, to the network can be readily envisioned. Conclusion This pilot study has demonstrated that increased coherence in Arterial Stiffness (AS) variability obtained from the pulse wave analysis of a continuous non-invasive blood pressure device appears to be an effective predictor of hypotension after spinal anesthesia in the obstetrics population undergoing C/S. This allowed us to predict specific dosing thresholds of phenylephrine required to maintain systolic blood pressure above 90 mmHg.

关键词： Arterial stiffness Cesarean section Finger cuff Hypotension Neural network Non-invasive predictive algorithm

来源：评论

学校读者我要写书评

暂无评论

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案：

请选择收藏分类：

通借通还

建议与咨询 留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

分类表

所选分类

限定检索结果

文献类型

馆藏范围

日期分布

学科分类号

主题

机构

作者

语言

请选择保存的检索档案： 新增检索档案 确定 取消

请选择收藏分类： 新增自定义分类 确定 取消

通借通还

建议与咨询留下您的常用邮箱和电话号码，以便我们向您反馈解决方案和替代方法

请选择保存的检索档案：

请选择收藏分类：