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Assessing vehicle interdiction strategies on a complex transportation network: A simulation-based study

SOCIO-ECONOMIC PLANNING SCIENCES 2024年 95卷

作者： Samanta, Sukanya Sen, Goutam Uniyal, Jatin Ghosh, Soumya Kanti Kyushu Univ Dept Informat Informat Sci & Elect Engn ISEE Fukuoka 8190395 Japan Indian Inst Technol Kharagpur Dept Ind & Syst Engn Kharagpur 721302 W Bengal India Indian Inst Technol Kharagpur Dept Comp Sci & Engn Kharagpur 721302 W Bengal India

The escape interdiction problem within the context of attacker activities on a transportation network is addressed in this study. In the absence of traffic within the network, the attacker attempts to flee the city by choosing one of the shortest paths from the crime scene to a randomly selected exit point. However, in the presence of traffic, the attacker strategically selects the optimal route that minimizes his time to reach a randomly selected exit point. On the other side, defenders try to interdict the attacker on his escape route. Defenders face the daunting challenge of interdicting the attacker's escape route while operating under limited resources. Dealing with a real city road network further adds complexity to the scenario. A simulation-based model is proposed for the optimal allocation of resources to tackle this issue. The focus then shifts to the development of an advanced search strategy that involves routing with optimal resource allocation. This paper presents the first comparative study for escape interdiction problems within a simulation environment, explicitly focusing on solution methodologies. An optimal resource allocation approach is proposed in the presence of traffic, constituting a novel contribution that has not been previously implemented in escape interdiction problems. In addition, the paper introduces a Genetic Algorithm (GA)-based meta-heuristic approach within a simulation environment. This approach generates optimal paths for defenders, wherein each node is associated with a fixed time window, representing the defender's waiting time. In this proposed methodology, defenders undertake a tour of the network rather than remaining stationary at a single location. This approach expands the network search capabilities, thereby requiring optimization to ascertain the optimal routes and schedules for the defender vehicles. A case study is conducted using the map of IIT Kharagpur, India, to evaluate the effectiveness of this approach. By e

关键词： Resource allocation Genetic algorithm Vehicle routing and scheduling Network optimization linear regression model Simulator software

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Effect of CGM in the HbA1c and Coefficient of Variation of glucose in a pediatric sample

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PRIMARY CARE DIABETES 2021年第2期15卷 289-292页

作者： Lou, Graciela Larramona, Gemma Montaner, Teresa Barbed, Sara Hosp Infantil Miguel Servet Unidad Diabet Pediat Paseo Isabel Catolica 1-3 Zaragoza 5009 Spain Ctr Salud Binefar Calle Mariano Pano 34 Binefar 22500 Spain Univ Zaragoza Fac Econ & Empresa Gran Via 2 Zaragoza 50005 Spain Univ Zaragoza Fac Econ & Empresa Zaragoza 50018 Spain Ctr Salud Ejea Caballeros Zaragoza 50600 Spain

Aim of the study: Previous studies have found no significant improvements in glycated hemoglobin (HbA1c), while using Continuous Glucose Monitoring (CGM), with children and adolescents. The aim of this paper is to measure the change in HbA1c, and the Coefficient of Variation in glucose levels, when using CGM, once the effect of other relevant variables, such as gender, actual age, the years the patient has had diabetes, use of an insulin pump, the presence of autoimmune disease, other associated pathologies, and weekly hours of exercise, are controlled for. Methods: This is a retrospective study that uses a linear regression model. Data was collected from Type 1 Diabetes Mellitus (T1DM), children diagnosed between 2003 and 2017 in the Pediatric Unit for Diabetes in Zaragoza, Spain. We used a linear regression and the method of estimation is Ordinary Least Squares. Results: Results show that the use of CGM decreased the HbA1c value by 3.5% and the Coefficient of Variation by 14%. Conclusions: The implication of these results is that this device helped in the management of diabetes, although more research is needed to distinguish between different devices in terms of their efficacy. (c) 2020 Primary Care Diabetes Europe. Published by Elsevier Ltd. All rights reserved.

关键词： Continuous Glucose Monitoring Type 1 Diabetes Mellitus Glycosylated hemoglobin A Coefficient of Variation linear regression model

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On-Site Sensor Calibration Procedure for Quality Assurance of Barometric Process Separation (BaPS) Measurements

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SENSORS 2023年第10期23卷 4615-4615页

作者： Munz, Hannah Ingwersen, Joachim Streck, Thilo Univ Hohenheim Inst Soil Sci & Land Evaluat Biogeophys Sect D-70599 Stuttgart Germany

Barometric process separation (BaPS) is an automated laboratory system for the simultaneous measurement of microbial respiration and gross nitrification rates in soil samples. To ensure optimal functioning, the sensor system, consisting of a pressure sensor, an O-2 sensor, a CO2 concentration sensor, and two temperature probes, must be accurately calibrated. For the regular on-site quality control of the sensors, we developed easy, inexpensive, and flexible calibration procedures. The pressure sensor was calibrated by means of a differential manometer. The O-2 and CO2 sensors were simultaneously calibrated through their exposure to a sequence of O-2 and CO2 concentrations obtained by sequentially exchanging O-2/N-2 and CO2/N-2 calibration gases. linear regression models were best suited for describing the recorded calibration data. The accuracy of O-2 and CO2 calibration was mainly affected by the accuracy of the utilized gas mixtures. Because the applied measuring method is based on the O-2 conductivity of ZrO2, the O-2 sensor is particularly susceptible to aging and to consequent signal shifts. Sensor signals were characterized by high temporal stability over the years. Deviations in the calibration parameters affected the measured gross nitrification rate by up to 12.5% and affected the respiration rate by up to 5%. Overall, the proposed calibration procedures are valuable tools for ensuring the quality of BaPS measurements and for promptly identifying sensor malfunctions.

关键词： sensor calibration calibration quality linear regression model sensor noise error analysis

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A direct error correction method for quantum machine learning

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QUANTUM INFORMATION PROCESSING 2023年第2期22卷 119-119页

作者： Ohno, Hiroshi Toyota Cent Res & Dev Labs Inc 41-1 Yokomichi Nagakute Aichi 4801192 Japan

In performing quantum machine learning on quantum computers (noisy intermediate-scale quantum devices), we need quantum noise and error mitigation methods to obtain reliable outcomes. We thus propose a correction method using learning-based error mitigation for hybrid quantum-classical machine learning, which can be used by experimenters without prior knowledge of the quantum noise channel models. A correction function using linear regression models inspired by the depolarizing noise channel is trained, and the function is to correct classical probabilities of each qubit of the quantum circuit for the quantum machine learning model. In constructing the training data for the correction function, we assume that quantum circuit simulators yield classical probabilities with the noise-free state as the ground truth. In the experiments, quantum generative models, quantum neural networks, and quantum circuit learning models were used for demonstrating the effectiveness of the proposed method on noisy quantum devices (simulation models) and real devices. Experimental results demonstrated that the proposed method worked well for the error correction, and the convergence speed of training the models was improved.

关键词： Hybrid quantum-classical machine learning Quantum error mitigation Quantum generative model Depolarizing noise channel linear regression model

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Quantitative evaluation of rejuvenation treatment of nasolabial fold wrinkles by regression model and 3D photography

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JOURNAL OF COSMETIC DERMATOLOGY 2021年第1期20卷 338-345页

作者： Fang, Rou-Yu Zhang, Han-Chao Liu, Yu-Guang Sun, Qiu-Ning Peking Union Med Coll Hosp Dept Dermatol 1 Shuaifuyuan Beijing 100730 Peoples R China Johns Hopkins Univ Dept Biostat Dept Appl Math Baltimore MD 21205 USA

Objective With the application of 3D photography, our study aimed to quantify parameters of static nasolabial fold wrinkles and establish mathematic regression model between parameters of wrinkles and age, further to quantitatively evaluate the effect of rejuvenation treatment in terms of age. Methods From October 2016 to May 2018, 433 Chinese female volunteers, aged 25-60 years old, were enrolled in this study. Antera 3D camera was used to collect four parameters of static nasolabial fold wrinkles on the left and right sides of the volunteers, including overall size, average depth (mm), average width (mm), and maximum depth (mm). For those presented a linear relationship with age, univariate linear regression fitting was performed, followed by residual analysis, goodness of fit test, and significance test. Results The results of univariate linear regression fitting showed there was a clear linear relationship between the maximum depth, average depth, overall size of nasolabial fold wrinkles and age, and the regression equations were established. The significance test of regression coefficients showed P values were less than .0001. Conclusions With application of the regression model between parameters of nasolabial fold wrinkles and age, the effect of rejuvenation treatment can be quantitatively evaluated in terms of age, which has certain reference and promotion value.

关键词： linear regression model nasolabial fold wrinkles quantitative evaluation rejuvenation treatment

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Combination of the modified Kibria-Lukman and the principal component regression estimators

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COMMUNICATIONS IN STATISTICS-SIMULATION AND COMPUTATION 2023年

作者： Huang, Dan Huang, Jiewu Bai, Dewei Moutai Inst Dept Business Adm Guizhou Peoples R China Guizhou Minzu Univ Coll Data Sci & Informat Engn Guizhou Peoples R China

Statistical inference with the ordinary least squares (OLS) estimator is frequently influenced when there is a multicollinearity in the linear regression model. In this article, to reduce these effects of multicollinearity, we generalize the modified Kibria-Lukman principal component (MKLPC) estimator in the linear regression model by combining the principal component regression (PCR) estimator and the modified Kibria-Lukman (MKL) estimator. Meanwhile, the necessary and sufficient conditions for the superiority of the MKLPC estimator over OLS, PCR, Ridge, r-k, Liu, r-d, k-d, KL, and MKL estimators in the mean squared error (MSE) criterion are derived. Furthermore, we conduct Monte Carlo simulation and empirical analysis to compare these estimators under the MSE criterion.

关键词： linear regression model Multicollinearity Principal component estimator MKL estimator Mean squared error

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Research on Software Defect Prediction Method Based on model Reuse 23

Research on Software Defect Prediction Method Based on Model...

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Proceedings of the 3rd International Conference on Electronic Information Technology and Smart Agriculture

作者： Xiong Li Wei Liang Soft School Hunan College of Information China Ideological and Political Theory Teaching Department Hunan International Business Vocational College China

ISBN: (纸本)9798400716775

Software reuse has been the most effective way to improve the quality and efficiency of software development for a long time. model-based software reuse(MBSR) development method can greatly improve the efficiency and quality of reuse by providing reusable software components and models for reuse. However, this reuse will inevitably lead to software defects, and software defect prediction has always been an important research direction in the field of software engineering. The accuracy of software defect prediction depends not only on the choice of prediction methods, but also on the metrics of software. Therefore, for software defect prediction based on model reuse, a software defect prediction method based on multi-metrics is proposed. The multi-metrics of the model are constructed by the characteristics of defect type, defect severity, possibility of defect generation, defect priority, defect state, defect source, defect root and so on. The correlation and significance methods were used to determine the decisive influencing factors. The defect samples are trained by the hybrid neural network to obtain the optimal solution of the training results, and the defect prediction model based on linear regression is constructed. Through the similarity screening of the historical data of the model library, the least square method is used to solve the regression coefficient to reduce the error of the predicted value and improve the accuracy of the defect prediction.

关键词： Hybrid neural network linear regression model model reuse Multi-metrics Software defect prediction

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Integrating Multiple linear regression and Infectious Disease models for Predicting Information Dissemination in Social Networks

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Journal of Electronic Research and Application 2023年第2期7卷 20-27页

作者： Junchao Dong Tinghui Huang Liang Min Wenyan Wang School of Computer Engineering Guilin University of Electronic TechnologyBeihai 536000Guangxi Zhuang Autonomous RegionChina School of Computer Science and Information Security Guilin University of Electronic TechnologyGuilin 541004Guangxi Zhuang Autonomous RegionChina The Youth Innovation Team of Shaanxi Universities Xi’an Jiaotong University City CollegeXi’an 710018Shaanxi ProvinceChina School of Computer Sciences Universiti Sains MalaysiaGelugor 11800PenangMalaysia

Social network is the mainstream medium of current information dissemination,and it is particularly important to accurately predict its propagation *** this paper,we introduce a social network propagation model integrating multiple linear regression and infectious disease ***,we proposed the features that affect social network communication from three ***,we predicted the node influence via multiple linear ***,we used the node influence as the state transition of the infectious disease model to predict the trend of information dissemination in social *** experimental results on a real social network dataset showed that the prediction results of the model are consistent with the actual information dissemination trends.

关键词： Social networks Epidemic model linear regression model

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Segregation of formulated powders in direct compression process and evaluations by small bench-scale testers

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INTERNATIONAL JOURNAL OF PHARMACEUTICS 2023年 647卷 123544页

作者： Deng, Tong Sousa, Lucas Massaro Garg, Vivek Bradley, Michael S. A. Univ Greenwich Fac Engn & Sci Wolfson Ctr Bulk Solids Handling Technol Cent Ave Chatham ME4 4TB England IFP Energies nouvelles Rond Point Echangeur Solaize F-69360 Solaize France

Powder segregation can cause severe issues in processes of pharmaceutical drugs for control of content uniformity if the powder is likely to be free or easy flowing. Assessing segregation intensity of formulated powders in a process is challenging at the formulation stage because of the limited availability of samples. An advanced segregation evaluation using small bench-scale testers can be useful for formulation decisions and suggestions of operation conditions in the process, which has not been practically investigated before. In this study, eight formulations (two co-processed excipients blended with one active pharmaceutical ingredient at different ratios) were used for the segregation study on two types of bench-scale testers (air-induced and surface rolling segregation tester), and a pilot simulation process rig as a comparative study. The results show that segregation measured on the bench-scale testers can give a good indication of the segregation intensity of a blend if the segregation intensity is not more than 20%. The comparison also shows that both the bench-scale testers have a good correlation to the process rig, respectively, which means either segregation tester can be used independently for the evaluation. A linear regression model was explored for prediction of segregation in the process.

关键词： Segregations in process Formulated powders Bench-scale testers linear regression model Direct compression process

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Learning based short term wind speed forecasting models for smart grid applications: An extensive review and case study

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ELECTRIC POWER SYSTEMS RESEARCH 2023年第1期222卷

作者： Saini, Vikash Kumar Kumar, Rajesh Al-Sumaiti, Ameena S. Sujil, A. Heydarian-Forushani, Ehsan Malaviya Natl Inst Technol Ctr Energy & Environm Jaipur India Malaviya Natl Inst Technol Dept Elect Engn Jaipur India Khalifa Univ Adv Power & Energy Ctr Dept Elect Engn & Comp Sci Abu Dhabi U Arab Emirates Rajarambapu Inst Technol Dept Elect Engn Uran Islampur Maharashtra India Qom Univ Technol Dept Elect & Comp Engn Qom Iran

This paper provides an extensive review of learning-based short-term forecasting models for smart grid applications. In addition to this, the paper also explores forecasting models including physical, statistical, hybrid, and uncertainty analysis models for wind speed forecasting. The learning-based models are classified into three broad categories, namely classical machine learning, advanced machine learning, and probabilistic learning. In this work, 41 different models are employed to forecast the wind speed. Dataset for this case study is collected from the site of Jodhpur, India. Dataset have 8759 sample with five features i.e., wind speed, pressure, humidity, temperature, and dew point. This forecast also includes the seasonal effects. model accuracy has been tested considering single and multiple features in the input data. A comparative analysis of the performance of these 41 learning-based models is conducted based on coefficient of regression and error indices. It is observed that the performance of these models varies with the variability in the season. On the basis of the evaluation of these models, future recommendations are also framed out. These recommendations target of energy storage planning, energy market and policymakers, and reliability and reserve sizing direction. These recommendations can be utilized by authorities for effective planning and coordination of power.

关键词： Renewable energy Learning based short term wind speed forecasting Machine learning models linear regression model

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