The Experts below are selected from a list of 25689 Experts worldwide ranked by ideXlab platform
J E Lombard - One of the best experts on this subject based on the ideXlab platform.
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Decision analysis model for paratuberculosis control in commercial dairy herds
Preventive Veterinary Medicine, 2006Co-Authors: Nathan C Dorshorst, Michael T. Collins, J E LombardAbstract:A previous economic test-and-cull Decision analysis model has been strengthened and updated with current epidemiologic information. Created using Excel 1 and PrecisionTree 1 software, the model incorporates costs and benefits of herd management changes, diagnostic testing, and different management actions based on test results to control paratuberculosis in commercial dairy herds. This novel ‘‘JD-Tree’’ model includes a herd management Decision Node (four options), a test/no test Decision Node (two options), a diagnostic test choice Decision Node (five options), test result chance Nodes (four levels of possible results), and test action Decision Nodes (three options; cull, manage, no action). The model culminates in a chance Node for true infection status. Outcomes are measured as a net cost‐benefit value to the producer. The model demonstrates that improving herd management practices to control infection spread (hygiene) is often more cost-effective than testing; not all herds should test as part of a paratuberculosis control program. For many herds, low-cost tests are more useful than more sensitive, higher cost tests. The model also indicates that test-positive cows in early stages of infection may be retained in the herd to generate farm income, provided they are managed properly to limit infection transmission. JD-Tree is a useful instructional tool, helping veterinarians understandthecomplexinteractions affectingtheeconomicsof paratuberculosis controland todefine the accuracy and cost specifications of better diagnostic tests. # 2006 Elsevier B.V. All rights reserved.
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Decision analysis model for paratuberculosis control in commercial dairy herds
Preventive Veterinary Medicine, 2006Co-Authors: Nathan C Dorshorst, Michael T. Collins, J E LombardAbstract:A previous economic test-and-cull Decision analysis model has been strengthened and updated with current epidemiologic information. Created using Excel and PrecisionTree software, the model incorporates costs and benefits of herd management changes, diagnostic testing, and different management actions based on test results to control paratuberculosis in commercial dairy herds. This novel "JD-Tree" model includes a herd management Decision Node (four options), a test/no test Decision Node (two options), a diagnostic test choice Decision Node (five options), test result chance Nodes (four levels of possible results), and test action Decision Nodes (three options; cull, manage, no action). The model culminates in a chance Node for true infection status. Outcomes are measured as a net cost-benefit value to the producer. The model demonstrates that improving herd management practices to control infection spread (hygiene) is often more cost-effective than testing; not all herds should test as part of a paratuberculosis control program. For many herds, low-cost tests are more useful than more sensitive, higher cost tests. The model also indicates that test-positive cows in early stages of infection may be retained in the herd to generate farm income, provided they are managed properly to limit infection transmission. JD-Tree is a useful instructional tool, helping veterinarians understand the complex interactions affecting the economics of paratuberculosis control and to define the accuracy and cost specifications of better diagnostic tests.
Nathan C Dorshorst - One of the best experts on this subject based on the ideXlab platform.
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Decision analysis model for paratuberculosis control in commercial dairy herds
Preventive Veterinary Medicine, 2006Co-Authors: Nathan C Dorshorst, Michael T. Collins, J E LombardAbstract:A previous economic test-and-cull Decision analysis model has been strengthened and updated with current epidemiologic information. Created using Excel 1 and PrecisionTree 1 software, the model incorporates costs and benefits of herd management changes, diagnostic testing, and different management actions based on test results to control paratuberculosis in commercial dairy herds. This novel ‘‘JD-Tree’’ model includes a herd management Decision Node (four options), a test/no test Decision Node (two options), a diagnostic test choice Decision Node (five options), test result chance Nodes (four levels of possible results), and test action Decision Nodes (three options; cull, manage, no action). The model culminates in a chance Node for true infection status. Outcomes are measured as a net cost‐benefit value to the producer. The model demonstrates that improving herd management practices to control infection spread (hygiene) is often more cost-effective than testing; not all herds should test as part of a paratuberculosis control program. For many herds, low-cost tests are more useful than more sensitive, higher cost tests. The model also indicates that test-positive cows in early stages of infection may be retained in the herd to generate farm income, provided they are managed properly to limit infection transmission. JD-Tree is a useful instructional tool, helping veterinarians understandthecomplexinteractions affectingtheeconomicsof paratuberculosis controland todefine the accuracy and cost specifications of better diagnostic tests. # 2006 Elsevier B.V. All rights reserved.
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Decision analysis model for paratuberculosis control in commercial dairy herds
Preventive Veterinary Medicine, 2006Co-Authors: Nathan C Dorshorst, Michael T. Collins, J E LombardAbstract:A previous economic test-and-cull Decision analysis model has been strengthened and updated with current epidemiologic information. Created using Excel and PrecisionTree software, the model incorporates costs and benefits of herd management changes, diagnostic testing, and different management actions based on test results to control paratuberculosis in commercial dairy herds. This novel "JD-Tree" model includes a herd management Decision Node (four options), a test/no test Decision Node (two options), a diagnostic test choice Decision Node (five options), test result chance Nodes (four levels of possible results), and test action Decision Nodes (three options; cull, manage, no action). The model culminates in a chance Node for true infection status. Outcomes are measured as a net cost-benefit value to the producer. The model demonstrates that improving herd management practices to control infection spread (hygiene) is often more cost-effective than testing; not all herds should test as part of a paratuberculosis control program. For many herds, low-cost tests are more useful than more sensitive, higher cost tests. The model also indicates that test-positive cows in early stages of infection may be retained in the herd to generate farm income, provided they are managed properly to limit infection transmission. JD-Tree is a useful instructional tool, helping veterinarians understand the complex interactions affecting the economics of paratuberculosis control and to define the accuracy and cost specifications of better diagnostic tests.
Michael T. Collins - One of the best experts on this subject based on the ideXlab platform.
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Decision analysis model for paratuberculosis control in commercial dairy herds
Preventive Veterinary Medicine, 2006Co-Authors: Nathan C Dorshorst, Michael T. Collins, J E LombardAbstract:A previous economic test-and-cull Decision analysis model has been strengthened and updated with current epidemiologic information. Created using Excel 1 and PrecisionTree 1 software, the model incorporates costs and benefits of herd management changes, diagnostic testing, and different management actions based on test results to control paratuberculosis in commercial dairy herds. This novel ‘‘JD-Tree’’ model includes a herd management Decision Node (four options), a test/no test Decision Node (two options), a diagnostic test choice Decision Node (five options), test result chance Nodes (four levels of possible results), and test action Decision Nodes (three options; cull, manage, no action). The model culminates in a chance Node for true infection status. Outcomes are measured as a net cost‐benefit value to the producer. The model demonstrates that improving herd management practices to control infection spread (hygiene) is often more cost-effective than testing; not all herds should test as part of a paratuberculosis control program. For many herds, low-cost tests are more useful than more sensitive, higher cost tests. The model also indicates that test-positive cows in early stages of infection may be retained in the herd to generate farm income, provided they are managed properly to limit infection transmission. JD-Tree is a useful instructional tool, helping veterinarians understandthecomplexinteractions affectingtheeconomicsof paratuberculosis controland todefine the accuracy and cost specifications of better diagnostic tests. # 2006 Elsevier B.V. All rights reserved.
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Decision analysis model for paratuberculosis control in commercial dairy herds
Preventive Veterinary Medicine, 2006Co-Authors: Nathan C Dorshorst, Michael T. Collins, J E LombardAbstract:A previous economic test-and-cull Decision analysis model has been strengthened and updated with current epidemiologic information. Created using Excel and PrecisionTree software, the model incorporates costs and benefits of herd management changes, diagnostic testing, and different management actions based on test results to control paratuberculosis in commercial dairy herds. This novel "JD-Tree" model includes a herd management Decision Node (four options), a test/no test Decision Node (two options), a diagnostic test choice Decision Node (five options), test result chance Nodes (four levels of possible results), and test action Decision Nodes (three options; cull, manage, no action). The model culminates in a chance Node for true infection status. Outcomes are measured as a net cost-benefit value to the producer. The model demonstrates that improving herd management practices to control infection spread (hygiene) is often more cost-effective than testing; not all herds should test as part of a paratuberculosis control program. For many herds, low-cost tests are more useful than more sensitive, higher cost tests. The model also indicates that test-positive cows in early stages of infection may be retained in the herd to generate farm income, provided they are managed properly to limit infection transmission. JD-Tree is a useful instructional tool, helping veterinarians understand the complex interactions affecting the economics of paratuberculosis control and to define the accuracy and cost specifications of better diagnostic tests.
Afshin Fallahi - One of the best experts on this subject based on the ideXlab platform.
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sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Ataollah Ebrahimzadeh, Maryam Najimi, Seyed Mehdi Hosseini Andargoli, Afshin FallahiAbstract:In this paper, an energy-efficient scheme is proposed for cooperative spectrum sensing in cognitive sensor networks. In our scheme, we introduce a technique to select the sensing Nodes and to set energy detection threshold so that energy saving can be accomplished in the Nodes. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on global probability of detection and probability of false alarm by determining the detection threshold and selection of the sensing Nodes. The energy detector is applied to detect the primary-user activity for the sake of simplicity. At first, it is assumed that the instantaneous signal-to-noise ratio (SNR) for each Node is known. Then, the optimal conditions are obtained, and a closed-form equation is expressed to determine the priority of Nodes for spectrum sensing, as well as the optimum detection threshold. This problem is also solved when the average SNRs of sensors are available according to real situations. To achieve more energy savings, the problem of joint sensing Node selection, detection threshold, and Decision Node selection is analyzed, and an efficient solution is extracted based on the convex optimization framework. Simulation results show that the proposed algorithms lead to significant energy savings in cognitive sensor networks.
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a novel sensing Nodes and Decision Node selection method for energy efficiency of cooperative spectrum sensing in cognitive sensor networks
IEEE Sensors Journal, 2013Co-Authors: Maryam Najimi, Ataollah Ebrahimzadeh, Seyed Mehdi Hosseini Andargoli, Afshin FallahiAbstract:In this paper, we address the problem of sensor selection for energy efficient spectrum sensing in cognitive sensor networks. We consider minimizing energy consumption and improving spectrum sensing performance simultaneously. For this purpose, we employ the energy detector for spectrum sensing and formulate the problem of sensor selection in order to achieve energy efficiency in spectrum sensing while reducing complexity. Due to the NP-complete nature of the problem, we simplify the problem to a more tractable form through mapping assignment indices from integer to the real domain. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of Nodes for spectrum sensing. In the next step, to save more energy, the Decision Node (DN) selection procedure is proposed to address the problem of direct transmissions to fusion center. Then, the problem of joint sensing Node selection and DN selection is analyzed and an efficient solution is extracted based on the convex optimization framework. The novelty of the proposed work is to address the selection of the best sensing Nodes while minimizing energy consumption. Simulation results show that significant energy is saved due to the proposed schemes in different scenarios.
Ataollah Ebrahimzadeh - One of the best experts on this subject based on the ideXlab platform.
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sensor selection and optimal energy detection threshold for efficient cooperative spectrum sensing
IEEE Transactions on Vehicular Technology, 2015Co-Authors: Ataollah Ebrahimzadeh, Maryam Najimi, Seyed Mehdi Hosseini Andargoli, Afshin FallahiAbstract:In this paper, an energy-efficient scheme is proposed for cooperative spectrum sensing in cognitive sensor networks. In our scheme, we introduce a technique to select the sensing Nodes and to set energy detection threshold so that energy saving can be accomplished in the Nodes. Our objective is to minimize the energy consumed in distributed sensing subject to constraints on global probability of detection and probability of false alarm by determining the detection threshold and selection of the sensing Nodes. The energy detector is applied to detect the primary-user activity for the sake of simplicity. At first, it is assumed that the instantaneous signal-to-noise ratio (SNR) for each Node is known. Then, the optimal conditions are obtained, and a closed-form equation is expressed to determine the priority of Nodes for spectrum sensing, as well as the optimum detection threshold. This problem is also solved when the average SNRs of sensors are available according to real situations. To achieve more energy savings, the problem of joint sensing Node selection, detection threshold, and Decision Node selection is analyzed, and an efficient solution is extracted based on the convex optimization framework. Simulation results show that the proposed algorithms lead to significant energy savings in cognitive sensor networks.
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a novel sensing Nodes and Decision Node selection method for energy efficiency of cooperative spectrum sensing in cognitive sensor networks
IEEE Sensors Journal, 2013Co-Authors: Maryam Najimi, Ataollah Ebrahimzadeh, Seyed Mehdi Hosseini Andargoli, Afshin FallahiAbstract:In this paper, we address the problem of sensor selection for energy efficient spectrum sensing in cognitive sensor networks. We consider minimizing energy consumption and improving spectrum sensing performance simultaneously. For this purpose, we employ the energy detector for spectrum sensing and formulate the problem of sensor selection in order to achieve energy efficiency in spectrum sensing while reducing complexity. Due to the NP-complete nature of the problem, we simplify the problem to a more tractable form through mapping assignment indices from integer to the real domain. Based on the standard optimization techniques, the optimal conditions are obtained and a closed-form equation is expressed to determine the priority of Nodes for spectrum sensing. In the next step, to save more energy, the Decision Node (DN) selection procedure is proposed to address the problem of direct transmissions to fusion center. Then, the problem of joint sensing Node selection and DN selection is analyzed and an efficient solution is extracted based on the convex optimization framework. The novelty of the proposed work is to address the selection of the best sensing Nodes while minimizing energy consumption. Simulation results show that significant energy is saved due to the proposed schemes in different scenarios.
