Category: News
URL suggestions randomly disappearing
This bug has been happening for probably a month or two.
Randomly once or twice a week, the address bar would stop showing any URL suggestions when I’m typing.
Regardless of what I type, it just doesn’t show anything. A simple restart fixes it, but clearly this is a bug.
This bug has been happening for probably a month or two.Randomly once or twice a week, the address bar would stop showing any URL suggestions when I’m typing. Regardless of what I type, it just doesn’t show anything. A simple restart fixes it, but clearly this is a bug. Read More
Face Difficulty when converting tensorflow model to Matlab
I have a part of tensorflow code that I need to translate to matlab, but fail to do that. I have checked deep learning toolbox and unable to resolve the issue. If someone can help me this question, it is very helpful.
My tensorflow code (Python) is the following:
def get_r(model,tw,xw,a_data_n,mean_a,mean_u,kP,dim1,dim2,w1,stdt,stdx):
% A tf.GradientTape is used to compute derivatives in TensorFlow
with tf.GradientTape(persistent=True) as tape: % This makes you record the gradients on the tape for the parameters defined
tape.watch(tw) % This is needed to ‘follow’ the time, for automatic differentiation with respect to time
tape.watch(xw) % This is needed to ‘follow’ the position, for automatic differentiation with respect to position
a,u,p = model.net_u(tw,xw)
Px = tape.gradient(p, xw)
At = tape.gradient(a, tw)
ux = tape.gradient(u, xw)
ut = tape.gradient(u, tw)
My Matlab Code is the following: (notice that model.net_u input cannot accept dlarray format, dlarrya has to be done after model.net_u fucntion)
function get_r(model, tw, xw, a_data_n, mean_a, mean_u, kP, dim1, dim2, w1, stdt, stdx)
% Compute derivatives using the MATLAB automatic differentiation functionality
% Run the model
[a, u, p] = model.net_u(tw, xw);
a = dlarray(a);
tw = dlarray(tw);
xw = dlarray(xw);
% Compute gradients by iterating over each element
At = dlgradient(a, tw);
Px = dlgradient(p, xw);
ux = dlgradient(u, xw);
ut = dlgradient(u, tw);
end
Also, my a, u, p variable all have the shape 39600 * 1
My error message is
Error using dlarray/dlgradient (line 105)
Value to differentiate is not traced. It must be a traced real dlarray scalar. Use dlgradient inside a function called by dlfeval to
trace the variables.
Error in get_r (line 12)
At = dlgradient(sum(a, ‘all’), tw);
Can anyone point out how can I improve the code by adding dlfeval and other codes as well.
Additionally, sometimes when I add dlfeval, it will create the following error. What does this error mean?
Error using deep.internal.dlfevalWithNestingCheck (line 14)
Nested dlfeval calls are not supported. To compute higher derivatives, set the ‘EnableHigherDerivatives’ option of the dlgradient
function to true.
Error in dlfeval (line 31)
[varargout{1:nargout}] = deep.internal.dlfevalWithNestingCheck(fun,varargin{:});
Thanks for all suggestions!I have a part of tensorflow code that I need to translate to matlab, but fail to do that. I have checked deep learning toolbox and unable to resolve the issue. If someone can help me this question, it is very helpful.
My tensorflow code (Python) is the following:
def get_r(model,tw,xw,a_data_n,mean_a,mean_u,kP,dim1,dim2,w1,stdt,stdx):
% A tf.GradientTape is used to compute derivatives in TensorFlow
with tf.GradientTape(persistent=True) as tape: % This makes you record the gradients on the tape for the parameters defined
tape.watch(tw) % This is needed to ‘follow’ the time, for automatic differentiation with respect to time
tape.watch(xw) % This is needed to ‘follow’ the position, for automatic differentiation with respect to position
a,u,p = model.net_u(tw,xw)
Px = tape.gradient(p, xw)
At = tape.gradient(a, tw)
ux = tape.gradient(u, xw)
ut = tape.gradient(u, tw)
My Matlab Code is the following: (notice that model.net_u input cannot accept dlarray format, dlarrya has to be done after model.net_u fucntion)
function get_r(model, tw, xw, a_data_n, mean_a, mean_u, kP, dim1, dim2, w1, stdt, stdx)
% Compute derivatives using the MATLAB automatic differentiation functionality
% Run the model
[a, u, p] = model.net_u(tw, xw);
a = dlarray(a);
tw = dlarray(tw);
xw = dlarray(xw);
% Compute gradients by iterating over each element
At = dlgradient(a, tw);
Px = dlgradient(p, xw);
ux = dlgradient(u, xw);
ut = dlgradient(u, tw);
end
Also, my a, u, p variable all have the shape 39600 * 1
My error message is
Error using dlarray/dlgradient (line 105)
Value to differentiate is not traced. It must be a traced real dlarray scalar. Use dlgradient inside a function called by dlfeval to
trace the variables.
Error in get_r (line 12)
At = dlgradient(sum(a, ‘all’), tw);
Can anyone point out how can I improve the code by adding dlfeval and other codes as well.
Additionally, sometimes when I add dlfeval, it will create the following error. What does this error mean?
Error using deep.internal.dlfevalWithNestingCheck (line 14)
Nested dlfeval calls are not supported. To compute higher derivatives, set the ‘EnableHigherDerivatives’ option of the dlgradient
function to true.
Error in dlfeval (line 31)
[varargout{1:nargout}] = deep.internal.dlfevalWithNestingCheck(fun,varargin{:});
Thanks for all suggestions! I have a part of tensorflow code that I need to translate to matlab, but fail to do that. I have checked deep learning toolbox and unable to resolve the issue. If someone can help me this question, it is very helpful.
My tensorflow code (Python) is the following:
def get_r(model,tw,xw,a_data_n,mean_a,mean_u,kP,dim1,dim2,w1,stdt,stdx):
% A tf.GradientTape is used to compute derivatives in TensorFlow
with tf.GradientTape(persistent=True) as tape: % This makes you record the gradients on the tape for the parameters defined
tape.watch(tw) % This is needed to ‘follow’ the time, for automatic differentiation with respect to time
tape.watch(xw) % This is needed to ‘follow’ the position, for automatic differentiation with respect to position
a,u,p = model.net_u(tw,xw)
Px = tape.gradient(p, xw)
At = tape.gradient(a, tw)
ux = tape.gradient(u, xw)
ut = tape.gradient(u, tw)
My Matlab Code is the following: (notice that model.net_u input cannot accept dlarray format, dlarrya has to be done after model.net_u fucntion)
function get_r(model, tw, xw, a_data_n, mean_a, mean_u, kP, dim1, dim2, w1, stdt, stdx)
% Compute derivatives using the MATLAB automatic differentiation functionality
% Run the model
[a, u, p] = model.net_u(tw, xw);
a = dlarray(a);
tw = dlarray(tw);
xw = dlarray(xw);
% Compute gradients by iterating over each element
At = dlgradient(a, tw);
Px = dlgradient(p, xw);
ux = dlgradient(u, xw);
ut = dlgradient(u, tw);
end
Also, my a, u, p variable all have the shape 39600 * 1
My error message is
Error using dlarray/dlgradient (line 105)
Value to differentiate is not traced. It must be a traced real dlarray scalar. Use dlgradient inside a function called by dlfeval to
trace the variables.
Error in get_r (line 12)
At = dlgradient(sum(a, ‘all’), tw);
Can anyone point out how can I improve the code by adding dlfeval and other codes as well.
Additionally, sometimes when I add dlfeval, it will create the following error. What does this error mean?
Error using deep.internal.dlfevalWithNestingCheck (line 14)
Nested dlfeval calls are not supported. To compute higher derivatives, set the ‘EnableHigherDerivatives’ option of the dlgradient
function to true.
Error in dlfeval (line 31)
[varargout{1:nargout}] = deep.internal.dlfevalWithNestingCheck(fun,varargin{:});
Thanks for all suggestions! deep learning MATLAB Answers — New Questions
Deploy Dynamic Routing (BGP) between Azure VPN and Third-Party Firewall (Palo Alto)
This blog explains how to deploy dynamic routing (BGP) between Azure VPN and a third-party firewall. You can refer to this topology and deployment guide in scenarios where you need VPN connectivity between an on-premises third-party VPN device and Azure VPN, or any cloud environment.
Why BGP?
BGP is the standard routing protocol commonly used in the Internet to exchange routing and reachability information between two or more networks. When used in the context of Azure Virtual Networks, BGP enables the Azure VPN gateways and your on-premises VPN devices, called BGP peers or neighbors, to exchange “routes” that will inform both gateways on the availability and reachability for those prefixes to go through the gateways or routers involved. BGP can also enable transit routing among multiple networks by propagating routes a BGP gateway learns from one BGP peer to all other BGP peers.
Diagram Pre-Requisite
Firewall Network: Firewall with three interfaces (Public, Private, Management). Here, the LAB has configured with VM-series Palo Alto firewall.
Azure VPN Network: Test VM, Gateway Subnet
Test Network Connected to Firewall Network: Azure VM with UDR pointing to Firewall’s Internal Interface. The test network should be peered with firewall network.
Configuration
Part 1: Configure Azure VPN with BGP enabled
Create Virtual Network Gateway with below specification
Provide Name, Gateway type (VPN), VPN SKU, VNet (with dedicated Gateway Subnet), Public IP
Enable BGP and provide AS number
Create
Note: Azure will auto provision a local BGP peer with an IP address from Gateway Subnet. After deployment the configuration will look similar to below. Make a note of Public IP and Peer IP generated, we need this while configuring VPN at remote end
Create Local Network Gateway
Local Network Gateway represents the firewall VPN network Configuration where you should provide remote configuration parameters.
Provide Name, Remote peer Public IP
In the Address space specify remote BGP peer IP (Router ID in case of Palo Alto).
Please note that if you are configuring static route instead of dynamic you should advertise entire remote network ranges which you want to communicate through VPN. Here BGP making this process much simpler.
In Advanced tab enable BGP and provide remote ASN Number and BGP peer IP
create
Create Connections with default crypto profile
Once the VPN Gateway and Local Network Gateway has provisioned you can build connection which represents IPsec and IKE configurations.
Go to VPN GW and under Settings, Add Connection
Provide Name, VPN Gateway, Local Network Gateway, Pre-Shared Key
Enable BGP
If Required Modify IPsec and IKE Crypto setting, Else leave it as default
Create
Completed the Azure end configuration, now we can move to firewall side.
Part 2: Configure Palo Alto Firewall VPN with BGP enabled
Create IKE Gateway with default IKE Crypto profile
Provide Ike Version, Local VPN Interface, Peer IP, Pre-shared key
Create IPSec Tunnel with default IPsec Crypto profile
Create Tunnel Interface
Create IPsec Tunnel: Provide tunnel Interface, IPsec Crypto profile, IKE Gateway
By this configuration your tunnel should be UP
Now finish the remaining BGP Configurations
Configure a Loopback interface to represent BGP router, we have provided 10.0.17.5 IP for the interface.
Configure virtual router Redistribution Profile
Configure Redistribution Profile as below, this configuration ensures what kind of routers needed to be redistributed to BGP peer routers
Enable BGP and configure local BGP and peer BGP parameters
Provide Router ID, AS number
Make user to enable Install Route Option
Configure EBGP Peer Group and Peer with Local BGP Peer IP, Remote (Azure)BGP Peer IP and Remote (Azure) BGP ASN Number.
Also Specify Redistribution profile, also make sure to enable Allow Redistribute Default Route, if you need to propagate default route to BGP peer router
Create Static route for Azure BGP peer
Commit changes
Test Results
Now we can test the connectivity, I have already configured necessary NAT and default route in Firewall. You can see the propagated route in both azure VPN gateway and Palo Alto firewall.
FW NAT
Name
Src
Zone
Dst
Zone
Dst
I/F
Dst
Addr
Service
Translated Packet Source Translation
Translated Packet Destination Translation
nattovm1
any
Untrust
any
untrust_int_ip
3389
none
destination-translation;address: vm1
nattovm2
any
Untrust
any
untrust_int_ip
3000
none
destination-translation;address: vm2;port: 3389
natto internet
any
Untrust
ethernet1/1
default
0.0.0.0/0
dynamic-ip-and-port;ethernet1/1
none
Stattic Route configured:
Azure VPN GW Connection Status and Propagated routes
Azure Test VM1 (10.0.0.4) Effective routes
Palo Alto BGP Summary
Palo Alto BGP connection status
Palo Alto BGP Received Route
Palo Alto BGP Propagated Route
Final Forwarding table
Ping and trace result from Test VM1 to test VM2
Conclusion:
BGP simplifies the route advertisement process. There are lot more configuration options that we can try in BGP to smooth functioning of routing. BGP also enables automatic redundancy and high Availability. Hence it is always recommended to configure BGP when it comes to production grade complex networking.
This blog explains how to deploy dynamic routing (BGP) between Azure VPN and a third-party firewall. You can refer to this topology and deployment guide in scenarios where you need VPN connectivity between an on-premises third-party VPN device and Azure VPN, or any cloud environment.
Why BGP?
BGP is the standard routing protocol commonly used in the Internet to exchange routing and reachability information between two or more networks. When used in the context of Azure Virtual Networks, BGP enables the Azure VPN gateways and your on-premises VPN devices, called BGP peers or neighbors, to exchange “routes” that will inform both gateways on the availability and reachability for those prefixes to go through the gateways or routers involved. BGP can also enable transit routing among multiple networks by propagating routes a BGP gateway learns from one BGP peer to all other BGP peers.
Diagram Diagram:BGP Between Azure VPN and Third-Party FirewallPre-Requisite
Firewall Network: Firewall with three interfaces (Public, Private, Management). Here, the LAB has configured with VM-series Palo Alto firewall.
Azure VPN Network: Test VM, Gateway Subnet
Test Network Connected to Firewall Network: Azure VM with UDR pointing to Firewall’s Internal Interface. The test network should be peered with firewall network.
Configuration
Part 1: Configure Azure VPN with BGP enabled
Create Virtual Network Gateway with below specification
Provide Name, Gateway type (VPN), VPN SKU, VNet (with dedicated Gateway Subnet), Public IP
Enable BGP and provide AS number
Create
Note: Azure will auto provision a local BGP peer with an IP address from Gateway Subnet. After deployment the configuration will look similar to below. Make a note of Public IP and Peer IP generated, we need this while configuring VPN at remote end
Create Local Network Gateway
Local Network Gateway represents the firewall VPN network Configuration where you should provide remote configuration parameters.
Provide Name, Remote peer Public IP
In the Address space specify remote BGP peer IP (Router ID in case of Palo Alto).
Please note that if you are configuring static route instead of dynamic you should advertise entire remote network ranges which you want to communicate through VPN. Here BGP making this process much simpler.
In Advanced tab enable BGP and provide remote ASN Number and BGP peer IP
create
Create Connections with default crypto profile
Once the VPN Gateway and Local Network Gateway has provisioned you can build connection which represents IPsec and IKE configurations.
Go to VPN GW and under Settings, Add Connection
Provide Name, VPN Gateway, Local Network Gateway, Pre-Shared Key
Enable BGP
If Required Modify IPsec and IKE Crypto setting, Else leave it as default
Create
Completed the Azure end configuration, now we can move to firewall side.
Part 2: Configure Palo Alto Firewall VPN with BGP enabled
Create IKE Gateway with default IKE Crypto profile
Provide Ike Version, Local VPN Interface, Peer IP, Pre-shared key
Create IPSec Tunnel with default IPsec Crypto profile
Create Tunnel Interface
Create IPsec Tunnel: Provide tunnel Interface, IPsec Crypto profile, IKE Gateway
By this configuration your tunnel should be UP
Now finish the remaining BGP Configurations
Configure a Loopback interface to represent BGP router, we have provided 10.0.17.5 IP for the interface.
Configure virtual router Redistribution Profile
Configure Redistribution Profile as below, this configuration ensures what kind of routers needed to be redistributed to BGP peer routers
Enable BGP and configure local BGP and peer BGP parameters
Provide Router ID, AS number
Make user to enable Install Route Option
Configure EBGP Peer Group and Peer with Local BGP Peer IP, Remote (Azure)BGP Peer IP and Remote (Azure) BGP ASN Number.
Also Specify Redistribution profile, also make sure to enable Allow Redistribute Default Route, if you need to propagate default route to BGP peer router
Create Static route for Azure BGP peer
Commit changes
Test Results
Now we can test the connectivity, I have already configured necessary NAT and default route in Firewall. You can see the propagated route in both azure VPN gateway and Palo Alto firewall.
FW NAT
Name
Src Zone
DstZone
DstI/F
DstAddr
Service
Translated Packet Source Translation
Translated Packet Destination Translation
nattovm1
any
Untrust
any
untrust_int_ip
3389
none
destination-translation;address: vm1
nattovm2
any
Untrust
any
untrust_int_ip
3000
none
destination-translation;address: vm2;port: 3389
natto internet
any
Untrust
ethernet1/1
default
0.0.0.0/0
dynamic-ip-and-port;ethernet1/1
none
Stattic Route configured:
Azure VPN GW Connection Status and Propagated routes
Azure Test VM1 (10.0.0.4) Effective routes
Palo Alto BGP Summary
Palo Alto BGP connection status
Palo Alto BGP Received Route
Palo Alto BGP Propagated Route
Final Forwarding table
Ping and trace result from Test VM1 to test VM2
Conclusion:
BGP simplifies the route advertisement process. There are lot more configuration options that we can try in BGP to smooth functioning of routing. BGP also enables automatic redundancy and high Availability. Hence it is always recommended to configure BGP when it comes to production grade complex networking.
Read More
Error message using SPM command spm_jobman
Hi everyone,
I try to run an adapted preprocessing script (got it from an collegue) for a fMRI analysis. Unfortunatelly, I’m stuck on the command spm_jobman and get the following error message:
Error using spm_jobman>fill_run_job
No executable modules, but still unresolved dependencies or incomplete module inputs.
Error in spm_jobman (line 247)
sts = fill_run_job(‘run’, cjob, varargin{3:end});
Error in s03_preprocessing_with_slice_timing_batch_NICCUE (line 79)
spm_jobman(‘run’, matlabbatch);
It would be really helpful, if somebody could tell me what the problem is.
Thank you very much!
FranziskaHi everyone,
I try to run an adapted preprocessing script (got it from an collegue) for a fMRI analysis. Unfortunatelly, I’m stuck on the command spm_jobman and get the following error message:
Error using spm_jobman>fill_run_job
No executable modules, but still unresolved dependencies or incomplete module inputs.
Error in spm_jobman (line 247)
sts = fill_run_job(‘run’, cjob, varargin{3:end});
Error in s03_preprocessing_with_slice_timing_batch_NICCUE (line 79)
spm_jobman(‘run’, matlabbatch);
It would be really helpful, if somebody could tell me what the problem is.
Thank you very much!
Franziska Hi everyone,
I try to run an adapted preprocessing script (got it from an collegue) for a fMRI analysis. Unfortunatelly, I’m stuck on the command spm_jobman and get the following error message:
Error using spm_jobman>fill_run_job
No executable modules, but still unresolved dependencies or incomplete module inputs.
Error in spm_jobman (line 247)
sts = fill_run_job(‘run’, cjob, varargin{3:end});
Error in s03_preprocessing_with_slice_timing_batch_NICCUE (line 79)
spm_jobman(‘run’, matlabbatch);
It would be really helpful, if somebody could tell me what the problem is.
Thank you very much!
Franziska spm spm_jobman fmri MATLAB Answers — New Questions
Combine Google Authentication & JWT Tokens using Cookie Authorization
* Edit: project is only a web-api, front-end will be developed in react(next.js)
Hi guys!
I’m a TypeScript Fullstack developer and I’m new to .NET and ASP.NET and basically this is one of my first projects using C# and .NET.
My project’s architecture requires a kind of different than out-of-the-box way of identifying users.
I want Google Authentication which will create a local account (which I can relate other entities to)
And JWT Tokens (access, refresh) generated and sent as cookies to the client and each protected endpoint to be using these cookies to authorize the requesting user.
I haven’t figured out a way of implementing this and still using mostly OOTB tools in order to keep modifications little as possible.
Any tips and tricks on how to implement it?
* Edit2:
Just to be clear, I know how to create endpoints, and protected endpoints using the built in identity framework, using roles, etc… Its this combination of Google Auth, generating tokens, sending them to client as cookies basically is where my difficulty relies.
* I have almost a blank new project, already set up with only google credentials
* Edit: project is only a web-api, front-end will be developed in react(next.js) Hi guys! I’m a TypeScript Fullstack developer and I’m new to .NET and ASP.NET and basically this is one of my first projects using C# and .NET. My project’s architecture requires a kind of different than out-of-the-box way of identifying users.I want Google Authentication which will create a local account (which I can relate other entities to)And JWT Tokens (access, refresh) generated and sent as cookies to the client and each protected endpoint to be using these cookies to authorize the requesting user. I haven’t figured out a way of implementing this and still using mostly OOTB tools in order to keep modifications little as possible. Any tips and tricks on how to implement it? * Edit2: Just to be clear, I know how to create endpoints, and protected endpoints using the built in identity framework, using roles, etc… Its this combination of Google Auth, generating tokens, sending them to client as cookies basically is where my difficulty relies. * I have almost a blank new project, already set up with only google credentials Read More
Lenovo Thinkpad keeps getting stuck in boot error
I have a Lenovo ThinkPad and recently it got stuck in a boot loop. After setting the Secure Boot option to “Enabled” my laptop worked fine, but every time I put it to sleep/hibernate mode it gets into the loop again. This is getting really annoying (because the Secure Boot option, for some reason doesn’t work unless my battery drops to 0% and then I do the whole process again) and inconvenient. Is there a solution to this?
I have a Lenovo ThinkPad and recently it got stuck in a boot loop. After setting the Secure Boot option to “Enabled” my laptop worked fine, but every time I put it to sleep/hibernate mode it gets into the loop again. This is getting really annoying (because the Secure Boot option, for some reason doesn’t work unless my battery drops to 0% and then I do the whole process again) and inconvenient. Is there a solution to this? Read More
how to modify this code to estimate LCOE using the parameters for offshore wind?
CAPEX ESTIMATION (KUSD)
% CAPEX OF WIND TURBINE
USD = 1000; Pwt = 400; dp = 30; Nwt = 50; Lcc = 10620; Acc = 6691; Loffs = 10; Lonsh = 2;
CAPwt = 1497.9 * Pwt^0.87; T = 20; WACC = 0.10;
% CAPEX OF BUILDING
CAbui = (0.9181 * dp^2 – 31.43 * dp + 747.4) * 1.09 * Pwt;
% CAPEX OF PROJECT MANAGEMENT
CApm = 130.82 * Pwt;
% CAPEX OF SUPERVISORY CONTROL AND DATA ACQUISITION
CAscda = 555.1 * Nwt;
% CAPEX OF ACQUISITION OF WIND POWER CONNECTION CABLES
CAawpc = ((4.26 * 10^-4 * Acc) + (2.324 * 10^-1)) * 1.09 * Lcc;
% INSTALLATION OF WIND POWER CONNECTION CABLES
CAiwpc = ((-2.2684 * 10^-3 * Nwt) + (3.8018 * 10^-1)) * 1.09 * Lcc;
% ACQUISITION OF OFFSHORE TRANSMISSION CABLES
CAaotc = ((3.3565 * 10^-3 * Pwt) + (8.3872 * 10^-2)) * Loffs * 1.09;
% CAPEX OF INSTALLATION OF OFFSHORE TRANSMISSION CABLES
CAiotc = ((-6.318 * 10^-4 * Pwt) + (3.8125 * 10^-1)) * 1.09 * Loffs;
% CAPEX OF ACQUISITION OF ONSHORE TRANSMISSION CABLES
CAaontc = ((-3.3565 * 10^-3 * Pwt) + (8.3872 * 10^-2)) * 0.27 * Lonsh;
% CAPEX OF INSTALLATION OF ONSHORE TRANSMISSION CABLES
CAiontc = 0.578 * Lonsh;
% Total CAPEX
CApexT = (CAPwt + CAbui + CApm + CAscda + CAawpc + CAiwpc + CAaotc + CAiotc + CAaontc + CAiontc);
CAXNEW = CApexT – 0.2 * CApexT;
% OPEX
Dexex = 0.003 * CApexT;
% TOTAL CAPEX IN NAIRA
CApexTN = CApexT * USD * 10^3;
% TOTAL OPEX AND DEXEX IN NAIRA
DexexN = Dexex * USD * 10^3;
Opex = 0.035 * CApexT;
OpexN = Opex * USD * 10^3;
n = 20; Rb = 10; AEPv = 28298304; Civ = 668860000; Cdex = 2006600; Cpex = 23410000;
% Calculate LCOE
AEP = 28298304;
lcoe = calculate_lcoe(CAXNEW, Opex, WACC, AEP, T);
% Display the LCOE
disp([‘The LCOE is ‘, num2str(lcoe), ‘ currency units per kWh’]);
% Function definition
function lcoe = calculate_lcoe(CAXNEW, Opex, WACC, AEP, T)
% Calculate the numerator of the LCOE formula
numerator = CAXNEW;
for t = 1:T
numerator = numerator + Opex / (1 + WACC)^t;
end
% Calculate the denominator of the LCOE formula
denominator = 0;
for t = 1:T
denominator = denominator + AEP / (1 + WACC)^t;
end
% Calculate the LCOE
lcoe = numerator / denominator;
endCAPEX ESTIMATION (KUSD)
% CAPEX OF WIND TURBINE
USD = 1000; Pwt = 400; dp = 30; Nwt = 50; Lcc = 10620; Acc = 6691; Loffs = 10; Lonsh = 2;
CAPwt = 1497.9 * Pwt^0.87; T = 20; WACC = 0.10;
% CAPEX OF BUILDING
CAbui = (0.9181 * dp^2 – 31.43 * dp + 747.4) * 1.09 * Pwt;
% CAPEX OF PROJECT MANAGEMENT
CApm = 130.82 * Pwt;
% CAPEX OF SUPERVISORY CONTROL AND DATA ACQUISITION
CAscda = 555.1 * Nwt;
% CAPEX OF ACQUISITION OF WIND POWER CONNECTION CABLES
CAawpc = ((4.26 * 10^-4 * Acc) + (2.324 * 10^-1)) * 1.09 * Lcc;
% INSTALLATION OF WIND POWER CONNECTION CABLES
CAiwpc = ((-2.2684 * 10^-3 * Nwt) + (3.8018 * 10^-1)) * 1.09 * Lcc;
% ACQUISITION OF OFFSHORE TRANSMISSION CABLES
CAaotc = ((3.3565 * 10^-3 * Pwt) + (8.3872 * 10^-2)) * Loffs * 1.09;
% CAPEX OF INSTALLATION OF OFFSHORE TRANSMISSION CABLES
CAiotc = ((-6.318 * 10^-4 * Pwt) + (3.8125 * 10^-1)) * 1.09 * Loffs;
% CAPEX OF ACQUISITION OF ONSHORE TRANSMISSION CABLES
CAaontc = ((-3.3565 * 10^-3 * Pwt) + (8.3872 * 10^-2)) * 0.27 * Lonsh;
% CAPEX OF INSTALLATION OF ONSHORE TRANSMISSION CABLES
CAiontc = 0.578 * Lonsh;
% Total CAPEX
CApexT = (CAPwt + CAbui + CApm + CAscda + CAawpc + CAiwpc + CAaotc + CAiotc + CAaontc + CAiontc);
CAXNEW = CApexT – 0.2 * CApexT;
% OPEX
Dexex = 0.003 * CApexT;
% TOTAL CAPEX IN NAIRA
CApexTN = CApexT * USD * 10^3;
% TOTAL OPEX AND DEXEX IN NAIRA
DexexN = Dexex * USD * 10^3;
Opex = 0.035 * CApexT;
OpexN = Opex * USD * 10^3;
n = 20; Rb = 10; AEPv = 28298304; Civ = 668860000; Cdex = 2006600; Cpex = 23410000;
% Calculate LCOE
AEP = 28298304;
lcoe = calculate_lcoe(CAXNEW, Opex, WACC, AEP, T);
% Display the LCOE
disp([‘The LCOE is ‘, num2str(lcoe), ‘ currency units per kWh’]);
% Function definition
function lcoe = calculate_lcoe(CAXNEW, Opex, WACC, AEP, T)
% Calculate the numerator of the LCOE formula
numerator = CAXNEW;
for t = 1:T
numerator = numerator + Opex / (1 + WACC)^t;
end
% Calculate the denominator of the LCOE formula
denominator = 0;
for t = 1:T
denominator = denominator + AEP / (1 + WACC)^t;
end
% Calculate the LCOE
lcoe = numerator / denominator;
end CAPEX ESTIMATION (KUSD)
% CAPEX OF WIND TURBINE
USD = 1000; Pwt = 400; dp = 30; Nwt = 50; Lcc = 10620; Acc = 6691; Loffs = 10; Lonsh = 2;
CAPwt = 1497.9 * Pwt^0.87; T = 20; WACC = 0.10;
% CAPEX OF BUILDING
CAbui = (0.9181 * dp^2 – 31.43 * dp + 747.4) * 1.09 * Pwt;
% CAPEX OF PROJECT MANAGEMENT
CApm = 130.82 * Pwt;
% CAPEX OF SUPERVISORY CONTROL AND DATA ACQUISITION
CAscda = 555.1 * Nwt;
% CAPEX OF ACQUISITION OF WIND POWER CONNECTION CABLES
CAawpc = ((4.26 * 10^-4 * Acc) + (2.324 * 10^-1)) * 1.09 * Lcc;
% INSTALLATION OF WIND POWER CONNECTION CABLES
CAiwpc = ((-2.2684 * 10^-3 * Nwt) + (3.8018 * 10^-1)) * 1.09 * Lcc;
% ACQUISITION OF OFFSHORE TRANSMISSION CABLES
CAaotc = ((3.3565 * 10^-3 * Pwt) + (8.3872 * 10^-2)) * Loffs * 1.09;
% CAPEX OF INSTALLATION OF OFFSHORE TRANSMISSION CABLES
CAiotc = ((-6.318 * 10^-4 * Pwt) + (3.8125 * 10^-1)) * 1.09 * Loffs;
% CAPEX OF ACQUISITION OF ONSHORE TRANSMISSION CABLES
CAaontc = ((-3.3565 * 10^-3 * Pwt) + (8.3872 * 10^-2)) * 0.27 * Lonsh;
% CAPEX OF INSTALLATION OF ONSHORE TRANSMISSION CABLES
CAiontc = 0.578 * Lonsh;
% Total CAPEX
CApexT = (CAPwt + CAbui + CApm + CAscda + CAawpc + CAiwpc + CAaotc + CAiotc + CAaontc + CAiontc);
CAXNEW = CApexT – 0.2 * CApexT;
% OPEX
Dexex = 0.003 * CApexT;
% TOTAL CAPEX IN NAIRA
CApexTN = CApexT * USD * 10^3;
% TOTAL OPEX AND DEXEX IN NAIRA
DexexN = Dexex * USD * 10^3;
Opex = 0.035 * CApexT;
OpexN = Opex * USD * 10^3;
n = 20; Rb = 10; AEPv = 28298304; Civ = 668860000; Cdex = 2006600; Cpex = 23410000;
% Calculate LCOE
AEP = 28298304;
lcoe = calculate_lcoe(CAXNEW, Opex, WACC, AEP, T);
% Display the LCOE
disp([‘The LCOE is ‘, num2str(lcoe), ‘ currency units per kWh’]);
% Function definition
function lcoe = calculate_lcoe(CAXNEW, Opex, WACC, AEP, T)
% Calculate the numerator of the LCOE formula
numerator = CAXNEW;
for t = 1:T
numerator = numerator + Opex / (1 + WACC)^t;
end
% Calculate the denominator of the LCOE formula
denominator = 0;
for t = 1:T
denominator = denominator + AEP / (1 + WACC)^t;
end
% Calculate the LCOE
lcoe = numerator / denominator;
end my research MATLAB Answers — New Questions
choose B or C or D if A does not exist – how to write this?
Hi all,
I have the following:
switch joint
case ‘Knee’
prox = ‘HipJC’;
dist = ‘LEPI’;
wand = ‘THPA’ ;
end
side = {‘R’,’L’};
for i = 1:length(side)
wandMk = data.([side{i},wand]).line;
end
I got the following error: Unrecognized field name "LTHPA", which is because ‘LTHPA’ does not exist in my structure.
I could use ‘THPP’ or ‘THDA’ or ‘THDP’ instead of ‘THPA’, because I know these exist. Hence, how could I rewrite the above for this to run?
For example:
if ‘THPA’ does not exist then
wand = ‘THDA’ or ‘THDA’ or ‘THDP’
Thanks!Hi all,
I have the following:
switch joint
case ‘Knee’
prox = ‘HipJC’;
dist = ‘LEPI’;
wand = ‘THPA’ ;
end
side = {‘R’,’L’};
for i = 1:length(side)
wandMk = data.([side{i},wand]).line;
end
I got the following error: Unrecognized field name "LTHPA", which is because ‘LTHPA’ does not exist in my structure.
I could use ‘THPP’ or ‘THDA’ or ‘THDP’ instead of ‘THPA’, because I know these exist. Hence, how could I rewrite the above for this to run?
For example:
if ‘THPA’ does not exist then
wand = ‘THDA’ or ‘THDA’ or ‘THDP’
Thanks! Hi all,
I have the following:
switch joint
case ‘Knee’
prox = ‘HipJC’;
dist = ‘LEPI’;
wand = ‘THPA’ ;
end
side = {‘R’,’L’};
for i = 1:length(side)
wandMk = data.([side{i},wand]).line;
end
I got the following error: Unrecognized field name "LTHPA", which is because ‘LTHPA’ does not exist in my structure.
I could use ‘THPP’ or ‘THDA’ or ‘THDP’ instead of ‘THPA’, because I know these exist. Hence, how could I rewrite the above for this to run?
For example:
if ‘THPA’ does not exist then
wand = ‘THDA’ or ‘THDA’ or ‘THDP’
Thanks! for loop, indexing, if statement MATLAB Answers — New Questions
Browser updated
Your Browser has been updated has come up on my screen, and asks me to continue, if I do continue and update. (A) What, and if anything will change ? (B) When I switch the computer on, will it be the same as before I updated ?
Your Browser has been updated has come up on my screen, and asks me to continue, if I do continue and update. (A) What, and if anything will change ? (B) When I switch the computer on, will it be the same as before I updated ? Read More
Access the emoji list using the Win + . shortcut
Is there a method, possibly through editing the registry, to set up an emoji shortcut that directly opens the full emoji list rather than displaying the recently used emojis? I primarily utilize emojis and find it cumbersome to access the complete emoji list by clicking on the icon each time. It would be more convenient to have quick access to all emojis, considering that the recently used emojis are already accessible within the full list and through the search feature.
Is there a method, possibly through editing the registry, to set up an emoji shortcut that directly opens the full emoji list rather than displaying the recently used emojis? I primarily utilize emojis and find it cumbersome to access the complete emoji list by clicking on the icon each time. It would be more convenient to have quick access to all emojis, considering that the recently used emojis are already accessible within the full list and through the search feature. Read More
Overwhelmed by the Number of Files?
Greetings everyone,
Following my usage of Windows Defender, I ran a scan that detected a total of 1,127,605 files. This figure seems strangely high to me. Upon inspecting the Windows files for “Old Windows,” I found that it was not present. If this issue is not related to the absence of “Old Windows,” it must be originating from another source.
I would appreciate hearing your thoughts on this matter.
Greetings everyone, Following my usage of Windows Defender, I ran a scan that detected a total of 1,127,605 files. This figure seems strangely high to me. Upon inspecting the Windows files for “Old Windows,” I found that it was not present. If this issue is not related to the absence of “Old Windows,” it must be originating from another source. I would appreciate hearing your thoughts on this matter. Read More
“Pinning Files to Taskbar Apps for Easy Access”
I am exploring a different challenge that arises from my transition to a Windows 11 machine. While I have successfully pinned files/folders/apps to the taskbar, a new issue has emerged. Specifically, I rely on Open Office to manage our bills through a custom spreadsheet I recreate annually. In Windows 10, I could easily drag a new file over the Open Office app on the taskbar to add it to the pinned list (as shown in the attached image).
However, this function no longer works in Windows 11, and the instructions available are focused on adding items directly to the taskbar. This change has resulted in two identical icons on the taskbar, making it challenging to differentiate or change them. I am seeking advice on whether this feature has been deactivated and how to address the duplication issue. Any assistance you can provide would be greatly appreciated. Thank you.
I am exploring a different challenge that arises from my transition to a Windows 11 machine. While I have successfully pinned files/folders/apps to the taskbar, a new issue has emerged. Specifically, I rely on Open Office to manage our bills through a custom spreadsheet I recreate annually. In Windows 10, I could easily drag a new file over the Open Office app on the taskbar to add it to the pinned list (as shown in the attached image). However, this function no longer works in Windows 11, and the instructions available are focused on adding items directly to the taskbar. This change has resulted in two identical icons on the taskbar, making it challenging to differentiate or change them. I am seeking advice on whether this feature has been deactivated and how to address the duplication issue. Any assistance you can provide would be greatly appreciated. Thank you. Read More
“What are Input Switch Notifications?”
I enabled the settings and received a notification prompt asking if I wished to activate it, and it appears to be already activated. It seems like just the usual pop-up messages to me. But what does “Input” refer to? Input of what exactly? And what is meant by “Switch”?
I tried searching for “input switch Windows 11,” but all the results relate to keyboard functionalities and nothing about what I am witnessing.
I enabled the settings and received a notification prompt asking if I wished to activate it, and it appears to be already activated. It seems like just the usual pop-up messages to me. But what does “Input” refer to? Input of what exactly? And what is meant by “Switch”? I tried searching for “input switch Windows 11,” but all the results relate to keyboard functionalities and nothing about what I am witnessing. Read More
ADF copy data with pagination headers not allowing dynamic filename for sink.
ADF copy data with pagination headers not allowing dynamic filename for sink.
In my ADF pipeline there is a single copy data activity with pagination headers to use pagination by using cursor parameter from body.
But the source is working for api request but the sink is not allowing dynamic filenames and only storing single file with all data instead of multiple files based on cursor
ADF copy data with pagination headers not allowing dynamic filename for sink.In my ADF pipeline there is a single copy data activity with pagination headers to use pagination by using cursor parameter from body.But the source is working for api request but the sink is not allowing dynamic filenames and only storing single file with all data instead of multiple files based on cursor Read More
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SORTBY always sorts by row whereas SORT can sort by row and can sort by column.
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how to get a good estimate of the positive parameters that will give a good fit of the curves to real data?
clear
close all
clc
% Données spécifiques
specific_data = [
2009 2 8;
2010 10 22;
2011 30 45;
2012 111 75;
2013 125 96;
2014 255 192;
2015 379 227;
2016 384 238
2017 360 279;
2018 399 229;
2019 235 128
];
% Utilisez les données spécifiques
tdata = specific_data(:, 1);
Hdata = specific_data(:, 2);
HSdata = specific_data(:, 3);
tforward = 2009:1:2019;
%tmeasure = [ 1:100:1001]’;
% initial values
gamma = 1.5;
phi_S = 0.0006; % transmission prob
phi_H = 0.000051; % trans proba
c1=3;
c2=1.5;
theta1 = 100; % djustment parameters for syph
theta2 = 4; %djustment parameters for hi
alpha = 0.6; % progression rate
beta = 0.2; %Complications rate
rho1 = 0.4; % adjustment parameters
rho2 = 1.5; % adjustment parameters
rho3 = 1.5; % adjustment parameters
k0 = [phi_S phi_H c1 c2 theta1 theta2 alpha beta rho1 rho2 rho3 ];
% solve equ with initial value of parameters
[t, Y] = ode23s(@(t, y)modelhs(t, y, k0), tforward, [ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
yintM = Y(:,1);
yintS = Y(:,2);
yintH1 = Y(:,3);
yintH2 = Y(:,4);
yintH1S=Y(:,5);
yintH2S=Y(:,6);
Hh=phi_H * ( yintH1 + c1 * yintH1S ).*yintM + rho1 * gamma * yintH1S + alpha* yintH2; % new cases from monoH
HShs=theta1*phi_S * ( yintS + c2 * yintH1S ).*yintH1 + theta2*phi_H * ( yintH1 + c1 * yintH1S ).*yintS+ rho2*alpha*yintH1S; %new case of co h-s
%H2q = Y(:,4);% assignts the y-coordinates of .
% Plotting specific data and solutions
% Display the results
figure(1)
%subplot(1,2,1);
plot(tdata, Hdata, ‘r*’);
hold on
plot(tdata, Hh, ‘b-‘);
xlabel(‘time in days’);
ylabel(‘Number of monhiv cases’);
axis([2009 2019 0 500]);
figure(2)
%subplot(1,2,1);
plot(tdata, HSdata, ‘r*’);
hold on
plot(tdata, HShs, ‘b-‘);
xlabel(‘time in days’);
ylabel(‘Number of Coinfection cases’);
axis([2009 2019 0 500]);
% Minimization routine using Nelder & Mead Simplex algorithm (a derivative-free method)
% Assigns the new values of parameters to k and the error to fval
% Minimization routine using Nelder & Mead Simplex algorithm (a derivative-free method)
% Assigns the new values of parameters to k and the error to fval
[k,fval] = fminsearch(@moderHS,k0)
%print final values of alpha and beta
disp(k);
%Draw the data with the final ODE
[T, Y] = ode45(@(t,y)(modelhs(t,y,k)),tforward,[ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
yintM = Y(:,1);
yintS = Y(:,2);
yintH1 = Y(:,3);
yintH2 = Y(:,4);
yintH1S=Y(:,5);
yintH2S=Y(:,6);
Hh=phi_H * ( yintH1 + c1 * yintH1S ).*yintM + rho1 * gamma * yintH1S + alpha* yintH2; % new cases from mono-HIV
HShs=theta1*phi_S * ( yintS + c2 * yintH1S ).*yintH1 + theta2*phi_H * ( yintH1 + c1 * yintH1S ).*yintS+ rho2*alpha*yintH1S; %new case of coinfection hiv+syphilis
%H2q = Y(:,4);% assignts the y-coordinates of …
residuals = (Hdata+HSdata – Hh-HShs)./2;
%subplot(1,2,2);
figure(3)
plot(tdata,Hdata,’r*’);
hold on
plot(tdata,Hh,’b-‘);
xlabel(‘Time in days’);
ylabel(‘Number of mono-HIV cases’);
axis([2009 2019 0 1000]);
legend(‘Data’, ‘Model estimation’);
figure(4)
plot(tdata,HSdata,’r*’);
hold on
plot(tdata,HShs,’b-‘);
xlabel(‘Time in days’);
ylabel(‘Number of Coinfection cases’);
axis([2009 2019 0 1000]);
legend(‘Data’, ‘Model estimation’);
function dy=modelhs(~,y,k)
delta = 0.01; % Taux de mortalité
delta_S = 0.05; % Taux de mort de Syphilis.
delta_H = 0.4;
Lambda =4.04 *100;
gamma=1.5;
phi_S =k(1);
phi_H =k(2);
c1 = k(3);
c2=k(4) ;
theta1 =k(5) ;
theta2 = k(6);
alpha = k(7);
beta = k(8);
rho1 = k(9);
rho2= k(10);
rho3=k(11);
dy = zeros(7,1);
%lambda_s=phi_S * ( y(2) + c2 * y(5))
%lambda_H= phi_H * ( y(3) + c1 * y(5) )
dy(1) = Lambda + gamma * y(2) – (phi_S * ( y(2) + c2 * y(5)) + phi_H * (y(3) + c1 * y(5)) + delta ) * y(1) ;%M
dy(2)= phi_S * ( y(2) + c2 * y(5) ) * y(1) – ( gamma + theta2 * phi_H * ( y(3) + c1 * y(5) ) + delta_S ) * y(2) ;%S
dy(3) = phi_H * ( y(3) + c1 * y(5) ) * y(1) + rho1 * gamma * y(5) – (theta1 * phi_S * ( y(2) + c2 * y(5)) + delta + delta_H + alpha) * y(3) ;%H1
dy(4) = alpha * y(3) – (beta + delta + delta_H) * y(4) ;%H2
dy(5) = theta2 * phi_H * ( y(3) + c1 * y(5) ) * y(2) + ( theta1 * phi_S * ( y(2) + c2 * y(5) ) ) * y(3) – ( rho1 * gamma + rho2 * alpha + delta_H + delta_S + delta ) * y(5) ;%H1S
dy(6)= rho2 * alpha * y(5) – ( rho3 * beta + delta_S + delta_H + delta ) * y(6) ;%H2S
dy(7)= beta * y(4) + rho3 * beta * y(6) – ( delta + delta_H ) * y(7) ;%C
end
function error_in_data = moderHS(k)
% Données spécifiques
specific_data = [
2009 2 8;
2010 10 22;
2011 30 45;
2012 111 75;
2013 125 96;
2014 255 192;
2015 379 227;
2016 384 238
2017 360 279;
2018 399 229;
2019 235 128
];
gamma = 1.5;
phi_S =k(1);
phi_H =k(2);
c1 = k(3);
c2=k(4) ;
theta1 =k(5) ;
theta2 = k(6);
alpha = k(7);
beta = k(8);
rho1 = k(9);
rho2= k(10);
rho3=k(11);
% Utilisez les données spécifiques
tdata = specific_data(:, 1);
Hdata = specific_data(:, 2);
HSdata = specific_data(:, 3);
tforward = 2009:1:2019;
[T, Y] = ode23s(@(t,y)(modelhs(t,y,k)),tforward,[ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
M = Y(:,1);
S = Y(:,2);
H1 = Y(:,3);
H2 = Y(:,4);
H1S=Y(:,5);
H2S=Y(:,6);
H=phi_H * ( H1 + c1 * H1S ).*M + rho1 * gamma*H1S + alpha* H2; % new cases from mono-HIV
HS=theta1*phi_S * ( S + c2 * H1S ).*H1 + theta2*phi_H * ( H1 + c1 * H1S ).*S+ rho2*alpha*H1S; %new case of coinfection hiv+syphilis
%H2q = Y(:,4);% assignts the y-coordinates of …
%the solution at
D=mean(Hdata).^2;
D1=mean(HSdata).^2;
A=(H – Hdata).^2;
B=(HS – HSdata).^2;
error_in_data =sum(A)./(11*D)+ sum(B)./(11*D1);
%%
endclear
close all
clc
% Données spécifiques
specific_data = [
2009 2 8;
2010 10 22;
2011 30 45;
2012 111 75;
2013 125 96;
2014 255 192;
2015 379 227;
2016 384 238
2017 360 279;
2018 399 229;
2019 235 128
];
% Utilisez les données spécifiques
tdata = specific_data(:, 1);
Hdata = specific_data(:, 2);
HSdata = specific_data(:, 3);
tforward = 2009:1:2019;
%tmeasure = [ 1:100:1001]’;
% initial values
gamma = 1.5;
phi_S = 0.0006; % transmission prob
phi_H = 0.000051; % trans proba
c1=3;
c2=1.5;
theta1 = 100; % djustment parameters for syph
theta2 = 4; %djustment parameters for hi
alpha = 0.6; % progression rate
beta = 0.2; %Complications rate
rho1 = 0.4; % adjustment parameters
rho2 = 1.5; % adjustment parameters
rho3 = 1.5; % adjustment parameters
k0 = [phi_S phi_H c1 c2 theta1 theta2 alpha beta rho1 rho2 rho3 ];
% solve equ with initial value of parameters
[t, Y] = ode23s(@(t, y)modelhs(t, y, k0), tforward, [ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
yintM = Y(:,1);
yintS = Y(:,2);
yintH1 = Y(:,3);
yintH2 = Y(:,4);
yintH1S=Y(:,5);
yintH2S=Y(:,6);
Hh=phi_H * ( yintH1 + c1 * yintH1S ).*yintM + rho1 * gamma * yintH1S + alpha* yintH2; % new cases from monoH
HShs=theta1*phi_S * ( yintS + c2 * yintH1S ).*yintH1 + theta2*phi_H * ( yintH1 + c1 * yintH1S ).*yintS+ rho2*alpha*yintH1S; %new case of co h-s
%H2q = Y(:,4);% assignts the y-coordinates of .
% Plotting specific data and solutions
% Display the results
figure(1)
%subplot(1,2,1);
plot(tdata, Hdata, ‘r*’);
hold on
plot(tdata, Hh, ‘b-‘);
xlabel(‘time in days’);
ylabel(‘Number of monhiv cases’);
axis([2009 2019 0 500]);
figure(2)
%subplot(1,2,1);
plot(tdata, HSdata, ‘r*’);
hold on
plot(tdata, HShs, ‘b-‘);
xlabel(‘time in days’);
ylabel(‘Number of Coinfection cases’);
axis([2009 2019 0 500]);
% Minimization routine using Nelder & Mead Simplex algorithm (a derivative-free method)
% Assigns the new values of parameters to k and the error to fval
% Minimization routine using Nelder & Mead Simplex algorithm (a derivative-free method)
% Assigns the new values of parameters to k and the error to fval
[k,fval] = fminsearch(@moderHS,k0)
%print final values of alpha and beta
disp(k);
%Draw the data with the final ODE
[T, Y] = ode45(@(t,y)(modelhs(t,y,k)),tforward,[ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
yintM = Y(:,1);
yintS = Y(:,2);
yintH1 = Y(:,3);
yintH2 = Y(:,4);
yintH1S=Y(:,5);
yintH2S=Y(:,6);
Hh=phi_H * ( yintH1 + c1 * yintH1S ).*yintM + rho1 * gamma * yintH1S + alpha* yintH2; % new cases from mono-HIV
HShs=theta1*phi_S * ( yintS + c2 * yintH1S ).*yintH1 + theta2*phi_H * ( yintH1 + c1 * yintH1S ).*yintS+ rho2*alpha*yintH1S; %new case of coinfection hiv+syphilis
%H2q = Y(:,4);% assignts the y-coordinates of …
residuals = (Hdata+HSdata – Hh-HShs)./2;
%subplot(1,2,2);
figure(3)
plot(tdata,Hdata,’r*’);
hold on
plot(tdata,Hh,’b-‘);
xlabel(‘Time in days’);
ylabel(‘Number of mono-HIV cases’);
axis([2009 2019 0 1000]);
legend(‘Data’, ‘Model estimation’);
figure(4)
plot(tdata,HSdata,’r*’);
hold on
plot(tdata,HShs,’b-‘);
xlabel(‘Time in days’);
ylabel(‘Number of Coinfection cases’);
axis([2009 2019 0 1000]);
legend(‘Data’, ‘Model estimation’);
function dy=modelhs(~,y,k)
delta = 0.01; % Taux de mortalité
delta_S = 0.05; % Taux de mort de Syphilis.
delta_H = 0.4;
Lambda =4.04 *100;
gamma=1.5;
phi_S =k(1);
phi_H =k(2);
c1 = k(3);
c2=k(4) ;
theta1 =k(5) ;
theta2 = k(6);
alpha = k(7);
beta = k(8);
rho1 = k(9);
rho2= k(10);
rho3=k(11);
dy = zeros(7,1);
%lambda_s=phi_S * ( y(2) + c2 * y(5))
%lambda_H= phi_H * ( y(3) + c1 * y(5) )
dy(1) = Lambda + gamma * y(2) – (phi_S * ( y(2) + c2 * y(5)) + phi_H * (y(3) + c1 * y(5)) + delta ) * y(1) ;%M
dy(2)= phi_S * ( y(2) + c2 * y(5) ) * y(1) – ( gamma + theta2 * phi_H * ( y(3) + c1 * y(5) ) + delta_S ) * y(2) ;%S
dy(3) = phi_H * ( y(3) + c1 * y(5) ) * y(1) + rho1 * gamma * y(5) – (theta1 * phi_S * ( y(2) + c2 * y(5)) + delta + delta_H + alpha) * y(3) ;%H1
dy(4) = alpha * y(3) – (beta + delta + delta_H) * y(4) ;%H2
dy(5) = theta2 * phi_H * ( y(3) + c1 * y(5) ) * y(2) + ( theta1 * phi_S * ( y(2) + c2 * y(5) ) ) * y(3) – ( rho1 * gamma + rho2 * alpha + delta_H + delta_S + delta ) * y(5) ;%H1S
dy(6)= rho2 * alpha * y(5) – ( rho3 * beta + delta_S + delta_H + delta ) * y(6) ;%H2S
dy(7)= beta * y(4) + rho3 * beta * y(6) – ( delta + delta_H ) * y(7) ;%C
end
function error_in_data = moderHS(k)
% Données spécifiques
specific_data = [
2009 2 8;
2010 10 22;
2011 30 45;
2012 111 75;
2013 125 96;
2014 255 192;
2015 379 227;
2016 384 238
2017 360 279;
2018 399 229;
2019 235 128
];
gamma = 1.5;
phi_S =k(1);
phi_H =k(2);
c1 = k(3);
c2=k(4) ;
theta1 =k(5) ;
theta2 = k(6);
alpha = k(7);
beta = k(8);
rho1 = k(9);
rho2= k(10);
rho3=k(11);
% Utilisez les données spécifiques
tdata = specific_data(:, 1);
Hdata = specific_data(:, 2);
HSdata = specific_data(:, 3);
tforward = 2009:1:2019;
[T, Y] = ode23s(@(t,y)(modelhs(t,y,k)),tforward,[ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
M = Y(:,1);
S = Y(:,2);
H1 = Y(:,3);
H2 = Y(:,4);
H1S=Y(:,5);
H2S=Y(:,6);
H=phi_H * ( H1 + c1 * H1S ).*M + rho1 * gamma*H1S + alpha* H2; % new cases from mono-HIV
HS=theta1*phi_S * ( S + c2 * H1S ).*H1 + theta2*phi_H * ( H1 + c1 * H1S ).*S+ rho2*alpha*H1S; %new case of coinfection hiv+syphilis
%H2q = Y(:,4);% assignts the y-coordinates of …
%the solution at
D=mean(Hdata).^2;
D1=mean(HSdata).^2;
A=(H – Hdata).^2;
B=(HS – HSdata).^2;
error_in_data =sum(A)./(11*D)+ sum(B)./(11*D1);
%%
end clear
close all
clc
% Données spécifiques
specific_data = [
2009 2 8;
2010 10 22;
2011 30 45;
2012 111 75;
2013 125 96;
2014 255 192;
2015 379 227;
2016 384 238
2017 360 279;
2018 399 229;
2019 235 128
];
% Utilisez les données spécifiques
tdata = specific_data(:, 1);
Hdata = specific_data(:, 2);
HSdata = specific_data(:, 3);
tforward = 2009:1:2019;
%tmeasure = [ 1:100:1001]’;
% initial values
gamma = 1.5;
phi_S = 0.0006; % transmission prob
phi_H = 0.000051; % trans proba
c1=3;
c2=1.5;
theta1 = 100; % djustment parameters for syph
theta2 = 4; %djustment parameters for hi
alpha = 0.6; % progression rate
beta = 0.2; %Complications rate
rho1 = 0.4; % adjustment parameters
rho2 = 1.5; % adjustment parameters
rho3 = 1.5; % adjustment parameters
k0 = [phi_S phi_H c1 c2 theta1 theta2 alpha beta rho1 rho2 rho3 ];
% solve equ with initial value of parameters
[t, Y] = ode23s(@(t, y)modelhs(t, y, k0), tforward, [ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
yintM = Y(:,1);
yintS = Y(:,2);
yintH1 = Y(:,3);
yintH2 = Y(:,4);
yintH1S=Y(:,5);
yintH2S=Y(:,6);
Hh=phi_H * ( yintH1 + c1 * yintH1S ).*yintM + rho1 * gamma * yintH1S + alpha* yintH2; % new cases from monoH
HShs=theta1*phi_S * ( yintS + c2 * yintH1S ).*yintH1 + theta2*phi_H * ( yintH1 + c1 * yintH1S ).*yintS+ rho2*alpha*yintH1S; %new case of co h-s
%H2q = Y(:,4);% assignts the y-coordinates of .
% Plotting specific data and solutions
% Display the results
figure(1)
%subplot(1,2,1);
plot(tdata, Hdata, ‘r*’);
hold on
plot(tdata, Hh, ‘b-‘);
xlabel(‘time in days’);
ylabel(‘Number of monhiv cases’);
axis([2009 2019 0 500]);
figure(2)
%subplot(1,2,1);
plot(tdata, HSdata, ‘r*’);
hold on
plot(tdata, HShs, ‘b-‘);
xlabel(‘time in days’);
ylabel(‘Number of Coinfection cases’);
axis([2009 2019 0 500]);
% Minimization routine using Nelder & Mead Simplex algorithm (a derivative-free method)
% Assigns the new values of parameters to k and the error to fval
% Minimization routine using Nelder & Mead Simplex algorithm (a derivative-free method)
% Assigns the new values of parameters to k and the error to fval
[k,fval] = fminsearch(@moderHS,k0)
%print final values of alpha and beta
disp(k);
%Draw the data with the final ODE
[T, Y] = ode45(@(t,y)(modelhs(t,y,k)),tforward,[ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
yintM = Y(:,1);
yintS = Y(:,2);
yintH1 = Y(:,3);
yintH2 = Y(:,4);
yintH1S=Y(:,5);
yintH2S=Y(:,6);
Hh=phi_H * ( yintH1 + c1 * yintH1S ).*yintM + rho1 * gamma * yintH1S + alpha* yintH2; % new cases from mono-HIV
HShs=theta1*phi_S * ( yintS + c2 * yintH1S ).*yintH1 + theta2*phi_H * ( yintH1 + c1 * yintH1S ).*yintS+ rho2*alpha*yintH1S; %new case of coinfection hiv+syphilis
%H2q = Y(:,4);% assignts the y-coordinates of …
residuals = (Hdata+HSdata – Hh-HShs)./2;
%subplot(1,2,2);
figure(3)
plot(tdata,Hdata,’r*’);
hold on
plot(tdata,Hh,’b-‘);
xlabel(‘Time in days’);
ylabel(‘Number of mono-HIV cases’);
axis([2009 2019 0 1000]);
legend(‘Data’, ‘Model estimation’);
figure(4)
plot(tdata,HSdata,’r*’);
hold on
plot(tdata,HShs,’b-‘);
xlabel(‘Time in days’);
ylabel(‘Number of Coinfection cases’);
axis([2009 2019 0 1000]);
legend(‘Data’, ‘Model estimation’);
function dy=modelhs(~,y,k)
delta = 0.01; % Taux de mortalité
delta_S = 0.05; % Taux de mort de Syphilis.
delta_H = 0.4;
Lambda =4.04 *100;
gamma=1.5;
phi_S =k(1);
phi_H =k(2);
c1 = k(3);
c2=k(4) ;
theta1 =k(5) ;
theta2 = k(6);
alpha = k(7);
beta = k(8);
rho1 = k(9);
rho2= k(10);
rho3=k(11);
dy = zeros(7,1);
%lambda_s=phi_S * ( y(2) + c2 * y(5))
%lambda_H= phi_H * ( y(3) + c1 * y(5) )
dy(1) = Lambda + gamma * y(2) – (phi_S * ( y(2) + c2 * y(5)) + phi_H * (y(3) + c1 * y(5)) + delta ) * y(1) ;%M
dy(2)= phi_S * ( y(2) + c2 * y(5) ) * y(1) – ( gamma + theta2 * phi_H * ( y(3) + c1 * y(5) ) + delta_S ) * y(2) ;%S
dy(3) = phi_H * ( y(3) + c1 * y(5) ) * y(1) + rho1 * gamma * y(5) – (theta1 * phi_S * ( y(2) + c2 * y(5)) + delta + delta_H + alpha) * y(3) ;%H1
dy(4) = alpha * y(3) – (beta + delta + delta_H) * y(4) ;%H2
dy(5) = theta2 * phi_H * ( y(3) + c1 * y(5) ) * y(2) + ( theta1 * phi_S * ( y(2) + c2 * y(5) ) ) * y(3) – ( rho1 * gamma + rho2 * alpha + delta_H + delta_S + delta ) * y(5) ;%H1S
dy(6)= rho2 * alpha * y(5) – ( rho3 * beta + delta_S + delta_H + delta ) * y(6) ;%H2S
dy(7)= beta * y(4) + rho3 * beta * y(6) – ( delta + delta_H ) * y(7) ;%C
end
function error_in_data = moderHS(k)
% Données spécifiques
specific_data = [
2009 2 8;
2010 10 22;
2011 30 45;
2012 111 75;
2013 125 96;
2014 255 192;
2015 379 227;
2016 384 238
2017 360 279;
2018 399 229;
2019 235 128
];
gamma = 1.5;
phi_S =k(1);
phi_H =k(2);
c1 = k(3);
c2=k(4) ;
theta1 =k(5) ;
theta2 = k(6);
alpha = k(7);
beta = k(8);
rho1 = k(9);
rho2= k(10);
rho3=k(11);
% Utilisez les données spécifiques
tdata = specific_data(:, 1);
Hdata = specific_data(:, 2);
HSdata = specific_data(:, 3);
tforward = 2009:1:2019;
[T, Y] = ode23s(@(t,y)(modelhs(t,y,k)),tforward,[ 5000.0 20.0 2.0 0.0 6.0 2.0 0.0 ]);
M = Y(:,1);
S = Y(:,2);
H1 = Y(:,3);
H2 = Y(:,4);
H1S=Y(:,5);
H2S=Y(:,6);
H=phi_H * ( H1 + c1 * H1S ).*M + rho1 * gamma*H1S + alpha* H2; % new cases from mono-HIV
HS=theta1*phi_S * ( S + c2 * H1S ).*H1 + theta2*phi_H * ( H1 + c1 * H1S ).*S+ rho2*alpha*H1S; %new case of coinfection hiv+syphilis
%H2q = Y(:,4);% assignts the y-coordinates of …
%the solution at
D=mean(Hdata).^2;
D1=mean(HSdata).^2;
A=(H – Hdata).^2;
B=(HS – HSdata).^2;
error_in_data =sum(A)./(11*D)+ sum(B)./(11*D1);
%%
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