Sharing KnowledgeEight fold noble path of the middle way.
1 Right view
2 Right thought
3 Right speech
4 Right behaviour
5 Right livelihood
6 Right effort
7 Right mindfulness
8 Right concentration
Five evils in the world.
There is also a similar saying in Sikhism these are kaam (lust), krodh (anger), lobh (greed), moh (attachment) and ahankar (ego).
There are more than 200 volcanoes in Japan. The zip file contains the list of volcanoes in google kml file, csv file and a shape file format. The data on elevation and the year of its last eruption is provided. It could be helpful to study geology.
https://drive.google.com/file/d/1eJdNalQhocOkjgZUunRJHtym2uCVKdI4/view?usp=sharing
Japan is rich in water resources. The average annual precipitation is about 650 billion m3, out of which 420 billion m3 is the theoretical maximum that can be utilized for various purposes including hydropower generation. In 1951, the electricity business of Japan was handed to 10 regional power utilities. In 1995, the independent power producer (IPP) were allowed to sell electricity. According to NEF, by 2010 there were 1754 small and medium power plants (less than 30 MW). The feed-in tariffs for various types of hydro electricity, set after Fukoshima disaster, are as follows. (Ref: https://www.meti.go.jp/english/press/2016/0318_03.html)
The feed in tariffs are lucrative. However, the hydropower development in Japan is not straightforward due to fragmented government offices requiring permissions from multiple agencies.
River legislation and management
The new “River Law” started since 1964 to manage water resources in Japan. It has been revised multiple times.
The River Law classifies the rivers into two categories- A and B. Class A rivers are administered by the Minister of Construction (MLIT). There are 17798 rivers grouped into 109 river systems in this category totaling to about 87,150km. Class B rivers are administered by the prefectural government. There are 6,631 rivers grouped in 2,691 river systems in this category totaling to about 35,700 km. Some section of rivers (both A and B) may be administered by local cities or villages.
Once the water is extracted from the river, it is managed by other laws. A list of ministries and agencies handling the water resource are listed in table below:
| MILIT | It is responsible for over all water resource development |
| MOE | Development of guidelines, policy and planning for water conservation and setting of water quality standards. |
| Ministry of health | Regulation of domestic water supply facilities |
| METI | Regulation of industrial water supply facilities |
| Ministry of agriculture | Regulation of agricutural water and conservation of forest water resorces |
| Japan water agency | It is responsible for the supply of safe, quality water at a reasonable price. It is engaged in the construction and refurbishment of major dams for water utilization (for domestic, industrial and agricultural use) and river management purposes (flood control, maintenance and environmental flow). |
| Inter-ministerial Liaison Council | This is responsible to study how procedures could be simplified fir water resource development |
Base on:
While conducting multi variable analysis, it may be useful to see how the output changes due to change in inputs. For example humidity, temperature and water-level may change the deflection of dam. Cobweb plot combined with normal distribution will help to visualize the risks easily. An example of such plot is shown below.
An octave (or matlab) code to generate such plot is given below. Basically, it requires to plot each entity separatly. The data should be normalized to set equal scale.
%shaded plot
clear all;clf; % clear figure (just in case something was there before)
epdata=[-0.0008959 -0.00059726 -0.0013775 -0.00080342 -0.002345 -0.0012454 -0.0014801 -0.00097711 -0.00096319 -0.00030882 -0.0015608 -0.00099932 -0.00082923 -0.00105 -0.0013915 -0.00021562 -0.0011739 -0.0015718 -0.00087249 -0.0016794 -0.00036352 -0.00059309 -0.0011483 -0.0010326 -0.00034531 -0.0020304 -0.0014554 -0.00092798 -0.0012557 -0.0016694 -0.0010306 -0.001245 -0.0013191 -0.0016508 -0.0010848 -0.0018287 -0.0010912 -0.0014441 -0.0012839 -0.00089405 -0.0024034 -0.0015758 -0.00026981 -0.0014717 -0.0023293 -0.0020914 -0.00076314 -0.00056843 -0.0014572 -0.00083516 -0.0012168 -0.0013884 -0.0012994 -0.0010078 -0.00045422 -0.0015285 -0.00061293 -0.00091066 -0.0014499 -0.0010975 -0.001501 -0.0012706 -0.0019573 -0.0019029 -0.0013711 -0.0019286 -0.00099036 -0.0016533 -0.001049 -0.0015944 -0.0016858 -0.0014946 0.000314 -0.0016982 -0.0012176 -0.0016377 -0.0012109 -0.0015727 -0.001435 -0.0010661 -0.0012172 -0.0020125]
tcdata=[31.007 17.279 71.559 42.942 30.757 64.937 56.682 34.429 48.118 51.053 46.382 70.517 49.346 56.116 38.708 39.931 61.857 52.765 58.071 55.669 34.416 38.345 36.623 42.745 66.059 83.693 40.504 62.218 64.917 20.47 65.233 37.162 69.894 27.246 3.5394 65.416 65.68 43.018 95.189 59.176 59.944 56.573 53.296 38.949 41.054 39.729 28.056 43.599 59.708 46.291 49.449 48.566 57.104 51.747 68.345 48.032 63.941 41.263 26.673 48.541 31.849 56.63 39.846 49.557 80.801 37.334 37.459 36.248 57.355 39.958 32.182 24.017 75.422 61.597 43.402 11.902 58.988 42.176 17.418 44.48 56.916 41.854]
tldata=[61.235 77.001 86.213 75.22 43.764 67.211 76.459 52.866 77.028 54.373 34.241 90.087 80.787 60.298 75.159 107.12 84.327 39.385 104.76 66.38 62.929 42.629 88.112 83.559 59.862 85.192 50.859 55.306 66.787 56.848 82.743 95.426 112.39 44.012 70.265 54.671 76.631 76.642 88.841 39.25 56.877 60.893 104.51 59.836 63.706 71.222 63.624 32.153 70.074 79.584 72.205 72.182 73.233 67.998 80.533 40.931 63.258 42.223 79.662 81.851 52.266 80.383 83.354 57.363 93.174 64.228 87.165 93.264 79.508 67.241 98.564 86.5 37.035 59.61 64.813 68.867 72.937 81.84 61.348 92.968 57.552 64.609]
dpdata=[-0.016616 0.0012712 -0.018897 -0.01016 -0.092141 -0.024361 -0.023951 -0.026684 -0.013089 -0.0056231 -0.065296 -0.011672 -0.0096546 -0.023527 -0.018468 0.0022514 -0.015527 -0.056125 -0.0052227 -0.0348 -0.0046483 -0.020469 -0.0072833 -0.010084 -0.0052599 -0.030438 -0.044018 -0.022032 -0.024795 -0.031457 -0.014002 -0.0055563 -0.0096733 -0.063971 0.003394 -0.046408 -0.017231 -0.019991 -0.016868 -0.028567 -0.061399 -0.036514 0.00094998 -0.034091 -0.050397 -0.03461 -0.011083 -0.023391 -0.027504 -0.0096521 -0.020398 -0.023604 -0.022343 -0.018593 -0.0046534 -0.055475 -0.011241 -0.03279 -0.0084095 -0.013231 -0.043265 -0.018161 -0.020591 -0.04901 -0.016887 -0.038989 -0.0064535 -0.0089957 -0.014825 -0.029465 -0.0045653 -0.0031735 0.013363 -0.040035 -0.024299 -0.00078227 -0.020854 -0.018003 -0.015243 -0.0074379 -0.02939 -0.042169]
% normalize
ep=epdata/min(epdata);
tc=tcdata/max(tcdata);
tl=tldata/max(tldata);
dp=dpdata/min(dpdata);
x=[0 1 2 3.5]*7;
x1=ones(1,length(ep))*x(1);
x2=ones(1,length(ep))*x(2);
x3=ones(1,length(ep))*x(3);
x4=ones(1,length(ep))*x(4);
%calculate mean
m_ep=mean(ep);
m_tc=mean(tc);
m_tl=mean(tl);
m_dp=mean(dp);
%standard deviation
sd_ep=std(ep);
sd_tc=std(tc);
sd_tl=std(tl);
sd_dp=std(dp);
%coords to plot normal distribution
nd_x_ep=[m_ep-4*sd_ep:sd_ep/20:m_ep+4*sd_ep]
nd_y_ep=1/sd_ep/(2*pi())^0.5*exp((nd_x_ep-m_ep).^2/-2/sd_ep^2)
nd_x_tc=[m_tc-4*sd_tc:sd_tc/20:m_tc+4*sd_tc]
nd_y_tc=1/sd_tc/(2*pi())^0.5*exp((nd_x_tc-m_tc).^2/-2/sd_tc^2)
nd_x_tl=[m_tl-4*sd_tl:sd_tl/20:m_tl+4*sd_tl]
nd_y_tl=1/sd_tl/(2*pi())^0.5*exp((nd_x_tl-m_tl).^2/-2/sd_tl^2)
nd_x_dp=[m_dp-4*sd_dp:sd_dp/20:m_dp+4*sd_dp]
nd_y_dp=1/sd_dp/(2*pi())^0.5*exp((nd_x_dp-m_dp).^2/-2/sd_dp^2)
figure 1
% plot vertical lines
hold on;
plot (x1,ep,"o");
plot (x2,tc,"o");
plot (x3,tl,"o");
plot (x4,dp,"o");
%plot connecting lines
p=[ep; tc; tl; dp ];
plot (x, p, "color",[.8 .8 .8] );
%plot the normal disribution
patch(nd_y_ep*2+x(1),nd_x_ep,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_tc*2+x(2),nd_x_tc,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(nd_y_tl*2+x(3),nd_x_tl,'facecolor',[1 1 0.8],'edgecolor',[1 0 0], 'facealpha',0.3)
patch(-nd_y_dp*2+x(4),nd_x_dp,'facecolor',[1 1 0.8],'edgecolor',[0 0 1], 'facealpha',0.3,"linewidth", 3)
%yaxis lines
ymin=-0.5;ymax=1.5;
plot ([x(1) x(1)],[ymin ymax],"color",[0 0 0] );
plot ([x(2) x(2)],[ymin ymax],"color",[0 0 0] );
plot ([x(3) x(3)],[ymin ymax],"color",[0 0 0] );
plot ([x(4) x(4)],[ymin ymax],"color",[0 0 0] );
%other properties
ylim([ymin ymax])
axis off;
set(gca, "linewidth", 2, "fontsize", 12);
%labels
%xlabels
text(x(1)+0.3,ymin,"Strain","color","black","fontsize", 18)
text(x(2)+0.3,ymin,"TC","color","black","fontsize", 18)
text(x(3)+0.3,ymin,"TL","color","black","fontsize", 18)
text(x(4)-3.5,ymin,"Max. Deflection","color","black","fontsize", 18)
%informations
text(x(1)+0.3,ymax-.2,["Mean=" num2str(-0.0012, "%5.2e") char(10) "SD=" num2str(0.005, "%5.2e")],"color","black","fontsize", 15)
text(x(2)+0.3,ymax-.2,["Mean=" num2str(50, "%5.0f") char(10) "SD=" num2str(15, "%5.0f") " years"],"color","black","fontsize", 15)
text(x(3)+0.3,ymax-.2,["Mean=" num2str(70, "%5.0f") char(10) "SD=" num2str(20, "%5.0f") " years"],"color","black","fontsize", 15)
text(x(4)-3.5,ymax-.2,["Mean=" num2str(mean(dpdata), "%5.2e") char(10) "SD=" num2str(std(dpdata), "%5.2e") " m"],"color","black","fontsize", 15)
hold off;
print -dpng figure1.pngSome salt required.
Once born we slowly evolve physically by gaining tissues and bones. The food we eat is transformed into the machinaries of body. This indicates that the entropy or randomness decreases over time to create a useful thing. But once we cross certain age decay starts and the biological machinaries starts to disfunction. It seems decay and dissolution is the final destiny. And in long term degeneration is imenent.
If so how about the evolution theory? Why should a simple being evolve to a complex one? More natural way would be a complex being degenerate into simple ones. Because this is the way we observe, its easy to break things than to create one. May be we human too are degenerated form of more complex superhumans, not the complex form of monkeys.
Sharing Knowledge 