Optimisation of a tubular linear machine with permanent magnets for wave energy extraction Purpose -The purpose of this paper is to optimise the cost-based performance of a tubular linear generator and to minimise cogging forces. Design/methodology/approach -Optimisation of a tubular linear generator with longitudinal flux topology has been undertaken using a finite element method. The computational models used have been verified experimentally. Findings -The use of an oversized stator linear generator design as opposed to an oversized translator design has the potential to increase the output electromotive force per unit material cost by 25 per cent for slotless iron core topologies and approximately 14 per cent for air core topologies. For cogging force minimisation, optimisation of the length of the stator core is an effective technique for both oversized stator and oversized translator constructions. Comparisons of magnet materials also indicate that the higher cost of rare earth magnets to ferrites is compensated by their superior specific performances. Originality/value -In this paper, a broader range of design parameters than in previous investigations has been optimised for the slotless iron core and air core topologies. The result relating to cogging force reduction and cost savings (in particular) has the potential to make direct drive wave energy extraction a more competitive technology in terms of reliability and cost. In recent years, linear generators have been proposed and studied in the context of marine wave power generators. The concept of such electrical machines is relatively simple. Moreover, the lack of crank shafts or any rotary parts make the design robust and suitable for direct drive conversion. A fairly recent concept is one of a tubular iron-core linear generator, the main advantage of which is the fact that stator elements are loop-closed around the permanent magnets mounted on the moving part of the machine known as the translator (or rotor). In this arrangement, most of the available magnetic flux is utilised resulting in higher efficiency than that of a planar linear generator. However, the use of high-performance magnets in such tubular generator structures would create extremely high cogging forces if a classical design with teeth were to be used (Janssen et al., 2007) . These cogging forces need to be reduced in order to reduce the losses (Kimoulakis et al., 2009) and meet the support structure requirements (Baker et al., 2004) . The removal of iron teeth from the magnetic circuit in these generators appears to be a reasonable method to reduce the cogging forces; however, it will also reduce flux density through the coils and will therefore reduce the induced electromotive force (EMF). For such generators to be shown to be competitive, the economic aspects need to be included; we have therefore undertaken a cost-based study. Our aim was to understand the cost implications for a design that maximises EMF while minimising the cogging force. Finite element modelling was used to conduct a cost-based optimisation of slotless iron core and air core topologies for both oversized stator and oversized translator constructions of a tubular linear generator. This focused mainly on the optimisation of non-dimensional geometric design parameters, but also included comparison between different magnet materials and types. The results indicate that an oversized stator topology using rare earth axial magnets is the most suitable, as a consequence of its high cost-based performance. The experimental results of prototype linear generators were used to confirm the accuracy of the computational models used in the design optimisation. Wave energy extraction

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O


An algorithm for solving the obstacle problems Abstract--ln this paper, we propose an algorithm for solving the obstacle problem. We try to find the approximated region of the contact in the obstacle problem by iteration. Numerical examples are given for the obstacle problem for a membrane and the elastic-plastic torsion problem. (~) Let f/C ~2. Consider the obstacle problem: find u E K, such that E(u) = inf E(v), If the solution u ~ C2(£t) N H01(f~), then, we have f~ : f~l U f~2 --the coincidence set ~1 and its complement f~2, u=~b, and -Au-f>0, ing~l, (1.5) u>~b, and -Au-f=0, in~2. (1.6) Notice that the region of contact ~l={Xea!,,(x) =~(x)} is an unknown a priori.. The obstacle problem is a typical example of the elliptic variationa! inequality of the first kind. Many important problems, such as the torsion of an elastic-plastic cylinder, the Stefan problem can be formulated by transformation to an obstacle problem. Several comprehensive monographs can be consulted for the theory and numerical solution of variational inequalities, e.g., [1] [2] [3] [4] [5] [6] . Since obstacle problems are highly nonlinear, it is difficult for the computation of approximate solutions. The approximate solution of obstacle problems is usually solved by variable projection method, for example, the relaxation method [3] , multilevel projection method [7] , multigrid method [8] [9] [10] [11] , and projection method [12, 13] for nonlinear complementarity problems.. In this paper, we will propose an algorithm to solve the obstacle problem (1.1)-(1.3) in a different way. We try to find the approximated region of the contact domain ~1 by iteration. We present the algorithm in the next section and report some numerical experiments in Section 3.

O B O O O O O O O O O O O B O O B I I O O O O O O O O O O O B I I O O O O O O O B I I O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O B I I O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O


Towards a hybrid testing process unifying exploratory testing and scripted testing Given the current state of the art in research, practitioners are faced with the challenge of choosing scripted testing (ST) or exploratory testing (ET). This study aims at systematically incorporating strengths of ET and ST in a hybrid testing process to overcome the weaknesses of each. We utilized systematic review and practitioner interviews to identify strengths and weaknesses of ET and ST. Strengths of ET were mapped to weaknesses of ST and vice versa. Noblit and Hare's lines-ofargument method was used for data analysis. The results of the mapping were used as input to codesign a hybrid process with experienced practitioners. We found a clear need to create a hybrid process as follows: (i) both ST and ET provide strengths and weaknesses, and these depend on some particular conditions, which prevents preference of one approach to another; and (ii) the mapping showed that it is possible to address the weaknesses in one process by the strengths of the other in a hybrid form. With the input from literature and industry experts, a flexible and iterative hybrid process was designed. Practitioners can clearly benefit from using a hybrid process given the mapping of advantages and disadvantages. Software testing aims to verify whether software behaves as intended and identifies potential problems. A recent survey [37] indicates that testing is the main approach being used in industry to identify defects. Hence, there is a need to understand how to improve the efficiency and effectiveness of testing approaches. Two widely used testing processes in industry are scripted testing (ST) (also referred to as prescriptive or test case based testing in International Organization for Standardization/International Electrotechnical Commission [ISO/IEC] 29119 Software Testing Standard) and exploratory testing (ET) [24] .. Scripted testing follows a prescriptive process, in which test cases are designed prior to test execution to structure and to guide the testing tasks. Many of the existing studies on ST have a focus on automated test case design, generation and prioritization, or testing technique selection [8, 12, 28, 45] . In a sense, ST is a plan-driven process for testing. *Correspondence to: Cigdem Gencel, Free University of Bolzano-Bozen, 39100 Bolzano-Bozen, Bolzano, Italy.. With the input of the previous phase, we designed the HT process in the third phase (P3). We identified the process fragments and high-level structure of the process as suggested in [21] . The initial design was created by mapping the activities of ET and ST to the strengths and weaknesses identified. Having designed an initial version of the solution (HT process), we iteratively improved the design of the process with the practitioners' input (see solution [HT] and practitioner feedback on design). Codesigning the HT process with very experienced practitioners in both HT and ET improves the credibility of the solution proposed. The details of this step are given in Section 4.. As the outcome towards a practically applicable and useful HT process, we provide valuable directions based on making strengths and weaknesses between the two processes as well as how they relate to each other explicit. Furthermore, the HT process proposed was designed with practitioner input. In future work (dynamic validation in Figure 1 ), the process should be further evolved in controlled experiments, case studies, and action research.. Followed by our design steps presented in Sections 2, 3, and 4, we present the threats of validity to this study (Section 5) and provide the conclusion in Section 6. The conclusion provides answers to the RQs, implications for practitioners and researchers, as well as directions for future work. The conclusion is divided into two parts. The first part summarizes the results, and the second part presents the implications for practitioners and researchers. In future work, we highlight the importance to evaluate the HT process first in controlled experiments and in industrial environments. An experimental setup should focus on comparing ET, ST, as well as HT in relation to testing effectiveness (ability to identify critical defects) and efficiency (time needed for test design and execution).. Industrially focused studies need to focus on practitioners executing the process and learning how the process is tailored based on the context (e.g., different organizational test policies, types of system, and so forth). Earlier, we mentioned two types of tailoring, namely process structure (activities to be executed) and process flow (order of activities and relative effort spent on them).

O O O O O O B I O B I O O O O O O O O O O O O O O O O O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O B I I I O O O O O O O O O O O O O B I O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


Design

of

an

autonomous

intelligent

Demand-Side

Management

system

using

stochastic

optimisation

evolutionary

algorithms

Demand-Side

Management

systems

aim

to

modulate

energy

consumption

at

the

customer

side

of

the

meter

using

price

incentives.

Current

incentive

schemes

allow

consumers

to

reduce

their

costs,

and

from

the

point

of

view

of

the

supplier

play

a

role

in

load

balancing,

but

do

not

lead

to

optimal

demand

patterns.

In

the

context

of

charging

fleets

of

electric

vehicles,

we

propose

a

centralised

method

for

setting

overnight

charging

schedules.

This

method

uses

evolutionary

algorithms

to

automatically

search

for

optimal

plans,

representing

both

the

charging

schedule

and

the

energy

drawn

from

the

grid

at

each

time-step.

In

successive

experiments,

we

optimise

for

increased

state

of

charge,

reduced

peak

demand,

and

reduced

consumer

costs.

In

simulations,

the

centralised

method

achieves

improvements

in

performance

relative

to

simple

models

of

non-centralised

consumer

behaviour.

EU

policy

aims

to

reduce

greenhouse

gas

emissions

and

reduce

dependency

on

imported

fossil

fuels.

The

targets

mandate

the

reduction

in

member

states

to

below

the

emission

levels,

the

supply

of

of

all

energy

from

renewable

energy

sources

(RESs)

and

a

reduction

in

energy

consumption

by

by

the

year

Electric

vehicles

(EVs)

are

viewed

as

playing

a

role

in

reducing

emissions

in

the

transport

sector,

but

their

usage

causes

an

increase

in

electricity

demand.

The

use

of

RESs

also

causes

problems

for

the

efficient

operation

of

a

power

plant

Increased

cycling

(starting

up

and

shutting

down)

of

the

power

system

results

in

increased

wear

and

tear

on

the

plant

and

can

cause

an

increase

of

greenhouse

gas

emissions..

Therefore,

new

methods

are

required

to

increase

electricity

grid

efficiency

and

reduce

emissions.

The

smart

grid

(SG)

is

one

main

approach.

A

SG

is

a

type

of

electrical

power

grid

whose

goal

is

to

respond

to

the

behaviour

and

actions

of

energy

suppliers

and

consumers

to

efficiently

deliver

economic,

reliable

and

sustainable

electricity

services.

Multiple

research

areas

have

been

explored

in

SG

over

recent

years

as

a

result

of

different

challenges

that

have

been

posed

to

the

electrical

grid.

One

of

the

most

explored

areas

in

SG

is

Demand-Side

Management

(DSM)

systems

as

shown

by

the

increasing

number

of

publications,

ranging

from

the

use

of

intelligent

algorithms

(e.g.,

game

theory

Monte

Carlo-based

methods

evolutionary

algorithms

multi-agent

systems

real-time

systems

up

to

challenges

and

in-depth

surveys

on

the

area

DSM

is

a

set

of

measures

to

improve

the

energy

system

at

the

consumer

side.

DSM

ranges

from

improving

energy

efficiency

through

the

use

of

better

insulation

or

better

materials

up

to

the

use

of

autonomous

systems

to

control

energy

resources

DSM

programs

include

different

approaches,

e.g.,

manual

conservation

and

energy

efficiency

programs

and

Residential

Load

Management

(RLM)

RLM

programs

based

on

smart

pricing

are

amongst

the

most

popular

methods..

The

motivation

for

smart

grid

tariff

structures

is

twofold.

They

allow

consumers

to

reduce

their

electricity

costs.

At

the

same

time,

the

utility

company

achieves

a

reduction

in

the

peak-to-average

ratio

(PAR)

in

load

demand

resulting

from

the

shifted

consumption

If

no

special

measures

are

taken

to

avoid

them,

high

PAR

values

come

about

naturally

because

consumer

electricity

demand

follows

a

diurnal

pattern,

with

increased

load

in

the

morning,

a

dip

in

the

afternoon,

a

rise

in

the

evening,

and

a

stronger

dip

in

the

middle

of

the

night..

Some

of

these

smart

pricing

methods

are

very

popular.

In

particular,

time-of-use

(ToU)

pricing

has

been

widely

adopted

in

some

Contents

lists

available

at

ScienceDirect

journal

homepage:

European

countries

Other

smart

pricing

types

include

criticalpeak

pricing,

extreme

day

pricing,

and

smart

grid

real-time

pricing..

Motivated

by

smart

price-based

approaches,

we

are

interested

in

developing

an

autonomous

intelligent

DSM

system

that

shifts

electricity

consumption

of

electric

vehicles

(EVs).

To

this

end,

we

use

stochastic

optimisation

evolutionary

algorithms

(EAs).

The

main

contribution

of

this

work

focuses

on

the

notion

of

load

shifting,

borrowed

from

popular

smart

pricing-based

methods.

In

contrast

to

typical

DSM

approaches

such

as

dynamic

pricing,

which

are

based

on

an

interaction

between

the

utility

and

the

user,

we

use

a

centralised

approach,

wherein

the

consumption

schedule

is

set

centrally

based

on

complete

information

of

all

EVs.

The

motivation

is

to

achieve

improvements

in

performance.

To

do

so,

we

use

EAs

to

automatically

generate

(optimal)

solutions.

The

use

of

all

EVs

is

considered

in

the

solution

representation

used

in

our

EAs

(described

in

detail

in

Section

We

also

use

this

in

the

evaluation

of

candidate

solutions..

To

test

this

idea,

we

considered

a

dynamic

scenario

of

simulated

days,

with

the

charging

period

from

to

divided

into

time-slots

of

min

each.

An

action

(switching

EV

charging

on

or

off)

can

be

taken

at

the

beginning

of

each

time-slot.

We

defined

three

different

goals:.

(a)

that

EVs'

batteries

are

as

fully

charged

as

possible;

(b)

we

add

an

extra

goal

to

(a)

by

aiming

for

a

low

fluctuation

at

the

transformer

load

(i.e.,

low

PAR);

and

finally,

(c)

we

add

a

third

and

final

goal

that

aims

to

reduce

electricity

costs

to

the

consumer

by

using

a

pricing

signal

based

on

ToU..

To

achieve

these

three

goals,

we

propose

three

fitness

functions.

Each

will

be

used

independently

in

our

EA

and

will

guide

our

evolutionary

search

to

automatically

create

an

(optimal)

plan.

The

core

elements

in

this

work

are

the

following:.

We

study

the

impact

of

the

representation

and

functions

proposed

in

this

work

when

scaling

the

problem

up

(i.e.,

from

using

a

few

EVs

to

using

dozens

of

them)

by

measuring

the

transformer

load,

the

initial

and

final

state

of

charge

(SoC),

the

PAR

and

electricity

costs.

To

do

so,

we

used

two

EA

approaches:

a

genetic

algorithm

and

an

evolution

strategy

and

compared

their

performance

against

three

non-intelligent

approaches

(i.e.,

Greedy,

Midnight

and

Random

methods),

each

of

them

simulating

a

specific

user

behaviour.

A

dynamic

scenario

was

used

to

study

all

these

approaches

by

allowing

having

a

variety

of

changes,

i.e.,

different

SoC

for

each

EV

for

each

of

the

simulated

days,

over

a

simulated

period.

We

have

implemented

two

variants

of

evolutionary

algorithms

(EAs):

a

and

a

population-based

EA

to

search

for

efficient

charging

schedules

for

fleets

of

EVs,

achieving

good

results

in

terms

of

reducing

peak

demand

and

reducing

consumers'

electricity

costs,

while

maintaining

a

high

overall

state

of

charge

of

EVs'

batteries.

We

have

tested

these

approaches

on

small

to

medium

fleet

sizes

and

EVsusing

realistic

data

generated

by

a

state

of

the

art

grid

simulator,

over

the

course

of

simulated

days..

We

have

found

that

the

1þ

is

capable

of

slightly

outperforming

a

population-based

EA,

referred

to

in

this

work

as

EA.

We

have

also

shown

that

both

the

EA

and

the

approach

exhibit

better

performance

compared

against

the

non-intelligent

methods

(i.e.,

Greedy,

Midnight

and

Random

approaches)

used

in

this

work.

This

is

a

significant

result

because

each

of

these

nonintelligent

methods

reflects

likely

default

behaviour

for

most

consumers:

in

the

Greedy

approach,

the

EV

is

simply

plugged

in

and

charged

up

fully

as

soon

as

it

arrives

home

each

evening;

in

the

Midnight

approach,

the

EV

is

plugged

in

at

midnight

to

take

advantage

of

the

cheapest

electricity

cost;

and

in

the

Random

approach,

the

EV

could

be

charged

at

any

time

during

the

simulated

period

for

any

length

of

time.

In

contrast,

either

of

the

EAs

used

in

this

work

produces

plans

which

take

advantage

of

lower-cost

pricing

in

the

middle

of

the

night,

and

at

the

same

time

reduce

peak

demand..

Although

numbers

of

EVs

are

projected

to

be

in

the

thousands

or

millions,

Demand

Aggregators

may

have

much

smaller

fleet

sizes.

Therefore,

the

smaller

fleet

sizes

considered

here

represent

an

important

real-world

case.

The

slight

disimprovement

in

the

EA's

results

noted

in

Section

for

the

case

is

not

a

cause

for

great

concern.

However,

in

future

work

we

hope

to

scale

our

results

up

further..

It

is

an

assumption

of

this

work

that

customers

are

willing

to

submit

their

charging

schedules

to

a

central

authority,

e.g.,

a

Demand

Aggregator.

The

intelligent

algorithms

used

in

this

work

(i.e.,

þ

and

EA)

require

centralised

knowledge

(the

number

of

EVs

and

their

initial

SoC)

and

centralised

control

(specification

of

when

each

EV

should

charge,

up

to

granularity).

This

assumption

is

not

unrealistic.

As

we

have

seen,

customers

will

do

better

through

centralised

control

than

through

the

most

likely

individualised

behaviour,

the

Greedy

approach.

However,

more

informed

and

more

price-conscious

customers

will

be

willing

to

deviate

from

the

Greedy

approach

Table

The

Equalised

Cost

is

calculated

based

on

the

number

of

kilowatt-hours

drawn

during

peak

and

off-peak

hours,

equalized

between

schedules

by

taking

into

account

only

electricity

up

to

a

minimum

SoC.

The

Equalised

Cost

per

EV

is

calculated

based

on

the

Equalised

Cost

and

the

number

of

EVs.

Evolutionary

Algorithms

(EAs)

also

known

as

Evolutionary

Computation

systems,

are

influenced

by

the

theory

of

evolution

by

natural

selection.

These

algorithms

have

been

with

us

for

some

decades

and

are

very

popular

due

to

their

successful

application

in

a

range

of

different

problems,

ranging

from

the

automated

design

of

an

antenna

carried

out

by

NASA

the

automated

optimisation

of

game

controllers

the

automated

design

of

combinational

logic

circuits

to

automated

optimal

localisation

for

building

seismic

sensing

stations

EAs

are

"black-box",

that

is,

they

do

not

require

any

specific

knowledge

of

the

fitness

function.

They

work

even

when,

for

example,

it

is

not

possible

to

define

a

gradient

on

the

fitness

function

or

to

decompose

the

fitness

function

into

a

sum

of

per-variable

objective

functions.

The

fitness

functions

used

in

our

work

(described

in

Section

are

not

amenable

to

analytic

solution

or

simple

gradient-based

optimisation,

hence

search

algorithms

such

as

EAs

are

required..

The

idea

behind

EAs

is

to

automatically

generate

(nearly)

optimal

solutions

by

"evolving"

potential

solutions

(individuals

forming

a

population)

over

time

(generations)

by

using

bioinspired

operators

(e.g.,

crossover,

mutation).

More

specifically,

the

evolutionary

process

includes

the

initialisation

of

the

population

Pð0Þ

at

generation

g

The

population

consists

of

a

number

of

individuals

which

represent

potential

solutions

to

the

particular

problem.

At

each

iteration

or

generation

(g),

every

individual

within

the

population

ðPðgÞÞ

is

evaluated

using

a

fitness

function

that

determines

its

fitness

(i.e.,

how

good

or

bad

an

individual

is).

Then,

a

selection

mechanism

takes

place

to

stochastically

pick

the

fittest

individuals

from

the

population.

Some

of

the

selected

individuals

are

modified

by

genetic

operators

and

the

new

Source:

Last

accessed

date:

Links

of

strength

less

than

(in

(a))

or

(in

(b))

are

filtered

out.

Details

on

how

this

was

produced

can

be

found

in

population

Pðg

þ

1Þ

at

generation

g

þ

is

created.

The

process

stops

when

some

halting

condition

is

satisfied.

Further

details

on

how

these

stochastic

optimisation

algorithms

work

can

be

found

in

O O O O O O O O O O O B I B I I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O


SC-FDMA-based resource allocation and power control scheme for D2D communication using LTE-A uplink resource Device-to-device (D2D) communication-enabled cellular networks allow cellular devices to directly communicate with each other without any evolved NodeB (eNB). D2D communication aims to improve the spectral efficiency and increases the overall system capacity. For future mobile networks, intelligent radio resource allocation and power control schemes are required to accommodate the increasing number of cellular devices and their growing demand of data traffic. In this paper, a combined resource allocation and power control scheme for D2D communication is proposed. In the proposed scheme, D2D communication reuses the uplink (UL) resources of conventional cellular user equipments (CUEs); therefore, we have adopted single-carrier frequency division multiple access (SC-FDMA) as UL transmission scheme. The proposed scheme uses fractional frequency reuse (FFR)-based architecture to efficiently allocate the resources and mitigate the interference between CUEs and D2D user equipments (DUEs). In order to guarantee the user fairness, the proposed scheme uses the well-known proportional fair (PF) scheduling algorithm for resource allocation. We have also proposed an intelligent power control scheme which provides equal opportunity to both CUEs and DUEs to achieve a certain minimum signal-to-interference and noise ratio (SINR) value. The performance evaluation results show that the proposed scheme significantly improves the overall cell capacity and achieves low peak-to-average power ratio (PAPR). Recent years have witnessed rapid increase in the demand of ubiquitous communication services [1, 2] . Because of their almost ubiquitous nature, cellular networks have been massively deployed and used ever since their introduction in 1980s. Several cellular technologies have surfaced over the past some time. Most recently, the longterm evolution (LTE) and its successor LTE-advanced (LTE-A) have attracted considerable attention in the cellular domain. LTE-A is essentially an improvement on LTE, which aims at offering a variety of new techniques such as carrier aggregation, multiple-input multiple-output (MIMO), device-to-device (D2D) communication, etc. Unlike conventional cellular technology that conveys information between two user equipments (UEs) via evolved NodeB (eNB), D2D communications allows UEto-UE data exchange on direct links. By relieving the eNBs from too much relaying, D2D communications improves the overall system capacity and end user data rates [3] . The idea is that the eNB shall help the two UEs in establishing a direct connection between them, and then let the communication session evolve on one-hop D2D link. The UEs that are capable of communicating directly are referred to as D2D UEs (or DUEs). In certain situations, it may be imperative for the UEs to send information through the eNB. For instance, if the distance between two UEs is very large, direct communication cannot take place. Therefore, a cellular network that supports D2D communications has two kinds of UEs. One is the DUEs that transmit and receive data without eNB relaying, and the other is conventional cellular UEs (or CUEs) that communicate via the base station [4] . The 3rd Generation Partnership Project (3GPP) has considered two modes of D2D communications: mode-1 and mode-2 [5] . In mode-1 D2D communications, D2D users are located in an eNB coverage zone (i.e., in coverage). Thus, the eNB is responsible for D2D connection establishment, resource allocation, monitoring, and so on. On the other hand, in mode-2 D2D communications, D2D users are not located in the coverage zone of an eNB. Therefore, D2D users select radio resources autonomously. For D2D service scenarios such as content sharing, proximity-based advertisements, etc., Mode-1 communications is preferred [6] [7] [8] [9] . In this paper, we focus on mode-1 D2D communications. Figure 1 shows an example of D2D communications underlying cellular network.. It is intuitive that D2D communications under 3GPP mode-1 category shall result in severe interference if CUEs and DUEs are operating on the same frequency band. This so-called underlay technique requires a systematic frequency reuse strategy in order to prove beneficial [3, 10] . The other technique that allows CUEs and DUEs to coexist within a cell is the overlay technique. The overlay technique splits the frequency band allocated to a cell and distributes it among the CUEs and DUEs. In the overlay mode, CUEs and DUEs operate at two different frequency sub-bands. While the overlay technique works best in reducing interference, it results in the underutilization of the frequency resource [3, 10] . Almost all previous works on D2D communications prioritize the underlay technique because it can accommodate more UEs on a given frequency band. In order to ensure that interference is avoided in the underlay technique, various previous works have focused on proposing techniques that (i) intelligently allocate frequency resources to CUEs and DUE [11] and (ii) smartly control the transmit power of CUEs and DUEs [12] . This paper focuses on proposing a combined method that allows CUEs and DUEs to exist on the same frequency band in a typical LTE-A network.. LTE-A networks use single-carrier frequency division multiple access (SC-FDMA) for uplink transmissions [13] . An SC-FDMA system uses different subchannels to transmit the information symbols in parallel with each other. Compared with OFDMA, SC-FDMA significantly reduces the envelope fluctuations in the transmitted waveform. Thus, SC-FDMA signals have inherently lower peak-to-average power ratio (PAPR) than the OFDMA signals. In SC-FDMA, throughput depends on how the information symbols are applied to the subchannels. There are two possible implementation approaches to SC-FDMA: SC-FDMA with constraints (also known as localized SC-FDMA) and distributed SC-FDMA. In SC-FDMA with constraints, each terminal uses a set of adjacent subcarriers to transmit its symbols. On the other hand, in distributed SC-FDMA, the subcarriers are spread over the entire signal band. It has been shown in [14] [15] [16] that SC-FDMA with constraints achieves higher throughput than the distributed SC-FDMA. Therefore, this work employs SC-FDMA with constraints for uplink transmissions. For this kind of SC-FDMA, it has two critical constraints in relation to resource allocation. The first is exclusivity which maintains that a subchannel can only be allocated to one UE. The second constraint is adjacency, which states that if multiple subchannels are assigned to a UE, all of them must be adjacent to each other in the frequency domain. D2D communications reusing the uplink resources of cellular users can significantly improve the network capacity. In this paper, we have proposed an FFR-based resource allocation scheme for D2D communications using SC-FDMA and PF scheduler as a multiple access technique and scheduling algorithm, respectively. The proposed scheme also adopts an intelligent power control mechanism for D2D users which does not only guarantees a certain SINR for CUEs but also provides equal opportunity for DUEs to maintain a minimum SINR value. The simulation result shows that the proposed scheme alleviates the throughput of all UEs and improves the overall cell capacity. Furthermore, due to the use of SC-FDMA, the proposed scheme also alleviates the high PAPR problem. As a future research work, this approach can be extended by considering the reuse of both uplink and downlink radio resources for D2D communications. Considerable research has been conducted on spectrum sharing between CUEs and DUEs and their transmit power control. Some of the relevant works are briefly highlighted in the following.

O B I O B I O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O B I O O O O O O O O O O O O O O O O O O O O O O O B I O B I O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B I O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O B B O O O O O O B O O B O B O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O B O O O O O O O O O B O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O


A framework for designing sustainable urban communities The objectives of the paper are to show how the sustainability of urban settlements can be improved by treating as a variable the design of: (a) property rights to realty, corporations and currencies and: (b) their communication and control governance architecture. System science provides the basis for showing that the governance of complexity is improved by increasing the richness and variety of communication and control channels. The new variables introduced also provide a way to integrate the design of the built environment into the design of its governance architecture. The scope of orthodox economic analysis is extended to include the value of assets and liabilities to provide additional feedback signals. This more holistic economic framework increases the richness of the "semiotic" channel of social communication and control that complements those based on senses, words and prices. The analysis reveals self-reinforcing feed forward and feedback channels between the use and maintenance of the built environment and its governance architecture not available in less holistic design frameworks. This identifies the need for urban planners to extend their discipline to become governance architects and how the knowledge of system scientists can be applied to improve the design of capitalism. The analysis indicates how a design paradigm that does not accept the nature of property rights as a given, but a design variable, can enhance the ability towns or suburbs to become self-financing, self-governing political units. It also shows how capitalism can be made more efficient, equitable, responsive and democratic. This paper shows how the ability of an urban community to be sustainable is facilitated by a design framework that introduces time limits, as found in all living things, for property rights to realty, corporations and money. Ownership and control rights that terminate over time or "die", as exist for all intellectual property, are described as "ecological". The framework also introduces as a variable the design of the communication, control and decision making architecture of communities -referred to as its cybernetic or governance architecture.. Social analysis is traditionally undertaken on the assumption that the nature of property rights is fixed, rather than being a variable subject to design. By making property rights and the cybernetic architecture of communities a design variable, a different framework or paradigm is introduced to extend the study of economics, urban design and other disciplines that implicitly assume that the existing designs represents the "natural order of things".. Instead of implicitly assuming that property rights are static, exclusive and perpetual the innovation of this paper is that they can be designed to become dynamic, inclusive and time limited. Dynamic rights increase the variety of feedback and feed forward channels to increase the importance of the science of communication and control in the design of sustainable urban communities. This makes it important to also design their governance architecture.. The process of design is defined by Ashby (1968: 252) as "what determines the final model, of how it comes to be selected" (emphasis in the original). The act of designing means that maker of a machine or the architect of a social organization is involved in selecting from all possible varieties. Ashby describes the design process as selecting in stages and this contribution follows this approach by first selecting a framework that determines how the more traditional details of design are executed.. The criteria for the economy of an urban precinct to become self-financing also provide design criteria for the built environment. The interdependency of the visible and invisible structures of society requires that town planners acquire the knowledge of system scientists. System scientists in turn need to acquire knowledge of: (i) the operating characteristics of the four cybernetic channels (senses, semiotics, words and prices) that modern societies use to co-ordinate and govern their activities, along with (ii) the limitations of humans to reliably receive, store, process and transmit signals. With this knowledge they can apply the laws of requisite variety of communication channels, decision making and control to design organizations that can mitigate the unreliability and inconsistency in human communications, control and decision making.. To increase the richness and variety of social cybernetic channels, property rights to realty, corporations and money are considered a design variable. To take into account changing economic values introduced by dynamic property rights when transactions do not occur to generate a price signal, the scope of orthodox economic analysis is extended to include the value of assets and liabilities. The traditional remit of economics has been limited to the production and exchange of goods and services that ignore the windfall gains that accrue in property and how investors can get overpaid (Turnbull 2006: 451) .. The holistic approach to both economics and property rights creates a design framework for improving the economic efficiency and equity in sustaining urban communities. The framework also provides a way of improving the responsiveness and participation of citizens in governing themselves in using, managing, maintaining, and developing the built environment.. For a town or suburb to manage its affairs on a sustainable basis it needs a degree of political independence to take into account feedback information from its host environment, its citizens and trading partners. Political independence of an urban community is dependent upon resident ownership and control of its land, sites, services and productive processes. It also depends on the ability of its residents and their local government to operate on a self-financing basis to make them independent of, or being excessively controlled by, higher orders of government or private sector agents.. Self-financing urban communities can be viewed like a micro economy and/or a "holon" (Koestler 1967 ). Politically they could represent a local government body, suburb or a component of one. To further their financial and so political independence communities needs to eliminate or minimize the loss of economic value from: (i) imports exceeding exports. (ii) migration of its citizens; (iii) wages, salaries, and fees paid to guest workers; (iv) rents, profits, dividends, royalties, and fees paid to external property owners; and (v) interest payments to external lenders. These considerations provide criteria for designing the built environment, its governance architecture and its economic institutions, each of which provides feed forward and feedback information to each other as recognized by Howard (1946) in designing both the visible and invisible structures of Letchworth.. The design of the invisible communication and control architecture of urban communities needs to take into consideration the limited ability of humans to receive, store, process and transmit data, and its higher derivatives of information, knowledge and wisdom. Humans have five senses of taste, touch, smell, sound and sight for detecting signals from others and their environment. The amount of data that can be communicated in bytes/second by each of these five senses has been measured by the Research Laboratories of British Telecom (Cochrane 2000) . No matter what technology is used to communicate between humans their ability to receive and transmit data and so information, knowledge and wisdom is limited by the physiology of their input and output channels.. The export of data from humans is in practice limited to body movements and sounds because it is difficult to vary ones taste and smell and like touch requires intimacy. Communication through movement creates signs and symbols described as "semiotics". A highly specialized form for semiotics developed by humans is writing/typing and an even more specialized communication channel is through the use of prices. The data intensity or "band width" of each channel decreases as its specialization increases.. The richest input channels are the sense of sight and sound, then semiotics, words and price. The band widths, operating characteristics, limits and benefits of each of the four channels provide criteria for designing the invisible cybernetic architecture of urban communities as identified by Turnbull (2000b: 96) . Different combinations of the four channels create different social outcomes that are characteristic of different political systems (Turnbull 2000b: 277) .. Humans also have limited ability to store or process bytes (Kurzweil 1999) . The design of human social organizations must therefore accommodate their limited neural ability to store and process bytes in this context 1 (Turnbull 2000b: 111) .. Not only do humans have physiological and neural limits in transacting bytes but they can be inconsistent in how they interpret, process or use the data and/or create omissions, biases and errors in their communications with others. However, the cybernetic laws of requisite variety in communications, decision making and control provide the organizational architect with design strategies to mitigate these problems.. The manner in which humans convert bytes into data, information, knowledge and wisdom is determined by nature and nurture with nurture being subjected to cultural conditioning. For example the meaning of ownership is culturally determined. Traditionally nomadic Australian Aboriginals did not even have a word for ownership (Turnbull 1986 ). The concept of ownership has evolved over time from the use and/or control of land. In squatter settlements the rule is "if you do not use it you loose it". This rule introduces a dynamic dimension to the concept of ownership that is consistent with the efficient and equitable use of land or any other resource. An option not considered by George (1912) who sought the same objective but assumed that the rules of ownership were fixed rather than variable. As a result his solution of taxing the underutilized value of land introduced the dead weight cost of valuing each individual land title and administrating a tax system.. Time limits promote efficiency because they can avoid a resource not being used. Time limits promote equity as it avoids a non-user-owner being over-paid by receiving rents in perpetuity or from non-owner-users being excessively exploited.. Property rights without time limits only exist in regards to realty and corporate shares. All productive assets wear out, all intellectual property has time limits, and ownership of art and other collectable is limited to the span of human life. The introduction of time limited ownership of realty and corporations minimizes the loss of value from communities to external owners and so furthers their ability to become self-financing. It also increases the efficiency and equity of allocating resources within the community to support the hypothesis of this paper that ecological ownership facilitates the sustainability of urban communities.. Another way value can drain out of communities is through interest payments. Households may spend up to a third of their income in paying mortgage costs or rent. Over half of household income can be spent on other goods and services. A community currency provides a way of identifying these costs and/or reducing payments outside the community.. During the Great Depression (1929−1939) when the US banking system did not provide sufficient credit, thousands of communities created their own currencies described as "Stamped Script" (Fisher 1934 ) that had ecological property rights. Stamp Scrip was not created by banks but by local government authorities or Chambers of Commerce in the form of a voucher that lost all its value unless a stamp was fixed to it at a specified interval. Various rules were designed in different communities but typically a voucher or script valued at one dollar would loose all its value every Wednesday midnight unless a one cent stamp was attached each week.. Stamp scrip was given away to citizens who could redeem them for their face value of one dollar after two years. However, during this time the issuer of the scrip would collect income from selling stamps for 104 weeks that would amount to $1.04. So a 4% gross profit was made on redeeming the scrip that was given away! Merchants accepted the 1% cost of the stamp per week as it represented only a fraction of the charge from a modern credit card that cost around 3% per transaction. Citizens accepted scrip and its 1% "demurrage" charge per week because it cost them nothing to acquire the scrip. The cost of the demurrage charge per transaction decreased with use of the scrip to make it more attractive than official money − even when it was available. If Stamped Scrip was used 20 times in a week its 1% cost reduced to 1/20 th or 0.05% per transaction.. The adoption of Stamped Script spread rapidly because it created credit without an interest charge. For this reason it posed a serious threat to the banks licensed by the Federal Reserve Act of 1913 who created credits that generated profits from earning an interest rate in perpetuity rather than fees for a limited time. As a result, local currencies were squeezed out by "The New Deal" legislation introduced in 1933. However, local currencies are now appearing again around the world (CC, 2007) .. A currency with a demurrage charge provides an incentive to convert money into tangible assets that increase in value over time like trees, breeding cattle and man made productive assets. Demurrage money inhibits monetary speculation and eliminates discounting the future. This provides additional support to the hypothesis that ecological property rights facilitate sustainable communities.. The ability of money to earn interest undermines sustainability because future values are discounted from the ability to earn interest today. As recognized by Islamic banking, the ability of money to increase in value from interest independently of any increases in real assets is inconsistent with the laws of nature. It would be difficult to explain to a visitor from another planet why an artificial token should be designed in such a way.. Especially when it is the cost of money that disadvantages investment in sustainable energy sources as discussed in Section 3. As a result the design of the current monetary systems is creating market forces to exacerbate climate change.. Another design problem in the current monetary system is that it is based on central banking that represents a specialized form of central planning. As described by Jacobs (1985: 156−81) a centralized monetary system in a diversified economy can misallocate resources in different regions from the feedback signals that it creates which must be based on the aggregate of all regions. This problem would be overcome with competing regional currencies as described Hayek (1976a, b) .. The following Section 2 considers a framework for designing self-financing self-governing urban precincts. The attraction for introducing a local currency defined in terms of sustainable energy is considered in Section 3. Section 4 illustrates how urban design framework creates the need for urban planners to become system scientists and how cybernetics provides criteria for designing self-governing sustainable urban communities. Concluding remarks on the interdependency of "cybernetics and design" is presented in Section 5. Because the CLB provides a framework for furthering the self-governance of urban citizens it also allows them to be responsive to the needs of their host bio-region. In this way signals from the natural environment can provide feed forward and feedback information to allow contributions from nature to govern society to introduce elements of "Environmental Republicism".. If urban communities are to maintain themselves over generations on a sustainable basis they need to receive not only feed forward and feedback information from nature but from all stakeholders on whom they depend for their existence. Residents are the primary stakeholders but external investors, and stakeholders in resident firms and trading partners represent secondary stakeholders on who the community depends for its existence. Beside these "lateral" stakeholders there are also "vertical" stakeholders such as higher levels of government. In this way urban communities designed as a CLB can become a "holon" in a "holarchy" (Koestler 1967) to mimic the architecture of nature as illustrated by examples in Turnbull (2000b: 130; 221) .. The CLB can itself be composed of almost self-governing holons that could be represented by apartment complexes, shopping centers, firms, schools, hospitals and community centers. A rich diversity of activities assists in making a CLB self-financing as discussed earlier. This introduces complexity. Nature governs complexity by adopting holonic architecture because "The reduction in data transmission, and in data complexity, achieved by the holonic architecture, is prodigious" (Mathews 1996: 30) .. This observation provides an answer to the first question posed by the guest editor for the current issue of this journal who asked: "How does cybernetics throw light on design, and leads to developments and improvements in our understanding of an ability to act in design". The observation by Mathews means that holonic architecture needs to be designed into the governance systems of complex activities found in a CLB. Holonic architecture also provides a framework for designing requisite variety in communications, control and decision making to govern complexity as reliably as required. A corollary is that centrally organized command and control systems cannot deliver requisite variety to govern complexity.. The second question raised by the guest editor was "How does design inform us in our understanding of cybernetics and its potential to parallel and throw light on design?" The answer to the question is provided by the architecture of the MCC which provides an outstanding example of a governance architecture that mimics nature with nested holonic networks supported by lateral holonic networks as found in living things (Turnbull 2000b: 221) . In this way the MCC informs us of the efficiency and effectiveness of following the architecture of nature as the MCC has proven to be more efficient, competitive and sustainable than hierarchical controlled firms. Cybernetics in turn explains why the architecture of the MCC is successful for the reasons noted by Mathews and because its networks provide a way to de-compose decision making labor to a level sufficiently simple to allow humans to govern complexity on a sustainable basis.. The third question raised by the guest editor was: "What is the mutualism that may hold between them when questions 1) and 2) are seen as part of the same whole?" As pointed out above, the CLB creates a design framework for designing both the visible and invisible structures of society to create a virtuous self-reinforcing process to sustain each other. It does this in way to enrich the participation of citizens in the decision making processes of their community to create a more efficient, equitable and democratic form of capitalism as indicated in Turnbull (1976) .. Another element of mutualism arises from designing dynamic property rights to realty, firms and money so that they take on ecological characteristics. As indicated in the last Section, dynamic tenure can fundamentally change decision making in the use and control of realty, firms and money. It introduces continuous change but at the same time introduce incentives to maintain sustainable equilibrium between opposing interests. Mutuality arises as this can only be achieved by designing a governance architecture that can use and act on the emergent behavior that dynamic tenure introduces. However the most compelling reason for introducing dynamic ecological property rights is to stop economic value being drained out of communities.. Mutuality is inherent in the design of property rights, the nature of the built environment and its governance architecture for creating self-governing sustainable communities. This provides a compelling reason for town planners to become system scientists and governance architects.

O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O B O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O B O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O B O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O B I O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O B O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O B O O O O O O O B I O O O O O O O O B O O O O O B O O O O O O O O O O O O O B O


LabeledIn:

Cataloging

labeled

indications

for

human

drugs

Drug-disease

treatment

relationships,

i.e.,

which

drug(s)

are

indicated

to

treat

which

disease(s),

are

among

the

most

frequently

sought

information

in

PubMed

Ò

Such

information

is

useful

for

feeding

the

Google

Knowledge

Graph,

designing

computational

methods

to

predict

novel

drug

indications,

and

validating

clinical

information

in

EMRs.

Given

the

importance

and

utility

of

this

information,

there

have

been

several

efforts

to

create

repositories

of

drugs

and

their

indications.

However,

existing

resources

are

incomplete.

Furthermore,

they

neither

label

indications

in

a

structured

way

nor

differentiate

them

by

drug-specific

properties

such

as

dosage

form,

and

thus

do

not

support

computer

processing

or

semantic

interoperability.

More

recently,

several

studies

have

proposed

automatic

methods

to

extract

structured

indications

from

drug

descriptions;

however,

their

performance

is

limited

by

natural

language

challenges

in

disease

named

entity

recognition

and

indication

selection.In

response,

we

report

LabeledIn:

a

human-reviewed,

machine-readable

and

source-linked

catalog

of

labeled

indications

for

human

drugs.

More

specifically,

we

describe

our

semi-automatic

approach

to

derive

LabeledIn

from

drug

descriptions

through

human

annotations

with

aids

from

automatic

methods.

As

the

data

source,

we

use

the

drug

labels

(or

package

inserts)

submitted

to

the

FDA

by

drug

manufacturers

and

made

available

in

DailyMed.

Our

machine-assisted

human

annotation

workflow

comprises:

(i)

a

grouping

method

to

remove

redundancy

and

identify

representative

drug

labels

to

be

used

for

human

annotation,

(ii)

an

automatic

method

to

recognize

and

normalize

mentions

of

diseases

in

drug

labels

as

candidate

indications,

and

(iii)

a

two-round

annotation

workflow

for

human

experts

to

judge

the

precomputed

candidates

and

deliver

the

final

gold

standard.In

this

study,

we

focused

on

highly

accessed

drugs

in

PubMed

Health,

a

newly

developed

public

web

resource

for

consumers

and

clinicians

on

prevention

and

treatment

of

diseases.

These

drugs

corresponded

to

more

than

drug

labels

unique)

in

DailyMed

in

which

candidate

indications

were

pre-tagged

by

an

automatic

tool.

After

being

reviewed

independently

by

two

experts,

indications

were

selected,

and

additional

(missed

by

computer)

were

manually

added,

with

an

inter-annotator

agreement

of

as

measured

by

the

Kappa

coefficient.

Our

final

annotation

results

in

LabeledIn

consist

of

drug-disease

treatment

relationships

where

drugs

are

represented

as

a

triplet

of

ingredient,

dose

form,

and

strength.A

systematic

comparison

of

LabeledIn

with

an

existing

computer-derived

resource

revealed

significant

discrepancies,

confirming

the

need

to

involve

humans

in

the

creation

of

such

a

resource.

In

addition,

LabeledIn

is

unique

in

that

it

contains

detailed

textual

context

of

the

selected

indications

in

drug

labels,

making

it

suitable

for

the

development

of

advanced

computational

methods

for

the

automatic

extraction

of

indications

from

free

text.

Finally,

motivated

by

the

studies

on

drug

nomenclature

and

medication

errors

in

EMRs,

we

adopted

a

fine-grained

drug

representation

scheme,

which

enables

the

automatic

identification

of

drugs

with

indications

specific

to

certain

dose

forms

or

strengths.

Future

work

includes

expanding

our

coverage

to

more

drugs

and

integration

with

other

resources.The

LabeledIn

dataset

and

the

annotation

guidelines

are

available

at

LabeledIn/.Published

by

Elsevier

Inc.

This

is

an

open

access

article

under

the

CC

BY-NC-SA

license

Drug-disease

treatment

relationships

are

among

the

top

searched

topics

in

PubMed

Ò

established

during

the

drug

discovery

and

development

process,

which

establishes

the

therapeutic

intent

of

a

given

drug

based

on

its

properties

and

target

patient

characteristics.

The

primary

application

of

such

information

is

to

inform

healthcare

professionals

and

patients

for

questions

like

''what

drugs

may

be

prescribed

for

hypertension

or

''what

are

the

indications

of

Fluoxetine''

These

relationships

are

also

used

for

feeding

the

Google

Knowledge

Graph,

developing

computational

methods

for

predicting

and

validating

results

of

novel

drug

indications

and

drug

side

effects

assisting

PubMed

Health

Ò

pubmedhealth/)

editors

to

cross-link

drug

and

disease

monographs

More

recently,

such

information

is

found

to

be

critical

in

validating

patient

notes

and

medication-problem

links

in

electronic

medical

records

(EMRs)

Given

this

variety

of

applications,

it

is

important

to

have

a

comprehensive

gold

standard

of

drugdisease

treatment

relationships,

that

is

(i)

accurate

and

derived

from

a

credible

source,

and

(ii)

structured

to

support

computer

processing,

and

(iii)

normalized

to

precise

concepts

in

standard

vocabularies,

such

as

UMLS

and

RxNorm

to

facilitate

semantic

understanding

and

interoperability.

The

third

desired

property

deserves

further

explanation.

To

precisely

represent

the

treatment

relationship,

it

is

necessary

that

diseases

and

drugs

are

normalized

to

the

most

appropriate

abstraction

levels:.

Disease

Normalization:

The

diseases

should

be

normalized

to

the

most

specific

concepts.

For

instance,

if

a

drug

is

used

for

treating

''respiratory

tract

infections,''

mapping

to

the

generic

concept

''infections''

would

not

only

be

imprecise

but

also

inaccurate

since

the

drug

may

not

treat

all

kinds

of

infections.

Drug

Normalization:

A

drug

can

be

represented

at

several

levels

of

granularity

based

on

its

properties.

While

the

therapeutic

intent

of

a

drug

is

largely

determined

by

its

active

ingredient

(IN),

there

is

evidence

showing

that

it

may

also

be

dictated

by

its

dose

form

(DF)

and

strength

(ST)

For

example,

the

indications

of

Ketorolac

oral

tablet

are

different

from

those

of

the

ophthalmic

solution

(see

Table

Several

existing

knowledge

bases

such

as

DrugBank

and

MedicineNet

already

contain

drug-disease

relationships.

However,

they

are

unstructured

(i.e.

described

in

free

text),

and

thus,

do

not

allow

automatic

computer

analysis.

Google's

Freebase

is

a

structured

resource,

but

the

drugs

are

coarsely

represented

as

ingredients,

and

the

diseases

are

not

normalized.

The

NDF-RT

provides

structured

and

normalized

information.

However,

it

is

found

incomplete

with

respect

to

the

list

of

drug

indications

and

the

drug-disease

relationships

are

not

separately

labeled

according

to

different

dose

forms

or

strengths.

For

instance,

the

Ketorolac

drug

is

manufactured

in

multiple

dose

forms,

each

serving

a

different

purpose,

e.g.

injectable

solution

is

used

for

pain,

and

ophthalmic

solution

for

conjunctivitis.

Despite

this,

the

NDF-RT

links

all

the

different

forms

of

this

drug

to

the

same

set

of

diseases:

inflammation,

allergic

conjunctivitis,

photophobia,

and

pain..

In

addition,

there

have

been

multiple

attempts

to

use

automated

methods

for

extracting

computable

(i.e.

structured

and

normalized)

indication

information

from

existing

textual

resources

(e.g.

the

DailyMed

website

using

knowledge-based

approaches.

SIDER

is

a

public

resource

focused

on

identifying

adverse

drug

reactions

and

indications

from

the

FDA

drug

labels

and

public

documents.

The

method

used

to

extract

indications

is

based

on

a

UMLS-based

lexicon

lookup

technique

followed

by

side

effects

filtering.

The

October

version

of

SIDER

contains

indications

for

drugs

labels.

Neveol

and

Lu

used

text

mining

techniques

to

extract

indications

from

FDA

drug

labels,

and

automatically

extract

relationships

between

ingredients

and

diseases

using

SemRep

with

precision

of

Wei

and

colleagues

created

an

ensemble

indication

resource

called

MEDI

by

integrating

information

from

four

resources:

SIDER

NDF-RT,

MedlinePlus,

and

Wikipedia.

A

subset

of

MEDI

was

sampled

and

reviewed

by

two

physicians

in

two

rounds

to

further

determine

the

automatic

inclusion

strategy

for

a

high

precision

dataset.

The

final

computer-generated

dataset

(MEDI-HPS)

contains

ingredient-indication

pairs

corresponding

to

ingredients

with

an

estimated

precision

and

recall.

Fung

et

al.

designed

a

DailyMed-based

indication

extraction

system

(SPL-X)

for

decision

support

in

electronic

medical

records.

SPL-X

uses

MetaMap

negation

removal,

and

semantic

reclassification

techniques

for

disease

concept

identification.

SPL-X

was

applied

to

unique

drug

labels,

a

subset

of

which

was

evaluated

by

seven

physicians

showing

in

precision

and

in

recall..

As

found

in

the

abovementioned

studies

automatic

methods

alone

are

not

yet

sufficient

to

deliver

a

gold

standard

due

to

the

challenges

in

natural

language

processing

(NLP),

including:

(a)

the

difficulties

with

automatic

disease

recognition

and

normalization

and

(b)

the

presence

of

disease

mentions

other

than

indications

in

drug

labels.

To

illustrate

these,

Table

contains

the

indication

fields

of

two

sample

drug

labels

(dl

and

dl

same

ingredient

but

different

dose

forms)

in

DailyMed

which

houses

the

most

up-to-date

drug

labels

submitted

to

the

FDA

by

drug

manufacturers.

Table

also

shows

the

disease

names

found

by

a

state-of-the-art

NLP

tool

(column

in

italics)

and

the

final

indications

after

human

revision

(column

in

italics).

As

can

be

seen,

recognition

of

disease

mentions

is

not

trivial

(''ocular

itching''

vs.

''itching''

in

dl

''severe

pain''

vs.

''moderate

to

severe

pain''

in

dl

In

addition,

drug

labels

could

contain

negative

and

irrelevant

(''cataract''

mention

in

dl

''analgesia''

mention

in

dl

disease

mentions..

Unlike

previous

studies

in

this

work

we

resort

to

human

annotation

to

create

a

gold

standard

of

drug

indications

with

the

aids

from

automatic

text-mining

tools,

as

they

have

been

shown

to

be

useful

for

assisting

manual

curation

This

study

is

built

on

a

pilot

study

in

which

we

evaluated

the

feasibility

of

our

semi-automated

annotation

framework

on

a

small

set

of

drug

labels

with

two

human

annotators.

Based

on

the

error

analysis

of

the

previous

study,

we

have

significantly

improved

the

annotation

framework,

revised

the

annotation

guidelines,

and

produced

a

resource

covering

DailyMed

drug

labels

in

the

current

study.

Another

unique

aspect

of

the

current

work

is

that

each

indication

is

linked

to

a

specific

textual

location

in

the

source

drug

label(s).

Not

only

does

this

provide

evidence

and

context

of

selected

indications,

such

as

linked

textual

information,

but

also

can

serve

as

training

data

for

the

development

of

supervised

machine-learning

methods

for

automatic

indication

extraction.

Finally,

this

study

uses

a

finer-grained

scheme

where

each

drug

is

represented

as

a

<Active

Ingredient

(IN),

Dose

Form

(DF),

Drug

Strength

(ST)>

when

linking

to

its

labeled

indications.

We

have

conducted

a

study

of

annotating

FDA

drug

labels

using

a

semi-automatic

method.

Deleger

et

al.

previously

annotated

disease

mentions

from

FDA

drug

labels.

Our

study

produced

a

comparable

inter-annotator

agreement

but

differs

in

that

we

yielded

drug

indications

as

opposed

to

all

the

diseases

mentioned

in

a

drug

label.

Distinguishing

indications

from

other

disease

mentions

is

a

non-trivial

problem

requiring

human

judgment

as

we

observed

that

approximately

of

automatically

identified

In

addition

to

supporting

computer

processing,

LabeledIn

is

linked

to

the

source

drug

labels

such

that

it

captures

the

rich

context

in

which

a

disease

is

judged

as

an

indication.

This

enables

systematic

comparison

with

existing

sources

and

enhancing

automatically

generated

resources

(e.g.

SIDER

in

Section

Also,

because

drug

indications

in

LabeledIn

are

linked

to

the

source

text,

they

enable

the

development

of

supervised

machine-learning

methods

for

automatic

drug

indication

extraction

from

free

text.

Furthermore,

an

immediate

application

of

the

humanvalidated

treatment

relationships

in

LabeledIn

is

to

improve

accessibility

in

online

health

resources

(e.g.

PubMed

Health)

by

enriching

hyperlinks

between

drug

and

disease

monographs..

While

most

existing

studies

represent

a

drug

by

its

active

ingredient,

the

theoretical

definition

of

''therapeutic

equivalence''

suggests

that

the

information

about

dose

form

and

drug

strength

is

also

critical

in

ensuring

effective

and

correct

treatment

Such

information

is

also

important

in

controlling

documentation

malpractices,

including

prescription,

medication,

drug

nomenclature

errors

and

medication-problem

linking

errors

in

the

EMRs.

Hence,

this

study

regards

indications

as

a

function

of

all

the

key

properties

of

a

drug,

and

represents

a

drug

as

a

<Active

Ingredient

(IN),

Dose

Form

(DF),

Drug

Strength

(ST)>.

Furthermore,

the

automatic

analysis

of

our

annotation

results

helped

identify

the

candidate

drugs

in

DailyMed

for

which

the

indications

may

in

practice

be

dictated

by

dose

forms

and/or

strengths.

The

validation

of

such

advanced

information

about

the

candidate

drugs,

however,

requires

further

analysis

by

domain

experts

in

medicine

and

pharmacy..

There

are

several

limitations

of

the

current

work.

First,

LabeledIn

currently

contains

information

about

highly

accessed

drugs

and

covers

nearly

of

the

human

prescription

FDA

drug

labels

accordingly.

For

future

work,

we

would

like

to

expand

to

more

ingredients

and

keep

LabeledIn

current

with

new

releases

of

DailyMed.

As

an

estimate,

we

studied

the

effective

differences

between

the

existing

version

of

LabeledIn

(August

and

the

current

version

of

DailyMed

(April

for

our

drugs.

We

found

that

only

about

new

drug

labels

need

to

be

annotated

for

a

period

of

months.

Second,

LabeledIn

only

contains

labeled/marketed

indications.

On

the

other

hand,

an

existing

resource

MEDI

provides

computable

information

regarding

off-label

indications

from

Wikipedia

and

MedlinePlus,

in

addition

to

labeled

indications

from

SIDER

and

NDF-RT.

Hence,

in

the

future

we

plan

to

investigate

ways

to

integrate

our

results

with

existing

resources

such

as

MEDI.

Lastly,

certain

annotated

drug

indications

(e.g.

''inflammation''

in

Table

are

specific

to

certain

procedures/conditions

which

are

not

currently

captured.

Given

LabeledIn

is

linked

to

the

source

drug

labels,

in

future

work

we

plan

to

extract

and

organize

such

information

in

structured

and

computable

format

in

order

to

further

enrich

our

resource.

In

summary,

we

have

produced

LabeledIn,

a

resource

containing

the

labeled

indication

information

for

frequently

accessed

human

drugs.

We

believe

our

human

annotation

results

are

useful

in

a

wide

variety

of

applications,

and

are

complementary

to

existing

resources.

O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I I O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O B I O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O