A convex formulation for joint RNA isoform detection and quantification from multiple RNA-seq samples Background: Detecting and quantifying isoforms from RNA-seq data is an important but challenging task. The problem is often ill-posed, particularly at low coverage. One promising direction is to exploit several samples simultaneously.We propose a new method for solving the isoform deconvolution problem jointly across several samples. We formulate a convex optimization problem that allows to share information between samples and that we solve efficiently. We demonstrate the benefits of combining several samples on simulated and real data, and show that our approach outperforms pooling strategies and methods based on integer programming. Conclusion: Our convex formulation to jointly detect and quantify isoforms from RNA-seq data of multiple related samples is a computationally efficient approach to leverage the hypotheses that some isoforms are likely to be present in several samples. The software and source code are available at http://cbio.ensmp.fr/flipflop. We proposed a multi-sample extension of FlipFlop, which implements a new convex optimization formulation for RNA isoform identification and quantification jointly across several samples. Experiments on simulated and real data show that an appropriate method for joint estimation is more powerful than a naive pooling of reads across samples. We also obtained promising results compared to MiTie, which tries to solve a combinatorial formulation of the problem.. Accurately estimating isoforms in multiple samples is an important preliminary step to differential expression studies at the level of isoforms [36, 37] . Indeed, isoform deconvolution from single samples suffers from high false positive and false negatives rates, making the comparison between different samples even more difficult if isoforms are estimated from each sample independently. Although the FlipFlop formulation of joint isoform deconvolution across samples provides a useful solution to define a list of isoforms expressed (or not) in each sample, variants of FlipFlop specifically dedicated to the problem of finding differentially expressed isoforms may also be possible by changing the objective function optimized in (1) .. Finally, as future multi-sample applications such as jointly analyzing large cohorts of cancer samples or many cells in single-cell RNA-seq are likely to involve hundreds or thousands of samples, more efficient implementations involving in particular distributed optimization may be needed. Most genes in eukaryote genomes are subject to alternative splicing [1] , meaning they can give rise to different mature mRNA molecules, called transcripts or isoforms, by including or excluding particular exons, retaining introns or using alternative donor or acceptor sites. Alternative splicing is a regulated process that not only greatly increases the repertoire of proteins that can be encoded by the genome [2] , but also appears to be tissue-specific [3, 4] and regulated in development [5] , as well as implicated in diseases such as cancers [6] . Hence, detecting isoforms in different cell types or samples is an important step to understand the regulatory programs of the cells or to identify splicing variants responsible for diseases.. Next-generation sequencing (NGS) technologies can be used to identify and quantify these isoforms, using the RNA-seq protocol [7] [8] [9] . However, identification and Full list of author information is available at the end of the article quantification of isoforms from RNA-seq data, sometimes referred to as the isoform deconvolution problem, is often challenging because RNA-seq technologies usually only sequence short portions of mRNA molecules, called reads. A given read sequenced by RNA-seq can therefore originate from different transcripts that share a particular portion containing the read, and a deconvolution step is needed to assign the read to a particular isoform or at least estimate globally which isoforms are present and in which quantity based on all sequenced reads.. When a reference genome is available, the RNA-seq reads can be aligned on it using a dedicated splice mapper [10] [11] [12] , and the deconvolution problem for a given sample consists in estimating a small set of isoforms and their abundances that explain well the observed coverage of reads along the genome. One of the main difficulty lies in the fact that the number of candidate isoforms is very large, growing essentially exponentially with the number of exons. Approaches that try to perform de novo isoform reconstruction based on the read alignment include Cufflinks [13] , Scripture [14] , IsoLasso [15] , NSMAP [16] , SLIDE [17] , iReckon [18] , Traph [19] , MiTie [20] , and FlipFlop [21] . However, the problem is far from being solved and is still challenging, due in particular to identifiability issues (the fact that different combinations of isoforms can correctly explain the observed reads), particularly at low coverage, which limits the statistical power of the inference methods: as a result, the performance reported by the state-of-the-art is often disappointingly low.. One promising direction to improve isoform deconvolution is to exploit several samples at the same time, such as biological replicates or time course experiments. If some isoforms are shared by several samples, potentially with different abundances, the identifiability issue may vanish and the statistical power of the deconvolution methods may increase due to the availability of more data for estimation. For example, the state-of-theart methods CLIIQ [22] and MiTie [20] perform joint isoform deconvolution across multiple samples, by formulating the problem as an NP-hard combinatorial problem solved by mixed integer programming. MiTie avoids an explicit enumeration of candidate isoforms using a pruning strategy, which can drastically speed up the computation in some cases but remains very slow in other cases. The Cufflinks/Cuffmerge [13] method uses a more naive and straightforward approach, where transcripts are first predicted independently on each sample, before being merged (with some heuristics) in a unique set.. In this paper, we propose a new method for isoform deconvolution from multiple samples. When applied to a single sample, the method boils down to FlipFlop [21] ; thus, we simply refer to the new multi-sample extension of the technique as FlipFlop as well. It formulates the isoform deconvolution problem as a continuous convex relaxation of the combinatorial problem solved by CLIIQ and MiTie, using the group-lasso penalty [23, 24] to impose shared sparsity of the models estimated on each sample. The group-lasso penalty allows to select a few isoforms among many candidates jointly across samples, while assigning sample-specific abundance values. By doing so, it shares information between samples but still considers each sample to be specific, without learning a unique model for all samples together as a merging strategy would do. Compared to CLIIQ or MiTie, FlipFlop addresses a convex optimization problem efficiently, and involves an automatic model selection procedure to balance the fit of the data against the number of detected isoforms. We show experimentally, on simulated and real data, that FlipFlop is more accurate than simple pooling strategies and than other existing methods for isoform deconvolution from multiple samples. The deconvolution problem for a single sample can be cast as a sparse regression problem of the observed reads against expressed isoforms, and solved by penalized regression techniques like the Lasso, where the 1 penalty controls the number of expressed isoforms. This approach is implemented by several of the referenced methods, including IsoLasso [15] and FlipFlop [21] . When several samples are available, we propose to generalize this approach by using a convex penalty that leads to small sets of isoforms jointly expressed across samples, as we explain below.

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


Dependency

Chart

Parsing

Algorithm

Based

on

Ternary-Span

Combination

This

paper

presents

a

new

span-based

dependency

chart

parsing

algorithm

that

models

the

relations

between

the

left

and

right

dependents

of

a

head.

Such

relations

cannot

be

modeled

in

existing

spanbased

algorithms,

despite

their

popularity

in

dependency

corpora.

We

address

this

problem

through

ternary-span

combination

during

the

subtree

derivation.

By

modeling

the

relations

between

the

left

and

right

dependents

of

a

head,

our

proposed

algorithm

provides

a

better

capability

of

coordination

disambiguation

when

the

conjunction

is

annotated

as

the

head

of

the

left

and

right

conjuncts.

This

eventually

leads

to

state-of-the-art

performance

of

dependency

parsing

on

the

Chinese

data

of

the

CoNLL

shared

task.

key

words:

dependency

chart

parsing,

span-based

parsing,

factor-based

parsing,

dependency

parsing

algorithm,

averaged

perceptron,

syntactic

analysis

Chart

parsing

is

frequently

used

to

derive

the

syntactic

structure

of

a

given

sentence

and

various

chart

parsing

algorithms

are

available

for

dependency

parse

tree

derivation

(Sect.

Among

them,

Eisner's

algorithm

is

notable

for

its

efficiency,

with

a

time

complexity

of

O(n

In

Eisner's

algorithm

has

been

successfully

augmented

with

current

learning

methods

in

the

frame

of

graph-based

datadriven

dependency

parsing.

Eisner's

algorithm

was

further

extended

in

the

works

of

and

and

all

of

these

parsers

reported

a

state-of-the-art

performance

on

the

English

dependency

treebank

that

is

converted

from

the

English

Penn

Treebank

We

refer

to

these

algorithms

as

span-based

algorithms

because

they

all

perform

parsing

by

composing

spans

(details

in

Sect.

Our

algorithm

is

another

extension

of

Eisner's

algorithm,

and

aims

to

model

the

relations

between

the

left

and

right

dependents

of

a

head.

It

is

quite

common

that

a

head

simultaneously

dominates

the

left

and

right

dependents,

e.g.,

the

case

in

which

a

predicate

verb

takes

subject

and

object

as

its

left

and

right

dependents

in

a

Subject-Verb-Object

(SVO)

language,

or

the

case

of

the

coordinate

structures

in

which

the

conjunction

is

annotated

as

the

head

of

the

leftand

right-side

conjuncts.

Despite

their

popularity

and

the

importance

of

modeling

such

relations

in

dependency

parsing,

they

have

not

yet

been

modeled

in

existing

span-based

dependency

parsing

algorithms..

In

the

proposed

parsing

algorithm,

we

derive

subtrees

by

augmenting

three

spans,

and

the

relations

between

the

left

and

right

dependents

are

modeled

during

the

spanaugmentation

(Sect.

Experimental

results

show

that

the

proposed

algorithm

provides

a

better

coordination

disambiguation

when

the

conjunctions

are

annotated

as

heads,

and

that

this

improved

coordination

disambiguation

eventually

improves

the

overall

parsing

performance

(Sect.

This

paper

is

organized

as

follows.

We

describe

the

spans

and

present

our

algorithm

in

Sect.

In

Sect.

we

compare

our

algorithm

with

other

dependency

chart

parsing

algorithms.

In

Sect.

we

briefly

discuss

various

dependency

annotation

schemes

and

their

corresponding

parsing

algorithms.

Section

presents

our

experimental

results

and

finally,

Sect.

provides

some

concluding

remarks

regarding

our

propose.

This

paper

proposed

a

new

span-based

dependency

chart

parsing

algorithm,

that

is

suitable

for

a

language

or

corpus

where

modeling

the

left

and

right

dependents

is

essential

to

achieve

a

high

parsing

performance..

By

modeling

the

relation

between

the

left

and

right

dependents

of

a

head,

the

algorithm

provides

a

solution

for

coordination

disambiguation

when

the

conjunction

is

annotated

as

the

head

of

its

left

and

right

conjuncts.

When

applied

to

the

Chinese

data

of

the

shared

task,

our

algorithm

achieved

a

better

parse

performance

on

the

coordinate

structures,

which

eventually

improved

the

overall

parsing

performance..

We

believe

our

algorithm

can

also

achieve

better

performances

for

Arabic,

Czech,

and

Slovene

corpora

of

CoNLL

than

existing

span-based

algorithms

through

an

improved

coordination

disambiguation,

since

the

coordinations

in

these

corpora

are

also

of

CCH-type.

In

case

of

SVO

langauges,

the

proposed

algorithm

also

provides

a

platform

to

model

the

relation

between

the

subject

and

object

with

regard

to

their

common

predicate

verb.

We

evaluated

our

system

on

Chinese

data

and

the

English

data

converted

from

the

English

Penn

Treebank.

For

the

Chinese

data,

we

used

the

original

included

training

and

test

sets;

for

the

English

data,

we

used

the

sections

for

training

and

the

section

for

test

as

convention.

For

both

data

sets,

we

used

the

gold

standard

part-of-speech

tags

in

the

treebanks.

is

an

automatic

tool

for

converting

a

Penn

Treebank

style

phrase

structure

tree

into

dependency

tree.

In

case

of

a

nominal-coordination,

the

right-most

conjunct

is

annotated

as

the

head

in

the

English

data

with

We

evaluated

the

performance

based

on

an

Unlabeled

Attachment

Score

(UAS),

following

the

measure

defined

in

the

CoNLL

shared

task

Most

data-driven

dependency

parsers

adopt

two-stage

for

labeled

parsing

by

deriving

dependency

labels

at

the

second-stage

upon

the

unlabeled

dependency

tree

of

the

first-stage.

Multi-labeled

classifiers

are

often

adopted

to

label

each

dependency

arc.

In

this

sense,

the

proposed

algorithm

has

little

effect

on

dependency

labelling,

we

did

not

evaluate

with

Labeled

Attachment

Score

(LAS)

in

this

paper..

The

parsing

model

in

this

paper

is

conventionally

defined

by

selecting

the

maximum

score

parse

tree.

The

score

of

the

parse

tree

is

the

sum

of

the

scores

of

the

dependency

arcs.

The

dependency

function

dep

(included

in

Lines

and

of

Fig.

is

defined

as:.

Feature(

represents

the

features

related

to

the

target

dependency

arc,

while

Weight(

is

the

associated

weight

function,

which

is

learned

through

the

average

perceptron

The

average

perceptron

allows

for

fast

learning

with

a

respectable

performance

and

a

simple

implementation,

compared

with

other

structured

training

algorithms,

such

as

the

max-margin

model

the

margin

infused

relaxed

algorithm

(MIRA)

and

the

log-linear

model

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


Recent advances in data-driven modeling of remote sensing applications in hydrology Artificial neural networks (ANNs) are very effective statistical models for (1) extracting significant features or characteristics from complex data structures and/or for (2) learning nonlinear relationships involved in any input-output mapping. Another interesting aspect of ANN modeling is the fact that overall performance of these models is not greatly hampered by the presence of error-corrupted values in some input nodes. ANNs have gained interest in remote sensing applications as valuable inverse models that can retrieve physical characteristics of interest, such as precipitation, from remote sensing measurements collected from radars or satellites. The spatial coverage and high resolution of remote sensing measurements relative to ground-based measurements can improve the hydrological modeling of the water cycle at both local and global scales. This review paper intends to present recent advances in artificial neural network modeling of remote sensing applications in hydrology. This paper focuses on precipitation and snow water equivalent (SWE) retrievals from remote sensing data. Data-driven models, and more specifically ANNs, are very attractive tools that are used in hydrology and remote sensing applications. Indeed, ANNs are very powerful models that can learn the nonlinear relationships characterizing many processes of hydrological, climatological or weather prediction models. Herein, the focus is on remote sensing applications that could improve the hydrological modeling at different scales (local, regional and global scales). A huge number of hydrological models are applied all over the world. Even if they can be assigned to deterministic, stochastic or event-based types of models, they all need input data to forecast the flows at the outlet of a basin through the modeling of specific processes providing the state variables at each modeling step. One of the key input components of hydrologic models is precipitation. The key state variables that could benefit from the advances in ANN modeling in remote sensing are soil moisture and SWE.. The physical characteristics of a basin are crucial and necessary information for distributed hydrological models.. Remote sensing can provide valuable information on soil classification and land cover. Overall, remote sensing can supply valuable information relative to a basin at time and spatial scales that match the requirements of distributed hydrological models. Reliable information on SWE, for instance obtained from neural network-based retrieval algorithms, can be used to update the state variables of numerical forecasting models (hydrological, climate and weather prediction models) through a data assimilation procedure.. This review paper focuses on recent advances in artificial neural network modeling of remote sensing doi: 10.2166/hydro.2009.036 applications involving precipitation and SWE retrievals that could be used in hydrologic modeling for operational water management or runoff and stream flow forecasting purposes. Papers published since 2000 are particularly of great interest. This paper reviews recent advances in artificial neural network modeling of remote sensing applications that could be beneficial to hydrology. A rather large number of applications involving both precipitation and SWE retrieval from remote sensing data have been developed since 2000.. The need for precipitation estimation at high resolution over basins of different scales is of major concern in distributed hydrological modeling or in urban drainage studies in the case of extreme rainfall events. Reliable snow water equivalent retrievals are needed for operational streamflow forecasting in snow-dominated areas as well as in meteorological and climatological modeling. The launching of sophisticated and high resolution satellites and radars (NASA's Global Precipitation Measurement mission, for instance) will provide remote sensing measurements that are very useful information used as inputs to neuralnetwork-based retrieval models. There are some interesting research perspectives concerning the development of satellite-based precipitation and snow monitoring techniques that will provide high spatial and temporal resolutions products that could benefit hydrological applications such as flood monitoring.

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


e-Business strategy and firm performance: a latent class assessment of the drivers and impediments to success Among many leading organizations, in all sectors of industry, commerce and government, there is considerable evidence of e-business being deployed to achieve strategic goals. Where this deployment has been most successful, there is a strong case that the organization has taken an integrated approach that both builds on the organization's strengths and pays careful attention to the process of change within the organization. However, in the literature most empirical work has either studied e-business strategy and performance from the perspective of strategy content -which highlights positioning and/or unique bundles of resources -or from the perspective of strategy process -which captures human influence and e-business implementation. In this study, we integrate these two perspectives to develop a more holistic understanding of the underlying drivers of e-business performance. Further, latent class modeling techniques are used to show that the variables in our study are heavily influenced by the unobservable heterogeneity across firms. Four distinct types of firms populate our data, and the relationship between performance and its underlying determinants varies greatly between them. The implication is that a single model cannot explain the relationship between environment, structure, feasibility, managerial beliefs and performance. This is critical to our understanding of e-business as it implies that there is far less homogeneity at the individual firm level than is normally assumed in the literature.: e-business strategy, information technology, performance, latent class models E-BUSINESS STRATEGY AND FIRM PERFORMANCE: A LATENT CLASSASSESSMENTAmong many leading organizations, in all sectors of industry, commerce and government, there is considerable evidence of e-business being deployed to achieve strategic goals.Where this deployment has been most successful there is a strong case that the organization has taken an integrated approach that both builds on the organization's strengths and pays careful attention to the process of change within the organization. However, in the literature most empirical work has either studied e-business strategy and performance from the perspective of strategy content-which highlights positioning and/or unique bundles of resources-or from the perspective of strategy process-which captures human influence and e-business implementation. In this study we integrate these two perspectives to develop a more holistic understanding of the underlying drivers of e-business performance. Further, latent class modeling techniques are used to show that the variables in our study are heavily influenced by the unobservable heterogeneity across firms. Four distinct types of firms populate our data, and the relationship between performance and its underlying determinants varies greatly between them. The implication is that a single model cannot explain the relationship between environment, structure, feasibility, managerial beliefs and performance. This is critical to our understanding of e-business as it implies that there is far less homogeneity at the individual firm level than is normally assumed in the literature.Running head: e-Business Performance Despite the dot.com collapse, there remains a strong belief that e-business-with its resurgent potential for creating new transactional opportunities between firms, suppliers, complementary product/service providers, and customers-will ultimately contribute significantly to the future performance of many established firms. A number of impressive examples come to mind: Tesco in the U.K., Otto Versand in Germany, Dell Computers and Cisco Systems in the U.S.. 1 For organizations such as these, e-business is more than a tool but part of a deeply held strategic disposition that enables them to outperform the competition. Advances in e-business applications and technologies present many opportunities for contemporary businesses to redefine their strategic objectives and enhance or transform products, services, markets, work processes and business communication. By integrating strategy content and process perspectives we begin to more fully explain why, when and how certain firms are successful with e-business systems while others remain hesitant, unwilling or unable to change. The empirical results tell us that e-business performance differs as external pressures and capabilities (i.e., human, technological and business) vary. However, the exact extent of these capabilities is ex ante indeterminant. Most importantly, the study shows that variation in managerial beliefs regarding the perceived benefit of e-business tells us much about performance. These belief structures are determined based on a shared understanding of the environmental context, the strategic orientation sought, and the feasibility of executing the type of organizational change required.. Further, the cross sectional sample of firms in our study strongly suggests that firm differences explain much of the propensity for organizational change and the e-business performance outcomes of such change. This heterogeneity is perhaps most pronounced in ebusiness settings where fickle markets, rapid technological change and financial constraints strongly effect the organizational reasoning that takes place to determine e-business strategy and the subsequent implications for firm development and survival. For scholars, this raises several questions concerning published studies that employ models based on mean scores that fail to take into account variance within each construct. As we have shown, directional differences exist that cancel out statistical significance at aggregate levels and therefore, potentially mask the very essence of e-business strategy. Effect sizes based on significant effects (from Table 2 ) are shown in bold † Excluding the joint effect of external pressures and managerial beliefs, but including the direct effect of the variable in question Including the joint effects of external pressures and managerial beliefs We began this paper with an important question: Why does e-business performance vary between organizations that operate within the same line of business and have access to the same information and technologies? Although earlier research has examined the factors driving adoption of IT functions (e.g., relational databases, CASE and object-oriented technologies) or administrative processes (e.g., office, groupware or decision support tools), there has been little research on the drivers of more complex e-business systems (Chatterjee, et al. 2002) . That IT is aligned with the business is a given, but what is poorly understood is the extent to which IT business value is dependent upon organizational structures, management practices and the macro environment (Melville, et al 2004) . This paper makes a unique contribution to the e-business literature by capturing the contingent nature of these factors.. For example, the empirical results tell us that as content variables (i.e., external pressure for ebusiness and internal firm capabilities) vary, so too will the firm's e-business performance.. More importantly, the results reveal that process variables (i.e., managerial beliefs and organizational impediments) matter most. Successful adopters quite clearly draw on strong beliefs and commitment by managers to an e-business future. For example, in our first OLS model the regression weights signify, with certain limitations, the importance attached to managerial beliefs. This construct explains considerable variation in performance. In the second OLS model the influence of managerial beliefs on performance is moderated by the interaction with external pressures and financial costs. In other words, the perceived feasibility of e-business change is moderated by external factors and organizational impediments. This is entirely consistent with Swanson and Ramiller's (2004) notion of "mindful" innovation in IT.. Furthermore, we see evidence of mindless innovation in IT (op cit) in the latent class segments. For example, the direct effect of managerial beliefs is strongest in segment 2 and 3. (the worst performing firms). In the case of segment 2, managers are strongly pessimistic, while in segment 3 there is unjustified optimism. These results systematically reveal the variance and importance of managerial beliefs. Conceptually, these results also support Swanson and Wang's (2005) finding that know-how is an important driver of packaged software success. But as we know, there are many aspects of know-how that influence the success of packaged software and e-business (Lucas 2005 ).. The study also hints at what we may be able to ignore. For example, data pooling techniques based on observed factors-firm size, industry type, and corporate status-offer little insight.. Only when the unobservable factors that characterize modern business are taken into account does our model's predictive power increase. By using segmentation techniques we are able to provide an explanation for why proposition 4 was not supported in the standard regression analysis. Examination of the coefficients for each segment reveals that most paths are highly significant, but directional differences between the various segments cancel out statistical significances at the aggregate level. Clearly, hypothesis testing based on assumptions of homogeneity across a data set deserves more careful consideration. The results of the OLS and mixture regressions are presented in Table 3 with the effect size estimates shown in Table 4 . The OLS estimates provide a basis of comparison to show the extent of heterogeneity in the data and what the failure to account for this implies. Based on the OLS results alone we find support for hypotheses 1, 2, and 3. Firms can most likely expect significant e-business performance increases when environmental pressures are high (H1), when the IT capability within the firm is at an advanced stage (H2), and when the managerial beliefs concerning the value of e-business are high (H3). On the other hand, feasibility constraints (H4) were not found to influence performance.. ----Insert Table 3 here ----One of the central points of our model (figure 1) is that the influence of external pressures on performance will be mediated by managerial beliefs. To test for mediation we follow a threestep regression method (Baron and Kenny 1986) . The results confirm the existence of a mediation effect. 5 However, the presence of an interaction effect between managerial beliefs and the feasibility constraint also suggests a moderating influence between these variables.. When Baron and Kenny's (1986) combined framework procedure is applied, the results confirm the existence of moderated mediation. 6 In other words, the mediating effect of managerial beliefs on the relationship between environmental pressure and performance varies across different levels of organizational impediments. This result is not unexpected given increased managerial awareness of e-business and the well-documented difficulties firms experience when executing e-business strategy.. OLS regression assumes that a single model is appropriate for all firms. Given that there is no indication that this might be true, we applied latent class modeling. The identification of the segments and simultaneous estimation of coefficients within each segment allow us to identify unobserved relationships directly. The latent class models shown in Table 2 offer considerable improvement in explanation, with a 60 percentage point increase in R 2 (from 30% to 90%) and reveal an interesting mixture of different models for different groups of firms. According to all the information criteria presented in Table 3 , the four-segment solution provided the clearest between segment distinctions. This is based on a combination of the lowest information criteria measures, the highest entropy measure, and the theoretical meaning of the results. Table 3 here ----Just comparing the significance of coefficients in latent class models can be misleading as the means of the dependent and independent variables will be different across the different classes. Hence, to increase the interpretability of our results, we compute 'effect size'

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


Variability and misclassification of worker estimated hand force Ergonomic studies often use worker estimated hand force reproduced on a dynamometer to quantify force exposures but this method has not been well-studied in real work settings. This study evaluated the validity of worker estimates of hand force in a field study and determined the misclassification of worker estimated hand force exposures compared to directly measured forces. Eight experienced sheet metal assemblers completed ¼-inch diameter fastener installations using 6 different pneumatic tools. Grip forces were recorded by a pressure mat and were compared to worker estimated forces demonstrated on a dynamometer. Directly measured and worker estimated readings showed moderate correlations (0.53-0.67) for four installation tools and fair to moderate for two tools. The coefficient for variation of force estimates was 65% within repeated subject trials and 78% between averaged subject trials but 69% between subject trials during actual tool installations. Misclassification of worker estimated exposures varied by two cut-points: 29% using 4.0 kg and 49% using 6.0 kg. The force match procedure may provide adequate differentiation of high and low exposures in some settings, but is likely to result in substantial misclassification in other settings.Force Measurement; Hand Force; Exposure Rating Introduction Ergonomic studies depend upon quantified physical exposures of work activities to guide prevention efforts in the management of work-related musculoskeletal disorders. Many exposures are difficult to collect in the work environment, and gathering information on hand force exposures present some unique challenges. Force has been most consistently linked to the development of many work-related musculoskeletal disorders (Panel on Musculoskeletal Disorders and the Workplace, 2001) so it is important to explore appropriate methods for quantifying force exposures in various work settings. The challenge of quantifying measures of hand force is caused by its complex nature. Force is transferred through direct physical contact with tools and other objects in multiple directions. A variety of factors including grip forces of the palm and fingers and feed forces that push through the arm and palm combine to produce force on a tool or object (Welcome et al., 2004). Force is also affected by tool characteristics including handle design, mass, center of gravity, and the typical duration of the operation, making it important to measure force during performance of actual tasks (Lin et al., 2003) . There are several types of data collection methods used to quantify exposures including direct measurement, observation and subjective methods. There is a trade-off in validity as well as cost and practibility of use between these methods. Collecting force data using direct measurement involves placing sensors in or on the work equipment or attaching them to the worker's body to record the physical effort produced by the muscles of the body (Bao and Silverstein, 2005; DiDomenico and Nussbaum, 2008) . Generally, data collection using direct measurement occurs in the laboratory since Ergonomic studies depend upon quantified physical exposures of work activities to guide prevention efforts in the management of work-related musculoskeletal disorders. Many exposures are difficult to collect in the work environment, and gathering information on hand force exposures present some unique challenges. Force has been most consistently linked to the development of many work-related musculoskeletal disorders (Panel on Musculoskeletal Disorders and the Workplace, 2001) so it is important to explore appropriate methods for quantifying force exposures in various work settings. The challenge of quantifying measures of hand force is caused by its complex nature. Force is transferred through direct physical contact with tools and other objects in multiple directions. A variety of factors including grip forces of the palm and fingers and feed forces that push through the arm and palm combine to produce force on a tool or object (Welcome et al., 2004) . Force is also affected by tool characteristics including handle design, mass, center of gravity, and the typical duration of the operation, making it important to measure force during performance of actual tasks (Lin et al., 2003) . There are several types of data collection methods used to quantify exposures including direct measurement, observation and subjective methods. There is a trade-off in validity as well as cost and practibility of use between these methods. Collecting force data using direct measurement involves placing sensors in or on the work equipment or attaching them to the worker's body to record the physical effort produced by the muscles of the body (Bao and Silverstein, 2005; DiDomenico and Nussbaum, 2008) . Generally, data collection using direct measurement occurs in the laboratory since the instrumentation is cumbersome and often invasive to the work environment; but laboratory settings may lose some of the workplace environmental factors that influence exposure levels such as the stability and orientation of the work materials, speed of production, and tool performance. Direct measurement is considered the most precise and accurate, but is costly to collect. Observation methods that are commonly used to record repetitive motions and awkward postures are less useful for capturing the true measure of muscular effort.. Observer-ratings on an ordinal scale can discriminate between jobs requiring higher and lower hand forces but the force rating may not represent the actual force level. Subjective methods offer a simple way to collect force estimates such as worker self-reported ratings on a Borg scale or worker demonstrated force estimations on a hand-held dynamometer (Borg,1990 , Bao 2005 , but generally produce less accurate estimates of force level than direct instrumentation (Buchholz et al., 2008) . Validity of the results from subjective methods depend on a worker's ability to accurately estimate the physical exertion used during task performance.. Hand grip "force matching" is one subjective method commonly used by ergonomists in field studies. In this method, the worker estimates the physical hand grip requirements of a task by simulating the force required on an instrument such as a hand force gauge or dynamometer (Bao et. al, 2006; Casey et al., 2002; King and Finet, 2004) . The worker typically performs the force estimation immediately following performance of the actual task to produce the best recall (Lowe, 1995) . Controversy exists as to how accurately individuals perceive their own level of exertion and their ability to replicate the force required, particularly when other factors such as object size and shape, motion, vibration, and posture come into play (Buchholz et al., 2008; King and Finet, 2004) .. Most studies evaluating the usability of the hand grip force match procedure have been conducted in a controlled laboratory environment (Bao and Silverstein, 2005; Koppelaar and Wells, 2005; Marshall et al., 2004) . These studies have explored volunteers' ability to replicate specific hand force levels (King and Finet, 2004; Kumar et al., 1997; Lowe, 1995) , the accuracy of simulating force from simple tasks that were familiar to volunteers, and the effect of training in the force match procedure to improve the precision of estimates. Results have varied regarding the accuracy and precision of force matching when compared to known forces in instrumented handles or tools (Koppelaar and Wells, 2005) . Few studies have examined forces generated by tools used in specific work environments (McGorry et al., 2004) . Despite limited testing of the force match procedures in field studies, the methods are commonly used to collect hand force information for prevention and intervention studies. It is important to examine the usability of methods in actual work environments even if the methods show promise in laboratory testing. In this study, we evaluated the validity of worker estimates of hand force among experienced sheet metal assemblers using fastener installation tools, and determined the degree of exposure misclassification that could be caused by using worker estimates instead of directly measured force values. This study evaluated the validity of force matching in a field study using hand force estimation procedures commonly used by ergonomists (Bao et al., 2006) . The subjects selected for this study were unfamiliar with the force match procedure and received only a brief introduction and opportunity for practice prior to data collection which is the typical scenario in many field studies. The results showed that subjects had inconsistent responses and large coefficients of variation among repeated estimation trials as well as among multiple trials of direct measurement of the actual fastener installation. The force estimation method produced reasonably valid results for three of the six tools as evidenced by the low level of estimation errors and modest correlations. The three tools with high estimation errors may be due to a poor interface of the tool and pressure mat (tool 5) or caused by the nature of the tool operation (tools 4 and 6). Tool 5 showed significant differences in area of coverage of the mat on the handles of the tools and the dynamometer and this affected the pressure readings. Tool 4 was a light weight tool that required a low hand force to operate the tool during the installation; tool 6 was a heavier tool (4.3 kg) and used a pull force with a "break" at the end of the operation that may have been difficult to replicate. These two issues were unique to these two tools so it is unknown if this would be repeated with different tools that have the same characteristics.. In order to relate physical exposures to adverse health outcomes, it is important to have valid exposure estimates. Many studies have reported large within subject and/or between subject variability of physical exposures (Burdorf, 1993; Kromhout et al., 1993) . The variability may be due to the nature of the work tasks, the environment, or the method for measuring the physical exposures. Force matching gives a subjective estimation of the perceived effort and even though the estimate does not represent the actual force, the values may be adequate for use in epidemiological studies to show a relationship to poor health outcomes. Using cut-points to categorize the force estimates as high or low exposure can result in misclassification of cases so researchers should be cautious in applying defined cut-points to estimated values.. The current study was intended to mimic the way data collection occurs in ergonomic field studies since most companies cannot afford to have workers taken off the assembly line for extended periods of time. The worker volunteers were given specific instructions about the force match procedures and they had the opportunity to practice the protocol before the start of data collection. The procedure for testing initially had each subject install four fasteners with a single tool to help them adjust to the test set-up before performing the first force match trial. Despite these efforts, there was large within subject variability, and this occurred within the estimation and the directly measured trials. Factors contributing to this variability may have included the short time (2-4 seconds) required for installation of each fastener, the low force required to hold and operate the tool during the installation, and subtle differences between arm and fastener position in successive trials. Some researchers have found a tendency for more frequent misclassification and exposure underestimation in tasks with low force levels (Kumar et al., 1997; Lowe, 1995; McGorry et al., 2004) and this may account for the large estimation errors found in tool 4 of the current study. McGorry and colleagues (2004) reported a high mean error of estimation (194.7%) in more complex tasks such as meat cutting even when performed by experienced meat cutters. The high variability of grip force measured among individual experienced workers while performing the same task with the same tool demonstrates the inherent variability of hand force exposures in workplace settings, a fact that poses a significant challenge for all methods of exposure assessment.. We found high estimation errors within some tools and these variable responses occurred more frequently in four of the eight subjects. This indicates that some subjects were less able to perceive and reproduce efforts, particularly for some tools. This is not surprising as there is a wide range in human abilities (memory, physical strength, cognition) so it is likely that some subjects are more aware of physical performance and therefore able to replicate forces during estimation (Casey et al., 2002) . The large errors among some subjects may indicate the lack of understanding of the force match process. Training was not a part of this protocol; some studies that have emphasized training and used a reference for force estimation report improved precision of estimates (Marshall et al., 2004; Spielholz, 2006) but others found no difference (McGorry et al., 2004) . Ideally, several workers would provide estimated values on several trials of a particular task to reduce the effects introduced by worker variability.. This study had several limitations. Using the pressure mat may have altered the normal tool-hand interface and the tactile feedback from the fastening operation and therefore contributed to some of the variability in the measurement, In addition, the mat sensor was designed to detect perpendicular forces, so shear forces that may have been present with some operations would have been missed. Most tools used a rotational mechanism to install the ¼-inch diameter threaded fasteners; the small fastener size, light weight tools, and relatively low hand forces would have produced minimal shear forces during the installations. A comparison of the mat on the dynamometer to the dynamometer readings showed a 23% lower force value indicative of some loss in detected pressures, although the area of coverage of the mat on the tool handles were similar to the dynamometer handle mat area for most tools. Lower measured pressures from nonperpendicular forces may lead to lower correlations and larger estimation errors. These problems likely contributed to the lack of agreement shown in some of the tools. In addition, the limited number of workers tested may have contributed to some of the variability in results. The installation tools used in this fastening operation required a generally low hand force and a short duty cycle, both of which may have affected workers' ability to reproduce estimated forces.. The strengths of the study are the measurements taken from experienced workers, using the tools, materials, and work procedures that exist in an actual work setting. We tested the validity of force matching, a method commonly used by researchers and consultants to obtain hand force estimates from work tasks in manufacturing settings.. Our results suggest that force matching provides a reasonable estimate of hand grip forces for some tools under field research settings. Further investigation is needed to determine the validity of using the force match method to collect force estimates on tools requiring low forces to operate or with impulsive mechanisms. Use of direct measurement techniques will provide more accurate estimates of exposures, but in many settings may result in fewer workers or work tasks being evaluated. Common to epidemiological study designs is the trade-off between obtaining high quality exposure data on few subjects versus lower quality data on many subjects. Force matching gives acceptably accurate estimates for some tools and tasks, and will allow more workers or work tasks to be studied. All eight subjects were right-handed males with a mean age of 56 years and an average employment time at the company of 24 years (range 10-45). The subjects' average right hand grip of 468.5 N (95% CI 434.3-502.8) was 4% higher than population grip strength for age and gender (Mathiowetz et al., 1985) . The duration of installation time was between two and four seconds per trial for all tools. Time between trials ranged from 21 to 528 seconds (41seconds mean; 32 seconds median). Validity testing showed good results with high correlations of the simultaneous recordings of the mat and dynamometer (r = 0.97, n=165) as graphically shown in figure 2. A linear regression of the mat sensor readings obtained while holding the dynamometer to the readings off the dynamometer dial accounted for 93% of the variance with a beta estimate of 0.77 and an intercept of -15.95.. The estimated force readings on the dynamometer shown in table 2 had a wide range of values and the directly measured peak and mean force readings are lower than the estimate force values. Comparison of the mean of directly measured peak force readings to the mean of the force estimated readings showed good correlation values for four tools with lower correlations for the other two tools as shown in table 2. Results were similar for the comparisons of the directly measured mean values to estimated force values (correlations ranged from 0.04 to 0.68) but the estimation errors were larger for the mean force trials (range of values 18% to 207%).. The coefficient of variation (CV) that describes the variability within repeated trials showed high variability within the force estimation trials across all subjects (CV mean = 65%) and across all tools (CV mean = 78%). There were similar inconsistencies found during the directly measured tool trials despite using experienced workers (CV mean = 69%). Figure 3 shows the range of force values for the directly measured peak values and the dynamometer force estimations of each tool. The results of the estimation of error [(actual-estimate)/actual] used as a measure of validity was quite varied between tools with an error range of 2% to 176%. Examination of estimation errors by subjects across all tools showed four individuals had error rates of less than 20% but the other subjects' error rates were remarkably higher (91%, 170%, 205%, 253%). Subjects with high estimation errors occurred with at least two of three specific tools (Tools 4, 5 and 6). A comparison of the number of active sensors of the pressure mat that were in contact with the hand and tool during the directly measured trials to the active area from the dynamometer force estimations for each tool showed no significant difference in total area except with tool 5 (p=0.01), indicating similar hand posture between the tool grip and the grip on the dynamometer for five of six tools. There were few occurrences of sensors reaching the maximum pressure (0.00027%). Video recordings of 210 (44%) of the installation trials showed no meaningful postural changes for four tools (Tool 2, 3, 5, 6). Tool 1 showed modest postural changes of the wrist for most trials (93%) and tool 4 showed minimal wrist movement but these postural changes occurred at the end of the installations when the fastener was seated.. We evaluated the number of trials that classified the estimated force similar to the directly measured force. Each estimated and directly measured force was assigned a value as above or below a cut-point. The selected cut-points have been used to identify high and low hand force levels in studies investigating an association between hand force and hand and wrist disorders in workers. Using a 2X2 table, we showed that the proportion of cases with the same classification (high or low force) was 71% with the 4.0 kg cut-point and 51% with the 6.0 kg cut-point. We examined the occurrence of misclassification of trials for the 4.0 kg cut-point and found 4% of the trials under-estimated the force level on the dynamometer compared to the directly measured values and 25% over-estimated the force. For the 6.0 kg cut-point, 41% under-estimated the force level and 8% over-estimated the force level of the misclassified cases. Eight employees from a local company, experienced with the use of fastener installation tools, volunteered for the study and provided informed consent approved by the Institutional Review Boards of both our university and the company sponsor. The data used in the current study was collected as part of a larger project whose purpose was to design a protocol for measuring workers' physical exposures from tools used by sheet metal assemblers at this company. The methods pertinent to the current study are described below.

O O O O 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 B I 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 B I 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 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 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O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O O 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 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 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 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 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 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An Intelligent Inference System for Robot Hand Optimal Grasp Preshaping This paper presents a novel Intelligent Inference System (IIS) for the determination of an optimum preshape for multifingered robot hand grasping, given object under a manipulation task. The IIS is formed as hybrid agent architecture, by the synthesis of object properties, manipulation task characteristics, grasp space partitioning, lowlevel kinematical analysis, evaluation of contact wrench patterns via fuzzy approximate reasoning and ANN structure for incremental learning. The IIS is implemented in software with a robot hand simulation. Robot grasping is one of the major research areas of robotics since it is required for many applications, from handling hazardous material to surgical operations. A review of robot grasping is given by Bicchi and Kumar. 1 One of the main problems in robot grasping is the dexterous manipulation of a multifingered robot hand. An overview of dexterous manipulation is given by Okamura and Cutkosky. 2 Dexterous Manipulation (DM) can be decomposed into individual phases such as preshaping (grasp planning and approach), grasping and manipulation. 1, 2 However, a human like dexterous manipulation requires phases of grasp planning, approach and grasping to be analyzed and handled all together. In the literature related to dexterous manipulation of a robot hand, one of the major problems that remains to be solved is the determination of an appropriate preshape for grasping, given an object coupled to a predefined manipulation task. In order to have a successful grasp execution, the robot control system should initiate grasp as the continuum of a successful grasp planning and approach of the preshaping phase. Grasp planning and grasp preparation works in literature are generally grouped into two major approaches: grasp synthesis (mechanical or analytical grasp based) and heuristic grasp planning approaches (physiological or knowledge based).. The grasp synthesis approach is mathematically based and seeks to develop computational algorithms for the low-level control of the hand. Low-level control includes computation of finger contact positions, controlling finger joints through finger force and torque. These approaches assume well-defined models and precise sensory information. Shimoga 3 provides a survey and overview of grasp synthesis techniques including the algorithms used. Many of the grasp synthesis methods deal with finding optimal contact points to satisfy a force closure property of the possible grasp using linear and nonlinear optimization techniques, such as Li and Sastry's work. 4 Coelho and Grupen 5 proposed a robust control for an online grasp synthesis to achieve statically stable grasp configurations given instantaneous contact information. Arimoto 6 implemented an intelligent control scheme for grasping and manipulation of an object by multi-fingered robot hands with fingers inducing rolling contacts with the object in 2D. In addition, grasp synthesis methods based on the concepts of wrench spaces were analyzed in the literature in terms of the set of all wrenches that can be applied to the object through grasp contact, such as Grasp Wrench Space (GWS) proposed by Ferrari and Canny 7 , Object Wrench Space (OWS) introduced by Pollard 8 and Task Wrench Space (TWS) developed by Borst and Fischer 9 . These methods utilize the convex hull of task wrenches in order to match task and object in terms of wrench cones and ellipsoids, which yielded a scalar value for grasp matching between task and object and some grasp quality measures. However, these methods do not give a direct measure for an arbitrary grasp and do not link the high-level task to low-level grasp kinematics. Synthesis methods are in general, computationally intensive and do not yield effective results for mapping of high-level task, object and low-level grasp kinematics. The other group of works include heuristic grasp planning approaches, which are knowledge based, and utilize hand preshaping of human grasps. In general, a combination of the object geometry and task properties is the main factor for determining the grasp. In early phase of robotic grasps, many researchers have studied human grasp preshapes for various types of tasks. Cutkosky 10 illustrates a grasp taxonomy for hand preshape matching for manufacturing tasks. Iberall 11 gives a task oriented approach to hand preshaping and VF concept. In a work by Kang and Ikeuchi 12 , the robotic system observes a human while performing the task and then classifies the human grasp and transfers to the manipulator. Further heuristic methods are developed using fuzzy expert systems to represent human experience for grasp preshaping, such as in the work by Bowers and Lumia 13 , a heuristic fuzzy expert system to grasp non-modelled objects using vision based noisy data. In addition, Gorce and Rezzoug 14 propose a heuristic approach for hand posture configuration using neural networks for inverse kinematics and reinforcement learning in order to optimize hand position and orientation without direct combination of task and object. Miller et al. 15 introduced a method for grasp planning using shape primitives, by demonstrating grasp simulations. Berenson et al. 16 performed a work on grasp planning in complex scenes using grasp analysis, manipulation planning techniques and grasp databases. In a recent work, Ciocarlie et.al 17 analyzed low dimensional grasp postures for grasp synthesis. Among these methods, heuristic grasp planning methods are efficient for grasp posture formation utilizing hand preshapes however they are lack of low-level kinematics for execution of the grasp. Neither of two approaches alone constitutes a global solution method for grasp preparation and execution for dexterous manipulation. Hence, another approach is necessary to utilize from heuristic planning and grasp synthesis approaches, to form an intelligent inference architecture. Such an approach can combine low-level kinematics from grasp synthesis approach and preshape formation from heuristic planning approach to complement each other.. In this paper, we use a novel approach to deduce a successful preshape for an optimal initialization of the grasping task, considering the problem of preshaping and grasp kinematics as a sole continuum. It involves grasp planning and grasp synthesis using the attributes coming from each grasp phases. It is foreseen that preshape estimation must be planned virtually in a decision control unit, before any actual movement. This perspective is somewhat similar to physiological grasp situations in human infants and primates, observed by Arbib and Oztop. 18 Further details of our approach are illustrated in the methodology section. The remainder of the paper is organized as follows. Section 2 presents our methodology. Section 3 illustrates the structure our Intelligent Inference System model and the computer implementation for the model developed. Section 4 contains the implementation results. Finally, Section 5 gives the conclusion of the paper. In this work, a Hybrid Intelligent Inference System (HIIS) for optimal preshaping and grasping of robot hand is designed and the model is implemented in a software simulation environment. The HIIS model is developed such that it includes grasp planning coming from task requirements, approaching and grasping which is related to object, its pose and environment , as a sole continuum controller. The HIIS uses attributes coming from each grasp phases to deduce a successful preshape for a good initialization of a subsequent grasp task. Object pose, shape , together with environment constraints are also analyzed to select approaching and grasp characteristics. It takes inputs from the virtual environment and yields a resultant grasp preshape, after handling decomposed task attributes, and selecting a virtual approach to the predefined object by an analysis of possible virtual grasps with kinematic estimations and grasp quality measures developed using screw systems.. The results show us that our proposed method combining high level task and low level kinematics for preshape estimation works as a intelligent system. The designed HIIS was implemented using 3D computer simulation environment which demonstrated us that it could be implemented in real hardware environment. HIIS executes attributes in symbolic forms, which can be easily integrate a higher level robot control mechanisms. The HIIS for preshaping demonstrated satisfactory results for enough robustness, incremental learning of new objects and tasks .It also handled some uncertainity in tasks. The hand grasp simulation results are encouraging to work further in this approach, especially to implement HIIS in a real hardware environment. In our preshape estimation methodology, given in figure 1 , first, the grasping task is analyzed and decomposed into primitives of axial translational and axial rotational displacements, as done in some of the previous works, such as Michelman's experiments. 19 Object pose and shape are also analyzed to select approaching and grasp characteristics, by using object 3D model. In addition, hand grasp space is partitioned by the Approach Plane method to combine task primitives and hand posture. Task primitives are interpreted as spatial displacements represented by wrenches in a partitioned grasp space. Moreover, using screw systems properties in existing robot hand contact models, representation pattern of the virtual grasps in wrench form is obtained by estimating kinematical effects of the possible virtual grasps on object 3D model. Utilizing task primitives and virtual grasp patterns in wrench form; a method for high-level task mapping to low-level kinematical actions is developed. We also introduce screw system based grasp measures, which are stability index and manipulability index. These measures support our inference system in justification and inferencing stages.. The inference system uses the all estimated attributes as inputs, some of which are in forms of Fuzzy Sets. Using approximate reasoning based on fuzzy logic, we evaluate the matching of the desired task and possible grasp patterns, all represented in wrench form. Learning is achieved incrementally by means of developed ANN structure. All above techniques are integrated into an Intelligent Inference System. In the following sub-sections, we present detailed explanations for individual parts of our method.

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


Extracting physical homogeneous regions out of irrigation networks using fuzzy clustering method: a case study for the Ghazvin canal irrigation network Improving the current operation and maintenance activities is one of the main steps in achieving higher performance of irrigation networks. Improving the irrigation network management, influenced by different spatial and temporal parameters, is confronted with special difficulties.One of the controversial issues often faced by decision-makers is how to cope with the spatial diversity of irrigation systems. Homogeneous area detection out of the irrigation networks could improve the current management of networks. The idea behind this research is to present a quantitative benchmark for exploring the homogeneous areas with similar physical attributes out of the network region. Five physical attributes, such as length, capacity, number of intakes, number of conveyance structures and the covered irrigated area for each canal reach, are used for spatial clustering. Two fuzzy clustering algorithms, namely FCM and GK, are applied to the Ghazvin irrigation network. Using a clustering validity index, SC, shows that the GK algorithm is the more appropriate tool for clustering of the considered dataset. According to the results the optimal number of clusters for the Ghazvin irrigation project is derived as nine clusters and the irrigated district is classified into nine homogeneous areas. Physical homogeneous regions provide a context for better and easier decision-making.Key words 9 9 9 9 fuzzy clustering, irrigation network, regionalization

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The marginal social cost of travel time variability This paper investigates the cost of travel time variability for car users at the peak hour. In particular, we derive the marginal social cost of travel time variability, which takes the feedback of travel time unreliability on the congestion profile into account. This is in contrast with the value of travel time variability, which treats congestion as an exogenous phenomenon. Congestion is modeled using the standard bottleneck model of road congestion, which we amend by adding a random delay. For individuals with (a, b, c) preferences and uniformly distributed delays, the marginal social cost of travel time variability is strictly lower than the value of travel time variability. Moreover, we show that the former tends toward the latter when r, the standard deviation of the random delay, tends toward +1. For normally distributed delays, numerical application leads to similar conclusions.Analysis of data from the Paris area suggests that given the plausible range of r, the marginal social cost of travel time variability is markedly lower than the value of travel time variability. When appraising the economic benefits of reliability improvements, one should prefer the marginal social cost of travel time variability for the peak period, and the value of travel time variability for the off-peak period. Travel time reliability is an important consideration when people make travel decisions (Li et al., 2010; Carrion and Levinson, 2013) . Bates et al. (2001) distinguish two main ways in which unreliable travel times impact people. First, it may disturb their activity schedule by causing early or late arrivals at destination. When excessive, the delay can even lead to the cancellation of an activity: for instance, an unpredicted traffic jam can cause one to arrive too late at the theater and miss the play. Second, people could dislike travel time variability per se. The unpredictability of the travel time would generate some form of anxiety, or cause some additional planning costs (Noland et al., 1998) .. The valuation of travel time variability has accordingly become an increasing concern in transport economics, to the extent that the value of travel time variability (also referred to as the value of reliability) is now along with the value of time one of the most important values obtained from travel demand studies (Carrion and Levinson, 2012) . The work of Fosgerau and Karlström (2010) has been central in this regard. In line with Polak (1987) and Noland and Small (1995) , the authors study the choice of departure time for a traveler facing variable travel times. Their major contribution lies in deriving the value of travel time variability (VTTV) in a case relatively general, thereby providing a sound theoretical foundation to this notion. Still, one element limits the extent of their results. In line with the others before them, Fosgerau and Karlström (2010) study the behavior of one traveler only and model congestion as an exogenous phenomenon. Hence, the VTTV does not capture the impact that changes in travel time variability have on congestion. Likely limited for the off-peak period, this feedback effect could be sizable at rush hour when congestion becomes a primary feature of traffic. As all individuals (and not only one) adapt their departure time in response to changes in travel time variability, the congestion time profile is likely to change. To address this shortcoming of the VTTV, we propose an alternative indicator, the marginal social cost of travel time variability (MSCTTV), which differs from the VTTV in that it does capture the equilibrium mechanism just mentioned.. This paper studies the cost of travel time variability for car users when congestion is modeled as an endogenous phenomenon. We are especially interested in the spread between the value of travel time variability and the marginal social cost of travel time variability: for a given individual, it reflects the difference between the short term and long term (i.e. before and after other individuals adjust their departure time) costs of a change in the variability of travel times. We model congestion using the standard bottleneck model of peak-load congestion, which we amend by introducing a random delay. Derivation of the Nash equilibrium in departure times yields first the equilibrium expected scheduling disutility as a function of the standard deviation of the delay, then the MSCTTV by differentiation. We focus the analysis on the case of uniformly distributed delays as it leads to closed-form solutions. This allows a direct analytical comparison of the VTTV and the MSCTTV. The more realistic case of a normal law is also considered using numerical simulation, analytical derivation of the equilibrium solution being unfeasible in this case.. The primary impact of travel time variability on the Nash equilibrium is peak spreading. Departures are spread more evenly over the rush hour than under certain travel times, resulting in less congestion. As a result of this mitigating mechanism, the MSCTTV is strictly lower than the VTTV. When the scale of the random delay is small enough compared to the total length of the rush hour, the MSCTTV is even null in the case of a uniform law, and close to null in the case of a normal law. Individual adjustments regarding the departure time offset, partly or even entirely, the direct cost of an increase in travel time variability. As far as cost-benefit analysis is concerned, this means that direct uses of the VTTV, in particular in 4-step models, overestimate the benefits of policies aiming to reduce travel time variability on a road network, and likely strongly so. One should consider the effect of such policies on the departure time profile (and thus on the O-D matrix when using a 4-step model) to avoid this bias, one solution being to use the MSCTTV. Additionally, our relatively strong result that the MSCTTV is null in some cases tends to corroborate the finding of Börjesson et al. (2012) , which is that (a, b, c) preferences only capture part of the cost that travel time variability puts on travelers. Future research might want to delve into this issue.. The layout of the paper is as follows. Section 2 reviews the prior literature. The model is presented in Section 3. Section 4 elaborates on the case where the random delay follows a uniform law, while Section 5 addresses more succinctly the case of a normal law, using numerical simulation. Section 6 discusses the results, and Section 7 concludes.

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