*4.9. Isolation of Soluble Plasma Membrane Proteins*

The soluble fractions of PM proteins were obtained by ultracentrifugation, as previously described [55]. After normocapnia or hypercapnia treatment, cells were detached, homogenized in homogenization buffer (10 mM, 1 mM EDTA, 1 mM EGTA, 100 μg/mL N-tosyl-l-phenylalanine chloromethyl ketone, phosphatase, and protease inhibitors), and centrifuged at 500× *g* at 4 ◦C. Next, supernatants were collected and centrifuged at 100,000× *g* for 1 h at 4 ◦C. Afterward, the pellet, which contained the crude membrane fraction, was resuspended in homogenization buffer supplemented 1% Triton X-100 and centrifuged at 100,000× *g* for 30 min at 4 ◦C. After centrifugation, a supernatant containing a soluble membrane fraction was collected and used for the experiments.

### *4.10. Measurement of Na,K-ATPase Enzymatic Activity*

To measure ouabain-sensitive Na,K-ATPase activity, a high-sensitivity ATPase assay kit (Innova Biosciences, Cambridge, United Kingdom) was used, according to the instructions of the manufacturer, and was performed as described previously [55]. After treatment with normocapnia or hypercapnia, cells were detached, homogenized, and the PM fraction was isolated. To measure ouabain-sensitive ATPase activity, ouabain was applied to the soluble PM fractions and absorbance, which was measured at 600 nm by an Infinite M200 Pro reader (TECAN, Männedorf, Switzerland). Na,K-ATPase specific activity was calculated by subtraction of ouabain-sensitive from total ATPase activity.

#### *4.11. Detection of the Protein Oxidation*

Total protein oxidation was measured by using the OxyBlot protein oxidation detection kit (Merck Millipore, Darmstadt, Germany), according to instructions of the manufacturer. After normocapnia or hypercapnia treatment, cells were lysed, and an equal amount of proteins were derivatized by adding 1X 2,4-dinitrophenylhydrazine (DNPH) solution for 15 min. Afterward, a neutralization solution was added, samples were subjected to SDS-PAGE and transferred to nitrocellulose membranes. After incubation with antibodies from the kit, protein oxidation was determined by chemiluminescence and the density of detected protein oxidation was calculated by using the Image J software and normalized to the total protein amounts assessed by Coomassie staining.

#### *4.12. Measurement of Intracellular ATP Levels*

ATP levels after hypercapnia exposure were measured by an ATP bioluminescence assay kit HSII (Roche Diagnostics, Mannheim, Germany) following the instructions of the manufacturer. Cells were treated with normal or elevated CO2 levels, detached, and lysed in the lysis buffer provided with the kit. Next, samples were centrifuged at 10,000× *g* for 60 s and the supernatant was transferred to a fresh tube. Lastly, 50 μL of supernatants were transferred to 96-well plates. Additionally, 50 μL of the luciferase reagent was added to the samples and luminescence was measured immediately by Infinite M200 Pro reader. The obtained values were normalized to the protein amount as assessed by the Bradford assay.

#### *4.13. Measurement of Cellular Viability*

Cell viability was assessed by using the CASY cell counter and analyzer model TT (Roche Innovatis AG, Reutlingen, Germany) following the instructions of the manufacturer, as determined by an automatized measurement of plasma membrane integrity via an electric impulse. After exposure to normocapnic or hypercapnic conditions, cells were detached by incubation with 0.25% trypsin-EDTA, resuspended in culture medium, diluted in CASY tone solution, and the membrane integrity measurement was performed.

#### *4.14. Statistics*

Data are presented as mean ± SD unless otherwise indicated. Comparison of two groups was performed by paired (dependent) or unpaired (independent) two-tailed Student's t-test. Comparisons between more than two groups were performed by one-way or two-way analysis of variance (ANOVA) with a Dunnet test for multiple comparisons. For statistical analysis and data visualization, GraphPad Prism 6 (GraphPad Software, San Diego, CA, USA) was used. A *p*-value of <0.05 was considered to be statistically significant.

**Author Contributions:** Conceptualization, V.K. and I.V. Data curation, V.K., R.E.M., S.H., W.S., O.V., and L.A.D. Formal analysis, V.K., O.V., and L.A.D. Funding acquisition, I.V. Investigation, V.K. and M.W. Methodology, V.K., M.W., O.V., and L.A.D. Project administration, M.W. and I.V. Resources, R.E.M., S.H., W.S., J.I.S., and I.V. Supervision, I.V. Validation, V.K., O.V., L.A.D., and J.I.S. Visualization, V.K. and I.V. Writing—original draft, V.K. and I.V. Writing—review & editing, V.K., R.E.M., S.H., W.S., O.V., L.A.D., J.I.S., and I.V. All authors have read and agreed to the published version of the manuscript.

**Funding:** Grants from the Federal Ministry of Education and Research (German Center for Lung Research [DZL/ALI 3.1]), the Hessen State Ministry of Higher Education, Research and the Arts (Landes-Offensive zur Entwicklung Wissenschaftlichökonomischer Exzellenz [LOEWE]), the von Behring Röntgen Foundation (Project 66-LV07) and the German Research Foundation (DFG/KFO309, P5 and The Cardio-Pulmonary Institute (EXC 2026; Project ID: 390649896) (to I.V.) and a MD/PhD start-up grant (DFG/KFO309, MD/PhD) (to V.K.) as well as the National Institutes of Health (HL-147070) (to J.I.S.) supported this work.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study, in the collection, analyses, or interpretation of data, in the writing of the manuscript, or in the decision to publish the results.


**3. Survey of Earth Observation Sensors and Measurements Requirements to Cover the Future Gaps on Copernicus**

EO satellites have revolutionized the study of the environment, and are contributing to a more rational use of the natural resources, and environmental protection. The applications of the data supplied by these systems are enormous: disaster monitoring, weather forecast, maritime safety, marine resources monitoring, forestry, vegetation state, water cycle, energy budget, pollution control, water quality, climate change, and security; using radars, microwave and optical/IR radiometers, optical imagers or scanners. Table 4 presents the generic classification of the remote sensors. Instruments are classified in the following four categories: active or passive, either microwave or optical. Optical sensors measure the signals received around the visible part of the spectrum, from the Ultra-Violet (UV) to the Thermal Infrared (TIR). Microwave sensors use the signals in the microwave and millimetre-wave parts of the spectrum, typically from 1 GHz to 1 THz. Passive systems are based on the collection of the electromagnetic waves that are emitted/scattered by external sources, such as the Sun or other bodies. On the other hand, active systems such as radars and lidars, transmit an electromagnetic wave, either radio or laser, and measure the scattered/reflected signal from the Earth's surface or atmosphere. Microwave sensors do not rely on the Sun as source of illumination. These particular characteristics are especially important in Polar Regions that have extended dark periods in winter. In addition, microwaves are mostly unaffected by the cloud cover, except in some specific bands. This feature makes microwave sensors more suitable than optical sensors in these regions.

This section presents a survey of the selected EO technologies. In order to identify the potential EO sensors to improve the Copernicus space infrastructure, EO technologies are analyzed in depth based on the measurements with identified gaps, and the technological limitations. A total of 77 instruments have been surveyed, and their parameters (mass, power consumption, spatial resolution, swath, frequency bands, aperture, and orbit altitude) have been compiled from the Observing Systems Capability Analysis and Review (OSCAR) Tool [24], the Earth Observation Portal Directory [25], and companies websites (Appendix B). The best instruments in terms of data quality and suitable for the small platform are identified for each technology.

**Table 4.** Instrument categorization: potential instruments to complement the Copernicus system [6].

*a* multi-static radar using satellite navigation signals of opportunity (SoOp). *b* e.g., Direct Broadcast Satellite (DBS) television at Ku-band or X-band.


**2. Industrial machine tool data**

In the following, the term "machine data" defines an information message emitted by any "data source" associated with a machine tool (for example a sensor), which concerns an event that occurred during the activity of the machine itself. Machine data are the log files emitted by the machine itself, but also all data emitted by external entities such as event sensors, sensors for speed, temperature, acceleration and so on.

In order to reconstruct in detail the "history" of the messages, machine tool data must be raw and immutable (as opposed to classical structured and aggregated Relational Databases data). Data is never deleted or updated (except in very rare cases, for example to comply with regulations), it is only added. The main disadvantage of this management is that the stored data tends to become very large. From this point of view we can talk about big data. To avoid misinterpretation of the data, they are not stored in a free format, but are "semantically normalized", that is, remodeled in a standard format, even if not strictly structured.

Machine tools data are represented through the "fact-based model" (see **Figure 1**): a graph where each message corresponds to a single fact. In this graph, each node corresponds to a machine tool data source entity (e.g. Sensor Y on machine M) and each arc can represent information about an entity (dashed connecting line) or a relationship between two entities (continuous line). Each single message is identified by its timestamp plus the identification of the machine (M) and the identification of the entity that emits the message (Sensor Y).

*Text Mining for Industrial Machine Predictive Maintenance with Multiple Data Sources DOI: http://dx.doi.org/10.5772/intechopen.96575*

check, or after a certain amount of work, the state of use of the machine, without any signs of behavioral anomalies having actually occurred. According to [8], if the maintenance strategy only involves interventions that react to failures, the maintenance costs are relatively low but the losses could be high. If preventive and predictive maintenance is introduced, maintenance costs increase: for example, some activities must be carried out using overtime, detectors for predictive maintenance are introduced, time is dedicated to training activities for operators and maintenance workers. Some clues for defining the algorithms on which the cyber-physical system presented here is based derive from research in the field of computational linguistics, which have appeared on papers presented at various important interna-

*Advances in Dynamical Systems Theory, Models, Algorithms and Applications*

Here we present a discrete dynamic system, based on events represented as textual messages to which an alert level has been associated and whose data structure is a graph. The system is dynamic because the data structure adjusts itself without additional computational costs if a new message is issued by the machine, which has never appeared before and that is not yet included in the set of known messages. The new message must in any case be validated by a human expert, who

This paper is structured as follows: in section 2 we present the main characteristics of the data emitted by the data sources associated with an industrial machine tool, and how they are represented in the system. In section 3 we present the system. In particular, in section 3.1 we present the model for a machine tool, in section 3.2 we give an overview of the system, while in section 3.3 and in section 3.4 we present the two main phases of the system: pre-processing, to be performed only once, and runtime; in section 3.5 and in section 3.6 we present the main algorithms and their theoretical performances; in section 4 we report a brief summary of the results of a prototype of the system applied to a simple, but real, case study. The

In the following, the term "machine data" defines an information message emitted by any "data source" associated with a machine tool (for example a sensor), which concerns an event that occurred during the activity of the machine itself. Machine data are the log files emitted by the machine itself, but also all data emitted by external entities such as event sensors, sensors for speed, temperature, accelera-

In order to reconstruct in detail the "history" of the messages, machine tool data

must be raw and immutable (as opposed to classical structured and aggregated Relational Databases data). Data is never deleted or updated (except in very rare cases, for example to comply with regulations), it is only added. The main disadvantage of this management is that the stored data tends to become very large. From this point of view we can talk about big data. To avoid misinterpretation of the data, they are not stored in a free format, but are "semantically normalized",

Machine tools data are represented through the "fact-based model" (see **Figure 1**): a graph where each message corresponds to a single fact. In this graph, each node corresponds to a machine tool data source entity (e.g. Sensor Y on machine M) and each arc can represent information about an entity (dashed connecting line) or a relationship between two entities (continuous line). Each single message is identified by its timestamp plus the identification of the machine

(M) and the identification of the entity that emits the message (Sensor Y).

that is, remodeled in a standard format, even if not strictly structured.

must associate the message with an adequate alert level.

paper ends in section 5 with some conclusions.

**2. Industrial machine tool data**

tion and so on.

**2**

tional conferences [9–14].

Physically, we assume that each source message emitted by a machine tool is in a semi-structured text format (a sort of simplified JSON): that is, it is a succession of text fields, divided by a field terminator. This provides simplicity and flexibility with the greatest possible space savings. It is possible to store anything within the main dataset, as long as each data has the same information placed in the same order, with the same data type format; otherwise it will be necessary to carry out a pre-processing of the source messages before storing them, downloading the data that do not conform to the expected format in a separate archive.


**Part 2**

**Energy Efficiency on Demand Side**

116 Energy Efficiency – A Bridge to Low Carbon Economy

Rothschild, M., Stiglitz, J.E., 1973. Some further results on the measurement of inequality.

Sinn, H.W., 2007. Public policies against global warming. NBER Working Paper No.W13454,

Spreng, D., 2005. Distribution of energy consumption and the 2000 W/capita target. Energy

United Nations Development Programme (UNDP)–Human Development Report Office.

United Nations Environment Project (UNEP), 2006. Kenya: Integrated assessment of the

CESifo (Center for Economic Studies and Ifo Institute for Economic Research)/

1998-2009. "The Human Development Index (HDI)." New York. Available online

Journal of Economic Theory, 6, 188–204.

Policy, 33, 1905–1911.

Sen, A., 1973. On economic inequality. Oxford Univ. Press, Oxford.

NBER (National Bureau of Economic Research).

at: http://hdr.undp.org/ en/statistics/hdi/.

Energy Policy. Nairobi, UNEP. Available online at: www.unep.ch/etb/areas/pdf/Kenya%20ReportFINAL.pdf

**6**

*Finland*

**Effect of an Electric Motor on the Energy**

**Efficiency of an Electro-Hydraulic Forklift**

Mobile working machines play an important role in modern industry. These machines are widely used for instance in the mining, process and goods manufacturing industry, forest harvesting and harbour terminal work. Figure 1. illustrates typical examples of mobile

(a) (b) (c)

photo: Cargotec photo: Rocla photo: Sandvik Fig. 1. Examples of mobile working machines: a) straddle carrier, b) forklift truck and c)

Mobile working machines can be classified as light mobile machines that operate by battery power and heavy machines that work using a diesel engine. Both of these types include mechanical structures, which are driven by hydraulics. With the rising concern in global scale environmental issues, energy saving in vehicles and mobile machines is an important subject and reduction of fuel consumption is strongly required (Petrone, 2010 ; Saber, 2010 ; Bhattacharya, 2009 ; Montazeri-Gh, 2010 ; Mapelli, 2010, Liu, 2010). Hybrid propulsion concepts in working machines are emerging to improve their fuel economy and reduce CO2 emissions (Fakham, 2011; Hui, 2010, Paulides, 2008). In addition, now there are government mandated Tier IV reduction regulations for harmful exhaust gases for diesel powered

**1. Introduction**

working machines.

mine underground truck.(Minav, 2011d)

Tatiana Minav, Lasse Laurila and Juha Pyrhönen *Lappeenranta University of Technology/LUT Energy*


**1. Introduction**

Obstructive Sleep Apnea (OSA) was described as early as 1837 in "The Posthumous Papers of the Pickwick Club". Dickens, a British author, described "Joe", the main character, as a fat boy who falls asleep easily and involuntarily (Figure 1). [1]

In The Pickwick Papers (c. 1836–1837)

**Figure 1.** Artist (Hablot Knight Browne- Phiz) rendering of Joe, Charles Dickens' character.

© 2015 The Author(s). Licensee InTech. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and eproduction in any medium, provided the original work is properly cited.

Later on, Osler (1914) used the term "Pickwickian syndrome" to describe obese and sleepy patients, in homage to Dickens' character "Joe". As early as 1956, Bickelmann et al [2] reported that the "Pickwickian syndrome" was associated with extreme obesity and alveolar hypoventilation.

Gastaut's research group described three different types of apnea, namely, obstructive apnea central apnea, and mixed apnea. In 1973, Guilleminault introduced the apnea-hypopnea index (AHI), which refers to the total number of apnea and hypopnea episodes per hour of sleep, and proved, along with Dement, that obesity is not a prerequisite for OSA. In 1977, Guillemi‐ nault and Dement used the term "sleep apnea syndrome", in association with hypertension and electrocardiographic pathologies. [3]

Recently, much research on OSA has been conducted with a view to help elucidate the characteristic features of OSA. Sleep is a process through which the body restores energy used during the day. Not much is known about its biological purpose, but its evaluation can be undertaken by muscle and brain electrical activity, and ocular movement. Good-quality sleep entails several functions; these include physical recovery, biochemical refreshment, memory consolidation and psychological well-being. [4]

In adults, sleep is regulated by a cycle of five periods. The first four periods belong to nonrapid eye movement sleep (light and deep stage) and the fifth period is named the rapid-eyemovement (REM) or paradoxical sleep (active stage). The progression from the first stage to the RAM constitutes one sleep cycle. Generally, there are four to six sleep cycles per night; during which activities of the brain, muscles, and the cardio-respiratory system fluctuate (Figure 2, 3). [4]

**Figure 2.** A sleep cycle (non-REM to REM stages). [4]

During these sleep stages, several sleeping disorders can occur. International classification of Sleep Disorders (ICSD-3), revisited in 2014, includes the following broad categories: [5]

**Figure 3.** Consecutive wave of non REM to REM sleep cycles (I to IV). Throughout the night, REM becomes longer than slow wave sleep (stage 3and 4). MT: movement time, WT: wake time. [4]

**•** Insomnia

Later on, Osler (1914) used the term "Pickwickian syndrome" to describe obese and sleepy patients, in homage to Dickens' character "Joe". As early as 1956, Bickelmann et al [2] reported that the "Pickwickian syndrome" was associated with extreme obesity and alveolar

Gastaut's research group described three different types of apnea, namely, obstructive apnea central apnea, and mixed apnea. In 1973, Guilleminault introduced the apnea-hypopnea index (AHI), which refers to the total number of apnea and hypopnea episodes per hour of sleep, and proved, along with Dement, that obesity is not a prerequisite for OSA. In 1977, Guillemi‐ nault and Dement used the term "sleep apnea syndrome", in association with hypertension

Recently, much research on OSA has been conducted with a view to help elucidate the characteristic features of OSA. Sleep is a process through which the body restores energy used during the day. Not much is known about its biological purpose, but its evaluation can be undertaken by muscle and brain electrical activity, and ocular movement. Good-quality sleep entails several functions; these include physical recovery, biochemical refreshment, memory

In adults, sleep is regulated by a cycle of five periods. The first four periods belong to nonrapid eye movement sleep (light and deep stage) and the fifth period is named the rapid-eyemovement (REM) or paradoxical sleep (active stage). The progression from the first stage to the RAM constitutes one sleep cycle. Generally, there are four to six sleep cycles per night; during which activities of the brain, muscles, and the cardio-respiratory system fluctuate

During these sleep stages, several sleeping disorders can occur. International classification of Sleep Disorders (ICSD-3), revisited in 2014, includes the following broad categories: [5]

hypoventilation.

(Figure 2, 3). [4]

and electrocardiographic pathologies. [3]

362 A Textbook of Advanced Oral and Maxillofacial Surgery Volume 2

consolidation and psychological well-being. [4]

**Figure 2.** A sleep cycle (non-REM to REM stages). [4]

Sleep apnea is the most common sleep disorder related to breathing. There are 3 types of sleep apnea: Obstructive, central, and mixed (a combination of both forms). Obstructive sleep apnea (OSA) is caused by partial or complete obstruction at multiple levels of the upper airway, producing reduction (hypopnea), or cessation (apnea) of airflow. Due to the lack of adequate alveolar ventilation, oxygen saturation may drop and partial pressure of CO2 may occasionally increase. Snoring and sleep fragmentation are common with OSA that can be graded as mild, moderate and severe.[6] Adults and children are equally affected. However, the prevalence, etiology and pathophysiology of the disorder differ from one group to another. It is important to note that the physiopathology and etiology of OSA are poorly understood.

Furthermore, OSA is associated with neuropsychological impairment, sexual dysfunction, metabolic and cardiovascular co-morbidities; and causes an increase in mortality. Quality of life and economic potential are also affected: snoring affects the sleeping pattern of the partner, and frequent arousals at night result in relative sleep deprivation and can cause excessive daytime sleepiness, loss of concentration and motor vehicle accidents.[4]-[7] Therefore, OSA is regarded as a public health condition and increases the consumption of health care resources.

Continuous positive airway pressure (CPAP) is considered a golden standard treatment; oral appliances and surgical procedures for upper airway soft tissues and maxilla-mandibular advancement are other alternatives. Hence OSA treatment requires a multidisciplinary management. [8] Orthodontists, sleep specialists and surgeons should all be involved in managing and treating OSA. This chapter gives a comprehensive account of the literature on OSA and underlines the role of orthodontists in managing OSA with a view to improve the physical, mental and social status of patients diagnosed with OSA.


Others (e.g., Luthar et al., 2000) suggest that positive adaptation simply refers to the ability to meet the demands faced during adversity. Furthermore, others assert that positive adaptation may be a combination of the previous definitions; Leipold and Greve (2009) suggest that positive adaptation refers to "An individual's stability or quick recovery (or even growth) under significant adverse conditions" (p. 41). Thus, the definitional debate in the resilience literature seems to surround the second core process of adaptation. Luthar and colleagues (Luthar et al., 2000; Luthar, 2006) suggest that positive adaptation may be a function of the severity of the adverse event, and what constitutes positive adaptation might be context specific.

Alongside definitional confusion, there has been considerable debate about the basic conceptualization of resilience. Although all people possess some degree of resilience, not everyone is equal in this regard. While some people have difficulty overcoming commonplace hassles, others react positively in the face of even the most challenging situations (Bonanno, 2004). In search of an explanation for this variance, early resilience researchers sought to identify factors that protect individuals from experiencing adverse effects after a setback. In this regard, resilience can be conceptualized as an amalgamation of protective factors, or traits, that "influence, modify, ameliorate, or alter a person's response to some environmental hazard that predisposes to a maladaptive outcome" (Rutter, 1985, p. 600). This conception was originally suggested by Block and Block (1980), using the

term "ego resilience" to reflect traits such as resourcefulness, character, and flexibility. Those high on ego resilience were found to be energetic, optimistic, and had the ability to detach in order to problem solve (Block and Block, 1980). Since the origination of this work, there seems to be general agreement that the construct of resilience implies a protection against future stressors (Fletcher and Sarkar, 2016).

Several specific protective factors have been examined by resilience researchers, including: positive emotions (Tugade and Fredrickson, 2004), hardiness (Bonanno, 2004), self-efficacy (Gu and Day, 2007), extraversion (Campbell-Sills et al., 2006), selfesteem (Kidd and Shahar, 2008), positive affect (Zautra et al., 2005), and spirituality (Bogar and Hulse-Killacky, 2006).

#### TEAM RESILIENCE

Given the growth of teamwork within organizations, resilience researchers have recently shifted their focus from the individual and community levels to the team level (Norris et al., 2008; Alliger et al., 2015). As recently suggested by Brodsky et al. (2011), "a focus on the individual is not enough" (p. 233). In line with Alliger et al. (2015), we purport that individual and team resilience while related, are distinct constructs. A team comprised of resilient members does not necessarily make the team resilient. At the team level, resilience has been characterized by variables including collective efficacy, creativity, cohesion, social support, and trust (Gittell et al., 2006; Norris et al., 2008; Blatt, 2009). Moreover, teams that encompass a broader perspective in the face of adversity have a greater likelihood of positive adaption (Bennett et al., 2010). In support of the notion that team resilience research is critical, Bennett et al. (2010) purports that, "resilience may be viewed as much a social factor existing in teams as an individual trait" (p. 225). This would suggest that teams have the capacity for positive adaptation through collective interactions, rather than as isolated individuals. As stated by West et al. (2009), "Team resilience may prove to be an important positive team level capacity that aids in the repair and rebound of teams when facing potentially stressful situations. Teams which display the ability to either thrive under high liability situations, improvise and adapt to significant change or stress, or simply recover from a negative experience are less likely to experience the potentially damaging effects of threatening situations" (p. 254).

#### ORGANIZATIONAL RESILIENCE

As noted by Maynard and Kennedy (2016), research is lacking on the effect of organizational-level inputs on team resilience. Work by Gibson and Birkinshaw (2004) have suggested organizational context to be a pre-cursor to team ambidexterity. More specifically, the more supportive the context, the greater the ambidexterity. Team ambidexterity "allows teams to reconcile the tensions between alignment and adaptability" (Maynard and Kennedy, 2016, p. 12). Moreover, Gibson and Birkinshaw (2004) found that ambidexterity is a mediator between context and unit performance. Thus, the contextual inputs at the organizational level seem to facilitate unit adaptation.

As defined by Vogus and Sutcliffe (2007), resilience at the organizational level refers to the ability to maintain positive adjustment to difficult situations, such that the result is a stronger and more resourceful organization. Since organizations that are resilient as a whole have greater resources, this may allow their individual teams to also be more resilient as they have access to a greater repertoire of resources when faced with a difficult situation. "Difficult situations" include crises, unexpected events, deviations from boundary conditions (i.e., deviations from normal functioning), strains, and emerging risks. It is important to note that the amalgamation of small stresses, deviations, or interruptions can pose a significant risk to system functioning just as readily as a more catastrophic event (Rudolph and Repenning, 2002). Adjustment to adversity at the organizational level has been said to strengthen individual teams through "a hierarchical integration of behavioral systems whereby earlier structures are incorporated into later structures in increasingly complex forms" (Egeland et al., 1993, p. 518). Alternatively stated, resiling from difficult conditions necessitates the activation of latent resources. Therefore, resilience encompasses more than a specific adaption. Competence in the face of one adversity implies a greater likelihood of competence in the face of the next adversity. In order to be resilient, a team must be prepared for hardship, which requires an "improvement in overall capability, i.e., a generalized capacity to investigate, to learn, and to act, without knowing in advance what one will be called to act upon" (Wildavsky, 1991, p. 70). In this light, resilience greatly depends on learning from previous experiences and adversities which facilitates future learning. However, because resilience is independent of learning activities, it represents a greater repertoire of capabilities.

Several resilience processes at the organization level have been identified by Brodsky et al. (2011), which include: a sense of community, positive team culture, reframing of stressors, striving to achieve the organization's mission, shared values, and malleable team structures (Fletcher and Wagstaff, 2009; Wagstaff et al., 2012). This supports the contention of Chan (1998), who suggested that although constructs may fall under the same domain, they manifest differently at different levels (i.e., individual or team). A similar position has been advocated more recently by Morgan et al. (2013).

#### AN INPUT-MEDIATOR-OUTCOME (IMO) MODEL OF TEAM RESILIENCE

What follows is our attempt to synthesize past work to create a model of team resilience by employing a modified Input-Process-Outcome (I-P-O) framework advocated by Ilgen et al. (2005): the Input-Mediator-Output-Input (I-M-O-I) framework. According to Ilgen et al. traditional I-P-O models failed to account for the dynamic complexity that characterizes team behavior. Using Marks et al.'s (2001) notion of emergent state described above, they substitute the term "mediator" for "process" in the original I-P-O framework. In doing so, these authors contend that it "reflects the broader range of variables that are important mediational influences with explanatory power for explaining variability in team performance and viability," (Ilgen et al., 2005, p. 520). The following sections first summarize past work into the inputs, processes and mediators, and outcomes associated with resilience as the individual, team, and organizational levels. We included the individual levels in our review because they are part of the dynamic system that effects team resilience. Our contention is that it is essential to maintain this multi-level view in order to understand the full complexity of team performance and outcomes. Based on this review, we conclude by offering a comprehensive model of those things that contribute to development of resilience and outcomes that can be expected as a result of achieving resilience. We hope this model will stimulate further thinking and research.

#### BEGINNING WITH THE END: DEFINING OUTCOMES OF RESILIENCE

To begin specification of an I-M-O model of resilience, we reviewed literature summarizing the outcomes that are expected to result from resilient behavior. Our goal here is to synthesize what has been theorized about the expected outcomes of resilience at the individual, team and organizational levels (see **Table 1**). Implicit in all of these outcomes is that they must occur during a period of stress that is sufficient to interrupt performance.

## DEFINING INPUTS OF RESILIENCE

The inputs to resilience vary greatly depending on the level at which it is being considered. **Table 2** summarizes the major inputs that enable resilience, again ordered by whether they occur at the individual, team, or organizational level. At the individual level, inputs to resilient behavior are most often considered to be individual traits. These traits serve to buffer individuals to the effects of a stressor and/or allow him or her to bounce back quickly. At the team level, inputs to resilience are not traits, rather they are factors that exist at the team level. However, they operate in a similar manner to individual inputs in that they can have a buffering effect on the team's experience of stress and/or equip them to cope with the stress. Finally, at the organizational level, input factors are similar to team-level inputs in that they exist at the organizational level and serve to set the stage for coping behaviors by the organization.

# PROCESSES ASSOCIATED WITH RESILIENCE

Similar to input factors, the processes associated with resilience behavior vary greatly depending on the level being considered. At the individual level, resilient processes are most often conceived of as adaptive behaviors. At the team and organizational levels, resilient processes are more closely associated with collective behavior by team members. **Table 3** summarizes our review of the literature regarding processes associated with resilience.

#### TABLE 2 | Inputs that enable resilience.

#### A COMPREHENSIVE MODEL OF TEAM RESILIENCE

**Figure 1** displays a summary of the variables included in the tables above. As noted previously, we conceptualize team resilience as a second order mediator. That is, team resilience is best thought of as enabled by a combination of other team emergent states including cohesion, collective efficacy, culture, shared mental models, familiarity, and adaptability (see **Table 4**). Our conclusion is based on the notion that resilience is the result of these other states and it enables the team to achieve either positive or negative outcomes. It is this quality of resilience that is unique in that it can act as a buffer for negative outcomes and also as an enabler of positive ones.

Inspection of the model in **Figure 2** reflects what we have discussed above. According to this model, team resilience is a second order emergent state that is situated between other team emergent states (see **Figure 1**) and outcomes (see **Figure 1**). Team emergent states are the result of various team processes (see **Figure 1**) and those, in turn, are driven by input

#### TABLE 4 | Team emergent states.

factors at the individual, team, and organizational level. We believe that this conceptualization is reflective of the complex, multi-level, dynamic relationship among variables at the team level.

#### RESEARCH DIRECTIONS

The construct of emergent states allows researchers to propose hypotheses that better represent the dynamic, evolving nature of team processes and performance. However, given that this is a relatively new approach, empirical research to identify key emergent states is in its infancy. As this theoretical position is articulated, and as we develop new statistical tools to allow us to validate these models, we are learning more about the complex nature of team processes and the psychological states that result from team interactions.

In this paper, we have suggested a "second-order" emergent state of team resilience which might help us to understand how certain teams are able to cope with extreme stressors and to maintain their performance. More specifically, as an emergent state, this suggests that resilience may be the result of a number of team actions or processes, rather than a process in it of itself. Additionally, given the process vs. state debate in the literature, the nature of the construct of team resilience is certainly

unclear. Thus, a new conceptualization of team resilience is warranted. As articulated throughout the present work, viewing team resilience as an emergent state may offer insight into the nature of team resilience that prior conceptualizations have failed to achieve. Ultimately, this is a hypothesis that will need to be validated using modeling approaches. However, it is important to articulate specific relationships that will be the foundation of these models. To that end, we propose the following first-order emergent states that we hypothesize will be related to the second-order emergent state of team resilience.

et al., 2002). Adherence to group norms is an element that is thought to be a critical element in maintaining team performance under periods of high stress (Stevens et al., 2015). Cohesion is often included in theories of team resilience (Hind et al., 1996; Meredith et al., 2011). For example, Morgan et al. (2013) describe it as an element of collective efficacy. However, we might argue that it is better included in their construct of group identity. Nevertheless, it seems reasonable to suggest that cohesion is related to the emergence of resilience.

work, these tasks are also critical but equally distributed across team members. That said, there is no question that adaptability is a critical emergent state for the development of team resilience.

#### REFERENCES

#### AUTHOR CONTRIBUTIONS

All authors listed have made a substantial, direct and intellectual contribution to the work, and approved it for publication.

Jackson, S. E., and Dutton, J. E. (1988). Discerning threats and opportunities. Adm. Sci. Q. 370–387. doi: 10.2307/2392714

**Conflict of Interest Statement:** The authors declare that the research was conducted in the absence of any commercial or financial relationships that could be construed as a potential conflict of interest.

Copyright © 2017 Bowers, Kreutzer, Cannon-Bowers and Lamb. This is an openaccess article distributed under the terms of the Creative Commons Attribution License (CC BY). The use, distribution or reproduction in other forums is permitted, provided the original author(s) or licensor are credited and that the original publication in this journal is cited, in accordance with accepted academic practice. No use, distribution or reproduction is permitted which does not comply with these terms.


**Acknowledgements**

The author thanks Dr. Girish Kumar for help with flow cytometry, Randy Bidinger for making the apparatus that securely housed the pressure pump device, Dr. Stephen J. Merrill for help with statistical analysis, and Dr. Shelby Skoog for critically reviewing this manuscript.


*4.1.2. Lars Ulrich*

these were distant dreams or more like jokes. They just wanted to set up a band and play their favorite music. They liked the same kind of music and worked hard for their passion. Furthermore, they wanted to differ musically from other bands. Both Ulrich and Hetfield want to control their own life. They are stubborn personalities that have also been called control freaks. Ultimately controlling has been one of Metallica's most prominent success factors. In addition to common features, the duo also has significant personal and background

According to Ulrich, Hetfield was the only person in the United States who was inspired by the same type of music at the beginning of 1980s. In addition, Ulrich saw Hetfield's talent and passion for music. On the other hand, he regarded Hetfield as the shyest person he ever had met. Ulrich felt immediately the connection and thought they fit together because they complement each other. Hetfield did not warm up so quickly because of Ulrich's inadequate drummer talents, but he also understood they shared the same taste for music, and Ulrich's

Hetfield was raised to believe in Christian science, in which God cares for everything, and doctors are not needed. People started talking about him being different. Hetfield grew a silent child who wanted to be alone and forget about the surrounding world. He was a misfit and did not want to belong to any group. Listening to music and later playing it helped him. On the other hand, music also helped Hetfield to come out of his shell. It became his escape, therapy and rescue. He tells that without music he would probably be dead or in

James's father left the family in 1976, and his mother died of cancer in 1979 after refusing to believe the doctors and taking medicines. Hetfield's next of kin left him either by their own will or by their death. This increased his need to try to control things and people around him. Hetfield felt fear and hatred of authority because they could take control of him. He has admitted having the habit of suffocating his loved ones: Do not go anywhere, do not leave. Hetfield lost trust in people and started treating everyone as possible enemies. He was seen as a rude and stubborn introvert, who came out of his shell only when drinking. He did not want to talk about his own past, but his creativity came from internal anxiety. Hetfield was controversial even in the sense that on stage he was fierce and extroverted, but very cautious outside the stage. His stage persona was his protection and security. He could hide behind it. Later it became clear that alcohol had greatly contributed to this, but at the same time gave him the wrong feeling of power. Hetfield had a lot of unsolved stuff from his childhood and he started to live again as someone else

At the beginning, Hetfield thought that he did not have the voice, appearance or charisma needed by the lead of Metallica. This could be influenced by the extroverted, social Ulrich and guitarist Dave Mustaine, who left Hetfield in their shadow. The band was looking for a new singer until Hetfield's self-esteem began to rise in 1984 with the release of the gig and record

differences [12–14].

138 Organizational Conflict

*4.1.1. James Hetfield*

prison [12, 14, 41, 42].

[12, 14, 38, 41–45].

huge energy and great networks [12, 14, 39].

Just like Hetfield, Ulrich also fought strongly against the authorities, which he picked up from his father who loathed them. Ulrich has defined his life's philosophy as controlling his own life and destiny rather than allowing someone else to do it, which is very much the same as Hetfield's "live freely or not at all". Ulrich's parents of Danish descent belonged to the Danish elite. Ulrich had no siblings but grew up with adults. His father was involved in many progressive and experimental issues, which led Lars to meet a lot of artist from different fields. He also traveled around the world due to his father's tennis training. Through this, he developed an adventurous, curious, enthusiastic and open-minded worldview, which later played a part in his desire to experiment with music as well. He was never brainwashed with ideologies, but instead, he was free to experience and find answers himself [12, 14, 49, 50].

After having moved to the United States, Ulrich was shocked about all strict rules after the freedom in Denmark. Furthermore, he was nobody after the move to the United States, while in Denmark everybody knew him because of his father. Ulrich had to re-emerge in one way or another, and this happened to be rock and Metallica. Ulrich grew into a dream partner in the music industry, a good speaker and a person who would be welcome in any band. He charms and observes all those present naturally and sincerely. On the other hand, if something is kept secret from Ulrich, he feels threatened and uncertain. He does not want to be excluded. He wanted to show his own ability to his very successful father. Besides of his father, Ulrich's competitive edge also comes from his tennis player history. Ulrich works spontaneously and quickly when he is excited about something. He will not be stopped by anything before he gets what he wants. He is confident and grans each chance without the fear of rejection. With most bands it was "if we get a record deal". With Ulrich it was "when we get a record deal". The word "no" bears little meaning to Ulrich. He lives in the moment. The America's goaloriented thinking was strange for him [12–14, 38, 41, 46, 48, 51].

wait a long time. Metallica calls this delay as "Metal-time". The dispute ultimately ends up with a compromise that everyone can adapt to. Although the band speaks of compromises, it seems that in the 1980s and the early 1990s it was more a question of handling conflicts in a manner that combined competition and collaboration that resulted in completely new perspectives [12, 38, 41, 51, 53]. Letus examine more specific what conflicts have occurred within Metallica's inner circle during the band's lifecycle, and how these conflicts have been

Conflicts as Springboard for Metallica's Success http://dx.doi.org/10.5772/intechopen.71579 141

The tensions within the band started already before the release of the first album in 1983, and even though the tensions contributed to creativity, the band also came close to splitting. No one was safe in the band; everyone was under the thread to be kicked out. Metallica's first lineup included drummer Ulrich and vocalist-guitarist Hetfield, lead guitarist Dave Mustaine and bassist Ron McGovney. As McGovney felt that others were not honoring him, he resigned in 1982. He later admitted that he was not fully committed to Metallica. This was not okay for Ulrich and Hetfield—especially when McGovney was not a very skilled bass player. Ulrich and Hetfield were perfectionist who did not allow themselves to be comfortable. They demanded the same attitude from other band members and also from external stakeholders. They challenged Metallica to make everything as good as it could be. Ulrich and Hetfield found what they hoped for, Clive Burton as the new bassist. Metallica was like a speeding car without brakes. When Burton joined in the band it got a professional driver to better control

Hetfield and Ulrich thought that Metallica would not succeed with Dave Mustaine either. More versatile players were needed. There were other problems with Mustaine as well. He became unpredictable when drinking. The last drop was Mustaine driving their tour bus drunk into a ditch and endangering the whole band. Mustaine was kicked out from Metallica in 1983. According to Ulrich, Metallica was more important than relationships. Without kicking Mustaine out, Metallica would probably have died in internal conflicts. Metallica's charm was based originally on Mustaine. Ulrich did not like Mustaine's and Hetfield's macho-attitudes. When Mustaine was around, Hetfield paid less attention to Ulrich. After Mustaine left, Hetfield was able to take on his role in Metallica and was no longer shadowed by Mustaine. Mustaine was replaced by Kirk Hammett, who was a more easy-going than Mustaine. This dramatically

At the time of the Ulrich-Burton-Hetfield-Hammett line-up, the band had four completely different powerful and individual personalities with totally different philosophies. When all the different ideas and characters were merged, the result was the most acclaimed line-up of Metallica. In addition, members of the band were influenced by very different types of music. The progress of Metallica's music was based on this equation. Metallica's second album, Ride the Lightning, was more sophisticated than its predecessor and began separating Metallica from other thrash bands. This immediate shift in musical maturity is widely credited to the talent Burton. Hammett also brought new musical dimensions to the band. Metallica's good

*4.2.1. Years 1981–1986: through the struggles to the ideal line-up*

handled.

energy [12, 38, 42, 53].

changed the band's dynamics [12, 40, 42 46, 53].

Ulrich had the clearest view of Metallica at the beginning of the band. He has been the engine for Metallica's ambition. Ulrich wanted Metallica to spread everywhere. He eats, sleeps and breathes Metallica. According to him, the only way to progress was to write and record an album that was bigger and more bombastic than the last. Satisfaction kills the progress. Ulrich's enthusiasm is likely to stem from his childhood—he was free to go to different gigs among other things, but he had to earn his money and lift there [12, 38, 42, 45, 52–54].

Hetfield's and Ulrich's common ground is the struggle against the authorities, a strong desire for control, the need for constant progress, and love for heavy music. These four factors form the "Metallica spirit" that stems from Ulrich and Hetfield's intrapersonal conflicts. This spirit has not only enabled Metallica to succeed, but it has also kept the band alive in the midst of quarrels. Metallica has been regarded as a deranged self-centered band due to Ulrich and Hetfield's strong desire for control, which has caused considerable conflicts both within the band and with external stakeholders. Let us examine Metallica's conflicts within its inner cycle that consists of artists, and producer Bob Rock and performance coach Phil Towle, who have managed to get into inner circle.