**3.2. Scene and model development**

The scene developed in Unity3D is modeled after a subsea production system placed on the seabed 900 m below the surface. It consists of manifolds, pipelines, BOPs (BlowOut Virtual Simulation Platform for Training Semi-Autonomous Robotic Vehicles' Operators http://dx.doi.org/10.5772/intechopen.79600 93

**Figure 2.** Unity3D interface.

**Figure 3.** GameObject with various components.

**Figure 4.** Map of simulated environment.

Preventer) and PLETs (Pipeline End Termination). A map of the simulated environment is shown in **Figure 4**. The scene was developed using models found online and crafted within Unity3D.

The ROV used in the simulation was modeled using an actual ROV used in the industry to fulfill requirement stated by the classification society DNV. It indicates that ROV models available in the simulation should be similar to the ROV in real life. In this proposed simulator, the TRV-M is a light work class ROV manufactured by Submersible Systems. The TRV-M in **Figure 5** can dive up to 1000 m deep that possesses the capacity to carry a payload up to 27 kg in any environments.


**4. Discussion**

The precipitations of the BP simulated by the climate model ICHEC\_KNMI are underestimated compared to those observed at theSetif and BBA stations during the BP. Thus, the simulated precipitations are underestimated for all months of the year except June, where P were overestimated. Romeraet al. [40] suggested that the high variability of rainfall and the weak network of observation stations in the Maghreb region made the rainfall simulations in the EURO-CORDEX database full of uncertainties (or bias). The underestimation of Nr in spring and summer at both stations was possibly caused by the climate model overestimation of cloud cover as suggested by [41].

The decline in Ps under Sc1 compared to the BP at both stations is attributed to the shortening of the Mexicali cultivar's GSL. This is induced by the increase in TS, as PS is expected to increase throughout the Mexicali cultivar's GSL. In addition, the increase in spring's precipitations and the lengthening of the Mexicali cultivar's GSL under Sc2 explain why PS is expected to increase in this scenario compared to the BP. The NrS decreases in Sc1 could be due to the shortening of the Mexicali cultivar's GSL and the aerosol pollution projected under these two RCPs scenarios, as suggested by [42]. Meanwhile, the decrease in NrS in Sc2 could be attributed to the atmospheric pollution caused by the presence of aerosols in the atmosphere as the Mexicali cultivar's GSL was prolonged in this last scenario.

The wheat grain yield increase projected under both RCPs scenarios is due to the fertilizing effect of the air enrichment with CO2. This result is more consistent at the BBA site, where the evolution of the grain yield curve perfectly follows that of CO2 concentrations. Thus, the Mexicali cultivar's grain yields projected in Sc2 are higher than those projected in Sc1 at the BBA site because the CO2 concentrations expected under RCP 8.5 are higher than those projected under RCP4.5. However, at the Setif site, the average grain yield simulated in Sc1 is higher than that simulated under Sc2. This could be explained by the decline in TS by −0.3 ◦C in Sc2 because it is possible that the lower temperatures reduce the fertilizing effect of CO2 on durum wheat grain yields. However, under Sc1, the projected grain yields at Setif are better by comparison with those projected at the BBA site. This result can be explained by the combined negative effect on wheat grain yield of very severe water stress and the thermal stress projected under Sc1 at the BBA site (Table 3). According to the Pearson correlation test results, in Sc1 and Sc2, there is a negative and statistically significant correlation between TS and Mexicali cultivar's grain yields at the Setif site. Meanwhile, at the BBA site, the correlation is negative and statistically insignificant. On the contrary, the PS and NrS's correlation is positive and statistically significant with Mexicali cultivar's grain yield simulated by AquaCrop by both future scenarios.

Therefore, the fertilizing effect of CO2 offsets the negative effects of rising TS and decreasing PS and NrS on durum wheat yields projected, under Sc1 and Sc2, at the Setif and BBA experimental sites. These results are compatible with the conclusions of recent studies carried out by ([43–45] respectively, in China, Germany, and Morocco. Likewise,

Pugh et al. [46] reported that rainfed wheat in arid and semi-arid regions located in low latitudes would benefit from the fertilizing effect of CO2, but less in temperate regions located in high latitudes. Moreover, Long et al. [47] reported that 550 ppm high CO2 concentration in experiments in a Free Air CO2 Enrichment (FACE) and in closed chamber experiments, resulted in a wheat yield enhancement of +31 and +13%, respectively, in both these experimental devices. Tubiello et al. [48] suggested that the fertilizing effects of a high CO2 concentration might be overestimated in crop models because their simulation of yield enhancement induced by a high CO2 concentration was much greater than that observed in FACE studies. However, they suggested that the magnitude of these effects is still under debate. In China, the study [49], proved that the fertilizing effect of the CO2 enrichment in the atmosphere slows the negative effects of warm air temperatures and precipitation decline on wheat yield under RCP 4.5 and RCP 8.5, at the beginning of the 21st century in China. Likewise, Xiong et al. [50] found that the CO2 enrichment in the atmosphere enhanced wheat yield by +0.9% by offsetting the negative effect of the drop in solar radiation, but without this fertilizing effect of CO2, the wheat yield would decrease by −9.7%. Under RCP 8.5, in Egypt, the increase in atmospheric CO2 concentrations will act as a growth stimulant. The simulated irrigated wheat yield across Egypt was projected to increase slightly (2.4%) in the 2030s and will decline slowly toward the end of the century (−1.7% by 2050s and−4.0% by 2080s). This result was attributed to the increase in negative impacts of the projected warm temperature [51]. In Jordan, the ESCWA [52] reported that under RCP 4.5 and RCP 8.4, with a fixed CO2 concentration, the rainfed wheat yields simulated with the AquaCrop model will increase by an average of about +33.8 and +48.3% in 2025 and 2045 future periods. Meanwhile, with elevated CO2 concentration, the simulated wheat yield will be enhanced by +53.5 and +81.6%inboth above future periods, respectively, with respect to the baseline yield.

The Pearson correlation test revealed the existence of a negative and statistically significant correlation between Mexicali cultivar's GSL and TS during the BP and under both Sc1 and Sc2.This result is compatible with that of [45] in Morocco and [53] in the entire Mediterranean region. Furthermore, in Palestine and Jordan, the rainfed wheat growth cycle period simulated under RCP 8.5 is projected to shorten by 2030 and 2050 [52].

In addition to these above results, the Pearson correlation test proved that there is a positive and statistically significant correlation between Mexicali cultivar's GSL and PS during the BP and under both Sc1 and Sc2 at the Setif and BBA sites. Thus, the lengthening of the Mexicali cultivar's GSL by 10 days under Sc2 at the Setif site could also be explained by the increase in PS by +65.6 mm. Despite the shortening of Mexicali cultivar's GSL by the effect of the expected increase in TS. The Mexicali cultivar's grain yield is projected to enhance under Sc1.Tao et al. [54] and Liu et al. [55], explained that the shortening of wheat's GSL is due to the vegetative development stage's length reduction, meanwhile the duration of the reproductive stage remained intact. So, this negated the yield losses reported by Zheng et al. [16], who recommended wheat cultivars flower early in order to prevent wheat crops from the risk of yield loss which could be induced by very warm temperatures in late spring throughout the period of grain formation.

The ET0 drop predicted under Sc1 is due to the shortening of the Mexicali cultivar's GSL. This result is consistent with that of [56] on rice in Bangladesh and [45] on wheat in Morocco. However, under Sc2, the lengthening of the Mexicali cultivar's GSL did not avoid the ET0 decrease, which could be explained by the stomatal regulatory effect ofdurum wheat, which made it possible to reduce water losses by evapotranspiration, as it was suggested by [45], under the fertilizing effect of the elevated CO2 concentrations in the atmosphere, projected under Sc2.

WP is the ratio of the amount of durum wheat biomass produced (in kg) to the amount of water lost by evapotranspiration during durum wheat's growing cycle. Thus, according to the AquaCrop model simulations, the WP enhancement under both Sc1 and Sc2 scenarios is due to the increase in Mexicali cultivar's above-ground biomass induced by the fertilizing effect of the enrichment of the atmosphere with CO2. This induced the acceleration of photosynthetic activity and the decrease in water loss by evapotranspiration, thanks to stomatal regulation under Sc2, and the shortening of the Mexicali cultivar's GSL induced by the TS increase under Sc1. According to [52], the AquaCropcrop model, with a fixed CO2 concentration, predicted an enhancement of rainfed wheat's WP by an average of +17.8 and +30% for the 2025 and 2045 future periods, whereas in the case of elevated CO2, an increase of +3 and +56% are projected by both future horizons, respectively, under RCP 4.5 and RCP 8.5.

According to [46], the fertilizing effect of a high CO2 concentration can improve the WP of C3 plants (such as wheat) by stimulating their photosynthetic activity. Ainsworth et al. [57] reported that across a range of FACE experiments, with a variety of plant species, the growth of plants at elevated CO2 concentrations of 475–600 ppm leads to increasing leaf photosynthetic rates by an average of +40%. These last authors explained that CO2 concentrations are essential in regulating the openness of stomata, the pores that allow plants to exchange gasses with the exterior environment. Thus, open stomata allow CO2 to diffuse into leaves for photosynthesis, but also provide a way for water to circulate out of leaves. Plants, therefore, regulate the degree of stomatal opening, a measure called stomatal conductance, which is used as a compromise between the aims of maintaining high rates of photosynthesis and low rates of water loss. So, as CO2 concentrations increase, plants can maintain high photosynthetic rates with relatively low stomatal conductance. Added to that, they also reported that across a multitude of FACE experiments, growth under elevated CO2 concentrations decreases stomatal conductance of water by an average of −22%. However, Taub et al. [58] suggested that generally, the magnitude of the effect of CO2 on crop water use would depend on how it affects other determinants of plant water use, such as plant size, morphology, and leaf temperature.

At both experimental sites, under the climate conditions projected by 2035–2064, under RCP 4.5 and RCP 8.5, the earlier sowings of mid-September and mid-October lead to the best yields because the earlier sowing dates allow the wheat crop to take advantage of the increase in precipitations predicted in the late summer and early fall. That will allow the achievement of the vegetative development stage of the Mexicali cultivar's plant from November until the beginning of February. In addition, this early sowing allows the flowering stage to take place between the period from the end of February until the beginning of April, which allows the Mexicali cultivar's plant to avoid the high temperaturesin May and June. These results are compatible with those of [59], who also predicted the adaptation of wheat to early sowings in 2031–2060 climate conditions, under both RCP 4.5 and RCP 8.5 scenarios in the Mediterranean area. However, late sowing in mid-November and mid-December resulted in poor yields, as they led to the achievement of the durum wheat's flowering and grain-filling stages through the high-temperature period of the mid-April and early June period, which induce durum wheat's grain yield losses by scalding. This result is in concordance with those of [60], who reported that the early maturing cultivar did not show a yield reduction on any sowing dates, thanks to the earliness of the anthesis stage, on which risk of crop exposure to heat stress during the sensitive grain-filling stage is decreased or avoided. In Ethiopia, [61], reported that by the middle and the end of the 21st century, one wheat cultivar is adapted to late sowing date, under low CO2 emissions of RCP 4.5, meanwhile another cultivar is well adapted to early sowing date, under elevated CO2 emissions of RCP 8.5. So, it is important to assess the adaptation of wheat sowing dates under future CC scenarios by simulating different wheat cultivars yields with the crop model in order to select the best-adapted wheat cultivar to the projected CC.


**3.3.1 Calculation of the realizable modulation depth**

The modulation functionality is studied with the following link (Fig.13.). The optical signal from the laser diode is intensity modulated by SOA with time dependent optical gain. The intensity modulated optical signal is detected by traditional PIN photodiode.

Fig. 13. Simplified block diagram of the link for calculation of modulation behaviour

The modulation operation can be derived based on the slope of the measured optical gain curve (md) and the average optical gain (G0).

The current and the gain of the device are:

96 Selected Topics on Optical Amplifiers in Present Scenario

gain the beat noises give the dominating contribution. So the signal-to-noise ratio (SNR) increases up to a certain gain, reaches a maximum, finally it decreases. There exists an optimum amplifier bias point corresponding to maximum SNR of the SOA-detector. Although higher gains would yield higher responsivity, the SNR decreases and it is not

Fig. 12. Calculated noise and signal powers versus input optical power (a) and optical gain

The optical gain of the amplifier depends on the bias current and taking advantage of this effect could be used as an external modulator. The bias current of the SOA is modulated; therefore the material gain and the intensity of the output signal are modulated [Mork]. If small signal current modulation is considered, the electrical signal contains an invariant and a modulation parts, hence the number of charge carriers and photons are also time dependent [Conelly]. The magnitude and purity of the signal depend on the modulation signal, the bias current, the input power and the operation parameters. The SOA modulator requires low modulation power, and the detected electrical power is high because of the optical gain in contrary to the optical insertion loss of other modulators. However the SOA

The modulation functionality is studied with the following link (Fig.13.). The optical signal from the laser diode is intensity modulated by SOA with time dependent optical gain. The

intensity modulated optical signal is detected by traditional PIN photodiode.

G=G0(1+mcos(t))

Imod0+Imodcos(t)

DC Information

GPLD

Fig. 13. Simplified block diagram of the link for calculation of modulation behaviour

**LD detector**

a

aGPLD

Idet0+Idetcos(t)

desirable for the system.

(b), calculation based on measured data

has noticeable optical noise [Udvary1].

CW

**3.3.1 Calculation of the realizable modulation depth**

PLD **SOA modulator**

**3.3 Modulation function**

$$I(t) = I\_0 + \Delta I\_{mod} \cdot \cos(\alpha t) \qquad G(t) = G\_0 + \Delta G \cdot \cos(\alpha t) \tag{8}$$

where I0 is the constant (dc) current, ΔI is the current modulation amplitude, G0 is the constant optical gain of SOA, ΔG is the modulation part. Hence the optical signal at the output of the SOA-modulator takes the form

$$P\_{out} = G\_0 \cdot P\_{in} \cdot \left(1 + m \cdot \cos\left(\alpha t\right)\right) \tag{9}$$

where the modulation index (m) is

$$m = \frac{\Delta G}{G\_0} = \frac{m\_d \cdot \Delta I\_{mod}}{G\_0} = \frac{m\_d}{G\_0} \cdot \sqrt{\frac{2 \cdot P\_{mod}}{Z}}\tag{10}$$

where Pmod is the modulation electrical power, Z is the microwave impedance of SOA. The output signal of the SOA-modulator is detected by an optical-electrical converter, Pdet is the detected electrical modulation power

$$P\_{det} = \eta^2 \cdot \frac{P\_{in}^2}{a^2} \cdot m\_d^2 \cdot P\_{mod} \tag{11}$$

is the detection efficiency, a is the optical loss between the SOA and the detector.

The modulation depth is proportional to the slope of the gain curve and the electrical modulation power, but it is in inverse relation to the average optical gain (Eq.16). However, the detected electrical power increases with the modulation power (direct relation), the slope of the gain curve and the input optical power of the SOA-modulator (quadratic relation) increase (Eq.17). Same conclusions can be observed from the experiments.

Naturally, the modulation depth realized by the SOA-modulator can be computed from the measured detected electrical power with the knowledge of the modulation power and the input average optical power of the detector:

$$\eta m = \frac{\mathbf{2} \cdot \mathbf{a}^2 \cdot \mathbf{P} \, \det}{\mathbf{P}\_{\text{in}}^2 \cdot \mathbf{G}\_0^2 \cdot \mathbf{\eta}\_{\text{det}}^2 \cdot \mathbf{Z}} = \frac{\mathbf{2} \cdot \mathbf{P}\_{\text{det}}}{\mathbf{P}\_{\text{det}}^{opt} \cdot \mathbf{\eta}\_{\text{det}}^2 \cdot \mathbf{Z}} \tag{12}$$

Where *opt det P* is the average optical power at the input of the detector.

Based on this calculation the experimentally realized optical modulation depth is 5-10 percent with sufficiently low (-30dBm) modulation power.


**Marine Toxins from Harmful Algae and Seafood Safety**

Editor

**Shauna Murray**

MDPI ' Basel ' Beijing ' Wuhan ' Barcelona ' Belgrade ' Manchester ' Tokyo ' Cluj ' Tianjin

*Editor* Shauna Murray School of Life Sciences University of Technology Sydney Sydney Australia

*Editorial Office* MDPI St. Alban-Anlage 66 4052 Basel, Switzerland

This is a reprint of articles from the Special Issue published online in the open access journal *Toxins* (ISSN 2072-6651) (available at: www.mdpi.com/journal/toxins/special issues/Seafood Safety).

For citation purposes, cite each article independently as indicated on the article page online and as indicated below:

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**ISBN 978-3-0365-6194-3 (Hbk) ISBN 978-3-0365-6193-6 (PDF)**

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*Review* **Optimal Planning of Battery Energy Storage Systems by Considering Battery Degradation due to Ambient Temperature: A Review, Challenges, and New Perspective**

**Chico Hermanu Brillianto Apribowo 1,2, Sarjiya Sarjiya 1,3,\*, Sasongko Pramono Hadi 1 and Fransisco Danang Wijaya 1**

**Abstract:** In recent years, the goal of lowering emissions to minimize the harmful impacts of climate change has emerged as a consensus objective among members of the international community through the increase in renewable energy sources (RES), as a step toward net-zero emissions. The drawbacks of these energy sources are unpredictability and dependence on nature, leading to unstable load power supply risk. One way to overcome instability in the power supply is by using a battery energy storage system (BESS). Therefore, this study provides a detailed and critical review of sizing and siting optimization of BESS, their application challenges, and a new perspective on the consequence of degradation from the ambient temperature. It also reviews advanced battery optimization planning that considers battery degradation, technologies, degradation, objective function, and design constraints. Furthermore, it examines the challenges encountered in developing the BESS optimization model and evaluates the scope of the proposed future direction to improve the optimized BESS, especially its battery.

**Keywords:** battery energy storage system; sizing; optimal planning; battery degradation; ambient temperature; renewable energy sources


Due to the low incidence of GBCa, most of the data on the impact of adjuvant therapy on survival of patients with GBCa are derived from studies done on patients with any biliary tract cancer. While initial randomized control trials with gemcitabine-based adjuvant therapy failed to show a survival benefit in biliary tract cancers, examination of adjuvant capecitabine (BILCAP trial), suggested positive trends towards survival when adjusted for nodal positivity and tumor grade [78–80]. These studies also prompted dedicated examination of patients with GBCa. Retrospective analysis of large cohorts, including two studies that utilized National Cancer Database and a subsequent meta-analysis of more than 20,000 patients have shown an association between adjuvant chemotherapy and prolonged survival in patients with GBCa, especially in the presence of node positive disease [81–84].

There is also an increased interest in the use of adjuvant chemoradiotherapy in gallbladder cancer. Recent studies including propensity matched analysis of patients receiving adjuvant chemoradiotherapy versus chemotherapy have noted improved survival and reduced local recurrence associated with the use of adjuvant chemoradiation. These findings are especially noted in patients with tumor stage T2 or lymph node positive disease [85, 86]. In addition, a secondary analysis of the phase II intergroup trial, SWOG S0809—that evaluated adjuvant capecitabine and gemcitabine followed by radiotherapy and concurrent capecitabine—demonstrated improved OS in patients with extrahepatic cholangiocarcinoma and GBCa compared to historical controls. Furthermore, the data suggested that adjuvant chemoradiation positively impacted local control in patients with node positive disease [87].

### **4.3 Peri-operative chemotherapy**

There is now a great interest in the use of peri-operative therapy in patients with incidental GBCa (i.e., prior to formal oncological resection in patients who are diagnosed with incidental GBCa after a cholecystectomy). It may be hypothesized that timing of systemic therapy prior to formal resection would allow downstaging of the residual disease, allowing complete resection of the tumor. While no definitive data exist to suggest the superiority of either regimen (i.e., peri-operative versus adjuvant) there are currently two randomized control trials underway to ascertain the merits of the two approaches. Both the OPT-IN trial and the ACO-GAIN trial are examining the difference in oncological outcomes of patients treated with peri-operative gemcitabine- cisplatin therapy compared to those who undergo radical resection without any intervening systemic therapy [88, 89].

### **5. Surgical resection of GBCa**

All GBCa with T-stage including T1b to T3 warrant a radical cholecystectomy after having ascertained the absence of distal lymphadenopathy (i.e., periaortic, celiac, and *Gallbladder Cancer: Diagnosis and Surgical Management DOI: http://dx.doi.org/10.5772/intechopen.109208*

retropancreatic) and metastatic disease. Similar to the operative principles used in patients undergoing re-resection, patients diagnosed pre-operatively and those found to have GBCa on intra-operative frozen section should undergo cholecystectomy with hepatic resection of segment 4b and 5, along with lymphadenectomy with bile duct resection reserved only for obtaining negative margins [45]. Additional aspects of the surgery may include bile duct resection, vascular resection and extended hepatectomy, all of which are performed with the single goal of obtaining negative resection margins. There is currently no role of port site resection in patients who were diagnosed following a previous laparoscopic cholecystectomy, as this practice does not impact disease-free or overall survival [90].

Traditionally, the concern for port site seeding, chimney effect and concern for peritoneal dissemination, led to radical cholecystectomy being done as an exclusively open procedure [91–93]. However, studies comparing minimally invasive and open radical cholecystectomies have noted no oncological differences between laparoscopic and open surgery, with improved intra-operative and peri-operative outcomes in patients undergoing laparoscopic resection [94, 95]. Thus this procedure is now performed both laparoscopically and robotically. While most of the data on robotic oncological safety is derived from studies on laparoscopic radical cholecystectomy, there is an increasing trend of utilization of the robotic platform for this surgical procedure [96, 97].

### **6. Conclusion**

Gallbladder cancer, although rare, is an aggressive malignancy and the most common biliary tract cancer. With the increased cholecystectomy rate, most patients in the western world are diagnosed incidentally. Pathological evaluation of the gallbladder not only establishes diagnosis, but also guides further treatment planning, based on the accurate knowledge of the T-stage and of the cystic duct margin. The early systemic recurrence and poor overall survival—even after complete resection—warrant the use of multimodality treatment with chemotherapy and immunotherapy. Systemic therapy is currently the first line treatment in patients unable to undergo surgical resection, moreover, it is increasingly being advocated in the neoadjuvant and perioperative period to improve resection rates and possibly disease-free survival.

### **Conflict of interest**

The authors declare no conflict of interest.

*Biliary Tract – Review and Recent Progress*

### **Author details**

Asmita Chopra1 and Alessandro Paniccia2 \*

1 Department of Surgery, University of Toledo, Toledo, OH, USA

2 Department of Surgery, University of Pittsburgh, Pittsburgh, PA, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2022 The Author(s). Licensee IntechOpen. 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 reproduction in any medium, provided the original work is properly cited.

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*Gallbladder Cancer: Diagnosis and Surgical Management DOI: http://dx.doi.org/10.5772/intechopen.109208*

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**Abstract**

Anemia is a serious health issue throughout the world affecting both sexes of any age group. This nutritional disease is more common among the pregnant women of developing countries, where it is a major cause of maternal death and negative outcome of pregnancy. Among all anemic types, IDA is most prevalent one and is comprises of about 95% of all anemic cases around the world. In many developing countries it is more common in women of low socio-economic background and with no record of antenatal checkup. There is need for further health educational programs to overcome anemia especially for pregnant females.

**Keywords:** iron deficiency anemia, pregnancy outcomes, iron supplements


Preface

Leaders and managers are popular conversation topics when people meet. Especially bad leaders. And that is not that strange. We spend a great part of our lives in the workplace. How we feel at work is dictated to a great extent by our leaders and managers. They have the power to assign us our tasks and responsibilities, while deciding over benefits such as salaries and bonuses.

Scientifically, negative leadership has also gained more attention in recent years. It is well documented what behaviors are considered destructive and what the consequences of such behaviors are for the leader, the subordinates, and the organization. More focus has also been directed at the fact that the leader is a part of a context that influences the leader's be‐ haviors and how the subordinates perceive the leader. The organizational culture and struc‐ tures all play a part in this process.

In this book we have nine chapters, all addressing the issue of the dark side of organization‐ al behavior and leadership. We gain knowledge and reflections from different contexts such as the military, the public sector, architectural design offices, and educational settings. Indi‐ vidual and organizational barriers are described as well as the social context in which nega‐ tive leadership occurs. Negative leadership is also connected to people-centric approaches to management and service leadership theory.

> **Maria Fors Brandebo and Aida Alvinius** Department of Security, Strategy and Leadership Swedish Defence University Karlstad, Sweden

**Chapter 1**

**Provisional chapter**

**Introductory Chapter: Dark Sides of Organizations and**

**Introductory Chapter: Dark Sides of Organizations and**

DOI: 10.5772/intechopen.81550

**Leadership - An Integrative Approach and Definitions**

The purpose of this introductory chapter is to define and integrate previous research on negative organizational structures and destructive leadership in order to understand how negative organizational features can be framing factors for negative leadership behavior. This is a necessary theoretical grip in order to fully understand the dark sides of organizational and

Negative aspects of organizational structures have been previously studied in the area of management and organizational behavior and slightly within the area of destructive leadership [1–5]. However, the focus has primarily been either on the individual level or on the structures within the organization. For example, there are studies of the impact of adverse working conditions in terms of health [6] and job satisfaction [7]. Other studies focusing on individual organizational members suggest that organizational dysfunction is the result of dysfunctional individual behavior as shown in organizational settings [8]. Besides the impact of the individual on organizational challenges, the other widely studied aspect in relation to dysfunctional organizational aspects is organizational culture [9]. This is essentially an endogenous explanation. Researchers draw similarities between dysfunctional organizations and dysfunctional individuals arguing that culture is a pivotal factor in how organizations function internally. Similarly, organizational culture is seen in many studies as that which creates or destroys an organization [8]. Despite such interest and attempts to understand organizational culture and its role in managing organizational challenges, we still know little about the processes that spur dysfunctional organizational behavior—the exogenous factors—and

**Leadership - An Integrative Approach and Definitions**

© 2016 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 reproduction in any medium, provided the original work is properly cited.

© 2018 The Author(s). Licensee IntechOpen. 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 reproduction in any medium, provided the original work is properly cited.

Maria Fors Brandebo and Aida Alvinius

individual behavior at the workplace in general.

how it affects individuals within the organization.

Maria Fors Brandebo and Aida Alvinius

http://dx.doi.org/10.5772/intechopen.81550

**1. Introduction**

Additional information is available at the end of the chapter

Additional information is available at the end of the chapter

#### **Introductory Chapter: Dark Sides of Organizations and Leadership - An Integrative Approach and Definitions Introductory Chapter: Dark Sides of Organizations and Leadership - An Integrative Approach and Definitions**

DOI: 10.5772/intechopen.81550

Maria Fors Brandebo and Aida Alvinius Maria Fors Brandebo and Aida Alvinius

Additional information is available at the end of the chapter Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/intechopen.81550


*3.2.2 AEKF based on ANFIS combined model (ANFIS-ANFIS) performance accuracy analysis*

The performance analysis is made on the information provided by the battery SOC and terminal voltage residuals errors shown in **Figure 19a** and **b**. During the steady state, more precisely after 347 seconds, the SOC residual error is less than 1% smaller

**Figure 20.** *System-level flowchart diagram.*

than usual SOC residual error of 2% value reported in the literature field. Since the SOC residual error of AEKF based on ARX ECM battery model that is less than 1% during the steady state after 600 s, as is shown in **Figure 13c**, is obviously that the AEKF based on ANFIS battery model performs better. For a complete information about the suitability of AEKF SOC estimator based on ANFIS battery model is built in the **Table 2**, which incorporates all the statistics of criteria values RMSE (IC1), MSE (IC2), MAE (IC3), and MAPE (IC4), the most common criteria that have been used in the literature field to measure model performance and select the best model from a set of potential candidate models [7, 29, 30]. Comparing both statistics criteria values (third and fourth columns) is straightforward that the AEKF SOC estimator based on ANFIS battery model performs better than AEKF SOC estimator based on ARX battery model.

By comparing the terminal battery voltage residuals shown in **Figure 12** for AEKF based on ARX model and AEKF based on ANFIS model depicted in **Figure 19b**, they are ranged inside the intervals (�0.2, 0.3) volts and (�0.01, 0.08) volts, respectively; thus, the second SOC estimator based ANFIS battery model performs better than the first one. Based on the performance analysis of SOC accuracy, robustness to changes in SOCini value and terminal battery voltage prediction accuracy it can conclude that the AEKF SOC estimator based on ANFIS model is the most suitable SOC estimator for HEVs/EVs applications.

A system-level flowchart/flow diagram is shown in **Figure 20**. It indicates the major steps involved in the key sections of the last two chapters to provide an overview of the differences in steps between generic ECM, ARX ECM, ANFIS ECM, hybrid (ARX-ANFIS), and combined (ANFIS-ANFIS) models.

A detailed description for each small block of the overall diagram of the models along with a flow of the equations is also considered in the overall diagram shown in **Figure 20**.