Impact of Working Capital Management on Profitability: A Case Study of Trading Companies

*Rafathunnisa Syeda*

## **Abstract**

The success of any business depends on its profitability, liquidity, and solvency. Liquidity plays an important role in the successful running of a business. Many prior studies have been conducted to measure the relationship between working capital and profitability. The results showed that the high investment in inventories and receivables is associated with lower financial performance. They found a negative relationship between Return on Assets and Inventory turnover and Cash conversion cycle the present study is designed to know the direct impact of working capital on profitability by choosing the days of collection, days of payment, days inventory converts to sales and finally the cash conversion cycle. This study examines the association between the profitability and working capital using the data of 15 US trading companies for the period of 2015 to 2019. The key points in this study are firstly there exists a negative relationship between the profitability and the average collection period, the lower the average collection period higher will be the profitability, indicating that a decrease in the number of days a firm receives payment from sales affects the profitability of the firm positively. Secondly there is a highly significant positive relationship between average payment period and profitability. This implies that the longer a firm makes the payment to its creditors, the more profitable it is. Thirdly the cash conversion cycle decreases it will lead to an increase in profitability of the firm, and managers can create a positive value for the shareholders which indicates that it has been maintained. The regression analysis showed the value for the R-squared in the model is 0.584, i.e., 58.4% of the variation in the dependent variable Net Profitability is explained by the independent variables.

**Keywords:** net profitability, trading companies, working capital management, average collection period, average payment period, inventory turnover days, cash conversion cycle

#### **1. Introduction**

An attempt has been made in this empirical study to know the impact of working capital management on profitability, both the factors are important concerns of management. If working capital is not managed perfectly it will reduce the liquidity of the company and ultimately effects profitability.

The working capital should be maintained at a desired level depending upon the size of the firm, excessive working capital leads to the unnecessary accumulation of inventories causing losses and wastages. The large debtors indicate the defective credit policy which might lead to bad debts. On the other hand, with the inadequate working capital, the firm will not be in a position to pay short-term liabilities. The firm may not be able to pay its day-to-day expenses which creates inefficiencies and reduces profits.

The success of any business depends on its profitability, liquidity, and solvency. Liquidity plays an important role in the successful running of a business. The crucial functions of financial managers to ensure the liquidity of a firm, that it must be in a position to meet its short-term obligation without which it cannot survive. The working capital which consists of current assets and current liabilities which measure the liquidity has been chosen as the main independent variable to study its relationship with the profitability. The collection period, payment period, inventory days and cash conversion cycle has been used as a measure of working capital.

Many prior studies have been conducted to measure the relationship between working capital and profitability as examined by Azhar [1]. The impact of liquidity and management efficiency on the profitability of select power sectors using different ratios as independent variables, where debtor turnover ratio, collection efficiency, and interest coverage showed a significant impact. Rathiranee and Sangeetha [2] examine the impact of working capital on financial performance in select trading firms where the regression analysis results showed that the high investment in inventories and receivables is associated with lower financial performance i.e., Return on Assets (ROA). They found a negative relationship between Return on Assets and Inventory turnover and Cash conversion cycle for the trading firms listed in Colombo Stock Exchange. Mansoori and Muhammad [3] have studied the same picture with the evidence from Singapore found that Management performance would be improved by managing working capital efficiently. Their results demonstrate that firm's profitability is increased by decreasing in receivable conversion period and inventory conversion period. Saradhadevi found in her study that there exists a highly significant negative relationship between the profitability and cash conversion cycle and a highly significant positive relationship between the time it takes the firm to pay its (Average payment period) which implies the longer a firm takes to pay its creditors the more profitable it is.

Keeping in view the above scenario the present study is designed to know the direct impact of working capital on profitability by choosing the days of collection, days of payment, days inventory converts to sales and finally the cash conversion cycle.

Many studies have been conducted for manufacturing companies, cement and textile companies, oil and gas companies only a few have been focused on trading companies. Hence the present study has its focus on working capital management and its impact on profitability in relation to trading sector.

#### **2. Review of literature**

1.Working capital management and profitability [4]: This study aims to find out the impact of working capital management on profitability. Return on assets, Current ratio, debt to equity ratio, operating profit to debt ratio, and inventory turnover ratios of the firms are the variables that are used in this study carried out for electrical equipment firms listed on Karachi stock exchange for a period of six years i.e. 2007–2012. Regression analysis was applied to the data. Normality and linearity test was also applied. Results showed significant positive

results. T-test is applied to see for individual variable significance, it tells that each variable is significant. It is concluded that working capital management has positive significant impact on profitability of the firms.

years from 2001 to 2010. The study uses Regression analysis. The findings indicate that there exists a significant negative linear relationship between inventory conversion period and profitability. It was also found that when profitability increases with the decrease in the financial debt ratio. Further, it showed a positive relationship between profitability and firm size, as the profitability increases with an increase in firm size. Lastly, the relationship between the current ratio and profitability was negative.

*Impact of Working Capital Management on Profitability: A Case Study of Trading Companies DOI: http://dx.doi.org/10.5772/intechopen.99912*

focusing on real estates and construction companies from the Abu Dhabi market. The finding of this study presented that there is a negative relationship between cash conversion cycle and profitability; longer the CCC, the profitability decreases. Another finding showed that the amount of payable days is negatively related to profitability.

#### **3. Research question**

The main objective of any business is to earn profit and manage the funds efficiently and effectively which has direct impact on profits. So, working capital is the major constituent to measure liquidity. This study examines the association between the profitability and working capital using the data of 15 US trading companies for the period of 2015 to 2019.

#### **4. Hypotheses development**

Working capital is an important issue during financial decision making. The crucial part in managing working capital is required to maintain its liquidity in dayto-day operation for the smooth running of business and meeting its obligations in time. Thus, working capital is selected as one of the independent variables to know that how it effects profitability.

H1: There is a significant relationship between Working Capital Management and profitability.

H2: Working capital management has strong impact on profitability.

Keeping in view the above studies the following objectives have been outlined for the present study.

#### **5. Objectives of the study**

**Table 1.**

*Showing the key research variables.*

## **6. Methods**

The choice of the variables for the present study is influenced by the previous studies on working capital management. They include dependent, independent and some control variables. The profitability in terms of Return on assets, Gross profit ratio, Operating profit and Net income are taken as dependent variable in previous studies.

The dependent variable is the one which is affected during the experiment, for the present study profitability is taken as dependent variable i.e., in terms of Net Income. The independent variable is the one which effects the dependent variable. Average collection period, cash conversion cycle, average payment period, inventory turnover ratio, current ratio, liquid ratio, etc. were taken as independent variables in previous studies. For this study the independent variables are the average collection period, average payment period, inventory turnover days and cash conversion cycle. The study aims at to find out the association between the variables through different statistical analysis (**Table 1**).

The following equation is used to estimate the impact of working capital on profitability.

*NI it ACP it APP it ITD it CCC it* ( ) =+ + + + â0 â1 â2 â3 â4 ( ) ( ) ( ) ( )

NI (it) = profitability of the firms at time 5 years, i = 15 firms. β0 = the intercept of an equation. β = coefficients of independent variables. T = time 5 years 2015–2019. Average collection period ACP. Average payment period APP. Inventory turnover days ITD. Cash conversion cycle CCC.

In the above general equation, the Profitability is the dependent variable, and it is influenced by the independent variables i.e., ACP, APP, ITD and CCC.

#### **7. Sample and data collection**

The main source of data is the S&P Capital IQ website. Many studies have been conducted to examine the relationship between the financial performance and working capital management. These studies have been done relating to the cement companies, trading companies, manufacturing companies, pharmaceutical *Impact of Working Capital Management on Profitability: A Case Study of Trading Companies DOI: http://dx.doi.org/10.5772/intechopen.99912*

companies and only a few have been carried out about trading companies. So for the present study sample is taken from trading companies.

The present study aims at to provide some empirical evidence of impact of working capital management on profitability for a sample of 15 trading companies for the period of five years from 2015 to 2019. These companies are randomly selected from all listed companies in the New York Stock Exchange (NYSE). The sample companies includes: 1) Applied Industrial Technologies Inc. (AIT), 2) DXP Enterprises Inc., 3) Eco Shift Power Corp. (ECOP), 4) EVI Industries Corp., 5) General Finance Corp. (GFN), 6) Gypsum Management and Supply Corp. (GMS), 7) W.W. Grainger (GWW), 8) H&E Equipment Inc. (HEES), 9) HD Supply Inc. (HDS) 10) Houston Wire and Cable Company (HWCC), 11) Huttig Building Products Inc. (HBP) 12) Kaman Corporation (KAMN), 13) MRC Global Inc., 14) MSC Industrial Direct Co. Inc. (MSM), 15) ProShares Ultra Health Care (RXL).

The Net Profitability is taken as the dependent variable and the average collection period (ACP), average payment period (APP), inventory days converted to sales (ITD) ad cash conversion cycle (CCC) are considered as independent variables.

#### **8. Results**

The analysis of data is done using descriptive statistics, correlation matrix and regression analysis.

The following observations are made from the above **Table 2** compiled with five years data for 15 trading companies:

#### **Table 2.**

*Descriptive statistics of 15 companies for the years from 2014 to 2019.*

## **9. Correlation analysis**

Correlation analysis is used to measure the degree of association between different variables under consideration. Correlation matrix of all variables included in the analysis is presented in **Table 3** which is calculated based on data of 15 trading companies for a period of five years 2015 and 2019.

An attempt has been made to find the relationship between profitability and WC, for this purpose Pearson's coefficient of correlation analysis is done. As indicated in the above studies there exist a negative correlation between the profitability and the collection period, the lower the average collection period higher will be the profitability. The correlation between average payment period and profitability is 0.127 which shows a positive relationship if there is an increase in payment period it leads to an increase in profitability. There exist a negative correlation between profitability and the cash conversion cycle is −0.271 which indicates an increase in collection period leads to increase in CCC and vice versa and ultimately effects profitability The correlation between inventory conversion days and profitability is positive which is beyond expectation. There exists a negative correlation between cash conversion cycle and average payment period.

It is recommended that the companies should avoid an increasing cash conversion cycle because it means that the business is becoming more operating inefficient, locking more and more cash into its processes. They should maintain a lowest cash conversion cycle compared to their peers, or at least a decreasing one. A decreasing CCC represents a more efficient company that converts its inventories faster as well as gets paid faster and probably is paying its suppliers later thus, holding cash for more time (**Table 4**).

#### **Table 3.**

*Correlation matrix of 15 companies for the year 2015 and 2019.*

#### **Table 4.**

*Regression results of 15 companies for the year 2015 to 2019.*

*Impact of Working Capital Management on Profitability: A Case Study of Trading Companies DOI: http://dx.doi.org/10.5772/intechopen.99912*

### **10. Regression analysis**

The regression analysis showed the value for the R-squared in the model is 0.584, i.e., 58.4% of the variation in the dependent variable (NI) is explained by the independent variables working capital of the model, which is represented by CCC, APP, ACP, and ITD and 42% is affected by other factors.

#### **11. Summary and conclusions**

The study is carried out for a sample of 15 trading companies for the period of five years from 2015 to 2019. These companies are randomly selected from all listed companies in the New York Stock Exchange (NYSE).

This study examined the relationship between Net Profitability and several variables of working capital management such as average collection period, average inventory turnover in days, average payment period and cash conversion cycle. The results showed that there exist a negative relationship between the profitability and the average collection period, the lower the average collection period higher will be the profitability. The correlation between average payment period and profitability is 0.127 which shows a positive relationship if there is an increase in payment period it leads to an increase in profitability. It is found that the cash conversion cycle decreases it will lead to an increase in profitability of the firm, and managers can create a positive value for the shareholders which indicates that it has been maintained.

The results of this study show that there is a strong relationship between the working capital and profitability of the firms. It means if the financial managers keep an eye on the liquidity it will lead to profitability. So, it is recommended that Companies should always maintain a sound collection policy and it is further suggested that managers can create value for their shareholders by reducing the number of days accounts receivable, increasing the number of days accounts payable and inventories to a reasonable minimum.

The hypotheses is accepted for working capital management that it has strong impact on profitability. There is a significance relationship between Working Capital Management and profitability. The study has examined the impact in terms of average collection period, average payment period, inventory turnover days and cash conversion cycle on profitability.

Furthermore, On the basis of the above analysis the results can be further strengthened if the firms manage their working capital in more efficient ways. Management of Working capital means the management of current assets and current liabilities. If these firms efficiently manage their cash, accounts receivables, accounts payables, and inventories, this will ultimately increase profitability of these companies.

### **12. Limitations of the study**

*Accounting and Finance Innovations*

## **Author details**

Rafathunnisa Syeda Northeastern Illinois University, Chicago, IL, USA

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 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.

*Impact of Working Capital Management on Profitability: A Case Study of Trading Companies DOI: http://dx.doi.org/10.5772/intechopen.99912*

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[2] Yogendrarajah, Rathiranee. Working capital management and its impact on firms' financial performance: An analysis of trading firms. Research Gate 2011. https://www.researchgate.net/ publication/256088750\_Working\_ Capital\_Management\_and\_its\_Impact\_ on\_Firms'\_Financial\_Performance\_An\_ Analysis\_of\_Trading\_Firms

[3] Mansoori, Ebrahim. The effect of working capital management on firm's profitability: Evidence from Singapore. Interdisciplinary Journal of Contemporary Research in Business 2012. https://papers.ssrn.com/sol3/ papers.cfm?abstract\_id=2185840

[4] Rimsha Khalid, Tehreem Saif, Abdul Rehman Gondal, and Hamza Sarfraz. Working capital management and profitability. Mediterranean Journal of Basic and Applied Sciences (MJBAS) 2018;2(2):117-125.

[5] Nasser A. Alsulayhim. The relationship between working capital management and profitability. International Business Research 2019;12(8). DOI:10.5539/ibr. v12n8p142

[6] Kafeel, Ali, J., Ud Din, M., Waris, A., Tahir, M., and Khan, S. Working capital management and firms' profitability: dynamic panel data analysis of manufactured firms. Journal of Financial Risk Management 2020;9:494-517. DOI:10.4236/ jfrm.2020.94027

[7] Arnaldo Arnaldi, Barbara Novak, Remo Roscigno Wu Zhan. Working capital management and profitability: Empirical evidence. International Journal of Business Management and Economic Research (IJBMER) 2021;12(2).

[8] Ul Haq, Ikram, Muhammad Sohail, Khalid Zaman, and Zaheer Alam. The relationship between working capital management and profitability: A case study of cement industry in Pakistan. Research Gate 2011. https:// www.researchgate.net/ publication/288946070

[9] Panigrahi, CMA (Dr.) Ashok. Relationship between inventory management and profitability: An empirical analysis of Indian cement companies. SSRN 2013. https://papers. ssrn.com/sol3/papers. cfm?abstract\_id=2342455.

[10] Karaduman, Hasan Agan, Halil Emre Akbas, Arzu Ozsozgun, and Salih Durer. Effects of working capital management on profitability: The case for selected companies in the Istanbul stock exchange (2005-2008). Research Gate 2010. https://www. researchgate.net/publication/ 290164649\_Effects\_Of\_Working\_ Capital\_Management\_On\_Profitability\_ The\_Case\_For\_Selected\_Companies\_In\_ The\_Istanbul\_Stock\_ Exchange\_2005-2008

[11] Thappa, Sankar. Working capital management in Indian oil and gas industry – A case study of Reliance Industries LTD. Academica Science Journal 2014. https://www.researchgate. net/publication/334442075\_ Working\_Capital\_Management\_In\_ Indian\_Oil\_Gas\_Industry\_-\_A\_Cas\_ Study\_of\_Reliance\_Industries\_LTD

[12] Safdar Sial, Muhammad, and Aqsa Chaudhry. Relationship between

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working capital management and firm profitability: Manufacturing sector of Pakistan. Research Gate 2012. https:// www.researchgate.net/ publication/255855851\_Relationship\_ between\_Working\_Capital\_ Management\_and\_Firm\_Profitability\_ Manufacturing\_Sector\_of\_Pakistan

[13] Asif Iqbala and Zhuquan Wang. Effect of working capital management on profitability. 2018 5th International Conference on Key Engineering Materials and Computer Science (KEMCS 2018).

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## Section 8


**2.4. PAT functions in addition to palmitoylation**

its intracellular localization; however, a defined targeting signal present within this region of

PATs have already been linked, in varying degrees, to human disease despite their recent discovery. At least 7 genes encoding PATs have been implicated in human disorders, in‐ cluding *ZDHHC8* with schizophrenia [33], *ZDHHC17* with Huntington's disease [49], *ZDHHC15* and *ZDHHC9* with X-linked mental retardation [32, 50], and *ZDHHC2*, *ZDHHC9*, *ZDHHC17*, and *ZDHHC11* with cancer [29, 51-53]; most of the demonstrated and putative

Overexpression of some PATs has also been shown to alter cancer-related signaling. DHHC17 (HIP14) is oncogenic. DHHC9 and DHHC11 display characteristics that strongly suggest they also are oncogenic. Overexpression of DHHC17 has the ability to induce colo‐ ny formation and anchorage-independent growth in cell culture and tumors in mice [53]. It has been shown that these effects occur, at least in part, by DHHC17-mediated palmitoyla‐ tion of H-, N-, and K2A- RAS proteins [53]. DHHC9 is strongly upregulated in some adeno‐ carcinomas of the gastrointestinal tract at the transcript and protein levels [52] and has also been shown to palmitoylate H- and N- RAS proteins *in vitro* [31]. *ZDHHC11* has a high inci‐ dence of additional genomic copies in cases of non-small cell lung cancer and bladder cancer in which it is strongly linked to high-grade, advanced stage and disease progression [51].

Conversely to the behavior of the oncogenic PATs, a failure to express *ZDHHC2* results in an increase in metastasis in an *in vivo* model leading to the suggestion that *ZDHHC2* is a tu‐ mor/metastasis suppressor [29]. This absence of expression suggests that substrates of DHHC2 are no longer palmitoylated, and that whatever role palmitoylation had in signaling downstream from that event has been disrupted. Such is the case of DHHC2, where due to a lack of palmitoylation, one of its substrates, CKAP4, is no longer normally palmitoylated. One consequence of this is that CKAP4 no longer traffics efficiently (or at all) to the cell sur‐ face where it acts as a receptor for antiproliferative factor (APF) [37] [or presumably its other two known ligands, tissue plasminogen activator [54] and surfactant protein A [55]]. With‐ out surface expression of CKAP4, APF is unable to initiate a wide range of downstream ef‐ fects, including halting cellular proliferation and altering the expression of genes related to

CD9 and CD151, both tetraspanin proteins, have also been identified as substrates of DHHC2 [56]. CD9, which has been suggested to be a tumor suppressor [57, 58], is palmitoy‐ lated on multiple cysteines, but which of these are palmitoylated by DHHC2 is not known. Nonetheless, it is clear that suppression of DHHC2-mediated palmitoylation of CD9 in A431 cells affects cell behaviors that are consistent with it playing a role in tumor suppression. In particular, the cells undergo what appears to be epithelial-mesenchymal transition (EMT) a process in which epithelial cells lose epithelial morphology and markers and gain a fibro‐ blastic morphology during tumor progression [59-61]. It is not yet clear whether this change in cellular behavior was mediated solely by the reduction in CD9 palmitoylation or through reduced palmitoylation of this and other DHHC2 substrates such as CKAP4. It will be inter‐

DHHC2 and in other DHHC proteins has yet to be defined.

**2.3. PAT genes, palmitoylation and human disease**

connections are with cancer.

256 Drug Discovery

the progression of cancer [44].

It is not surprising that a disruption in the homeostatic balance of protein palmitoylation, in either direction, can have pathophysiological consequences. However, one must remain mindful that palmitoylation may not be the sole function of these proteins. Recently, two PATs—HIP14 (DHHC17) and HIP14L (DHHC13)—have been shown to mediate the trans‐ port of Mg2+ [67]. The first indication that these PATs were involved in Mg2+ regulation was that the abundance of their corresponding mRNAs was increased in cells grown in medium with reduced Mg2+ concentration. The authors then showed that Mg2+ (but not Ca2+) trans‐ port was both electrogenic and voltage dependent, and that the transport required palmitoy‐ lation of the PAT. The authors concluded that these two PATs fall into a category of enzymes called "chanzymes" or ion channels that also have enzymatic activity; a type of protein previously represented only by the transient receptor potential melastatin (TRPM) family of transporters [68, 69]. The fact that GODZ (DHHC3) does not appear to mediate Mg2+ transport [70] but can mediate the transport of Ca2+ [71] suggests that this is not a gen‐ eral property of all PATs. The discovery that these PATs transport Mg2+ was astonishing es‐ pecially in light of the fact that the DHHC-CRD motif appears, by sequence and predicted structure, to be a Zn2+-binding protein; (a divalent cation with an atomic radius similar to Mg2+)—not Mg2+. However, Goytain and colleagues also found that HIP14 and HIP14L transported Zn2+ with approximately half the efficiency as Mg2+. The role of these and other PATs in binding to and/or transporting Zn2+ remains to be elucidated, but demonstrates the importance of not limiting ones view of PAT function (or many other proteins for that mat‐ ter) only to palmitoylation.

#### **2.5. Enzymatic mechanisms of palmitoylation**

The physical and chemical mechanisms that result in enzymatic palmitoylation have yet to be defined clearly, but some progress has been made using purified proteins. It has been es‐ tablished that mutation of the cysteine in the DHHC motif of all PATs studied to date abol‐ ishes autoacylation of the PAT and palmitoylation of the substrate [23, 56, 62, 72]. This literature as well as discussion of potential physical mechanisms for the reaction have been reviewed recently [3, 73].

with these algorithms is the complexity of the PAT-substrate recognition that is encoded by residues outside of those that immediately surround the palmitoyl-cysteine; the higher or‐

Discovery of Selective and Potent Inhibitors of Palmitoylation

http://dx.doi.org/10.5772/52503

259

The unique physical and biochemical nature of the thioester bond that links palmitate to cysteine residues is the basis for the design of many recent assays for palmitoylation. The cysteine residue is among the most nucleophilic entities in a cell [79] and is the most com‐ mon site of palmitoylation. Other residues can be modified by palmitate, but their occur‐ rence is relatively rare and the bond chemistries are different [2, 80-83]. Palmitoylation can also occur in other ways, for example, on an amine of an N-terminal cysteine as is the case with Hedgehog [2, 83, 84], a secreted signaling protein. An example of palmitate modifying the weaker –OH nucleophile of threonine occurs on the carboxyl terminus of a spider toxin [81]. The ε-amino group of lysine can also be modified by palmitate linked by an amide

The reactivity of the thiolate anion of cysteine residues makes it a key component in the structure and function of many proteins by stabilizing higher order structures via disulfide bridges and post-translational modifications like nitrosylation, prenylation, and acylation [85-87]. The high degree of reactivity has also provided a well-characterized, indispensable target for modification by synthetic, thiol-reactive ligands, allowing capture and characteri‐ zation of proteins [88]. An exceptionally useful application of such thiol-specific chemistry is isotope-coded affinity tags (ICAT) for mass spectrometric determination of relative protein or peptide abundance among two or more samples [89-91]. With these probes, changes in abundance of identified proteins or peptides are determined by changes in the ratio of heavy to light-isotope-modified peptides from mixed samples. Combining ICAT technology with functional genomics methods like siRNA-mediated PAT-gene knockdown is one of

In healthy cells the cytoplasm is generally a reducing environment, meaning that solventexposed cysteine side chains are not typically disulfides and thus available to engage in re‐ actions with other molecules [92]. The reactivity of a free cysteine depends on the pKa of the cysteine which is a function of the local environment surrounding the residue within the context of the whole protein. Unlike other residues with nucleophilic side chains (-OH or – NH2), thiol side chains undergo conjugations, redox, and exchange reactions [85]. Conjuga‐ tion reactions (in addition to fatty acylation) include nitric oxide (NO) or S-nitrosylation, re‐ active oxygen species (ROS), and reactive nitrogen species (RNS) forming bonds that are not susceptible to cleavage by hydroxylamine at neutral pH. Hydroxylamine is a reagent used to selectively remove thioester-linked palmitate [93]. Importantly, we know that hydroxyla‐ mine does not perturb disulfides [94], and that it efficiently cleaves thioesters in a quantita‐

In addition to the linkage of palmitate to cysteines, another thioester bond that occurs in cells is the transient association between ubiquitin and the E1, E2, and certain E3 ubiquitina‐ tion enzymes [87, 96]. However, these thioester bonds are easily distinguished from the thio‐

bond. This occurs in several secreted proteins including a bacterial toxin [80].

several mechanisms that will allow us to identify substrates of PATs [37].

der components of the recognition sites.

tive manner [95].

**3.1. The physical properties of cysteines and thioester bonds**


**2.3.1 The Ziegler–Nichols tuning method**

The Ziegler–Nichols tuning method is a heuristic method of tuning a PID controller. It was proposed by John G. Ziegler and Nichols in the 1940's. It is performed by setting I (integral) and D (derivative) gains to zero. The P (proportional) gain, Kp is then increased (from zero) until it reaches the ultimate gain Ku, at which the output of the control loop oscillates with a constant amplitude. Ku and the oscillation period Tu are used to set the P, I, and D gains depending on the type of controller used [3,4]:

There are several prescriptive rules used in PID tuning. The most effective methods generally involve the development of some form of process model, and then choosing P, I,

Requires experienced

Some trial and error, process upset and very aggressive

Good only for first order

Some cost and training

personnel

Some math

processes

involved

Very slow

tuning

This can be very useful to remove unstable poles.

and D based on the dynamic model parameters.

Manual Online method

Ziegler-Nichols Online method

We present here four tuning methods for a PID controller [2,3].

Software tools Online or offline method,

Algorithmic Online or offline method,

**2.3.1 The Ziegler–Nichols tuning method**

depending on the type of controller used [3,4]:

Method **Advantages Disadvantages**

Proven method

Cohen-Coon Good process models Offline method

Very precise

No math expression

consistent tuning, Support Non-Steady State tuning

Consistent tuning, Support Non-Steady State tuning,

The Ziegler–Nichols tuning method is a heuristic method of tuning a PID controller. It was proposed by John G. Ziegler and Nichols in the 1940's. It is performed by setting I (integral) and D (derivative) gains to zero. The P (proportional) gain, Kp is then increased (from zero) until it reaches the ultimate gain Ku, at which the output of the control loop oscillates with a constant amplitude. Ku and the oscillation period Tu are used to set the P, I, and D gains

**2.3 Tuning methods**

We can realise a PID controller by two methods: First, an analog PID controller Second, a digital PID controller

1. Circuit diagram below (figure.5) shows an analog PID controller. In this figure, we present an analog PID controller with three simple op amp amplifier, integrator and differentiator circuits.

Fig. 5. Electronic circuit implementation of an analog PID controller

PID Control Theory 221

While PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or even tuning, they can perform poorly in some

Fractional order systems are characterized by fractional-order differential equations. Fractional calculus considers any real number for derivatives and integrals. The FOPID controller is the expansion of the conventional integer-order PID controller based on

The PIDs are linear and in particular symmetric and they have difficulties in the presence of non-linearities. We can solve this problem by using a fractional-order PID (FOPID)

( ) *I P ID*

*K KS K KSS Gs K K S S S*

>

 (14)

*P D*

Figure.7 describes the possibilities a FOPID for the different controllers.

Fig. 7. Generalization of the FOPID controller: from point to plane.

to the Riemann-Liouville definition.

There are several methods to calculate the fractional order derivative and integrator of a fractional order PID controller. For this purpose we present a real order calculus according

applications, and do not in general provide optimal control.

**3.1 Fractional-order PID (FOPID) controller**

controller. A FOPID controller is presented below [7-9]:

**3. Fractional systems**

fractional calculus [7,8].

Finally, we need to add the three PID terms together. Again the summing amplifier OP4 serves us well. Because the error amp, PID and summing circuits are inverting types, we need to add a final op amp inverter OP5 to make the final output positive.

2. Today, digital controllers are being used in many large and small-scale control systems, replacing the analog controllers. It is now a common practice to implement PID controllers in its digital version, which means that they operate in discrete time domain and deal with analog signals quantized in a limited number of levels. Moreover, in such controller we do not need much space and they are not expensive. A digital version of the PID controller is shown in figure 6 [5,6].

Fig. 6. Digital PID Controller

In its digital version, the integral becomes a sum and the deferential a difference. The continuous time signal e(t) is sampled in fixed time intervals equals a determined sample period, here called Tc (in figure 6 Tc = 1). An A/D (analog to digital) converter interfaces the input and a D/A (digital to analog) converter interfaces the output. This sampled and digitalized input, called eD[j], exists only in time instants *C t kT* for all 0 *k Z* . A lower bound for the sample period is the computing time of a whole cycle of the digital PID (which includes the A/D and D/A conversion).

Finally, we need to add the three PID terms together. Again the summing amplifier OP4 serves us well. Because the error amp, PID and summing circuits are inverting types, we

2. Today, digital controllers are being used in many large and small-scale control systems, replacing the analog controllers. It is now a common practice to implement PID controllers in its digital version, which means that they operate in discrete time domain and deal with analog signals quantized in a limited number of levels. Moreover, in such controller we do not need much space and they are not expensive. A digital version of

In its digital version, the integral becomes a sum and the deferential a difference. The continuous time signal e(t) is sampled in fixed time intervals equals a determined sample period, here called Tc (in figure 6 Tc = 1). An A/D (analog to digital) converter interfaces the input and a D/A (digital to analog) converter interfaces the output. This sampled and digitalized input, called eD[j], exists only in time instants *C t kT* for all 0 *k Z* . A lower bound for the sample period is the computing time of a whole cycle of the digital PID

need to add a final op amp inverter OP5 to make the final output positive.

the PID controller is shown in figure 6 [5,6].

Fig. 6. Digital PID Controller

(which includes the A/D and D/A conversion).

While PID controllers are applicable to many control problems, and often perform satisfactorily without any improvements or even tuning, they can perform poorly in some applications, and do not in general provide optimal control.


**5. Conclusion**

the OC phase, an ordinary offset cancellation technique is applied on the comparator and buffers. In the conversion phase, successive approximation algorithm first finds the segment of interest, encoded by b6b5b4, in 3 clock cycles. These 3 bits are converted to a 7-bit thermometer code, T7~T1, which will be used in the *Segment Selection* block to generate two analog voltages associated with the endpoints of the segment of interest. An in-segment linear A/D conversion process is then performed to determine the remained 4 LSBs, b3b2b1b0, in 4 clock cycles. Finally, an end of conversion signal is generated to reset the ADC and prepare for the next conversion cycle. A low-voltage band-gap reference (BGR) was designed to generate the required baseline,

> Nonlinear Cap. Arrays

220 µm

SAR & Switch Control Logic

**Experimental Results.** The presented exp-ADC was fabricated in a standard 0.18-μm CMOS process. A chip photograph is shown in Figure 18, in which the chip occupies a total size of

linearity (INL) are +0.8/-0.9 LSB and +4.3/-2.1 LSB, respectively. Table 1 summarizes the specifications of the NLADC and compares it with some of the nonlinear and linear ADCs

A proof-of-concept prototype of a 4-channel neural recording system based on anti-logarithmic quantization was reported in [6]. As shown in the block diagram of Figure 19, a time-domain multiplexer (TDM) shares an anti-logarithmic ADC (AL-ADC) between 4 channels (each sampled at 20kSps). The output digital codes are then packed by the data packaging block to be transmitted to the outside world via a wireless link. At the external host, received signal is first recovered and then converted back to analog via a PC software. This inverse conversion is performed using a logarithmic DAC. Quantization characteristic function for the DAC is an exact inverse for that of the NLADC used in the recording system. To evaluate the operation of the system, neural signals recorded from the auditory cortex of a Guinea pig are used for in-vitro tests. Figure 20 shows the input signal to one of the neural channels before entering the nonlinear quantization process on the implant side along with the associated signal on the external setup after reconstruction. To verify the concept of the noise reduction caused by

Linear Cap. Array

. The measured worst-case differential non-linearity (DNL) and integral non-

Comp, UGBs

T1 T2 T3 T4 T5 T6

2.15Cu

2.15Cu

3.02Cu

3.02Cu

**Segment Selection**

1.67Cu

1.15Cu

T7

1.15Cu

Cu

Cu

1.36Cu

1.36Cu

**VREF VBL**

T1 T2 T3 T4 T5 T6 T7

1.67Cu

for the exp-ADC.

BGR

220 µm

Figure 16. The process of B-Noise reduction as the signal passes through the NLADC, formulated by eqs. (14)-(16)

**In-Segment Digitization**

8Cb 2Cb Cb Cb 4Cb

b3 b1 b0 b2

SD Phase OC Phase

**V** PH1 **in**

ADC Characteristic curve

Background

Input average

noise PDF Pni(ni

noise power ni

Output average no noise power 2 (t) Pni(ni

VLSB,max

Analog Input (ni

Baseline

)

PH1

 **Vin** PH2

> **PH0 PH1 PH2**

**CLK**

PH0

PH1

**VBL**

PH1

UGB

UGB

)

2 (t) Pni(ni )

PH1

19.11Cu

5.14Cu

5.14Cu

PH2

19.11Cu

**VREF**

**VBL**

Digital Output (no)

)

Figure 17. Circuit schematic for the two-step SAR ADC designed to implement exp-ADC and its simplified timing diagram

Dummy Text **Circuit Design.** To realize the proposed exp-ADC with reasonable power and silicon area, successiveapproximation register (SAR) architecture was chosen [31]. In order to facilitate the realization of the exponential quantization function needed for the ADC, a piecewise-linear (PWL) approximation of the required function was implemented.

Segment Selection b6 b5 b4 b7

**VTH**

T7-T1

In-Segment Digitization b3 b2 b1 b0

Conversion Phase

**SAR & Switch Control Logic**

b3-b0 b7-b0

SAR b7- b0

Binary to Thermometer Converter

> End of Conversion

Digital Output

b6 - b4

range in which the input voltage is located. In the OC phase, an ordinary offset cancellation technique is applied on the

reference and threshold voltages for the exp-ADC.

**Figure 18.** Chip photograph of the proposed NLADC

220 ×230 μm2

310 Advances in Bioengineering

reported in the literature.

To overcome bandwidth limitation in the wireless telemetry of recorded neural data, a wide variety of data reduction techniques has been reported. These techniques range from spike reporting approaches such as spike detection and spike sorting techniques to mathematical approaches such as the discrete wavelet transform. Although it is proven that spike reporting approaches contain enough information to actuate prosthetic devices [33], in some other applications, such as neuroscientific studies, they are not satisfactory due to considerable loss of important information, e.g., spike wave shapes. Mathematical approaches, on the other hand, have been successful from the standpoint of data compression, while preserving wave shape of the spikes. Nonetheless, increasing the number of recording channels can result in the potential problems of these approaches: large silicon area and high power consumption. In contrast with all of the mentioned techniques, hardware approaches are capable of data reduction without adding any extra block to the microsystem. This is achieved by modifying the present hardware of the implant. An implementation of one of these approaches focusing on the ADC circuit of the system was presented and discussed in detail. The proposed method results in considerable reduction of bit-rate in multi-channel neural recording microsystems. Thus, efficient design of application-specific circuits for building blocks of neural implants should be taken into account as an appropriate method of data reduction.

**Figure 20.** Recorded neural signal and reconstructed neural signal

[1] Sodagar, A.M., Perlin, G.E., Yao, Y., Najafi, K. and Wise, K.D. An Implantable 64-Channel Wireless Microsystem for Single-Unit Neural Recording. IEEE Journal of Solid-State Circuits 2009;

[2] Harrison, R., Watkins, P., Kier, R., R. Lovejoy, Black, D., Normann, D. and Solzbacher, F. A Low-Power Integrated Circuit for a Wireless 100-Electrode Neural Recording System. IEEE Interna-

[3] Chae, M.S., Yang, Z., Yuce, M.R. , Hoang, L. and Liu, W. A 128-Channel 6 mW Wireless Neural Recording IC with Spike Feature Extraction and UWB Transmitter. IEEE Transactions on Neural

[4] Wise, K.D., Angell, J. B. and Starr, A. An Integrated Circuit Approach to Extracellular Microelec-

[5] Sodagar, A.M. Integrated Circuit and System (ICAS) Lab Internal Report. K. N. Toosi University

[6] Akhavian, A., Judy, M. and Sodagar, A.M. Anti-Logarithmic Quantization for Data Reduction in Multi-Channel Intra-Cortical Neural Recording Implants. IEEE-EMBS International Conference on

[8] Eberhart, R.C, Dobbins, R.W. and Webber, W.R.S. EEG Waveform Analysis Using CaseNet. Proceedings of the Annual International Conference of the IEEE Engineering in Medicine and Biolo-

tional Solid-State Circuits Conference 2006; 38. 958-965.

Systems and Rehabilitation Engineering 2009, 17, 312-21.

trodes. IEEE Transactions on Biomedical Engineering 1970; 17, 238-247.

Figure 21. (a) Measured quantization error in the time domain (b) Distributions of the measured **Figure 21.** quantization error and the measured B-Noise (a) Measured quantization error in the time domain (b) Distributions of the measured quantization error and the measured B-Noise


**10. 11β-HSD and metabolite syndrome - clinical importance**

Based on human data and genetic models, 11β-HSD1 seems to be cause and promising pharmaceutical target for the treatment of metabolic disease. In mice, the increased enzyme activity in adipose tissue enhances local glucocorticoid levels and produces a metabolic syndrome [196], whereas the decreased enzyme activity protects against obesity and the metabolic syndrome [200, 201]. In human, 11β-HSD1 expression is elevated in adipose tissue in obesity [206], whereas inhibition of 11β -HSD1 enhances insulin sensitivity and provides a new approach to treat type 2 diabetes [207-209]. Polymorphisms in the HSD11B1 gene that encodes 11β-HSD1 have been associated with type 2 diabetes [203] and hypertension [204, 205]. On the other hand, a polymorphism that predicts 11β-HSD1 deficiency may protect against obesity and its metabolic consequences because of impaired regeneration of cortisol in adipose tissue [180]. 11β-HSD1 inhibition is a tempting target for treatment of the metabolic syndrome and its complications. Selective 11beta-HSD1 inhibitors in rodents cause weight loss, improve insulin sensitivity and delay progression of cardiovascular disease [210-212]. Pharmacological inhibition of 11b-HSD1 with the anti-ulcer drug carbenoxolone has provided evidence that cortisol regeneration influences insulin sensitivity, particularly glycogen turnover in healthy human subjects and in patients with type 2 diabetes [207, 208]. This corroborated the notion that the enzyme may be an attractive option to treat the metabolic disease [108, 190, 202, 212, 213]. Moreover, 11β-HSD1 gene knock-out (11β-HSD1-/-) mice exhibited cardioprotective phenotype with improved glucose tolerance and lipid profile, reduced weight and visceral fat accumulation in condition of chronic high-fat feeding [190, 200, 201, 214]. These data support the beneficial effects of 11β-HSD1 inhibitors to lower intracellular glucocorticoid levels and to treat both obesity and its metabolic complications.

136 Dehydrogenases

mediates this activation, locally within tissues, is 11β-HSD1 that converts inactive metabolite cortisone to active cortisol, thereby amplifying local glucocorticoid action [104]. 11β-HSD1 expression in adipose tissue was first reported by Monder and White [144] and it is thought to be a dehydrogenase. Studies in leptin-resistant obese rats revealed that obesity was associated with an increase in 11β-HSD1 in abdomenal adipose tissue [191]. In human subcutaneous abdominal adipose tissue, 11β-HSD1 activity is increased both *in vivo* and *in vitro* and the enhanced 11β-HSD1 activity in biopsies is accompanied by elevated 11β- HSD1 mRNA levels [108]). It is interesting to note, that increased subcutaneous adipose 11β-HSD1 is associated with insulin resistance in obesity, but it is not linked specifically with visceral fat accumulation or hypertension [192]. The mechanisms underlying the increase in adipose 11β-HSD1 activity in obesity and metabolic syndrome are still not fully inderstood. 11β-HSD1 transcription is regulated by many factors like cytokines, sex steroids, growth

The key question is whether increased 11β-HSD1 in adipose tissue is a cause or a consequence of obesity and it is associated with metabolic syndrome. In order to determine this, mice over-expressing 11β-HSD1 selectively in adipose tissue have been generated, using the adipocyte fatty acid binding protein (aP2) promoter [196, 197]. The adiposeselective 11β-HSD1 transgenic mice exhibited elevated intra-adipose, but not systemic corticosterone levels, as well as the major features of the metabolic syndrome-abdominal obesity, hyperglycaemia, insulin resistance, dyslipidaemia and hypertension. Conversely, transgenic mice with overexpression of 11β-HSD1 in liver showed an attenuated metabolic syndrome with modest insulin resistance and hypertriglyceridemia, hypertension and fatty liver, but with normal body weight [198]. 11β-HSD1-knock-out mice fed on a high-fat diet are protected from obesity and metabolic complications [199-201]. Recently, polymorphisms in *HSD11B1*, the gene encoding 11β-HSD1, have been associated with components of the metabolic syndrome [186, 202-205]. Moreover, subjects with single nucleotide polymorphisms (SNPs) in *HSD11B1* gene exhibit increased adipose 11β-HSD1 expression and increased whole-body 11β-HSD1 activity, associated with increased prevalence of the metabolic syndrome. These findings strengthen the view that variations in 11β-HSD1 activity influence the metabolic profile and provide a new evidence that *HSD11B1* gene

hormone, insulin and induced weight loss [193-195].

influence enzyme activity *in vivo* [186].

**10. 11β-HSD and metabolite syndrome - clinical importance**

Based on human data and genetic models, 11β-HSD1 seems to be cause and promising pharmaceutical target for the treatment of metabolic disease. In mice, the increased enzyme activity in adipose tissue enhances local glucocorticoid levels and produces a metabolic syndrome [196], whereas the decreased enzyme activity protects against obesity and the metabolic syndrome [200, 201]. In human, 11β-HSD1 expression is elevated in adipose tissue in obesity [206], whereas inhibition of 11β -HSD1 enhances insulin sensitivity and provides a new approach to treat type 2 diabetes [207-209]. Polymorphisms in the HSD11B1 gene that encodes 11β-HSD1 have been associated with type 2 diabetes [203] and hypertension [204, 205]. On the other hand, a polymorphism that predicts 11β-HSD1 deficiency may protect


**4. Prostaglandins and pelvic pain**

Endometriosis has been traditionally included among the most important causes of pelvic pain in women of reproductive age. A recent case-control study described that 73% of women with endometriosis reported experiencing abdominopelvic pain, dysmenorrhea, or menorrhagia compared with only a 20% of the controls (Ballard et al., 2008).

The role of PGs has been examined in women suffering from heavy menstrual bleeding and dysmenorrhea (Maybin et al., 2011). Dysmenorrhea is defined as the excessive pain during menstruation; and menorraghia or heavy menstrual bleeding is the excessive menstrual blood loss during the menstrual periods. These may be primary disorders or secondary to endometrial pathology such as endometriosis (Maybin et al., 2011; Tietjen et al., 2006).

Involvement of Prostaglandins in the Pathophysiology of Endometriosis 123

One of the most inhabilitating symptoms of endometriosis for carrying a normal life is the elevated pelvic pain patients experience. It includes pain before and during periods, during sexual intercourse, while urinating or defecating and during menstruation. It has been of great importance to provide the patient with a better quality of life ameliorating pain symptoms. Elevated concentrations of PGE2 in peritoneal fluid from endometriosis patients is the major

There have been done a large number of studies focusing on the inhibition of COXs activity. Whether it is blocking simultaneously COX-1 and COX-2 or with selective COX-2 inhibitors,

NSAIDs, among them: ibuprofen, naproxen, diclofenac or aspirin; are used primarily for pain treatment, from a headache to menstrual cramps, from a backache to treating an inflammation due to a sprain. These NSAIDs are non-selective COX inhibitors; this means that they prevent the synthesis of PGs from both COXs. Of course, as a constitutively and ubiquitously expressed enzyme, COX-1 inhibition has side effects that should be avoided. Gastrointestinal ulcer is not a rare effect after long term inhibition of COX-1 (Wadman,

Celecoxib belongs to the family of NSAIDs with high selectivity for COX-2 inhibition. Other coxibs have been developed too (rofecoxib, valdecoxib) but were withdrawn from the market in the mid 2000s by the Federal Drug and Food Administration (FDA) of the United States of America because they were proven to have serious cardiovascular adverse events. Coxibs were and are mostly used in arthritis; the one still available for purchase, celecoxib, is prescribed in familial adenomatous polyposis and as an adjuvant in breast cancer (Basu et al., 2006; Falandry et al., 2009; Iwama, 2009; Jankowski & Hunt, 2008; Lynch et al., 2010). Celecoxib has not been approved yet for the treatment of endometriosis. There is one study that evaluated the effectiveness of a COX-2 specific inhibitor on relieving pain symptoms associated to endometriosis after a conservative surgery. Cobellis and coworkers demonstrated in their study that rofecoxib was effective for the management of pain and no

Ferrero and coworkers, recently published a review where they compilated information on the use of AIs and how they contributed to diminish endometriosis related pain. This systematic study shows that the AIs, letrozole and anastrozole, are effective in treating pain symptoms; when withdrawn symptoms reappear, but they cannot be used for a long term therapy because of the adverse effects these compounds have on bone density (Ferrero et al., 2011). Endometriosis patients need chronic treatment, and this could be achieved combining

cause thought to be involved in pain and inflammation processes (Wu et al., 2002).

**5.1 PGE2 synthesis inhibition and pain treatment**

the ultimate goal is to lower the concentrations of PGE2.

2007); this is why COX-2 selective inhibitors have been developed.

recurrence occurred during the six months of treatment (Cobellis et al., 2004).

AIs with a hormonal therapy to reduce the loss of bone density (Ferrero et al., 2011).

**5.2 PGE2 synthesis inhibition and the control over endometriosis development**

There have been conducted several studies in which AIs were used and the progression of endometriosis was evaluated. When letrozole or anastrozole were added to endometrial epithelial cells from endometriosis patients in culture, cell proliferation was inhibited and apoptosis augmented (Meresman et al., 2005). When the same two compounds were used for the treatment of surgically induced endometriosis in mice, not only cell proliferation was diminished and apoptosis increased within the endometriotic like lesion, but PGE was also

The pelvic pain of dysmenorrhea has been demonstrated to be mediated through the action of PGE2 and a direct relationship between the severity of dysmenorrhea and the production of PGs has been observed in endometriosis (Coco, 1999; Koike et al., 1992; Nasir & Bope, 2004). As well, analysis of menstrual fluid from women suffering from dysmenorrhea revealed augmented levels of PGE2 and PGF2α (Dawood & Khan-Dawood, 2007a, 2007b; Lumsden et al., 1983).

Increased synthesis of PGE2 and PGF2α in the endometrium has important implications for menstruation (Baird et al., 1996). PGE2 is a potent vasodilator leading to increased oedema and contributing to pain at time of menstruation. Increased COX-2 and enhanced PGE2–EPinduced cAMP production has been found by Smith and coworkers in the endometrium of women with objective heavy menstrual bleeding. These authors suggest that the increased expression of the rate-limiting COX enzymes in the endometrium of women with heavy menstrual blood loss will lead to an increase in PG production and to a magnified inflammation (Smith et al., 2007).

In addition, there is well reported evidence for the hyperalgesic properties of PGs; and EP receptors have been shown elevated in sensory neurons that lead to increased pain perception (Bley et al., 1998; Levine & Taiwo, 1990). Wienecke and coworkers also demonstrated that PGE2 induces headache in healthy subjects by sensitization of cranial perivascular sensory afferents (Wienecke et al., 2009). Therefore, PG inhibition usually resolves the pain and many studies have demonstrated the efficacy of NSAIDs and specific COX-2 inhibitors in relieving dysmenorrheic pain (Coco, 1999; Hayes & Rock, 2002). Suprofen, ibuprofen and acetaminophen were shown to be efficient not only for pain relief but for menstrual fluid PGs suppression as well (Dawood & Khan-Dawood, 2007a, 2007b).

A recent study suggests that the major modality to substantially alleviate pain in endometriosis is suppression of ovarian function and induction of a steady hormonal condition, anovulation and, eventually, amenorrhea (Vercellini et al., 2011). Hormonal manipulation and surgery have been found to be efficient in the management of pelvic pain associated to endometriosis (Vercellini et al., 2011). Oral contraceptives, GnRH agonists, danazol and progestins have been shown to reduce the production of PGs, which are responsible in large part for pelvic pain (Crosignani et al., 2006; Venturini et al., 1997).

Given that, further investigations should focus on how to inhibit the production of PGs in endometriosis to control the pain and the development of the pathology.


*4.1. Parameter Setting*

In the following experiments, the parameters are setting as *m* = 50, *m*1 = 40, *m*2 = 10, *α* = 1, *β* = 2, *ρ*=0.05, *Q*=1, *N*max = 2000, *g* = 0.5 for ACO-MaxIt, *R*0 = 0.6 while *R* ∈ (0, 1) for ACO-Rand. Twenty cycles of experiments are carried out for each example independently. Then, minimum solution *S*min, maximum solution *S*max, average solution *Savg*, standard deviation *Std*, and average runtime *Tavg* for different examples of 20 times are given in the tables, where minimum solution *S*min, maximum solution *S*max, and average solution *Savg* are the percentage value deviation against the known optimal solution. The minimum value in each result is bolded in the tables.

#### *4.2. Experimental Results Comparison Based on AS*

First, we employ AS to three kinds of opposite based ACO, called AS-Index, AS-MaxIt, and AS-Rand, to verify the effectiveness of the improved algorithm. Twenty-six TSP examples are divided into three main categories, the small-scale, the medium-scale, and the large-scale according to the number of cities, respectively.

Small-scale city example sets are selected from TSPLIB, including eil51, st70, pr76, kroA100, eil101, bier127, pr136, pr152, u159, and rat195. The results are shown in Table 1.

From Table 1, the proposed AS-Index, AS-MaxIt, and AS-Rand show superior performances over AS for the examples, st70, kroA100, eil101, bier127, pr136, and u159. For other examples, the proposed algorithms outperform AS in general, except eil51. Meanwhile, stability by standard deviation is the not the primary concern when evaluating an algorithm. Compared among three proposed algorithms, AS-MaxIt illustrates superior performances for most cases.

To show more details in the process of evolutionary, curves for different stages are given in Figure 1 based on the case of kroA100.

According to Figure 1, AS shows faster convergence speed than the other three proposed methods in early iterations, while AS-Index, AS-MaxIt, and AS-Rand all surpass AS in average path length in later iterations. Meanwhile, AS-MaxIt performs best among all these algorithms, which also verifies the results in Table 1.

In the early stage, opposite path information introduced by OBL has negative impact on the convergence speed for all three proposed algorithms; however, it can provide extra information which guarantees the boost in accuracy for the later stage. The results lie in the fact that introducing extra information of opposite paths help to increase the diversity of the population, which balances the exploration and exploitation of solution space.

Medium-scale city example sets are selected from TSPLIB, including kroA200, ts225, tsp225, pr226, pr299, lin318, fl417, pr439, pcb442, and d493. The results are shown in Table 2.

From Table 2, it can be found that the proposed algorithms outperform AS in all the cases except ts225. Among all three algorithms, AS-Index and AS-MaxIt perform similarly, but better than AS-Rand generally. From these results, it can be seen that, with the help of extra information from opposite paths, three proposed methods all improve the original AS in solution accuracy.

**Table 1.** Results comparison for small-scale example sets.

Taking fl417 as the example, evolutionary curves in detail for different iteration stages are given in Figure 2, accordingly. According to Figure 2, AS also converges faster than the other three proposed methods in early iterations—for example before 1000 iterations. In addition, in later iterations, the other three proposed methods all exceed AS in average path length. This further validates the conclusions obtained from the analysis of Figure 1.

**Figure 1.** Evolutionary curves for different iteration periods based on kroA100.

**Table 2.** Results comparison for medium-scale example sets.

**Figure 2.** Evolutionary curves for different iteration periods based on fl417.

Large-scale city example sets are selected from TSPLIB, including att532, rat575, d657, u724, vm1084, and rl1304. The results are shown in Table 3.

From Table 3, it can be discovered that the AS-Index shows the obvious superior performance over all the other algorithms, which reveals a fact that the advantages of AS-Index appears as the scale of the example increases based on these results.

Taking vm1084 as the example, evolutionary curves in detail for different iteration stages are given in Figure 3, accordingly. According to Figure 3, AS still shows faster convergence speed than the other three proposed methods in early iterations, but AS-Index outperforms all the others in the end.

Based on all the tables and figures, it can be found that, in most scenarios, at least one of AS-Index, AS-MaxIt, and AS-Rand outperforms AS in average path length. For small-scale examples, AS-MaxIt shows better performance, while, for medium-scale cases, AS-Index and AS-MaxIt perform similarly better than the others. For large-scale city sets, AS-Index is the best algorithm, while AS-Rand ranks in the middle for most cases regarding figures, and it illustrates its stability to some extent. Therefore, it can be drawn that the strategy to introduce OBL into AS provides more information, namely better exploration capability, which explains the superiority of these proposed methods over the original AS. By comparing the results of the running time from Tables 1–3, we can also find that the running time of the three improved algorithms is not significantly increased compared with AS. It also validates our previous discussion on time complexity.

#### *4.3. Experimental Results Comparison Based on PS-ACO*

To further verify the effectiveness of the proposed algorithm, we employed another PS-ACO to three kinds of opposite based ACO, PS-ACO-Index, PS-ACO-MaxIt, and PS-ACO-Rand to verify the effectiveness of the improved algorithm. The number of ants is 50, and the other parameters are the same as in [26]. Twelve sets of TSP examples are eil51, st70, kroA100, pr136, u159, rat195, tsp225, pr299, lin318, fl417, att532, and d657. The results are given in Table 4.

From Table 4, the proposed PS-ACO-Index, PS-ACO-MaxIt, and PS-ACO-Rand show superior performances over PS-ACO for the examples, eil51, st70, rat195, tsp225, and pr299. For other examples, the proposed algorithms outperform PS-ACO in general, except lin318 and fl417. Compared among three proposed algorithms, PS-ACO-Rand illustrates superior performances for most cases. By comparing the results of the running time, we can also find that the running time of the three improved algorithms is not significantly increased compared with PS-ACO.

**Table 3.** Results comparison for large-scale example sets.

**Figure 3.** Evolutionary curves for different iteration periods based on vm1084.

**Table 4.** Comparisons of PS-ACO, PS-ACO-Index, PS-ACO-MaxIt, and PS-ACO-Rand.


2. Description of the Switched Generalized Function Projective Synchronization and Two New Hyperchaotic Systems

Consider the following drive and response systems:

$$\begin{cases} \dot{x} = f(x) \\ \dot{y} = g(y) + u(t, x, y) \end{cases} \tag{1}$$

where x, y ∈ Rn are the state vectors, f(x), g(x) : Rn → Rn are differentiable vector functions, and u(t, x, y) is the controller vector to be designed.

The error states between the drive and response systems are defined as

$$e\_i = y\_i - \phi\_i(x)x\_j, (i, j = 1, 2, \dots, n, \ i \neq j) \tag{2}$$

where φi(x) : Rn → R(i = 1, 2, ..., n) are scaling function factors, and are continuous differentiable bounded , which compose the scaling function matrix φ(x), φ(x) = diag{φ1(x), φ2(x), ..., φn(x)}.

Definition 1. For the two systems described in Equation (1), we say that they are switched generalized function projective synchronous with respect to the scaling function matrix φ(x) if there exists a controller vector u(t, x, y) such that

$$\lim\_{t \to \infty} \|e\_i\| = \lim\_{t \to \infty} \|y\_i - \phi\_i(x)x\_j\| = 0, (i, j = 1, 2, \dots, n, i \neq j) \tag{3}$$

which implies that the error dynamic system (2) between the drive and response systems is globally asymptotically stable.

Remark 1. For the SGFPS, we define i = j in the above Equation (3). If i = j, the SGFPS degenerates to the GFPS [25].

Recently, Li *et al*. [45] proposed a new hyperchaotic Lorenz-type system described by

$$\begin{cases} \dot{x} = a(y - x) \\ \dot{y} = bx - xz - cy + w \\ \dot{z} = xy - dz \\ \dot{w} = -ky - rw \end{cases} \tag{4}$$

where a, b, c, d, k and r are positive constant system parameters. When a = 12, b = 23, c = 1, d = 2.1, k = 6 and r = 0.2, and with the initial condition [1, 2, 3, 4]T , system (4) is hyperchaotic and its attractor is shown in Figure 1.

Lately, Dadras *et al*. [46] reported the following four-wing hyperchaotic system, which has only one unstable equilibrium

$$\begin{cases} \dot{x} = ax - yz + w \\ \dot{y} = xz - by \\ \dot{z} = xy - cz + xw \\ \dot{w} = -y \end{cases} \tag{5}$$

where a, b and c are positive constant system parameters. When a = 8, b = 40 and r = 14.9, and with the initial condition [10, 1, 10, 1]T , system (5) is hyperchaotic and its attractor is shown in Figure 2.

Figure 1. Hyperchaotic attractor of system (4) with a = 12, b = 23, c = 1, d = 2.1, k = 6 and r = 0.2: (a) x − y − z space; (b) x − y plane; (c) x − z plane; (d) x − w plane.

Figure 2. Hyperchaotic attractor of system (5) with a = 8, b = 40 and r = 14.9: (a) x − y − z space; (b) x − y plane; (c) x − z plane; (d) y − w plane.

For more information on the dynamical behaviors of these two systems, please refer to [45,46].


**5.2 In situ upgrading**

Other strategy to improve the properties of the bio-oil obtained by pyrolysis process is the modification in situ, i.e., inside the own reactor by change of the variables and the parameters. Two strategies that have gained prominence and show good results in obtaining a bio-oil with good qualities are hydrogenation and catalytic pyrolysis, and the hybrid process with both.

The addition of H2 as a pyrolysis gas agent in the absence of catalysts promotes hydrogenation reactions mainly with the volatiles released and may have various effects such as increased formation of less oxygenated species, greater selectivity in phenolic compounds among others. For example, the effect of adding hydrogen in the carrier gas (up to 15% vol) in the pyrolysis of food waste in a dawndraft reactor promoted the formation of a bio-oil more selective in aromatic, phenolic compounds while reducing the amount of heterocycline nitrogenous species such as (quinoline) [55]. Other biomasses, such as poplar wood showed the same behavior upon pyrolysis in H2 atmosphere (6% vol) compared to pure N2 atmosphere obtaining higher yield and selectivity in phenolic species [56]. Another important effect of the addition of H2 in the pyrolysis process is that, regardless of the presence of catalysts, it can promote the production of bio-oil with higher H/C ratio which is highly beneficial for the use of bio-oil as liquid fuel [57].

Recycling the pyrolysis gas to the reactor as a reactive atmosphere is a potential alternative, although its effect is not yet clear. When fast pyrolysis of biomass takes place under a N2 atmosphere, the main gaseous products of pyrolysis are CO, CO2, H2, CH4 and low levels of light hydrocarbons, therefore, when this gas is recycled to the reactor, it can promote a reactive atmosphere and not more inert. Studies in fluidized bed reactors, in the temperature range of 430–500°C, show that the increase in gas recycling rates can lead to a decrease in the production of organic liquids, but some biomasses, such as oak and switch grass, show good results. Deoxygenation effect and HHV increase in your bio-oil with gas recycling rates of up to 80%. However, other biomasses, such as *Pennycress Presscake* and pine wood, had no effect on the yield and composition of bio-oil with the pyrolysis gas recycled in the reactor [58, 59].

Among all the in situ alternatives to improve the obtainment of higher quality bio-oil, catalytic pyrolysis stands out as the most promising. The process basically consists of the insertion of a solid catalyst in the pyrolysis reactor capable of acting in the set of pyrolysis reactions, promoting the formation of bio-oil with better properties and selectivity in the components of interest of interest. Several authors have already published works testing different catalysts, in different process configurations, attesting to diverse improvements in obtaining bio-oil. Among these improvements in the use of catalysts in the fast pyrolysis process, it is worth mentioning: [60].

Despite promising experimental results, the fast pyrolysis process using catalysts still needs to overcome some obstacles to reach the industrial level. The basis for these challenges is the development of catalytic reactors capable of supporting the pyrolysis atmosphere without expressive rates of catalyst deactivation, expanding the selection of desired products in bio-oil, in addition to efficient developments in the recovery and regeneration of catalysts [41, 60]. **Table 4** below shows a set of

### **Table 4.**

*Some research about catalytic pyrolysis.*

works on catalytic pyrolysis of biomass at different scales, the characteristics of the catalysts used as well as the yield in bio-oil.