**5. Conclusions**

In this paper, considering that the GA representation contains physical interpretations and complete information of incident signals, we use the idea of the traditional ESPRIT algorithm to find multiple EM signals in the direction finding method in GA. In particular, the model for DPULAs was built in GA and GA-ESPRIT was successfully derived using new calculation rules to achieve the two-dimensional DOA estimation. Compared with the previous ESPRIT algorithms, due to the robustness to sensor-model error and correlated noise, our proposed approach has potential in many practical situations, such as military radar in difficult environments. According to the experimental results, we have confirmed that the GA-ESPRIT has improved accuracy in two-dimension DOA estimation and can resist environmental interference to some extent. More importantly, the proposed algorithm achieves a reduction of more than 1/3 of the memory requirements while the time complexity is also greatly decreased.

Future works on the GA-ESPRIT will include polarization parameter estimation by optimizing matrix operations in GA and the ability of DOA recognition when facing coherent EM signals. It is expected that the proposed GA-ESPRIT will be an efficient DOA estimator.

**Author Contributions:** Conceptualization, R.W. and W.C.; methodology, R.W. and Y.W.; software, Y.W.; data curation, Y.W. and Y.Y.; writing—original draft preparation, Y.W. and Y.L.; project administration and funding acquisition, R.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Natural Science Foundation of China (NSFC) under Grant No. 61771299, 61771322 and Shenzhen foundation for basic research JCYJ<PHONE_NUMBER>0815125.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.


2. Objectives

comply with the requirements for emission reduction. The energy independence and security act of 2007 (US) requires the production of 36 billion gallons of renewable fuels including ethanol-based fuels by 2022. This constitutes a 75% increase in their production over the next

Biofuels constitute a renewable source of energy derived from biomass (the primary source). These include all three states of matter: solid, liquid and gas. They fall into the following categories, for liquid biofuels: (a) bioalcohols, (b) vegetable oils and biodiesel, and (c) biocrude and synthetic oils. It is expected that their demand in the future will rise because they replace petroleum [3]. Biofuels can be produced from bio-based materials through the biochemical processes such as pyrolysis, gasification, or liquefaction. Thermal chemical reforming of biomass concerns the process of catalytic or non-catalytic pyrolysis and gasification with a goal of maximizing in the production of energetically exploitable liquids or gases. Biofuels include

Oxygenated fuels including alcohols constitute one of the categories of the biofuels that have attracted research for many years due to their cleaner properties when blended with the conventional fuels [5]. Oxygenated fuels drew the attention of many researchers, due to their ability to drastically reduce the particulate matter (PM) emission without altering the emission levels of NOx, UHC, and CO [6, 7]. Oxygenated additives such as ethers, esters, alcohols have been added to diesel fuel [8]. Alcohols are used in internal-combustion engines as alternative fuels. If the source of alcohol is based on biomass then one can reduce greenhouse gas emissions, and exhaust emissions as well as enhance the overall energy efficiency [1, 3, 9–11]. N-butanol possesses certain qualities that make it more suitable to use in internal combustion engines than ethanol or methanol. These include a higher heating value of (28.4 MJ/L) for n-butanol and (21.2 MJ/L) for ethanol; higher cetane number (CN), lower auto-ignition temperature, better flow ability. For ethanol-diesel blend, an emulsifying agent is used to satisfy the homogeneity of the ethanol-diesel fuel mixture to prevent phase separation [12]. However, n-butanol is easily miscible in diesel fuel [13]. Within the functional groups of butanol isomers, n-butanol has the highest flame speed which enhances the combustion process [14]. Some researchers have argued that n-butanol-diesel blends prolong the ignition delay (ID), thereby affecting the combustion duration in internal combustion engines. However, others have stated that burning n-butanol-diesel fuel blends reduces emissions [14]. Therefore, comparisons are necessary on how the combustion characteristics differ between blends of n-butanol

In internal combustion engines such as diesel engines, diesel fuel is atomized into droplets using injectors at high pressure. These droplets entrain air as they evaporate into proportions that are combustible in the combustion chamber. The air temperature and pressure are above the fuel's ignition point. Therefore, after a short while: or ID the mixture auto ignites. ID is a time lag that is influenced by the physical chemical properties of the fuels measured in time (s) or CADs in engine cycles. This affects the combustion process in internal combustion engines

The simultaneous use of biodiesel, ethanol, and butanol in diesel is an interest that has arisen recently by researchers. However, since biodiesel is characterized by high viscosity, lubricity,

including combustion duration and in-cylinder pressure and temperatures.

bioethanol and biomethanol, Fischer-Tropsch liquids and biogas [4].

and biodiesel in diesel fuel, respectively.

14 years [2].

38 Improvement Trends for Internal Combustion Engines

The general objective is to reduce the negative impacts of petroleum oil-based fuels in reciprocating engines on the environment through the use of oxygenated (alcohol) blends, without deteriorating the engine performance. The specific objective of the study is as follows:


**4. Results**

It was found out that mean age of the patients was 66.03 years (SD= 11.33), 50.7 % was male, 58.7% belonged to ≥ 65 age group, 72.0% was married, 49.7% was illiterate, 46.3 % was housewives, 27.9% was retired, 74.3% had a moderate income level. It was explored that 30.7% of the patients had the disease for ≥12 years (disease length ≥12 years), 38.6% moderate COPD, 77.3% was repeatedly hospitalized and 89.3% said to use their medications regularly and 47.0% identified their health condition as bad.

Table 1 demonstrates dyspnea severity and fatigue-experience of the patients. All of the patients said to have dyspnea and the analysis made using MRC dyspnea scale revealed that 73.3%of the patients had severe dyspnea. 99.3% of the patient told to experience fatigue. It was explored that 49.0% of the patients had always fatigue experience.

Their total fatigue score'mean was 60.36+ 20.57, mean score of activities of affected by fatigue was 40.22 + 14.37, and MRC dyspnea mean score was 3.59 + 1.31.

Evaluation of Dyspnea and Fatigue Among the COPD Patients 265

< 44 51.93±22.22 35.60±13.17 2.73±1.48 45-64 58.30±22.15 38.60±15.68 3.38±1.34 65+ 62.35±19.18 41.61±13.49 3.79±1.23 F, p 2.66 >0.05 2.30 >0.05 6.89 >0.05

Female 61.72±20.00 41.00±14.17 3.64±1.29 Male 59.03±21.09 39.46±14.58 3.54±1.33 t, p 0.156 >0.05 0.001 >0.05 0.828 >0.05

1-3 Years 53.05±22.81 35.23±16.20 3.29±1.36 4-7 Years 59.57±19.61 40.61±13.44 3.46±1.35 8-11Years 62.77±17.46 42.00±12.01 3.90±1.28 12+ Years 66.51±18.57 43.77±13.21 3.76±1.20 F, p 7.358 <0.05 6.187 <0.05 3.493 >0.05

Stage I(Mild) 52.58±21.65 54.46±15.28 2.96±1.28 Stage II Moderate) 56.30±21.25 37.64±14.75 3.26±1.27 StageIII(Severe) 67.21±17.22 44.97±12.19 4.14±1.13 Stage IV(Very severe) 69.78±15.60 46.43±10.43 4.34±1.11 F, p 10.615 <0.05 10.413 <0.05 17.928 <0.05

No 52.29±23.52 34.57±16.26 3.11±1.37 Once 55.76±19.89 36.63±14.05 3.22±1.24 Twice 61.31±21.28 40.96±14.60 3.82±1.23 Three times 67.77±15.82 45.40±11.03 3.97±1.15 Four times and more 71.42±11.41 48.40±7.92 4.22±1.17 F, p 9.695 <0.01 10.631 <0.01 8.931 <0.01

Good 47.00±23.35 32.96±16.48 3.04±1.33 Normal 58.39±21.27 38.19±15.02 3.45±1.29 Bad 62.02±18.90 40.91±13.49 3.63±1.31 Very Bad 76.60±9.50 50.25±6.52 4.86±0.52 F, p 8.986 <0.01 5.953 <0.01 8.054 <0.01

Sometimes 46.50±23.90 31.58±16.61 2.93±1.36 Often 57.31±17.58 37.45±12.25 3.16±1.20 Always 67.95±17.27 45.49±12.39 4.13±1.13 F, p 29.305 <0.01 26.231 <0.01 2.864 <0.01 Table 2. Mean scores of dyspnea, fatigue levels and levels of the daily activities affected by

**Fatigue X ± SD**

**Mean Scores**

**Activities affected by fatigue X ± SD**

**Dyspnea X ± SD**

**Characteristics**

**Age**

**Gender**

**Disease Length**

**Disease Severity**

**Health Condition**

**Frequency Of Fatigue Experience**

fatigue accoding to some characteristics

**Repeated Hospitalization**

Table 1. Dyspnea severity and fatigue experience of the patients

It was concluded in our research that there was a positive correlation between dyspnea and fatigue (r=0.636, p<0.01) and as dyspnea scores increased so did mean fatigue scores. Also, there was a significant negative correlation between the measured FEV1 values of the patients and dyspnea scores (r=-.341 p<0.01) and fatigue scores (r=-.260 p<0.01).

Table 2 demonstrates mean scores of dyspnea, fatigue levels and levels of the daily activities affected by fatigue accoding to some socio demographic and disease characteristics.

It was explored that there was not any statistically significant difference between fatigue levels, levels of the daily activities affected by fatigue and dyspnea scores in terms of age and sex(p>0.05). It was found out that there was statistically significant difference between fatigue level and levels of the daily activities affected by fatigue and disease length (year) (p<0.05).

It was found out that there was statistically significant difference between fatigue level and levels of the daily activities affected by fatigue and dyspnea according disease severity, the number of repeated hospitalization, patients' perception about their health condition and frequency of fatigue-experience. High fatigue score and score of the daily activities affected by fatigue were presented by those who had the disease for ≥ 12 years. High fatigue score and score of the daily activities affected by fatigue and dyspnea score were presented by who fourth stage of COPD, who were repeatedly, had hospitalized for ≥ 4 times a year and who identified their own health condition as very bad and frequency of fatigue as always.


*3.4. Trypanosomatidae*

#### 3.4.1. *Lotmaria passim*

*Lotmaria passim* is a trypanosomatid with a single flagellum, capable to colonize the digestive tract of *A. mellifera* [185,186]. The parasite spreads within the colony by fecal contact, and the transmission occur via the oro-faecal route [187,188]. The infection impacts the colony by altering behaviour and lifespan of the infected bees [141,189]. *L. passim* is spread worldwide. The colonization implied the replacing of the other honey bee trypanosomatids *Crithidia mellificae* [190,191].

Besides, *L. passim* is present in bumblebee species (Supplementary Table S1) namely th South American *B. funebris, B. dalhbomii, B. opifex, B. ruderatus* and *B. terrestris* [92,147].

*L. passim* was found also in the small hive beetle, *A. tumida*, as a possible result of the feeding behaviour of this scavenger [81,148].

#### 3.4.2. *Crithidia mellificae*

*Crithidia mellificae* is another trypanosomatid which can replicate in the honey bee intestine to survive [185,186]. Transmission route and impact on bees are very similar to the other parasite *L. passim* [187,188,192]. *C. mellificae* was almost completely replaced by *L. passim* and its infection has been rarely observed [185,193,194].

Despite that, one spillover case was observed in *A. tumida*, that live in contact with bee colony debris [81].

#### 3.4.3. *Crithidia bombi*

*Crithidia bombi* is a trypanosomatid infecting *B. terrestris* colonies [195,196]. The infection occurs during the external activity of the forager bumblebees [197,198] and, back to the nest, it spreads by fecal contamination to the other workers [199,200]. *C. bombi* may harm the bumblebee populations as hibernating queens may reduce the success in founding the colonies and remarkably lower their fitness [201]. On queen emergence from the diapause, *C. bombi* infections grow together with the colony that is being established [200].

*C. bombi* is transmitted during the foraging activity. The pathogen was detected in the wild on *A. vaga* and *O. bicornis* individuals [37] and in small hive beetles collected from the nest of honey bee colonies [148]. Artificial infections showed that *C. bombi* can replicate in *O. lignaria*, *M. rotundata* and *H. ligatus* [202,203].

#### *3.5. Neogregarine*

#### Apicystis bombi

*Apicystis bombi* is a parasite found primarily in bumblebees. It was found to occur also in honey bees from Europe and North America [204–206]. Upon the ingestion of the oocytes by the bee, the sporozoites develop and migrate to the fat body, where they develop, multiply and disrupt the adipose tissue. The infection increases the worker mortality rate and, due to the fat body disruption, both queen survival to hibernation and colony foundation success are impaired [84,207,208].

Likely, the infection occurs via contact on contaminated flowers [208]. Indeed, *A. bombi* was found in wild species also, namely *A. vaga*, *A. ventralis*, *H. truncorum*, *O. bicornis* and *O. cornuta* [37].


**Author details**

B.C. Anu and Ramanuj Vishwakarma\* Department of Entomology, Bihar Agricultural University, Bhagalpur, India

\*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.


**Photonic Microwave Signal Processing Based on Opto-VLSI Technology**

Feng Xiao1, Kamal Alameh1,2 and Yong Tak Lee2,3

*1Electron Science Research Institute, Edith Cowan University, 2Department of Nanobio Materials and Electronics, Gwangju Institute of Science and Technology, 3Department of Information and Communications, Gwangju Institute of Science and Technology, 1Australia 2,3Korea*


**7. Conclusions**

Water industry needs to address the spent membrane management, keeping in mind to provide integrated solution. The AOPs are effective techniques to treat high degree industrial wastes. The aspects of depolymerisation and mineralisation were investigated. The variables studied have improved the degree of depolymerisation. Hybrid systems based on different techniques needs to be developed. These techniques have bright prospect in nearby future due to ongoing research initiatives. Best Available Technologies needs to be explored to supplement the conventional biological and chemical methods.