\r\n\tThe objective of the proposed book is to give a multi-perspective view on role of autophagy in injury, infection and cancer diseases. The book chapters aim to elucidate autophagy pathways in sustaining the host defense mechanisms, adaptive homeostasis as well as in remodeling and regeneration events that are essential for recuperation of the affected tissues. A specific subject for discussion will be up-regulation and/or impairment of autophagy and crinophagy in phagocytes/granulocytes and adult stem cells.
\r\n
\r\n\tRationale: \r\n\tThe cell/tissue responses to acute stress, trauma/injury or pathogens are mediated by expression and release of plethora of paracrine and endocrine effectors including DAMPs, PAMPs and inflammatory cytokines, chemokines, defensins, and reactive intermediate species. These effectors drive the integrative interactome constituted by hubs of the acute phase response modules, the inflammatory response modules, the module of the adaptive homeostatic response in the damaged parenchymal cells, vascular cells, immunocompetent cells and emerging stem cells. Among these defense mechanisms is autophagy – the lysosomal pathway for processing of compromised cell constituents and/or bacterial and viral pathogens. In this light, explication of the role of autophagy in cellular pathology may arouse R&D of new modalities for management of devastating diseases such as injury, acute infections or cancer.
",isbn:null,printIsbn:"979-953-307-X-X",pdfIsbn:null,doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!1,hash:"3daed6048bc8ff8368c4279558f109d7",bookSignature:"Dr. Nikolai Gorbunov",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/7997.jpg",keywords:"Autophagy-related Genes, Autophagy-related Proteins, Organelle Network, Signaling Mechanisms and Modulators, Cell Damage, Tissue Damage, PAMP and DAMP, Inflammasome, Autophagy Evasion, Cancer Stem Cells, Cancer Target Therapy, Disease",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:0,numberOfDimensionsCitations:0,numberOfTotalCitations:0,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"September 23rd 2019",dateEndSecondStepPublish:"October 14th 2019",dateEndThirdStepPublish:"December 13th 2019",dateEndFourthStepPublish:"March 2nd 2020",dateEndFifthStepPublish:"May 1st 2020",remainingDaysToSecondStep:"a year",secondStepPassed:!0,currentStepOfPublishingProcess:5,editedByType:null,kuFlag:!1,biosketch:null,coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"180960",title:"Dr.",name:"Nikolai",middleName:null,surname:"Gorbunov",slug:"nikolai-gorbunov",fullName:"Nikolai Gorbunov",profilePictureURL:"https://mts.intechopen.com/storage/users/180960/images/system/180960.jpg",biography:"Dr. Gorbunov obtained his Ph.D. degree in Biology from the Russian Academy Sciences. Then, he was a recipient of the NRC NAS (http://sites.nationalacademies.org/pga/rap/) and the Department of Energy fellowship awards to pursue postdoctoral training in translational science at the University of Pittsburgh and the Pacific Northwest National Laboratory (https://www.emsl.pnl.gov/emslweb Washington, USA). His translational research area has encompassed molecular pathology of trauma and countermeasures against acute radiation injury that was explored at the Walter Reed Army Institute of Research (http://wrair-www.army.mil) and the Uniformed Services University of the Health Sciences. His research interests are the disease-specific mechanisms driving alterations and defense responses in organelles, cells and tissues constituting biological barriers. With this perspective, the main objectives of his research are : i) to define the key components and pathways which regulate adaptive homeostasis and sustain intrinsic resistance to the harmful exposures and mediate recovery from the produced stress, cytotoxicity and damage; and (ii) to employ the acquired knowledge for advancement of injury-specific therapeutic modalities.",institutionString:"Henry M. Jackson Foundation for the Advancement of Military Medicine",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"2",totalChapterViews:"0",totalEditedBooks:"2",institution:{name:"Uniformed Services University of the Health Sciences",institutionURL:null,country:{name:"United States of America"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"6",title:"Biochemistry, Genetics and Molecular Biology",slug:"biochemistry-genetics-and-molecular-biology"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"270941",firstName:"Sandra",lastName:"Maljavac",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/270941/images/7824_n.jpg",email:"sandra.m@intechopen.com",biography:"As an Author Service Manager my responsibilities include monitoring and facilitating all publishing activities for authors and editors. From chapter submission and review, to approval and revision, copyediting and design, until final publication, I work closely with authors and editors to ensure a simple and easy publishing process. I maintain constant and effective communication with authors, editors and reviewers, which allows for a level of personal support that enables contributors to fully commit and concentrate on the chapters they are writing, editing, or reviewing. I assist authors in the preparation of their full chapter submissions and track important deadlines and ensure they are met. I help to coordinate internal processes such as linguistic review, and monitor the technical aspects of the process. As an ASM I am also involved in the acquisition of editors. Whether that be identifying an exceptional author and proposing an editorship collaboration, or contacting researchers who would like the opportunity to work with IntechOpen, I establish and help manage author and editor acquisition and contact."}},relatedBooks:[{type:"book",id:"5295",title:"Autophagy in Current Trends in Cellular Physiology and Pathology",subtitle:null,isOpenForSubmission:!1,hash:"e16382542f283b73017bdb366aff66ad",slug:"autophagy-in-current-trends-in-cellular-physiology-and-pathology",bookSignature:"Nikolai V. 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Venkateswarlu",coverURL:"https://cdn.intechopen.com/books/images_new/371.jpg",editedByType:"Edited by",editors:[{id:"58592",title:"Dr.",name:"Arun",surname:"Shanker",slug:"arun-shanker",fullName:"Arun Shanker"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"54156",title:"Performance Analysis of Three Routing Protocols in MANET Using the NS-2 and ANOVA Test with Varying Speed of Nodes",doi:"10.5772/66521",slug:"performance-analysis-of-three-routing-protocols-in-manet-using-the-ns-2-and-anova-test-with-varying-",body:'\n
1. Introduction
\n
The existing literature on MANETs is very extensive. An extremely comprehensive work is presented in Refs. [1, 2], which extensively covers most issues related to the subject, whereas in Refs. [3, 4], authors provide a brief introduction. MANET design issues such as a routing architecture in the light of the nature of MANETS, unidirectional link support, QoS routing, and multicast support are discussed in Refs. [5, 6]. In Ref. [7], the authors cover some of the same design issues as mentioned in Ref. [5], but they augment them with some additional ones, such as limited bandwidth, energy constrained operation, and limited physical security.
\n
Communication networks are evolving with a great pace witnessing increase in infrastructure and applications too. A mobile ad hoc network is the latest outcome in this research. The mobile ad hoc network, also known as MANET [8], is a network without any available infrastructure.
\n
Nodes are mobile and can move whenever and wherever they want, because there is no centralized control or any other infrastructure is needed in any MANET. Each node in an MANET must be capable of functioning as a router to relay the traffic of other nodes.
\n
A number of protocols have been developed for accomplish this task. Various dedicated routing protocols have been proposed to the Internet Engineering Task Force (IETF) MANET Working Group [8]. Some of these protocols have been studied, and their performances have been analyzed in detail. Broch et al. [9] evaluated four protocols using mobility and traffic scenarios similar to those we used. They focused on packet loss, routing message overhead, and route length. In Ref. [10], Johansson et al. compare three routing protocols, over extensive scenarios, varying node mobility, and traffic load. They focus on packet loss, routing overhead, throughput, and delay, and introduce mobility measures in terms of node relative speed. Finally, in Ref. [11], Das and coworkers compare the performance of two protocols, focusing on packet loss, packet end-to-end delay, and routing load. They obtained simulation results consistent with previous works and conclude with some recommendations for improving protocols. In this chapter, we measure and compare three performance parameter behaviors of two routing protocols, respectively, ad hoc on demand distance vector (AODV) [12] and destination-sequenced distance vector (DSDV).
\n
2. MANET routing protocols
\n
This is the leading routing protocol proposed so for in the category of on demand or reactive routing protocols. Unlike table-driven protocols, it does not maintain status of the network via continuous updates [13]. This approach assists in minimizing the flooded messages and also size of route tables. It was designed after a distance vector routing protocol (DSDV) but is much efficient than DSDV. Actually, AODV is a combination of DSDV and dynamic source routing (DSR). It has the actual on-demand technique of discovering the route and also route maintenance from DSR but uses sequence numbering and also the periodic beacons of DSDV. New routes are found through the process of RREQ and RREP where RREQ packets are broadcast and RREPs are unicast in nature. While route maintenance uses RERR packets for remedy of route breaks, routing information is kept afresh by the usage of sequence numbers, which is the idea borrowed from DSDV [14].
\n
The DSDV [15] is a proactive routing algorithm based upon a well-known classical distance vector algorithm of Bellman-Ford. Routing tables are maintained and updated accordingly, so broadcast periodic routing table update packets consume the bandwidth. So, the main weakness of DSDV is that when network grows these packets also increase. The main improvement here to the Bellman-Ford algorithm is loop freedom, which is made possible by assigning the sequence number to each entry in the routing table, which avoids stale routes.
\n
The dynamic source routing (DSR) [10] is an on-demand or reactive routing protocol. Therefore, unlike other proactive routing protocols, DSR involves no updates of whichever type at any stage inside the network. The DSR uses source routing for forwarding data packets, which distinguishes DSR from other reactive routing protocols. It is lightweight on inner routers due to source routing, the maintaining routing information is not needed at every host. The sender becomes aware of complete destination address before transmission and appends this address in the header of the routing data packet at the beginning. It is loop free due to source routing. Extensive use of cache and promiscuously listening are the main optimizations to DSR when network is at low mobility.
\n
3. Simulation model
\n
The simulation software used in this chapter is the network simulator, NS-2 [16, 17]. The software version used is the latest release at the time of the commencement of simulation, namely, ns-2.34, which can be downloaded from Ref. [17]. In addition, many existing ad hoc routing protocol modules have already been implemented in NS-2. Three such protocols are AODV, DSR, and DSDV. NS-2 is a discrete-event-driven simulation software targeted for network simulation. This software is currently maintained by the Information Science Institute of University of Southern California.
\n
3.1. Simulation evaluation methodology
\n
In order to analyze and compare the performance of the three routing protocols AODV, DSR, and DSDV, simulation experiments were performed. The purpose of the simulations was to compare the efficiency of the routing protocols based on different simulation parameters. The focus was concentrated on four performance metrics:
NAM is a Tcl/TK-based animation tool for viewing network simulation traces and real-world packet traces. Taking data from network simulators (such as ns) or live networks, NAM was one of the first tools to provide general purpose, packet-level, and network animation, before starting to use NAM, a trace file needs to create [16]. This trace file is usually generated by NS. Once the trace file is generated, NAM can be used to animate it. A snapshot of the simulation topology in NAM for 15 mobile nodes is shown in Figure 1, which is visualized the traces of communication or packet movements between mobile nodes [17].
Figure 1.
A simple NAM file output.
\n
The NAM file output for packet dropping is shown in Figure 2.
Figure 2.
A NAM output with packet dropping.
\n
4. Simulation results and observation
\n
4.1. Packet delivery ratio (PDR)
\n
Packet delivery ratio (PDR) is defined as the ratio of data packets delivered successfully to destination nodes and the total number of data packets generated for those destinations. PDR characterizes the packet loss rate, which limits the throughput of the network. The higher the delivery ratio, better the performance of the routing protocol. The ratio of the data delivered to the destination to the data sent out by the source. PDR is determined as
\n
PDR=(Received packetsSent packets)*100E1
Figures 3–6 clearly indicate that the AODV routing protocol outcomes are better with the CBR traffic. AODV protocol performs better in comparison of other two selected routing protocols in such a network environment with varying speeds of nodes. So, we conclude that AODV is better in most of the PDR cases.
Figure 3.
Packet delivery ratio (PDR) at 3 m/s.
Figure 4.
Packet delivery ratio (PDR) at 10 m/s.
Figure 5.
Packet delivery ratio (PDR) at 25 m/s.
Figure 6.
Packet delivery ratio (PDR) at 50 m/s.
\n
4.2. Throughput
\n
Throughput is defined as the ratio of the total data reaches a receiver from the sender. The time it takes by the receiver to receive the last message is called as throughput. Throughput is expressed as bytes or bits per sec (byte/sec or bit/sec). Some factors affect the throughput as; if there are many topology changes in the network, unreliable communication between nodes, limited bandwidth available, and limited energy. A high throughput is absolute choice in every network. Throughput can be represented mathematically as in equation. This represents the number of packets received by the destination within a given time interval. It is a measure of effectiveness of a routing protocol.
\n
Throughput=File sizeTransmission time (bps)E2
\n
Transmission time (bps)=File sizeBandwidth (sec)E3
The analysis of Figures 7–10 shows that performance of AODV is better than DSR and DSDV. Another characteristic that has come to the notice is that pause time does not have significant bearing on the throughput, whereas the performance is dictated only by the density of the network.
Figure 7.
Throughput at 3 m/s.
Figure 8.
Throughput at 10 m/s.
Figure 9.
Throughput at 25 m/s.
Figure 10.
Throughput at 50 m/s.
\n
4.3. Normalized routing overhead
\n
This is the ratio of routing-related transmissions (RREQ, RREP, RERR, etc.) to data transmissions in a simulation. A transmission is one node either sending or forwarding a packet. Either way, the routing load per unit data successfully delivered to the destination.
\n
It is the total number of control or routing (RTR) packets generated by routing protocol during the simulation. All packets sent or forwarded at network layer is consider routing overhead.
\n
Routing overhead = Number of RTR packetsE4
Based on the result of simulation, Figures 11–14 show that the performance of DSDV is better than AODV and DSR. At all the considered mobility, DSDV is the best protocol as compared to other protocols.
Figure 11.
Normalized routing overhead at 3 m/s.
Figure 12.
Normalized routing overhead at 10 m/s.
Figure 13.
Normalized routing overhead at 25 m/s.
Figure 14.
Normalized routing overhead at 50 m/s.
\n
4.4. Jitter
\n
The term jitter is often used as a measure of the variability over time of the packet latency across a network. A network with constant latency has no variation (or jitter). Packet jitter is expressed as an average of the deviation from the network mean latency. However, for this use, the term is imprecise [13]. Or in other words, jitter is the variation of the packet arrival time. In jitter calculation, the variation in the packet arrival time is expected to minimum. The delays between the different packets need to be low if we want better performance in mobile ad hoc networks.
\n
Based on the result of simulation, Figures 15 and 16 show that the performance of AODV and DSR gives the better result. Figures 17 and 18 show that DSR gives the better performance.
Figure 15.
Jitter at 3 m/s.
Figure 16.
Jitter at 10 m/s.
Figure 17.
Jitter at 25 m/s.
Figure 18.
Jitter at 50 m/s.
\n
5. ANOVA test
\n
Analysis of variance (ANOVA) is a collection of statistical models used to analyze the differences between group means and their associated procedures (such as “variation” among and between groups), in which the observed variance in a particular variable is partitioned into components attributable to different sources of variation [18].
\n
In this chapter, we have use one-way ANOVA. One-way ANOVA is used to study the effect of (k > 2) levels of a single factor. A factor is defined as characteristics under consideration, thought to influence the measured observation. A level is defined as a value of a factor.
\n
5.1. Output of the test for different parameters
\n
5.1.1. Packet delivery ratio
\n
The packet delivery ratio (PDR) is very much related to the throughput metric. The destination records the number of data packets it received and estimates the PDR delivery ratio in the network from the count of the data packets sent. The ANOVA hypothesis test is shown in Table 1, there is sufficient evidence to reject the null hypothesis. We see that there is a significant different in PDR performance when the network adopts different routing methods (P-value > 0.05).
Groups
Count
Sum
Average
Variance
AODV
23
802.8693
34.90736
121.3164733
DSDV
23
744.1666
32.35507
171.2711624
DSR
23
729.8366
31.73203
56.06334723
Table 1.
Summary of packet delivery ratio.
\n
The one-way ANOVA test for PDR is shown in Table 2.
Source of variation
SS
Df
MS
F
P-value
Fcrit
Between groups
130.21925
2
65.10963
0.560241875
0.573763
3.135918
Within groups
7670.3216
66
116.217
Total
7800.5409
68
Table 2.
ANOVA of packet delivery ratio.
\n
In this case, Fcrit=3.135918 at α=0.05. Since F=0.560241875<3.135918, the results are significant at the 5% significance level. So, we will accept the null hypothesis, and conclusion can be drawn that there is strong evidence that the expected values in the three groups do not differ. The variation is quite small and can be eliminated at this significance level. The P-value for this test is 0.573763.
\n
5.1.2. Throughput
\n
Data throughput is defined as the total number of packets delivered over the total simulation time. ANOVA statistical computation shows that we do not reject the null hypothesis. That is, there is no significant difference for the different methods in terms of throughput performance (P-value > 0.05) (Table 3).
Groups
Count
Sum
Average
Variance
AODV
23
7990941
347432.2
8201779957
DSDV
23
8094695
351943.3
20237752574
DSR
23
7267943
315997.5
5554377965
Table 3.
Summary of throughput.
\n
The one-way ANOVA test for throughput is shown in Table 4.
Source of variation
SS
df
MS
F
P-value
Fcrit
Between groups
1.764E+10
2
8.82E+09
0.778278814
0.463364
3.135918
Within groups
7.479E+11
66
1.13E+10
Total
7.655E+11
68
Table 4.
ANOVA of throughput.
\n
In this case, Fcrit=3.135918 at α=0.05. Since F=0.778278814<3.135918, the results are significant at the 5% significance level. So, we will accept the null hypothesis, and conclusion can be drawn that there is strong evidence that the expected values in the three groups do not differ. The variation is quite small and can be eliminated at this significance level. The P-value for this test is 0.463364.
\n
5.1.3. Normalized routing overhead
\n
Using the ANOVA hypothesis testing, the simulation results show a significant difference among methods used in terms of normalized routing overhead (P-value > 0.05). Thus, normalized routing overhead can be used as a metric to measure the performance of different algorithms (Table 5).
Groups
Count
Sum
Average
Variance
AODV
23
2.19207
0.095307
0.01199388
DSDV
23
13.66286
0.594037
0.846600923
DSR
23
0.528887
0.022995
0.000264522
Table 5.
Summary of normalized routing overhead.
\n
The one-way ANOVA test for normalized routing overhead is shown in Table 6.
Source of variation
SS
df
MS
F
P-value
Fcrit
Between groups
4.4470485
2
2.223524
7.766781596
0.000935
3.135918
Within groups
18.894905
66
0.286286
Total
23.341954
68
Table 6.
ANOVA of normalized routing overhead.
\n
In this case, Fcrit=3.135918 at α=0.05. Since F=7.766781596>3.135918, the results are significant at the 5% significance level. So, we will reject the null hypothesis, and conclusion can be drawn that there is strong evidence that the expected values in the three groups differ significantly. The P-value for this test is 0.000935.
\n
5.1.4. Jitter
\n
The term jitter often used as a measure of the packet of the variability over time of the packet latency across a network. A network with constant latency has no variation (or jitter). Packet jitter is expressed as an average of the derivation from the network mean latency. ANOVA statistical computation shows that we do not reject the null hypothesis. That is, there is no significant difference for the different methods in terms of throughput performance (P-value > 0.05) (Table 7).
Groups
Count
Sum
Average
Variance
AODV
23
0.129171
0.005616
2.07E−06
DSDV
23
0.125752
0.005467
5.9E−06
DSR
23
0.131442
0.005715
1.91E−06
Table 7.
Summary of jitter.
\n
The one-way ANOVA test for jitter is shown in Table 8.
Source of variation
SS
df
MS
F
P-value
Fcrit
Between Groups
7.14E−07
2
3.57E−07
0.108241
0.89757
3.135918
Within Groups
0.000218
66
3.3E−06
Total
0.000218
68
Table 8.
ANOVA of jitter.
\n
In this case, Fcrit=3.135918 at α=0.05. Since F=0.108241<3.135918, the results are significant at the 5% significance level. So, we will accept the null hypothesis, and conclusion can be drawn that there is strong evidence that the expected values in the three groups do not differ. The variation is quite small and can be eliminated at this significance level. The P-value for this test is 0.89757.
\n
6. Conclusion
\n
The results indicate that the performance is better especially when the number of nodes in the network is higher. In this chapter, we have used a simulator that provides the virtual environment for the testing different parameters. Reactive routing protocol AODV performance is the best considering due to its ability to maintain connection by periodic exchange of information. Using NS-2 simulator we created the scenarios under which using tcl script, it is run. After analyzing the X-graphs, we concluded that AODV indicates its highest efficiency and performance under high mobility than DSR and DSDV, and the performance of TCP and UDP packets with respect to normalized routing overhead, jitter, throughput, and PDR, and the performance of AODV is better than DSDV and DSR routing protocol for real-time applications from the simulation results.
\n
After that in one-way ANOVA test, AODV exhibits better routing performance compared with conventional routing methods such as DSDV and DSR. By performing an ANOVA analysis at the initial stage, we conclude that there is a significant difference in the performance metrics when using different routing algorithms. From there, we analyze the difference of the means and boundaries in 95% confidence interval. In all simulation scenarios, we see that AODV shows a lower packet loss and lower delay. It offers higher throughput and ensures higher packet delivery ratio.
\n',keywords:"AODV, DSR, DSDV, MANET, throughput, packet delivery ratio, jitter, NS-2, ANOVA",chapterPDFUrl:"https://cdn.intechopen.com/pdfs/54156.pdf",chapterXML:"https://mts.intechopen.com/source/xml/54156.xml",downloadPdfUrl:"/chapter/pdf-download/54156",previewPdfUrl:"/chapter/pdf-preview/54156",totalDownloads:1462,totalViews:673,totalCrossrefCites:2,totalDimensionsCites:3,hasAltmetrics:0,dateSubmitted:"March 29th 2016",dateReviewed:"October 25th 2016",datePrePublished:null,datePublished:"May 11th 2017",dateFinished:null,readingETA:"0",abstract:"In this chapter, we analyzed ad hoc on demand distance vector (AODV), dynamic source routing (DSR), and destination-sequenced distance vector (DSDV) routing protocols using different parameters of QoS metrics such as packet delivery ratio (PDR), normalize routing overhead, throughput, and jitter. The aim of this chapter is to determine a difference between routing protocol performance when operating in a large-area MANET with high-speed mobile nodes. After the simulations, we use AWK to analyze the data and then Xgraph to plot the performance metric. After that we use one-way ANOVA tools to confirm the correctness of the result. We use NS-2 for the simulation work. The comparison analysis of these protocols will be carrying out and in the last, we conclude that which routing protocol is the best one for mobile ad hoc networks.",reviewType:"peer-reviewed",bibtexUrl:"/chapter/bibtex/54156",risUrl:"/chapter/ris/54156",book:{slug:"ad-hoc-networks"},signatures:"Subhrananda Goswami, Subhankar Joardar, Chandan Bikash Das,\nSamarajit Kar and Dibyendu Kumar Pal",authors:[{id:"187556",title:"Mr.",name:"Subhrananda",middleName:null,surname:"Goswami",fullName:"Subhrananda Goswami",slug:"subhrananda-goswami",email:"subhrananda_usca@yahoo.co.in",position:null,institution:{name:"West Bengal University of Technology",institutionURL:null,country:{name:"India"}}},{id:"187557",title:"Dr.",name:"Chandan Bikash",middleName:null,surname:"Das",fullName:"Chandan Bikash Das",slug:"chandan-bikash-das",email:"cdas_bikash@yahoo.co.in",position:null,institution:null},{id:"187558",title:"Mr.",name:"Subhankar",middleName:null,surname:"Joardar",fullName:"Subhankar Joardar",slug:"subhankar-joardar",email:"subhankarranchi@yahoo.co.in",position:null,institution:null},{id:"204666",title:"Dr.",name:"Dibyendu",middleName:null,surname:"Kumar Pal",fullName:"Dibyendu Kumar Pal",slug:"dibyendu-kumar-pal",email:"paldibaec@gmail.com",position:null,institution:null},{id:"204667",title:"Dr.",name:"Samarjit",middleName:null,surname:"Kar",fullName:"Samarjit Kar",slug:"samarjit-kar",email:"samarjit.kar@maths.nitdgp.ac.in",position:null,institution:null}],sections:[{id:"sec_1",title:"1. Introduction",level:"1"},{id:"sec_2",title:"2. MANET routing protocols",level:"1"},{id:"sec_3",title:"3. Simulation model",level:"1"},{id:"sec_3_2",title:"3.1. Simulation evaluation methodology",level:"2"},{id:"sec_4_2",title:"3.2. Results",level:"2"},{id:"sec_5_2",title:"3.3. NAM file output",level:"2"},{id:"sec_7",title:"4. Simulation results and observation",level:"1"},{id:"sec_7_2",title:"4.1. Packet delivery ratio (PDR)",level:"2"},{id:"sec_8_2",title:"4.2. Throughput",level:"2"},{id:"sec_9_2",title:"4.3. Normalized routing overhead",level:"2"},{id:"sec_10_2",title:"4.4. Jitter",level:"2"},{id:"sec_12",title:"5. ANOVA test",level:"1"},{id:"sec_12_2",title:"5.1. Output of the test for different parameters",level:"2"},{id:"sec_12_3",title:"Table 1.",level:"3"},{id:"sec_13_3",title:"Table 3.",level:"3"},{id:"sec_14_3",title:"Table 5.",level:"3"},{id:"sec_15_3",title:"Table 7.",level:"3"},{id:"sec_18",title:"6. Conclusion",level:"1"}],chapterReferences:[{id:"B1",body:'Murthy R.S.C. and Manoj B.S. Ad Hoc Wireless Networks- Architectures. Prentice Hall, Upper Saddle River, NJ, 2004.'},{id:"B2",body:'Goswami S., Joardar S., Das C.B., and Das B. A simulation based performance comparison of AODV and DSDV mobile ad hoc networks. Information Technology and Computer Science. 2014;6(10):11–18.'},{id:"B3",body:'Remondo D. Tutorial on wireless ad hoc networks [Internet]. 2004. Available from: www.comp.brad.ac.uk/het-net/HET-NETs04/CameraPapers/T2.pdf'},{id:"B4",body:'Goswami S., Joardar S., and Das C.B. Performance Comparison of Routing Protocols of MANET Using NS-2. 1st ed. Germany: LAP LAMBERT Academic Publishing; 2014, 141p.'},{id:"B5",body:'Chun Y.L. and Lin S.M. Routing protocols overview and design issues for self-organizednetwork [sic]. International Conference on Communication Technology Proceedings; Beijing, China. 2000, pp. 1298–1303.'},{id:"B6",body:'Goswami S., Joardar S., and Das C.B. Reactive and proactive routing protocol performance metric comparison in mobile ad hoc networks using NS 2. International Journal of Advanced Research in Computer and Communication Engineering. 2014;3(1):4908–4914.'},{id:"B7",body:'Macker J.P. and Corson M.S. Mobile ad hoc networking (MANET):Routing protocol performance issues and evaluation considerations (Internet-draft), in: Mobile Ad-hoc Network (MANET) Working Group, IETF. 1998.'},{id:"B8",body:'Internet Engineering Task Force MANET Working Group Charter. Available from: www.ietf,org/html.charters/manet-charter.html; 1999'},{id:"B9",body:'Broch J., Maltz D.A., Johnson D.B., Hu Y.C., and Jetcheva J. A performance comparisonof multi-hop wireless ad hoc network routing protocols. Proceedings of the Fourth Annual ACM; IEEE International Conference on Mobile Computing and Networking; 1998, pp. 85–97.'},{id:"B10",body:'Broch J., Maltz D.A., and Johnson D.B. The dynamic source routing protocol for mobilead hoc networks. Internet Draft, MANET Working Group. 1999.'},{id:"B11",body:'Perkins C.E., Royer E.M., and Das S.R. Performance comparison of two on-demand routing protocols for ad hoc networks. in IEEE Personal Communications. vol. 8. no. 1. pp. 16–28. 2001.'},{id:"B12",body:'Perkins C.E. and Royer E.M. Ad hoc on-demand distance vector (AODV) routing.Internet Draft, MANET Working Group. 2000.'},{id:"B13",body:'Belding-Royer E.M., Perkins C.E., and Chakeres I. Ad hoc On-Demand Distance Vector (AODV) Routing: Work in progress. July 2004. Internet Draft, RFC 3561bis-01, http://moment. cs. ucsb. edu/pub/draft-perkins-manet-aodvbis-02. txt.'},{id:"B14",body:'Papadimitratos P. and Haas Z.J. Secure on-demand distance vector routing in ad hoc networks. IEEE/Sarnoff Symposium on Advances in Wired and Wireless Communication. Princeton, NJ, 2005, pp. 168–171.'},{id:"B15",body:'Gorantala K. Routing protocols in mobile ad hoc networks [thesis]. Sweden: Umea University; 2006.'},{id:"B16",body:'nsnam web pages. Network simulator-ns-2 [Internet]. Available from: www.isi.edu/nsnam; 1989.'},{id:"B17",body:'NS Manual/Documentation-The VINT Project Collaboration between researchers at UC Berkeley, LBL, USC/ISI and Xerox. PARC.KevinFall_kfall@ee.lbl.gov, Kennan Varadhan_kannan@catarina.usc.edu, 1996.'},{id:"B18",body:'Barman S., Ghosh A., and Biswas S. A transparent tree root identification scheme to support route-optimization and network mobility in PMIPv6 Domain. IEEE 2nd International Conference on Recent Trends in Information Systems (ReTIS), Kolkata. 2015. pp. 532–537.'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Subhrananda Goswami",address:"subhrananda_usca@yahoo.co.in",affiliation:'
Department of Information Technology, Global Group of Institutions, Haldia, Purba Midnapore, West Bengal, India
Department Of Computer Application, Asansol Engineering College, Asansol, Burdwan, West Bengal, India
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1. Introduction
Research methodology is the path through which researchers need to conduct their research. It shows the path through which these researchers formulate their problem and objective and present their result from the data obtained during the study period. This research design and methodology chapter also shows how the research outcome at the end will be obtained in line with meeting the objective of the study. This chapter hence discusses the research methods that were used during the research process. It includes the research methodology of the study from the research strategy to the result dissemination. For emphasis, in this chapter, the author outlines the research strategy, research design, research methodology, the study area, data sources such as primary data sources and secondary data, population consideration and sample size determination such as questionnaires sample size determination and workplace site exposure measurement sample determination, data collection methods like primary data collection methods including workplace site observation data collection and data collection through desk review, data collection through questionnaires, data obtained from experts opinion, workplace site exposure measurement, data collection tools pretest, secondary data collection methods, methods of data analysis used such as quantitative data analysis and qualitative data analysis, data analysis software, the reliability and validity analysis of the quantitative data, reliability of data, reliability analysis, validity, data quality management, inclusion criteria, ethical consideration and dissemination of result and its utilization approaches. In order to satisfy the objectives of the study, a qualitative and quantitative research method is apprehended in general. The study used these mixed strategies because the data were obtained from all aspects of the data source during the study time. Therefore, the purpose of this methodology is to satisfy the research plan and target devised by the researcher.
2. Research design
The research design is intended to provide an appropriate framework for a study. A very significant decision in research design process is the choice to be made regarding research approach since it determines how relevant information for a study will be obtained; however, the research design process involves many interrelated decisions [1].
This study employed a mixed type of methods. The first part of the study consisted of a series of well-structured questionnaires (for management, employee’s representatives, and technician of industries) and semi-structured interviews with key stakeholders (government bodies, ministries, and industries) in participating organizations. The other design used is an interview of employees to know how they feel about safety and health of their workplace, and field observation at the selected industrial sites was undertaken.
Hence, this study employs a descriptive research design to agree on the effects of occupational safety and health management system on employee health, safety, and property damage for selected manufacturing industries. Saunders et al. [2] and Miller [3] say that descriptive research portrays an accurate profile of persons, events, or situations. This design offers to the researchers a profile of described relevant aspects of the phenomena of interest from an individual, organizational, and industry-oriented perspective. Therefore, this research design enabled the researchers to gather data from a wide range of respondents on the impact of safety and health on manufacturing industries in Ethiopia. And this helped in analyzing the response obtained on how it affects the manufacturing industries’ workplace safety and health. The research overall design and flow process are depicted in Figure 1.
Figure 1.
Research methods and processes (author design).
3. Research methodology
To address the key research objectives, this research used both qualitative and quantitative methods and combination of primary and secondary sources. The qualitative data supports the quantitative data analysis and results. The result obtained is triangulated since the researcher utilized the qualitative and quantitative data types in the data analysis. The study area, data sources, and sampling techniques were discussed under this section.
3.1 The study area
According to Fraenkel and Warren [4] studies, population refers to the complete set of individuals (subjects or events) having common characteristics in which the researcher is interested. The population of the study was determined based on random sampling system. This data collection was conducted from March 07, 2015 to December 10, 2016, from selected manufacturing industries found in Addis Ababa city and around. The manufacturing companies were selected based on their employee number, established year, and the potential accidents prevailing and the manufacturing industry type even though all criterions were difficult to satisfy.
3.2 Data sources
3.2.1 Primary data sources
It was obtained from the original source of information. The primary data were more reliable and have more confidence level of decision-making with the trusted analysis having direct intact with occurrence of the events. The primary data sources are industries’ working environment (through observation, pictures, and photograph) and industry employees (management and bottom workers) (interview, questionnaires and discussions).
3.2.2 Secondary data
Desk review has been conducted to collect data from various secondary sources. This includes reports and project documents at each manufacturing sectors (more on medium and large level). Secondary data sources have been obtained from literatures regarding OSH, and the remaining data were from the companies’ manuals, reports, and some management documents which were included under the desk review. Reputable journals, books, different articles, periodicals, proceedings, magazines, newsletters, newspapers, websites, and other sources were considered on the manufacturing industrial sectors. The data also obtained from the existing working documents, manuals, procedures, reports, statistical data, policies, regulations, and standards were taken into account for the review.
In general, for this research study, the desk review has been completed to this end, and it had been polished and modified upon manuals and documents obtained from the selected companies.
4. Population and sample size
4.1 Population
The study population consisted of manufacturing industries’ employees in Addis Ababa city and around as there are more representative manufacturing industrial clusters found. To select representative manufacturing industrial sector population, the types of the industries expected were more potential to accidents based on random and purposive sampling considered. The population of data was from textile, leather, metal, chemicals, and food manufacturing industries. A total of 189 sample sizes of industries responded to the questionnaire survey from the priority areas of the government. Random sample sizes and disproportionate methods were used, and 80 from wood, metal, and iron works; 30 from food, beverage, and tobacco products; 50 from leather, textile, and garments; 20 from chemical and chemical products; and 9 from other remaining 9 clusters of manufacturing industries responded.
4.2 Questionnaire sample size determination
A simple random sampling and purposive sampling methods were used to select the representative manufacturing industries and respondents for the study. The simple random sampling ensures that each member of the population has an equal chance for the selection or the chance of getting a response which can be more than equal to the chance depending on the data analysis justification. Sample size determination procedure was used to get optimum and reasonable information. In this study, both probability (simple random sampling) and nonprobability (convenience, quota, purposive, and judgmental) sampling methods were used as the nature of the industries are varied. This is because of the characteristics of data sources which permitted the researchers to follow the multi-methods. This helps the analysis to triangulate the data obtained and increase the reliability of the research outcome and its decision. The companies’ establishment time and its engagement in operation, the number of employees and the proportion it has, the owner types (government and private), type of manufacturing industry/production, types of resource used at work, and the location it is found in the city and around were some of the criteria for the selections.
The determination of the sample size was adopted from Daniel [5] and Cochran [6] formula. The formula used was for unknown population size Eq. (1) and is given as
E1
where n = sample size, Z = statistic for a level of confidence, P = expected prevalence or proportion (in proportion of one; if 50%, P = 0.5), and d = precision (in proportion of one; if 6%, d = 0.06). Z statistic (Z): for the level of confidence of 95%, which is conventional, Z value is 1.96. In this study, investigators present their results with 95% confidence intervals (CI).
The expected sample number was 267 at the marginal error of 6% for 95% confidence interval of manufacturing industries. However, the collected data indicated that only 189 populations were used for the analysis after rejecting some data having more missing values in the responses from the industries. Hence, the actual data collection resulted in 71% response rate. The 267 population were assumed to be satisfactory and representative for the data analysis.
4.3 Workplace site exposure measurement sample determination
The sample size for the experimental exposure measurements of physical work environment has been considered based on the physical data prepared for questionnaires and respondents. The response of positive were considered for exposure measurement factors to be considered for the physical environment health and disease causing such as noise intensity, light intensity, pressure/stress, vibration, temperature/coldness, or hotness and dust particles on 20 workplace sites. The selection method was using random sampling in line with purposive method. The measurement of the exposure factors was done in collaboration with Addis Ababa city Administration and Oromia Bureau of Labour and Social Affair (AACBOLSA). Some measuring instruments were obtained from the Addis Ababa city and Oromia Bureau of Labour and Social Affair.
5. Data collection methods
Data collection methods were focused on the followings basic techniques. These included secondary and primary data collections focusing on both qualitative and quantitative data as defined in the previous section. The data collection mechanisms are devised and prepared with their proper procedures.
5.1 Primary data collection methods
Primary data sources are qualitative and quantitative. The qualitative sources are field observation, interview, and informal discussions, while that of quantitative data sources are survey questionnaires and interview questions. The next sections elaborate how the data were obtained from the primary sources.
5.1.1 Workplace site observation data collection
Observation is an important aspect of science. Observation is tightly connected to data collection, and there are different sources for this: documentation, archival records, interviews, direct observations, and participant observations. Observational research findings are considered strong in validity because the researcher is able to collect a depth of information about a particular behavior. In this dissertation, the researchers used observation method as one tool for collecting information and data before questionnaire design and after the start of research too. The researcher made more than 20 specific observations of manufacturing industries in the study areas. During the observations, it found a deeper understanding of the working environment and the different sections in the production system and OSH practices.
5.1.2 Data collection through interview
Interview is a loosely structured qualitative in-depth interview with people who are considered to be particularly knowledgeable about the topic of interest. The semi-structured interview is usually conducted in a face-to-face setting which permits the researcher to seek new insights, ask questions, and assess phenomena in different perspectives. It let the researcher to know the in-depth of the present working environment influential factors and consequences. It has provided opportunities for refining data collection efforts and examining specialized systems or processes. It was used when the researcher faces written records or published document limitation or wanted to triangulate the data obtained from other primary and secondary data sources.
This dissertation is also conducted with a qualitative approach and conducting interviews. The advantage of using interviews as a method is that it allows respondents to raise issues that the interviewer may not have expected. All interviews with employees, management, and technicians were conducted by the corresponding researcher, on a face-to-face basis at workplace. All interviews were recorded and transcribed.
5.1.3 Data collection through questionnaires
The main tool for gaining primary information in practical research is questionnaires, due to the fact that the researcher can decide on the sample and the types of questions to be asked [2].
In this dissertation, each respondent is requested to reply to an identical list of questions mixed so that biasness was prevented. Initially the questionnaire design was coded and mixed up from specific topic based on uniform structures. Consequently, the questionnaire produced valuable data which was required to achieve the dissertation objectives.
The questionnaires developed were based on a five-item Likert scale. Responses were given to each statement using a five-point Likert-type scale, for which 1 = “strongly disagree” to 5 = “strongly agree.” The responses were summed up to produce a score for the measures.
5.1.4 Data obtained from experts’ opinion
The data was also obtained from the expert’s opinion related to the comparison of the knowledge, management, collaboration, and technology utilization including their sub-factors. The data obtained in this way was used for prioritization and decision-making of OSH, improving factor priority. The prioritization of the factors was using Saaty scales (1–9) and then converting to Fuzzy set values obtained from previous researches using triangular fuzzy set [7].
5.1.5 Workplace site exposure measurement
The researcher has measured the workplace environment for dust, vibration, heat, pressure, light, and noise to know how much is the level of each variable. The primary data sources planned and an actual coverage has been compared as shown in Table 1.
Table 1.
Planned versus actual coverage of the survey.
The response rate for the proposed data source was good, and the pilot test also proved the reliability of questionnaires. Interview/discussion resulted in 87% of responses among the respondents; the survey questionnaire response rate obtained was 71%, and the field observation response rate was 90% for the whole data analysis process. Hence, the data organization quality level has not been compromised.
This response rate is considered to be representative of studies of organizations. As the study agrees on the response rate to be 30%, it is considered acceptable [8]. Saunders et al. [2] argued that the questionnaire with a scale response of 20% response rate is acceptable. Low response rate should not discourage the researchers, because a great deal of published research work also achieves low response rate. Hence, the response rate of this study is acceptable and very good for the purpose of meeting the study objectives.
5.1.6 Data collection tool pretest
The pretest for questionnaires, interviews, and tools were conducted to validate that the tool content is valid or not in the sense of the respondents’ understanding. Hence, content validity (in which the questions are answered to the target without excluding important points), internal validity (in which the questions raised answer the outcomes of researchers’ target), and external validity (in which the result can generalize to all the population from the survey sample population) were reflected. It has been proved with this pilot test prior to the start of the basic data collections. Following feedback process, a few minor changes were made to the originally designed data collect tools. The pilot test made for the questionnaire test was on 10 sample sizes selected randomly from the target sectors and experts.
5.2 Secondary data collection methods
The secondary data refers to data that was collected by someone other than the user. This data source gives insights of the research area of the current state-of-the-art method. It also makes some sort of research gap that needs to be filled by the researcher. This secondary data sources could be internal and external data sources of information that may cover a wide range of areas.
Literature/desk review and industry documents and reports: To achieve the dissertation’s objectives, the researcher has conducted excessive document review and reports of the companies in both online and offline modes. From a methodological point of view, literature reviews can be comprehended as content analysis, where quantitative and qualitative aspects are mixed to assess structural (descriptive) as well as content criteria.
A literature search was conducted using the database sources like MEDLINE; Emerald; Taylor and Francis publications; EMBASE (medical literature); PsycINFO (psychological literature); Sociological Abstracts (sociological literature); accident prevention journals; US Statistics of Labor, European Safety and Health database; ABI Inform; Business Source Premier (business/management literature); EconLit (economic literature); Social Service Abstracts (social work and social service literature); and other related materials. The search strategy was focused on articles or reports that measure one or more of the dimensions within the research OSH model framework. This search strategy was based on a framework and measurement filter strategy developed by the Consensus-Based Standards for the Selection of Health Measurement Instruments (COSMIN) group. Based on screening, unrelated articles to the research model and objectives were excluded. Prior to screening, researcher (principal investigator) reviewed a sample of more than 2000 articles, websites, reports, and guidelines to determine whether they should be included for further review or reject. Discrepancies were thoroughly identified and resolved before the review of the main group of more than 300 articles commenced. After excluding the articles based on the title, keywords, and abstract, the remaining articles were reviewed in detail, and the information was extracted on the instrument that was used to assess the dimension of research interest. A complete list of items was then collated within each research targets or objectives and reviewed to identify any missing elements.
6. Methods of data analysis
Data analysis method follows the procedures listed under the following sections. The data analysis part answered the basic questions raised in the problem statement. The detailed analysis of the developed and developing countries’ experiences on OSH regarding manufacturing industries was analyzed, discussed, compared and contrasted, and synthesized.
6.1 Quantitative data analysis
Quantitative data were obtained from primary and secondary data discussed above in this chapter. This data analysis was based on their data type using Excel, SPSS 20.0, Office Word format, and other tools. This data analysis focuses on numerical/quantitative data analysis.
Before analysis, data coding of responses and analysis were made. In order to analyze the data obtained easily, the data were coded to SPSS 20.0 software as the data obtained from questionnaires. This task involved identifying, classifying, and assigning a numeric or character symbol to data, which was done in only one way pre-coded [9, 10]. In this study, all of the responses were pre-coded. They were taken from the list of responses, a number of corresponding to a particular selection was given. This process was applied to every earlier question that needed this treatment. Upon completion, the data were then entered to a statistical analysis software package, SPSS version 20.0 on Windows 10 for the next steps.
Under the data analysis, exploration of data has been made with descriptive statistics and graphical analysis. The analysis included exploring the relationship between variables and comparing groups how they affect each other. This has been done using cross tabulation/chi square, correlation, and factor analysis and using nonparametric statistic.
6.2 Qualitative data analysis
Qualitative data analysis used for triangulation of the quantitative data analysis. The interview, observation, and report records were used to support the findings. The analysis has been incorporated with the quantitative discussion results in the data analysis parts.
6.3 Data analysis software
The data were entered using SPSS 20.0 on Windows 10 and analyzed. The analysis supported with SPSS software much contributed to the finding. It had contributed to the data validation and correctness of the SPSS results. The software analyzed and compared the results of different variables used in the research questionnaires. Excel is also used to draw the pictures and calculate some analytical solutions.
7. The reliability and validity analysis of the quantitative data
7.1 Reliability of data
The reliability of measurements specifies the amount to which it is without bias (error free) and hence ensures consistent measurement across time and across the various items in the instrument [8]. In reliability analysis, it has been checked for the stability and consistency of the data. In the case of reliability analysis, the researcher checked the accuracy and precision of the procedure of measurement. Reliability has numerous definitions and approaches, but in several environments, the concept comes to be consistent [8]. The measurement fulfills the requirements of reliability when it produces consistent results during data analysis procedure. The reliability is determined through Cranach’s alpha as shown in Table 2.
Table 2.
Internal consistency and reliability test of questionnaires items.
K stands for knowledge; M, management; T, technology; C, collaboration; P, policy, standards, and regulation; H, hazards and accident conditions; PPE, personal protective equipment.
7.2 Reliability analysis
Cronbach’s alpha is a measure of internal consistency, i.e., how closely related a set of items are as a group [11]. It is considered to be a measure of scale reliability. The reliability of internal consistency most of the time is measured based on the Cronbach’s alpha value. Reliability coefficient of 0.70 and above is considered “acceptable” in most research situations [12]. In this study, reliability analysis for internal consistency of Likert-scale measurement after deleting 13 items was found similar; the reliability coefficients were found for 76 items were 0.964 and for the individual groupings made shown in Table 2. It was also found internally consistent using the Cronbach’s alpha test. Table 2 shows the internal consistency of the seven major instruments in which their reliability falls in the acceptable range for this research.
7.3 Validity
Face validity used as defined by Babbie [13] is an indicator that makes it seem a reasonable measure of some variables, and it is the subjective judgment that the instrument measures what it intends to measure in terms of relevance [14]. Thus, the researcher ensured, in this study, when developing the instruments that uncertainties were eliminated by using appropriate words and concepts in order to enhance clarity and general suitability [14]. Furthermore, the researcher submitted the instruments to the research supervisor and the joint supervisor who are both occupational health experts, to ensure validity of the measuring instruments and determine whether the instruments could be considered valid on face value.
In this study, the researcher was guided by reviewed literature related to compliance with the occupational health and safety conditions and data collection methods before he could develop the measuring instruments. In addition, the pretest study that was conducted prior to the main study assisted the researcher to avoid uncertainties of the contents in the data collection measuring instruments. A thorough inspection of the measuring instruments by the statistician and the researcher’s supervisor and joint experts, to ensure that all concepts pertaining to the study were included, ensured that the instruments were enriched.
8. Data quality management
Insight has been given to the data collectors on how to approach companies, and many of the questionnaires were distributed through MSc students at Addis Ababa Institute of Technology (AAiT) and manufacturing industries’ experience experts. This made the data quality reliable as it has been continually discussed with them. Pretesting for questionnaire was done on 10 workers to assure the quality of the data and for improvement of data collection tools. Supervision during data collection was done to understand how the data collectors are handling the questionnaire, and each filled questionnaires was checked for its completeness, accuracy, clarity, and consistency on a daily basis either face-to-face or by phone/email. The data expected in poor quality were rejected out of the acting during the screening time. Among planned 267 questionnaires, 189 were responded back. Finally, it was analyzed by the principal investigator.
9. Inclusion criteria
The data were collected from the company representative with the knowledge of OSH. Articles written in English and Amharic were included in this study. Database information obtained in relation to articles and those who have OSH area such as interventions method, method of accident identification, impact of occupational accidents, types of occupational injuries/disease, and impact of occupational accidents, and disease on productivity and costs of company and have used at least one form of feedback mechanism. No specific time period was chosen in order to access all available published papers. The questionnaire statements which are similar in the questionnaire have been rejected from the data analysis.
10. Ethical consideration
Ethical clearance was obtained from the School of Mechanical and Industrial Engineering, Institute of Technology, Addis Ababa University. Official letters were written from the School of Mechanical and Industrial Engineering to the respective manufacturing industries. The purpose of the study was explained to the study subjects. The study subjects were told that the information they provided was kept confidential and that their identities would not be revealed in association with the information they provided. Informed consent was secured from each participant. For bad working environment assessment findings, feedback will be given to all manufacturing industries involved in the study. There is a plan to give a copy of the result to the respective study manufacturing industries’ and ministries’ offices. The respondents’ privacy and their responses were not individually analyzed and included in the report.
11. Dissemination and utilization of the result
The result of this study will be presented to the Addis Ababa University, AAiT, School of Mechanical and Industrial Engineering. It will also be communicated to the Ethiopian manufacturing industries, Ministry of Labor and Social Affair, Ministry of Industry, and Ministry of Health from where the data was collected. The result will also be availed by publication and online presentation in Google Scholars. To this end, about five articles were published and disseminated to the whole world.
12. Conclusion
The research methodology and design indicated overall process of the flow of the research for the given study. The data sources and data collection methods were used. The overall research strategies and framework are indicated in this research process from problem formulation to problem validation including all the parameters. It has laid some foundation and how research methodology is devised and framed for researchers. This means, it helps researchers to consider it as one of the samples and models for the research data collection and process from the beginning of the problem statement to the research finding. Especially, this research flow helps new researchers to the research environment and methodology in particular.
Conflict of interest
There is no “conflict of interest.”
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New York: John Wiley & Sons Inc; 2000'},{id:"B2",body:'Saunders M, Lewis P, Thornhill A. Research Methods for Business Student. 5th ed. Edinburgh Gate: Pearson Education Limited; 2009'},{id:"B3",body:'Miller P. Motivation in the Workplace. Work and Organizational Psychology. Oxford: Blackwell Publishers; 1991'},{id:"B4",body:'Fraenkel FJ, Warren NE. How to Design and Evaluate Research in Education. 4th ed. New York: McGraw-Hill; 2002'},{id:"B5",body:'Danniel WW. Biostatist: A Foundation for Analysis in the Health Science. 7th ed. New York: John Wiley & Sons; 1999'},{id:"B6",body:'Cochran WG. Sampling Techniques. 3rd ed. New York: John Wiley & Sons; 1977'},{id:"B7",body:'Saaty TL. The Analytical Hierarchy Process. Pittsburg: PWS Publications; 1990'},{id:"B8",body:'Sekaran U, Bougie R. Research Methods for Business: A Skill Building Approach. 5th ed. New Delhi: John Wiley & Sons, Ltd; 2010. pp. 1-468'},{id:"B9",body:'Luck DJ, Rubin RS. Marketing Research. 7th ed. New Jersey: Prentice-Hall International; 1987'},{id:"B10",body:'Wong TC. Marketing Research. Oxford, UK: Butterworth-Heinemann; 1999'},{id:"B11",body:'Cronbach LJ. Coefficient alpha and the internal structure of tests. Psychometrika. 1951;16:297-334'},{id:"B12",body:'Tavakol M, Dennick R. Making sense of Cronbach’s alpha. International Journal of Medical Education. 2011;2:53-55. DOI: 10.5116/ijme.4dfb.8dfd'},{id:"B13",body:'Babbie E. The Practice of Social Research. 12th ed. Belmont, CA: Wadsworth; 2010'},{id:"B14",body:'Polit DF, Beck CT. Generating and Assessing Evidence for Nursing Practice. 8th ed. Williams and Wilkins: Lippincott; 2008'}],footnotes:[],contributors:[{corresp:"yes",contributorFullName:"Kassu Jilcha Sileyew",address:"jkassu@gmail.com",affiliation:'
School of Mechanical and Industrial Engineering, Addis Ababa Institute of Technology, Addis Ababa University, Addis Ababa, Ethiopia
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This information is important to allow exploration of natural forests sustainably or to subsidize conservation actions. In planted and native forests, the biogeochemical cycling of nutrients predominantly occurs through production and decomposition of litterfall. The information provided in this chapter, in particular with regard to nutrient cycling, is an important basis for understanding the structure and dynamics of nutrients in the ecosystem. We characterized nutrient stocks and elucidate some aspects of forest growth and productivity. This information is important to enhance biodiversity conservation and generate ecosystem goods and services in the Atlantic Forest Biome. 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UK Research and Innovation (former Research Councils UK (RCUK) - including AHRC, BBSRC, ESRC, EPSRC, MRC, NERC, STFC.) Processing charges for books/book chapters can be covered through RCUK block grants which are allocated to most universities in the UK, which then handle the OA publication funding requests. It is at the discretion of the university whether it will approve the request.)
Wellcome Trust (Funding available only to Wellcome-funded researchers/grantees)
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