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More than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\\n\\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\\n\\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\\n\\nAdditionally, each book published by IntechOpen contains original content and research findings.
\\n\\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\\n\\n\\n\\n
\\n"}]',published:!0,mainMedia:null},components:[{type:"htmlEditorComponent",content:'
Simba Information has released its Open Access Book Publishing 2020 - 2024 report and has again identified IntechOpen as the world’s largest Open Access book publisher by title count.
\n\nSimba Information is a leading provider for market intelligence and forecasts in the media and publishing industry. The report, published every year, provides an overview and financial outlook for the global professional e-book publishing market.
\n\nIntechOpen, De Gruyter, and Frontiers are the largest OA book publishers by title count, with IntechOpen coming in at first place with 5,101 OA books published, a good 1,782 titles ahead of the nearest competitor.
\n\nSince the first Open Access Book Publishing report published in 2016, IntechOpen has held the top stop each year.
\n\n\n\nMore than half of the publishers listed alongside IntechOpen (18 out of 30) are Social Science and Humanities publishers. IntechOpen is an exception to this as a leader in not only Open Access content but Open Access content across all scientific disciplines, including Physical Sciences, Engineering and Technology, Health Sciences, Life Science, and Social Sciences and Humanities.
\n\nOur breakdown of titles published demonstrates this with 47% PET, 31% HS, 18% LS, and 4% SSH books published.
\n\n“Even though ItechOpen has shown the potential of sci-tech books using an OA approach,” other publishers “have shown little interest in OA books.”
\n\nAdditionally, each book published by IntechOpen contains original content and research findings.
\n\nWe are honored to be among such prestigious publishers and we hope to continue to spearhead that growth in our quest to promote Open Access as a true pioneer in OA book publishing.
\n\n\n\n
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\r\n\tPhysical activity (PA) is very important and has many health benefits for people. Through physical activity, people can increase their mobility and reduce the risk of disease while improving the quality of their daily lives. Changing adults’ behavior by increasing physical activities could reduce illness and decrease costs to society concerning health problems. PA has been linked to a reduction in the risk of developing heart disease, stroke, high blood pressure, cancers, diabetes and osteoporosis, helps control weight, and even helps to ease stress.
\r\n\r\n\tThe purpose of this book is to quantify the state of progress in the use of the latest technologies applied to practice and performance for high-level athletes and how these tools can be used to measure the impact of sports on humans - from sedentary lifestyles to high-level sports performance. Technologies examined can be artificial intelligence, virtual reality, modeling, simulation, and computing in the musculoskeletal system.
",isbn:"978-1-83969-141-6",printIsbn:"978-1-83969-140-9",pdfIsbn:"978-1-83969-142-3",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,hash:"725573ecd45f3f984f173d7d6dba0433",bookSignature:"Prof. Redha Taiar",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/10615.jpg",keywords:"Sports Performance, Sports Medicine, Sports Analysis, Gait Analysis, Gait and Posture, Injury in Sport, Augmented Human, Mixed Reality Simulation, Artificial Intelligence in Sport, Healthcare, Wearable Technologies, Musculoskeletal System",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"October 19th 2020",dateEndSecondStepPublish:"December 28th 2020",dateEndThirdStepPublish:"February 22nd 2021",dateEndFourthStepPublish:"May 13th 2021",dateEndFifthStepPublish:"July 12th 2021",remainingDaysToSecondStep:"22 days",secondStepPassed:!0,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Prof. Taiar is a specialist in the biomechanics of health disease and rehabilitation. In industry, his last work was the conception and validation of anti-fatigue mats Notrax society, and in sport, his last work focused on the development of swimsuits for a triathlon, swimming, and skiing for Brazil Olympic Games (2016) and winter Sotchi 2014 Olympic Games. Currently, he is Head of Redha Taiar Biomechanical Engineering and a Vice head of the congress of Physical Medicine and Rehabilitation Society.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"81693",title:"Prof.",name:"Redha",middleName:null,surname:"Taiar",slug:"redha-taiar",fullName:"Redha Taiar",profilePictureURL:"https://mts.intechopen.com/storage/users/81693/images/system/81693.jpeg",biography:"Prof. Redha Taiar, Ph.D. Biomechanics is a Professor at the University of Reims, Champaign France. He is Head of the Redha\nTaiar Biomechanical Engineering Society developed for sport\nand medical advice for the industry. His research focuses on the\nindustry of engineering for medicine and sport. He is an engineer for different industries such as Arena for sport and Sidas,\nMedicapteur for medical development. For industry workers,\nhis last project was for the Notrax Society (Conception and validation of anti-fatigue mats). For sport, his last project focused on the development of a swimsuit for\ntriathlons and swimming for Brazil Olympic Games (2016) and the suit fabrics for\nskiing at the Olympic Games at Sotchi in 2014. He is a specialist in the biomechanics\nof health disease and rehabilitation.",institutionString:"University of Reims Champagne-Ardenne",position:null,outsideEditionCount:0,totalCites:0,totalAuthoredChapters:"3",totalChapterViews:"0",totalEditedBooks:"1",institution:{name:"University of Reims Champagne-Ardenne",institutionURL:null,country:{name:"France"}}}],coeditorOne:null,coeditorTwo:null,coeditorThree:null,coeditorFour:null,coeditorFive:null,topics:[{id:"16",title:"Medicine",slug:"medicine"}],chapters:null,productType:{id:"1",title:"Edited Volume",chapterContentType:"chapter",authoredCaption:"Edited by"},personalPublishingAssistant:{id:"185543",firstName:"Maja",lastName:"Bozicevic",middleName:null,title:"Ms.",imageUrl:"https://mts.intechopen.com/storage/users/185543/images/4748_n.jpeg",email:"maja.b@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. 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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"}},{type:"book",id:"878",title:"Phytochemicals",subtitle:"A Global Perspective of Their Role in Nutrition and Health",isOpenForSubmission:!1,hash:"ec77671f63975ef2d16192897deb6835",slug:"phytochemicals-a-global-perspective-of-their-role-in-nutrition-and-health",bookSignature:"Venketeshwer Rao",coverURL:"https://cdn.intechopen.com/books/images_new/878.jpg",editedByType:"Edited by",editors:[{id:"82663",title:"Dr.",name:"Venketeshwer",surname:"Rao",slug:"venketeshwer-rao",fullName:"Venketeshwer Rao"}],productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}]},chapter:{item:{type:"chapter",id:"39777",title:"Adjustment Methodology in a Regional Densification of a Terrestrial Reference Frame",doi:"10.5772/50548",slug:"adjustment-methodology-in-a-regional-densification-of-a-terrestrial-reference-frame",body:'The measurement of geodetic networks by satellite positioning is becoming more and more common, given the advantages of this technology to solve the main problems of geodesy:
Planimetric geo-reference to a geocentric system
Independence of intervisibility between points
Increase of relative precision.
The purpose of working under the Network scheme is to have superabundant observations that allow adjustment by estimating parameter (coordinates) values and precisions.
Given the versatility of the method of indirect or parametric observations, it has become the most widely used as a method of geodetic adjustment.
The Adjustment Method by Non-Conditioned Indirect Observations, also known as Method of Coordinate Variation, or Parametric Method has been widely accepted in Geodesy. by means of the use of observation equations, in which the parameters are the coordinates.
This adjustment made under the application of the method of minimum squares, estimates parameters as well as their precisions.
It allows to incorporate equations of coordinates, which are essential when introducing the reference frame and generating an external control.
In the adjustment, an appropriate management of the coordinates requires:
The introduction of fiducial coordinates with a precision better than the precision of the network to be adjusted.
The adoption of appropriate weights to these coordinates, in order not to create unnecessary deformations.
These objectives, as will be shown in this chapter, are strongly correlated.
The strict frame introduction by fixing the fiducial points could often result in an unfavorable option because it induces loss of precision to the network, caused by internal deformation. On the contrary it is possible to generate a free or quasi free adjustment omitting the introduction of the datum in order not to cause deformation to the network. Finally, it is possible to achieve a condition of balance by adopting the appropriate strategy, the optimum quantity and distribution of fiducial points. This solution must achieve the required precisions and the densification of the selected reference frame [2].
Before the datum input it is necessary to estimate the internal precision of the network to be adjusted. Knowledge of network internal precision will allow the definition of tolerances when datum is introduced.
This analysis is possible because of the existence of superabundant observations that is to say in the presence of degrees of freedom. In the satellite positioning techniques the adjustment over observations made in different periods or days is very important since these observations are affected by factors that change through time such as the tropospheric and ionospheric delay, oceanic and terrestrial tides, etc. This is the reason why it is necessary to measure the points of the networks in different sessions (2 or more are recommended). The analysis of network precision is carried out on such points of over-occupation.
A “free” adjustment to the network is performed (assigning a very low a-priori weight to the coordinates of the points). This adjustment combines the total group of sessions in the so called multisession adjustment. After the application of similarity transformation (Fig.1), this result is compared with the network solution of each daily session. The internal precision of the free network is estimated from the quantification of the residuals obtained (Fig.2).
The methodology for performing the analysis on the internal accuracy is summarized in the following items and Fig. 2 outlines the procedure.
A free weekly adjustment is performed incorporating each of daily networks.
Transformation parameters are estimated between the weekly combined network and each daily networks.
The transformation parameters are applied to each of the daily solutions.
The residuals are calculated by comparing the coordinates arising from the previous processing and the coordinates of the weekly solution.
The obtained residuals were analyzed according to predetermined tolerances. If it is necessary stations are reduced.
The knowledge of the internal precision of the network makes possible to perform the tie of the network to a certain reference frame.
Similarity transformation.
Analysis of the internal precision of the network.
Once the analysis of the internal precision of the network to be adjusted is carried out, the second adjustment, “weighted adjustment” takes place, in which the control points are selected and a-priori errors are set.
The datum input strategies are mainly based on the adhesion or not to the equations of coordinates for fiducial points. An appropriate weight is added to them in order to link to a greater or lesser extent the network vectors to the positions determined by the values of the coordinates mentioned.
The most common strategies in the adjustment of normal equations, with the objective to link the network to a certain reference frame are the followings [5][6]:
Minimum constraint solution: this kind of solution adjusts the network by performing a roto-translation in the measured and processed network towards the reference frame desired to be used as control or Datum, materialized in the fiducial coordinate group. Similarity parameters are applied, calculated from the coordinates of fiducial points and their corresponding of the free network.
Coordinates constrained: this strategy allows you to force the coordinates of the points taken as fiducial network to their corresponding coordinates in the reference frame. The latter should be assigned a weight according to their precisions.
Coordinates fixed: with this option the coordinates of the selected stations are completely fixed as fiducial, accepting that they do not receive any correction.
Entering the control coordinates with a certain weight produces a double effect: it ties the network to the reference system and consequently causes certain deformation. Such effects are strongly related. A greater weight at the fiducial coordinates introduces the frame with greater accuracy at the expense of greater distortion of the network. For these reasons there is no one adjustment strategy only. Different adjustments are made by varying both the adjustment strategy as the corresponding weights. Among the various solutions will choose the optimal strategy from a careful analysis of results [3].
The choice of the solution to be adopted is based on a double analysis, aiming at a balance between the reference frame input and the minimum network deformation. This is accomplished by controlling, on the one hand, the fact that the coordinates of the support points do not vary more than the error value with which they were determined, keeping the precision of the reference frame; and, on the other hand, that this adjustment does not create deformations to the network in the points that exceed its internal precision [3].
The proposal to analyze the solutions is based, on the one hand, on the estimation of similarity transformations between the coordinates obtained from free adjustment of the network and those obtained from weighted adjustment on the other hand. The purpose of the application of this transformation to the weighted network to be assimilated to the free network is to filter the systematic effects that may be affecting it, mainly the differences between reference systems. Only the residual effects that show the type of deformation generated by the weighted adjustment are left. This deformation can be quantified by means of the calculus of the residuals between the coordinates of the weighted network and the free network transformed [4]
In order to achieve balance the analysis of these deformations must be considered on one of the sides of the scale. On the other side of the scale it is necessary to quantify the precision of the frame over the adjusted network. This is observed in the magnitude of corrections of the estimated coordinates (new coordinates minus a-priori coordinates) in the fiducial points. They should not exceed the error value with which they were determined in order to preserve the precision of the reference frame that they intend to densify.
The steps to be followed in this analysis methodology are mentioned below.
A quasi free adjustment is made, by assigning a very low a-priori weight to all point coordinates.
A weighted adjustment is made, by selecting a weighting strategy and assigning a-priori weights to the coordinates of the control points.
Seven similarity transformation parameters between the coordinates of the points of the free network and the weighted network are established.
The similarity transformation is applied to the free network in order to take it to the reference frame of the weighted network.
The residuals are calculated after the application of the transformation mentioned above.
The calculated residuals are analyzed.
The extent of the estimated corrections to the a-priori coordinates of the fiducial points is analyzed.
The process is repeated, modifying the adjustment strategy and the weights to the fiducial coordinates. It is also repeated changing the fiducial reference frame in case there is the possibility to choose more than one.
The optimum weighted adjustment is selected, based on the comparison of the results obtained for each case in points 6 and 7, or decisions are taken as regards the control network to be adopted.
Figure 3 outlines the proposed analysis methodology. Where it comes to finding the balance between the distortion introduced by the reference frame to be densified and accuracy on the same network densification.
In order to present this theory in practice, the Methodology used for the processing of a regional continuous GNSS network in South America (SIRGAS-CON-D-South), is described. In this point the models used in the processing, the adjustment methodology and the result analysis are described[7].
As the main objective is to obtain a high precision network, it was necessary for the observations gathered by observation GNSS permanent stations to undergo a strict processing, modeling in a precise form the physical phenomena which affect the observations and estimating those parameters that cannot be modeled [5].
The observations of 98 continuous GNSS stations were gathered and precise ephemerides were used together with Earth Orientation Parameters (EOPs) provided by IGS (International GNSS Service.)
Methodology for analysis of results. Balance between accuracy and deformation.
The total 2.5 year-period of observation to be processed was divided into GPS weeks, witch provided data redundancy and over-occupation, in order to allow the corresponding accuracy analysis week by week and then the total that includes the whole period.
Other variables considered in the processing were: published satellite problems and maneuvers, as well as corrections to the antenna phase centers used. The latter were consistent with the offset of satellite antennas, on the one hand, and with the specific equipment (receiver, antenna, radome, off set, etc.) of each GNSS continuous station on the other. In addition, a-priori coordinates and velocities for all processed stations were incorporated to the processing.
Taking into account all these elements, a data pre-control and pre-processing were carried out, which detected outliers and inconsistencies in the data. In certain cases, it was necessary to reduce or cut data which did not comply with the required quality criteria.
Once the pre-processing was accomplished, the vectors network was constituted and also the parameters estimation was made. In this processing, models of ocean loading, gravitational attraction, nutation and precession were considered and improved by EOPs, calculated by IGS and only tropospheric parameters, ambiguities and corrections to a-priori coordinates were estimated.
The most important parameters introduced in the processing are detailed as follows [8]:
Observations | Double differenced |
Sampling rate | 30 sec |
Elevation cutoff | 03º |
Baselines strategy | MAX-OBS |
Observations weighting | cos Z |
Orbits/EOP | IGS final - IGS08 and EOP week |
A-priori Troposphere model | Niell dry component |
Troposphere | Zenith delay estimated each 2 hours (12 daily corrections p/station) A-priori sigma applied with respect to Niell prediction model (wet component) -first parameter +/- 5 m absolute and +/- 10 cm relative |
Ambiguities | QIF strategy, no ionosphere model applied |
Ocean tide model | FES2004 |
Phase center variation | Absolute (IGS_08) |
Coordinates and velocities | IGS05_R |
Daily solution | NEQ files, free network solution (s=±1m) |
Week solution | SINEX files, Free network solution (s=±1m) |
Parameters incorporated in the processing of GNSS observables
The daily normal equations generated after parameter estimations, were subjected to a quasi-free weekly adjustment, with which the analysis of the internal precision of the network was performed. In this adjustment the normal equations obtained for each of the seven days of the week are combined in a single system of equations. All the point coordinates are constrained using a 1.00 meter a-priori sigma. In this way, the network is only tied to the frame established by the orbits at the moment of observation.
The solution of each of the 7 days was analyzed together with the combined weekly solution, starting from the comparison of coordinates after the application of similarity transformation. Residuals were calculated after the transformation and were verified to ensure that they were within the pre-established tolerances. This process was described in detail in point 2.1, Fig.1 and Fig.2.
It is worth noting that these tolerances of internal precision are determined in each case according to the repeatability analysis of coordinates. The latter was possible thanks to the observation redundancy and a particularly varied sampling according to the time (Fig. 4), which is relevant due to its influence on a vast quantity of factors taking part.
Internal precision. SIRGAS-CON-D-South network – Period: 127 weeks.
In the presented case these tolerances were considered for north and east components in 15 mm. and for a up component of 30 mm. For residuals larger than the tolerances, it was concluded that the main causes were: insufficient observations (shorter than 10 hours), too high offset of the receiver clock or too many outliers.
In some cases, it was necessary to partially or completely reduce the observations of such stations. Once the control was finished, the internal precision of the network in 2 mm. was estimated for the horizontal components and for up of 4 mm. (Fig. 4.)
Two of the three strategies mentioned were used: “Coordinates constrained” and “Minimum constraint solution”.
The “Fixed Coordinates” method was discarded because it is a very precise network (2mm), as expressed in the previous paragraph, not counting to the date of this adjustment with fiducial coordinates that would present an order of precision better than the mentioned one.
Among available high precision global frames this selection was performed taking into account the precision quality of the network to be adjusted.
The availability and the distribution of processed points which had coordinates in one of the ITRF [9][10] defined by the IERS or in one of the frames defined by the IGS [11] were analized.
The epoch of reference frame was also taken into account, since it is necessary to update the coordinates to the date of the observations to be adjusted [12].
Of the most recent global frames to the date of the adjustment (2008.1), ITRF2005 and IGS05, IGS05 was selected, since it presents a good availability and distribution of fiducial points over the network to be adjusted (Fig.5). In this selection, the ITRF2005 was discarded because it had been calculated with relative antenna phase centre corrections whereas IGS05 was adopted because absolute antenna phase centre corrections had been used in its calculation.
Stations with coordinates in ITRF00, ITRF05 and IGS05.
The possibility of adopting as frame IGS weekly solutions was also considered. IGS weekly coordinates refer to the IGS05. They coincide with the epoch of each week to adjust. In this case it was not necessary to update coordinates by velocities regardless of introducing error. The error introduced by the update coordinates for velocities is produced at the stations having in the time series of coordinates, nonlinear variations (mainly in the Up component). GNSS stations show significant seasonal position variations resulting from a combination of geophysical loading and systematic errors [2][5].
As an example (Fig. 6) shows the time series of the “up” component in the stations BRAZ and LPGS.
Seasonal position variations in the Up component. Stations: BRAZ and LPGS.
A total of 98 points made up the network to be adjusted. Of these 14 points were considered fiducial.
Summarizing, four different adjustments were calculated for each of the selected weeks:
"Coordinates constrained" strategy with an a-priori sigma of 0.00001 m. in fiducial coordinates and their corresponding velocities in IGS05.
"Minimum constraint solution" strategy, by the application Not-Net-Rotation and Not-Net-Translation, both with an a-priori sigma of 0.0000001 m without applying scale factor. Coordinates and their corresponding velocities in IGS05.
“Coordinates constrained” method, with an a-priori sigma of 0.00001 m, using as fiducial coordinates the weekly IGS solutions. These were obtained by a three-week delay in the site: ftp: // igscb.jpl.nasa.gov/pub/product/
Finally it was established as strategy of adjustment “Minimum constraint solution" with an a-priori sigma of 0.0000001 m without applying scale factor, by IGS weekly coordinates.
In order to identify which of two adjustment strategies used and which feasible group of coordinates used to introduce the datum was turning out to be most adapted to adjust the network object of this work, a comparison of results was made. 127 weeks of observations were included, from the 1400 GPS week, (November, 2006), up to the 1527 week, (March, 2009), two and a half years of observation).
SIRGAS-CON-D-South and fiducial IGS stations.
The analysis was realized on the deformations caused to the loosely constrained network by the introduction of the datum. The deformations were calculated based on the quasi-free network corresponding to every week. It was considered that given the precision of this network, it was the best boss to analyze the deformations, since it is free of the systematic effects of rotation and translation introduced by the reference frame.
To estimate the deformations, each of the 4 solutions (obtained by applying the different two adjustment strategies and two frames), were compared, after the application of Helmert transformation to the quasi-free solution (2.2.1). It is to be noted that the parameters were calculated only with the groups of coordinates of the fiducial points, and then they were applied to the totality of the network for every week of calculation.
The residuals resulting from the totality of the comparisons were plotted for each of the stations, according to three components, north, east and up [5].
The major deformations were observed on the fiducial points and on the points of the contour of the network (Fig.7)\n\t\t\t\t\t
Fig. 8 and Fig. 9 show the residuals calculated in two fiducial stations, CONZ and BRAZ. In both graphs it can be observed that the strategy that produces the minimum deformation to the network is “Minimum constraint solution” (Not-Net-Rotation and Not-Net-Translation), both with IGS05 coordinates updated by velocities (in blue), and for the IGS Weekly coordinates (light blue).
In the same way, it can be observed that the strategy that major deformation offers is “Coordinates constrained” with IGS05 coordinates and velocities (red color). This response is mainly observed in those stations in which their behavior is not contemplated by a linear velocity. For example the BRAZ station is affected by seasonal variations principally in its Up coordinate. The CONZ station shows residuals in its three components, that increase as time passes (Fig. 8).
Residual of Helmert transformation in the fiducial station CONZ (Chile).
Residual of Helmert transformation in the fiducial station BRAZ (Brasil).
In the results, the behaviors observed in the variation of the coordinates it wasn´t linear. Which allowed to conclude in the need to calculate velocities determined in precise form and that they accompany on the real movement of the coordinates answering to a model defined with better precision.
In order to realize a quantitative analysis there were calculated the average values of these residuals by every station of reference for 127 analyzed weeks, in each of four strategies (table 2 and Fig.10).
Finally there was calculated the average value of these residuals for the whole network according to the strategy, with its corresponding standard deviation (table 1 and Fig. 11.). It is necessary to mention that the residuals in north and east components have combined in a residual called "Horizontal".
It was concluded that the strategy that minor deformation was causing to the network was “Minimum constraint solution” since this type of adjustment tries to support the quasi-free original network without deforming it and only torn it moves trying to accommodate to the frame of reference, producing of this form the minor residuals.
RMS in the reference points according to strategy of adjustment.
RMS (127 weeks) | ||||||||
Station | IGS05+vel CC (mm) | IGS05+vel NNR-NNT | IGSweekly CC | IGSweekly NNR-NNT | ||||
Horizontal | Up | Horizontal | Up | Horizontal | Up | Horizontal | Up | |
BRAZ | 7,916 | 5,686 | 3,487 | 1,930 | 3,569 | 3,438 | 2,878 | 0,999 |
CHPI | 7,470 | 5,531 | 3,862 | 1,757 | 3,704 | 2,711 | 3,402 | 1,054 |
CONZ | 5,179 | 3,692 | 3,133 | 1,685 | 3,461 | 3,543 | 3,142 | 1,379 |
CRO1 | 5,795 | 4,980 | 5,314 | 2,429 | 4,224 | 3,894 | 4,620 | 1,886 |
GLPS | 4,388 | 7,013 | 4,012 | 2,491 | 4,589 | 3,664 | 4,163 | 1,375 |
ISPA | 7,694 | 7,064 | 3,643 | 1,848 | 5,105 | 3,904 | 2,696 | 1,344 |
LPGS | 4,561 | 4,639 | 3,670 | 2,560 | 2,708 | 2,281 | 3,515 | 2,366 |
MANA | 15,239 | 9,955 | 4,515 | 4,485 | 4,680 | 3,636 | 4,391 | 2,365 |
SANT | 3,660 | 4,787 | 2,545 | 1,229 | 3,298 | 3,631 | 2,634 | 1,046 |
SCUB | 6,892 | 4,318 | 4,495 | 1,893 | 3,503 | 3,450 | 4,341 | 1,387 |
UNSA | 4,169 | 4,302 | 1,621 | 0,891 | 2,315 | 3,018 | 1,712 | 0,818 |
Mean value | 6,633 | 5,633 | 3,663 | 2,109 | 3,741 | 3,379 | 3,408 | 1,456 |
Standard deviation | 3,235 | 1,790 | 1,002 | 0,938 | 0,846 | 0,507 | 0,907 | 0,530 |
RMS in the reference points according to strategy of adjustment.
Analyzing the average values of residuals, they confirm that the minor values obtain in both cases in which there was applied the strategy Minimum constraint (NNR-NNT). Between these two options the one that minor deformation produces is the one that uses the IGS weekly coordinates (last column, table 1 and Fig.11.). It presented an average residual of 3.40 mm in horizontal component and 1.45 mm in height.
The solutions obtained by means of coordinates constrain linking to the weekly coordinates of the IGS also they present minimal deformations. The strategy that should have discarded was CC with coordinates IGS05 updated by speeds, since in this case they were duplicating and even they were trebling the values of introduced deformation.
Deformation of the network according to the strategy of adjustment.
In the previous section we arrived at the solution that allows us to deform as little as possible the network. However, this solution will not be adequate if the accuracy of the frame is lost. It\'s time to reach equilibrium in the balance. Finding the point at which the chosen solution provides lower distortion and greater accuracy
In order to quantify the accuracy of the frame over the adjusted network, the extent of estimated corrections of the coordinates (new coordinates - prior coordinates) in the fiducial points were analyzed. They should not exceed the error value with which they were determined in order to preserve the precision of the reference frame that intend to densify.
The estimated corrections were analyzed for each of the four strategies of adjustment used. In each case they were compared to the a-priori error of fiducial coordinates and the optimum solution was selected together with the analysis of deformation. In the above mentioned solution 80% of the fiducial points presented accuracy to the reference frame within 0.005m., thus complying with the objective pursued. (Fig.12).
Final result was selected as better strategy of adjustment to link the network SIRGAS-CON-D-South to the strategy “Minimum constraint” adopting as reference coordinates the correspondents to the IGS weekly solution.
Frame accuracy. Estimated corrections in the fiducial points.
The absence of result analysis in the adjustment can bring about one of the following problems: the adoption of an inappropriate frame, its loss of accuracy, or an unnecessary loss of the precision if the Network to be adjusted.
To ignore seasonal variations at reference stations can introduce systematic errors in the datum realization and the networks can be significantly deformed [13].
These effects are larger in regional networks than in the global net, especially in regions with strong seasonal variations such as Latinamerica [2].
The geometry of the quasi free network is always deformed, when the geodetic datum is introduced. This deformation is specifically larger at the remote sites of the network, and in fiducial points
Updating strategies based on the linear movement of the reference stations introduce important errors into the station positions, mainly at the reference stations. This is a consequence of constraining a seasonal signal to be a linear trend [13].
It is necessary that the reference frame definition include, together with the usual linear terms, seasonal variations in order to improve the modelling of the reference site motions and to make it more reliable.
In the mean time, weekly solutions of regional reference frames shall be aligned to IGS frames by constraints to the IGS weekly coordinates [5].
The modern global economy is on the brink of a new industrial revolution, as evidenced by many actual trends, following the provisions of modern economic theory (economic cycle theory, crisis theory, innovation theory). Overcome the global crisis, a new wave of innovation has needed where Industry 4.0 will become a new global industrial landmark and standard of development in the real economic sector of the entire global economic system [1]. In line with the goals of society 5.0, Industry 4.0 revolutionizes the production industry by increasing operational efficiency, new business models, services, and products. Society 5.0 focuses on using technology developed by Industry 4.0 to benefit humankind by utilizing advanced technology to solve problems and the economy. This study identifies that disruption of innovation through environmental turbulence offers opportunities to drive business development. Society 5.0 focuses on people and changes from innovation to technological transformation through industrial automation 4.0 [2, 3]. In this study, environmental turbulence refers to exogenous factors that give rise to uncertain and volatile situations that impact business performance [4, 5].
\nWhile some companies view environmental instability as a risk that threatens performance, it has understood as a threat and an opportunity for more entrepreneurial firms. It opens opportunities for the growth of various new businesses accompanied by increasingly fierce competition [6]. The desire to find ways to overcome turbulence and create growth and success from turbulence drives companies to think and act. Rapid technological advances have led to significant business developments and changes [7]. Likewise, as reported by The Global Competitiveness Report 2017–2018, the interruption and disruption of technological change create extraordinary possibilities and challenges which strengthen by using the convergence of digital, physical and organic technology that has characterized the rise of the Fourth Industrial Revolution [8].
\nEnvironmental turbulence can occur in several factors: technology turbulence, competition intensity, market turbulence, and regulation. The biggest factor is due largely to rapid technological changes and the high intensity of competition marked by customer composition changes, behavior, and preferences. In Indonesia, over the top service (OTT) penetration was long before Covid-19 eroded revenue providers [9, 10]. Even though the operator is experiencing growth, the Telco industry requires a large investment, but the income is low. Although growing, digital services have not replaced declining operator revenues [11, 12]. When environmental turbulence occurs, it causes a direct disruption to company performance. Thus, the company’s initial response must be fast to reduce accumulated losses. In the firm’s predetermined response model, management reactions are initiated without delay on the rational trigger point, as illustrated in Figure 1 [13]. Due to the environment’s turbulent characteristics, both the base model and the Determinant model of responses are triggered after the event has occurred.
\nDecisive model of firm response to a crisis event.
In this study, the methodology used is a qualitative systematic literature review on various aspects of the topics discussed, including strategy, distinctive capability, environmental turbulence, and business model strategies. The basic ideas on carrying out such a systematic review had borrowed from leading scholars in the field [14]. Both integrative and interpretive techniques had used to uncover new insights into strategy. Various studies on dynamic strategies and volatile business environments have collected it from various primary qualitative studies conducted using this methodology. Several in-depth studies of the subject matter have carried it out so that conclusions have drawn it. Thus, the literature review’s qualitative synthesis has used it as the basis for the arguments discussed in this chapter.
\nThis study is also measured by asking respondents’ perceptions through explanatory studies and preliminary surveys on market predictability, innovation, and customer preferences. Disruptive innovation through environmental turbulence is a major creative driver for reengineering businesses. It is not just innovation but agile innovation that can take advantage of economic downturns by developing new creative business models by making new paradigm shifts [15, 16]. As globalization and digitization evolve, organizations must respond quickly to changing market demands. According to Milan Krstic, 2018 agile innovation is a collaborative process that connects innovation, people, technology, projects, and outputs that impact paradigm shifts driven by competition in the market and rapid technological advances [16]. Agile innovation emphasizes creative and adaptable teamwork in solving complex problems; Lean development focuses on eliminating all losses continuously so that the innovation process is significantly faster [15]. Thus, the company can be fast responding to Changes in Environmental Turbulence to encourage business growth in the face of deteriorating company performance.
\nExploratory case studies and literature reviews are using to test environmental turbulence’s antecedents, Distinctive Innovation Capabilities, through business model innovation strategies to firm performance [17, 18].
\nThis study fills the gap from the previous study. Since 2000, most business models have focused on organizations’ mediating role to mediate technology capabilities and firm performance. Only a handful of research publications so far have examined how organizations innovating should have structured collaborations and interact fluidly [19, 20]. There are no studies that elaborate on how organizations exploit internal resources and structures and how to access, configure, and utilize external resources embedded in business model strategies to produce the ability to manage business turbulence in the industrial evolution of the 4.0 era through Distinctive Innovation Capabilities [21, 22]. This research’s authenticity lies in how management produces a practically oriented framework of how organizations shape to be innovative and competitive by building a common business model. However, this study has limitations due to its qualitative nature and conceptual framework, which needs further investigation through large-scale surveys by quantitative research. The conceptualization and investigation of the independent and combined effects of business model innovation, environmental turbulence, and Distinctive Innovation Capabilities structures on organizational agility highlight the importance of complementarity between antecedents and add to the cumulative pool of knowledge in this research area.
\nFrom a managerial perspective, this study provides a comprehensive view of environmental turbulence impact, how to develop a Distinctive Innovation Capability strategy for developing an organization’s business model, how to achieve it, what variables contribute, and how to relate it to performance. Institutional cooperation requires collaboration in an innovation environment where consumers, companies, universities, and public authorities work together to produce innovation (Quadruple Helix). It means that the resulting research can apply to national development, and higher education is responsive to industrial, customer and social needs [23, 24].
\nCompanies are operating in a more dynamic environment that affects environmental turbulence that occurs influence business model transformation and business performance. Many studies confirm that several conditions in the Study of Environmental Turbulence and Model Transformation & Business Models affect company performance [25, 26, 27, 28]. The more turbulent an environment, companies must rely on innovation to neutralize the outdated threats of products and technology and take advantage of new technological opportunities to create new business models that can support company performance [29, 30]. However, according to [31] pragmatically, some problems will be missed and become strategic surprises even at the most sophisticated environmental scanning method. The recent economic downturn and current pressures on the market have increased the need for leaders to have a strategic business model plan [32]. Organizations whose historical business models have based on organic growth must now strategically position themselves taking into account the new ‘rules of the game’ and develop a posture that increases strategic, managerial aggressiveness [33].
\nThe process of forming a volatile environment is inseparable from the influence of technology, the emergence of a new economic order, changing values and lifestyles, and the availability of an exchange of information flows, goods and services with a decreasing price trend accompanied by the high speed of the flow of goods/services and the dissemination of information. The emergence of an economic model of sharing transportation and accommodation has disrupted business establishment. Several studies have confirmed the relationship between environmental turbulence and business strategy models and then show that it successfully competitive advantage. Besides, it mediates the role of business model innovation to show that there is a relationship, and the results of several studies suggest that business model innovation has a positive effect on company performance [34, 35].
\nThe description above means the higher the organization’s ability to provide the right solutions with a good business model innovation strategy that can provide added value to the organization. Then the innovative business model will perform better in facing competitive advantages in the telecommunications industry. Therefore, it had assumed that environmental turbulence influences business innovation. According to [36], human resources and capabilities are important components of a company’s competitive advantage in a resource-based view. The concept of turbulence was initially introduced in 1965 by Emery and Trist. The business environment has been influencing by several components (competition, customers, suppliers, shareholders, public markets, regulatory bodies, legislative bodies, technology, economics, and society) [37]. Environmental turbulence influences the external environment or changes in the future [38, 39]. Based on the contingency theory [40, 41], organizations react to environmental changes, and organizational strategies are determined based on environmental conditions that occur. Figure 2 shows that many studies on environmental turbulence strategies with a link and difference focus have described it as pedigrees for the theoretical foundation of environmental turbulence from the market and organization perspective.
\nTheoretical foundation of environmental turbulence. Source: Various publication
This study ends with the definition of environmental turbulence for the Telecommunication Industry. Organizational ability to project business planning creatively, flexibly, and adaptively to anticipate potential disruptors, new regulations, technology changes, competition, social patterns, and consumer lifestyles. The dimension of environmental turbulence in this study is; Dynamics - the extent to which the company’s environmental components’ elements remain the same over time or a continuous flux [42]. The degree of dynamism (instability) has influenced it by changing environmental change and change intensity. Complexity - Increasing number of elements and their interdependence [43], homogeneity/heterogeneity [44] contribute to environmental complexity. Predictability - uncertainty, or ignorance, is a dimension related to the extent of causation and the availability of information about change contributing to the level of uncertainty [39, 45].
\nThe literature on strategic management shows different capabilities or competencies as an important part of an organization’s resources and competitive advantage. Mintzberg and Quinn [46] argue that Distinctive Innovation capability, as a competitive advantage source, provides the basis for a sustainable competitive advantage [43]. Meanwhile, Drucker [47] and Cavusgil et al. [48] discussed the importance of the ability of innovation for organizations to survive in an unstable environment [46, 47]. Innovation capability had proposed as a high-level integration capability, namely, forming and managing various capabilities [48]. Organizations with this innovation capability can integrate their company’s core capabilities and resources to stimulate innovation successfully [49, 50].
\nAccording to Amabile [51] and quoted by Zhao et al. [52] states that innovation capability is the application of knowledge relevant to the achievement of value in an organization. Teece and Pisano [53] and Teece et al. [54] suggested that the winners in the global market are companies that demonstrate timely responsiveness and innovation that is fast and flexible [55, 56], together with management’s ability to coordinate effectively and re-apply internal and external competencies [57]. However, Miranda and Figueiredo [58] distinguish routine and innovative companies based on technological innovation capabilities with the factors that shape vision and strategy capabilities [51, 52], utilization of competency bases, organizational intelligence, creativity and idea management, organizational structures and systems, culture and climate and technology management [53]. Saunila defines innovation capabilities by influencing an organization’s ability to manage innovation [53]. These aspects include participatory leadership culture, ideation, organizational structure, climate, worker welfare, knowledge development, regeneration, external knowledge [54], and individual activity [59].
\nThis study concludes that Distinctive Innovation capability is the company’s ability to develop and manage fast and flexible innovation by leveraging technology management’s creative capabilities, knowledge development, integrating capabilities, and core company resources to stimulate innovation and provide added value for companies to gain competitive advantage.
\nAny organization interested in competing in a dynamic market must create unique benefits that enable it to gain a competitive advantage over competitors. However, [60] argues that business has become a global and global market, which increasingly encourages an Innovation Business Model because companies must overcome global challenges. The way to outperform competitors is through innovation. Innovation as a strategy involves creating new ideas, processes, and methods to improve current goods and services or bring them into new products and services [37]. Innovation is needed to maintain the existing competitive position and the competitive advantage that has obtained it. Most studies conclude that business model innovation increases competitive advantage and contributes significantly [58, 61]. Previous studies examined cloud and digital computing as disruptive innovations. Building on this reflection, to better understand business model innovation resulting from accepting a cloud benefit model [36, 62].
\nTechnology has changed organizations must realize that a classic business model, such as buying and selling at a premium price, will no longer work [63]. Technological changes provide the necessary context for understanding the transformation of new digital technologies [64, 65, 66]. This article will help understand why this new business model’s strategy innovation is important and why everyone should understand the most successful business models. Looking at the business model is essential to understanding how to position a company and generate additional revenue [58, 60, 67]. New business models can also help companies become more resilient to market dynamics and diversify business strategy [68, 69]. Successful disruptive business models are often customer focused. Based on the description above, the higher the organization’s ability to provide the right solutions with a good business model innovation strategy that can provide sustainable Business Models, Added value to the organization, the high-level innovation will perform better in the face of competitive advantage [69, 70]. Therefore, a business model paradigm’s concept logic can help understand specific innovation to transformation challenges that companies will face when introducing new business models, a simplified schematic overview strategy Sustainability Business Model, reconceptualized from Figure 3 [71].
\nSustainability business model transformation.
An extensive literature review guided the choice of variables for the conceptual framework. Identify the dominant antecedents considered to form the business model innovation strategy and firm performance by developing it through exploratory case studies. Thus, in line with this study, the objectives are to examine the effect of Environmental Turbulence (ET), Distinctive Innovation Ability, and business model innovation influence firm performance (FP). The literature review of the concepts above will answer the questions in this study by explaining how the relationship between Environmental Turbulence (ET), Distinctive Innovation capabilities, and business model innovation affects company performance (FP)?
\nTherefore, based on the study literature and explanatory case study, this study defined several hypotheses. The following hypotheses have been compiled by modeling the relationships between the variables examined in detail in this section, and a conceptual research model developed, as shown in Figure 4.
Business model innovation has a positive effect on company performance.
Environmental Turbulence (ET) has a positive effect on company performance (FP) directly and indirectly through a business model innovation strategy.
Typical Innovation Capabilities have a positive influence on company performance (FP) directly or indirectly through a business model innovation strategy.
Conceptual model.
The explanation above and based on the previous literature means that the higher the organization’s ability to provide the right solution with a good environmental turbulence strategy that can add value to firm performance, either directly or through an innovation model business strategy. Meanwhile, based on a previous study, the higher its ability to carry out business model innovation and a good distinctive innovation capability, the higher its performance. Therefore, it should have assumed that based on the hypothesis in Figure 4, there is an influence between environmental turbulence, business innovation strategy, and distinctive innovation capability on company performance in the telecommunications industry.
\nExploratory case studies and systematic literature reviews prove that business strategy innovation and firm performance are essential for business success when faced with intense environmental turbulence. Typical Innovation Capabilities and Firm Performance are determined by appropriate revenue in the ongoing market and the expansion of technology and innovation in products for new information for decision-making capabilities [70]. Therefore, innovative portfolio business models are at the center of strategic decision-making capabilities, especially when the complexity of innovation and dynamics increases. This study conceptually develops a framework that connects environmental turbulence, distinctive innovation capabilities, business model innovation, and tests its effect on company performance. This study uses information processing theory to propose and test the antecedents and consequences of a business model innovation strategy.
\nInnovative business model capabilities are a continuous change from new business strategies to keep pace with market developments, competitive advantage, and performance. Therefore, innovation capabilities are not simply the ability to successfully run a new business or manage mainstream capabilities. However, innovation capability is about synthesizing three paradigms, enabling innovative companies to produce new products and services that are quality-focused, efficient, and responsive.
\nThis study proposes the idea of a Typical Innovation capability. We have used the innovation management literature and several case studies as examples of Cisco Systems to demonstrate how innovation capabilities synthesize new and mainstream flows to achieve effective innovation performance. The next section identifies the elements of makeup innovation capability. Therefore, the innovation capability model aims to build a theoretical framework that highlights the actions managers can take to influence innovation success. However, several attempts have made it use a dynamic capabilities approach to produce a holistic innovation capability model. Organizations focus on innovation and innovation output as a competitive strategy.
\nBesides, this study has been able to fill the gaps and enhance previous studies on business model innovation strategies. It has explained that the distinctive Innovation capabilities by utilizing the external organizational structure’s capabilities and flexibility can face business turbulence. It allows organizations to achieve an increase in business strategy innovation capabilities, high-quality company performance in the face of economic decline, and at the same time, add to this cumulative knowledge study. Investigations of a set of antecedents of distinctive Innovation capabilities, business model innovation strategies, and environmental turbulence on company performance have highlighted the importance of complementarity as critical factors developed in future research.
\nThis research has limitations in telecommunications information, communication, and technology in Indonesia. As an antecedent of the Service Innovation has identified it, and its relationship has presented, large-scale quantitative surveys will test the conceptual framework and further explore each variable’s role. Furthermore, future research expands to consider the international implications of research and the broader scope.
\nFrom a managerial perspective, this research gives organization management a comprehensive view of enhancing innovation business model strategy and consecutively providing a solution to customers and influencing it and their relationships. This study will benefit the telecommunications companies that want to move to digital transformation, which is still an expensive and lengthy process, by using strengthening integrative capability to perfecting internal monetization, but especially overcoming external monetization from the opportunity of environmental turbulence to leap forward. Portfolio managers have suggested it intensify the information system’s use as it positively impacts an organization’s innovation business performance by intensifying information exchange with internal and external stakeholders.
\nFrom the social impact perspective, with the sustainable growth of technology-based companies, through innovative business models, Distinctive Innovation capability and environmental turbulence capabilities, companies can provide general benefits from the microeconomic side to customers and at a macro level to the performance of the company and industry in general. Thus, it can be a driving force for the advancement of digitalized technology knowledge and adopted by the community to prepare development towards society 5.0.
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He is an expert in structural, absorptive, catalytic and photocatalytic properties, in structural organization and dynamic features of ionic liquids, in magnetic interactions between paramagnetic centers. The author or co-author of 3 books, over 200 articles and reviews in scientific journals and books. He is an actual member of the International EPR/ESR Society, European Society on Quantum Solar Energy Conversion, Moscow House of Scientists, of the Board of Moscow Physical Society.",institutionString:null,institution:{name:"Semenov Institute of Chemical Physics",country:{name:"Russia"}}},{id:"62389",title:"PhD.",name:"Ali Demir",middleName:null,surname:"Sezer",slug:"ali-demir-sezer",fullName:"Ali Demir Sezer",position:null,profilePictureURL:"https://mts.intechopen.com/storage/users/62389/images/3413_n.jpg",biography:"Dr. Ali Demir Sezer has a Ph.D. from Pharmaceutical Biotechnology at the Faculty of Pharmacy, University of Marmara (Turkey). 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