Chronology of developments in Love wave biosensors and Love wave seismology.
\\n\\n
IntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\\n\\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\\n\\nLaunching 2021
\\n\\nArtificial Intelligence, ISSN 2633-1403
\\n\\nVeterinary Medicine and Science, ISSN 2632-0517
\\n\\nBiochemistry, ISSN 2632-0983
\\n\\nBiomedical Engineering, ISSN 2631-5343
\\n\\nInfectious Diseases, ISSN 2631-6188
\\n\\nPhysiology (Coming Soon)
\\n\\nDentistry (Coming Soon)
\\n\\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\\n\\nNote: Edited in October 2021
\\n"}]',published:!0,mainMedia:{caption:"",originalUrl:"/media/original/132"}},components:[{type:"htmlEditorComponent",content:'With the desire to make book publishing more relevant for the digital age and offer innovative Open Access publishing options, we are thrilled to announce the launch of our new publishing format: IntechOpen Book Series.
\n\nDesigned to cover fast-moving research fields in rapidly expanding areas, our Book Series feature a Topic structure allowing us to present the most relevant sub-disciplines. Book Series are headed by Series Editors, and a team of Topic Editors supported by international Editorial Board members. Topics are always open for submissions, with an Annual Volume published each calendar year.
\n\nAfter a robust peer-review process, accepted works are published quickly, thanks to Online First, ensuring research is made available to the scientific community without delay.
\n\nOur innovative Book Series format brings you:
\n\nIntechOpen Book Series will also publish a program of research-driven Thematic Edited Volumes that focus on specific areas and allow for a more in-depth overview of a particular subject.
\n\nIntechOpen Book Series will be launching regularly to offer our authors and editors exciting opportunities to publish their research Open Access. We will begin by relaunching some of our existing Book Series in this innovative book format, and will expand in 2022 into rapidly growing research fields that are driving and advancing society.
\n\nLaunching 2021
\n\nArtificial Intelligence, ISSN 2633-1403
\n\nVeterinary Medicine and Science, ISSN 2632-0517
\n\nBiochemistry, ISSN 2632-0983
\n\nBiomedical Engineering, ISSN 2631-5343
\n\nInfectious Diseases, ISSN 2631-6188
\n\nPhysiology (Coming Soon)
\n\nDentistry (Coming Soon)
\n\nWe invite you to explore our IntechOpen Book Series, find the right publishing program for you and reach your desired audience in record time.
\n\nNote: Edited in October 2021
\n'}],latestNews:[{slug:"webinar-introduction-to-open-science-wednesday-18-may-1-pm-cest-20220518",title:"Webinar: Introduction to Open Science | Wednesday 18 May, 1 PM CEST"},{slug:"step-in-the-right-direction-intechopen-launches-a-portfolio-of-open-science-journals-20220414",title:"Step in the Right Direction: IntechOpen Launches a Portfolio of Open Science Journals"},{slug:"let-s-meet-at-london-book-fair-5-7-april-2022-olympia-london-20220321",title:"Let’s meet at London Book Fair, 5-7 April 2022, Olympia London"},{slug:"50-books-published-as-part-of-intechopen-and-knowledge-unlatched-ku-collaboration-20220316",title:"50 Books published as part of IntechOpen and Knowledge Unlatched (KU) Collaboration"},{slug:"intechopen-joins-the-united-nations-sustainable-development-goals-publishers-compact-20221702",title:"IntechOpen joins the United Nations Sustainable Development Goals Publishers Compact"},{slug:"intechopen-signs-exclusive-representation-agreement-with-lsr-libros-servicios-y-representaciones-s-a-de-c-v-20211123",title:"IntechOpen Signs Exclusive Representation Agreement with LSR Libros Servicios y Representaciones S.A. de C.V"},{slug:"intechopen-expands-partnership-with-research4life-20211110",title:"IntechOpen Expands Partnership with Research4Life"},{slug:"introducing-intechopen-book-series-a-new-publishing-format-for-oa-books-20210915",title:"Introducing IntechOpen Book Series - A New Publishing Format for OA Books"}]},book:{item:{type:"book",id:"1640",leadTitle:null,fullTitle:"Applied Biological Engineering - Principles and Practice",title:"Applied Biological Engineering",subtitle:"Principles and Practice",reviewType:"peer-reviewed",abstract:"Biological engineering is a field of engineering in which the emphasis is on life and life-sustaining systems. Biological engineering is an emerging discipline that encompasses engineering theory and practice connected to and derived from the science of biology. The most important trend in biological engineering is the dynamic range of scales at which biotechnology is now able to integrate with biological processes. An explosion in micro/nanoscale technology is allowing the manufacture of nanoparticles for drug delivery into cells, miniaturized implantable microsensors for medical diagnostics, and micro-engineered robots for on-board tissue repairs. This book aims to provide an updated overview of the recent developments in biological engineering from diverse aspects and various applications in clinical and experimental research.",isbn:null,printIsbn:"978-953-51-0412-4",pdfIsbn:"978-953-51-6175-2",doi:"10.5772/2101",price:159,priceEur:175,priceUsd:205,slug:"applied-biological-engineering-principles-and-practice",numberOfPages:676,isOpenForSubmission:!1,isInWos:null,isInBkci:!1,hash:"add21c7eaac031f28b31bb2fcf116e94",bookSignature:"Ganesh R. 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Naik received his B.E. degree in electronics and communication engineering from the University of Mysore, Mysore, India, in 1997, M.E. degree in communication and information engineering from the Griffith University, Brisbane, Australia, in 2002, and PhD degree in the area of digital signal processing from RMIT University, Melbourne, Australia, in 2009.\nHe is currently an academician and researcher at RMIT University. As an early career researcher, he has authored more than 60 papers in peer reviewed journals, conferences and book chapters over the last five years. His research interests include pattern recognition, blind source separation techniques, audio signal processing, biosignal processing, and human–computer interface. Dr. Naik was the Chair for the IEEE Computer Society CIT08 Conference, Sydney and a member of the organising committee for IEEE BRC2011 conference, Vitoria, Brazil. He was a recipient of the Baden–Württemberg Scholarship from the University of Berufsakademie, Stuttgart, Germany (2006–2007). 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Recent Advances, New Perspectives and Applications",subtitle:null,reviewType:"peer-reviewed",abstract:"\r\n\tIn mathematics, operator theory is the study of linear operators on function spaces, beginning with differential operators and integral operators. The operators may be presented abstractly by their characteristics, such as bounded linear operators or closed operators and consideration may be given to nonlinear operators. The study, which depends heavily on the topology of function spaces, is a branch of functional analysis. If a collection of operators forms an algebra over a field, then it is an operator algebra. The description of operator algebras is part of operator theory. Single operator theory deals with the properties and classification of operators, considered one at a time. For example, the classification of normal operators in terms of their spectra falls into this category.
\r\n\r\n\tThe theory of operator algebras brings algebras of operators such as C*-algebras to the fore. Many operators that are studied are operators on Hilbert spaces of holomorphic functions, and the study of the operator is intimately linked to questions in function theory. For example, Beurling's theorem describes the invariant subspaces of the unilateral shift in terms of inner functions, which are bounded holomorphic functions on the unit disk with unimodular boundary values almost everywhere on the circle. Beurling interpreted the unilateral shift as multiplication by the independent variable on the Hardy space. The success in studying multiplication operators, and more generally Toeplitz operators (which are multiplication, followed by projection onto the Hardy space) has inspired the study of similar questions in other spaces, such as the Bergman space. Hence, operator theory has a connection with complex analysis. Additionally, this book will be intended to be an illustration of the use of operator theory when applied to solve specific problems in pure and applied mathematics, engineering, physics, or science in general.
\r\n\t
It is interesting to note that many outstanding physicists (Kelvin, Michelson, and Jolly) expressed in the second half of the nineteenth century an opinion that classical physics (how we name it nowadays) is in principle completed and nothing interesting or significant rests to be discovered. Needless to say, forecasting development of future events was always and still is a very risky business, especially in physical sciences and engineering. Indeed, in these disciplines of human endeavors, one must take into account not only an inherently volatile human factor but also the impact of potential discoveries of unknown yet laws of nature, which often open new unanticipated possibilities and horizons. We may try to justify such an obvious complacency, attributed to the abovementioned scientists, by the historical spirit of the Belle Époque (1870–1914), that believed in harmony, good taste, optimism, unlimited progress and generally in positivistic philosophical ideas.
Anyway, not waiting for the revolution heralded by quantum mechanics (1900) or general theory of relativity (1917), classical physics was already shaken by the emergence of the theory of chaos (Poincaré 1882 and Hadamard 1898), which later on in the twentieth century will effectively eliminate deterministic description from many physical problems, such as weather forecasting, etc. Another new significant achievement of the classical physics (although not revolutionary) was the discovery of surface waves. At first, elastic surface waves were discovered in solids (Rayleigh 1885 and Love 1911) and then in electromagnetism (Zenneck 1907 and Sommerfeld 1909).
In fact, the existence of surface waves in solids was predicted mathematically by the celebrated British scientist Lord Rayleigh in 1885, who showed that elastic surface waves can propagate along a free surface of a semi-infinite body. By contrast to bulk waves, the amplitude of surface waves is confined to a narrow area adjacent to the guiding surface. Since surface waves are a type of guided waves, they can propagate often longer distances than their bulk counterparts and in addition, they are inherently sensitive to material properties in the vicinity of the guiding surface. It will be shown in the following of this chapter that these two properties of surface waves are of crucial importance in geophysics and sensor technology.
First, seismographs were constructed by British engineers in 1880, working in Japan for Meiji government. Consequently, the first long distance seismogram was registered in 1889 by German astronomer Ernst von Rebeur-Paschwitz in Potsdam (Germany), who was able to detect seismic signals generated by an earthquake occurred in Japan, some 9000 km away from Potsdam (Berlin). It was obvious soon that long distance seismograms display two different phases. First (preliminary tremor), a relatively weak signal arriving with the velocity of bulk waves (P and S) and second (main shock) with a much higher amplitude arriving with the velocity close to that of Rayleigh surface waves. However, this Rayleigh wave hypothesis was not satisfactory, since large part of the main shock energy was associated with the shear horizontal (SH) component of vibrations, absent by definition in Rayleigh surface waves composed of shear vertical (SV) and longitudinal (L) displacements. This dilemma was resolved in 1911 by the British physicist and mathematician Augustus Edward Hough Love by a brilliant stroke of thought [1]. Firstly, Love postulated that the SH component in the main shock is due to the arrival of a new type of surface waves (named later after his name) with only one SH component of vibrations. Secondly, Love assumed that SH surface waves are guided by an extra surface layer existing on the Earth’s surface, with properties different than those in the Earth’s interior. Using contemporary language, we can say that he made a direct hit.
It is noteworthy that the existence of Rayleigh and Love surface waves was first predicted mathematically prior to their experimental confirmation. This shows how beneficial can be the mutual interaction between the theory and experiment. Indeed, the theory indicates directions of future experimental research and the experiment confirms or renders the theory obsolete. It is worth noticing that the existence of a new type of electromagnetic surface waves was predicted mathematically quite recently, i.e., in 1988, and soon confirmed experimentally.
It is interesting to note that Love surface waves have direct counterparts in electromagnetism (optical planar waveguides) and quantum mechanics (particle motion in a quantum well). By contrast, a similar statement is not true for Rayleigh surface waves, which therefore remain a unique phenomenon within the frame of the classical theory of elasticity.
Surface waves of the Love type have a number of unique features. Firstly, they have only one SH component of vibrations. As a result, Love surface waves are insensitive to the loading with liquids of zero or negligible viscosities. Thus, Love surface waves can propagate long distances without a significant attenuation. Indeed, Love waves propagating many times around the Earth’s circumference have been observed experimentally. On the other hand, it was discovered much later (1981) that Love waves are very well suited for measurements of viscoelastic properties of liquids. Secondly, the mathematical description of Love surface waves is much simpler than that for Rayleigh surface waves. A relative simplicity of the mathematical model enables for direct physical insight in the process of Love wave propagation, attenuation, etc.
The idea to employ Love surface waves for measurements of viscoelastic properties of liquids was presented for the first time in 1981 by Kiełczyński and Płowiec in their Polish patent [2]. In 1987, the theory of the new method was presented by Kiełczyński and Pajewski on the international arena at the European Mechanics Colloquium 226 in Nottingham, UK [3]. In 1988, they presented this new method with equations and experimental results at the IEEE 1988 Ultrasonic Symposium in Chicago [4]. In 1989, Kiełczyński and Płowiec published a detailed theory and experimental results in the prestigious Journal of the Acoustical Society of America [5]. It is noteworthy that subsequent publications on Love wave sensors for liquid characterization appeared in USA not earlier than in 1992 [6], but nowadays, we witness about 100 publications per year on that subject [7].
We hope that the reader, after studying this chapter, will agree that the nature has many different faces and that the same physical phenomenon can be sometimes deadly (earthquakes) and in different circumstances, can be beneficiary (biosensor technology). As a consequence, SH surface waves of the Love type are an interesting example of an interdisciplinary research.
This chapter is organized as follows. Section 2 presents main characteristics and properties of Love surface waves, including basic mathematical model and examples of dispersion curves and amplitude distributions. More advanced mathematical treatment of the Love surface waves can be found, for example, in [8]. Section 3 shows the importance of Love surface waves in geophysics and seismology. Section 4 describes applications of Love surface waves in biosensors used in biology, medicine, chemistry, etc. Section 5 contains discussion of the chronological development of SH ultrasonic sensors starting from bulk wave sensors and then first surface wave sensors. We show also that the results of research conducted in Seismology and geophysics can be transferred to biosensor technology and vice versa. Conclusions and propositions for future research in biosensor technology employing Love surface waves are given in Section 6.
In addition to biosensors, Love surface waves are used in chemosensors, in non-destructive testing (NDT) of materials, and in sensors of various physical quantities such as:
humidity of air [9];
spatial distribution of elastic parameters in solid functionally graded materials (FGM) [10];
elastic parameters of nanolayers [11];
porosity of the medium [12]; and
dielectric constant of liquids [13].
Recently, Love surface waves were also employed in the construction of the magnetic field sensor system with outstanding characteristics (sensitivity, dynamic range, etc.) [14].
Shear horizontal (SH) surface waves of the Love type are elastic waves propagating in a surface waveguide, which is composed of a surface layer rigidly bonded to an elastic substrate, see Figure 1. The existence of an elastic surface layer is a necessary condition for propagation of Love surface waves, since it can be easily shown that on an elastic half-space alone, SH surface waves cannot exist. The extra surface layer must also be “slower” than the substrate, i.e., the following condition must hold [15]:
where
Basic structure of a free Love wave waveguide, not loaded with a viscoelastic liquid. An elastic surface layer of thickness “h” and a shear velocity
Mechanical displacement
where the function
Substitution of Eq. (2) into Newton’s equation of motion leads to the Helmholtz differential equation for the transverse amplitude
where
and A is an arbitrary constant. In isotropic solids, Love surface waves have two stress components,
where
The mechanical displacement
Using Eq. (4), one can rewrite Eq. (7) in a more explicit form as:
Eq. (8) shows that the unknown phase velocity
The dispersion relation Eq. (8) is a transcendental algebraic equation for the unknown phase velocity
The dispersion relation [Eq. (8)] reveals that phase velocity
The amplitude
Amplitude of the fundamental (n = 0) Love wave mode, as a function of the normalized depth
The total derivative of the implicit function
Since group velocity
As a consequence, using Eqs. (7) and (10), one can show [8, 15, 16, 17, 18, 19] that group
Eqs. (7) and (11) show that phase
The phase velocity resulting from the solution of Eq. (8) and the group velocity determined by Eq. (11) of the fundamental mode of Love surface waves, as a function of the normalized frequency
Phase
It is noteworthy that in waveguides loaded with a lossy, viscoelastic liquid, the wavenumber k of the Love surface wave is a complex quantity, i.e.,
Since Love surface waves were originally discovered in seismology, we give here a brief description of their applications in seismic and geophysical research.
Propagation of Love surface waves on the Earth’s surface is made possible by layered structure of the Earth. The outermost layer of the Earth, the crust, is made of solid rocks composed of lighter elements. Thickness of the crust varies from 5 to 10 km under oceans (oceanic crust) to 30–70 km under continents (continental crust). The crust sits on mantle, which in turn covers the outer and inner core. The destructive power of earthquakes is mainly due to waves traveling in this thin crustal layer [23].
As predicted by Love, the velocity of SH bulk waves increases with depth [24], i.e., as a function of distance from the free surface of the Earth.
The frequency of Love waves generated by earthquakes is rather low comparing to that used in sensor technology and ranges typically from 10 mHz to 10 Hz.
Love and Rayleigh surface waves travel along great circle paths around the globe. Surface waves from strong earthquakes may travel several times around the Earth without a significant attenuation. They are termed global Rayleigh wave impulses [25]. An example of surface waves traveling multiply around the Earth [26] is given in Figure 4.
Illustration of a seismogram of Rayleigh surface waves triggered by an earthquake. Note that, Rayleigh wave packet traveled 8 times around the Earth’s circumference.
Seismic waves, generated both by natural earthquakes and by man-made sources, have delivered an enormous amount of information about the Earth’s interior (subsurface properties of Earth’s crust). In classical seismology, Earth is modeled as a sequence of uniform horizontal layers (or spherical shells) having different elastic properties and one determines these properties from travel times and dispersion of seismic waves [27].
Love surface waves have been successfully employed in a tomographic reconstruction of the physical properties of Earth’s upper mantle [28] as well as in diamond, gold, and copper exploration in Australia, South America, and South Africa [29].
Surface waves generated by earthquakes or man-made explosions were used in quantitative recovery of Earth’s parameters as a function of depth. These seismic inverse problems helped to discover many fine details of the Earth’s interior [30, 31, 32].
It is noteworthy that many theoretical methods were initially originated in seismology and geophysics before their transfer to the surface wave sensor technology (see Table 1 in Section 5.5).
Developments | Seismology | Biosensors |
---|---|---|
Basic theory | Love [1] | Kiełczyński [3] |
Multilayered waveguides (transfer matrix method) | Haskell [72] | Kiełczyński [8] |
Viscoelastic waveguides (theoretical analysis) | Sezawa [73] | Kiełczyński [74] |
Inverse problems | Dorman [76] | Kiełczyński [77] |
Nonlinear waves | Kalyanasundarm [78] | — |
Phased arrays | Frosch [79] | — |
Tomography | Nakanishi [80] | — |
Higher-order modes | Haskell [81] | — |
Solitary waves | Bataille [82] | — |
Energy harvesting | Qu [83] | — |
Waveguides with nanomaterials | — | Penza [84] |
Piezoelectric waveguides | — | Kovacs [6] |
Resonators | — | Kovacs [67] |
Delay lines | — | Tournois [19] |
Chronology of developments in Love wave biosensors and Love wave seismology.
An example of structural damages made by surface waves of the Love type is shown in Figure 5. It is apparent that railway tracks were deformed by strong shear horizontal SH forces parallel to the Earth’s surface. Love surface waves together with Rayleigh surface waves are the most devastating waves occurring during earthquakes.
Twisted railroad tracks, an example of structural damages due to SH displacement of Love surface waves in the aftermath of an earthquake.
It is interesting to note that recently developed earthquake engineered metamaterials open a new way to counterattack seismic waves [33, 34]. The metamaterials actively control the seismic waves by providing an additional shield around the protected building rather than reconstructing the building structure. Compared with common engineering solutions, the advantage of the metamaterial method is that it can not only attenuate seismic waves before they reach critical targets, but also protect a distributed area rather than an individual building. The periodic arrangement of metamaterial structure creates frequency band gaps, which effectively prevent surface waves propagation on the Earth’s surface via a Bragg scattering mechanism.
A biosensor can be described as a device which can generate a signal (usually electrical) that is proportional to the concentration of a particular biomaterial or chemicals in the presence of a number of interfering species [35]. This can be accomplished using biological recognition elements such as enzymes, antibodies, receptors, tissues, and microorganisms as sensitive materials because of their selective functionality for target analytes along with an appropriate transducer.
High sensitivity of Love surface wave sensors can be explained by spatial concentration of the energy of Love waves. Indeed, it was shown in Section 2 that the energy of Love surface waves is localized mostly in the vicinity of the free guiding surface (Figure 1), looking in both sides from it. Moreover, the amplitude of Love surface waves reaches maximum at the free guiding surface
Surface waves of the Love type are especially suited to measure parameters of viscoelastic liquids, polymers, gels, etc., providing that they can form a good mechanical contact (absorption and adhesion) with free surface of the waveguide. Since Love surface waves are, in principle, mechanical waves, they can measure the following mechanical parameters of an adjacent medium: density, modulus of elasticity, and viscosity. In waveguides composed of piezoelectric elements (substrate and/or surface layer), dielectric constant of the adjacent medium will also affect the propagation of Love surface waves. In practice, we are interested in detection and quantification other more specific properties of biological and chemical materials, such as concentration and presence of proteins, antibodies, toxins, bacteria, viruses, size and shape of DNA, etc. Therefore, the next step in the development of Love wave sensors is to correlate (experimentally or analytically) the abovementioned specific properties of the measured analytes with changes in density, viscosity, and elastic moduli of the surface (sensing) layer. Finally, we have to measure changes in phase velocity and attenuation of Love surface waves, which are due to changes in density, viscosity, and elastic moduli of this surface layer. It should be noticed that part of Love wave energy enters into the measured liquid to some distance (penetration depth) from the guiding surface. Such an energy redistribution changes certainly the phase velocity and attenuation of the Love surface wave. In practice, we often adopt a more empirical approach, i.e., we measure directly changes in phase velocity and attenuation of Love waves, as a function of the aforementioned specific properties of the measured material, such as the concentration of proteins and so on, without referring to changes in density, viscosity or elastic modulus of the measured material. However, the former step is indispensable during modeling, design, and optimization of Love surface wave sensors.
As with other types of wave motion, we can measure in principle two parameters of Love surface waves, i.e., their phase and amplitude. Polarization of SH surface waves of the Love type is constant and therefore does not provide any additional information about the medium of propagation. Phase
Similarly, amplitude A measurements are correlated with the coefficient of attenuation
where
Phase and amplitude characteristics of Love surface waves can be measured in a closed loop configuration by placing Love wave delay line in a feedback circuit of an electrical oscillator (resonator). Another possibility is to use network analyzer, which provides phase shift and insertion loss of the Love wave sensor working in an open loop configuration, due to the load of the sensor with a measured material. The typical frequency range used by Love wave sensors is from 50 MHz to 500 MHz [7].
The structure and cross section of a typical Love wave biosensor is shown in Figure 6a and b. A relatively thick (0.5–1.0 mm) substrate provides mechanical support for the whole sensor. Often the substrate material is piezoelectric (AT-cut quartz material [41]). In this case, a pair of interdigital transducers (IDTs) can be deposited on the substrate to form a delay line of the sensor. The guiding layer (SiO2, ZnO, PMMA, etc.), deposited directly on the substrate, provides entrapment for surface wave energy. The sensing layer, made of gold (Au) or a polymer, usually very thin (˜50-100 nm), serves as an immobilization area for the measured biological material. This thin-sensing layer interacts directly with the measured material (liquid) and serves often as a selector of the specific target substance, such as antigen, to be measured.
a) Layered structure of a typical Love wave sensor not yet connected to the external driving circuit and b) cross-section of this sensor structure + loading liquid.
An interesting solution for Love wave sensors was proposed in [42], where the Love wave sensor works in a wireless configuration without an external power supply. This design has many unique advantages, i.e., the sensor can be permanently implanted in a patient body to monitor continuously the selected property of a biological liquid. Readings of the sensor can be made on demand, totally noninvasively by a reading device connected to a broader computer system of patient monitoring. Another implementation of a remotely controlled wireless Love wave sensor was presented in [43]. The proposed sensor can measure simultaneously two different analytes using Love surface waves with a frequency of 440 MHz.
Wireless bioelectronics sensors may be used in a variety of fields including: healthcare, environmental monitoring, food quality control, and defense.
To apply the measured analyte to the Love wave sensor, the sensor is often equipped with a flow cell, which separates interdigital transducers from sensing area of the waveguide [44]. A laboratory grade Love wave sensor equipped with a flow cell is shown in Figure 7.
An example of a laboratory grade Love wave sensor with a flow cell [
A prototype of an commercial ready Love wave sensor was presented in 2015 in Ref. [45]. A 250 MHz delay line Love wave immunosensor was designed on the ST quartz substrate with a thin gold layer of thickness ˜90 nm used as a guiding and sensing area, for antibodies or antigens can be easily immobilized on a gold surface. The changes of Love wave velocity and attenuation were due to antibodies-antigens interactions. A disposable test cassette with embedded Love wave immunosensor is connected to a handheld electronic reader, which in turn is connected wirelessly via bluetooth to a smartphone or a computer. This device is a strong candidate for clinical and personnel healthcare applications.
Love wave biosensors have been used in measurement and detection of a large number of substances (analytes) [44]. As representative examples, we can mention the following:
concentration of bovine serum albumin [46];
real-time detection of antigen-antibody interactions in liquids (immunosensor) [47];
simultaneous detection of Legionella and
virus and bacteria detection in liquids [49];
detection of pathogenic spores
investigation of lipid specificity of human antimicrobial peptides [51];
Sin Nombre Virus detection at levels lower than those typical for human patients suffering from hantavirus cardiopulmonary syndrome [52];
detection of nanoparticles in liquid media [53];
okadaic acid detection [54];
study of protein layers [55];
antibody binding detection [56];
toxicity of heavy metals [57];
size and shape of DNA [58];
real-time detection of hepatitis B [59];
liquid chromatography [60];
immunosensors for detection of pesticide residues and metabolites in fruit juices [61];
detection of cocaine [62]; and
detection of carbaryl pesticide [63].
This rather impressive list of achievements in R&D activities on biosensor technology suggests that biosensors employing Love surface waves have a huge potential. However, in order to compete with other types of biosensors, such as optical sensors based on the surface plasmon resonance [64], the biosensors employing Love surface waves should possess the following characteristics:
high sensitivity to the measured property (measurand);
high selectivity to the measured property (measurand);
low limit of detection;
zero temperature coefficient (high-thermal stability);
high repeatability and stability;
possibility of multiple reuse; and
cost-effectiveness.
At present, none of the above targets have been fully achieved. Love wave biosensors are, in general, still in the laboratory research phase, where most developments are focused on the proof of concept and construction of a working prototype. Only one European company offers today commercially available Love wave sensors [7]. Nevertheless, as it was shown in this section, Love wave biosensors can be used to measurements of a surprisingly large number of biological substances (analytes) with a quite remarkable accuracy and sensitivity. Therefore, in our opinion, Love wave biosensors will reach soon an industrial grade level with numerous real-life applications in biology, medicine (clinical practice), and chemistry.
It is interesting to note that first acoustic sensors for measurements of viscoelastic properties of liquids used to this end bulk (not surface) SH waves propagating in a solid buffer, loaded on one side with a measured viscoelastic liquid. This idea appeared in 1950 in works of such prominent ultrasonic scientists Mason and McSkimmin [65]. However, the main drawback of the bulk wave sensors was their inherent low sensitivity. For example, to perform measurements with a water-loaded sensor, one had to observe about 50 consecutive reflections in the solid buffer.
The breakthrough came with a proposition to employ to this end SH surface waves of the Love and Bleustein-Gulyaev types. This idea was first articulated by Kiełczyński and Płowiec in 1981 in their Polish patent no 130040 [2]. In 1987, the theory of the new method was presented by Kiełczyński and Pajewski on the international arena at the European Mechanics Colloquium 226 in Nottingham, UK [3]. In 1988, this new method, with equations and experimental results, was presented by Kiełczyński and Pajewski at IEEE 1988 Ultrasonic Symposium in Chicago [4]. In 1989, Kiełczyński and Płowiec published detailed theory and experimental results in the prestigious Journal of the Acoustical Society of America [5]. Their theory [3, 4, 5] was based on the Auld’s perturbative technique [66] and gave satisfactory results for liquids of viscosities up to ˜10 Pas. The main advantage of the Love surface wave sensors is their very high sensitivity, namely the sensitivity of a few orders of magnitude (102 to 104) higher than that of their bulk SH waves counterparts [3, 4, 5]. As a result, measurements of the viscosity of water (˜1 mPas) and other biological substances (based largely on water) was no longer a challenge, what was the case with bulk SH wave sensors. In other words, due to the employment of SH surface waves, the way for development of the corresponding biosensors was widely open.
It should be noticed that next publications on the Love wave sensors for liquid characterization appeared in the open literature not earlier than in 1992 [6]. In fact, in papers published in 1992, Kovacs and Venema [67], and, in 1993, Gizeli et al. [68] confirmed our earlier discovery [3, 4, 5] that Love surface waves are much more sensitive to viscous loading than other types of SH waves. In another paper published in 1992, Gizeli et al. [69] developed theoretical analysis for Love wave sensors, using the same Auld’s perturbative technique [66] as that employed by us in papers [3, 4, 5].
It is interesting to note that two other types of SH waves, i.e., leaky SH SAW waves and plate SH waves, were also tried to measure viscosity of liquids. Leaky SH SAW waves were proposed in 1987 [70] by Moriizumi et al. and SH plate waves in 1988 by Martin et al. [71]. However, these two types of SH waves were quickly abandoned, since the corresponding viscosity sensors were of inherently low sensitivity, difficult in practical realization and difficult in theoretical analysis (leaky SH SAW waves). In fact, the energy of SH plate waves is uniformly distributed across the whole thickness of the plate. Therefore, SH plate waves are not so sensitive to viscous loading as Love surface waves, whose energy is highly concentrated in the surface layer of the waveguide. On the other hand, leaky SH SAW waves are not pure SH waves and contains in principle all three components of vibrations, not only the SH one. In particular, the component perpendicular to free surface of the waveguide will continuously radiate energy into the adjacent liquid. This will cause an additional attenuation for leaky SH SAW waves, which will be indistinguishable from that due to the viscous loading measured.
R&D activities in seismology and biosensor technology using Love surface waves focus inevitably on different problems and challenges. The main reason for these differences is the nature and scale of Love surface waves used in seismology and biosensor technology, i.e., in seismology, they are a natural phenomenon and in biosensors, they are controlled within man-made devices. It is instructive to compare the chronology of developments made in seismology and in biosensor technology (see Table 1). In fact, the theory of Love waves published in 1911 [1] was developed for the simplest surface wave waveguide, namely for that composed of linear, isotropic, and lossless materials (surface layer on a substrate). Since loading viscoelastic liquids are always lossy, the corresponding theory of Love wave sensors had to use perturbative [3] or numerical methods [37]. The theory of Love waves in multilayered waveguides, developed in Seismology [72], uses a conventional transfer-matrix method based on the elementary matrix algebra. By contrast, the theory developed for biosensors extends the transfer-matrix method to a more advanced formalism of matrix differential equations with eigenvectors and eigenvalues and operator functions [8]. First theories of Love waves propagating in viscoelastic waveguides, were developed in Seismology [73], long before the advent of modern fast digital computers. By contrast, the corresponding theory developed for biosensors [74] in 2016 heavily relates on numerical methods.
Examination of Table 1 reveals that a number of R&D activities already well established in Seismology were not yet initiated in biosensor technology. As examples, one can mention the applications of nonlinear Love waves, higher-order Love wave modes or solitary waves. This suggests that in future research, it may be advantageous to employ higher-order modes, nonlinear Love waves, metamaterials, etc., to increase biosensors sensitivity [75] or lower their limit of detection. Other technologies not yet used in biosensor technology are phased array and tomography. Indeed, applied to biosensors they may allow for a 2D characterization of the analyte distribution, electronic beam steering, focusing, etc. These indications for future research in biosensor technology show clearly advantages of multidisciplinary R&D activities, in this case seismology and biosensor technology. Indeed, it is much easier to adapt an existing technology already developed in other fields to a new domain than to invent a new technology from scratch without any prior feedback.
Despite the fact that the first theory of Love surface waves was published as early as in 1911 [1], surprisingly, a large number of problems concerning the theory of Love surface waves have not yet been solved.
This chapter contains theoretical foundations and calculation results regarding the propagation of the Love wave in various media. A new interpretation of the Love wave dispersion equation was given. This equation is presented in the form of an implicit function of two variables, i.e., (
The obtained results can be employed in the design and optimization of not only biosensors but also chemosensors and sensors of physical quantities that use Love waves. In addition, the obtained results can be used in seismology and geophysics for the interpretation of seismograms and determining the distribution of elastic parameters of the Earth’s crust.
This chapter contains also a novel comparison of milestones in developments made in Love wave seismology and Love wave biosensors (see Section 5.4). Since Love wave biosensors appeared exactly 70 years [2] after emergence of Love surface waves in seismology [1], it is not surprising that many discoveries and developments were made first in seismology and then transferred to biosensors (see Table 1). This cross-pollination between the two seemingly distant branches of science is very beneficial and can significantly accelerate developments made in either of them.
In this limited space chapter, it was impossible to address or even mention all interesting problems relevant to the properties and applications of Love surface waves in seismology and biosensor technology. Instead, we tried to present only main properties of the Love surface waves, such as their dispersive nature, phase and group velocities, amplitude distribution, etc., as well as their most iconic applications in seismology and biosensor technology. We think that presentation of the Love surface waves R&D activities in a broader historical perspective gives an invaluable insight in the process of developments made in this fascinating interdisciplinary domain of research.
In this chapter, we attempted to present a variety of aspects that can be attributed to SH surface waves of the Love type. As a matter of fact, Dr. Jekyll and Mr. Hyde Love surface waves possess simultaneously two diametrically different faces, i.e., first benign (biosensors) and second deadly (earthquakes). The good news is that developments made in one of these domains can be easily transferred to the second one and vice versa. In fact, Love surface waves were first discovered in seismology (1911). They finally enabled for precise interpretation of seismograms registered in the aftermath of earthquakes. Beneficiary applications (biosensors) of Love surface waves were announced exactly 70 years later (1981) in a Polish patent.
Since earthquake is a natural phenomenon, we have little or no influence on its occurrence and dynamics. By contrast, the construction and the operation of biosensors can be optimized by mathematical modeling and experimental studies. At present, the mathematical modeling of Love wave biosensors is an active domain of research. On the other hand, progress in electronics and computer technology will lead to development of new compact and reliable instrumentation working in conjunction with Love wave biosensors.
Despite their centennial heritage, Love surface waves are subject of an intensive research activity. For example, one can mention the application of inverse problem techniques to recover material parameters of surface layers from measurements of velocity and attenuation of Love surface waves. Inverse problem techniques have been successfully employed in seismology and geophysics [25] and recently also pioneered by the authors [74, 77, 85] and others [86, 87] in the biosensor technology.
Other open problems in the theory and technique of Love surface waves are non-linear Love waves, extremely slow Love waves [88], Love waves in layered nanostructures [89], energy harvesting with Love waves, and metamaterial-based seismic shielding, [33, 34], etc.
Finally, coming back to the idea expressed at the beginning of the introduction in this chapter, we want to assure the reader that there exist still many significant unresolved problems in the theory and technique of the Love surface waves, which deserve to be addressed in future R&D activities. We hope that this chapter may be helpful in this endeavor.
According to Russell Ackoff [1], a systems theorist and professor of organizational change, the content of the human mind can be classified into three categories:
IF temperature < = 0° C THEN cold = true;
Cold IF == right THEN notify personnel to remove ice from aircraft.
Indeed, knowledge is the appropriate collection of information such that it intends to be useful. Knowledge is a deterministic process. Memorization of information leads to knowledge. Knowledge represents a pattern and provides a high level of predictability regarding what is being described or will happen next.
Ex: If the humidity is very high and the temperature drops drastically, the atmosphere is unlikely to hold the humidity so that it rains.
This knowledge has a useful meaning, but its integration in a context will infer new knowledge. For example, a student memorizes or accumulates knowledge of the multiplication Table. A student can answer 2 × 2 because this knowledge is in the multiplication table. Nevertheless, when asked for 1267 × 300, he cannot answer correctly because he cannot dip into the multiplication table. To answer such a question correctly requires a real cognitive and analytical capacity that exists in the next level … comprehension. In computer jargon, most of the applications we use (modeling, simulation, etc.) use stored knowledge.
The system is an aggregated “whole” where each component interacts with at least one other component of the system. The components or parts of a system can be real or abstract.
All system components work toward a standard system goal. A system can contain several subsystems. It can be connected to other systems.
A system is a collection of elements or components that interact to achieve goals. The elements themselves and the relationships between them determine how the system works. Systems have inputs, processing mechanisms, outputs, and feedback mechanisms. A system processes the input to create the output [3].
Input is the activity of collecting and capturing data.
Processing involves the transformation of inputs into outputs such as computation, for example.
Output is about producing useful information, usually in the form of documents and reports. The output of one system can become the input of another system. For example, the output of a system, which processes sales orders, can be used as input to a customer’s billing system. Computers typically produce output to printers and display to screens. The output can also be reports and documents written by hand or produced manually.
Finally, feedback or feedback is information from the system used to modify inputs or treatments as needed.
An information system (IS) is a set of interrelated components that collect, manipulate, store and disseminate information and provide a feedback mechanism to achieve a goal. The feedback mechanism helps organizations achieve their goals by increasing profits, improving customer service [3], and supporting decision-making and control in organizations [4].
Companies use information systems to increase revenues and reduce costs.
In organizations, information systems are structured around four essential elements, proposed in the 1960s by Harold Leavitt (Figure 1). The pattern is known as the “Leavitt Diamond.”
Leavitt’s diamond: A socio-technical view of IS.
A company has systems to support the different managerial levels. These systems include transaction processing systems, management information systems, decision support systems, and dedicated business intelligence systems.
Companies use information systems so that accurate and up-to-date information is available when needed [5].
Within the same organization, executives at different hierarchy levels have very different information requirements, and different types of information systems have evolved to meet their needs. A common approach for examining the types of information systems used within organizations is to classify them according to their roles at different organizational structure levels, and this approach is called a vertical approach. Indeed, the organization is considered a management pyramid at four levels (Figure 2):
Information Systems types according to managerial level.
At the operational level, managers need systems that keep track of the organization for necessary activities and operations, such as sales and material flow in a factory. A transaction processing system is a computer system that performs and records the routine (daily) operations necessary for managing affairs, such as keeping employee records, payroll, shipping merchandise, keeping records, accounting and treasury.
At this level, the primary purpose of systems is to answer routine questions and monitor transactions flow through the organization.
At the operational level, tasks, resources, and objectives are predefined and highly structured. The decision to grant credit to a customer, for example, is made by a primary supervisor according to predefined criteria. All that needs to be determined is whether the client meets the criteria.
Middle managers need systems to help with oversight, control, decision making, and administrative activities. The main question that this type of system must answer is: is everything working correctly?
Its role is to summarize and report on essential business operations using data provided by transaction processing systems. Primary transaction data is synthesized and aggregated, and it is usually presented in reports produced regularly.
DSS supports decision-making for unusual and rapidly evolving issues, for which there are no fully predefined procedures. This type of system attempts to answer questions such as: What would impact production schedules if we were to double sales for December? What would the level of Return on investment be if the plant schedule were delayed by more than six months?
While DSSs use internal information from TPS and MIS systems, they also leverage external sources, such as stock quotes or competitor product prices. These systems use a variety of models to analyze the data. The system can answer questions such as: Considering customer’s delivery schedule and the freight rate offered, which vessel should be assigned, and what fill rate to maximize profits? What is the optimum speed at which a vessel can maximize profit while meeting its delivery schedule?
ESS helps top management make decisions. They address exceptional decisions requiring judgment, assessment, and a holistic view of the business situation because there is no procedure to be followed to resolve a given issue at this level.
ESS uses graphics and data from many sources through an interface that senior managers easily understand. ESS is designed to integrate data from the external environment, such as new taxes or competitor data, and integrate aggregate data from MIS and DSS. ESSs filter, synthesize and track critical data. Particular attention is given to displaying this data because it contributes to the rapid assimilation of these top management figures. Increasingly, these systems include business intelligence analysis tools to identify key trends and forecasts.
Decision-making in companies is often associated with top management. Today, employees at the operational level are also responsible for individual decisions since information systems make information available at all company levels.
So decisions are made at all levels of the company.
Although some of these decisions are common, routine, and frequent, the value of improving any single decision may be small, but improving hundreds or even thousands of “small” decisions can add value to the business.
Not all situations that require decisions are the same. While some decisions result in actions that significantly impact the organization and its future, others are much less important and play a relatively minor role. A decision’s impact is a criterion that can differentiate between decision situations and the degree of the decision’s structuring. Many situations are very structured, with well-defined entrances and exits. For example, it is relatively easy to determine the amount of an employee’s pay if we have the appropriate input data (for example, the number of hours worked and their hourly wage rate), and all the rules of relevant decision (for example, if the hours worked during a week are more than 40, then the overtime must be calculated), and so on. In this type of situation, it is relatively easy to develop information systems that can be used to help (or even automate) the decision.
In contrast, some decision situations are very complex and unstructured, where no specific decision rules can be easily identified. As an example, consider the following task: “Design a new vehicle that is a convertible (with a retractable hardtop), has a high safety rating, and is esthetically pleasing to a reasonably broad audience. No predefined solution to this task finalizing a design will involve many compromises and require considerable knowledge and expertise.
Examples of Types of decisions, according to managerial level, are presented in Table 1.
Decision level | Characteristics of decisions | Examples of decisions |
---|---|---|
Top Management | Unstructured | Decide whether or not to come into the market |
Approve the budget allocated to capital | ||
Decide on long-term goals | ||
Intermediate management | Semi-structured | Design a marketing plan |
Develop a departmental budget | ||
Design a website for the company | ||
Operational management | Structured | Determine the overtime hours |
Determine the rules for stock replenishment | ||
Grant credit to customers | ||
Offer special offers to customers |
Types of decisions according to managerial level.
Generally speaking, structured decisions are more common at lower levels of the organization, while unstructured problems are more common at higher business levels.
The more structured the decision, the easier it is to automate. If it is possible to derive an algorithm that can be used to make an efficient decision and the input data to the algorithm can be obtained at a reasonable cost, it generally makes sense to automate the decision.
Davenport and Harris [6] proposed a framework for the categorization of applications used for decision automation. Most of the systems they describe include some expert systems, often combined with DSS and/or EIS aspects. The categories they provided include Solution Configuration, Optimization of Performance, Routing or Segmentation of Decisions, Business Regulatory Compliance, Fraud Detection, Dynamic Forecasting, and Operational Control.
Many business decision situations are not very structured, and therefore cannot (or should not) be fully automated.
Data visualization tools allow users to see patterns and relationships in large amounts of data that would be difficult to discern if the data had been presented in tabular form, for example.
Geographic Information Systems (GIS) helps decision-makers visualize issues requiring knowledge about people’s geographic distribution or other resources. GIS software links the location data of points, lines, and areas on a map. Some GIS have modeling capabilities to modify data and simulate the impact of these modifications. For example, GIS could help the government calculate response times to natural disasters and other emergencies or help banks identify the best replacement for installing new branches or ATMs of tickets.
Geographic (or geospatial) information refers not only to things that exist (or are being planned) on specific locations on the Earth’s surface but also to events such as traffic congestion, flooding, and other events such as an open-air festival [7].
Its scope and granularity characterize this information:
Location, extent, and coverage are essential aspects of geographic information.
Granularity, for example, geometric information, can be concise or fuzzy depending on the application.
GIS is used to capture, store, analyze, and visualize data that describes part of the Earth’s surface, technical and administrative entities, and the results of geosciences, economics, and ecological applications.
It is a computer system with a database observing the spatial distribution of objects, activities, or events described by points, lines, or surfaces.
It is a comprehensive collection of tools for capturing, storing, extracting, transforming, and visualizing real-world spatial data for applications.
It is an information system containing all the data of the territory, the atmosphere, the surface of the Earth, and the lithosphere, allowing the systematic capture, the update, the manipulation, and the analysis of these data standardized reference framework.
It is a decision support system that integrates spatial data into a problem-solving environment.
Other definitions of GIS exist depending on the point of view of application [7], a GIS can be considered as
A collection of spatial data with storage and retrieval functions
A collection of algorithmic and functional tools
A set of hardware and software components necessary for processing geospatial data
A particular type of information technology
A gold mine for answers to geospatial questions
A model of spatial relations and spatial recognition.
Typically, a GIS provides functions for the storage and retrieval, interrogation and visualization, transformation, geometric and thematic analysis of information.
Indeed, geographic/geospatial information is ubiquitous, as seen on mobile devices such as cell phones, maps, satellite images, positioning and routing services, and even 3D simulations, gaining popularity from increasingly essential segments of the consumers.
Technological advances in recent years have transformed classical GIS into new forms of geospatial analysis tools, namely:
Web-based and service-oriented approaches have led to a client–server architecture.
Mobile technology has made GIS ubiquitous in smartphones, tablets, and laptops (opening up new markets).
IS applications cover functional areas and focus on the execution of business processes across the enterprise, including all management levels.
There are several categories of business applications: Enterprise Resource Planning (ERP), Supply Chain Management systems (SCM), Customer Relationship Management systems (CRM), electronic commerce or e-commerce, Knowledge Management systems or KM, and Business Intelligence or BI. The categories of business applications dealt with in this section cover all managerial levels since KMS are mainly intended for top management (ESS), SCMs, CRMs, and BI for mid-level management (MIS and DSS), ERP and e-commerce dedicated to the transactional level (TPS or basic or operational).
However, it is useful to specify that some ERP systems, such as the global giant SAP, offer versions of its software package covering these different categories, including SCM and CRM.
ERPs allow business processes related to production, finance and accounting, sales and marketing, and human resources to be integrated into a single software system. Information that was previously fragmented across many different systems is integrated into a single system with a single, comprehensive database that multiple business stakeholders can use.
An ERP system centralizes an organization’s data, and the processes it applies are the processes that the organization must adopt [8]. When an ERP provider designs a module, it must implement the rules of the associated business processes. ERP systems apply best management practices. In other words, when an organization implements ERP, it also improves its management as part of ERP integration. For many organizations, implementing an ERP system is an excellent opportunity to improve their business practices and upgrade their software simultaneously. Nevertheless, integrating an ERP represents a real challenge: Are the processes integrated into the ERP better than those currently used? Furthermore, if the integration is booming, and the organization operates the same as its competitors, how do you differentiate yourself?
ERPs are configurable according to the specificities of each organization. For organizations that want to continue using their processes or even design new ones, ERP systems provide means for customizing these processes. However, the burden of maintenance falls on the organizations themselves in the case of ERP customization.
Organizations will need to consider the following decision carefully: should they accept the best practice processes embedded in the ERP system or develop their processes? If the choice is ERP, process customization should only concern processes essential to its competitive advantage.
Electronic commerce is playing an increasingly important role in organizations with their customers.
E-commerce enables market expansion with minimal capital investment, improves the supply and marketing of products and services. Nevertheless, there is still a need for universally accepted standards to ensure the quality and security of information and sufficient telecommunications bandwidth.
The three main categories of e-commerce are Business-to-Consumer (B2C), Business-to-Business (B2B), and Consumer-to-Consumer (C2C).
Business-to-Consumer (B2C) e-commerce involves the retailing of products and services to individual customers. Amazon, which sells books, software, and music to individual consumers, is an example of B2C e-commerce.
Business-to-Business (B2B), e-commerce involves the sale of goods and services between businesses. The ChemConnect website for buying and selling chemicals and plastics is an example of B2B e-commerce.
Consumer-to-Consumer (C2C), this type of e-commerce involves consumers selling directly to consumers. For example, eBay, the giant web-based auction site, allows individuals to sell their products to other consumers by auctioning their goods, either to the highest bidder or through a fixed price.
Information systems for the management of the supply chain or SCM make it possible to manage its suppliers’ relations. These systems help suppliers and distributors share information about orders, production, inventory levels, and delivery of products and services so that they can source, produce and deliver goods and services efficiently.
The ultimate goal is to get the right amount of products from their suppliers at a lower cost and time. Additionally, these systems improve profitability by enabling managers to optimize scheduling decisions for procurement, production, and distribution.
Anomalies in the supply chain, such as parts shortages, underutilized storage areas, prolonged storage of finished products, or high transportation cost, are caused by inaccurate or premature information. For example, manufacturers may stock an excessive amount of parts because they do not know precisely the dates of upcoming deliveries from suppliers. Alternatively, conversely, the manufacturer may order a small number of raw materials because they do not have precise information about their needs. These supply chain inefficiencies squander up to 25 percent of the company’s operating costs.
If a manufacturer has precise information on the exact number of units of the product demanded by customers, on what date, and its exact production rate, it would be possible to implement a successful strategy called “just in time” (just-in-time strategy). Raw materials would be received precisely when production needed them, and finished products would be shipped off the assembly line with no need for storage.
However, there are always uncertainties in a supply chain because many events cannot be predicted, such as late deliveries from suppliers, defective parts or non-conforming raw materials, or even breakdowns in the production process. To cope with these kinds of contingencies and keep their customers happy, manufacturers often deal with these uncertainties by stocking more materials or products than they need. The safety stock acts as a buffer against probable supply chain anomalies. While managing excess inventory is expensive, a low stock fill rate is also costly because orders can be canceled.
CRM aims to manage customer relationships by coordinating all business processes that deal with customers’ sales and marketing. The goal is to optimize revenue, customer satisfaction, and customer loyalty. This collected information helps companies identify, attract and retain the most profitable customers, and provide better service to existing customers and increase sales.
The CRM captures and integrates the data of the company’s customers. It consolidates data, analyzes it, and distributes the results to different systems and customer touchpoints throughout the company. A point of contact (touchpoint, contact point) is a means of interaction with the customer, such as telephone, e-mail, customer service, conventional mail, website, or even a sales store, by retail.
Well-designed CRM systems provide a single view of the company’s customers, which is useful for improving sales and customer service quality. Such systems also provide customers with a single view of the business regardless of their contact point or usage.
CRM systems provide data and analytical tools to answer these types of questions: “What is the value of a customer to the business” “Who are the most loyal customers?” “Who are the most profitable customers” and “What products are profitable customers buying?”
Businesses use the answers to these questions to acquire new customers, improve service quality, support existing customers, tailor offerings to customer preferences, and deliver escalating services to retain profitable customers.
Some companies perform better than others because they know how to create, produce, and deliver products and services. This business knowledge is difficult to emulate, is unique, and can be leveraged and deliver long-term strategic benefits. Knowledge Management Systems or KMS enable organizations to manage processes better to collect and apply knowledge and expertise. These systems collect all the relevant knowledge and experiences in the company and make them available to everyone to improve business processes and decision management.
Knowledge management systems can take many different forms, but the primary goals are: 1) facilitating communication between knowledge workers within an organization, and 2) to make explicit the expertise of a few and make it available to many.
Consider an international consulting firm, for example. The company employs thousands of consultants across many countries. The consultancy team in Spain may be trying to resolve a client’s problem, very similar to a consultancy team in Singapore that has already been solved. Rather than reinventing the solution, it would be much more useful for the Spain team to use the Singapore team’s knowledge.
One way to remedy this situation is to store case histories from which employees worldwide can access (via the Internet) and search for cases (using a search engine) according to their respective needs. If the case documentation is of good quality (accurate, timely, complete), the consultants will share and benefit from each other’s experiences, and the knowledge gained.
Unfortunately, it is often difficult to get employees to contribute meaningfully to the knowledge base (as they are probably more concerned with moving forward on their next engagements with customers rather than documenting their past experiences). For such systems to have any chance of success, the work organization must change, such as establishing a reward system for cases captured and well documented.
The term Business Intelligence (BI) is generally used to describe a type of information system designed to help decision-makers learn about trends and identify relationships in large volumes of data. Typically, BI software is used in conjunction with large databases or data warehouses. While the specific capabilities of BI systems vary, most can be used for specialized reporting (e.g., aggregated data relating to multiple dimensions), ad-hoc queries, and trend analysis.
As with knowledge management systems, the value of business intelligence systems can be hampered in several ways. The quality of the data that is captured and stored is not guaranteed. Besides, the database (or data warehouse) may lack essential data (for example, ice cream sales are likely to correlate with temperature; without the temperature information, it may be difficult to identify why it is. There has been an increase or decrease in sales of ice cream). A third challenge is the lack of mastery of data analysts over the context of the organization’s operations, even if they are proficient in BI software. In contrast, a manager has mastery of the organization but does not know how to use BI software. As a result, it is common to have a team (a manager associated with a data analyst) to get the most information (and/or knowledge) from a business intelligence system.
Unlike physical assets, the information does not necessarily disappear when it has been stolen. If an organization holds confidential information such as a new manufacturing process, it may be uploaded by an unauthorized person and remain available to the organization.
Exposing information to unauthorized personnel constitutes a breach of confidentiality.
Another type of system failure happens when the integrity of information is no longer guaranteed. In other words, rather than unauthorized exposure of information, there are unauthorized changes of information. A corporate website containing documentation on how to configure or repair its products could suffer severe financial harm if an intruder could change instructions, leading to customers misconfigure or even ruin the purchased product.
Finally, the denial of access to information or the unavailability of information represents another type of information failure. For example, if a doctor is prevented from accessing a patient’s test results, the patient may suffer needlessly or even die. A commercial website could lose significant sales if its website were down for an extended period.
Understanding the potential causes of system failure enables appropriate action to be taken to avoid them. There are a wide variety of potential threats to an organization’s information systems.
Human threats are the most complicated to manage because they include a wide variety of behaviors. To illustrate how the level of detail can vary, some relevant subcategories include:
Accidental behavior by members of the organization, technical support staff, and customers of the organization
Malicious behavior by someone inside or outside the organization
Other categories of threats include:
A natural event: flood, fire, tornado, ice storm, earthquake, pandemic flu
Environmental elements: chemical spill, gas line explosion.
Technical Threat: Hardware or software failure
Operational Threat: a faulty process that unintentionally compromises the confidentiality, integrity, or availability of information. For example, an operational procedure that allows application programmers to upgrade software without test or notification system operators can result in prolonged outages.
It is possible to categorize the various checks intended to avoid a failure, such as:
Management controls management processes that identify system requirements such as confidentiality, integrity, and availability of information and provide for various management controls to ensure that these requirements are met.
Operational controls: include the day-to-day processes associated with the provision of information services.
Technical controls: concern the technical capacities integrated into the IT infrastructure to support the increased confidentiality, integrity, and availability of information services.
A widely cited Gartner research report concludes that “people directly cause 80% of downtime in critical application services. The remaining 20% are caused by technological failures, environmental failure or a natural disaster”.
Often, these failures are the result of software modifications such as adding new features or misconfiguring servers or network devices.
IT professionals should ensure that system changes are prioritized and tested and that all interested parties are notified of proposed changes.
Perceptible benefits can be quantified and assigned a monetary value. Imperceptible benefits, such as more efficient customer service or improved decision making, cannot be immediately quantified but can lead to quantifiable long-term gain [4].
System performance can be measured in different ways.
Efficiency is often referred to as “doing the things right” or doing things right. Efficiency can be defined as the ratio of output to input. In other words, a company is more efficient if it produces more with the same amount of resources or if it produces the same amount of output with a lower investment of resources, or - even better - produces more with less input. In other words, the company achieves improvements in terms of efficiency by reducing the waste of resources while maximizing Productivity.
Each time an item is sold or ordered, the manager updates the quantity of the item sold in the inventory system. The manager needs to check the sales to determine which items have been sold the most and restocked. This considerably reduces the manager’s time to manage his stock (limit input to achieve the same output). So efficiency is a measure of what is produced divided by what is consumed [3].
Effectiveness is measured based on the degree achieved in achieving system objectives. It can be calculated by dividing the objectives achieved by the total of the objectives set.
Effectiveness is denoted as “doing the right thing” or doing the things necessary or right. It is possible to define effectiveness as an organization’s ability to achieve its stated goals and objectives. Typically, a business more significant is the one that makes the best decisions and can carry them out.
For example, to better meet its various customers’ needs, an organization may create or improve its products and services founded on data collected from them and information accumulated from sales activities. In other words, information systems help organizations better understand their customers and deliver the products and services that customers desire. Collecting customer data on an individual basis will help the organization provide them with personalized service.
The manager can also ask customers what kind of products and services customers would like to buy in the future, trying to anticipate their needs. With the information gathered, the manager will order the customers’ products and stop ordering unpopular products.
In what follows, we present several formulas established to measure efficiency and effectiveness resulting from the information systems use. Indeed, the impact of an information system on an organization can be assessed using financial measures.
When the information system is implemented, management will certainly want to assess whether the system has succeeded in achieving its objectives. Often this assessment is challenging to achieve. The business can use financial metrics such as Productivity, Return On Investment (ROI), net present value, and other performance metrics explained in the following:
Return on investment, denoted as a Return rate, is a financial ratio that measures the amount gained or lost compared to the amount initially invested.
An information system with a positive return on investment indicates that this system can improve its efficiency.
The advantage of using Return on investment is that it is possible to quantify the costs and benefits of introducing an information system. Therefore, it is possible to use this metric to compare different systems and see which systems can help the organization be more efficient and/or more effective.
Developing information systems that measure Productivity and control is a crucial element for most organizations. Productivity is a measure of produced output divided by required input. A higher production level for a given entry-level means greater Productivity; a lower output level for a given entry-level means lower Productivity. Values assigned to productivity levels are not always based on hours worked. Productivity may be based on the number of raw materials used, the quality obtained, or the time to produce the goods or services. According to other parameters and with other organizations in the same industry, Productivity’s value has to mean only compared to other Productivity periods.
Another measure of the SI value is the increase in profit or the growth in realized profits. For example, a mail-order company installs an order processing system that generates 7 percent growth in profits over the previous year.
Market share is the percentage of sales of a product or service relative to the overall market. If installing a new online catalog increases sales, it could help increase the company’s market share by, for example, 20 percent.
Although customer satisfaction is difficult to quantify, many companies measure their information systems performance based on internal and external feedback. Some companies use surveys and questionnaires to determine whether investments have resulted in increased customer satisfaction.
Another way to measure the value of information systems has been developed by the Gartner Group and is called the Total Cost of Ownership (TCO). This approach allocates the total costs between acquiring the technology, technical support, and administrative costs. Other costs are added to the TCO, namely: retooling and training costs. TCO can help develop a more accurate estimate of total costs for systems ranging from small computers to large mainframe systems.
The evolution of information technologies leads to the reflection on new approaches that set up more flexible, more scalable architectures to meet its agility needs. The urbanization of information systems is one such approach.
The company’s information system’s urbanization is an IT discipline consisting of developing its information system to guarantee its consistency with its objectives and business. By taking into account its external and internal constraints while taking advantage of the opportunities of the IT state of the art.
This discipline is based on a series of concepts modeled on those of the urbanization of human habitat (organization of cities, territory), concepts that have been reused in IT to formalize or model the information system.
Town planning defines rules and a coherent, stable, and modular framework, to which the various stakeholders refer for any investment decision relating to the management of the information system.
In other words, to urbanize is to lead the information systems’ continuous transformation to simplify it and ensure its consistency.
The challenges of urbanization consist of managing complexity, communicating and federating work, considering organizational constraints, and guiding technological choices.
Define and frame the objectives of the project, define the scope, develop the schedule.
Carry out the inventory, organize the work, and present the deliverables. More precisely, list the assets and map the different layers (business, functional, application, and technical):
Business Architecture
Identify “business processes”: Who does what and why? The description of the processes is done with BPMN, EPC formalisms, etc. This step is tricky and may require the use of exploration methods. However, it does improve the overall understanding and increase the possibilities for optimization
Functional architecture
Identify the “functional block”: What do we need to carry out the business processes? Here, we are based on a classic division into zones (exchanges, core business, reference data, production data, support activities, management). This step’s difficulty lies in choosing the right level of detail and remaining consistent with business processes. However, it provides a hierarchical presentation and makes it easier to break down the work.
Application Architecture
Identify the applications: How to achieve the functionalities? This step is based on a classic N-Tiers division. However, it is not easy to provide value and solutions compared to functional architecture. This stage lays the foundations for the realization (major technological choices, etc.).
System Architecture
Identify the technical components: With what and where the applications work, it is based on a classic division into technical areas (security, storage, etc.). It is not easy to make the connection between applications and servers. This step brings concrete and structuring and is essential to assess the cost of the system.
Impact on the different layers, consideration of constraints (human, material, etc.), design of costed scenarios, and arbitration of the choice of a target.
How to organize the work, frame and then refine the budgets, design and plan projects, define the support strategy, set up an organization, contributions, roles, and responsibilities of actors.
At the end of this process, a Land Use Plan (LUP) is defined. It is a report consisting of:
Summaries of the orientations chosen as well as the justifications for the options selected.
A definition of areas, neighborhoods, and blocks.
Existing and target maps (process, functional, application, and technical mapping).
Additional documents (interview reports, list of people and organizational entities, etc.)
The goal is to identify the gaps between the existing and the principles of urbanization and establish changes by describing the actions and their corresponding cost.
In practice, the urbanization process is very cumbersome to implement. On the one hand, it requires the participation of many actors in the organization, and on the other hand, the analysis is very long. As a result, needs to change, and LUP is no longer necessarily suitable.
The reasons for a successful or unsuccessful IS implementation are complex and contested by different stakeholders and from the various perspectives involved. Developers tend to focus on the system’s technical validity in terms of execution, operation, and evolution. Other qualities are often considered, such as security, maintainability, scalability, stability, and availability. All of these criteria are considered to be signs of successful IS Development.
The failure of an IS can be defined as: either the system put in place does not meet the user’s expectations or does not function properly. The reasons for failure are as divergent as the projects.
The perspective of project management, on the other hand, tends to focus on the consumption of resources. The project delivered with the initial budget and within the allotted time is considered a successful project. Nelson [9] analyzed 99 SI projects and identified 36 classic errors. He categorized these errors into four categories: process, people, product, and technology. The last category concerns the factors leading to IS failures based on the misuse of modern technologies.
The seminal article by DeLone and McLean [10] suggested that IS success should be the preeminent dependent variable for the IS domain. These researchers proposed a taxonomy of six interdependent variables to define the IS’ success as the system’s quality, the quality of information, the IS, user satisfaction, individual impact, and organizational impact.
One of the significant extensions to this proposition is the dimension of the IT department’s quality of service [11].
Either way, the use of the system is seen as a sign of its success. The IS use level is incorporated into most IS success models [11, 12]. These models show the complexity of measuring user satisfaction because, even in the same organization, some user groups may be more or less enthusiastic than others to use the new information system.
In the current global context of the covid pandemic, it appears clear that information systems that integrate web and mobile technologies can positively contribute to the monitoring of contaminated cases and therefore minimize the risks of contamination provided that users adhere to this movement for the benefit of all [13]. A truly global, rapid, and efficient decision-making process is enabled by the integration of information systems from distributed sources [14].
To conclude this introductive chapter, we present its key ideas:
Levels of information are data, information, and knowledge.
The system is an aggregated “whole” where each component interacts with at least one other system component to achieve a goal.
An information system can be defined as a set of interconnected components that gather, process, store and dispense information to support decision making and control in an organization. An IS can be seen as a socio-technical system. The technical part includes the technology and the processes, while the social part includes the people and the structure.
The role of information systems is to solve an organization’s problems concerning its information needs
A company has systems to support the different managerial levels: transaction processing systems, management information systems, decision support systems, and systems dedicated to business intelligence.
Decisions can be operational or strategic.
There are several categories of business applications: enterprise resource planning, supply chain management systems, customer relationship management systems, knowledge management systems, and business intelligence.
Among the failures that can affect IS a violation of confidentiality, integrity, and availability of information.
The controls intended to avoid the IS’s security failures include management controls, operational controls, and technical controls.
The information system’s performance can be measured according to efficiency, effectiveness, Return on investment, Productivity, customer satisfaction, etc.
Urbanizing an information system means directing its continuous transformation to guarantee its consistency
The reasons for a successful or unsuccessful implementation of an IS are complex and contested by the various stakeholders and from the various perspectives involved.
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