Systemic sclerosis classification criteria 2013.
\r\n\tThe objective of this book is to provide a state-of-the-art review of the use of timber in building construction from various perspectives, including manufacturing, fabrication, modeling, design, and construction of residential and other types of buildings. Of special interest will be contributions related to new developments in timber technologies, design, construction, testing, sustainability, LCA, building envelope, and the performance of timber buildings in natural and man-made hazard conditions.
",isbn:"978-1-83768-263-8",printIsbn:"978-1-83768-262-1",pdfIsbn:"978-1-83768-264-5",doi:null,price:0,priceEur:0,priceUsd:0,slug:null,numberOfPages:0,isOpenForSubmission:!0,isSalesforceBook:!1,isNomenclature:!1,hash:"356565153fc7e43f1bf0cb7ba5e7b28a",bookSignature:"Prof. Ali M. Memari",publishedDate:null,coverURL:"https://cdn.intechopen.com/books/images_new/12057.jpg",keywords:"Wood, Lumber, Timber Industry, Home Building, Glue-Laminated Wood, Cross-Laminated Timber, Plywood, Fire Resistance, Sustainability, Fabrication, Panelized/Modular, Material Properties",numberOfDownloads:null,numberOfWosCitations:0,numberOfCrossrefCitations:null,numberOfDimensionsCitations:null,numberOfTotalCitations:null,isAvailableForWebshopOrdering:!0,dateEndFirstStepPublish:"May 31st 2022",dateEndSecondStepPublish:"June 28th 2022",dateEndThirdStepPublish:"August 27th 2022",dateEndFourthStepPublish:"November 15th 2022",dateEndFifthStepPublish:"January 14th 2023",dateConfirmationOfParticipation:null,remainingDaysToSecondStep:"9 days",secondStepPassed:!0,areRegistrationsClosed:!1,currentStepOfPublishingProcess:3,editedByType:null,kuFlag:!1,biosketch:"Dr. Memari is a Professor and Bernard and Henrietta Hankin Chair in Residential Building Construction in the Departments of Architectural Engineering and Civil and Environmental Engineering. During his 30 years of teaching in structural engineering, his research focused on the behavior of structural, architectural, and enclosure components of residential and commercial buildings under natural hazard loading and environmental conditions. He has published over 300 publications.",coeditorOneBiosketch:null,coeditorTwoBiosketch:null,coeditorThreeBiosketch:null,coeditorFourBiosketch:null,coeditorFiveBiosketch:null,editors:[{id:"252670",title:"Prof.",name:"Ali",middleName:null,surname:"M. Memari",slug:"ali-m.-memari",fullName:"Ali M. Memari",profilePictureURL:"https://mts.intechopen.com/storage/users/252670/images/system/252670.jpg",biography:"Dr. Memari is a Professor and Bernard and Henrietta Hankin Chair in Residential Building Construction in the Departments of Architectural Engineering and Civil and Environmental Engineering at Penn State, and Director of The Pennsylvania Housing Research Center. During his 30 years of teaching and research experience, he has taught various courses related to structural\r\nengineering. He has focused his research on full-scale laboratory testing characterization and evaluation of residential and commercial buildings with respect to structural, architectural, and envelope components under gravity and lateral loads that simulate natural hazards (earthquakes/wind-storms), as well as environmental effects involving building science aspects (heat transfer, air leakage and moisture transport) through building enclosure. 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It is indicated in patients with suspect of microangiopatia [1].
It gives precise information about capillaries’ conditions and related diseases to rheumatologist.
Recently, after the development of new systemic sclerosis classifications criteria [2], the role of capillaroscopy became more important. In fact a score of two out of nine is assigned in the case of significant capillaroscopic abnormalities to diagnose this disease (Table 1).
Systemic sclerosis classification criteria 2013.
In the past, the study of capillaries was performed by instruments that could zoom, take pictures, or film the blood microcirculation, such as the ophthalmoscope and the dermatoscope, the stereo microscope, and tools for macrophotography.
The modern capillaroscopy, equipped with optical probes, is today commonly used in rheumatological and dermatological practice, and it is able to zoom (magnificat) capillaries in order of 200×, obtaining much qualitative measurements that could be reproducible.
The instrument consists of an optical probe fitted with an adjustable magnification and illumination ring for focusing and a personal computer (with high resolution color screen) with software for data processing. Several new models interface with iOS or Android system, and they are more portable (Figure 1).
Example of Wi‐fi capillaroscope.
Although capillaroscopic examination is easy to perform, it is essential that the operator has been properly trained about the instrument’s functioning and correct method of image acquisition to avoid misinterpretation [3].
Capillaroscopy should be always performed in conditions of constant temperature. By convention, it is considered that the patient needs an acclimatization of at least 15 min at the temperature of 21–24°C before performing the examination.
When booking the videocapillaroscopy, it is necessary to give to the patient a few suggestions, especially to avoid the manicure in 7–10 days prior to the examination and application of nail polish.
At the time of the examination, the patient should be sat with his hands gently resting on the table on the palm with the fingers slightly apart.
Before starting the exam, it is necessary to apply a drop of oil (usually cedar oil) on the nail fold of each district that should be examined. At this level, the capillaries shall run parallel to the skin plane and, therefore, in normal subjects, are visible an afferent branch, a loop and an efferent branch (capillary hairpin).
Usually, they are investigated four districts in each hand leaving the first finger that usually has a bad view of the vascular widespread and nonspecific alterations. Furthermore, several studies have shown that, even in the presence of Raynaud phenomenon exclusively localized at the foot, capillaroscopy of the hands provides the same information that would be obtained by investigating the lower limbs and, therefore, for convenience, the examination, even in these cases, is usually performed exclusively in the hand level.
Modern software let you select on the monitor the investigation from time to time in the district, so you can compare any changes in respect of each finger. Also, they include measuring systems for dimensional analysis [4].
Although there are some parameters to indicate a normal/healthy capillaroscopic, it is important to consider that there is great variability in the capillary structure both interindividual and intraindividual. This variability depends on many factors such as employment, racial, and environmental.
In particular, in patients underwent to repeated microtrauma for professional reasons, such as typists, jackhammer users, pianists, etc., it is not uncommon to observe widespread phenomena of microhemorrhages and neoangiogenesis. As well as, patients who smoke have often shortened and tortuous capillaries. Moreover, in patients with dark or very dark skin color (e.g., for racial factors), it is often difficult to correctly visualize the capillaries and almost impossible to see the subpapillary venous plexus. Modern capillaroscopies with editable light intensity solve this problem in part. It is also important that the capillaroscopic be set from time to time for each patient calibrating on the “contrast”, “range,” and “saturation” functions to obtain an image that is as clear as possible [5].
A good capillaroscopic examination is achieved by positioning the probe plumb to the district under consideration and to obtain the correct visualize of the dermal papilla roughly between the middle and the upper third of the monitor (photo 2—correct assessment of the image). The area of interest is in fact just the dermal papilla and the capillaries residing there, or should reside within it, although the outside alterations are also important [6–8].
During capillaroscopy, the following parameters are evaluated:
Density
Structure
Microhemorrhages
Bloodstream
Edema (soft focus effect)
Subpapillary venous plexus
Example of normal pattern.
(a) and (b) Black arrows indicate papillae vacuous.
Normal structure: hairpin shape.
You can observe the modest structural disorganization even in healthy individuals. Instead, the complete subversion of it is attributable to pathological conditions.
They should not be present ramifications that often indicate a poor vascularization with angiogenesis to provide the blood supply of avascular areas.
Tortuosities are often found, for the most in apical zone, and the capillary has a distorted aspect. It manifested as single or multiple cross/overs and/or patterns described as “trefoil,” “antler,” “glomerular loop,” and “treble clef.” These anomalies can be isolated or diffuse (Figure 5: tortuosities).
Tortuosity in apical zone.
The apical tortuosities, by themselves, are not a pathologic finding. They are frequently found in heavy smokers, in patients underwent to repeated microtrauma, in patients with psoriasis and in various other conditions.
Otherwise, the tortuosity can be contextualized within a framework frankly altered or a real scleroderma pattern.
The capillary branches (afferent and efferent loop) of the healthy subject normally have a diameter between 8 and 20 μm depending on the capillary portion considered. In fact, usually, the loop efferent, due to venous stasis, is slightly larger than that afferent.
They are defined “enlarged” capillaries with a diameter between 30 and 50 μm measured at the level of the two branches and the loop, “mega” capillaries with diameter >50 μm at the level of the two branches and the loop, and “giant” capillaries with diameters >100 μm wide and the two branches of the loop (Figure 6: megacapillaries and giant capillaries).
(a) and (b) Megacapillaries and giant capillaries.
Morphostructural minor anomalies (e.g., tortuosity) are found in approximately 10–20% of healthy subjects.
Among irregularly enlarged capillaries, loop size can vary considerably in different segments, with normal portions alternating with extremely enlarged areas, sometimes giving a “microaneurysmatic” or “rosary‐like” appearance (Figure 7).
Capillaries with “rosary‐like” aspect.
The extravasation blood of pathological capillaries assumes a characteristic aspect in the supply chain “a strung pearls” (Figure 8a and b: microhemorrhages to “strung pearls”), or mold on the capillary (Figure 8c and d: “Napoleonic hat”).
(a) and (b) Microhemorrhages to “strung pearls”; (c) and (d) microhemorrhages to “Napoleonic hat.”
It can get an idea more or less realistic age of bleeding based on the analysis of the same color that tends to move from dark red to light yellow before disappearing altogether.
In the case of disease patterns may be encountered persistent deposits hemosiderin standing.
Nailfold microbleeds may also be related to capillary thrombosis which can occur in some pathological conditions and is often misinterpreted as hemorrhages. A key distinguishing feature of capillary thrombosis is the configuration of the dark area, which mirrors that of the capillary loops.
A capillary with continuous blood flow is normal index. It will appear constantly full, and in the case when pressure is exerted by the probe on the same papilla, these will quickly fill the cessation of the stimulus. In the case of slowed blood flow, this will assume a granular appearance in particular in correspondence of the capillary walls and, in the case a pressure is exerted with the capillaroscopic probe the capillary will fill up slowly to cease the stimulus.
In the case where there is capillary thrombosis, typical of giant capillaries or megacapillari, the flow will appear static, and neither the pressure exerted by the probe nor the subsequent stop of the stimulus will show variations in the capillary which will appear always full.
Occasionally, it is seen in healthy subjects. Alone it does not constitute a significant fault (Figure 9: flou effect).
Black arrow → indicates flou effect.
(a) and (b) Good visibility of the subpapillary venous plexus.
The venous plexus vessels have a perpendicular progress to the capillaries and are larger.
In cases of serious and widespread destruction capillaries, such as in the advanced stages of systemic sclerosis, the subpapillary venous plexus may constitute the only identifiable vascular element.
In healthy individuals, it is not always detectable.
Secondary Raynaud’s phenomenon refers to the clinical manifestation in the presence of an underlying systemic disease.
Among the diseases of rheumatologic interest, the more strongly and inseparably linked to Raynaud’s phenomenon is the systemic sclerosis, even if this appears also in the presence of other autoimmune diseases such as systemic lupus erythematosus, dermatomyositis, undifferentiated connective, and mixed connective, in a small percentage of cases, in the presence of rheumatoid, psoriatic, or juvenile idiopathic arthritis.
Capillaroscopy does not constitute, in itself, a diagnostic test. Especially not usefull to diagnostic Raynaud’s phenomenon, whose diagnosis is exclusively linked to the clinic. The examination provides a specific view on the state of the microcirculation and in particular on its integrity and is always related to the clinical and laboratory data. Then we can determine whether the Raynaud’s phenomenon is referable to a damage of the microcirculation [8, 9].
However, capillaroscopy plays a key role in the diagnosis of connective tissue diseases and especially of the scleroderma spectrum disorders, which include, in addition to systemic sclerosis, the dermatomyositis, the undifferentiated, and mixed connective tissue disease [10, 11].
In fact, historically, the typical capillaroscopic alterations for scleroderma spectrum disorders have always played a fundamental role in the diagnosis of systemic sclerosis, being included in all the diagnostic criteria formulated over the years for this condition, including the latest ACR criteria of 2013.
In contrast to what happens in the case of primitive Raynaud’s phenomenon, in the presence of secondary Raynaud’s phenomenon, the capillaroscopy is able to reveal specific anomalies.
Over the years, we have been implemented many efforts in an attempt to identify specific capillaroscopic paintings for a specific pathology. These efforts have hesitated in identifying a capillaroscopic pattern defined as “scleroderma pattern.” More than 95% of patients with overt systemic sclerosis have morphological markers of microvascular disorganization, including giant capillaries, microhemorrhages, loss of capillaries, avascular areas, and angiogenesis [12–14].
Specific capillaroscopic alterations are found in the majority of cases of systemic sclerosis and often several years before diagnosis. The key element that distinguishes the scleroderma pattern is megacapillare. Another common finding is typical microhemorrhage called “pearls strung” or “Napoleon hat” usually overlying dilated capillaries or megacapillaries. Third distinctive element is represented by avascular areas. In 2000, the group of Genoa, headed by professor Cutolo, has identified three types of scleroderma pattern, referred to as “early,” “active,” and “late” [15–17].
We analyze below a capillaroscopic examination as a “scleroderma pattern.”
Generally, the presence of megacapillari in the context of apparently normal capillaries, with few or absent microhemorrhages, characterizes the earliest stages of the disease and it is identified by Cutolo et al. as “early.” Instead, in the presence of numerous or ubiquitous megacapillari in the context of rare normal capillaries or ectatic capillaries, in the presence of numerous microhemorrhages in more districts, we will be faced to a capillary framework called “active.”
In the late stages of systemic sclerosis are sometimes pathognomonic capillary ramifications, with elongated and bizarre capillaries, or overgrowth of the subpapillary venous plexus, last attempt to make up for the total or almost total disappearance of the subpapillary capillaries. This framework configures the “late” scleroderma pattern.
Other detectable morphostructural abnormalities in scleroderma pattern are ectasia (capillaries in the range from 30 to 50 μm), the tortuosities, identifying, based on the forms as “staghorn,” “a clef,”“a glomerulus,”“ball,” etc. and microaneurysms. These alterations are not specific and can also be found in healthy subjects.
Microhemorrhages: They constitute an extremely common finding in the scleroderma pattern and, especially in the one called “active”. The typical hemorrhages of the scleroderma pattern may look as a mold, overlooking a megacapillare, or a giant capillary, or an aspect to “strung pearls” that are stacked in succession in the subpapillary and extrapapillary, never in deep seat. The microhemorrhages indicated breaking of capillary wall and are a sign of microvascular damage. There may be bleeding in the absence of megacapillari. The lonely bleeding are not considered sufficient for the identification of scleroderma pattern although, the presence of diffuse bleeding, even in the absence of further alterations, suggests necessarily the follow up.
The microhemorrhages should not be confused with traumatic hemorrhages that have a very heterogeneous presentation, generally overlying or adjacent to completely normal capillaries. Usually, they have a greater extension and can be found in the subpapillary‐, deep‐or extra‐papillary segment.
Early scleroderma pattern (Figure 11(a–c)): framework characterized by the presence of dilated capillaries and some megacapillaries. Microhemorrhages are poorly represented or absent. No reduction in capillary density.
Active scleroderma pattern (Figure 12(a) and (b): framework characterized by the widespread presence of megacapillaries and/or giant capillaries. A large number of microhemorrhages. No reduction in capillary density or occasional finding of “papilla vacua” in the presence of angiogenesis phenomena.
Late scleroderma pattern (Figure 13(a) and (b): framework characterized by the presence of rare lasts megacapillaries, abundant ramifications. Rare or absent microhemorrhages. Large avascular areas. Exuberance of the subpapillary venous plexus.
(a–c) Few megacapillaries without microhemorrhages.
(a) and (b) Diffuse megacapillaries with microhemorrhages.
(a) and (b) “Papilla vacua,” diffuse or localized loss of capillaries.
As mentioned, all of rheumatic diseases included in the scleroderma spectrum disorders may present a capillaroscopic framework suggesting for scleroderma pattern. However, in some diseases, there are typical capillary feature presentations. For example, in rheumatoid arthritis, extremely elongated capillaries are the principal findings (Figure 14) [18, 19].
Capillaroscopy in rheumatoid arthritis.
In dermatomyositis, the most frequent findings are dilated and giant capillaries with tree‐like appearance (Figure 15(a) and (b)).
(a) and (b) Tree pattern in dermatomyositis.
Even in the case of psoriatic arthritis, capillaries appear rather short and stubby (Figure 16).
Capillaroscopy pattern in psoriatic arthritis.
However, these features are not enough specific to identify a defined framework.
In present‐day clinical practice, capillaroscopic surveys are usually analyzed qualitatively in order to show patterns of disease (as previously stated). However, some authors share the idea that a normalization of the capillaroscopic pattern may be positive.
Different scoring methods have been proposed to prospectively evaluate both the trend and the gravity of the scleroderma microangiopathy.
One of these methods is the semiquantitative assessment which contemplates the analysis of the following capillaroscopic parameters:
Loss of capillaries: reduction of the number of capillaries to less than 9 per mm
Disorganization of the capillary architecture: irregular loops distribution, orientation, and morphology
Tree‐like capillary network: capillaries with skein‐like or shrub‐like branched loops.
Each parameter is given a score based on the given impairment:
0: no alteration
1: capillary impairment up to 33%
2: capillary impairment higher than 33% and up to 66%
3: capillary impairment higher than 66%
The average score for each parameter comes from the analysis of four conterminous capillaroscopic areas (each area consisting of a 1 mm2 surface) in the central part of the II, III, IV, and V finger of each hand.
The final score of each parameter is given by adding up the average scores of each finger; then the result is divided by 8. Lastly, the sum of the three scores constitutes the
On the basis of the capillaries loss only, such score is easier to determine and has been proposed as a predictor of digital ulcers. An average score of the capillaries number decrease is obtained by using capillary density as only parameter if a 1 mm2 surface on eight fingers is analyzed. Scores that appear to be higher than 1.67 are shown to be a predictive factor of digital ulcers (Se 70%, Sp 69.77%, with a positive likelihood ratio of 2.32 and a negative likelihood ratio of 0.43) since such ulcers occur within 6–12 months after the capillaroscopic evaluation [20] (Figure 17).
Number of fields studied: gold standard (F32) and successive simplifications (F16‐F8‐F4). (A) F32: eight fingers (arrows), four fields of 1 mm per finger, giving a total of 32 fields. (B) F16: eight fingers (arrows), two fields of 1 mm per finger, giving a total of 16 fields. (C) F8: eight fingers (arrows), one filed of 1 mm per finger, giving a total of eight fields. (D) F4: one finger (arrows), four fields of 1 mm in that finger, giving a total of four fields [
This method needs a strict standardization to be reproducible and comparable through time. As of today, the only quantitative score to be validated for both replicabilities and the predictive value is the
The calculation is carried out by analyzing the whole nailfold area (from the II to the V finger of each hand, saving at least an image for each finger). Of all selected images, the one with the highest number of capillaries and the one with the lowest number of megacapillaries are to be considered; then the following formula has to be used:
Examples of capillaroscopic finding measurements. (A) 4 capillaries, 3 giant capillaries (1 ramified giant capillaries occupying both dermal papillae). (B) 8 capillaries, 5 giant capillaries (every capillary was counted in the distal row even if it was not on the same level). (C) 13 capillaries, 1 giant capillary (1 ramified giant capillary computed as 2 in the total number count). (D) 12 capillaries, 1 giant capillaries (every capillary was counted in the distal row even if it was not on the same level) [
Algorithm for capillaroscopic skin ulcer risk index (CSURI) evaluation. D, Maximum diameter of mega capillary; M, number of megacapillaries (diameter ≥ 50 μm); N, number of capillaries [
Capillaroscopic parameters are defined in a strict way so that reproducibility and replicability are optimized:
Number of capillaries: all the capillaries in the first row (the ones closest to the papilla) must be counted even if they are all different depths.
Megacapillary: maximum measurable diameter in the first row (microaneurysms should not be included).
Tree‐like morphology: a tree‐like capillary is equivalent to the number of taken papillae or to the number of observable loops.
The CSURI value per patient is the maximum computable if the whole nailfold area is analyzed so that the highest score of microangiopathy is defined. PROs: number of false negatives <3%. CONs: a higher risk of increasing the false positives.[21].
The
Prognostic parameters of PRINCE index.
Presence of megacapillaries
Presence of microhemorrhages
Numbers of capillaries per mm
The inclusion of antinuclear antibodies allowed to develop an additional predictive model with the following risk categories: high (50+ %), medium (10–50%) and low risk (<10%) (Figure 20) [22].
Prognostic Index for Nailfold Capillaroscopic Examination (PRINCE). A, D, G, and J: possible combination of giant loops and microhemorrhages (0 = absent, 1 = present). The score (on the
The role of capillaroscopy has been considered over the years more and more attention, especially in the early diagnosis of systemic sclerosis. Then, to detect valid predictors of early systemic sclerosis, the European Scleroderma Trials and Research group (EUSTAR) identified three red flags, thanks to the VEDOSS program (very early diagnosis of systemic sclerosis): Raynaud’s phenomenon (RP), antinuclear antibodies (ANA) positivity, and puffy fingers are the main elements to suspect systemic sclerosis. In the case of these three flags performing, further tests to confirm the diagnosis, in particular nailfold video‐capillaroscopy and evaluation of specific disease antibodies (anticentromere and antitopoisomerase I), are mandatory. The challenge of VEDOSS program is to identify patients who will develop an established systemic sclerosis.
Very recently, the first results of the VEDOSS project were processed and new EULAR/ACR (American College of Rheumatology) classification criteria have been validated and published (2013), in which the capillaroscopic characteristic changes have been included (requiring at least two, or better, all four items to be present) (Figure 21) [23, 24].
A behavioral flow chart for patients in whom the very early diagnosis of systemic sclerosis (SSc) should be considered is proposed. Red flags should trigger the differential diagnosis of SSc and guide the general practitioner to send the patient to the referral center where capillaroscopy and specific autoantibodies are ordered and the diagnosis of very early SSc is made. HRCT, high resolution CT; PFT, pulmonary function tests [
Capillaroscopy is an easily tolerable, noninvasive, important angiologic examination method. In the case of Raynaud’s phenomenon, associated or not with signs or symptoms suggestive for connective tissue disorders or in the presence of autoantibodies (in particular, antinuclear antibodies (ANA) and extractable nuclear antigens (ENA)), capillaroscopy is a crucial examination that adds irreplaceable information to formulate a diagnosis. In the meantime, capillaroscopy has achieved a firm status in the early diagnosis of systemic sclerosis (SSc).
The report should include capillaroscopic terms understandable to nonexperts and should be as standardized as possible, using both qualitative and quantitative parameters that reliable and establishing normal limits (Figure 22). In the presence of capillaroscopic, more alterations (megacapillaries, microhemorrhages, neoangiogenesis, density decrease,) a greater degree of detail is required, indicating although these alterations are present on only one or a few fingers.
Example of Capillaroscopic Template.
Nowadays, many research efforts have been concentrated on the efficiency and security of IoT devices to raise the performance and the level of protection for IoT data and detect possible attacks. It is significant to understand the types of data delivery challenges in IoT. The challenges related to wireless sensor networks (WSN), cyber-physical systems (CPS), and machine-to-machine (M2M) continue to appear within the context of IoT since the basic components of IoT networks include WSNs, CPS, and M2M. One of the challenges is the difficulty in providing communications using infrastructure-based wireless systems because of the high cost of deploying and maintaining this infrastructure with the rapid growth of IoT users and devices [1]. Furthermore, the IoT system is mobile and dynamic; thus, its perimeters are not well-defined. It is also robustly heterogeneous concerning the devices, protocols, and communication medium.
The other concern is that IoT system is vulnerable to malicious cyber-attacks. One of these aggressive attacks is a distributed DoS (DDoS) attack, which intends to bring down a victim system by preventing legitimate devices of service from accessing it. DDoS attackers may also aim to gain unlimited access to the victim machines and cause more damage consequently. These attacks are made not to be significantly distinct from the usual behavior practiced by the system. One of the techniques that can be used to detect cyber-attacks is intrusion detection (ID). Yet, the advanced ID schemes utilizing machine learning techniques struggle to detect some of the cyber-attacks. These attacks are made not to be significantly distinct from the usual behavior practiced by the system. Therefore, there is a need for an anomaly-based IDS combined with artificial intelligence and machine learning due to its ability to classify and identify earlier hidden attacks. This kind of IDS will help in detecting multi-stage DDoS attacks. Current schemes in the development of ID investigate artificial intelligence and machine learning in academia and industry, such as artificial neural networks and fuzzy logic.
IoT advanced systems can achieve high performance with a human being’s supervision for defining how to perform their duties. They also can automatically detect unusual patterns of web traffic with malicious activities and learn the patterns by themselves over time. Previous studies in the wireless network security area focused on ID based on a single hidden Markov model (HMM) and multi-class system classifier (MCSC) [2, 3]. Here, we study the potential applicability of the hierarchical hidden Markov model (HHMM) for intrusion detection in IoT systems in which the problem space can be several magnitudes higher than in wireless networks. And, we propose a probabilistic hierarchical hidden Markov model that reduces the high state-space without compromising classification accuracy. The proposed scheme shows better outcomes for detecting the DoS and DDoS attack patterns compared to the state-of-the-artwork.
The main contributions of the work are:
We propose a PHHMM model that translates high-dimensional IoT data to a discrete set of reliable data to be securely delivered with the ability to detect DDoS attacks.
We propose a method that learns and efficiently analyzes large amounts of data for classifying DDoS patterns in IoT traffic.
We conducted a performance comparison of our PHHMM with the baseline HHMM, Neural Network, and Naive Bayes models on the benchmark dataset from the CICIDS2019 database that contains 11 types of DoS and DDoS attacks collected over real-time for validating the proposed model.
The rest of this paper is organized as follows. Section II investigates the state of the art of some IoT data delivery used methods and presents the related work, followed by the background in Section III. Then, the model details are demonstrated in section IV and section V. Next, we show and discuss the experiments and simulation results in sections VI and VII. Finally, section V ends the paper, outlining some suggestions for future work.
To design an efficient protocol in IoT networks is a risky task due to their characteristics. The efficient routing protocol has to respond to the changes that may happen in the topology as same as the bandwidth constraint. Most of the proposed protocols are only sub-optimal. Forster et al. [4] discuss three popular machine learning techniques on the communication layers in the WSNs. These algorithms are used in distributed environments to solve different problems such as ad hoc routing. They are categorized into three groups; reinforcement learning, supervised, and unsupervised. The aim is to find out a convergent mapping function that helps in prophesying the output results for any new input. Routing in IoT environments, as mentioned earlier, is associated with protocols in wireless sensors and ad hoc networks. One existing routing protocol for IoT networks is IP6 overpower personal area networks (6LoWPAN), which are used to route the data among non-IP sensors through networks with high processing capabilities. Its topology consists of a set of reduced function sensors that are linked to full function sensors [5]. It helps to support low cost, different length addresses, low bandwidth, different topologies, energy consumption, and lengthy sleep time. This protocol supports the multi-hop data delivery and reduces transmission overhead by providing header compression enclosing IPv6 long headers in the IEEE802.15.4 small packets [6]. Many of the real-world machine learning algorithms use both supervised and unsupervised learning as hybrid learning or semi-supervised learning to take advantage of the strengths of these main categories and minimize their cons [7]. Another standard protocol in IoT is the Routing Protocol for Low-Power and Lossy Networks (RPL) [8], which is a distance-vector protocol based on IPV6 that can prop lots of data-link protocols. It builds a destination-oriented directed acyclic graph (DODAG). It has only one path from each node to the root, and all the communications will be through that root. All nodes advertise themselves as the root by broadcasting a DODAG information object (DIO), and then the DODAG is gradually built. For the cognitive networks, as an extension of RPL, which is the Cognitive RPL Protocol (CORPL) is designed. It uses the DODAG topology generation. Constrained Application Protocol (CoAP) [6] is another IoT protocol that produces a lightweight RESTful (HTTP) interface to reduce overhead and power consumption. The next protocol is the Message Queue Telemetry Transport (MQTT), which was introduced for providing embedded connectivity between the party of middlewares and applications and the party of networks and communications. It is a publish/subscribe design that includes three parts: publishers, which are the sensors that connect to the broker to send their data, subscribers, which are the sensory data or applications, and the broker, which sends the data to the subscribers after classifying them in topics. Secure MQTT (SMQTT) [6] is an extension of MQTT to enhance its security features. It is encryption-based, where each message is encrypted and delivered to multiple nodes, which is common in IoT applications. For supporting a large range of IoT applications, ZigBee smart energy [6] is used. It has a wide star topology, peer-to-peer topology, or cluster-tree network topology. It also allows implementations with low memory and processing power. In addition, the Advanced Message Queuing Protocol (AMQP) [9] is designed for the financial industry. It is a publish/subscribe design built over TCP, but the broker here is divided into two main components: exchange and queues. The exchange receives publisher messages and distributes them to queues based on pre-defined conditions. Moreover, the long-term evolution advanced protocol (LTE-A) [9] is used for IoT applications in wireless networks. LTE-A design has a core network (CN) to control mobile devices, a radio access network (RAN) to establish data planes and control the wireless connections, and mobile nodes.
Because of the lack of training datasets, the current IoT intrusion detection systems are incapable of detecting the latest DoS and DDoS attacks [10], such as Network Time Protocol (NTP) attack, Network BIOS (NetBIOS) attack, UDP lag (delay). The authors in [11] proposed a hidden Markov model for predicting and detecting multi-stage attacks. Their work is not applicable for IoT systems as it fails on the high dimension state space since the incoming network traffic in IoT will have largely hidden states. The approach developed by [12] has a high detection rate as it identified most of the occurred attacks. However, they did not consider DDoS attacks. Authors in [2] proposed an anomaly detection module that uses Long Short-term memory for detecting both known and unknown attacks with a low false-positive rate. Their work shows high recognition rates. In [3], researchers discussed the multi-stage attack and its prediction. They proposed a multi-stage Naive Bayes model that can predict each stage of the multi-stage attack scenarios. However, schemes in [2, 3] are not suitable for predicting multiple attack intents in heteroecious environments. Besides, the authors in [13] propose a Hierarchical Hidden Markov Model (HHMM), which is an extension of the hidden Markov model (HMM), as the method for activity recognition. They analyzed the accuracy rate of their model with the Naive Bayes and HMM schemes. The comparison showed that the HHMM has the highest accuracy rate among others. However, they did not take into consideration the nature of IoT systems. The authors in [14] described routing-specific attacks in the IoT systems and concentrated on identifying the malicious node’s location and neighborhood to inform the network administrator. In [15], the researchers proposed an ID scheme to detect flood attacks in IoT networks. Their proposed model identifies the attacks through the back-propagation neural network model. Table 1 summarizes the properties of the major types of existing ID schemes.
Paper | HHMM | HMM | DDoS detection | Up-to-date dataset | Applicable for IoT |
---|---|---|---|---|---|
[1] | No | Yes | Yes | No | No |
[2] | No | Yes | No | N/A | No |
[3] | No | Yes | No | Yes | No |
[4] | No | Yes | Yes | Yes | No |
[5] | Yes | Yes | Yes | Yes | No |
[6] | No | No | Yes | N/A | Yes |
[7] | No | No | Yes | N/A | Yes |
Intrusion detection schemes.
This section provides definitions for the used terms in this paper:
DDoS is one of the potential attacks in IoT where attackers coordinate the utility of many machines connected to the network to send an overwhelming amount of unwanted requests to a targeted server [16]. They try to disrupt the traffic of the server with a flood of unwanted requests. Besides, DDoS reaches effectiveness by using various compromised devices as the roots of attack traffic. The more hacked devices, the more damage is caused to the servers. Thus, the attacker examines remote machines for security gaps using some tools such as worms to find their vulnerabilities and inject them with the attack code. Then, these compromised machines become zombies, which the attacker uses to send malicious packets to the targeted victim. DDoS may yet cause a long-term memory consumption of the relaying nodes in IoT environments due to nodes’ restricted resources. There are various DDoS attack types used to degrade the performance or availability of targeted services on the Internet. Some of these attacks are Botnet attacks, Spoof-packet flood attacks, Multi-Vector Attacks, and Misused Application Attacks. Besides, there are various schemes used to defend against DDoS attacks, which are under three categories; policy-based schemes, application-based schemes, and machine learning–based schemes. The policy-based defense scheme is placed in the switch to define the traffic that is allowed to be forwarded and the other ones are defined as malicious. It requires analyzing collected data samples of the network to classify malicious traffic. Numerous policy algorithms use different measurements such as standard deviation or measure the chi-square statistic of the sample to classify the packets as malicious or legitimate. Secondly, the application-based schemes handle and control packets in the network by the user interface layer. Finally, the machine Learning-based defense schemes deploy machine learning algorithms to investigate and classify the traffic to detect the DDoS attack.
The Hierarchical hidden Markov model (HHMM) is a multi-level stochastic process derived from the Hidden Markov model (HMM) by making each of the hidden states a self-contained autonomous probabilistic model. It is a statistical framework for modeling a sequence of observations. Each observation is emitted from a hidden state within the system by recursive activation. The basic idea of HHMM is that the upper-level states produce sequence states called “abstract” states [17]. And, the lower-level states produce single observations called “concrete” states [17]. The observations are governed by each of the sub-states (sub-HMMs). The process of recursive activations ends when reaching a state that produces output symbols like an HMM [17].
For estimating HHMM parameters, we define the generalized forward (
where
We also define the generalized horizontal (
The model is represented as
A probability transition matrix (
where
N is hidden states.
An emission matrix (
where
M is observable states.
An initial state distribution (
In this section, we first explain the structure of the traditional HHMM model then we illustrate the framework of our proposed model.
Learning, decoding, and evaluating are the three principal HHMM objectives as described in [18]. Briefly, the techniques applied to achieve these objectives are as follows:
This probabilistic hierarchical hidden Markov model should overcome the problem of the heterogeneity of IoT data. However, it suffers from high computational costs as the data increases in an exponential manner due to its used algorithms. Applying this scheme to IoT data undeviatingly will contribute to a problem of high state space. We, therefore, need to find a way to reduce the high state space without compromising the classification quality.
Our proposed model uses clustering and dimension reduction techniques to partition the massive incoming network traffic to overcome the problem of largely hidden states, before applying HHMM for classification. It follows the framework described in Figure 1 and achieves the objectives [18] and techniques are applied as follows:
The PHHMM detection model.
In our PHHMM model, applying dimension reduction techniques is a challenging step due to the lack of a standard approach for reducing the dimensionality of the observed IoT network traffic. It requires identifying the principal components and linear combinations of variables that describe the highest contrast in the massive data without compromising this data. Determining the principal components given a covariance matrix is computationally expensive as it claims the eigenvalue decomposition that requires the calculation of the covariance matrix. To overcome this challenge, we present an approach that avoids the direct computation of the covariance matrices but delivers the efficient subspace dimension. Our model applies the singular value decomposition (SVD) for calculating PCA to circumvent this expensive operation. The participating nodes in the algorithm use the PCA with the SVD learning mechanism to estimate principal components of the data traffic.
This work contributes to improving and resolving the common flaws in the application of HHMM in massive data by reducing the data dimensions based on traffic from both of the two streams being compared instead of depending only on some training data of normal traffic. Using only the most significant principal components, we could avoid the computation of the entire subspace. We can estimate a reduced number of principal components that are sufficiently effective in detecting malicious traffic. Our model allows the subsequent use of only the number of dimensions necessary at any given time.
This section explains the methods and techniques that we apply to the tasks in the tasks of the proposed model in Figure 1.
Collecting a large amount of attack traffic and normal traffic in a large real-time network is time and money-consuming. It needs significant resources, a diversity of normal IoT traffic, and a diversity of attack traffic. Instead, there are publicly available network traffic datasets, which can be used for this task. We analyze some of the available datasets based on the following:
Real-time network traffic
The most advanced DoS and DDoS attacks
The CICIDS2019 includes inbound and outbound traffic of the most advanced DoS and DDoS attacks. It contains lots of network flow-related characteristics and different types of current DoS and DDoS IoT attacks traffic collected over real-time networks.
The CICISD dataset has a huge number of attributes, selecting a subset of them is necessary for eliminating redundant and irrelevant ones. This helps in improving detection accuracy for DDoS attack detection. To better train our model for detecting the attack patterns, we have selected packet features that indicate DDoS attacks, which are useful for classification to distinguish between normal IoT traffic and DDoS IoT traffic:
The k-means clustering is a method of vector quantization that aims to partition n observations into k clusters in which each observation belongs to the cluster with the nearest mean based on a similarity metric [20].
Let
The clustering method works as follows:
Randomly initialize K:
Select K features as the primary cluster centers.
Allocate each feature
Determine the new center
Repeat until the cluster centers do not move anymore.
When deploying k-mean to cluster IoT traffic, we reduce the dimension data to quantize the data into single-dimensional data. We classify the traffic flow depending on similar characteristics and identify clusters of homogeneous traffic flows and define their borders. It needs to have high intra-cluster homogeneity and inter-cluster heterogeneity.
For determining the optimal (fitting) value of K, we use the elbow algorithm repeatedly applying different values of K and plotting their heterogeneity. When the curve begins to flatten, it reaches the optimal value of K.
After clustering the data, initially, we have n states
To achieve dimension reduction by applying PCA, it requires placing the eigenvalues from the highest to lowest by their value. This ordering stores the elements in order of weight to the variance of the initial data matrix. This will allow us to drop the less important elements. Thus, we keep most of the information and lose a little noise. We can reduce the dimension of the original data. For instance, for any data of d dimensions, we only take the first r eigenvectors:
Singular value decomposition of Y [
Assume that the data matrix Y is centered, i.e., the column means have been subtracted to be equal to zero. The covariance matrix (C) is calculated by:
Because the covariance matrix is symmetric, it can be diagonalized by:
where V is an eigenvectors matrix and L is a diagonal matrix with eigenvalues
As the result, the eigenvectors of C are the same as the matrix V (the right singular vectors of Y) and the eigenvalues of C can be defined from the singular values
The principal components are defined by:
In short, the PCA is calculated as follows:
Obtain the maximum covariance in the data.
Keep meaningful information only to reduce data size.
Simplify the representation of the data:
The variance of the data is maximized.
Analyze the construction of the features and observations.
Based on Oja’s algorithm for stochastic PCA optimization [25], the primary concept of our algorithm is to implement stochastic m updates by uniformly sampling the columns
We use the variance-reduced stochastic schemes for convex optimization [23] to reduce the stochastic variance. Let
The algorithm is burst into periods f = 1, 2, 3,. .., wherein all period we do a single exact power iteration by computing
1:
2:
3:
4:
5:
6:
7: Set
8:
9:
This is to ensure that
10:
11:
12:
Similar to HHMM [17], the PHHMM model uses the Baum-Welch algorithm to calculate the likelihood-maximizing parameters of the model given the observed data. It comprises four phases: the initial phase where the
The traditional HHMM constitutes multi-single states that are considered as self-contained probabilistic models [18]. However, due to the heterogeneity of IoT traffic, we design each state to have multiple separate lower HMM layers and one upper HMM layer, each lower state constitutes three levels:
Learning: The observations of the first level train the
Decoding: The observations of the second level trains the
Evaluation: The observations of the third level trains the
The model has one upper HMM state for predicting DDoS attacks that use the attack sequence from the lower states to learn new patterns of DDoS attacks by the DDoS detection algorithm. Thus, we can detect the multistage DDoS attacks in this extended mode, unlike the standard HHMM.
Model Training (Baum-Welch algorithm) Baum-Welch algorithm [19] is a recursive Expectation–Maximization method for estimating un-observed hidden parameters in an HHMM model. This algorithm facilitates the complex challenges of analytically applying maximum likelihood estimation. It trains the HHMMs to find the optimal
By this algorithm, for HHMM models (
• The state transition matrix
• The Observation likelihood sequence
1:
2: Estimate
3: Calculate the expected probability
4:
5: Calculate forward variable
6: Calculate backward variable
7: Calculate downward and upward variable
8: Estimate the state probability
9: Compute the optimal state sequence
10: Estimate
11: Calculate
12:
13:
Model Decoding (Viterbi algorithm) Viterbi decoding algorithm [26] predicts the hidden traffic states. This algorithm only uses state-optimized joint likelihood for observation data and the underlying Markovian state sequence as the objective function for estimation. Opposed to the BW algorithm, it does not update all likely paths for all states in the HHMM.
1:
2: Obtain the model (
3:
4: Split
5:
6:
where,
7: Normalize row sums of
8: Estimate
9:
10:
11:
Model Detection algorithm This algorithm uses prior knowledge to learn about the previous attack behavior and track the attack alerts. It gets the likelihood probability of the observation sequence
1:
2: Calculate the probability of
3: Calculate the probability of observation sequence (
4: Calculate the likelihood sequence of the observable sequence (O) obtained by the model:
5: If (
6:
7:
In this section, the performance of the PHHMM based anomaly detection approach was tested on traffic combining DDoS attack data with normal data from the prepared dataset. We place the prepared dataset into our PHHMM model to identify DDoS attack intentions and predict the possible attacks. The performance of implementing our proposed model is obtained through MATLAB R2020b simulations. To remove duplicate alerts, we wrote a script for extracting necessary fields such as IP Addresses, Alert ID, Destination Port, Source Port, and timestamp from Snort IDS alerts.
We analyze and evaluate the performance on the common metrics for IDS performance evaluation; Accuracy (the rate of true results including true negatives and true positives), Precision (positive predictive value), Sensitivity (true positive rate), Specificity (false positive rate), and False Negative Rate (error rate) [27], all in an average sense (see Table 2).
Predicted class | |||
---|---|---|---|
Attack | Non-attack | ||
Actual class | Attack | TP | FN |
Non-attack | FP | TN |
Two-Class Case Confusion Matrix.
After generating the likely state sequences, we compare them to the known state sequences to define true positive (TP), false positive (FP), true negative (TN), and false-negative (FN) parameters [27]. The accuracy (ACC) is obtained by the following equation:
The precision (PR), the fraction of the total number of positive cases that are correctly identified as attacks to the total number of attacks, is obtained by the following equation:
Sensitivity (SN) or the true positive rate, the fraction of the total number of classified true positive that are accurately identified as attacks to the total number of positive cases, is calculated by the following equation:
We use F
The following equation is used to identify the error rate (ER) for false negative predictions:
Compared to the original system, our model constructs an equivalent system with a minimal number of constraints over real-valued variables consisting of bounds on variations. This helps in reducing the high state space and improving the classification accuracy and time complexity as well.
The above results show that our proposed model obtains satisfactory results with regard to attack detection rate. The proposed model has 98.9
Models (Training 80 | |||||
---|---|---|---|---|---|
Neural network | 0.92 | 0.90 | 0.94 | 0.92 | 0.05 |
Naive Bayes | 0.45 | 0.68 | 0.56 | 0.61 | 0.06 |
HHMM | 0.975 | 0.92 | 0.979 | 0.94 | 0.03 |
PHHMM | 0.989 | 0.979 | 0.99 | 0.985 | 0.02 |
Experiment results summary I.
We perform the tests using different window sizes to understand their influence on the detection. It shows that increasing the size of the window results in better accuracy.
Figure 3 shows the ROC curves for the performance of our proposed model, compared to the HHMM, NN, and NB models. ROC curves help identify the balance between the true-positive rate and the false-positive rate for all possible thresholds. It illustrates the model’s strength to differentiate between attack and non-attack classes (see Figure 4).
Roc curves of models performance.
Comparison of average models performance.
Computation time is not associated instantly with classification; however, it describes the training time taken by the model. Table 4 shows that our model has a lower computation time compared to the HHMM. In [28], The time complexity of calculating the probability of a sequence and estimating the HHMM parameters as the model depends on the length of the observation equals
Models | Computation time (sec) |
---|---|
HHMM | 8.3 |
PHHMM | 5.6 |
Comparison of computation time.
Time complexity of HHMM vs. PHHMM algorithms.
In this work, we propose a probabilistic hierarchical hidden Markov model (PHHMM) applied for IoT intrusion detection which is more efficient than the existing HHMM without compromising classification accuracy. The main idea of our model is to reduce the huge problem state space of IoT traffic through dimensionality reduction by PCA and SVD. The proposed model is tested on the CICISD2019 dataset to detect and predict DDoS attacks. We evaluated our model on major performance metrics including Accuracy, Precision, Sensitivity, and False Negative Rate, Specificity and shows that our scheme has better detection accuracy and low error rates compared to Naive Bayes and neural network classification algorithms. It shows that PHHMM achieves a comparable accuracy as HHMM, better than NB and NN, and better efficiency than HHMM.
We would like to thank the Ministry of Higher Education in Saudi Arabia and the University of Adelaide for partially supporting this research.
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\\n\\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\\n\\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\\n\\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\\n\\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
\\n"}]'},components:[{type:"htmlEditorComponent",content:"Pristupom na stranicu www.intechopen.com slažete se s ovim odredbama, sa svim primjenjivim zakonskim odredbama, te se slažete s poštovanjem svih lokalnih zakona. Korištenje i/ili pristup ovoj stranici temelji se na potpunom prihvaćanju ovih odredbi. Svi materijali na ovoj stranici zaštićeni su primjenjivim zakonima o autorskim pravima i žigu.
\n\nSljedeća terminologija odnosi se na Odredbe i uvjete, te na sve naše ugovore:
\n\nKlijent, stranka, vi, vaš odnosi se na vas, osobu koja pristupa ovoj stranici i prihvaća IntechOpenove Odredbe i uvjete;
\n\nKompanija, tvrtka, mi, naše odnosi se na tvrtku IntechOpen;
\n\nStranke, strane odnosi se na klijenta i na nas, ili samo na klijenta ili nas.
\n\nSve odredbe koje se odnose na ponudu, prihvat ili razmatranje plaćanja, a za koja mi pružamo asistenciju klijentu, bilo na ugovoreni ili fiksni način, a s ciljem da se ostvare potrebe i želje klijenta u svezi s našim uslugama, su podložne zakonskim odredbama Ujedinjenog Kraljevstva.
\n\nOsim ako nije suprotno navedeno, IntechOpen i/ili svi davatelji licence vlasnici su intelektualnog vlasništva nad svim materijalima na www.intechopen.com. Sva prava intelektualnog vlasništva su pridržana. Stranice sa www.intechopen.com možete gledati, preuzimati, dijeliti, dijeliti poveznice i printati za osobnu uporabu, a temeljem pravila sadržanih u ovim Odredbama i uvjetima.
\n\nMi koristimo kolačiće. Korištenjem IntechOpenove stranice slažete se s korištenjem kolačića u skladu s IntechOpenovom Politikom privatnosti. Većina modernih, interaktivnih stranica koristi kolačiće kako bi omogućila ponovno pronalaženje korisničkih detalja kod svakog posjeta. Na našoj stranici kolačići se uglavnom koriste kako bi omogućili funkcionalnost i olakšali posjetiteljima korištenje stranice.
\n\nIntechOpen ili njegovi suradnici niti u jednom slučaju neće biti odgovorni za štete (štete uključuju gubitak podataka ili profita, druge poslovne prekide, te sve ostale štete) koje nastanu zbog korištenja materijala na IntechOpenovoj stranici ili nemogućnosti da se iste koriste, čak i ako je IntechOpen ili njegov predstavnik o takvoj šteti obaviješten pismenim ili usmenim putem. Neke jurisdikcije ne dozvoljavaju ograničenja garancija ili ograničenja obveza za posljedične ili slučajne štete pa se u tom slučaju ova ograničenja možda ne odnose na vas.
\n\nMaterijali koji se pojavljuju na IntechOpenovoj stranici mogu sadržavati manje greške, tipfelere ili fotografske greške. IntechOpen može napraviti promjene na bilo kojem materijalu koji se nalazi na stranici u bilo koje vrijeme.
\n\nIntechOpen nije formalno povezan niti s jednom vanjskom stranicom čije poveznice vode na www.intechopen.com, osim ako to nije izravno navedeno. Iz tog razloga IntechOpen nije odgovoran za sadržaj koji se pojavljuje na takvim stranicama. Poveznica na IntechOpenovu stranicu ne implicira povezanost sa IntechOpenom. Korištenje takvih poveznica isključiva je odgovornost korisnika.
\n\nZadržavamo pravo vlasništva nad cjelokupnom stranicom www.intechopen.com i nad svim materijalom na toj stranici. Koristeći se našim uslugama, slažete se da maknete sve poveznice na našu stranicu odmah nakon što to od vas zatražimo. Također, zadržavamo pravo da ove Odredbe i uvjete, i politiku o poveznicama izmjenimo u bilo koje vrijeme. Koristeći se poveznicama na naše stranice slažete se s ovim Odredbama i uvjetima.
\n\nAko smatrate da je bilo koja poveznica na našoj stranici sumnjiva iz bilo kojeg razloga, molimo vas da nas kontaktirate. U tom slučaju razmotrit ćemo micanje poveznice s naše stranice, iako nismo obvezni to napraviti.
\n\nBez prethodne privole i izričite pisane dozvole, ne možete stvarati okvire oko naših stranica ili koristiti druge tehnike koje na bilo koji način mogu promijeniti prezentaciju ili izgled naše stranice.
\n\nIntechOpen može ove Odredbe izmijeniti u bilo koje vrijeme i bez prethodne obavijesti. Koristeći ovu stranicu vi se slažete s trenutnim Odredbama i uvjetima koje su na snazi.
\n\nOve Odredbe i uvjeti su sastavljeni u skladu s odredbama prava Ujedinjenog Kraljevstva, a za sve sporove nadležan je sud u Londonu, Ujedinjeno Kraljevstvo.
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by",editors:[{id:"182871",title:"Prof.",name:"Angelo",middleName:null,surname:"Paone",slug:"angelo-paone",fullName:"Angelo Paone"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"9992",title:"Updates in Volcanology",subtitle:"Transdisciplinary Nature of Volcano Science",isOpenForSubmission:!1,hash:"c9f71037866aa5450cf23c0fb74711d1",slug:"updates-in-volcanology-transdisciplinary-nature-of-volcano-science",bookSignature:"Károly Németh",coverURL:"https://cdn.intechopen.com/books/images_new/9992.jpg",editedByType:"Edited by",editors:[{id:"51162",title:"Dr.",name:"Károly",middleName:null,surname:"Németh",slug:"karoly-nemeth",fullName:"Károly Németh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"7677",title:"Forecasting Volcanic Eruptions",subtitle:null,isOpenForSubmission:!1,hash:"5afd431dd1f4f5081355b017fd17f237",slug:"forecasting-volcanic-eruptions",bookSignature:"Angelo Paone and Sung-Hyo Yun",coverURL:"https://cdn.intechopen.com/books/images_new/7677.jpg",editedByType:"Edited by",editors:[{id:"182871",title:"Prof.",name:"Angelo",middleName:null,surname:"Paone",slug:"angelo-paone",fullName:"Angelo Paone"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"6104",title:"Volcanoes",subtitle:"Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health",isOpenForSubmission:!1,hash:"a11586252b4ac42153a8b2bc9a8fcf08",slug:"volcanoes-geological-and-geophysical-setting-theoretical-aspects-and-numerical-modeling-applications-to-industry-and-their-impact-on-the-human-health",bookSignature:"Gemma Aiello",coverURL:"https://cdn.intechopen.com/books/images_new/6104.jpg",editedByType:"Edited by",editors:[{id:"100661",title:"Dr.",name:"Gemma",middleName:null,surname:"Aiello",slug:"gemma-aiello",fullName:"Gemma Aiello"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5311",title:"Updates in Volcanology",subtitle:"From Volcano Modelling to Volcano Geology",isOpenForSubmission:!1,hash:"a579041bbfa682d2376a58326d0483e6",slug:"updates-in-volcanology-from-volcano-modelling-to-volcano-geology",bookSignature:"Karoly Nemeth",coverURL:"https://cdn.intechopen.com/books/images_new/5311.jpg",editedByType:"Edited by",editors:[{id:"51162",title:"Dr.",name:"Károly",middleName:null,surname:"Németh",slug:"karoly-nemeth",fullName:"Károly Németh"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5694",title:"6th International Maar Conference Abstracts",subtitle:null,isOpenForSubmission:!1,hash:"f96e1339bf34deb5cb0228dba907b1b3",slug:"6th-international-maar-conference-abstracts",bookSignature:"Jiaqi Liu",coverURL:"https://cdn.intechopen.com/books/images_new/5694.jpg",editedByType:"Edited by",editors:[{id:"194433",title:"Dr.",name:"Jiaqi",middleName:null,surname:"Liu",slug:"jiaqi-liu",fullName:"Jiaqi Liu"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"5104",title:"Environmental Applications of Remote Sensing",subtitle:null,isOpenForSubmission:!1,hash:"6f91748e9b1463ce5e7352ea982c3128",slug:"environmental-applications-of-remote-sensing",bookSignature:"Maged Marghany",coverURL:"https://cdn.intechopen.com/books/images_new/5104.jpg",editedByType:"Edited by",editors:[{id:"96666",title:"Prof.",name:"Dr. Maged",middleName:null,surname:"Marghany",slug:"dr.-maged-marghany",fullName:"Dr. Maged Marghany"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}},{type:"book",id:"409",title:"Updates in Volcanology",subtitle:"A Comprehensive Approach to Volcanological Problems",isOpenForSubmission:!1,hash:"39ff133e87b1d1f1a07d872ff755762b",slug:"updates-in-volcanology-a-comprehensive-approach-to-volcanological-problems",bookSignature:"Francesco Stoppa",coverURL:"https://cdn.intechopen.com/books/images_new/409.jpg",editedByType:"Edited by",editors:[{id:"57017",title:"Prof.",name:"Francesco",middleName:null,surname:"Stoppa",slug:"francesco-stoppa",fullName:"Francesco Stoppa"}],equalEditorOne:null,equalEditorTwo:null,equalEditorThree:null,productType:{id:"1",chapterContentType:"chapter",authoredCaption:"Edited by"}}],booksByTopicTotal:8,seriesByTopicCollection:[],seriesByTopicTotal:0,mostCitedChapters:[{id:"25980",doi:"10.5772/25264",title:"Hydrovolcanic vs Magmatic Processes in Forming Maars and Associated Pyroclasts: The Calatrava -Spain- Case History",slug:"hydrovolcanic-vs-magmatic-processes-in-forming-maars-and-associated-pyroclasts-the-calatrava-spain-c",totalDownloads:2862,totalCrossrefCites:8,totalDimensionsCites:25,abstract:null,book:{id:"409",slug:"updates-in-volcanology-a-comprehensive-approach-to-volcanological-problems",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - A Comprehensive Approach to Volcanological Problems"},signatures:"F. Stoppa, G. Rosatelli, M. Schiazza and A. Tranquilli",authors:[{id:"57017",title:"Prof.",name:"Francesco",middleName:null,surname:"Stoppa",slug:"francesco-stoppa",fullName:"Francesco Stoppa"},{id:"62737",title:"Dr.",name:"Gianluigi",middleName:null,surname:"Rosatelli",slug:"gianluigi-rosatelli",fullName:"Gianluigi Rosatelli"},{id:"62738",title:"Mr",name:"Mariangela",middleName:null,surname:"Schiazza",slug:"mariangela-schiazza",fullName:"Mariangela Schiazza"},{id:"62739",title:"Mr",name:"Andrea",middleName:null,surname:"Tranquilli",slug:"andrea-tranquilli",fullName:"Andrea Tranquilli"}]},{id:"51948",doi:"10.5772/64129",title:"Fumarolic Minerals: An Overview of Active European Volcanoes",slug:"fumarolic-minerals-an-overview-of-active-european-volcanoes",totalDownloads:2270,totalCrossrefCites:8,totalDimensionsCites:24,abstract:"The fumarolic mineralogy of the Icelandic active volcanoes, the Tyrrhenian volcanic belt (Italy) and the Aegean active arc (Greece) is investigated, and literature data surveyed in order to define the characteristics of the European fumarolic systems. They show broad diversity of mineral associations, with Vesuvius and Vulcano being also among the world localities richest in mineral species. Volcanic systems, which show recession over a longer period, show fumarolic development from the high-temperature alkaline halide/sulphate, calcic sulphate or sulphidic parageneses, synchronous with or immediately following the eruptions, through medium-temperature ammonium minerals, metal chlorides, or fluoride associations to the late low-temperature paragenesis dominated by sulphur, gypsum, alunogen, and other hydrous sulphates. The situation can be different in the systems that are not recessing but show fluctuations in activity, illustrated by the example of Vulcano where the high-temperature association appears intermittently. A full survey of the mineral groups and species is given in respect to their importance and appearance in fumarolic associations.",book:{id:"5311",slug:"updates-in-volcanology-from-volcano-modelling-to-volcano-geology",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - From Volcano Modelling to Volcano Geology"},signatures:"Tonči Balić-Žunić, Anna Garavelli, Sveinn Peter Jakobsson, Kristjan\nJonasson, Athanasios Katerinopoulos, Konstantinos Kyriakopoulos\nand Pasquale Acquafredda",authors:[{id:"183593",title:"Dr.",name:"Tonci",middleName:null,surname:"Balic-Zunic",slug:"tonci-balic-zunic",fullName:"Tonci Balic-Zunic"},{id:"183700",title:"Prof.",name:"Anna",middleName:null,surname:"Garavelli",slug:"anna-garavelli",fullName:"Anna Garavelli"},{id:"183701",title:"Dr.",name:"Sveinn Peter",middleName:null,surname:"Jakobsson",slug:"sveinn-peter-jakobsson",fullName:"Sveinn Peter Jakobsson"},{id:"183702",title:"Prof.",name:"Athanasios",middleName:null,surname:"Katerinopoulos",slug:"athanasios-katerinopoulos",fullName:"Athanasios Katerinopoulos"},{id:"188833",title:"Dr.",name:"Kristjan",middleName:null,surname:"Jonasson",slug:"kristjan-jonasson",fullName:"Kristjan Jonasson"},{id:"188834",title:"Dr.",name:"Konstantinos",middleName:null,surname:"Kyriakopoulos",slug:"konstantinos-kyriakopoulos",fullName:"Konstantinos Kyriakopoulos"},{id:"188835",title:"Dr.",name:"Pasquale",middleName:null,surname:"Acquafredda",slug:"pasquale-acquafredda",fullName:"Pasquale Acquafredda"}]},{id:"51105",doi:"10.5772/63486",title:"How Polygenetic are Monogenetic Volcanoes: Case Studies of Some Complex Maar‐Diatreme Volcanoes",slug:"how-polygenetic-are-monogenetic-volcanoes-case-studies-of-some-complex-maar-diatreme-volcanoes",totalDownloads:1943,totalCrossrefCites:5,totalDimensionsCites:15,abstract:"The increasing number of field investigations and various controlled benchtop and large‐scale experiments have permitted the evaluation of a large number of processes involved in the formation of maar‐diatreme volcanoes, the second most common type of small‐volume subaerial volcanoes on Earth. A maar‐diatreme volcano is recognized by a volcanic crater that is cut into country rocks and surrounded by a low‐height ejecta rim composed of pyroclastic deposits of few meters to up to 200 m thick above the syn‐eruptive surface level. The craters vary from 0.1 km to up to 5 km wide and vary in depth from a few dozen meters to up to 300 m deep. Their irregular morphology reflects the simple or complex volcanic and cratering processes involved in their formation. The simplicity or complexity of the crater or the entire maar itself is usually observed in the stratigraphy of the surrounding ejecta rings. The latter are composed of sequences of successive alternating and contrastingly bedded phreatomagmatic‐derived dilute pyroclastic density currents (PDC) and fallout depositions, with occasional interbedded Strombolian‐derived spatter materials or scoria fall units, exemplifying the changes in the eruptive styles during the formation of the volcano. The entire stratigraphic sequence might be preserved as a single eruptive package (small or very thick) in which there is no stratigraphic gap or significant discordance indicative of a potential break during the eruption. A maar with a single eruptive deposit is quantified as monogenetic maar, meaning that it was formed by a single eruptive vent from which only a small and ephemeral magma erupted over a short period of time. The stratigraphy may also display several packages of deposits separated either by contrasting discordance surfaces or paleosoils, which reflect multiple phases or episodes of eruptions within the same maar. Such maars are characterized as complex polycyclic maars if the length of time between the eruptive events is relatively short (days to years). For greater length of time (thousands to millions of years), the complex maar will be quantified as polygenetic. These common depositional breaks interpreted as signs of temporal interruption of the eruptions for various timescales also indicate deep magma system processes; hence magmas of different types might erupt during the formation of both simple and complex maars. The feeding dikes can interact with groundwater and form closely distributed small craters. The latter can coalesce to form a final crater with various shapes depending on the distance between them. This observation indicates the significant role of the magmatic plumbing system on the formation and growth of complex and polygenetic maar‐diatreme volcanoes.",book:{id:"5311",slug:"updates-in-volcanology-from-volcano-modelling-to-volcano-geology",title:"Updates in Volcanology",fullTitle:"Updates in Volcanology - From Volcano Modelling to Volcano Geology"},signatures:"Boris Chako Tchamabé, Gabor Kereszturi, Karoly Németh and\nGerardo Carrasco‐Núñez",authors:[{id:"51162",title:"Dr.",name:"Károly",middleName:null,surname:"Németh",slug:"karoly-nemeth",fullName:"Károly Németh"},{id:"62029",title:"Dr.",name:"Gabor",middleName:null,surname:"Kereszturi",slug:"gabor-kereszturi",fullName:"Gabor Kereszturi"},{id:"182834",title:"Dr.",name:"Boris",middleName:null,surname:"Chako Tchamabé",slug:"boris-chako-tchamabe",fullName:"Boris Chako Tchamabé"},{id:"183809",title:"Dr.",name:"Gerardo",middleName:null,surname:"Carrasco-Núñez",slug:"gerardo-carrasco-nunez",fullName:"Gerardo Carrasco-Núñez"}]},{id:"49656",doi:"10.5772/61974",title:"Optical Satellite Remote Sensing of the Coastal Zone Environment — An Overview",slug:"optical-satellite-remote-sensing-of-the-coastal-zone-environment-an-overview",totalDownloads:2469,totalCrossrefCites:7,totalDimensionsCites:15,abstract:"Optical remote-sensing data are a powerful source of information for monitoring the coastal environment. Due to the high complexity of coastal environments, where different natural and anthropogenic phenomenon interact, the selection of the most appropriate sensor(s) is related to the applications required, and the different types of resolutions available (spatial, spectral, radiometric, and temporal) need to be considered. The development of specific techniques and tools based on the processing of optical satellite images makes possible the production of information useful for coastal environment management, without any destructive impacts. This chapter will highlight different subjects related to coastal environments: shoreline change detection, ocean color, water quality, river plumes, coral reef, alga bloom, bathymetry, wetland mapping, and coastal hazards/vulnerability. The main objective of this chapter is not an exhaustive description of the image processing methods/algorithms employed in coastal environmental studies, but focus in the range of applications available. Several limitations were identified. The major challenge still is to have remote-sensing techniques adopted as a routine tool in assessment of change in the coastal zone. Continuing research is required into the techniques employed for assessing change in the coastal environment.",book:{id:"5104",slug:"environmental-applications-of-remote-sensing",title:"Environmental Applications of Remote Sensing",fullTitle:"Environmental Applications of Remote Sensing"},signatures:"Ana C. Teodoro",authors:[{id:"18485",title:"Dr.",name:"Ana",middleName:null,surname:"Teodoro",slug:"ana-teodoro",fullName:"Ana Teodoro"}]},{id:"49851",doi:"10.5772/62122",title:"Detection of Tree Crowns in Very High Spatial Resolution Images",slug:"detection-of-tree-crowns-in-very-high-spatial-resolution-images",totalDownloads:3240,totalCrossrefCites:8,totalDimensionsCites:13,abstract:"The requirements for advanced knowledge on forest resources have led researchers to develop efficient methods to provide detailed information about trees. Since 1999, orbital remote sensing has been providing very high resolution (VHR) image data. The new generation of satellite allows individual tree crowns to be visually identifiable. The increase in spatial resolution has also had a profound effect in image processing techniques and has motivated the development of new object-based procedures to extract information. Tree crown detection has become a major area of research in image analysis considering the complex nature of trees in an uncontrolled environment. This chapter is subdivided into two parts. Part I offers an overview of the state of the art in computer detection of individual tree crowns in VHR images. Part II presents a new hybrid approach developed by the authors that integrates geometrical-optical modeling (GOM), marked point processes (MPP), and template matching (TM) to individually detect tree crowns in VHR images. The method is presented for two different applications: isolated tree detection in an urban environment and automatic tree counting in orchards with an average performance rate of 82% for tree detection and above 90% for tree counting in orchards.",book:{id:"5104",slug:"environmental-applications-of-remote-sensing",title:"Environmental Applications of Remote Sensing",fullTitle:"Environmental Applications of Remote Sensing"},signatures:"Marilia Ferreira Gomes and Philippe Maillard",authors:[{id:"177110",title:"Dr.",name:"Philippe",middleName:null,surname:"Maillard",slug:"philippe-maillard",fullName:"Philippe Maillard"},{id:"177172",title:"Ph.D.",name:"Marilia",middleName:"Ferreira",surname:"Gomes",slug:"marilia-gomes",fullName:"Marilia Gomes"}]}],mostDownloadedChaptersLast30Days:[{id:"66703",title:"P-Wave Teleseismic Tomography: Evidence of Imprints of Deccan Mantle Plume below the Kachchh Rift Zone, Gujarat, India",slug:"p-wave-teleseismic-tomography-evidence-of-imprints-of-deccan-mantle-plume-below-the-kachchh-rift-zon",totalDownloads:2602,totalCrossrefCites:2,totalDimensionsCites:2,abstract:"The Indian plate had experienced the Deccan volcanism at 65 Ma when it moved over the Re-union hotspot, which has altered lithospheric structure below the Kachchh rift zone (KRZ). To quantify the influence of Deccan volcanism on the crust-mantle, the present chapter focuses on the delineation of the upper mantle structure below the KRZ, through the modeling of crust corrected P-residuals and P-wave teleseismic tomography. The crust corrected normalized P-residuals suggest dominant negative residuals associated with the central KRZ, indicating crustal and lithospheric thinning below the KRZ. A low velocity down to a depth of 170 km below the central KRZ is detected through the teleseismic tomography using these P-residuals. However, these residuals also show positive values for the surrounding un-rifted zones. Note that a low shear velocity zone extending from 100–120 km to 170–220 km depth beneath the central KRZ has already been revealed by the modeling of P-RFs. This reduction in seismic velocity in the upper mantle could be explained by the presence of trapped carbonatite/partial melts related to the Deccan volcanism. The influx of volatile CO2 emanating from the carbonatite melts in the asthenosphere might be generating lower crustal earthquakes occurring in the KRZ.",book:{id:"7677",slug:"forecasting-volcanic-eruptions",title:"Forecasting Volcanic Eruptions",fullTitle:"Forecasting Volcanic Eruptions"},signatures:"Prantik Mandal",authors:[{id:"279344",title:"Dr.",name:"Prantik",middleName:null,surname:"Mandal",slug:"prantik-mandal",fullName:"Prantik Mandal"}]},{id:"49608",title:"Remote Sensing of Mountain Glaciers and Related Hazards",slug:"remote-sensing-of-mountain-glaciers-and-related-hazards",totalDownloads:2386,totalCrossrefCites:1,totalDimensionsCites:5,abstract:"Mountain glaciers are highly sensitive to temperature and precipitation fluctuations and active geomorphic agents in shaping the landforms of glaciated regions which are direct imprints of past glaciations, providing reliable evidence of the evolution of the past Cryosphere and contain important information on climatic variables. But most importantly, glaciers have aroused a lot of concern in terms of glacier area changes, thickness change, mass balance and their consequences on water resources as well as related hazards. The contribution of glacier mass loss to global sea-level rise and increasing number of glacier-related hazards are the most important and current socioeconomic concerns. Therefore, understanding the dynamics of the changes and constant monitoring of glaciers are essential for studying climate, water resource management and hydropower and also to predict and evade glacier-related hazards. The recent advances in the techniques of earth observations have proved as a boon for investigating glaciers and glacier-related hazards. Remote sensing technology enables extraction of glacier parameters such as albedo/reflectance/scattering, glacier area, glacier zones and facies, equilibrium line, glacier thickness, volume, mass balance, velocity and glacier topography. The present chapter explores the prospective of remote sensing technology for understanding and surveying glaciers formed at high, inaccessible mountains and glacier-induced hazards.",book:{id:"5104",slug:"environmental-applications-of-remote-sensing",title:"Environmental Applications of Remote Sensing",fullTitle:"Environmental Applications of Remote Sensing"},signatures:"Pratima Pandey, Alagappan Ramanathan and Gopalan\nVenkataraman",authors:[{id:"18342",title:"Prof.",name:"Ramanathan",middleName:null,surname:"Alagappan",slug:"ramanathan-alagappan",fullName:"Ramanathan Alagappan"},{id:"177179",title:"Dr.",name:"Pratima",middleName:null,surname:"Pandey",slug:"pratima-pandey",fullName:"Pratima Pandey"},{id:"178231",title:"Prof.",name:"Gopalan",middleName:null,surname:"Venkataraman",slug:"gopalan-venkataraman",fullName:"Gopalan Venkataraman"}]},{id:"60548",title:"Volcanic Glass and its Uses as Adsorbent",slug:"volcanic-glass-and-its-uses-as-adsorbent",totalDownloads:1601,totalCrossrefCites:1,totalDimensionsCites:3,abstract:"Volcanic glasses are an amorphous phyllosilicates formed by the fast cooling of the magma. The physicochemical properties of volcanic glasses are directly related to their chemical composition. Thus, the rhyolitic magma, which presents the highest SiO2 percentage, displays a high viscosity, which leads to explosive eruptions by the ex-solution of H2O, CO2, and SO2, when the pressure diminishes generates a macroporous structure with interesting applications in construction, as abrasive, acoustic, filter as well as in the agriculture field. The macroporosity of volcanic glass allows to host large molecules as biomolecules, tensoactives, or dyes. On the other hand, the existence of hydroxyl groups in this amorphous aluminosilicate also favors the adsorption of cations and anions, so the volcanic glass is an economical adsorbent to retain heavy metals or radioactive cations.",book:{id:"6104",slug:"volcanoes-geological-and-geophysical-setting-theoretical-aspects-and-numerical-modeling-applications-to-industry-and-their-impact-on-the-human-health",title:"Volcanoes",fullTitle:"Volcanoes - Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health"},signatures:"Juan Antonio Cecilia, Miguel Armando Autie-Pérez, Juan Manuel\nLabadie-Suarez, Enrique Rodríguez Castellón and Antonia Infantes\nMolina",authors:[{id:"126325",title:"Dr.",name:"Enrique",middleName:null,surname:"Rodríguez-Castellón",slug:"enrique-rodriguez-castellon",fullName:"Enrique Rodríguez-Castellón"}]},{id:"57502",title:"The Characteristics of Volcanic Eruption in Indonesia",slug:"the-characteristics-of-volcanic-eruption-in-indonesia",totalDownloads:1846,totalCrossrefCites:1,totalDimensionsCites:1,abstract:"This chapter discusses the unique characteristics of the volcanic eruptions in Indonesia. We know that Indonesia has 147 volcanoes and 76 of them are active volcanoes and spread along the islands of Java, Lesser Sunda, Sumatra, and Celebes. The characteristics of Indonesian volcanoes are quite unique in terms of the formation process, eruption phenomenon, and the resulting natural disasters. Most volcanoes in Indonesia consist of stratovolcanoes, but this does not mean that the resulting eruptions are always explosive and they have a long period. This can be seen from the activity of Semeru that always erupts effusively every day, Sinabung that has a very short eruption period, Tangkuban Perahu eruption that occurs suddenly with the lack of early signs, and Merapi and Kelud that have eruption period that is getting shorter. Based on the results of our study it can be known that the types of volcanic eruption are influenced by the structure of the constituent rocks of the volcanoes. However, the presence of external control factors in the form of large-scale earthquakes will affect their periodicity. The large earthquakes can affect the stability of the magma chamber that can trigger a premature eruption.",book:{id:"6104",slug:"volcanoes-geological-and-geophysical-setting-theoretical-aspects-and-numerical-modeling-applications-to-industry-and-their-impact-on-the-human-health",title:"Volcanoes",fullTitle:"Volcanoes - Geological and Geophysical Setting, Theoretical Aspects and Numerical Modeling, Applications to Industry and Their Impact on the Human Health"},signatures:"Eko Hariyono and Liliasari S",authors:[{id:"214360",title:"Dr.",name:"Eko",middleName:null,surname:"Hariyono",slug:"eko-hariyono",fullName:"Eko Hariyono"},{id:"219699",title:"Prof.",name:"Liliasari",middleName:null,surname:"S",slug:"liliasari-s",fullName:"Liliasari S"}]},{id:"51105",title:"How Polygenetic are Monogenetic Volcanoes: Case Studies of Some Complex Maar‐Diatreme Volcanoes",slug:"how-polygenetic-are-monogenetic-volcanoes-case-studies-of-some-complex-maar-diatreme-volcanoes",totalDownloads:1939,totalCrossrefCites:5,totalDimensionsCites:15,abstract:"The increasing number of field investigations and various controlled benchtop and large‐scale experiments have permitted the evaluation of a large number of processes involved in the formation of maar‐diatreme volcanoes, the second most common type of small‐volume subaerial volcanoes on Earth. A maar‐diatreme volcano is recognized by a volcanic crater that is cut into country rocks and surrounded by a low‐height ejecta rim composed of pyroclastic deposits of few meters to up to 200 m thick above the syn‐eruptive surface level. The craters vary from 0.1 km to up to 5 km wide and vary in depth from a few dozen meters to up to 300 m deep. Their irregular morphology reflects the simple or complex volcanic and cratering processes involved in their formation. The simplicity or complexity of the crater or the entire maar itself is usually observed in the stratigraphy of the surrounding ejecta rings. The latter are composed of sequences of successive alternating and contrastingly bedded phreatomagmatic‐derived dilute pyroclastic density currents (PDC) and fallout depositions, with occasional interbedded Strombolian‐derived spatter materials or scoria fall units, exemplifying the changes in the eruptive styles during the formation of the volcano. The entire stratigraphic sequence might be preserved as a single eruptive package (small or very thick) in which there is no stratigraphic gap or significant discordance indicative of a potential break during the eruption. A maar with a single eruptive deposit is quantified as monogenetic maar, meaning that it was formed by a single eruptive vent from which only a small and ephemeral magma erupted over a short period of time. The stratigraphy may also display several packages of deposits separated either by contrasting discordance surfaces or paleosoils, which reflect multiple phases or episodes of eruptions within the same maar. Such maars are characterized as complex polycyclic maars if the length of time between the eruptive events is relatively short (days to years). For greater length of time (thousands to millions of years), the complex maar will be quantified as polygenetic. These common depositional breaks interpreted as signs of temporal interruption of the eruptions for various timescales also indicate deep magma system processes; hence magmas of different types might erupt during the formation of both simple and complex maars. The feeding dikes can interact with groundwater and form closely distributed small craters. The latter can coalesce to form a final crater with various shapes depending on the distance between them. 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In this context, digitization has the potential to disrupt processes, with significant implications for the environment and sustainable development. There are numerous challenges associated with sustainability and digitization, the need to consider new business models capable of extracting value, data ownership and sharing and integration, as well as collaboration across the entire supply chain of a product. In order to generate value, effectively developing a complex system based on sustainability principles is a challenge that requires a deep commitment to both technological factors, such as data and platforms, and human dimensions, such as trust and collaboration. Regular study, research and implementation must be part of the road to sustainable solutions. Consequently, this topic will analyze growth models and techniques aimed at achieving intergenerational equity in terms of economic, social and environmental well-being. 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