5.1 Component lifetime

The lifetime of fusion reactors must be very long, and similar to that of a fission reactor, for example, >40 years, and, like fission reactors, they will operate continuously between outages for maintenance, which will occur not more frequently than yearly, and the outage time has to be as short as possible, for example, <1 month, see, for example [14]. The time that the NBI system on a reactor must operate, both in total and between maintenance periods, is orders of magnitude beyond any NBI system designed so far, and that means that many new considerations enter into play, such as the lifetime of components which are subjected to sputtering by the beams and thermal fatigue. It is also evident that as the reactor and the injectors will operate for 1 year or more between maintenance periods, any component that is designed to be replaced routinely must be able to operate for more than 1 year before the replacement becomes necessary.

#### 5.2 Lifetime and fatigue

The injector components should be designed to have a fatigue life that is greater than the life of the fusion reactor, that is, about 40 years with essentially continuous operation. When there is a breakdown in the accelerator, the beam will be reestablished in ≈180 ms, which is short compared to the thermal response time of the components, and the components "see" only a small part of a thermal cycle. Therefore fatigue failure will arise from the on-off cycles of the beam, when the components will experience the complete thermal cycle. The number of on-off cycles will probably be dominated by the regeneration cycle of the cryopumps. With a photon neutraliser and a gas flow into the ion source that is reduced by a factor 3, the cryopumps should be regenerated after ≈3 h of operation (see Figure 2). As, with continuous operation of the reactor, ≈3000 regenerations will be needed per year, there will be ≈1.2 105 cycles in the reactor lifetime, 40 years. The injector will almost certainly require conditioning pulses after a regeneration in order to regain full performance. The low power operation used at the start of the reconditioning does not contribute significantly to fatigue, and about five full power pulses should suffice to complete the conditioning. Thus the injector components will "see" ≈6 105 full thermal cycles in the reactor lifetime.

The relatively low extracted current assumed above, together with the reduction in the stripping losses by a factor 3 (due to the reduced source gas outflow), means that the power load to the grids should be easily handled and fatigue should not be a problem. Similarly, the power to the residual ion dump will be quite low because of the high neutralisation expected with a photon neutraliser and designing the residual ion dump to have the required fatigue life not be a problem. The reduced gas flow into the injector will significantly reduce the re-ionisation loss, hence the loads to the panels in the duct leading to the reactor, and fatigue should not be a problem. However, even in the reduced current density design considered in Section 1.2, the beamline calorimeter, which is essential for the commissioning and recommissioning of the beam source, will receive a similar power density to that received by the calorimeter of the heating neutral beams of ITER, and the fatigue life of the optimised design of that component is calculated to be ≈7.5 104 cycles, so either a non-negligible improvement of the design is needed or the calorimeter will have to be replaced after about 20 years, i.e. once in the lifetime of the reactor.

It is worth emphasising that, as assumed above, it is reasonable to conclude that the power "seen" by the beamline calorimeter of an injector on a reactor cannot be higher than that "seen" by the calorimeter of the heating injectors of ITER, which precludes increasing the accelerated D current density to >100 A/m2 when a high efficiency neutraliser is used. Another consequence is that any increase in the beam energy would necessitate a decrease in the accelerated current density in order to keep the power to the calorimeter at the acceptable level.


**3.2.7 Operational Taxonomic Units diversity**

Each band with specific migration distance was cut from the gel and the eluted DNA was reamplified, sequenced and compared to genetic databases for OTU affiliation. For the five patients presented here, we detected 16 different OTUs (Table 1). Four major OTU, *Staphylococcus* sp., *Flavobacteriaceae*, *Comamonadaceae* and *Propionibacterium* sp., were detected in all patients. *Acinetobacter* sp. and *Corynebacterium* sp. are also frequently detected. In contrast, OTUs such as *Bosea* sp. and *Bacillus* sp. were specific of one single patient. Most major OTUs were present in all patient at all step of the surgery operation and in all sampling sites. *Flavobacteriaceae* and *Comamonadaceae* were atypical Gram-negative bacilli recently described in the skin metagenome (Grice et al., 2009). These atypical Gram-negative bacilli were particularly represented in subcutaneous and in deep tissues (Figure 4). Band identification by sequencing allowed refining the crude DI by the elimination of multiple bands corresponding to a single OTU, as observed for bacteria that displayed 16S rRNA genes heterogeneity. In some cases, the DI dramatically decreased as shown in Figure 3.

#### **3.3 Culture-dependent approach: Use of PCR-TTGE to assess the impact of antimicrobial prophylaxis on the whole skin microbiome**

#### **3.3.1 Methods**

Sternal skin microbiome was sampled with cotton swabs, before hospitalization, in four patients requiring CABG surgery. This sample reflects the normal resident skin microbiome of the patients. Sternal skin samples were streaked on 5% blood and nutrient agar plates supplemented with different concentrations of cefamandole (0, 0.125, 0.25, 0.5, 2, 4, 8 and 32 g/mL) and incubated for 24h at 37°C in anaerobiosis and aerobiosis. The resulting colonies

from 4.2 to 7.6. The increase of crude DI was also observed at each site of sampling: 4.2 to 7.6 for skin, 7.4 to 8.2 for subcutaneous tissues, 4.4 to 5.2 for sternum banks and 5.4 to 6.8 for

Fig. 3. Representative evolution of crude and optimized DI during the course of the surgical

Each band with specific migration distance was cut from the gel and the eluted DNA was reamplified, sequenced and compared to genetic databases for OTU affiliation. For the five patients presented here, we detected 16 different OTUs (Table 1). Four major OTU, *Staphylococcus* sp., *Flavobacteriaceae*, *Comamonadaceae* and *Propionibacterium* sp., were detected in all patients. *Acinetobacter* sp. and *Corynebacterium* sp. are also frequently detected. In contrast, OTUs such as *Bosea* sp. and *Bacillus* sp. were specific of one single patient. Most major OTUs were present in all patient at all step of the surgery operation and in all sampling sites. *Flavobacteriaceae* and *Comamonadaceae* were atypical Gram-negative bacilli recently described in the skin metagenome (Grice et al., 2009). These atypical Gram-negative bacilli were particularly represented in subcutaneous and in deep tissues (Figure 4). Band identification by sequencing allowed refining the crude DI by the elimination of multiple bands corresponding to a single OTU, as observed for bacteria that displayed 16S rRNA genes heterogeneity. In some cases, the DI dramatically decreased as shown in Figure 3.

**3.3 Culture-dependent approach: Use of PCR-TTGE to assess the impact of**

Sternal skin microbiome was sampled with cotton swabs, before hospitalization, in four patients requiring CABG surgery. This sample reflects the normal resident skin microbiome of the patients. Sternal skin samples were streaked on 5% blood and nutrient agar plates supplemented with different concentrations of cefamandole (0, 0.125, 0.25, 0.5, 2, 4, 8 and 32 g/mL) and incubated for 24h at 37°C in anaerobiosis and aerobiosis. The resulting colonies

**antimicrobial prophylaxis on the whole skin microbiome**

mediastin. Figure 3 showed a representative DI dynamics for a patient.

operation of the patient AD.

**3.3.1 Methods**

**3.2.7 Operational Taxonomic Units diversity**

Table 1. Repartition of the OTUs detected in the skin microbiota of five patients.

Fig. 4. Number of patients carrying the major OTUs according to the step of the surgery procedure.

were counted and the whole bacterial cultures were then harvested for DNA extraction. DNA extraction, single PCR HDA, TTGE migration and band affiliation to an OTU were performed as described in previous section.

#### **3.3.2 Results: Impact of antimicrobial prophylaxis on skin microbiota**

Most samples displayed cultivable microbiota susceptible to cefamandole, i.e. no culture was observed on agar plates with 8mg/L, which was the cefamandole critical concentration used to define susceptibility versus resistance. At lower concentrations (from 0.125 mg/L to 4 mg/L), we observed a decrease of colonies in number and in diversity, comparatively to

Temporal Temperature Gel Electrophoresis to Survey

**medical microbiologist?**

strains as performed currently.

very low.

Pathogenic Bacterial Communities: The Case of Surgical Site Infections 305

concentrations in the surgical wound (0.125mg/L to 4mg/L), TTGE profiles showed a modification of the microbiota with loss of bands affiliated to the genus *Staphylococcus* and appearance of other bands affiliated to the genera *Corynebacterium* and *Micrococcus* or to *Bacillus cereus* group. These bands corresponding to OTUs with a decreased susceptibility to cefamandole revealed minor OTUs that were undetected in the total cultivable microbiota, i.e. on plates without antibiotics. For instance in the patient CB (Figure 5), a band corresponding to *Bacillus* sp. was observed from 0,5 à 4mg/L cefamandole and a band

corresponding to *Corynebacterium* sp. was observed in at least 8mg/L cefamandole.

**4. Conclusion: Why should 'microbiomology' be on the benchtop of the**

In this experiment we applied PCR TTGE to the study of microbiome dynamics in skin and tissues during a cardiac surgery procedure. We present the results obtained for 5 patients in a culture-independent approach and in 4 patients in a culture-dependent approach. Our results enable us to describe the bacteria involved in surgical site colonization during the procedure. The bacteria detected belonged to OTUs previously described in the skin microbiome (Grice et al., 2009). Gram-positive bacteria belonged to *Bacilli* and *Clostridia* in the phylum *Firmicutes*, and to *Corynebacterium*, *Propionibacterium* and *Micrococcaceae* in the phylum *Actinobacteria*. Gram-negative bacteria mainly belonged to *Proteobacteria* but also to *Flavobacteria.* This result confirmed that the nested-PCR TTGE approach gave a good representation of the skin microbiome even after antisepsis when the bacterial load is

We have showed for the first time that the skin microbiota contaminated the surgical wound at all steps of the surgery in quantities that can be detected by molecular methods. The PCR-TTGE appeared as a suitable method to follow-up the microbiology of a clean wound such as a surgical wound. The approach could also give insights about the activity of antibiotics, e.g. agents used for antibioprophylaxis, at the community level and not only on isolated

In order to find relationships between the dynamics of microbiome and the clinical evolution of patients, a cohort of 120 patients will be studied for their surgical wound microbiota and their clinical outcome. Such clinical studies are necessary to understand to physio-pathological mechanism of wound re-colonization and SSI. PCR-TTGE appeared as easy and cost-effective enough to provide microbiological arguments at the community level in large clinical studies. Other very efficient methods such as pyrosequencing are until

More generally the study presented here underlines that is now time for microbiome studies to exit from research labs and to take place on the benchtop of the medical microbiologist. Current medical microbiology still rests on the shoulders of Leeuwenhoek, Koch and Pasteur. Although every microbiologist knows that a bacterium does not live alone but in complex communities, routine practice still consists in watching a small subset of isolated microbes under microscope and on Petri dishes. It is now clear that the human microbiome has profound effects on health and disease. Ambitious project are in progress such as the international Human Microbiome Consortium and the Human Microbiome Project. Highthroughput sequencing and metagenomics allow us to explore new fields of the

now not convenient for clinical studies on large cohorts of patients.

the control plate. The mean colony counts went from 80 CFU/plate without antibiotics to 30 CFU/plate with 0.125 mg/L cefamandole and to 6 CFU/plate with 1mg/L cefamandole. All the colonies were bulked and analyzed by 16S rRNA gene PCR-TTGE. Profiles comparison allowed to determine the concentration of antimicrobial agents that inhibited 25%, 50%, 75% and 90% of the microbial diversity and to identify the resistant bacteria to the species level. These data will be related to dynamics of wound re-colonization after surgery.

Fig. 5. TTGE gel illustrating the culture-dependent approach on skin sample of the patient CB3. The gel shows differences in banding patterns according to the concentration of cefamandole and the culture atmosphere. Lanes 1 and 8, culture without cefamandole; Lanes 2 and 9, cefamandole 0.125mg/L; Lanes 3 and 10, cefamandole 0.25mg/L; Lanes 4 and 11, cefamandole 0.5mg/L; Lanes 5 and 12, cefamandole 1 mg/L; Lanes 6 and 13, cefamandole 2mg/L; Lanes 7 and 14, cefamandole 4mg/L; Lane 15, cefamandole 8mg/L. Bands corresponding to OTUs are shown with arrow: a for *Staphylococcus* sp., b for *Bacillus*

sp., c for *Corynebacterium* sp. and d for *Propionibacterium* sp.

The TTGE profiles from colonies harvested on control plates without antibiotics showed the predominant cultivable population. For the four patients studied herein, the corresponding bacteria belonged to the genera *Staphylococcus* (4 to 6 species) and *Propionibacterium*. For two patients, the cultivable skin microbiota was susceptible to cefamandole because no colonies were observed on plates with more than 8 mg/L, which was the cefamandole critical concentration used to define susceptibility versus resistance. For the two other patients, colonies were observed for 8 mg/L plates incubated in aerobiosis and even for 32 mg/L plates incubated in anaerobiosis. Resistant bacteria belonged to the genera *Bacillus* and *Corynebacterium*. For lower concentrations, which could correspond to the tissue

the control plate. The mean colony counts went from 80 CFU/plate without antibiotics to 30 CFU/plate with 0.125 mg/L cefamandole and to 6 CFU/plate with 1mg/L cefamandole. All the colonies were bulked and analyzed by 16S rRNA gene PCR-TTGE. Profiles comparison allowed to determine the concentration of antimicrobial agents that inhibited 25%, 50%, 75% and 90% of the microbial diversity and to identify the resistant bacteria to the species level.

Fig. 5. TTGE gel illustrating the culture-dependent approach on skin sample of the patient CB3. The gel shows differences in banding patterns according to the concentration of cefamandole and the culture atmosphere. Lanes 1 and 8, culture without cefamandole; Lanes 2 and 9, cefamandole 0.125mg/L; Lanes 3 and 10, cefamandole 0.25mg/L; Lanes 4 and 11, cefamandole 0.5mg/L; Lanes 5 and 12, cefamandole 1 mg/L; Lanes 6 and 13, cefamandole 2mg/L; Lanes 7 and 14, cefamandole 4mg/L; Lane 15, cefamandole 8mg/L. Bands corresponding to OTUs are shown with arrow: a for *Staphylococcus* sp., b for *Bacillus*

The TTGE profiles from colonies harvested on control plates without antibiotics showed the predominant cultivable population. For the four patients studied herein, the corresponding bacteria belonged to the genera *Staphylococcus* (4 to 6 species) and *Propionibacterium*. For two patients, the cultivable skin microbiota was susceptible to cefamandole because no colonies were observed on plates with more than 8 mg/L, which was the cefamandole critical concentration used to define susceptibility versus resistance. For the two other patients, colonies were observed for 8 mg/L plates incubated in aerobiosis and even for 32 mg/L plates incubated in anaerobiosis. Resistant bacteria belonged to the genera *Bacillus* and *Corynebacterium*. For lower concentrations, which could correspond to the tissue

sp., c for *Corynebacterium* sp. and d for *Propionibacterium* sp.

These data will be related to dynamics of wound re-colonization after surgery.

concentrations in the surgical wound (0.125mg/L to 4mg/L), TTGE profiles showed a modification of the microbiota with loss of bands affiliated to the genus *Staphylococcus* and appearance of other bands affiliated to the genera *Corynebacterium* and *Micrococcus* or to *Bacillus cereus* group. These bands corresponding to OTUs with a decreased susceptibility to cefamandole revealed minor OTUs that were undetected in the total cultivable microbiota, i.e. on plates without antibiotics. For instance in the patient CB (Figure 5), a band corresponding to *Bacillus* sp. was observed from 0,5 à 4mg/L cefamandole and a band corresponding to *Corynebacterium* sp. was observed in at least 8mg/L cefamandole.


**1. Introduction**

Soil is the uppermost layer of Earth's crust, which is produced at the rate of a few centimeters per thousand years by the continuous transformation of solid crust material. According to FAO, the soil consists of mineral particles, organic matter, water, air, and living organisms [1]. It is one of the most essential, complex, and non-renewable natural resources. It provides humanity with a wide range of ecological, economical, and cultural services. These include provisional services: food, fiber, raw materials; regulating services: mitigation against flood, drought, carbon storage, support hydrological and nutrient cycle, recycling of wastes; cultural services: recreational, esthetic, heritage values, and cultural identity [2]. According to McBratney, 2017 soil provides around US\$ 11.4 trillion of ecosystem services [3].

Soil conditions underpin food security, habitat for various organisms, bioeconomies, and above-ground biodiversity. It is the major variable in regulating the climate, hydrological, and nutrient cycles. However, anthropogenic activities including industrialization and urbanization have polluted the environment extremely and deteriorating the quality of life for all living organisms. There is enormous pressure on this finite, non-renewable natural resource. Further, inappropriate land-use management severely impacts the functions of soil, which is amplified by climate change. These stresses lead to degradation processes of soil like erosion, contamination, and degradation [4].

#### **1.1 Soil pollution**

In the era of the Anthropocene, the imprudent discharge of waste, and chemicals in the ecosystem has led to the increase of concentration of contaminants to critical levels. According to FAO. "Soil pollution" refers to the presence of a chemical or substance out of place and/or present at a higher than the normal concentration that has adverse effects on any non-targeted organism [1]. Although there is the contribution of contaminants through natural sources like, volcanic, seepage from parental rock, biogenic, and forest emissions, the widespread soil contamination and degradation are caused by anthropogenic activities. The rapid and injudicious industrialization, intensive agricultural practices, faulty mining practices and waste disposals are the major causes of heavy metal contamination of soil.

The pollutants introduced in soil by anthropogenic activities can arise from a plethora of sources. These might be discrete point sources or diffuse sources. The emission of heavy metals from point sources includes thermal power plants, coal mines, gold mines, smelting, electroplating, textiles, leather, and e-waste processing; and non-point sources include soil erosion, agricultural run-off, vehicular emissions, ash fallout, combustion of fuel, acid deposition, mining tailings, heavy metal mining and smelting, mismanaged radionuclides waste, and open freight storage (**Figure 1**).

One of the major concerns is the contamination of heavy metals in agricultural soil. It has increased tremendously in the soil system since the last decade. Although most of the heavy metals exist geologically, the emission of them in the ecosystem through anthropogenic sources like increased chemical discharge through the indiscriminate usage of pesticides and fertilizers into the agricultural soil has led to the accumulation of heavy metals concentration to dangerous levels. As soil holds the largest terrestrial pool for carbon, thus degrading soil will only worsen the phenomenon of climate change. The conditions of soil also underpin various Sustainable Development Goals (SDGs) set by the United Nations (**Figure 2**).

In view of these facts, strategies for remediation of contaminated soil must be implemented. Various remediation techniques have been developed to solve or minimize the influences of contamination. These technologies include physical, chemical, and biological methods.

#### **1.2 Heavy metals**

Heavy metals and metalloids are generally referred to as a group of elements that have densities >5 g cm−3. These include lead (Pb), chromium (Cr), arsenic (As), zinc (Zn), cadmium (Cd), copper (Cu), mercury (Hg), and nickel (Ni). They are naturally occurring elements, whose natural concentration in the soil ecosystem is primarily dependent on parent rock material [5]. Some heavy metals, like Zn, Cu, Fe, Ni, Mn, Mo, and Cr are essential for the functioning of structural and biochemical processes in living organisms and are required in trace concentrations, hence called micronutrients. They can cause harmful effects to plants if absorbed in higher concentrations. While non-essential heavy metals, including Pb, Hg, As have unknown biological functions but are used for various processes in modern industrial applications. The non-essential heavy metals are toxic to plants even at low concentrations [6]. However, the emission rate of pollutants through anthropogenic sources has increased the concentration of heavy metals in soil to hazardous amounts.

Heavy metals speciation plays an important role in their long-lasting presence in the environment, as mobile forms are easily leachable thus making them to spread ubiquitously in different media, and the bioavailable heavy metals are easily *Conventional and Contemporary Techniques for Removal of Heavy Metals from Soil DOI: http://dx.doi.org/10.5772/intechopen.98569*

#### **Figure 1.**

*Various anthropogenic sources of soil pollution.*

#### **Figure 2.** *The negative impacts of soil pollution on SDGs.*

absorbed by living organisms. They are non-biodegradable and non-thermodegradable so their accumulation in living organisms can cause biomagnification of heavy metals, that is they can affect organisms throughout all levels of the food chain. Particularly humans, as they are at the top of the food chain. The physicochemical properties of soil, like pH, cation exchange capacity and soil texture, also play a key role in the accumulation and availability of heavy metals [7]. Once heavy metals are exposed to humans, via inhalation, ingestion, or absorbed through the skin, they can accumulate in vital organs such as the kidney, brain, liver where they can be a threat to the health of humans [8]. Heavy metal contamination in agricultural soils may cause disturbance in the structure of soil, interfere with plant growth, and be harmful for human health via entering the food chain [9], posing health problems for all living organisms [10]. Furthermore, degradation of agricultural soil will impact crop yield and will put the most vulnerable people at higher risk of economic loss and malnutrition [4].

Soil Pollution by heavy metals is now a global concern. Europe has been found with 2.8 million sites that are potentially contaminated with heavy metal soil pollution, in China 19% of agricultural soil contain harmful pollutants exceeding the standards of environmental quality [11]. In India, heavy metal pollution in soil cover is approximately 80% by anthropogenic origin in the states of Maharashtra, Gujarat, and Telangana [12]. Therefore, the studies on agricultural soils which are contaminated with heavy metals are of much concern, especially due to two reasons. Firstly, ingestion is the main source of heavy metal exposure to humans and the agricultural food chain is the primary source of various food products for humans [8]. Secondly, densely accumulated heavy metals in agricultural soil can percolate through pore spaces and enter groundwater systems, consequently deteriorating the groundwater quality [13].


3.1.2. Fabrication of Pectin-Coated Composite Particles

HZ-chi particles prepared with 1% Na2CO3 were further coated with pectin to form a second layer. The addition of pectin did not influence particle PDI (Table 1). When the concentration of chitosan was 0.5 mg/mL, the size of HZ-chi particles increased from 87 to 170 and 180 nm upon adding 0.01 and 0.025 mg/mL of pectin, respectively. When the concentration of chitosan was 2 mg/mL, the size of HZ-chi particles increased from 84 to 194 and 192 nm upon adding 0.01 and 0.025 mg/mL of pectin, respectively. Meanwhile, a reduction in the ζ-potential positive values was observed, showing insufficient pectin to fully cover the surface of particles. With further increasing the pectin concentration to 0.05 and 0.1 mg/mL in the presence of 0.5 mg/mL chitosan and to at 0.05 mg/mL pectin in the presence of 2 mg/mL, the particle dispersion became unstable with the formation of precipitates, due to charge neutralization [7]. The neutralization was observed at 0.01 and 0.025 mg/mL pectin in the presence of 1 mg/mL chitosan (Table 1). However, in the presence of 1 mg/mL chitosan, an increment in size to 248 and 219 nm was observed

at 0.05 and 0.1 mg/mL pectin, respectively, and ζ-potentials changed to negative values, suggesting the formation of pectin surface layer. An absolute value of ζ-potential above 20 mV is so high enough to ensure the physical stability of biopolymer-based particles [32]. Therefore, the hollow zein particles prepared with 1 mg/mL chitosan and 0.1 mg/mL pectin were used for further study on their physicochemical and functional attributes.

**Table 1.** Effect of pectin on size, PDI and ζ-potential of hollow zein particles coated with chitosan.

Values with different letters (upper case A and B for the concentration of chitosan; lower case a, b and c for the concentration of pectin) are significantly different in rows (*p* < 0.05); – represents the aggregation followed by precipitation of nanoparticles.


**2. Background**

This section provides a brief overview of the Digital Twin concept, presenting its main building blocks and relating them to IPPODAMO. Next, we provide a concise and contextual survey of big data computing frameworks, part of the technological ecosystem adopted in this work.

### *2.1. Digital Twin Concept*

Grieves is recognized to be the first that coined the term Digital Twin, using it to describe the digital product lifecycle management process [11]. Since then, thanks to the availability of modern computing platforms, engineering and management paradigms and practices, the term has gained in popularity, promoting DT technology as a key enabler of advanced digitization.

To date, many such applications have been successfully implemented in different domains, ranging from manufacturing [12] and logistics [13] to smart cities [14], etc. However, currently, there is no common understanding of the term, and in this respect, a taxonomy would help to demarcate the conceptual framework.

To this end, the authors in [4] provide a classification of the individual application areas of Digital Twins. As part of a comprehensive literature review, they develop a taxonomy for classifying the domains of application, stressing the importance and prior lack of consideration of this technology in information systems research. Building on this, the authors of [15] undertake a structured literature review and propose an empirical, multi-dimensional taxonomy of Digital Twin solutions, focusing on the technical and functional characteristics of the technological solution. As an add-on, the proposal allows for the classification of existing Industry 4.0 standards enabling a particular characteristic.

What unites the various DT approaches is the presence of some key technological building blocks that need to be considered and are crucial to its success.

1. Data link: the twin needs data collected from its real-world counterpart over its full lifecycle. The type and granularity of the data depend on the scope and context of deployment of the technology.

IPPODAMO relies on a multitude of heterogeneous data sources, available at different granularities. Each data source has a dedicated (automatic) ingestion process used to transform the data, enriching the IPPODAMO data layer. For more information on the data sources and some computational aspects, we refer to the reader to Sections 3 and 4, respectively.

2. Deployment: the twin can be deployed and span the entire cloud-to-thing continuum, starting from the thing (IoT devices), the edge and/or the cloud. The specific deployment criteria depend on the scenario requirements, typically based on latency/bandwidth and/or security/privacy constraints.

IPPODAMO is not directly involved in the raw data collection process. The system relies on third-party data providers, which collect, extract and fetch the data in dedicated ingress, cloud-backed machines. The raw data are anonymized and temporally retained in the system.

3. Modeling: the twin may contain different and heterogeneous computational and representational models pertaining to its real-world counterpart. This may range from first-principle models adhering to physical laws; data-driven; geometrical and material, such as Computer-Aided Design/Engineering (CAD, CAE); or visualizationoriented ones, such as mixed-reality.

Our Digital Twin solution provides a consolidated view of heterogeneous urban data (descriptive Digital Twin), while at the same time, relying on historical and (near-)real-time data to perform near-to-mid-term predictions on the activity indexes (predictive Digital Twin). This allows operators to perform simulation scenarios aimed at minimizing predetermined indexes during routine and scheduled interventions. More information on the use cases and, in particular, the UFM scheduling is provided in Section 5.

4. APIs: the twin needs to interact with other components, e.g., twins in a composite system or an operator consulting the system. To facilitate these interactions, various APIs must be available to allow for information collection and control between the twin and its real-world counterpart.

IPPODAMO presents the UFM operator an intuitive, high level user interface abstracting low-level system interfaces. At the same time, IPPODAMO has a programmatic interface, exposing well-defined ReST APIs through which other systems and operators could integrate and interact. The rationale behind this choice is to enable future potential integrations of IPPODAMO into a larger, federated ecosystem of smart city platforms. This aspect of the study is beyond the scope of this article.

5. Security/privacy: considering the role and scope of the DT, physical-to-digital interactions require security/privacy mechanisms aimed at securing the contents of the twin and the interaction flows between the twin and its physical counterpart. The solution is deployed on a state-of-the-art virtualized environment equipped with all the necessary security features. To this end, different administrative levels are provisioned, for accessing both the virtualized system and the system functionalities.

In the following, we provide a concise survey on some big data computing frameworks, motivating the choice of the identified technological ecosystem.

### *2.2. Big Data Computing*

Smart cities generate and require, more than often, the collection of massive amounts of geo-referenced data from heterogeneous sources. Depending on the process and purpose of the system(s), these data must be quickly processed and analyzed to benefit from the information. To this end, big data computing frameworks have gained tremendous importance, enabling complex and online processing of information, allowing us to gain insights about phenomena in (near-)real time.

Cluster computing frameworks such as Apache Hadoop, based on the MapReduce compute model, are optimized for offline data analysis and are not ideal candidates for fast and online data processing due to the overhead of storing/fetching data at intermediate computation steps [16]. Current efforts in the relevant state-of-the-art have shifted toward promoting a new computing paradigm referred to as Stream Processing [17]. This paradigm adheres to the dataflow programming model, where computation is split and modeled as a directed acyclic graph and data flow through the graph, subjected to various operations [18].Micro-batching represents a middle ground in this continuum: the idea is to discretize the continuous flow of data as a set of continuous sequences of small chunks of data, delivered to the system for processing. In this context, frameworks such as Apache Spark adopt this philosophy [19]. While Apache Spark is not a purely streaming approach, it introduces a powerful abstraction—the resilient distributed dataset (RDD [20])—allowing for distributed and in-memory computation. This programming abstraction supports efficient batch, iterative and online micro-batching processing of data, and it is a perfect match for the dynamics of our UFM scenario. Moreover, the cluster computing platform offers a wide range of libraries and computation models, as well as the geographical extensions needed to handle spatial data [21].

### **3. The IPPODAMO Platform**

In this section, we start by presenting the data sources used by the Digital Twin, discussing their spatial and temporal characteristics. Next, we provide a high-level overview of the functional components comprising the technical solution.

### *3.1. Data Sources*

IPPODAMO relies on a multitude of heterogeneous data sources provided, in part, by the project partners, including a telco operator and a company operating in the UFM sector.

Referring to Figure 1, the twin relies on (anonymized) vehicular and human presence data, combined to extract a measure of the activity inside an area of interest. The data sources are geo-referenced, embodying different levels of granularity, subject to different data processing steps and aggregation procedures aimed at building a composable activity level index (refer to Section 5). At the time of writing, the system has processed and stores two years of historical data and actively processes (near) real-time updates from each data source. These data have important value and enable IPPODAMO to gain (near-)real-time insights into the activities, but also to simulate near-to-mid-term evolutions of the activity level by exploiting the historical data.

**Figure 1.** IPPODAMO data sources.

Other important data relate to the UFM process itself, which comprises data generated from: (i) the urban monitoring activity, e.g., the status of an urban asset assessed periodically through field inspections, (ii) annual planning and scheduled operations, e.g., repair interventions, (iii) geographical data concerning public utilities such as hospitals, schools, cycling lanes, etc. The data are provided and updated by the company operating in the UFM sector, having a vested interest in accurately depicting and monitoring the status of the urban assets.

Additional data sources are available and extracted from the open data portal, curated and maintained by the municipality of Bologna, Italy, and these include: (i) city events and (ii) other public utility maintenance operations.

All the above-mentioned data sources undergo dedicated processing steps and are stored in a logically centralized system, providing a consolidated and multi-source data layer. A rich set of visualizations can be built, guiding the UFM operator in their work. At the same time, more advanced functionalities, relying on the historical data to predict future evolutions of the phenomena inside an area of interest, are possible, and this topic is discussed in Section 5.

### *3.2. Technological Ecosystem*

The platform (Figure 2) is structured in four main conceptual layers: (i) the ingestion layer, which interacts with the data providers, continuously acquiring new data, performing syntactic transformations, pushing them upwards for further refinements; (ii) the big data processing layer, which performs semantic transformation and enrichment of raw data fed from the ingestion points; (iii) the storage layer, providing advanced memorization and query capability over (near-)real-time and historical data, and (iv) the analytics layer, presenting to the customer an advanced (near-)real-time layer with query capabilities, aggregate metrics and advanced representations of data.

**Figure 2.** Technological components of the IPPODAMO platform.

For each data source used by the Digital Twin and, in particular, for the vehicular and presence data, there is a custom ingestion process tasked with reading the raw data, performing some syntactic transformations and pushing them towards the big data cluster. The various data sources are retrieved and pushed in parallel to specific Kafka topics, which identify also the semantic processing pipeline. Indeed, to enable the reliable and fast delivery of data from the ingestion points to our analytics and storage platform, we rely on Apache Kafka, an open-source, distributed, message-oriented middleware [22]. This choice is driven by the capabilities of this platform, including, but not limited to, its capability to gracefully scale the processing in the presence of high-throughput and low-latency ingress data. This matches the requirements of our domain, where data are constantly and periodically collected from many heterogeneous sources, e.g., vehicle black boxes, cellular, public transport, etc.

To provide advanced and fast processing capabilities for spatial data, the technological stack integrates and relies on Apache Sedona [23]. Apache Sedona is a distributed processing library, which builds on the in-memory computation abstraction of the Apache Spark framework and is able to provide advanced and fast queries over spatial data. Thanks to this spatial processing framework, we are able to blend and elaborate different data sources, creating rich representations of various phenomena in an area of interest.

Once the data have been processed, they are stored in Elasticsearch, a fast and scalable no-SQL database with advanced capabilities of indexing and querying key-value data [24]. Thanks to the advanced integration between Spark and Elasticsearch, we can use the solution as both a data provider and as a distributed storage system. The data are subject to further refinements and algorithmic processes aimed at creating different layers of aggregations, calculating synthetic indexes, etc., which are then used by the planner functionality to identify suitable time intervals during which to schedule urban operations. The raw data and information extracted from the data are presented to the end-users in different forms through advanced visualization dashboards available through Kibana, part

of the Elasticsearch ecosystem. Last is the JobServer component, an optimized RESTful interface for submitting and managing Apache Spark tasks [25]. A job request, e.g., for a suggestion on a maintenance operation schedule, is a JSON-formatted request, triggering the execution of a specific algorithm resulting in a JSON-formatted output visualized through a web-based user interface. This component composes part of the IPPODAMO programmatic API, which could be used to integrate the solution into a larger, federated ecosystem of platforms in a smart city context.

### **4. Big Data Processing**

In this section, we provide some technical details on the big data processing pipelines dedicated to the transformation and enrichment of incoming raw data. Next, we present and analyze the performance trend of two distinct data processing pipelines, providing in-depth insights into some system mechanisms.

### *4.1. Processing Pipeline(s)*

The data sources introduced in Section 3 are subject to different processing pipelines due to the inherent syntactic and semantic differences that they embody. Concerning the vehicular and presence data, of relevance to the UFM process is the measure of the activity level inside a particular area in time. To this end, both pipelines are finalized to implement a counting technique measuring the activity volume. The geographical granularity of the presence data is accounted on a tile basis, a square-shaped geographical covering an area of 150 m × 150 m, while the vehicular data are point data—latitude and longitude—and can be accounted for at any meaningful granularity. These data resolutions are imposed by the data provider. It is important to note that in scenarios where both vehicular and presence data need to be accounted for, the granularity of this aggregation operation can be a tile or a multiple of tile entities.

Referring to Figure 3, both vehicular and presence data sources are initially subjected to some syntactic transformations before being forwarded from the Kafka producer, simplifying the ingestion process on the Spark cluster. From here on, the data are subject to semantic transformation processes, enriching the source data with relevant geographical information. In particular, the last operation in the pipeline accounts for the activity index information, retaining it in a distributed memory support. This operation should be done in a fast and efficient way; otherwise, it risks becoming a bottleneck for the overall system, delaying the ingestion performance.

To this end, we rely on a spatial partitioning mechanism, dividing the interest area among the cluster nodes. The area of interest to the project is initially saved in the underlying distributed file system and programmatically loaded and partitioned using the GeoHash spatial partitioning scheme [26]. This allows us to distribute topological data and index thereof among the cluster nodes. Indeed, in scenarios where ingress data are uniformly distributed inside the area of interest, this allows us, on average, to equally distribute and scale the computation among available cluster nodes. In particular, the data are accounted for by performing distributed point-in-polygon (PiP) and k-Nearest-Neighbor (kNN) operations, and these operations are enclosed by the *GeoRDDPresence* and *GeoRDDVehicular* functional components, respectively, for the presence and vehicular data. The data, once retained, are then subjected to additional processes, aggregating and slicing the data in the time and space domains (refer to Section 5). Other sources of information are those containing topological information concerning urban assets such as cycling and bus lanes, hospitals, schools, etc., and open data offered by the municipality, e.g., city events. Topological data have a dedicated batch processing pipeline; at its crux is a geo-join operation, aimed at enriching the IPPODAMO baseline topological map with additional information on urban assets. A dedicated update procedure is available whereby old information is discarded and only the fresh information is considered.

Last are the data sources containing operational information on the facility management process. UFM data have spatial and time information and are currently handled by a similar processing pipeline to the topological data.

**Figure 3.** IPPODAMO (big) data processing pipelines and information flow.

### *4.2. Performance Analysis*

The periodicity of the individual data sources imposes some operational constraints on the data processing pipelines, and this consideration applies to the vehicular and presence data. As an example, when a new batch of vehicular data enters the system, the corresponding end-to-end processing pipeline comprising the various syntactic and semantic transformations should take no more than 60 min (refer to Figure 1). Failure to do so would create a backlog of data that increases over time, slowing down the ingestion performance.

In particular, among the operations shown in Figure 3, the one embodying the highest computational burden is the *GeoRDDVehicular* operation, which requires the execution of a kNN operation for each data point present in the hourly dataset. We report that the end-to-end sequential processing of an hourly vehicular dataset containing, on average, 15,000 records (trips) often results in a violation of its operational constraint.

To address the issue, we leverage some specific constructs of the Apache Sedona framework, aimed at distributing the computational effort among the various cluster nodes. As anticipated, the proposed solution makes use of the (i) GeoHash spatial partitioning scheme, allowing the partitioning of a geographical area of interest among cluster nodes, and the (ii) broadcast primitive implementing a distributed, read-only shared memory support. In particular, the ingress vehicular dataset is enclosed as a broadcast variable, shared among worker nodes, where each node is responsible for the computation of a kNN operation over a subset of the points contained in the original dataset. This approach allows the parallel computation of individual kNN operations, whose outcome is later merged and retained in ElasticSearch. Figure 4 shows the performance trend of the optimized vehicular data processing pipeline. In particular, Figure 4a plots the processing time with a varying number of input records. The resulting trend is monotonic, allowing for the timely processing of the input dataset, adhering to the operational constraints imposed by the data source periodicity. Figure 4b puts the processing time into a greater context, accounting for additional operations occurring before and after the *GeoRDDVehicular* processing step, comprising data (de)serialization and output communication to the driver node, with the kNN processing step accounting for nearly 91% of the processing time.

**Figure 4.** Vehicular processing pipeline performance. The experiments were carried out in a testbed comprising 4 VMs—1 driver and 3 workers—each equipped with 8 vCores, 32 GB vRAM and 150 GB data SSD support. (**a**) Overall processing time under varying number of ingress records. (**b**) Processing time decomposition for the 15,000 record configuration.

### **5. A Decision Support System**

At its core, the IPPODAMO platform serves as a decision support system, aiding UFM operators in their daily activity. In this section, we start by discussing a set of identified use cases best targeting the needs of the UFM operator. Next, we discuss the concept behind the activity level index, a synthetic index used by some underlying system functionalities. Then, we provide a brief description of the algorithmic details of the scheduler functionality, along with a validation study showcasing its capabilities.

### *5.1. Use Cases*

While preserving the general aspect of our study and without loss of generality, we identified two broad use cases, which showcase the capabilities of the solution in the following directions:

Concerning the first use case, the system provides a rich set of configurable visualizations, allowing the UFM operator to consult and confront the historical and current trend of data inside an area of interest. The second use case aims to provide the UFM planner with a proactive decision tool, guinding the scheduling decisions. The last use case allows the platform administrator to perform an *a posteriori* evaluation of the annual planning schedule, dictated by the data that IPPODAMO has ingested. Through this functionality, we would like also to be able to perform a qualitative evaluation of the algorithmic decisions made by IPPODAMO, confronting them with the knowledge of the UFM specialist. At the core of these use cases is the activity level index, which is discussed in the following section.

### *5.2. Activity Level Index*

Once the data are processed, they are stored in ElasticSearch and are subjected to further periodic and event-based algorithmic processes, aggregating and slicing the data in the time and space domains. Intuitively, the activity index is a measure of the activity level inside an area of interest.

At first, vehicular data are stored at their finest granularity, contributing to the traffic volume in a specific point of the underlying road topology. These point-wise data are aggregated and accounted on a road-arch basis, a constituent of the road topology. The data are also sliced in the time domain, accounting for the daily traffic volume and the traffic volume on some configurable rush hours. Once aggregated, the data are normalized and stored at different scales, e.g., for better visualization. This computed quantity constitutes the activity level index derived from the vehicular data.

The human presence data are subjected to a similar workflow. The difference is in the granularity (tile entity) that the data are accounted for. Recall that this granularity is imposed on us by the data provider. Once the individual indexes are computed, they can be composed and weighted according to some criteria. The current implementation allows an operator to simulate different scenarios by specifying the weights accordingly.

These indexes are periodically updated and maintained, and they constitute the input for the, e.g., UFM scheduler. Currently, three implementations for the activity level index are available, and the planners' behavior is parametric on the index type:

Concerning the predictive index, we rely on state-of-the art algorithms, capable of inferring seasonality and local phenomena from the data. Currently, we are evaluating some practical design considerations that can occur in a dynamic and time-varying scenario such as ours.

### *5.3. UFM Scheduling Algorithm*

This functionality is used in the first use case, and aids the UFM operator in searching for a suitable timeframe during which schedule a maintenance intervention. A maintenance operation may consist of a minor/major repair operation of an urban asset; it has fixed coordinates in space, a predicted duration, and an optional timeframe in which it needs to be scheduled. The scheduling criteria vary, and, depending on the objective, one would like to avoid or minimize disturbance to nearby activities. To this aim, the system has the capability to express and consider all these and other constraints when performing the search for a suitable timeframe.

Once a request is issued, the system receives all the constraints expressed by the operator, including the list of attributes, e.g., coordinate, expected duration, etc., for each intervention. The algorithm then exploits the geographical coordinates to compute the activity level index inside an area of interest and gathers all the potential interferences with nearby ongoing activities. To this aim, the functional component relies on the Spark SQL library and geographical primitives available in Apache Sedona. Recall that the algorithms' behavior is parametric on the index type, and different types of indexes are available. The final outcome of this computation is the construction of the index history and its hypothetical evolution in time.

Once the activity level index is computed, and all interferences have been retrieved, a final index is processed for the time horizon under consideration served as input to the scheduling algorithm. The exact algorithmic details and final index composition are beyond the scope of this article.


**4. Microbiology—urine culture**

Urinary tract colonization often follows colonization of the urogenital tract, perineum or urethra with enteropathogenic microorganisms [66, 67]. In a study of 15 adult men with SCI and other neurogenic urinary dysfunctions, the normal flora of the perineum, penis and urethra regions was compared with the flora of 10 control men without neurogenic urinary disorders [68]. The predominant microorganisms with respect to the control group were Grampositive granules and diphtheroids. In the individuals with the neurogenic urinary disorder, the microorganisms isolated from the skin flora include species such as Enterobacteriaceae, Pseudomonas, Acinetobacter, and Enterococcus [68]. In addition, other studies of individuals with SCI as compared to non-injured patients, the presence of *E.coli* microorganisms and Klebsiella spp. are less, and have a higher frequency of infections than Pseudomonas, Proteus, and Serratia. Esclarin De Ruz et al. [20] reported that *E. coli*, 36% enterobacteria, 15% *Pseudomonas aeruginosa*, 15% *Acinetobacter* spp., 12% Enterococcus, 6% other microorganisms, and 26% multiple strains were isolated in 45%. In another study in 43 of 50 individuals with SCI, the same types of microorganisms as those from various areas of the skin, including perineal, peripubic, and perinatal regions, were isolated in urine [69]. In 50% of the cases, the same microorganism was isolated from the anterior urethra and from the bladder [70]. Also, the catheter insertion mode is also considered to be significant, which appears to cause an increase of approximately 10 times the number of bladder colonies [70]. The above results demonstrate the important role of bacterial colonization of the skin and urethra as a source of vaccination, through the catheters, of the bladder with microorganisms.

When a UTI is suspected, it is important that the urine specimen is obtained in an appropriate manner in order to prevent contamination and a potential false-positive result. For patients with indwelling catheters (either the urethral catheter or suprapubic), the indwelling catheter should be changed to a new catheter, and the specimen should be obtained from the new catheter after capping the catheter for a few minutes to allow a small amount of urine to collect in the bladder. The urine specimen should then be collected by uncapping the catheter. For patients with external catheters or those who perform IC, the specimen should be collected by catheterization with a new sterile catheter.


**Gully Erosion in Southeastern Nigeria: Role of Soil Properties and Environmental Factors**

C.A. Igwe

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/51020


**Appendix A. Preliminary Calibration Stage**

Consider an agricultural region in which farmers grow *O* crops. The region has access to freshwater, TWW, and brackish water, the regional consumptions of which are limited to the amounts denoted *Qf* , *Qh* and *Qb*, respectively, and their respective prices are *pf* , *ph*, and *pb*, where *pf* > *ph* > *pb*. Let *q f o* , *qh*, and *qb o* denote the per hectare annual water applications of freshwater, TWW, and brackish water to crop *o* (*o* = 1, ... , *O*), respectively, where the sets **q***f* = *q f* 1 ,..., *q f O* , **q***h* = *qh* 1,..., *qh O* , and **q***b* = *qb* 1,..., *qb O* are defined accordingly. The total per hectare annual application to crop *o*, *wo*, is considered constant. The production function is given by *θoeo q f o* , *qb o*, *qh* , in which *θo* is a parameter for calibration, and *eo*(•) is the evapotranspiration function of crop *o*, which is taken from Slater et al. [35]. The salinity of brackish water is higher than that of TWW, the salinity of which is higher than that of freshwater; therefore, *∂eo ∂q f o* > *∂eo ∂qh o* > *∂eo ∂qb o* > 0. We denote by *xo*, the land allocated to crop *o*, which is fixed at the preliminary stage. With the above setting, we first solve the nonlinear optimization problem

$$\max\_{\mathbf{q}^f, \mathbf{q}^b, \mathbf{q}^h} \pi = \sum\_{i=1}^I \mathbf{x}\_o \left[ p\_o \left( \theta\_o \mathbf{c}\_o \left( q\_o^f, q\_o^h, q\_o^b \right) \right) - p^f q\_o^f - p^h q\_o^h - p^b q\_o^b \right] \tag{A1}$$

s.t.

$$q\_o^f + q\_o^h + q\_o^b = w\_o \; \forall \; o = 1, \dots, O \tag{A2}$$

$$\sum\_{o=1}^{O} \mathbf{x}\_o q\_o^f \le Q^f \tag{A3}$$

$$\sum\_{o=1}^{O} \mathbf{x}\_o q\_o^h \le Q^h \tag{A4}$$

$$\sum\_{o=1}^{O} \mathbf{x}\_o q\_o^b \le Q^b \tag{A5}$$

$$\mathbf{q}^f, \mathbf{q}^h, \mathbf{q}^b \ge 0 \tag{A6}$$

where the initial values of *q f o* , *qh o* , and *qb o* are set based on the shares of *Qf* , *Qh*, and *Qb* in the total regional water *Qf* + *Qh* + *Qb*, and the parameter *θo* is set so as to equate the computed yield to the observed one *Y*ˆ *o*:

$$
\theta\_o \mathfrak{e}\_o \left( q\_o^f, q\_o^h, q\_o^b \right) = \hat{Y}\_o \; \forall \; o = 1, \ldots, O \tag{A7}
$$

The resultant optimal water allocation sets **q***f* ∗, **q***h*∗, and **q***b*∗ are then used fto recalibrate *θo* based on Equation (A7).