3. The effect of obesity on clinical outcomes

#### 3.1. In the adjuvant setting

As we mentioned earlier, several studies have established a link between obesity and colon cancer risk, but there is little information about the effects of obesity on clinical outcomes after diagnosis and surgical treatment. In the first publication, Dignam et al. reported a significant increased risk for recurrence and death from colon cancer in very obese patients (BMI ≥35 kg/m2 ) who receive adjuvant chemotherapy [146]. They investigated the association of BMI with the outcomes of 4288 patients with Dukes B and C colon cancer. This study cohort was from cooperative group clinical trials. They observed 2074 events in this study population and hazard ratio (HR) for risk of recurrence was 1.38 (95% confidence interval [CI] = 1.10–1.73), and HR for risk of mortality was 1.28 (95% CI = 1.04–1.57) in very obese patients compared with normal weight patients.

The Adjuvant Colon Cancer Endpoints database also compared the outcomes in patients (N = 25,291) with stage II and stage III colon cancer receiving adjuvant chemotherapy, and they showed inferior outcomes both for obese as well as underweight patients [147]. Men with class 2 and 3 obesity (BMI ≥ 35.0 kg/m2 ) had a statistically significant reduction in DFS (HR: 1.16; 95% CI = 1.01–1.33; p = .0297) compared with normal-weight patients. These worse outcomes appeared to be cancer-related.

On the other hand, Cancer and Leukemia Group B (CALGB) 89803 trial investigators did not find any significant associations between an increased risk of recurrence and mortality and BMI in patients with stage III colon cancer [148]. In this study, they observed 369 events in 1053 patients. The difference between studies should be related to statistical power. Another explanation is related to suboptimal adjuvant therapy (calculating dose according to a maximum body surface area of 2.0 m2 ) in very obese patients in the study by Dignam et al.

#### 3.2. In metastatic disease

Clustering of clinical findings made up of abdominal obesity (apple-shaped body), hyperglycemia with insulin resistance, dyslipidemia (high triglyceride, low high-density lipoprotein), and hypertension is accepted as metabolic syndrome [23]. It is clear that one of the conse-

Insulin, IGFs PI3K-Akt Evading apoptosis (resisting cell death) Insulin, IGFs RAS-RAF-MAPK-Cyclin D1 Sustaining proliferative signaling IGFs Disruption of E-cadherin/catenin Activating invasion and metastasis Leptin JAK2-STAT3-MAPK Sustaining proliferative signaling

As we mentioned earlier, several studies have established a link between obesity and colon cancer risk, but there is little information about the effects of obesity on clinical outcomes after diagnosis and surgical treatment. In the first publication, Dignam et al. reported a significant increased risk for recurrence and death from colon cancer in very obese patients (BMI ≥35

The Adjuvant Colon Cancer Endpoints database also compared the outcomes in patients (N = 25,291) with stage II and stage III colon cancer receiving adjuvant chemotherapy, and they showed inferior outcomes both for obese as well as underweight patients [147]. Men with

1.16; 95% CI = 1.01–1.33; p = .0297) compared with normal-weight patients. These worse out-

On the other hand, Cancer and Leukemia Group B (CALGB) 89803 trial investigators did not find any significant associations between an increased risk of recurrence and mortality and BMI in patients with stage III colon cancer [148]. In this study, they observed 369 events in 1053 patients. The difference between studies should be related to statistical power. Another

) had a statistically significant reduction in DFS (HR:

) who receive adjuvant chemotherapy [146]. They investigated the association of BMI with the outcomes of 4288 patients with Dukes B and C colon cancer. This study cohort was from cooperative group clinical trials. They observed 2074 events in this study population and hazard ratio (HR) for risk of recurrence was 1.38 (95% confidence interval [CI] = 1.10–1.73), and HR for risk of mortality was 1.28 (95% CI = 1.04–1.57) in very obese patients compared with

quences of metabolic syndrome is the increased risk of carcinogenesis.

Growth factors Pathways Hallmarks of cancer

Leptin/VEGF PI3K-MAPK Inducing angiogenesis

Leptin, TNF-alpha, IL-6 Multiple pathways Tumor promoting inflammation

Table 4 summarizes obesity-related mechanisms of carcinogenesis.

3. The effect of obesity on clinical outcomes

Table 4. Obesity-related mechanisms of carcinogenesis.

3.1. In the adjuvant setting

86 Body-mass Index and Health

normal weight patients.

class 2 and 3 obesity (BMI ≥ 35.0 kg/m2

comes appeared to be cancer-related.

kg/m2

In advanced colorectal cancer, there are more trials than in the adjuvant setting evaluating the role of obesity on clinical outcomes. In a pooled analysis from four large, prospective studies, among 6128 patients with metastatic CRC treated with first-line bevacizumab and chemotherapy, patients with the lowest BMI (<25 kg/m2 ) experienced the lowest median OS [149]. This study is presented at the World Congress on Gastrointestinal Cancer 2015. This observation concerning the relationship between worse outcome and the lowest BMI does not mean that obesity is an advantage in patients with mCRC [150]. Probably, in patients with mCRC with a lower BMI, the effects of cancer-related cachexia may be more deleterious. These patients may have less tolerance to the treatments. According to another explanation, obesity may promote angiogenesis and bevacizumab, which is an anti-VEGF monoclonal antibody and may have a vital role in obese patients.

There are also other trials with contradictory results [151–155]. In a trial investigating the influence of BMI on outcomes in advanced CRC patients receiving chemotherapy with or without targeted therapy, BMI was shown as an independent prognostic factor for survival in patients receiving chemotherapy (CT), but not in patients receiving CT plus targeted therapy [151]. In patients receiving only CT, median OS was 19.5 months for BMI category >30 kg/m2 versus 8 months for BMI category <18.5 kg/m2 (p = 0.001). In patients receiving CT plus targeted therapy, median OS was 21.4 months for BMI category >30 kg/m2 versus 16.6 months for BMI category <18.5 kg/m2 (p = 0.8). In another trial, the authors speculate that higher circulating levels of VEGF may confer resistance to bevacizumab [152]. In our retrospective trial, we found better time to progression (TTP) in patients who have BMI < 25 kg/m2 compared to patients who have BMI > 25 kg/m2 ; median TTP was 11.7 months versus 6 months, respectively (p = 0.004) [153]. All patients had been treated with fluoropyrimidine-based combination CT plus bevacizumab. We think that patients with high BMI may require higher dosages. In another similar trial, the investigators compared OS across the BMI groups for CRC, and they found that the OS was shorter for patients who were underweight and overweight compared to normal in the group of patients receiving CT + targeted therapy. There was no difference in OS for CT alone [154]. In a recent multicentric study, we demonstrated that obesity serves as a prognostic factor for mCRC patients who have been treated with bevacizumab-based regimens. In particular, among Kras wild-type left-sided tumor patients with bevacizumab-based regimens, the prognosis could be worse for obese patients than that for non-obese patients [155].

#### 4. Conclusion

High BMI or obesity is an established risk factor in colorectal carcinogenesis. Adipose tissue excess, particularly abdominal obesity is linked to hyperinsulinemia, insulin resistance, hyperglycemia, and to the development of type 2 diabetes which are the components of metabolic syndrome. Chronic low-grade inflammation is a hallmark of obesity, and obesity supports many hallmarks of carcinogenesis. It is clear that one of the consequences of metabolic syndrome is the increased risk of carcinogenesis. Targeting obesity and metabolic syndrome should be beneficial in the prevention and the treatment of colorectal cancer.

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**Honghua Yang 1,2, Linwei Ma 1,2,\* and Zheng Li 1**

Received: 28 February 2020; Accepted: 19 April 2020; Published: 21 April 2020

**Abstract:** To systematically analyze energy-related carbon emissions from the perspective of comprehensive energy flow and allocate emissions responsibility, we introduce energy allocation analysis to carbon flow process based on Sankey diagrams. Then, to quantitatively compare different diagrams and evaluate the structural changes of carbon flow, we define changes from three dimensions including total amount change, relative growth rate and occupation ratio change (TRO), propose TRO index. The method is applied to China's case study from 2005 to 2015. We map China's energy-related carbon flow Sankey diagrams with high technical resolution from energy sources, intermediate conversion, end-use devices, passive systems to final services, and conduct TRO index decomposition by stages. The results indicate that in energy sources, the emission share of coal has declined due to energy transition although coal is still the largest contributor to China's energy-related carbon emissions. In passive systems, the factory passive systems are the largest contributors, among them, emission reduction should focus on the steel, non-ferrous and chemical industries; the building passive systems should pay attention to household appliances; the vehicle passive systems should focus on cars. In final services, the demand for structural materials is the strongest driving force for carbon emissions growth.

**Keywords:** energy-related carbon emissions; carbon flow; Sankey diagrams; structural changes; TRO index; energy allocation analysis; energy system


vidual valuation method could be based on individuals' willingness to pay for data protection and privacy [22]. Both approaches remain incomplete as market valuation methods rely on indicators that are insufficiently precise while individual valuation methods are no incentive compatible. Defining the value of personal data therefore remains an ethical, legal and practical challenge without an apparent optimal solution. To overcome this epistemic uncertainty, more research into fair and ethical pricing mechanisms is needed to educate individuals on the value of personal data. As technology and the data economy are under constant change, the exact value of data at the point of collection is likely to continue to be unknown for most parties involved in the transfer of the data. Thus, the most probable and promising route would aim at developing adequate proxies and indicators that allow for an approximation of the data's value. Renewed legislation could provide examples and frameworks for measuring the value of data in order to compensate data subjects.

#### **The Value of Data - Counter-Performance Practices**

*Context:* Currently, it is unclear whether or not a form of trade that does not involve money transfers but rather the monetarization of the data, i.e., the process of converting personal data into currency, is lawful [83]. As the value of the EU data economy is continuing to grow [23] and many internet service providers are opting to monetise personal data instead of charging a fee for their service or platform [78], this matter also requires ethical consideration.

*Ethical Issue and Example:* Presently, individuals oftentimes pay with personal data for the usage and experience of digital products and services. An example are popular social media or communication services such as Facebook, WhatsApp or TikTok. While these services are advertised as "free" to individuals, they are paying indirectly by consenting to the processing of their personal data by the service providers. Most often, users do not have the chance to chose between paying with money or their data and personal information. Indeed, it is possible that individuals are not even aware about this possible choice or would simply opt for the payment using personal data as they do not have the means to pay with money, while still relying on the service or product. The ethical issue lies therefore in the question whether personal data should be accepted as a form of payment and under what considerations.

*Risk Assessment and Discussion:* Kitchener [43] identified several moral principles that can serve as ethical guidelines for this issue. The principles are defined as *beneficence, non-nmaleficence, autonomy, justice* and *fidelity*. As the principle of fidelity does not fit the context of counter-performance practices, the principle of *explicability* is used instead, which has already been used in related work on ethics in AI [26]. It can be seen that data as counter-performance for a service contributes to the welfare of the individual, satisfying the beneficence criterion. Similarly, *non-maleficenc*e be seen as fulfilled, assuming that the service provider does not offer the service to intentionally harm the individual or others. In relation to fundamental right for privacy, a service provider needs to take measure to prevent accidental or deliberate harm to the data subject when processing personal data. Regulation such as the GDPR acts therefore as a control mechanism for the non-maleficence principle.

The principle of *autonomy* relates to independence, allowing an individual freedom of choice in its actions. In this case, individuals are, in principle, free to choose a service that asks for data as counter-performance for a service. Individuals could decline such an offer, choose no service at all, or search for other service providers that offer the same or a similar service for a monetary fee. To do so, individuals must firstly understand how their decisions impact them and others in a society. Secondly, this decision must be able to be sound and rational, that is, children for instance can-not be expected to make a sound and rational decision on this matter. The Covid-19 crisis demonstrated that access to services that gather personal data as payment is not always a free choice but a necessity. During worldwide lockdowns, especially school-children and students were forced to use services to connect with each other as well as with education institutions in order, as the alternative would have been to not receive any kind of education. For children and students from less fortunate households and countries, a paying-with-money option would not have been a better alternative, given that many individuals were not even able to afford laptops, let alone a functioning working and learning environment. Additionally, individuals are often not aware of the value of their data, as discussed in other sections of this work, or unaware of the potential implications and processing activities that are conducted with personal data. As data can be stored indefinitely, individuals might forget about consent that they gave in the past, preventing them to exercise their right of erasure or to withdraw their consent. Thus, the use of data as counter-performance for a service can therefore not be considered a completely autonomous decision in many circumstances. This however does also apply to instances in which individuals consent to the processing of data for a service. According to the GDPR, consent has to be freely given to act as a legal basis for the processing of personal data. If an individual is not able to use a service or product without consenting to the processing of their personal data, this consent is not freely given. Oftentimes, for instance in the case of website cookies or ubiquitous internet services, individuals are not able to effectively withdraw their consent. There are a number of reasons for this. Especially in the case of cookie notifications, organizations use techniques such as nudging and dark patterns to push individuals into accepting them. While the opt-in choice is easy to choose, finding an opt-out choice is often a tedious and frustrating task. Organizations might also decline website visits or only offer limited functionality if individuals not fully consent to the whole cookie policy. The recent EDPB Statement 03/2021 on the ePrivacy Regulation reiterates the importance of enforcing more strict consent requirements for cookies and similar technologies in the upcoming ePrivacy regulation [6]. However, individuals might have no other option than to consent to the processing of data as they effectively need, feel or are obliged to use a product or a service. This could be because individuals are socially pressured in the case of monopolistic or oligopolistic services or because individuals are not able to use costly alternatives that collect no or less personal data, demonstrating that individuals are often unable to take autonomous decisions on the processing of personal data in general.

Kitchener [43] defines justice as "treating equals equally and unequals unequally but in proportion to their relevant differences". Treating individuals differently therefore requires a rationale that explains the appropriateness of this treatment to promote fairness and impartiality [27]. In principle it could be argued that individuals could receive different degrees of services for their data, depending on how the service providers values the data. Similarly, the justice principle could also encompass the option of giving individuals the option to pay with data or with money for a specific service. However, this could not be regarded as fair practice given the power imbalance be-tween individuals and service providers in instances outlined above. The ethical is-sues related to the use of data as counter-performance for a service are however not automatically solved if the provider of a service gives the individual the option to pay with money for this particular service. Less wealthy individuals are likely to always prefer data as payment, as money could be used for other goods and services while the same type of data could be used to "pay" for multiple services. Similarly, as the value of data is oftentimes not observable at the time of data collection, but through the combination with other data points, a price discrimination between individuals cannot satisfy the justice principle in this case.

Finally, *explicability* acts as an enabler for the aforementioned principles by promoting intelligibility, accountability and transparency. Individuals should be enabled to understand what data is being processed, how exactly it is being processed, by whom and for how long. Only then can individuals gain an understanding on the current and future value of their data. Service providers need to be held accountable for their processing activities. Again, current regulation such as the GDPR aim to increase explicability by fostering transparency and accountability when personal data is being processed. Under the condition that regulation allows for data as payment for a service and regulates it, the explicability principle can be affirmed.

Overall, the use of data as payment for a service poses both, ethical and legal issues. From an ethical point of view, the simple prohibition of this matter does not solve the underlying power imbalance between individuals and service providers. Instead, it may hinder individuals in gaining an understanding over the value of personal data. Clear contractual agreements and regulation could allow for data as payment while complying with legal and ethical requirements.

## **5 Discussion**

Based on the analysis of the ethical issues identified and elaborated upon in the prior section, Table 2 provides an assessment of the ethical principles of cybersecurity potentially violated in each step of a digital service chain. Moreover, for each ethical issue the negatively affected party as well as the party that could potentially resolve the issue is identified. These parties are namely the user, or individual, or the organization that is providing a specific service to a user. We deliberately decided not to include the state, that could introduce additional legislation to overcome ethical issues, in the table. Instead, the aim of this work is to assess whether and how organizations and individuals may overcome these ethical issues without further regulation.

**Table 2.** Ethical issues and potentially violated ethical cybersecurity principles in digital service chains

The results suggest that different ethical issues in different steps of digital service chains affect different ethical cybersecurity principles. Consequently, there cannot exist a one-size-fits-all solution to solve these ethical issues. Moreover, in all cases, the individual is the entity negatively affected through the specific ethical issue. Organizations are largely found to be potentially in the position to resolve or mitigate ethical issues, although there exist issues were both entities may be negatively affected or able to resolve an issue. We find that each cybersecurity ethics principle is violated through at least one ethical issue identified in this work. Similarly, for each step in the digital service chain, at least one ethical issue could be identified. As discussed in the beginning of this work, the objective has not been to provide a comprehensive list of all ethical issues but to focus on the ones derived through the methodology of this work.

Schoentgen and Wilkinson [77] noted that while digital technologies should be used for the benefit of individual people and the society, most of them are rather designed for commercial benefits. They sketch the transition of digital services prioritising ethics through a feedback loop. In order to have companies incorporating ethics, it is essential that they are rewarded for their efforts, i.e. benefits need to exceed costs. To benefit from ethical services, it is necessary that customers notice that organizations take ethical compliance serious and that individuals benefit from this compliance. Increased ethical awareness can be achieved through increased transparency and accountability, but requires ethics to be measurable to allow customers to compare digital services on their ethical conduct.

Consequently, stronger ethical conduct can build users' trust which in turn will increase engagement and consumption of digital technologies which could reward the company. These observation are in line with findings of Hagendorff [33] who concluded that there are currently no consequences if an organization is not considering ethical issues when developing and offering their services.

However, Schoentgen and Wilkinson [77] also note that users of digital services face the ethical dilemmas of self-responsibility and choice making and that the best way to drive awareness of ethics is education and data literacy. In particular for privacy enhancing technologies, this is not new, as for Tor11 and Jondonym12, two tools safeguarding against mass surveillance, trust in the technology has been shown to be one of the major drivers [37–39,41]. The trust in the technology was driven by online privacy literacy [40] supporting Schoentgen and Wilkinsons' theory. In accordance with Schoentgen and Wilkinson [77] is also the result of a study [35] investigating incentives and barriers for the implementation of privacy enhancing technologies from a corporate view where ethics and reputation of the company were among the named incentives. Another incentive mentioned is to charge for more privacy friendly services. This is a business model which is currently popular among German publishers who require online users to either pay a fee or agree to accept cookies for targeted advertising. However, besides the question whether users are willing to pay [36], offering privacy-friendly services only with additional charge may amplify other ethical issues. In particular, if users can opt-out from their data being used if they pay for it, this will most likely cause biased data since one will expect that only more wealthy people would afford to pay for their privacy. As sketched in the previous section any bias in the data, which might be used to train machine learning models, may cause algorithms to fail causing other problems.

Nonetheless, even if an organization wants to provide services in an ethical way, the resulting trade-offs are sometimes difficult to overcome. Examples are the storage of personal data, where it is not per se clear if data on a local device is necessarily more secure than if stored in the cloud [69]. Although it might seem logical that the users keeps as much data as possible on their devices, with manufactures not providing updates for still used devices and current malware targeting mobile users, it may be that data stored in a trustworthy environment, such as a cloud, where professionals operate and secure the systems might be more secure. Another example is the provision of open data for the benefit of the society. While it might allow the creation of new services, research or just transparency, it might on the other hand threaten the users' privacy if its possible

11 https://www.torproject.org/.

12 https://anonymous-proxy-servers.net/.

to link the data with existing datasets [70]. Thus, even well-intentioned ideas might backfire to the disadvantage of the users. For instance, questioning users directly on their preferences might be an obvious ethical solution. However, this solution requires well-educated users being able to make informed decisions and being willing to take the time and effort necessary to do so, which can not be assumed in any context [44].

## **6 Conclusion and Future Work**

This work demonstrates the importance of the consideration of ethical issues in digital service chains. As such service chains strongly rely on data and information for the creation, aggregation, distribution, transport and experience of an organizations' products and services, ethical issues are highly related to the data and information that is used in these steps. Using workshops and encounters with ethical experts as well as experts in data protection from both, a technological and a legal point of view, multiple ethical issues were identified. When analyzing these ethical issues it became apparent that ethical issues arise particularly in the steps of data creation and aggregation as well as in the last step, digital experience. While the first steps need to carefully consider how data is obtained, created and combined, the last step of a digital service chain needs to consider how the service, and its underlying use of data and information are to be valued. Following the classification of Hagendorff [33], all ethical cybersecurity principles were at least once potentially violated in the ethical issues identified in this work. The analysis of the differing ethical issues demonstrated that their exists no one-size-fits-all solution to solve these ethical issues. Various different ethical frameworks for cybersecurity as well as different approaches to overcome or mitigate the ethical issues exist. Prior research indicated that different approaches can lead to differing and conflicting solutions. In this work, we did not focus on the introduction of additional regulation to overcome ethical issues but aimed to elaborate on them in the context of digital service chains. Here, organizations create content and information by aggregating and processing personal data to offer new, digital services to users. Not surprisingly, we find that in particular users are the ones that may be harmed through the identified issues while organizations are in the position to overcome these issues.

*Limitations and Future Work.* The objective of this work has not been to develop or list all possible ethical issues with regards to cybersecurity and digital service chains. While experts were asked to state their most pressing issues, there might exist other ethical issues that were not discussed in this work. Moreover, changes and advancements in technology could lead to the rise of other, more pressing, issues, constituting a limitation of this work. Additionally, we did not discuss potential solutions to the ethical issues but rather aimed to create awareness on them in the context of digital service chains and cybersecurity. Several opportunities for future work could be identified. Further research could build up on the results of this work by developing a framework on how best to overcome the identified ethical issues. Here, it could be assessed whether the violation of different ethical principles, as outlined in the previous section, could require differing approaches to mitigate the identified ethical issues. Such frameworks should incorporate and assess economic benefits for organizations to comply with ethical principles [77]. Lastly, future work might focus on the empowerment of users in the context of digital service chains as the mere awareness and education on ethical issues could lead to a change in behavior and might help in mitigating the identified ethical issues.

**Acknowledgements.** This work was supported by H2020 Science with and for Society Programme's projects PANELFIT (grant no. 788039) and CyberSec4Europe (grant no. 830929).

## **References**

**Open Access** This chapter is licensed under the terms of the Creative Commons Attribution 4.0 International License (http://creativecommons.org/licenses/by/4.0/), which permits use, sharing, adaptation, distribution and reproduction in any medium or format, as long as you give appropriate credit to the original author(s) and the source, provide a link to the Creative Commons license and indicate if changes were made.

The images or other third party material in this chapter are included in the chapter's Creative Commons license, unless indicated otherwise in a credit line to the material. If material is not included in the chapter's Creative Commons license and your intended use is not permitted by statutory regulation or exceeds the permitted use, you will need to obtain permission directly from the copyright holder.

## **Author Index**

Al-Shargabi, Zhanin 202

Bajraktari, Agron 136

Carrega, Alessandro 1, 32 Carretero, José Ignacio 1 Coppa, Giovanni 56

Grieco, Giovanni 1 Grzonka, Daniel 56

Hopmann, Cornelio 56

Kirov, Atanas 202 Kołodziej, Joanna 56, 162 Kozhuharova, Denitsa 202 Krzyszto ´n, Mateusz 83

Landauer, Max 103, 136 Lew, Marcin 83

Lima, Tomas 1 Löbner, Sascha 222

Marks, Michał 83

Pape, Sebastian 222 Papoutsakis, Manos 1

Rannenberg, Kai 222 Repetto, Matteo 1, 32

Skopik, Florian 103, 136 Striccoli, Domenico 1 Szynkiewicz, Paweł 120

Tronnier, Frédéric 222

Widłak, Adrian 56 Wilczy ´nski, Andrzej 162 Wurzenberger, Markus 103, 136


**2. Numerical Model**

A scheme of the steam pipeline connecting the boiler with the turbine in a 120 MW unit is shown in Figure 1. Superheated steam from the last superheater stage flows into the outlet chambers, connected from both sides of the boiler with steam pipelines (2 *rin =* 245 mm, *sw* = 30 mm). Next, the pipelines are connected through a T-pipe to the main steam pipeline (2 *rin =* 324 mm, *sw* = 40 mm).

**Figure 1.** The pipeline connecting the boiler and turbine.

First, the analyzed domain consisting of steam pipeline and steam was divided into control volumes (Figure 2). In the radial direction, the pipeline is divided into *n* finite volumes, and in the longitudinal direction into *m* finite volumes. Only half of the pipeline was analyzed due to symmetry.

**Figure 2.** Division of the computational domain into finite volumes.

The equation of the transient heat transfer for the pipeline wall has the form

$$\rho\_w \ c\_{p,w} \frac{\partial T\_w}{\partial t} = \nabla \cdot [k\_w(T\_w) \ \nabla T\_w] \tag{1}$$

The heat balance Equation (1) in a cylindrical coordinate system is as follows

$$\rho\_{w}\ c\_{pw}\frac{\partial T\_{w}}{\partial t} = \frac{1}{r}\frac{\partial}{\partial r}\left[r\,k\_{w}(T\_{w})\frac{\partial T\_{w}}{\partial r}\right] + \frac{\partial}{\partial z}\left[k\_{w}(T\_{w})\frac{\partial T\_{w}}{\partial z}\right] \tag{2}$$

Heat balance equations were formed for each node, including nodes in control volumes near the boundary with the steam. For example, the equation for a node *i* for a control volume located in the wall area is

$$\begin{split} \frac{dT\_{\overline{w}\_{i}}}{dt} = \frac{a\left(T\_{w\_{i}}\right)}{k\_{\text{w}}\left(T\_{w\_{i}}\right)} \left| \frac{r\_{j}}{\Delta r} \frac{k\_{\text{w}}\left(T\_{w\_{i}}\right) + k\_{\text{w}}\left(T\_{w\_{(i-1)}}\right)}{r\_{j+1}^{2} - r\_{j}^{2}} \left(T\_{w\_{(i-1)}} - T\_{w\_{i}}\right) + \frac{r\_{j+1}}{\Delta r} \frac{k\_{\text{w}}\left(T\_{w\_{(i+1)}}\right) + k\_{\text{w}}\left(T\_{w\_{i}}\right)}{r\_{j+1}^{2} - r\_{j}^{2}} \left(T\_{w\_{(i+1)}} - T\_{w\_{j}}\right) \right. \\ & \left. + \frac{k\_{\text{w}}\left(T\_{w\_{(i+1+n+1)}}\right) + k\_{\text{w}}\left(T\_{w\_{i}}\right)}{2\left(\Delta z\right)^{2}} \left(T\_{w\_{(i+1+n)}} - T\_{w\_{i}}\right)} \right. \\ & \left. + \frac{k\_{\text{w}}\left(T\_{w\_{(i-1-n)}}\right) + k\_{\text{w}}\left(T\_{w\_{i}}\right)}{2\left(\Delta z\right)^{2}} \left(T\_{w\_{(i-1-n)}} - T\_{w\_{i}}\right) \right] \end{split} (3)$$

where:

$$
\Delta r = \frac{r\_{\text{out}} - r\_{\text{in}}}{n}, \ \Delta z = \frac{L}{m} \tag{4}
$$

The symbol *n* denotes the number of the control volumes in radial directions in the pipeline wall. Over the length of the pipeline, we have *m* finite volumes of length Δ*z*. Similarly, the heat balance equations for the steam region were formed. For example, after transformation, the heat balance equation for a node *i* takes the form

$$\frac{dT\_{f\_{i+1}}}{dt} = -\frac{\dot{m}}{\rho\_f \left(T\_{f\_i}\right)} \frac{T\_{f\_{i+1}} - T\_{f\_{i\cdot}}}{\Delta z} - \frac{h\_{\text{in}} \left(T\_{\text{cz}\_{\text{ci}}}\right) \mathcal{U}\_{\text{in}}}{A \,\rho\_f \left(T\_{f\_{i\cdot}}\right) \, c\_{p,f} \left(T\_{f\_{\cdot}}\right)} \left[\frac{T\_{f\_{i\cdot+1}} + T\_{f\_{\cdot i}}}{2} - \frac{T\_{\text{w,i}(n+1)+1} + T\_{\text{w,i}(i-1)\cdot(n+1)+1}}{2}\right] \tag{5}$$

After formulating the energy conservation equation for all control volumes, a system of ordinary differential equations was obtained. The Runge-Kutta method of the fourth order was used to solve the formed system of ordinary differential equations.

The following boundary and initial conditions were assumed

$$\left.T\_f\right|\_{t=0} = T\_0\tag{6}$$

$$T\_w|\_{t=0} = T\_{w,0} \tag{7}$$

$$\left.T\_f\right|\_{z=0} = f(t) \tag{8}$$

$$\left.k\_w \frac{\partial T\_w}{\partial r}\right|\_{r=r\_{in}} = h\_{in} \left( |T\_w|\_{r=r\_{in}} - T\_f \right) \tag{9}$$

$$\left.k\_{w}\frac{\partial T\_{w}}{\partial r}\right|\_{r=r\_{out}}=0\tag{10}$$

$$\left.k\_w \frac{\partial T\_w}{\partial z}\right|\_{z=0} = 0\tag{11}$$

$$\left.k\_w \frac{\partial T\_w}{\partial z}\right|\_{z=L} = 0\tag{12}$$

The heat transfer coefficient *hin* (Figure 2) on the inside surface of the pipeline was determined from the following formula

$$h\_{in} = \frac{\mathcal{N}u \, k\_f}{d\_{in}} \tag{13}$$

The correlation proposed by Taler [28] was used to determine Nusselt numbers *Nu* in Equation (13)

$$Nu = Nu\_{m,q} (Re = 2300) + \frac{\left(\frac{\gamma}{8}\right) (Re - 2300) Pr^{1.008}}{1.08 + 12.4 \left(\frac{\gamma}{8}\right)^{\left(\frac{1}{2}\right)} \left(Pr^{\left(\frac{2}{3}\right)} - 1\right)} \left[1 + \left(\frac{d\_{in}}{L}\right)^{\left(\frac{2}{3}\right)}\right] \tag{14}$$

$$2300 < Re < 10^6, \ 0.1 < Pr < 1000$$

The friction factor *ξ* in Equation (14) is given by the correlation of Taler [29]

$$\xi = \left[ 1.2776 \log(Re) - 0.406 \right]^{-2.246} \tag{15}$$

The symbol *Num*,*q*(*Re* = 2300) designates the Nusselt number at *Re* = 2300 for laminar flow at the tube with constant wall heat flux [30]. At the beginning of the transitional flow, i.e., the end of the laminar flow for Reynolds number *Re* = 2300, the second term on the right-hand side of Equation (14) is equal to zero.

## **3. Inverse Problems in Heating up the Pipeline Connecting the Boiler with the Turbine during the Start-Up of the Unit**

The paper presents a numerical method to determine the steam temperature as a function of time at the pipeline inlet *Tf*(*t*)|z=0m, at which the steam temperature at the pipeline outlet *Tf*(*t*)|z=45m (turbine inlet) is known *f*(*t*) from the measurement. The problem formulated in this way is an inverse transient heat transfer problem. The inverse problem is much more difficult to solve than the direct one, as random errors influence the stability and accuracy of the determination of the inlet steam temperature in the measured transient steam temperature at the outlet of the pipeline.

Random measurement errors influence the solution of IHP significantly. Therefore, the measured temperature variation was approximated by a local polynomial of third-degree with respect to time. Similarly, the accuracy of approximation of the time derivatives from measured steam temperature *f*(*t*) is essential. In numerical methods, for example, moving digital filters or so-called future steps are used to reduce the impact of random measurement errors.

The following assumptions were adopted in the solution of the IHP:

Random measurement errors from the measured temperature and its first-order derivative after time are partly eliminated using moving digital filters. The Beck future time steps [24] in solving IHCP are also applied. Future steps are an effective tool for increasing the stability of solutions to inverse problems and making it possible to determine the time variation of the inlet temperature with a smaller time step. Beck's concept of future time steps is extended in this paper. The time step of the solution of the inverse problem is several to a dozen times larger than the integration step of the system of differential equations present in the solution of the direct problem. The single time step in solving the

inverse problem is divided into smaller time steps, the size of which is derived from the stability condition for the solution of the direct problem.

The temperature *Tf*(*t*)|*z*=0 = *Tf*1 of the steam at the pipeline inlet was determined sequentially (Figure 2).

The steam inlet temperature in the time interval *t*M−1 < *t* < *tM+F* (Figure 3) was determined using the least squares method with *F* future time steps. The following squared differences between the calculated steam temperature *Tcalc f* ,*m*+1(*t*), and measured fluid temperatures *Tmeas f* ,*m*+1(*t*) over the time interval [*t*M−1, *t*M+F] must be minimum (Figure 3).

$$S\left[T\_{f,1}^{\text{calc}}(t\_M)\right] = \int\_{t\_{M-1}}^{t\_{M+F}} \left[T\_{f,m+1}^{\text{calc}}(t) - T\_{f,m+1}^{\text{meas}}(t)\right]^2 dt + w\_r \left(\frac{dT\_{f,1}^{\text{calc}}(t)}{dt}\bigg|\_{t=t\_M}\right)^2 \to \min \tag{16}$$

**Figure 3.** Approximation the time changes of the steam at the pipe inlet by stepwise curve (**a**), and the concept of future time steps, *F* =3(**b**).

The second term on the right-hand side of Equation (16) represents the regularization term of order one in the Tikhonov regularization [31] that is used in this paper. The inlet steam temperature *Tf,*1(*tM*) was determined with a basic time step equal to Δ*tb* = *tM* − *tM*−1 = *kb* Δ*t*. The time step Δ*tb* is a multiple of the step Δ*t* used to solve a direct heat transfer problem using the finite volume method. At the time *t* = *tM*−1, the steam inlet temperature *Tf,*1(*tM*−1) is known, while the steam temperature *Tf,1*(*tM*) at the time *t* = *tM* was sought. The time step Δ*tb* should be chosen so that the change of steam temperature at the pipeline inlet *Tf,*1 at time *t* = *tM*−1 caused the change of steam temperature at the pipeline outlet at time *t* = *tM*.

Step Δ*tb* is *kb* times larger than step Δ*t* used in determining the temperatures of the pipeline wall and steam from Equations (3) and (5), respectively. The step Δ*t* must not be too large for the solution of the system of Equations (3)–(5) to be stable.

To ensure the stability of determining the wall and steam temperature should be the Fourier stability condition for the wall, and the Courant-Friedrichs-Lewy condition for the steam should be satisfied. The allowable time step Δ*t* results from the Courant-Friedrichs-Lewy condition [32].

$$\frac{w\_{z,i}\Delta t}{\Delta z} \le 1, \qquad i = 1, \ldots, m+1 \tag{17}$$

The steam velocity *wi* at the *i*-th finite volume inlet is calculated from the following equation

$$w\_{z,i} = \frac{4\,\dot{m}}{\pi\,\rho\_i\,d\_{in}^2} \tag{18}$$

where the symbol *ρi* denotes the steam density at the *i*-th finite volume inlet.

The steam temperature at the pipeline inlet was determined with the time step Δ*tb* based on the preset steam temperature at the pipeline outlet. The steam temperature at the pipeline outlet was calculated with a time step Δ*t* using the mathematical model of the pipeline developed. The steam temperature *Tcalc f* ,1 (*tM*) was determined by minimizing the sum given by Equation (16).

$$\mathbb{E}\left[T\_{f,1}^{\text{calc}}(t\_M)\right] = \sum\_{i=1}^{k\_b(F+1)} \left[T\_{f,m+1}^{\text{calc}}(t\_i) - T\_{f,m+1}^{\text{mas}}(t\_i)\right]^2 + w\_r \left(\frac{T\_{f,1}^{\text{calc}}(t\_{M+F}) - T\_{f,1}^{\text{calc}}(t\_{M-1})}{t\_{M+F} - t\_{M-1}}\right)^2 \tag{19}$$

where *wr* = *w*, *r*/Δ*t*, *ti* = *tM*−1 + *i*Δ*t*, *i* = 1, . . . , *k*2(*F* + 1).

Equation (19) is the discrete form of Equation (16), calculating the integral in Equation (16) using the rectangular method.

For too small values of basic step Δ*tb*, it is not possible to determine the steam temperature variation *Tf*,1 at the pipeline inlet in the time interval *tM*−1 ≤ *t* ≤ *tM* based on the set or measured outlet steam temperature variation *Tmeas f* ,*m*+1(*t*). For a too small time step Δ*tb*, there are instabilities in the determined pipeline inlet temperature.

For the solution stabilization, the future time interval *tM* ≤ *t* ≤ *tM+F* is used (Figure 3). By increasing the analyzed time interval from *tM*−1 ≤ *t* ≤ *tM* to *tM*−1 ≤ *t* ≤ *tM+F,* there is a measurable change in steam temperature at the pipeline outlet *Tcalc f* ,*m*+1(*t*) as a result of the temperature change of the fluid at the pipeline inlet *Tcalc f* ,1 (*t*) during the time *tM*−1. After determination of the fluid temperature *Tf,*1(*tM+F*) at time point *tM+F*, it is assumed that this temperature value occurs only in the basic range *tM*−*1* ≤ *t* ≤ *tM.* The analysis at the next time step [*tM*, *tM*+1] is repeated with the time *tM* as the starting point and not the time point *tM+F*.

The temperature of the fluid *Tf*,1 (*tM*) was determined by the golden-section search method [32], for which the sum *S Tcalc f* ,1 (*tM*) given by Equation (19) attained minimum.

Figure 4 shows the block diagram of the program for the sequential steam temperature at the pipeline inlet *Tf,*1 using the golden section method.

First, the limits of the interval [*Tf,MIN*, *Tf,MAX*] are set in which the desired inlet temperature *Tcalc f* ,1 (*t*) lies, for which the S sum is defined by Equation (19) reaches a minimum. The steam temperature calculated values at points *XL* and *XR* takes values in the range *Tf,MIN* < *XL* < *XR* < *Tf,MAX*. The value of the factor *k* is *k* ≈ 0.61803398. The golden division factor *k* is a constant factor that reduces the interval value at each iteration until the condition (*Tf,MAX* − *Tf,MIN*) ≤ is satisfied.

**Figure 4.** Block diagram of a program to determine the fluid temperature using the golden division method.


*4.6. Statistical Analyses*

Statistical calculations of F- and *t*-Tests were performed in Microsoft Excel 2007 program.

**Author Contributions:** Conceptualization, J.K. (József Kukolya); methodology, J.K. (Judit Kosztik), I.B.-V., M.M. and A.S.; validation, M.M.; investigation, J.K. (Judit Kosztik) and I.B.-V.; writing—Original draft preparation, J.K. (Judit Kosztik), I.B.-V. and M.M.; writing—Review and editing, I.B.-V. and A.S.; supervision, J.K. (József Kukolya) and A.S.; funding acquisition, J.K. (József Kukolya). All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the National Research, Development and Innovation Office in Hungary, projects NVKP-16-1-2016-0009, NVKP-16-1-2016-0049 and OTKA K116631.

**Conflicts of Interest:** The authors declare no conflict of interest.


**4. Base excision repair of oxidized bases**

 Base excision repair (BER) is responsible for repairing most oxidized base lesions, AP sites, and DNA SSBs. The basic mechanism of BER first elucidated in *Escherichia coli* is broadly conserved across all organisms, as highlighted in several reviews [43–46]. BER requiring only four or five enzymes in the basic reaction steps is initiated with excision of the damaged base by a monofunctional DNA glycosylase (DG), for example, uracil-DNA glycosylase (UDG) or 3-methyladenine-DNA glycosylase, generating an abasic apurinic/apyrimidinic (AP) site due to hydrolysis of the N-glycosidic bond of the damaged base. The AP endonuclease (APE1 in mammalian cells) cleaves the resulting AP site in the second step and generates 3′ OH and 5′ deoxyribose phosphate (dRP) termini. The DNA polymerase in the third step fills in the single nucleotide gap. In mammalian cells, DNA polymerase β (Pol β) also has intrinsic dRP lyase activity, which cleaves the dRP residue and generates 5′ phosphate; the resulting nick after incorporation of the correct base is sealed by DNA ligase III (Lig III) complexed with XRCC1 in the final step.

 The BER initiating DGs for oxidized bases, on the other hand, are bifunctional with intrinsic AP lyase activity. The bifunctional oxidized base-specific DGs further process the AP site via β or βδ lyase reaction. The Nth family of DGs, OGG1, and NTH1, via β eliminations generates 3′ phospho α,β-unsaturated aldehyde (3′ PUA; formally named 3′ phospho 4-hydroxylpentenal) and 5′ phosphate at the strand break. NTH1 prefers oxidized pyrimidines as substrates, and 8-oxoG and ring opened guanine, that is, formamidopyrimidine (Fapy-G), are preferred substrates for OGG1. The Fpg/Nei family DGs NEIL1, NEIL2, NEIL3, discovered by us and others [47–51] catalyze βδ elimination and remove the deoxyribose residue to produce a 3′ phosphate and 5′ phosphate at the strand break. NEILs prefer modified pyrimidine substrates, NEIL1 having preference for ring-opened purines, for example, Fapy-A and Fapy-G. The activity and substrate specificity of NEILs depend on the DNA structure, and NEILs have significant 5-OHU excision activity with single-stranded or bubble, forked DNA. In contrast, OGG1 and NTH1 prefer double-stranded DNA substrates. Usually, the base excision and lyase reactions act in a concerted sequence. However, due to weak lyase activity of OGG1, intact AP sites are the major product after OGG1 catalyzed cleavage of 8-oxoG [52, 53]. All these bifunctional DGs have broad and overlapping substrate range and possess backup activity for many base lesions. This accounts for the fact that only few DGs have been discovered so far for much larger number of oxidized bases and for the nonessentiality of individual DGs.

The 3′ phosphate generated by the NEILs by βδ elimination is a poor substrate for mammalian APE1 and is processed by polynucleotide kinase phosphatase (PNKP) [54–57]. Thus, for oxidized bases, the DGs actually define the subsequent steps. APE1 is responsible for processing the β elimination product of OGG1 and NTH1, whereas PNKP is required for generating 3′-OH termini from 3′ phosphate, a βδ elimination product of NEILs. Furthermore, AP sites and 3′ PUA generated by other DNA glycosylases can also be processed through a NEIL-PNKP-dependent pathway [53, 57]. This alternative repair route provides the functional redundancy in mammalian BER for genome safeguarding against a plethora of endogenous and induced oxidative damages.

*Regulation of Oxidized Base Repair in Human Chromatin by Posttranslational Modification DOI: http://dx.doi.org/10.5772/intechopen.81979*

BER, in the simplistic model, generates a 1-nucleotide gap after excision of the damaged base and has been termed single nucleotide BER (SN-BER) or short-patch BER (SP-BER). In contrast, long-patch BER (LP-BER) involves repair synthesis of two to eight deoxynucleotides. The 5′ blocking group after oxidation of AP sites cannot be removed by Pol β via its dRP lyase activity. Instead it is removed by 5′-flap endonuclease 1 (FEN-1), which is normally required for removing the 5′ RNA primers from Okazaki fragments during DNA replication. Thus, the subsequent steps of LP-BER are identical to that of DNA replication, utilizing DNA replication machinery, involving DNA polymerases δ/ε (Pol δ/ε) and DNA ligase I (Lig I). These enzymes including FEN-1 are recruited by the sliding clamp PCNA, loaded by replication factor-C (RFC), as in replication [58]. Thus, the choice of LP-BER vs. SN-BER depends on the 5′-terminus at the base cleavage site. With unaltered aldehyde group in deoxyribose, Pol β could carry out SN-BER by excising the 5′-dRP. LP-BER becomes necessary for repairing the oxidized AP sites, which cannot be processed by the 5′ end cleaning lyase activity of Pol β. The nuclear replicative Pol δ/ε lack dRP lyase activity and thus repair synthesis by these enzymes have to follow the LP-BER subpathway. Because Pol β-depleted cells are resistant to oxidative stress, Pol δ/ε can substitute for DNA Pol β and carry out the preferred LP-BER. The BER subpathways are schematically shown in **Figure 1**, adapted from [44].

#### **Figure 1.**

*A schematic representation of oxidized base-specific BER subpathways. The damaged base is represented as . BER is initiated by the DGs: OGG1, NTH1, NEILs, and converge to common steps for end cleaning, followed by repair synthesis and ligation. See text for details.*


**1. Introduction**

In marine settings, the waves trapped near the fluid–solid interface are called Scholte waves [1]. Scholte waves are a kind of interface wave, and they are expected to have a longer traveling path or less transmission loss than waves in water [2,3]. These characteristics make Scholte waves a great prospect in underwater detection and communication, leading to research interests in ocean acoustics. Currently, Scholte waves have been applied in many areas, such as geoacoustic inversion [4–8], acoustic source localization [9,10], and imaging [11]. Therefore, how to detect Scholte waves effectively has significant meaning in ocean detection.

In existing methods, the seismic field is often applied to detect Scholte waves. Seismic measurement equipment such as Ocean Bottom Seismometers (OBSs) [12–14] and geophones [6,9] is used to record the seismic signal at the ocean bottom. Then, the velocity characteristics in the fields can be extracted from the recorded signal [2,8]. Finally, Scholte waves are identified according to the velocity characteristics.

In the measurement process, the seismic measurement equipment mainly records the seismic wave signal on the seafloor through the coupling between the equipment and seafloor, so their measurements are susceptible to the bottom type and seafloor topography [15,16]. Compared with seismic equipment, the hydrophone is immune to these issues, making it more suitable for measuring underwater acoustic signals.

In the stage of signal processing, the velocity characteristics of Scholte waves are used to identify Scholte waves as mentioned above. The velocities of Scholte waves have two features. One is slower speed, and their speeds are slower than other waves in the acoustic field. A later arriving signal with high energy can be observed in the time

**Citation:** Liang, M.; Wang, L.; Yu, G.; Ren, Y.; Peng, L. Study on a Detection Technique for Scholte Waves at the Seafloor. *Sensors* **2022**, *22*, 5344. https://doi.org/10.3390/s22145344

Academic Editors: Yuxing Li and Luca Fredianelli

Received: 13 June 2022 Accepted: 16 July 2022 Published: 18 July 2022

**Publisher's Note:** MDPI stays neutral with regard to jurisdictional claims in published maps and institutional affiliations.

**Copyright:** © 2022 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

海水

海冰

沉积层

基底层

玄武岩层

series [9,12,17,18]. Another is that the velocities of Scholte waves are dispersive in the waveguide [12]. Investigators have proposed many practical techniques to extract the dispersion curve of Scholte waves, the most extensive of which is the approach based on domain transformation [5,7,8,19–21]. The extracted dispersion curve can be used to identify Scholte waves at the seafloor. These velocity-based approaches can identify Scholte waves effectively when the Scholte wave velocity differs largely from the acoustic waves in water. However, they will be invalid when the velocities are close to each other.

This paper introduces a new technique to detect Scholte waves based on acoustic pressure field measurement and researches the detection of Scholte waves theoretically and experimentally. Acoustic models with the multilayer elastic bottom are established. The Scholte wave is identified according to the excitation and propagation characteristics. Scaled-down tank experiments for detecting Scholte waves are designed and carried out. Firstly, the detection principle is introduced. The characteristics of Scholte waves that vary with the source depth and propagation distance are researched. Then, the scaleddown experiment is introduced in detail. Acoustic field features in the water tank are analyzed. Measurement and analysis results of this experiment are presented. Furthermore, the research in conditions with sediment layers is introduced. Finally, the summary and conclusions are presented.

#### **2. Detection Principle**

#### *2.1. Acoustic Model*

Ocean acoustic studies generally assume the ocean bottom as the fluid bottom [22], but this assumption is not suitable for the research in this paper. The elastic bottom plays an indispensable role in the excitation and propagation of Scholte waves, so the ocean bottom is an elastic medium with shear waves in this research. The marine environment is simplified into a three-layer model consisting of seawater, basalt, and peridotite, as shown in Figure 1, based on Hamilton's studies [23] and the results of geological surveys [24].

**Figure 1.** Schematic of the acoustic model for the marine environment. *H*1 and *H*2 are the interface depth for "water–basalt" and "basalt–peridotite".

The typical deep ocean environment is researched in this study. Referring to related research [25], the media parameters in the model are shown in Table 1. The KRAKENC program is used to analyze the acoustic field in this model. The phase velocities of the Scholte wave and normal modes at 10 Hz are calculated and listed in Table 2. The Scholte wave in the acoustic field is treated as the zeroth mode. These data show that the velocities of the Scholte wave and normal modes are extremely close, which means that the Scholte wave in the field cannot be identified using velocity features.

A new technique is proposed to detect Scholte waves according to the excitation and propagation characteristics.

**Table 1.** Parameters of the marine environment.

#### *2.2. Elastic Normal Modes*

In order to interpret the detection principle, an elastic normal mode solution is presented for this range-independent model with the elastic bottom. Assume that all layers in the marine environment are isotropic media. Subscripts *i* = 1, 2, 3 are used to represent acoustic parameters in seawater, basalt, and peridotite, respectively. *ρi* is density; *c*1 is seawater sound velocity; *cp*2 and *cp*3 are compressional wave velocities (P wave); *cs*2 and *cs*3 are shear wave velocities (S wave). *H*1 and *H*2 represent the interface depths for "water–basalt" and "basalt–peridotite".

The acoustic field is expressed by velocity potentials. *φ*1 is the potential in water; *φi* and Ψ*i*~*ey* (*i* = 2, 3) are compressional and shear potentials in the elastic bottom, respectively. Potentials in the water and bottom satisfy the wave equations [26].

$$\frac{\partial^2 \phi\_1}{\partial z^2} + \frac{\partial^2 \phi\_1}{\partial x^2} = \frac{1}{c\_1^2} \frac{\partial^2 \phi\_1}{\partial t^2} \tag{1}$$

$$\frac{\partial^2 \phi\_i}{\partial z^2} + \frac{\partial^2 \phi\_i}{\partial x^2} = \frac{1}{c\_{pi}^2} \frac{\partial^2 \phi\_i}{\partial t^2} \tag{2}$$

$$\frac{\partial^2 \psi\_i}{\partial z^2} + \frac{\partial^2 \psi\_i}{\partial x^2} = \frac{1}{c\_{si}^2} \frac{\partial^2 \psi\_i}{\partial t^2} \tag{3}$$

A time–harmonic plane wave with the time dependence *e* −*iωt* is considered. Subsequently, all potentials can be represented as follows:

$$\phi\_i = Z\_i(z)e^{i(k\mathbf{x}-\omega t)}\tag{4}$$

$$
\psi\_i = Z\_{\rm si}(z)e^{i(\mathbf{k}\cdot\mathbf{x}-\omega t)}\tag{5}
$$

where *ω*=2*π f* is the angular frequency and *k* = *ω*/*c* is the wavenumber. *Zi*(*z*) and *Zsi*(*z*) are depth-dependent functions for compressional and shear waves; they satisfy the homogeneous depth-separated wave equations.

$$
\left[\frac{d^2}{dz^2} + (k\_i^2 - k^2)\right] Z\_i(z) = 0\tag{6}
$$

$$
\left[\frac{d^2}{dz^2} + (k\_{si}^2 - k^2)\right] Z\_{si}(z) = 0\tag{7}
$$

The general solutions for potentials can be obtained by the free surface condition and radiation condition at infinity.

$$\phi\_1 = -2A \sinh(k\xi\_1 z) e^{i(kx - \omega t)}\tag{8}$$

$$\Phi\_2 = \left( B\_1 e^{-k\sharp\_2 z} + B\_2 e^{k\sharp\_2 z} \right) e^{i(k\mathbf{x} - \omega t)} \tag{9}$$

$$\psi\_2 = \left(\mathbb{C}\_1 e^{-k\_{\rm s2}^\ddagger z} + \mathbb{C}\_2 e^{k\_{\rm s2}^\ddagger z}\right) e^{i(k\boldsymbol{x} - \omega t)}\tag{10}$$

$$\phi\_3 = \left(De^{-k\xi\_3 z}\right)e^{i(kx-\omega t)}\tag{11}$$

$$\psi\_3 = \left(\mathbb{E}e^{-k\mathbb{E}\_{s3}z}\right)e^{i(k\mathbb{x}-\omega t)}\tag{12}$$

where *k* 2 *i* − *k* 2 ≡ −*k* 2 *ξ* 2 *i* , *k* 2 *si* − *k* 2 ≡ −*k* 2 *ξ* 2 *si*, and *k*1 = *ω*/*c*1 is the wavenumber in water, *ki* = *ω cpi*, *i* = 2, 3 are compressional wavenumbers, and *ksi* = *ω*/*csi*, *i* = 2, 3 are shear wavenumbers in the elastic seabed. *A*, *B*1, *B*2, *C*1, *C*2, *D*, and *E* are undetermined coefficients for potentials.

Here, it is assumed that **u** is the displacement and **T** is the stress tensor. *uix* is the horizontal displacement; *uiz* is the vertical displacement; *Tizz* is the normal stress; *Tizx* is the shear stress.

The displacements are determined from the velocity potentials as follows:

$$u\_{\rm ix} = \frac{1}{-i\omega} \left( \frac{\partial \phi\_i}{\partial \mathbf{x}} - \frac{\partial \psi\_i}{\partial z} \right), \\ u\_{\rm iz} = \frac{1}{-i\omega} \left( \frac{\partial \phi\_i}{\partial z} + \frac{\partial \psi\_i}{\partial \mathbf{x}} \right) \tag{13}$$

According to the stress–strain constitutive relation, the following equations can be obtained:

$$T\_{\rm izz} = \rho\_i c\_{pi}^2 \left(\frac{\partial u\_{\rm ix}}{\partial \mathbf{x}} + \frac{\partial u\_{\rm iz}}{\partial z}\right) - 2\rho\_i c\_{\rm si}^2 \frac{\partial u\_{\rm ix}}{\partial \mathbf{x}},\\ T\_{\rm izx} = \rho\_i c\_{\rm si}^2 \left(\frac{\partial u\_{\rm ix}}{\partial \mathbf{z}} + \frac{\partial u\_{\rm iz}}{\partial \mathbf{x}}\right) \tag{14}$$

The boundary condition at the interface between water and basalt (*z* = *H*1) is satisfied by the continuity of the normal displacement, normal stress, and zero tangential stress.

$$T\_{1zz} = T\_{2zz}, \mu\_{1z} = \mu\_{2z}, T\_{2xz} = 0 \tag{15}$$

The boundary condition at the interface between basalt and peridotite (*z* = *H*2) is satisfied by the continuity of displacements and stresses.

$$
\mu\_{2\mathbf{2}} = \mu\_{3\mathbf{2}\prime} \mu\_{2\mathbf{x}} = \mu\_{3\mathbf{x}\prime} T\_{2\mathbf{2}\mathbf{2}} = T\_{3\mathbf{2}\prime} T\_{2\mathbf{x}\mathbf{2}} = T\_{3\mathbf{x}\mathbf{2}} \tag{16}
$$

Substituting the general solutions for potentials into the boundary conditions, Equations (15) and (16), a system of homogeneous linear equations can be obtained. Here, we present the equations in matrix form:

$$
\begin{bmatrix}
a\_{11} & a\_{12} & a\_{13} & a\_{14} & a\_{15} & 0 & 0 \\
a\_{21} & a\_{22} & a\_{23} & a\_{24} & a\_{25} & 0 & 0 \\
0 & a\_{32} & a\_{33} & a\_{34} & a\_{35} & 0 & 0 \\
0 & a\_{42} & a\_{43} & a\_{44} & a\_{45} & a\_{46} & a\_{47} \\
0 & a\_{52} & a\_{53} & a\_{54} & a\_{55} & a\_{56} & a\_{57} \\
0 & a\_{62} & a\_{63} & a\_{64} & a\_{65} & a\_{66} & a\_{67} \\
0 & a\_{72} & a\_{73} & a\_{74} & a\_{75} & a\_{76} & a\_{77} \\
\end{bmatrix}
\begin{bmatrix}
A \\
B\_1 \\
B\_2 \\
C\_1 \\
D\_2 \\
D\_3 \\
E \\
\end{bmatrix} = \begin{bmatrix}
0 \\
0 \\
0 \\
0 \\
0 \\
0 \\
0 \\
0 \\
\end{bmatrix} \tag{17}
$$

Elements in the first matrix represent the known coefficients in the equations of the boundary conditions. They are a function of *k* such that Equation (17) can be simplified as follows:

$$\mathbf{G}(k)\mathbf{M} = \mathbf{O} \tag{18}$$

O represents the zero vector. **M** represents the undetermined coefficients for the potentials, and the values cannot be zero. The non-zero solutions for the coefficients in vector **M** exist only when the determinant of matrix **G**(*k*) (det|**G**(*k*)| = 0) is zero. The values of *k* that make det|**G**(*k*)| = 0 are therefore the eigenvalues for this question. Once an eigenvalue has been found, the undetermined coefficients in **M** can be calculated by solving the linear equations. There have been many methods for figuring out the values of *k*, such as the Newton method [27] and bisection [25]. In order to accurately determine the wavenumber *k*, the KRAKENC program [28] is used. Substituting the wavenumber *k* into Equation (17), the undetermined coefficients in **M** can be calculated by solving the equations. Then, the normal stresses, *Tzz*, can be computed by the relations between the stresses and potentials. Acoustic pressure in water is just the negative of *Tzz*, according to the definition of acoustic pressure, *p* = 1 *iω ρ*1*cp*1 2∇2*φ*1. Eventually, the pressure mode shape function for each *k* can be calculated. This processing of obtaining the pressure mode shape function will be used in subsequent analysis.

The pressure field for a single point source can be represented as a sum of the normal modes. The Scholte wave is treated as the zeroth mode. Then, the pressure can be written as [25]:

$$p(r,z) = \sum\_{m=0}^{\infty} \Phi\_m(r) \Psi\_m(z) \tag{19}$$

where Ψ*m*(*z*) is a mode shape function, Φ*m*(*r*) is a mode coefficient, and the subscript *m* is the order of modes. Here, the variable Φ0(*r*) is used to represent the amplitude of the Scholte wave in the acoustic field. We assume that there is no continuous spectrum so that the modes form a complete set. The coefficient Φ*m*(*r*) can be calculated by applying the operator (Equation (20)) to Equation (19), where *ρ*(*z*) is the media density.

$$\int\_0^\infty (\cdot) \frac{\Psi\_m(z)}{\rho(z)} dz \tag{20}$$

Mode analyses based on the elastic normal mode solution are applied to the acoustic field in the model. The pressure mode shape functions Ψ*m*(*z*) at a source frequency of f = 10 Hz are illustrated in Figure 2, and the normal stress *Tzz* is shown in the basalt and peridotite layer. The mode shape of the Scholte wave shows that a part of the energy is distributed in the pressure field in seawater. It can be indicated that the excitation intensity of Scholte waves Φ0(*r*) can be affected by source depths and propagation distances. (a) (b) (c)

**Figure 2.** Pressure mode shape functions for a 10 Hz source in the ocean acoustic model. (**a**) Scholte wave. (**b**) Mode 1. (**c**) Mode 2.

The finite element method software, COMSOL Multiphysics [29], was applied to simulate the acoustic field in the constructed model. The environmental parameters in the simulation were kept in line with Table 1. The source frequency was f = 10 Hz, and a vertical receiving array was set to obtain pressure in water and normal stress in the bottoms at intervals of 10 m from 0 m to 3500 m. It should be noted that the upper limit of the integral in Equation (20) is infinity, but in this model, it can be found from the mode shape function in Figure 2 that the model distribution at a depth of 3500 m is approximate zero, so it is reasonable to truncate at this depth. Then, the Scholte wave amplitude in the pressure field can be obtained using the mode decomposition method. Figure 3a shows Scholte wave amplitudes, with different source depths, at a 3000-m horizontal distance from the source. The result indicates that the excitation intensity of the Scholte wave increases gradually with the increase of the source depth. It could also explain that Scholte waves can be excited efficiently when the source is at the seafloor or very close to the seafloor. Moreover, Scholte wave amplitudes can vary with range during propagation. When the source depth is SD = 2950 m, the Scholte amplitudes drop monotonically with propagation distance increasing, as shown in Figure 3b. (a) (b)

**Figure 3.** (**a**) Scholte wave amplitude versus source depth. The red dotted line is the mode shape for the Scholte wave and the black line is the simulation result. (**b**) Scholte wave amplitude versus propagation distance.

The simulation results indicate that the amplitude of the Scholte wave increases with an increase in source depths and decreases with an increase in propagation distance. These two characteristics can be applied to detect the Scholte wave, and a water tank experiment is designed according to this principle.

#### **3. Tank Experiment**

## *3.1. Acoustic Field Analysis for the Laboratory Environment*

The acoustic model in Figure 1 is scaled down to laboratory size based on the similarity principle. Due to the close velocities, basalt and peridotite in the seabed are modeled by brass and iron slabs. Table 3 lists a scale model presented at a scale of 1:5000, where the depth and frequency were appropriately modified. Here, the source frequency is 50 kHz, and the water depth is 0.6 m.

**Table 3.** Parameters of the marine environment.

Figure 4 shows the pressure mode shape functions Ψ*m*(*r*) of the Scholte wave and normal modes for a 50 kHz source in the water tank. It is illustrated that the mode shape functions in two environments agree well by comparing the results in Figures 2 and 4. Moreover, it reveals that the parameters for the scale model experiment are correct. (a) (b) (c)

**Figure 4.** Pressure mode functions for a 50kHz source; the red circles represent the receiver positions. (**a**) Scholte wave. (**b**) Mode 1. (**c**) Mode 2.

#### *3.2. Experiment Settings*

The scale model experiment was performed in a water tank (L:3 m × W:2 m × H:1 m). Reflection waves from the tank's walls are absorbed by absorbing wedges. The tank is filled with water to a height of 0.6 m, where the sound speed in water is measured to be 1485 m/s. A spherical transducer, the source level of which is measured at approximately 140 dB (re 1 µPa.m/V) in the frequency range used in the experiment, is used as the source. An RHCA-7 hydrophone is used as the receiver. The sensitivities of the hydrophones are about −210 dB (re 1 V/µPa) in the frequency range from 20 to 100 kHz. The vertical array, including 119 receiver elements, is obtained by the synthetic aperture method. The source and receiver hydrophones are positioned in water using a robotic apparatus (with an accuracy of 0.01 mm), which allows for accurate positioning. The hydrophone measures depth from 0.5 to 59.5 cm; the depth interval is 0.5 cm. Figure 4 shows the receiver positions in the form of red circles, and the configuration of the experiment equipment is demonstrated in Figure 5.

#### *3.3. Experimental Data Analysis*

The source radiates ten cycles of sine waves at a frequency of f = 50 kHz in 1 s time intervals. Figure 6a shows the output signal of a power amplifier, which is used as a reference signal for a measurement with a vertical array of 119 hydrophones. Figure 6b demonstrates the received waveform of a hydrophone, which represents the temporal correlation between the received sound pressure by the hydrophone and the reference signal. By the temporal correlation, the virtual receiving array plays the same role as real receiving arrays of the same length. Therefore, the received waveforms by the virtual array are used to obtain the complex sound pressure required for identifying Scholte waves. To eliminate the effects of reverberation in the water tank, received waveforms are truncated by assuming that the pulse duration of the received signal from the source is almost the same for each hydrophone, and the truncated waveform is shown in Figure 6c.

**Figure 5.** Diagram of the experimental system setup.

**Figure 6.** (**a**) Reference signal for a vertical array measurement; (**b**) the received waveform of a hydrophone with a source depth of SD = 55 cm (temporal correlation between the received sound pressure and the reference signal); (**c**) the truncated waveform adopted for identifying Scholte waves.

By the fast Fourier transform of truncated waveforms, we obtain the sound pressure *p*(*r*, *z*) for the hydrophone at different positions. The sound pressure at 50 kHz is extracted to compose the matrix **P**, including 119 elements in the depth direction. The mode shape functions in the water tank are demonstrated in Figure 4. Finally, the amplitudes of the Scholte waves can be extracted using the mode decomposition.

Ten positions were set evenly for the source in the depth range from 0.1 to 0.55 m; the hydrophone array was at a horizontal distance of 0.6 m away from the source. The result in Figure 7a shows the normalized amplitudes of the Scholte wave and normal modes for a source depth of SD = 55 cm. It was demonstrated that the Scholte wave has the largest amplitude for this source depth, and the amplitudes of each mode in the experiment are in excellent agreement with the simulation result. This result shows that the water tank experiment is feasible and the processing for experimental data is correct. The Scholte wave amplitudes in the experiment field that vary with source depth are presented in Figure 7b. The results reveal that the tendency of the excitation amplitudes of Scholte waves in the experiment is consistent with the theoretical calculations. (a) (b)

**Figure 7.** (**a**) Normalized amplitudes of the Scholte wave (0th order) and normal modes for the source depth, SD = 55 cm. The blue circle is the result of the water tank experiment and the red line is the simulation result. (**b**) Normalized amplitudes of the Scholte wave versus source depth. The blue cross is the result of the water tank experiment; the black line is the simulation result; the red dotted line is the mode shape function of the 0th mode.

Seven positions were set evenly for the receiving array within the horizontal range of 0.15–1.05 m away from the source, and the source was maintained at a depth of SD = 0.55 m. Scholte wave amplitudes in the experiment field that vary with the propagation distance are presented in Figure 8. It is shown that the Scholte wave amplitudes decrease as the propagation distance increases. The tendency of Scholte wave amplitudes in the experiment agrees well with the simulation result. These results show that the detection technique proposed in this paper can detect the Scholte wave at the seafloor successfully.

**Figure 8.** Scholte wave amplitude versus propagation distance. The blue cross is the result of the water tank experiment, and the black line is the simulation result.

## **4. Sediment Effect**

#### *4.1. Experiment Setting*

In practice, there are many types of seafloor sediments. Here, silt was used as the sediment in the water tank experiment. Two cases, a sediment layer thickness of 1 mm and 12 mm, were considered in this research. The corresponding thickness is 5 m and 60 m in the ocean environment according to the scale of 1:5000. The sediment layer in the experiment was very thin, so it was treated as a liquid sediment layer. The model parameters for a 1 mm thickness are listed in Table 4. Here, the thickness of silt is 1 mm. The sediment layer in the water tank experiment and the result of thickness measurement are shown in Figure 9.

**Table 4.** Media parameters and sizes in the tank experiment.

**Figure 9.** (**a**) The silt sediment layer in the water tank experiment. (**b**) Silt sediment with a thickness of 1 mm.

The study above proved that the detection technique is valid for detecting Scholte waves at the seafloor. The same technique was applied to the condition with a sediment layer. Mode shapes in this acoustic model can be obtained by the elastic mode solution presented in the theoretical analysis. The process of experimental measurement is similar to the previous experiment, so no more details are provided here.

#### *4.2. Experimental Results*

Figure 10 shows the dependence of the mode amplitude for the Scholte wave and normal modes in the sound field on source depth. The hydrophone array was at a horizontal distance of 0.6 m away from the source. Here, the theory is the mode shape function Ψ*m*(*r*) for each mode. It can be seen from the figure that the results of the experiment and theory agree well with each other. This result indicates that treating the silt as fluid sediment is reasonable, and the Scholte wave can still be excited under this condition.

The experiment was repeated by changing the thickness of the sediment layer to 12 mm. Figure 11 shows the experimental results for the thickness of the sediment layer being 12 mm. It can be found that no Scholte wave is excited in the sound field under this condition, and the experimental results of normal waves in water are in good agreement with the theory.

**Figure 10.** Mode amplitude versus source depth for the Scholte wave and normal modes (thickness of sediment is 1 mm, source frequency f = 50 kHz). The blue cross is the experimental result, and the

**Figure 11.** Mode amplitude versus source depth for the Scholte wave and normal modes (thickness of sediment is 12mm, source frequency f = 50 kHz). The blue cross is the experimental result, and the black line is the theoretical result (mode shape function). (**a**) Mode 1. (**b**) Mode 2. (**c**) Mode 3.

The presented results show that the sediment will affect the excitation of Scholte waves. When the sediment layer is thin (the thickness of sediment is 1 mm, and the acoustic wavelength is about 3 cm), the existence of the sediment layer does not affect the excitation of Scholte waves. When the thickness of the sediment layer increases, there is no Scholte wave mode in the acoustic field. Moreover, The results also show that the Scholte wave detection method proposed in this paper is feasible in the sedimentary layer environment.

#### **5. Discussion**

Unlike existing methods, this paper proposed a new detection technique based on the acoustic pressure field measurement for identifying Scholte waves according to the excitation and propagation characteristics of Scholte waves. The experimental results show that the detection technique can detect Scholte waves at the seafloor. There are certain discrepancies in the details between the experiments and calculations in this study. Here, the error analysis was performed. Due to the amplitudes of Scholte waves being extracted by mode decomposition in this research, the orthogonality between Scholte and normal modes waves is essential for the results' accuracy. Theoretically, the upper limit of the integral in Equation (20) is infinity, but only the range of the water depth can be measured in practice, so the modes cannot be strictly orthogonal. Figure 12 shows the orthogonal coefficients of the Scholte mode (0th mode) and each mode in the water column. The Scholte mode is not strictly orthogonal to higher-order normal modes. Therefore, this could be the cause of errors in the amplitudes of Scholte waves.

**Figure 12.** Orthogonal coefficients of the Scholte mode (0th mode) and each mode in water.

The acoustic field in this experiment would change when the sediment is added to the model as one layer of the multilayer medium. The normal modes were also changed due to the existence of the sediment layer. This problem still needs further theoretical and experimental research for different seafloor environmental conditions.

#### **6. Conclusions**

This paper introduces a new detection technique for Scholte waves at the seafloor. According to the actual ocean environment, a series of laboratory experiments with the scaled model of the elastic ocean bottom were designed and performed to detect Scholte waves at the seafloor. The study shows that Scholte wave amplitudes depending on different source depths and propagation distances are in good agreement with the theoretical results. These results indicate that the Scholte wave at the seafloor was detected successfully by the technique.

Furthermore, treating the silt layer in the laboratory experiment as fluid sediment is valid in this study. Some conclusions about the effect of sediment can be reached. When the sediment layer is thin, the existence of the sediment layer does not affect the excitation of Scholte waves. When the thickness of the sediment layer increases, there is no Scholte wave mode in the acoustic field. Moreover, The results also show that the Scholte wave detection method proposed in this paper is feasible in the sedimentary layer environment.

**Author Contributions:** Conceptualization, L.P. and G.Y.; methodology, M.L., G.Y. and L.W.; software, M.L. and L.W.; validation, M.L. and L.W.; formal analysis, M.L. and G.Y.; investigation, M.L., L.W. and L.P.; resources, G.Y., Y.R. and L.W.; writing—original draft preparation, M.L. and L.W.; writingreview and editing, L.P., M.L. and L.W.; visualization, M.L., L.W. and Y.R.; supervision, L.P., G.Y. and Y.R.; project administration, L.P. and G.Y.; funding acquisition, L.P. and L.W. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by the Nation Natural Science Foundation of China (Grant No. 11474258) and the State Key Laboratory of Acoustics, Chinese Academy of Sciences (Grant No. SKLA202206).

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Conflicts of Interest:** The authors declare no conflict of interest.

#### **References**

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**2.2 Extrusion**

 Bioink is extruded from a micro-nozzle to build a three-dimensional structure in layer-by-layer fashion. Extrusion-based printing is either mechanically (piston or screw) or pneumatically driven [9]. This fabrication technique uses highly viscous bioink and does not require any chemical additives to enable the curing of the material [9]. As inks are viscous, and therefore more concentrated, fabrication rates are relatively quick. However, the more viscous a bioink is, the higher the induced shear-stress during extrusion, which can result in a higher cell apoptotic activity. Out of the different extrusion bioprinting techniques, pneumatic systems have demonstrated the best cell viability post printing [17]. Extrusion-based techniques are promising as they create scaffolds with high structural integrity using bioinks containing high cell densities and can achieve resolutions around 200 μm [9, 18].


*Review* **Cracking the Monoubiquitin Code of Genetic Diseases**

### **Raj Nayan Sewduth 1,2,**†**, Maria Francesca Baietti 1,2,**† **and Anna A. Sablina 1,2,\***

Received: 7 April 2020; Accepted: 23 April 2020; Published: 25 April 2020

**Abstract:** Ubiquitination is a versatile and dynamic post-translational modification in which single ubiquitin molecules or polyubiquitin chains are attached to target proteins, giving rise to monoor poly-ubiquitination, respectively. The majority of research in the ubiquitin field focused on degradative polyubiquitination, whereas more recent studies uncovered the role of single ubiquitin modification in important physiological processes. Monoubiquitination can modulate the stability, subcellular localization, binding properties, and activity of the target proteins. Understanding the function of monoubiquitination in normal physiology and pathology has important therapeutic implications, as alterations in the monoubiquitin pathway are found in a broad range of genetic diseases. This review highlights a link between monoubiquitin signaling and the pathogenesis of genetic disorders.

**Keywords:** ubiquitin system; genetic diseases; ubiquitin ligase; deubiquitinases; monoubiquitin signaling; vesicular tra fficking; protein complex formation