monitoring adjustment according to the validation results building upon [107]. Furthermore, I was involved in the conception of the derivation of the architectural model based on source code and the self-validation concepts as an extension of [172]. All this I developed together with David Monschein while supervising his master thesis [183].

# **5 Architecture-based Change Impact Analysis in Cross-disciplinary Automated Production Systems**

The paper is available at the following reference.

Robert Heinrich, Sandro Koch, Kiana Rostami, Suhyun Cha, Ralf Reussner, Birgit Vogel-Heuser. Architecture-based Change Impact Analysis in Crossdisciplinary Automated Production Systems. Journal of Systems & Software, 146:167–185, Elsevier, 2018. ISSN: 0164-1212. DOI: https://doi.org/10. 1016/j.jss.2018.08.058.

# **6 Architecture-based Change Impact Analysis in Information Systems and Business Processes**

The paper is available at the following reference.

Kiana Rostami, Robert Heinrich, Axel Busch, Ralf Reussner. Architecturebased Change Impact Analysis in Information Systems and Business Processes. IEEE International Conference on Software Architecture, pages 179–188, IEEE, 2017. DOI: https://doi.org/10.1109/ICSA.2017.17.

# **7 Data-driven Software Architecture for Analyzing Confidentiality**

The paper is available at the following reference.

Stephan Seifermann, Robert Heinrich, Ralf Reussner. Data-driven Software Architecture for Analyzing Confidentiality. IEEE International Conference on Software Architecture, pages 1–10, IEEE, 2019. DOI: https://doi.org/10. 1109/ICSA.2019.00009.

# **8 Detecting Violations of Access Control and Information Flow Policies in Data Flow Diagrams**

The paper is available at the following reference.

Stephan Seifermann, Robert Heinrich, Dominik Werle, Ralf Reussner. Detecting Violations of Access Control and Information Flow Policies in Data Flow Diagrams. Journal of Systems & Software, 184, Elsevier, 2022. ISSN: 0164-1212. DOI: https://doi.org/10.1016/j.jss.2021.111138.

# **9 Architectural Attack Propagation Analysis for Identifying Confidentiality Issues**

The paper is available at the following reference.

Maximilian Walter, Robert Heinrich, Ralf Reussner. Architectural Attack Propagation Analysis for Identifying Confidentiality Issues. IEEE International Conference on Software Architecture, pages 1–12, IEEE, 2022. DOI: https: //doi.org/10.1109/ICSA53651.2022.00009.

# **10 A Layered Reference Architecture for Metamodels to Tailor Quality Modeling and Analysis**

The paper is available at the following reference.

Robert Heinrich, Misha Strittmatter, Ralf Reussner. A Layered Reference Architecture for Metamodels to Tailor Quality Modeling and Analysis. IEEE Transactions on Software Engineering, 47(4):775–800, IEEE, 2019. ISSN: 0098- 5589. DOI: https://doi.org/10.1109/TSE.2019.2903797

# **11 Integrating Business Process Simulation and Information System Simulation for Performance Prediction**

The paper is available at the following reference.

Robert Heinrich, Philipp Merkle, Jörg Henss, Barbara Paech. Integrating Business Process Simulation and Information System Simulation for Performance Prediction. Software & Systems Modeling, 16:257–277, Springer, 2017. ISSN: 1619-1366. DOI: https://doi.org/10.1007/s10270-015-0457-1.

# **12 Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications**

The paper is available at the following reference.

Robert Heinrich. Architectural Run-time Models for Performance and Privacy Analysis in Dynamic Cloud Applications. ACM SIGMETRICS Performance Evaluation Review, 43(4):13–22, ACM, 2016. ISSN: 0163-5999. DOI: https: //doi.org/10.1145/2897356.2897359.

# **13 Architectural Runtime Models for Integrating Runtime Observations and Component-based Models**

The paper is available at the following reference.

Robert Heinrich. Architectural Runtime Models for Integrating Runtime Observations and Component-based Models. Journal of Systems & Software, 169, Elsevier, 2020. ISSN: 0164-1212. DOI: https://doi.org/10.1016/j.jss. 2020.110722.

# **14 Enabling Consistency between Software Artefacts for Software Adaption and Evolution**

The paper is available at the following reference.

David Monschein, Manar Mazkatli, Robert Heinrich, Anne Koziolek. Enabling Consistency between Software Artefacts for Software Adaption and Evolution. IEEE International Conference on Software Architecture, pages 1–12, IEEE, 2021. DOI: https://doi.org/10.1109/ICSA51549.2021.00009.

## **15 Conclusion**

This chapter concludes the thesis with a summary and gives an outlook of future areas of research to extend and improve the approaches described before.

### **15.1 Summary**

This cumulative habilitation thesis presented approaches to architecturebased evolution of dependable software-intensive systems. We focused on three important quality properties of dependable software-intensive systems — performance, maintainability, and confidentiality. These quality properties strongly depend on architectural design decisions. However, we have shown that there is little support for reasoning about quality properties, especially maintainability and confidentiality, early in development. This little support for architecture-based quality analysis may result in quality issues identified only late or even occur once the system is in operation, unfavourable design decisions that are costly to be revised later, or the need for prototypes. We proposed approaches to modelling and analysing maintainability of softwareintensive systems based on architectural models in Chapter 5 and Chapter 6 in order to answer Research Question 1.1. Moreover, we proposed approaches to modelling and analysing confidentiality of software-intensive systems based on architectural models in Chapter 7, Chapter 8, and Chapter 9 in order to answer Research Question 1.2.

Besides the software-intensive system itself also the modelling languages and analysis techniques to reason about the system have to evolve to satisfy emerging or changing requirements due to novel system properties. However, we have shown that there is little flexibility for purpose-specific creation, extension, and reuse of modelling languages and analysis techniques with respect to the specifics of given quality properties or disciplines in approaches

to model-driven engineering. In order to answer Research Question 2, we investigated concepts for decomposition and composition known from objectoriented software design and transferred them to modelling languages for quality modelling and analysis of software-intensive systems in Chapter 10. Moreover, we investigated the composition of analysis techniques with a special focus on performance simulation of information systems and business processes in Chapter 11.

Once a software-intensive system is in operation it typically drifts away from its initial development models due to adaptive and evolutionary changes. Observing the system in operation is a feasible approach to keep track of the system. However, there is a gap of abstraction between the data elicited by observation in operation and the architectural models used in development. This gap results in loss of architectural knowledge and thus makes the initial development models increasingly less useful or even entirely useless for reasoning about quality properties of the system for upcoming changes. Source code changes without changing the architectural model is another reason for systems drifting away from their architectural models. We have shown that existing approaches are limited to single aspects relevant for bridging the divergent levels of abstraction in modelling between development and operation. In order to answer Research Question 3, we proposed approaches to bridge the divergent levels of abstraction between data of the operation phase, architectural models (in Chapter 12 and Chapter 13) and source code (in Chapter 14) of the development phase.

### **15.2 Outlook**

An outlook of possible future research activities with respect to architecturebased evolution of dependable software-intensive systems is given in this section. The outlook is structured by the objectives of this thesis.

#### **15.2.1 Architecture-based Quality Modelling and Analysis**

In this thesis, we presented approaches to architecture-based modelling and analysis of software-intensive systems for the quality properties maintainability, confidentiality, and performance. An obvious topic of future work is to consider additional quality properties of the ISO/IEC 25010 quality models

[128] in our research. Reliability is a candidate for a quality property to be considered next as there is already a large body of work on modelling and analysing reliability in different disciplines available that, however, needs to be investigated for applicability on the level of the system architecture. Moreover, developing approaches to analyse trade-offs between different quality properties of software-intensive systems, such as performance versus confidentiality or maintainability versus security, and to identify optimal design decisions with respect to these trade-offs seems to be an interesting topic of future work. There is already a large body of work for software systems (e.g., [141, 10]) that needs to be investigated for applicability on the level of the system architecture. For both, the concepts proposed in this thesis serve as foundations for future research.

An overview of future work regarding architecture-based modelling and analysis of maintainability is given by Busch [50] as summarised in the following.

We have presented approaches to architecture-based change propagation analysis in automated production systems as well as in business processes and information systems. In [108], we introduced and further detailed in [50] a methodology for domain-spanning change impact analysis that generalises the approaches presented in this thesis and allows to be instantiate in various disciplines. The instantiation of this methodology in new disciplines is expected to reveal further insights on the applicability of the methodology. Instantiation to modelling and analysing systems from the automotive domain seems to be an interesting future application areas that promises new insights due to the various heterogeneous disciplines involved. Instantiating the methodology in different ways in a given discipline would provide further insights on the applicability of the methodology. The instances can be developed based on influencing factors discussed in [50] to systematically analyse the effects of the influencing factors and to develop guidelines for the creation of possible future instances of the methodology. Moreover, the effects of different influencing factors on analysis results can be investigated further.

The approaches to change propagation analysis presented in this thesis are based on the approach in [210, 239]. The approach in [210, 239] was evaluated in an empirical study that indicated an automated approach helps lessexperienced users to estimate change effort more completely and precisely. The approaches presented in this thesis were evaluated using community

case studies [119, 259] from different domains as we focused on the demonstration that architecture-based change propagation analysis can be used to reason about various kinds of software-intensive systems. In the future, empirical studies can be conducted for the approaches proposed in this thesis to investigate how these approaches can help domain experts in analysing change propagation in different domains. These empirical studies can be set-up similar to the previous study in [210, 239].

Another interesting question to be answered in the future is how the automated approaches to change propagation analysis proposed in this thesis scale, for instance, for different numbers of model elements, different types of change propagation rules, or different types of dependencies between model elements. Moreover, the effort required to develop the modelling languages and analysis algorithms for instantiating the methodology at different abstraction levels as well as the effort required to create models and analyse the change propagation, both manually and using an automated approach, can be investigated to better understand when applying a model-based and automated approach to change propagation analysis pays off.

The approach presented in Chapter 6 uses fairly generic modelling languages to represent business processes and information systems. Consequently, fairly generic change propagation rules have been specified based on these languages. Using modelling languages tailored to more specific kinds of processes or systems would allow for defining more specific change propagation rules and thus promises to result in more precise analysis results. However, more specific modelling languages and rules come with the disadvantage that they may not be applicable to an arbitrary process or system and development of these languages and rules may be more costly. This needs to be further investigated — also for other disciplines — in the future.

The approaches to change propagation analysis proposed in this thesis result in a set of potentially affected model elements which is used by domain experts to estimate the effort of implementing a given change request. In the future, we may further investigate the needs of domain experts, for example, in terms of the granularity of the set of model elements for effort estimation.

In the presented approaches, domain experts have to indicate seed modifications in the model based on a change request. An interesting question to be investigated in the future is whether these seed modifications can be identified automatically based on a given representation of a change request. Ways to automated identification of seed modifications obviously depend

on the representation of the change request. For textual representation of a change request the application of approaches to natural language processing seem to be promising.

The approaches to change propagation analysis presented in this thesis apply models of the system architecture. These models may be extracted automatically by approaches like those proposed in this thesis to address Challenge 3 (cf. Chapter 12, Chapter 13, and Chapter 14) or related approaches discussed in Section 3.3 and be extended automatically with technical and organisational artefacts other than those of the system structure in the future. A first attempt to the automated extension with technical and organisational artefacts has been proposed in [212] but needs to refined in the future.

An overview of future work regarding architecture-based modelling and analysis of confidentiality is given by Seifermann [226] as summarised in the following.

The approaches presented in this thesis are focused on confidentiality. However, considering only one security objective is not enough to secure a system. A topic of future work is to investigate how the modelling languages and analysis techniques proposed in this thesis can be applied and extended to reason about other security objectives. As a next step investigating the security objective integrity seem to be promising because the mechanisms access control and information flow control discussed in this thesis can also be applied to protect the integrity of information.

For the approaches presented in this thesis we assumed the models adequately represent the system under study. However, software-intensive systems will further evolve to process data in highly dynamic contexts and will show dynamically changing system structure and behaviour in the future. The high level of heterogeneity, complexity, and dynamicity of future softwareintensive systems make these systems different from traditional systems as the sheer number of possible situations that may occur during operation will result in high level of uncertainty, for example about system structure, behaviour, and contexts. Ensuring confidentiality in such a system requires significant paradigm shift with respect to existing approaches. The high level of dynamicity deprecates existing techniques to static modelling and analysis. The high level of uncertainty results in even more significant challenges as it collides with the traditional interpretation and modelling of confidentiality where, for example, access/deny decisions are sharp and fully determined. In future systems fraught with uncertainty the rigid interpretation of access

control causes many problems. Instead, confidentiality must be understood in a "fluid" sense, and not be determined by rigid rules, but rather as continuous space where risk and loss associated with confidentiality mechanisms and together are tied to dynamic situations. The combination of dynamicity and uncertainty creates new challenges for modelling and analysing softwareintensive systems which need to be addressed in the future. In the ongoing research project FluidTrust [88] funded by the German Research Foundation, we take a first stance in this new direction of research by providing modelling languages and analysis techniques for reasoning about the system in development as well as for enforcing confidentiality in operation in highly dynamic and uncertain systems. First attempts to consider uncertainty in the context [39, 40] and in the structure and behaviour [270, 103] of a system have been developed based on the approaches presented in this thesis. These first attempts need to be improved and extended in the future.

Parts of our approaches to modelling and analysing confidentiality depend on a particular system and others can be reused for reasoning about other systems as well, as discussed in [228]. Security engineers often use catalogues to provide reusable artefacts to the community. In the future, we may investigate how parts of our approaches can be provided in form of catalogues and understand how engineers can apply such catalogues to provide and/or use partial analysis definitions. This is related to future work on the decomposition and composition of modelling languages and analysis techniques discussed in the following section.

The approach to attack propagation analysis may be extended in the future by investigating the automated creation of vulnerability models by combining existing vulnerability analysis techniques and reverse engineering approaches for technology-induced architectures, such as [145].

Another topic of future work is to investigate the scalability of our approaches to modelling and analysing confidentiality. Especially the approach to attack propagation analysis shows potential for further research as for every newly gained credential and newly compromised element all connected elements have to be checked because they may provide new attack possibilities. To address this, more efficient data-structures may be used in the future. Also our approaches to confidentiality analysis based on data flows proposed in this thesis can be examined for scalability in the future, for instance, depending on the number of model elements or the kind of label propagation. Moreover, like discussed before, the effort required to create models and analyse confidentiality issues, both manually and using the automated approaches, may be investigated also for our approaches to modelling and analysing confidentiality to better understand when applying a model-based and automated approach pays off.

### **15.2.2 Decomposition and Composition of Modelling Languages and Analysis Techniques**

In the Dagstuhl Seminar 19481 [77], we brought together members of the software engineering and formal methods communities and representatives from industry with the goal of establishing the foundations for a common understanding of the decomposition and composition of model-based analysis tools. We identified several challenges with respect to the decomposition and composition of model-based analyses and proposed first solutions to address these challenges in a book [114] created by the community as a result of the seminar. We collected topics of future work related to the decomposition and composition of model-based analyses and arranged them in form of a research roadmap. In the following, we discuss an excerpt of this research roadmap while the complete roadmap is described in [114].

Further research to understand the compositionality of specific quality properties and their analyses is required. While for some properties research has already advanced well, many other important properties remain for which compositionality is still not well understood. For example, research advances on compositionality of performance properties have been achieved in the last decades but compositionality of security properties is still not well understood [114].

Further investigation on the composition of analysis techniques is required in the future. We have introduced different forms of composition in the context of model-based analysis (black-, white-, and grey-box) in [246]. Getting a detailed understanding under which conditions each of them should be used and the consequences of use still remains a topic for future research. In this thesis, we gave examples with a special focus on performance simulation of information systems and business processes and described how these forms of composition can be implemented in existing simulators by discussing specific composition operators in Chapter 4. However, there is still a lot of research required to come to a general understanding. Investigating these composition operators for other types of analysis techniques and other quality properties is one topic of future work. Moreover, further insights on the applicability of these composition operators can be gathered in case studies for analysis techniques in other disciplines we will conduct in the future.

An important challenge that needs to be addressed in the future is the decomposition of analysis techniques along the analysis features they provide. Based on first experience with analysis decomposition our hypothesis is that the decomposition of a model-based analysis technique can follow the decomposition of the modelling language it is based on like we proposed in this thesis. However, further research is necessary and case studies for different types of analysis techniques need to be conducted in the future to develop guidelines for the decomposition of analysis techniques similar to those we proposed for the decomposition of modelling languages in this thesis.

We have introduced fundamental concepts for the integration and orchestration of analysis tools in our previous work [116]. A comprehensive study of orchestration strategies and the contexts in which these are most appropriate, however, is a topic of future research. A related challenge is to develop a language providing appropriate primitives for the specification and operationalisation of new orchestration strategies building on the foundational principles introduced in our previous work [116]. Implementing orchestration strategies efficiently requires substantial future research. For example, a safe and general approach to modular language development across concrete and abstract syntax as well as semantics is missing. This requires future research in language engineering and tool support.

Getting a better understanding of the compositionality of semantics is an important topic of future work. The decomposition and composition concepts proposed in this thesis are a good foundation to investigate the compositionality of semantics when decomposing and composing model-based analyses along different quality properties and disciplines. The concepts proposed in this thesis are starting point based on which fundamental questions on semantically correctness, validity, semantics and property preservation, and proper executability when decomposing and composing model-based analyses can be answered.

An overview of additional future work on the decomposition and composition of modelling languages is given by Strittmatter [241] as summarised in the following.

Besides metamodels, grammars are commonly used to specify the syntax of a modelling language. A topic of future work is to apply the reference architecture described for metamodels in this thesis to grammar-based languages. Starting points are described, for example, in [53]. Moreover, the layers of the reference architecture proposed in the thesis are specific to languages for quality modelling and analysis. Similar reference architectures showing (some) other layers may be applied for modelling languages of other application areas. For these languages the specific layers of the reference architecture may change while the decomposition and composition concepts described in this thesis will remain as they are applicable to metamodels in general.

Another topic of future work refers to the decomposition of metamodels. Language features may be identified by analysing a large amount of models and, for example, clustering metaclasses that are instantiated by groups of models into language features. First attempts to clustering metamodels have been shown, for example in [243], and need to be further examined.

In the future, our decomposition and composition approach as well as the reference architecture may be better align with related initiatives like MPM [186] or ideas we had for COLD [59]. Moreover, applying language interfaces [68] can allow for information hiding and decoupling of tools from modelling languages.

Moreover, our decomposition and composition concepts as well as the reference architecture may be applied to refactor and consolidate various existing metamodels and thus gather new insights on the applicability of our approach in the future. For example, metamodels that support related concepts, such as the Palladio Component Model [206] and the Descartes Modeling Language [152], can be consolidated to share a common basis. We may also consider modelling languages that cover new quality properties and/or disciplines and new types of analyses. For example, we are currently working on basing aforementioned approaches to confidentiality modelling and analysis on a modular version of the Palladio Component Model. Furthermore, applying our approach to new domains, such as automotive, promises interesting future insights.

Another topic of future work is further investigation on the extension mechanisms for composing modelling languages. The extension mechanisms proposed in this thesis are based on EMOF [181]. In the future, we may survey and examine other forms of extension mechanism for modelling languages, both metamodel-based and grammar-based modelling languages, by using and adapting the comparison criterion for extension mechanisms presented in [241]. The compatibility of different extension mechanisms may also be investigated in the future.

### **15.2.3 Bridging the Divergent Levels of Abstraction between Development and Operation**

Our approaches to bridging the divergent levels of abstraction in modelling between development and operation can be extended and revised in the future as described in the following.

We will further broaden our approaches to better support the planning and execution phases of the MAPE control loop model. We have proposed first attempts in [111, 110, 200]. As next steps, we (i) will further investigate design space exploration and optimisation techniques to identify optimal candidate architectural models, (ii) will further investigate the execution of adaptation plans to allow for a maximum degree of automation where adaptation is possible without human intervention, and (iii) will examine approaches to efficient operator-in-the-loop adaptation in case of trade-off decisions, lack of information, or criticality of decisions. Live visualisation approaches have demonstrated support for human operators in cases where human intervention is required. In the future, we may extend our visualisation approach sketched in [110] by additional views to further improve support of human operators.

Another topic of future work is expanding the approaches proposed in this thesis by considering, besides monitoring data of the software application, additional kinds of data gathered in operation, such as sensor data of an automated production plant, and represent this data in architectural runtime models. These architectural runtime models then need to adhere to corresponding modelling languages. The modelling languages for automated production systems we proposed in Chapter 5, for example, can serve as a basis which then need to be extended for specific purposes. Furthermore, correspondence relations must be specified accordingly.

Our incremental model calibration may be extended in the future. We may apply genetic algorithms to analyse the entire behaviour and additional quality properties of services instead of focusing on performance parameters.

Another topic of future work is to extend the scalability experiments of the proposed approaches. Extensions to the scalability experiments may comprise the consideration of influence factors like variation in the complexity of user behaviour or optimisations using genetic algorithm.

Currently, the implementation of our consistency preservation rules are focused on the Java programming language and other specific technologies. However, the Vitruvius approach [147] our consistency preservation approach is based on allows for defining domain-specific metamodels and consistency preservation rules. In the future, we may adjust the metamodels and consistency preservation rules to make our approach independent of specific technologies.

## **Bibliography**

## **The Karlsruhe Series on Software Design and Quality**

ISSN 1867-0067

#### Band 25 Sebastian Michael Lehrig Efficiently Conducting Quality-of-Service Analyses by Templating Architectural Knowledge. ISBN 978-3-7315-0756-7

#### Band 26 Georg Hinkel

Implicit Incremental Model Analyses and Transformations. ISBN 978-3-7315-0763-5

#### Band 27 Christian Stier

Adaptation-Aware Architecture Modeling and Analysis of Energy Efficiency for Software Systems. ISBN 978-3-7315-0851-9

#### Band 28 Lukas Märtin

Entwurfsoptimierung von selbst-adaptiven Wartungsmechanismen für software-intensive technische Systeme. ISBN 978-3-7315-0852-6

#### Band 29 Axel Busch

Quality-driven Reuse of Model-based Software Architecture Elements. ISBN 978-3-7315-0951-6

#### Band 30 Kiana Busch

An Architecture-based Approach for Change Impact Analysis of Software-intensive Systems. ISBN 978-3-7315-0974-5

#### Band 31 Misha Strittmatter

A Reference Structure for Modular Metamodels of Quality-Describing Domain-Specific Modeling Languages. ISBN 978-3-7315-0982-0

## The Karlsruhe Series on Software Design and Quality

**Edited by Prof. Dr. Ralf Reussner**

Ensuring dependability of increasingly heterogeneous and complex softwareintensive systems requires to address the following challenges. There is (i) little support for reasoning about dependability in early development, (ii) little flexibility for evolving modelling languages and analysis techniques, and (iii) a gap of abstraction between data of the operation phase, architectural models and source code of the development phase which results in loss of architectural knowledge and deprecates models.

This work, proposes concepts for (i) modelling and analysing dependability based on architectural models of software-intensive systems early in development, (ii) decomposition and composition of modelling languages and analysis techniques to enable more flexibility in evolution, and (iii) bridging the divergent levels of abstraction between data of the operation phase, architectural models and source code of the development phase.

**38**

**Robert Heinrich**

**Evolution of Dependable Software-intensive Systems**

ISSN 1867-0067 ISBN 978-3-7315-1294-3

Gedruckt auf FSC-zertifiziertem Papier


**2. History**

The history of ICPwf is centuries-old. For understanding, it is necessary to go back to the year 1783, after researcher Alexander Monro [1], who started studies of intracranial structures. His work was later completed by George Kellie [2], giving rise to the Monro-Kellie doctrine. This doctrine showed that the volume of intracranial components (blood, CSF and brain tissue) and the bone box that contains them is fixed. The volume of these components needs to be under balance, if there is an increase in one, the others need to compensate by reducing their volumes. Thus, intracranial hypertension (ICH) emerges when this compensation ceases to happen [3]. The stiffness of the skull was later challenged by the work of Sérgio Mascarenhas in 2012 [4].

Angelo Mosso, at the end of the 19th century, presented results showing the influence of brain activity on its blood flow. For the first time, it was possible to observe the brain pulses, through a system that captured these pulses and recorded them on paper. These figures can be seen in Zago's manuscript [5]. Many years later these pulses were related to ICP and CSF [6].

Langfitt [7] brought an important contribution showing the mathematical hypothesis for the relationship between ICP and intracranial volume, making it easier to understand the importance of intracranial compliance (ICC=V/ P) in critical care. Marmarou [8] in 1975 added to the pressure-volume curve the information about the increase in the amplitude of ICPwf as the mean value of ICP increases, and demonstrated that ICPwf contains unique information on intracranial contents, this data has already proven useful in several diseases such as stroke, hydrocephalus, idiopathic intracranial hypertension and brain injuries. It is important for gathering information on cerebrovascular hemodynamics and also of the cerebrospinal compensatory system. In 1983, Cardoso [9] showed that with the increase in ICP, in addition to the increase in the amplitude of its pulse, there was also a change in its configuration, as morphology became pyramidal with an increase in amplitude in the middle region of the pulse. Cardoso also shows the ICPwf configuration: P1 - systolic peak, P2 - tidal wave and P3 - venous return (explained in detail below).

Hu [10], understanding the possibility of using ICPwf as information to aid in diagnosis, created an algorithm called MOCAIP (Morphological Clustering and Analysis of Continuous Intracranial Pressure). This software is able to turn the waveform into numbers to facilitate the interpretation of the medical team. Nucci [11], in 2016, presented results of a software to calculate ICPwf parameters and clusters of waveforms for different stages of pathology.

Ballestero [12] and Bollela [13], in 2017, presented studies with hydrocephalus and meningitis respectively, that showed the applicability of a new noninvasive sensor to monitor the ICPwf, in the Ballestero study the ICPwf was analyzed through the relationship between the amplitude of the P2 and P1 pulses.

ICPwf is an important information, increasingly disseminated among physicians. New methods and analyses facilitate the use of this parameter, which is useful and disseminating over clinical institutions. The next chapters will provide more details and clinical applications of ICPwf.

**3**

damage [19, 20].

**Figure 1.**

*Intracranial Pressure Waveform: History, Fundamentals and Applications in Brain Injuries*

ICP is determined by the intracranial components volumes, as the brain tissue, the vascular or cerebral blood flow (CBF) and cerebrospinal fluid (CSF) [14, 15], and relations between them in a semi-rigid skull box, the Monroe-Kellie doctrine. Each cardiac beat corresponds to an ICP waveform composed of three peaks; arterial pulsation- P1, cerebral venous flow, secondary to autoregulation-derived cyclic fluctuations of arterial blood volume, reflecting intracranial compliance- P2, and

These cardiac-derived pulsatile signals are overlapped in time domain with the respiratory cycle, with influence in the cerebral venous pulsation by means of intrathoracic pressure generated by breathing, disclosed as slow waves [16] (**Figure 1b**). Moreover, ventilation plays a direct and remarkable role over CBF [17]. Thus, ICP is influenced by many physiological factors from extra and intracranial compartments. Moreover, factors as age, body posture, time of day as well as the clinical condition also are considerable variables, although in absence of disturbances, mean ICP is kept mostly within a range between 7 and 15 mmHg for adults, 3 and

There is an existing volume of reserve in the brain which is around 60–80 mL in young persons and approximately 100–140 mL in geriatric population, because of ongoing cerebral atrophy with age. However, in normal conditions and for short time observations, the brain volume is typically static, with mean ICP varying mainly according to the CBF and the balance between production and absorption or outflow of the CSF. The relation observed between these intracranial components is named intracranial compliance (ICC). Compensatory mechanisms exist to maintain intracranial volume homeostasis by extrusion of the CSF or venous blood, in order to preserve ICC, otherwise, these efforts may be insufficient in pathological conditions (i.e. traumatic brain injury) with intracranial hypertension (ICH) and ICC impairment producing primary or secondary brain tissue

*(a) One single arterial pulse transmitted to the intracranial compartment, with three peaks observed; systolic peak (P1), tidal peak (P2) and aortic valve closure peak (P3). (b) Respiratory slow waves overlapping cardiac*

Langfitt et al. characterized the transmission of pressure across the intracranial compartments as the intracranial elastance curve, observing an exponential behavior between ICP and intracranial volume [21], from a stable ICP vs. volume relation until

6 mmHg in children, and between 1.5 and 6 mmHg in term infants [18].

**3. Intracranial pressure- waveform pathophysiology**

*DOI: http://dx.doi.org/10.5772/intechopen.94077*

the aortic valve closure- P3 (**Figure 1a**).

*intracranial pulses spectrum (from Hall et al. [16]).*

**3.1 Waveform components**

*Intracranial Pressure Waveform: History, Fundamentals and Applications in Brain Injuries DOI: http://dx.doi.org/10.5772/intechopen.94077*


**4.5 Awareness and cost of installing biogas facility**

Findings show that one household plant could cost USD 550- 675 with wide standard deviation suggesting a high variation for the cost of installation depending on the expertise availability and the size of the biogas facility. The size for biogas plants ranged from 6- 12m3.

A comparison across wealth ranks shows a significant difference (Table 5). The *slightly welloff* respondents had significantly less installation cost compared to *less poor* (p<5%) and the

The source of energy varied from one category to another across the three wealth ranks. Nevertheless, fuelwood dominated energy sources in all the three categories, where over 77% of the respondents were using fuelwood for cooking (Table 4), followed by biogas, very few of the respondents were using charcoal. No-one was using electricity for

The respondents were asked whether they would like to have a biogas facility in their homes or not, and almost all (96%) said yes, they are willing to install biogas facilities.

Fuelwood 89.5 83.3 60.6 77.8 Biogas 15.9 18.6 30.3 21.6 Biogas and charcoal 2.7 6.1 16.7 8.5 Electricity/LPG 0 0 0 0

The sources of fuelwood in the district are communal forests, private forests, farms and timber residues. The distance to the fuelwood sources ranged from 1.1km for the less poor and slightly well-off households and 2.25 km for the 'poor' categories. The average distance for all respondents was 1.40km. The short distance for accessing fuelwood by the slightly well off and less poor is partly because a high proportion of them have private forests near their homes. Most people in the district use fuelwood from their own planted trees.

A high proportion of the communal forests have been severely degraded which makes fuelwood not easily available. Women spend 3-4 hours looking for fuelwood. This means that households with biogas facility were saving 3-4 hours wasted in collecting fuelwood. The saved time is used for other economic activities (e.g. farming and marketing) as well as leisure (e.g. resting and listening to the news and other entertainment). On the other hand, if the use of biogas for cooking will increase the demand for fuelwood in the district may decrease which is likely to benefit the poor because most of them do not have dairy cattle for

Findings show that one household plant could cost USD 550- 675 with wide standard deviation suggesting a high variation for the cost of installation depending on the expertise availability and the size of the biogas facility. The size for biogas plants ranged from 6-

A comparison across wealth ranks shows a significant difference (Table 5). The *slightly welloff* respondents had significantly less installation cost compared to *less poor* (p<5%) and the

Table 4. Wealth Categories and Sources of Energy for Cooking (%)

**The Poor Less Poor Slightly Well-off Average**

**4.3 Sources of energy for cooking**

cooking.

Source: Survey data 2006

**4.4 Sources of fuelwood**

biogas plant installation.

12m3.

Communal land is very limited in the district.

**4.5 Awareness and cost of installing biogas facility**

*poor* (p<2%) categories. However, there was no significant difference of cost of installation between the *less poor* and the *poor* respondents. A major explanation to this is that a high proportion of the *slightly well off* respondents benefited from the pilot project in 1996 when the biogas facilities were installed at half cost by the Danish volunteers. This was a strategy used to sensitise and raise awareness and demand for the biogas facilities. Unfortunately, many people from the *less poor* and the *poor* categories could not take up this opportunity because of many reasons, one of them being risk averse. They wanted to learn from others how it worked and what the advantages were to be. However, by the time they were convinced by the technology and started adopting it, the price had gone back to the market price levels. Another reason for not adopting it during the promotion period was that they had other more pressing issues than biogas, such as a need for cash to carter farming activities and paying for education and health services. Various studies have shown that poor people are always risk averse and therefore it takes time for them to adopt a new technology. Many of the studies about technology adoption conclude that the pace of adopting a new technology in developing countries has been slow among the poor.1 Feder et al., (1985) have identified factors such as aversion to risk and limited access to information as reasons that could partly explain why adoption is slow. Individual characteristics such as education, access to credit, the capacity to bear risk, availability of other inputs and access to information may play a big role in the adoption of the technology.

NS =not significant, \*\* Significant at p<5%, \*\*\* Significant at p<2%

Table 5. A comparison of cost (USD) of installation across wealth ranks


*3.7. Head Space-Solid Phase Microextraction (HS-SPME) and Chromatographic Analysis*

Head Space–Solid Phase Microextraction (HS-SPME) and chromatographic analysis was conducted in accordance with the method described by Matłok et al. [36]. The analyses were performed in three replications.


*3.4. Electrochemical—Open Circuit Potential Measurements*

The results of the open circuit potential measurements (OCP) of all types of bare or coated specimens after 1 or 4 days of exposure are shown in Figure 9.

**Figure 9.** Open circuit potential measurements of all types of bare or coated specimens after 1 day (**a**) and 4 days (**b**) of exposure in a corrosive environment of 3.5 wt % NaCl solution.

Open circuit potential measurements were used for a preliminary and indicative prediction of the corrosion behavior. As it is known, the general criterion for the estimation of corrosion behavior, based on open circuit potential values, is that the more anodic (lower negative value) the OCP, the lower the corrosion susceptibility. However, this criterion is not absolute. The estimation depends on the variations of the potential during the monitoring period and an indication of better corrosion behavior is the stability of the corrosion potential with time [2,16,23,29]. From the open circuit potential with time diagram, after 1 day of exposure (Figure 9), it can be seen that the more anodic value of OCP was obtained for the I.30E nanocomposite coating (−595 mV) and the corresponding more cathodic value was obtained for bare steel (−670 mV). All curves moved linearly towards the cathodic direction and oscillations were observed mainly in the case of the I.28E nanocomposite coating. This decrease of OCP indicated a continuous dissolution of the surface layers of the steel specimens. From the open circuit potential with time diagram, after 4 days of exposure (Figure 9), it can be seen that the more anodic value of OCP was obtained for both the I.30 and I.28 nanocomposite coatings (−625 mV) and the corresponding more cathodic value was obtained for bare steel (−680 mV). OCP curves of bare and resin coated specimens move initially in the anodic direction and after that continuously towards more cathodic values, indicating the formation of a non-passive oxide layer (in the case of the bare specimen) or the non-passive behavior of the coating (in the case of the resin coated specimen) that cracks and so the potential decreases and corrosion increases. The I.30 nanocomposite coating curve only decreased slightly (from −605 to −625 mV), indicating an increased stability of the coating and better corrosion behavior.

The electrochemical impedance response is a fundamental characteristic of an electrochemical system. Knowledge of the frequency dependency of impedance for a corroding system enables a model equivalent electrical circuit describing that system to be created [29–32]. Processing of the experimental data and fitting of the electrochemical impedance measurements was based on the equivalent circuit shown in Figure 10 for bare and coated steels, using a non-linear regression analysis. The results are presented in Figures 11–16 (Nyquist diagrams) and Table 4 (Resistance values).

**Figure 10.** Electrical equivalent circuit model simulating a corroding system metal/coating/electrolyte, *R*s = solution resistance, *R*1, *R*2 = ohmic resistances, CPE1, CPE2 = constant phase elements with admittance *Y*0(*j*ω) *n*.

**Figure 11.** Nyquist plots of impedance spectra, *R*e (Z), real part of impedance, and *I*m (Z), imaginary part of impedance, for bare specimens after 0, 1, 2, or 4 days of exposure in a corrosive environment of 3.5 wt % NaCl solution.

**Figure 12.** Nyquist plots of impedance spectra, *R*e (Z), real part of impedance, and *I*m (Z), imaginary part of impedance, for epoxy coated specimens after 0, 1, 2, or 4 days of exposure in a corrosive environment of 3.5 wt % NaCl solution.

**Figure 13.** Nyquist plots of impedance spectra, *R*e (Z), real part of impedance, and *I*m (Z), imaginary part of impedance, for I.28 nanocomposite coated specimens after 0, 1, 2, or 4 days of exposure in a corrosive environment of 3.5 wt % NaCl solution.

**Figure 14.** Nyquist plots of impedance spectra, *R*e (Z), real part of impedance, and *I*m (Z), imaginary part of impedance, for I.30 nanocomposite coated specimens after 0, 1, 2, or 4 days of exposure in a corrosive environment of 3.5 wt % NaCl solution.

**Figure 15.** Nyquist plots of impedance spectra, *R*e (Z), real part of impedance, and *I*m (Z), imaginary part of impedance, for bare and coated specimens after 1 day of exposure in a corrosive environment of 3.5 wt % NaCl solution.

**Figure 16.** Nyquist plots of impedance spectra, *R*e (Z), real part of impedance, and *I*m (Z), imaginary part of impedance, for bare and coated specimens after 4 days of exposure in a corrosive environment of 3.5 wt % NaCl solution.

**Table 4.** Resistance values (Ω cm2) for bare and coated specimens.

From the results of the electrochemical impedance measurements (Figures 11–16 and Table 4), it follows that all the coatings used increased the total resistance and both nanocomposites had greater total resistance values than the pristine glassy epoxy polymer, indicating improved protection properties. Thus, the total resistance value, Rtot, after 4 days exposure in the corrosive environment, increased from 1.03 × 102 of bare steel, to 5.34 × 103 in the case of pristine resin, to 7.40 × 103 in the case of I.28E, and to 2.96 × 104 (Ω cm2) in the case of I.30E coated specimens. In the case of bare steel, the total resistance values decrease continuously with exposure time and the relation between these two factors was observed to be linear, indicating a constant corrosion rate. A high total resistance value in the case of pristine resin coated steel in conditions of no prior exposure in the corrosive environment (0 days) was observed, 6.80 × 105 Ω cm2, that decreased quickly in any conditions of exposure, 5.34 × 103 Ω cm2 after 4 days, indicating high corrosion rate values and the fast evolution of corrosion. In the case of nanocomposite coated steel, the total resistance values decreased after four days of exposure, from 7.99 × 104 to 7.40 × 103 Ω cm2 in the case of I.28E and from 2.03 × 105 to 2.96 × 104 Ω cm2 in the case of I.30E, as it was expected due to corrosion initiation. However, the decrease is much smaller and so the corrosion evolution is also lower.

The superior protection performance, indicated from the higher total resistance value, of the epoxy nanocomposite with the I.30E organo-clay compared to that with the I.28E organic-clay, can be attributed to the higher dispersion of the clay nanolayers in the former nanocomposite (XRD and TEM results), which also induced slightly improved barrier properties.

#### **4. Conclusions**

The mechanical, thermomechanical, and barrier properties of all the epoxy—organoclay nanocomposites were improved compared to those of the pristine epoxy polymer. Both the pristine epoxy and the epoxy nanocomposite coatings offered substantial protection to steel from corrosion. The protective properties of the nanocomposite coatings were superior compared to those of the pristine epoxy polymer, as it was revealed from the weight loss results, the optical and microscopy examination of the specimens after the exposure in the corrosive environment, the open circuit potential measurements, and the electrochemical impedance spectroscopy measurements. The protective properties of the nanocomposite coatings varied with the organo-clay used. The epoxy—montmorillonite clay modified with primary octadecylammonium ions, Nanomer I.30E, had a better behaviour than the clay modified with quaternary octadecylammonium ions, Nanomer I.28E. This was attributed to the higher dispersion of the nanolayers in the nanocomposite formed

with the I.30E organoclay compared to that formed with I.28E. The enhanced mechanical properties and thermal stability of both epoxy—clay nanocomposites, in combination with their high protection efficiency, renders them as attractive candidates for various demanding coating applications.

**Author Contributions:** Panagiotis Giannakoudakis, Konstantinos Triantafyllidis and Panagiotis Spathis conceived and designed the experiments; Domna Merachtsaki and Panagiotis Xidas performed the experiments; Domna Merachtsaki, and Panagiotis Xidas analyzed the data; Panagiotis Giannakoudakis, Konstantinos Triantafyllidis and Panagiotis Spathis contributed reagents/materials/analysis tools; Domna Merachtsaki, Panagiotis Xidas and Panagiotis Spathis wrote the paper.

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


**Author details**

Ramya Ramadoss1 \*, Rajkumar Krishnan2 , Lekshmy Jayan2 and Priyadharini Shankaran2

1 Department of Oral Pathology, Saveetha Dental College, Chennai, India

2 Department of Oral Pathology, SRM Dental College, Chennai, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*2.4. Intervention*

During the intervention phase of the study, participants used weighted blankets for 14 consecutive nights. These weighted blankets were the SensaCalm ® brand, custom made, and were provided by the PI. The weighted blankets were designed to be 10% of each child's body weight adhering to the prototypical weighted blanket protocol [13–16]. The SensaCalm ® blankets used for the study ranged from 3–7 pounds to accommodate the varying weights of potential participants and ranged between USD 40.00 and 80.00. The blanket brand was chosen based upon the a ffordable cost, durability, equal distribution of weight across the blanket. When the weighted blankets were provided, caregivers were given instructions on safely and e ffectively using them. Caregivers were instructed to only use the blankets at night (i.e., not during nap time or quiet time); only use the blanket if the child was able to remove it on their own; cover the child's body, arms, and feet but not their head or face; check on the child occasionally while using the blanket; adjust other bedding while using the weighted blanket to ensure the child was not too hot, and to contact the PI if the weighted blanket was showing signs of wear. Additionally, caregivers of the participants reported that they slept in their own bed (as opposed to the caregivers, or another location) through this study's duration.

### *2.5. Method of Analysis*

Data were analyzed through visual analysis of repeated measure graphs generated using Microsoft Excel, version 16, as described by Kennedy [31]. Visual analysis is widely accepted as a mechanism to analyze data for single-subject designs [32]. The literature supports visual inspection as the preferred method of analysis among single-subject designs because it is sensitive and able to capture intervention e ffects significant to clinicians working outside research labs within clients' natural or typical context [32]. Moreover, the visual analysis approach is preferred because it has lower error rates and is conservative enough to identify reliable treatment e ffects [32].

In addition to visual analysis, this study used the percentage of non-overlapping data (PND) [33] as an additional analysis tool. PND is a statistical method widely used in behavioral science research, particularly for analyzing the small data sets, which are commonplace with single-subject design studies. PND is calculated by identifying the most extreme data point in the baseline phase (either the highest or lowest value depending on whether the intervention is intended to reduce or increase a behavior). The PND is the percentage of data in the intervention phase, which falls above or below this point based on its intended outcome.


This article proposes a simple method to calculate the carbon emissions of ports, which only considers the carbon emissions of ships. Other equipment will not be considered, such as harbor railway, quayside container crane, locomotive, and other special machinery. We calculate the carbon emissions of ships using fuel oil and shore power and add the carbon emissions to the economic value, including the port cost. Then, the tripartite economic game models under the two carbon mechanisms are established, respectively, and the impact of the power supply service price, the carbon price, and the proportion of the time using shore power to the docking time on the social welfare, government benefits, port benefits, and ship benefits is discussed.

#### *2.2. Assumptions*

To simplify the problem and facilitate modeling and subsequent analysis and discussion, the following assumptions are proposed for the research content:

#### *2.3. Methodology*

In order to address climate change, the Ministry of Ecology and Environment of China has formulated "Implementation Plan for Setting and Allocating the Total Amount of Carbon Emission Trading Quotas for 2019–2020 (Power Generation Sector)", which includes enterprises or other economic organizations that emit 26,000 ton of carbon dioxide equivalent or more in any one year from 2013 to 2019. As carbon emissions trading management has only just started, the central government is currently only regulating the power sector. However, in places such as Guangdong Province and Shanghai, it has already started to cover industries such as steel, chemicals, cement, paper, and aviation. The allocation methods are mainly the historical intensity method and the historical emissions method. The former is applicable to industrial enterprises with a high correlation between product output and carbon emissions, and well measured. The latter is suitable for industrial enterprises where the boundary has changed significantly in recent years and it is difficult to apply the industry baseline method or the historical intensity method.

In the historical intensity method, the annual base quota for enterprises is equal to the historical intensity base multiplied by the annual business volume. The historical intensity base is the weighted average of the enterprise's annual business volume of carbon emissions in the previous three years. The annual business volume is the business volume data of the enterprise for the current year verified by a third party verification agency and validated and confirmed by the relevant departments. In the historical emissions approach, an enterprise's annual base allowance is equal to the historical emissions base. In this article, port companies use the historical intensity method and carbon emissions from ports only consider carbon emissions from ships, not from other equipment such as shoreside cranes and locomotives. Thus, carbon emissions are only relevant to the activities of the ship, and the oil or electricity consumed per unit of power has a relevant carbon emission factor to calculate carbon emissions.

#### *2.4. Parameter Descriptions*

The following is a description of the parameters that appear in the mathematical model of the three-way game.

$$\begin{cases} \mathbf{P}^{k,i} = \mathbf{W}\_{\mathbf{sp}}^{k,i} / T\_{\mathbf{sp}}^{k,i} \\\\ \mathbf{W}\_{\mathbf{sp}}^{k} = \sum\_{i=1}^{N\_i} \mathbf{W}\_{\mathbf{sp}}^{k,i} \\\\ \mathbf{W}\_{oil}^{k} = \sum\_{i=1}^{N\_i} \mathbf{P}^{k,i} \left( T^{k,i} - T\_{\mathbf{sp}}^{k,i} \right) \\\\ \mathbf{W}^{k} = \mathbf{W}\_{\mathbf{sp}}^{k} + \mathbf{W}\_{oil}^{k} \end{cases} \tag{1}$$

where *k* denotes the year; *i* denotes the type of ships; *P k*,*i* is the power of the ship's auxiliary engine, kWh; *Wk sp* is the power consumed by the ship using shore power, kWh; *Wk oil* is the power consumed by the ship using fuel oil, kWh; *Wk* is the total power consumption of the ship, kW h; *T k*,*i* is the berthing time of the ship, h; *T k*,*i sp* is the time when the ship uses shore power, h.

$$\mathsf{C}\_{dj}^{k} = \mathsf{C}\_{\text{grid}}^{k} + \mathsf{C}\_{\text{serve}}^{k} \tag{2}$$

$$\begin{cases} \mathsf{C}\_{\text{e}}^{k} = \mathsf{W}\_{\text{sp}}^{k} \mathsf{C}\_{\text{grid}}^{k} \\\\ \mathsf{C}\_{\text{sp}}^{k} = \mathsf{W}\_{\text{sp}}^{k} \mathsf{C}\_{dj}^{k} \\\\ T\_{\text{e}}^{k} = \mathsf{C}\_{\text{e}}^{k} / (1 + 16\%) \times 16\% \\\\ T\_{\text{s}}^{k} = \mathsf{W}\_{\text{sp}}^{k} \mathsf{C}\_{\text{serve}}^{k} / (1 + 6\%) \times 6\% \end{cases} \tag{3}$$

where *C k grid* is the electricity basic price, CMY/kWh; *C k serve* is the electricity service price, CMY/kWh; *C k dj* is the electricity actual price, CMY/kWh; *C k e* is the cost of purchasing electricity for the port, CMY; *C k sp* is the ship paying the port for electricity, CMY; *T k e* is to pay value-added tax to the government as the power grid provides electricity to the port, CMY; *T k s* is the port that makes a profit from providing shore power service to the ship and pays value-added tax to the government, CMY.

$$\begin{cases} \mathsf{C}\_{oil,sp}^{k} = 10^{-6} \mathsf{W}\_{sp}^{k} E\_{1} \mathsf{C}\_{per-oil} \\\\ \mathsf{C}\_{oil,oil}^{k} = 10^{-6} \mathsf{W}\_{oil}^{k} E\_{1} \mathsf{C}\_{per-oil} \end{cases} \tag{4}$$

where *C k oil*,*sp* represents the fuel cost savings by the ship using shore power, CMY; *C k oil*,*oil* represents the cost of fuel oil used by the ship, CMY; *E*1 is the fuel consumption per unit of electricity emitted by the auxiliary engine, g/kWh; *Cper*−*oil* is the price of marine fuel oil, CMY/Mt.

$$\begin{cases} \begin{aligned} T\_{ep,sp}^k &= 10^{-3} \text{C}\_{dl} \mathcal{W}\_{sp}^k \sum\_{n=1}^{N\_n} \text{F}\_n / E\_{dl}^n \\\ T\_{ep,oil}^k &= 10^{-3} \text{C}\_{dl} \mathcal{W}\_{oil}^k \sum\_{n=1}^{N\_n} \text{F}\_n / E\_{dl}^n \end{aligned} \end{cases} \tag{5}$$

where *n* denotes the type of pollutant; *T k ep*,*sp* is the environmental protection tax saved by ships using shore power, CMY; *T k ep*,*oil* is the environmental protection tax paid by ships using fuel oil, CMY; *Cdl* is the pollution factor pollutant discharge fee standard per unit of pollution equivalent, CMY/equivalent; *Fn* is the emission factor of pollutants discharged from the fuel oil of the ship's auxiliary engine, g/kWh; *E n dl* is the pollution equivalent value of pollutants, kg.

$$\begin{cases} \begin{aligned} V\_{sp}^k &= 10^{-6} \mathcal{W}\_{sp}^k F\_\varepsilon \\ V\_{oil}^k &= 10^{-6} \mathcal{W}\_{oil}^k F\_\varepsilon \\ V\_{actual}^k &= V\_{sp}^k + V\_{oil}^k \end{aligned} \tag{6}$$

$$\begin{cases} F\_{\rm cf}^k = 10^6 V\_{\rm actual}^k / W^k \\\\ F\_{\rm wef}^k = 10^6 \frac{\Sigma \left( V\_{\rm actual}^{k-3} + V\_{\rm actual}^{k-2} + V\_{\rm actual}^{k-1} \right)}{\Sigma \left( W^{k-3} + W^{k-2} + W^{k-1} \right)} \end{cases} \tag{7}$$

where *V k sp* and *V k oil* are the CO2 emissions of the ship using shore power and fuel oil, respectively, Mt; *V k actual* is the total CO2 emissions of the ship, Mt; *Fe* is the annual average power supply emission factor of the regional power grid, g/kWh; *Fc* is the emission factor of carbon dioxide pollutants emitted by marine auxiliary engine fuel oil, g/kWh; *F k cef* is the comprehensive CO2 emission factor of the ship, g/kWh; *F k wef* is the ship's weighted CO2 emission factor, g/kWh. Equation (6) gives the calculation of the carbon emissions from the use of shore power and the use of oil, respectively, as well as the total emissions for the year. In Equation (7), the ship CO2 weighted emission factor is taken as the weighted average of the carbon emissions per unit of business of the port in the previous three years, which is used as the base of the historical carbon emission intensity.

$$\begin{cases} \begin{aligned} \boldsymbol{V}\_{\text{cap}}^{k} &= 10^{-6} \boldsymbol{\eta} \boldsymbol{F}\_{\text{unf}}^{k} \boldsymbol{W}^{k} \\ \boldsymbol{T}\_{\text{c1}}^{k} &= \left( \boldsymbol{V}\_{\text{actual}}^{k} - \boldsymbol{V}\_{\text{cap}}^{k} \right) \times \boldsymbol{P}\_{\text{c1}}^{k} \end{aligned} \tag{8}$$

where *V k quato* is the port's carbon emission cap, Mt; *η* is the annual decline coefficient, taking 1; *T k* c1 is the total amount of carbon trading, CMY; *P k c*1 is the carbon price in the carbon trading market, CMY/Mt CO2. Since only the power sector is currently subject to government regulation and other sectors are not yet subject to excessive restrictions, the annual decline coefficient in Equation (8) is set to 1. This gives the port room to strengthen its efforts to reduce carbon emissions. Carbon emission allowances for the year were calculated, as well as the fees paid in excess of the allowances.

$$T\_{c2}^{k} = V\_{actual}^{k} \times P\_{c2}^{k} \tag{9}$$

where *T k* c2 is the carbon emission tax paid by the port, CMY; *P k c*2 is the carbon tax price, CMY/Mt CO2.

$$T^k\_{\mu} = \mathcal{W}^k\_{sp} / \mathcal{S}\omega \cos \varphi \tag{10}$$

where *T k u* is the annual utilization hours, h; *S* is the total installed capacity of the shore power system, kVA; *ω* means that there is a certain margin between the actual use of the shore power capacity of the terminal and the planned construction capacity of shore power; cos *ϕ* is the comprehensive power factor of the shore power frequency conversion equipment and the ship's load. We experimentally set *ω* = 0.8 and cos *ϕ* = 0.7. *ω* is needed to make sure that the shore power equipment is working properly under the load. Part of the shore power equipment must be suspended during equipment repairs and maintenance in order to prolong the equipment's useful life. cos *ϕ* is related to the nature of the load: different devices have different power factors, so, here, the integrated power is used instead. When *Wk sp* and *S* are fixed, obviously, the larger *ω* and cos *ϕ*, the smaller *T k u* .

#### **3. Game Research under Two Carbon Mechanism**

#### *3.1. Game Research on the Use of Shore Power under Cap and Trade*

Figure 2 explains the three-way game model in the case of cap and trade. The government defines carbon emission rights as a commodity and establishes carbon emission caps for ports. By incorporating the value of carbon emissions into port costs, ports can directly buy or sell allowances in the carbon trading market. Ships can voluntarily choose to use shore electricity while they are docked in port and the amount of time they utilize it has been rising every year.

**Figure 2.** Tripartite benefit analysis under cap and trade.

1. Government Benefit Analysis Model

$$\begin{cases} \begin{aligned} B\_{\mathcal{S}} &= T\_{\mathcal{e}} + T\_{\mathcal{s}} + T\_{\mathcal{ep},sp} + T\_{\mathcal{ep},\rho\mathcal{il}} \\ &\mathcal{C}\_{\mathcal{S}} = \mathcal{C}\_1 \mathfrak{a}\_1 + \mathcal{C}\_2 \mathfrak{a}\_2 + T\_{\mathcal{ep},\rho\mathcal{il}} \\ &F\_{\mathcal{S}} = B\_{\mathcal{S}} - \mathcal{C}\_{\mathcal{S}} \end{aligned} \end{cases} \tag{11}$$

where *Bg* is the government income; *Cg* is the government cost; *Fg* is the government profit; *α*1 and *α*2 are the subsidy rates for shore power equipment transformation given by the government to the port and the ship, respectively; *C*1 and *C*2 are the transformation costs of the port and the ship, respectively, CMY.

2. Port Benefit Analysis Model

$$\begin{cases} \mathcal{B}\_p = \mathsf{C}\_{sp} + \mathsf{C}\_1 \mathsf{a}\_1 \\\\ \mathsf{C}\_p = \mathsf{C}\_1 + \mathsf{C}\_\varepsilon + T\_s + T\_{c1} \\\\ F\_p = B\_p - \mathsf{C}\_p \end{cases} \tag{12}$$

where *Bp* is the port income; *Cp* is the port cost; *Fp* is the port profit.

3. Ship Benefit Analysis Model

$$\begin{cases} \text{ } B\_{\text{s}} = \text{C}\_{2}\text{a}\_{2} + \text{C}\_{\text{oil},sp} + T\_{ep,sp} \\\\ \text{ } \text{C}\_{\text{s}} = \text{C}\_{sp} + \text{C}\_{2} + \text{C}\_{\text{oil},oil} + T\_{ep,oil} \\\\ F\_{\text{s}} = B\_{\text{s}} - \text{C}\_{\text{s}} \end{cases} \tag{13}$$

where *Bs* is the ship income; *Cs* is the ship cost; *Fs* is the ship profit.

$$SW\_1 = F\_p + F\_s + T\_e + T\_s - T\_{c1} \tag{14}$$

where *SW*1 refers to social welfare, including consumer surplus, producer surplus, government tax, and environmental benefits [31]. *Tep*,*sp* represents the environmental benefit, which not only reduces the environmental protection fee levied on the ship but also is included in the government income. Note that social welfare is only counted once and does not accumulate repeatedly.

The use of shore power is abandoned after the ship converts to shore power owing to the high price. In the worst case, all ships consume fuel for power supply. In this way:

$$\begin{cases} \mathsf{B}\_{\mathrm{s},\rho\mathrm{il}} = \mathsf{C}\_{2}\mathsf{a}\_{2} \\ \mathsf{C}\_{\mathrm{s},\rho\mathrm{il}} = \mathsf{C}\_{\mathrm{oil},\rho\mathrm{il}} + \mathsf{C}\_{\mathrm{oil},\mathrm{sp}} + T\_{\mathrm{sp},\rho\mathrm{il}} + T\_{\mathrm{ep},\mathrm{sp}} + \mathsf{C}\_{2} \\ \quad F\_{\mathrm{s},\rho\mathrm{il}} = B\_{\mathrm{s},\rho\mathrm{il}} - \mathsf{C}\_{\mathrm{s},\rho\mathrm{il}} \end{cases} \tag{15}$$

where *Bs*,*oil* is the ship income; *Cs*,*oil* is the ship cost; *Fs*,*oil* is the ship profit.

When some ships use shore power, the benefits of the entire ship are improved:

$$F\_{\rm s,save} = F\_{\rm s} - F\_{\rm s,oil} \tag{16}$$

When the ships use all fuel oil, the costs come from the fuel costs and the environmental protection taxes paid. The income comes from freight and is unincluded in the scope of the three-party game, so the ship's benefit is negative, which is similar to social welfare.

#### *3.2. Game Researches on the Use of Shore Power under the Carbon Tax Policy*

Figure 3 illustrates the three-way game model in the case of carbon tax policy. The government sets the carbon tax rate, and ports must offer the government a carbon tax for every metric ton of carbon dioxide they emit.

**Figure 3.** Tripartite benefit analysis under carbon tax policy.

1. Government Benefit Analysis Model

$$\begin{cases} \mathcal{B}\_{\mathcal{S}} = T\_{\varepsilon} + T\_{\text{s}} + T\_{c2} + T\_{ep,sp} + T\_{ep,oil} \\\\ \mathcal{C}\_{\mathcal{S}} = \mathcal{C}\_{1}\mathfrak{a}\_{1} + \mathcal{C}\_{2}\mathfrak{a}\_{2} + T\_{ep,oil} \\\\ F\_{\mathcal{S}} = B\_{\mathcal{S}} - \mathcal{C}\_{\mathcal{S}} \end{cases} \tag{17}$$

where *Bg* is the government income; *Cg* is the government cost; *Fg* is the government profit.

2. Port Benefit Analysis Model

 *Bp* = *Csp* + *C*1*α*1 *Cp* = *C*1 + *Ce* + *Ts* + *Tc*2 *Fp* = *Bp* − *Cp* (18)

where *Bp* is the port income; *Cp* is the port cost; *Fp* is the port profit.

3. Ship Benefit Analysis Model

$$\begin{cases} \text{ } B\_s = \text{C}\_2\text{a}\_2 + \text{C}\_{oil,sp} + T\_{ep,sp} \\\\ \text{ } \text{C}\_s = \text{C}\_{sp} + \text{C}\_2 + \text{C}\_{oil,oil} + T\_{ep,oil} \\\\ F\_s = B\_s - \text{C}\_s \end{cases} \tag{19}$$

where *Bs* is the ship income; *Cs* is the ship cost; *Fs* is the ship profit.

Social welfare is given by:

$$SW\_2 = F\_p + F\_s + T\_\varepsilon + T\_s \tag{20}$$

If all types of ships are powered by fuel oil, the benefit analysis of the ship is as follows:

$$\begin{cases} \mathsf{B}\_{\mathrm{s},\rho\mathrm{il}} = \mathsf{C}\_{2}\mathsf{a}\_{2} \\ \mathsf{C}\_{\mathrm{s},\rho\mathrm{il}} = \mathsf{C}\_{\mathrm{oil},\rho\mathrm{il}} + \mathsf{C}\_{\mathrm{oil},\mathrm{sp}} + T\_{\mathrm{ep},\rho\mathrm{il}} + T\_{\mathrm{ep},\mathrm{sp}} + \mathsf{C}\_{2} \\ \quad \mathsf{F}\_{\mathrm{s},\rho\mathrm{il}} = \mathsf{B}\_{\mathrm{s},\rho\mathrm{il}} - \mathsf{C}\_{\mathrm{s},\rho\mathrm{il}} \end{cases} \tag{21}$$

where *Bs*,*oil* is the ship income; *Cs*,*oil* is the ship cost; *Fs*,*oil* is the ship profit.

Some ships are converted from oil to electricity, and the overall benefit of the ship is improved:

$$F\_{\rm s,save} = F\_{\rm s} - F\_{\rm s,oil} \tag{22}$$

#### **4. Data Selection**

#### *4.1. Government Data Acquisition*

According to the "Interim Measures for the Management of Subsidy Funds for Ports, Ship Shore Power Facilities and Marine Low Sulfur Oil Subsidy Funds in Shenzhen", the subsidy will be provided for the reconstruction of port shore power facilities, which will not exceed 30% of the project construction costs [32].

#### *4.2. Port Data Acquisition*

According to the survey, a port has built 14 sets of shore power systems, covering a total of 23 berths. The installed capacity of the shore power system has reached 11,600 kVA. Including the equipment purchase fee and construction installation fee, the investment and renovation costs of the power equipment are CMY55,144,134. Taking the service life as 30 years, the interest rate of the annualized cost is 8%, which is equivalent to the annual renovation cost:

$$\mathcal{C}\_1 = \frac{8\% \times (1 + 8\%)^{30}}{(1 + 8\%)^{30} - 1} \times 55, 144, 134 = 4,898, 312 \text{ CMY}$$

#### *4.3. Ship Data Acquisition*

The investment and transformation cost of onboard electrical equipment, referring to the report of the European Commission Environment Directorate (ECDGE) [33], converted into unit power is 1530 CMY/kW.

The berthing time of the ship in the port, the length of the use of shore power in a certain year, and the electricity consumed by the use of shore power are shown in Figure 4.

**Figure 4.** Ship information.

#### *4.4. Electricity Price Acquisition*

According to the "Notice on Clarifying the Electricity Price and Service Price of Ship's Shore-based Power Supply Facilities" of the price bureau of Jiangsu province, *C k grid* takes the electricity price of large industrial electricity at 0.6601 CMY/kWh. The maximum price of shore power used by ships in Taizhou is 1.20 CMY/kWh [34].

In order to standardize the accounting of carbon dioxide emissions implied by electricity consumption by regions, industries, enterprises, and other units, and to ensure comparability of results, the government organized a study to determine the average carbon dioxide emission factor for regional power grids in China. It refers to the carbon emissions generated by one unit of electricity used in the grid, and is obtained by dividing the total emissions of the entire grid by the total electricity generation. As the port study is in the southern region, the average CO2 emission factor for the southern regional grid was used. *Fe* is the annual average power supply emission factor of the regional power grid, taking 527.1 g/kWh.

#### *4.5. Pollutant Data Acquisition*

According to the literature [33], *E*1 is taken as 213 g/kWh. According to the "2020 Implementation Plan for the Global Sulfur Restriction Order for Marine Fuel Oil" issued by the China Maritime Safety Administration, those entering the country's inland river ships' air pollutant emission control areas should use fuel oil with a sulfur content of no more than 0.10% [35]. *Cper*−*oil* was taken as 3800 CMY/Mt.

According to the "Decision of the Standing Committee of the Jiangsu Provincial People's Congress on the Applicable Tax Amount of Environmental Protection Tax for Air Pollutants and Water Pollutants", the tax rate in Nanjing is CMY8.4 per pollution equivalent of air pollutants [36].

The annual emission factors and pollution equivalent values of various pollutants caused by marine auxiliary engine fuel are shown in Table 1 [37,38].

**Table 1.** Annual emission factors and pollution equivalent values of pollutants.

#### *4.6. Carbon Price Data Acquisition*

The carbon price data from 2020 to 2050 comes from the "2020 China Carbon Price Survey" [39]. Figure 5 reveals that there has been a steady increase in the carbon price in China since 2020.

**Figure 5.** The expected price of the national carbon emissions trading market in 2021–2050.

#### **5. The Impact of Various Factors on the Benefits of Each Party**

*5.1. Comparison of the Impact of Subsidy Rates*

Based on the maximization of social welfare, we explore the impact of subsidy rates under two carbon mechanisms on the optimal price and the benefits to all parties. Restrictions:

$$\begin{cases} \ 0 \le a\_1 \le 30\% \quad 0 \le a\_2 \le 30\% \quad \mathcal{C}\_{\text{service}} \ge 0 \quad 0 \le \mathcal{C}\_{d\text{j}} \le 1.2\\\ \quad F\_{\mathcal{S}} \ge 0 \quad F\_p \ge 0 \quad F\_{\text{s.save}} \ge 0 \end{cases} \tag{23}$$

Objective function:

$$\text{max.}SW\tag{24}$$

Tables 2 and 3 state the impact of the subsidy rate on the economic and environmental benefits of each party in the three-way game model under the two carbon regimes, respectively.

**Table 2.** Comparison of the impact of subsidy rates on economic benefits to all parties under the two carbon regimes.

**Table 3.** Comparison of the impact of subsidy rates on environmental benefits to all parties under the two carbon regimes.

As can be seen from Figure 6a, whether under the cap-and-trade or the carbon tax policy, social welfare decreases when the electricity service price rises with roughly a linear negative correlation between the two. The cost of power supply services is rising, which does not promote social welfare, and there is clear resistance to the use of shore power. In addition, the lowest value of social welfare under cap and trade is much higher than the maximum value under a carbon tax policy. As a result, cap and trade offers a significant benefit in this case and merits consideration. From Figure 6b–e, we can observe that the subsidy rate a1 affects social welfare, port benefit, and ship benefit to a larger extent than a2 affects all three. The social welfare and ship benefits rise as the subsidy rate a1 rises. The effects of a1 and a2 on government benefits are identical. Government benefits rise with an increase in a2, while they decline with an increase in a1. A comparison of the two results reveals that the cap-and-trade group reported far more social welfare and ship benefits than the other one. On the contrary, the government benefit and port benefit under cap and trade are in every case short of what they are under the other system.

**Figure 6.** The relationship between subsidies and the benefits to all parties. (**a**) The relationship between electricity service price and social welfare; (**b**) The relationship between subsidy and social **Figure 6.** The relationship between subsidies and the benefits to all parties. (**a**) The relationship between electricity service price and social welfare; (**b**) The relationship between subsidy and social welfare; (**c**) The relationship between subsidy and government benefit; (**d**) The relationship between subsidy and port benefit; (**e**) The relationship between subsidy and ship benefit.

than the maximum value under a carbon tax policy. As a result, cap and trade offers a significant benefit in this case and merits consideration. From Figure 6b–e, we can observe that the subsidy rate a1 affects social welfare, port benefit, and ship benefit to a larger extent than a2 affects all three. The social welfare and ship benefits rise as the subsidy rate a1 rises. The effects of a1 and a2 on government benefits are identical. Government benefits rise with an increase in a2, while they decline with an increase in a1. A comparison of the two results reveals that the cap-and-trade group reported far more social welfare and ship benefits than the other one. On the contrary, the government benefit and port benefit under cap and trade are in every case short of what they are under the other system.

#### *5.2. Carbon Price Impact Comparisons*

We evaluated how the price of carbon affects the best price and the gains for all parties under two carbon mechanisms based on the maximization of social welfare.

Restrictions:

$$\begin{cases} \begin{array}{ll} 20 \le P\_{\mathcal{C}} \le 100 & \mathcal{C}\_{\text{server}} \ge 0 & 0 \le \mathcal{C}\_{dj} \le 1.2 \end{array} \\\\ F\_{\mathcal{S}} \ge 0 & F\_{p} \ge 0 & F\_{\text{s.save}} \ge 0 \end{cases} \tag{25}$$

Objective function:

#### max.*SW* (26)

Tables 4 and 5 indicate the impact of the carbon price on the economic and environmental benefits of each party in the three-way game model under the two carbon regimes, respectively.

**Table 4.** Comparison of the impact of carbon price on economic benefits to all parties under the two carbon regimes.

**Table 5.** Comparison of the impact of carbon price on environmental benefits to all parties under the two carbon regimes.

Figure 7 compares the outcomes gained from the analysis of the two carbon mechanisms. Under both the cap-and-trade and carbon tax policies, social welfare decreases as electricity service prices rise. The social welfare under cap and trade is always greater than that under the carbon tax policy. Figure 7b–e shows that the price of carbon has a significant impact on social welfare, government benefits, port benefits, and ship benefits under the carbon tax policy. The four are not significantly impacted by the carbon price under cap and trade. According to the cap-and-trade model, while government benefits decline as the price of carbon rises, social welfare and ship benefits increase. The carbon tax policy states that when carbon prices rise, the government gains more advantages whereas social welfare and ship benefits decline. Social welfare and ship benefits under cap and trade tend to be invariably greater than those under the carbon tax approach. In comparison to a carbon tax, the government's benefit under cap and trade is always less. The port efficiency fluctuates positively around 0.

**Figure 7.** The relationship between carbon price and the benefits to all parties. (**a**) The relationship between electricity service price and social welfare; (**b**) The relationship between carbon price and social welfare; (**c**) The relationship between carbon price and government benefit; (**d**) The relationship between carbon price and port benefit; (**e**) The relationship between carbon price and ship benefit.

#### *5.3. Comparison of Time-Proportional Effects of Using Shore Power*

This study investigates the impact of the ratio of time spent using shore power to total docking time on the best pricing and the gains for all parties under two carbon mechanisms, based on the maximization of total social welfare.

$$T\_{\mu}^{k} = \lambda \sum\_{i=1}^{N\_{\bar{i}}} T^{k,i} P^{k,i} / S\omega \cos \varphi \tag{27}$$

where *λ* is the ratio of using shore power to the berthing time. As the total port call time is constant, *T k u* and *λ* are proportional. *T k u* decreases, so *λ* decreases, which affects all aspects of electricity prices, social welfare, government benefits, port benefits, ship benefits, etc. Restrictions:

*Cserve* ≥ 0 0 ≤ *Cdj* ≤ 1.2 *Tu* ≤ 8760 (28)

Objective function:

$$\max.SW\tag{29}$$

Tables 6 and 7 explicate the impact of the time-proportional on the economic and environmental benefits of each party in the three-way game model under the two carbon regimes, respectively.

**Table 6.** Comparison of the impact of time-proportional on economic benefits to all parties under the two carbon regimes.

**Table 7.** Comparison of the impact of time-proportional on environmental benefits to all parties under the two carbon regimes.

What stands out in Figure 8a is the price of electricity supply service changes regularly between 0 and 0.5399 CMY/kWh, and social welfare is also changing. The service price must be 0.5399/kWh under cap and trade in order to reach its maximum value, and it must be 0 under the carbon tax policy in order to reach its minimum value. In Figure 8b–d, the similarity between the two carbon mechanisms is highlighted above. The overall level of social welfare rises as the ratio of time spent utilizing shore power to docking time grows. Under the two carbon mechanisms, the change curves for overall social welfare, government benefits, and port benefits are comparable and can be attained by moving up and down. The curve of ship benefit with the time proportion of using shore power is exactly the same in Figure 8e. With the share of time spent utilizing shore power increasing, the overall level of social welfare rises monotonically. However, government benefits, port benefits, and ship benefits show an upward trend with the increase in the proportion of time when shore power is used.

**Figure 8.** The relationship between time proportion and the benefits to all parties. (**a**) The relationship between electricity service price and social welfare; (**b**) The relationship between time proportion and social welfare; (**c**) The relationship between time proportion and government benefit; (**d**) The relationship between time proportion and port benefit; (**e**) The relationship between time proportion and ship benefit.

#### **6. Conclusions**

The two systems of cap and trade and carbon tax are clear and easy to understand, but too many similar parameters appear in the modeling process, leading to easy confusion. In both cap and trade and carbon tax, the specific components of tripartite benefits and social welfare are different and are simply represented by the same parameters, and the specific values change as the electricity service price, carbon tax, and the ratio of using shore power to the berthing time change. Also note that the government is happy to see and promote the use of shore power, which is in line with the plan to reduce carbon emissions; the port is to take the responsibility of a state-owned enterprise, respond to the national policy, take responsibility for emission reduction and establish a good image among the public. Ships have the least public pressure and social responsibility, and they are oriented by economic interests, so they are more unstable, and they need government guidance and support because they have to face technical and economic difficulties in the "oil-to-electricity" conversion. In the model, we should focus on understanding the cost saving of some ships after "oil-to-electricity" conversion, which is the key to decide whether ships should insist on using shore power. There is a difference between the CO2 emitted from using oil to meet the power and the CO2 emitted from switching to shore power to replace this power, and the focus is on calculating the difference and the accompanying economic and environmental benefits.

Depending on the port's yearly business volume and historical carbon emission intensity base, the government calculates the annual carbon cap for the port. The comprehensive CO2 emission factor of ships in the first three years is greater than the current year's CO2 emission factor. Therefore, the carbon cap is frequently higher than the actual CO2 emission, according to calculations of the port's real energy usage. Currently, compared to other businesses, the government's criteria for reducing carbon emissions in the maritime sector are not stringent enough.

In summary, these results indicate that the annual hours of shore power facility use, the amount of environmental protection tax saved by utilizing shore power, and the reduction in pollutants and carbon dioxide are all equivalent as long as the ships' energy consumption is constant. The social welfare, governmental benefits, port benefits, and ship benefits appear to vary depending on the subsidy rate, carbon price, and percentage of time using shore power.

It is reasonable for the port to set the shore electricity price within the range of 0.6601 to 1.2 CMY/kWh, taking into consideration both its own transformation costs and government subsidies. As long as ships use electricity instead of fuel, the economic benefits will be significantly improved, no matter what changes in various carbon mechanisms and influencing factors. The primary source of improvement may partly be related to reduced fuel costs. The government's benefits are always greater than zero, mostly due to the environmental benefits of decreasing pollutant emissions. Low economic advantages are expected for the port, as a result of high costs of self-renovation and government limits on the actual electricity price.

The major limitation of this study is that ships are assumed to be equivalent to a virtual ship although they have distinct types and numbers when in berth. It is a macro analysis for multiagent games; hence, it cannot accurately reflect the benefit to an individual ship. The three-party game is dynamic and played repeatedly, and ships are allowed to choose whether to utilize shore power or not, which makes it challenging to calculate the best electricity price. By examining the status of the tripartite game in electricity substitution, this study offers a particular reference value for the cycle planning and benefit distribution of electric energy replacement projects in the future.

**Author Contributions:** Conceptualization, Y.H. and Y.Z.; methodology, Y.H.; software, Y.H.; validation, Y.H.; formal analysis, Y.H.; investigation, Y.H.; resources, Y.Z.; data curation, Y.H.; writing—original draft preparation, Y.H.; writing—review and editing, Y.Z.; visualization, Y.H.; supervision, Y.Z.; project

administration, Y.Z.; funding acquisition, Y.Z. All authors have read and agreed to the published version of the manuscript.

**Funding:** Guangxi Special Fund for Innovation-Driven Development (AA19254034).

**Data Availability Statement:** Publicly available datasets were analyzed in this study. These data can be found in the References section.

**Conflicts of Interest:** The authors declare no conflict of interest. The funders had no role in the design of the study; in the collection, analyses, or interpretation of data; in the writing of the manuscript; or in the decision to publish the results.

#### **References**

**Disclaimer/Publisher's Note:** The statements, opinions and data contained in all publications are solely those of the individual author(s) and contributor(s) and not of MDPI and/or the editor(s). MDPI and/or the editor(s) disclaim responsibility for any injury to people or property resulting from any ideas, methods, instructions or products referred to in the content.


**2. Literature review**

#### **2.1 Tumor etiology**

Canine oral melanoma is the most frequently diagnosed malignant tumor in dogs [2]. Oral melanoma is associated with a high local infiltration, metastatic rate, and poor prognosis. Biological behavior and presentation of melanoma vary remarkably and are influenced by anatomic site, stage, and histological features [1, 2]. Increasing age, with no relation to sex, is also a determinant for canine oral melanoma aggressiveness [2, 3, 6].

The etiology of canine oral melanoma is multifactorial, including environmental and genetic factors [6]. In cutaneous melanoma, hairy skin and sunlight exposure could be considered risk factors [2]. However, sunlight exposure cannot be considered a risk factor for oral melanoma. Chronic inflammation or trauma, deep bacterial infections, intralesional necrosis, chemical exposure, and burns are factors associated with canine melanoma development [6, 8].

Highly pigmented oral mucosa and purebred dogs, such as Airedale Terrier, Boston Terrier, Boxer, Chihuahua, Chow, Cocker Spaniel, Doberman Pinscher, English Springer Spaniel, Golden Retriever, Irish Setter, Miniature Schnauzer, Scottish Terrier, Poodles, Beauce Shepherds, Rottweilers, and Labrador Retrievers, are predisposed to melanocytic tumors, including oral melanoma [3–5]. This suggests that melanoma in dogs may have genetic factors [7, 8].


*3.4. In Silico Docking with IKK*β

The docking calculations were carried out using the protein structure of IKKβ (The Protein Data Bank code; 4KIK.pdb). Using the Sybyl program, the apo-protein of IKKβ was obtained by removing the original ligand **K252a** contained in 4KIK.pdb. The original ligand K252a was again docked to the apo-protein, to confirm that the flexible docking procedure worked well. Through a flexible docking procedure repeated 30 times, 30 complexes between apo-protein and **K252a** were obtained. Their binding energy ranged from −28.44 to −10.24 kcal/mol and their binding poses were good to be comparable with 4KIK.pdb.The binding pocket of IKKβ was determined using the LigPlot software as previously reported [48]. They are composed of 16 residues; 14 residues, namely Leu21, Gly22, Thr23, Val25, Ala42, Lys44, Glu61, Val74, Met96, Tyr98, Glu149, Asn150, Ile165, and Asp166 are involved in hydrophobic interactions, and two residues, Glu97 and Cys99 are involved in hydrogen bonds. Using the three-dimensional structure of compound **5** obtained in this study, docking with apo-protein was performed in the same way as the original ligand. The binding energy generated by the 30 iterations ranged from −15.92 to −13.09 kcal/mol. The interactions between IKKβ and compound **5**

*3.3. Hirshfeld Surface analysis of Compound 5*

interactions between the molecules [47].

were analyzed using the LigPlot progam. Six residues including Thr23, Val29, Glu61, Met65, Met96, and Ile165 showed the hydrophobic interactions with the ligand and three residues including Gly27, Lys44, and Asp166 formed hydrogen bonds (H bonds) with the ligand (Figure 9A). The binding pocket of compound **5** resided in IKKβ was visualized using the PyMOL program (PyMOL Molecular Graphics System, version 1.0r1, Schrödinger, LLC, Portland, OR, USA). Isoflavone compound **5** in IKKβ exhibited a slightly different binding pattern from those of the original ligand **K252a**. However, both isoflavone **5** and original ligand **K252a** have been shown to bind well at the active site of the IKKβ protein (Figure 9B). **Figure 6.** (**A**) Hirshfeld surfaces of molecule **5I** mapped with dnorm, shape index and curvedness. (**B**) Hirshfeld surfaces of molecule **5II** mapped with dnorm, shape index and curvedness. According to two-dimensional fingerprint plots analysis, the dominant interaction in each molecule **5I** and **5II** originates from H···H contacts, which are the major contributors of 43.5% and 42.5% to the total Hirshfeld surface, respectively. The contribution from the O···H/H···O contacts of 25.1% and 29.1% of each molecule **5I** and **5II** is represented by a pair of sharp spikes that are characteristic of hydrogen-bonding interactions. Other meaningful interactions include C···H/H···C with contributions of 17.8% and 16.7% from **5I** and **5II**, respectively (Figure 7A–H).

*Crystals* **2020**, *10*, x FOR PEER REVIEW 9 of 15

In order to quantify the intermolecular interactions in the crystals of the titled compound **5**, a Hirshfeld surface (HS) analysis was carried out. Based on the Hirshfeld analysis on all conformers, two independent molecules (**5I** and **5II**) showed different dnorm, shape index (SI) and curvedness, however, each set of conformers **A** and **B** revealed the same Hirshfeld analysis results [46]. The 3D Hirshfeld surfaces of two independent molecules (**5I** and **5II**) were illustrated in Figure 6A,B, which maps dnorm, shape index and curvedness. The deep red spots on the dnorm Hirshfeld surfaces of each molecule represent the close contact interactions, which are mainly responsible for the significant intermolecular C–H···O interactions. Shape index and curvedness can also be used to identify the characteristic packing modes. The shape indexes of **5I** and **5II** show red concave regions on the surface around the acceptor atoms and blue regions around the donor H atoms. The maps of curvedness for **5I** and **5II** show no flat surface patches representing that there are no stacking

**Figure 7.** Two-dimensional fingerprint plots of the most important intermolecular contacts in each molecule **I** and **II**. For **I**: full (**A**) and resolved into O···H (**B**), H···H (**C**), C···H (**D**). For **II**: full (**E**) and resolved into O···H (**F**), H···H (**G**), C···H (**H**). **Figure 7.** Two-dimensional fingerprint plots of the most important intermolecular contacts in each molecule **I** and **II**. For **I**: full (**A**) and resolved into O···H (**B**), H···H (**C**), C···H (**D**). For **II**: full (**E**) and resolved into O···H (**F**), H···H (**G**), C···H (**H**).

The overall contribution to the total Hirshfeld surface is illustrated in Figure 8.

**Figure 8.** Summary of the overall detailed intermolecular interactions and their contribution to each crystal structure **I** and **II**. For **I**; H···H ; 43.5%, O···H; 25.1%, O···C; 6.4%, C···H; 17.8%, C···C; 5.7%, O···O; 1.5%, For **II**; H···H ; 42.5%, O···H; 29.1%, O···C; 5.6%, C···H; 16.3%, C···C; 5.7%, O···O; 0.8%. crystal structure **I** and **II**. For **I**; H···H; 43.5%, O···H; 25.1%, O···C; 6.4%, C···H; 17.8%, C···C; 5.7%, O···O; 1.5%, For **II**; H···H; 42.5%, O···H; 29.1%, O···C; 5.6%, C···H; 16.3%, C···C; 5.7%, O···O; 0.8%.