**6. Mentorship and gender**

Mentorship is important in guidance regarding setting and managing expectations for daily practice and career trajectory. As such, it is a very effective method for achieving better professional outcomes and retention. This type of intervention is especially needed to address women leaving medicine, a trend that is increasingly costly and dangerous for the medical system and patients. Of concern, nearly 40% of women physicians decide to pursue part-time work or leave medicine altogether within six years of completing residency [53]. Among reasons for this phenomenon are discrimination, salary inequity, and harassment, but the main challenge to women in medicine continues to be the difficulty of balancing work with family demands [54–56]. While systemic changes, such as expanded parental leave

policies, need to be made to ease this tension for female physicians, mentorship is one of many tools that can help. Mentorship provides an avenue for women (and men) to come together and discuss the real challenges that women face in medicine, help younger trainees manage their expectations, and may ultimately yield increased research projects to create useful solutions [57–59].

There are important additional gender-specific considerations. For example, research shows that it may be more challenging for women to find mentors [42]. One proposed reason for this is the lack of availability of mentors with similar backgrounds and/or experiences. For example, there are fewer women mentors available to mentor surgery residents [60]. This is not only due to the relatively fewer women in the field, but also due to the additional unique time pressures in personal life faced by women surgeons [61]. Similar considerations apply to other male-dominated disciplines where there is already a lack of female mentors, and women in those fields often do not have the time to commit to mentorship due to competing priorities and external demands [62, 63]. Women may also feel somewhat uncomfortable reaching out to male mentors due to gender dynamics [64]. It naturally follows that the paucity of female mentors in certain areas is deleterious to diversity in the medical profession, since it has been noted that specialty fellows-in-training consistently indicate that they chose their subspecialty largely due to having had a mentor in the field [42].

As a consequence, without sufficient representation of female mentors in subspecialties, fewer women have the support and the opportunity to enter those fields, which ultimately perpetuates the cycle of exclusion. Institutional support such as dedicated time for mentorship could be a helpful factor in increasing numbers of female mentors. It is also harder for individuals from underrepresented groups to find mentors [65, 66], and despite the best intentions the experiences of microaggressions or outright bias continue to occur [67, 68]. Peer mentorship has been especially helpful in bridging this gap for underrepresented minority trainees, since successive classes of trainees have been increasingly diverse [69, 70]. Due to the uneven opportunity for organic mentorship, formal mentorship programs have also been important to ensure fair and equitable access to mentors. In summary, there continues to be an unmet need for mentors, with large numbers of residents and other medical trainees reporting that they wish they had mentors or that they had difficulty initiating a mentorship [42, 71]. This crisis is negatively affecting the medical profession and requires urgent and durable resolution.

## **7. Organization of mentorship**

#### **7.1 Senior mentorship**

The senior mentorship model is the traditional mentorship model in which the mentor is a well-established faculty member who can provide guidance informed by personal experience and professional connections [72]. Senior mentors are well positioned to serve as sponsors and can provide mentees with more opportunities for professional advancement. A major barrier to the success of this type of relationship is the presence of an institutional hierarchy – and thus power gradient(s) - that insidiously influences all interactions between mentor and mentee [73–75]. The power dynamic results in mentees feeling uncomfortable showing vulnerabilities, speaking honestly, and challenging the mentor when appropriate [75, 76]. It also can lead mentees to overextend themselves in a pursuit to meet their mentors' expectations, which ultimately may degrade the relationship [77]. Finally, there is much less diversity among senior attendings, thus limiting the diversity among available mentors.

#### **7.2 Peer mentorship**

Peer mentorship is a very successful approach that facilitates access to mentorship experience for individuals early on in their careers, thus increasing the likelihood that they will continue to serve as mentors throughout their career [67]. It also eliminates a certain level of formality and hierarchical barriers that exist in traditional mentorship relationships, thus providing a more flexible and relaxed environment. For example, it is not as daunting to reschedule a meeting with a peer as it may be to do the same with a senior leader. In various studies, residents have noted that it is easier to go to a peer resident mentor than to a faculty member [78]. This is potentially due to increased approachability and lessened fear of being criticized or judged.

Peer mentorship has been especially successful for underrepresented minority students who may experience cultural challenges in medicine, especially when there is a lack of representative attendings or faculty mentors [79, 80]. Connecting residents at different levels of training addresses some of the barriers to diversity in mentorship. Beyond technical skills, there are certain 'unspoken rules' that residents must pick up in the hospital and postgraduate environment. Having a peer mentor can help assuage this discomfort and facilitate learning these unspoken rules and expectations, especially related to being a minority in medicine [67]. The peer mentorship model helps both parties gain confidence, connect with colleagues, broaden professional networks, and can be a powerful tool for experience-based knowledge sharing between senior and junior residents [67]. Such peer mentorship programs have been implemented successfully for residents and staff from underrepresented minority groups [67, 81, 82].

The peer mentoring model is also an effective way to build a growing cadre of female mentors – a factor important in addressing some of the gender-based issues associated with male predominance across certain medical disciplines [83, 84]. The increased flexibility of a peer-based mentoring approach makes it more attractive for female residents to mentor each other and can make mentees feel more at ease by eliminating the hierarchy.

#### **7.3 Individual model**

Also known as one-on-one mentorship, this is a more traditional approach in which there is one mentor and one mentee, usually working together in a long-term professional relationship. The primary advantage of this model is the opportunity to invest deeply in a single, more dedicated relationship [85]. However, because time constraint is a concern for many mentors, it is important to note that weekly and monthly meetings were both shown to have equal success rates, which may make it easier for individual mentors to commit to more mentees [86]. A significant drawback of this method is the lack of diversity offered from a single mentor.

### **7.4 Group model**

Group, 'team-based,' or 'multiple-mentor' model is an approach where a mentee has several mentors, each facilitating growth in different, often complementary areas [87, 88]. This method allows for more diversity in both content and perspectives; however, it is possible that mentors and mentees are more likely to develop a more superficial bond through this practice. This model also puts a higher burden on mentees as they must coordinate logistics and time-manage multiple mentors, all while requiring less time from individual mentors. Furthermore, as discussed pre previous sections, it can be difficult to find one mentor, let alone multiple,

*Senior mentors provide more experience and connections but have limited availability and less candid conversations. Peer mentors provide less formality and more candid discussion but offer fewer career progress opportunities. One-toone mentorships provide the opportunity to develop deep relationships but can be more time consuming for mentors and provide less diverse perspectives to the mentee. Multiple mentorship reduces the strain on mentors, and offers more diverse perspectives, but increases the logistical work for mentees. All combinations of mentorships organization outlined in the table can be adapted to a virtual environment which can potentially lead to larger and more global mentorship programs.*

#### **Table 1.**

*Mentorships include either a peer or senior mentor and either a one-to-one or multiple mentorship structure.*

so supply of mentors remains a major limiting factor. A version of multiple mentoring is a 'team-based' approach in which the various mentors communicate among themselves in order to facilitate more efficient mentoring of an individual mentee [78]. A summary of commonly employed mentorship models is provided in **Table 1**.

#### **8. Approaches and techniques for mentorship**

#### **8.1 Micromentorship**

Micromentorship is a model proposed by Waljee, et al., in which the mentorship relationship changes based on goals, and focuses on frequent, brief, informal communication and feedback, targeting improvement in very specific areas [89]. This is better suited to younger generations who have grown up in the technology era, as it has been shown that they are more purpose-driven, show preference toward collaboration and horizontal/flat social structures, are more focused on end product

#### *Mentorship in Postgraduate Medical Education DOI: http://dx.doi.org/10.5772/intechopen.98612*

deliverables, and above all are accustomed to instant access to information [89]. It also provides benefits to the mentor – primarily because time constraint is a major concern – by decreasing the amount of time set aside for mentorship meetings. Under this paradigm, a simple intervention such as a quick text message, email, or phone call may be sufficient to meet a particular mentee's needs and expectations. The micromentorship model can be adapted and scaled to include the increasingly virtual social interaction landscape, with informal meetings, which typically involve less planning, and the ability to more readily connect people across the globe.

Micromentorship is highly compatible with the group mentorship model in which a mentee has many mentors, all focused on providing guidance in diverse areas. This also prepares the mentees for more effective participation in modern team-based medicine approaches, addresses some issues of isolation among residents, and strengthens the feeling of community [39]. This model also empowers mentees by reducing the effects of the hierarchical structure of the traditional mentorship model and by eliminating a level of formality which, within the medical system, can be very beneficial to sharing knowledge, experience, and bidirectional feedback. By providing trainees with a greater stake in their community and collective decision-making, institutions will likely reduce attrition and improve retention of talent at the same time.

#### **8.2 Adapting Maslow's hierarchy of needs to mentorship**

Maslow's Hierarchy of Needs is a general principle stating that foundational needs must be met before higher level developmental processes like self-actualization can be met. Hale, et al., adapted this hierarchy to help tackle the issue of burnout among medical residents and to address critical wellness issues [90]. In their model, mentorship is placed at the highest tier as a method of accomplishing self-actualization. However, this model could be reasonably expanded to view mentorship as a tool to address various levels of needs rather than just self-actualization (**Figure 1**).

For example, when initially setting general goals for the mentor-mentee relationship, there should be a discussion of where to focus efforts so that the needs can be optimally met. If the mentor and mentee agree to emphasize wellness, then implementing reflection and wellness check-ins during regularly scheduled meetings could address the corresponding domain components [91, 92]. Implementation of reflective practices may help emphasize wellness, with benefits in both mental and physical health domains [92–95]. Mentorship can

#### **Figure 1.**

*Remapping Maslow's Hierarchy of Needs to represent general focus areas of mentorship. Mentorship practices can focus on fostering a sense of belonging, esteem, self-actualization, safety, and improving physiologic conditions.*

target self-actualization through discussions about professional identity or career mapping. Building of one's esteem can be accomplished by treating each other with respect and fairness and working on various projects (including research) together to reinforce the value that each member of the team adds to the final outcome [96, 97]. The general domain of "safety" can be addressed by exchanging advice on practical life matters, including financial topics, as well as having candid conversations about personal boundaries [97–99]. While this framework is not universally applicable across all mentorship relationships, it may help in setting goals and creating actionable items for the pair to work on. This model also works very well with the 'multiple mentor' model, where different mentors could be responsible for addressing different aspects of the mentee's growth. This method could also be helpful in addressing the need for diversity within a mentorship team; for example, matching a female mentor with a female mentee to discuss work-life balance or wellness [77].

### **8.3 Intentional mentorship**

Prior to engaging in mentorship, mentors should reflect on their own education and experiences at various levels of training [100]. They should identify what skills, behaviors, or thought patterns they found helpful and formative. They should reflect on good advice given to them by their own mentors or colleagues. Do they remember how they felt as a student, so they can understand what (and how) the mentee is experiencing? What were elements of successful mentorships and professional interactions that they have had? What do they wish they had been taught? What characteristics do they hope their own doctors possess? What do they hope for the future of medicine? What do they want to pass on to the next generation? Mentors should use this thought exercise to inform their mentorship technique and goals. They should revisit their answers regularly to ensure they are staying on track and mentoring with intention [34, 101].

#### **8.4 Motivation and life-long learning**

Among key benefits of a fruitful mentor-mentee relationship is the generally higher intrinsic motivation among mentees [102]. This is important because intrinsic motivation is positively associated with ongoing focus on self-improvement and life-long learning. Thus, measures to increase intrinsic motivation amongst medical trainees could have positive implications for one's entire career. Opportunities that support autonomous learning were shown to cultivate intrinsic motivation, which could be implemented in mentorship. For example, the mentor could invite his or her mentee to conduct research and present information on any topic of their choosing. This, in turn, provides the mentee with valuable skills related to selfdirected, independent work.

#### **8.5 Division of responsibilities between mentor and mentee – best practices**

#### *8.5.1 Mentee responsibilities*

Mentees must enter into a mentorship knowing that the onus of cultivating the relationship rests primarily on them [77]. First, they must identify a potential mentor with consideration of personality fit, field of expertise, career and life experience, and professional network. In the absence of an organized mentorship structure, the mentee should initiate contact and set up a meeting to discuss

#### *Mentorship in Postgraduate Medical Education DOI: http://dx.doi.org/10.5772/intechopen.98612*

the viability of a potential mentorship relationship. If both parties agree to move forward, a series of meetings should outline the general goals of the mentorship, specific goals and topics of interest, frequency and type of communication, as well as various work and learning styles [77]. Both parties should consider outlining expectations for the relationship and for each other – something discussed in more detail in a subsequent section on mentorship malpractice. After that, general and specific goals can be set, optimally in an orderly, well-outlined fashion.

The mentee must come to subsequent meetings prepared with discussion points, including challenges that they seek guidance on, as well as the status of any projects they are working on with their mentor. Optimally, they should leave the meeting with a list of 'action items' to complete by the next meeting, as well as a mutually agreed date for the next meeting. Mentees should seek feedback at regular intervals, frequently enough to ensure continuity of experience. Feedback should encompass mentee-specific goals and objectives. Some guiding questions to help evaluate the relationship include [103]:

Evaluation of these answers could be on a graded scale from 0 to 5 as suggested by Wadhwa, et al. [103]. In terms of attitude, mentees should be generally appreciative and show gratitude for their mentor's time. They should be honest about their limitations, take initiative, follow through with tasks, take risks and be willing to leave their comfort zone [77]. They should be eager to learn from their mentors and show respect in all interactions.

#### *8.5.2 Mentor responsibilities*

In order to ensure high quality of mentors and excellent mentee experience, involvement in a mentorship program must be voluntary, otherwise an advisingtype relationship tends to emerge [86]. While there could be incentives such as dedicated time for mentorship in more academically-oriented institutions, mentors have to be willing to engage for personal reasons. Likewise, mentorship pairs must not be imposed but rather mutually chosen [78]. Most mentees choose mentors based on personality/style rather than academic achievements.

Before mentoring commences, individuals should reflect as outlined in the intentional mentoring section above. Mentors should also be aware that the best indicator of a successful mentorship relationship is when mentees feel that their mentors are invested in their day-to-day progress [104]. Among the most common reasons for mentorship failure is 'mentor neglect,' thus mentors must be effective communicators to minimize this threat [104, 105]. For example, one way to avoid neglect is to communicate effectively if meetings need to be cancelled and even give a clear message that mentorship might not be possible under a specific set of circumstances or conditions; leaving mentees in suspense is the primary mode of neglect and is largely avoidable. Another common threat to the success of mentorships is the power dynamic and negative impacts of hierarchy (e.g., 'the power gradient') within the relationship [106, 107]. To mitigate the above issues, mentors need to clearly indicate that mentees should avoid engaging in activities or projects that do not align well with their interests, skill set, or capacity to complete. This may be challenging for mentees because of the above-mentioned 'power gradient' and potentially mentee concerns of appearing ungrateful or unappreciative for opportunities offered to them.

To help optimize mentor-mentee interactions, mentors should make efforts to make routine information exchange less formal and to reduce either the presence or the appearance of hierarchical barriers. For example, mentors could preface the mentorship with statements of support and encouragement, while emphasizing that the ultimate signs of strength, maturity, and leadership include the ability to self-assess, know when to seek advice or help, and estimating one's ability to take on more work. A mentor should also inquire early on about the mentee's preferred work and motivational styles [77]. There should be mutual awareness of reasonable expectations, comfort levels, resources and generally speaking support (e.g., both in terms of resources and encouragement). At the same time, a balance should be struck between the amount and relative proportions of encouragement, support, and praise. Imbalance among those three factors may lead to mismatched expectations (e.g., praising poor effort will likely be counter-productive).

Throughout the entire mentorship process, mentors should embrace opportunities for mentees to engage in reciprocal teaching. This both enhances the mentee's teaching (and leadership) skills and provides the mentor a fresh perspective on mutually relevant topics. This input can be solicited, for example, by asking a mentee a question about technology or changes to medical education. Mentors should embrace their commitment to life-long learning and regularly and frequently seek feedback about their performance as a mentor and the effectiveness of the mentorship on the whole. Other factors in creating a successful mentorship include implementing micro-motivational behaviors as well as awareness and avoidance of exposing mentees to unintentional microaggressions [108, 109]. In terms of their general approach, mentors should keep an open mind and be eager to learn from their mentees [77]. They should treat mentees with respect and view them as valuable colleagues. Mentors should be honest with mentees while at the same time exhibiting patience and generosity (**Figure 2**).

#### *Mentorship in Postgraduate Medical Education DOI: http://dx.doi.org/10.5772/intechopen.98612*

#### **Figure 2.**

*Mentees bear the majority of work in mentorship and have the responsibility for cultivating the relationship; however, actionable items for both mentor and mentee can help facilitate a successful relationships. Having clearly defined tasks and responsibilities increases likelihood of success [86].*

#### *8.5.3 Mentorship malpractice*

Mentorship malpractice is an important topic within the overall context of this chapter. It is critical that both mentors and mentees understand the scope of their mentorship relationship, and that education regarding manifestations of "bad mentorship" is provided to all stakeholders. For example, Chopra, et al., grouped mentorship malpractice into active and passive types [110]. The authors further categorize active mentorship malpractice into three subtypes – the hijacker who takes hostage a mentee's idea, project, or grant, often for self-gain; the exploiter who torpedoes a mentee's success by saddling them with low-yield activities; and the possessor who dominates the mentee across various areas of collaboration [110]. Passive mentorship malpractice can be divided into the following three subtypes – the 'bottleneck' mentor who is preoccupied with own competing priorities and does not have the bandwidth or the desire to attend to mentees; the 'country clubber' mentor who focuses on conflict evasion and avoids difficult but necessary conversations; and the 'world traveler' mentor who spends little to no time or effort on mentoring while often exploiting the mentee for self-promotion [110]. In addition to educational efforts, more formal 'mentor-mentee agreements' may help enforce accountability within the overall relationship [111]. Finally, active prevention of mentorship malpractice requires mentees to be proactive, including the establishment of a 'mentorship team,' setting boundaries, communicating needs, knowing when to walk away, and not being complicit by facilitating negative mentor traits [110].

### **9. Challenges to mentorship**

It is generally accepted that physicians at all levels of training carry tremendous amounts of responsibility and face significant time constraints due to multiple competing clinical and non-clinical priorities [112–114]. A major concern among potential mentors is the time commitment required for a successful mentorship.

This is a valid concern, and while new approaches to mentorship like 'micromentorship' and group mentorship provide avenues to lessen the time demand, mentorship is still an added responsibility. Consequently, it is up to the individual to evaluate if the benefits of mentorship are worth the time commitment [2, 115, 116].


Raton, FL:CRC Press, Inc.),1982. **Part 3**

**Materials Science**

88 Gamma Radiation

Whicker, F.W. and Schultz, V. Radioecology Nuclear Energy and the Environment(Boca

**6**

**Gamma Radiation as a Novel Technology for Development**

**of New Generation Concrete**

and Fernando Ureña-Nuñez3

*La Marquesa Ocoyoacac,*

*Mexico*

Gonzalo Martínez-Barrera1, Carmina Menchaca Campos2

*Facultad de Química, Universidad Autónoma del Estado de México,*

*2Centro de Investigación en Ingeniería y Ciencias Aplicadas (CIICAp), Universidad Autónoma del Estado de Morelos, Cuernavaca Morelos*

*Km.12 de la Carretera Toluca-Atlacomulco, San Cayetano,*

*1Laboratorio de Investigación y Desarrollo de Materiales Avanzados (LIDMA),*

*3Instituto Nacional de Investigaciones Nucleares, Carretera México-Toluca S/N,*

Global development demand new technological creations at less costs. Inside the specialized industry, composite materials usage is well known. That is why it is of major importance research and development of new composite materials for technological applications, as in

The composite materials usage has increased rapidly during the last few decades in building construction; showing different properties as reduction in weight, reduction on the fatigue and corrosion. As we know, a key issue with composite technology is that the final characteristics of a material are established at the time of fabrication of the goods. Therefore, part design, fabrication development and material characterization must proceed

The production of polymer concrete can be developed for semi-industrial and industrial scales for its economical advantages, as well as environmental benefits if its main raw materials are wastes. In such composite materials the polymeric materials are able to compete and contribute substantially to the development of better, cheaper and more

In polymer concrete some engineering polymers are widely used due to their excellent mechanical, thermal and chemical properties. These properties are direct consequence of their composition as well as their molecular structure. Nowadays, mechanical properties of polymeric materials are of great interest and can be improved by composition and/or morphological modifications, in order to change the softening temperature and get their

**1. Introduction**

concurrently.

functional products.

elastic solid state back [Menchaca et al., 2011].

the construction industry.


Bismuth Advanced Applications and Defects Characterization

*Edited by Ying Zhou, Fan Dong and Shengming Jin*

**BISMUTH - ADVANCED**

**APPLICATIONS AND**

**CHARACTERIZATION**

Edited by **Ying Zhou, Fan Dong**

**DEFECTS**

and **Shengming Jin**


**Abstract**

The chapter is divided into 5 parts. The first part describes what the satellite data is and describes the levels of its processing. In the second part, attention is paid to sea surface temperature anomalies, the conditions for the appearance of the most significant anomalies and their influence on the behavior and survival of aquatic organisms are described. The third part is devoted to calculating linear trends in ocean surface temperature from a 20-year series of satellite data. It is shown that the heat content of the surface layer of Okhotsk Sea decreases, most significantly in its northern and western parts. This trend is especially pronounced in the spring, which may be due to a decrease in ice cover and a more significant cooling of the waters due to winter convection. In the fourth part, periodic fluctuations in the temperature of the surface of the ocean are considered. It is demonstrated how, using the calculated trend and several basic harmonics, one can try to predict the temperature next year. And the last part concludes the chapter.

**Keywords:** satellite data, sea surface temperature, Tsushima current, Okhotsk Sea, regional effects of global warming, SST anomalies, trends, harmonic, cyclicity

## **1. Introduction**

The study of the thermal regime of various water areas is one of the most important oceanological problems, since the spatio-temporal variability of water temperature reflects complex processes of formation, transformation and dynamics of water masses. In addition, temperature is one of the key parameters that determine the conditions for the existence and development of most species of aquatic organisms; therefore, the study of this problem is also of key importance for hydrobiology. The zoning of water areas by the nature of temperature conditions, as well as their forecasting, taking into account the peculiarities of seasonal and interannual variability, is an important scientific task, which also has a pronounced applied aspect associated with the fact that accumulations of some species of commercial fish are confined to the zones of separation of water masses with different characteristics.

Direct measurements are a traditional source of water temperature data. Their accuracy depends only on the accuracy of the device, and the discreteness depends on the specific task (in some cases it can reach a fraction of a second). They can also be used to construct vertical profiles with high vertical resolution. However, recently the number of expeditions has been steadily declining, and the data obtained through direct measurements are point and irregular and are not suitable for studying large-scale phenomena or for obtaining data from hard-to-reach places.

Satellite data, on the other hand, are regular and allow covering the entire water area of the studied basin. Therefore, they are very good sources for studying seasonal and interannual variations in the temperature of the surface layer (the thickness of which ranges from 1 to 10 meters). The disadvantage of these data is the strong dependence of accuracy on cloudiness (especially in the infrared and visible ranges). Ideally, they should be regularly compared with direct measurements in order to identify errors in data interpretation.

For scientific purposes, data are usually used from satellites located either in a geosynchronous (in a particular case, geostationary orbit) or in a heliosynchronous orbit. The advantage of the geostationary orbit is obvious: you can get a picture of the same area with discreteness of up to half an hour, and after pointing to the satellite, a constant correction of the antenna position is not required. It is very useful for telecommunication systems as well as for obtaining meteorological data. The disadvantage is the lack of coverage at polar latitudes.

This disadvantage is easily eliminated by satellites in a polar sun-synchronous orbit. A sun-synchronous orbit is important to science because it keeps the angle of incidence of sunlight on the Earth's surface more or less constant, although the angle will change with the change of seasons. Without a sun-synchronous orbit, you would have to account for changes in shadow and lighting angles, making it difficult to track changes over time. It would simply be impossible to collect the information needed to study climate change.

In 1997, a TeraScan satellite receiving station was installed at SakhNIRO, with the help of which data are received from the NOAA, Metop, Aqua and Terra satellites in a polar sun-synchronous orbit (**Figure 1** [1]). At the moment, a 21-year series of satellite data on the temperature of the surface of the Sea of Okhotsk and adjacent waters has been accumulated, which makes it possible to analyze the interannual variations of this parameter.

**Figure 1.**

*Example of a sun-synchronous orbit for a NOAA satellite [1].*

*Analysis of Spatiotemporal Variability of Surface Temperature of Okhotsk Sea and Adjacent… DOI: http://dx.doi.org/10.5772/intechopen.94918*

This chapter is organized as follows:


**7. Easy preparation of reduced GO‐based conducting polymer composite via organogel**

Recently, we have reported that a highly electrically conducting graphene (reduced GO, rGO) based polymer composite was successfully prepared via organogels using an organically dispersible electrically conductive polyaniline (polyANI) and a low molecular‐weight organogelator consisting of cholesterol derivatives: organogelator used was complex salt of cholesterol hydrogen succinate with 1, 10-diaminodecane [33].

The plain gel was prepared by heat treatment of a mixture of toluene and the organogelator. PolyANI/rGO organogel was successfully obtained by the addition of polyANI and rGO to the plain gel and mixing them, as shown in **Figure 17**.

**Figure 17.** Easy preparation of polyANI/rGO organogel.

Similarly to the plain gel, polyANI/rGO organogels showed thixotropic behavior. It became apparent that these gels show a good recovery of viscoelasticity after the application of mechanical stress. Three‐dimensional networks consisting of organogelators, polyANI aggregates, and rGO particles dispersed in the gel state were confirmed by the microscopic studies.

The value of electrical conductivity of the polyANI/rGO organogel is very low: it could be detected at 2.5 × 10⁻ 5 S/cm. The conductivity of dried polyANI/rGO composites synthesized via organogels was determined to be 3.2 S/cm. It is interesting to note that the contribution of the organogelator to form effective and complementary conducting pathways by polyANI and rGO.

A ballpoint pen was filled with the polyANI/rGO organogel and used to produce lines similar to the ones produced by commercially available ballpoint pen. It is interesting to note that the line showed electrical conductivity.


**3.1 Transition metal oxide–based electrode materials**

Transition metal oxides (TMOs) are well studied by researchers in the energy storage field. Pseudocapacitive nature of these materials is due to the fast and reversible redox reactions at the surface of electrode material [17]. Theoretical specific capacitance of some TMOs is shown in **Figure 2**.


3.3.5. Autoencoder (AE)

AE is a type of AI algorithm based on deep neural networks that use unsupervised learning for encoding and decoding the input data and are commonly utilized for feature extraction and denoising [68]. Two different processes are performed by AE: encoding and decoding. Hence, its structure is symmetrical. The input data are passed through three different layers: the input, latent, and output layers. These layers make up the AE architecture (Figure 9). The input and output layers have the same size, and the latent layer has a smaller size than the input layer [69]. Encoding and decoding are achieved with the following equations, respectively:

$$\mathbf{e} = f\_{\theta}(\mathbf{x}) = s(\mathcal{W}\mathbf{x} + b) \tag{10}$$

$$
\widetilde{\mathfrak{X}} = \mathfrak{g}\_{\ \mathfrak{G}'}(e) = s \left( \mathcal{W}' e + b' \right) \tag{11}
$$

where *x* is the input vector, *e* ∈ [0, 1] d represents the latent vector, and *x* ∈ [0, 1] D is the produced vector. From the input layer to the latent layer, the encoding process is repeated. Next, the decoding process is repeated from the latent layer to the output layer. *W* and *W* represent the weight from the input to the latent and from the latent to the output layers, respectively. *b* and *b* denote the bias vectors of the input layer and the latent layer. The activation functions of the latent layer neurons and the output layer neurons are represented with *fθ* and *g* θ , respectively. The weight and bias parameters are learned in

the AE structure after reducing the reconstruction error. Equation (12) is used to measure the error between the reconstructed *x* and the input data *x* for individual instances:

$$J(\mathcal{W}, b', \mathbf{x}, \tilde{\mathbf{x}} \mid) = \frac{1}{2} \left\| h\_{w, b}(\mathbf{x}) - \tilde{\mathbf{x}} \right\|^2 \tag{12}$$

**Figure 9.** The structure of the auto-encoder (AE) model.

In a training dataset including *D* instances, the cost function is defined as follows:

$$\sum\_{l=1}^{n\_l} \sum\_{i=1}^{n\_l} \sum\_{j=1}^{c\_l} \left( \mathcal{W}\_{ji}^{(l)} \right)^2 = \left[ \frac{1}{D} \sum\_{i=1}^{D} (\frac{1}{2} \left\| h\_{w\beta} \left( \mathbf{x}^{(i)} - \hat{\mathbf{x}}^{(i)} \right) \right\|^2 \right] + \frac{\lambda}{2} \sum\_{l=1}^{n\_l} \sum\_{i=1}^{c\_l} \sum\_{j=1}^{c\_l+1} \left( \mathcal{W}\_{ji}^{(l)} \right)^2 \tag{13}$$

where *D* refers to the total number of instances, *s* to the number of neurons in layer *l*, *λ* represents the weight attenuation parameter, and the square error is the reconstruction error of each training instance.


*5.1. Experimental Platform*

For the purpose of verifying the practicability of ONAC, a hardware experiment was carried out while the experimental platform is illustrated by Figure 5. A strong AC/DC power supply was used to provide stable three phase AC and DC voltages. The power supply was connected with the converter by an IGBT converter [27] with an inductive filter. IGBT converters can be driven by pulse width modulation (PWM) signals. Compared with the conventional PWM or sinusoidal pulse width modulation (SPWM) techniques, the space vector modulation (SVM) can increase 15% more of the maximum output voltage and reduce the switching times. The voltage/current isolators measured the voltages and currents, which were then sent to the dSPACE simulator through analogue/digital (A/D) blocks [28].

**Figure 5.** The experimental platform based on the dSPACE platform.

The dSPACE simulator had quad-core AMD (advanced micro devices) processors (DS1104) and operated with DS5202 ACMC board, which was capable of generating high-frequency PWM [29] signals and providing high-speed A/D interfaces. The IGBT converter, inductive filter, voltage/current isolators, and AC/DC power supply were all from the Lab-Volt company, which provides various equipment with the user-friendly interfaces, accurate system parameters, and reliable hardware protections. The DC bus of the IGBT converter was protected by adopting a 100 Ω damping resistor with 1 kW rated power connected to the damping circuit built in to the IGBT converter to avoid potential damages caused by overvoltage or overcurrent in the DC bus. Finally, Table 2 demonstrates the guaranteed measurement error of DS1104 board.

Offset drift 40 μV/K 130 μV/K Gain drift 25 ppm/K 25 ppm/K Signal-to-noise ratio Multiplexed channels: >80 dB >80 dB Parallel channels: >65 dB

**Table 2.** The error specification of quad-core AMD (advanced micro devices) processors (DS 1104) board [30].

The controller was embedded in the dSPACE platform (dSPACE Inc., Paderborn, Germany) shown in Figure 5, which measured the DC voltage, reactive power, and active power as inputs. Then, the PWM signals were generated with various duty cycles as controller outputs to the IGBT converter. Furthermore, the sampling frequency *f* s and the PWM frequency *f* PWM with SVPWM are given in Table 3. Here, sampling frequency was determined by the performance of industrial microprocessors of VSC, which usually ranges from 1 kHz to 3 kHz. Note that a lower sampling frequency might degrade the control precision while a higher sampling frequency might increase the computation burden. To make a trade-off between the control precision and computation burden, this paper adopted the median sampling frequency (2 kHz) to validate the controller implementation feasibility. MSSA was run 30 times and the optimal outcomes (the controller gains and observer gains leading to the lowest fitness function) were undertaken in ONAC. The parameters of the controller are provided in Table 4, while the control inputs were limited as |*u*d*i*| ≤ 50 V and |*u*q*i*| ≤ 50 V, *i* = 1,2, respectively. Based on SVPWM and ignoring the resistance in the steady state, the minimum DC voltage must satisfy the following inequality to ensure the converter is controllable and can work properly as [16]

$$V\_{\rm dci} \ge \sqrt{3} \sqrt{\left(u\_{\rm sdi} + \omega L \dot{u} \dot{q} \dot{i}\right)^2 + \left(u\_{\rm sqi} - \omega L \dot{u}\_{\rm di}\right)^2} \tag{37}$$

At last, the IGBT converter was used as the DC/AC converter, the antiparallel diodes were combined with the IGBT converters such that it could be operated as either a rectifier or an inverter.

**Table 3.** System parameters used in the experiment.

**Table 4.** Optimized controller gains and observer gains tuned by MSSA.

#### *5.2. Rectifier Controller Experiment*

The configuration of the rectifier controller experiment is illustrated by Figure 6, in which the DC bus was connected to a resistive load to achieve a specific evaluation of control performance when the active power flowed from the AC grid to the DC cable.

**Figure 6.** The configuration of the rectifier controller experiment.

The effectiveness of the ONAC-based rectifier controller was tested at first, in which the initial DC voltage *V*dc1 and reactive power *Q*1 were adjusted to be 100 V and 0 Var, respectively. The DC voltage remained at 100 V for the whole period of the experiment. The reference of reactive power was set to be 15 Var, then dropped to 5 Var and was restored to 15 Var. After reactive power was stabilized at 15 Var, a resistive load *R*L was connected to the DC bus given in Figure 6 to represent the DC cable. Then a 50% voltage drop of the AC grid occured to evaluate the system transient performance.

The experiment results are provided in Figure 7. It demonstrates that both the reactive power and DC voltage were rapidly regulated, while the DC voltage was able to be restored after the AC grid voltage drop occurred, which validates the effectiveness of the ONAC-based rectifier controller.

**Figure 7.** System responses obtained in the rectifier controller experiment: (**a**) Average active power *P*1; (**b**) average active power *Q*1; (**c**) DC voltage *V*dc1; (**d**) control input *V*dq.

#### *5.3. Inverter Controller Experiment*

The configuration of the inverter controller experiment is shown in Figure 8, where a DC power supply was utilized to output a stable DC voltage (representing a constant DC voltage regulated by the rectifier controller) such that the IGBT converter could work properly.

**Figure 8.** The configuration of the inverter controller experiment.

The effectiveness of the ONAC-based inverter controller was then tested, the initial value of reactive power and active power were zero. The DC voltage *V*dc2 was maintained at 60 V for the whole period of the experiment. The controller was activated to increase the reactive power *Q*2 to be 20 Var at first; after reactive power *Q*2 was stabilized the active power *P*2 was increased to be 10 W, then the reactive power *Q*2 was decreased to be 10 Var. At last a 33.3% voltage drop of the AC grid occurred to evaluate the system transient performance.

The experiment outcomes are given in Figure 9. Obviously, the reactive power and active power were able to be regulated independently, and the system could also be restored after the voltage drop, which verified the effectiveness of the ONAC-based inverter controller.

**Figure 9.** System responses obtained in the inverter controller experiment. (**a**) Average active power *P*2; (**b**) average active power *Q*2; (**c**) DC voltage *V*dc2; (**d**) control input *V*dq.

#### *5.4. Set point Tracking*

In order to evaluate the set point tracking performance of ONAC, both the simulation (set point curve) and experiment are compared in Figure 10. One can clearly observe that the experiment can closely track the set point, while their tiny difference resulted from the transmission delay of control signals or measurement noises during the experiment.

**Figure 10.** System responses obtained under set point tracking. (**a**) DC voltage *V*dc1 of rectifier; (**b**) average reactive power *Q*1 of rectifier; (**c**) DC voltage *V*dc2 of inverter; (**d**) average reactive power *Q*2 of inverter.

#### *5.5. Disturbance Rejection*

Disturbances are very common in many industrial processes which often have a malignant impact on the predesigned control performance of the studied system. As a result, disturbance rejection performance is a very crucial property for advanced controller design in industries [31–33]. This test aimed to evaluate the disturbance rejection performance of ONAC, while the perturbation estimation performance of HGPO under 20% voltage drop lasting for 15 ms (t = 0.1 s–0.115 s) of VSC under rectifier mode was recorded and illustrated in Figure 11. As shown in Figure 11, both perturbations Ψr1 and Ψr2 could be rapidly estimated by HGPO s under the voltage drop of VSC as the perturbation estimation error could be efficiently converged to 0. As a result, the disturbance was able to be effectively rejected as its real-time estimate could be rapidly obtained and then fully compensated by ONAC. Here, only the rectifier results are provided as it involved both the second-order HGPO (Figure 11a) and third-order HGPO (Figure 11b), while similar results could be found in inverter mode. Note that the transient relatively large variation of perturbation when the disturbance occurred was due to the high gains used in HGPO.

**Figure 11.** Disturbance rejection performance obtained under 20% voltage drop of rectifier. (**a**) Perturbation Ψr1 and (**b**) perturbation Ψr2.

#### *5.6. Robustness to Parameter Uncertainties*

Lastly, the robustness to parameter uncertainties was investigated in this section. Here, a series of plant model mismatches of equivalent resistance *R*1 and inductance *L*1 within ±20% variation around their nominal value were undertaken. Then, a 50% voltage drop lasting 100 ms at VSC was applied, in which the peak value of output power |*P*| was recorded. Figure 12 shows that the variation of peak value of active power |*P*| obtained by FLC [10] and ONAC were 73.4% and 26.5%, which clearly demonstrated that ONAC provided the highest robustness against VSC parameter uncertainties thanks to the real-time compensation of perturbation, while FLC was vulnerable to parameter uncertainties as it required an accurate VSC system model.

**Figure 12.** Peak value of active power |*P*| obtained under a 50% voltage drop lasting 100 ms at VSC with 20% variation of the equivalent resistance *R*1 and inductance *L*1 of FLC and ONAC. (**a**) FLC and (**b**) ONAC.

#### **6. Conclusions**

This paper designed an ONAC-based rectifier controller and ONAC based inverter controller. It can provide significant robustness as various modelling uncertainties were aggregated into a perturbation, which was rapidly evaluated by HGPO and then compensated for by the proposed controller. Besides, ONAC merely needs the active power and reactive power, as well as DC voltage need to be measured, while a precise VSC system model is not required, thus the proposed controller can be readily implemented in practice. Moreover, the controller gains and observer gains are optimally tuned by an emerging and promising biology based metaheuristic algorithm called MSSA, which can effectively achieve an appropriate trade-off between global exploration and local exploitation, thus a satisfactory control performance and perturbation estimation performance could be guaranteed. Finally, for the sake of verifying the practicability of the proposed controller, a hardware experiment utilizing dSPACE platform was undertaken. The experiment results showed that ONAC could achieve satisfactory control performance.

Future studies will apply ONAC-based VSC on renewable energy systems, e.g., PV inverter, wind energy conversion systems, as well as electric energy storage systems.

**Author Contributions:** Preparation of the manuscript has been performed by Y.J., X.J., H.W., Y.F., W.G., B.Y., and T.Y.

**Funding:** The authors gratefully acknowledge the support of National Natural Science Foundation of China (51777078), and Yunnan Provincial Basic Research Project-Youth Researcher Program (2018FD036).

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

#### **Nomenclature**

#### **References**

1. He, Y.Q.; Chen, Y.H.; Yang, Z.Q.; He, H.B.; Li, L. A review on the influence of intelligent power consumption technologies on the utilization rate of distribution network equipment. *Prot. Control Mod. Power Syst.* **2018**, *3*, 183–193. [CrossRef]

© 2019 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).


2.3.1. Alcoholic Liver Disease

In alcoholic fatty liver disease, viable bacteria, endotoxin, and LPS have been observed in the blood of both animal models and ALD patients, for which there are many potential mechanisms [49,50]. Small intestinal bacterial overgrowth (SIBO) in chronic alcoholics is more prevalent than healthy controls [51], perhaps due to extended orocaecal transit time in alcoholics compared to social drinkers [52]. SIBO has been suggested to mediate translocation due to differences in mucosal defense in the small intestine, primarily a reduction in mucus secretion. Increased intestinal permeability has also been reported in alcoholic cirrhosis, ALD, and even in non-cirrhotic alcoholics using in vivo assays [53–55].

In human studies, a reduction in the number of small intestinal villi, goblet cells, and TJ protein ZO-1 expression in the colon were observed in chronic alcoholics [56–58]. Furthermore, cell culture and animal studies suggest that ethanol and its metabolite, acetaldehyde, can alter intestinal barrier function by (1) inducing epithelial cell apoptosis, (2) disrupting TJs by downregulating ZO-1, occludin, and claudin and redistributing ZO-1 into cytoplasm, and (3) displacing the cytoplasmic skeleton [59–62].

Lastly, the production of intestinal IgA and the quantity of immune cells have been shown to be significantly altered in cases of chronic alcohol consumption and in ALD patients. While systematic IgA is increased in alcoholic liver diseases due to intestinal permeability, fecal IgA and IgA-producing B cells within the lamina propria are reduced in animal models of ALD [63,64]. Recently, a significant decrease in the number and activation state of mucosa-associated invariant T (MAIT) cells, a key component in antibacterial immune defense, was shown in ALD patients [65].