**6. Acknowledgments**

198 Mass Transfer in Chemical Engineering Processes

The optimum separation time and separation cycles of the extraction can be estimated. The

Fig. 19 is a plot of the integral concentrations of Cu(II) against time to determine the reaction order (n) and the forward reaction rate constant (kf). The rate of diffusion and/or rates of chemical changes may control the kinetics of transport through liquid membrane depending on transport mechanisms (diffusion or facilitated). The reaction rate constants of first-order

Reaction order (n) Reaction rate constant (kf) R-squared % Deviation First-order 0.393 min-1 0.813 61.233 Second-order 0.708 L/mgmin 0.911 1.453

Table 4. R-squared and percentages of deviation for first-order and second-order reactions The percentage of copper ion extraction is calculated by Eq. (27). The percentage of

> f,in f,out f,in

(27)

(28)

C C % extraction 100 C

j Expt. Theo.

C C C % deviation 100

function of initial concentration of the target species in feed and also feed flow rate.

experimental data at the average percentage of deviation of 2%.

i 1 Expt. i

j

The optimum separation time for the prediction of separation cycles can be estimated by the model based on the optimum conditions from the plot of percentage of extraction as a

In this work, at the legislation of Cu(II) concentration in waste stream of 2 mg/L, the calculated separation time is 10 min for about 15-continuous cycles. The percentage of extraction calculated from this reaction flux model is much higher than the results from other works which applied different extractants and transport mechanisms. Types of extractants and their concentrations are significant to the separation of metal ions. For example, a hard base extractant can extract both dissociated and undissociated forms in a basic or weak acidic condition but dissociated forms are high favorable. While a neutral extractant normally reacts with undissociated forms, but in an acidic condition it can react with dissociated forms. It is noteworthy to be aware that not only types of the extractants (single or synergistic), in this case LIX84I for Cu(II), but also the transport mechanism, e.g., facilitated transport mechanism attributes to the extraction efficiency. The model results are in good agreement with the

Facilitated transport of the solutes or target species benefits the separation process by liquid membrane with a non-equilibrium mass transfer and uphill effect. It is more drastic chemical changes of the target species with the presence of a suitable extractant or carrier (sometimes by synergistic extractant) in liquid membrane to form new complex species

model was verified with the experimental extraction results and other literature.

(n = 1) and second-order (n = 2) are 0.393 min-1 and 0.708 L/mgmin, respectively.

deviation is calculated by Eq. (28).

**5. Conclusions**

The authors are highly grateful to the Royal Golden Jubilee Ph.D. Program (Grant No. PHD50K0329) under the Thailand Research Fund, the Rare Earth Research and Development Center of the Office of Atoms for Peace (Thailand), Thai Oil Public Co., Ltd., the Separation Laboratory, Department of Chemical Engineering, Chulalongkorn University, Bangkok, Thailand. Kind contributions by our research group are deeply acknowledged.


**3. Results**

#### *3.1. Social Network Analysis of Study Linking Cancer and Vaping Published Research (October 2019)*

Figure 1 below shows a social network analysis of tweets from early October 2019, which relates to a time period when the first academic study was published linking vaping to cancer in mice. Figure 2 is zoomed into Group 2 and labels the influential users within the group. From group 3 onwards we have abbreviated the word 'Group' to 'G'.

**Figure 1.** Tweets related to e-cigarettes in October 2019.

**Figure 2.** Zooming into Group 2.

The network graph highlights that there were different clusters of discussion taking place on Twitter during this time with two large groups and several smaller groups indicating a number of communities that had emerged related to this topic. The ten largest groups were labelled. There was also a sizeable isolates group (Group 1), which indicated that a number of Twitter users were tweeting about e-cigarettes without mentioning or replying to other Twitter users. In Figure 2, we zoom into Group 2 in which discussions were formed around a number of influential user accounts such as President Donald Trump, the World Health Organisation, and Fox News. Alongside this network graph, we were also able to identify the most frequently used words, hashtags, websites, and most influential users in the network, which are described below.

#### *3.2. Most Frequently Occurring Words*

Table 1 below displays the most frequently occurring words during this time period. It highlights that mentions of e-cigarettes were made in conjunction with the scientific work that linked them to cancer.

**Table 1.** Most frequently occurring words.

The most frequently occurring words in Group 1 are displayed in Table 2 below. Here, it can also be seen that many of these words centred on the news story linking vaping to cancer.

**Table 2.** Most frequently occurring words in Group 1.

The most frequently occurring words in Group 2 (the second largest cluster of Twitter users) are highlighted in Table 3 below. It is important to note that the word-count numbers listed above would also contain hashtags that used those words.

**Table 3.** Most frequently occurring words in Group 2.

These words in Group 2 highlighted the polarisation on Twitter as certain hashtags were against the idea of any restrictions on vaping by governments, and Twitter users expressed this through the use of the hashtag "#wevapewevote", whereas those against e-cigarettes would use hashtags such as "#vapeban".

#### *3.3. Most Frequently Occurring Hashtags*

The most frequently occurring hashtags overall are summarized in Table 4 below. These hashtags highlighted division and polarisation on Twitter as some hashtags related to campaigns against vaping such as "vapeban", whereas other hashtags such as "wevapewevote" were pro-vaping. There were more Twitter users using pro-vaping based hashtags than against them.

**Table 4.** Most frequently occurring hashtags.

#### *3.4. Most Frequently Occurring Websites*

The top URL shared on Twitter during this time was entitled "Lung Damage From Vaping Resembles Chemical Burns, Report Says" published in the New York Times (*n* = 341) [18]. The other URLs consisted of an article by the Consumer News and Business Channel (CNBC) (*n* = 229) titled Researchers find e-cigarettes cause lung cancer in mice in first study tying vaping to cancer. Further websites also appeared such as an article titled Expert reaction to study on ecig vapour and cancer in mice, which was published by the Science Media Centre (*n* = 124). Interestingly, the article contained reactions from two professors with expertise in tobacco research, and both noted that the study linking e-cigarettes and cancer to mice was potentially seriously flawed in its relevance for human vapers. Another article titled Juul Is Sued by School Districts That Say Vaping Is a Dangerous Drain on Their Resources published by the New York Times was also shared (*n* = 88). These results are summarized in Table 5 below.

**Table 5.** Most frequently occurring hashtags.

#### *3.5. Influential Users Ranked by Betweenness Centrality*

Influential users were ranked by the betweenness centrality algorithm using NodeXL. Influential users consisted of CNBC, which had 3.3 million followers, and the news anchor of Columbia Broadcasting System's (CBS) Nightly Business Report, who had 31.3 thousand followers. The reason for the prominence of CNBC was because they had published an article on October 7th titled '*Researchers find e-cigarettes cause lung cancer in mice in first study tying vaping to cancer'.* A citizen who tweeted the study and noted the findings of the study for the reason for "not following trends" received over 400 retweets and 1.8 thousand likes also became influential and had 6.1 thousand followers. The user was characterizing e-cigarettes as a societal trend with dissent towards those who were using e-cigarettes. A user replied to this tweet indicating that the fear around e-cigarettes seemed irrational when traditional e-cigarettes were still being sold. Other influential accounts consisted of The Centres for Disease Control (CDC) with 1.24 million followers who tweeted about cases of lung damage of users of mostly illicit e-cigarettes containing the Tetrahydrocannabinol (THC), and Gregory Conley, with 18.1 thousand followers, who is a tobacco harm-reduction advocate and supporter of e-cigarettes who was tweeting at this time. Table 6 below provides a summary of the influential users in the network.

**Table 6.** Influential users ranked by betweenness centrality.

#### *3.6. Pro-Vaping and Anti-Vaping Themes*

Much of the content around this time related to Twitter users sharing the news story linking vaping and cancer in mice. Furthermore, our tweet clustering, word, and hashtag analysis revealed two further main themes taking place within the overall network related to Twitter users who were, broadly speaking, either pro-vaping or anti-vaping. In order to confirm this finding, we utilised content analysis to categorize tweets until thematic saturation occurred, and we found two additional main themes related to pro-vaping or anti-vaping views.

Twitter users who were supporting vaping tweeted how using vaping products had helped them stop smoking and also highlighted how the current issue around vaping was very specific to certain illicit types of vaping products that contained Tetrahydrocannabinol (THC). Arguments were also made towards vaping serving a function in harm-reduction as it was comparably safer than smoking. Twitter users against vaping requested for people to stop using e-cigarettes and for tougher legalisation on vaping products. Below, we provide anonymized tweets related to these two main themes.

*3.7. Pro-Vaping Tweet Extracts*

There were a number of Twitter users who would share their own positive personal experiences of using e-cigarette devices:

*"I was a heavy smoker for many decades and knew I'd be dead in my 50 s and never get a chance to see my grandchildren. But then 5 years ago I started to use e-cigarettes and I may be able to see my grandchildren now"*

Other Twitter users were concerned about the increased focus on younger people using e-cigarettes:

*"The dangers of vaping and youth is completely overblown! Car crashes are actually the leading cause of death in younger people"*

There were also Twitter users who noted the dangers of cigarettes compared to e-cigarettes:

*"Right then, they will ban e-cigs because it causes deaths, but regular cigarettes don't seem to be a problem, lol!"*

There were also Twitter users who criticised the study linking cancer and vaping:

*"If they re-ran the study and compared vaping and real cigarettes then vaping would be the safer alternative! #vapingsavedmylife"*

In the above tweet extract, the user employed the hashtag "vapingsavedmylife", which was used by Twitter users to indicate the life-saving potential of vaping.

#### *3.8. Anti-Vaping Tweet Extracts*

There were also Twitter users who called for people to quit vaping:

*"Please stop vaping! – read this news story Lung Damage From Vaping Resembles Chemical Burns, Report Says"*

Other Twitter users called for parents to inform their children that e-cigarettes were unsafe:

*"Let your children know that vaping is NOT SAFE, doesn't matter what their friends say, they've been brainwashed by big Tobacco!"*

There were also a number of general comments noting that e-cigarette use was likely to be dangerous:

*"I am not a medical professional, but most likely that vaping is not good for you"*

Another Twitter user noted:

*"I used to smoke 50 cigarettes a day and managed to just quit, that's the best way to quit. Vaping is not safe."*

From the tweet extracts above, we can see that there was a diverse range of views and comments noting that e-cigarettes were unsafe.


Waste Water

Treatment Technologies and Recent Analytical Developments

*Edited by Fernando S. García Einschlag and Luciano Carlos*

**WASTE WATER -**

**DEVELOPMENTS**

**Einschlag** and **Luciano Carlos**

**TECHNOLOGIES AND**

**RECENT ANALYTICAL**

Edited by **Fernando Sebastian García**

**TREATMENT**


**1. Introduction**

The fat accumulation in sub-mental area is a problem at the forefront of many minds, since excess adipose tissue leads to the drooping of lower face and causes further ptosis. These changes affect a full esthetic perception of the face, since a clear oval shape of the face is associated with young age and the smoothing of lines and corners with old age [1].

Plastic surgery offers a variety of ways to solve this problem, among which are different variations of platysmaplasty, combined lifts in combination with liposuction and other. However, all of them are tempered by significant stress of an invasive procedure, a long rehabilitation period, providing anesthetic procedures and a high risk of complications [2].

The realities of modern life and modern lifestyle determine that when choosing an operative measure most patients want a light invasion, a short rehabilitation period and no general anesthesia [3].

We use laser-assisted liposuction to correct the excess fat accumulation in submental area and at the lower face as well as to correct age-related alterations [4].

Many studies conducted by various authors on the effects of laser emission in tissues have shown that lipolysis was followed by small blood vessels coagulation in adipose tissue, cell lysis (bursting), reorganization of the reticular dermis and collagen coagulation in adipose tissue. Histological tests have demonstrated some of the advantages of using the Nd: YAG laser. These include formation of new collagen, reduction of intraoperative and postoperative hemorrhage as well as a reduction of adipocyte quantity [3–6].

Furthermore, a dermal flap retracts after laser, which in turn allows for reaching the lifting effect. In 2002 Badin published an article entitled "Laser Lipolysis: skin flaccidity under control", in which he demonstrated histological changes of tissue after laser treatment. During the operation of a laser, adipocyte membranes break down, blood vessels coagulate and collagen fiber restructures [7].

The research demonstrated that laser lipolysis with selective exposure to ND: YAG laser radiation with a wavelength of 1064 nm is less traumatic and is accompanied by tissue reactions, which appear by neocallagenesis and reduction of the dermal flap. In order to maximize the impact and minimize the negative consequences of laser, Badin recommended temperature control of the skin integument in the surgical area, range of safe temperatures is between 38-41C [7]. The laser lipolysis allows performing a surgery on the so-called hazardous areas, which include lower legs, arms, face [3, 8].

Thus, the development of algorithms for using laser-assisted liposuction in hazardous areas such as the lower face and the submental area continues to hold its relevance for greater effectiveness and minimum complications from this surgery.


*Article* **Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network**

**Weiliang Qiao 1,\*, Hongtongyang Guo 1, Enze Huang 1, Wanyi Deng 2, Chuanping Lian 3 and Haiquan Chen 1**

**Abstract:** To investigate the human-related factors associated with suffocation on ships during docking repair, a comprehensive analysis model composed of a Bayesian network (BN) and a complex network (CN) is proposed in the present study. The principle of event tree analysis (ETA) is firstly applied to identify the hazardous events involved in the accident according to the accident report, based on which the CN would then be developed with the logic relationships among the hazardous events. The improved K-shell decomposition algorithm is utilized to determine the criticality of nodes in the CN, the results of which are then used to develop the BN model within the framework of a human factor analysis classification system (HFACS). Then, the developed BN model can be simulated with the probability distribution of all the nodes within the BN, which are obtained on the basis of node criticality. Finally, the results of the BN simulation are interpreted from the perspectives of a brief analysis, backward analysis and sensitivity analysis. The results are verified with existing studies and the accident investigation report issued by authority, which are presented as evidence to verify the effectiveness of the proposed methodology to evaluate the human-related risk involved in the suffocation on ships. The methodology proposed in this study integrates the advantages of BN and CN to investigate the human-related hazardous events involved in maritime accidents, which can be seen as the main innovation of this work.

**Keywords:** suffocation on ships; human-related hazardous events; Bayesian network; complex network; HFACS

#### **1. Introduction**

The operational scenarios in shipyards are characterized by high risk level due to the frequent interaction among various stakeholders and the different work types involved during docking repair [1–3]. During docking repair, it is common that a lot of professional and dangerous operations are carried out simultaneously in the same space, meaning that risk factors are highly interconnected and harmful, especially human-related factors [4]. In Singapore, accidents and incidents which occur during docking repair are receiving attention, and a legislative amendment was passed by the attorney general's chambers [5] to prevent or minimize the occurrence of such accidents. Later, in 2019, the International Labor Organization (ILO) adopted a revised code of practice for safety and health in shipbuilding and ship repair at the 329th session [6], which aimed to improve safety and health practices by providing good practice for governments, employers, and workers. However, occupational incidents and accidents still occur in the shipyard during docking repair, which indicates that safety management processes during docking repair need to be

**Citation:** Qiao, W.; Guo, H.; Huang, E.; Deng, W.; Lian, C.; Chen, H. Human-Related Hazardous Events Assessment for Suffocation on Ships by Integrating Bayesian Network and Complex Network. *Appl. Sci.* **2022**, *12*, 6905. https://doi.org/10.3390/ app12146905

Academic Editor: José A. Orosa

Received: 17 June 2022 Accepted: 5 July 2022 Published: 7 July 2022

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

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

further improved [7]. In addition, with the influence of some important regulations and codes adopted by International Maritime Organization (IMO), such as the revised MARPOL Annex VI [8] and the International Convention for the Control and Management of Ships Ballast Water and Sediments (BWM) [9], a large number of operating ships have to be refitted during docking repair to satisfy the new regulations. It can be reasonably predicted that the ship repair industry will continue to run with relatively high demand. For instance, according to the statistics of the China Association of the National Shipbuilding Industry (CANSI), in 2019, the shipbuilding industry and ship repair industry accounted for 65.4% and 4.2% of the overall business income in China, respectively. Compared with the figures of 2015, the proportion of these increased significantly [10]. It is important and urgent to reduce the rate of occupational accidents and accurately and efficiently promote industrial safety management.

#### *1.1. Related Studies*

In the scientific literature, the general characteristics and common causes of accidents can be summarized in terms of the time, place, type of work, age of workers, work experience, etc., through statistics and research on historical accidents [4,11,12]. When the average temperature is above 25 ◦C from June to September every year, the death toll is the highest. These aspects can be taken as accident-related risk factors for analyzing the cause and effect of occupational accidents from a comprehensive perspective [13,14]. However, such studies often lack pertinence due to the variety of accident types; thus, some studies began to focus on causal analysis based on a typical accident or operation category [15–17]. Still, accidents that may occur in confined spaces such as cargo holds have not received enough attention. In fact, it is estimated that there are 0.05–0.08 deaths per 100,000 workers in such working conditions [18]. In addition, nearly 38 people die of poisoning or suffocation accidents in confined spaces every year [19]. In Virginia, the highest death rate per million employees is occupational confined-space accidents in shipbuilding and repair facilities, with a probability of 23.2% [20]. Therefore, it is especially necessary to analyze and solve the safety dilemma surrounding this kind production activity.

When there is a production accident, people tend to investigate the causes of accidents on the surface, rather than analyze the root causes, which leads to simply attributing the accidents to worker violations or errors. As a result, the risk control strategy is always inadequate, and accidents endangering the safety of employees occur repeatedly. Consistent with these observations, organizational and management factors such as the weak safety awareness of workers, high work pressure, inadequate professional familiarization and training, and lax supervision of processes [1,17,21] are increasingly being recognized as the deep-seated causes of accidents that need to be acknowledged. These factors are closely related to human behavior, not only human errors. Here, the related risks are collectively referred to as human factors. It is generally acknowledged that, with the economic environment and advanced science and technology providing technical guarantees and a material basis for the realization of safe conditions, human factors play an increasingly prominent role in the occurrence and evolution of accidents [22,23].

In addition, the risks that lead to incidents and accidents in shipbuilding and repair, including human factors, are extremely complex and uncertain. Therefore, it is crucial to systematically study the formation mechanism of accidents and the causal relationship between risk factors. Risk assessment is a useful technology in the field [3,13,24]. Some qualitative research methods involving literature reviews, field surveys of practitioners, and expert interviews can assist in the identification of potential risk factors related to accidents [25]. They provide factual information about working conditions, occupational characteristics, and management vulnerabilities that may not be covered in official accident reports, promoting a further understanding of the risks and hazards faced by employees. Meanwhile, studies have shown that the causes of accidents are multifaceted and variable [23,26,27]; hence, there is a need to classify these factors from different research perspectives. The decomposition and classification of human factors have been studied

and widely applied in various fields by [28] and other researchers. The HFACS is widely regarded as having great value in the systematic analysis of the causes of accidents, which takes the continuous influence of high-level factors on low-level factors into account [29]. However, the complex logic relationships among factors allocated at different hierarchical layers under the HFACS framework are frequently ignored. Therefore, this study considers the logic relationships among different factors to implement risk evaluation.

The methodologies employed to investigate the causation for shipyard accidents are mainly represented by statistics-based techniques, such as those used in [1,4,12,30,31]. To further analyze shipyard accidents, the hybrid methodologies proposed in recent years for marine accident analysis can be referred to for investigation of the potential risks involved in shipyard accidents. Some of these hybrid methodologies are listed in Table 1.

**Table 1.** Hybrid methodologies used within the scope of marine accident analysis in recent years.

Lots of complex causal relationships and uncertain risks are involved in shipyard accidents, which can hardly be addressed by statistics-based approaches. Therefore, it is necessary to consider hybrid methodologies to improve the safety management in shipyards by analyzing human-factor-related accidents. With reference to similar studies in the maritime industry, as listed in Table 1, it can be observed that BN is widely accepted as an effective technique to model maritime accidents. To implement the Bayesian inference, the probability distribution for the nodes within the BN is required; for this purpose, fuzzy theory and expert elicitation are frequently applied. However, in these cases, the subjective bias and knowledge limitation may inevitably reduce the accuracy of the Bayesian inference. Therefore, in the present study, the exploratory application of a CN approach is conducted to quantify the probability of all the nodes within BN. The focus of this study is to establish a comprehensive accident analysis and risk assessment model that can reflect the risk propagation path, and then combine with the fuzzy algorithm and information theory to quantify and address the uncertainty of human factors in a ship repair system. Moreover, an accident that occurred in a Chinese shipyard is used as an empirical case to verify the proposed method. A classic method of accident analysis, fault tree analysis (FTA), is used and combined with fuzzy extent AHP to systematically establish the causal relationship of accidents and address the randomness of risk factors in this study. In addition, BN is adopted to predict the probability of risk because of its powerful learning and reasoning capability in many intelligent algorithms. In particular, the introduction of canonical probabilistic models has come to be recognized as an ingenious method to reduce the difficulty of obtaining the conditional probability between nodes in a BN, and it can simplify and deal with the problems of complex systems.

#### *1.2. Innovative Contribution*

The purpose of this study is to develop a comprehensive, human-related hazardous events assessment methodology with full consideration of the influences from social and technical aspects. Using this methodology, the risks stemming from on-site operations and management level can be quantitatively analyzed for accidents which occur during docking repair. For this purpose, a suffocation accident which occurred in a Chinese shipyard is exampled as a case study to illustrate the application of the proposed methodology. By the application of the methodology, the accident report is firstly qualitatively interpreted according to the basic principle of ETA to identify the human-related hazardous events involved in the suffocation accident. Additionally, the logic relationships among the identified hazardous events are analyzed to establish the CN, which is then evaluated by the improved K-shell decomposition algorithm to obtain the node criticality. Meanwhile, the identified hazardous events are reorganized within the framework of the HFACS with reference to the node criticality to develop the framework of the BN. Subsequently, the above-obtained node criticality is utilized to calculate the probability distribution of the nodes involved in the BN, which is essential for Bayesian inference. The innovative contribution of the proposed methodology is summarized as follows.

#### *1.3. Organization*

The remainder of this paper is organized as follows. Section 2 describes the materials and methods for the evaluation of human-related hazardous events which contribute to suffocation. The results and discussion are presented in Section 3. Finally, the paper is concluded in Section 4.

#### **2. Materials and Methods**

#### *2.1. Risk Scenario and Overview for the Proposed Methodology*

#### 2.1.1. Risk Scenario Description

On 5 May 2021, at approximately 14:45, a newly built 180,000-tonnage bulk carrier named "H1502" suffered from a poisoning and suffocation accident caused by high-density nitrogen during docking repair in a shipyard in Shanghai. Unfortunately, this accident caused two deaths. The following details were extracted from the accident report which can be accessed from Office of Emergency Management of Shanghai [43].

In the morning of the 5 May, the operators were assigned to purge the pipes with pure nitrogen in the stem of the ship by the coordinator on site, in the absence of a representative of the ship's owner. After noon, the operators on site implemented the purge in the cabin containing the bathymeter, a space regarded as a typical enclosure space, without taking effective safety measures. At approximately 14:30, two hydraulic pipes which connected together with the "U" pipe, marked as A and B, respectively, were purged fully with nitrogen. Then, the outlet valve of nitrogen was closed. Subsequently, the operators on site dismantled the "U" pipe connecting the hydraulic pipes A and B, and intended to purge hydraulic pipe A. For this purpose, the outlet valve of nitrogen was opened again. At this moment, a large amount of nitrogen leaked into the cabin. Five minutes later, the operators were found syncope in the cabin.

#### 2.1.2. Overview for the Proposed Methodology

In the present study, a novel methodology integrated by CN and BN is proposed to investigate the human-related hazardous events involved in the suffocation on ships from the perspective of the complex social–technological system. The general principle for the proposed methodology is illustrated in Figure 1, and there are four main steps for its implementation.

**Figure 1.** Overview for the proposed methodology.

Step 1—Risk scenario description: The human-related hazardous events involved in the accident report are firstly identified based on the principle of ETA. Then, the directed CN is developed on the basis of hazardous events, with the nodes and logic relationships among hazardous events being directed edges. In addition, the topological description for the developed CN is also conducted with the aspects of weights and degrees.

Step 2—Human-related hazardous events: The criticality of the nodes involved in the directed CN is then calculated by means of an improved K-shell decomposition algorithm based on the values of weights and degrees.

Step 3—Development of BN: The framework of the BN is first developed by the integration of HFACS and the results of node criticality. Then, the prior probability distribution for root nodes and the conditional probability tables for other nodes can be determined with application of the probability-related method proposed in this study, on the basis of node criticality.

Step 4—Human-related causation analysis based on BN simulation: The developed BN is finally simulated to investigate the human-related hazardous events involved in the suffocation accident, which is beneficial in improving the safety management for operation on board the ships.

#### *2.2. Development of CN*

In the present study, the CN is developed by the human-related hazardous events identification and causal logic analysis among these events. The principle of ETA is utilized to identify the human-related hazardous events involved in the accident report. As a result, these events are considered as the nodes within the CN, and the causal logic among the events would be regarded as the directed edges.

#### 2.2.1. Human-Related Hazardous Event Identification

According to the principle of ETA, the suffocation accident report was interpreted in detail to identify the human-related hazardous events contributing to the occurrence of the objective accident. Finally, a total of 40 hazardous events were identified, all of which are presented in Table 2.

#### **Table 2.** Hazardous events identified from the accident report.

#### **Table 2.** *Cont.*

#### 2.2.2. Topology for the CN

The logic relationships among various human-related hazardous events listed in Table 2 were used to develop a CN. For this purpose, five experts were employed in this study to determine the logic relationships, whose basic information is summarized in Table 3. To conduct the expert elicitation, a safety meeting was held, during which the accident report was introduced. The logic relationships are summarized in Figure 2.

**Table 3.** *Cont.*

**Figure 2.** CN obtained from the accident report.

#### *2.3. Calculation for the Node Criticality*

#### 2.3.1. Basic Criticality Calculation for Nodes within CN

The topology of the CN can be described by the parameters associated with degree and weight. It is noticeable that the weight-related parameter is equal to 1 in the case of a single accident report. To implement the topological description for the developed directed CN, the network is represented as:

$$G = (V, E) \tag{1}$$

where *V* = {*v*1, *v*2, ..., *vN*} and *E* = {*e*1,*e*2, ...,*eM*} represent the set of nodes and the set of edges, respectively. In the present study, *vi* indicates the identified hazardous event, while *ei* denotes the logic relationship. Then, an adjacency matrix A is employed to quantitatively describe *G* = (*V*, *E*), and the elements in the adjacency matrix are determined by:

$$\mathcal{A}\_{ij} = \begin{cases} \begin{array}{c} a\_{ij} \cdot w\_{ij} \ \dot{t} \rightarrow \dot{f} \\ 0 \ \text{else} \end{array} \tag{2} $$

where *aij*(*i*, *j* ∈ *V*) is equal to 1 when *vj* is triggered by *vi*, and is zero otherwise. The weight between the node *vi* and the node *vj* is expressed by *wij*. The meaning of weight is to make the connection strength between the nodes are quantified. The weight is divided into two kinds: in-weight and out-weight. In general, the characteristics of the CN are obtained by node strength and cross-weight and can be determined by the correlation calculation of the weight.

$$T\_{\mathcal{W}}(i) = \mathcal{S}\_{\text{in}}(i) + \mathcal{S}\_{\text{out}}(i) = \sum\_{j \in V} w\_{ji} + \sum\_{j \in V} w\_{ij} \tag{3}$$

$$\mathbf{C\_w}(i) = \mathbf{S\_{in}}(i) \cdot \mathbf{S\_{out}}(i) \ = \sum\_{j \in V} w\_{ji} \cdot \sum\_{j \in V} w\_{ij} \tag{4}$$

Another important parameter for a typical, directed CN refers to the degree of node, which is defined as the sum of nodes connecting or being connected to the objective node. In the present study, the degree of the objective node is denoted by *d*(*i*). Similarly, the degree is also divided as in-degree and out-degree, which can be determined, respectively, by:

$$d\_{\rm in}(i) = \sum\_{j \in V} a\_{ji} \tag{5}$$

$$d\_{\text{Out}}(i) = \sum\_{j \in V} a\_{ij} \tag{6}$$

According to the equations expressed in (5) and (6), the in-degree of a node is the total number of nodes affecting the objective node, and the out-degree of a node refers to the total number of nodes affected by the objective node. Then, the total degree of the objective node can be obtained by:

$$T\_{\mathbf{d}}(i) = d\_{\text{in}}(i) + d\_{\text{out}}(i) = \sum\_{j \in V} a\_{ji} + \sum\_{j \in V} a\_{ij} \tag{7}$$

The total degree is generally used to evaluate the activeness of nodes within a network according to graph theory; however, the difference between nodes characterized by the same total degree cannot be distinguished from the perspective of activeness. Therefore, the concept of the cross-degree is introduced in this study, which is mainly applied to distinguish nodes with the same total degree:

$$\mathbb{C}\_{\mathbf{d}}(i) = d\_{\text{in}}(i) \cdot d\_{\text{out}}(i) = \sum\_{j \in V} a\_{ji} \cdot \sum\_{j \in V} a\_{ij} \tag{8}$$

In the present study, node criticality was determined by the improved K-shell decomposition algorithm, which is implemented with the precondition of calculating the basic criticality as the input for the improved K-shell decomposition algorithm. To calculate the values of basic criticality for the identified nodes, a group of control equations associated with degree and weight are proposed here, which are expressed as:

$$f\_1(w\_{\vec{\mu}i}, w\_{\vec{\imath}j}) = \left| \sum\_{\vec{\jmath} \in V} w\_{\vec{\jmath}i} - \sum\_{\vec{\jmath} \in V} w\_{\vec{\imath}j} \right| \tag{9}$$

$$\left[f\_2\left[d\_{\rm in}(i), d\_{\rm out}(i)\right] = \left|d\_{\rm in}(i) - d\_{\rm out}(i)\right|\right] \tag{10}$$

$$f\_{\mathfrak{Z}}(w\_{j\bar{\imath}\iota}w\_{\bar{\imath}\jmath}) = \sum\_{j \in V} w\_{j\bar{\imath}} + \sum\_{j \in V} w\_{\bar{\imath}\jmath} \tag{11}$$

$$f\_4[d\_{\rm in}(i), d\_{\rm out}(i)] = d\_{\rm in}(i) + d\_{\rm out}(i) \tag{12}$$

The nodes are generally characterized as critical in case of being valued by larger *f*3 and *f*4, and by smaller *f*1 and *f*4. However, the ideal values of *f*1 and *f*4 cannot be obtained simultaneously. Therefore, it is essential to search for a balance between *f*1 and *f*4. For this purpose, two balance coefficients are defined as [27]:

$$B\_{\rm d}(i) = \left[ 1 - \left( \frac{d\_{\rm in}(i)}{d\_{\rm in}(i) + d\_{\rm out}(i)} - 0.5 \right)^2 \right] \tag{13}$$

$$B\_{\mathbf{w}}(i) = \left[ 1 - \left( \frac{\sum\_{j \in V} w\_{ij}}{\sum\_{j \in V} w\_{ij} + \sum\_{j \in V} w\_{ji}} - 0.5 \right)^2 \right] \tag{14}$$

where *Bd*(*i*) is the degree balance coefficient, and *B*w(*i*) is the weight balance coefficient. When the control Equation (10) and cross-strength of the node is considered synthetically, the weight balance index is defined as:

$$\Psi\_{\mathbf{w}}(i) = B\_{\mathbf{w}}(i) \cdot \left[ T\_{\mathbf{w}}(i) + \mathbb{C}\_{\mathbf{w}}(i) \right] \tag{15}$$

The degree balance index is similar to the weight balancing index, which can be obtained with the cross-degree and control Equation (11):

$$\Psi\_{\mathbf{d}}(i) = B\_{\mathbf{d}}(i) \cdot \left[ T\_{\mathbf{d}}(i) + \mathbb{C}\_{\mathbf{d}}(i) \right] \tag{16}$$

As a result, the basic criticality of node can be determined by:

$$
\Psi\_{\mathbf{B}}(i) = \Psi\_{\mathbf{d}}(i) \cdot \Psi\_{\mathbf{w}}(i) \tag{17}
$$

2.3.2. Node Criticality Determination by Improved K-Shell Decomposition Algorithm

In the present study, the aforementioned basic criticality would be considered as the input of K-shell decomposition algorithm to determine the node criticality. The K-shell decomposition algorithm is widely applied to analyze the node criticality based on the topology of the CN [44]. Generally, the basic K-shell decomposition algorithm is applicable for undirected unweighted networks, and aims to decompose the node set into subsets based on node centrality [45]. Meanwhile, a layer sequence number *ξ*K is assigned to each node according to its centrality value in the network and used to assess the criticality of objective nodes [27]. Nodes assigned larger *ξ*K values are closer to the center of the network, and nodes assigned smaller *ξ*K values tend to be at the periphery of the network. The basic K-shell decomposition algorithm can be improved to be applicable for the directed, weighted CN.

In the present study, the obtained basic criticality of the objective node is considered as the decomposition function, which is then embedded into K-shell decomposition algorithm, as a result, the layer sequence number can be determined as:

$$\Psi\_{\mathbf{K}}(i) = \mathbb{K}[\Psi\_{\mathbf{B}}(i)] \tag{18}$$

According to the principle of the K-shell decomposition algorithm, *Ψ*K(*i*) is the criticality value of the layer containing the *i*th node, and the layer sequence number is denoted by *ξ*K. All the nodes at the same layer are regarded as a set of *Gξ*K = (node *i*|*ξ*K(*i*) = *ξ*K ). To distinguish the nodes sharing the same layer sequence number, in the present study, an iteration factor is introduced into the traditional K-shell decomposition algorithm. As a result, the improved K-shell decomposition algorithm can be obtained and is expressed as:

$$
\Psi\_{\mathbf{K}i}(i) = \Psi\_{\mathbf{K}}(i) + \Gamma\_{\tilde{\mathbf{g}}\mathbf{K}}(i) \tag{19}
$$

where *Ψ*K*i*(*i*) is the criticality of the objective node, and the iteration factor is represented by *Γξ*K (*i*), which can be obtained by [27]:

$$\Gamma\_{\mathsf{F}\_{\mathsf{K}}}(i) = \left\{ \begin{array}{c} \frac{(\sigma\_{i} - 1)}{\gamma} \cdot (\mathbb{Y}\_{\mathsf{K} + 1} - \mathbb{Y}\_{\mathsf{K}}) \text{ node } i \in \mathcal{G}\_{\mathsf{F}\_{\mathsf{K}'}} \mathcal{J}\_{\mathsf{K}} \text{ is not the outermost layer} \\\ \frac{(\sigma\_{i} - 1)}{\gamma} \cdot (\mathbb{Y}\_{\mathsf{K}} - \mathbb{Y}\_{\mathsf{K} - 1}) \text{ node } i \in \mathcal{G}\_{\mathsf{F}\_{\mathsf{K}'}} \mathcal{J}\_{\mathsf{K}} \text{ is the output layer} \end{array} \right. \tag{20}$$

where *γ* is the number of nodes contained in layer *ξ*K(*i*), and *σi* is the order in which node *vi* is assigned to layer *ξ*K(*i*).

According to the improved K-shell decomposition algorithm represented by Equations (9)–(20), the algorithm is coded and implemented with the application of MAT-LAB. Then, the criticality for nodes within the CN can be obtained, and the results of all the nodes in the CN are summarized in Table 4. It is noticeable that the criticality value for the node coded by "N\_19" (personnel suffocation) is marked as 1. Moreover, in this study, the "N\_19" node is regarded as the top event in the BN in the following section.

**Table 4.** Criticality values for nodes.

*2.4. Bayesian Inference*

2.4.1. Development of Topology for BN

It is difficult to transfer the CN directly into BN due to the complex logic relationships in the CN. Therefore, in this study, the HFACS was introduced to connect the CN and the BN. In detail, the human-related hazardous events involved in the CN were categorized under the framework of HFACS in the aspects of unsafe acts, precondition for unsafe acts, unsafe supervision and organizational influences. As a result, in the developed BN, the human-related hazardous events are considered as the root nodes, the four aspects of HFACS serve as the middle nodes and the accident is the top node. It is notable that not all the nodes within the CN can be transferred into the BN as the root nodes.

To develop a BN, the human-related hazardous events listed in Table 2 were first categorized according to the principle of HFACS model. Under the framework of HFACS, all the human-related factors contributing to the accident are summarized in the aspects of unsafe acts, precondition for unsafe acts, unsafe supervision and organization influence. In the present study, those nodes characterized by the least 10 importance are deleted for the establishment of the BN, and the results are summarized in Table 5.

**Table 5.** HFACS framework for the identified hazardous events.

According to the category illustrated in Table 5, the "N\_19" node (personnel suffocation) is regarded as the top node in the BN; the four aspects of HFACS are considered as the intermediate nodes; and the nodes contributing to every aspect of the HFACS are set as the root nodes. As a result, the basic framework of the BN is developed as shown in Figure 3.

**Figure 3.** BN developed for the human-related factors analysis.

2.4.2. Determination of Probability Distribution for Nodes within BN Based on Criticality

The basic principle for Bayesian inference is presented in [23]. In the present study, all the nodes within the BN are considered as simulation nodes rather than discrete nodes. Therefore, the traditional conditional probability tables for the middle nodes are presented as the continuous probability distribution functions. Additionally, the probability distribution of all the nodes needed for the BN simulation in this study are determined with reference to [42]. It is assumed that the probability distribution of all the nodes, including the root nodes, intermediate nodes and top-level nodes, obeys the normal distribution. For the root nodes, the mean value of probability distribution can be calculated by normalizing the maximum value of the criticality for the root nodes, which is expressed as:

$$P\_{\rm Ki}(i) = \frac{\Psi\_{\rm Ki}(i)}{\mathbf{Max}(\Psi\_{\rm Ki})} \tag{21}$$

where *Ψ*Ki(*i*) denotes the node criticality, and *Ψ*Ki(*i*) can be valued according to the algorithms proposed in Section 2.3. For the intermediate nodes within the BN, the mean values of probability distribution can be obtained on the basis of probability distribution of the parent nodes contributing to the objective intermediate node, which is expressed as:

$$\chi(j) = \sum \omega\_{\rm Ki}(i) \cdot \Lambda(i) \tag{22}$$

where *Λ*(*i*) denotes the sampling value of parent nodes directing to the *j*th intermediate node. In the present study, *Λ*(*i*) refers to the mean value of the probability distribution for the *i*th node. The weight of *i*th root node is represented by *ω*Ki(*i*) that can be calculated by:

$$
\omega\_{\rm Ki}(i) = \frac{\Psi\_{\rm Ki}(i)}{\sum \Psi\_{\rm Ki}} \tag{23}
$$

Similarly, the mean value of probability distribution for the top nodes can be calculated by the following equation:

$$
\Omega(t) = \sum \lambda\_{\text{Ki}}(j) \cdot \chi(j) \tag{24}
$$

where *λ*Ki(*j*) denotes the weight of the intermediate node, and can be calculated by the following equation:

$$
\omega\_{\mathbf{K}i}(j) = \sum\_{i \in j} \frac{\Psi\_{\mathbf{K}i}(i)}{\sum\_{i \in R} \Psi\_{\mathbf{K}i}(i)} \tag{25}
$$

#### (1) Determination of probability distribution for root nodes:

According to the methodology proposed here, the prior probability for root nodes can be determined on the basis of the node criticality. In the present study, the prior probability distribution of the root nodes is assumed to be normal distribution, which can be expressed as:

$$F\_i \sim \mathcal{N}(P\_{i\prime}\sigma) \tag{26}$$

where F*i* is the prior probability distribution of the *i*th root node, and *Pi* denotes the mean value of the probability distribution, which is valued as the node criticality presented in Table 4. The variance of normal distribution is donated by *σ*, which is determined as onethird of the mean value according to the principle of 3*σ* (standard deviation). According to Equation (26), the prior probability distribution for the root nodes in the BN can be obtained, and the results are summarized in Table 6.

**Table 6.** The criticality and probability distribution for root nodes.

(2) Determination of probability distribution for intermediate nodes and the top node:

The conditional probability distribution for the child nodes (intermediate nodes and the top node) in the developed BN can be determined based on the algorithm proposed in this section. Specifically, the probability distribution of the root nodes listed in Table 6 is set as the input for Equations (21)–(25). Then, the probability distribution for the four intermediate nodes and the top node can be determined, which is the function of the probability distribution of the root nodes, as shown in Table 7. It is notable that the probability distribution expressions presented in Table 7 can be then entered directly into the software of AgenaRisk for the simulation of BN.

**Table 7.** Probability distribution expression for intermediate nodes and the top node.

#### **3. Results and Discussion**

*3.1. Results*

#### 3.1.1. Topology Analysis of CN

According to the calculation principle for the degree and weight described in Section 2.3, the degree distribution and weight distribution can be discussed according to [44]. In the present study, the weight is set as 1 because the analysis sample is limited to a single accident report; therefore, the topology analysis for the developed CN is limited to the degree distribution. According to [44], the degree of each node in the developed CN can be determined. Following this, the frequencies for the in-degree and out-degree can be plotted, as illustrated in Figure 4.

**Figure 4.** Degree distribution for the developed CN. (**a**) Degree distribution of the CN; (**b**) phase diagram of degree distribution.

The phase diagram of Figure 4b is essentially the projection of Figure 4a along the Y–Z axis. Based on the contents of Figure 4, the in-degree and out-degree of most nodes are less than 4, and the number of nodes with d*in* = 2, *dout* = 2 is found to be larger than other nodes. In addition, some nodes illustrated in Figure 4 are supposed to receive significant attention. For instance, the nodes with large in-degree are characterized by being caused or affected easily, which indicates that these nodes are difficult to control; meanwhile, the nodes with large out-degree are capable of affect other nodes easily, which shows that these nodes are important in improving the safety level. To distinguish the nodes with different in-degree and out-degree, the degrees for the nodes involved in the CN are then calculated, and the results are illustrated in Figure 5.

#### 3.1.2. Belief Propagation Analysis

According to [46], the belief propagation analysis of Bayesian inference usually contains forward-propagation and backward-propagation analysis. The forward-propagation analysis is mainly aimed at investigating the influence of parent nodes on their child nodes, while the backward-propagation analysis focuses on the sensitivity of the parent nodes to their child nodes, which is implemented by updating the probability distribution of the parent nodes by setting their child nodes as "evidence". In the present study, the criticalitybased values are utilized to assess the human-related factors involved in the accident, and the higher values of a node, the more important the objective factors. The results presented in Figure 6 are set as the base for comparison, which can be found in Table 8. According to the results of criticality evaluation summarized in Table 4, the nodes with largest criticality value within each category of human-related factor under the framework of HFACS are selected as the given evidence for the backward-propagation analysis. As a result, these nodes coded by "N\_13", "N\_16", "N\_17", "N\_18", "N\_25", "N\_29" and "N\_40" were selected as the given evidence in the present study. Then, different risk scenarios are developed by decreasing the criticality values of these selected nodes by 50% one by one, and the different scenarios are summarized in Table 8. Subsequently, the various risk scenarios are simulated by the developed BN on the software of AgenaRisk, and the variations in the node of "personnel suffocation" and the four aspects of HFACS are presented in Table 8.

**Figure 5.** Node evaluation from degree.

**Figure 6.** The results of running the BN model.

**Table 8.** Different scenarios for propagation analysis.

Note: ↓ indicates the reduction of a certain variable value in the specific scenario compared with the value in the base case.

#### 3.1.3. Sensitivity Analysis

The sensitivity analysis was implemented in the present study to quantify the sensitivity of the identified human-related hazardous events to the personnel suffocation accident using the backward-propagation analysis of the developed BN. During the sensitivity analysis, the top event, which refers to the personnel suffocation, was set as the given evidence, and the developed BN was simulated with AgenaRisk. In the present study, sensitivity nodes were selected according to the node criticality, and the top ten root nodes were considered as the sensitivity nodes for the sensitivity analysis. The mean value of the target node (referring to personnel suffocation) was selected to indicate the summary statistics. Finally, the results of the sensitivity analysis are presented by the tornado graph, which is illustrated in Figure 7. The criticality of the selected nodes is larger than 0.5526, and some nodes share the same criticality, such as "N\_16", "N\_17" and "N\_18". According to the basic principle of the sensitivity analysis, the sensitivity level of the nodes is represented by the length of the blue bars illustrated in Figure 7. The longer the blue bar is, the more sensitive the objective node is, and the nodes characterized by high sensitivity would be paid much attention to improve the safety management system to prevent the similar accidents.

**Figure 7.** Sensitivity analysis for the root nodes.

In the present study, the sensitivity analysis was implemented to investigate the sensitivity of the intermediate nodes which were set as the sensitivity nodes, while the top event was again regarded as the target node. The results are shown in Figure 8 in the form of a tornado graph, which was obtained by simulating the developed BN with the mean value of the top node criticality representing the summary statistics.

**Figure 8.** Sensitivity analysis of the intermediate nodes.

#### *3.2. Discussion*

In the aspect of the topology of the CN, according to Figures 4 and 5, it is clear that the node of "N\_25" is valued as the maximum in terms of in-degree, which indicates that the risk-prevention measures taken by the operators on site should have been supervised and instructed by many other stakeholders. In addition, the node of "N\_13" can be observed as the maximum in terms of out-degree, and it can be reasonably inferred that the supervision of the operators on the site of the shipyard is critical for accident prevention. Moreover, most operators on site in the shipyard are affiliated with subcontractors, which has been identified as a primary risk according to [12]. In addition, it can also be seen that the safety culture, especially during the holiday season, is essential for safety, which is proved by the second largest out-degree of "N-40", which is similar with the study conducted by the authors of [12]. In their study, the lunch effect attenuated the concentration of the operators on-site, and the holiday effect also functioned by a similar principle.

The results of belief propagation analysis summarized in Table 8 can be utilized to assess the sensitivity of the human-related hazardous events for the suffocation accident. Taking the comparison of the base and scenario 1 as an example, the status of UA would decrease from 0.538 to 0.497 in the case of lowering the node criticality of "N\_25" by 50%, which would subsequently cause the change of the top event by 2.1% (from 0.675 to 0.661). According to the above-mentioned principle of the improved K-shell decomposition algorithm, the decrease in the node criticality indicates that the corresponding node would be less active in the network. According to the contents in Table 8, the influence of "N\_13" on the top event is the most obvious, which would lower the probability of the "personnel suffocation" by 4.0%. Meanwhile, the node of "N\_13" is also identified as the most active node in terms of out-degree discussed in Section 3.1.1. Therefore, the performance of the shipyard's supervision of the operators, strictly implementing the safety management system, is essential for safe operation, which was identified as the most important factor in the field of OI (organization influence under the framework of the HFACS). Similar advice was presented in [12] for ensuring a safe work environment. The second most influential nodes coded by "N\_16", "N\_17" and "N\_18" would lower the probability of personnel suffocation by 3.9%, all of which are categorized as US (unsafe supervision). Even though

it is difficult to distinguish these nodes in terms of their influence on personnel suffocation, the correlation between these nodes and the personnel suffocation accident can be observed according to the results summarized in Table 8, which indicates that the superintendents and the officers of the operation department are critical to the supervision procedures on site. The last influential nodes presented in Table 8 were found to be "N\_25" and "N\_29", both of which are categorized as UA (unsafe acts), which implies the concentration of unsafe acts from operators in controlling and managing human-related risk factors is difficult to improve the safety level.

In the aspect of sensitivity analysis, according to the tornado graph illustrated in Figure 7, the top three sensitive nodes coded by "N\_16", "N\_17" and "N\_18" are associated with the superintendents and officers in the civil marine project affiliated with the shipyard, especially the risks which occur in relation to the supervision by superintendents and officers of the operators run by subcontractors. Actually, the ILO identified the contracting service of subcontractors as one of main hazards in shipyard operations, and supervision for risk-prevention measures should be implemented at regular intervals [6]. It is interesting to find that, even though the three most sensitive nodes share the same criticality, the sensitivity level of N\_18 is slightly lower than that of "N\_16" and "N\_17", which indicates that the identification and prevention of the risks associated with the planned operation on-site are more important than the management for the potential risks associated with the similar operation, although the latter is also important in the safety management system. In addition, the sensitivity of the node coded by "N\_13" is ranked behind that of the nodes of "N\_16", "N\_17" and "N\_18", even though the hazardous event denoted by "N\_13" (shipyard failed to supervise operators on-site to implement strictly the safety management system) is regarded as being active in the developed CN. It is also noticeable that the absence of risk-prevention measures taken by the operators on-site coded by "N\_25" is characterized by high in-degree and low sensitivity level, which indicates that the humanrelated risk management should focus on the causation for "N\_25", such as "N16" and "N\_17". It can be observed that the unsafe supervision coded by US in Figure 8 is found as the most sensitive node for the personnel suffocation, which implies the well-implemented safety management system is essential for the operation on-site. The similar conclusions were obtained by Fragiadakis et al., who argued that "bad information of subcontractors about safety rules" played a key role in shipyard injury accidents [13]. Therefore, the supervision for the operators from subcontractors is an effective way to maintain safe operations in a shipyard. The sensitivity level of US is followed by UA, which can be interpreted as the initial causation for the personnel suffocation in the unsafe actions of operators on site. However, the unsafe actions of the operators were not identified as the most active or sensitive events in the present study, which indicates that the consequences caused by the unsafe acts of the operators can be eliminated by the supervision or monitoring arrangements which followed. In addition, according to the contents of Figure 8, the organizational influence (coded by OI) is less sensitive than that of unsafe supervision and unsafe acts based on the accident report, and the difference in terms of sensitivity level between US and OI is as high as 3.17 times; therefore, much more attention should be paid to the factors involved in unsafe supervision rather than factors related to organizational influence. Finally, the least sensitive aspect under the HFACS framework is the preconditions for unsafe acts (UP) which is valued as 0.355, which is followed by the OI with the sensitivity value of 0.487. The sensitivity difference between UP and OI is not considerable for the case in this study, which indicates that the external environment has little influence on the occurrence of the personnel suffocation accident.

Overall, the following lessons can be obtained from the case study:

(1) The significance of "N\_13" (shipyard failed to supervise the operator on site) was identified by the proposed methodology in relation to risk propagation based on the perspective of the CN analysis. Therefore, the effective supervision of the shipyard operators on site may be able to intercept risk propagation, despite the occurrence of unsafe acts.

#### **4. Conclusions**

The present study proposes an innovative methodology to bridge the CN and the BN on the basis of an event tree analysis under the HFACS framework. This methodology quantitatively assesses the human-related hazardous events involved in maritime accidents. For this purpose, a personnel suffocation accident on board a ship was selected as a case study, and the ETA was applied to identify the human-related hazardous events according to the accident report. Then, the logic relationships among the identified hazardous events were mapped into an event tree. As a result, the CN was developed. The improved K-shell decomposition algorithm was subsequently proposed to quantify the criticality of the nodes contained in the developed CN. Meanwhile, the framework of the BN was established under the framework of the HFACS with reference to the results of the criticality evaluation. To implement the Bayesian inference, the criticality of the root nodes was used to determine their prior probability distribution with the assumption of the normal distribution. The conditional probability distribution for other nodes in the BN can be also determined on the basis of the probability distribution of the root nodes. Finally, the developed BN was simulated in AgenaRisk, by which the scenarios were simulated, and the sensitivity analyses were implemented.

The advantages of the proposed methodology are mainly characterized by its elimination of the traditional potential bias. This is due to the expert elicitation and our compensation of the constraints of the ETA, without considering the logic relationships among hazardous events. However, the performance of the proposed methodology can be improved by further study in the near future. For instance, the logic relationships among hazardous events can be described in greater detail by a more effective method to aggregate the expert elicitation rather than the safety meeting which was adapted in the present study. In addition, the performance of the proposed methodology can be verified by the implementation of numerous accident reports associated within the industry in the near future.

**Author Contributions:** Methodology and original draft, W.Q.; Formal analysis and modelling, H.G.; Investigation, E.H.; Modelling and calculation, W.D.; Data curation and inveistigation, C.L.; Resource, H.C. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by National Key Research and Development program of China (grand No. 2019 YFB1600602), the Fundamental Research Funds for the Central Universities (Grand No.<PHONE_NUMBER>) and Program of Innovative Talents of Dalian (Grand No. 2021RQ043).

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

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

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

#### **References**

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*Editorial* **New Advances into Nanostructured Oxides**

**Roberto Nisticò**

Department of Materials Science, INSTM, University of Milano-Bicocca, Via R. Cozzi 55, 20125 Milano, Italy<EMAIL_ADDRESS>Tel.: +39-02-6448-5111

Inorganic nanostructured (metal) oxides are a large class of inorganic materials extensively investigated for their unique and outstanding properties that allow for their use within a multitude of technological fields of emerging interest, such as (photo)catalysis, environmental remediation processes, energy storage, controlled transport and/or release of drugs and chemicals, biomedicine, sensing, development of smart materials, stimuliresponsive materials, and nanocomposites [1–6]. The large applicability of nanostructured (metal) oxides is primarily due to the possibility of easily exerting a high level of control in the design of their morphology, in particular in terms of particle dimensions, shapes, and surface porosities, by means of proper synthetic routes and innovative templating processes [7–9]. However, exerting morphological control might be not always enough. In fact, even surface residual functionalities and surface reactivity play key roles in the determination of a nanomaterial's final properties [10]. Hence, for this reason, nanostructured (metal) oxides often require surface functionalization with specific chemical moieties (thus, eventually forming inorganic–organic hybrid systems) to further extend their field of application.

Nowadays, the research on this class of inorganic materials is still very dynamic, as documented by the recent achievements in the field, and it is expected to further increase in the coming years, with particular attention on the continuous search for more ecofriendly synthesis and templating approaches alternative to the traditional inorganic chemistry routes [11]. This Special Issue, entitled "New Advances into Nanostructured Oxides", is specifically dedicated to the recent advancements in the design of the most promising nanostructured (metal) oxides and their technological application. Altogether, this Special Issue collects nine original research articles and three review papers, covering diverse nanostructured (metal) oxides and different technological applications.

Palma et al. [12] reported the effects of the experimental parameters over the solution combustion synthesis of Ce-doped Sr ferrates with the perovskite structure. The authors demonstrated that the full gelification of the *sol* is crucial to obtain single-phase perovskite materials, avoiding secondary phase formation and cerium oxide segregation, whereas the presence of secondary phases might induce the formation of Sr carbonate at the perovskite's surface that negatively affects the material's catalytic activity. Interestingly, the authors also evaluated that the presence of segregated cerium oxide has a positive effect on the availability of oxygen vacancies in low temperature conditions, thus favoring the interaction with polar substrates and bettering the material's catalytic activity. Concerning the thermocatalytic activity of these nanostructured materials, tests were performed against two different target substrates, namely: Orange II dye and Bisphenol A at alkaline pH (to avoid metal leaching from perovskites). Experimental results indicated that the best performances are obtained for samples containing the highest amount of segregated cerium oxide, probably due to the formation of a higher number of oxygen vacancies, which are available during the catalytic process.

The formation of heterojunctions between C3N4 and nanostructured (metal) oxides is a very promising technological solution to exploit sunlight (i.e., visible frequencies) in a multitude of photocatalytic processes, such as fuel production (i.e., CO2 photo-reduction, water photo-splitting), and photo-degradation of contaminants in wastewater. In this

**Citation:** Nisticò, R. New Advances into Nanostructured Oxides. *Inorganics* **2023**, *11*, 130. https:// doi.org/10.3390/inorganics11030130

Received: 9 March 2023 Revised: 14 March 2023 Accepted: 17 March 2023 Published: 20 March 2023

**Copyright:** © 2023 by the author. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (https:// creativecommons.org/licenses/by/ 4.0/).

context, Actis et al. [13] systematically investigated different synthesis techniques used to prepare bare C3N4 and combined C3N4/ZnO mixed systems. In particular, the authors compared different synthetic protocols, namely: (i) the formation of combined C3N4/ZnO mixed systems from supramolecular precursors, (ii) the direct growth of C3N4 on preformed ZnO nanoparticles, and (iii) the ultrasonic/mechanical solid-state mixing of both pre-formed C3N4 and ZnO nanopowders. Experimental data pointed out a trend existing between these three different synthetic processes. In detail, in the case of the synthesis from supramolecular precursors, a tighter association between the two phases forming the heterojunction has been recognized (with the appearance of adsorption bands in the visible region) coupled with low crystallinity. An opposite trend was registered in the case of ultrasonic/mechanical solid-state mixing. Crystalline materials with a modified morphology are obtained this way, with the consequent formation of a high-crystalline heterojunction characterized by lower visible light harvesting, deriving from the minor cooperation between the two components at the interface. Lastly, the approach based on C3N4 growth on pre-formed ZnO shows an intermediate condition in terms of both crystallinity and optical activity under visible frequencies. Chebanenko et al. [14] reported the production of ZnO-decorated graphitic carbon nitride (g-C3N4) nanocomposites synthesized following a three-step process, namely: (i) heat treatment of urea, (ii) ultrasonic exfoliation of the colloidal solution by introducing different ratios of ZnO precursor, and (iii) final thermal treatment. The authors reported a very interesting photocatalytic system by placing g-C3N4 in close contact with ZnO nanoparticles, since the formation of a heterojunction between these two components allows for the achievement of both excitation by visible light and a better charge separation with the formation of O-containing radicals. Nanocomposites were tested in the photocatalytic abatement of Methylene Blue dye as a target molecule under visible light. Experimental results indicated that the best performances were obtained for the nanocomposite containing 7.5 wt.% of ZnO. Moreover, Said et al. [15] reported the synthesis of a mesoporous g-C3N4/CaO nanocomposite using a step-by-step ultrasonication technique. Furthermore, such a composite was successfully tested in the capture of Basic Fuchsin dye by means of a sorption mechanism. After the optimization of the sorption conditions, it was demonstrated that: (i) dye sorption is pH-independent, (ii) interaction between nanocomposite and dye follows the Freundlich model, (iii) sorption capacity increases with contact time (reaching an equilibrium after ca. 30 min), and (iv) maximum sorption capacity at the optimized condition is quantified as 813 mg/g. Furthermore, the authors experimentally demonstrated that the adsorption mechanism involves both hydrogen bonds and π-π stacking interactions.

Pereira et al. [16] reported the preparation of V-doped titania/WO3 (1:1 molar ratio) nanocomposites with different dopant concentrations and dispersed them in high-surfacearea fibrous polymeric membranes obtained by means of electrospinning deposition. Evidence demonstrated that: (i) V5+ ions doped the atomic structure of titania (anatase phase), (ii) the titania crystallite sizes slightly increased by increasing the dopant concentration, and (iii) both W6+ and V5+ occupies Ti4+ vacancies in the anatase crystal lattice, allowing for a greater degree of local vibrations, which favors the occurrence of photocatalytic processes. Furthermore, the introduction of V into the TiO2/WO3 system caused a significant decrease in the titania bandgap as it generates an n-type extrinsic semiconductor. Electrospun fibrous membranes containing the nanocomposites were successfully tested in the photocatalytic abatement of Rhodamine B dye from the water environment under visible light. Experimental results indicated that the best performances (92% dye degradation after 2 h of visible light irradiation) are reached by the nanocomposite containing 5 wt.% of vanadium oxide, as it shows an improved photoresponse and charge separation efficiency, coupled with the presence of high active site density.

Peralta et al. [17] reported novel ecofriendly routes for the synthesis of nanostructured core–shell magnetic particles based on the polyol method and using biowaste-derived biobased substances (BBSs) as both stabilizers and structure-directing agents. Subsequently, the covering of such BBS-stabilized magnetite nanoparticles with either mesoporous silica shell

or titanium dioxide was performed by means of a sol–gel method carried out in alkaline environment. Experimental results evidenced that the size/shape and the aggregation of the magnetite nanoparticles strongly depend on the starting BBS concentration (the higher the concentration of BBS, the smaller the sizes of the magnetite nanoparticles). Additionally, the presence of BBS is also crucial concerning the growth of both the mesoporous silica shell and the homogeneous titania shell, probably due to interactions involving the carboxylic groups of BBS. Such core–shell systems were successfully tested as nanocarriers of ibuprofen and adsorbing agents against Methylene Blue dye from aqueous solution, and as photocatalysts in the degradation of Methylene Blue dye. In particular, the mesoporous silica core–shell systems show an ibuprofen loading capacity of 13% and a very fast adsorption capacity of Methylene Blue dye, achieving 76% of dye removal within the first 15 min, whereas the titania core–shell systems show a photocatalytic degradation efficiency against Methylene Blue dye, analogous to bare titania. This last result is very surprising (and potentially a breakthrough), as the direct contact between magnetite and titania typically brings an unfavorable heterojunction, which accelerates the recombination of the electron–hole pairs and weakens the photocatalytic activity of the catalyst.

Dekyvere et al. [18] reported a novel strategy for the fabrication of nano zero-valentiron (nZVI) carbon-based mesoporous materials derived from iron-containing metalorganic frameworks (MOFs) using zinc oxide nanorods as sacrificial nuclei. Here, the breakthrough idea is the utilization of the nanostructured zinc oxide under the shape of nanorods (i.e., aspect ratio 10) as sacrificial consumable nuclei for shaping the final highly porous materials (with specific surface area in the 185–270 m2/g range). The mesoporous materials thus obtained were tested as adsorbing agents against Methylene Blue dye, reaching an impressive sorption of 78% after only 2 min and close to total sorption (99%) after 6 min. The best-performing system also showed excellent recyclability, with 99% of abatement after 60 min of contact time for 10 consecutive cycles. As highlighted by the authors, the high number of mesopores produced by the sacrificial zinc oxide rods significantly enhanced the adsorption capability of the nanomaterial by increasing the contact area with the adsorbate. Furthermore, this innovative strategy opens the possibility of exerting further control over the pore size when forming the final porous material by changing the morphology of the starting nanostructured sacrificial nuclei.

Newar et al. [19], instead, tested the use of different commercial (metal) oxide nanoparticles as sorbing systems against low specific activity 99Mo radiotracer solution. The commercial (metal) oxides investigated were: silica, titania, zirconium oxide, cerium oxide, tin oxide, and mixed systems, such as an aluminosilicate, cerium aluminum oxide, aluminum titanium oxide, and cerium zirconium oxide. The authors evaluated the adsorption behavior under different experimental conditions (pH, initial concentration of Mo, contact time, and temperature). Experimental results showed that: (i) the optimum adsorption pH for all systems is pH 2–4, (ii) the Freundlich isotherm model fitted the experimental data (mainly physisorption), (iii) the maximum adsorption capacity at optimized conditions was registered by zirconium oxide (73 mg/g), followed by cerium oxide and titania. This study clearly demonstrates that the investigated nanostructured (metal) oxides showed higher static sorption capacities than conventionally used alumina (2–20 mg/g) and potentially opens up the possibility of further improving the sorption capacity of these systems by varying both morphologies and porosities of these nanostructured (metal) oxides.

Chang et al. [20] evaluated the possibility of using a metal matrix nanocomposite reinforced with titania nanoparticles towards a potential endodontic instrument using a co-deposition approach with pulse electroplating for dental applications. In this study, the authors registered that the electrodeposition of the nanocomposite produced a smooth sidewall and surface, thus maintaining structural integrity and revealing a promoted formability, whereas cytotoxicity tests revealed high cell viability, high cell proliferation behavior and excellent biocompatibility.

Sfameni et al. [21] contributed to this Special Issue with an interesting overview dealing with the surface modification of textile fabrics to confer specific implemented and new

properties by means of inorganic sol–gel approaches. In this review, different examples and methodologies based on sol–gel inorganic coatings for textile finishing are evaluated, considering a wide range of inorganic precursors and functional additives to fabricate textiles showing different/implemented properties. Moreover, with respect to conventional textile finishing processes, synthetic routes based on functional nanoparticles and nanosols represent a more ecofriendly and safe approach than the most commonly employed formulations containing harmful substances. In the present review, sol modification techniques and functional applications for textile materials are classified into three sections, properly discussed with examples taken from the literature. Inorganic protocols are organized into methods for improving wear resistance of textile fabrics, for UV protection textile finishing, and for antimicrobial textile finishing, with a further classification based on the active nanoparticles involved.

Scarpelli et al. [22] reviewed the recent advancements in the synthesis of nanostructured iridium oxide. Iridium oxide is a very peculiar (metal) oxide as it is characterized by showing metallic-type conductivity and also displaying a low surface work function (which is the energy required for moving an electron from the Fermi level to the local vacuum level), high chemical stability, and good stability under the influence of high electric fields. Iridium oxide can be synthesized as various nanostructures, which significantly affect its surface work function. In this regard, the authors provided a very interesting overview of the nanostructuring of iridium oxide, with a special emphasis on the different strategies to drive the synthesis toward specific nanostructures. In this context, the authors organized the text into four sections, dedicated to the description of the different synthesis methods to obtain spherical nanoparticles, one-dimensional nanostructures (e.g., nanotubes, nanorods, nanowires, and nanofibers), unusual shapes (e.g., urchin-like, and nanoneedles), and thin films.

Nisticò [23] reviewed the recent advances in the exploitation of naturally occurring clays (phyllosilicates), including more non-conventional applications. Within the entire review, the author analyzed clays' peculiar properties, such as their ability to exchange (capture) ions, their layered structure, surface area and reactivity, and their biocompatibility, pointing out the deep correlation existing between the field of application and the structure–property relationships involved. After providing an introduction mainly focused on the economic analysis of the global trade in clays, the review was organized into two mains sections. Section I is dedicated to the classification of clays based on their structural and chemical composition, together with a schematic summary of the main relevant structure-induced properties, which are strongly correlated to the nature and quantity of chemical species at the interlayer, exchange capacity, interlayer thickness, surface area, and hydration/gel-forming capacity. Section II, instead, is dedicated to the analysis of the unconventional advanced uses of clays in technological fields of emerging interest, providing a further organization into biomedical applications (ranging from the development of scaffolds and drug delivery systems to cancer diagnosis and therapy), environmental applications (wastewater treatments and membrane technology), additive manufacturing, and sol–gel processes.

Personally, the hope is that readers will enjoy this Special Issue, now edited as a book. Many examples of interesting and challenging research are collected in this "scientific compilation". In some cases, the focus was on the nanostructuring of (metal) oxides, in others, their exploitation towards target advanced applications, but the common thread remains the continuous progress of this important class of inorganic materials.

**Acknowledgments:** I personally would like to thank all authors, reviewers, and the entire editorial staff of *Inorganics* who assisted me in the realization of the present Special Issue.

**Conflicts of Interest:** The author declares no conflict of interest.


**Molecular Targets of Aspirin and Prevention of Preeclampsia and Their Potential Association with Circulating Extracellular Vesicles during Pregnancy**

#### **Suchismita Dutta 1, Sathish Kumar 2, Jon Hyett 3 and Carlos Salomon 1,4,5,\***

Received: 19 May 2019; Accepted: 26 August 2019; Published: 5 September 2019

**Abstract:** Uncomplicated healthy pregnancy is the outcome of successful fertilization, implantation of embryos, trophoblast development and adequate placentation. Any deviation in these cascades of events may lead to complicated pregnancies such as preeclampsia (PE). The current incidence of PE is 2–8% in all pregnancies worldwide, leading to high maternal as well as perinatal mortality and morbidity rates. A number of randomized controlled clinical trials observed the association between low dose aspirin (LDA) treatment in early gestational age and significant reduction of early onset of PE in high-risk pregnant women. However, a substantial knowledge gap exists in identifying the particular mechanism of action of aspirin on placental function. It is already established that the placental-derived exosomes (PdE) are present in the maternal circulation from 6 weeks of gestation, and exosomes contain bioactive molecules such as proteins, lipids and RNA that are a "fingerprint" of their originating cells. Interestingly, levels of exosomes are higher in PE compared to normal pregnancies, and changes in the level of PdE during the first trimester may be used to classify women at risk for developing PE. The aim of this review is to discuss the mechanisms of action of LDA on placental and maternal physiological systems including the role of PdE in these phenomena. This review article will contribute to the in-depth understanding of LDA-induced PE prevention.

**Keywords:** pregnancy; placentation; preeclampsia; low dose aspirin; exosomes


*3.5. TEM Analysis*

TEM image of the SA106B carbon steel sample was depicted in Figure 10a. Figure 10a, b show TEM bright-field micrograph and selected-area electron diffraction (SAED) patterns, respectively, in the top surface layer of the sample RASP-processed for 5 min. One can note that the crystal grains have been refined into nanocrystal grains of relatively equiaxed and uniformly distributed compared with the irregular crystal grains of the matrix. The mean grain size is 25 nm (Figure 10c). Note that the SAED pattern of the sample consists of rings. It is known that the more continuous the rings, the smaller the grain sizes within the selected field of the view and the more uniform the distribution of grains [29]. The result demonstrates that the grain size of the steel can be markedly reduced when they are RASP-processed for 5 min. It is noteworthy that some spots spreading along the rings (Figure 10b) were found, indicating that there are also some coarse grains in the samples.

**Figure 10.** (**a**) TEM image of the top surface layer of the sample RASP-processed for 5 min; (**b**) the corresponding selected-area electron diffraction (SAED) pattern in (**a**); (**c**) grain size distribution in the top surface layer.

Figure 11 shows typical TEM images of the SA106B low-carbon steel, (a) RASP-processed for 0 min, (b) RASP-processed for 5 min and (c) shows the corresponding SAED pattern. In the carbon steel specimens untreated by the peening, one can see that the cementite is parallel, evenly distributed and the width of lamellar spacing is about 200 nm, as shown in Figure 11a. As shown in Figure 11b, the main result concerns the cementite phase which underwent a dissolution, at least partial, during SPD. The parallel distributions were changed severely. The diffraction spot, as shown in Figure 11c, confirmed the formation of supersaturated carbon atom ferrite. It is plausible that because the dislocation activity and cementite dissolution occurred simultaneously during plastic deformation, the carbon atoms can be dragged out of the cementite by mobile dislocations [30]. One study [31] has shown that cementite dissolution takes place through a global mechanism involving the whole volume of each individual lamellae, resulting in a carbon concentration gradient from cementite to ferrite. Indeed, as cementite partly dissolves, a large amount of carbon atoms is released, and must therefore be partitioned in the ferrite. Owing to the importance of the dissolution, the average carbon content in the ferrite may reach 1–2 at.%. After the cementite dissolution, a solid solution formed, which increased the electrode potential of the matrix [32]. Thus, the number of micro batteries reduced and the corrosion film was more stable and the surface layer of the sample was flattened.

**Figure 11.** TEM images of the SA106B low-carbon steel. (**a**) RASP-processed for 0 min; (**b**) RASP-processed for 5 min; (**c**) The corresponding selected-area electron diffraction (SAED) pattern in (b).

#### **4. Conclusions**

In the present work, the influence of corrosion resistance of a SA106B low-carbon steel with gradient nanostructure produced by RASP was investigated. The main conclusions can be drawn as follows:

(3) Prominent micro-cracks and holes were produced in the steel when the RASP was processed more than 5 min, resulting in the decrease of corrosion resistance.

**Author Contributions:** Writing-original draft preparation, figures and data curation, C.L.; data analysis, C.L., X.C., Y.C. and B.Y.; literature search, X.C.; study design, Y.L. and B.Y.; Writing—review and editing, B.Y.

**Funding:** This research was founded by the Beijing Municipal Natural Science Foundation under No 2162026 and the Beijing Municipal Science and Technology Commission under No Z181100005218005.

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