*Article* **Cenozoic Depositional Evolution and Stratal Patterns in the Western Pearl River Mouth Basin, South China Sea: Implications for Hydrocarbon Exploration**

**Entao Liu 1,\*, Yong Deng 2,3,\*, Xudong Lin 1, Detian Yan 4, Si Chen 4 and Xianbin Shi 5**

**Abstract:** Investigating the deposition evolution and stratal stacking patterns in continental rift basins is critical not only to better understand the mechanism of basin fills but also to reveal the enrichment regularity of hydrocarbon reservoirs. The Pearl River Mouth Basin (PRMB) is a petroliferous continental rift basin located in the northern continental shelf of the South China Sea. In this study, the depositional evolution process and stacking pattern of the Zhu III Depression, western PRMB were studied through the integration of 3D seismic data, core data, and well logs. Five types of depositional systems formed from the Eocene to the Miocene, including the fan delta, meandering river delta, tidal flat, lacustrine system, and neritic shelf system. The representative depositional systems changed from the proximal fan delta and lacustrine system in the Eocene–early Oligocene, to the tidal flat and fan delta in the late Oligocene, and then the neritic shelf system in the Miocene. The statal stacking pattern varied in time and space with a total of six types of slope break belts developed. The diversity of sequence architecture results from the comprehensive effect of tectonic activities, sediment supply, sea/lake level changes, and geomorphic conditions. In addition, our results suggest that the types of traps are closely associated with stratal stacking patterns. Structural traps were developed in the regions of tectonic slope breaks, whereas lithological traps occurred within sedimentary slope breaks. This study highlights the diversity and complexity of sequence architecture in the continental rift basin, and the proposed hydrocarbon distribution patterns are applicable to reservoir prediction in the PRMB and the other continental rift basins.

**Keywords:** depositional system; sequence architecture; stacking pattern; Pearl River Mouth Basin; hydrocarbon exploration


**2. Construction of SMAPP Model**

The sentiment is people's attitude towards objective things and corresponding reaction behavior. Ortony, Clore, and Collins proposed the OCC model, which divides emotions into 11 pairs of basic emotions [23,24]. In employee emotion and management, Zhang Jian introduced five important employee emotion phenomena, namely, emotional intelligence, emotional work, occupational stress, emotion and creativity, and emotion and decision. This paper mainly refers to Wang Chunxue's research on the impact of emotions on safety sign recognition, and defines emotions as positive (happiness), neutral, and negative (sadness, anger, and fear), assuming that positive emotions promote the performance of safety behaviors [25].

Motivation is a kind of internal power, which affects the direction, intensity, and endurance of individual behavior. It is formed under self-regulation so that the internal needs of individuals and external incentives match. According to American psychologist Herzberg's theory of motivation–health care in 1959, motivation is divided into motivating factors and healthcare factors. Health factors mainly include policy and management, supervision, wages, colleague relations, and working conditions. These are non-work factors that, if satisfied, can eliminate dissatisfaction and maintain productivity, but do not motivate people to behave more positively. Motivating factors include achievement, promotion, development, etc. If these factors are satisfied, they can generate great incentives for people; if they are not satisfied, they will not generate dissatisfaction such as health factors. This paper defines motivation as progress, development, achievement, salary, and policy.

Ability is the degree to which an individual can perform a task or task as required. Ren Hao believes that a person's ability includes IQ, EQ, creativity, and moral quotient; Stephen believes that ability can be divided into intellectual ability and physical ability. Intellectual ability mainly includes language understanding, logical reasoning, understanding and expression, vocabulary use, associative memory, imagination, etc. Physical abilities are mainly strength, vitality, endurance, and coordination. According to the existing research, abilities are divided into physiological, cognitive, and technical levels, as well as learning, creativity, and social abilities.

Personality mainly refers to the relatively stable characteristics that affect people's behaviors. According to John Holland's personal–job fit theory, personality is divided into six parts [26]. According to Hippocrates' humoral dominance theory, personality is divided into four parts: bilious, sanguine, mucous, and melancholic. In this paper, according to the big five personalities of the ocean theory of personality, personality is defined as openness, neuroticism, conscientiousness, extroversion, and agreeableness. See Figure 1 for the manifestations of each type of personality.

**Figure 1.** Big Five personality.

With the quickening pace of society, today, most people live under high pressure. While the right amount of stress is good for people's performance, excessive stress can certainly take a toll on people's physical and mental health. There are many reasons for people's stress. Su Yong divided stress into four parts: task-related, role-related, interpersonal, and working conditions [27]; or, simply stated: work, family, society, relationships, and health.

The human behavior model is established according to emotion, motivation, ability, personality, pressure, and the corresponding secondary influencing factors selected, as shown in Figure 2.

**Figure 2.** Human behavior model.

#### **3. Build SMAPP Model Simulation Process Framework**

In the aspect of the realization of human model simulation, there are three main methods. The first is a mathematical method, using mathematical formulas to predict human behavior, but this traditional simulation method can only get a simplified description of the whole system. It does not fully emphasize the complex hierarchical structure of the system, and sometimes it is difficult to extract formulas [28]. The second model is a rules-based system, which is a mature and widely used approach, as we only need to extract the interaction between different attributes and describe it in the form of rules [29]. Thirdly, agent-based modeling (ABM), seeks the simplest explanation for the observed complex system, modeling individuals such as people, organizations, and enterprises as agents. Agents have their state and rules of behavior. Through the behavior and interaction of micro-agents, sudden phenomena, dynamic equilibrium, and nonlinear results can be reproduced to deduce the macroscopic phenomena and operation results of the system [30]. There is no universally recognized definition of an agent. Currently, the cognitive architecture BDI (belief–desire–intention) model [31] is widely used in agent modeling (generally, it is believed that the thinking state of an agent includes belief, desire, and intention) and is too rational for describing complex and changeable human beings. For example, ACT-R believes that the cognitive process of human beings requires the participation of different modules, which correspond to the physiological structure of the human brain. The modules work independently and are coordinated by the central production system, and it is understandable to take rule-based reasoning as its core part to predict the output [32]. In this paper, the simulation framework of the SMAPP model is built based on rules; see Figure 3. The specific process is as follows:

**Figure 3.** SMAPP model simulation process framework.

**Table 1.** Corresponding score interval of fuzzy state.

Different application scenarios have different definitions of environment. To verify the simulation results of the model on unsafe behaviors, the environment is defined as a task, and three attributes are defined for it. The first is Difficulty, which is divided into five levels as before, but with different names: Very Hard, Hard, Medium, Easy, and Very Easy. The second property is the status value; it records the output value of the task, used to reflect the performance of the employee, and will affect the state of the employee. The third is unsafe behavior, which is consistent with the corresponding score of fuzzy state in Table 1 above, and divided into five levels: extremely unsafe (more than 90 points), unsafe (70–90 points), generally safe (40–70 points), safe (20–40 points) and very safe (less than 20 points). In the unsafe behavior of mining operations, "T" represents the unit of time in weeks.

According to the simulation architecture of the SMAPP model, Python language rules are used to describe the relationship between different attributes. For example:

if (isHigh(self.management) and isHigh(self.ability)):

self. progress = ActionUtil.increase (self. progress)

if (isHigh(self.openness) and isHigh(self.sentiment)):

self.creativity = ActionUtil.increase(self.creativity)

if (isVeryLow(self.promotion) and isVeryLow(self.extraversion)):

self.positive = ActionUtil.highDecrease(self.positive)

if (isVeryLow(self.promotion) and isLow(self.extraversion)):

self.positive = ActionUtil.highDecrease(self.positive).

The condition of the above rule is the state of the person and the environment, and the execution part is to change the state value in five update modes. The rule contains all possible states related to unsafe behavior in mine operations. The synergetic influence relationship of the SMAPP model is shown in Figure 4. The output of different factors is represented by different colors: purple for stress, green for sentiment, yellow for personality, red for motivation, blue for ability, light blue for safety tasks instead of the external environment, and black lines for human output. The different colored lines indicate the influence of factors on other factors, such as extraversion promoting sociability, with a yellow line going from extraversion to sociability. Ability will affect the individual's progress but will also affect the sense of achievement; there is a blue line pointing to progress and sense of achievement; and emotion will affect people's creativity and directly affect individual behavior (unsafe behavior). There is a green line pointing to creativity and unsafe behavior; motivation will promote individuals to improve their technical level; and a red line pointing to technology.

**Figure 4.** Synergistic influence diagram of SMAPP model attribute factors.


*6.8. Therapeutic Effects of PTS against COVID-19 Infection*

The COVID-19 pandemic, caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV2), triggered a major setback to global human health and the economy in the 21st century [125]. Resveratrol was tested and proved to have effective therapeutic value against MERS-CoV infection by decreasing cell death [126]. Screening studies have indicated that stilbenes inhibit complex formation between the spike protein and ACE-2 receptor, thereby blocking viral entry into the host cell [127]. Based on these studies, stilbene derivatives could be considered important drug candidates for COVID-19 [127]. PTS has been demonstrated to actively inhibit SARS-CoV-2 virus replication in infected African green monkey kidney cells. Antiviral activity was seen for up to five rounds of replication, which indicates a long-lasting therapeutic effect.

Moreover, in human primary bronchial epithelial cells isolated from healthy volunteers, PTS showed an antiviral effect for up to 48 h after infection. These data promote the use of PTS as a potent drug against COVID-19 and warrant further clinical trials to prove its antiviral efficacy early in COVID-19 [125]. Furthermore, PTS, co-administered with zinc, has also been identified as a potential COVID-19 adjuvant therapy for managing moderate–severe disease [128]. However, further clinical trials are required to back the pharmacotherapeutic potential of PTS. COVID-19 patients with comorbidities related to metabolic syndromes, such as diabetes, obesity, hypertension, and cardiovascular disease, have low levels of the HO antioxidant enzyme. Higher HO-1 expression has been associated with reduced susceptibility to COVID-19 infection [129]. Considering that COVID-19 patients are susceptible to the induction of overt inflammatory processes and cytokine storms, PTS treatment could exert anti-inflammatory and cytoprotective effects by increasing HO-1 expression [130].


*Article* **Monitoring of the 2015 Villarrica Volcano Eruption by Means of DLR's Experimental TET-1 Satellite**

**Simon Plank 1,\*, Michael Nolde 1, Rudolf Richter 2, Christian Fischer 3, Sandro Martinis 1, Torsten Riedlinger 1, Elisabeth Schoepfer 1 and Doris Klein 1**

Received: 17 July 2018; Accepted: 28 August 2018; Published: 30 August 2018

**Abstract:** Villarrica Volcano is one of the most active volcanoes in the South Andes Volcanic Zone. This article presents the results of a monitoring of the time before and after the 3 March 2015 eruption by analyzing nine satellite images acquired by the Technology Experiment Carrier-1 (TET-1), a small experimental German Aerospace Center (DLR) satellite. An atmospheric correction of the TET-1 data is presented, based on the Advanced Spaceborne Thermal Emission and Reflection Radiometer (ASTER) Global Emissivity Database (GDEM) and Moderate Resolution Imaging Spectroradiometer (MODIS) water vapor data with the shortest temporal baseline to the TET-1 acquisitions. Next, the temperature, area coverage, and radiant power of the detected thermal hotspots were derived at subpixel level and compared with observations derived from MODIS and Visible Infrared Imaging Radiometer Suite (VIIRS) data. Thermal anomalies were detected nine days before the eruption. After the decrease of the radiant power following the 3 March 2015 eruption, a stronger increase of the radiant power was observed on 25 April 2015. In addition, we show that the eruption-related ash coverage of the glacier at Villarrica Volcano could clearly be detected in TET-1 imagery. Landsat-8 imagery was analyzed for comparison. The information extracted from the TET-1 thermal data is thought be used in future to support and complement ground-based observations of active volcanoes.

**Keywords:** volcanic thermal anomalies; change detection; Villarrica Volcano; small satellites; FireBIRD; TET-1