**5. Conclusions**

Body temperature neither correlated with crSO2/cTOI nor with SpO2 and this finding was independent from gestational age. We found a weak negative correlation between prSO2 and body temperature in preterm neonates and a weak positive correlation between HR and body temperature in the whole cohort, as expected. Body temperature during immediate postnatal transition was statistically lower in preterm neonates compared to term neonates, but the difference was small and probably clinically irrelevant. Nevertheless, hypothermia was significantly more often observed in preterm neonates than in to term neonates. Preterm infants >28+0 and <32+0 weeks of gestation had the highest prevalence of hypothermia, whereas hyperthermia was most frequent in preterm infants born below 28+0 weeks of gestation. Therefore, attention to stringent body temperature management should be paid not only to extremely preterm neonates but also to very and late preterm infants.

**Author Contributions:** Conceptualization: M.B., L.P.M., and A.R.; data curation: M.B. and L.P.M.; formal analysis: M.B. and A.R.; investigation: L.P.M., A.R., N.B.-S., B.S., and C.B.-H. Methodology: M.B., L.P.M., A.R., and G.P. Project administration: L.P.M. Resources: B.U. Supervision: G.P. Visualization: M.B. and L.P.M. Writing—original draft: M.B. and L.P.M. Writing—review and editing: M.B., L.P.M., A.R., N.B.-S., B.S., C.B.-H., B.U., and G.P. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research received no external funding.

**Acknowledgments:** We thank the parents for their trust, so we were allowed to investigate their infants. We also thank all the staff members, especially Evelyn Ziehenberger, for contributing to this study.

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


*4.4. Data Analysis*

The viability dose–response curve of the compounds was analyzed using the Emax model (Equation (1)).

$$\% \text{ Cell viability} = (100 - \text{R}) \times \left(1 - \frac{[\text{D}]^m}{K\_d^m + [\text{D}]^m} \right) + \text{R} \tag{1}$$

where R is the residual unaffected fraction (the resistance fraction) which is deduced from fitting concentration versus viability on the *Emax* equation (Equation (1)) described above, [D] is the drug concentration used, *Kd* is the drug concentration that produces a 50% reduction in the maximum inhibition rate and *m* is a Hill-type coefficient. IC50 was defined as the drug concentration required to reduce optical density to 50% of that of the control (i.e., *Kd* = IC50 when R = 0 and *Emax* = 100 − R) [68].

### *4.5. Apoptosis*

Annexin V conjugates allow for the identification of cell surface changes that occur early during the apoptotic process using flow cytometry. Early in the apoptotic process, phosphatidylserine emerges from within the cytoplasmic membrane and becomes exposed on the cell surface, which is thought to be important for macrophage recognition of cells undergoing apoptosis. The binding of Annexin V to phosphatidylserine is calcium-dependent, reversible, and specific with a *Kd* of approximately 5 × 10 − 10 M [69].

#### *4.6. Assessment of Active Caspase-3 Concentration*

To assess the effect of GCB, terrein, and their combination on apoptosis, the active caspase-3 concentration was measured using a Quantikine® caspase-3 ELISA Kit (R&D Systems, Inc., Minneapolis, MN, USA). Briefly, the cells were exposed to the predetermined IC50s of test compounds (single or combined treatments) or drug-free media (control group) for 24 h. Cells were harvested and washed twice with PBS, then incubated with the biotin-ZVKD-fmk inhibitor for 1 h. Cells were transferred into the wells of a microplate precoated with a monoclonal antibody specific for caspase-3. Following a wash to remove any unbound substances, streptavidin conjugated to horseradish peroxidase was added to the wells and bound to the biotin on the inhibitor. Following a wash to remove any unbound streptavidin–HRP, a substrate solution was added to the wells. The enzyme reaction yields a blue product that turned yellow when a stop solution was added. The optical density of each well was determined within 30 min, using a microplate reader set to 450 nm with a wavelength correction at 540 nm or 570 nm. The concentrations of active caspase-3 were calculated from a standard curve constructed with known concentrations of active caspase-3. Caspase concentration was expressed as ng/mg protein. Proteins were determined by the Bradford method using purified bovine serum albumin as a standard protein.

#### *4.7. Autophagy*

Acridine orange (AO) is a cell-permeable green fluorophore that can become hydronated and consequently absorbed by acidic vesicular organelles. Its metachromatic shift from green to red fluorescence is highly dependent on its concentration, which causes AO to fluoresce from green to red in acidic organelles, such as lysosomes. Lysosomes tend to increase in number and volume when autophagy occurs; AO staining is a quick and reliable method for the assessment of autophagy [70].


*Edited by Prathna Thanjavur Chandrasekaran*

Floods and droughts brought about by the vagaries of nature cause extensive damage to the livelihoods of people as well as the environment. Knowledge of the need to adopt sustainable practices toward water use, reuse, and management is critical and the scope of this book deals precisely with sustainable practices in water management. The purpose of this book is to provide the reader with abundant and relevant information on all aspects related to water sustainability: water reuse, dynamics of transboundary waterways, economic tools to analyze sustainability, water-energy-food nexus, computer simulation models to study watershed models, and so forth. Although this book may not provide readers with comprehensive information on all aspects related to water sustainability, it will provide constructive data and content on the current trends and advancements in sustainable practices related to water. The book is intended to further motivate readers and scientists alike to look further and make concerted efforts toward promoting better and effective water management.

Published in London, UK © 2019 IntechOpen © NiseriN / iStock

Water and Sustainability

Water and Sustainability

*Edited by Prathna Thanjavur Chandrasekaran*


**Abstract**

Over the years, technology has become increasingly dominant in every sector of the world, and the impact of digital technology on educational institutions has not been left behind. The concept of "digital education," which entails using technological tools for both learning and teaching in a classroom context, has changed the traditional classroom environment for both teachers and students. In the present era of technological advancements, digital education is essential because it makes learning efficient through individualized learning, open access to an unlimited amount of information, flexible teaching methods, etc. Digital learning makes it possible for students to communicate and study with a greater variety of materials without having to rely solely on the limited resources available in a traditional classroom setting. Students can study and explore information more easily with the aid of basic digital skills and technology. A student's academic performance and engagement in active learning both improve with a positive attitude and motivation. Digital education has various pros and cons in the teaching environment and its impact on different beneficiaries. Through a literature search, materials from public, educational, and expert sources were obtained. In this chapter, each of these will be explored and conclusions drawn.

**Keywords:** digital education, digital students, digital teachers, digital curriculum, digital classroom


# **Artificial Intelligence in Image-Based Screening, Diagnostics, and Clinical Care of Cardiopulmonary Diseases**

Edited by Sameer Antani and Sivaramakrishnan Rajaraman Printed Edition of the Special Issue Published in *Diagnostics*

www.mdpi.com/journal/diagnostics


**3. Concept vehicle for Mars entry, descent, and landing**

To date, lifting body architectures have not been employed yet for Mars EDL missions. Therefore, only *a-priori* knowledge based on Earth re-entry vehicles can be assumed. In this work, a conceptual lifting body configuration, suitable for a manned entry mission starting from a Mars low orbit is considered [8, 9]. The vehicle is derived from a design procedure supported by a multidisciplinary optimization (MDO) analysis. By accounting in the design loop several sub-disciplines, a blended wing body with a double-delta planform configuration with low wing loading, and capable to perform a long gliding trajectory (i.e., *γ* ≪ 1° ) can be derived (**Figure 1**) [9, 10].

The vehicle is designed to exploit a specific guidance law modulating the AoA versus Mach number in order to attain a favorable value of convective heat flux profile and promote the cooling down of the passive TPS exclusively by thermal radiation.

Reference geometric parameters of vehicle are shown in **Table 1**.

**Figure 1.** *Entry vehicle representation.*


*2.2. AIS Signal Power Simulation*

The AIS signal power calculation formula is shown in Equation (5):

$$P = P\_t + G\_t + G\_r - L\_1 - L \tag{5}$$

where *Pt* is the transmission power of the AIS system, which is 41 dBm. *Gt* is the transmit antenna gain, *Gr* is the receiving antenna gain, *L* is the propagation loss of AIS in an atmospheric environment, and *L*1 is the cable transmission loss of AIS receiving equipment.

The propagation loss of the AIS signal in an atmospheric environment was obtained using the parabolic equation method [23]. Parabolic equations are divided into the narrowangle parabolic equation and wide-angle parabolic equation. We employed the narrowangle parabolic equation, suitable for the calculation of radio wave propagation with an elevation angle of less than 10 degrees. The expression of the narrow-angle parabolic equation is shown in Equation (6).

$$\frac{\partial^2 u(\mathbf{x}, z)}{\partial z^2} + 2ik \frac{\partial u(\mathbf{x}, z)}{\partial \mathbf{x}} + k^2 (n^2(\mathbf{x}, z) - 1)u(\mathbf{x}, z) = 0 \tag{6}$$

where *u*(*x*, *z*) is the component of the electric or magnetic field, *k*0 is the wave number, and *n*(*x*, *z*) is the atmospheric refractive index at different distances and heights. The Split-Step Fourier Transform (SSFT) method is the main method for solving parabolic equations [24]. The SSFT solution of the narrow-angle parabolic equation is shown in Equation (7) [23].

$$u(x+\Delta x, z) = e^{\frac{ik(w^2-1)\Delta x}{2}} \Im^{-1} \left\{ e^{\frac{-i\pi^2 p^2 \Delta x}{2k}} \Im u(x, z) \right\} \tag{7}$$

where *αe* is the radius of the Earth, *p* is the transform domain variable, and −1 are Fourier transform and inverse transform respectively. The equation of AIS signal path propagation loss obtained from Equation (8) is:

$$L = 20 \text{lg}f + 10 \text{lg}r - 20 \text{lg}|u(x, z)| - 27.6\tag{8}$$

where *L* is propagation loss, *f* is AIS signal frequency, and *r* is the propagation distance.

### *2.3. AIS Signal Receiving Test*

In June 2020, the China Research Institute of Radiowave Propagation carried out an AIS signal receiving test in the coastal area of Nantong, China. AIS signal-receiving equipment are often used to receive AIS signals in coastal areas and to collect meteorological sounding data in the test area. The sounding data were obtained twice a day at 08:00 and 20:00 Beijing time respectively. The test area is shown in Figure 3. Parameters of AIS signal-receiving equipment are shown in Table 1.

**Figure 3.** AIS signal receiving test position.

**Table 1.** Parameters of AIS signal-receiving equipment.

We selected three typical atmospheric environments: no atmospheric duct, surface duct, and elevated duct. The corresponding atmospheric duct profile is illustrated in Figure 4.

AIS signal data were selected at the same time as sounding data, and the signal position and power distribution are shown in Figure 5. The x-axis is the longitude direction distance, the y-axis is the latitude direction distance, and the AIS signal-receiving equipment is located at point 0 of the y-axis. When there is no atmospheric duct, the AIS signal is distributed within 100 km as seen in Figure 5. When the surface duct appeared, the maximum distance of the AIS signal was over 500 km, and the signal power was strong. The signal power beyond 100 km was about −80 dBm. When the elevated duct appeared, the AIS signal was distributed within 200 km, and the signal power was weak (about −100 dBm).

Using sounding data and Equation (5), the AIS signal power variation with distance was determined in three atmospheric environments and was compared with the actual received AIS signal power, as shown in Figure 6. The red dotted line shows the sensitivity of AIS signal-receiving equipment (−112 dBm); the solid green line is the simulated AIS signal power variation curve with distance; the blue points are the distribution of measured AIS signal power with distance. Figure 6 illustrates that the AIS simulation results are in good agreement with the measured data, thereby revealing the effectiveness of the AIS signal power simulation algorithm used in our study.

**Figure 4.** Atmospheric duct profile calculated by sounding data.

**Figure 5.** AIS signal distribution in different atmospheric ducts.

**Figure 6.** Comparison between simulated AIS signal power and measured AIS signal power.

From the above analysis, we observed obvious differences in AIS signal distribution in different atmospheric environments, mainly as follows:

These show that AIS signals can be used to invert atmospheric ducts, and the types of atmospheric ducts can be distinguished since surface and elevated duct have different influences on AIS.

### **3. Modeling of Duct Parameters Classifying-Inversion Model**

In this section, we introduced two artificial intelligence methods: genetic algorithm (GA) and DNN, as well as the modeling process of the classifying-inversion model of atmospheric duct parameters using AIS data.

### *3.1. Artificial Intelligence Method for Atmospheric Duct Inversion*

From previous studies, the main artificial intelligence methods used for atmospheric duct parameter inversion were GA and DNN. DNN is a deep learning network structure. GA is designed according to the evolution law of organisms in nature, and the optimal solution is searched by simulating the natural evolution process. In this algorithm, the problem-solving process is transformed into the evolutionary process of biological chromosome genes through mathematical means and computer simulation. GA has been widely used in combinatorial optimization, machine-learning, signal processing, adaptive control,

and artificial life [25]. GA consists of three steps: selection, crossover, and mutation. The GA flow chart is shown in Figure 7.

### **Figure 7.** GA flow chart.

DNN is composed of input, hidden, and output layers. The hidden layer can have multiple layers that enhance the expression ability of DNN. The neurons in the output layer have multiple outputs that flexibly apply to classification regression, dimensionality reduction, and clustering. The schematic diagram of DNN is shown in Figure 8. The DNN layer is fully connected with the other layers, and any neuron in the *i* layer must be connected with a neuron in the *i* + 1 layer.

**Figure 8.** The schematic diagram of DNN.

### *3.2. Classifying-Inversion Flow of Atmospheric Duct*

In this study, we employed the idea of "classification before inversion" for atmospheric duct parameters inversion. The first step was to establish a classification model of atmospheric duct, use the received AIS signal to judge the occurrence of atmospheric ducts, and distinguish the types of atmospheric duct occurrence. Secondly, the surface duct parameter inversion model and the elevated duct parameter inversion model were established respectively. The flow chart of atmospheric duct Classifying-inversion model is shown in Figure 9.

**Figure 9.** Flow chart of atmospheric duct Classifying-inversion model.

The atmospheric duct classification model adopted DNN, and the atmospheric duct parameters inversion model adopted GA and DNN respectively, and two classifyinginversion models were established as Model-1 and Model-2. In addition, we used GA to establish the traditional atmospheric duct inversion model (Model-3) and compared it with the aforementioned models. The model information is illustrated in Table 2.

**Table 2.** The model information.


**5. Outcomes and results**

With the combination of sound liposuction technique and energy-based technologies we have seen excellent results with improvement in skin laxity. Whenever possible, we treat the trunk circumferentially. Below we demonstrate our abdominal contouring results with and without energy-based technologies, with the addition of an abdominoplasty, and with the addition of abdominal etching.

#### **5.1 Case 1: circumferential trunk liposuction with SAFE technique**

This is a 33-year-old female who desired circumferential trunk contouring. We performed circumferential trunk liposuction with the SAFE technique using the MicroAire PAL handle and 4 mm Mercedes tip and basket tip cannulas.  Each buttocks was grafted with 250 cc and the breasts were grafted with 250 cc and 200 cc. A total of 1660 cc of excess fat was removed (**Figure 3**).

#### **5.2 Case 2: circumferential trunk liposuction with VASER and Renuvion**

Case 2 is a 36-year-old female who desired trunk contouring. Circumferential trunk liposuction with VASER and Renuvion was performed.  The total VASER time was 5 minutes and 53 seconds to the posterior trunk. Renuvion was performed in 4–5 passes per treatment area. The total excess fat removed was 1360 cc (**Figure 4**).

#### **5.3 Case 3: full abdominoplasty with circumferential trunk liposuction**

This 42-year-old patient desired trunk contouring. She presented with rectus diastasis, excess abdominal skin, and abdominal striae; necessitating skin removal

**Figure 3.** *Preoperative photographs. Patient is shown 3 months postoperatively with significant improvement in abdominal contour.*

and repair of her diastasis. She underwent circumferential trunk liposuction and full abdominoplasty with rectus muscle plication. VASER was used to treat the posterior trunk (**Figure 5**).


**5. Methanogenesis stage**

Methanogenesis is the microbial process, whereby CO2, acetate, or methyl compounds are converted to CH4 in order to generate ATP through the buildup of a sodium ion or proton gradient [31]. Methanogenesis is one of the most metal-rich enzymatic pathways in biology [93]. The contents of Cu, Fe, Ni, and Zn in methanogens (including 10 species of *Methanosarcina*, *Methanococcus*, *Methanobacterium*, *Methanobrevibacter*, etc.) were determined as <10–160 ppm, 0.07–0.28%, 65–180 ppm, and 50–630 ppm, respectively [36].The key enzyme complex in producing biogas from acetate is CODH [15, 94]. CODH cleaves the C-C and C-S bonds in the acetyl moiety of acetyl-CoA, oxidizes the carbonyl group to CO2, and transfers the methyl group to coenzyme M. MCR catalyzes the enzymatic reduction of methyl-coenzyme M to CH4 in methanogenesis, which includes a Ni-containing cofactor called F430 [15, 31, 40]. Besides, the CODH complex is also involved in the formation of acetate by acetogens from H2/CO2 and methanol [95]. MCR is found exclusively in methanogenic archaea [96].

### **5.1 Biogas yields**

The stimulatory effect of trace metal supplementation in certain ranges on anaerobic digestion has been widely reported [10, 21, 46]. Depending on the methanogenic pathway, the general trends of metal requirements are as follows: Fe is the most abundant metal, followed by Ni and Co and smaller amounts of Mo (and/orW) and Zn [31].

Low concentrations of Fe have been shown to markedly increase the conversion of acetic acid to CH4 [97]. Fe2+ in concentrations of up to 20 mM has been shown to increase the conversion of acetate to CH4 [98]. It was found that Fe2+ marginally stimulated biogas yield and CH4 content at 37°C and the addition of Fe2+ increased VFA utilization but enhanced H2 utilization considerably [99]. The addition of Fe resulted in a stable process, and its combination with Co contributed to higher biogas production (+9%), biogas production rates (+35%), and reduced VFA concentration while simultaneously degrading the organic fraction of municipal solid waste and slaughterhouse waste [34]. The promoting effect of Fe2+ addition on biogas yields of mixed *Phragmites* straw and cow dung was mainly attributed to the extension of the gas production peak stage and the improvement of cellulase activities [46].

The optimum or stimulatory concentrations of Ni for batch cultures of methanogens were reported to range between 12 mg/m3 and 5 g/m3 [92]. Pobeheim et al. observed an increase in CH4 production of 25% at day 25 of operation following addition of 10.6 μM Ni [100]. Ni addition of 1–200 μM enhanced the methane production from anaerobic conversion of acetate by 6.30–44.6% compared with the control, respectively [101]. Furthermore, the limitation of Ni in the fermenters led to process instability and was proven to reduce biogas generation [102]. On the other hand, the addition of Ni was found to be beneficial to the methanation process. Ni had increased the ratio of CH4:CO2 [103].

Besides Fe, Ni, and Co, other trace metals like Cu, Cr, and Cd were shown to promote biogas production. Lower concentrations of Cu (1.82 ± 0.01 μg/g dry wt.) and Cr (0.89 ± 0.04 μg/g dry wt.) better served as micronutrients for methanogenic bacteria and might have enhanced the process of methanogenesis and thus CH4 content in the product biogas [9]. Cao et al. harvested five types of plant from Cu-contaminated land, including *Phytolacca americana* L., *Zea mays* L., *Brassica*

**141**

*Biodegradability during Anaerobic Fermentation Process Impacted by Heavy Metals*

*napus* L., *Elsholtzia splendens*, and *Oenothera biennis* L. and investigated the effects of Cu on anaerobic digestion of these plants. Compared to normal plants with low Cu content, the plants used in remediation with increased Cu levels (100 mg/kg) not only required a shorter anaerobic digestion time but also increased the CH4 content in biogas [25]. 30 and 100 mg/L Cu2+ addition increased the cumulative biogas yields by up to 43.62 and 20.77%, respectively [22]. In another study, 30, 100, and 500 mg/L Cr6+ addition increased the cumulative biogas yields by up to 19.00, 14.85, and 7.68%, respectively, while bringing forward the daily biogas peak yield [21]. Investigations on the anaerobic fermentation of five contaminated crops showed that less than 1 mg/L of Cd in plants promoted or at least had no inhibitory effect on cumulative biogas yields [11]. Jain et al. noted that at low concentrations, Cd and Ni had a favorable effect on the rate of biogas production and its CH4 content, but with increase in concentrations, the rate of biogas production and CH4

Biogas is composed of CH4, CO2, and other trace compositions like H2. Methanogens using H2/CO2 as the matrix usually contain two hydrogenases: one is a hydrogenase that uses coenzyme F420 as the electron acceptor called coenzyme F420-reducing hydrogenase, and the other is coenzyme F420-nonreducing hydrogenase [105]. Hence, the presence of metals in the fermenters influences the biogas composition by impacting the pathways. For example, Cu2+ and Cr6+ addition stimulated biogas production and the generation of CH4 by enhancing the activities

Previously, Fe was found in acetyl-CoA synthase, CH4 monooxygenase, NO-reductase, and nitrite reductase [15]. Fe, together with Ni, was found in hydrogenases of *Methanosarcina barkeri*, which consumes H2 to provide electrons

As we studied, the compositions of biogas varied with temperatures in the presence of heavy metals. The impacts of Cd addition on biogas compositions of anaerobic co-digestion of acid-pretreated corn and fresh cow dung under different fermentation temperatures are shown in **Figure 1**. When temperature increased, the CH4, CO2, and H2 contents also increased, but N2 contents decreased in both Cd-added and control groups. The CH4 contents reached plateau after the fourth day in 55°C group and seventh day in 45°C group (**Figure 1A**). The increase of CO2 contents slowed down after the fourth day in both 55 and 45°C groups (**Figure 1B**). The tendency of CH4 and CO2 contents in 35 and 25°C groups was not detected fully as the biogas yields were too low to be collected by gas bag. Similar contents of H2 were observed in 45, 35, and 25°C groups while lower in 55°C group (**Figure 1C**). The N2 contents decreased more rapidly when fermentation temperature was increased (**Figure 1D**). The results indicated that elevated temperatures accelerated

Cd addition improved the CH4 contents by approximately 6% after the fourth day in 55°C group. Taking the other biogas compositions into account, it was found that Cd addition decreased the CO2 contents in the biogas while having little influences on H2 and N2 contents. However, the impact of Cd on biogas compositions in other three temperature groups was not significant. Therefore, thermophilic fermentation (55°C) promoted the CH4 generation in the presence of Cd in the present study. Low temperature hindered the production of CH4 which agreed with

a previous study that used swine manure as substrate [108].

*DOI: http://dx.doi.org/10.5772/intechopen.87161*

content decreased [104].

**5.2 Biogas compositions**

of coenzyme F420 and methanogenesis [21, 22].

for the reduction of CO2 to CH4 [106, 107].

the start-up of fermentation.

*Biodegradability during Anaerobic Fermentation Process Impacted by Heavy Metals DOI: http://dx.doi.org/10.5772/intechopen.87161*

*napus* L., *Elsholtzia splendens*, and *Oenothera biennis* L. and investigated the effects of Cu on anaerobic digestion of these plants. Compared to normal plants with low Cu content, the plants used in remediation with increased Cu levels (100 mg/kg) not only required a shorter anaerobic digestion time but also increased the CH4 content in biogas [25]. 30 and 100 mg/L Cu2+ addition increased the cumulative biogas yields by up to 43.62 and 20.77%, respectively [22]. In another study, 30, 100, and 500 mg/L Cr6+ addition increased the cumulative biogas yields by up to 19.00, 14.85, and 7.68%, respectively, while bringing forward the daily biogas peak yield [21]. Investigations on the anaerobic fermentation of five contaminated crops showed that less than 1 mg/L of Cd in plants promoted or at least had no inhibitory effect on cumulative biogas yields [11]. Jain et al. noted that at low concentrations, Cd and Ni had a favorable effect on the rate of biogas production and its CH4 content, but with increase in concentrations, the rate of biogas production and CH4 content decreased [104].

#### **5.2 Biogas compositions**

*New Advances on Fermentation Processes*

exclusively in methanogenic archaea [96].

nogens were reported to range between 12 mg/m3

process. Ni had increased the ratio of CH4:CO2 [103].

**5.1 Biogas yields**

activities [46].

(and/orW) and Zn [31].

Methanogenesis is the microbial process, whereby CO2, acetate, or methyl compounds are converted to CH4 in order to generate ATP through the buildup of a sodium ion or proton gradient [31]. Methanogenesis is one of the most metal-rich enzymatic pathways in biology [93]. The contents of Cu, Fe, Ni, and Zn in methanogens (including 10 species of *Methanosarcina*, *Methanococcus*, *Methanobacterium*,

The stimulatory effect of trace metal supplementation in certain ranges on anaerobic digestion has been widely reported [10, 21, 46]. Depending on the methanogenic pathway, the general trends of metal requirements are as follows: Fe is the most abundant metal, followed by Ni and Co and smaller amounts of Mo

Low concentrations of Fe have been shown to markedly increase the conversion of acetic acid to CH4 [97]. Fe2+ in concentrations of up to 20 mM has been shown to increase the conversion of acetate to CH4 [98]. It was found that Fe2+ marginally stimulated biogas yield and CH4 content at 37°C and the addition of Fe2+ increased VFA utilization but enhanced H2 utilization considerably [99]. The addition of Fe resulted in a stable process, and its combination with Co contributed to higher biogas production (+9%), biogas production rates (+35%), and reduced VFA concentration while simultaneously degrading the organic fraction of municipal solid waste and slaughterhouse waste [34]. The promoting effect of Fe2+ addition on biogas yields of mixed *Phragmites* straw and cow dung was mainly attributed to the extension of the gas production peak stage and the improvement of cellulase

The optimum or stimulatory concentrations of Ni for batch cultures of metha-

observed an increase in CH4 production of 25% at day 25 of operation following addition of 10.6 μM Ni [100]. Ni addition of 1–200 μM enhanced the methane production from anaerobic conversion of acetate by 6.30–44.6% compared with the control, respectively [101]. Furthermore, the limitation of Ni in the fermenters led to process instability and was proven to reduce biogas generation [102]. On the other hand, the addition of Ni was found to be beneficial to the methanation

Besides Fe, Ni, and Co, other trace metals like Cu, Cr, and Cd were shown to promote biogas production. Lower concentrations of Cu (1.82 ± 0.01 μg/g dry wt.) and Cr (0.89 ± 0.04 μg/g dry wt.) better served as micronutrients for methanogenic bacteria and might have enhanced the process of methanogenesis and thus CH4 content in the product biogas [9]. Cao et al. harvested five types of plant from Cu-contaminated land, including *Phytolacca americana* L., *Zea mays* L., *Brassica*

and 5 g/m3

[92]. Pobeheim et al.

*Methanobrevibacter*, etc.) were determined as <10–160 ppm, 0.07–0.28%, 65–180 ppm, and 50–630 ppm, respectively [36].The key enzyme complex in producing biogas from acetate is CODH [15, 94]. CODH cleaves the C-C and C-S bonds in the acetyl moiety of acetyl-CoA, oxidizes the carbonyl group to CO2, and transfers the methyl group to coenzyme M. MCR catalyzes the enzymatic reduction of methyl-coenzyme M to CH4 in methanogenesis, which includes a Ni-containing cofactor called F430 [15, 31, 40]. Besides, the CODH complex is also involved in the formation of acetate by acetogens from H2/CO2 and methanol [95]. MCR is found

**5. Methanogenesis stage**

**140**

Biogas is composed of CH4, CO2, and other trace compositions like H2. Methanogens using H2/CO2 as the matrix usually contain two hydrogenases: one is a hydrogenase that uses coenzyme F420 as the electron acceptor called coenzyme F420-reducing hydrogenase, and the other is coenzyme F420-nonreducing hydrogenase [105]. Hence, the presence of metals in the fermenters influences the biogas composition by impacting the pathways. For example, Cu2+ and Cr6+ addition stimulated biogas production and the generation of CH4 by enhancing the activities of coenzyme F420 and methanogenesis [21, 22].

Previously, Fe was found in acetyl-CoA synthase, CH4 monooxygenase, NO-reductase, and nitrite reductase [15]. Fe, together with Ni, was found in hydrogenases of *Methanosarcina barkeri*, which consumes H2 to provide electrons for the reduction of CO2 to CH4 [106, 107].

As we studied, the compositions of biogas varied with temperatures in the presence of heavy metals. The impacts of Cd addition on biogas compositions of anaerobic co-digestion of acid-pretreated corn and fresh cow dung under different fermentation temperatures are shown in **Figure 1**. When temperature increased, the CH4, CO2, and H2 contents also increased, but N2 contents decreased in both Cd-added and control groups. The CH4 contents reached plateau after the fourth day in 55°C group and seventh day in 45°C group (**Figure 1A**). The increase of CO2 contents slowed down after the fourth day in both 55 and 45°C groups (**Figure 1B**). The tendency of CH4 and CO2 contents in 35 and 25°C groups was not detected fully as the biogas yields were too low to be collected by gas bag. Similar contents of H2 were observed in 45, 35, and 25°C groups while lower in 55°C group (**Figure 1C**). The N2 contents decreased more rapidly when fermentation temperature was increased (**Figure 1D**). The results indicated that elevated temperatures accelerated the start-up of fermentation.

Cd addition improved the CH4 contents by approximately 6% after the fourth day in 55°C group. Taking the other biogas compositions into account, it was found that Cd addition decreased the CO2 contents in the biogas while having little influences on H2 and N2 contents. However, the impact of Cd on biogas compositions in other three temperature groups was not significant. Therefore, thermophilic fermentation (55°C) promoted the CH4 generation in the presence of Cd in the present study. Low temperature hindered the production of CH4 which agreed with a previous study that used swine manure as substrate [108].

**Figure 1.**

*Biogas composition under Cd stress with fermentation temperature of 55, 45, 35, and 25°C (A) CH4 contents, (B) CO2 contents, (C) H2 contents, and (D) N2 contents.*

#### **6. Conclusions**

This book chapter reviewed the past findings in the impacts of metals on different stages of the anaerobic degradation process. The requirements of metals by the enzymes involved in the anaerobic process resulted in the different performances with varied metal species and bioavailability. In general, metals in certain concentrations were able to promote the lignocellulose degradation, the generation and consumption of organic components in the fermenters like LCFAs and VFAs, the biogas production, as well as the CH4 contents. The mechanisms of metals were studied by many scientists, focusing on the enzyme activities, microbial communities, etc.

Although a large amount of research has been carried out on individual stages or the entire anaerobic fermentation process, there are still challenges on controlling metal-stressed anaerobic degradation process for optimal utilization of metalcontaminated biowastes. Further work on bioavailability of metals during anaerobic fermentation process and detailed compositions of intermediary products, the relationships between microbial functions and metal species, etc. are recommended for better understanding of the metal-stressed anaerobic degradation process.

#### **Acknowledgements**

This work was funded by the Major Science and Technology Program for Water Pollution Control and Treatment (No. 2017ZX07101-003, 2015ZX07204-007, 2015ZX07203-011), the Fundamental Research Funds for the Central Universities (2018MS051).

**143**

**Author details**

\*, Huayong Zhang1

provided the original work is properly cited.

North China Electric Power University, Beijing, China

\*Address all correspondence to<EMAIL_ADDRESS>
1 Research Center for Engineering Ecology and Nonlinear Science,

2 Marine Biology Institute, Shantou University, Shantou, Guangdong, China

© 2019 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium,

and Edmond Sanganyado2

Yonglan Tian1

*Biodegradability during Anaerobic Fermentation Process Impacted by Heavy Metals*

*DOI: http://dx.doi.org/10.5772/intechopen.87161*

**Conflict of interest**

No conflict of interest.

*Biodegradability during Anaerobic Fermentation Process Impacted by Heavy Metals DOI: http://dx.doi.org/10.5772/intechopen.87161*


*6.4.4. Post-"MediaMente Bullo" project*

For the questionnaire "My Life in School" [68, 69]: physical bullying: 82.1% never; 13.2% one time; 4.7% more than once; *verbal bullying:* 80% never, 11.8% one time; 8.2% more than once; *indirect bullying*: 80.9% never, 11.3% one time; 7.8% more than once; *prosocial behavior:* 16.3% never, 23.7% one time; 60% more than once.

For the questionnaire "You and the conflict": 16.1% *avoidant*; 4.5% *aggressive, 22.3% compliant;* 14.1% *accommodating*; 43% *shopkeeper.*

The general index of bullying of the sample is calculated by the average of these scales for the three bullying (physical, verbal and indirect) indices. Data analysis shows significant differences between before and after the project in bullying, mediation skills and prosocial behavior. After the evaluation of scores, distribution data did not follow a normal distribution that is, a Gaussian distribution. The main tests for the assessment of normality Kolmogorov– Smirnov (K-S) was used to investigate it. According to the available literature, nonparametric statistical tests were considered. Considering this assumption, we applied the Wilcoxon test. We found significant differences between *Pre* and *Post* "MediaMente Bullo" project in *physical bullying (p < .005); verbal bullying (p < .001); indirect bullying (p < .01); prosocial behavior (p < .05); avoidant (p < .05); aggressive (p < .05); shopkeeper (p < .001).*

Descriptive results of percentages and data analysis indicate significant decrease in bullying scales (*physical bullying; verbal bullying; indirect bullying)* and increase prosocial scores and mediation skills after the path of project "MediaMente Bullo."

These results indicate that this path, in which students have experienced empathy and mediation, can contribute to decrease phenomenon of prejudice, bullying, social exclusion for a system of inclusion.