Preface

As we face the challenges of an energy crisis, it is crucial to seek out next-generation energy sources that are economical, sustainable (renewable), and clean (environmentally friendly). There are many types of energy that can be utilized in industry, such as nuclear energy, radiation solar energy, hydrogen energy, and so on. In most cases, all energy is converted into electricity before it can be utilized. Heat plays a key role in energy conversion and utilization. Heat is traditionally used to create electricity, but the process is complex and noisy and creates large amounts of waste. With the advantage of first-hand solid-state conversion to electrical power from thermal energy, especially from the reuse of waste heat, thermoelectricity has valuable applications in reusing resources, making it costeffective. Compared to the traditional methods of converting heat to electricity, thermoelectric devices have many advantages, such as all-solid-state features, no mechanical moving parts, long lifetime, and more. Consequently, thermoelectricity has many potential applications. For example, based on nuclear radiation with a long half-life, thermoelectricity can supply electricity for space stations and spaceships in deep space. In addition, thermoelectricity can also supply electricity for flexible wearable devices. As such, thermoelectricity has received extensive attention and is expected to be helpful for combating environmental pollution and saving energy.

The wide applications of thermoelectrics in commercial and industrial fields have long been limited by thermoelectric conversion efficiency. The continuous improvement of thermoelectric performance and the strive to increase the power output under the same heat source are the key foci in thermoelectric technology. Many efforts have been dedicated to improving thermoelectric efficiency, which is a very challenging task because of the strongly correlated properties involved in thermoelectrics. Efforts to boost thermoelectric efficiency focus on two aspects. The first approach focuses on improving electric performance while not affecting thermal transport. The second approach focuses on suppressing thermal transport while not affecting electric properties. All methods for boosting thermoelectric efficiency include designing or searching for novel materials with excellent properties, especially low-dimensional materials, and regulating performance via techniques such as strain engineering, doping, nanostructuring, and so on. Significant progress has been made in the past few years and thermoelectric efficiency has been largely improved. Future studies are expected to improve thermoelectric efficiency even further.

This book presents a comprehensive overview of the progress made in thermoelectrics over the past few years with a focus on charge and heat carrier transport from both theoretical and experimental viewpoints. It also presents new strategies to improve thermoelectricity. In addition, the book discusses device physics and applications to guide the research community.

This book project has lasted more than two years and in that time many things have happened. I would like to acknowledge my wife and daughter for their support. Also, I would like to thank my sister for her helpful suggestions and contributions during the editing of this book.

## **Guangzhao Qin**

State Key Laboratory of Advanced Design and Manufacturing for Vehicle Body, College of Mechanical and Vehicle Engineering, Hunan University, Changsha, China

## **Chapter 1**


**Acknowledgements**

The author thanks members of the BCPH Unit of Molecular Physiology for technical help and discussion. The author also thanks professional peer reviewers for critical comments on this manuscript. This study was thankfully funded by donators for the Fukushima Project.


3.2.3. Cation Exchange

Cation exchange influenced the main cations concentration, and it was judged by using the binary phase diagram of (Na+ − Cl−) vs. (Ca2+ + Mg2+ − SO4 2− − HCO3 −) and Chlor–Alkali indices [4,16].

$$\text{(1)}\quad \text{(Na}^+-\text{Cl}^-\text{)}\text{ vs. (Ca}^{2+}+\text{Mg}^{2+}-\text{SO}\_4{}^{2-}-\text{HCO}\_3{}^-\text{)}$$

If cation exchange was the dominant process influencing the contents of Na+ , Ca2+ and Mg2+, the relationship between the two parameters was negative linear, with a slope of −1.0. As shown in Figure 7a, there was a certain linear relationship between (Na+ – Cl−) and (Ca2+ + Mg2+ − SO4 2− − HCO3 −) in groundwater, but the correlation coefficient was low. It was indicated that cation exchange existed in the hydrogeochemical process, but it did not play a dominant role in the changes in the contents of Na+ , Ca2+ and Mg2+ .


**2. Model of the interaction**

It will be assumed here that a thermal spot (plasma region) has been created via a discharge at a distance in front of a stationary solid body and the spot's continuous expanding ceases by the time of interaction. This could be a case of a relatively slow evolving plasma bubble or when thermal energy is deposited far enough from the body, so the system had enough time to achieve a thermal equilibrium state. The plasma region is allowed to move with the hypersonic flow toward the bow shock formed in front of the body. The interaction starts when the plasma region arrives at the shock location, and that is when the time *t* in the model relations starts to be counted. For blunt bodies that are common in the experiments, a spherical shock wave and interface geometry can be an adequate approximation.

In **Figure 1**, the spherical interface (dashed curve) separates the hot plasma (medium 2) and the surrounding cold gas (medium 1). Both media will be treated as ideal gases with initially equal pressures on both sides of the interface.

The cold gas temperature *T*1 will be always distributed homogeneously, and *T*2 is the plasma temperature right behind the interface with varied parameter distribution along the shock motion direction. The gas temperature can change across the interface abruptly (stepwise) or smoothly (distributed over a distance) [34], and *T*2 > *T*1. The radii *Rb* and *Rs* are for the plasma boundary and the shock front correspondingly, and other parameters are shown in the figure. In the reference frame stationary for the plasma region, the shock wave is incident on the interface from left to right, center to center, with constant horizontal velocity *V*1. If the hotter medium (plasma) is uniform, a portion of the shock front crossing the interface accelerates to velocity *V*2 in a steplike manner, and after this its velocity remains constant. Due to the acceleration, this portion advances faster compared to the

#### **Figure 1.**

*Shock-plasma interaction diagram in the vertical plane of symmetry. As the initially spherical shock progresses through the spherical interface (dashed curve), its shape gradually deforms (green curve). Only the upper part of the diagram is shown.*

**104**

*Wave Drag Modification in the Presence of Discharges DOI: http://dx.doi.org/10.5772/intechopen.86858*

in experiments and thus fills the gap in understanding of this phenomenon. The shock refraction on an interface will be considered there as a mechanism [33] that triggers the chain of subsequent flow transformations leading to the wave drag reduction. The model has an advantage of pointing at the origin of the complex phenomena and describing each of the consecutive stages of its development in

It will be assumed here that a thermal spot (plasma region) has been created via a discharge at a distance in front of a stationary solid body and the spot's continuous expanding ceases by the time of interaction. This could be a case of a relatively slow evolving plasma bubble or when thermal energy is deposited far enough from the body, so the system had enough time to achieve a thermal equilibrium state. The plasma region is allowed to move with the hypersonic flow toward the bow shock formed in front of the body. The interaction starts when the plasma region arrives at the shock location, and that is when the time *t* in the model relations starts to be counted. For blunt bodies that are common in the experiments, a spherical shock

In **Figure 1**, the spherical interface (dashed curve) separates the hot plasma (medium 2) and the surrounding cold gas (medium 1). Both media will be treated as

The cold gas temperature *T*1 will be always distributed homogeneously, and *T*2 is the plasma temperature right behind the interface with varied parameter distribution along the shock motion direction. The gas temperature can change across the interface abruptly (stepwise) or smoothly (distributed over a distance) [34], and *T*2 > *T*1. The radii *Rb* and *Rs* are for the plasma boundary and the shock front correspondingly, and other parameters are shown in the figure. In the reference frame stationary for the plasma region, the shock wave is incident on the interface from left to right, center to center, with constant horizontal velocity *V*1. If the hotter medium (plasma) is uniform, a portion of the shock front crossing the interface accelerates to velocity *V*2 in a steplike manner, and after this its velocity remains constant. Due to the acceleration, this portion advances faster compared to the

*Shock-plasma interaction diagram in the vertical plane of symmetry. As the initially spherical shock progresses through the spherical interface (dashed curve), its shape gradually deforms (green curve). Only the upper part*

wave and interface geometry can be an adequate approximation.

ideal gases with initially equal pressures on both sides of the interface.

adequate timing order.

*Aerodynamics*

**Figure 1.**

**104**

*of the diagram is shown.*

**2. Model of the interaction**

reminder that is still propagating in the colder media thus resulting in the continuously increasing front stretching toward the hotter medium [18].

The shock front development proceeds in two stages, first being affected by the conditions on the interface and second in the plasma volume. The shock refraction resulting in an increase of the absolute value of the shock velocity along with its vector rotation (at refraction angle *γ*) occurs at the moment when the shock front crosses the plasma interface. As the refracted shock continues to propagate in hotter medium, its dynamics is determined by the parameter distribution in the plasma volume [17, 19, 33, 35]. Even though the changes in the shock structure become visible only during this time, they are the still consequences of the interaction at both stages: the conditions on the interface are necessary to trigger the front instability, and the gas volume effects provide the means necessary for its positive dynamics.

The relationship between the incident (*x*i, *y*i) and refracted (*X*i, *Y*i) shock front coordinates at a point of the interaction *i* has been derived in [33]. To recast it in a dimensionless form, the coordinates can be scaled with the plasma sphere radius *Rb*, the shock velocity with *V*1, gas temperature with *T*1, Mach number with *M*1*n*, and time *t* with the characteristic time *τ* = *Rb*/*V*1:

$$\overline{X\_i} = (\overline{\nu}\cos\chi - \mathbf{1})(n - (\overline{\mathbf{x\_i}} + \overline{\mathbf{x\_b}})) + \Delta\overline{\mathbf{x\_i}} \\ \overline{Y\_i} = \overline{\gamma\_i} - \overline{\nu}\sin\chi \bullet (n - (\overline{\mathbf{x\_i}} + \overline{\mathbf{x\_b}})) \tag{1}$$

Here *n* = *t*/*τ* is the dimensionless time, 0 < *n* < 2, *α* is the local incidence angle at the interaction point (**Figure 1**), *xb* ¼ 1 � cos *α*, Δ*x* ¼ ð Þ *Rs=Rb* ð Þ cos *β* � *η* , the parameter

$$\eta = \frac{2(R\_\sharp + R\_b)(R\_\sharp - nR\_b) + n^2 R\_b^2}{2R\_\sharp[(R\_\sharp + R\_b) - nR\_b]} \tag{2}$$

and the bar over the variable means its dimensionless equivalent. The dimensionless shock velocity

$$
\overline{v} = V\_2 / V\_1 = \sqrt{\overline{T} \left(\overline{M}\right)^2 \cos^2 a + \sin^2 a} \tag{3}
$$

and the refraction angle *γ* ¼ *α* � tan �1 ffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffiffi *TM* tan *α* p Þ � are determined by the problem geometry, heating intensity *T* ¼ *T*2/*T*1, and the ratio of normal components of Mach numbers in the two media *M* ¼ *M*2n/*M*1n that account for the shock reflections off the interface.

The Mach number ratio for normal incidence can be obtained using the refraction equation from [36] that was derived assuming steplike temperature *T*2/*T*1 changes across the interface (a "sharp" interface)

$$\frac{1}{M\_{1n}(k-1)}\left\{\left[2kM\_{1n}^2 - (k-1)\right]\left[(k-1)M\_{1n}^2 + 2\right]\right\}^{\frac{1}{2}} \times \left\{\left[\frac{2kM\_{2n}^2 - (k-1)}{2kM\_{1n}^2 - (k-1)}\right]^{\frac{k-1}{2n}} - 1\right\}.$$

$$= M\_{1n}\left(1 - \frac{1}{M\_{1n}^2}\right) - M\_{2n}\left(1 - \frac{1}{M\_{2n}^2}\right)\left(\frac{T\_2}{T\_1}\right)^{\frac{1}{2}}$$

and the normal and tangential components of the Mach numbers are related as *M*1*n* ¼ *M*1 cos *α*, *M*1*t* ¼ *M*1 sin *α*, and *M*2*t* ¼ *M*1*t* ffiffiffiffiffiffiffiffiffiffiffiffiffi *T*1*=T*2 p . The "sharp" interface

assumes back reflections off it and associated losses of the shock energy; thus, the ratio *M*2/*M*1 < 1. For an interface of a "smooth" type, when parameters across its thickness change slower than 1/*x*2 , there are no losses associated with the shock reflection. Thus, *M*2/*M*1 approaches the unit, and the refraction effects become stronger [33]. An intermediate boundary type, when the parameters across its thickness can change faster than 1/*x*2 (an "extended" interface), has been considered in [34]. For a particular case of the exponential gradient in the parameter change, Eq. (3) can be replaced with more general *V*2*x=V*1 ¼ ð Þ *T*2ð Þ *x =T*1 *β* , where *β* ¼ 1*=*2 � 1*=*Σ and *Σ* ¼ ∑10 *n*¼0 *nαn* þ ∑*m mαm* ¼ 53*:*58 is the numerical constant calculated from the fit coefficients *α*n and *α*m in the equation:

$$\frac{T\_2}{T\_1} = \sum\_{n=0}^{10} \alpha\_n (\frac{M\_1}{M\_2})^n + \sum\_{\text{w}} \alpha\_\text{w} (\frac{M\_1}{M\_2})^n \tag{5}$$

The main difference in the treatment imposed by the nonhomogeneous plasma parameter distributions is that the refracted shock velocity becomes time dependent and the system of Eqs. (1)–(3) must be substantially modified. While the plasma parameter distribution will vary, the cold gas parameters will be always considered as distributed homogeneously. For easier comparison, all the simulation results presented below will be obtained for the same incident shock strength, heating intensity, and the interface parameters, with *M*1 = 1.9,*T*1 = 293 K,*T*1/*T*2 = 0.10, equal radii *Rs* = *Rb*, adiabatic index *k* = 1.4 (air), and smooth boundary type. The timely order in the sequence of the flow modification stages demonstrated below follows from the model logic and agrees well with that observed in experiments: the shock front distortion and its weakening; flow parameter redistribution and pressure drop in the post-shock flow, followed with the body drag reduction; vortex generation in the plasma volume; and finally deformation or collapse of the plasma bubble.

When the temperature/density in plasma is distributed uniformly, the relations Eqs. (1)–(3) apply. Results of numerical simulation in **Figure 2** obtained for this case demonstrate the shock front distortion as it progresses through the hot plasma sphere, at different propagation times starting at *n* = 0.05 (the most left curve) through Δ*n* = 0.05 time intervals. To highlight the size of the interface effect, the results are plotted comparatively, with the upper part of the diagram corresponding

The initially spherical front acquires a nearly conical shape stretched in the propagation direction, in good agreement with the experimental observations. The most central part of the front (near the longitudinal symmetry axis) is affected by significant stretching due to longer interaction times and smaller angle of incidence. The curvature sign changes from negative to positive, with the inflection point location tending the intermediate area off the axis. Comparison between upper and lower curves shows that both types of the boundary produce identically shaped

*The shock front modification in homogeneous plasma, at several interaction times. The shock is incident from*

*left to right. The outside part of the shock remains spherical (not shown in the picture).*

**3.1 Shock front distortion**

**Figure 2.**

**107**

to the smooth, lower, and sharp type of the interface.

*Wave Drag Modification in the Presence of Discharges DOI: http://dx.doi.org/10.5772/intechopen.86858*

fronts; however, the smooth boundary results in stronger effect.

The numerical constants are valid within the limits for *M*1 = (1.6–2.4), *T*2/*T*1 = (0–75), and *k* = 1.4 (air). The power coefficients *m* in the second sum run the values *m* = 1/16, 1/8, ¼, and ½. In Eq. (5), the coefficient *β* approaches exactly ½ for a limiting case of a smooth boundary and a value much smaller than ½ for another limit of a sharp boundary [34].


**1. Introduction**

Concurrent training is defined as the combination of resistance and endurance training as part of a periodised physical training model [1]. The simultaneous development of strength, power and endurance is required by many athletic and exercising populations to meet the physical demands of their chosen sporting discipline (e.g., soccer, rugby, hockey) or exercise activity (e.g., circuits, cross-fit training) [2–6]. Similarly, recruits undergoing arduous military training routinely engage in concurrent training so as to meet the training and operational demands of military life [7–13]. Military recruit training programmes are designed to transform civilians into trained soldiers, therefore, physical training is necessarily arduous, involving a combination of aerobic training, strength and conditioning, obstacle courses, swimming, circuit training and loaded marching [14,15]. Despite the requirement of concurrent training in athletic and military recruit populations, and the positive effects protein supplementation may have on training outcomes, the majority of systematic reviews and meta-analyses have focused mainly on the effects of protein

**Citation:** Chapman, S.; Chung, H.C.; Rawcliffe, A.J.; Izard, R.; Smith, L.; Roberts, J.D. Does Protein Supplementation Support Adaptations to Arduous Concurrent Exercise Training? A Systematic Review and Meta-Analysis with Military Based Applications. *Nutrients* **2021**, *13*, 1416. https:// doi.org/10.3390/nu13051416

Academic Editor: Shanon L. Casperson

Received: 19 March 2021 Accepted: 20 April 2021 Published: 23 April 2021

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

**Copyright:** © 2021 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/).

supplementation when either resistance or endurance training are studied in isolation with no specific population in particular being studied [16–19].

In untrained individuals, a bout of endurance exercise upregulates muscle protein synthesis (MPS) in mitochondrial and myofibrillar proteins, whereas, a bout of resistance training elicits an increase primarily in myofibrillar protein synthesis [20]. Moreover, a period of chronic (10 weeks) endurance or resistance training refines the MPS response following exercise in proteins specific to each mode of training. Resultantly, chronic endurance training improves the oxidative capacity of muscle, which can increase whole-body oxygen uptake, leading to a more fatigue-resistant muscle, whereas resistance training develops muscle strength [21]. Both modes of exercise have been shown to increase the phosphorylation of protein in the protein kinase B-mammalian target of rapamycin-p70 ribosomal protein S6 kinase (Akt-mTOR-p70S6K) pathway, leading to an increase of MPS [20]. Indeed, studies have suggested an interference effect when both modes of training are conducted concurrently within the same training programme [22–28], however, others have disputed this interaction [29–31]. Mechanistically, endurance exercise stimulates a rise in adenosine monophosphate-activated protein kinase (AMPK) [32], which may inhibit mTOR through the activation of the tuberous sclerosis complex (TSC) [2]. This has the potential to reduce the post-exercise MPS response, and subsequently attenuate muscle strength adaptations [27] when individuals undertake concurrent endurance training [28].

MPS has been shown to be maximised when protein is consumed in 20–40 g doses immediately-post resistance training [33]. Studies have also shown that concurrent resistance and aerobic training stimulates myofibrillar protein synthesis to a similar degree compared to when resistance training is performed in isolation [34]. This response is further augmented when 25 g of protein is ingested in the immediate post-exercise period [35]. Elevated levels of amino acids in the blood upregulate the localisation and activation of mTOR by deactivating the TSC [27]. The concept of "nutrient sensing" has also been suggested, whereby other proteins such as VPS34 may be key at stimulating the mTOR pathway and myofibrillar protein synthesis in response to elevated blood amino acid concentrations [27,36,37]. As such, an elevated protein intake during arduous concurrent training may be an effective strategy for attenuating the interference effect of endurance exercise [27,28], by maximising mTOR activity and the MPS response to resistance training [33,38–43], thus supporting muscle strength adaptations. Moreover, individuals undertaking arduous concurrent training with limited recovery time between exercise sessions (i.e., military recruit training) may benefit further from strategies which elevate the amount of energy and protein in the diet to support muscle adaptations [44]. In addition to muscle endurance, military recruits are required to pass strength-based tests during basic training [7,8]. Therefore, strategies which support the development of muscle strength and/or attenuate the interference effect are likely to be advantageous, particularly when considering strength is a key determinant of occupational performance [7].

To our knowledge, no study has systematically evaluated the literature to establish the effects of protein supplementation on training adaptations during arduous concurrent training. Therefore, the aim of this systematic review and meta-analysis was to evaluate the literature on protein supplementation and its effects on adaptations to arduous concurrent exercise training in healthy individuals with potential applications to recruits undergoing military training.


*2.4. Technologies Based on the Method of Curing Material by Direct Energy Supply (LENS, EBAM)*

This technology allows elements to be produced by directly melting materials and depositing them on the workpiece, layer by layer. The LENS process must take place in a hermetically sealed, argon-filled chamber so that the oxygen and moisture levels are kept very low. This keeps the manufactured item clean and prevents oxidation. The metal powder is delivered directly to the head which deposits the material. After depositing a single layer, the head which deposits the material advances to the next layer. The whole section is constructed by building successive layers. Once completed, the element is removed and can be processed or finished in any way. EBAM is an additive manufacturing technology that produces large-scale metal structures. The EB gun deposits the metal with a wire, layer by layer, until the element reaches a grid-like shape and is ready for its final treatment [11–13,22,24,25,27].

This article offers an explanation of the influence the AM technology use on increase the manufacturing company's competitiveness. On the basis of the current analysis of the literature on the subject and of the defined dimensions of the AM technology use a questionnaire was developed, based on which, pilot studies of the first stage were conducted, followed by the main research into the Polish automotive and metal manufacturing companies, regarding the use of additive manufacturing technology. This study assumes also that those workers who were involved in the survey realize at least 80% of the defined dimensions associated with the dimensions of the of the AM technology use. The surveys applied for testing the research model (Figure 1) were developed by defining scales to fit the knowledge codification meaning.

Factors of dimensions of the AM technology use were based on feedback surveys and their sources are listed here: The dimensions of the of the Additive Manufacturing Technology Use: I know that in my company the use AM technologies during the business processes, namely in automotive industry: DA11-production processing of machinery and equipment parts made of plastic and metal, including high-precision mechanical parts and components, DA12-production process of functional prototypes and co-operating mechanisms, DA13-production process of models used in strength tests, tests and modelling; in metal industry: DM11-prototype and model production process, DM12-production process of injection moulds, foundry moulds, highly precise metal constructions and other elements with a complex geometry or requiring high mechanical properties, the production of which would

often not be possible by means of foundry technologies, DM13-process of repair and regeneration of complex damaged metal parts is: factor0: not very important/ factor1: very important.

**Figure 1.** Research model.

The Polish manufacturing company's competitiveness: I know that in my company the realization of the work activities using Additive Manufacturing Technology, is: factor0: not very important/factor1: very important for C1-reduction of production costs, C2-effective use of material, C3-freedom in product design, C4-no assembly stage, C5-product personalisation to meet specific customer requirements, C6-quick response to market needs, C7-optimisation of product functions, C8-development, C9-elimination of human labour, C10-waste reduction/lower energy consumption.

The data for this study were collected from 250 Polish manufacturing enterprises between October to November, 2019.


**4. Simulation results**

*Green Energy Advances*

**Figure 8.**

energy consumptions:

*Example of load and SOC profiles in case of basic BMS.*

In the other case, if the energy stored in the batteries is slightly lower than loads, the charge is used both for baseload supply and peak shaving (BMS Strategy #3). Nevertheless, the exact time schedule of loads is not predictable and it is not possible to know when the load peaks will occur. In the worst case, storage will be discharged soon in the evening, while the peak will be in the next early morning, when batteries are already empty. For this reason, the SBMS limits the discharge of batteries during time with the definition of different levels of minimum *SOC*min,x for an user-defined number of time slots *x*, in which the *TDT* is divided. According to the procedure proposed in Section 3.2, in case of *TDT* = 12 h, the number of time slots *x* = 2, otherwise with *TDT* = 36 h, the time slots are 5 (*x* = 5). The *SOC*min,x limits are defined in order to distribute the stored energy proportionally to the provisional energy consumption. Thus, *SOC*min,x limits are calculated starting from the *SOC* of the storage, measured in real time by the BMS, and the provisional

where *E*load, slot x is the provisional energy that will be required by loads in the time slot *x*. For example, let us suppose that the TDT is 12 h and the overall required load will be 10 kWh. In particular, during the evening (from 6:00 p.m. to midnight), the required load will be 4 kWh, and during the next night (from midnight to 6:00 a.m.), the load will be 6 kWh. The stored energy will be discharged as follows: 40% during the evening and 60% during the night. In this example, the

**Figure 8** shows an example of load and *SOC* profiles in case of a basic battery management. In this case, the storage is charged when PV production is higher than loads and batteries are empty; on the contrary, storage is discharged if PV production is lower than loads [23]. The only limitation in charge/discharge is performed to avoid fast degradation of batteries, by limiting the *SOC* in a safety range *SOC*min,safety < SOC < *SOC*max,safety. For sake of simplicity, it is considered a rainy day and the production from the PV generator is negligible. In case of lithium batteries (**Figure 8**), the minimum level *SOC*min,safety generally corresponds to *SOC*safety ≈ 20%, while in case of lead-acid batteries, it can reach 50% [41, 42]. In the example of **Figure 8**, the storage supplies the loads until 10:50 a.m., when the *SOC*min,safety is reached. After that, only the grid supplies the load and the highest

storage is considered initially full and with a minimum *SOC*min,safety = 0.2.

*Eload*.*TDT*) (8)

*SOCmin*,*slot x* = *SOC* ∙ (1 − *Eload*,*slot x* \_\_\_\_\_\_\_\_\_\_\_

**84**

absorption peak is not limited ≈2.9 kW.

#### **4.1 Inputs parameters and constraints of the simulations**

Simulations of the PV-storage system are performed for the entire month of December with a 1-min time step for both basic and proposed BMS to compare their performance. During winter, the PV production is low, batteries are often empty, and the development of an efficient BMS is necessary to reduce the absorption peaks from the grid. On the contrary, in summer, PV generation generally charges storage and directly supplies part of the loads.

The optimal management of the storage is investigated in case of different sizes of the PV system *P*PV,r and different capacities of the battery *C*bat. Regarding *P*PV,r, it ranges between 2 and 6 kWp with a step of 1 kW, while the storage capacity *C*bat is in the range 1–5 kWh (step of 1 kWh). The management parameters are the power value *P*load,max beyond which the peak shaving strategy works and the threshold *R*thres. The power limitation *P*load,max ranges between 0.5 and 2 kW with a step of 0.5 kW, while the user-defined threshold *R*thres varies between 50 and 80% (step of 10%). Regarding the loads, the measured consumption profile of a domestic user (a family composed of two persons) located in Northern Italy (45.05° Nord, 7° 40' Est) is used. The annual consumption of

the domestic user analyzed in the case study is ≈2800 kWh/year and its loads correspond to typical home appliances (e.g., hairdryer, oven, personal computer, lighting, and electric water heater).

### **4.2 Case study**

The results of the simulation show that the proposed BMS decreases the peaks of absorption from the grid with respect to a traditional management. The results are interesting especially in case of a small storage, while in case of higher storage capacity, there are negligible differences between the two managements. **Figure 10** shows case #1: it corresponds to the analysis of 2 days of simulation for a PV system with *P*PV,r = 4 kW and a storage system with *C*bat = 2 kWh. In the graphs, in case of battery discharge, the sign of the power supplied by the storage to the loads is negative. The 2 days are characterized by cloudy and rainy conditions, and the PV production is low. The proposed BMS calculates the provisional energy balance and a huge lack in storage is predicted; thus, the peak shaving method is used (BMS Strategy #2). Before 6:00 p.m., all the loads are supplied by PV and storage; then, peak shaving is applied and only the quota exceeding *P*load,max = 2 kW is satisfied by batteries. The saved energy is then preserved and used to shave loads during the second day, with the result of keeping the absorption from the grid always ≤2 kW.

On the contrary, if a standard BMS is used (**Figure 11**), all the stored energy is consumed before the end of the evening of the first day; furthermore, there is no energy from storage to supply the load peaks during the second day. The result is a maximum absorption peak of ≈4.2 kW: during these days, the proposed SBMS reduces the absorption peak of ≈50%.

**Table 2** shows the energy balance of the case #1 related to **Figures 10** and **11**. With the proposed SBMS, the maximum power absorbed from the grid is half, while the deviations in terms of self-sufficiency and injected energy into the grid are negligible. Nevertheless, there is an increase in grid absorption: to guarantee power for peak shaving, a residual energy is kept in the storage, and at 6:00 p.m. of the second day *SOC*≈0.7.

A second simulation is shown in **Figure 12**. The case #2 is characterized by two different days with respect to case #1: a negligible PV production occurs in both days, while the sizes of PV and storage systems and loads are the same of case #1. The provisional energy balance predicts that the energy in the storage will supply great part of the loads, but it will be not sufficient to supply them totally. The

**87**

**Table 2.**

*Energy results for case #1.*

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw…*

provisional self-sufficiency parameter is *R*suff > *R*thres (with *R*thres *=* 50%); thus, the converter selects the BMS Strategy #3. The most interesting part corresponds to the time window 6:00 a.m.–6:00 p.m. of the second day. Batteries start discharging at 6:00 a.m. and when peaks occur (10:00–12:00 a.m.), only the quota exceeding *P*load,max = 2 kW is satisfied by batteries. In the same way, the other absorption peak occurring at 9:00 p.m. is shaved, thanks to the preserved energy in the storage. The

On the contrary, in the same conditions, a traditional BMS would discharge the battery before 10:00 a.m. and the absorption peak would be 2.8 kW (higher than

Finally, in **Table 3**, the above-described combination #A and other three combinations of PV and storage sizes, which permit to obtain significant improvements, are presented. The power and energy results of the proposed SBMS are compared to the standard BMS. In all the other cases, the improvement in terms of maximum absorption from the grid is confirmed, ranging from ~9 to ~10%. Regarding the maximum injection into the grid and the energy quantities, their deviations are negligible. The combination #A shows much better results, confirming that the

Load (kWh) 11.45 11.45 Self-consumption (kWh) 3.2 3.2 Grid absorption (kWh) 5.5 4.58 Grid injection (kWh) 0.52 0.52 Self-sufficiency/load (%) 28 28 Self-consumption/PV production (%) 43 43 Grid injection/load (%) 4.5 4.5 *P*load,max (kW) 2 4.22 Injection peak (kW) −0.81 −0.81

**Proposed BMS Standard BMS**

*DOI: http://dx.doi.org/10.5772/intechopen.80562*

maximum absorption peak is 2.4 kW.

*Power profiles for case #1 with standard BMS.*

the proposed SBMS of ≈14%).

**Figure 11.**

**Figure 10.** *Power profiles for case #1 with the proposed SBMS.*

*A Smart Battery Management System for Photovoltaic Plants in Households Based on Raw… DOI: http://dx.doi.org/10.5772/intechopen.80562*

**Figure 11.** *Power profiles for case #1 with standard BMS.*

*Green Energy Advances*

**4.2 Case study**

puter, lighting, and electric water heater).

reduces the absorption peak of ≈50%.

*Power profiles for case #1 with the proposed SBMS.*

the second day *SOC*≈0.7.

the domestic user analyzed in the case study is ≈2800 kWh/year and its loads correspond to typical home appliances (e.g., hairdryer, oven, personal com-

The results of the simulation show that the proposed BMS decreases the peaks of absorption from the grid with respect to a traditional management. The results are interesting especially in case of a small storage, while in case of higher storage capacity, there are negligible differences between the two managements. **Figure 10** shows case #1: it corresponds to the analysis of 2 days of simulation for a PV system with *P*PV,r = 4 kW and a storage system with *C*bat = 2 kWh. In the graphs, in case of battery discharge, the sign of the power supplied by the storage to the loads is negative. The 2 days are characterized by cloudy and rainy conditions, and the PV production is low. The proposed BMS calculates the provisional energy balance and a huge lack in storage is predicted; thus, the peak shaving method is used (BMS Strategy #2). Before 6:00 p.m., all the loads are supplied by PV and storage; then, peak shaving is applied and only the quota exceeding *P*load,max = 2 kW is satisfied by batteries. The saved energy is then preserved and used to shave loads during the second day, with

On the contrary, if a standard BMS is used (**Figure 11**), all the stored energy is consumed before the end of the evening of the first day; furthermore, there is no energy from storage to supply the load peaks during the second day. The result is a maximum absorption peak of ≈4.2 kW: during these days, the proposed SBMS

**Table 2** shows the energy balance of the case #1 related to **Figures 10** and **11**. With the proposed SBMS, the maximum power absorbed from the grid is half, while the deviations in terms of self-sufficiency and injected energy into the grid are negligible. Nevertheless, there is an increase in grid absorption: to guarantee power for peak shaving, a residual energy is kept in the storage, and at 6:00 p.m. of

A second simulation is shown in **Figure 12**. The case #2 is characterized by two different days with respect to case #1: a negligible PV production occurs in both days, while the sizes of PV and storage systems and loads are the same of case #1. The provisional energy balance predicts that the energy in the storage will supply great part of the loads, but it will be not sufficient to supply them totally. The

the result of keeping the absorption from the grid always ≤2 kW.

**86**

**Figure 10.**

provisional self-sufficiency parameter is *R*suff > *R*thres (with *R*thres *=* 50%); thus, the converter selects the BMS Strategy #3. The most interesting part corresponds to the time window 6:00 a.m.–6:00 p.m. of the second day. Batteries start discharging at 6:00 a.m. and when peaks occur (10:00–12:00 a.m.), only the quota exceeding *P*load,max = 2 kW is satisfied by batteries. In the same way, the other absorption peak occurring at 9:00 p.m. is shaved, thanks to the preserved energy in the storage. The maximum absorption peak is 2.4 kW.

On the contrary, in the same conditions, a traditional BMS would discharge the battery before 10:00 a.m. and the absorption peak would be 2.8 kW (higher than the proposed SBMS of ≈14%).

Finally, in **Table 3**, the above-described combination #A and other three combinations of PV and storage sizes, which permit to obtain significant improvements, are presented. The power and energy results of the proposed SBMS are compared to the standard BMS. In all the other cases, the improvement in terms of maximum absorption from the grid is confirmed, ranging from ~9 to ~10%. Regarding the maximum injection into the grid and the energy quantities, their deviations are negligible. The combination #A shows much better results, confirming that the


**5. Revised results**

**Figure 5.** Constraints on image data processing

336 Advances in Vibration Engineering and Structural Dynamics

The initial results of these measurements were reported in another paper in this series (Oda et al., 2011). Fig. 6 shows typical results. It shows an offset of a few millimeters appeared when solar array paddle illuminated by the Sun and when in an eclipse. However, these re‐ sults pose certain difficulties in explaining the phenomena, as the tendency of the solar array paddle to bend does not agree with the observation results and conventional research.

Sun illuminated Eclipse

**Figure 6.** Motion of the solar array paddle's tip position as estimated from onboard camera images

**4. Previous results**

Fig. 6 shows a sudden offset in the motion of the solar array paddle. When the solar array paddle is straight, the value of displacement may indicate -12 or -11 [mm] in Fig.6. Although we attempted to identify cause of this offset, we could not imagine a proper mechanism that would produce such a direction. We therefore assumed that the previous image data proc‐ essing contained unidentified data processing errors, and consequently modified the algo‐ rithm that identifies the target markers. The earlier version of the algorithm used to identify locations of the target markers assumed such highly illuminated areas as those of the target markers. This algorithm works well when the markers are brightly illuminated. When the target markers are weakly illuminated, however, we found that this algorithm produces some data processing errors.

Fig. 7 and Fig. 8 illustrate the difference described above. Fig. 7 is based on the previous al‐ gorithm. The areas enclosed by a yellow line are pixels that are brighter than the threshold level and thus can be assumed to be the target markers as based on sub-pixel level image data processing. The red cross indicates the center position of the marker.

**Figure 7.** Target markers estimated by the previous algorithm under weak illumination. The areas of target markers assumed by the previous algorithm are smaller than the actual target marker size.

Fig. 7 shows that the sizes of the estimated target markers are smaller than the actual target markers. We therefore modified the algorithm used to estimate the area of a target marker so that the size of the predicted target marker is similar to that calculated from the actual target marker. This modification worked well to identify the target marker and its displace‐ ment. Fig. 8 is a result based on the revised algorithm. In this revised algorithm, the areas to be considered target makers are decided based on the brightness level of each pixel and also on the size of the areas considered to be target markers. When an assumed target marker is too small, then the threshold level of brightness is automatically adjusted to meet the possi‐ ble size of the target markers.

From these data, we can conclude that the temperature of the solar array paddle changes steadily but not drastically, depending on its thermal capacity. And we also assume like as

Vibration of Satellite Solar Array Paddle Caused by Thermal Shock When a Satellite Goes Through the Eclipse

http://dx.doi.org/10.5772/52626

339

**1.** When the solar array paddle is illuminated by the Sun, its temperature is governed by

**2.** When in an eclipse, the solar array paddle shows lower temperature depending on its thermal capacity. When the satellite's LST (Local Sun Time) is around noon, the solar cell side of the panels faces the Earth and receives solar radiation from Earth. In the eve‐ ning and the early morning of satellite LST, however, both sides of the solar panels face

In order to verify whether the observation results described above are correct, and to under‐ stand the features of thermal snap on the solar array paddle, we conducted numerical simu‐ lation of the solar array paddle. There are many studies analyzing the thermal snap (Thornton, 1996; Boley, 1972; Johnston, 1998; Lin, 2004; Xue2007), we develop the method using a thermal model and a structural model and revising these models by applying the observed data. This section presents the analytical system that we constructed, the result of a thermal-structural analysis and its problems. To solve the problems, we have developed a new model in considering the effects of hinge/latch mechanisms and friction. The result of

This section describes the thermal snap analysis procedure. Fig. 12 shows a flowchart of thermal snap analysis. The analysis can be broken down into to three parts: construction of the structural model, calculation of temperature distribution, and thermal snap analysis for

In the first part, a structural model of the solar array paddle is constructed for thermal snap analysis. To verify the structural model, we conducted modal analysis to obtain the natural frequency and vibrational mode of the solar array paddle model. These results will be com‐ pared with an on-orbit preliminary experiment, and if necessary, we will then revise the model (see Section 6.2 for details). In the second part, a thermal model is developed for the solar array paddle. Thermal analysis for the whole orbit is then conducted to verify the ther‐ mal model. The thermal analysis results will be applied to GOSAT's trajectory information, and also compared with data obtained by GOSAT, in order to verify accuracy. After accura‐ cy is verified, thermal analysis will focus on GOSAT during its integration and testing. In order to improve the accuracy of thermal analysis, a profile of thermal input was deter‐

mined based on the brightness of the solar paddles (see Section 6.3 for details).

solar energy from the Sun (as well as that reflected from Earth).

toward space, resulting in a rapid drop in temperature.

following.

**6. Numerical simulation**

using new model is also introduced.

**6.1. Thermal snap analysis procedure**

the penumbra.

Finally, Fig. 9 shows the motion of a target marker as based on the revised algorithm. We can see that the estimated motion of the solar array paddle has less dispersion.

**Figure 8.** Target markers estimated by the revised algorithm

**Figure 9.** Displacement of GOSAT's solar array paddle when going into eclipse

From these data, we can conclude that the temperature of the solar array paddle changes steadily but not drastically, depending on its thermal capacity. And we also assume like as following.


**1. Introduction**

Agro-industrial residues are important sources of lignocellulosic biomass, the main feedstock to be used as a substitute for petrol in a circular bioeconomy. These organic materials are generated in large volumes as a result of agricultural and agro-industrial activities and are in great part subtilized, especially in developing countries that are the main suppliers of commodities. According to Magalhães Jr. et al. [1], around 900 million tons of biomass from agro-industrial residues such as sugarcane bagasse, cereal straws and oil palm solid wastes will be generated in South America in 2025, and their most probable destination, if not left in the field, is burning for energy generation and composting, the most traditional processing methods. However, considering that more than 60% of their mass can be converted to fermentable sugars, these residues could be valorized through microbial processes

to produce second generation (2G) ethanol and other biofuels, organic acids, biomaterials and many other commercial bioproducts.

Nowadays, 2G ethanol is the product with the most mature stage of technological development obtained in lignocellulose biorefineries. However, the cost of 2G ethanol is still not competitive with that of petroleum-based liquid fuels [2], in part because of the cost of enzymes that are necessary to convert fibrous carbohydrates into fermentable sugars. Another step that impacts the overall cost and lacks technological maturity is the pretreatment, necessary to prepare the recalcitrant structure of lignocellulosic biomass to enzymatic hydrolysis or saccharification. In general, the aim of the pretreatment is to enhance the recovery of glucose and other reducing sugars from lignocellulosic biomass in the saccharification step, and this is usually accomplished through the decrease of the crystallinity of cellulose, degree of polymerization, lignin content and moisture content, associated to an increase of available surface area [3].

Several methods have been reported to pretreat lignocellulosic biomass, most of them thermochemical processes. These include steam explosion with or without alkali washing, dilute acid hydrolysis, alkaline pretreatment, organosolv, ammonia fiber expansion, liquid hot water, wet oxidation and others [3]. Alkaline pretreatment is especially efficient in solubilizing lignin, while mild acid pretreatment is more directed to the hemicellulose fraction.

In 2018, the global sugarcane production was of 1.9 billion tons, and Brazil was the main producer accounting for 39% of the total production, followed by India with 20% [4]. In the harvest of 2018/2019, India has surpassed Brazil producing more than 700 million tons, but for 2019/2020 the projections are very similar for both countries [5]. The Brazilian production estimative for the harvest of 2019/2020 is of around 643 million tons [6]. Sugarcane bagasse is a porous residue of cane stalks generated after the crushing and extraction of sugarcane juice. It is composed mainly by cellulose (32–44%), hemicellulose (27–32%), lignin (19–24%) and ashes (4.5–9%). Sugar mills generate approximately 270–280 kg of bagasse (with 50% moisture) per metric ton of sugarcane [7,8].

Soybean is one of the most important sources of vegetable protein for human and animal nutrition and one of the most economical sources of oil for food and biofuel production. According to the United States Department of Agriculture [9], the world production of soybean for 2019/2020 is estimated at 338 million metric tons. The world's leading producer is Brazil and the second is the United States, responsible for 37% and 27% of the global production, respectively. From one ton of soybean with around 13% moisture, 50 kg of soybean husks are obtained [10]. The composition of soybean husks depends on the dehulling process, so they may contain varying amounts of cellulose (29–51%), hemicelluloses (10–25%), lignin (1–4%), pectins (4–8%), proteins (11–15%) and minor extractives [11].

The global production of oil palm is estimated at 73 million metric tons for the period of 2019/2020, Indonesia and Malaysia being the leading producers with 58% and 26% of the total, respectively [9]. The milling of oil palm to extract the oil generates a solid fibrous residue, named oil palm empty fruit bunches (EFB). It is estimated that for each ton of fresh fruit bunches processed, 220 kg of EFB are generated. This residue is composed of around 30, 25 and 25% (mass percentages) of cellulose, hemicellulose and lignin, respectively [12].

The present work aimed at evaluating the effect of different enzyme preparations and combinations in the saccharification of these three important agro-industrial residues, the sugarcane bagasse, soybean husks and oil palm empty fruit bunches, pretreated by mild acid and alkali. The enzyme pools were represented by cellulase and xylanase complexes of *Penicillium verruculosum* (B1 host preparation), by the B1-XylA preparation obtained through the recombinant expression of *Penicillium canescens* xylanase A in the *P. verruculosum* B1 host strain, and by the F10 preparation obtained by recombinant expression of *Aspergillus niger* β-glucosidase in the B1 host strain. All preparations were crude, which could significantly reduce the cost associated to the enzymatic saccharification step.


**6.2 Investigate the indication of liver transplantation for uncontrollable liver trauma**

In conclusion, we largely agree with the aforementioned reports. The therapeutic option of liver transplantation also needs to be accessible for patients with liver injuries caused by trauma. However, not least due to the mentioned poor transplantation results in severely injured patients, indication for transplantation needs to be critically proposed by the attending surgeons.

#### **6.3 Try to answer the question – Is transplantation a valuable option or just a "waste of organs" in polytraumatized patients with liver injury –**

It is essential to sensibly and appropriately allocate the organs so that the shortage of donor organs is not further enlarged. In patients where no hemodynamic stabilization can be achieved despite exhaustion of all extensive care measures, transplantation should not be considered any further. Although, there is a fundamental difference regarding the timeframe after trauma during which patients are to be transplanted. It has shown, that especially patients with acute, non-controllable liver injuries as described by us have clinically changed for the worse rapidly after transplantation and have died in MOF. Therefore, we postulate that indication for transplantation in these patients may only be provided after critically reviewing every single case as not to "waste of organs".

#### **6.4 Identifying new approaches to improving organ donation**

It should be noted that the success of transplantation medicine with a simultaneously increasing shortage of donor organs will only be assured if all available resources are exploited. Increasing acceptance of "expanded criteria donor" organs appears more justified than ever under these circumstances, but also with sustained good results despite constantly deteriorating organ quality. Approaches to increase the transplant quality, not only of extended criteria donor (ECD) organs, offer further developed possibilities that support perfusion such as machine perfusion and optimized perfusion solutions. Moreover, shortened ischemic periods are achieved through further improved logistics and allocation processes, which together with individualized, medicinal immune suppression ultimately benefit the transplant and the organ recipient. In addition to this continuously improving and thus optimized use of postmortal organs, it must also be the common objective of the medical profession and politics to increase the overall number of donor organs and to improve their quality.

In order to achieve this, priority should be given to an improved exploitation of the existing organ donation potential through hospital-based advising, for example by a contact person for organ donation at each intensive care unit. Information and advising by Eurotransplant and the transplant centers for physicians, nursing staff and the population are of key importance here. Furthermore, to an ever great extent it will be the task of all parties involved in the field of transplantation to present organ donation, the allocation and transplantation of organs, and all the decisive aspects relating to the readiness of organ donation such as trust, safety and equity in a transparent manner. Conducting advisory discussions on the topic of organ donation with the relatives of the deceased is a special task for physicians. One measure should include involving a physician with special communicative expertise. It remains to be hoped that in this way a higher acceptance rate for organ donation will be more successfully achieved throughout all population groups in the future. In tapping into all resources and approaches for the optimized exploitation of donated postmortal organs, it should be possible to assure the medical care mandate of transplantation medicine in Germany in the future as well.

Based on previous studies, the recording of organ donors with "expanded criteria" in a targeted analysis is useful and necessary for new ways of improving organ donation. Further local, national and international analyses are additionally necessary to identify the limits to expanding donor acceptance criteria.


**4. Conclusion**

We are at the end of the beginning in the field of stem cell research and stem cell regenerative medicine. Particularly, the development of iPSCs and gene editing techniques have opened a new era of disease modeling and personalized

**115**

*Innovations in Human Stem Cell Research: A Holy Grail for Regenerative Medicine*

medicine. However, although it is feasible, the time and cost for production and validation of autologous iPSC-derived cellular therapeutics have made such personalized medicine unpractical, especially for diseases requiring an immediate therapeutic intervention. Therefore, a more feasible use of iPSCs and iPSCderived cell products would rely on an HLA-matched allogeneic setting. Moreover, targeted differentiation of PSCs into distinct cell subpopulations and proper cell maturation remain to be a challenge in the development of iPSC therapies. In the end, whether the cell products are derived from iPSCs or adult stem cells, they face the same challenges as cellular therapies, that is, large number of cell death after transplantation and poor functional integration of the survived cells. The current efforts on tissue engineering and organoid system have demonstrated promises in overcoming these difficulties. Understanding tissue microenvironment is also the key to develop effective therapies that ensure exogenous cell engraftment and integration and/or augment endogenous tissue stem cell func-

With the excitement of stem cell research, more subtypes of stem cells are entering into clinical studies, and there is a growing interest in commercializing and marketing of these stem cell products. However, caution should be maintained to ensure the quality of cell products and the scientific rationale and rigor for their clinical translation. Lastly, a randomized and controlled clinical trial with large sample size and multiple surrogate endpoints are essential to determine the safety

The authors would like to thank Erin Morris, RN, for assistance in the preparation of this chapter. This work was supported by grants from the Pediatric Cancer Research Foundation to MSC, DEBRA International funding to MSC, and National

*DOI: http://dx.doi.org/10.5772/intechopen.88790*

tion for regeneration.

**Acknowledgements**

and efficacy of stem cell therapy.

Natural Science Foundation of China (81472141) to HZ.

*Innovations in Human Stem Cell Research: A Holy Grail for Regenerative Medicine DOI: http://dx.doi.org/10.5772/intechopen.88790*

medicine. However, although it is feasible, the time and cost for production and validation of autologous iPSC-derived cellular therapeutics have made such personalized medicine unpractical, especially for diseases requiring an immediate therapeutic intervention. Therefore, a more feasible use of iPSCs and iPSCderived cell products would rely on an HLA-matched allogeneic setting. Moreover, targeted differentiation of PSCs into distinct cell subpopulations and proper cell maturation remain to be a challenge in the development of iPSC therapies. In the end, whether the cell products are derived from iPSCs or adult stem cells, they face the same challenges as cellular therapies, that is, large number of cell death after transplantation and poor functional integration of the survived cells. The current efforts on tissue engineering and organoid system have demonstrated promises in overcoming these difficulties. Understanding tissue microenvironment is also the key to develop effective therapies that ensure exogenous cell engraftment and integration and/or augment endogenous tissue stem cell function for regeneration.

With the excitement of stem cell research, more subtypes of stem cells are entering into clinical studies, and there is a growing interest in commercializing and marketing of these stem cell products. However, caution should be maintained to ensure the quality of cell products and the scientific rationale and rigor for their clinical translation. Lastly, a randomized and controlled clinical trial with large sample size and multiple surrogate endpoints are essential to determine the safety and efficacy of stem cell therapy.


**1. Introduction**

*Uraria crinita* (L.) Desv. ex DC. (UC) belongs to the family of Leguminosae. It is a popular and commercially important medicinal plant, distributed widely in Taiwan and cultivated mostly in Mingjian Township, Nantou (approximately 20–60 hectares per year). UC roots, also known as "Taiwanese ginseng" due to the similar potency and aroma of its decoction and ginseng, have been traditionally used to coordinate the gastrointestinal system, thanks to their detumescent and antipyretic effects, indicating immunomodulatory activity [1]. UC roots are used as dietary supplements for treating childhood skeletal dysplasia. UC roots have therefore been developed as valuable and commercial functional food in Taiwan. This herb has been shown to have antioxidant [2] and antidiabetic activities [3] and the potential to stimulate bone formation and regeneration [4]. Previous phytochemical investigations on UC roots led to the isolation of fatty acids, steroids, triterpenoids, phenolics, lignans, flavonoids, and isoflavonoids [2,4–6]. However, little is known about the association between the immunomodulatory activity and the metabolites in this herb.

Dendritic cells (DCs), acting as antigen-presenting cells (APCs), are the major leukocytes, with a critical role in regulating adaptive immune responses [7]. Immature DCs, characterized by a high antigen uptake ability and poor antigen-presenting function, reside in the peripheral tissues, where they regularly uptake and process self-antigens and maintain self-tolerance [7]. Upon activation, immature DCs undergo maturation and migrate to adjacent lymph nodes or to the lymph organs, after the recognition of pathogen-associated molecular patterns and damage-associated molecular patterns by pattern recognition receptors, mostly Toll-like receptors (TLRs) [8]. This process is accompanied by the upregulation of the expression of major histocompatibility complex (MHC) class II molecules and several co-stimulatory molecules (CD40, CD80, and CD86) on the surface of cells [9]. Mature DCs generate more pro-inflammatory cytokines (TNF-α, IL-6, and IL-12) required for T cell activation and have the ability to present antigens to T cells, linking the innate and adaptive immune systems [9]. Therefore, targeting DCs is a promising strategy for immunomodulation. Medicinal herbs, which modulate the function of DCs, can potentially be developed into botanical drugs for treating immune disorders.

The promising potential of the herbal industry can only be achieved through the standardization of the composition of herbal products and the assurance of proper quality control [10]. However, the variable constituents of herbal products, owing to genetic, cultural, and environmental factors, have made the quality of herbal medicines di fficult to control. Nuclear magnetic resonance (NMR) has been demonstrated as one of the best analytical approaches to identifying metabolites and providing both qualitative and quantitative information [11]. While NMR su ffers from a relatively poor sensitivity compared to mass spectrometry (MS), it has some unique advantages over MS-based approaches, including a non-destructive nature, high robustness, high reproducibility, high reliability, and powerful ability to provide structural information for unknown metabolites [12]. NMR spectroscopy could o ffer structural elucidation, achieved by the chemical shift, multiplicity, coupling constant, and integration of (primary or secondary) metabolite signals in crude extracts. Additionally, the 1H NMR signal intensity, proportional to the relative number of protons, could provide useful information about the quantity of the di fferent metabolites in herbal extracts. Therefore, NMR has been successfully used for fingerprinting and the metabolite discrimination of herbs [13,14]. Currently, NMR-based metabolomic analysis is widely used in studies of nutrients, environment, plant physiology, drug metabolism, toxicology, as well as for diagnoses and for the quality control of herbal products [10,15,16].

In this study, a combination of bioactivity-guided fractionation and NMR-based identification led to the elucidation of the central role of the immunomodulatory isoflavone genistein present in UC root methanolic extract (UCME) against the activation and maturation of lipopolysaccharide (LPS)-stimulated DCs. This strategy could prevent unnecessary time-consuming isolation procedures and provide a rapid tool for the identification of the active ingredients of herbs. Our findings suggest that UC roots can be applied as an immunosuppressive functional food, of which genistein can be a chemical marker for quality control. The standardization of UC roots could therefore be improved.

#### **2. Results and Discussion**

#### *2.1. The EtOAc-Soluble Fraction from UCME Inhibited LPS-Stimulated DC Activation*

The immunomodulatory e ffects of UC roots on DCs have not been reported. Here, the Gram-negative bacterial endotoxin LPS was used to stimulate bone marrow-derived dendritic cell (BMDC) activation, as a model for investigating the immunomodulatory e ffects of UC roots on DCs. First, the immunomodulatory e ffects of UCME and its EtOAc-, *n*-BuOH-, and H2O-soluble fractions were evaluated against the production of pro-inflammatory cytokines, including TNF-α and IL-6, which is a hallmark of DC activation. As shown in Figure 1A, LPS-stimulated BMDC activation was suppressed by UCME, and UCME ability to inhibit DC activation was mainly associated with its EtOAc-soluble fraction. In addition, the treatment with UCME and its various partitioned fractions, at concentrations below 100 μg/mL, did not exhibit any cytotoxicity in BMDC (data not

shown). In summary, our results revealed that the EtOAc-soluble fraction of UCME may contain immunomodulatory phytochemicals which attenuate the activity of DCs.

**Figure 1.** The effects of UC methanolic extract and of its EtOAc-, *n*-BuOH-, H2O-soluble fractions and subfractions of the EtOAc-soluble fraction on pro-inflammatory cytokine production in LPS-stimulated dendritic cells (DCs). DCs were untreated or treated with LPS (100 ng/mL, white bar). (**A**) Methanolic extract and EtOAc, *n*-BuOH, and H2O subfractions (25 μg/mL, gray bar, or 100 μg/mL, black bar) were used. (**B**) EtOAc and Fr. A to I subfractions (50 μg/mL, black bar) were used. (**C**) Fr. D and D-1 to D-6 subfractions (10 μg/mL, hatch bar, 25 μg/mL, gray bar, or 50 μg/mL, black bar) were used. Supernatants were collected 6 h after the treatment. The production of cytokines (TNF-α and IL-6) was measured by ELISA. The data shown are the mean ± SD of three independent experiments; ### *p* < 0.001; \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001 (Scheffe's test) for comparisons of the treated and untreated LPS-stimulated DC samples.

#### *2.2. Bioactivity-Guided Fractionation and NMR-Based Identification of the EtOAc-Soluble Fraction of UCME*

The EtOAc-soluble fraction of UCME was subjected to silica gel column chromatography (EtOAc/*n*-hexane/MeOH, gradient), and each collected fraction was analyzed by thin-layer chromatography (TLC) and assign to one of nine main fractions (Fr. A to I, Figure 2).

**Figure 2.** Bioactivity-guided fractionation and NMR-based identification of genistein from the roots of *Uraria crinita* (UC). \* indicates the most potent subfractions or constituents against pro-inflammatory cytokine production in lipopolysaccharide (LPS)-stimulated DCs. UCME: UC root methanolic extract, BMDCs: bone marrow-derived dendritic cells.

Among them, fraction D significantly inhibited the production of TNF-α and IL-6 in BMDC (Figure 1B). Furthermore, the subfractions D-4 and D-5 from fraction D indicated the most potent inhibitory effects against DC activation (Figure 1C).

In order to elucidate the association between bioactivity and metabolites in subfractions D-1 to D-6, 1H NMR spectroscopy was conducted (Figure 3). This could offer structural elucidation, achieved by the chemical shift, multiplicity, coupling constant, and integration of metabolite signals in the mixtures.

**Figure 3.** Selected 1H NMR profiling (acetone-*d*6, 500 MHz) of subfractions D-1 to D-6. \* indicates the characteristic singlet signals, δH 8.13, of isoflavones; ↓ indicates the 1H spectral data of genistein: δH 7.44 (2H, d, *J* = 8.7 Hz), 6.88–6.94 (overlaps), 6.40 (1H, d, *J* = 2.2 Hz), and 6.28 (1H, d, *J* = 2.2 Hz).

According to the 1H NMR profiling of subfractions D-1 to D-6, the characteristic singlet signals (δH 8.13) indicated the presence of isoflavonoids in D-3, D-4, D-5, and D-6. The inhibitory activity of subfractions D-4 and D-5 was then related to the additional signals [δH 7.44 (2H, d, *J* = 8.7 Hz), 6.88–6.94 (overlaps), 6.40 (1H, d, *J* = 2.2 Hz), and 6.28 (1H, d, *J* = 2.2 Hz)], which were not visible for subfractions D-3 and D-6. A pair of aromatic protons with a *meta* coupling (*J* = 2.2 Hz) indicated the presence of a 1,3,4,5-tetrasubstituted aromatic ring. An aromatic proton signal at δH 7.44 (2H, d, *J* = 8.7 Hz) revealed that the other proton signal might be overlapping at δH 6.88–6.94 ppm, suggestive of a 1,4-disubstituted aromatic ring. To determine the overlapping peaks at δH 6.88–6.94 ppm, the 13C NMR and 2D NMR experiments (Figures S1–S3), including heteronuclear single-quantum correlation (HSQC) and heteronuclear multiple-bond correlation (HMBC), were then conducted. On the basis of these results, genistein was identified [17] from the genistein-containing subfractions (D-4 and D-5). Bioactive fractions, together with the active ingredient, were rapidly obtained from the combination of bioactivity-guided fractionation and NMR-based identification of the UCME EtOAc-soluble fraction.

#### *2.3. The Role of Genistein in Modulating LPS-Stimulated DC Activation of UCME*

To clarify the role of genistein in modulating LPS-stimulated DC activation, the genistein-containing subfractions D-4 and D-5 were further isolated by normal-phase semipreparative HPLC to isolate genistein (24 mg/1.136 kg dry material) and genistein-knockout subfractions (D-4 *w*/*o* and D-5 *w*/*o*, Figure S4). The yield of genistein was 15 times higher than that achieved in a previous study [6]. These subfractions were then evaluated in terms of their inhibitory activity by measuring cytokine production in LPS-stimulated DCs. As shown in Figure 4A, the genistein-containing subfractions (D-4 and D-5) significantly suppressed the production of cytokines (IL-6 and TNF-α), while the genistein-knockout subfractions D-4 *w*/*o* and D-5 *w*/*o* showed lower inhibitory activity. As illustrated in Figure 4B, concerning TNF-α production, the inhibition percentage of the genistein-knockout subfraction D-4 *w*/*o* (9% at 25 μg/mL and 18% at 50 μg/mL) was dramatically lower than that of the genistein-containing subfraction D-4 (35% at 25 μg/mL and 55% at 50 μg/mL).

**Figure 4.** The effects of the subfractions, containing or not containing genistein, on the production of pro-inflammatory cytokines in LPS-stimulated DCs. (**A**) DCs were untreated or treated with LPS (100 ng/mL, white bar), LPS + genistein-containing subfractions, or LPS + genistein-knockout subfractions (25 μg/mL, gray bar or 50 μg/mL, black bar), as indicated. Supernatants were collected 6 h after the treatment. The production of cytokines (TNF-α and IL-6) was measured by ELISA. The data shown are the mean ± SD of three independent experiments. ### *p* < 0.001; \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001 (Scheffe's test) for comparisons of the treated and untreated LPS-stimulated DCs. (**B**) The inhibition percentage (%) of the subfractions, with and without genistein, of cytokine production was derived from the data in (A).

In addition to the identification of genistein, the fractionation and HPLC isolation of UCME identified eight compounds, including an isoflavone (lupinalbin A [18]), three phenolic acids (*p*-hydroxybenzoic acid [19], salicylic acid [20], and vanillic acid [19]), two fatty acids (monomethyl succinate [21] and 1,10-decanedioic acid [22]), and two steroids (a mixture of β-sitosterol and stigmasterol [23]). Their structures (Figure 5) were identified by comparing their spectroscopic data with data in the literature. Among the isolates, lupinalbin A, *p*-hydroxybenzoic acid, vanillic acid, monomethyl succinate, and monomethyl succinate were isolated from this herb for the first time. All isolates were also assessed in terms of their inhibitory effect on DC activation. However, only the analogue of genistein, lupinalbin A, showed a moderate inhibitory activity against LPS-stimulated DC activation (Figures S5 and S6).

**Figure 5.** Chemical structures of the compounds identified in this study.

1H NMR signal intensity is absolutely proportional to the relative number of protons. Therefore, the relative quantity of the different metabolites could easily be observed in the 1H NMR spectra (Figure 3). The major metabolites (identified by signals with a stronger intensity) in subfractions D-4 and D-5 are shown in Table 1. Interestingly, the 1H NMR profiling of subfractions D-4 and D-5 revealed the presence of other unknown genistein derivatives, which might contribute slightly to the immunomodulatory effects, because of their low abundance or poor activity. Additionally, the well-known bioactive isoflavonoids, including daidzein, formononetin, equol, and glycitein, were not present as major metabolites in fraction D. Therefore, genistein was suggested as having the central role in the modulation by UCME of LPS-stimulated DC activation.

#### *2.4. LPS-Stimulated DC Maturation was Impaired by Genistein at Non-Cytotoxic Concentrations*

To exclude the possibility that genistein caused cytotoxicity, the cell viability of DCs was determined via the CCK8 assay. As shown in Figure 6, genistein induced a significant level of DC death at 40 μM. Thus, the significant inhibition of cytokine production by 40 μM genistein should be attributed to its cytotoxicity in DCs. However, the LPS-stimulated production of pro-inflammatory cytokines

(TNF-α, IL-6, and IL-12) was suppressed by genistein below 20 μM, suggesting that genistein possesses an immunosuppressive activity. Therefore, we selected concentrations below 20 μM of genistein for further assessment of DC maturation. The complex process of DC maturation is accompanied by the upregulation of the expression of MHC class II molecules and three major co-stimulatory molecules (CD40, CD80, and CD86) on the surface of DCs. As shown in Figure 7, LPS stimulation upregulated the expression of MHC class II and also of the co-stimulatory molecules (CD40, CD80, and CD86) in DCs, while genistein treatment significantly decreased the expression levels of all these molecules. These data indicated that genistein indeed impaired LPS-stimulated DC maturation at non-cytotoxic concentrations.

**Table 1.** Chemical shifts (δH) and assignment of major compounds present in the D-4 and D-5 subfractions.

**Figure 6.** The effect of genistein on cell viability (A) and the production of pro-inflammatory cytokines in LPS-stimulated DCs (B, C, and D). (**A**) DCs were treated with genistein at various concentrations for 24 hours, and then their viability was measured by the CCK-8 assay (Sigma). DCs were untreated or treated with LPS (100 ng/mL) and LPS + genistein (2.5, 5, 10, 20, and 40 μM), as indicated. Supernatants were collected 6 h after treatment. The production of the cytokines TNF-α (**B**), IL-6 (**C**), and IL-12p70 (**D**) was measured by ELISA. The data shown are the mean ± SD of three independent experiments. ### *p* < 0.001; \* *p* < 0.05; \*\* *p* < 0.01; \*\*\* *p* < 0.001 (Scheffe's test) for comparisons of the genistein-treated and untreated LPS-stimulated DCs.

**Figure 7.** Genistein-mediated suppression of a maturation-associated surface marker on LPS-stimulated DCs. DCs were untreated or treated with LPS or LPS + genistein for 24 h. The suppression of major histocompatibility complex (MHC) class II and of co-stimulatory molecules (CD40, CD80, and CD86) was analyzed by flow cytometry. All of the data shown were gated on CD11c+ cells. All results are representative of three independent experiments.

Isoflavones, including genistein, daidzein, and glycitein, are generally found in leguminous plants and have been reported as antioxidants and immunosuppressant agents, capable of suppressing the allergic sensitization to peanuts by regulating human monocyte-derived dendritic cell function [24]. The majority of the dietary isoflavonoids are present in inactive glycosides forms (e.g., genistin) and then converted to active aglycone forms (e.g., genistein) by the bacterial microbiote in the digestive tract. Thus, DCs have direct access to dietary antigens and are therefore poised to uptake isoflavones directly from the lumen [24]. Previously, genistein was shown to have promising activities, such as neuroprotective effects by improving hippocampus neuronal cell viability and proliferation in vitro [25], antioxidant capacity by regulating β-oxidation and energy metabolism in vivo [26], and anti-inflammatory effects by inhibiting the ERK pathway [27] and NF-κB-dependent gene expression in TLR4-stimulated DCs [28].

In this study, the association between genistein and the immunomodulatory effect of UCME was carefully elucidated through the combination of bioactivity-guided fractionation and NMR-based identification. An 1H NMR-based metabolomics approach was applied to partially purified subfractions D-1 to D-6 from UCME. 1H NMR profiling suggested the presence of genistein in the D-4 and D-5 subfractions, which exhibited a stronger inhibitory activity against cytokine production in LPS-stimulated DCs. Genistein was therefore supposed to be a possible marker of the immunosuppressive activity of UCME. This suggestion was supported by the results of the genistein-knockout subfractions, which showed a much lower inhibitory activity. Moreover, HPLC isolation of the D-4 and D-5 subfractions provided other compounds with poor inhibitory activity. On the basis of this evidence, genistein could be used as a chemical marker for the quality control of the potentially immunosuppressive functional food UC roots. A literature survey disclosed that the 1H NMR-based metabolomics approach for screening bioactive secondary metabolites has not been widely used [29,30]. Our study provides a powerful tool for discovering the active ingredients in immunosuppressive functional foods. This approach can be applied for investigating the active ingredients of herbal products.

#### **3. Materials and Methods**

#### *3.1. General Experimental Procedures*

Column chromatography (CC) was performed on Silica gel 60 (40–63 μm, Merck, Darmstadt, Germany). Thin-layer chromatography (TLC) was performed on silica gel 60 F254 plates (200 μm, Merck). High-performance liquid chromatography (HPLC) was performed, using Keystone Spherisorb silica (5 μm, 250 × 10 mm), on a Knauer Smartline 2400 refractive index (RI) detector and a Knauer Smartline 100 pump. The NMR experiments were performed on a Bruker DRX-500 NMR spectrometer (Bruker, Rheinstetten, Germany). Flow cytometry was conducted using a BD FACSCanto II Flow Cytometer (BD Biosciences, CA, USA).

#### *3.2. Sample Preparation and Isolation*

UC was purchased from Mingjian Township, Nantou, Taiwan. The procedure of extraction and isolation is summarized in Figure 2. Briefly, the roots (1.136 kg) were pulverized into a fine powder and extracted with methanol (12 L). The supernatant was collected and concentrated under reduced pressure to obtain the methanolic extract (UCME, 80 g). A portion of the residue (60 g) was suspended in H2O and sequentially fractionated with EtOAc and *n*-BuOH. The EtOAc-soluble fraction (9.6 g) was then subjected to silica-gel column chromatography (150 g, 70–230 mesh), using a gradient solvent system of *n*-hexane, EtOAc, and MeOH as a mobile phase. Each fraction, from which a sample was collected for the immunomodulating assessment, was analyzed by thin-layer chromatography (TLC) and assigned to one of 9 main fractions (Fr.A to Fr.I). A mixture of β-sitosterol and stigmasterol was obtained from fraction C (*n*-hexane/EtOAc = 9/1). The subfractions of fraction D (*n*-hexane/EtOAc = 7/3) were further analyzed by 1H NMR, indicating the presence of genistein in the D-4 and D-5 subfractions. The genistein-knockout subfractions (D-4 *w*/*o* and D-5 *w*/*o*) were obtained by HPLC and evaluated in terms of their immunomodulating activity.

#### *3.3. HPLC Conditions Used for the D-4 and D-5 Subfractions*

D-4 and D-5 were isolated by semipreparative HPLC (dichloromethane/acetone = 85/15, flow rate = 3 mL/min) to obtain the genistein-knockout subfractions D-4 *w*/*o*, D-5 *w*/*o* and genistein (24.0 mg, tR = 8.0 min). The genistein-knockout subfractions D-4 *w*/*o* and D-5 *w*/*o* were further isolated by semipreparative HPLC (*n*-hexane/acetone = 2/1, flowrate = 3 mL/min) to obtain salicylic acid (tR = 8.3 min), lupinalbin A (tR = 11.0 min), 1,10-decanedioic acid (tR = 11.3 min), monomethyl succinate (tR = 13.2 min), *p*-hydroxybenzoic acid (tR = 16.7 min), and vanillic acid (tR = 18.5 min).