*Article* **Battery Energy Storage System Dimensioning for Reducing the Fixed Term of the Electricity Access Rate in Industrial Consumptions**

**Jorge Nájera \* , Miguel Santos-Herran , Marcos Blanco , Gustavo Navarro , Jorge Torres and Marcos Lafoz**

> Centro de Investigaciones Energéticas, Medioambientales y Tecnológicas (CIEMAT), Government of Spain, 28040 Madrid, Spain<EMAIL_ADDRESS>(M.S.-H<EMAIL_ADDRESS>(M.B<EMAIL_ADDRESS>(G.N<EMAIL_ADDRESS>(J.T<EMAIL_ADDRESS>(M.L.) **\*** Correspondence<EMAIL_ADDRESS>Tel.: +34-91-335-7194

**Abstract:** Industrial buildings account for very few high peaks of power demand. This situation forces them to contract a high fixed electricity term to cover it. A more intelligent use of the energy in industrial buildings, together with an improved efficiency of the transmission and distribution of the energy along the electric power grid, can be achieved by reducing the peak consumption of industrial buildings. Energy storage systems, and lithium ion (Li-ion) batteries in particular, are one of the most promising technologies for reducing this peak consumption. However, selecting a proper Li-ion battery requires a dimensioning process in terms of energy and power which is not straightforward. This paper proposes a dimensioning methodology that takes into consideration both technical and economic implications, and applies it to a case example with real industrial consumption data and a commercial battery. Results show that implementing batteries for reducing this peak consumption can lead to a cost–benefit improvement.

**Keywords:** battery energy storage system; dimensioning methodology; industrial consumption; electricity fixed term

#### **1. Introduction**

The fee that electric energy consumers pay for being able to make use of it, commonly known as electricity access rate, is split into two terms in the majority of European countries: a variable term and a fixed term [1]. The variable term is associated with the energy consumed by the user (kWh), while the fixed term corresponds to the contracted power (kW), defined as the maximum power that the user can consume before the power-limit switch is triggered.

Commonly, the fixed term is over-dimensioned, meaning that the usual consumption along a day is considerably lower than the power consumption limit, the latter being reached only during a few instants per day. This behaviour is specially representative of industrial consumption, forcing them to contract a high fixed electricity term due to very few high power consumption peaks [2].

Modifying this electrical consumption pattern could lead to a more intelligent use of the energy in industrial buildings, to the fostering of the "prosumer" behaviour among industrial consumers, and to the improvement of the efficiency of transmission and distribution of the electricity across the power grid. Among the different solutions proposed in the literature for achieving this goal, the inclusion of photovoltaic panels is presented as a feasible solution in [3,4], although the vast majority of studies opt for implementing an energy storage system (ESS) so that the joint consumption profile of the ESS and the industrial building is modified, as in [5–7] among other studies.

**Citation:** Nájera, J.; Santos-Herran, M.; Blanco, M.; Navarro, G.; Torres, J.; Lafoz, M. Battery Energy Storage System Dimensioning for Reducing the Fixed Term of the Electricity Access Rate in Industrial Consumptions. *Appl. Sci.* **2021**, *11*, 7395. https://doi.org/10.3390/ app11167395

Academic Editors:Luis Hernández-Callejo, Víctor Alonso Gómez, Sergio Nesmachnow, Vicente Leite, Javier Prieto and Ângela Ferreira

Received: 30 June 2021 Accepted: 6 August 2021 Published: 11 August 2021

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**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/).

Battery energy storage systems (BESS) arise as the most promising ESS candidates for their connection in buildings and infrastructures, both industrial and residential, considering that this is one of their most prominent and leading uses [8]. The benefits of including BESS encompass the majority of the electricity value chain, including peak shaving and continuity of supply for residential and industrial customers, ancillary and power quality services for transmission and distribution system operators, and flexibility and curtailment minimization for renewable generation companies [9].

Nevertheless, selecting a BESS for its connection to an industrial consumption for reducing the peak power consumption is not straightforward. A wide variety of solutions is offered by battery manufacturers, so a dimensioning process based on a techno-economic assessment is required in order to help decision makers. This techno-economic assessment will incorporate the load characteristics, the BESS technical features and its cost.

In this regard, BESS dimensioning for reducing the electricity access fee in industrial buildings has been studied together with photovoltaic in [10,11], where a dimensioning process is proposed for a BESS and photovoltaic installation by using a brute force methodology, and applied to peak shaving and prosumer purposes, and for working isolated from the grid. In addition, the authors in [12] analyse the economic viability of BESS integration under different tariff structures and systems configurations, although a generalized and simplified ageing for the Li-ion BESS has been assumed for the yearly cost calculations. Thus, the importance of the BESS charge/discharge profile has not been taken into account properly. A similar approach is proposed in [13,14] for residential consumption. Both papers assess the economic viability of integrating BESS at a residential level with and without subsidies. However, these studies lack a detailed analysis of the BESS lifespan, since they neglect its influence in the overall yearly costs. Further studies, such as [15], analyse the peak shaving of decentralised residential BESS for load peak shaving. The proposed BESS model includes the battery efficiency and SoC estimation, but an over-simplified battery ageing assumption is made, where the payback period of the battery varies linearly with the battery size.

Regarding alternative dimensioning methodologies, stochastic models for improving the decision making are developed in [16], where the methodologies are applied to reach the optimal BESS investment for industrial buildings. As identified in the previously mentioned papers, there is no detailed battery ageing analysis, so the influence of this variable is underestimated.

The BESS dimensioning studies mentioned above employ different selection criteria. Those criteria are based only on technical issues, or on techno-economic issues but using generic ageing data to calculate the BESS lifespan. Moreover, they analyse the complete compensation of technical issues under study, e.g., feed an isolated load with a combination of a BESS together with photovoltaic or eliminate the consumption peaks via smart charging of electric vehicles, which may not be the most feasible scenario faced by residential or industrial consumers. As a main contribution and further development, this paper proposes a BESS dimensioning methodology for diminishing the fixed term of the electricity access rate in real industrial consumption, employing a techno-economic criterion for selecting the optimum solution. The proposed methodology includes a detailed evaluation of the BESS ageing, which is included in the cost–benefit analysis as a key component. This improved BESS ageing assessment highly influences the time for a BESS replacement and, hence, the yearly cost (BESS cost and fixed electricity access fee).

Hence, the applicability of this methodology encompasses industrial consumers with an irregular consumption accounting for several peaks of consumption, as well as BESS manufacturers aiming for specific developments for industrial consumption. The applicability of the developed methodology is exemplified in this paper for an existing industrial consumption and on a typical day in summer and in winter, considering a commercial battery and standard prices for the economic analysis.

The paper is organized as follows: the BESS dimensioning methodology is described in Section 2, including a series of steps to be followed. The different simulation models used along the different steps determined by methodology are detailed in Section 3, including a power grid model, an industrial consumption model, a power converter model, and a battery model. At last, a case example with real industrial consumption data, commercial BESS and standard prices is presented in Section 4, while conclusions are drawn in Section 5.

#### **2. BESS Dimensioning Methodology**

The BESS dimensioning methodology described in this section aims at reducing the fixed term of the electricity access rate by introducing a BESS that deals with the peaks of the industrial consumption. A few assumptions made by the authors in the development of the BESS dimensioning methodology are presented in the following.

Firstly, both the BESS (*PBESS*) and the electrical grid (*PGRID*) must cover the customers' demand (*PLOAD*) and the losses (*PLOSS*):

$$P\_{BESS} + P\_{GRID} = P\_{LOAD} + P\_{LOSS} \tag{1}$$

Moreover, the BESS must account for enough energy to cover the energy to fulfill the extra energy demand not supplied/covered by the grid. Besides, a BESS must be able to fully recharge the same amount of energy as dispatched in a specific scenario. If not, the mean SoC will be decaying as the scenario repeats over time, being eventually unable to fulfil the load power demand.

The methodology comprises a set of steps, as seen in the flow chart of Figure 1.

**Figure 1.** Flow chart of the proposed BESS dimensioning methodology.

#### **3. Industrial Consumption, BESS, and Grid Modelling**

The mathematical and simulation models used in Section 4 for performing a case example of the aforementioned methodology are described in this section. Three main models are considered: an industrial consumption, a BESS, and a power grid. The BESS is connected to the same point as the industrial consumption, the point of common coupling (PCC), and both of them are connected to the grid, as it can be seen in the general model diagram in Figure 2. The complete model outputs several electrical-variables time series (P, Q, V, and I) for the three main components. Additional electrical variables such as frequency or harmonic distortion are also evaluated and calculated by the model, but the authors decided not to output them since they are not relevant for the purpose of this paper.

The models are developed with MATLAB Simulink, under the Simscape Electrical environment. When developing simulation models, a higher complexity and completeness usually implies a higher accuracy [17,18]. However, complex models tend to require a substantial computational effort to run them, and a huge amount of information to parametrize them. Moreover, they are commonly valid only for a specific device, making them non-generalizable [19,20].

On the other hand, simplified models, usually composed by combination of resistors, inductors, and capacitors, are easily parametrized, they require low computational effort, and are valid for several devices with similar characteristics. Furthermore, even though they are not as accurate as complex models, they can mimic the behaviour of the device with sufficient precision, allowing for the extraction of general conclusions [21,22].

Hence, given the objective of this paper, the approach taken by the authors was to develop simplified models, that are generalizable and easily parametrized, which is sufficient for obtaining accurate general conclusions when dimensioning BESS.

**Figure 2.** General model diagram.

#### *3.1. Industrial Consumption Model*

The industrial consumption is modelled as a PQ load, i.e., it consumes the desired active and reactive power independently of the voltage at the PCC. Hence, it needs to be modelled as a controlled current source, as depicted in Figure 3. The inputs of the model, named as P and Q in Figure 3, correspond to the 24-hour active and reactive power profile on a 1-second basis.

The Simulink diagram in Figure 3 shows, from left to right, the inputs of the model P and Q together with the measured voltage (Vabc). From those inputs, the complex values of the phase currents are calculated, and then redirected to the corresponding current controlled source. A resistor with a very high value is also connected in parallel, which acts as a snubber. At last, the industrial consumption model is linked to the rest of the model via connectors A, B, C, and N.

**Figure 3.** Industrial consumption model in MATLAB Simulink.

#### *3.2. BESS Model*

The BESS model includes both a battery model and a power electronic converter model, the latter being necessary to manage the battery behaviour and to connect the battery to the AC grid.

#### 3.2.1. Electronic Converter Model

For the purpose of this work, there is no need to model the power electronic converter in detail, since neither efficiency nor power converter ageing are incorporated into the economic evaluation of the methodology. Thus, an ideal voltage source converter (VSC) has been selected, with no conduction or switching losses and no power limitations, so the battery can dispatch as much power as required. As a result, the VSC is modelled as a controlled three-phase voltage source whose reference voltage is given by the converter control, which manages the power that flows in and out of the battery.

The control of a VSC can be modelled as explained in [23], consisting of two levels: a high-level control and a low-level control. The high-level control is in charge of selecting, based on a specific strategy, the power dispatched or consumed by the battery. Several strategies have been proposed in the literature, from those based on rules [24] to more complex strategies [25,26]. For this paper, a simple strategy based on rules has been selected, which sets the reference power for the battery (*PBATT*) based on the maximum power that the industrial consumption can absorb from the grid (*PLIMIT*), the actual power that the industrial load is demanding (*PLOAD*), and the battery state of charge (*SoCBATT*). The flow diagram for the high-level control is shown in Figure 4.

**Figure 4.** Flow diagram for the VSC high-level control.

The BESS recharge profile has been defined as the minimum current that allows the BESS to recharge the same amount of energy that it has provided to the load along the day. Moreover, reactive power reference for the battery has been set to zero, so no contribution is expected from it.

On the other hand, the low-level control is in charge of setting the voltage at the VSC terminals so that the power management commanded by the high-level control can be performed. The low-level control is a dq axis control, shown in Figure 5, which has been developed building on [27]. The control is duplicated for P and Q, with minor variations. Each branch accounts for an outer PI loop control, which sets the battery power equal to the reference power coming from the high-level control, outputting a reference current. Then, there is an inner PI loop control that adjusts the current flow through the VSC with the reference current, so a reference voltage is outputted. This voltage is finally regulated so that it corresponds to the VSC voltage at its terminals. In order to properly control the VSC, the inner control must be considerably faster than the outer control. Detailed explanation about the implemented dq axis control for a VSC can be found in [23,27].

**Figure 5.** VSC low-level control model in MATLAB Simulink.

#### 3.2.2. Battery Model

The battery model controlled by the VSC is represented as an equivalent circuit model which, as mentioned above, sacrifices accuracy in favour of generality. Nevertheless, equivalent circuit models account for errors lower than 5% [28], sufficient for obtaining general conclusions. The battery equivalent circuit is depicted in Figure 6, where *u* is the instantaneous battery voltage [V], *i* is the battery current [A] (*i* > 0 discharging; *i* < 0 charging), *ISELF* is the self-discharge current, *ROHM* is the ohmic internal resistance [Ω], *RPOL* is the polarization internal resistance [Ω], and *CPOL* is the polarization capacitor [F].

**Figure 6.** Battery equivalent circuit model.

The selected battery model has been developed and validated by the authors in [18,29]. The model comprises three parts: a voltage/runtime model, a thermal model, and an ageing model.

#### Battery Voltage/Runtime and Thermal Model

The lithium-ion battery model used in this paper is a modification of the MATLAB Simulink model in [30]. The battery equivalent circuit is based on the Shepherd equation [31], which was experimentally validated later in [32]. The Shepherd model keeps the error between 1% and 5% and it can be parametrizable without testing the battery in the laboratory: it suffices with the manufacturer datasheet information. For the work developed in this paper, both voltage/runtime and thermal models have been parametrized via a leastsquare method. Data can be obtained from typical manufacturer's curves: voltage-capacity curve as a function of different C rates (with constant temperature), and voltage-capacity curve at different temperatures (with constant C rate). The detailed parametrization process is described in [33]. The voltage/runtime equations that govern the model, considering thermal dependencies, can be written as follows:

$$E(\text{SoC}, T) = E\_0(T) - K(T) \cdot Q\_{MAX}(T) \cdot \left(\frac{100}{\text{SoC}} - 1\right) + A \cdot e^{\left(-B \cdot Q\_{MAX}(T) \cdot \left(1 - \frac{\text{SoC}}{100}\right)\right)} \tag{2}$$

$$R\_{POL-DISCHORE}(So\mathbb{C}, T) = K(T) \cdot \frac{100}{So\mathbb{C}}\tag{3}$$

$$R\_{\rm FOL-CHARG}(So\mathbb{C}, T) = K(T) \cdot \frac{1}{1.1 - \frac{So\mathbb{C}}{100}}\tag{4}$$

$$E\_0(T) = E\_0(T\_0) - \frac{dE}{dT} \cdot (T - T\_0) \tag{5}$$

$$Q\_{MAX}(T) = Q\_{MAX}(T) - \frac{dQ}{dT} \cdot (T - T\_0) \tag{6}$$

$$K(T) = K(T\_0) - e^{\kappa \cdot (\frac{1}{T} - \frac{1}{T\_0})} \tag{7}$$

$$R\_{OHM}(T) = R\_{OHM}(T\_0) - e^{\beta \cdot (\frac{1}{T} - \frac{1}{T\_0})} \tag{8}$$

where *E* is the open-circuit nonlinear voltage (V), *E*0 is the open-circuit constant voltage (V), *K* is the polarization parameter (Ah−1 ), *QMAX* is the maximum capacity (Ah), *A* is the exponential voltage constant (V), *B* is the exponential capacity constant (Ah−1 ), *T* is the temperature (K), *T*0 is the parameter reference temperature (K), and *SoC* is the state of charge (%).

The thermal model comprises two different parts: a heat generation model and a heat evacuation model. The heat generation model can be expressed as follows [34]:

$$H = (E\_0 - E) \cdot i + T \cdot \frac{dE}{dT} \cdot i + (R\_{OHM} + R\_{POL}) \cdot i^2 \tag{9}$$

where *H* is the generated heat (W), and *dE*/*dT* the change of the equilibrium potential with temperature (V·K −1 ).

The heat evacuation model occurs due to convection and radiation mechanisms, the latter being negligible [35]. Assuming that the temperature in the battery is uniform (reasonable approximation according to [36]), the following expression is obtained [35]:

$$H = m \cdot c\_p \cdot \frac{dT}{dt} + \frac{1}{R\_{OUT}} \cdot (T - T\_0) \tag{10}$$

$$R\_{OUT} = \frac{1}{h \cdot Ar} \tag{11}$$

where *m* is the mass of the battery (kg), *cp* is the specific heat capacity (J·kg−1 ·K−1 ), *h* is the convective heat transfer coefficient (W·m−2 ·K−1 ), *Ar* is the external surface area (m2 ), and *ROUT* is the equivalent thermal resistance with the ambient (K·W−1 ).

Hence, the temperature variation can be calculated as:

$$T(s) = \frac{H \cdot R\_{OUT} + T\_0}{1 + m \cdot c\_p \cdot R\_{OUT} \cdot s} = \frac{H \cdot R\_{OUT} + T\_0}{1 + t\_{th} \cdot s} \tag{12}$$

where *tth* is the thermal time constant (s), and *s* is the Laplace complex frequency parameter.

#### Battery Ageing Model

The ageing estimator implemented in the battery model comprises two parts: a cycling ageing model and a calendar ageing model. In order to parametrize both models, a leastsquare method has been applied, so the error between the model output and the real battery data is minimized. The data for parametrizing the models need to be obtained from experimental tests. The complete parametrization process is detailed in [33]. A minimum set of 4 tests at different SoC and temperature are needed for the calendar ageing, and a minimum of 5 tests with different combinations of C rate, DoD and temperature are needed for the cycling model. Cycling ageing is related to the capacity fade when the battery is subject to charging and discharging cycles, i.e., when the current is flowing through the battery. It depends on several factors, among which *Crate*, temperature, and DoD are the most relevant [37]. The cycling ageing model published in [38] for lithium-ion batteries has been implemented:

$$Q\_{loss-cyc} = (a \cdot T^2 + b \cdot T + c) \cdot e^{(d \cdot T + e) \cdot \mathbb{C}\_{rate}} \cdot Ah \tag{13}$$

where *Qloss*−*cyc* is the cycling loss capacity (Ah), *Ah* the Ah that have flown through the battery (in or out), and *a*, *b*, *c*, *d*, and *e* are constants depending on the battery chemistry.

On the other hand, calendar ageing is related to the capacity fade in the battery because of the time passed from the moment it was manufactured. It is dependent on the *SoC* of the battery, the temperature, and the time [37]. The calendar ageing model published in [39] has been implemented:

$$Q\_{\rm loss-cal} = f \cdot e^{\rm g\cdot So\mathcal{C}} \cdot e^{\frac{h}{\hbar}} \cdot t^z \tag{14}$$

where *a*, *b*, and *c* are constants that depend on the battery chemistry, and *z* can take values between 0.5 and 1 and depends on the chemistry as well.

Apart from the ageing factors previously mentioned, additional ageing factors such as DoD or mean SoC along a cycle are indirectly considered by the model. The Ah dependency included in the cycling ageing is indirectly considering the DoD, since the higher the DoD the higher the Ah that flow through the battery. Moreover, the mean SoC along a cycle is considered in the calendar ageing equation, since the latter is constantly computing the battery SoC contribution.

#### *3.3. Grid Model*

The grid is modelled as an infinite power grid, so both the frequency and voltage at the PCC are kept constant. A Three-Phase Source model has been selected for this purpose, with a swing generator type, high short-circuit power, and low X/R ratio.

#### **4. Case Example**

In order to illustrate how the described methodology could be implemented, a case example is analysed. The models developed in Section 3 are used for performing the simulations. The same structure of steps defined in Section 2 is applied:

#### *4.1. Step 1. Parametrize Models*

At first, the simulations models need to be parametrized. For the grid model, the following parameters have been selected, so an almost infinite power grid is implemented (Table 1).

**Table 1.** Grid model parameters.

The battery model can be parametrized following the methodology defined in [29,32,33]. A commercial lithium polymer battery cell from Kokam, model SLPB100255255HR2, has been selected for this case study, which is a standard lithium-ion pouch cell [40]. The experimental data for calculating the ageing model parameters have been obtained from [40] and [33], where the complete theoretical and experimental validation for the selected battery cell has been performed. The battery ageing model performs with an error below 5%, as detailed in [33]. The selected battery cell main parameters are shown in Table 2.

**Table 2.** Battery model data and parameters [33,40].

The load needs a power consumption profile as input. For this case example, two 24-h profile on a 1-second basis have been selected, corresponding to a typical day in summer and a typical day in winter in an industrial building. The selected building is the CEDEX facility in Madrid, located at Julián Camarillo 30, 28037 Madrid (Spain). For the analysis performed in this paper, the load corresponds to a three-phase load with no unbalance between phases, whose active power profiles are shown in Figure 7 for both summer and

winter. The cumulative energy consumed by the load for each typical day is also shown in Figure 8.

**Figure 7.** Industrial consumption profiles for summer and winter scenarios.

**Figure 8.** Cumulative energy consumed by the load in summer and winter scenarios.

#### *4.2. Step 2. Define Test Cases*

Test cases are defined by the BESS control parameter, which is the power limit that the load is allowed to consume from the grid (*PLIMT*). Hence, as *PLIMIT* decreases, the battery will increase its contribution. Scenarios are divided between summer scenarios (S) and winter scenarios (W). SB and WB correspond to the base case scenario for each season, which is the current scenario at the real world facility, with a contracted power of 90 kW. Steps of 10 kW have been selected, so the different scenarios are defined in Table 3.

**Table 3.** Scenarios defined for the case example.

#### *4.3. Step 3. Simulate Scenarios and Obtain BESS Requirements and Configuration*

As previously explained in Sections 2 and 3, BESSs that are not able to recharge as much energy as they have delivered along the day, are considered as not valid. Once the simulations are performed, BESS power profiles for the valid configurations are obtained. For the case example presented in this paper, valid BESS power profiles are shown in Figures 9 and 10.

**Figure 9.** BESS power profile in summer for different values of *PLIM*.

**Figure 10.** BESS power profile in winter for different values of *PLIM*.

As expected, the lower the *PLIMIT*, the higher the contribution from the BESS. Since the BESS deals with high demand peaks, it follows a similar distribution as the top part of the industrial load profile for the corresponding scenario. Scenarios SB and WB, i.e., when the grid delivers as much power as demanded by the load, implies no contribution from the BESS.

For *PLIMIT* values below 30 kW, BESSs are not able to recharge the same amount of energy delivered during the high-demand period, both for summer and winter scenarios. It must be noticed that *PLIMIT* also affects the maximum power available for recharging, i.e., for a *PLIMIT* value of 20 kW, the maximum power available for recharging is 20 kW since that recharging power is coming from the grid, which is not sufficient for the case with *PLIMIT* values below 30 kW.

Based on the BESS power profiles, the power and energy requirements (*PBESS*−*req* and *EBESS*−*req*) for the valid scenarios are shown in Table 4.

Once the requirements are calculated, the most suitable BESS configuration must be selected for each scenario (number of cells connected in series and in parallel). Commercial three-phase inverters for 400 V grids demand 48 V in the DC side. Hence, based on the standard cell parameters, the number of cells in series so the DC side voltage reaches 48 V equals:

$$\frac{48V\_{DC-side-voltage}}{3.7V\_{Nominal-cell-voltage}} \approx 13\tag{15}$$

The number of cells in parallel depends on the BESS energy requirements. Given that one branch of 13 cells accounts for:

$$13\text{ cells} \cdot 3.7 \text{ V/cell} \cdot 55 \text{ Ah} = 2.64 \text{ kWh} \tag{16}$$

The following table can be filled:

**Table 4.** BESS requirements for the different scenarios.

Scenarios S6 to S4 and W8 to W6 can satisfy their energy requirements with just one BESS branch in parallel. It must be noticed that, given the BESS configuration and the power demand shown in Table 4, the BESS in scenario S4 will be more demanded than in scenarios S5 and S6, and the BESS in scenario W6 will also be more demanded than scenarios W7 and W8, i.e., it will work with higher C rates.

#### *4.4. Step 4. Calculate BESS Ageing*

BESS ageing is closely related to its C rate and temperature (among other factors), as previously explained in Section 3. The higher the C rate, the higher the temperature augmentation in the battery. Hence, the BESS configuration has a huge influence on the BESS ageing, since batteries with a higher number of cells in parallel work with lower C

rates, i.e., with less stress. This implies that "bigger" BESSs may last longer than "smaller" BESSs, which may be beneficial from a cost–benefit point of view.

Four configurations have been tested for each BESS: the minimum number of cells so that the energy requirements are satisfied, and three more configurations increasing one extra branch each time, so that the C rate is decreased.

Commonly, a BESS is considered as aged when it loses 20% of its capacity retention.

As seen in Figure 11, Scenario S4 presents a very different behaviour from the minimum configuration to the minimum plus one. This happens because the C rate is diminished to a half when the branches in parallel are increased by one (from 1 to 2), which is a big difference according to the ageing model. A higher number of branches in parallel do not improve that much the behaviour, although the ageing is further reduced, as expected.

Scenario S3, which is the summer scenario with more energetic demand from the BESS, does not present a high ageing. This happens because the highest C rates are close to 1C, and there are few moments where those C rates are reached, even though the BESS is dispatching power during longer periods along a day. However, ageing for Scenario S3 is higher than Scenario S6, the BESS is working with a less intermittent profile, implying that temperature is higher during a cycle. Scenario S5 presents a higher ageing that Scenario S3 for the minimum BESS configuration, given that the C rate is higher (close to 3C), although it works for shorter times with that C rate. In any case, a low ageing is expected for every scenario except for Scenario S4.

For winter scenarios, as seen in Figure 12, Scenarios W8 and W7 present a low ageing due to the few periods of time under which the BESS is dispatching power, even though the C rate for W7 and the minimum BESS configuration reaches 5C. Besides, a low ageing is expected for Scenarios W3 and W4 which, being the most energetic form the point of view of the BESS, are hugely over-dimensioned in terms of power (very low C rates).

**Figure 11.** Days until ∇*Q* = 20% as a function of the BESS configuration in summer.

Scenario W6 presents a similar behaviour as S4, where increasing the number of branches in parallel from 1 to 2 prevents from an excessive ageing, which is produced as a consequence of working with C rates higher than 8C for a few moments along a day. Scenario W5 steadily improves its ageing as the branches in parallel increase, although it suffers from the highest ageing except for the minimum BESS configuration.

**Figure 12.** Days until ∇*Q* = 20% as a function of the BESS configuration in winter.

#### *4.5. Step 5. Cost–Benefit Analysis*

Once the BESS ageing for different configurations and scenarios has been calculated, a cost–benefit analysis must be performed, considering both the BESS cost and fixed term energy cost. Among the different reports found in the literature that analyse energy storage costs, the one published in [41] has been selected as the most reliable study.

Moreover, electricity access fees for industrial consumption for the regulated market in Spain can be found in [42]. The fixed term of the access fee considers three time periods of time-discrimination (P1, P2, and P3). For further information about the Spanish regulated market, and time discrimination periods, the authors recommend the work published in [42].

The variable term of energy is kept constant for every scenario, i.e., the BESS is recharged from the grid, and the BESS energy recharged form the grid plus the industrial load demand matches the total energy consumed by the industrial load in scenarios SB and WB, so the overall energy consumption remains constant. Hence, it is not included in the cost. Costs are summarized in Table 5 [42].

**Table 5.** Summarized costs.

Considering this, the cost equation can be written as:

$$\text{Cost}(\frac{euro}{year}) = (P1 + P2 + P3) \cdot P\_{LIMIT} + BESS\_{project-cost} \tag{17}$$

The contracted power (*PLIMIT*) is considered constant for the three time-discrimination periods (as is requested to industrial consumption).

The yearly BESS project cost has been calculated based on [43–45]. The annualized cost of the BESS project has been defined as:

$$\mathbf{C}\_{ann} = \mathbf{C}R F(i, R\_{proj}) \cdot \mathbf{C}\_{NPC} \tag{18}$$

where *Cann* is the annualized cost, *CNPC* is the net present cost, and *CRF* is the capital recovery factor, which depends on the annual real discount rate (*i*) and on the project lifetime (*Rproj*). *CNPC* is calculated as the present value of all the costs of installing and operating the battery over the project lifetime, minus the present value of all the revenues earned over the project lifetime.

Hence, the total yearly cost is calculated as follows:

$$\text{Cost}(\frac{\text{error}}{\text{year}}) = (P1 + P2 + P3) \cdot P\_{\text{LINIT}} + \text{C}\_{\text{ann}} = (P1 + P2 + P3) \cdot P\_{\text{LINIT}} + \text{CRF}(i, \mathbb{R}\_{\text{proj}}) \cdot \text{C}\_{\text{NPC}} \tag{19}$$

The constants for calculating the BESS project cost are listed in Table 6 [41,43],

**Table 6.** Summarized costs for the BESS project.

where *COM* is the operation and maintenance cost, and *Nproj* is the number of years over which the cost is annualized. The total cost for each scenario is shown in Tables 7 and 8, where:

$$\text{Total}(\frac{euro}{year}) = \text{Fixed} - access - fee + BESS - project \tag{20}$$

**Table 7.** Costs for the summer scenarios.

**Table 8.** Costs for the winter scenarios.

As it can be seen in Tables 7 and 8, results indicate that introducing a BESS into an industrial consumption for diminishing the fixed term of the electricity fee, can reduce the overall yearly cost. Moreover, this reduction can enhance an intelligent use of the energy in industrial buildings, as well as the improvement of the energy efficiency along the electric power grid, since consumption peaks are eliminated.

Tables 7 and 8 show that the minimum reduction in the fixed power limit is the best solution for this particular case example, both for summer and winter, from the point of view of the industrial consumer. Every configuration in scenario S6, and two of them in Scenario S5, reduce the yearly price of having a higher contracted power in summer. A similar behaviour is obtained in Table 8 for Scenarios W8, W7 and W6. The rest of the scenarios do not improve the yearly cost for this case example, although some interesting conclusions can be drawn.

Scenario S4 shows that the minimum configuration might not be the optimal solution, since configuration 13s2p provides a higher cost reduction than the minimum BESS configuration. A higher number of branches in parallel allows for a reduced C-rate, DoD, and temperature in each scenario. Hence, the BESS lifespan is extended and a higher cost reduction is achieved. A similar result would be expected for Scenario W6, given the ageing behaviour shown in Figure 12. However, in this case, the ageing is sufficiently low with the minimum BESS configuration, so the extra benefit of the further reduction in ageing does not improve that much the yearly cost.

Costs are not reduced in scenarios S4, S3, W5, W4, and W3 with respect to the base case scenario, since the BESS is dedicated not only to reducing the peak consumption, but to dealing with an important part of the load demand. This forces an over-dimensioning in the BESS's number of cells in parallel (given that the number of cells in series is fixed), and indicates that those BESS configurations may not be the best option for feeding an industrial infrastructure by themselves since, even though technically viable, it is costly.

#### **5. Conclusions**

This paper presents a BESS dimensioning methodology for reducing the fixed term of the electricity access fee for industrial consumption, considering battery ageing and the cost of the BESS and electricity access fees. By connecting a BESS together with a consumption, the power demand peaks can be reduced, so there is no need for contracting a high fixed term power to cover the high demand peaks.

A five-step dimensioning methodology is described, finishing with a cost–benefit analysis to facilitate the final decision making. The methodology is easily implemented and can be adapted to different electricity market conditions, as well as particularized to different BESS or load conditions. The parameter that defines the different scenarios of the methodology is the power contracted from the grid, being the decision variable the combined cost of the BESS and the fixed electricity fee.

Results drawn from the two case examples show that implementing a small battery to limit the consumption peaks can lead to considerable cost reduction for the industrial consumer. In fact, the best techno-economical solution corresponds to a small BESS that limits the peak consumption. The low C rate, DoD, and steady temperature experienced by a BESS that only limits the top part of the peak consumption, guarantees a long lifespan that highly reduces the overall yearly costs. Given that BESS are over-dimensioned in energy in several scenarios (S6, S5, W8, W7, and W6), a higher contribution from the BESS could be imposed in order to reduce the fixed power contracted from the grid, achieving a higher peak reduction. However, this leads to an increased ageing (higher C rate, DoD, and temperature profile) that has an impact on the techno-economical decision, turning those solutions into non-optimum.

Apart from the previous conclusion, depending on the case example and scenario under study, it could be beneficial from the economical point of view to increase the size of the BESS with more cells in parallel. This situation occurs when the BESS energy is suited to the BESS energy requirements (scenario S4). This forces the BESS to work with a high DoD and high C rates, deriving in a premature ageing. Thus, increasing the BESS size will allow it to work under less stress, extending the time needed to a BESS replacement and reducing the overall yearly costs.

One important conclusion drawn form the case examples results is that the optimum BESS is over-dimensioned in both power and energy. This may indicate that other energy storage technologies different than Li-ion batteries could be more beneficial from the techno-economic point of view. Supercapacitors, which can last millions of cycles until they need a replacement, or flow batteries, which can dissociate energy and power, may be suitable for their inclusion in a further study. In addition, if the industrial consumption is facing a scenario with low consumption peaks, the optimum solution could be discarding the usage of an energy storage system at all. As previously stated, the presence of high consumption peaks is the reason that justifies the inclusion of a BESS together with the industrial consumption.

Consequently, the proposed methodology is of potential interest for industrial consumers, since the cost-benefit analysis may reduce the yearly cost of their infrastructure, and also for TSOs and DSOs, since the peak reduction improves the efficiency of the transmission and distribution of the energy along the electric power grid. Moreover, BESS manufacturers could also find benefits in this methodology for the development of specific products related to this industrial demand peak and fixed term fee reduction.

Suggested future works include the parametrizations of the grid with frequency and voltage variations, so that further analysis considering BESS contributions to the grid in terms of frequency and voltage regulation can be performed. Moreover, time discrimination pricing can be added to electricity energy prices, so that the BESS can recharge during low price periods, providing an even higher reduction than the one shown in this work. At last, a techno-economic evaluation considering other energy storage technologies such as supercapacitors or flow batteries may result in interesting conclusions.

**Author Contributions:** Conceptualization, J.N. and M.L.; methodology, J.N., M.B. and G.N.; software, J.N. and M.B.; validation, J.T. and M.B.; formal analysis, J.N.; investigation, J.N.; data curation, M.S.-H., J.T. and M.B.; writing—original draft preparation, J.N.; writing—review and editing, M.S.-H., M.L. and G.N.; visualization, J.N., J.T. and M.S.-H.; supervision, M.L. All authors have read and agreed to the published version of the manuscript.

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

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

**Informed Consent Statement:** Not applicable.

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

#### **Abbreviations**

The following abbreviations are used in this manuscript:

#### **References**


**4. Conclusion**

*Arctic Studies - A Proxy for Climate Change*

Diseases of the musculoskeletal system and connective tissue (DMSSCT)

*wind. Coefficient values marked in red color correspond to the level of significance p < 0.05.*

The infections of the skin and subcutaneous tissue (ISST)

**64**

**Table 5.**

*polar night (PN).*

**Figure 6.**

**Table 4.**

*(B); Y axis: all normalized parameters.*

**Period M ± δ NM δ**

**phi-**

*wind angle parameters, deg.; Pl beta—plasma beta (Beta = [(T \* 4.16/105*

*values marked in red color correspond to the level of significance p < 0.05.*

**δ theta**

*The stable links between injury rates and poisoning on the way to work and from work with similar geocosmic agents during the polar day (A) and the polar night (B). A, B. Dynamics of the monthly values of incidence of the injury and poisoning on the way to work and from work (1), the monthly average magnitudes of the solar radio emission with wavelength 10.7 cm (2), and the solar wind parameter "sigma-phi-V"(3). X axis: the months from January 1985 to December 1989, where months from March to September are included in the period of the polar day (A); the months from October to February are included in the period of the polar night*

**Period M ± δ NM IMF FV|<B>| Bz,GSM δ-B δ-By δ-Bz Na/Np**

PD 3.66 ± 2.48 −0.63 0.44 0.38 −0.39 0.47 0.47 0.44 0.46 PN 3.42 ± 2.95 −0.25 0.26 0.31 −0.21 0.15 0.17 0.21 0.16

PD 1.83 ± 1.61 −0.20 0.23 0.25 −0.12 0.16 0.13 0.12 0.15 PN 1.76 ± 1.27 −0.50 0.57 0.53 0.53 0.50 0.47 0.47 0.50 *NM—count rate of ground based on neutron monitor (counts/s); IMF—field magnitude Avg, <F>, nT; FV|<B>| magnitude of average, field vector, |<B>|, nT; Bz, GSM-Bz—component INF in the geocentric solar-magnetospheric coordinate systems; δ-B—variability of the magnetic field strength; δ-By—Sigma By—variability of By—component of IMF, nT; δ-Bz—Sigma Bz—variability of Bz—component of IMF, nT; Na/Np—alpha/proton ratio in the solar*

*Correlation coefficients between monthly values of incidents of the diseases of the musculoskeletal system and connective tissue (DMSSCT), the infections of the skin and subcutaneous tissue (ISST), and the monthly average magnitudes of the parameters of geocosmic agents during the polar day (PD) and polar night (PN).*

> **Pl beta**

*number (Ma = (V \* Np0.5)/20 \* B); Kp \* 10-index of geomagnetic activity (GMA); R—sunspot number; f10.7-index of the solar radio flux with wavelength 10.7 cm in solar flux units (s.f.u.), (10<sup>−</sup>22), Watts/meter sq/hertz; PC(N)—high latitude index of GMA; MMN—magnetosonic mach number = V/Magnetosonic\_speed. Coefficient*

*Correlation coefficients between monthly values of incidents of the injury and poisoning on the way to work and from work and the monthly average magnitudes of the geocosmic agents during the polar day (PD) and*

PD 1.75 ± 1.97 −0.52 0.48 0.46 −0.38 −0.43 0.26 0.61 0.58 0.39 −0.43 PN 1.26 ± 1.07 −0.55 0.54 0.51 −0.55 −0.61 0.47 0.67 0.72 0.58 −0.55 *NM—count rate of ground based on neutron monitor (counts/s); δ phi, δ-theta—sigma-phi-V, sigma-theta-V—solar*

**AMN Kp \* 10 R f10.7 PC(N) MMN**

*; AMN—Alfven mach*

*) + 5.34] \* Np/B2*

A geophysical feature of the arch. Spitsbergen is its location in the cusp region a kind of funnel on the dayside of the magnetosphere with near zero magnetic field magnitude. The open field lines of the cusp are connected with those of the interplanetary magnetic field (IMF), which allows the shocked solar wind plasma of the magnetosheath to enter the magnetosphere and to penetrate the ionosphere.

In the cusp areas, the impacts of the solar wind (SW) on the Earth's magnetosphere manifest most strongly, and multiple phenomena originating as consequences of such interactions are referred to as the space weather. The magnetosphere and the ionosphere of the Earth are sources of electromagnetic oscillations and waves, many of which are detected in the form of radiation outside the region of generation, in particular, on the surface of the Earth.

The feature of the cusp is the existence of the geomagnetic pulsations not only in the period of geomagnetic disturbances but also during the quiet period. One can see that narrow band waves at frequencies 0.2 to 3 Hz are a permanent feature in the vicinity of the polar cusp. The waves have been found in the magnetosphere adjacent to the cusp (both poleward and equatorward of the cusp) and in the cusp itself. It is an established fact that the daytime polar cusp latitudes are typically characterized by long-period (T ~4–60 min) geomagnetic pulsations observed both in daytime and nighttime hours. Diverse radiation (from ULF to VHF) and waves in the field of polar cusp, covering the entire range of the body rhythms, give credit for studying the effects of space weather in the field of polar cusp. The study of the dependence cases of diseases on effects of space weather has shown that diverse forms of morbidity varied synchronously and they are associated with variations of space weather agents. Assessment of the relationship between the dynamics of the monthly morbidity in Russian settlements and indicators of space weather revealed that, practically, all forms of morbidity are associated with solar activity: with F10.7 index, with variations of solar wind parameters, and with indices characterizing the local geomagnetic activity in the polar cusp.

It has been found that mental disorders are associated with the variability of the solar wind and the radio emission of the Sun, as well as the frequency of injuries and poisoning at the work and at the home. A high degree of association of the diseases of arteries, arterioles, and veins with the parameters of the solar wind and the geomagnetic indices, characterizing the local geomagnetic activity in the polar cusp, was shown.

A high sensitivity of the female organism to variations of space weather in the polar cusp was revealed. This phenomenon is manifested in the increase of pregnancy complications, cases of inflammation of the genital organs, etc., according to the increase in geomagnetic activity in the polar cusp.

The revealed differences in the nature of the links between the morbidity of the population in the Barentsburg during the polar day and the polar night show that the diverse diseases are associated with a combination of separated characteristics of the SV, MMP, GMA, and SA, the significance of which for the morbidity varies with the season.

However, it has been found that certain diseases are associated only with the polar day or with the polar night. This allows selecting the physical agents that could modulate morbidity rate in the alternative season. In particular, agents such as long-period oscillations, with the frequency range that coincides with the range of the ultraslow fluctuations of the constant potential (USFCP) in the brain, could modulate the morbidity of the MD and DAAV in the polar day.

The absence of solar radiation during the polar night, such as UV radiation, and the association of the incidence of the inflectional diseases of skin with GMA only during the polar night indicate the role of UV in suppressing the growth of pathogenic microflora. Correlations of the inflectional diseases of skin with GMA in the absence of UV radiation demonstrate the significance of GMA for the microorganism growth.

In general, it should be noted that, probably, many of the bioeffective agents associated with CA were left out of consideration. The health of the population most likely depends on a combination of geophysical agents, some of which are simply not registered and are not reflected in the indicators of the database (OMNI). On the other hand, the state of the human body during the periods of the polar day and the polar night may also differ in sensitivity to the effects of geophysical agents. In general, the polar day is characterized by a larger number of influencing physical agents on the human body, than the polar night.

The found links between the morbidity of the population and the peculiarities of space weather will make it possible to develop prognoses of the morbidity for preventive measures aimed at reducing the morbidity in high latitudes.

The task of studying the labor activity in the difficult arctic conditions demands the need to develop criteria for determining the mental state of a person and his working capacity, as well as predicting a shift in the functional state of the CNS. The solution of such a problem should take into account the possibility of modulation of the mental and of the physiological state of people of the dangerous professions by the high latitude geocosmic agents, the effects of which might also express in the seasonal manifestation of morbidity.


2.1.2. Variations in Vegetation Cover

Vegetation cover is also influenced by temperature (Figure 1); an increase in this, particularly in Mediterranean areas, can cause a reduction in vegetation with a consequent increase in erodibility. However, this temperature variation should lead to an upward shift of the tree vegetation limit in mountains [35] with a consequent reduction in erosion rates in the upper parts of catchment areas where, due to the greater relief, the production of sediments is greater [36].

Variations of this type certainly occurred in the Holocene and may have modified, even significantly, the sedimentary balance of some coasts. Unfortunately, studies of this nature are extremely scarce due to the objective difficulties of appreciating a phenomenon that is largely overshadowed by a much more relevant one that in turn is linked to variations of the vegetation cover induced by anthropic intervention. Forests cutting to convert land to pasture and agriculture have been recognized to be the main cause of coastal progradation in historical time [37–39].

Conversely, agricultural abandonment can lead to forest growth with a reduction of soil erosion [40–42], but where this favored collapse of terrace stone walls, as in many parts of the Mediterranean countries, this process increased [43].


**4. Discussion**

(**Figure 4a**). In contrast, no bone tissue was observed on the implant surface in the control group of animals (**Figure 4b**). Representative histological specimens of un-decalcified tissues with Villanueva bone staining are shown (**Figure 5a–d**). In the BMP-treated group, the implant was covered by a new bone that was continuous with the original ilium, and the new bone was found to have entered pores inside the implant (**Figure 5c**). The dough material pasted on the implant surface was not observed in histological specimens obtained 12 weeks postoper‐ atively. This indicates biodegradation during the experimental period. No inflammatory

**Figure 5.** Histological sections at 12 weeks after surgery [lower (\*20) and higher magnification (\*200), *scale bar* indicates 100μm, 8μm in each magnification]. (a, b) Boxed area of reconstructed axial CT image shows a histological picture of (c) (BMP-treated group) and (d) (control group), respectively. (c) In the BMP-treated group, the implant was covered by a new bone that was continuous with the original ilium, and the new bone was observed in the pore of the implant. d. In the control group, no bone tissue was observed. (arrowhead: new bone, asterisk: IP-CHA implant, cross: ilium,

In the BMP-treated group, the volume of the calcified new bone tissue around the implant on CT slice images of the pelvis peaked 3 weeks postoperatively, then decreased over time, and

arrow: fibrous tissue, circle: osteoid, box: calcified bone).

**3.4. Changes over time in the amount of the new bone formed**

response was observed around the implant over the experimental period.

150 New Trends in 3D Printing

BMPs are established osteoinductive proteins, and BMP-2 and BMP-7, in particular, are used in combination with animal-derived collagen as a carrier for spinal fusion, pseudarthrosis treatment, and open fractures of the tibia [6–8]. These cytokine therapies accomplish a high rate of bone union and have reduced the frequency of bone grafting. However, the currently available carrier material of BMP is collagen derived from animals, which carries a possibility of infectious pathogens. We established a new DDS of BMP-2; efficacy of the rhBMP-2 retaining putty for repair of the large bone defect had been presented previously by our group in other bone defect models in beagles and posterolateral spinal fusion models in rabbits. In those experimental studies, a special A-B-A type of block copolymer (molecular weight 9,800, A;-*dl polyethylene glycol* and B; *polylactic acid)* was successfully used as the carrier material for BMP-2. The polymer was originally produced by us in collaboration with a chemical company as an optimal delivery system for rhBMP-2. Because this polymer itself was highly sticky and difficult to handle, an equal weight of biodegradable β-TCP powder was mixed with the polymer to transform it to a slightly sticky putty or dough for easy implantation. Thus far, with respect to the use of this carrier material in experimental animals, no local or systemic adverse events were noted, and no significant inflammatory reaction has been observed on histology. This synthetic carrier material is a candidate for efficacious use of rhBMP-2 to repair or reconstruct the damaged bone after extensive safety check and clinical trials. Regarding the rhBMP-2 dose added to the implant, 100 μg of rhBMP-2 in combination with 400 mg of the delivery system (carrier material) per animal was used in the current study. This formulation was fixed based on our previous experimental results obtained in beagle models. As widely known, a minimal dose of BMP-2 to elicit local new bone formation in *in vivo* condition depends on the animal species, and humans are less sensitive to BMP-2. Therefore, a higher BMP-2 dose to the order of a few mg will be required for bone defect repair as observed in clinical reports of rhBMP-2 used for spinal body fusion, non-union of fracture, and other off-label use of rhBMP-2 in humans.

Recent reports have described successful reconstruction of maxillary bone defects in rabbits and bone union achievement in rats with pseudarthrosis using a combination of BMP genetransfected cells and an artificial material or using sheets of cultured mesenchymal stem cells [34, 35]. However, an expensive facility equipped with a special culture system is required to establish a cell culture system for clinical application in humans. Conversely, BMPs can be conveniently used anywhere, provided that the user has adequate storage expertise. In this study, we accomplished the accurate skeletal reconstruction after resection in a virtual iliac bone tumor model in beagle dogs by combining BMP with a 3D printer, CAD, CAM, and a computer navigation system. In future, skeletal reconstruction will become possible for more specialized skeletal tissue, such as the vertebral body and load-bearing sites.

In wide resection surgery of malignant musculoskeletal tumors, optimal tumor margins and resection margin of the bone and soft tissue would be decided preoperatively on the basis of CT and/or MRI to avoid exposing the tumor tissue to the surgical field and later local recurrence of the tumor. But the accurate resection as planned preoperatively has often proven difficult. In this experimental study, a CAS system of CT-based navigation was used as a guide for bone resection margin, and the results indicated that it is possible to perform bone resection exactly as planned using the CAS system. But planning of the exact resection margin of the soft tissue covering the tumor was not attempted in this series, and similar studies based on CT and/or MRI will be necessary to determine optimal resection margin of the soft tissue.

The 3D configuration of the defect on the left iliac bone after resection of the virtual tumor was depicted on screen by subtraction of the 3D image of the left iliac bone from the 3D mirror image of the right iliac bone. CT data of the defect images were then transferred to the CAM system to drive the system to fabricate an IP-CHA implant to fill and fit the bone defect generated in the left iliac bone. The shape and size of the implants thus fabricated were acceptable and matched the defects well. However, the IP-CHA implant alone did not repair the bone defect with a new bone, as observed in control animals, in spite of its high osteoin‐ ductive capacity due to its fully interconnected porous structure. To overcome this disadvant‐ age of the IP-CHA implant and to enhance regenerative potential of the implant, rhBMP-2 with its potent bone-inducing capacity in combination with a specific delivery system (carrier material for BMP-2) was added to the surface of the IP-CHA implant. This successfully elicited new bone formation on the surface of the implant and restored normal anatomical surface contour of the iliac bone in 12 weeks using the IP-CHA non-degradable block within the new bone mass. Replacement of the non-biodegradable IP-CHA block by a porous β-TCP implant might have resulted in complete regeneration of the bone defect without remnant biomaterials. However, because of the highly fragile nature of the porous TCP and easy breakage of the implant during fabricating and surgical procedures, the IP-CHA implant was used in this study, and the bone mass with stable connection to the original iliac bone and porous implant was created.

Recent developments in 3D printing have expanded its range of application to include production of implants, scaffolds for tissue engineering, and instruments for DDSs in addition to visualization of surgical sites preoperatively and casting of surgical tools on the basis of the additive manufacturing technology used in the present study. In addition, it has become possible to directly print biological materials onto solvent-free, aqueous-based 3D scaffolds for tissue transplantation [36–38]. Using CAD/CAM techniques, spatially defined implants can be constructed by attaching functional living cells, physiological substances, and bioactive factors in layers to organ scaffolds created on the basis of MRI or CT. For this bioprinting technique, three major modes are currently available: inkjet, micro-extrusion, and laserassisted bioprinting. These modes differ in various properties, such as the range of material viscosity accommodated, gelling method, viability, and concentration of cells to be handled. Skin and cartilage regeneration by the inkjet method, creating heart valves and blood vessels by the micro-extrusion method, and creating skin containing the heart tissue or cells by the laser-assisted method have been reported [37, 38]. We expect that for cases involving extensive tissue defects or requiring organ transplantation, printing technologies, scaffold development, cell surgical techniques, and the use of growth factors will advance further and the scope of clinical application will be further broadened if necessary tissues can be created in a shorter period of time in the future.


**2.1.2 The Golden Horn Estuary (Istanbul)**

The Golden Horn Estuary has been heavily polluted by industrial and domestic wastes since 1950. Five million cubic meters of sludge has been removed during the last 10 years of restoration works. After the rehabilitation project, decreases in level of bacteria were reported (Altuğ and Balks 2009).


*4.4. Reclassification of GA A07 Strain*

In total, 882 *Bacillus* genomes were downloaded to identify the most closely related species. FastANI v1.2 [41] was leveraged to identify the most closely related species (i.e., the ones with the highest average nucleotide identity compared to the GA A07 genome). Average amino acid identity was also checked by the following steps: (1) Prodigal v2.6.3 [25] was used to in batch-predict protein-coding genes from the genomes, and genes were converted into amino acid sequences; (2) amino acid identities between the GA A07 genome and all downloaded *Bacillus* genomes were compared using BLASTP [26] (parameters: -max\_target\_seqs 1 -evalue 1e-10); and (3) the mean value of the best-hit identities was calculated and was regarded as the average amino acid identity between the two genomes. The Tetra Correlation Search (TCS) function implemented in the JSpeciesWS webserver [27] was also pursued to find the most closely related bacterial genome based on tetra-nucleotide composition evidence.

Only genomes with better assembly quality (defined as genomes with at most ten scaffolds) were used to build the phylogenetic tree. The tree was generated using ezTree [42], which is capable of identifying single-copy marker genes among input genomes, thereby creating a concatenated alignment of all marker genes, and using FastTree 2 [43] with the Jones–Taylor–Thornton (JTT) evolutionary model and 1000 resampling tests to construct a reliable tree. The nwk file of the phylogenetic tree was then visualized using Molecular Evolutionary Genetics Analysis (MEGA) X software [31].

#### *4.5. Identification and Analysis of GT Genes*

The dbCAN2 webserver [44] was employed to identify potential GTs from the *B. thuringiensis* GA A07 genome. An unrooted phylogenetic tree of all extracted GT protein sequences was constructed using MEGA X software [31] with the maximum-likelihood method, 500 bootstrap replications, the general reversible mitochondrial model [30], and partial deletion.

#### *4.6. Fermentation and Biotransformation of GAA*

*Bacillus subtilis* ATCC 6633 or *B. thuringiensis* GA A07 was cultivated in a 250-mL baffled Erlenmeyer flask containing 20 mL of LB medium with 5% of glucose at 180 rpm and 28 ◦C. When the OD600 of the cell culture reached 0.6, 1 mg/mL of GAA was added to the broth. Cultivation was carried out for another 32 h, and fermentation broth (0.5 mL) of the culture was taken at predicted time intervals and used for the UPLC analysis to measure the biotransformation activity.

#### *4.7. UPLC Analysis*

The UPLC system (Acquity UPLC H-Class, Waters, Milford, MA, USA) was equipped with an analytic C18 reversed-phase column (Kinetex® C18, 1.7 μm, 2.1 i.d. × 100 mm, Phenomenex, Torrance, CA, USA). The operating conditions of UPLC for analysis of GAA, antcin K, and celastrol were

consistent with those of our previous study [4] except for the 430-nm absorbance detection of celastrol. Operating conditions for flavonoids were from our previous study [11].

#### *4.8. Expression and Purification of GT from GA A07 Strain*

Genomic DNA of the GA A07 strain was isolated using the commercial kit Geno *Plus*TM (Viogene, Taipei, Taiwan). Candidate GT genes were amplified from genomic DNA using a PCR with specific primer sets (Table S2). The amplified GT genes were subcloned into the pETDuet-1™ vector through suitable restriction enzyme sites (Table S2) to obtain the expression vector, pETDuet-BtGT (Figure S1a). Expression vectors were transformed into *E. coli* BL21 (DE3) via electroporation to obtain recombinant *E. coli*.

Recombinant BtGT\_16345, BtGT\_19840, and BtGT\_19010 were produced and purified from the recombinant *E. coli*, and analyzed by SDS-PAGE (Figure S1b–d). The protein concentration was determined by a Bradford assay using bovine serum albumin as the standard. The experimental procedures were the same as those in our previous study [4].

#### *4.9. In Vitro Biotransformation Assay*

In vitro biotransformation was performed using purified GT proteins. In a 0.1mL standard reaction mixture, 1 μg of purified GT protein, 0.02 mg/mL of GAA, 1 mM of UDP-glucose, 10 mM of MgCl2, and 50 mM of Tris at pH 8.0 were added. The reaction was carried out at 40 ◦C for 30 min, stopped by adding 0.9 mL of methanol, and analyzed by UPLC.

For optimization experiments, different pH values, temperatures, and metal ions were replaced in the standard reaction, where 1 mg/mL of GAA was used. For pH testing, PB at pH 6.0 to 7.5, and Tris buffer at pH 8.0 and pH 9.0 were used. For metal ion testing, 10 mM of MgCl2, CaCl2, or MnCl2 was used. The relative activity was obtained by dividing the area of the product peak of the reaction in the UPLC profile by that of the reaction with Tris pH 8.0, at 40 ◦C, and with 10 mM of MgCl2.

For the substrate specificity assay, 25 mg/mL of the substrate soluble in dimethyl sulfoxide conditions, all tested substances were soluble in the reaction buffer. 1 mg/mL of different test compounds was mixed with 1 μg of purified GT protein, 10 mM of UDP-glucose, 10 mM of MgCl2, and 50 mM of PB pH 7.0 in a 0.1mL reaction mixture and incubated at 30 ◦C for 30 min. After incubation, the reaction mixture was analyzed by UPLC.

For the kinetic experiments, different concentrations of GAA were mixed with 10 μg of purified GT protein, 10 mM of UDP-glucose, 10 mM of MgCl2, and 50 mM of PB at pH 7.0 for BtGT\_16345, or 50 mM of Tris at pH 8.0 for BsUGT398 and BsUGT489 in a 1-mL reaction mixture and incubated at 30 ◦C for BtGT\_16345, or 40 ◦C for BsUGT398 and BsUGT489 for 20 min. During incubation, samples from each reaction were taken out and analyzed by UPLC every 2 min. The amount of GAA-15-*O*-β-glucoside produced from the reaction was calculated from the peak area of the UPLC analysis normalized to a standard curve. The reaction velocity at each concentration of GAA was obtained from the slope of the plot of time versus the amount of the product. The kinetic parameters were calculated by nonlinear regression analysis applied to Michaelis-Menten equation using SigmaPlot 14.0 software (Systat Software, San Jose, CA, USA). The kcat values were calculated using the predicted molecular mass for each recombinant enzyme.

#### **5. Conclusions**

A novel GT28 family enzyme, BtGT\_16345, from a new genome assembly of the *B. thuringiensis* GA A07 strain, was identified that can biotransform GAA into GAA-15-*O*-β-glucoside. To our knowledge, BtGT\_16345 is the first GT28 family enzyme with triterpenoid glycosylation activity.

*Int. J. Mol. Sci.* **2019**, *20*, 5192

**Supplementary Materials:** Supplementary materials can be found at http://www.mdpi.com/1422-0067/20/20/5192/s1. The following materials are available online. Table S1: Single copy marker genes used for building the phylogenetic tree; Table S2: Nucleotide sequences of the primers used for amplification of glycosyltransferase (GT) genes in the present study; Figure S1: Expression and purification of glycosyltransferases (GTs) from *Bacillus thuringiensis* GA A07 in *E. coli*.

**Author Contributions:** Conceptualization, T.-S.C., T.-Y.W., and Y.-W.W.; data curation, Y.-W.W., T.-Y.H., Y.-W.L., H.-M.C., W.-X.C., and T.-S.C.; methodology, Y.-W.W., C.-M.C., T.-Y.W., J.-Y.W., and T.-S.C.; project administration, T.-S.C.; Writing—original draft, Y.-W.W., C.-M.C., T.-Y.W., J.-Y.W., and T.-S.C.; writing—review and editing, Y.-W.W., C.-M.C., T.-Y.W., J.-Y.W., and T.-S.C.

**Funding:** This research was financially supported by grants mainly from the Ministry of Science and Technology of Taiwan (Project No. MOST108-2221-E-024-008-MY2 to T.-S.C.) and partially from MOST-108-2628-E-038-002-MY3 to Y.-W.W. and Academia Sinica to T.-Y.W.

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


**8. Conclusions**

The main motivation of this work is to contrast low-consumption wireless technologies applied to IoT that are characterized by transmitting small amounts of information in a reliable and flexible way, consume little battery in devices, and have great scalability in the communications system.

Concerning data, we can control the information management of applications in our computers such as photos, videos, mails, etc. But for IoT, it does not work in the same way. It captures information at every moment it considers necessary, instead of capturing it when requested. People usually become subjects for data collecting, instead of users of IoT services, and most cases they are not aware of it. Because it is not easy for people to know when sensors are activated. The privacy stack framework bridge of today's Internet of Things between IoT and user, starts with awareness. It concerns on how IoT services might open communication channels to users and subjects. The IoT protocol work has not gone into privacy data standardization, in other words, the bridge between privacy and public status is minimal. Second, the inference part proposes users to be conscious of the constantly grow of inferences, because data and IoT learning techniques rise their capabilities every day. Inferences helps users to understand what IoT devices learn about them and helps the system to improve privacy with a natural language to understand which are our privacy preferences.

**Author Contributions:** The methodology was proposed by C.D.-V.-S., the investigation and the formal analysis work were done by L.J.V. and C.D.-V.-S., the validation work was done by R.V., the data analysis work was finished by J.-C.L.-P. and L.R.-D., and the original draft was finished by L.J.V., C.D.-V.-S. and R.V.

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

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


**1. Introduction**

Molten thermotropic liquid crystalline polymers have physical properties that are dependent on both small molecule liquid crystals and flexible chain polymers. Liquid crystalline polymers (LCPs) are typical examples of self-ordered polymeric systems, due largely to their intrinsic molecular anisotropy. The properties of LCPs are strongly influenced by flow-induced changes in the degree of molecular orientation during processing [1].

Blends containing small amounts of a thermotropic liquid crystalline polymer (TLCP) in a matrix of thermoplastic have attracted technical interest in recent years for two main reasons. Firstly, by the use of TLCP to enhance the mechanical properties of the matrix polymer through in situ formation of fibrous TLCP dispersion during processing, it may be possible to develop 'self-reinforced' composites that exploit the outstanding tensile properties of fibres made from LCPs. Secondly, it is known that TLCP can act as a flow modifier, resulting in a substantial reduction in pressure drop during melt extrusion. Previous studies by Chan et al.[2] have shown that a small amount of TLCP (1.0 wt %) added to high molecular weight polyethylene (HMMPE, Chevron Phillips Marlex® HXM TR570) caused drastic bulk viscosity reduction (> 95.0%) to occur at 190 oC, when TLCP was in its nematic phase. A significant improvement in extrudate surface smoothness has also been observed, coupled with an increase in the processing window from 34 1/s to up to 1000 1/s. Whitehouse et al. [3] blended 0.2%, 0.5% and 2.0% TLCP with high density polyethylene (HDPE, Chevron Phillips Marlex® HMN 6060), and the blends were then rheologically

characterized at 185 oC when the TLCP was in the nematic regime; substantial viscosity reductions of between 85% and 90% compared with pure HDPE were observed.

Micro-Rheological Study on Fully Exfoliated Organoclay Modified Thermotropic Liquid Crystalline Polymer and Its Viscosity Reduction Effect on High Molecular Mass Polyethylene 277

In this study, the organoclay-modified TLCP (TC3 white) is prepared by a method combining ultra-sonication, centrifugation, solution casting and heating-shearing separation. TC3 white has a very high degree of dispersion of organoclay but without any loss in the degree of liquid crystallinity of TLCP. The effects of the fully exfoliated organoclay on the thermal and rheological properties of TLCP are investigated in detail. Based on this material, we characterize the rheological behaviour of purified TLCP and TC3 white with 1 wt% in HMMPE matrix by a capillary rheometer at 190 oC and 230 oC, where the TLCP has its nematic and nematic-isotropic biphase structures. Schematic drawings of the conformation of organoclay, TLCP molecules and polyethylene molecules before and after yielding are shown. A binary flow pattern model with simulated results is presented and the predicted results show good consistency with

The HMMPE, Chevron Phillips Marlex® HXM TR571, with a melt flow index (MFI) of 2.5g/10 min (ASTM D1238, 190 ºC/21.6 kg) was kindly supplied by Phillips Petroleum International Inc., USA. The TLCP, a copolymer containing 30% p-hydroxybenzoic acid, 35% hydroquinone and 35% sebacic acid (HBA/HQ/SA), used here was synthesized and kindly supplied by B. P. Chemicals Ltd, UK. The as-received TLCP is a light brown powder that has been characterized previously [20]. The Organoclay, Closite 20A modified by dimethyl dihydrogenated tallow ammonium ions, was kindly supplied by

The as-received dried TLCP powder was dissolved into chloroform and followed by mechanically stirred at room temperature overnight. The deionized water was added to the mixture and was mechanically stirred for several minutes. Water extraction was repeated several times to remove any water soluble content, e.g. pure sebacic acid; the subsequent solution was subjected to centrifugal separation (KUBOTA 2010, Japan) at a speed of 3,800 rpm for 1800 seconds. Three layers of solution were obtained. The bottom layer was a brownish precipitate which was believed to be a mixture of HBA, HBA rich TLCP, catalyst or other heavier components in the TLCP powder. The top layer was a white cloudy layer, which was believed to be a SA rich TLCP component. The middle colorless portion was extracted. To obtain more thorough purification, the whole centrifugal separation and extraction processes were repeated several times. The TLCP powder was precipitated out by dropping the extracted solution into methanol. The TLCP fine powder was filtered out and

dried at 120 oC for 3 days to remove any residual solvent contents.

experimental data.

**2. Experimental**

*2.1.1. Materials*

Southern Clay Products.

*2.1.2. Purification of as-received TLCP*

**2.1. Materials and samples preparation**

It is worth noting that even in early studies of polymer blends containing TLCP, researchers had already attempted to introduce inorganic reinforcements into such blend systems [4]. The addition of inorganic fillers not only enhanced the mechanical properties of the blends but also reduced the anisotropy of the resulting materials [5]. Rheological characterization revealed that TLCP could reduce the melt viscosity of glass-filled thermoplastics [6]. Much work has been published on TLCP systems containing different inorganic solid reinforcements, such as glass fibres [7], carbon black [8], whiskers [9] and silica [10]. Most of the studies have used high inorganic solid reinforcement content and have focused on the balance between the mechanical properties and processability of such blends.

Layered silicates have long been used as fillers in polymeric systems to improve mechanical, thermal and other properties in the resulting polymer composites. In layered silicate itself, the local interaction between layers causing the presence of domains similar to those found in studies of liquid crystalline and ordered block copolymer systems have been analyzed by many researchers [11-14].

It seems that intercalation or exfoliation of layered silicates in polymers should induce nanocomposites to exhibit similar rheological behaviour to that found in the nematic state structures in LCPs. Nanocomposites based on thermotropic liquid crystalline polymer and organically modified layered silicate have been studied by several groups with different foci. Zhang et al. [15] synthesized a series of liquid-crystalline copolyester/organically modified montmorillonite nanocomposites by intercalation polycondensation with different surfactant modified clay in LCP. X-ray diffraction and transmission electron microscope studies indicated that, after ion exchange with suitable surfactants, clay formed delaminated morphology and was well dispersed in LCP. Chang et al. [16] reported nanocomposites of TLCP with an alkoxy side-group and an organoclay (Closite 25 A) prepared by the melting intercalation method above the melt transition temperature of TLCP, with liquid crystallinity of the hybrids being lost when the organoclay content exceeded 6.0 %. Vaia et al. [17] directly annealed a powder mixture of TLCP and clay within the nematic region of TLCP under high hydraulic pressure. Reversible intercalation formed, with a loss of liquid crystallinity which was attributed to the confinement of LCP chains on the clay pseudo-2D geometry. An extensive study of a series of nanocomposites with a segmented main-chain liquid crystalline polymer having a pendent pyridyl group (PyHQ12) or a pendent phenylsulfonyl group (PSHQ12) and commercial Closite 20 A or 30B clays by examining rheological and other properties was reported by Huang and Han [18, 19] to demonstrate whether functionality of TLCP was essential to obtain highly dispersed clay in nanocomposites with TLCP as matrix. Only intercalated morphology formed when the nanocomposites were based on a TLCP without functionality. The formation of hydrogen bonds caused a very high degree of dispersion but a considerable loss in the degree of liquid crystallinity in a PyHQ12/30B nanocomposite. From the above studies, it can be seen that functionality in TLCP is necessary to obtain highly dispersed nanocomposites, but at the same time, there is the possibility of loss some degree of liquid crystallinity in the TLCP.

In this study, the organoclay-modified TLCP (TC3 white) is prepared by a method combining ultra-sonication, centrifugation, solution casting and heating-shearing separation. TC3 white has a very high degree of dispersion of organoclay but without any loss in the degree of liquid crystallinity of TLCP. The effects of the fully exfoliated organoclay on the thermal and rheological properties of TLCP are investigated in detail. Based on this material, we characterize the rheological behaviour of purified TLCP and TC3 white with 1 wt% in HMMPE matrix by a capillary rheometer at 190 oC and 230 oC, where the TLCP has its nematic and nematic-isotropic biphase structures. Schematic drawings of the conformation of organoclay, TLCP molecules and polyethylene molecules before and after yielding are shown. A binary flow pattern model with simulated results is presented and the predicted results show good consistency with experimental data.


**7. Mechanism of oxidative stress and DNA damage due to micronutrient deficiency**

Micronutrients are referred to as nutrients required in very small amounts that do not have calorific values but are extremely important for the maintenance of health. They comprise the vitamins and trace elements that regulate vital metabolic and molecular pathways and

If the induced mutations occur at one or a relatively few critical sites on the DNA, then the cells may be converted from a "normal" to a preneoplastic state. Chemically-induced mutations are not limited to critical genes, different pretumor cells may demonstrate a variety of different growth potentials due to the range of altered " non-critical" genes, thus enabling those cells with most favourable properties to transform most rapidly to clinically apparent tumours. Alternatively, the carcinogens may act indirectly through the formation of reactive oxygen species or nitrogen radicals, some types of which are also highly reactive with macromolecules such as DNA. The process of raised cellular proliferation is also multifactorial in its genesis. It may arise from, for instance, direct hormone-like stimulation of specific cell types, from perturbation of tissue processes that lead to a balance between cell proliferation and cell death (apoptosis), it may alternatively arise from massive cell-killing or cytotoxicity followed by proliferative regeneration to maintain the physiological functioning of the affected tissues. A yet further way by which excess, tumor-inducing cellular division may be induced is that exhibited by the urinary bladder. Oyasu and his colleagues (1981) and in his subsequent studies (1995) showed by using heterotopical transplant rat bladder technique that urine by itself, but not water can induce proliferation.

It was however conceived that urine contained epithelial growth factors that stimulate cell division and that such factors would penetrate the epithelium should it be injured by the presence of a foreign agent in the bladder. A fourth type of mechanism of carcinogenesis may arise from the ability of the agent to form a complex with a specific protein. This complex (ligand-protein) may have the property of altering the expression of specific and important region in the DNA. There is the emerging but incompletely understood involvement of epigenetics; alteration in the genetic processes not involving DNA base sequence. Many epimutagens have already been identified and a number of existing

Cancers caused by environmental agents frequently occur in tissues with the greatest surface of exposure to the agents: lung, gastrointestinal tract, and skin. Recently, the study of chemical carcinogenesis has merged with studies on the molecular changes in cancer cells, thus generating biological markers to assess altered metabolic pathways and providing new targets for therapy. Although these are exciting areas, they may be peripheral to attacking the primary causes of the most common human cancers. As more and more mutations are catalogued in cancer cells and more and more changes in transcription regulation, it becomes increasingly apparent that we need to understand what generates these changes. The fact that chemicals cause random changes in our genome immediately implies that our efforts need to be directed to quantifying these changes, reducing exposure, and developing approaches to chemoprevention (Extensively reviewed by Pereira, 1997).

**7. Mechanism of oxidative stress and DNA damage due to micronutrient**

Micronutrients are referred to as nutrients required in very small amounts that do not have calorific values but are extremely important for the maintenance of health. They comprise the vitamins and trace elements that regulate vital metabolic and molecular pathways and

The mechanism by which this happens is not quite clear.

**6. The environment and cancer**

**deficiency**

chemicals such as cadmium are also known to act through this pathway.

processes. Some of them play very vital roles in DNA and RNA metabolism either as coenzymes or cofactors involved in their metabolism or as components of systems intimately interacting with these molecular regulators. The micronutrients are basically supplied in the diet or may be taken as supplements. Very few of them are synthesized endogenously enough to meet physiological requirements hence they are mostly essential.

Based on public data emanating from the Healthy People 2010 Project, it has been estimated that about 80% of colon and prostate cancers, may be influenced by diet, nutrition, and life style. It has been proposed that DNA damage induced by dietary micronutrient deficiency accounts for about 33% of preventable cancers (Ames, 2001; Ames and Wakimoto, 2002). Owing to the fact that micronutrient deficiencies can induce DNA damage in a manner similar to those induced by ionizing radiation and reactive oxygen species (ROS), it has been suggested that oxidative stress and the associated DNA breaks are critical targets for nutritional control of carcinogenesis (Cheng, 2009) and perhaps a marker for early detection. When DNA lesions are left unrepaired they can promote accumulation of mutations that facilitate the process of carcinogenesis. Micronutrients may act directly on the genome to prevent mutations, or indirectly as enzyme cofactors in cellular processes that modulate transformation (Hanahan and Weinberg, 2000; Sjoblom et al., 2006). By yet incompletely defined mechanisms, micronutrients at levels higher than nutritional requirements may also activate DNA damage response or senescence, which are processes that are recognised to eliminate cancer cells or limit the progression of precancerous cells (Gorgoulis et al., 2005; Bartkova et al., 2005, 2006). The essential microminerals copper, iron, selenium and zinc play important roles in genome stability. In particular, these microminerals have significant impact on oxidative DNA damage and the corresponding repair pathways.

Micronutrient intakes below recommended levels are known to be unusually widespread in poor countries, though also some segments of the population in economically advanced nations such as the United States, especially among the poor, children, adolescents (Anetor, 2009; Ames, 2010). It has been hypothesised that two of the many insidious but measurable consequences of moderate micronutrient inadequacy are increased DNA damage (precursor of cancer) and mitochondria decay which can cause mutagenic oxidant release also involved in future carcinogenesis. Studies indicate that sensitive assays targeted at these end points have a high probability of detecting changes in individuals with micronutrient deficiencies(Ames, 2010) This again appears instructive to scientists and technologists in the biomedical engineering field.


**5.1 Epidermis**

It consists of a layer of stratified squamous epithelium that is in continuous renewal. It presents different cell types: keratinocytes (95%), melanocytes, Langerhans cells (immune function), and Merkel cells (transmitters of mechanical stimuli). The epidermis is an avascular layer.

### **Figure 2.**

*Anatomy of the epidermis. Image taken from: https://commons.wikimedia.org/wiki/File:502\_Layers\_of\_ epidermis.jpg#filehistory OpenStax college: J. Gordon Betts, Peter Desaix, Eddie Johnson., CC BY 3.0 <https:// creativecommons.org/licenses/by/3.0>, via Wikimedia commons.*

The keratinocyte moves from the basal membrane to the surface of the skin, forming different epidermal layers with different morphologies: basal or germinative stratum, lucidum stratum, spinosum stratum, granular stratum, and corneum stratum. Among other functions, epidermis prevents moisture loss from the skin (**Figure 2**) [8].


*Article* **Potential Effects of Sucralose and Saccharin on Gut Microbiota: A Review**

**Susana del Pozo 1,\*, Sonia Gómez-Martínez 2, Ligia E. Díaz 2, Esther Nova 2, Rafael Urrialde 3,4 and Ascensión Marcos 2**

**Abstract:** Artificial sweeteners are additives widely used in our diet. Although there is no consensus, current evidence indicates that sucralose and saccharin could influence the gut microbiota. The aim of this study was to analyze the existing scientific evidence on the effects of saccharin and sucralose consumption on gut microbiota in humans. Different databases were used with the following search terms: sweeteners, non-caloric-sweeteners, sucralose, splenda, saccharin, sugartwin, sweet'n low, microbiota, gut microbiota, humans, animal model, mice, rats, and/or in vitro studies. In vitro and animal model studies indicate a dose-dependent relationship between the intake of both sweeteners and gut microbiota affecting both diversity and composition. In humans, long-term study suggests the existence of a positive correlation between sweetener consumption and some bacterial groups; however, most short-term interventions with saccharin and sucralose, in amounts below the ADI, found no significant effect on those groups, but there seems to be a different basal microbiotadependent response of metabolic markers. Although studies in vitro and in animal models seem to relate saccharin and sucralose consumption to changes in the gut microbiota, more long-term studies are needed in humans considering the basal microbiota of participants and their dietary and lifestyle habits in all population groups. Toxicological and basal gut microbiota effects must be included as relevant factors to evaluate food safety and nutritional consequences of non-calorie sweeteners. In humans, doses, duration of interventions, and number of subjects included in the studies are key factors to interpret the results.

**Keywords:** saccharin; sucralose; gut microbiota; acceptable daily intake; short-term studies; longterm studies; short-chain fatty acids


**2. Material and Methods**

#### *2.1. Study Site*

This study focuses on a 2.3 ha semi-natural meadow in the Osnabrück district, Lower Saxony, Germany (52.18°N, 8.10°E), as visible in Figure 1. According to the official soil survey map [37], the soil is predominantly gley, with part of the northern area being plaggic anthrosol. The climate is temperate oceanic, with an annual precipitation of 835 mm and a mean air temperature of 8.8 °C [38].

This survey covers the first growth (*G*1) of plants from the beginning of May 2021 until the first mowing in the middle of June 2021, and the second growth (*G*2) until the second mowing at the end of August 2021. The SNG can be assigned to the class *Molino-Arrhenatheretea* and the order *Arrhenatheretalia* [2]. Over the past 5 years, the study site was used twice a year for hay production according to the agri-environmental measure for low-intensity use of grasslands in Lower Saxony GL11 [6]. Before that, it had been used as a cattle pasture for about 30 years and a heterogeneous structure of plant communities had developed.

**Figure 1.** Location of the study site in Germany (**top left**) and the district of Osnabrück (**bottom left**). Orthomosaic and grassland vegetation of one plot of 06/08/2021 (**right**).


**4. Generalization**

12 Semantics – Advances in Theories and Mathematical Models

Fig. 3. Physical and Semantic Information interaction block-schema.

interrelated in a general information processing system.

system.

definition of semantic information makes it crystal clear – "Knowledge" is "Semantic information" brought from the outside and memorized into the information processing

And yet, the time is ripe to verify what does it means "semantic information processing"? My answer is depicted in Fig. 3 where I show how physical and semantic information are Approaching the end of the chapter, I would like to generalize the partial clarifications that were just given above. My research motivation was inspired by home security visual scene analysis and understanding goals. Therefore, my main concern was with visual information processing. However, I think it would be wise to broaden the scope of my findings.

I can faithfully state now that every information gathering starts with sensor data acquisition and accumulation. The body of data is not a random collection of data elements, but exhibits undeniable structures discernible in the data. These structures emerge as a result of data elements agglomeration shaped by similarity in their physical properties. Therefore, such structures could be called primary or physical data structures.

In the eyes of an external observer these primary data structures are normally grouped and tied together into more larger and complex aggregations, which could be called secondary data structures. These secondary structures reflect human observer's view on the arrangement of primary data structures, and therefore they could be called meaningful or semantic data structures. While formation of primary data structures is guided by objective (natural, physical) properties of data elements, ensuing formation of secondary structures is a subjective process guided by human habits and customs, mutual agreements and conventions.

Description of structures observable in a data set has to be called "Information". Following the given above explanation about the structures discernible in every data set, two types of information must be declared therefore – Physical Information and Semantic Information. They are both language-based descriptions; however, physical information can be described with a variety of languages, while semantic information can be described only with natural human language used.

Every information description is a top-down evolving coarse-to-fine hierarchy of descriptions representing various levels of description complexity (various levels of description details). Physical information hierarchy is located at the lowest level of the semantic hierarchy. The process of data interpretation is reified as a process of physical

Let Us First Agree on what the Term "Semantics"

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information extraction from the input data, followed by a process of input physical information association with physical information stored at the lowest level of a semantic hierarchy. In this way, input physical information becomes named with an appropriate linguistic label and framed into a suitable linguistic phrase (and further – in a story, a tale, a narrative), which provides the desired meaning for the input physical information.