**4. Development of pneumococcal vaccines**

620 The Complex World of Polysaccharides

evidence and speculation.

phosphate).

1. Non-housekeeping nucleotide sugar biosynthesis.

It should be noted that not all polysaccharides behave as TI-2 antigens. Zwitterionic polysaccharides such as *S. pneumoniea* type 1 polysaccharide: [3)--AATGal-(14)--D-Gal*p*A-(13)--D-Gal*p*A-(1]n with a right-handed helix with repeated zwitterionically

**Figure 2. Representation of the Wzx/Wzy-dependent pathway for biosynthesis of CPS 9A** (Adopted from Bentley. S.D. et al [21]). Representation of the Wzx/Wzy-Dependent Pathway Pictured is a hypothetical model for capsule biosynthesis in *S. pneumoniae* based on a mixture of experimental

2. The initial transferase (WchA in this case) links the initial sugar as a sugar phosphate (Glc-P) to a membrane-associated lipid carrier (widely assumed to be undecaprenyl

3. Glycosyl transferases sequentially link further sugars to generate repeat unit. 4. Wzx flippase transports the repeat unit across the cytoplasmic membrane. 5. Wzy polymerase links individual repeat units to form lipid-linked CPS.

charged grooves elicit potent T cell responses *in vivo* and *in vitro* [30, 31].

Although the first pneumococcal vaccines, including the application of the principle of conjugate vaccination, were already initiated in the beginning of the previous century, most of these developments stopped when antibiotics were introduced. Existing vaccines were even withdrawn from the market. By now, in many parts of the world, the antibiotic resistance of *S. pneumoniae* bacteria has increased: America [32, 33], Africa [34], Europe [35, 36], Asia [37-39], and Australia [40]. This makes treatment of pneumococcal infections more difficult and stresses the importance of the development of effective vaccines as a strategy to reduce morbidity and mortality caused by *S. pneumoniae* infection worldwide.

## **4.1. Pneumococcal polysaccharide-based vaccines.**

Currently two vaccine types against *S. pneumoniae* are commercially available: a pneumococcal polysaccharide vaccine (PPV) and a pneumococcal conjugate vaccine (PCV) [41]. The first multivalent pneumococcal polysaccharide vaccine (PPV) contains 23 purified capsular polysaccharides (25 µg of each capsule type; Pneumovax®, PPV23: 1, 2, 3, 4, 5, 6B, 7, 8, 9N, 9V, 10A, 11A, 12F, 14, 15B, 15F, 18C, 19A, 19F, 20, 22F, 23F, 33F ) which is licensed for use in adults and children older than 2 years of age [42]. This vaccine was shown to be moderately effective in young adults [43] but not in young children [44] and elderly [45] and also not in immunocompromised patients, e.g HIV infected people [46, 47].

In early 2000, a polysaccharide-protein conjugate vaccine targeting seven pneumococcal serotypes was licensed in the United States for use in young children (Prevnar, PCV7: 4, 6B, 9V, 14, 18C, 19F, 23F). The polysaccharides are conjugated to the non-toxic cross reactive material from diphtheria toxin, CRM197 and each dose contains 2µg of each capsule type, except for 6B, for which 4 µg is included in every vaccine dose[48]. The PCV7 vaccine produces a significant effect regarding prevention of invasive pneumococcal disease in children younger than 24 months (based on a meta-analysis of published data from trials on pneumococcal vaccine) [49]. Large scale introduction of PCV7 has resulted in an overall decline in infectious pneumococcal disease (IPD). However, IPD caused by the non-vaccine serotypes serotypes 1, 19A, 3, 6A, and 7F has increased (replacement disease), highlighting the need for inclusion of these serotypes in future improved vaccine formulations [50]. Apart from the CRM197 based PCV7, several new candidate pneumococcal conjugate vaccines have been developed to cover more serotypes with different protein carriers and most of them are in clinical trials, such as PCV10 vaccine (1, 4, 5, 6B, 7F, 9V, 14, 18C, 19F, 23F) [51, 52] and PCV13 vaccine (1, 3, 4, 5, 6A, 6B, 7F, 9V, 14, 18C, 19A, 19F, 23F) [53].

#### **4.2. Pneumococcal protein-based vaccines**

An alternative vaccine strategy focuses on the use of pneumococcal surface-associated proteins which are to be assumed to elicit protection in all age groups against all, or nearly all, pneumococcal serotypes (Fig. 1). Protection induced by the proteins should be serotypeindependent and possibly cheaper and thus within reach of developing countries [54]. Currently, several surface pneumococcal proteins are investigated as a candidate vaccine against *S. pneumoniae* infection with single or combination of recombinant proteins, such as pspA family fusion protein [55]; pneumolysis and pspA1/pspA2 combined [56]. Recently new candidate protein antigens were discussed at the 8th International Symposium on Pneumococci and Pneumococcal Diseases at Iguaçu Falls, Brazil (2012), phtD (pneumococcal histidin triad protein D) and PcpA (pneumococcal choline binding protein A) [57].

## **4.3. Pneumococcal synthetic oligosaccharide-based vaccines**

The current polysaccharide conjugate vaccines are based on natural polysaccharides, purified form bacterial cultures. Synthetic oligosaccharide–protein conjugates (neoglycoconjugate), involving functional mimics of the natural polysaccharide antigens have emerged as an attractive option [58]. The advantages of neoglycoconjugates are well-defined chemical structures (chain length, epitope conformation, and carbohydrate/protein ratio) as well as a lack of the impurities present in polysaccharides obtained from bacterial cultures [59, 60].

The chemical synthesis of oligosaccharide fragments however is complex. According to the sequence in the natural polysaccharide, monosaccharide residues have to be linked in such a way that they form an oligosaccharide with the required stereospecificity (epitope). Various methodologies and strategies for synthesis of carbohydrates have successfully been used for production of experimental neoglycoconjugates, as reviewed by Kamerling [16]. In 2001, the first automated synthesis of oligosaccharides was reported by Plante, O.J. et al [61].

Neoglycoconjugates have been prepared for saccharides of different microorganisms. In 2004, Verez Bencomo et al., reported the large-scale synthesis and the introduction of a synthetic oligosaccharide vaccine for *Haemophilus influenzae* type b for use in humans in Cuba [9]. The immunogenicity of the synthetic oligosaccharide fragment of the O-specific polysaccharide (O-PS) of *Vibrio cholera* O1, serotype Ogawa, conjugated to bovine serum albumin has been investigated in a mouse model [62, 63]. A multimeric bivalent synthetic hexasaccharide fragment of the O-specific polysaccharide of *Vibrio cholera* O1, serotype Ogawa, in combination with Inaba:1 or a synthetic disaccharide tetrapeptide peptidoglycan fragment as adjuvant were prepared and conjugated to recombinant tetanus toxin H(C) fragment as protein carrier [64]. The immunogenicity of synthetic oligosaccharides mimicking the Oantigen of the *Shigella flexneri* 2a lipopolysaccharide (LPS) was also investigated in mice [65, 66]. Immunization of mice with synthetic hexasaccharide of glycosylphosphatidylinositol malarial toxin conjugated to a protein carrier was reported to protect the mice from an otherwise lethal dose of malaria parasites [67]. A fully synthetic carbohydrate-based antitumor candidate vaccine for the common T-synthase was recently reported [68].

Meanwhile we and other groups have been working on improving the immunogenicity of neoglycoconjugates against different *S. pneumoniae* serotypes in animal models: Di-, tri-, and tetrasaccharides related to polysaccharide type 17F conjugated to keyhole limpet hemocyanin (KLH) protein[69, 70] and tri- and tetrasaccharides related to type 23 conjugated to KLH protein [71]; Di-, tri-, and tetrasaccharides related to type 6B conjugated to KLH protein [72]; Di-, tri-, and tetrasaccharide related to type 3 conjugated to the crossreactive material of diphteria toxin (CRM197) protein [60] and most recently overlapping oligosaccharide varying from tri- to dodecasaccharides related to polysaccharide type 14 conjugated to CRM197 protein [73, 74].


*2.3. Online Identification of Parameters for a Second-Order RC Circuit*

From Figure 1, the KVL (Kirchhoff's voltage law) equation of the circuit can be obtained:

$$\mathcal{U}\_{\rm OC} = \mathcal{U}\_{p1} + \mathcal{U}\_{p2} + \mathcal{R}\_0 I\_L + \mathcal{U}\_L \tag{5}$$

According to Equation (1), taking the Laplace transform on both sides:

$$\mathcal{U}\_{\rm CC}(s) = (\frac{R\_{p1}}{R\_{p1}\mathcal{C}\_{p1}s + 1} + \frac{R\_{p2}}{R\_{p2}\mathcal{C}\_{p2}s + 1} + R\_0)I(s) + \mathcal{U}\_L(s) \tag{6}$$

Let the time constants be *τp*1 = *Rp*1*Cp*1 and *τp*2 = *Rp*2*Cp*2, and let *a* = *τp*1*τp*2, *b* = *τp*1 + *τp*2, *c* = *Rp*1 + *Rp*2 + *R*0; then, Equation (6) can be simplified as:

$$a l L\_{\rm OC} \mathrm{s}^2 + b l L\_{\rm OC} \mathrm{s} + l L\_{\rm OC} = a R\_0 I\_\mathrm{s}^2 + d I\_\mathrm{s} + c I + a l I\_\mathrm{s}^2 + l I\_\mathrm{L} \tag{7}$$

Equation (8) can be obtained via discretion using Equation (7) as follows:

$$\mathcal{U}(k) = \frac{-bT - 2a}{T^2 + bT + a} \mathcal{U}(k - 1) + \frac{a}{T^2 + bT + a} \mathcal{U}(k - 2) + \frac{cT^2 + dT + aR\_0}{T^2 + bT + a} \mathcal{I}(k) + \frac{-dT - 2aR\_0}{T^2 + bT + a} \mathcal{I}(k - 1) + \frac{aR\_0}{T^2 + bT + a} \mathcal{I}(k)$$

After simplification, the expression (8) becomes:

$$\mathcal{L}I(k) = k\_1 \mathcal{U}(k-1) + k\_2 \mathcal{U}(k-2) + k\_3 I(k) + k\_4 I(k-1) + k\_5 I(k-2) \tag{9}$$

Substituting Equation (9) into FFRLS, the value of parameter *θ*(*k*)=(*k*1, *k*2, *k*3, *k*4, *k*5) can be calculated, and then the circuit model parameter *R*0, *Rp*1, *Rp*2, *Cp*1, *Cp*2 from the identification results can be deduced as follows:

$$\begin{aligned} \tau\_{p1} &= \frac{T}{2(k\_1 + k\_2 + 1)} [\sqrt{k\_1^2 - 4k\_2} - k\_1 - 2k\_2] \\ \tau\_{p2} &= -\frac{T}{2(k\_1 + k\_2 + 1)} [\sqrt{k\_1^2 - 4k\_2} + k\_1 + 2k\_2] \end{aligned} \tag{10}$$

Combining Equations (4), (8) and (10), Equation (11) can be obtained as follows:

$$\begin{array}{l} R\_0 = \frac{k\_5}{k\_2} \\ R\_{p1} = \frac{\tau\_{p1}\frac{k\_3 + k\_4 + k\_5}{k\_1 + k\_2 + 1} + \tau\_{p2}R\_0 + \frac{T(k\_4 + 2k\_5)}{k\_1 + k\_2 + 1}}{\tau\_{p1} - \tau\_{p2}} \\ R\_{p2} = \frac{k\_3 + k\_4 + k\_5}{k\_1 + k\_2 + 1} - R\_{p1} - R\_0 \\ C\_{p1} = \frac{\tau\_{p1}}{R\_{p1}} \\ C\_{p2} = \frac{\tau\_{p2}}{R\_{p2}} \end{array} \tag{11}$$

#### **3. SOC Estimation Method Based on AEKF**

*3.1. Adaptive Extended Kalman Filter State Equation*

The Kalman filter algorithm applies to linear systems. If it is used in a nonlinear system, the Taylor expansion formula needs to be used to locally approximate the nonlinear state equation as a linear equation. The method used for nonlinear systems is called the extended Kalman filter algorithm. Nonlinear systems can be represented by Formula (12):

$$\begin{cases} \mathbf{x}\_{k+1} = f(\mathbf{x}\_{k'} \boldsymbol{u}\_k) + \boldsymbol{w}\_k\\ \boldsymbol{y}\_k = g(\mathbf{x}\_{k'} \boldsymbol{u}\_k) + \boldsymbol{v}\_k \end{cases} \tag{12}$$

where *xk* is the state variable, *yk* is the observation variable, *f*(*xk*, *uk*) is the nonlinear state function, *g*(*xk*, *uk*) is the nonlinear observation function, and *wk* and *vk* are gaussian white noise with zero means and covariances of *Qk* and *Rk*, respectively. The EKF algorithm takes advantage of the local linearity property of nonlinear functions by locally linearizing both nonlinear functions. As described in the previous section, *x*ˆ*k*/*k* this is the optimal estimated value of the state variable at *k* time. By performing a first-order Taylor expansion of the nonlinear state function *f*(*xk*, *uk*) around *x*ˆ*k*/*k*, Equation (13) can be obtained as follows:

$$f(\mathbf{x}\_k, u\_k) = f(\mathbf{\hat{x}}\_{k/k}, u\_k) + \frac{\partial f}{\partial \mathbf{x}\_k}|\_{\mathbf{x}\_k = \mathbf{\hat{x}}\_{k/k}} (\mathbf{x}\_k - \mathbf{\hat{x}}\_{k/k}) + o(\mathbf{x}\_k - \mathbf{\hat{x}}\_{k/k}) \tag{13}$$

In Equation (13), ignoring the high-order term *o*(*xk* − *x*ˆ*k*/*k*) and letting *∂ f ∂xk xk*=*x*ˆ*k*/*k* = *Fk*, Equation (2) can be simplified as follows:

$$\mathbf{x}\_{k+1} = f(\mathbf{\hat{x}}\_{k/k\prime}\boldsymbol{u}\_k) + F\_k(\mathbf{x}\_k - \mathbf{\hat{x}}\_{k/k}) + w\_k \tag{14}$$

Expanding the nonlinear observation function *g*(*xk*, *uk*) around the prior state estimate *x*ˆ*k*/*k*−1 at *k* time by a first-order Taylor series, Equation (15) can be obtained as follows:

$$\log(\mathbf{x}\_{k\prime}\boldsymbol{u}\_{k}) = \mathbf{g}(\mathbf{\hat{x}}\_{k/k-1\prime}\boldsymbol{u}\_{k}) + \frac{\partial \mathbf{g}}{\partial \mathbf{x}\_{k}}\Big|\_{\mathbf{x}\_{k} = \mathbf{\hat{x}}\_{k/k-1}} (\mathbf{x}\_{k} - \mathbf{\hat{x}}\_{k/k-1}) + o(\mathbf{x}\_{k} - \mathbf{\hat{x}}\_{k/k-1}) \tag{15}$$

Ignoring higher-order terms in *o*(*xk* − *x*ˆ*k*/*k*−1) and setting *∂g ∂xk xk*=*x*ˆ*k*/*k*−1 = *Gk*, Equation (14) can be simplified as follows:

$$y\_k = \mathcal{g}(\mathfrak{x}\_{k/k-1}, \mathfrak{u}\_k) + \mathcal{G}\_k(\mathfrak{x}\_k - \mathfrak{x}\_{k/k-1}) + v\_k \tag{16}$$

If both the nonlinear state equation and the observation equation are linear, then Equation (11) can be rewritten as follows:

$$\begin{cases} \mathbf{x}\_{k+1} = F\_k \mathbf{x}\_k + f(\mathbf{f}\_{k/k'} \boldsymbol{\mu}\_k) - F\_k \mathbf{f}\_{k/k} + \boldsymbol{w}\_k \\\ y\_k = \mathbf{G}\_k \mathbf{x}\_k + \mathcal{g}(\mathbf{f}\_{k/k-1}, \boldsymbol{\mu}\_k) - \mathbf{G}\_k \mathbf{f}\_{k/k-1} + \boldsymbol{v}\_k \end{cases} \tag{17}$$

Here, matrices *Fk* and *Gk* can be obtained by calculating the Jacobian matrices of *f* and *g*. If the state variable *x* is *n*-dimensional, i.e., *x* = [*x*1, *x*2, ..., *xn*] *T*, then the solution for matrices *Fk* and *Gk* is as follows.

$$F\_k = \frac{\partial f}{\partial x} = \begin{bmatrix} \frac{\partial f\_1}{\partial x\_1} & \frac{\partial f\_1}{\partial x\_2} & \cdots & \frac{\partial f\_1}{\partial x\_n} \\ \frac{\partial f\_2}{\partial x\_1} & \frac{\partial f\_2}{\partial x\_2} & \cdots & \frac{\partial f\_2}{\partial x\_n} \\ \vdots & \vdots & \cdots & \vdots \\ \vdots & \vdots & \cdots & \vdots \\ \frac{\partial f\_n}{\partial x\_1} & \frac{\partial f\_n}{\partial x\_2} & \cdots & \frac{\partial f\_n}{\partial x\_n} \end{bmatrix} \tag{18}$$

$$G\_k = \frac{\partial g}{\partial x} = \begin{bmatrix} \frac{\partial g\_1}{\partial x\_1} & \frac{\partial g\_1}{\partial x\_2} & \cdots & \frac{\partial g\_1}{\partial x\_n} \\ \frac{\partial g\_2}{\partial x\_1} & \frac{\partial g\_2}{\partial x\_2} & \cdots & \frac{\partial g\_2}{\partial x\_n} \\ \vdots & \vdots & \cdots & \vdots \\ \vdots & \vdots & \cdots & \vdots \\ \frac{\partial g\_n}{\partial x\_1} & \frac{\partial g\_n}{\partial x\_2} & \cdots & \frac{\partial g\_n}{\partial x\_n} \end{bmatrix} \tag{19}$$

The EKF algorithm requires advanced calibration of the covariance matrices for observation noise and process noise, which is often calculated by experience, and the covariance matrices *Qk* and *Rk* are fixed values. However, in high-rate conditions of lithium iron phosphate batteries, the noise often changes due to the internal chemical reaction and resulting temperature variation and is no longer a fixed value. To improve the accuracy of SOC estimation, the AEKF algorithm is introduced. Based on the EKF algorithm, the AEKF adds the Sage–Husa adaptive filtering algorithm, enabling the observation noise covariance matrix and the process noise covariance matrix in the EKF algorithm to be adaptively updated, thus improving the accuracy of the SOC estimation. The steps of the AEKF algorithm are listed as follows:

Step 1. Initialization

Set *x*ˆ0 = *x*0, *y*0, *P*0, *Q*0, *R*0 when *k* = 0, where *x*ˆ0 is the initial estimate of the state variables, *y*0 is the initial observation value, *P*0 is the initial value of the error covariance matrix, and *Q*0 and *R*0 are the initial values of the process covariance matrix and observation noise covariance matrix, respectively.

Step 2. State prediction

$$\pounds\_{k/k-1} = f\left(\pounds\_{k-1/k-1\prime}\mu\_{k-1}\right) \tag{20}$$

Step 3. Prediction of error covariance

$$P\_{k/k-1} = F\_{k-1} P\_{k-1/k-1} F\_{k-1} \, ^T + Q\_{k-1} \tag{21}$$

Step 4. Calculate the Kalman gain

$$K\_k = P\_{k/k-1} G\_k^T \left[ G\_k P\_{k/k-1} G\_k^T + R\_{k-1} \right]^{-1} \tag{22}$$

Step 5. State estimation

$$\pounds\_{k/k} = \pounds\_{k/k-1} + K\_k[y\_k - \lg(\pounds\_{k/k-1} \mu\_k)] \tag{23}$$

Step 6. Update the noise covariance matrix

$$\mathfrak{e}\_k = \mathfrak{y}\_k - \mathfrak{g}(\mathfrak{x}\_{k/k-1}, \mathfrak{u}\_k) \tag{24}$$

$$\begin{cases} \; E\_k = E\left(\mathfrak{e}\_k \mathfrak{e}\_k^T\right) \\ \; d = \mathfrak{e}\_k^T E\_k^{-1} \mathfrak{e}\_k \end{cases} \tag{25}$$

In Equations (24) and (25), *ek* is called the innovation matrix, and *d* is the estimated residual value expressed using the adaptive window factor in the windowing function, which is used to calculate the observation dimension *M*. According to the principle of the windowing function, if *M* is small, the computational burden of the adaptive algorithm is reduced, but the accuracy of the algorithm is lower. Conversely, if *M* is large, the accuracy of the algorithm is significantly improved, but the computational burden is too high, which is not suitable for recursive estimation algorithms. Therefore, *M* needs to be adjusted according to the convergence time, as shown in Equation (26).

$$\begin{cases} \begin{array}{l} M=1, d=1\\ M=k, d=0 \end{array} \\ \begin{array}{l} M=k \times \eta^d, 0 < d < 1 \end{array} \end{cases} \tag{26}$$

In Equation (26), *η* is the window convergence rate, which has a range of 0 < *η* < 1. After calculating *M*, the noise covariance matrix can be updated as shown in Equations (27) and (28):

$$H\_k = \frac{1}{M} \sum\_{i=k-M+1}^{i=k} e\_k e\_k^T \tag{27}$$

$$\begin{cases} \begin{array}{l} \mathcal{R}\_{k} = H\_{k} - G\_{k} P\_{k/k - 1} \mathcal{G}\_{k}^{T} \\ \mathcal{Q}\_{k} = K\_{k} H\_{k} \mathcal{K}\_{k}^{T} \end{array} \end{cases} \tag{28}$$

Step 7. Estimation of error covariance.

$$P\_{k/k} = (I - K\_k G\_k) P\_{k/k - 1} \tag{29}$$

Subsequently, repeat step 2~step 7 for recursive estimation to obtain the optimal estimate of the state variables.

#### *3.2. Discretization of State Space Equation*

Taking the operating current *IL* as the input variable, battery SOC, battery electrochemical polarization voltage *Up*1, and concentration difference polarization voltage *Up*2 as state variables, and *UOC* as the observation variable, Equation (30) can be obtained by combining Equations (8) and (9):

$$
\begin{pmatrix}
\text{SOC}(t) \\
\frac{d\mathcal{U}\_{p1}(t)}{dt} \\
\frac{d\mathcal{U}\_{p2}(t)}{dt}
\end{pmatrix} = \begin{pmatrix}
1 & 0 & 0 \\
0 & -\frac{1}{\mathcal{R}\_{p1}(t)\mathcal{C}\_{p1}(t)} & 0 \\
0 & 0 & -\frac{1}{\mathcal{R}\_{p2}(t)\mathcal{C}\_{p2}(t)}
\end{pmatrix} \begin{pmatrix}
\text{SOC}\_{t0} \\
\mathcal{U}\_{p1}(t) \\
\mathcal{U}\_{p2}(t)
\end{pmatrix} + \begin{pmatrix}
\frac{1}{\mathcal{C}\_{p1}(t)} \\
\frac{1}{\mathcal{C}\_{p2}(t)}
\end{pmatrix} I(t) \tag{30}
$$

$$\mathcal{U}L\_L(t) = \mathcal{U}\_{\text{OC}}[\text{SOC}(t)] - I(t)\mathcal{R}\_0(t) - \mathcal{U}\_{p1}(t) - \mathcal{U}\_{p2}(t) \tag{31}$$

Equations (30) and (31) are the continuous state equation and continuous observation equation, respectively. Here, *UOC*[*SOC*(*t*)] is the open-circuit voltage at time *t* obtained using the OCV-SOC relation function.

Setting the Coulomb efficiency *η* to be 1 and defining the sampling period as *T*, Equations (30) and (31) are discretized as follows:

$$
\begin{pmatrix}
\text{SOC}(k+1) \\
\text{II}\_{p1}(k+1) \\
\text{II}\_{p2}(k+1)
\end{pmatrix} = \begin{pmatrix}
1 & 0 & 0 \\
0 & e^{\frac{-\overline{T}}{p\_1 \text{1}(k)}} & 0 \\
0 & 0 & e^{\frac{-\overline{T}}{p\_2 \text{1}(k)}}
\end{pmatrix} \begin{pmatrix}
\text{SOC}(k) \\
\text{II}\_{p1}(k) \\
\text{II}\_{p2}(k)
\end{pmatrix} + \begin{pmatrix}
R\_{p1}(k)(1 - e^{\frac{-\overline{T}}{p\_1 \text{1}(k)}}) \\
R\_{p2}(k)(1 - e^{\frac{-\overline{T}}{p\_2 \text{1}(k)}})
\end{pmatrix} I(k)
$$

$$\mathcal{U}\_L(k) = \mathcal{U}\_{\rm OC}[\rm SOC(k)] - \mathcal{U}\_{p1}(k) - \mathcal{U}\_{p2}(k) - I(k)\mathcal{R}\_0(k) \tag{33}$$

The vector composed of *SOC*(*k*), *Up*1(*k*), and *Up*2(*k*) in the above equation is the state vector *x*k of the system at *k* time in Equation (11), while *UL*(*k*) is the observation vector *y*k of the system at *k* time. Equation (32) is a linear equation, while the nonlinearity of the battery system state space equation is reflected in the *U*OC[*SOC*(*k*)] part of Equation (33). Therefore, the Kalman filtering algorithm for SOC estimation is carried out using both EKF and AEKF, and the results are compared with the coulomb counting method for SOC estimation.


**1. Introduction**

A significant increase of new heating and heating/cooling installations that is based on heat pumps and borehole heat exchangers (BHE) has been recently observed in many countries, including Switzerland [1], Germany [2], Sweden [3], Canada [4], and the United States [5]. Borehole thermal energy storage (BTES) permits the extraction of heat from the ground for heating in winter and the extraction of cool (i.e., the input of heat) for air conditioning in summer [6,7]. A BTES is a type of geoenergetic system, which also includes energy systems that are based on geothermal waters. Geothermal energy utilisation is usually more problematic when it is connected with geothermal water rather than the ground.

The energy efficiency of a BHE mostly depends on the thermal conductivity of the underground rock mass. Other construction parameters also influence the energy efficiency. There are various types of BHEs, with the most typical being:

BHEs can be vertically drilled. Alternatively, the BHE construction technology of Geothermal Radial Drilling (GRD) allows for directional (oblique) wells [13]. GRD provides the possibility of using the ground under buildings for thermal energy storage by the placing of boreholes. The effectiveness for various BHEs of different constructions has been compared. For instance, the effective thermal conductivity and performance in closed-loop vertical ground heat exchangers have been compared [14] and evaluated with TRTs [15], while the performances of the U-tube, concentric tube, and standing column well ground heat exchangers have been compared while using simulation [16]. Additionally, the thermal performances have been assessed for various types of underground heat exchangers [17] and for borehole heat exchangers specifically using TRTs [18]. The latter study included an analysis and comparison of interpretation methods.

BTES efficiencies in the literature consider such factors as borehole array geometry [19], heat transfer from the surroundings [20,21], grout parameters [22], freezing of underground water [23], and underground water flow. Studies that account for underground water flow include examinations of the influence of groundwater on: pile geothermal heat exchanger with cast-in spiral coils [24], closed-loop ground-source heat pump systems [25], the heat transfer in ground heat exchangers [26], the optimization of large-scale ground-coupled heat pump systems [27] and vertical closed-loop geothermal systems [28], the simulation of borehole heat exchangers [29], and the performance of geothermal heat exchangers [30]. Reference [31] describes many factors concerning coaxial BHEs.

The thermal response test (TRT) is an accurate and conventional method for the determination of the thermal properties of different cases of borehole heat exchangers [32]. A TRT is typically performed in large installations (over 100 kW) for an exploration BHE. According to [33], the TRT is an economic test for a lower capacity limit of about 30 kW. When the thermal parameters of the analyzed borehole heat exchanger are known, it is possible to establish the number of boreholes that are needed to satisfy heating and/or cooling demands.

Much work is being carried out to improve TRTs. For instance, a distributed TRT approach is described in [34]. The measuring process during a disturbed TRT (i.e., one with thermal sensors inside the BHE) affects the results. Sensors inside a BHE cause local turbulences and pressure losses, rendering the measuring unsettled/distorted. New methods for the interpretation of TRT results using statistics are described in [35,36]. The factors that are considered in a TRT are described in [37], for example, outdoor air temperature. Error analysis for a TRT is described in [38].

A TRT can be performed using various methods, e.g., the method that is described by Eskilson [39] and the computer code Earth Energy Designer (EED), which was developed following that method. Additionally, the extent of an underground thermal energy storage, i.e., the number and distribution of borehole heat exchangers, can be determined with numerical modeling, e.g., [40] or with commercial simulators [41].

Thermal response tests and mathematical modeling can also be used for determining the energy efficiency for thermal purposes of oil and gas wells, which have the potential for conversion to borehole heat exchangers [42]. This application has attracted increasing interest in recent years, especially for deep borehole heat exchangers, and it has correspondingly been subject to analysis by energy specialists in academe and industry.

BHE modeling also needs to account for thermal stresses. Doing so is essential for large installations that are connected with thermal waters and heat recovery from enhanced geothermal systems [43,44] and large BHE fields. The methods of thermal stress calculation that are used in the oil and gas industry can be applied for geothermal wells (and BHEs) [13].

In this article, we propose and verify a new method of establishing effective thermal conductivity of BHEs and assessing the usefulness of this method for utilization with thermal response tests. Existing methods provide the BHE thermal resistivity (*Rb*) as a function of the time of the TRT, whereas it should be constant in time. The method that is proposed here rectifies this shortcoming. That is, it is often possible to observe a change in *Rb* with time during a TRT [45]. Sometimes the change is an increase and at other times a decrease. Since the formula includes the effective thermal conductivity of the BHE (*λeff*), the value of *λeff* can be found, which yields a constant function *Rb* with time.

The novelty of this article lies in it presenting a new enhancement to a methodology. The methodology is described in the paper and verified while using the analyses of two TRTs. The main contribution and scientific significance is that it assists efforts to interpret TRTs better, so as to avoid tests providing erroneous or problematic results. Problematic TRTs occur quite often in practice.

The proposed new approach is based on the equation for the thermal resistivity of a BHE *Rb* in the function of the thermal conductivity of a rock mass *λ* or effective thermal conductivity *λeff*, as determined on the basis of a TRT. The new methodology is based on the assumption that *Rb* does not depend on the duration of the TRT, an assumption that corresponds to reality.

The basic assumption of the new approach can be expressed by the formula *Rb* = *f*(*t*) = constant. The equation *Rb* = *f*(*t*) can be developed into the dependence of *Rb* = *kt* + *b*. According to the model assumption *kt* + *b* = constant, the equation is spilled if and only if *k* = 0. The determination of thermal conductivity *λx* and BHE thermal resistivity *Rb* is reduced in this methodology to determining such a value of *λx*, to obtain *k* = 0. Subsequently, we get *Rb* = *b*.

#### **2. Thermal Response Test Mathematical Background**

According to the well-established Kelvin infinite line source theory thermal response test was developed [46]. The TRT methodology is based on the partial differential equation form of the Fourier thermal conductivity equation, which describes the dynamic dependence of temperature *T* on the distance from heat exchanger *r* and duration of the test *t*, i.e., determines *T* = *T*(*r*, *t*). The equation has the following form:

$$\frac{\partial^2 T}{\partial r^2} + \frac{1}{r} \frac{\partial T}{\partial r} = \frac{\rho c\_p}{\lambda} \frac{\partial T}{\partial t} \tag{1}$$

One method of solving such a partial differential equation involves substitution. This transforms the partial differential Equation (1) to an ordinary differential equation. Perina [47] has used this approach to describe the Theis equation in hydrogeology, which gives the pressure distribution *p* = *p*(*r*,*t*). To use this approach, we let

$$
\mu = \frac{r^2 \rho c\_p}{4t\lambda} \tag{2}
$$

and

$$
\rho c\_p = \frac{\lambda}{\alpha} \tag{3}
$$

Subsequently, we can show that

$$
\mu = \frac{r^2}{4\alpha t} \tag{4}
$$

and Equation (1) assumes the following form:

$$\frac{\partial^2 T}{\partial r^2} + \frac{1}{r} \frac{\partial T}{\partial r} = \frac{1}{a} \frac{\partial T}{\partial t} \tag{5}$$

Returning to the Substitution (4), we finally obtain

$$T(r,t) = T\_0 + \frac{q}{k\tau\lambda} \int\_{\frac{r^2}{4a\lambda}}^{\infty} \frac{e^{-x}}{x} dx \tag{6}$$

In view of the substitution in Equation (4), and by substituting the integral in (6) with an approximate expression, we obtain

$$T(r,t) = T\_0 + \frac{q}{4\pi\lambda} \left[ \ln\left(\frac{4\alpha t}{r^2}\right) - \gamma \right] \tag{7}$$

Regarding initial and boundary conditions, it is noted that the solution obtained is not numerical. Rather, it is analytical and the idea of the mathematical model of the TRT is based on an infinite linear heat source. Accordingly, we do not solve the differential equation in a finite region. The (linear) source has a length that corresponds to the borehole depth. The time of the TRT is limited (max. 100 h). The initial temperature corresponds to the natural temperature distribution, as seen in Figure 1, but it is normally approximated with one initial temperature: *To*.

#### **3. Two Thermal Response Tests**

The Thermal Response Test (TRT), which is sometimes called the Geothermal Response Test (GRT), is a suitable method in determining the effective thermal conductivity of the ground and the borehole thermal resistance (or the thermal conductivity of the borehole fill). A temperature curve is obtained, which can be evaluated by several methods. The resulting thermal conductivity is based on the total heat transport in the ground. Other effects, like convective heat transport (in permeable layers with groundwater), and further disturbances are automatically included, so it may be more correct to speak of an "effective" thermal conductivity *λeff*. The test equipment can be made in such a way that it can be easily transported to the site, e.g., on a light trailer (Figure 2) [48]. In short, a TRT relies on forcing the closed circulation of a heat carrier that is heated with a constant heating power. The temperature change with time is analyzed.

The TRT involves introducing and collecting energy from a borehole heat exchanger (Figure 2). During the tests, the heat carrier is most frequently heated at a constant heating power *P*, which is measured, and the temperatures *T*1 and *T*2 are then recorded. The heating power is maintained by switching the heaters on and off. The automation system takes into account the variability with the temperature of the heat carrier density *ρ* and the specific heat at constant pressure *cp*.

This section describes two actual thermal response tests. The Laboratory of Geoenergetics, Faculty of Drilling, Oil, and Gas, AGH University of Science and Technology in Krakow, Poland performed the tests and analyses [49,50]. The tests were carried out to illustrate and compare the results from the old and new methods of TRT interpretation.

For the sake of interpretation, it is noted that the TRT was performed for a borehole heat exchanger, the geological profile of which is presented in Table 1. The first test was performed in Zar ˙ ów (Dolno´sl ˛askie Region, Poland) in 2011. The average heating power during the test was *P* = 5920 W and the volumetric flow rate of the carrier was 16 dm3·min−1 (Figure 3a). The control of the volume value of the heat carrier volumetric flow rate was carried out with a rotary (windmill) flowmeter with an accuracy of 0.25 dm3 per one impulse. The flow measurements have the task of only visual control of the correctness of the TRT execution, where the relationship Q = const should be satisfied. Similarly, for the value of heating power P, which is calculated on the basis of temperature measurements from the dependence *P* = *Qρc*Δ*T*, the visual relationship (Figure 3a) should be observed during the entire test to ensure *P* = const.

The borehole heat exchanger is *H* = 120 m deep, so the heat exchange per unit of depth is *q* = 49.34 W·m−1. In the linear heat source model, the unit heating power is assumed to be uniform. A constant temperature is also assumed in the model for the heat carrier and the ground. Table 2 presents the design of the borehole heat exchanger.

A second TRT was performed in the BHE at the Laboratory of Geoenergetics in the university, where the lithological profile is as described in Table 3.

345

**Figure 2.** Schematic of thermal response test devices and operation. Legend: 1–thermometer, with absolute error 0.1 ◦C, 2–flowmeter, 3–pump, 4–control computer (stabilisation of thermal power and record the data), 5–set of heaters, 6–current source, 7–heater control signal, 8–borehole heat exchanger, *Q*–flow rate of heat carrier, *P*–heat flow rate (power), *T*1–temperature of heat carrier (outflow from borehole heat exchangers (BHE)), T1–temperature of heat carrier (inflow to BHE), *ρf* = f(*T*)–density of heat carrier as a function of temperature, and *cf* = f(*T*)–specific heat of heat carrier as a function of temperature.

**Figure 3.** (**a**) Variation during Thermal Response Test (TRT) of heating power and carrier volumetric flow rate with time; (**b**) results of thermal response test.

(**b**)

**1.** Lithological profile of borehole in ˙ Zarów, Poland.

**Table**

**Table2.**Designofboreholeheatexchangerin˙ ZarówandatLaboratoryofGeoenergetics,FacultyofDrilling,Oil,andGas,AGHUniversityofScienceandTechnology

**Table 2.** *Cont.*

*Energies* **2019**

, *12*, 1072

The values of *Do*, *dz*, *Hb*, *b,* and *H* (in Equation (8)) in Table 2 were provided by a company making a BHE without any data on measurement accuracy. The value of *λg* comes from the literature [51].

Figure 3b presents the curves illustrating the data during the TRT test, where the dependence on the duration of the test is observed. The average thermal conductivity that is assumed for rocks of 2.15 W·m−1·K−1 is based on data in the literature [51], as is the average volumetric specific heat of rocks in the profile of 2.17 MJ·m−3·K−1 [52,53]. A 35% propylene glycol solution is used as the heat carrier. At 20 ◦C, the specific heat of the carrier is 3810 J·kg−1·K−1 and its density is 1028 kg·m−3.

The average temperature of the rock mass is determined on the basis of the heat carrier circulation, without heating (i.e., before the TRT heating phase begins). The return flow temperature is 11.1 ◦C and the mean measured air temperature is 16.1 ◦C. The mean natural temperature in the borehole can also be determined on the basis of temperature logging [54]. The mean temperature of the rock mass, based on the temperature profile (Figure 1), is 11.00 ◦C. The NIMO-T (Non-wired Immersible Measuring Object for Temperature) was used for temperature profiling in BHE. The relative error of the temperature measurement was 0.0015 ◦C and the absolute error was 0.1 ◦C [54].

The thermal diffusivity *α* = 0.97·10−6 m2·s−1 is calculated using data from the literature [51] and Equation (3).

The mean temperature of the heat carrier flowing into the BHE during the test is 25.48 ◦C and the mean return flow temperature is 19.82 ◦C. Thus, the mean temperature difference is 5.67 ◦C and the mean temperature of the heat carrier is 22.65 ◦C.

Figure 4 shows the characteristic times that are used for the interpretation of TRT results. There, the points are denoted, as follows: *t*0 the beginning of heating phase of the test (heaters on), *t*1 the slope point of the curve (time of the first complete circulation loop of the heat carrier), *t*2 the time corresponding to t = 5 *r*2 *oα*−1, *t*3 the time corresponding to t =20 *r*2 *oα*−1, *t*4 the half-time of the heating phase of the test, and *t*5 the end of the heating phase of the test (heaters off).

The following values were obtained during the test: *t*0 = 0, *t*1 = 480 s (0.13 h), *t*2 = 46348 s (12.87 h), *t*3 = 185391 s (51.50 h), *t*4 = 180660 s (50.18 h), and *t*5 = 361320 s (100.37 h). Linear regression analysis is used for determining coefficients of line slope in the semi-logarithmic system (log *t*) for the following time intervals:

The values of the slope coefficient *k* and the effective thermal conductivity *λeff* are calculated on the basis of Equation (7), as follows:

$$
\lambda\_{eff} = \frac{P}{4\pi Hk} = \frac{q}{4\pi k} \tag{8}
$$

Table 4 lists the results, including the average values of the BHE thermal resistivity *Rb* and values for various data intervals. The value of *Rb* is calculated, as follows:

$$R\_b = \frac{1}{q} [T\_{d\overline{\nu}}(t) - T\_0] - \frac{1}{4\pi\varepsilon\lambda} \left[ \ln \frac{4\alpha t}{r\_o^2} - \gamma \right] \tag{9}$$

Here, values for *λ* can be taken from the literature (*λ* = 2.15 W·m−1·K−1 from Table 1) or calculated with Equation (7).

In Table 5, the TRT results are shown for a BHE belonging to the Laboratory of Geoenergetics, Faculty of Drilling, Oil, and Gas AGH–University of Science and Technology for a heat carrier volumetric flow rate of 12 dm3·min−1 and heating power *P* = 4000 W. The lithological profile of the borehole was described earlier (Table 5), as was the design of the borehole heat exchanger (Table 2).

The correlation coefficient for two TRTs was calculated. It concerned the temperatures dependence of the heat carrier and the duration of TRT. In both cases, the correlation coefficient had a higher value than 0.925.

**Figure 4.** Variation of temperature of heat carrier flowing out of BHE with (**a**) time and (**b**) logarithm of time, showing key characteristic times.

*Energies* **2019** , *12*, 1072

˙

#### *Energies* **2019** , *12*, 1072

#### **4. A New Way of Determining Parameters from TRTs**

Although the test (Figure 3b) was carried out almost ideally, there are discrepancies in the BHE thermal conductivity and thermal resistivity values. These discrepancies stem from the different ranges of data that are assumed for the analyses. Therefore, a new way of determining the parameters *λeff* and *Rb* is proposed.

Figure 5 presents a graph showing the dependence of BHE thermal resistivity on test duration. The curves for *Rb*1 and *Rb*4 are more 'linear' in Figure 5 than the curves for *Rb*2, *Rb*3, and *Rb*5. Both of the curves only differ in the value of *λ* from Equation (8).

The proposed approach involves determining a value of *λ* for which linear regression that is based on *Rb = f*(*t*) assumes the form of a function *Rb = kt + b* with a slope coefficient *k* of zero. The task of determining *λ* and *Rb* values reduces to finding a *λ* value, for which *k* = 0, after which we have *Rb = b*. For the TRT that was performed in Zar ˙ ów, we determined the following pairs of values meeting this requirement: *λeff =* 2.77 W·m−1·K−1 and *Rb* = 0.129 m·K·W−1. This contrasts with the values obtained with the traditional method of *λ* = 2.81 W·m−1·K−1 and *Rb* = 0.130 m·K·W−1.

For the TRT performed at the BHE of the Laboratory of Geoenergetics, we find *λ* = 1.98 W·m−1·K−1 and *Rb* = 0.134 m·K·W−1 (whereas with the traditional method *λ* = 1.97 W·m−1·K−1 and *Rb* = 0.128 m·K·W−1).

Figure 5 shows the relation of *Rb* with time from the TRT for the test in Zar ˙ ów. The curves of *Rb = f*(*t*) only vary due to the value of *λ* in Equation (9). The waveforms represent the following:

The values of *Rb* and *λeff* for various time intervals calculated based on the new methodology are listed in the Table 6 for the TRT at Zar ˙ ów and in Table 7 for the TRT at the Laboratory of Geoenergetics.

*Energies* **2019**, *12*, 1072

**Figure 5.** Thermal resistivity of BHE vs. TRT time, where *Rb*1, *Rb*2, *Rb*3, *Rb*4, and *Rb*5 are described in the text.


**2.1 Types of stimuli-responsive microgels**

### *2.1.1 Microgels responsive to temperature*

Several classes of polymers, including poly(N-isopropylacrylamide) and poly(ethylene glycol), demonstrate swelling/deswelling changes in response to temperature [19, 20]. With increase in temperature, these systems have reduced solvency and pronounced deswelling. Nolan et al. [21] demonstrated higher insulin release from poly (N-isopropyl acrylamide), with increasing temperature. Temperaturedependent aggregation property of such thermosensitive microgel systems may also be utilized in drug delivery, e.g., at elevated temperature; due to aggregation of PNIPAM microgels particle inside the cancerous cell, toxicity was observed [22].

## *2.1.2 Microgels responsive to particular compounds*

Microgels can be designed to be triggered by the concentration of particular compounds, like insulin [23, 24]. For example, insulin containing poly (diethyl aminoethyl methacrylate) microgels conjugated with glucose oxidase [25]. The enzymatic conversion of glucose to gluconic acid causes pH-responsive swelling of the polymer network leading to release of insulin. In another study Sui et al. [26] reported trifluoperazine triggered volume transition in calmodulin-based hydrogels.

### *2.1.3 Microgels responsive to external fields*

Microgel systems may also respond to external fields (ultrasound, light, and magnetic fields). Patnaik et al. [27] investigated photoresponsive drug release in azo-dextran nanogels based on (trans-cis) photoisomerization of an azobenzene present in the cross-linker. For this system, the release of drug was slower for transconfiguration while faster for cis-configuration.

Metal nanoparticles may be used for optical or magnetic heating. When temperature-responsive microgels are combined with metals, heat induced by the external fields may result in deswelling, leading to release of the absorbed drugs. Using this perspective, Wong et al. [28] explored Fe-containing PNIPAM microgels. The microgels showed ability to manifest local heating attributed to an oscillating magnetic field. With increasing temperature microgels deswelled. Similar kind of triggering was also manifested in other studies, where light-originated heating of absorbed metal nanoparticles was used to induce local heat, provoking permeability variations in temperature-responsive polymers [29–31].

#### *2.1.4 Microgels responsive to degradation*

Microgel degradation in response to stimuli offers another way of controlled drug delivery [32, 33]. Such systems are commonly based on biodegradable microgels, occasionally surrounded by a shell impermeable to the drug. In later case, microgel degradation causes increased osmotic pressure, finally breaking the

**87**

*pH-Responsive Microgels: Promising Carriers for Controlled Drug Delivery*

shell and drug release. Examples include dextran microgels coated by different polyelectrolyte multilayer systems [34] and lipid-coated microgels for the release of

Biodegradable acrylamide/bisacrylamide microgels containing acetal linkers were investigated by Murthy et al. [36]. Biodegradation stimulated by low pH, resulting from acid-catalyzed hydrolysis of acetal linkage, was responsible for drug release. Similarly, Bromberg et al. [37] investigated poly (acrylic acid)-containing microgels cross-linked with disulfide groups. The chemical reduction of the disul-

pH-responsive microgels represent one of the major approaches for microgel-based delivery of biomacromolecular drugs. Of the many stimuli, alteration in pH is markedly fascinating because of the availability of pH gradients admissible for drug targeting. For example, pH gradients between normal tissues and some pathological sites, between the extracellular environment and some cellular compartments, and along the gastrointestinal (GI) tract are well characterized [38]. Orally administered drug encounters a pH gradient as it move from the stomach (pH 1–2, fasted state) to the duodenum (pH of about 6) and along the jejunum and ileum (pH 6–7.5) [39, 40]; therefore, attempts to avoid deterioration of drug and/or to promote intestinal absorption by exploiting this pH gradient is promising. pH-responsive polymeric networks, hence, have been

pH-responsive polymers are generally fabricated by inserting pendant acidic or basic functional groups to the backbone of the polymer. These functional groups either accept or release protons in response to appropriate pH and changes in the ionic strength of the surrounding aqueous media [42]. Polymers with acidic groups are unexpanded at low pH values, since the acidic groups are protonated and unionized. While increasing pH acidic groups are ionized, the resulting negatively charged polymer expands. The opposite behavior will be observed in the case of polybasic polymers [43, 44]. These systems can form polyelectrolytes with water, and microgels fabricated from weak polyelectrolytes demonstrate a pHresponsive volume phase changes. On the basis of the framework of polyelectrolyte, pH-responsive microgels can be classified as cationic, basic, or amphoteric. For instance, poly(acrylic acid) and polyethylenimine are weak polyacid and a poly-

Methods used for the synthesis of microgels can be divided into two major ideas:

The first approach is based on the investigations of Staudinger [45], who prepared

inter- and intramolecularly cross-linked microgels by free radical cross-linking copolymerization of monomers in dilute solutions. However, the resulting internal structure of microgels was not well established, but investigations performed on these systems were key step to understand the process of gel formation [46].

(a) The synthesis of microgels in homogeneous phase

(b) The synthesis of microgels in heterophase

**3.1 Synthesis of microgels in homogeneous phase**

extensively studied for the design of efficient carriers for drug delivery [41].

*DOI: http://dx.doi.org/10.5772/intechopen.82972*

fide bonds manifested the swelling of these systems.

doxorubicin [35].

base, respectively.

**3. Synthesis of microgels**

*2.1.5 Microgels responsive to pH*

#### *pH-Responsive Microgels: Promising Carriers for Controlled Drug Delivery DOI: http://dx.doi.org/10.5772/intechopen.82972*

shell and drug release. Examples include dextran microgels coated by different polyelectrolyte multilayer systems [34] and lipid-coated microgels for the release of doxorubicin [35].

Biodegradable acrylamide/bisacrylamide microgels containing acetal linkers were investigated by Murthy et al. [36]. Biodegradation stimulated by low pH, resulting from acid-catalyzed hydrolysis of acetal linkage, was responsible for drug release. Similarly, Bromberg et al. [37] investigated poly (acrylic acid)-containing microgels cross-linked with disulfide groups. The chemical reduction of the disulfide bonds manifested the swelling of these systems.


**14. Discussion**

There are many technological applications being made focused on the area of medicine, but not all of them are structured under artificial intelligence and expert systems. This led to evaluate current healthcare technologies in terms of artificial intelligence and expert systems.

#### **Figure 5.**

*Development of artificial intelligence application by medical specialty.*

**Figure 6.** *Decision making by artificial intelligence specialty.*

Important findings were obtained with respect to the interventions that have been applied in the clinical part.

Among the most relevant findings, it was recognized that the areas of artificial intelligence where different medical applications were developed were automatic reasoning, computer vision, expert systems, and deep learning with neural networks.

The articles identified as highly concordant were in English, whose applications were implemented in developed countries. This demonstrates the great void of projects and studies on technologies in developing countries, where there is little economic investment in technology and research.

In addition, the epidemiological studies that apply to each of the areas of artificial intelligence were identified, represented by observational, descriptive, analytical, predictive, and experimental studies.

When comparing this article with another research conducted on artificial intelligence in communicable diseases, we can observe that the total number of articles found according to the degree of agreement was 70, compared to 146 in this study; 16 studies were highly concordant compared to 28 in this research. The trend by areas of artificial intelligence focused mainly on automatic reasoning, computer vision, expert systems, and neural networks; in this study, the same result was obtained according to the approach.

In both studies, it could be observed that the area of neural networks is the least developed, the most published and very concordant studies were in the English language in developed countries and in the Medline database, which demonstrates the economic, educational, and research investment that these countries have made in comparison with developing countries.

In addition, it reflects the transformation and technological advances that these countries have made in different areas. The areas of health where there was greater development were public health and epidemiology in infectious diseases, unlike this study, which was in the area of internal medicine.

On the other hand, it made it possible to identify which clinical issues have been evaluated and do not require further research, as well as which texts or topics do not require further research. It also allowed professionals to keep up to date on current trending topics.

We were able to assess the consistency of studies and explore the main sources of variability in studies with apparently beneficial results. Different predictive models of morbidity were identified.

The quality assessment of the body of evidence obtained was performed by two reviewers or peers independently, and the differences in the results of the evaluation of each aspect were discussed until a consensus was reached. The differences between the peers between each of the filters were minimal.


*3.3.1 Stack effect*

*Zero and Net Zero Energy*

*<IP_ADDRESS> Landscape*

**Figure 4.**

in summer day as seen in **Figure 5**.

*Library building at University of Nottingham, UK (author).*

design, facade design, and opening.

**Variables Notes**

**3.3 Removal**

Trees and landscape can improve the thermal environment and reduce the temperatures of interior spaces and surfaces in buildings. Monitoring tree effects on buildings showed that trees and landscapes do not only provide shade and reduce air temperature but also prevent buildings' materials from gaining and storing heat and radiating it back later as seen in **Table 4**. A study conducted in Jordan University of Science and Technology compared two identical glazed corridors of orientation and materials, but one of them with trees and landscape providing shade, and one without trees. The study showed that shaded building's surfaces with high trees recoded a lower temperature of 34°C than identical spaces with temperature of 41°C

Designing with avoidance systems requires analysis of the solar angles laterally with the dimensions and orientation of the building's site to identify the type of shading devices with integration and function and daylight strategies. Designer's decision of the building's form should consider self-shading building, orientation, elevation design, and relation to landscape to enhance avoidance actions. Performance evaluation of avoidance actions required integration of cooling decisions with all building elements, such as glazing area, orientation form and mass

Removing, as a passive cooling action, refers to the removal of undesirable gained heat in interior or exterior spaces in a building. Natural ventilation normally is the main strategy used to take unwanted heat out of the buildings. It depends mostly on pressure differences to circulate air between inner and outer spaces, allowing air to enter or escape from buildings. Devices like windows, openings,

Tree type Evergreen or deciduous trees affect the periods and area of shading on

Distance from building Location of shade on building, walls, walking area, and courtyards

Height and horizontal spread The pattern of shade provided by different types of trees

buildings, walls, courtyards, and outdoor spaces in summer and winter

**44**

**Table 4.**

*Landscape variables (author).*

Stack effect depends on temperature differences to circulate air, as hot air rises up and cool air sinks down (**Figure 6**). The design for ventilation that depends on stack principles is achieved by letting hot air rise up within spaces or specific devices and exhausting it from upper openings, which allows it to be replaced by cooler air from lower openings. The designer's role in the process is represented in designing air movement and its exhaustion and penetration, which includes the following methods:

**Figure 6.** *Stack effect (author).*


*2.4. Chromosomal Distribution of the UGlcAE Genes in Tomato*

To characterize the distribution of *UGlcAE* genes in the tomato genome, the physical locations of *UGlcAE* genes on the tomato chromosomes were obtained. According to the genomic sequences of *UGlcAE* genes, nine *SlUGlcAE* genes were mapped to eight chromosomes, including chromosome 1, 3, 5, 7, 8, 9, 10, and 12 without regularities of tandem duplication, whose positions were indicated *2.4. Chromosomal Distribution of the UGlcAE Genes in Tomato*

by the black lines in the tomato chromosomes (Figure 3). Two *UGlcAE* genes (*SlUGlcAE3-like1* and *SlUGlcAE6-like*) were located on chromosome 5, and the other seven genes (*SlUGlcAE1*, *SlUGlcAE1-like*, *SlUGlcAE2-like*, *SlUGlcAE3*, *SlUGlcAE3-like2*, *SlUGlcAE5*, and *SlUGlcAE6*) were assigned to different chromosomes, but no gene was mapped to chromosome 2, 4, 6, and 11. There were no tandem duplication events among *UGlcAE* family members of tomato, suggesting that the functional differentiation may exist among the *SlUGlcAE* family members. Almost all of the *UGlcAE* genes in tomato are located near the ends of the chromosome. the black lines in the tomato chromosomes (Figure 3). Two *UGlcAE* genes (*SlUGlcAE3-like1* and *SlUGlcAE6-like*) were located on chromosome 5, and the other seven genes (*SlUGlcAE1*, *SlUGlcAE1-like*, *SlUGlcAE2-like*, *SlUGlcAE3*, *SlUGlcAE3-like2*, *SlUGlcAE5*, and *SlUGlcAE6*) were assigned to different chromosomes, but no gene was mapped to chromosome 2, 4, 6, and 11. There were no tandem duplication events among *UGlcAE* family members of tomato, suggesting that the functional differentiation may exist among the *SlUGlcAE* family members. Almost all of the *UGlcAE* genes in tomato are located near the ends of the chromosome.

3, 5, 7, 8, 9, 10, and 12 without regularities of tandem duplication, whose positions were indicated by

*Int. J. Mol. Sci.* **2018**, *19*, x FOR PEER REVIEW 6 of 21

To characterize the distribution of *UGlcAE* genes in the tomato genome, the physical locations

**Figure 3.** Chromosomal localization of the UGlcAE family genes in tomato. The numbers above each chromosome indicates the chromosome number. **Figure 3.** Chromosomal localization of the UGlcAE family genes in tomato. The numbers above each chromosome indicates the chromosome number.

#### *2.5. Sequence Alignments and Hydrophilicity Analysis of SlUGlcAE Family 2.5. Sequence Alignments and Hydrophilicity Analysis of SlUGlcAE Family*

UGlcAE is one of the short-chain dehydrogenase/reductase (SDR) enzyme families, and therefore the amino acid sequences of SlUGlcAE and AtUGlcAE contained two conserved motifs that existed in SDR protein families [30,31]. As shown in Figure 4, the two motifs contain an N-terminal GxxGxxG (x represents any amino acid) sequence to bind to NAD (P)+, and a motif (YxxxK), which play a catalytic role [32]. UGlcAE is one of the short-chain dehydrogenase/reductase (SDR) enzyme families, and therefore the amino acid sequences of SlUGlcAE and AtUGlcAE contained two conserved motifs that existed in SDR protein families [30,31]. As shown in Figure 4, the two motifs contain an N-terminal GxxGxxG (x represents any amino acid) sequence to bind to NAD (P)<sup>+</sup> , and a motif (YxxxK), which play a catalytic role [32].

As indicated in Supplementary Materials Figure S1, all of the SlUGlcAE proteins were trans-membrane proteins. The result was consistent with previous reports [13,14,24]. Among the nine proteins, most of SlUGlcAEs had only one trans-membrane helice with more than 80% probability, except that SlUGlcAE1 and SlUGlcAE5 were more likely to contain two trans-membrane helices. As indicated in Supplementary Materials Figure S1, all of the SlUGlcAE proteins were trans-membrane proteins. The result was consistent with previous reports [13,14,24]. Among the nine proteins, most of SlUGlcAEs had only one trans-membrane helice with more than 80% probability, except that SlUGlcAE1 and SlUGlcAE5 were more likely to contain two trans-membrane helices.


**7. Summary and perspective**

132 Hydrodynamics – Advanced Topics

inhibited the HGF-mediated migration of MC-38 cells in a culture model of colon cancer invasion. This anti-invasive effect of NK4, obtained by *in vitro* studies, is demonstrated *in*

An hepatic metastatic model was prepared by the injection of mouse MC-38 cells into the spleen. During the progression of colon cancer in hepatic tissues, HGF was over-produced by hepatic sinusoidal cells, while MET tyrosine phosphorylation became evident, particularly around the front lines of invasive zones. Supplementation of NK4 in blood and livers via a single injection of NK4-cDNA containing plasmid (pCAGGS-NK4) resulted in the loss of MET tyrosine phosphorylation (**Fig. 6**). Under such a MET-inactivated condition by NK4 treatment, hepatic invasion by colon carcinoma was strongly inhibited (Wen *et al*.,

Fig. 6. Successful outcome of hydrodynamics-based NK4 gene therapy in a mouse model of colon cancer. The hepatic invasion model is prepared by intra-splenic inoculation of MC-38 colon carcinoma in mice. In the control group, invasion of carcinoma cells into neighboring hepatic areas becomes evident, along with an induction of MET tyrosine phosphorylation (p-MET) and an increase in vessel numbers. In contrast, NK4 suppresses tumor invasion by inhibiting MET tyrosine phosphorylation and reducing angiogenesis. As a result, NK4 gene

Repeated administrations of NK4-containing plasmid DNA also inhibited the malignant behaviors of colon carcinoma (Wen *et al*., 2007). Actually, NK4 repetitive gene therapy potently inhibited the muscular invasion of MC-38 carcinoma cells. Furthermore, angiogenesis in the colon cancer was markedly suppressed by NK4 repetitive therapy, along with an increase in tumor apoptosis. Overall, the number of hepatic metastatic nodules was

therapy prolongs the survival of these mice (Wen *et al*., 2004).

*vivo* in the following two studies.

2004).

NK4-related studies provided a proof-of-concept that MET signaling from stroma-derived HGF plays a pivotal role in eliciting tumor invasion and metastasis (Matsumoto & Nakamura., 2005; Nakamura *et al*., 2010). Human genetic studies also strengthened the important role of MET activation for tumor malignancy. There is now ample evidence to demonstrate the role of MET mutations in tumor malignancy (Lengyel *et al*., 2007; Matsumoto *et al*., 2008a,b; Pao *et al*., 2011). Of interest, mutation of the von-Hippel-Lindau (VHL) gene leads to renal clear cell carcinoma through constitutive MET tyrosine phosphorylation (Nakaigawa *et al*., 2006), hence suggesting a critical role of wild-type VHL in inhibiting MET over-activation as a negative regulator.

During the progression of malignant tumors, soluble MET is producible by carcinoma cells through an ectodomain shedding cascade (Wader *et al*., 2011). Soluble MET inhibits the HGF-MET complex and signaling transduction. Thus, MET shedding system is considered as a self-defense response that minimizes tumor metastasis. Likewise, an NK4-like fragment of the HGF α-chain can be secreted from human breast carcinoma, which inhibits MET tyrosine phosphorylation (Wright *et al*., 2009). Thus, "endogenous" soluble MET and NK4 like variant appear to reduce HGF-MET signaling and delay tumor progression, but this response is insufficient, allowing for tumor metastasis. Thus, supplemental therapy with NK4 is a reasonable strategy to completely block tumor metastasis.

The hope is that angiogenesis inhibition might control tumor metastasis (Yancopoulos *et al*., 1998). However, long-term use of angiogenesis inhibitors, such as VEGF inhibitor, results in hypoxia-resistance (Fischer *et al*., 2007), possibly due to hypoxia-induced MET up-regulation by cancer (Bottaro & Liotta, 2003). NK4 is an angiogenesis inhibitor with the ability to inhibit MET activation, and discovery of this fragment opened up a new avenue for the development of freeze-and-dormancy therapy (**Fig. 7**). Thus, NK4 is now defined as "Malignostatin". In addition to NK4, several anti-metastatic drugs have been proposed, with a major focus on small molecules that inhibit the tyrosine kinase activity of MET; ribozyme; small-interfering RNA; anti-HGF antibodies; soluble MET; and HGF-variant decoys (Jiang *et al*., 2005; Benvenuti & Comoglio, 2007; Eder *et al*., 2009; Underiner *et al*., 2010; Cecchi *et al*., 2010). HGF-MET targeting research will shed more light on cancer biology, pathology and new technologies to overcome host death due to cancer metastasis.

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**About the Editors**

**Chiara Colombero** has served as Researcher at the Department of Environment, Land and Infrastructure Engineering of Politecnico di Torino since March 2018. She graduated in Geology Applied to Engineering and Environment at the Earth Science Department of Universita degli Studi ` di Torino in 2012. From this same department, she received her PhD in Earth Sciences in March 2017, with her thesis in Applied Geophysics on microseismic and ambient noise monitoring of potentially unstable rock masses. She won the AGLC 2016 prize for a paper related to her PhD thesis. She currently works in the Applied Geophysics research group and is lecturer/teaching assistant for the courses Innovation Lab for Climate Change, Applied Geophysics, Exploration Geophysics, and Geophysics. Her research interests encompass seismic and non-seismic methods applied to a wide variety of fields, from rock mass stability to glacier characterization and monitoring, from surface-wave analyses for the detection of subsurface anomalies to geological and hydrogeological investigations in water-covered environments, and from engineering geology purposes to archaeological research and cultural heritage preservation and restoration. She is author of more than 40 publications in international journals and national/international conference proceedings and serves as guest editor and reviewer.

**Cesare Comina** is Associate Professor in Applied Geophysics at Universita degli Studi di Torino. ` He has also served as Research Associate at Politecnico di Torino where he previously graduated in Civil Engineering and was awarded his PhD in Geotechnical Engineering. In the past years, he has carried out his research at the Department of Structural and Geotechnical Engineering of Politecnico di Torino, where he collaborated on numerous funded research projects and participated in the scientific activities of the Geotechnical section. He is currently affiliated with the Department of Earth Sciences of Universita degli Studi di Torino and collaborates with the Applied Geology ` group. He has teaching experience in both Geophysics and Geotechnics. His research involves the use of different geophysical methods for geotechnical and geological characterization. He has been particularly involved in developing innovative testing apparatuses for geophysical monitoring of laboratory tests and has addressed the use of surface wave tests for dynamic site characterization. He is currently involved in research related to geothermal energy usage and exploration and has also worked on waterborne geophysical surveys for the characterization of riverbed or lake sediments. On these topics, he is author and co-author of more than 70 scientific and technical papers both in international journals and conferences.

**Alberto Godio** holds a degree in Mining Engineering (1988) and a PhD in Georesource Engineering from Politecnico di Torino (1993). He is currently Professor at Politecnico di Torino in Geophysics, Applied Geophysics and Design for Environmental Engineering. His main research interests are focused on the application of electrical and electromagnetic methods for prospection and monitoring in environmental, engineering, hydrogeology and geology, and glaciology. The main topics deal with numerical methods applied to the processing of geophysical data. He has been responsible for research projects within the framework of EU-funded programs; he was responsible for Life Project, a bilateral Italy–India project (2018–2020), and several national programs funded by the Italian government (PRIN, FIRB) and regional agencies. He has been responsible for industrial projects with partners such as Italcementi, Teksid, and San Pellegrino. As the Delegate of the Rector for student mobility (2015–2018), he has promoted international cooperation among European universities in the higher educational framework; nowadays, he is the Rector's Advisor for student mobility and Erasmus+ program. He is author and co-author of 70 papers in peer-reviewed journals and more than 120 contributions at national and international conferences.


**Preface to "Acoustic Properties of Absorbing Materials"**

Thanks to the progress made in materials research and to the introduction of innovative manufacturing technologies, a wide range of sound-absorbing elements are currently available to adjust the acoustic features of an environment. Nowadays, performance is only one of the required specifications, together with environmental compatibility, longevity, and affordable cost. This book collects the most recent advances in the broad-spectrum characterization of sound-absorbing materials used in civil, industrial, and tertiary applications, by means of experimental, numerical, or theoretical studies.

**Edoardo Alessio Piana, Paolo Bonfiglio, and Monika Rychtarikova**

*Editors*

### *Editorial* **Acoustic Properties of Absorbing Material**

**Edoardo Alessio Piana 1,\*, Paolo Bonfiglio 2 and Monika Rychtarikova 3**

	-<EMAIL_ADDRESS>**\*** Correspondence<EMAIL_ADDRESS>
#### **Overview of the Articles in This Special Issue**

Thanks to the progress made in materials research and to the introduction of innovative manufacturing technologies, a wide range of sound-absorbing elements are currently available to adjust the acoustic features of an environment. Nowadays, performance is only one of the required specifications, together with environmental compatibility, longevity, and affordable cost. The Special Issue, "Acoustic Properties of Sound-Absorbing Materials", collected the most recent advances in the broad-spectrum characterization of sound-absorbing materials used in civil, industrial, and tertiary applications, by means of experimental, numerical, or theoretical studies. Among many submissions, 11 articles were accepted and published.

The first published paper is an investigation by Dengke Li et al. [1] about the improvement of the sound absorption characteristics of a porous material coupled with a spherical structure. The aim was to improve the sound absorption of a specimen at relatively low frequencies, without increasing its thickness and keeping a good mid- to high-frequency sound absorption. Such behavior was obtained introducing a hollow perforated spherical structure, featuring extended tubes in a foam. The overall thickness of the specimen was less than 1/28 of the wavelength. Good agreements were observed between the simulated and the experimental results. The second paper, by Yaw-Shyan Tsay et al. [2], concerns the development of a resonant membrane panel absorber. The study focused on the improvement in acoustic quality parameters for auditoria, and showed how it is possible to manufacture a prototype, which proved to be particularly effective below 800 Hz. The tests were carried out during an experimental campaign, carried out in the Ge-Chi Hall of the National Cheng Kung University. The third paper, authored by Hasina Begum and Kirill Horoshenkov [3], studied the acoustical properties of fiberglass blankets impregnated with silica aerogel. The use of aerogel with fibrous blankets allows one to improve both acoustic absorption and thermal insulation performances. Since the mechanism influencing the acoustic performance of aerogel-impregnated blankets is still unclear and there is a lack of studies attempting to explain the measured absorption properties with a valid mathematical model, this paper contributed to this knowledge gap through a simulation that predicts the measured complex acoustic reflection coefficient of aerogel blankets with different filling ratios. The fourth contribution to the Special Issue, by Shtrepi et al. [4], is an experimental characterization of the sound absorption performances of "normal weight" and "lightweight" porous concrete aggregates. For each concrete type, three panel thicknesses were tested. Moreover, different mounting conditions were investigated, considering the combination of single panels in multiple layers, adding an air gap between the panel and the backing structure, and inserting a layer of rock wool in the air gap. The results show weighted absorption coefficients in the range of 0.30 to 0.75, depending on the thickness and mounting conditions. In the context of recycling materials that reached their end of life, but can still be used for other applications, Neri et al. [5] studied the sound absorption characteristics of low-cost insulating elements made of non-conventional materials. Given

**Citation:** Piana, E.A.; Bonfiglio, P.; Rychtarikova, M. Acoustic Properties of Absorbing Materials. *Appl. Sci.* **2022**, *12*, 4446. https://doi.org/ 10.3390/app12094446

Received: 11 March 2022 Accepted: 26 April 2022 Published: 28 April 2022

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

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

that household materials at their end of life (EoLHM) are free of costs and available also to the more disadvantaged population, they can be used to build acoustic panels for such contexts. The acoustic properties of EoLHM, such as cardboard, egg cartons, clothes, metal elements and combinations of them, are investigated by means of a four-microphones impedance tube. The measured sound absorption coefficient and transmission loss showed that EoLHM can be used for manufacturing acoustic panels. However, since none of the analyzed materials show good absorbing and insulating properties at the same time, EoLHM must be wisely selected. The sixth article is authored by Zihao Li et al. [6] and proposes the application of a technique based on semi-active structure of a shunted loudspeaker and a fully exhaustive backtracking algorithm, in order to obtain an optimized sound absorption in a specific frequency range. In the seventh article, Levi et al. [7] investigated the acoustic and non-acoustic properties of steelwork by-products. The inverse method adopted in the paper is founded on the Johnson–Champoux–Allard (JCA) model and uses a standard minimization procedure, based on the difference between the experimentally obtained sound absorption coefficients and the absorption coefficients predicted by the JCA model. The eighth paper, by Bettarello et al. [8], investigates the sound insulation properties of timber floors. Such structures must be properly designed in order to meet the requirements of indoor comfort and comply with current building regulations. This work presents the results obtained by in-field measurements developed using different sound sources on Cross-Laminated Timber floors (tapping machine, impact rubber ball, and airborne dodecahedral speaker), changing different sound insulation layering conditions (suspended ceiling and floating floors). The results clearly show that there is no available analytical model able to correctly predict the acoustic performances of Cross-Laminated Timber floors. In the ninth paper, Xin Li et al. [9] studied the low-frequency sound absorption of a Perforated Plate with Stepwise Apertures. Such panels can match the acoustic resistance of air and moderately increase the acoustic mass, especially at low frequencies. Some prototypes made by 3D printing technology were tested in an impedance tube. The measured results agree well with the predictions. The tenth paper, by Urban et al. [10], focuses on the determination of the acoustic properties (sound absorption and transmission coefficients) of membrane types of specimens. The characterization was made by means of a combination of incident plane wave sound pressure and membrane surface displacement information, measuring the sound pressure with a microphone and the membrane displacement by means of a laser Doppler vibrometer. The proposed method was compared with the conventional methods for sound transmission loss and absorption measurements in an impedance tube, both numerically and experimentally. Subsequently, the proposed method was tested in a laboratory environment. The last paper in the Special Issue is authored by Lamberto Tronchin et al. [11], and compares different methodologies that can be applied for the evaluation of the sound diffusion inside a room. This article considers the surface-scattering effects and the diffusion phenomena related to some types of MDF and plywood panels, tested by disposing the wells horizontally and vertically. The test results, undertaken inside a semi-reverberant room and inside a large reverberant room, were compared to highlight the success and the failure of the different measuring methodologies adopted.

**Author Contributions:** Conceptualization, E.A.P., P.B. and M.R.; writing—original draft preparation, E.A.P., P.B. and M.R.; writing—review and editing, E.A.P., P.B. and M.R. All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** We gratefully thank all authors, who made this Special Issue a success.

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

#### **References**


*Supporting open minds since 2005*

Advances in Candida Albicans http://dx.doi.org/10.5772/intechopen.87410 Edited by Xinhui Wang

Part of IntechOpen Book Series: Infectious Diseases, Volume 9 Book Series Editor: Shailendra K. Saxena

#### Contributors

Humam Kasem Kasem Hussein, Estela Ruiz Baca, Ana Lilia Martinez-Rocha, Rosa Isela Arredondo Sánchez, Karina Corral-Pérez, Angélica López-Rodríguez, Iván Meneses-Morales, Víctor Manuel Ayala-García, Snigdha Pattnaik, Laxmidhar Maharana, Manoj Kumar Sethi, Sandra Widaty, Eliza Miranda, Caroline Oktarina, Ahmad Ibrahim, Rashi Verma, Dibyabhaba Pradhan, Harpreet Singh, Arun Kumar Jain, Luqman Ahmad Khan

#### © The Editor(s) and the Author(s) 2021

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First published in London, United Kingdom, 2021 by IntechOpen IntechOpen is the global imprint of INTECHOPEN LIMITED, registered in England and Wales, registration number: 11086078, 5 Princes Gate Court, London, SW7 2QJ, United Kingdom Printed in Croatia

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**1. Introduction**

For a long time, the phenomenon that agricultural foods (agri-foods) with good quality do not sell at a good price is common, and the main reason for this is the credence property of agricultural products [1]. Farmers need to invest more in quality to get high-quality agri-foods compared with ordinary produce. However, the quality of the produce cannot be easily identified by consumers. Due to asymmetric information, consumers are not willing to pay high prices for high-quality produces [2]. As a result, agricultural producers often lack the motivation to improve the quality of agri-foods, leading to the prevalence of homogeneous "big-ticket" products in the field of agricultural supply [3].

With the development of economy and society, consumers' income level and consciousness of food safety are rising, and the consumption demand for agricultural produce is gradually changing from concern for supply quantity to supply quality and diversity [4]. Further, the consumers' demand for premium quality agri-foods is increasingly strong, which has driven more and more participants in the agricultural supply chain to increase investment for the improvement of agri-foods quality. Some measures such as establishing

**Citation:** Xu, J.; Cai, J.; Yao, G.; Dai, P. Strategy Optimization of Quality Improvement and Price Subsidy of Agri-Foods Supply Chain. *Foods* **2022**, *11*, 1761. https://doi.org/10.3390/ foods11121761

 Academic Editor: Maria Lisa Clodoveo

Received: 16 May 2022 Accepted: 10 June 2022 Published: 15 June 2022

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

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

production standard systems and adopting clean production technologies are used. After taking the quality improvement actions, agricultural operators apply for pollution-free, green, or organic certification as a way to increase the premium price of their produce and build agricultural brands. This information is ultimately passed on to the consumer in the form of labels on the produce [5–7]. Nowadays, in the field of practice, this quality improvement measure has been launched either by agricultural producers on their own initiative or by sellers who induce agricultural producers to improve their quality inputs in the form of high-price subscriptions [8]. However, there is still no relevant theory to guide the practice of which model is better. Therefore, which way is more beneficial to achieve quality and price of agri-foods? How do the participants in the agricultural supply chain make quality improvement and price subsidy decisions? Additionally, can appropriate contracts be designed to encourage producers and sellers to increase their investment in agricultural quality improvement actions? With these questions, we conducted this study.


**3. Effects of acrylamide and its by-products**

#### **3.1 Reproductive toxicity**

ACR can play a direct role in the toxicological effects of sperm morphology, motility, and production, as well as being an indirect cause of reproductive issues as shown by various studies done on ACR exposed male mice [16, 19]. Repeated injections of ACR (20 mg/kg) into male rats for 20 days resulted in dose-dependent reductions in testosterone and prolactin levels [21]. In another study it was observed, after a onemonth experiment, ACR exposure at levels of 1.25–24 mg/kg/day in their drinking water lowered fertility rates and litter sizes in mice, while increasing morphological anomalies of sperm and embryo resorption rates. In animal toxicity experiments

using ACR, decreased reproductive behaviour, testicular atrophy, aberrant spermatogenesis, and poor sperm quality are some of the symptoms [13]. Peripheral neuropathies caused by ACR, such as decreased hind-limb function, may impede copulatory behaviour, mounting responses, and intromission, eventually impact the sperm deposition in the vagina and uterus, as well as cause hormonal alterations. In terms of hormonal mechanisms of action, ACR decreased serum testosterone and prolactin levels, which could contribute to testicular shrinkage and sperm motility [22].