*Article* **Mechanical Properties and Microstructure of Polyvinyl Alcohol (PVA) Modified Cement Mortar**

#### **Jie Fan 1, Gengying Li 2,\*, Sijie Deng 1 and Zhongkun Wang 2**

Received: 1 May 2019; Accepted: 25 May 2019; Published: 28 May 2019

**Abstract:** The mechanical properties of cement mortars with 0~2.0% (by mass) polyvinyl alcohol (PVA) were experimentally studied, and the effects of PVA incorporation on the hydration products and microstructure of the cement mortar were determined with differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR) and scanning electron microscopy (SEM). The results show that the rational content of PVA formed evenly dispersed network-like thin films within the cement matrix, and these network-like films can bridge cracks in the cement matrix and improve the mechanical properties of the cement mortar. Over- incorporation of PVA may result in the formation of large piece polymer films that coat the cement particles, delay the hydration of the cement mortar and adversely affect its performance. The mechanical properties of the cement mortar show a significant increase and then decrease with a change in the PVA incorporation. When the PVA content was 0.6% and 1.0%, the mortar had the best compressive and flexural strengths, respectively. The compressive strength of the cement mortar increased by 12.15% for a PVA content of 0.6%, and the flexural strength of the cement mortar increased by 24.83% for a PVA content of 1.0%.

**Keywords:** polyvinyl alcohol; cement mortar; mechanical properties; microstructure

#### **1.  Brittleness makes the cement material easy to crack under the action of external force, which adversely affects the bearing capacity and durability of cement concrete structures. Polymers have good elastic deformation properties, flexibility, acid and alkali corrosion resistance, and good compatibility with cement materials, which can effectively improve the mechanical properties, deformation properties and durability of cement-based materials [4–9]. Therefore, polymers are widely used in repairing roads, bridges, reservoirs and dams and in the bonding engineering of facing materials [10–12].

Polyvinyl alcohol (PVA) is a typical water-soluble nonionic synthetic polymer containing vinyl [13,14]. It is harmless and, therefore, considered safe and relatively environmentally friendly [15]. Commercial- grade PVA is usually divided into two categories based on its degree of polymerization and hydrolysis: (1) fully hydrolyzed groups (98% by mole of acetate or more) where the PVA group has been replaced by an alcohol group, and (2) hydrolyzed groups (approximately 87–89% by mole of acetate group) where the PVA group has been replaced by alcohol groups. Completely hydrolyzed PVA is soluble in hot water and has good film-forming properties (the formed film is insoluble in low-temperature water) and good adhesion properties [15,16]. The polyvinyl alcohol after film formation has good deformation properties, toughness, and wear resistance [17,18]. The physical properties of the polyvinyl alcohol film system are very stable. It not only has good heat (no obvious change occurs under 140 ◦C), light, and chemical stability but also has excellent gas and water barrier

properties (the inside of polyvinyl alcohol can remain dry under high humidity) [19,20]. The good physical and chemical properties of polyvinyl alcohol and its formed films enable its wide use in the textile, construction, chemical, and papermaking industries, to name a few.

Owing to the excellent performance as described above, researchers have utilized PVA to modify the mechanical properties of a cement-based material. Singh et al. [21] found that the addition of 3% of PVA could increase the compressive strength of cement mortar about 12 % due to there exists chemical interaction between PVA and cement hydration. This chemical interaction is effective in improving the interfacial bond between cement hydration and aggregates. Moreover, the chemical products could fill the pores of the cement matrix, resulting in the mechanical properties of the cement matrix increase greatly. Similarly, Kapen et al. [22] demonstrated that the flexural strengths of cement mortar increased by 21% after loading 1% PVA when cured in dry condition, respectively. They stated that this improvement was mainly attributed to the formation of PVA film within the cement matrix. Kim et al. [23] found that the improvement in bond strength after loading PVA seems to arise from suppression of the porous interfacial transition zone and inhibition of calcium hydroxide nucleation on the aggregate surface.

Using PVA to enhance the properties of cement composites has attracted extensive attention. However, the previous research mainly focused on the hydration and mechanical properties after loading a mono content of PVA, there is limited information on the effect of PVA with different content on the fresh properties and strength development at the early ages. Moreover, the morphology and microstructure of cement composite with different PVA content lack systematic investigation. The goal of this study was to study the effect of PVA content on the compressive and flexural strength of mortar after 3 days, 7 days, 14 days and 28 days curing, in which PVA/binder weight ratios of 0% 0.2%, 0.6%, 1.0%, and 2.0% were adopted. Moreover, the fresh properties and water absorption of mortars were also investigated. Additionally, the morphology and microstructure of mortars after 28 days curing were also analyzed by using differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FTIR), and SEM.

#### **2. The Experiment**

#### *2.1. Materials and Mix Design*

The cement used was Type P.O 42.5 R Portland cement, produced by Guangdong Tapai Cement Co., Ltd. (Figure 1a). The chemical composition is listed in Table 1. Polyvinyl alcohol (PVA-124 AR) from Xilong-Chem was used. The molecular weight (Mw) was 105,000, and the degree of hydrolysis was 97% (Figure 1b). The physicochemical characteristics of this polyvinyl alcohol are given in Table 2. The fine aggregate was China ISO standard sand with the execution criterion of GSB08-1337-2001, from Xiamen ISO Standard Sand Co., Ltd. (Figure 1c). The chemical composition and particle size of standard sand are listed in Table 3. The defoaming agent was produced by Guangdong Defeng Chemical Industry Co., Ltd.

**Figure 1.** The raw materials: (**a**) Portland cement; (**b**) polyvinyl alcohol (PVA) power, (**c**) standard sand.

The water/cement/sand ratio by weight of the specimens was selected as 0.4:1:1.5, and the PVA was incorporated at concentrations of 0%, 0.2%, 0.6%, 1.0%, and 2.0% by weight of cement. The defoamer was added at the amount of 0.14% of cement to eliminate the entrained air bubble during the mixing process. The detailed mix design is shown in Table 4.

**Table 1.** Chemical composition of Portland cement.

**Table 3.** Chemical composition and particle size of standard sand.

**Table 4.** Mix proportions of cement mortar.

#### *2.2. Sample Preparation*

PVA powder was dispersed in cold water at room temperature for 10 min, and this mixture was heated to 95 ◦C with stirring to completely dissolve the PVA. Using a cement mortar mixer, cement and fine sand were first dry-mixed thoroughly for approximately three minutes. The prepared dispersed PVA solution and the remaining water were then poured into the cement mortar and mixed for two minutes with a mid speed stirring. The defoaming agent was then slowly added into the mix, and the mixing continued for three minutes with a high-speed stirring until the relatively homogeneous cement slurry was formed. The prepared mixture was poured into oiled molds and vibrated to remove air bubbles, then pouring the mixes into oiled molds (40 × 40 × 160 mm3). Afterwards, the specimens were removed from their molds after 24 h and cured in a moist room (relative humidity ≥97%) for 28 days. The detailed process of material preparation method is shown in Figure 2.

**Figure 2.** Illustration of the PVA modified cement mortar fabrication process.

#### *2.3. Testing Methods*

The flow table test was carried out on the freshly prepared mortars as described by GB/T2419-2005 [24]. The jumping table beats 25 times in 15 s and measures the average diameter of the fresh mortar on two mutually perpendicular sides.

The hardened density of the specimens was determined by using a water displacement method according to GB/T 11970-1997 [25].

The capillary water absorption test on cement mortar was conducted after 28-day standard curing and determined according to DIN 52617 [26]. Fifteen samples of size 40 × 40 × 80 mm3 were adopted in this test, and the surface of samples was wax-sealed, and then the molded surface was immersed in water, the distance between the height of the water surface and the bottom edge of the test piece was 5 mm, the schematic diagram is shown in Figure 3. The results are the average values of three specimens.

**Figure 3.** Capillary water absorption test schematic diagram (**a**) and measured diagram (**b**).

An electronic universal testing machine (CMT-5105, China) was used to measure flexural strength and compression strength of the composites (three samples of size 40 × 40 × 160 mm3 were adopted in this test). The flexural strength was tested at 3 days, 7 days, 14 days, and 28 days using a three-point method in accordance with GB/T 17671-1999 [27]. The broken pieces (portions of the prisms broken in the flexural strength test) were used to determine the cube compressive strength of the mortar samples in accordance with GB/T 17671-1999 [27].

Differential thermal analysis was using a Shimadzu DSC 50 thermal analyzer at a heating rate of 20 ◦C/min. The samples chamber was purged with nitrogen at a flow rate of 40 cc/min.

FT-IR spectra were carried out using a MAUNA-IR 750 spectrometer manufactured by Nikolai, USA. Powdered samples were mixed with KBr and pressed into pellets. The analyses were carried out in the frequency range of 500–4000 cm−1.

The microstructures of the cement mortar were observed using a scanning electron microscope (SEM, SU3500) at an accelerating voltage of 10 kV. The sample was coated with a thin layer of gold before observation.

#### **3. Results and Analysis**

#### *3.1. Flow Properties*

Fluidity is an important index that reflects the working performance of a cement mortar. The flow properties of cement mortar can be evaluated by using a flow table test. Figure 4 shows the test results of the slump value of all mortars, in which the w/c ratio was fixed at 0.4. Obviously, the incorporation of PVA greatly reduced the flow properties of mortars, as shown in Figure 4, the slump value of fresh mortars decreased from 219.4 mm to 108.5 mm with the PVA content increased from 0% to 2.0%. The incorporation of 0.2%, 0.6%, 1.0% and 2.0% PVA reduced the slump value by 13.9%, 31.5%, 41.1%, and 50.5%, as compared to the control one, respectively. This reduction possibly attributed to the strong, cohesive force of PVA, which limits the movement of cement particles during the mixing process. Moreover, the -OH bonds in the PVA matrix may react with the -OH bonds of water, which reduce the water participating in dispersing of cement particles. The working performance impacts the mechanical properties of cement mortar, and generally, a higher slump value leads to a better uniformity for the fresh mortar, which further results in a higher mechanical properties. Thus, the content of PVA for actual engineering applications should be as small as possible.

**Figure 4.** Effect of PVA content on the flow spread diameter of fresh mortar mixes.

#### *3.2. Flexural Strength*

Figure 5 shows the flexural strength of cement mortars with 0.2~2.0% PVA after 3~28 days of curing. Clearly, the flexural strength of mortars increased at first and then decreased with the increase of PVA content. PCM3 containing 1.0% PVA shows the highest flexural strength. The 7-day, 14-day and 28-day flexural strengths were about 31.9%, 28.8% and 24.8% higher than those of the control mixture without PVA. In addition, the curing ages also impacted the flexural strength development, which generally increased with the increase of curing age. However, the increasing rate was greatly influenced by the PVA content. When the PVA content was 0~0.6%, the increasing rate of flexural strength for different samples was very similar. However, when incorporating 1.0~2.0 wt. % PVA, the flexural strength slightly increased with the increase of curing ages. The 28-day flexural strength of PCM4 containing 2.0% PVA was just 12.5% higher than the three-day flexural strength, while for PCM2, the flexural strength after 28 days of curing was about 29.0% higher than that after three days of curing.

**Figure 5.** Effects of PVA content on the flexural strength of cement mortar.

#### *3.3. Compressive Strength*

Figure 6 shows the compressive strength development of cement mortars with 0~2.0% PVA at the early age. Clearly, the growth trend of compressive strength of all mortars was quite similar to that of flexural strength, in which the strength increased at first and then decreased with the increase of PVA content. However, Figure 6 shows that the optimal use of PVA was 0.6% of cement content by mass, and the compressive strength increased by 12.15% as compared to PCM0 after 28 days of curing. Moreover, it is worth noting that the curing age impacted the improvement effect of PVA, and the reinforcement effect was more remarkable at the early curing age. For example, the three-day compressive strength of PCM2 was about 17.6% higher than that of PCM0, while its 14-day strength just increased by 14.9%, as compared to PCM0. Additionally, the excessive adding of PVA might reduce the compressive strength. As shown in Figure 6, the 28-day compressive strengths of PCM4 were about 5.5% lower than those of PCM0.

**Figure 6.** Effects of PVA content on the compressive strength of cement mortar.

#### *3.4. Volume Density*

The mass per unit volume of a material that contains open and closed pores is referred to as the volume density, and the volume density can reflect the degree of internal compactness of materials. Figure 7 shows the effect of the PVA content on the volume density of the cement mortar. For the measured value, the volume density of the mortar increased with increasing PVA content until it reached an optimal amount of approximately 0.6 wt. %, and then started to decrease. This indicates that the incorporation of a proper amount of PVA can improve the internal pore structure of the mortar and improve the compactness of the mortar. The maximum density of 2148.8 kg/m3 corresponded to 0.6% PVA and compared with the mortar without PVA (2028.5 kg/m3), the increase in the density was determined to be 5.9%. When the PVA content continued to increase, it was difficult to uniformly mix the mortar during the stirring and shaking process due to its poor working performance, so the

internal pores increased, and the volume density decreased. The minimum density of 1992.1 kg/m3 corresponded to 2.0% PVA content.

**Figure 7.** Effects of PVA content on the volume density of cement mortar.

#### *3.5. Capillary Water Absorption*

The water absorption of a material depends not only on its hydrophily but also on its porosity and pore size distribution. Figure 8 shows the capillary water absorption of cement mortars containing 0.2~2.0% PVA. The capillary water absorption rate is plotted on the ordinate, and the square root of time t 0.5 is the abscissa. By analyzing the relationship between the capillary water absorption and the square root of the time, the pore characteristics of the mortar can be reflected. According to the test results in the figure, it can be found that the curve shape of water absorption of cement mortars with or without PVA was quite similar, and which can be divided into two stages: A rapid ascending phase (stage I), and a steady phase (stage II). At stage I, water diffuse along the micro-cracks or interconnecting holes and then fill the pores of surrounding mortar. Thus, the more micro-cracks and interconnecting holes, the faster this stage will ascend. Clearly, according to Figure 8, PCM0 had the highest micro-cracks or interconnecting holes, followed by PCM4, and PCM2 had the lowest micro-cracks and interconnecting holes as its ascending rate was the lowest. In stage II, the pores were filled by water to a certain level, the equilibrium between the rates of transporting moisture and filling pores is built. At this stage, the capillary water absorption value is related to the porosity, and a higher water absorption value means a higher porosity. The effect of PVA on the water absorption of mortar may be because (1) PVA reduces the hydrophily, and (2) PVA film fills the pores of the cement matrix. For this reason, the samples containing PVA had a much lower absorption over the control one. Especially for PCM2, the capillary water absorption of cement mortar was the lowest value of 3.21 kg/m2, which was 60% lower than that of PCM0. However, it should be noted that the addition of PVA will reduce the flow properties of cement mortar, as shown in Figure 4, the higher the PVA content, the lower the slump value was. Particularly for PCM4, the slump value reduced by about 50% as compared to PCM0. The poor flow behavior might cause the uneven dispersion of cement mortar, consequently, the interconnecting holes probably increased with the increase of PVA.

**Figure 8.** Capillary water absorption of cement mortar with different PVA contents (0~24 h).

#### *3.6. Thermal Analysis*

The above-mentioned test results indicate that the adding of 0.6% PVA had a considerable effect on increasing the mechanical property and reducing the water absorption of cement mortar, while the excessive incorporation of 2.0% PVA hardly affected the properties of cement mortar. To understand the mechanism behind these opposite levels of effectiveness of PVA on cement mortar, the microstructure of cement mortar incorporating of 0.6% and 2.0% PVA was studied, for comparison, the property of cement mortar without PVA was also investigated.

Firstly, the thermal decomposition performance of cement mortars containing 0%, 0.6%, and 2.0% PVA was investigated by using differential scanning thermal analysis (DSC), and the results are shown in Figure 9a. There are two major endothermic peaks in the three curves, as shown in Figure 9a. The first endoscopic endothermic peak is located at 95 ◦C, which is caused by the evaporation of adsorbed water. The second endoscopic endothermic peak is located at 430~450 ◦C, which is caused by the decomposition of Ca(OH)2. The two absorption peaks of the three curves are integrated in Figure 9b to assess the effect of PVA on the thermal decomposition of cement mortar. Comparing the first endothermic peaks of all DSC curves, it can be found that PCM0 without PVA is 18.245 J/g, and the incorporating 0.6% PVA increased the endothermic enthalpy values by 13.8%. However, the adding of 2.0% PVA reduced the first absorption peak by 9.3% as compared to PCM0 without PVA. This result indicates that the incorporation of 0.6% PVA might accelerate the hydration rate of the cement matrix, while the addition of 2.0% of PVA delay the hydration rate. Comparing the second absorption peak of the three samples, it can be found that PCM0 had the highest value of 22.145 J/g, followed by PCM2 having a peak value of 19.51J/g, and PCM4 had the lowest value of 17.243 J/g. Figure 9b shows that the first endothermic peak corresponds to the evaporation of adsorbed and interlayer water of PCM2 was much higher than that of PCM0, while the second peak corresponds to the decomposition of Ca(OH)2. This conflicting result may be due to the -OH bonds in PVA chain react with the -OH bonds of Ca(OH)2, leading to the reduction of the content of Ca(OH)2, which is further studied by using FTIR and reported in the next section.

**Figure 9.** Differential scanning calorimetry (DSC) analysis of cement mortars at 28 days. (**a**) DSC curves and (**b**) bound water and Ca(OH)2 content.

#### *3.7. FTIR Spectroscopy*

The FTIR analyses of cement mortar containing 0.6% and 2.0% PVA were conducted after 28 days of curing, and the spectra are shown in Figure 10 for comparison. As shown in Figure 10, all curves have obvious characteristic peaks at 3643, 1643, 1452, and 973 cm−1. The absorption peak near 3643 cm−1 corresponds to the characteristic peak of free hydroxyl in the Ca(OH)2 molecule produced by cement hydration. The characteristic absorption peak of Si-O of C-S-H of cement hydration products is at near 973 cm−1. The peak at 1452 cm−1 corresponds to the stretching of CaCO3. The FTIR spectra of PCM2 containing 0.6% PVA shows more intense absorption peaks at 973 cm−1 as compared to CM, indicating the positive effect of PVA on accelerating the hydration rate of the cement matrix. However, this peak of PCM4 was much lower than that of PCM0, indicating the excessive addition of PVA impacted the hydration of the cement matrix in a negative way. Moreover, Figure 10 shows that the addition of PVA reduced the spectral data at 3643 cm−1, which corresponds to the -OH band of Ca(OH)2. This result coincides well with the results of DSC.

**Figure 10.** Fourier transform infrared spectroscopy (FTIR) analysis of cement mortar with different PVA content.

#### *3.8. SEM Investigation*

Scanning electron microscopy is an important means of analyzing the internal microstructure of materials. Scanning electron microscopy images can be used to determine the composition of the pore structure inside the cement matrix, the internal shape of the cement matrix and the type of hydration products. Figure 11 shows the microstructure of the modified mortar with a PVA content of 0.6% for 28 days of curing. As shown in Figure 11, the internal structure, pore distribution, and pore size composition of the modified mortar changed with the incorporation of PVA. The polymer was evenly distributed in the cement matrix while the cement mortar was being agitated and intertwined with the cement hydration product. As the cement hydration reaction progressed, the polymer continuously precipitated in the cement to form a polymer film. The presence of these films can play the following roles: (1) The films can combine with the hydration product to enhance the connection between the hydration products, thereby improving the mechanical properties of the cement mortar. (2) Part of the film can become interspersed with the cement base. Between the pores, it can fill the pores and form bridges in the cement plate. Under a load, it can absorb the energy generated by the external force so that the mechanical properties of the cement matrix are improved [23]. (3) The presence of the polymer can refine the pores, making a large number of harmful pores decrease into medium-sized or even harmless pores, thereby enhancing the mechanical properties of the cement mortar. In addition, it can be clearly observed in Figure 11 that the polymer film spans the crack between the cement plates to form a load transfer bridge. Under the action of an external force, the film is torn, and the energy generated by the external load is absorbed so that the crack generation and development process of the cement plate are delayed.

**Figure 11.** Polymer film stretched in open space in 0.6% PVA modified mortar at 28 days.

Figure 12 shows the microstructure of the modified mortar with a PVA content of 1.0%. As shown in Figure 12, as the amount of PVA increased, polymer film combines with the hydration product in the cement matrix to form a stable continuous network-like structure. This structure improves the mechanical properties of the mortar, especially the bending performance.

**Figure 12.** Network-like structure in cement mortar with the PVA content of 1.0% at 28 days.

Figure 13 is the SEM image of PCM4 containing 2.0% PVA. Figure 13 shows that the morphology and microstructure of PCM4 are quite different from PCM2. The excessive incorporation of PVA led to the formation of heave polymer sheets within the cement matrix. These heavy PVA film might coat cement particles and prevent them from having contact with water. This coated effect of PVA might delay the hydration rate of cement, as demonstrated by DSC and FTIR test results as shown in Figures 9 and 10. The lower compressive strength of PCM4 over PCM0 might be due to its lower hydration rate caused by the coated effect of PVA. In addition, Figure 13 shows the positive effect of PVA on bridging cracks, which lead to the increase of flexural strength of mortar as shown in Figure 6, where the three-day flexural strength of PCM4 was about 36.6% and 1.8% higher than that of PCM0 and PCM2, respectively.

**Figure 13.** SEM image of cement mortar with 2.0% PVA content at 28 days.

The results of the scanning electron microscopy analysis show the following: (a) The proper amount of PVA incorporation (0.6%~1.0%) can be evenly distributed inside the matrix with an appropriate aggregate stirring process. A uniformly distributed polymer film can be formed that has a certain tensile strength. The polymer film bridging between the cement plates can delay or inhibit crack development of the cement mortar. Additionally, the film also can combine with hydration products to form a network-like structure. The structural system improves the mechanical properties of the cement mortar, especially for the flexural strength. (b) When the PVA content is too high (2.0%), the mortar has a poor working performance, and it is difficult to stir evenly. The incorporation of a large amount of PVA forms an uneven distribution inside the matrix. The cement curing process leads to the formation of a large and thick polymer film that coats the surface of the cement particles and hinders the hydration of the cement, thereby reducing the mechanical properties of the cement mortar.

#### **4. Conclusions**

This study investigated the strength development of cement mortars at the early age (3~28 days) after loading various polyvinyl alcohol (PVA) contents to obtain an optimum proportion. Then, the microstructure and morphology of PVA-modified cement mortar were studied by using DSC and FTIR to reveal its working mechanism. According to the test results in this study, the main achievements were:

**Author Contributions:** Conceptualization, J.F. and G.L.; data curation, S.D. and Z.W.; writing of the original draft, J.F. and Z.W; writing of review and editing, G.L.

**Funding:** This work is supported by the National Natural Science Foundation of PR China (No. 51708145, No. 51878299), the project of Natural Science Research Foundation of Guizhou Province (No. [2018]1064), and the Science and Technology Plan of Guangdong Province (2015A010105029).

**Acknowledgments:** This work is supported by the National Natural Science Foundation of PR China (No. 51708145, No. 51878299), the project of Natural Science Research Foundation of Guizhou Province (No. [2018]1064), and the Science and Technology Plan of Guangdong Province (2015A010105029). The authors are grateful for the above supporters.

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

#### **References**

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**5. Discussion and Concluding Remarks**

Coming long after a conjecture by Pomeau [1], empirical evidence is growing that the ultimate stage of decay of wall-bounded turbulent flows towards the laminar regime follows a directed-percolation scenario. The evidence comes from laboratory experiments and direct numerical simulation of the Navier–Stokes equations but this support is still far from a theoretical justification. The recognition of the globally subcritical character of nontrivial states away from laminar flow and the elucidation of the structure of coherent structures involved in these nontrivial states [29] were first steps in this direction. The next ones would be the elucidation of special phase space trajectories from sustained localized turbulence accounting for the decay to laminar regime, on one side, and to proliferation via splitting, on the other side, using specific algorithms for the detection of rare events and the determination of transition rates that can be attached to them (see [30] for an illustrative example and references). These are heavy, and possibly not much rewarding, tasks but it would be nice to be able to attach numbers to specific events such as the splittings illustrated in Figure 3 or Figure 4. We have chosen to short-circuit such studies through analogical modeling, by which seemed more appropriate to make further progress regarding the thermodynamic limit and associated universality issues. One should though consider this practice as providing hints and not a demonstration that the results will apply to the case under study.

In the present paper, the problem has been considered from this last viewpoint, assuming that the ultimate decay stages were amenable to the most abstract level of implementation in terms of probabilistic cellular automata [2], following [20,22]. We focussed on the specific case of channel flow that offers a particularly rich transitional range. Its upper part displays regular non-intermittent laminar–turbulent patterns that can better be described using the tools of pattern-forming theory [15,25,31]. The lower transitional range is characterized by their spatiotemporally intermittent disaggregation, to which the considered type of modeling is particularly relevant. The analogy alluded to above has, however, been severely constrained to fit the empirical observations. The main assumptions were the introduction of two types of active agents attached to each kind of localized turbulent bands propagating in one of the two possible orientations with respect to the stream-wise direction. Interactions were assumed local so that the probabilistic cellular automata evolved simple nearest neighbors von Neumann neighborhoods (Figures 5–7). Scrutiny of simulation results lead to the introduction of a certain number of probabilities governing the fate of single-occupancy neighborhoods. Multiple-occupancy was treated as a combination of single-occupancy configurations supposedly independent, reducing the number of parameters to be introduced and drastically simplifying the interactions (at any rate impractical to estimate in detail). A clear-cut physical interpretation was, however, given to each parameter in the set reduced to four, accounting for every possible stochastic event affecting the agents, namely, propagation, decay, and splitting, either longitudinal or transversal. A mean-field study of the model, neglecting the nontrivial effects of stochastic fluctuations, reproduced the empirical bifurcation diagram of channel flow at a qualitative level (Figure 14). Transitions have been studied quantitatively by numerical simulation of the stochastic model considering variations of these parameters as putative functions of the Reynolds number Re, highlighting three situations:

In the two first cases, the parameter *p* 4 associated with transversal splitting, i.e., the nucleation of a daughter with orientation opposite of its mother, was switched off, as inferred from observations for Re 800, where the single-sided regime is well established. The coarsening observed when starting from two-sided initial conditions was faithfully reproduced (Figure 8) and decay seen to follow the directed-percolation expectations. The specific conclusion was that, when parameter *p*2 is no-zero, with *p*2 attached to longitudinal but upstream-shifted splitting, the scenario is typical of a 2D system with a high level of confidence, whereas when it is strictly zero, i.e., the daughter strictly aligned with the mother, the decay is 1D. A cross-over is observed when *p*2 is reduced, that manifests itself as a transient reminiscent of 1D behavior, the longest the closest *p*2 is to zero. Simulations of channel flow have shown that exponent *β* controlling the ultimate decay of the turbulent fraction was that of 1D directed percolation [16]. Since parameter *p*2 is attached to the slight upstream trajectory shift experienced by a daughter upon splitting from its mother, this observation strongly suggests that the trajectory shift is mostly irrelevant and that localized turbulent bands propagate along independent tracks so that the end result is just a mean over the direction complementary to their propagation direction.

The last situation we have considered corresponds to *p* 4 = 0, with transversal splitting on. This parameter measures the frequency of transversal splitting and is expected to increase with Re. Accordingly, the system can change from one-sided when *p* 4 is zero or small, to two-sided when it is large. The transition has indeed been observed and mean-field predictions were well observed far from the transition point. Unfortunately, while the effect of fluctuations close to that point was obvious, strong size effects have forbidden us to approach it and evaluate critical corrections. This is the subject of on-going work within the framework of finite-size scaling theory [2,27,28]. This follow-up should allow us to establish the universality class to which this transition belongs. Here, the left-right symmetry of localized turbulent bands with respect to the stream-wise direction is reminiscent of the up-down symmetry of magnetic systems at thermodynamic equilibrium, which may lead to conjecture the relevance of the 2D Ising class [26]. This class appears also applicable to coupled map lattices with the same up-down symmetry when updated asynchronously, one site after the other, close to randomization by thermal fluctuations. In contrast, another universality class is obtained with synchronous update [27]. Here, the situation is unclear: on the one hand, configurations are treated as a whole in a simulation step, which tips the scales in favor of a synchronous

update model (in line with what is expected for a problem primitively formulated in terms of partial differential equations); on the other hand, spatial correlations generated by the deterministic dynamics governing the coupled map lattices are weakened by the independence of random drawings at the local scale, which can be viewed as a source of asynchrony in the probabilistic cellular automata. In its application to the symmetry-breaking bifurcation in channel flow, this uncertainty is, however, only of conceptual importance in view of size effects: owing to the large and unknown time-scale rescaling that allowed us to pass from flow structures to local agents in the model and to the narrowness of the region where critical corrections are expected, the mean-field interpretation developed in [14] appears amply sufficient.

In the three cases that were considered in detail (specific cuts in the parameter space), the transitions remained continuous. However, this may not always be the case since there are known example of similar systems displaying transitions akin to first-order ones [24]. Even while keeping the same general frame, a plethora of circumstances of physical interest can be mimicked: propagation can be made more stochastic by decreasing *p*5, splitting rules not observed in channel flow can be considered, e.g., with *p*3 or *p*4 different from zero, etc., though it seems hard to anticipate situations where the universal features pointed out here would not hold. In contrast, when dealing with highly populated configurations, even in the simple nearest-neighbor von Neumann setting, rules can be made more complicated by introducing the neighborhood's degree of occupation. This introduction might help us to account also for the upper part of the transitional range of wall-bounded flows characterized by the emergence of regular patterns in the same stochastic framework [11]. The construction of the present model is, of course, fully adapted to the study of universality in the framework of the theory of critical phenomena in statistical physics, especially directed percolation. Still, we are confident that the kind of approach illustrated here brings a valuable contribution to the understanding of the transition to turbulence, by rationalizing its key ingredients in an easily accessible way.

**Author Contributions:** Conceptualization, P.M., M.S.; methodology, P.M.; software, P.M., M.S.; validation, M.S.; formal analysis, P.M.; investigation, P.M.; resources, M.S.; data curation, M.S., P.M.; writing—original draft preparation, P.M.; writing—review and editing, P.M., M.S. All authors have read and agreed to the published version of the manuscript.

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

**Acknowledgments:** P.M. wants to thank H. Chaté (CEA-Saclay, Gif-sur-Yvette, France) for pointing out a model that could help one uncover the universal contents of the symmetry-breaking bifurcation on the same footing as the decay near Reg.

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


**4. Discussion**

The recurrence of GBS in pediatrics is rare, as well as the presentation of two clinical variants, which presented different clinical course and remission, despite receiving adequate treatment with intravenous immunoglobulin at a dose of 1 grkg. As previously mentioned, during the 1st episode with acute inflammatory demyelinating variant GBS, the patient presented remission of clinical symptoms without respiratory compromise. But this is not the same way in the 2nd episode of GBS, acute axonal motor variant, where it progresses with acute, torpid neurological evolution, with respiratory and bulbar involvement, meriting phase III ventilation and tracheostomy programming as a protective measure of airway and deficit motor in all 4 extremities for 5 weeks with gradual improvement. Upon discharge, the patient with great limitation to daily activities, staged according to the Hughes Scale in grade IV, found herself confined to bed and requiring the use of a wheelchair to perform daily activities, due to the involvement and motor involvement in the extremities lower, continuing with neurological rehabilitation to delimit the severity of the sequelae.

At present, it is rare to find a case of RGBS, this case being one of the few presentations with an axonal phenotype in a child.

Therefore, it is important for clinicians to recognize the various features of RBGS in recurrence [28].

Remembering that patients may present similar symptoms, but have different findings in the examination, clinical course and electrodiagnostic studies [28, 29].

RGBS may be an underdiagnosed and underrecognized entity in pediatric patients that deserves further study with regard to epidemiology and pathophysiology.


**Author details**

laboratory tests and therapeutic blood components, the current management of patients with disorders of hemostasis and thrombosis is based on the individual approach according to the

Sickle cell disease (SCD) is one of the most frequent inherited genetic disorders in the world. It predominantly affects people of African descent as well as individuals from the Middle East, India, and Mediterranean regions [28]. It is an autosomal-recessive disease caused by a point mutation in the hemoglobin beta gene found on chromosome 11p15.5, Hb S (HBB: c.20A>T) along with or without any other abnormal Hb gene, and results in a number of health problems, for example, anemia, acute and chronic pain, infection, acute chest syndrome, pulmonary hypertension, cardiac, CNS, gastrointestinal involvement, and so on, leading to significant morbidity and mortality. Significant advances in prophylactics and therapy achieved improved survival among children with sickle cell disease, with the majority of children attaining adulthood [29]. However, the median age at death of 39 years with only 35.0% surviving beyond age 35 years was reported by the Centers for Disease Control (CDC). Sickle cell disease substantially alters renal structure and function leading to nephropathy which is not only a chronic comorbidity but is also one of the leading causes of mortality in patients with sickle cell disease [30]. Knowledge of the natural progression of the disease, as well as identification of persons at risk, allows for timely intervention and improved outcomes. The search for biomarkers for the early diagnosis of the disorder and its outcomes is an area of intense contemporary research [31]. The current understanding of the presentation, diagnostic, and therapeutic challenges in sickle cell nephropathy is presented in detail by Inusa Baba et al. in the chapter 8. Risk factors for renal impairment and acute kidney injury are reviewed in detail. In addition, data coming from established mouse models are invaluable to elucidate the pathogenesis of SCD-associated multiple organ complications and to identify targets for

The continuous and rather extensive influx of new information regarding the key features and underlying mechanisms as well as treatment options of blood disorders requires a frequent updating of this topic. The primary objective of this book is to provide the specialists involved in the clinical management and experimental research in hematological diseases with comprehensive and concise information on some important theoretical and practical developments in the biology, clinical assessment, and treatment of patients, as well as on some molecular and pathogenetic mechanisms and the respective translation into novel therapies. Specific clinical scenarios such as myeloid sarcoma or sickle cell nephropathy are also within the scope of this book. An international panel of experts provides novel insights of various aspects of hematology and contributes their experience to an update of

Each chapter is a separate publication that reflects each author's views and concepts. However, this book presents an update and introduces novel insights in our current understanding of the biology and clinical presentation, the risk assessment, and therapeutic challenges in

**3.2. New sickle cell disease research shows improved patient outcomes**

individual patient.

10 Hematology - Latest Research and Clinical Advances

prevention and therapy.

patients with hematological diseases.

the field.

Gueorgui Balatzenko1 and Margarita Guenova2 \*

\*Address all correspondence to<EMAIL_ADDRESS>
1 Laboratory of Cytogenetics and Molecular Biology, National Specialized Hospital for Active Treatment of Hematological Diseases, Sofia, Bulgaria

2 Laboratory of Haemathopathology and Immunology, National Specialized Hospital for Active Treatment of Hematological Diseases, Sofia, Bulgaria


*3.2. Data Collection*

During the winter semester of 2019/2020, the data was collected through online surveys from individuals studying at the British University in Dubai (BUiD) from 15 January to 20 February 2020. The aggregated response rate was 93%; 400 questionnaires were circulated, out of which 372 were answered by respondents. This means that 372 questionnaires were filled out correctly and found to be useful, while 28 were rejected because of missing values. The prospective sample size was 306 respondents with respect to a population of 1500. Thus, the sample size of 372 correct responses was suitable, according to [71], because—bearing in mind the required sample size—the sample size of 372 is a higher figure. Thus, this sample size could be reviewed using structural equation modeling [72] to verify the hypotheses. It must be noted that hypotheses were based on the current theories and were adjusted to the e-learning context. In order to assess the measurement model, the researchers used structural equation modeling (SEM) [72]. Further treatment was performed using a final path model.

#### *3.3. Students' Personal Information/Demographic Data*

The assessment of personal/demographic data is covered in Table 2. The percentage of males was 53%, while for females it was 47%. A total of 33% of students had ages ranging from 18 to 29 years, while 67% of respondents were aged over 29. In terms of academic background, 39% were students from the Faculty of Engineering and IT, 35% were from the Faculty of Education and 26% belonged to the Faculty of Business and Law. The majority of respondents came from sophisticated families and held university degrees; 49% of participants had bachelor's degrees, 42% had master's degrees, and 9% had a doctoral degree. When the respondents were ready to volunteer and were easily approachable, the purposive sampling approach was used as per [3]. This sample was created by students coming from different faculties, with different ages, enrolling in diverse programs at different levels. Moreover, with the aid of IBM SPSS Statistics ver. 23, the demographic data was evaluated. Table 2 depicts the complete demographic data of the respondents.

**Table 2.** Demographic data of the respondents.

#### *3.4. Study Instrument*

The survey instrument used to validate the hypothesis was determined in this research. The survey, consisting of 30 items, was used for the measurement of seven constructs in the questionnaire. Table 3 depicts the sources of the constructs. The questions from prior studies were modified in order to enhance the appropriateness of the research.

Note: TPACK = Technological pedagogical content knowledge; TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; POS = Perceived organizational support; CTRLM = Controlled motivation; CU = Continuous intention to use e-learning platform.

#### *3.5. Pilot Study for the Questionnaire*

A pilot study was conducted to check the reliability of the questionnaire items. Approximately 40 students and teachers were chosen on a random basis from the given population to establish the pilot study. The sample size was set based on 10% of the aggregated sample size of this study (400 students and teachers) and thus adhered strictly to the research criteria. Cronbach's alpha test was utilized for the computation of internal reliability through IBM SPSS Statistics ver. 23, in order to judge the outcomes of the pilot study. Thus, the appropriate findings were shown for the measurement items. A value of 0.7 was taken to be an acceptable value for the reliability coefficient, considering the model for social science research [14]. Tables 4 and 5 show the Cronbach's alpha values for the seven measurement scales for teachers and students.

**Table 4.** Cronbach's alpha values for the pilot study (Cronbach's alpha ≥ 0.70) for teachers (Model A).

Note: TPACK = Technological pedagogical content knowledge; TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; POS = Perceived organizational support; CU = Continuous intention to use e-learning platform.

#### *3.6. Survey Structure*

The questionnaire survey given to students and teachers had two sections. Within the first part, personal data was given to gather information about students and teachers. The second section had a group of questions related to the main factors of the proposed models. The teachers' questionnaire had six sub-sections coinciding with the six factors proposed in the model. Similarly, the students' questionnaire had five sub-sections related to the five factors proposed in the model. With the help of the five-point Likert Scale, the 42

items were evaluated. The scales included the following: (1) strongly disagree, (2) disagree, (3) neutral, (4) agree, and (5) strongly agree.

**Table 5.** Cronbach's alpha values for the pilot study (Cronbach's alpha ≥ 0.70) for students (Model B).

Note: TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; CTRLM = Controlled motivation; CU = Continuous intention to use e-learning platform.

#### **4. Findings and Discussion**

#### *4.1. Data Analysis*

Along with the help of SmartPLS V.3.2.7 software, the partial least squares-structural equation modeling (PLS-SEM) was utilized to conduct the data analysis in this research [15]. The assessment approach had two steps of a structural model and a measurement model allowed to study the collected data [16]. There were various reasons for choosing PLS-SEM in the study. First, as the research is an extension of a current theory, PLS-SEM was considered the best option [17]. Second, the complex models within exploratory research can be effectively tackled with the help of PLS-SEM [18]. Third, PLS-SEM analyzes a complete model as a single unit, so there is no need to divide it [19]. Lastly, PLS-SEM provides concurrent analysis for measurement, as well as a structural model, leading to more accurate calculations [20].

#### *4.2. Convergent Validity*

In order to review the measurement model, it was suggested by [16] that construct reliability—including composite reliability (CR), Dijkstra–Henseler's rho (pA), and Cronbach's alpha (CA) and validity (including convergent and discriminant validity)—must be considered. Cronbach's alpha (CA) has values between 0.782 and 0.895, as Tables 6 and 7 show, in order to determine construct reliability. These statistics are higher than the threshold value of 0.7 [78]. According to Tables 6 and 7, the outcomes also show that the composite reliability (CR) has values from 0.796 to 0.882; these values are evidently bigger than the recommended value of 0.7 [79]. As an alternative, the construct reliability must be appraised by researchers by means of the Dijkstra–Henseler's rho (pA) reliability coefficient [80]. Like CA and CR, the reliability coefficient ρA must show 0.70 or higher in exploratory studies and values of more than 0.80 or 0.90 for further stages of research [78,81,82]. The reliability coefficient ρA of each measurement construct is above 0.70 according to Tables 6 and 7. According to these outcomes, the construct reliability is verified and all the constructs were considered to be accurate.

Convergent validity can be measured by testing the average variance extracted (AVE) as well as the factor loading [16]. Tables 6 and 7 suggest that all values of factor loadings exceeded the threshold value of 0.7. Moreover, Tables 6 and 7 show that the values obtained for the AVE were higher than the threshold value of 0.5, ranging from 0.509 to 0.718. Depending on the expected results, the convergent reliability can be obtained for all the constructs.

**Table 6.** Convergent validity results that ensure acceptable values (Factor loading, Cronbach's alpha, composite reliability (CR), Dijkstra–Henseler's rho (pA) ≥ 0.70 and average variance extracted (AVE) > 0.5) (Model A).

**Table 7.** Convergent validity results that ensure acceptable values (Factor loading, Cronbach's alpha, composite reliability, Dijkstra–Henseler's rho ≥ 0.70 & AVE > 0.5) (Model B).

**Table 7.** *Cont*.

### *4.3. Discriminant Validity*

The two criteria that were suggested should be measured to obtain the measurement of discriminant validity were the Fornell–Larcker measure and the Heterotrait–Monotrait ratio (HTMT) [16]. As per the findings of Tables 8 and 9, these needs have been verified by the Fornell–Larker criterion as each AVE value, together with its square root, exceeds the value of the correlation of AVE with other constructs [83].

**Table 8.** Fornell–Larcker Scale (Model A).

Note: TPACK = Technological pedagogical content knowledge; TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; POS = Perceived organizational support; CU = Continuous intention to use e-learning platform.

Note: TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; CTRLM = Controlled motivation; CU = Continuous intention to use e-learning platform.

Tables 10 and 11 show the outcomes of the HTMT ratio, indicating that the threshold value of 0.85 is bigger than the values of other constructs [27], and hence confirming the HTMT ratio. These outcomes play a role in the evaluation of the discriminant validity. The outcomes of the analysis indicated a smooth and simple assessment of the measurement model in terms of the model's validity and reliability. To conclude, it can be said that the collected data was appropriate for additionally evaluating the structural model.

**Table 10.** Heterotrait–Monotrait Ratio (HTMT) (Model A).

Note: TPACK = Technological pedagogical content knowledge; TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; POS = Perceived organizational support; CU = Continuous intention to use e-learning platform.

Note TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; CTRLM = Controlled motivation; CU = Continuous intention to use e-learning platform.

#### *4.4. Model Fit*

The subsequently mentioned fit measures are ensured by SmartPLS: the standard root mean square residual (SRMR), exact fit criteria, Euclidean distance (d\_ULS), geodesic distance (d\_G), Chi-square, Normed Fit Index (NFI), and RMS Theta show the model fit in PLS-SEM [84]. The difference between experimental correlations and the correlation matrix inferred from model [85] are indicated by SRMR, and values smaller than 0.08 are assumed to serve as good model-fit measures [86]. The NFI values that are higher than 0.90 point out a good model fit [87]. The NFI is a ratio of the Chi-square value of the proposed model to the null model (also known as the benchmark model) [88]. The NFI increases with larger parameters and therefore, the NPI is not suggested as a model-fit pointer [85]. The discrepancy between the empirical covariance matrix and the covariance matrix, inferred from the composite factor model, is indicated by the metrics of squared Euclidean distance (d\_ULS) and the geodesic distance (d\_G) [80,85]. The RMS Theta helps in the measurement of the degree of outer model residuals correlation and is appropriate for reflective models only [88]. The nearer the RMS Theta value is to zero, the more superior the PLS-SEM model, and their values of less than 0.12, are assumed to be a good fit, with anything other than this suggesting an absence of fit [89]. The saturated model evaluates the correlation between all constructs, as recommended by [29], while the approximate model takes all the effects and model structure into consideration. The RMS Theta value was 0.073 in Model A and 0.073 in Model B, as given in Tables 12 and 13, which gives an idea that the specific goodness-of-fit for the PLS-SEM model was big enough to prove global PLS model validity.

**Table 12.** Model fit indicators (Model A).

**Table 13.** Model fit indicators (Model B).

### *4.5. Hypotheses Testing Using PLS-SEM*

The interdependence between different theoretical constructs related to the structural model was studied by using a combination of the structural equation model with maximumlikelihood estimation and SmartPLS [38,39]. This indicates the procedure of analysis of the proposed hypothesis. About 83% and 71% variance were found within the continuous intention to use the e-learning platform, as shown in Tables 14 and 15, which indicates a high predictive power of Models A and B [37]. For all the proposed hypotheses, outcomes of the PLS-SEM technique provided the beta (β) values, *t*-values, and *p*-values, which have been stated in Tables 16 and 17. It is evident that each and every hypothesis is supported by all the researchers. The empirical data backs hypotheses H1, H2, H3, H4, H5, and H6 on the basis of the analyzed data. The standardized path coefficients and path significances are demonstrated in Figures 3 and 4.

**Table 14.** R2 of the endogenous latent variables (Model A).

**Table 15.** R2 of the endogenous latent variables (Model B).

In Model A, technological pedagogical content knowledge (TPACK), technology selfefficacy (TSE), perceived ease of use (PEOU), perceived usefulness (PU), and perceived organizational support (POS) have significant effects on continuous intention to use the e-learning platform (CU) ((β = 0.336, *p* < 0.001), (β = 0.426, *p* < 0.05), β = 0.589, *p* < 0.05), (β = 0.625, *p* < 0.05) and (β = 0.553, *p* < 0.001), respectively); hence, H1, H2, H3, H4, and H5 are supported.

In Model B, technology self-efficacy (TSE), perceived ease of use (PEOU), perceived usefulness (PU), and controlled motivation (CTRLM) have significant effects on continuous intention to use the e-learning platform (CU) ((β = 0.290, *p* < 0.001), (β = 0.357, *p* < 0.05), β = 0.465, *p* < 0.05) and (β = 0.243, *p* < 0.05), respectively); hence, H2, H3, H4, and H6 are supported.

**Table 16.** Hypotheses testing of the research model (significant at \*\* *p* < = 0.01, \* *p* < 0.05) (Model A).

Note: TPACK = Technological pedagogical content knowledge; TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; POS = Perceived organizational support; CU = Continuous intention to use e-learning platform.

**Table 17.** Hypotheses testing of the research model (significant at \*\* *p* < = 0.01, \* *p* < 0.05) (Model B).

Note: TSE = Technology self-efficacy; PEOU = Perceived ease of use; PU = Perceived usefulness; CTRLM = Controlled motivation; CU = Continuous intention to use e-learning platform.

**Figure 3.** Path coefficient of the model (significant at \*\* *p* <= 0.01, \* *p* < 0.05) (Model A).

**Figure 4.** Path coefficient of the model (significant at \*\* *p* < = 0.01, \* *p* < 0.05) (Model B).

#### **5. Discussion and Conclusions**

This study proposed two unique CU models that took into consideration factors that affect both instructors' and students' attitudes. The two models can be theoretically extended to enhance other technology-supported educational environments and instructional processes. The first research model of instructors' CU was proposed, taking into consideration certain social cognitive theory along with personal, behavioral, and environmental elements that are closely related to instructors' CU. In general, the results of SEM analysis has supported all the proposed hypothesis. From a practical perspective, this study has proven that POS is the most influential factor that affects instructors' CU of e-learning platforms.

Ref. [90–92] seem to agree with the current conclusion in stating that POS could motivate its staff members, leading to an upgrade of the organization. However, a study by [93] placed an emphasis on staff members' personalities and readiness to change. This implies that the lack of organizational support may have negative consequences. One of the studies by [60] has proposed that when instructors feel that there is no adequate organizational support, they are less likely to continuously use the technology, especially in an educational atmosphere where instructors are supposed to implement various in-class pedagogical changes to facilitate a better learning environment for the students.

In fact, POS is not the only factor that affects instructors' CU, but rather instructors' TPACK is another key factor that affects CU. Most of the previous research has proven that the organizational support may affect users' motivation to use the technology. A study by [94] put emphasis on the effect of PACK in facilitating the e-learning process by both teachers and adult learners. This seems to be in line with the results obtained from this study where TPACK affected, to great extent, the teachers' performance. It is assumed that whenever teachers' content, technological, and pedagogical knowledge is high, it implies that his or her ability to change the teaching material to suit the newly used technology will be more practical and effective. Obviously, teachers are more motivated when there is reliable technical support and IT staff that can facilitate the process of establishing new computer-supported knowledge [66,68].

Similarly, CTRLM has an effect on students' CU. CTRLM, along with technology selfefficacy (TSE), are the key factors that have a great impact on students' CU. The study has proven that CTRLM is connected deeply with the willingness to use the e-learning teaching platform. The higher the motivation is, the more effective results are obtained. Previous studies have tackled the effect of CTRLM on students' performance and have proven that there are both pedagogical and non-pedagogical elements that affect motivation [95–97]. These studies have indicated that technology development has placed a positive effect on motivation, as it urges students to get engaged in the new learning platform. This makes motivation very high and involvement in classes even higher, increasing the students' willingness to learn, and thus, using the technology continuously [98,99].


**9. Conclusion**

92 12 Chapters on Nuclear Medicine

therefore, differentiation between active and structural but indolent alterations following surgery or other interventions is difficult to differentiate and these modalities are generally of limited value in detecting early disease regardless of the cause. In evaluation of infectious process CT scans may assist in the assessment of disruption of the bony cortex and softtissue involvement. Furthermore, CT may also reveal edema, intra-osseous fistula and

CT is better suited for an evaluation of cortical bone, whereas MR imaging is more useful for the evaluation of internal architecture of structures such as the bone marrow, muscles, tendons, ligaments, cartilage etc. MR imaging has high accuracy in the acute osteomyelitis evaluation and detection primarily delineating adjacent soft tissue infection; particularly when no prior alterations in osseous or soft structure are present. However, in patients who have undergone previous surgical intervention, MR imaging may not be able to clearly distinguish signal abnormality secondary to bone marrow edema or enhancement related to a reactive phenomenon and that related to infection. Similarly, the diagnostic accuracies of both CT and MR imaging to evaluate osteomyelitis generally decrease in the presence of

MR imaging has a higher sensitivity and specificity than plain films and CT. Further findings become positive earlier in the disease process with MR than with plain films. MR imaging is particularly better at depicting bone marrow abnormalities with sensitivity of 82%-100% and specificity of 75%-95%. Vertebral osteomyelitis is one condition where MR has a characteristic pattern of confluent vertebral body and disk involvement; the diagnostic accuracy in such cases amount to 90%. MR imaging findings in osteomyelitis usually are related to the replacement of marrow fat with water secondary to edema, exudate, hyperemia, and bone ischemia. Findings include the following: decreased signal intensity in the involved bone on T1-weighted images, increased signal intensity in the involved bone on T2-weighted image, and increased signal intensity in the involved bone on short-tau inversion recovery (STIR) images. A decreased intensity on T1-weighted images with no change on T2-weighted images may indicate surgical or post-traumatic scarring of bone marrow. The MR imaging limitations are primarily due to the reason that findings of osteomyelitis are nonspecific, and similar changes may occur as a result of fractures, tumours, and a number of various intramedullary or juxtamedullary processes that result in

The future for infection imaging looks promising with the search for an ideal agent still on. There has been a progression in the development of leukocytes labelled in-vitro by F-18 FDG. Moreover, leukocytes labelled with Copper-64 (Cu-64) are being studied. Radiolabelled antibiotics are also in vogue. The theoretical reasoning for radiolabelled antibiotics would be that such would incorporate into and metabolized by bacteria, thereby, making it possible to localize the site of infection. Tc-99m Ciprofloxacin has been studied, some results published with regards to its use in post-operative infections, however, this did not go into routine clinical practice due to conflicting data. Tc-99m labelled anti-microbial peptides are also being studied including Tc-99m labelled recombinant human betadefensin-3 that exerts bactericidal effects on gram positive and gram negative bacteria as well as some yeasts, and Tc-99m ubiquicidin whose uptake is related to number of viable bacteria. Most recently (Lupetti et al., 2011) in a review have mentioned radiolabelled

cortical defects that lead to soft tissue sinus tracts.

metallic implants due to streak and susceptibility artefacts.

bone marrow edema.

**8. Future prospects and novel agents**

Radionuclide imaging plays a significant role in infection detection and localization. Currently the selection of infection imaging agent depends upon the availability and local expertise especially in cases of labelling leukocytes which require time consuming labelling procedures. SPECT-CT provides essential anatomical localization especially in cases of vertebral osteomyelitis, diabetic foot, and infected prosthesis, cardiovascular, abdominal and pulmonary infections. Gallium scintigraphy can be replaced with FDG PET where available. FDG PET is a preferred procedure of choice for pyrexia of unknown origin, vasculitis, sarcoidosis, infected grafts and inflammatory bowel disease, moreover quantitative FDG PET analysis appears another promising further advance. Finally, with the progress in hybrid imaging the diagnostic power of conventional scintigraphy in detection and localization of infection has greatly augmented and the development of newer infection seeking agents pave the way for improved patient management in future.


3.1.3. Using Substitution as the Allocation Method

Another point of debate is the use of substitution for allocation (and not as a system expansion method). This type of allocation has been mentioned in the literature with different names, such as substitution-based allocation [9] and "proxy-based disaggregation" by substitution [35] and various versions of this method have been proposed (e.g., see Hermansson et al. who applied two different versions of this method to assess Kraft lignin [64]). By many practitioners, this option is perceived as the attributional way of using substitution. PEFCR guidance and PEF guide [28,29] propose that, when a by-product of a multifunctional system directly substitutes another product, such substitution might be considered as an allocation reflecting physical relationships. When this is the case, such substitution has to be based on a direct and empirically demonstrable relationship [28,65]. Pelletier et al. [65] stated that this is different from substitution based on marginal market models applied in consequential LCAs [65]. An example of such a substitution is when "manure nitrogen is applied to agricultural land, directly substituting an equivalent amount of the specific fertilizer nitrogen that the farmer would otherwise

have applied" [28,65]. Hence, it is assumed that the impact caused in the system by the production of the substituted by-product corresponds to the impact of the production of the replaced product (as shown in [66]). With substitution, the impact of a by-product should equal that of the product it substitutes, and so is independent on the actual process that produces it. Moreover, the application of this substitution-based allocation can lead to a negative impact that in ALCA would mean that the model has been built inconsistently [35]. As an example, this happens when the wrong product is chosen as the main product of the multifunctional system [66] or the substituted product is not a minor product, even if representing less than 50% of market value [67]. Even if the substituted co-products are chosen carefully (i.e., they represent small percentages in physical and economical terms), this method sometimes fails in ALCAs assessing multiple impact categories, resulting in negative impacts for some of them [13,67]. Moreover, PEFCR guidance and PEF guide [28,29] also allow the possibility of using indirect substitution as a form of allocation based on "other relationship". "Indirect substitution may be modeled as a form of allocation based on some other relationship when a co-product is assumed to displace a marginal or average market-equivalent product via market-mediated processes" [28].

#### *3.2. Selection of the ISO Allocation Criterion*

The main discussion on the allocation criterion concerns the nature of the so-called ISO "physical relationships" and "other relationships" [7,42].

The authors in line with the socio-economic school argue that allocation can be based on physical relationships only when the ratio of the output products can be varied since this allows the establishment of physical causality between functional units by mathematical modeling [68–73]. For example, Bernier et al. [74] assessed the impact of Kraft lignin and applied the physical causality principle to allocate the impact between pulp and lignin "by varying the quantity of lignin precipitated and then observing direct variations in the environmental loads". They also specified that this type of allocation was selected based on ISO standards, which recommends this type of allocation over allocation based on mass, energy or economic values. This school, therefore, interprets "physical relationships" as "physical causality relationships" and interpret "other relationships (e.g., economic value)" as "other causal relationships". Accordingly, they consider the allocation by other relationships as the only possible approach when it is not possible to change the ratio of production of the functional outputs of the system [69,72]. The practitioners following the view of this school often argue that, at this level, economic allocation is the recommended option, and only when it is not possible to use economic allocation, the allocation can be based on a physical parameter that should be selected based on the best proxy for economic revenues (e.g., see [7]). For example, this happens when there is a lack of market prices for one specific product [75]. However, the approximations of these causal relationships have always been a debated scientific issue [76]. For example, these relationships can be based on the common function of all co-products (as done by [77]).

The text-mining process revealed that only 28% of the LCA case studies selected an allocation method based on ISO relationships interpreted as "causal relationships". The percentage of studies following this interpretation varied significantly depending on the sectors considered (see Figure 5). In particular, it was very low in the studies focusing on anaerobic digestion, bioenergy and bio-based materials (5%–16%). On the other hand, this interpretation is largely present in the fossil fuels sector, where the allocation of emissions to single products is often based on linear programming models calculating marginal emissions by varying the amount of functional units [78,79]. Another example where this interpretation is largely present is in the dairy sector. The main reason is that many practitioners assessing dairy products often selected their allocation choice based on the recommendations of the International Dairy Federation [80], which adopts this interpretation.

**Figure 5.** The percentage of studies in each research area that used causality as the principle for the allocation choices per (sub-)cluster and overall. The number of studies per sector: anaerobic digestion (21), bioenergy (185), fossil counterparts (34), agriculture (63), aquaculture (14), dairy and meat (79), waste management (50), bio-based materials (52).

Conversely, the practitioners belonging to the natural-science school often refer to an allocation by physical parameter as ISO-second level allocation by interpreting "physical relationships" as allocation based on a physical parameter, e.g., mass or energy value [8,16,81–83]. On this basis, they prefer allocation based on a physical parameter (e.g., mass) over economic allocation because "ISO 14044 standard mentions economic allocation when no other possibility is available" [84]. The economic allocation may be selected by the practitioners following this view if allocation based on physical parameter "result in the attribution of a large proportion of burdens to low-value co-products" [9]. The same school often argued that an allocation based on a physical parameter is preferred over economic allocation since it is not affected by price fluctuations [82,85]. As a response, authors in line with the socio-economic school argue that the price fluctuation is not the important parameter for the allocation method, but the ratio of prices among all products, which is much less variable because it mainly depends on the fluctuating price of the common inputs to the process [86].

The preference expressed by the natural-science school is adopted by PEFCR guidance and PEF guide, which prefer allocation based on physical keys (e.g., mass or energy) to economic ones [28,29]. In the PEF guide, ISO "physical relationships" might have been interpreted as allocation based on physical parameters (this emerges from our understanding of annex X of PEF guide), leading to the preference for physical allocation keys. On the contrary, the ILCD handbook adopts the interpretation of "ISO physical relationships" from the socio-economic school and states that only when it is not possible to find clear physical *causal* relationships between the co-functions, allocation based on economic relationships can be used [27]. However, differently from what is usually preferred by the socio-economic school, the ILCD handbook does not give preference to economic allocation over non-causal physical properties such as energy content [27]. The ILCD handbook also adds a footnote to remark that energy allocation is not an allocation based on ISO causal physical relationship but a simplified allocation based on a physical property that is not causal [27].

To make an example of the implication of adopting one interpretation or the other, we can consider a biorefinery example that produces fuels (e.g., ethanol) and chemicals for materials (e.g., lactic acid) [87]. The natural-science school would prefer energy or mass allocation (considered by them as ISO second level) over economic allocation (considered ISO third level). Conversely, the socio-economic school would prefer economic allocation, arguing that mass and energy allocations (all considered ISO third level) are meaningless for such a biorefinery because of not representing any causality mechanism. They would also argue that it is not appropriate to use energy allocation when not all the co-products

are used for their energy content or to use mass allocation when there are energy products among the co-products.


*2.1.1 Preoperative investigation and imaging studies*

Baseline preoperative laboratory investigation include a complete blood count, serum electrolytes, blood urea nitrogen, creatinine, glucose, and coagulation profile. An electrocardiogram (ECG), Echocardiogram, chest x-ray, and arterial blood gases may also be obtained. ECG abnormalities in MMD include ventricular enlargement and hypertrophy, ST-T wave changes, premature ventricular contraction, and right and left axis deviation [24–26]. A review of imaging studies especially cerebral angiography may help to identify the severity of the disease.


*Article* **Construction of Novel Nanocomposites (Cu-MOF/GOD@HA) for Chemodynamic Therapy**

**Ya-Nan Hao 1,†, Cong-Cong Qu 1,†, Yang Shu 1,\*, Jian-Hua Wang 1,\* and Wei Chen 2,3,\***

> 2

**Abstract:** The emerging chemodynamic therapy (CDT) has received an extensive attention in recent years. However, the efficiency of CDT is influenced due to the limitation of H2O2 in tumor. In this study, we designed and synthesized a novel core-shell nanostructure, Cu-metal organic framework (Cu-MOF)/glucose oxidase (GOD)@hyaluronic acid (HA) (Cu-MOF/GOD@HA) for the purpose of improving CDT efficacy by increasing H2O2 concentration and cancer cell targeting. In this design, Cu-MOF act as a CDT agent and GOD carrier. Cu(II) in Cu-MOF are reduced to Cu(I) by GSH to obtain Cu(I)-MOF while GSH is depleted. The depletion of GSH reinforces the concentration of H2O2 in tumor to improve the efficiency of CDT. The resultant Cu(I)-MOF catalyze H2O2 to generate hydroxyl radicals (·OH) for CDT. GOD can catalyze glucose (Glu) to supply H2O2 for CDT enhancement. HA act as a targeting molecule to improve the targeting ability of Cu-MOF/GOD@HA to the tumor cells. In addition, after loading with GOD and coating with HA, the proportion of Cu(I) in Cu-MOF/GOD@HA is increased compared with the proportion of Cu(I) in Cu-MOF. This phenomenon may shorten the reactive time from Cu-MOF to Cu(I)-MOF. The CDT enhancement as a result of GOD and HA effects in Cu-MOF/GOD@HA was evidenced by in vitro cell and in vivo animal studies.

**Keywords:** Fenton reaction; hydroxyl radicals; GSH depletion; hydrogen peroxide; glucose oxidase