*Article* **Area-E**ffi**cient FFT Kernel with Improved Use of GI for Multistandard MIMO-OFDM Applications**

#### **Song-Nien Tang 1,\* and Yuan-Ho Chen 2,\***

Received: 17 May 2019; Accepted: 16 July 2019; Published: 18 July 2019

**Abstract:** This study presents a fast Fourier transform (FFT) kernel for multistandard applications, which employ multiple-input, multiple-output orthogonal frequency-division multiplexing (MIMO-OFDM). The proposed design uses a mixed-radix, mixed-multipath delay-feedback (MRM2DF) structure, which enables 4/5/6-stream 64/128-point FFT. This approach allows the effective usage of guard intervals (GI) in conjunction with a novel resource-sharing scheme to improve area efficiency. An area-reduced constant multiplication unit and sorting buffer with minimal memory size further reduced an area overhead. A test chip was designed using UMC 90-nm technology, and was evaluated through post-layout simulation. The proposed design outperformed previous works in terms of the throughput per area.

**Keywords:** fast Fourier transform; FFT; kernel; MIMO; OFDM; multistandard


**5. Clustering of public RNA-seq data from recount2**

Recount2 is a multi-experiment resource of analysis-ready RNA-seq gene and exon count datasets. It contains 2041 different studies and over 70,000 human RNA-seq [25]. We selected for our study four different datasets based on the number of samples and the number of classes. We then performed sample-based clustering on each dataset and compared the results to the classes in the phenotype table in recount2 to evaluate the performance of each method. The methods used to classify the data are 3 subtypes of the hierarchical clustering with the Euclidean distance, hierarchical clustering with a Poisson model and k-medoids.


**2. Overview of optimization**

Optimization methods execute and compare iteratively to find solutions for the optimum solutions to be searched. Optimization is part of all problems in all fields. Common types of optimization methods adopted to find solutions are briefed.

#### **2.1 Stochastic optimization**

Stochastic optimization (SO) computation involves more vagueness and impreciseness because of randomness in function of minimization or maximization to lend for real-life scenarios. The involved unpredictability exists in form of noise in process of search by Monte Carlo randomness [8]. Stochastic annealing, approximation, programming, swarm-based algorithms are common involved techniques of SO. They include high non-linearity system noise and dimensional models. These models are present to analyze, solve, derive, and numerical extraction of information in resolving decision-making problems. Major investment of SO is in specific applications oriented towards long and short programs. Aircrafts, missile, drug design, and network traffic control applications are getting advantage of SO. Stochastic application tool can be

applied as a powerful modelling tool in a few applications, but estimation of real-life problems is another major uncertainity where solving through SO involves practical limitations. Another problem of SO is complete dependency on data available and modelling of it [3, 9].

### **2.2 Robust optimization**

The optimization model is robust based to deal with data to regulate uncertainty. Key features are deterministic, easy computational tractability and set based. Model includes global or local or non- probabilistic or probabilistic models. Any given problem will get involve all the features of robust optimization in order to search for a solution. The technique is also known as the min-max or worst-case approach. Provide a guarantee for solutions to problem application which involves more uncertainty in data. The parameters involved in process of estimation are to resolve estimation errors. One improved model for definition and interpretation is setting more robust constraints [10]. Engineering optimization design results mainly in reliability optimization and feasible input possible values to robust solution structure. Robust optimization gives same weight and values for parametric values in collection of uncertain data. Problems will be resolved with the formulation of cost savings and stability, qualitative and quantitative. Complex problems considered for optimization may extend complexity to a more significant level [6, 7].

### **2.3 Dynamic optimization**

Dynamic programming is another name for dynamic optimization which processes optimal profile of more than one parameter of a system used to find possible solutions for a problem given. Variations of dynamic optimization with optimization discrete time, calculus variation and extended static optimization. The implementation includes a system controller to perform criteria with algorithm to execute control. Dynamic optimization involves a system controller performing optimal substructure and overlapping sub-problems [8]. Dynamic optimization characterizes structure, recursively defines value, computes value and constructs an optimal solution for computation. Dynamic programming optimizes problems and recursively divide problem into sub-problems which can solve either bottom-up or top-down approach. The logic used is general and supple. It solves computation time and storage space [9]. Classification optimization based on different factors is summarized in **Table 1**.


**4.2.1 The cross tab analysis of religious and educational variables**

In this research, two research questions are addressed. The first research question is: "Whether a person's education is related with his or her religious activities." The analysis of the first question could be expressed in the following table:

Table 2. The Structure Tree of Analysis

Table 2 demonstrate the logic of our analysis: we analyse the correlation between one's highest degree and his or her religious activities. The religious activities are divided into two categories: the first category is the fact concerning one's religious belief such as how often one prays, takes part in religious activities, and in which religion is one raised. The second category is one's opinions considering how religious one person is. The questions include how fundamental one considers him or her to be, the strength of affiliation, etc. The corresponding results of the analysis will be shown in the result part.

Religious Participation and Educational Attainment: An Empirical Investigation 327

Followed by the empirical estimation, the results are described as two parts: the first part concerns about the association between one's highest degree achieved and his or her religious practices and beliefs. The second part of the results is about the association between other factors (age, education, family background, etc.) and one's religious practices.

**5.1 The association between one's highest degree achieved and his or her religious**

There is no significant association between a person's highest degree earned and "how often the person attends religious services". However, there is significant association between one's highest degree and their feelings about the bible. Besides, there is significant association between a person's highest degree and "allow anti-religionist to teach". There is also a significant association between one's highest degree earned and "how fundamentalist was one at age 16. Similarly, a significant relationship exists between one's highest degree earned and "how fundamentalist was one currently". Other variables that are significantly associated with "a person's highest degree earned" include the following variables: "feelings about the bible", "confidence in the existence of God", "Pope is infallible on matters of faith or morals", "how often does one pray", "whether one should agree that there can by Bible prayer in public schools or not", "whether one agrees that sinners must be punished or not", "whether one has ever had a 'born again' experience", "how often does one take part in religious activities", "whether religious experience changed one's life", "whether there is a turning point in life for religion", "one's religious preference", "religion in which one is raised", "strength of affiliation", "try to carry one's beliefs into other dealings", "whether there is any turning point when less committed to religion", "whether one considers himself or herself a religious person", "whether one tried to convince others to accept Jesus", "whether one thinks that he or she believes too much in science, not enough about faith", "how fundamentalist is spouse currently", "whether one considers herself or himself a spiritual person". Tables 3 to Table 11 describe some of the most important associations between one's highest degree achieved and his or her religious practice in detail. We classify the tables 3 to 11 as one's belief such as in which denomination is he or she raised and people's opinion of religious activities such as how fundamental the

Table 3-6 describes some of the facts concerning the association between one's highest

Table 3 shows the relationship between a person's highest degree and how often he or she prays. From the table, we can see that for people with less than high school education, most of them pray once a day (31.2%); for people with high school and junior college education, the majority pray several times a day; for people with bachelor and graduate degrees, the majority of them pray once a day. The largest percentage of people who never pray comes out of graduate degree holders (21.2%). The Pearson Chi-Square is 35.128 and the significance lever is 0.019, this means that a person's highest degree is significantly

**5. Results**

**practices and beliefs**

person considers himself to be.

degree and his religious practices.

associated with how often a person prays.

**5.1.1 Facts concerning one's religious belief**

Aanalyse this issue, crosstab analysis is applied. Crosstab method is usually applied to when the independent variables are categorical variables. The data is divided into several cells and the significance of association among different cells is estimated. To use the crosstab method, it is very crucial that there are no empty cells. Technically, there will be empty cells only if the variables are continuous. Since we don't have continuous variables in our model, we are not worried about this problem. We did the crosstabs with the response variable by each of the categorical predictor variables, and those tables looked good with cell size, as a result, the analysis is valid. The results of this part of the analysis are shown in Table 3 to Table 11. The meanings of the crosstab results and regression results are explained in detail to satisfy the needs of those who are less methodologically inclined (sociologists included) and those who are generally less familiar with the jargons of sociology (non-sociology readers)

#### **4.2.2 Regression analysis of religious, educational, occupational, and other variables**

The second part of the analysis applies regression analysis to analyze the relationship between religion and other social economics variables. First, I estimate the effect of education and other variables on the number of church attendances of individual i in year t: R is a variable indicating the number of days of attending religious activities. The constant term is denoted by α1. E is a categorical variable indicating the level of one's education. M is a variable indication the marital status of the person: whether he or she is married or not. N is a variable indicating number of children in the family; X is a vector of independent variables including age, family income, religion denomination, whether the person is divorced or not, ethnicity, whether the person feels happy or not, whether the person feels life is exciting or not, race, the type of household, whether the person is Hispanic or not, the marital status of the person, and the gender of the person. Since the independent variable of religious activity attendance is a continuous variable, the paper applies ordinary least square regression to estimate the model.

$$R\_{it} = \alpha\_1 + \delta\_1 E\_{it} + \delta\_2 M\_{it} + \delta\_3 N\_{it} + \sum\_{1}^{11} \delta\_m X\_{im} + \varepsilon\_{it}^1 \tag{1}$$

Second, I estimate the effect of occupation and other variables on people's religion activities. The estimation equation is as the following:

$$R\_{it} = \alpha\_2 + \lambda\_1 O\_{it} + \lambda\_2 M\_{it} + \lambda\_3 N\_{it} + \sum\_{1}^{11} \lambda\_m X\_{im} + \varepsilon\_{it}^2 \tag{2}$$

R is again the variable indicating religious attendances. The constant term is denoted as α2. O is a categorical variable representing a person's occupation. M is a variable indication whether the person is married or not. N is a variable representing the number of children in the family, X is a vector of control variables that include age, the family income, religious denomination, whether the person is divorced or not, ethnicity, whether the person is happy or not, whether the person feels life or not, race, the type of the household, whether the person is Hispanic or not, the marital status of the children, and gender. The independent variables from equation one and equation two are similar.


**3. Practical outlook of herbal drug discovery**

The following facets represent outlook of the stages involved in the development of bioactive molecule as pure drug from a plant source [12].

First of all, in order to study medicinal plants, selection of plant and which type of pharmacological activity is to be studied should be clear to the researcher. Five principles of selection of plants are very important to know which are the random, the taxonomic, the phytochemical, the ethno-medical and the information-managed approach (**Figure 2**)[14].

**Figure 2.**

*General procedure for obtaining active principles from plants.*


**6.2 Targeting matrix metallopeptidase 9 (MMP-9) in diabetic foot ulcers**

Wound healing can be hampered by stress. Cortisol, a steroid hormone, is increased during stress and chronic inflammation, and it slows wound healing Elevated Matrix metallopeptidase-9 levels that have been linked to higher cortisol levels in individuals with coronary artery disease. Prostaglandin E2 has been shown to increase cortisol release and to activate matrix metallopeptidase-9 [58]. One of the causes of the recalcitrance is the increased activity of the enzyme matrix metalloproteinase (MMPs). The preferred substrates of MMPs, such as gelatinases (MMP-2 and MMP-9), collagenases (MMP-1, MMP-8, and MMP-13), and stromelysins, are characterized in a variety of ways (MMP-3 and MMP-10). A second generation of orally bioactive broad-spectrum inhibitors was created as a result of the first generation of MMPIs being broad-spectrum zinc chelators with low bioavailability [59]. The medication becaplermin Gelatinase activity was reduced, but collagenase activity was *Targeting Matrix Metallopeptidase 9 (MMP-9) and Role of Quorum Sensing (QS) in Diabetic… DOI: http://dx.doi.org/10.5772/intechopen.106514*

unaffected; active MMP-9 was reduced, while active MMP-8 was unaffected [60]. MMP expression and activity may be significantly impacted by diabetic foot ulcer treatments that do not directly target matrix metallopeptidase. Most researches have concentrated on the effects on this proteinase specifically since MMP-9 has long been of interest in wound healing [61].


**1. Introduction**

Currently, the use of sodium chloride (NaCl) is widespread in a large number of countries, as a de-icing agent on winter roads. The impact that salt has on the environment has been extensively studied; the melting salt leads to the mobilization of heavy metals such as lead, cadmium, copper and zinc [1–4], along with increased chlorides (Cl−) [5,6]. However, salt is still used, due to its low cost, versatility and physical properties [7,8]. These physical properties make their use propitious in temperatures up to −21 ◦C, but as García [7] indicated, from −5 ◦C, in order to be more effective, they can be used in combination with calcium chloride (CaCl2).

Shi et al. [9] noted that the value of negative impacts on the environment and on vehicles must be taken into account when calculating the costs of de-icing agents, including NaCl. In this trend of reducing costs and negative impacts, Klein-Paste et al. [10] indicated that the amount of NaCl that is used in practice could be reduced by 40%, to prevent the pavement from being slippery, because the salt spread on the roads causes a weakening of the ice. Another aspect studied along the line of reducing the negative impact of NaCl is the creation of tools to decide more e fficiently when salt should be applied to roads [11,12].

There are also studies on the behaviour of bituminous mixtures in contact with the di fferent de-icing agents. Wang et al. [13] noted that, compared to other anti-icing agents, such as sand and quartz dust, salt does not produce a polished surface. Hassan et al. [14] indicated that the value of Indirect Tensile Strength (ITS) for asphalt mixtures exposed to salt after 25 freeze–thaw cycles is similar to other de-icing agents, such as potassium acetate, sodium formate or urea. However, after 50 cycles, the ITS result is even better for the mixtures exposed to salt than those submerged in distilled water.

Feng et al. [15] studied the impact of salt on the performance of bituminous mixtures when it remains submerged under seawater subjected to freeze–thaw cycles. They simulate this fact by adding salt to bitumen and subjecting various types of mixtures (AM-16, OGFC-19 and AC-16) to freeze–thaw cycles; their results show a decrease in the Water Sensitivity Test (ITSR), with results being lower in the case of the AC mixture.

Juli-Gándara et al. [16] investigated how NaCl influences the mechanical properties of three types of asphalt mixtures: a hot mix asphalt with conventional bitumen; and two porous mixtures, one manufactured with a conventional binder and another with a modified binder. The e ffect of the salt is analysed by three di fferent processes: immersing the specimens in salt water; adding salt as aggregate into the mixture; and submerging the aggregate in water with a certain concentration of salt, drying it and then making the mixture with it. The results show that the hot mix asphalt is scarcely a ffected when it is submerged in salt water.

The e ffects of freeze–thaw cycles in the bituminous mixture are widely studied. Goh and You [17] used an image-processing technique to indicate that the removal of fine and coarse aggregates on the surface increases when the asphalt mixture undergoes more freeze–thaw cycles. Tarefder et al. [18] tested an AC mixture to ITS and Fatigue Test after 5, 10, 15 and 20 freeze–thaw cycles, and the results showed a reduction of 30.7% in the fatigue life and a small amount of reduction of ITSR after five cycles. Özgan and Serin [19] indicated through the void ratio (Vh), the void ratio filled with asphalt (Vf) and the void ratio inside mineral aggregate (VMA) parameters; the ultrasonic velocity test; and the Marshall Stability (MS) that the e ffect of the freeze–thaw cycles on the asphalt concrete is highly important, especially for the hot mix asphalt design. Islam and Tarefder [20], who investigated the sti ffness and tensile strength degradation behaviour of asphalt concrete on long-term freeze–thaw samples in the laboratory, indicated that the flexural sti ffness decreases with the number of freeze–thaw cycles, whereas the ITS does not change significantly with the number of freeze–thaw cycles. Teltayev et al. [21] investigated, in laboratory conditions, the e ffect of cyclic freezing and thawing on the characteristics of the neat bitumen and bitumens modified with di fferent polymers, as well as stone mastic asphalt concretes. Their results of the SMA-20 Bit 100/130 show a 78% decrease in the value of ITS and a 270% increase in the Rut Depth (RD) after 50 freeze–thaw cycles.

Until now, no research has covered the results of mechanical behaviour such as mechanical strength, permanent deformations, dynamic modulus and fatigue life, when an asphalt concrete mixture is subjected to temperature changes, freeze–thaw cycles and the impact of salt. The purpose of this research is to fill this gap. The temperature interactions have been carried out for dry specimens, specimens submerged in distilled water and specimens submerged in salt water (NaCl).


**2.** *Eciton* **Army Ants**

Several ant genera within the subfamily Dorylinae are referred to as army ants, with the genera *Cheliomyrmex*, *Eciton*, *Labidus*, *Neivamyrmex* and *Nomamyrmex* forming a monophyletic group and found in tropical and subtropical regions in the American continent [43,44]. Army ants are characterised by presenting the "army ant syndrome" which includes the evolution of behavioural and reproductive traits that have allowed them to be key predators in neotropical forests. This suite of traits includes obligate collective foraging, nomadic lifestyle, and highly specialised queens which are characterised by being wingless and having large expanding abdomens capable of lying thousands of eggs [38,45–47]. Colony founding of *Eciton* army ants is by fission. When colonies attain a large size, a sexual brood is produced, containing a small number of new queens and hundreds of males. The parental colony will then divide into two daughter colonies, one of them headed by a new queen and the other one headed either by the old queen or another of the new queens [36,48]. Initial phylogenetic analyses based on data derived from three nuclear genes, one mitochondrial gene, and morphological analyses, suggested that the evolution of the "army ant syndrome" in army ants species in the Americas and Afro-Eurasia was as a result of inheritance of these traits from a unique Gondwanan common ancestor [46]. However, a recent comprehensive phylogenomic study based on 2,166 loci

has indicated multiple origins for the "army ant syndrome" in different continents [44]; therefore, supporting initial views of convergent evolution of this syndrome [38].

The genus is distributed from Mexico to Argentina [38], with 12 *Eciton* species, and several subspecies, described [49]. Genomic analyses using a large number of loci (> 4,000,000) for 146 specimens, collected from a wide distribution range and representing those nine species for which workers and queens are known, strongly supported interspecific relationships congruent with taxonomic classifications [50]. However, further taxonomic revisions are required to delimit species and subspecies boundaries. For example, a phylogeographic study based on mitochondrial DNA sequences indicated that genetic divergence of *E. burchellii foreli* and *E. b. parvispinum* colonies was higher than between other morphologically described species [51].

Foraging and emigration of *Eciton* army ants takes place above ground [36,52]. The species *E. burchellii* and *E. hamatum* are considered truly epigaeic, as they bivouac above ground. Other *Eciton* species such as *E. mexicanum*, *E. vagans* and *E. dulcium* tend to bivouac in more sheltered and darker places, inside or beneath logs, or in underground erosion chambers [36]. In these more hipogaeic species, raids and emigration columns can take place underground but only for short distances and when near the bivouac [53]. Knowledge on *Eciton* army ants is primarily based on research focused on *E. burchellii* and to less extent on *E. hamatum*, as these two species are the most epigaeic diurnal *Eciton* species. Most *Eciton* species are column raiders (e.g. *E. hamatum*, *E. vagans*), with hunting taking place by ants moving initially in narrow columns that later divide into several diverging branches. The size of the raid varies among species, from sparse raid front such as those in *Eciton dulcium,* often narrower than 2 m, to swarm fronts wider than 5 m in in the swarm raider *E. burchellii* [38,53,54].

A large-scale survey conducted at La Selva Biological Station (Costa Rica) examining the diets of sympatric neotropical army ants, including the species *E. burchellii*, *E. dulcium*, *E. hamatum*, *E. lucanoides*, *E. mexicanum* and *E. vagans* revealed high specialisation and small spatio-temporal niche overlap [55]. Using DNA barcoding to identify ant prey to the species level revealed differences in raiding preferences, with *E. dulcium* and *E. mexicanum* preferentially raiding ground-nesting ants and *E. hamatum* and *E. burchellii* arboreal-nesting ants. Furthermore, *E. hamatum* and *E. burchellii* colonies were found to have more diurnal raids whereas most of the other *Eciton* species had primarily nocturnal raids [55].

All *Eciton* species are predominantly predators of other ants (mainly their brood) although they specialise on different ant genera. *E. hamatum* predominantly preys on the brood of *Acromyrmex* leaf-cutting ants, *E. dulcium* on *Pachycondyla* and *Odontomachus* ants, and *E. burchellii* on *Camponotus* ants [14–20]. Although the diet of *E. burchellii* is more varied and includes a diverse range of non-ant litter arthropods, in particular the brood of other social insects [56–61], this non-ant component of the prey might be comparatively low [55]. The large colony size of *Eciton* species, with estimates ranging from 150,000 to 500,000 workers in *E. hamatum* and 500,000 to 2 million workers *in E. burchellii* [38], and the relatively large body size of of some of the workers imply high colony-energy demands [62]. Colonies of *Eciton* army ants have a nomadic-statary cycle, with periods of in which the bivouac (temporary nest) remains in a fixed location where the queen lays thousands of eggs [63]. During this period, the workers hunt prey locally in a regular manner. The statary phase is followed by a nomadic phase coinciding with the eggs hatching into larvae and, during this phase, the colony emigrates daily after the raids over the period of 2–3 weeks [36,64]. In the most studied species, *E. burchellii,* colonies concentrate their raids on invertebrate-rich forest patches [65] and a colony could capture c. 30,000 items of prey in a single day [66]. The nomadic-statary cycle of *Eciton* colonies is interpreted as an adaptation to avoid prey depletion by promoting population recovery of their prey [56]. The pattern of raids, as part of the nomadic lifestyle of *Eciton* army ants, therefore, exerts top-down regulation of leaf litter arthropod communities and creates a mosaic of habitat patches at different ecological succession stages which increases local species diversity [35,65,67].


**2.2 Applications of Blockchain**

Blockchain was originally employed in cryptocurrencies, with Bitcoin being the first to demonstrate its success. There are numerous Blockchain applications today, as summarized in the following **Figure 3**:

#### **Figure 2.**

*Types of Blockchain [9].*

#### **Table 1.**

*Differences between types of Blockchain [10].*

To be more specific, the following list will explain a little bit about the importance of the use of Blockchain in the most important domains [6]:

### *Information Systems Management*

**Figure 3.** *Blockchain applications tree [6].*


**1. Introduction**

Electric vehicles (EVs) have several advantages over conventional fossil fuel-powered vehicles such as zero tailpipe emissions, higher tank-to-wheel efficiency, low noise, and full torque at zero speed. With the increased use of electric vehicles in the form of e-bikes, electric cars, and electric buses, there is increased emphasis to make sure that the electricity used to charge the EVs are sustainable as well [1,2]. As shown in Figure 1, the average emission from electricity production for various European countries shows a wide variation of 13–819 g of CO2 equivalent per kWh based on the electricity generation mix in 2016 [3]. The European Union wide average is 296 g/kWh, which is primarily driven by the 43% share of fossil fuels in the gross electricity generation. Assuming a modest energy efficiency of an electric car to be 5 km/kWh, the CO2 emission of 13–819 g/kWh translates to a 2.6–163 g/km. Therefore, to make EVs truly sustainable, it is essential to charge EVs from sustainable sources of electricity, such as wind and solar energy [4–7].

Using solar energy for charging EVs is attractive due to the possibility of distributed photovoltaic (PV) generation in locations close to where EVs are parked. Specifically, charging electric vehicles from PV panels at workplaces has significant potential for the future due to several reasons.

**Figure 1.** CO2 emission from electricity production for selected European countries for 2016, in g CO2 equivalent/kWh.

Concurrently, the disadvantage of charging EVs at the workplace using solar energy is the low solar generation in winter months. Secondly, there is a lack of charging demand on the weekends for workplaces that have a weekday working week.

#### *1.1. Electric Bikes and Electric Scooters*

Electric bikes and electric scooters provide a convenient means of intra-city commute with a multitude of benefits such as door-to-door connectivity, low (indirect) emissions, reduced traffic, and parking congestion and a fraction of the energy usage of an electric car [11]. In 2016, around 30% of the 928,000 bikes sold in the Netherlands were e-bikes [12]. An e-bike can travel up to 25 km/h using a motor of up to 250 W (up to 1000 W for high-speed e-bikes) and uses a 12 V–48 V battery with an energy capacity of 0.2–1 kWh. Electric mopeds (including speed pedelec), on the other hand, can go up to 45 km/h using a motor of 1–4 kW and typically uses a 48 V battery of 1–5 kWh. Furthermore, e-bikes have an extremely low energy consumption in the range of 5–15 Wh/km depending on the drivetrain efficiency, riding behaviour, tire characteristics, and the combined weight of the bike and rider. This is much lower than the 150–200 Wh/km energy consumption of an electric car.

By providing a charging facility at the workplaces and powering it with solar energy, e-bikes can be made into a fully sustainable means of daily commute [13,14]. The focus of this paper is on the development of a charging station to sustainably charge e-bikes at the workplace using solar energy. The charge station is shown in Figure 2 and provides three modes of charging: AC, DC, and wireless charging, respectively.


**4. Experiences**

We have selected data from just one field using our irrigation scheduling platform as an example, to fully present its many applications. The authors have implemented irrigation scheduling professional consulting since 1982 in more than 150 horticultural plantations in Chile, Mexico, Peru, and Argentina, on a cultivated area estimated at 5500 hectares. Concepts, parameters, coefficients, and computer programs, developed and published extensively in scientific and professional journals and presented at countless congresses, courses, and workshops, today serve as the basis for the correct use of irrigation systems in horticultural plantations, carried out also by many other professionals and technicians in different countries; selected irrigation scheduling publications by the senior author are available on Internet [41].

**19**

**Figure 3.**

*Agronomic Operation and Maintenance of Field Irrigation Systems*

two climate evaporative demand conditions are presented (**Table 1**).

*Irrigation scheduling for two Cabernet sauvignon plots, November 15–December 31, 2018.*

Data for a 61.31-hectare vineyard, with two independent drip irrigation systems, three sectors each, planted in three different soils, with five different cultivars and

**Figures 3** and **4** present data on irrigation for Cabernet sauvignon for plots 1 and 6 (different evaporative demand conditions, due to site topographic positions within the vineyard, as well as different soil hydrodynamic characteristics), for a time frame from November 15 to December 31, 2018; the platform enables the user to select data for any plot or plot combination, for any time span, from 1 day to the whole irrigation season. Daily comparisons between calculated ETp (red columns) and actual water depth applied (blue columns) are provided in graphic format, indicating a correct operation of the irrigation system throughout both dates, with the exception of December 25th, when no irrigation was performed, followed by two intensive irrigation days, to recover the difference. Water depths applied during the specified time span are also provided, comparing calculated ETp and water depth applied. This information is a helpful tool to decision-making for water recovery or withhold, aimed to keep a constant soil water content in the root zone.

*DOI: http://dx.doi.org/10.5772/intechopen.84997*

#### **Table 1.**

*Vineyard data relevant for irrigation scheduling.*


**9. Virus CAR-Pooling to Tumours**

Although we have discussed primarily the use of viruses to improve various aspects of T cell anti-tumour function, so T cells can also be exploited to improve the efficiency of oncolytic virotherapy—most notably perhaps through helping to deliver the viruses to their active site. In vitro pre-loaded antigen specific T cells, and cytokine-induced killer cells, have been reported to traffic OVs, allowing replication and oncolysis within the tumour. This smuggling of viruses to tumours has been shown to be possible even in prevaccinated hosts, thus bypassing circulating anti-viral antibodies which have often proved to be the Achilles heel of systemic OV therapy [126–131]. Similarly, murine and human HER2 CAR T cells loaded with low doses of oncolytic VSV or vaccinia virus have been shown to deposit their cargo without compromising the function of the CAR T cells [132]. As discussed above, the TME represents a very unwelcoming parking place for (CAR) T cells. The same is true for highly immunogenic viruses trying to passage through and then exit selectively from a circulatory highway system heavily patrolled by neutralising antibodies, complement, and other anti-viral effectors. However, CAR-pooling of precisely engineered tumour-targeting viruses may overcome several of the barriers to effective combination CAR/OV therapy.


**2. General features of ostracods for taxonomy, ecology, and morphology**

Ostracods are a class of small crustaceans (Figures 1 and 2), the adult form of which is typically ca. 1.0 mm in length. They are not generally well known to many people except for researchers of fossils or living crustaceans due to their small size and lack of commercial significance compared to other crustaceans; *e*.*g*., shrimps and crabs.

Class Ostracoda consists of the following six orders: Podocopida, Platycopida, Palaeocopida, Leperditicopida, Myodocopida, and Halocypridina [10]. This chapter focuses mainly on ostracod species belonging to the order Podocopida, which consists of more than 20,000 named living and fossil species distributed globally, because Podocopida is the most diversified taxonomic group in class Ostracoda (Figure 3) [10]. The well-known bioluminescent organism 'the sea firefly' (called 'umi-hotaru' in Japanese) is a kind of ostracod but belongs to another order, Myodocopida.

Ostracods occur in most aquatic environments on earth (Figure 3), such as the deep sea to a depth of several thousands of meters, through to shallow seas on the continental shelf [5]. They also live in rock pools in intertidal zones, brackish water areas at river mouths, lagoons and estuaries, freshwater lakes, ponds, irrigated rice fields, and temporary puddles. Most species are benthic throughout their lives, and crawl on or through the surface sediment and among aquatic plants [10]. A number of interstitial species, which live between sediment particles, are also distributed globally [18] [19].

The most distinctive feature of ostracods is the calcareous bivalved carapace (or shell), consisting of two valves that completely envelop the soft body and all appendages (Figures 1 and 2) [10]. Various types of appendage are protruded between opened valves for locomotion, feeding, and reproduction. The two valves (termed right and left) are connected by a hingement running along the dorsal margin (Figure 2). The word ostracod (or 'ostracode') is derived from Greek word 'ostrakon', which means 'a shell'. This carapace (or shell) has various morphological characteristics (Figures 1 and 2) that allow detailed taxonomic and phylogenetic studies to be performed on both living and fossil specimens [20–22].

Like other crustaceans such as decapods, ostracods grow by moulting (ecdysis; Figure 4). For example, in Podocopida, there are usually eight moult stages between egg and adult [20]. Species with strongly calcified carapaces, such as most marine species, are relatively easily fossilised. Ostracods are abundant in sediments globally, beginning in the early Ordovician (ca. 490 Ma) [7]. The proteinaceous (= 'chitinous') soft body and appendages are rarely fossilised due to a lack of mineralised parts, with rare exceptions, such as preservation of the soft parts of Silurian fossils [23]. Therefore, an ostracod fossil typically consists only of the hard-calcified carapace; however this is sufficient for identification of both modern and fossil specimens to the species level based on various carapace morphological characteristics.

In particular, the surface ornamentation (reticulation, fossae, muri, eye tubercle, ridges and so on), hingement type, muscle scar morphology (Figures 1 and 2), and pore shape and numbers are useful for ostracod taxonomy [24]. With living specimens, the morphology of male copulatory organs and other appendages are also used for species identification, similar to identification techniques used for other crustaceans, such as decapods [24–26]. Fossil ostracods are commonly utilised by palaeontologists as important palaeoenvironmental and stratigraphic (geological age) indices, and have long been used in oil and gas exploration [4] [27–29].

The History of Sexual Dimorphism in Ostracoda (Arthropoda, Crustacea) Since the Palaeozoic 59

**Figure 7.** Carapace outlines of female and male of *Beyrichia salteriana* and *Bolbibollia labrosa* in Family Beyrichiidae (Palaeocopida) from Silurian deposits of Sweden and Canada respectively, modifed from

inferred that species of this family inhabited Palaeozoic shallow-marine shelf-water environments [7]. Thus, a reasonable interpretation of pouch function in this family is needed to clarify the detailed evolutionary processes of the ecology and reproductive modes

Carapace shape and size sexual dimorphism is common in ostracods, and males can be larger or smaller than females [4]. One example from the Mesozoic is a non-marine species of the podocopid genus *Cypridea* [15] [32]. This genus occurred only during the Jurassic and Cretaceous, and is not found in Cenozoic deposits. The shape and size of *Cypridea subvaldensis* (Figure 8) from Cretaceous sediments in northeastern China was studied, and the differences between the two forms were recognised to represent sexual dimorphism

Benson *et al.* (1961). Arrows indicate anterior.

*3.1.2. Mesozoic example in an extinct species*

within a single species [32].

of Palaeocopida in Palaeozoic shallow-marine water areas.


*<IP_ADDRESS> Concordance*

The amount of similarity in phenotype between a set of individuals. May be used to refer to the presence of the same trait in both members of a pair of twins. (Molecular biology -Glossary) [12].


**Acknowledgements**

bioreactors have been studied in recent research [78, 88-89]. Under oxic conditions, no methanogenesis was detected; however, once oxygen is depleted, methane production ensued. The results suggest that methanogenic archaea can be activated under anoxic conditions [88].

**Figure 4.** Schematic diagram of the two new types of WWTPs. (A) Submerged membrane bioreactor (MBR). (B) Sub‐

merged biofilter (SB).

40 Advances in Bioremediation of Wastewater and Polluted Soil

The composition of the wastewater is a key determinant of archaeal community composition in WWPTs [88, 90-91]: the microbial population in industrial wastewater treatments, rich in ammonia, phenol, and with high salinity, are closely related to *Methanobrevibacter smithii*, the predominant methanogen in human intestines [78]. The manufacturing of chemical com‐ pounds (pesticides, herbicides, explosives, etc.) usually generates effluents containing complex mixtures of salts and nitrate or nitrite. Also, the increase of salinity in soils and waters in the last few decades has given advantage to some species like *Hfx. mediterranei.* For example, *Hfx. mediterranei* is resistant to very high nitrate (up to 2 M) and nitrite (up to 50 mM) concen‐ trations, which are the highest described from a prokaryotic microorganism [92]. Therefore, it could be useful for bioremediation applications in sewage plants where high salts, nitrate, and nitrite concentrations are detected in wastewaters and brines. In a recent study [93], this haloarchaea was able to eliminate 60% of the nitrate and 75% of nitrite initially present in the brines (initial concentration was 40 mM nitrite). Moreover, it has also been described that nitrate reductase involved in denitrification reduces efficiently other oxyanions such us bromate and (per) chlorate [94]. These results suggest that *Hfx. mediterranei*, and in general, halophilic archaea, are able to carry out denitrification, thus providing excellent models to

This work was funded by a research grant from MINECO, Spain (CTM2013-43147-R).


*3.1. General Procedures*

1H-, 13C-NMR, 1Н, 1Н-COSY, HSQC, HMBC, NOESY (Nuclear Overhauser Effect Spectroscopy), and TOCSY spectra (in CD3OD or CDCl3) were recorded on Bruker Avance III HD 500 and Bruker Avance III 700 spectrometers (Bruker BioSpin, Bremen, Germany) at 125 MHz (13C), 500 (1H), and 700 ( 1H) MHz. Chemical shifts (ppm) were internally referenced to the corresponding residual solvent signals δH 3.306/ δC 49.6 for CD3OD. For the spectra of compounds in CDCl3, TMS was used as an internal standard. ESI mass spectra were recorded on a Q-TOF 6510 mass spectrometer (Agilent Technologies, Santa Clara, CA, USA). GC-MS analyses were carried out on a Hewlett Packard HP6890 GC System (Hewlett-Packard Company, Palo Alto, CA, USA) with an HP-5MS (J&W Scientific, Folsom, CA, USA) capillary column (30.0 m × 0.25 mm), helium as the carrier gas, and 70 eV ionizing potential. GC analyses were done on an Agilent 6850 Series GC System chromatograph (Agilent Technologies, Santa Clara, CA, USA) equipped with an HP-1 (Agilent technology, Santa Clara, CA, USA) capillary column (30 m × 0.32 mm), the carrier gas was helium (flow rate 1.7 mL/min), and the detector temperature was 300 ◦C (or 280 ◦C for per-acetylated (2*R*)-oct-2-yl glucosides). The GC-MS analyses of FA esters and per-acetylated sphingoid bases were done using the injector temperature of 270 ◦C and the temperature program 100 ◦C (1 min)–10 ◦C/min–280 ◦C (30 min). The GC analyses of per-acetylated (2*R*)-oct-2-yl derivatives of D-Glc and L-Glc were carried out using the injector temperature of 150 ◦C and the temperature program 100 ◦C (0.5 min)–5 ◦C/min–250 ◦C (10 min). Optical rotation was measured on a Perkin–Elmer polarimeter, model 343 (Perkin-Elmer

GmbH, Überlingen, Germany). Column chromatography was performed using silica gel (50/100 μm, Sorbpolimer, Krasnodar, Russia). RP-HPLC separations were performed using a Du Pont Series 8800 Instrument (DuPont, Wilmington, DE, USA) with an RIDK-102 refractometer (Laboratorni pristroje, Praha, Czechoslovakia). Two columns (Agilent Technologies, Santa Clara, CA, USA), Agilent ZORBAX Eclipse XDB-C8 (4 × 150 mm), and Agilent ZORBAX SB-C18 (4.6 × 250 mm), were used for the HPLC.


**4. Model Systems to Study DCM and DMD Cardiomyopathy**

To clarify the mechanisms responsible for the physiologic features of DCM and DMD cardiomyopathy, and to establish novel, preventative, and innovative therapies, several experimental animal and cell models of DCM and DMD cardiomyopathy have been developed and investigated. Most models recapitulate several clinical features of DCM in humans, typically exhibiting dilation of the left or both ventricles, severe impairment of systolic and diastolic left ventricle function, and thinning of the left ventricle wall [91]. This section will highlight both animal and cell models that have been developed to study DCM as an independent disease and models that study it as a pathology of DMD. An overview of this section is summarized in Table 1.

**Table 1.** Summary of model systems to study dilated cardiomyopathy (DCM) and Duchenne muscular dystrophy (DMD)-cardiomyopathy.

#### **Table 1.** *Cont*.

MLP: muscle LIM-protein; ECG: electrocardiogram; iPSCs-CMs: human induced pluripotent stem cells-derived *cardiac myocytes.*


*2.1. Test Materials*

Concrete was made of cement, mineral powder, fine aggregate, and coarse aggregate. The binder material comprised P.O42.5 ordinary Portland cement and mineral powder with densities of 3060 and 2890 kg/m3 respectively, in equal mass fraction. The chemical composition of cement is shown in Table 1. The fine aggregate was well-graded medium sand with a measured apparent density of 2623 kg/m3 (Table 2). The coarse aggregate had a 5–20 mm continuous particle gradation, a maximum particle size of 20 mm (Table 3), and a measured apparent density of 2710 kg/m3 . The fresh concrete was cast into 120 molds in dimensions of 100 mm × 100 mm × 100 mm. After curing at 20 ± 2 ◦C and 95% RH for 28 days, each specimen was cut horizontally into three pieces in a dimension of 100 mm × 100 mm × 30 mm. To avoid separation from vibration, only the middle piece was selected for testing. In total, 60 absorption and 60 desorption specimens were prepared. Table 4 provides the mix proportion of concrete in accordance with previous studies [19–21].

**Table 1.** Chemical compositions of cement in mass.

**Table 2.** Particle size distribution of fine aggregate.

**Table 3.** Particle size distribution of coarse aggregate.

The tests were performed as per ISO12571-2013 [22]. The specimens for the absorp-

261.0 562.0 514.0 960.0

**Fine Aggregate (sand) kg/m3**

**Coarse Aggregate kg/m3**

**Table 4.** Concrete mix ratio for the absorption and desorption specimens. tion test were first oven-dried at 100 °C for 24 h and then placed in a dryer to cool down

**Table 4.** Concrete mix ratio for the absorption and desorption specimens.

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**Binder Material kg/m3**

### *2.2. Test Method* with a towel and weighed. Then, the specimens were returned to the water for 24 h and weighed. This was repeated until the change in the specimen mass was less than 0.5% to

**Water kg/m3**

*2.2. Test Method*

The tests were performed as per ISO12571-2013 [22]. The specimens for the absorption test were first oven-dried at 100 ◦C for 24 h and then placed in a dryer to cool down to 20 ◦C (room temperature) and weighed. Subsequently, all specimens were oven-dried for another 12 h, cooled down to 20 ◦C in the dryer and weighed. These steps were repeated until the change in mass was less than 0.5% to obtain a dry state. For the desorption test, all dry specimens were placed in water for seven days to be saturated, surface-dried with a towel and weighed. Then, the specimens were returned to the water for 24 h and weighed. This was repeated until the change in the specimen mass was less than 0.5% to obtain a saturated state. obtain a saturated state. Afterward, the specimens were moved to glass containers in a constant-temperature/humidity chamber with a temperature accuracy of ±1 °C and 50 ± 2% RH as shown in Figure 1. During the test, saturated salt solutions of LiCl, MgCl2, KBr and KNO3 were added at the bottom of glass containers to secure 10%, 35%, 80% and 95% RH respectively. It should be noted that for the specimens at 50% RH, there was no saturated salt solution. The testing temperatures were 20 °C, 35 °C and 50 °C. Each specimen was weighed at designated time interval until the change in mass was less than 0.1%. It should be noted that since the mass change was decreasing over time,

Afterward, the specimens were moved to glass containers in a constant-temperature/ humidity chamber with a temperature accuracy of ±1 ◦C and 50 ± 2% RH as shown in Figure 1. During the test, saturated salt solutions of LiCl, MgCl2, KBr and KNO3 were added at the bottom of glass containers to secure 10%, 35%, 80% and 95% RH respectively. It should be noted that for the specimens at 50% RH, there was no saturated salt solution. The testing temperatures were 20 ◦C, 35 ◦C and 50 ◦C. the time interval of weight measurement in the early stage was shorter than in the later stage. In total, there were 12 weight measurements for the absorption test and 13 weight measurements for the desorption test. The specimens were designated as X-% and P-% for the absorption and desorption tests, respectively. The percentage represented the RH conditions. For example, specimens X-10% is the absorption specimens for 10% RH.

**Figure 1.** Isothermal adsorption devices (**a**) glass container, (**b**) constant-temperature/humidity chamber. **Figure 1.** Isothermal adsorption devices (**a**) glass container, (**b**) constant-temperature/humidity chamber.

*2.3. Determination of Saturation*  The moisture content of a specimen can be defined as follows [8]: Each specimen was weighed at designated time interval until the change in mass was less than 0.1%. It should be noted that since the mass change was decreasing over time, the time interval of weight measurement in the early stage was shorter than in the later stage. In total, there were 12 weight measurements for the absorption test and 13 weight measurements for the desorption test.

The specimens were designated as X-% and P-% for the absorption and desorption tests, respectively. The percentage represented the RH conditions. For example, specimens X-10% is the absorption specimens for 10% RH.

## *2.3. Determination of Saturation*

The moisture content of a specimen can be defined as follows [8]:

$$S = \frac{V\_S}{V\_\phi} \tag{1}$$

where *S* is the moisture saturation, *VS* is the volume occupied by water in the pores of the specimen, and *Vφ* is the volume of pores in the specimen.

In order to determine adsorption and desorption isotherms, the moisture saturation *S* can be expressed based on the water content [23]:

$$S = \frac{m\_{\rm W} - m\_{\rm d}}{m\_{\rm WS} - m\_{\rm d}} \tag{2}$$

where *mw* is the mass of specimen underwater (kg), *md* is the mass of the specimen in a dry state (kg) and *mws* is the mass of the specimen under saturation state (kg).

### **3. Results and Discussion**

### *3.1. Absorption–Desorption Curves*

Absorption mainly refers to physical absorption, which is the phenomenon that moisture enters concrete through capillary pressure, while desorption is the phenomenon that moisture in concrete is transferred from liquid to gas and released from concrete.

Absorption–desorption curves representing the changes in the moisture contents of the specimens over time at 20, 35, and 50 ◦C are shown in Figure 2.

Figure 2a shows that, at the same temperature, the absorption curves tended to be consistent between the different RH conditions, as the specimen saturation increased significantly during the initial stage of the absorption process. As absorption progressed, the saturation between the interior and exterior of concrete decreased due to decreased moisture absorption capacity and eventually stabilized. The moisture absorption behaviors of the specimens were similar at different temperatures. For a given temperature, a higher RH resulted in faster moisture absorption. When the RH was lower than 50%, the moistureabsorption process of the specimen was gradual, which reveals that temperature and RH had little effect on the time required to reach the equilibrium. The absorption rate and capacity increased sharply when the RH was greater than 50%. At 80% RH, the moisture absorption capacity and equilibrium time both increased as temperature decreased. Under low-RH conditions (i.e., less than 50% RH), the concrete exhibited less moisture absorption capacity with saturation less than 0.1, and temperature's effect on the moisture absorption rate was marginal. When RH was greater than 50%, temperature's effect on the moisture absorption became significant.

During the desorption test, a lower RH came with a greater saturation between the interior and exterior of concrete, and a longer time to reach equilibrium, as shown in Figure 2b. When the RH was lower than 80%, the increment of temperature increased desorption capacity and the time to reach equilibrium. When RH was higher than 80%, the effect of temperature on the moisture transport rate decreased. Regardless of temperature, the saturation at equilibrium was approximately 0.9.

**Figure 2.** Absorption and desorption over time: (**a**) 20 °C, (**b**) 35 °C, (**c**) 50 °C. **Figure 2.** Absorption and desorption over time: (**a**) 20 ◦C, (**b**) 35 ◦C, (**c**) 50 ◦C.

### *3.2. RH–Saturation Equilibrium 3.2. RH–Saturation Equilibrium*

The RH–saturation curves during absorption and desorption for different RH conditions are shown in Figure 3. The curves were substantially similar for different temperatures. As RH increased, the saturation gradually increased. When RH was below 50%, the absorption process was gradual. The absorption capacity significantly increased when RH was greater than 50%. Irrespective of temperature, 50% RH was an inflection point in the moisture absorption process. Based on the Kelvin equation, the maximum pore size that could be saturated was approximately 4 nm at 50% RH, and the boundary between the gel pores and capillary pores was generally 10 nm (i.e., gel pores < 10 nm < capillary pores). Therefore, when RH was below 50%, only part of the gel pores was saturated, while for RH above 50%, the capillary pores saturated gradually, thereby rapidly increasing the saturation [24]. Further, the saturation decreased with decreasing RH. As temperature decreased, the saturation considerably decreased. At 80% RH, the equilibrium saturation decreased substantially, which indicates that an RH greater than 80% could greatly affect the equilibrium of the moisture desorption process in concrete. The RH–saturation curves during absorption and desorption for different RH conditions are shown in Figure 3. The curves were substantially similar for different temperatures. As RH increased, the saturation gradually increased. When RH was below 50%, the absorption process was gradual. The absorption capacity significantly increased when RH was greater than 50%. Irrespective of temperature, 50% RH was an inflection point in the moisture absorption process. Based on the Kelvin equation, the maximum pore size that could be saturated was approximately 4 nm at 50% RH, and the boundary between the gel pores and capillary pores was generally 10 nm (i.e., gel pores < 10 nm < capillary pores). Therefore, when RH was below 50%, only part of the gel pores was saturated, while for RH above 50%, the capillary pores saturated gradually, thereby rapidly increasing the saturation [24]. Further, the saturation decreased with decreasing RH. As temperature decreased, the saturation considerably decreased. At 80% RH, the equilibrium saturation decreased substantially, which indicates that an RH greater than 80% could greatly affect the equilibrium of the moisture desorption process in concrete.

**Figure 3.** Absorption and desorption equilibrium curves: (**a**) 20 °C, (**b**) 35 °C, (**c**) 50 °C. **Figure 3.** Absorption and desorption equilibrium curves: (**a**) 20 ◦C, (**b**) 35 ◦C, (**c**) 50 ◦C.

In Figure 3, since the desorption curve is above the absorption curve, it indicates a higher saturation for a given RH. The desorption process displays a significantly hysteretic nature compared to the absorption process, which implies that the gas–liquid equilibrium in pores is nonhomogeneous. The hysteresis represents the "ink bottle effect," which refers to the effect that a small pore (bottleneck) exerts stress upon the water in a connected large pore (bottle) [25]. Larger concrete capillary pores require a higher RH to reach equilibrium saturation than small pores. For initially saturated pores, the liquid water in large pores transports through the liquid water in small pores without forming a gas–liquid interface. Therefore, the water in large pores can be discharged only when the ambient RH is below the saturated RH of small pores. For a given RH, the saturation equilibrium of the moisture absorption process is lower than that of the moisture desorption process In Figure 3, since the desorption curve is above the absorption curve, it indicates a higher saturation for a given RH. The desorption process displays a significantly hysteretic nature compared to the absorption process, which implies that the gas–liquid equilibrium in pores is nonhomogeneous. The hysteresis represents the "ink bottle effect," which refers to the effect that a small pore (bottleneck) exerts stress upon the water in a connected large pore (bottle) [25]. Larger concrete capillary pores require a higher RH to reach equilibrium saturation than small pores. For initially saturated pores, the liquid water in large pores transports through the liquid water in small pores without forming a gas–liquid interface. Therefore, the water in large pores can be discharged only when the ambient RH is below the saturated RH of small pores. For a given RH, the saturation equilibrium of the moisture absorption process is lower than that of the moisture desorption process [26].

[26]. For a given RH, the capillary condensation phenomenon is more likely to occur during the absorption process, and a higher saturation can be attained at a lower temperature. During desorption, at a higher temperature, the saturation is lower because the pore water evaporates more easily [27]. According to a study by Zeng [8], the relationship between For a given RH, the capillary condensation phenomenon is more likely to occur during the absorption process, and a higher saturation can be attained at a lower temperature. During desorption, at a higher temperature, the saturation is lower because the pore water evaporates more easily [27]. According to a study by Zeng [8], the relationship between the capillary pressure and RH is:

$$p\_{\mathbb{C}} = \frac{\rho\_{\mathbb{I}}RT}{M} \ln h \tag{3}$$

l c ln *RT p h M* ρ= (3) where *M* is the molar mass of water (kg/mol), *R* is the gas constant (given as 8.314 where *M* is the molar mass of water (kg/mol), *R* is the gas constant (given as 8.314 J/mol/K), *p*c is the capillary pressure (Pa), *h* is the RH, *T* is the absolute temperature (K) and *ρ*l is the density of liquid water (kg/m3 ).

J/mol/K), c *p* is the capillary pressure (Pa), *h* is the RH, *T* is the absolute temperature (K) and ρl is the density of liquid water (kg/m3). Using Equation (3), the isothermal equilibrium curve of RH and concrete saturation can be converted into a moisture characteristic curve, as shown in Figure 4. When the concrete was at the same saturation through absorption and desorption, the capillary pressure of the desorbed concrete was greater than that of the absorbed concrete, reflecting

the capillary pressure and RH is:

the hysteretic nature of the desorption process. When the temperature was lower during both the absorption and desorption processes, the capillary pressure was greater, as well as the equilibrium saturation corresponding to the same capillary pressure. the hysteretic nature of the desorption process. When the temperature was lower during both the absorption and desorption processes, the capillary pressure was greater, as well as the equilibrium saturation corresponding to the same capillary pressure.

Using Equation (3), the isothermal equilibrium curve of RH and concrete saturation can be converted into a moisture characteristic curve, as shown in Figure 4. When the concrete was at the same saturation through absorption and desorption, the capillary pressure of the desorbed concrete was greater than that of the absorbed concrete, reflecting

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## *3.3. RH and Saturation Equilibrium Model*

*3.3. RH and Saturation Equilibrium Model*  In [28], a model representing the absorption of water by the hygroscopic material and In [28], a model representing the absorption of water by the hygroscopic material and RH is shown as follows:

$$\mathcal{W} = \left[ \frac{-\ln(1 - h)}{A(T + B)} \right]^{\frac{1}{C}} \tag{4}$$

*C h W* where *W* is the equilibrium moisture content and *A*, *B* and *C* are coefficients.

1 ln(1 ) [ ] ( ) *AT B* − − = + (4) By replacing the equilibrium moisture content with the saturation, Equation (4) can be applied to find the inverse function. The RH and saturation relationship is:

$$h = 1 - \exp(-A(T+B)S^C) \tag{5}$$

be applied to find the inverse function. The RH and saturation relationship is: where *S* is the saturation.

RH is shown as follows:

=1 exp( ( ) ) *C h AT B S* −−+ (5) where *S* is the saturation. The model of the relationship between RH and equilibrium saturation can be obtained by substituting the data from the experimental results as seen in Figure 3 into Equation (5). Nonlinear regression was used to yield Equations (6) and (7) for the absorption and desorption processes, respectively.

$$h\_{\rm abs} = 1 - \exp\left(-0.03(T - 180.7) \text{S}^{0.837}\right) \qquad \mathbb{R}^2 = 0.91\tag{6}$$

$$h\_{\rm ds} = 1 - \exp(-0.05(T - 258.76)\mathbf{S}^{5.04}) \qquad \mathbb{R}^2 = 0.91 \tag{7}$$

law. For saturated concrete, the permeability coefficient is constant and a function of sat-

### and desorption processes, respectively. **4. Moisture Transport Model**

as *h TS R* =− − − = 1 exp( 0.03( 180.7) ) 0.91 (6) *4.1. Moisture Movement in Unsaturated Concrete*

uration for unsaturated concrete.

5.04 2 ds *h T SR* =− − − = 1 exp( 0.05( 258.76) ) 0.91 (7) The transport of liquid water in unsaturated concrete can be expressed by Darcy's law. For saturated concrete, the permeability coefficient is constant and a function of saturation for unsaturated concrete.

**4. Moisture Transport Model**  According to the Darcy–Buckingham equation, the relationship between transport rate of liquid water and capillary pressure is expressed as [11]:

$$v\_{\rm l} = -\frac{k\_{\rm l}}{\mu} \text{grad}(p\_{\rm c}) \tag{8}$$

0.837 2

where *v*1 is the rate (m/s); *k*1 is the effective permeability for moisture transport (m2 ), which is related to the moisture content in concrete and micro-geometric parameters of the pore occupied by moisture; *µ* is the dynamic viscosity coefficient of water (Pa·s).

If the conversion of liquid water to water vapor in the pores of concrete is neglected, the moisture transport in the concrete conforms to the law of mass conservation. The moisture transport equation in concrete can be written as below [29,30]:

$$\frac{\partial\theta}{\partial t} = \text{div}\left[\frac{k\_l}{\mu}\text{grad}(p\_\circ)\right] \tag{9}$$

Converting the left side of Equation (9) to pore moisture content and the right side into the pore moisture saturation gradient, it becomes:

$$\frac{\partial \theta}{\partial t} = \text{div} \left[ \frac{k\_1}{\mu} \frac{\partial p\_\mathbf{c}}{\partial S} \text{grad}(S) \right] \tag{10}$$

where *θ* is the moisture content of the concrete.

The effective permeability to moisture transport (*k*1) can be expressed as

$$k\_{\rm l} = k k\_{\rm rl} \tag{11}$$

Therefore, Equation (10) can be rewritten as follows:

$$\frac{\partial \theta}{\partial t} = \text{div} \left[ \frac{k k\_{\text{rl}}}{\mu} \frac{\partial p\_{\text{c}}}{\partial S} \text{grad}(S) \right] \tag{12}$$

where *k* is the intrinsic permeability of concrete (m2 ), and *k*rl is the relative permeability, which is a parameter associated with the saturation.

Equation (12) can be expressed as:

$$\frac{\partial \theta}{\partial t} = \text{div}[D(\mathcal{S})\text{grad}(\mathcal{S})] \tag{13}$$

$$D(S) = \frac{k}{\mu} k\_{\rm rl} \frac{\partial p\_{\rm c}}{\partial S} \tag{14}$$

where *D(S)* is the transport coefficient of liquid water (m2/s).

The relationship between the dynamic viscosity coefficient of water (*µ*), and temperature can be expressed as follows: [31]

$$\mu = \frac{0.001775}{\left[1 + 0.0837(T - 273.15) + 0.000221(T - 273.15)^2\right]}\tag{15}$$

The intrinsic permeability (*k)* of concrete can be determined based on the porosity, specific surface area and tortuosity of concrete using the Kozeny–Carman model [13]:

$$k = \frac{\phi^3}{F\_\text{s} \tau\_\text{c}^2 \Omega^2 \left(1 - \phi\right)^2} \tag{16}$$

where *F*s is the factor representing the influence of pore shape (2 for a circular and tubular pore), *τ*c is the tortuosity and Ω is the specific surface.

### *4.2. Relationship between the Moisture Transport Parameters*

By substituting Equations (6) and (7), which represent the equilibrium relationship between RH and saturation, into Equation (3), the relationship between the capillary pressure and saturation during the moisture absorption and desorption processes can be expressed as Equations (17) and (18), respectively.

$$p\_{\rm as} = \frac{\rho\_1 RT}{M} \ln(1 - \exp(-0.03(T - 180.7) \text{S}^{0.837})) \tag{17}$$

$$p\_{\rm ds} = \frac{\rho\_{\rm l}RT}{M} \ln(1 - \exp(-0.05(T - 258.76)S^{5.04})) \tag{18}$$

The relationship between the relative permeability (*k*rl(*S*)), and relative saturation can be established based on previous studies [14,15,32]:

$$p\_{\mathbb{C}} = A\_{\mathbb{A}} (\mathbb{S}^{-1/m} - 1)^{1-m} \tag{19}$$

$$k\_{\rm rl}(S) = \sqrt{S} \left[ 1 - \left( 1 - S^{1/m} \right)^{m} \right]^{2} \tag{20}$$

where *m* and *A*a are undetermined coefficients.

The value of *m* can be determined through regression of the capillary pressure versus saturation curve. The absolute value of capillary pressure *m* for absorption and desorption processes at different temperatures can be derived based on Equation (19) through nonlinear fitting.

Using the data in Table 5 to fit the relationship between *m* and *T* for the absorption and desorption processes gives Equations (21) and (22), respectively.

$$m\_{\rm as} = 1/(2.89 \ln(T) - 14.89) \tag{21}$$

$$m\_{\rm ds} = 1/(21.06 - 3.16 \ln(T))\tag{22}$$

**Table 5.** *m* values corresponding to the absorption and desorption process.

Equations (21) and (22) are then substituted into Equation (20) to express the relationship between the relative permeability and saturation during the moisture absorption process.

$$k\_{\rm rl-as}(S) = \sqrt{S} [1 - (1 - S^{2.89 \ln(T) - 14.89})^{1/[2.89 \ln(T) - 14.89]}]^2 \tag{23}$$

Thus, the relationship between the relative permeability and saturation during the moisture desorption process is:

$$k\_{\rm rl-ds}(S) = \sqrt{S} [1 - (1 - S^{21.06 - 3.16 \ln(T)})^{1/[21.06 - 3.16 \ln(T)]}]^2 \tag{24}$$

### *4.3. Moisture Transport Model*

The relationships between capillary pressure and saturation during the process of moisture absorption and desorption are shown in Equations (17) and (18). These equations are then substituted into Equation (13) to develop the moisture transport model for absorption and desorption processes.

$$\frac{\partial \phi S}{\partial t} = \text{div}[D(S)\text{grad}(S)]\tag{25}$$

where *φ* is the porosity of concrete.

Transport coefficient *D*(*S*) of Equation (25) in absorption and desorption processes are then represented as Equations (26) and (27), respectively.

$$D\_{\rm as}(S) = \frac{0.02511k k\_{\rm l-as} \rho\_l RT(T - 180.7)S^{-0.163} \exp(-0.03(T - 180.7)S^{0.837})}{\mu M (1 - \exp(-0.03(T - 180.7)S^{0.837}))} \tag{26}$$

$$D\_{\rm ds}(S) = \frac{0.252 k k\_{\rm l-ds} \rho\_l RT (T - 258.76) S^{4.04} \exp(-0.05 (T - 258.76) S^{5.04})}{\mu M (1 - \exp(-0.05 (T - 258.76) S^{5.04}))} \tag{27}$$

### *4.4. Heat Balance Equation*

The heat balance equation correlated to the saturation can be established based on Fourier's law [11,33]:

$$c\rho \frac{\partial T}{\partial t} + \nabla \cdot [-\lambda\_S \nabla T] = Q \tag{28}$$

where *c* is the specific heat of material (J/kg·K), *λS* is the thermal conductivity at different saturations (W/m·K), *ρ* is the material density (kg/m3 ) and *Q* is the heat flux (J/m2 ·s). The negative sign indicates that the direction of heat flow is opposite to the direction of the temperature gradient.

The transient plane source (TPS) technique was used to determine the thermal conductivity for the concrete in the same mix proportion as in the present study and at the same saturation values of 0, 0.3, 0.5, 0.7, 0.9 and 1.0 as in the moisture absorption and desorption tests described in Section 2.1. The relationship between the thermal conductivity and saturation is:

$$
\lambda\_S = (1 + 0.3432S^2)\lambda\_\text{a} \tag{29}
$$

where *λ*a is the thermal conductivity under dry state (W/m·K).

### **5. Numerical Simulation**

### *5.1. Model Parameters and Mesh Generation*

The concrete was considered as a uniform and isotropic continuous medium; thus the concrete skeleton was assumed as an impermeable material that did not react with gas or liquid phases. COMSOL®® software (COMSOL Inc., Stockholm, Sweden) was used to establish a three-dimensional numerical model based on Equations (25)–(28), and one-dimensional transmission was adopted. The physical parameters of heat and moisture transport are shown in Tables 6 and 7. The size of the simulated specimen was the same as that of the experimental specimen used in the test, namely 30 mm × 100 mm × 100 mm. A total of 9520 tetrahedral elements were used in COMSOL®® software to develop the finite element model with DOFs of 28,186 and convergence criteria of 10−5 , as shown in Figure 5.

**Parameter Dry Thermal Conductivity,**

**(m2)**

**Parameter Intrinsic Permeability,** *k*

**Parameter Dry Thermal Conductivity,**

**(W/m·K)**

**(W/m·K)**

**(m2)**

**Table 6.** Heat transfer physical parameters.

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**Table 6.** Heat transfer physical parameters.

Value 1.578 900 2297 0.1796

**Table 7.** Moisture transport physical parameters.

Value 1.578 900 2297 0.1796

**Table 7.** Moisture transport physical parameters.

**Water Density,**

**Water Density,**

**(kg/m3)**

Value 1.413 × 10−21 1000 8.3144 0.018

**(kg/m3)**

λa

λa

Value 1.413 × 10−21 1000 8.3144 0.018

**Specific Heat,** *c*  **(J/kg·K)**

**Specific Heat,** *c*  **(J/kg·K)**

**Gas Constant** *R*

**Gas Constant** *R*

**Concrete Density, (kg/m3)**

**(J/mol·K) Molar Mass of Water,** *M* **(kg/mol)**

**Concrete Density, (kg/m3)**

**(J/mol·K) Molar Mass of Water,** *M* **(kg/mol)**

**Porosity,**  *ϕ*

**Porosity,**  *ϕ*

**Figure 5.** Mesh elements division of concrete. **Figure 5.** Mesh elements division of concrete. librium saturation. The initial temperature of inside concrete was 283.15 K, and the simu-

### *5.2. Moisture Transport Model Validation 5.2. Moisture Transport Model Validation* lation time was the hygroscopic equilibrium time for the three temperatures, which was approximately 350 h. The simulated results were compared with the experimental results,

0.2

The experimental results shown in Figure 2 were simulated for 80% and 95% RH at 20, 35 and 50 °C to validate the moisture transport model. The initial internal saturation of the concrete was set as 0.08, and the boundary saturation was the corresponding equilibrium saturation. The initial temperature of inside concrete was 283.15 K, and the simulation time was the hygroscopic equilibrium time for the three temperatures, which was approximately 350 h. The simulated results were compared with the experimental results, as shown in Figure 6. The experimental results shown in Figure 2 were simulated for 80% and 95% RH at 20, 35 and 50 ◦C to validate the moisture transport model. The initial internal saturation of the concrete was set as 0.08, and the boundary saturation was the corresponding equilibrium saturation. The initial temperature of inside concrete was 283.15 K, and the simulation time was the hygroscopic equilibrium time for the three temperatures, which was approximately 350 h. The simulated results were compared with the experimental results, as shown in Figure 6. as shown in Figure 6. The simulated and experimental results show a similar trend, and the values also agreed well. The simulated results were slightly lower than the experimental results. Under the simulated conditions, the moisture absorption rate decreased, and the moisture absorption capacity was slightly lower than that in the experiment. This could be because the moisture transport model only considers the transport of liquid water, not the moisture vapor transport that occurred under low-saturation conditions.

of the concrete was set as 0.08, and the boundary saturation was the corresponding equi-

0.3 0.3 **Figure 6.** Test and simulation values of moisture absorption process: (**a**) is 80% RH, (**b**) is 95% RH.

0.2

 (**a**) (**b**) 0 0.1 0 40 80 120 160 200 240 280 320 360 Time(h) 0 0.1 0 40 80 120 160 200 240 280 320 360 Time(h) The simulated and experimental results show a similar trend, and the values also agreed well. The simulated results were slightly lower than the experimental results. Under the simulated conditions, the moisture absorption rate decreased, and the moisture absorption capacity was slightly lower than that in the experiment. This could be because the moisture transport model only considers the transport of liquid water, not the moisture vapor transport that occurred under low-saturation conditions.

The experimental results provided in Figure 2 were applied to validate the moisture desorption model. The simulation was performed under the conditions of 10% and 35% RH at 20, 35 and 50 ◦C. The initial temperature inside the concrete was 283.15 K. The concrete interior was initially saturated and the boundary saturation was the corresponding equilibrium saturation. The simulation time was 250 h.

Figure 7 provides a comparison of the simulated and experimental results, which shows that the simulated values were in good agreement with the experimental values under 35% RH and were slightly higher than the experimental values at 10% RH. The overall results demonstrated that the simulated model of moisture desorption was valid. This also indicates that the simulated results remained accurate when the concrete was at a higher saturation level. When the saturation decreased, the simulated moisture desorption

rate was slightly lower than the experimental rate, implying that the water vapor transport in the concrete exerted limited influence under the low-saturation condition. In the marine environment, it is difficult for concrete to reach a low saturation state due to high RH. Therefore, the simulated model of moisture transport was accurate for the applications in the high RH environment. a higher saturation level. When the saturation decreased, the simulated moisture desorption rate was slightly lower than the experimental rate, implying that the water vapor transport in the concrete exerted limited influence under the low-saturation condition. In the marine environment, it is difficult for concrete to reach a low saturation state due to high RH. Therefore, the simulated model of moisture transport was accurate for the applications in the high RH environment.

equilibrium saturation. The simulation time was 250 h.

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**Figure 6.** Test and simulation values of moisture absorption process: (**a**) is 80% RH, (**b**) is 95% RH.

The experimental results provided in Figure 2 were applied to validate the moisture desorption model. The simulation was performed under the conditions of 10% and 35% RH at 20, 35 and 50 °C. The initial temperature inside the concrete was 283.15 K. The concrete interior was initially saturated and the boundary saturation was the corresponding

Figure 7 provides a comparison of the simulated and experimental results, which shows that the simulated values were in good agreement with the experimental values under 35% RH and were slightly higher than the experimental values at 10% RH. The overall results demonstrated that the simulated model of moisture desorption was valid. This also indicates that the simulated results remained accurate when the concrete was at

**Figure 7.** Test and simulation values of moisture desorption process: (**a**) is 10% RH, (**b**) is 35% RH. **Figure 7.** Test and simulation values of moisture desorption process: (**a**) is 10% RH, (**b**) is 35% RH.

### *5.3. Absorption*

The upper, lower and right ends of the two-dimensional geometric model of concrete were defined as heat and moisture insulation. Therefore, moisture absorption was from left to inside. The effect of temperature on the moisture absorption process at 95% RH was evaluated under the temperatures of 293.15, 308.15 and 323.15 K and the boundary saturation values of 0.80, 0.72, and 0.39, respectively. The simulation had an initial temperature of 273.15 K, saturation of 0.2 and time of 500 days. The upper, lower and right ends of the two-dimensional geometric model of concrete were defined as heat and moisture insulation. Therefore, moisture absorption was from left to inside. The effect of temperature on the moisture absorption process at 95% RH was evaluated under the temperatures of 293.15, 308.15 and 323.15 K and the boundary saturation values of 0.80, 0.72, and 0.39, respectively. The simulation had an initial temperature of 273.15 K, saturation of 0.2 and time of 500 days.

The simulated results are shown in Figure 8. It can be seen that the saturation was the lowest at 323.15 K (i.e., the highest temperature) but the highest at 293.15 K (i.e., the lowest temperature). For a given RH, an increased temperature decreased boundary saturation, moisture transport rate, moisture transport capacity, moisture transport distance and moisture absorption. The saturation gradient is the main driving potential of moisture transfer. A higher temperature reduced moisture absorption rate. Meanwhile, with temperature increasing, the equilibrium saturation of the concrete boundary further decreased. Such a combined effect decreased moisture absorption of concrete. The simulated results are shown in Figure 8. It can be seen that the saturation was the lowest at 323.15 K (i.e., the highest temperature) but the highest at 293.15 K (i.e., the lowest temperature). For a given RH, an increased temperature decreased boundary saturation, moisture transport rate, moisture transport capacity, moisture transport distance and moisture absorption. The saturation gradient is the main driving potential of moisture transfer. A higher temperature reduced moisture absorption rate. Meanwhile, with temperature increasing, the equilibrium saturation of the concrete boundary further decreased. Such a combined effect decreased moisture absorption of concrete. *Crystals* **2021**, *11*, x FOR PEER REVIEW 13 of 17

*5.3. Absorption*

**Figure 8.** Simulated distribution at different temperatures. **Figure 8.** Simulated distribution at different temperatures.

The combined influences of the temperature and saturation in the moisture transport process were investigated at three conditions: high temperature and RH (boundary temperature *T* = 323.15 K, boundary saturation *S* = 0.9), medium temperature and RH (bound-The combined influences of the temperature and saturation in the moisture transport process were investigated at three conditions: high temperature and RH (boundary temperature *T* = 323.15 K, boundary saturation *S* = 0.9), medium temperature and RH (boundary

ary temperature *T* = 308.15 K, boundary saturation *S* = 0.75) and low temperature and RH

sults are provided in Figure 9, which shows the same trend among the three conditions. The transport rate is directly correlated with temperature and RH. It also shows that the saturation gradient mostly affected moisture transport rate. Although the saturation was large at high temperature, the moisture transport rate was notably high. Therefore, the effect of RH on the moisture transport rate in the concrete was greater than that of temperature. The transport process of moisture in concrete at the temperature of 293.15 K and saturation 0.6 for simulation at 500d is shown in Figure 10. It can be seen that the moisture is gradually transferred from the left end of the concrete to the interior, and the saturation

is reduced with depth increasing, which is coherent with the trend in Figure 9.

**Figure 9.** Saturation distribution during the absorption simulation.

temperature *T* = 308.15 K, boundary saturation *S* = 0.75) and low temperature and RH (boundary temperature *T* = 293.15 K, boundary saturation *S* = 0.6). The simulation had an initial temperature of 273.15 K, saturation of 0.2 and time of 500 days. The simulated results are provided in Figure 9, which shows the same trend among the three conditions. The transport rate is directly correlated with temperature and RH. It also shows that the saturation gradient mostly affected moisture transport rate. Although the saturation was large at high temperature, the moisture transport rate was notably high. Therefore, the effect of RH on the moisture transport rate in the concrete was greater than that of temperature. The transport process of moisture in concrete at the temperature of 293.15 K and saturation 0.6 for simulation at 500d is shown in Figure 10. It can be seen that the moisture is gradually transferred from the left end of the concrete to the interior, and the saturation is reduced with depth increasing, which is coherent with the trend in Figure 9. ary temperature *T* = 308.15 K, boundary saturation *S* = 0.75) and low temperature and RH (boundary temperature *T* = 293.15 K, boundary saturation *S* = 0.6). The simulation had an initial temperature of 273.15 K, saturation of 0.2 and time of 500 days. The simulated results are provided in Figure 9, which shows the same trend among the three conditions. The transport rate is directly correlated with temperature and RH. It also shows that the saturation gradient mostly affected moisture transport rate. Although the saturation was large at high temperature, the moisture transport rate was notably high. Therefore, the effect of RH on the moisture transport rate in the concrete was greater than that of temperature. The transport process of moisture in concrete at the temperature of 293.15 K and saturation 0.6 for simulation at 500d is shown in Figure 10. It can be seen that the moisture is gradually transferred from the left end of the concrete to the interior, and the saturation is reduced with depth increasing, which is coherent with the trend in Figure 9.

The combined influences of the temperature and saturation in the moisture transport process were investigated at three conditions: high temperature and RH (boundary temperature *T* = 323.15 K, boundary saturation *S* = 0.9), medium temperature and RH (bound-

**Figure 9. Figure 9.** Saturation distribution during the absorption simulation. Saturation distribution during the absorption simulation.

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**Figure 8.** Simulated distribution at different temperatures.

**Figure 10.** Cloud atlas of moisture in absorption process at temperature of 293.15 K and saturation of 0.6. **Figure 10.** Cloud atlas of moisture in absorption process at temperature of 293.15 K and saturation of 0.6.

### *5.4. Desorption 5.4. Desorption*

The upper and lower ends of the two-dimensional geometric model of concrete were treated as heat and moisture insulation. The direction of moisture desorption was from inside to both sides. The effect of temperature on the moisture desorption process at 10% The upper and lower ends of the two-dimensional geometric model of concrete were treated as heat and moisture insulation. The direction of moisture desorption was from inside to both sides. The effect of temperature on the moisture desorption process at 10%

RH was analyzed at temperatures of 293.15, 308.15 and 323.15 K and boundary saturation

Figure 11 presents the simulated results. It illustrates that, for a given RH, higher temperatures lowered the boundary saturation. When the internal saturation of concrete was higher, the difference in saturation was greater between the outside and inside of concrete, resulting in a significant loss of moisture in the concrete. The boundary saturation was lower at higher temperatures, which increased the moisture desorption rate as

Additionally, the combined effects of temperature and saturation on the moisture transport process during desorption were investigated for three conditions: low temperature and high RH (boundary temperature *T* = 293.15 K, internal saturation *S* = 0.9), medium temperature and RH (boundary temperature *T* = 308.15 K, internal saturation *S* =

**Figure 11.** Saturation distribution during the desorption simulation.

well as the moisture desorption capacity.

RH was analyzed at temperatures of 293.15, 308.15 and 323.15 K and boundary saturation values of 0.70, 0.46 and 0.36, respectively. The simulation time was 200 days with an initial temperature of 273.15 K and saturation of 0.9. values of 0.70, 0.46 and 0.36, respectively. The simulation time was 200 days with an initial temperature of 273.15 K and saturation of 0.9. Figure 11 presents the simulated results. It illustrates that, for a given RH, higher

The upper and lower ends of the two-dimensional geometric model of concrete were treated as heat and moisture insulation. The direction of moisture desorption was from inside to both sides. The effect of temperature on the moisture desorption process at 10% RH was analyzed at temperatures of 293.15, 308.15 and 323.15 K and boundary saturation

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**Figure 10.** Cloud atlas of moisture in absorption process at temperature of 293.15 K and saturation of 0.6.

*5.4. Desorption*

Figure 11 presents the simulated results. It illustrates that, for a given RH, higher temperatures lowered the boundary saturation. When the internal saturation of concrete was higher, the difference in saturation was greater between the outside and inside of concrete, resulting in a significant loss of moisture in the concrete. The boundary saturation was lower at higher temperatures, which increased the moisture desorption rate as well as the moisture desorption capacity. temperatures lowered the boundary saturation. When the internal saturation of concrete was higher, the difference in saturation was greater between the outside and inside of concrete, resulting in a significant loss of moisture in the concrete. The boundary saturation was lower at higher temperatures, which increased the moisture desorption rate as well as the moisture desorption capacity.

**Figure 11.** Saturation distribution during the desorption simulation. **Figure 11.** Saturation distribution during the desorption simulation.

Additionally, the combined effects of temperature and saturation on the moisture transport process during desorption were investigated for three conditions: low temperature and high RH (boundary temperature *T* = 293.15 K, internal saturation *S* = 0.9), medium temperature and RH (boundary temperature *T* = 308.15 K, internal saturation *S* = Additionally, the combined effects of temperature and saturation on the moisture transport process during desorption were investigated for three conditions: low temperature and high RH (boundary temperature *T* = 293.15 K, internal saturation *S* = 0.9), medium temperature and RH (boundary temperature *T* = 308.15 K, internal saturation *S* = 0.75) and high temperature and low RH (boundary temperature *T* = 323.15 K, internal saturation *S* = 0.6). The simulation time was 500 days with an initial temperature of 273.15 K and saturation of 0.2.

As shown in Figure 12, the three curves displayed a similar trend. The saturation values for the three conditions at the internal depth of 50 mm were 0.552, 0.481 and 0.417, respectively. Even at low temperature, the greatest moisture transport rate was observed at the highest RH. The temperature had a greater effect on the moisture desorption process than saturation. The change of saturation of moisture in the desorption process at a temperature of 308.15 K and saturation of 0.75 is shown for the simulation at 500d in Figure 13. This indicates that the moisture was uniformly desorbed to both sides. The rate of saturation reduction was high close to both sides, while it was slow in the middle. This is due to the fact that the boundary temperature is higher at the concrete's surface, resulting in faster moisture transport.

crete's surface, resulting in faster moisture transport.

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K and saturation of 0.2.

K and saturation of 0.2.

**Figure 12.** Internal average saturation distribution during the desorption process. **Figure 12.** Internal average saturation distribution during the desorption process. **Figure 12.** Internal average saturation distribution during the desorption process.

0.75) and high temperature and low RH (boundary temperature *T* = 323.15 K, internal saturation *S* = 0.6). The simulation time was 500 days with an initial temperature of 273.15

0.75) and high temperature and low RH (boundary temperature *T* = 323.15 K, internal saturation *S* = 0.6). The simulation time was 500 days with an initial temperature of 273.15

As shown in Figure 12, the three curves displayed a similar trend. The saturation values for the three conditions at the internal depth of 50 mm were 0.552, 0.481 and 0.417, respectively. Even at low temperature, the greatest moisture transport rate was observed at the highest RH. The temperature had a greater effect on the moisture desorption process than saturation. The change of saturation of moisture in the desorption process at a temperature of 308.15 K and saturation of 0.75 is shown for the simulation at 500d in Figure 13. This indicates that the moisture was uniformly desorbed to both sides. The rate of saturation reduction was high close to both sides, while it was slow in the middle. This is due to the fact that the boundary temperature is higher at the con-

As shown in Figure 12, the three curves displayed a similar trend. The saturation values for the three conditions at the internal depth of 50 mm were 0.552, 0.481 and 0.417, respectively. Even at low temperature, the greatest moisture transport rate was observed at the highest RH. The temperature had a greater effect on the moisture desorption process than saturation. The change of saturation of moisture in the desorption process at a temperature of 308.15 K and saturation of 0.75 is shown for the simulation at 500d in Figure 13. This indicates that the moisture was uniformly desorbed to both sides. The rate of saturation reduction was high close to both sides, while it was slow in the middle. This is due to the fact that the boundary temperature is higher at the con-

**Figure 13.** Cloud atlas of moisture in the desorption process at a temperature of 308.15 K and saturation of 0.75. **Figure 13.** Cloud atlas of moisture in the desorption process at a temperature of 308.15 K and saturation of 0.75.

**Figure 13.** Cloud atlas of moisture in the desorption process at a temperature of 308.15 K and saturation of 0.75.

### **6. Conclusions 6. Conclusions**

**6. Conclusions**  This study sought to elucidate the mechanisms of moisture transport in concrete. A This study sought to elucidate the mechanisms of moisture transport in concrete. A numerical model was developed. Based on the experimental and simulated results, the following conclusions can be drawn. This study sought to elucidate the mechanisms of moisture transport in concrete. A numerical model was developed. Based on the experimental and simulated results, the following conclusions can be drawn.

numerical model was developed. Based on the experimental and simulated results, the following conclusions can be drawn. (1) Under isothermal conditions, a higher RH increased the moisture absorption rate, and the moisture absorption capacity was greater at lower temperatures. When RH exceeded 50%, the moisture absorption rate increased significantly. Similarly, a lower RH resulted in a faster desorption, and higher temperatures increased the moisture desorption capacity.

(2) A model of the relationship between RH and equilibrium saturation in absorption and desorption processes was proposed. A formula in the function of capillary pressure and moisture saturation of concrete was established, and a model of moisture transport in the concrete was developed. The experimental results verified the accuracy of the moisture transport model.

(3) Based on the simulated results, an increased temperature reduced the rate of moisture absorption. The saturation had more effects on the absorption rate than temperature. Although an increment of temperature increased the moisture desorption rate, the saturation had more effects on the moisture desorption than temperature.

(4) Under low saturation, the transport of water vapor affected the moisture desorption rate in concrete. Hence, the transport of water vapor should be further considered in the moisture transport model.

**Author Contributions:** Conceptualization, Q.Z. and H.C.; investigation, Z.K.; data curation, Q.Z., Z.K. and K.L.; methodology, Q.Z.; visualization, Y.L.; formal analysis, Z.K.; software, K.L.; writing original draft, Z.K.; writing—review and editing, Y.L., Q.Z., Z.K., H.C. and K.L.; project administration, Q.Z.; funding acquisition, Q.Z.; validation, Q.Z., Y.L. and H.C.; resources, H.C. All authors have read and agreed to the published version of the manuscript

**Funding:** This work was financially supported by the National Natural Science Foundation of China [grant number 51509084]; the Foundation of Key Laboratory of Performance Evolution and Control for Engineering Structures of Ministry of Education, Tongji University [grant number 2018KF-2]; the Cultivation Plan for Youth Backbone Teachers of Institution of Higher Education by Henan Province [grant number 2019GGJS086]; the Fundamental Research Funds for the Henan Provincial Colleges and Universities in Henan University of Technology [grant number 2017RCJH03]; the Cultivation Plan for Youth Backbone Teachers by Henan University of Technology; and the Innovative Funds Plan of Henan University of Technology (Grant No. 2020ZKCJ05).

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

## **References**