**2. Neurophysiological Changes in Local Motor Circuits during Aging** *2.1. TMS Studies*

In the last decades, TMS has been extensively used to investigate the physiology of brain aging in a safe and non-invasive way, particularly with regards to the sensorimotor system. It is well known that a single TMS pulse results in multiple descending volleys, i.e., an early direct (D) followed by indirect (I) waves [29]. The physiological mechanisms underlying the generation of I-waves are still unclear and different hypotheses have been made, ranging from oscillating activation of corticospinal tract (CST) neurons, to reverberation of activity within interneuronal circuits and converging pyramidal output cells [30–32]. The motor-evoked potential (MEP) arises from the temporal summation of these descending volleys at the spinal level, but its basic characteristics (i.e., latency and amplitude) also reflect the combination of excitatory and inhibitory events occurring in a more complex synaptic network, at different levels of the motor pathway [31,33]. The easiest and most reproducible single-pulse (sp) TMS measure, used to probe the excitability of M1, is the resting motor threshold (RMT), which is defined as the stimulation intensity required to elicit MEPs in resting muscles of at least 50 µV in 5 out of 10 trials [30,34]. The RMT can be considered as an estimate of global cortical excitability, it reflects axonal excitability, likely of the intracortical elements activated by TMS, and it has been reported to have high test-retest reliability in healthy older adults, in particular when using monophasic rather than biphasic TMS pulses [35]. It is sensitive not only to cortical excitability, but also to the scalp-to-cortex distance, as this alters the amount of energy required to bring CST neurons to threshold [36]. Given that age-related cortical atrophy can increase the coil-to-cortex distance, the RMT can be used as a measure to explore brain aging [37,38]. Indeed, several studies reported an increase in RMT in older adults [39,40]; however, this has not been replicated by other authors [41–43]. This inconsistency can be due to the possibly different degree of individual cortical atrophy in elderly subjects and to the lack of a structural neuroimaging assessment in the various studies, which would allow a coil-to-cortex distance measurement in all participants and, accordingly, an ad-hoc RMT normalization. Another hypothesis proposed to explain the lack of consistency in agerelated differences in RMT is that this variable changes in discrete stages during the life span. Supporting this idea, Shibuya and colleagues (2016) recently evaluated the RMT in 113 subjects with an age ranging from 20 to 83 years; they found that the RMT increases

from age 20 to 50 and then decreases, roughly following a quadratic function [44]. Up to date, a long-term longitudinal RMT follow up is missing; this could potentially have more value, compared to cross-sectional measurements, in clarifying electrophysiological correlates of the gray matter volume loss in the aging brain.

In addition to RMT, the stimulus-response relationship may be intrinsically altered in the aged motor cortex. It is well known that the MEP amplitude increases sigmoidally with stimulation intensity, and that voluntary activation of the target muscle shifts this inputoutput curve to the left [45,46]. Pitcher and colleagues (2003) explored the input-output curve in young and elderly subjects and demonstrated a rightward shift in the latter group, due to the higher stimulation intensity necessary to achieve the maximum MEP. The slope of the curve and the RMT were not different between the two groups [47]. These data have been interpreted as reflecting a reduction in the amount of spinal motoneurons activated by TMS or in the stimulus-induced neuronal synchronization with aging [47,48].

Besides spTMS, more complex TMS protocols, which consist in delivering two stimuli via the same coil (paired-pulse TMS; ppTMS) [49,50] or by coupling a TMS pulse with peripheral electric stimulation [51] have been also applied in older subjects. While spTMS measures generally reflect the degree of the overall corticospinal excitability [52], these protocols allow to investigate the physiology of intracortical circuits which rely on different neurotransmitter classes [30,53,54]. More in detail, in ppTMS a conditioning stimulus (CS) precedes a test stimulus (TS) by an interstimulus interval (ISI) which varies according to the protocol used, allowing the evaluation of intra-cortical inhibitory and facilitatory circuits. Short-interval intracortical inhibition (SICI) is elicited when a subthreshold CS is followed by a suprathreshold TS with an ISI of 1–6 ms and likely reflects inhibition mediated by GABA-A receptors, while intracortical facilitation (ICF) entails longer ISIs (6–30 ms) and is likely mediated by NMDA receptors [30,49,53,55]. The investigation of ppTMS measures in physiological aging has produced inconsistent results. SICI has been reported to be either decreased [41,56–59], normal [42,43,60,61] or even increased [39,62] in elderly. Differently, ICF was found to be generally normal in the majority of [41,44,58,62], but not all [39,61], studies, suggesting intact NMDA-related glutamatergic neurotransmission in these subjects. Long-interval intracortical inhibition (LICI) is another ppTMS measure, which refers to inhibition of a test MEP using a suprathreshold CS applied 50–200 ms before, and likely depends on GABA-B receptors [53,63,64]. Similar to what observed with SICI, the effectiveness of LICI was found to be variable with aging, being either increased [39] or decreased [43,58] in different studies. Thus, ppTMS researches have not yet clarified whether GABA-ergic neurotransmission is generally preserved or impaired in physiological aging. Finally, short interval intracortical facilitation (SICF) can be elicited by a suprathreshold first stimulus followed by a second stimulus at about RMT [65,66], by an ISI compatible with I-waves period (i.e., 1.3–1.5 ms). In this protocol, the observed increase in MEP is likely due to summation of different I-waves at the M1 level and mainly reflects the activity of non-NMDA glutamatergic neurotransmission [17,31]. Clark and colleagues (2011) described a higher SICF in elderly than young subjects at ISI 1.5 ms, while the opposite result was present at ISI 2.5 ms. These apparently contrasting data confirm that physiological mechanisms underlying the first and later SICF peaks differ [17,31,67,68] and may suggest that they have different sensitivity to the aging process [69]. More recently, however, Opie and colleagues tested SICF in young and older adults in more detail and compared I-wave intervals that were optimal for I-waves summation in each group. Although they confirmed the reduction in the second SICF peak in the elderly, a similar change was also found in the other peaks, thus not corroborating the previous hypothesis and leaving the question open [70,71]. Additionally, these studies found a delayed latency of the third SICF peak with aging, which influences both plasticity induction and motor function [70,71].

Discrepancies in the effects of ppTMS protocols across studies can have several explanations. The first could be the choice of the ISI due the fact that paired-pulse protocol is associated with a relatively wide range of possible ISIs, and the timing optimal to obtain

the predicted changes in MEP may be different across subjects [30]. While some of ppTMS studies have used multiple ISIs [41,42,44,58], the majority employed only one ISI for all subjects [39,43,56,57,59–61]. As different subjects will probably manifest optimal responses at different ISI, the outcome of future experimental studies could be improved by the use of paired-pulse curves which may decrease the variability and improve the outcome in future studies. Furthermore, concerning SICI, many studies tested this measure only using an ISI of 3 ms, which, according to some reports [72], may be influenced by SICF; however, this has not been universally confirmed [66]. Another important factor contributing to the inconsistency of ppTMS protocols is the choice of CS intensity, which can be calibrated either based on the RMT, or the active motor threshold (AMT). The intensities used vary considerably, usually ranging from 50% to 95% RMT [39,41,42,44,55,56] or from 70% to 90% AMT [73–75], and this variability may contribute to divergent findings in SICI studies. Another point to take into account is that ppTMS measures may have different sensitivity to coil orientation [66,76], and that this pattern of susceptibility to current direction might change according to the age of subjects. In this regard, Sale and colleagues (2016) assessed SICI in young and older adults with TMS coil in posterior-to-anterior (PA) and, then, anterior-to-posterior (AP) position. Interestingly, they found no age-related differences in SICI with PA-directed TMS, while SICI was more effective in older than young subjects using AP-directed TMS [77]. Unfortunately, both the CS and TS were delivered with the same current direction, thus making it difficult to understand which one drives the effect. A further important issue is that ppTMS measures are known to be modulated by different brain states, including motor tasks [78,79], and that this change may be dependent on age. For instance, the amount of SICI during a reaction time task was related to manual dexterity in aging subjects [57]. Moreover, while SICI increases during motor stopping in young subjects, this phenomenon does not occur in older adults [80]. Analyzing taskrelated changes in ppTMS measures may thus increase their sensitivity in investigating possible age-related decline in intracortical motor circuits. Overall, future studies should standardize the methodology of ppTMS to help comparisons across different age groups.

Differently from ppTMS, short-afferent inhibition (SAI) has shown more consistent alterations in the aging brain. SAI is elicited by electrical stimulation of a peripheral nerve preceding contralateral M1 TMS by an ISIs slightly longer than the latency of the N20 component of the somatosensory evoked potential. This form of inhibition likely reflects intracortical sensorimotor interaction and involves cholinergic as well as GABA-A-ergic circuits [51,81–83]. Although a first study found that SAI was comparable between older and younger adults [42], later studies showed decreased SAI in the elderly [40,84–86], which was also correlated with the age of subjects [87]. These data overall point to a progressive impairment of cholinergic activity during aging.

#### *2.2. TMS-EEG Studies*

TMS-EEG allows to measure the perturbation induced by a TMS pulse on cortical activity, both at the local and network level. Thanks to its high temporal resolution, TMS-EEG can provide meaningful clues on the functional properties of cerebral circuits in physiological and pathological conditions [88–92]. Importantly, TMS–EEG allows the assessment of cortical physiology by discerning causal interactions from pure temporal correlations [93,94]. TMS-evoked EEG responses were first measured in the late nineties [95] and, subsequently, scalp topography and possible generator sources of TMS-evoked potentials (TEPs) have been described. It is now clear that TEPs represent genuine responses of the cerebral cortex to TMS, provided that indirect sources of brain activation, such as somatosensory and auditory stimulation, are appropriately reduced or suppressed [96–99]. TEPs occurring in the first 20–40 ms after the TMS pulse most likely reflect the responses of local cortical circuits, whereas longer-latency responses involve more distributed networks [100]. TMS-EEG studies which aimed to investigate age-related modifications in M1 generally showed decreased excitability, a finding which supports the outcomes of MEP investigations [42,47,101]. However TMS-EEG yields complex results, based on the metric

used for analysis [102]. For instance, the global-mean field power (GMFP), which provides a measure of the TMS-induced brain response on the whole scalp [103,104], has been shown to be smaller in the elderly, pointing towards a generalized cortical hypoexcitability. This is in agreement with analysis of single TEP peaks, which showed a decrease in the local P30 amplitude after M1 stimulation; however, an opposite trend was found in the ipsilateral prefrontal areas, reflected in an increased TEP amplitude. This has been interpreted as a true hyperexcitability of the prefrontal cortex, rather than a compensatory phenomenon to M1 hypoexcitability [102]. The result on P30 has not been confirmed by another study, which failed to identify an amplitude difference between groups, suggesting instead a decreased P30 latency in elderly subjects [105].

TMS-EEG allows the investigation of specific cortical neurotransmitter systems, with the assumption that different TEP components reflect selective activity of receptor classes. Data obtained in humans and non–human primates point to age-dependent changes in GABA-A and GABA-B receptor density and subunit composition, particularly in frontal cortices [106,107]. In particular, while GABA-ergic neurotransmission seems to become more efficient during the first years of postnatal development in the dorsolateral prefrontal cortex (DLPFC) [108], there is evidence for GABA-ergic neurons activity impairment in animal and human motor areas with aging [57–59,109–111]. Previous TMS-EEG data have found that the P30 and N45 peaks are related to GABA-A neurotransmission, whereas activity of GABA-B receptors shows a stronger link with later peaks, both in healthy and in diseased brains [82,90,100,112,113]. The amplitude of the N45 has been shown to be modulated by aging, albeit in opposite directions in two studies. It was reduced in Ferreri et al. [102], a data which supported a GABA-A-ergic neurotransmission deficit in M1 in older subjects, and increased in a following study [105]. As a further confounding factor, both P30 and N45 amplitude did not change when SICI, which measures GABA-A activity, was obtained by stimulation of DLPFC [114]. While the cause of these contradictory findings is currently unclear, it may reflect the variability observed for SICI in TMS studies in aging (see Section 2.1). Importantly, to date, no study has assessed SICI in M1 with concurrent EEG recording in older adults; this would help in better elucidating possible age-related changes in GABA-A mediated inhibition in the motor system. Only few studies used other ppTMS protocols during EEG recording in the elderly. Noda and colleagues (2017) tested NMDA receptor-mediated glutamatergic activity in the DLPFC using ICF; when comparing the results with a group of healthy controls, they found a reduction in N45 amplitude. The authors speculated that this result would reflect a functional decline in glutamatergic excitatory neurotransmission in older adults [114]. Finally, a recent study recorded TEPs during M1 LICI and demonstrated a greater inhibition of the N45 wave in older, compared to younger subjects; this may suggest that this component is particularly sensitive in detecting modulation of GABA-B-ergic inhibitory circuits which occurs during aging [105].


*Article* **The Narrow Mausolea at Conchada Cemetery as Part of Portuguese and European Architectural Heritage**

**Jorge Mascarenhas 1, Lurdes Belgas 2, Elisabete Vinhas 3 and Fernando G. Branco 4,\***

**Abstract:** Over millennia, death was the origin of great funerary constructions that have come down to us. These constructions aimed at ensuring eternity and perpetuating memory. Funeral art thus appears not in the service of death, but in the service of memory. In the modern age, funerary constructions do not have the dimensions and grandeur they did in ancient times, but there are still constructions with relevant architectural interest, built to perpetuate the memory of important families. In Conchada Cemetery, located in Coimbra, Portugal, a vast and diverse funeral heritage exists. Possessing various architectural styles, almost all built with limestone from the region, the narrow mausoleums stand out from this heritage. This work presents a study carried out on the architecture and construction of two types of narrow mausoleums, existing in the Conchada Cemetery, both of the Neo-Gothic style: one with an entrance from the front, and another from the back. As it is not possible to present the photographs of the burial vaults, since it would represent an intrusive approach to the families, the authors have resorted to representing them through Indian ink and watercolor illustrations.

**Keywords:** funeral heritage; mausoleum; architecture; construction; architectural styles


**3.1.1 Diaper dermatitis**

The term 'diaper dermatitis' refers to a multifactorial eruption in the region of the diaper and should not be confused with other diseases that are aggravated by diapers or occur in the same distribution (Scheinfeld, 2005). Factors contributing to primary diaper dermatitis include increased skin moisture and wetness, which create a warm and humid environment that makes infant skin more susceptible to breakdown and more permeable to chemicals

extracurricular activities. Consider the case of an 11-year-old female cellist with a three year history of an eruption on the right first, second and third digits. She patch tested positive to para-phenylenediamine, which the manufacturer of her bow verified was present in the

The association between atopic dermatitis and allergic contact dermatitis remains somewhat unclear. Several older studies that specifically investigated the prevalence of positive patch testing in children with atopic dermatitis suggested that contact allergy is less common in this population (Angelini & Meneghini, 1977). Jones et al. investigated sensitivity to Rhus in atopic and non-atopic patients. Patch tests to Rhus were positive in 61% of healthy patients and only 15% of those with atopic dermatitis (Jones et al., 1973). This correlation may be explained, as contact allergy is a Th1 response and atopic dermatitis patients have a decreased Th1 response (Mortz & Anderson, 1999). Alternatively, some studies suggest that allergic contact dermatitis is more frequent in atopic patients. Epstein and colleagues evaluated the frequency of positive patch tests in patients with atopic dermatitis versus those with psoriasis. Twenty-eight percent of those with atopic dermatitis had positive reactions versus 9% of those with psoriasis (Epstein & Mohajerin, 1964). Another study showed that patch tests were more frequently positive in those with atopic dermatitis versus controls without atopic dermatitis but with other allergic disease including allergic conjunctivitis and asthma (Lammintausta et al., 1992). Dotterud and Falk reported positive tests were significantly more common in schoolchildren with atopic dermatitis, 28.8%, versus 17.9% in controls (Dotterud & Falk, 1995). One explanation for increased risk in atopic patients is their defective skin barrier, which allows for increased exposure to antigens. Also, atopic patients may become sensitized to more allergens given their frequent use of topical agents including emollients, which often contain fragrances and preservatives (Mortz and Anderson, 1999). It should also be considered that atopic skin is readily irritated, which may lead to false positive patch testing results, especially in the case of metals (Dotterud & Falk, 1994). The latter concept is important as some recent studies did not detect a difference in the prevalence of positive patch testing between children with and

without atopic dermatitis (Balato et al., 1989; Motolese et al., 1995).

There are two categories of contact dermatitis that affect the pediatric population: irritant and allergic contact dermatitis. Irritant dermatoses have been diagnosed in children for

The term 'diaper dermatitis' refers to a multifactorial eruption in the region of the diaper and should not be confused with other diseases that are aggravated by diapers or occur in the same distribution (Scheinfeld, 2005). Factors contributing to primary diaper dermatitis include increased skin moisture and wetness, which create a warm and humid environment that makes infant skin more susceptible to breakdown and more permeable to chemicals

**3. Common causes of contact dermatitis in children**

bow stain (O'Hagan and Bingham, 2001).

**2.5 Atopic dermatitis**

**3.1 Irritant dermatitis**

**3.1.1 Diaper dermatitis**

many years, particularly diaper dermatitis.

and enzymes. An elevated pH results when bacterial ureases split urea in the urine to release ammonia, and this predisposes infant skin to dermatitis. Friction may also play a role, though this is likely a predisposing or exacerbating rather than dominant factor. Fecal enzymes including proteases and lipases have direct irritant action on the skin and their effects are increased by an alkaline environment. Finally, microorganisms, particularly candida, but also staphylococcus, peptostreptococcus, bacteroides, herpes virus, and dermatophytes can worsen irritant diaper dermatitis (Prasad et al., 2003; Wolf et al., 2000).

Other causes of dermatitis in the diaper region include seborrheic dermatitis, psoriasis, atopic dermatitis, congenital syphilis, acrodermatitis enteropathica (zinc deficiency), scabies, child abuse and miliaria. Finally, dermatitis of the diaper area may also be allergic contact dermatitis. Allergens to consider in this setting include sorbitansesquioleate, fragrances (mix I and balsam of peru), disperse dye, cyclohexlthiopthalimide, mercaptobenzothiazole, iodopropylcarbamate, bronopol and *p*-*tertiary*-butyl-phenolformaldehyde (Smith & Jacob, 2009).

Prevention and management of irritant diaper dermatitis revolves around keeping the occluded skin dry and limiting the amount of time that the skin is exposed to urine and feces. Removing diapers is one of the oldest and most effective measures in preventing and treating this condition. Frequent diaper changes are most helpful if done immediately after urination and bowel movements (every hour in neonates and every 3-4 hours in infants). Some experts recommend washing the area with mild soap, while others suggest that rinsing the area in lukewarm water is sufficient. New technology has allowed diapers to be much more absorbent and effective in keeping skin dry and with a normal pH. In terms of topical treatments, low potency steroids can be effective for inflamed skin. However, even if these are applied for a short time to acute disease, a waterproof emollient should be placed over them as a barrier to protect the skin. Ideally, emollients should be reapplied after every diaper change. Emollients effective in this setting are usually made of a large quantity of fine powder, such as zinc oxide, suspended in a greasy vehicle. For those eruptions which are superinfected with candida, topical antifungals may also be required (Wolf et al., 2000).

#### **3.1.2 Perianal dermatitis**

An entity that is distinct from diaper dermatitis is perianal dermatitis. Fecal components including fecal lipase and bile acids can cause degradation of the skin barrier perianally, leading to an erythematous irritant dermatitis limited to perianal skin (Ruselet-van Embden et al., 2004). There are several less common diagnoses that are thought to be related to irritant perianal dermatitis and some believe that these exist on a spectrum of one disease. These entities include granuloma gluteale infantum, pseudoverrucous papules and Jacquet's erosive dermatitis.

Granuloma gluteale infantum is thought to be multifactorial and related to occlusion, powder, topical halogenated steroids, *Candida* infection, urine and feces. It classically appears as oval, red-purple granulomatous nodules at sites of occlusion (Robson et al., 2006). This condition will improve with removal of inciting agents (Al-Faraidy & Al-Natour, 2010). Pseudoverrucous papules and nodules is a less common condition and was first reported in association with urostomy sites but may also be seen in children in a perianal distribution. Lesions are shiny, smooth, red, moist, flat-topped and round and may be

Contact Dermatitis in Children 135

Nickel is the most widespread allergen in the general population (Heim & McKean, 2009; Johnke et al., 2004) and is most often identified as the leading allergen in children (Tables 1 & 2). It accounts for up to 14.9% of positive patch tests (Dotterud and Falk, 1995) in asymptomatic children and is generally more frequent in females (Beattie et al., 2007; Brasch & Geier, 1997; Giordano-Labadie et al., 1999). Importantly, young infants may also be sensitized to nickel. In a study of 543 infants followed from birth to age 18 months, 8.6% showed a reproducible positive reaction to this metal (Johnke et al., 2004). Ear piercing is often considered the major risk factor for becoming sensitized to nickel (Smith-Sivertsen et al., 1999). Other sources include everyday items such as jewelry, eyeglass frames, belt buckles, jean snaps, zippers, coins, keys and even cell phones (Hsu et al., 2010). Another potential cause for sensitization is orthodontic devices (Temesvari & Racz, 1988; Veien et al., 1994). In this setting, the allergic contact dermatitis can present as cheilitis, perioral eczema and stomatitis. Other metals are also implicated in this setting including potassium dichromate (Veien et al., 1994). Typical locations for nickel dermatitis include the face, earlobes, wrist, neck and periumbilical skin with the last site being most common (Hsu et

While a localized contact dermatitis is most expected with nickel, id reactions may not be uncommon. Id reaction refers to involvement of skin lacking direct contact with the allergen, resulting from auto-sensitization from circulating immune cells. Such eruptions, sometimes confused with atopic dermatitis, present as pruritic papules distributed on the upper arms, thighs, knees and elbows. They tend to be more persistent than localized contact dermatitis, lasting up to months after localized plaques have cleared (Hsu et al., 2010). Silverberg and colleagues examined 30 pediatric patients with personal history of umbilical or wrist dermatitis or a family history of nickel allergic contact dermatitis. All patients developed a positive patch test to nickel and 50% of patients were reported to develop id reactions (Silverberg et al., 2002). Systemic contact dermatitis has also been reported with nickel. It may present as a generalized dermatitis despite contact with nickel at a limited body site. In some cases, it may result from oral ingestion of nickel, including

While a significant percentage of positive patch test results in children are attributed to cobalt, it should be recognized that this metal often co-sensitizes with other metals, particularly nickel and potassium dichromate (Goon & Goh, 2006; Lisi et al., 2003). At times, contamination of cobalt patch tests with nickel may also lead to false positive tests (Lisi et al., 2003). Yet, cobalt itself remains relevant for allergic contact dermatitis. One study attributes 2 of 17 cases of pediatric hand dermatitis to cobalt (Beattie et al., 2007). In 1971, a case was reported of an 11-year-old boy who presented with eczematous lesions at the site of his eyeglass frames, wrists and mouth. His dermatitis was attributed to cobalt in his

A common source for potassium dichromate exposure in children is its use in tanning leather, particularly in shoes (Sarma and Ghosh, 2010; Weston et al., 1986). In such cases, the

the small amount that is present in foods and tap water (Hsu et al., 2010).

watch, glasses and the ball point pen that he chewed (Grimm, 1971).

**3.2.1 Metals**

Nickel

al., 2010).

Cobalt

Potassium dichromate

mistaken for condyloma (Robson et al., 2006). Finally, Jacquet's erosive diaper dermatitis describes perianal papules that are well-demarcated, sometimes umbilicated and red-purple in color (2-5mm diameter). They evolve into slow-healing erosions and ulcers (Paradisi et al., 2009) and may have elevated borders (Robson et al., 2006). This usually occurs in infants older than six months. Treatment of this entity can be difficult, but therapeutic options include topical treatment with antibiotics, miconazole, zinc oxide and non-steroidal antinflammatory drugs (Paradisi et al., 2009).


**4. Results**

Low-rank lignite was combusted in a 1 MWth pilot plant to investigate the operational flexibility of CFB combustion. An APROS model was designed and adjusted to reproduce the experimental results. The underlying objective of this work is to create a model, which can predict the behavior of CFB combustion with di fferent fuels and fuel mixtures and at varying load. The data is presented here to provide valuable input for future research in the field of the flexibility of CFB combustion. The first part of this section describes the model tuning process. Additionally, the temperature profile, the pressure profile, and the gas composition of the tuned model are presented and compared to experimental data of the long-term steady-state test. The experimental data is averaged over the complete testing time and the standard deviation is used to illustrate the fluctuations during the test. After the tuning process, the validation process with five test points at part load is presented. The development of the pressure and temperature along the furnace height is presented on average and over time to evaluate the average part-load conditions as well as the dynamic behavior of the boiler. The validation process focusses on the hydrodynamics, the flue gas composition, and the dynamic behavior during load increasing and decreasing conditions.

#### *4.1. Steady-State Model Tuning*

The model was tuned with the experimental data of the 57-h steady-state test at a load of 845 kWth (82% load). Various model parameters were adjusted and chosen to meet the pressure profile, the temperature profile, and the gas composition of the experiment. Other parameters were tuned to find the best values for high numerical stability and good agreement with the experimental data. The most relevant parameters are listed in Table 10 with a short description. The riser is separated into 20 nodes. If the 20 nodes were equally distributed over the riser height, each node would have a height of 0.43 m. However, the bottom node of the riser has a height of 0.7 m. The bottom node represents the high-density bed zone. Here, no core-annulus approach is implemented due to the high inventory and the enhanced mixing of particles. For numerical stability, the bottom node must be higher than the bed height, which is calculated by APROS by dividing the mass of solid particles in the bed node by the average density of gas and solid particles in this node and the flow area of the node.

The heat transfer from the riser to the waterside depends on the temperature di fference, the surface area, and the heat transfer coe fficient. The heat transfer coe fficient is calculated using the Mattman-Molerus-Wirth correlation. It takes into account the Archimedes number and the local pressure drop/particle fraction [39]. This correlation is recommended for the prediction of heat transfer in circulating fluidized beds [24].

The mass flow of particles from the core to the annulus and vice versa depends on the radial velocities and the mass of solids in the calculation nodes. Without further tuning, the radial velocities of the solids would be set equal to the axial velocities of the solids in the core/annulus. However, in reality, radial and axial velocities are not equal. To adjust the radial velocities and thereby the mass flow rates of the solids, so-called global split coe fficients from the core to the annulus and from the annulus to the core are introduced. These coe fficients have values between 0 and 1. With a value of e.g., 1, the radial velocity is equal to the axial velocity. With a value of e.g., 0.1, the radial velocity is ten times smaller than the axial velocity. Thereby, the global split coe fficients have a great impact on the upflow/downflow of particles in the core/annulus and largely influence the pressure profile. The global split coe fficients were adjusted to adapt the pressure profile according to the measurement data.

**Table 10.** Attributes/parameters of the CFB combustor model.

Another large influence on the pressure profile is given by the so-called interface density. The amount of particles that are entrained from the high-density bottom node to the first core node directly depends on the axial solid's velocity and the interface density. The interface density is a theoretical value and can be set freely by the user. It represents the suspension density in the zone between the bed and the freeboard. Therefore, it physically represents the density in the splash zone of the fluidized bed. The density can be estimated from the experimental data by using the pressure drop in this area. From the experimental data, it is reasonable to define the location of the splash zone between the two pressure sensors at 0.58 m and 2.07 m, where the density of the bed changes from high-density zone to lean zone. In the steady-state case, the calculated splash zone density is 58.2 kg/m3. However, in the model, a value of 30 kg/m3 was chosen, as the entrainment was highly overestimated, when using a value of 58.2 kg/m3.

An average diameter has to be set to account for the particle size of the fluidized bed particles and no particle size distribution is implemented in the model. The average particle size is 0.211 mm, which is the average particle size of the bottom ash sample taken after the test period. To calculate the heat loss through the insulation to the environment, a boundary condition has to be set for the ambient temperature outside of the refractory lining. This temperature is set to 25 ◦C and is not adjusted during the simulations.

Figure 5 shows the pressure profile along with the furnace height during the long-term steady-state test with 845 kWth load. The average velocity according to the description in Section 2.2 is calculated to 4.2 m/s. In the model, the pressure drop in the freeboard is overestimated indicating that the entrainment is higher in the simulation compared to the experiment. The hydrodynamic conditions influence the chemical reactions, the heat transfer, and the temperature development in the furnace. Further improvements of the model are therefore useful to achieve better agreement in future studies—e.g., the interface density parameter can be set to smaller values. In this work, the interface density is already set 2 times smaller than the density in the splash zone. Therefore, it was chosen not to make the parameter even smaller, to have an appropriate compromise between the accuracy of the model and the physical foundation of the interface density. The agreement between experimental data and simulation is good both in terms of the total pressure drop and the reproduction of the typical CFB pressure profile. The figure also shows the standard errors for the experimental data calculated from the standard deviations in the measurement signals and the uncertainty of the measurement devices.

**Figure 5.** Pressure and temperature along with the reactor height (steady-state test).

The temperature profile is shown on the right in Figure 5. The temperatures in the simulation agree very well with the experiment. In the bed zone, the temperatures are almost constant due to the high level of mixing in the bottom region. At 2.7 m, secondary air is injected. However, the measurement shows decreasing temperatures from 2.4 to 6.3 m potentially for two reasons. Firstly, the cooling lances and the non-preheated secondary air might compensate for the combustion reactions. Secondly, no measurement is installed between these two locations. Therefore, the temperature might rise above the secondary air injection and it is just not shown by the available experimental data. However, the simulation does not show a temperature increase above 2.7 m either. In the experiment, the temperature increases above the secondary air injection at 6.0 m to the last measurement at 8.2 m. In the model, a temperature peak at 7.0 m is observed, before the temperature decreases again due to the impact of the cooling lances. At 8.2 m, experiment and simulation correspond. In addition to the riser temperatures, the temperature measurement after the cyclone is shown in Figure 5. In the experiment, the temperature increases between riser and cyclone outlet. It is reasonable, that the mixing of secondary air, volatiles, and char is considerably smaller in the experiment, as the penetration depth of the secondary air is not optimized in the pilot. Thereby, the combustion reactions are shifted to the top of the reactor and the cyclone. In the cyclone, the high velocity and the turbulent flow ensure

a strong mixing leading to the post-combustion of char. In the model, the mixing is very efficient, due to its 1-dimensional nature. Therefore, the combustion reactions are taking place only in the riser and the post-combustion is not reproduced sufficiently.

The flue gas composition is presented in Figure 6. On the left-hand side, the main gas components O2, CO2, and H2O are shown. The standard errors are presented, taking into account the measurement device uncertainty and the standard deviation of the fluctuating measurement signal. The simulation agrees very well with the experiment. Simulation results are always within the standard error of the experiment and the relative deviations are very small. Considering that the composition of the fuel is subject to fluctuations during the experiment, the agreement is considered very high.

**Figure 6.** Flue gas composition (steady-state test).

On the right-hand side of Figure 6, the content of the trace gases CO and SO2 is presented. There is no CO in the flue gas in the simulation. This supports the findings from the temperature profile of the simulation, where all combustion reactions are completed in the riser. In the experiment, the CO content is around 200 ppmwet, so the combustion reactions are not completed even after the post-combustion in the cyclone. The SO2 content depends on the varying sulfur content in the fuel and on the efficiency of the sulfur capture with calcium oxide from the fuel ash. The sulfur capture in a CFB combustor is a complex process, which depends on many parameters, such as the flue gas composition (locally reducing conditions, water vapour content), the temperature, the residence times and the properties of the CaO-particles (porosity, size, shape) [40–43]. Taking into account all these uncertainties and the fact, that especially the particle properties cannot be defined to this extent in the APROS model, the relative deviation of 41% between model and experiment is in a comprehensible range. It should be mentioned at this point that no tuning was carried out concerning the desulphurization reactions in the model, besides the definition of the S-content in the fuel and the CaO content in the fuel ash.

## *4.2. Model Validation*

After tuning with the steady-state test, no further tuning or modification of the model took place during the validation process. The model is validated with the measurement data of five test points, with a thermal load from 63% to 100%. The model validation focusses on the hydrodynamic conditions, the flue gas composition, and the dynamic behavior of the fluidized bed.

#### 4.2.1. Hydrodynamic Condition in the Furnace

The hydrodynamic conditions in a CFB furnace depend on the load, the bed material properties, the fuel, the combustion air, the heat exchangers inside the furnace, and other parameters, such as

the insulation of the furnace. All these parameters are considered in the presented APROS model. The temperature and pressure profiles are a suitable way to validate a process model in terms of the hydrodynamics. Figure 7 shows the pressure and temperature profile of the 100% load case (1032 kW) with a calculated average velocity of 4.60 m/s. The agreement between experiment and simulation is very good for the temperature profile. The bed temperature, as well as the temperature development along the reactor and the temperature at the riser top in the simulation, are within the standard errors of the experimental data. As in the steady-state case, the post-combustion in the cyclone does not happen in the model. Therefore, there is a temperature deviation of about 20 K between simulation and experiment for the temperature after the cyclone. The pressure profile shows greater deviations than the temperature profile. The overestimation of the pressure drop in the lean zone is similar to the steady-state test. However, the total pressure drop and the slope of the pressure profile in the upper reactor is still in good agreement.

**Figure 7.** Pressure and temperature along the reactor at high load.

Figure 8 presents the hydrodynamics at medium load (88%, 89%). The average calculated velocities are 3.70 m/s (88%) and 3.77 m/s (89%). The pressure profile is similar for ascending and descending load. The load is slightly higher than in the steady-state tuning test point. However, the velocities are smaller, as the excess air factor is lower in these cases. The pressure drop in the freeboard is overestimated by the model, which could potentially be improved by adjusting the interface density in the model. However, the numerical simulation is capable of predicting the reduced entrainment at decreasing velocities/load cases on the same scale as in the experiment. The agreement between experiment and simulation is good regarding the total pressure drop and the bed density.

**Figure 8.** Pressure and temperature along the reactor at medium load.

It is noticeable that the average temperature differs significantly at descending and ascending load changes, despite similar loads. The refractory and the inert bed material play a major role here. A large amount of energy is stored and is then slowly released when the load decreases. Therefore, the temperature level is higher in the case of descending load (89% load case), as long as steady-state conditions are not achieved. This mechanism is well shown also by the simulation, where this temperature difference is also present. It is necessary to mention here, that the walls of commercial CFB boilers are made of water walls. Therefore, the effect observed here will hardly affect industrial scale boilers.

Despite the different average temperatures, the temperature development along the riser is similar for both medium load tests. Partly, bed temperatures are higher at medium loads compared to high loads. Additionally, the temperature drop along the reactor increases at lower loads (see Figure 7), due to several reasons. Less particles (e.g., char) are entrained from the bed at lower loads/velocities. Thereby the combustion reactions are shifted to the bed zone, which increases the bed temperatures and decreases the temperatures in the upper reactor. When the circulation of particles decreases, fewer particles are cooled by contact with the cooling lances and the surrounding flue gas. This lack of particle-cooling can result in an increased temperature of the solids and thereby an increasing bed temperature. These mechanisms, in combination with the heat stored in the insulation, can lead to an increase in the bed temperature, although the load is reduced. The simulated temperatures match the experiment very well in the high-density bed region and at the reactor top. At 6.3 m height, there is a larger deviation between model and experiment. Potentially, the heat transfer to the cooling lances is not correctly reproduced at this low load. As already discussed, also the combustion reactions with the secondary air, which is injected at a height of 6.0 m, are completed much faster in the model due to the idealized mixing. This leads to a higher temperature in this area.

The hydrodynamic profiles of the tests with 63% and 68% load are illustrated in Figure 9. The calculated velocities are 2.78 m/s (63%) and 2.88 m/s (68%). In the experiment, there is only a minor pressure drop along the lean zone of the riser, while the simulation results still show significant entrainment. As discussed before, the model parameter interface density, which largely influences the entrainment, is kept constant for validation purposes. In the experiment, on the other hand, the density in the splash zone is slightly decreasing at lower loads (58.2 mbar at 82% load and 48.2 mbar at 63% load). It is therefore reasonable, that the deviation between the pressure profile of the simulation and the experiment may get worse at lower loads. The model gives a good indication of the total pressure loss of the experiment, even if the deviations are greater than at higher load.

**Figure 9.** Pressure and temperature along the reactor at low load.

Again, the temperature level is higher for the load reduction test (68% load) compared to the load elevation test (63%), which is partly due to the energy stored in the refractory and bed material. However, the load in the latter is also significantly lower, which is also responsible for the higher temperatures. There is an excellent agreement between experiment and simulation for the bed temperatures. The model also shows that the temperature along the reactor decreases much more than it did at higher loads, which matches the experimental data. At the top of the reactor and around 6 m, the deviation between experiment and simulation is higher than at high and medium load, whereby the agreement between experiment and model is much better for the temperature after the cyclone. In the experiment, there is a major level of post-combustion in the cyclone, which is not present in the model due to the reasons discussed before. As the combustion takes place entirely in the riser, the temperatures in the simulation are higher at 6.3 and 8.2 m.

#### 4.2.2. Flue Gas Composition

The flue gas composition is measured at several positions in the pilot plant. The FTIR measurement after the flue gas cooler includes all main species including water vapor and many trace gases. This measurement data is compared to the simulation results for the validation of the model. Figure 10 shows the flue gas composition of the part-load tests. In the simulation, the content of carbon dioxide and water vapor is within the standard error of the experiment for all tests. The oxygen content is always at or slightly below the lower limit of the standard error in the experiment. Regarding the fluctuating fuel composition, the model shows an excellent agreement with the experiment.

**Figure 10.** Flue gas composition at part load.

The CO content in dependency of the average riser temperature is shown in Figure 11. Large fluctuations are present for the CO content in the experiment. The CO content tends to decrease with increasing temperature, as high temperatures accelerate the combustion reactions. However, more parameters influence the CO content, such as the oxygen content and the efficiency of the post-combustion in the cyclone for instance. In the simulation, no carbon monoxide remains in the flue gas and the combustion reactions are completed inside the riser for all tests. As discussed before, the mixing of oxygen and burnable components (CO, char) is modeled as ideal in the 1.5 D APROS model. In the experiment, the mixing depends on several parameters, such as the injection speed of the secondary air and the conditions in the riser.

**Figure 11.** CO content in flue gas for varying riser temperature.


*2.5. Flux Dissolution*

Similarly to the decarburization rates at the impact zone, flux dissolution is modelled after Dogan et al. [27]. The flux particles are assumed to have a spherical shape, and the rate of flux dissolution is proportional to the rate of change of the particles' radius.

The rate of lime dissolution is given by Equation (41) [27,28], where *rL* is the particle radius, (%CaO) is the concentration of CaO in slag, (%*CaOsat*) is the saturation concentration of CaO in the slag, *kL* is the mass transfer coefficient, *ρs* and *ρL* are the slag and lime density, respectively. The parameter *αp L* is introduced here to account for deviations between the experimental conditions at which *kL* was derived, and the BOF operating conditions.

$$\frac{dr\_L}{dt} = a\_L^p k\_L \frac{\rho\_s}{100\rho\_L} ((\% \text{CaO}) - (\% \text{CaO}\_{sat})) \tag{41}$$

For dolomite, the rate of dissolution is given by [27,29]:

$$\frac{dr\_D}{dt} = \begin{cases} a\_D^p k\_D \frac{\rho\_s}{100\rho\_D}}{A} \left(1 + \frac{M\_{\rm MgO}}{M\_{\rm CaO}}\right) \left(\left(\% \rm CaO\right) - \left(\% \rm CaO\_{sat}\right)\right) & \left(\% \rm FeO\right) < 20\%\\ a\_D^p k\_D \frac{\rho\_s}{100\rho\_D}}{A\_D^p k\_D \frac{\rho\_s}{100\rho\_D}} \left(1 + \frac{M\_{\rm FeO}}{M\_{\rm MgO}}\right) \left(\left(\% \rm MgO\right) - \left(\% \rm MgO\_{sat}\right)\right) & \left(\% \rm FeO\right) \ge 20\% \end{cases} \tag{42}$$

where *MMgO* and *MCaO* are the molar mass of MgO and CaO, respectively, (%MgO) is the concentration of MgO in slag, (%*MgOsat*) is the saturation concentration of CaO in the slag and *kD* is the mass transfer coefficient. The rate constants *kL* and *kD* are calculated using the correlations proposed by Dogan et al. [27] with the parameter used to modify the Reynolds number *β* set to the nominal value of 1.

Data for the calcium oxide saturation in slag (%*CaOsat*) for different slag compositions were obtained using the Cell Model [30], a Matlab program that gives the saturation concentration of the individual species in the slag as a function of composition and temperature. Kadrolkar et al. [30] validated the Cell Model results against FactSage*TM* and ThermoCalc*TM*. The function 'fitnlm' in Matlab was then used to fit a curve to the generated data, yielding Equation (43).

$$(\% \text{CaO})\_{\text{sat}} = \frac{3.52 T\_s - 4,823.7 e^{-\frac{2.43}{100}(\% \text{SiO}\_2)} + 12.4(\% \text{FeO}) - 9.71(\% \text{MgO}) + 17.9(\% \text{CaO})}{100} \tag{43}$$

An R2 of 0.9 was obtained for the nonlinear regression. The goodness of Equation (43) to predict the calcium oxide saturation in slag was evaluated using the Cicutti data [13]. The calculated values for *CaOsat* using the Cell Model and Equation (43) are shown in Figure 4, where it can be seen that there is excellent agreement between them.

**Figure 4.** (%*CaOsat*) obtained using the Cell Model and Equation (43) for the Cicutti et al. [13] slag data.

The rate at which heat is absorbed by the flux particles is given by:

$$Q\_{flux} = 4\pi \sum\_{i} r\_i^2 h\_i n\_i (T\_s - T\_i) \quad i \in \{L, D\} \tag{44}$$

where *Ts* is the temperature of the slag, *Ti* is the flux temperature, *hi* is the heat transfer coefficient determined using data and *ni* is the number of flux particles. Assuming a uniform temperature for the flux particle, an energy balance gives Equation (45) for the temperature of an iron ore particle:

$$\mathcal{W}\_i \mathbb{C}\_{P,i} \rho\_i \frac{dT\_i}{dt} = 4\pi r\_i^2 h\_i (T\_5 - T\_i) \quad i \in \{L, D\} \tag{45}$$

where *CP*,*i* is the heat capacity and *Wi* = 4/3*ρiπr*3 is the mass of one single flux particle.


*Article* **Removal of Enantiomeric Ibuprofen in a Nanofiltration Membrane Process**

#### **Carlyn J. Higgins 1 and Steven J. Duranceau 2, \***

Received: 16 October 2020; Accepted: 26 November 2020; Published: 30 November 2020

**Abstract:** A study of the behavior of R- and S-enantiomers of ibuprofen (R-IBU and S-IBU) in aqueous solution by nanofiltration (NF) membranes revealed that up to 23% of the pharmaceutical was adsorbed onto the stainless steel equipment of a flat-sheet experimental unit. Mass balances disclosed that IBU's S-enantiomer was primarily responsible for the adsorption onto the equipment. Additional IBU adsorption was also experienced on the NF membrane coupons, verified by increased contact angle measurements on the surfaces. The IBU-equipment adsorptive relationship with and without the membrane coupon were best described by Freundlich and Langmuir isotherms, respectively. At a feed water pH of 4.0 units and racemic µg/L IBU concentrations, NF removal ranged from 34.5% to 49.5%. The rejection of S-IBU was consistently greater than the R-enantiomer. Adsorption onto the surfaces influenced NF rejection by 18.9% to 27.3%. The removal of IBU displayed a direct relationship with an increase in feed water pH. Conversely, the adsorption of IBU exhibited an indirect relationship with an increase in feed water pH.

**Keywords:** nanofiltration; ibuprofen; adsorption; enantiomer; chirality; removal

#### **1. Introduction**

The existence and subsequent discovery of chemicals of emerging concern (CECs) in aquatic environments has sparked interest in determining removal capabilities of specific water treatment technologies. Nanofiltration (NF) is a promising pressure-driven semipermeable membrane technology that can be employed to remove CECs from aqueous streams [1–5]. In a membrane process, the extent of solute removal is dependent on chemical properties, feed water matrix composition, membrane characteristics, and operational variables [1,2,6–8]. A strong research effort has attempted to elucidate the impact of CEC properties on solute removal through NF processes. It is widely accepted that molecular weight is an important parameter in the prediction of non-charged and non-polar compound rejection [1,9–11]. However, other solute characteristics such as chemical properties, solute geometry, and functional groups can also affect rejection of CECs [1,6,7]. In a membrane process, correlations between CEC removal and hydrophobicity [12], membrane adsorption [13], polarizability [14], polarity [15,16], and molecular size and shape [6,7,14,17,18] have been noted. The complexities of CEC mass transfer have been scrutinized and reported on over the years and serve as the basis for additional investigations such as those presented herein.

The position of functional groups in structural isomers has also been shown to have significant effects on rejection by reverse osmosis membranes [19]. This suggests that the spatial arrangement of atoms plays a larger role in membrane process removal than currently understood. A solute property that has received little attention regarding behavior in a membrane process is chirality. Chiral molecules, or stereoisomers, are molecules with the same molecular formula and chemical

bonding arrangement, but dissimilar spatial position of atoms. Enantiomers are pairs of stereoisomers that are non-superimposable mirror images. Although enantiomers have the same molecular formula and other chemical properties, some are known to behave differently.

A well-known example of a chiral molecule is ibuprofen (IBU). IBU is a weak propionic acid derivative and pharmaceutically active compound (PhAC) known for its non-steroidal anti-inflammatory (NSAID) properties. The molecule contains a chiral carbon, yielding two enantiomers, S-IBU and R-IBU. Although medically administered IBU is a racemic mixture of the two enantiomers, the S-form possesses most of the anti-inflammatory properties [20–22]. Physiochemical properties of IBU such as low Henry's law constant (1.5 × 10−7 atm-m3 /mol), moderately high log octanol-water partition constant (Kow; 3.97), and soil adsorption constant (log Koc; 2.60) suggest that the PhAC often persists in aquatic environments and can display adsorptive qualities to clay and other loamy solids [23]. Like other CECs, IBU has been detected in groundwater and surface waters from the ng/L to µg/L level [24,25]. The enantiomeric ratio (ER) of IBU in surface water has been recorded higher than 0.5, yielding disproportionate enantiomer concentrations in favor of the S-counterpart [26].

In this presented work, the capability of NF process to remove enantiomeric IBU (S-IBU, R-IBU) at acidic pH conditions is explored. The behavior of chirality with respect to removal by nanofiltration membranes has not been fully vetted. The results of this research provide insight into the differing behavior of chiral molecules, further elucidating the effect of solute properties on membrane rejection.

#### **2. Materials and Methods**

In this study, Dupont Filmtec NF270 (Edina, MN, USA) and Microdyn Nadir Trisep TS40 (Goleta, CA, USA) membranes were assessed. The polyamide thin-film composite NF270 and polypiperazine amide TS40 were acquired from Sterlitech Corp. (Kent, WA, USA). Membranes were received as flat sheets, cut to the appropriate size, and soaked in deionized water (DI) for at least 24 h prior to experimentation. Membrane operational parameters are listed in Table 1. Racemic IBU was purchased from Sigma-Aldrich (St. Louis, MO, USA) with reported purity of greater than 99%. A 400 mg/L standard solution was prepared in LC/MS grade methanol and sonicated for homogeneity. The standard was stored at 4 ◦C in a salinized amber bottle and used within one month.

**Table 1.** Membrane operational properties.

#### *2.1. Experimental Setup*

A bench-scale, flat-sheet membrane testing apparatus was used in this research. The unit consisted of a Wanner Engineering M-03S Hydra-Cell 6.81 L/min pump (Minneapolis, MN, USA) with a Control Techniques variable frequency drive (VFD) (Eden Prairie, MN, USA), a 19 L Sterlitech stainless steel conical feed tank, two Sterlitech CF042 acrylic cells to house the membrane coupons (operated in parallel for duplicity) with 42 cm2 effective membrane area, and accompanying appurtenances consisting of flowmeters, pressure gauges, check valves, and stainless steel braided hose. Two MyWeigh CTS-600 scales (Phoenix, AZ, USA) were utilized for permeate collection and flux measurements. Feed flow was controlled with the VFD and set at 1.0 L/min, corresponding to a crossflow velocity of 0.18 m/s. Feed pressure was controlled by the concentrate control valve. A chiller-coil system was utilized to sustain a feed water temperature of 20 ± 1 ◦C.

Prior to each experiment, membrane coupons were inserted into the bench-scale, flat-sheet unit and compacted at 6.9 bar (100 psi) with DI water for at least 24 h. After initial compaction, the mixture was replaced with a 10 L solution containing DI water spiked with racemic IBU and

adjusted to acidic conditions (feed water pH of 4.0 to 6.0 units) with 1 M sodium hydroxide or 5.8 M hydrochloric acid. Then, the bench-scale, flat-sheet unit was repressured with the experimental feed water matrix and operated for 24 h. Experiments were conducted in recycle mode, where permeate and concentrate streams recycled back to the feed reservoir. Feed water samples were taken at 0 and 24 h, where permeate and concentrate aliquots were taken at 24 h. The unit was flushed at least twice with 5 L of DI water in between experiments. A new set of membrane coupons was used for each experiment.

#### *2.2. Analytical Methods*

Samples were collected in 150 mL salinized amber bottles, stored in a 4 ◦C refrigerator, and extracted and analyzed within 48 h and 7 d, respectively. A solid phase extraction (SPE) method was utilized to extract and preconcentrate R- and S-IBU enantiomers [27]. Extractions were performed utilizing a Waters vacuum manifold and Waters Oasis HLB 3 mL, 60 mg cartridges (Milford, MA, USA). The R- and S-IBU enantiomers were analyzed via a Perkin-Elmer Series 200 high performance liquid chromatography (HPLC) instrument (Santa Clara, CA, USA). Separations were carried out on a Chiral Technologies, Inc., (West Chester, PA, USA) Chiralcel OJ-H column (4.6 × 150 mm, i.d., 5 µm particle size). The column was operated in polar phase mode, with an isocratic mobile phase consisting of methanol/formic acid (100:0.1, *v*/*v*) at a flow rate of 1 mL/min.

A ramé-hart Model 100 goniometer (Succasunna, NJ, USA) was utilized to determine membrane hydrophobicity via contact angle. Contact angle measurements were attained utilizing the sessile drop technique. Membrane coupons were dried and inserted on the stage with the active layer facing up. A micrometer syringe delivered a droplet of DI water onto the membrane surface, and a contact angle was measured by the goniometer. To obtain a representative contact angle of the entire membrane surface, ten contact angle measurements were taken on various areas of the membrane coupon and averaged.

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

Adequate mass balance tests are recommended in bench-scale membrane filtration experiments to confirm that rejection is not affected by solute behavior such as volatilization, adsorption, or a reaction with the feed water matrix. Consequently, prior to the series of pressurized filtration tests, a mass balance confirmation experiment was conducted by circulating a feed solution containing 100 µg/L IBU at feed water pH of 4.0 units through the flat-sheet equipment without a membrane coupon for 24 h. After analysis, 23% loss of IBU was observed during the experiment.

#### *3.1. E*ff*ect of Feed pH on Adsorption*

The test was repeated at a feed water pH range from 3.0 to 7.0 units, resulting in an inverse relationship between loss of enantiomeric IBU and the water quality parameter, aligning with the acid dissociation constant (pKa) of IBU (4.4). Figure 1 illustrates the results of the flat-sheet equipment adsorption experiments, where the total IBU concentration adsorbed is presented as a function of the total initial IBU concentration.

Due to the low Henry's law constant (1.5 × 10−7 atm-m3 /mol), volatilization could not explain observed IBU losses. However, in acidic conditions, IBU is known to adsorb onto and protect metal from corrosion [28,29], and IBU has been documented to attach to chromium-based metal organic frameworks [30]. It is noted that the flat-sheet test equipment is comprised mainly of components that consist of stainless steel (16% chromium, 10% nickel, 2% molybdenum, and less than 0.02% carbon) [31]. Existing literature suggests that IBU adsorption onto the flat-sheet stainless steel components (comprised of the reservoir, tubing, and chiller coil) could occur, as similar results have been realized with 9-anthracenecarboxylic acid [32]. Minimal to no attachment to the acrylic holding cells and permeate polyethylene tubing was observed. The bonding mechanism of IBU adsorption onto the surface was postulated to be between the hydrogen on the carboxylate functional group of the solute and adsorption of oxygen on the metal from the hydroxide moiety [33,34].

**Figure 1.** R- and S-enantiomers of ibuprofen (R-IBU and S-IBU) adsorption onto flat-sheet equipment as a function of feed water pH (temperature 20 ± 1 ◦C). Selectivity is a ratio of adsorption of S/R IBU.

−

#### *3.2. E*ff*ect of Feed Concentration on Adsorption*

Although significant adsorption onto the flat-sheet equipment was observed, IBU has also been known to attach onto membrane surfaces at pH values less than its pKa of 4.4 [35]. Hence, experiments deciphering the extent of IBU adsorption onto the flat-sheet equipment with and without a membrane coupon installed were conducted by repeating the experiment for a racemic solute concentration range from 100 µg/L to 1.50 mg/L. Feed samples were collected at 0 and 24 h. Triplicate feed concentration measurements were taken and averaged.

Results in Figure 2 indicate that the adsorption of IBU increases with initial feed concentration, which agree with prior PhAC-metal attachment studies [29]. It also appears that the sorbed IBU concentration may approach a saturated equilibrium in due course, and hence can be modeled by adsorption isotherms (see Supplementary Information).

An additional 19.6% to 39.2% IBU adsorption onto flat-sheet equipment with membrane coupon was recorded. These results suggest that additional IBU adsorption presumably occurred onto the membrane surface. IBU adsorption onto the membrane components was validated by an increase in hydrophobicity (measured by contact angle), illustrated in Figure 3. Hydrophobicity was found to have a positive direct relationship with the concentration of adsorbed IBU. Mechanisms of adsorption could include both hydrophobic interactions and the formation of hydrogen bonds between IBU and the membrane surface [36]. Previous studies have attributed IBU adsorption only to the membrane surface, neglecting to fully understand the behavior of IBU, hence, conceivably reporting inaccurate rejection values in flat-sheet studies [37–40].

At an initial racemic concentration of 1.08 mg/L, 83.7 µg/L IBU adsorbed onto the equipment and NF270 membrane. On the contrary, at an initial racemic content of 840 µg/L, 93.6 µg/L IBU adsorbed onto the equipment and TS40 membrane. Therefore, the TS40 membrane contained a slightly higher capacity to adsorb IBU. The difference of adsorption could not be explained by pore size or surface hydrophobicity. Although the NF270 membrane is more hydrophobic and has a larger MWCO, it did not adsorb as much IBU as the TS40 component. Others have postulated similar findings [35]. It should be noted that static batch experiments investigating IBU adsorptive capabilities on membrane coupons have been conducted elsewhere [35]. Batch adsorption experiments often do not represent actual attachment capacities of membrane while in pressurized operation [32], and thus were not included in the scope of this work.

**Figure 2.** Adsorbed R- and S-IBU onto (**A**) Flat-sheet equipment; (**B**) Flat-sheet equipment with NF270 coupon; (**C**) Flat-sheet equipment with TS40 coupon (feed water pH of 4.0 units, temperature 20 ± 1 ◦C). Selectivity is a ratio of adsorption of S/R IBU. Error bars represent minimum and maximum values from triplicate analysis.

**Figure 3.** Contact angle of NF270 and TS40 membranes as a function of IBU adsorption. Error bars represent one standard deviation of uncertainty. Contact angle snapshots provide a visual image of the linear relationship between hydrophobicity and adsorbed IBU.

An apparent difference between the adsorption of R- and S-IBU onto the metal surface was noted. At an initial racemic concentration of 100 µg/L, S-IBU adsorbed 4.82 times more than its R-counterpart. The ratio fell to 2.25 at an initial racemic concentration of 1.50 mg/L. Although current literature on the adsorption behavior of enantiomers is scarce, some have claimed IBU can enantioselectively adsorb onto chromium- and vanadium-based metal organic frameworks [30]. Additionally, S-IBU has been reported to adsorb up to 10 times more than the R-enantiomer on a liposome membrane [41]. In liposomes, enantioselectivity was ascribed to hydrogen bonding or hydrophobic interactions between the asymmetric carbons of the chiral molecule and the spherical vesicle.

A possible explanation for the disparate enantiomer behavior could reside in optimized molecular geometry between R- and S-IBU performed by density functional theory (DFT) computations [42,43]. In conjunction with experimental studies, DFT computations can provide insight to the contrary behavior of chiral molecules, as previously illustrated by D-alanine's enantioselective adsorptive behavior [44]. The DFT framework in this study utilized the gradient correction non-local correlation functional of Lee, Yang, and Parr (B3LYP) with a basis set of 6-31G\*, using the online GAMESS software [45,46]. Table 2 presents a comparison of the DFT-derived total energies and geometric properties of R- and S-IBU.

**Table 2.** Density functional theory (DFT) calculated energy and geometries of R- and S-IBU.

Results indicate approximately equal energies of R- and S-IBU, however, the surface area, volume, and dipole moment differ, which align with findings presented elsewhere [47]. The larger surface area, molecular volume, and smaller dipole moment of R-IBU suggest a bulkier, more hindered approach as compared with S-IBU. It should be noted that the ratio of S- to R-IBU dipole moments (2.68) compares well with the adsorption selectivity at initial racemic concentrations greater than 300 µg/L (2.46). Other conceivable explications for the dissonant chiral behavior include the Easson–Stedman three-point "lock and key" hypothesis between the chemical and binding site [48,49] or the slightly unequal opposite optical rotations of the enantiomers [50].

#### *3.3. Adsorption Isotherm Modeling*

Adsorption isotherms can be used to describe the relationship between the quantity of IBU attached on a solid surface in relation to its surrounding aqueous concentration at a constant temperature and pressure [51]. The concentration of IBU adsorbed to the solid surface at quasi-equilibrium (qe) is calculated by Equation (1):

$$\mathbf{q}\_{\mathbf{e}} = \frac{(\mathbf{C}\_{\mathbf{o}} - \mathbf{C}\_{\mathbf{e}})\mathbf{V}}{\mathbf{A}} \tag{1}$$

where qe is the concentration of IBU on solid surface (µg/cm2 ), Co is the initial concentration of IBU in aqueous solution (µg/L), Ce is the equilibrium concentration of IBU in aqueous solution (µg/L), V is the volume of aqueous solution (L), and A is the surface area of solid surface (cm2 ).

In this work, Langmuir, Freundlich, and Temkin isotherms were utilized to model the adsorption behavior of IBU [51–57]. Manipulations of qe and Ce for R- and S-IBU were plotted in accordance with the Langmuir, Freundlich, or Temkin isotherms to determine the best-fit model for the adsorption system. Isotherms were ascertained for error using coefficient of determination (r2 ), relative percent difference (RPD), some of square errors (ERRSQ), and root mean square error (RMSE) [56]. Derived parameters and statistical error are shown in Table 3. The variables KL and qa represent Langmuir adsorption constants, KF and n represents Freundlich adsorption constants, and the and KT and b represent Temkin adsorption constants.

From Table 3, the Langmuir, Freundlich, and Temkin isotherms yielded r2 values > 0.90, authenticating adsorption equilibrium tendencies for experimental data. Favorable adsorption was observed in the Freundlich isotherm as 1/n values were < 1 for R- and S-IBU. Adsorption intensities denoted by constants KL, KF, and KT were higher for S-IBU. Furthermore, greater Langmuir maximum adsorption capacities (qa) were also observed for S-IBU, aligning with the favorable adsorption presented herein. Larger concentrations of adsorbed IBU on the surface (qe) were experienced in the equipment-IBU-membrane system, highlighting the additional adsorptive capacity of the membrane surface.

The Langmuir, Freundlich, and Temkin isotherms yielded similar predictability for IBU equilibrium concentrations up to 350 µg/L but diverged as solute content increased. Error analysis revealed that the Langmuir isotherm best modeled the equipment-IBU relationship. A Langmuir adsorption model fit insinuates an equal quantity of attachment free-energy changes and a monolater coating of IBU on the surface. Similar results have been realized in applications utilizing stainless steel as the adsorbent [57–59]. Therefore, the equipment-IBU relationship can be modeled via Langmuir > Temkin > Freundlich. On the contrary, the Freundlich isotherm revealed the closest representation to the range of equipment-IBU-membrane system experimental data based on error analysis. A best-fit Freundlich adsorption isotherm suggests heterogeneous adsorption free-energy changes and a multilayer of IBU chemisorption. These findings align with existing literature denoting Freundlich-type adsorption on a membrane surface due to its laminose structure [35,60]. However, it should be noted that the Langmuir and Freundlich isotherms produced analogous r2 values for R-IBU (0.995 for NF270 and 0.996 for TS40), and contained similar error statistics. This suggests that as S-IBU has a stronger attachment affinity, weak interactions between R-IBU and the surface may yield a thinner adsorptive layer. A study of R- and S-IBU adsorption kinetics onto metal and membrane surfaces may elucidate the dissimilar

attachment mechanisms. For the purposes of this work, the equipment-S-IBU-membrane system can be modeled via Freundlich > Temkin > Langmuir, whereas the equipment-R-IBU-membrane system can be modeled via Freundlich = Langmuir > Temkin.

**Table 3.** R- and S-IBU Langmuir, Freundlich, and Temkin isotherm parameters derived from bench-scale, flat-sheet experiments (feed water pH of 4.0 units, temperature of 20 ± 1 ◦C).

#### *3.4. Rejection of Ibuprofen Enantiomers*

The effect of pH on IBU rejection via NF was investigated by altering the feed pH to 4.0, 5.0, or 6.0 units with an initial IBU concentration of 1.5 mg/L (R- and S-enantiomer concentrations of 750 µg/L). Figure 4 displays the rejection of R- and S-IBU from the NF270 and TS40 membrane at a feed pH of 4.0 units. The total NF270 and TS40 IBU rejection was 34.5% and 49%, respectively. However, the adsorption of IBU affected the rejection value based on the time of collection. Adsorption accounted for 14.3% to 23.4% and 23.6% to 31.3% of R-IBU and S-IBU rejection, respectively.

The NF270 and TS40 exhibited poor (<50%) rejection at a feed water pH of 4.0 units. However, removal efficacy increased with feed water pH, as illustrated in Figure 4, aligning with findings from others [37,38]. The feed water pH affects the speciation of IBU and the magnitude of negative charge on the NF membrane. It should be noted that feed water pH has an opposing effect on IBU adsorption. At feed water pH values higher than 4.4 units, the membrane surface is negatively charged and IBU is dissociated, primarily existing in the anionic form. Anionic IBU is believed to be rejected by electrostatic

repulsion and steric hindrance. Conversely, at a feed water pH less than 4.4 units, IBU principally exists as the neutral form, and the membrane is less negatively charged. Neutral IBU readily adsorbs onto stainless steel and the membrane surface. As available adsorptive sites become saturated, the NF membrane can partially reject neutral IBU due to size exclusion. Therefore, the mechanism of IBU removal at acidic conditions is postulated as initially adsorption and subsequently steric hindrance.

**Figure 4.** R- and S-IBU rejection as a function of feed water pH for NF270 membrane (**left**), TS40 membrane (**right**) (water flux 42.4 L/m2h, temperature 20 ± 1 ◦C).

It should be noted that the rejection of S-IBU was consistently greater than the R-enantiomer for the feed pH conditions examined, due to the preferential attachment onto the metal flat-sheet equipment, shown in Figure 5. Although CEC adsorption impacts the overall rejection, it should not impact the mechanism of removal by the membrane. This indicates that the membrane may have a slight affinity for the rejection of S-IBU. A possible explanation for the increased rejection lies in DFT calculations, which revealed a dipole moment of 2.02 and 5.40 Debeye for R- and S-IBU, respectively. Existing literature suggests that a molecule's polarity influences the orientation of the solute relative to the membrane [15,16]. A molecule with a lower dipole moment is less polar, and hence contains an orientation more perpendicular to the membrane surface, increasing the probability of the solute to travel through the material without being rejected. Others have also found a direct relationship between CEC dipole moment and rejection [14–16,61].

The influence of sample time is important when recording removal of hydrophobic CECs like IBU from a NF process. In this work, adsorption was recorded over 24 h, and rejection was collected at 24 h. Others have agreed that 24 h of operation was adequate for equilibration of hydrophobic compounds [12,13,32]. However, additional time may be required to confirm complete adsorption of the compound. If rejection is collected shortly after start-up, the value may not account for the adsorption of the CEC onto the membrane or equipment. Therefore, system equilibration is important in obtaining accurate removal capacities.

**Figure 5.** Rejection of R- and S-IBU at a feed water pH of 4.0 units (initial IBU concentration 400 µg/L, water flux 42.4 L/m2h, temperature 20 ± 1 ◦C). Error bars represent minimum and maximum values from triplicate analysis.

#### **4. Conclusions**

The results of this study revealed the behavior of IBU enantiomers in aqueous solutions treated by NF membranes. Feed water characteristics (such as pH) have a substantial influence on the rejection and adsorption mechanisms of IBU. At low feed pH values, S-IBU adsorbed up to five times more than its R-counterpart onto stainless steel and showed preferential rejection in a NF process. DFT calculations could provide insight into the differing behavior of the enantiomers in terms of molecular volume and dipole moment. In bench-scale membrane removal studies, it is important to conduct initial mass balance experiments to determine possible losses of compound, which may impact overall removal. Furthermore, equilibration time proves vital in determination of the true removal capabilities of membrane processes.

**Supplementary Materials:** The following are available online at http://www.mdpi.com/2077-0375/10/12/383/s1, Figure S1: Enantiomers of IBU, Figure S2: IBU speciation based on pKa value (4.4), Figure S3: Flat-sheet bench-scale unit schematic operated in (A) recycle mode, (B), permeate collection mode, Figure S4: HPLC IBU enantiomer chromatogram, Figure S5: Dipole moment of (A) R-IBU and (B) S-IBU produced by DFT computations using GAMESS software. Figures S6–S13: Linearized adsorption isotherms for R- and S-IBU. Figure S14: Adsorption isotherm curves of R- and S-IBU, Figure S15: Adsorption isotherm curves of R- and S-IBU and (A) flat-sheet equipment, (B) flat-sheet equipment and NF270 coupon, (C) flat-sheet equipment and TS40 coupon (feed water pH of 4.0 units, temperature 20 ± 1 ◦C). Error bars represent minimum and maximum values from triplicate analysis, Table S1: Chemical properties of IBU, Table S2: Operational parameters of NF270 and TS40 NF membrane coupons.

**Author Contributions:** C.J.H. conducted the investigation, methodology, software, formal analysis, data curation, validation, and writing—original draft preparation and visualization; S.J.D. provided and obtained the resources and funding acquisition for this study and conducted formal analysis, supported data curation, provided technical supervision, and project administration for the research. All authors have read and agreed to the published version of the manuscript.

**Funding:** This work was supported by Jupiter Water Utilities (17403 Central Blvd. Jupiter, FL 33458, USA) and Kimley-Horn & Associates, Inc. (1920 Wekiva Way Suite 200, West Palm Beach, FL 33411, USA) through UCF project 16208114.

**Acknowledgments:** The authors are in appreciation of funding provided by JupiterWater Utilities (Jupiter, FL, USA) and Kimley-Horn & Associates, Inc. The authors would like to thank the support offered from Sterlitech, Inc. (Kent, WA, USA), especially Kensen Hirohata. Additional thanks are offered to Laura Gallindo and Steven Coker

from Dupont Water Solutions (Edina, MN, USA) for providing FilmTec NF270 flat-sheet membrane coupons. Finally, the authors would like to thank the UCF Water Quality Engineering team for their assistance in this work. Any opinions, findings, and conclusions expressed in this material are those of the authors and do not necessarily reflect the view of UCF (Orlando, FL, USA) or its Research Foundation.

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

#### **References**

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

© 2020 by the authors. Licensee MDPI, Basel, Switzerland. This article is an open access article distributed under the terms and conditions of the Creative Commons Attribution (CC BY) license (http://creativecommons.org/licenses/by/4.0/).

*Review*


Meet the editors

Dr. Venketeshwer Rao, Professor Emeritus, Department of Nutritional Sciences, Faculty of Medicine, University of Toronto, has established a major focus in the area of diet and health. His research focuses on the role of phytochemicals in the prevention and management of human diseases including cancer, cardiovascular disease, and osteoporosis. The main area of his research is oxidative stress and antioxidant phytochemicals with particular

emphasis on the role of lycopene. He is credited for bringing international awareness to the role of lycopene in human health. In addition to carotenoids, his research interests also include plant polyphenols and the role of prebiotics and probiotics in human health. He has published extensively including research papers, reviews, and books. He has a distinguished academic career spanning over 52 years. He is popularly sought by the international media to express his opinions on the subjects of nutrition and health.

Dennis R.A. Mans (October 16, 1955, Paramaribo, Suriname) graduated as a Medical Biologist in 1984 at the Utrecht University, The Netherlands. He received his PhD diploma in 1991 from the Free University of Amsterdam, The Netherlands, with a thesis on the antineoplastic agent etoposide. Between 1992 and 2000, he worked in Brazil on plants with anticancer properties. He returned to Suriname in 2000 to hold an academic position at

the Anton de Kom University. In 2001, he became Head of the Department of Pharmacology, in 2006 he was appointed Associate Professor, and in 2008 Full Professor with a chair in Pharmacognosy. His scientific production amounts to more than 100 full papers and more than 200 scientific abstracts.

Dr. Leticia Rao is an Emerita Associate Professor of Medicine and former Director of the Calcium Research Laboratory, Department of Medicine, and St Michael's Hospital, Toronto, Ontario. Her degrees include BSc Chemistry, University of the Philippines; MSc Food Science, Oregon State University; and Ph.D. Biochemistry, University of Toronto. Her area of expertise is bone cell biology and osteoporosis with an emphasis on effects

of drugs and nutritional supplements. She has studied the effect of gravity on bone formation in osteoblast cells in an experiment sent to space with the Space Shuttle Columbia STS 107. Her research has been presented to national and international symposia and published in peer-reviewed scientific journals. She co-authored a book entitled "Bone Building Solution" and co-edited a book on phytochemicals.

Contents

**Section 1**

Their Bioactivities

*and Sariya Mapoung*

and Identification

Region (Caatinga)

**Section 2**

*by Robbert Bipat*

*Firdaus and Setiyo Gunawan*

*and Stephen Rathinaraj Benjamin*

One-Pot Synthesis of Coumarin Derivatives

*by Gabriela Tataringa and Ana Maria Zbancioc*

*by Inul Ansary and Abu Taher*

**Preface III**

Phytochemical Composition **1**

**Chapter 1 3**

**Chapter 2 27**

**Chapter 3 45**

**Chapter 4 73**

Coumarins: Bioactivity, Synthesis and Labelling **89**

**Chapter 5 91** From Rat Poison to Medicine: Medical Applications of Coumarin Derivatives

**Chapter 6 105**

**Chapter 7 139**

Coumarin Derivatives with Antimicrobial and Antioxidant Activities

The Phytochemical Composition of Medicinal Plants: Brazilian Semi-Arid

*by Iago Almeida da Ponte, Murugan Muthuvel, Sudarsini Saravanabhavan*

*by David Febrilliant Susanto, Hakun Wirawasista Aparamarta, Arief Widjaja,*

Anthocyanins and Proanthocyanidins in Natural Pigmented Rice and

*by Pornngarm Limtrakul (Dejkriengkraikul), Warathit Semmarath*

*Calophyllum inophyllum*: Beneficial Phytochemicals, Their Uses,

Analytical Methods of Isolation and Identification *by Weisheng Feng, Meng Li, Zhiyou Hao and Jingke Zhang*


**Predictive Solution for Radiation Toxicity Based on Big Data**

Suk Lee, Kwang Hyeon Kim, Choi Suk Woo, Jang Bo Shim, Yuan Jie Cao, Kyung Hwan Chang and Chul Yong Kim

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/67059

#### **Abstract**

Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patients' cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we are going to handle up prostate, lung, head, and neck cancer cases using machine learning algorithm in radiation oncology. And, the promising algorithms as the support vector machine, decision tree, and neural network, etc. will be introduced in machine learning. In conclusion, we explain a predictive solution of radiation toxicity based on the big data as treatment planning decision support system.

**Keywords:** big data, machine learning, radiation toxicity, predictive solution, radiation treatment planning

### **1. Introduction**

#### **1.1. Definition of big data and each clinical application overview**

Trifiletti et al. [1] describe the big data as follows: a lot of information and massive data sets or number of grains of sand in the earth for human analysis with 1012–1018 bytes [1].

Murdoch listed that the big data are the inevitable application in healthcare field as four things [10]:

This trend is called to be "big bang" to adapt and research for big data and machine learning in medicine. Especially, machine learning is widely used [4–6]. Radiotherapy is a treatment method using radiation for cancer treatment based on a patient treatment planning for each radiotherapy machine. At this time, the dose, volume, device setting information, complication, tumor control probability, etc. are considered as a single-patient treatment for each fraction during radiotherapy process. Thus, these filed-up big data for a long time and numerous patient cases are inevitably suitable to produce optimal treatment and minimize the radiation toxicity and complication. Thus, we describe various clinical cases and key machine learning algorithms in radiation oncology in this chapter.

First, what is the big data for a single patient in hospital? The data type and its size for each patient can be summarized in **Table 1**. In case of radiation oncology, imaging and treatment planning information could be a major treatable data [15].

**Table 1.** Data type and its size for each patient. In case of radiation oncology, imaging and treatment planning information could be the major treatable data [15].

Second, we would like to explain radiation treatment planning and decision support system in radiation oncology. When we set up treatment planning with parameters for patient cure in radiotherapy, it is based on the radiation treatment planning (RTP) system. The clinical target volume (CTV) and planning target volume (PTV) have to be targeted by maximum radiation, and critical organs have to be radiated by minimum. It is established based on the correlation between the dose and volume, also known as dose-volume histogram (DVH). At this process, considered parameters are the prescription dose (PD), dose distribution, dose fractionation, dose constraints at normal tissue, target volume, treatment machine setting values, etc. [2, 16].

Third, when the finish treatment planning has been completed, the DVH is acquired. The dose-volume distribution will be the basic information whether it could be use or not. But, these limited information do not give hot spot for target volume, conformity, homogeneity, and so on. And, the tumor control probability (TCP) and normal tissue complication probability (NTCP) have to be analyzed in parallel. As the knowledge-based judgment, other rival plans could be generated again [32]. Thus, some decision support system is needed to select the best treatment plan for personalized patient care. These decision support systems (BIOPLAN, CERR, DRESS, Slicer RT, etc.) that provide different functions to analyze treatment efficiency. And these were being researched and studied as the software program since the early 2000s to up to date [3, 26–28].

But now, these decision support systems are needed to add to specific function using machine learning and historical treatment results and previously mentioned big data information to predict patient toxicity or complication after radiation treatment.


*Article* **Mass Customization for Social Housing in Evolving Neighborhoods in Brazil**

#### **Luisa Felix Dalla Vecchia 1,\* and Branko Kolarevic 2**

Received: 22 September 2020; Accepted: 28 October 2020; Published: 30 October 2020

**Abstract:** Mass customization is being adopted in many housing contexts worldwide to provide families with dwellings that suit their individual needs at costs similar to mass-produced items. However, in many social housing contexts, there are barriers that can hinder the adoption of mass customization, despite the benefits it could bring to residents. This is the case in the Brazilian social housing context considering house units for families of the lowest income range. This paper explores the possibilities and limitations of applying mass customization in this context to improve the living conditions in these neighborhoods as they evolve over time. This study analyzes the ecology of the system of provision of social housing for the lowest income range, pre-occupancy, and post-occupancy in the neighborhood's development over time. This study argues that it would be more feasible and bring more and longer-lasting benefits to the stakeholders involved if mass customization were applied post-occupancy.

**Keywords:** mass customization; social housing; post-occupancy


**4.27 User to root attack**

User to root attack is a case of privilege escalation where a user gains a higher privilege than that authorized. This is not a class of attack as such, and it is the process of any attack. Every attack will do activities the attacker is not privileged to do.


tabletop exercise, 196 TAC. *See* Treaty of Amity and Cooperation in Southeast Asia tactic delaying, 88, 100 name and shame, 168 Taiwan, 29, 44, 45, 53, 59, 66, 94, 163, 165 Taiwan Strait, 171 Talbott, Strobe, 172 Tavares, Jose, 119 technical working groups, 101 teleconference, 180 Teluk Sepanggar Bay, 54 10-point consensus, 91 Teo, Chee Hean, 65 territorial disputes, ix, 4, 30, 31, 44, 48, 57, 61–63, 65, 67–69, 71, 72, 78, 80, 87, 90, 116, 163, 172, 173, 175, 176, 178, 182, 184, 190, 196, 199, 203, 204, 258 terrorism, 3, 4 counter-, 174, 194, 217 September 11 attacks, 2 Thailand, 32, 44, 54, 75, 79, 103, 115, 123, 161, 182

*The Philippine Star*, newspaper, 62 Thitu Island, 48, 54, 99, 100, 113 threat, 5, 26, 45, 47, 86, 102, 110, 171, 175, 186, 193, 199, 203, 210 perception, 117, 188, 248 Tillerson, Rex, 98 tourism/tourist, 59, 62, 95, 103 transnational crime, 30 Trans-Pacific Partnership (TPP), 21 transparency, 202, 213 Treaty of Amity and Cooperation in Southeast Asia, 163, 173, 182, 184, 189, 192–194, 203 limitations of, 185 signing of, 184, 192 Treaty of Southeast Asia Nuclear Weapon-Free Zone (SEANWFZ), 192 Tree Island, 103, 111 trilateral framework, 2 Trilateral Strategic Dialogue, 115 Triple Action Plan (TAP), 85, 168, 177 Triton Island, 89, 103 troop presence, 46 Trump, Donald, 21, 108, 117, 123, 209, 256 Truong, Tan Sang, 80 Turnbull, Malcolm, 95 2+7 cooperation framework, 82, 88, 97

### **U**

UNCLOS. *See* United Nations Convention on the Law of the Sea underground storage, 103 unilateral actions, 31, 59, 87, 89, 102, 115, 177 unipolarity, 77 united front, 92, 221

United Kingdom, 104, 107, 117, 121 United Nations Charter, 91 United Nations Commission on the Limits of the Continental Shelf, 63, 166, 194 United Nations Convention on the Law of the Sea (UNCLOS), 47 Annex VII, 79, 85 Article 121, 64 Article 287, 85 Article 298, 85 ratification of, 48, 164, 255 respect for, 84, 92, 165, 167, 174, 176, 207, 209, 216, 220 violation of, 92 United Nations General Assembly, 85 United Nations Secretary-General, 65, 121 United States accusations against, 111 accusations by, 64, 95, 106, 121, 178, 180 alliance with, 51 allies, 2, 47, 115, 176, 208, 209, 256, 257 and ASEAN, 47, 121 and Australia, 121 and China. *See* China, and the United States and Japan, 89 and the Philippines. *See* Philippines, the, and the United States and Vietnam, 104 behavior, 19, 83, 88, 99 Coast Guard, 70, 72, 115 commitment of, 43, 51, 66, 114, 118, 180, 205, 209, 212 decline of, 31 defense spending, 31 disengagement, 46, 190

freedom of navigation operations, 86, 88, 89, 96, 103, 106, 107, 114, 206 gross domestic product, 31 joining the East Asia Summit, 68, 200, 202 joint exercises with, 115 joint exploration projects, 77, 107 marines, 70 militarization, 88, 210 military, 30, 31, 70, 179, 206 national interests, 67, 203 national security strategy, 66, 179 naval exercise, 70 on freedom of navigation and overflight, 65, 109, 176, 203, 209, 255 policy, 46, 57 position on the South China Sea disputes, 51, 202. *See also* US Position on Maritime Claims in the South China Sea proposals by, 87, 177 rivalry with China, 2, 57, 66, 77, 122, 166, 180, 181, 194, 208, 211, 221, 222, 256, 257 sanctions, 122 skirmish with China, 43. *See also*  EP-3E incident; USNS *Impeccable* incident strategic focus, 58, 77, 179, 180 unipolarity, 77 unmanned aerial vehicles (UAVs), 115 US–China Diplomatic and Security Dialogue, 108, 109 US Position on Maritime Claims in the South China Sea, statement, 121

### **V**

Vanguard Bank, 111, 112, 116, 120 vessels/ships

BRP *Emilio Jacinto* (PS-35), 54 BRP *Gregorio del Pilar* (PS-15), 72 BRP *Rizal* (PS74), 54 BRP *Sierra Madre*, 105 capsize, 111 *CCG 5402*, 120 CMS 71, 72 coastguard, 72–74, 83, 86, 90, 111, 112, 118, 120, 124, 166 collision, 108, 113 cutter, 70, 72 *F/B Gem-Ver 1*, 113 fishing boats. *See* fishing boats *Haixun 31*, 64 *Haiyang Dizhi 4*, 120 *Haiyang Dizhi 8*, 111, 112, 120 *Hai Yang Shi You 981* (HD 981), 83 *Hakuryu-5*, 112 HMS *Albion*, 107 HMS *Prince of Wales*, 104 HMS *Queen Elizabeth*, 104 hydrographic survey, 49 JS *Izumo*, 104 JS *Sazanami*, destroyer, 104 *Kantan-3*, 49, 60 *Lanzhou*, 108 MCS 3008, 72 oil rig, 46, 49, 60, 112 patrol, 53, 54, 64, 72, 74, 80 SAR V002, 72 sinking, 110, 113 submarines, 54, 65, 70 supply, 82 USNS *Impeccable*, 64, 65, 77, 194.  *See* China, and Vietnam and Malaysia. *See* Malaysia, and Vietnam and Taiwan, 53 and the Philippines. *See* Philippines, and Vietnam and United States. *See* United States, and Vietnam anti-China demonstrations. *See*  protest(s), anti-China approach to the South China Sea disputes, 70 as ASEAN chair, 66, 118 as non-ASEAN member, 45, 47 construction of structures, 100 criticisms against, 59 criticisms by, 26 fishermen, 54, 71, 80 harassment against, 68, 180

joining ASEAN, 162. *See also*  ASEAN, membership expansion land reclamation, 85, 86, 103, 109, 180, 187 military, 53, 111, 180 navy, 71 proposal(s) by, 52 protest(s) against, 62, 109, 120, 173 protest(s) by, 49, 53 reinforcement of structures, 53, 57, 62, 163, 196 skirmishes with China, 43. *See also*  Johnson South Reef incident sovereignty, 49, 60, 62, 112, 120 territorial claims, 65 tourism plans, 62 unilateral action, 59 vessels, 112, 120 Vu, Dung, 62

### **W**

Wang, Yi, 85, 87, 89–91, 93, 101–103, 110, 111, 115, 123, 177, 180, 208, 210, 218 war drug, 95 of aggression, 50 trade, 117 Washington, 94 water cannons, 83 weapons anti-aircraft, 98 anti-missile, 98 anti-ship cruise missile system, 108 nuclear, 192 strategic, 210

weather observation stations, 108 Wen, Jiabao, 62 *West Capella*, ship, 120 Western Pacific Naval Symposium, 90 West Philippine Sea, 76 WHO. *See* World Health Organization Widodo, Joko, 118 Woody Island, 74, 89, 106, 120 Workshop Process on Managing Potential Conflict in the South China Sea, 163 Track-1, 45 Track-2, 163, 167 World Health Organization (WHO), 119 Wu, Bangguo, 58 Wu, Dawei, 62

### **X**

Xi, Jinping, 71, 80, 207 Xisha District People's Government, 120

### **Y**

Yang, Jiechi, 62, 65, 67, 75, 77, 175 Yang, Yanyi, 53, 54 Yang, Yi, 88 Yasay Jr., Perfecto, 95, 98, 99, 207

### **Z**

Zhang, Qiyue, 174 Zhu, Bangzao, 54, 55 Zlatoper, Ronald, 46 Zone of Peace, Freedom, Friendship and Cooperation (ZoPFFC), 70, 167