*4.2.4. Carbon dioxide data*

Both study periods reveal comparable trends in CO2 levels, highlighting a strong diurnal range. It is also clear that there is an identifiable difference between the general CO2levels in SP1 compared with SP2. Comparing identical bedrooms in House A and House B is also of interest.

### **Table 6.** Overall Bedroom CO2 Statistics

In SP1 CO2concentration reaches levels double the maximum recommended value high‐ lighting unhealthy IAQ. SP2 produced healthier results; however maximum values in each house still rose above Pettenkofer's Max. (Table 6) In general, bedrooms in House A and House B appear to function similarly in relation to CO2.

**Figure 3.** Unhealthy Bedroom Performance, SP1, House B, Bedroom 3

ditions, with max. and min. of 21.9o

strong instability evident within all three indicators.

**Figure 4.** Unhealthy Bedroom Performance, SP1, House A, Bedroom 2

The most unacceptable levels of CO2 found by the studies were recorded during SP1 in House B, Bedroom 3. (Figure 3) There was one occupant sleeping in this room. Levels of CO2reached over 1000ppm for approximately 13 hours of each day, more than half of the total study period. The maximum level recorded was 1819 and the minimum being 713ppm (Table 7). The range of 1106 is evidence of the occupancy pattern, however, the diurnal range is less defined in this instance and may show that the room was also occupied at times during the day. When the room was in use CO2levels in the air indicated a potentially harm‐ ful IAQ. When discussing IAQ, Ta and RH data recorded for this example support poor con‐

C and 16.7o

C, 67.5% and 36.4% respectively. There is

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**Figure 2.** Comparison CO2Levels, SP1, House A, Bedroom 2 & Living Room

The diurnal range is clearly visible on the graphical information produced for the study peri‐ ods. (Figure 2) This day to night change can be attributed to the bedrooms occupancy pattern, prescribed by their function. A graph showing a 24 hour period within a bedroom allows the rise in CO2levels to be identified as occurring during the night, and can be associated with peri‐ ods of sleep, and therefore occupancy. When CO2 levels are lower the room is most likely to be empty because respiration is not taking place. Occupancy patters within the living room, also identifiable through CO2 levels, are quite different to that of the bedroom.

**Figure 3.** Unhealthy Bedroom Performance, SP1, House B, Bedroom 3

*4.2.4. Carbon dioxide data*

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**Table 6.** Overall Bedroom CO2 Statistics

House B appear to function similarly in relation to CO2.

**Figure 2.** Comparison CO2Levels, SP1, House A, Bedroom 2 & Living Room

identifiable through CO2 levels, are quite different to that of the bedroom.

interest.

Both study periods reveal comparable trends in CO2 levels, highlighting a strong diurnal range. It is also clear that there is an identifiable difference between the general CO2levels in SP1 compared with SP2. Comparing identical bedrooms in House A and House B is also of

**Bedrooms Max (ppm) Min (ppm) Range Mean (ppm)** SP1 – House A 2007.00 478.00 1529.00 872.50 SP1 – House B 2006.00 445.00 1561.00 921.06 SP2 – House A 1300.00 367.00 933.00 605.26 SP2 – House B 1478.00 375.00 1103.00 633.82

In SP1 CO2concentration reaches levels double the maximum recommended value high‐ lighting unhealthy IAQ. SP2 produced healthier results; however maximum values in each house still rose above Pettenkofer's Max. (Table 6) In general, bedrooms in House A and

The diurnal range is clearly visible on the graphical information produced for the study peri‐ ods. (Figure 2) This day to night change can be attributed to the bedrooms occupancy pattern, prescribed by their function. A graph showing a 24 hour period within a bedroom allows the rise in CO2levels to be identified as occurring during the night, and can be associated with peri‐ ods of sleep, and therefore occupancy. When CO2 levels are lower the room is most likely to be empty because respiration is not taking place. Occupancy patters within the living room, also The most unacceptable levels of CO2 found by the studies were recorded during SP1 in House B, Bedroom 3. (Figure 3) There was one occupant sleeping in this room. Levels of CO2reached over 1000ppm for approximately 13 hours of each day, more than half of the total study period. The maximum level recorded was 1819 and the minimum being 713ppm (Table 7). The range of 1106 is evidence of the occupancy pattern, however, the diurnal range is less defined in this instance and may show that the room was also occupied at times during the day. When the room was in use CO2levels in the air indicated a potentially harm‐ ful IAQ. When discussing IAQ, Ta and RH data recorded for this example support poor con‐ ditions, with max. and min. of 21.9o C and 16.7o C, 67.5% and 36.4% respectively. There is strong instability evident within all three indicators.

**Figure 4.** Unhealthy Bedroom Performance, SP1, House A, Bedroom 2

Another bedroom performing particularly poorly was House A, Bedroom 2, again during SP1 (Figure 4).Although levels of CO2 were less frequently above 1000pm, compared with the pre‐ vious example, it did have the highest level of CO2 recorded over the whole investigation. The room was also occupied by one person but, it is worth noting that this bedroom was subject to approximately 30 hours of natural ventilation over the duration of the study period. The max‐ imum level recorded was 2007ppm and the minimum level was 497ppm, giving an excessive range, of 1510 (Table 7). Air quality in relation to CO2 levels was poor. Ta and RH data record‐ ed max. and min. values of 22.9o C and 17.6o C, 59.3% and 38.1% respectively.

for this bedroom was continuously below 1000ppm. The maximum level recorded was 939ppm and the minimum was 386ppm (Table 7). This produced an overall range in CO2 levels of 553ppm. Importantly, the room appears to respond better to occupation that in pre‐

With reference to the two latter examples, (Figures 4 and 5) both bedrooms are in House A and therefore have the same construction. They also have identical floor area and volumes, (Table 2) and appear to differ only in orientation. An influential disparity between the two

**Max (ppm) No. > 1000ppm Time > 1000ppm**

**(hr& min)**

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**Mean Time/**

**Day>1000ppm (hr& min)**

vious examples, with CO2 levels remaining within the prescribed limits.

House A – Bed 1 713 1398 1847 153hr 55 11hr 50 House A – Bed 2 497 2007 1513 126hr 5 9hr 42 House A – Bed 3\* 681 1316 197 16hr 25 1hr 16 House A – Bed 4 478 1176 711 59hr 15 4hr 33 Bedroom Total 2369 5897 4268 355hr 40 6hr 50 House B – Bed 1 585 1569 1317 109hr 45 8hr 27 House B – Bed 2 445 2006 690 57hr 30 4hr 25 House B – Bed 3 713 1819 2085 173hr 45 13hr 22 House B – Bed 4 550 1907 1220 101hr 40 7hr 49 Bedroom Total 2293 7301 5312 442hr 40 8hr 31 Total 44662 13198 9580 798hr 20 7hr 41

**Max (ppm)** No.

House A – Bed 1 386 939 0 0min 0min House A – Bed 2 393 1151 467 38hr 55 3hr 54 House A – Bed 3 374 1044 352 29hr 20 2hr 56 House A – Bed 4 367 1300 265 22hr 5 2hr 13 Bedroom Total 1520 4434 1084 90hr 20 2hr 16 House B – Bed 1 417 1226 334 27hr 50 2hr 47 House B – Bed 2 460 1075 56 4hr 40 28min House B – Bed 3 433 1478 774 64hr 30 6hr 27 House B – Bed 4 375 1055 18 1hr 30 9min Bedroom Total 1685 4834 1182 98hr 30 2hr 28 Total 3205 9268 2266 188hr 50 2hr 22

**Table 7.** CO2 Levels, SP1 and SP2 (\*House A – Bed 3 was not slept in, see Table 4.)

>1000ppm

examples is the study period in which the data was collected.

**(ppm)**

**(ppm)**

**SP1 Min**

**SP2 Min**

**Figure 5.** Healthy Bedroom Performance, SP2, House A, Bedroom 1

**Figure 6.** SP1, House A, Bedroom 1

The healthiest levels of CO2 were recorded during SP2 in House A, Bedroom 1. (Figure 5) This room had one occupant and was not subject to any natural ventilation. Data recorded for this bedroom was continuously below 1000ppm. The maximum level recorded was 939ppm and the minimum was 386ppm (Table 7). This produced an overall range in CO2 levels of 553ppm. Importantly, the room appears to respond better to occupation that in pre‐ vious examples, with CO2 levels remaining within the prescribed limits.

Another bedroom performing particularly poorly was House A, Bedroom 2, again during SP1 (Figure 4).Although levels of CO2 were less frequently above 1000pm, compared with the pre‐ vious example, it did have the highest level of CO2 recorded over the whole investigation. The room was also occupied by one person but, it is worth noting that this bedroom was subject to approximately 30 hours of natural ventilation over the duration of the study period. The max‐ imum level recorded was 2007ppm and the minimum level was 497ppm, giving an excessive range, of 1510 (Table 7). Air quality in relation to CO2 levels was poor. Ta and RH data record‐

The healthiest levels of CO2 were recorded during SP2 in House A, Bedroom 1. (Figure 5) This room had one occupant and was not subject to any natural ventilation. Data recorded

C, 59.3% and 38.1% respectively.

C and 17.6o

ed max. and min. values of 22.9o

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**Figure 6.** SP1, House A, Bedroom 1

**Figure 5.** Healthy Bedroom Performance, SP2, House A, Bedroom 1

With reference to the two latter examples, (Figures 4 and 5) both bedrooms are in House A and therefore have the same construction. They also have identical floor area and volumes, (Table 2) and appear to differ only in orientation. An influential disparity between the two examples is the study period in which the data was collected.

**Table 7.** CO2 Levels, SP1 and SP2 (\*House A – Bed 3 was not slept in, see Table 4.)

With this in mind, an obvious difference can be shown between the two study periods by comparing the best performing bedroom from SP2 with its counterpart in the first study. (Figure 5 and 6). SP2 shows much lower levels of CO2 than that recorded in the same bed‐ room in SP1. In fact SP1 results indicate unacceptable levels of CO2 within all the bedrooms of House A and B (Table 7). In SP2 only two of the eight bedrooms recorded levels above 1000ppm on a regular basis (>3 hours a day). Further to this, SP2's maximum levels are not as high, and the minimum levels were also lower.

produced, such as that seen prevalent in SP1 (Figure 3) is a concern. The bedrooms have ac‐ ceptable CO2 levels when empty but when they are occupied they fail to adapt. This obser‐ vation leads to an assumption that there is very little or no ventilation taking place during SP1. This conclusion is supported by unstable Ta and RH levels also presented in the CP. Reasons for poor ventilation could be attributed to the MVHR system not functioning as re‐ quired to produce the sufficient amount of air changes per hour needed for each room. There was no CP prior to SP2 to gain clarification from, however, a reduction in the diurnal

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Other than the probable identification of a poorly performing MVHR system, there are other

As highlighted in Figure 3, Bedroom 3 in House B SP1 produces unhealthy CO2 results. The room's size could be a contributing factor resulting in poor IAQ. Bedroom 3 has a volume less than half that of the largest bedroom (Table 2). It has no additional system design re‐ quirements, such as a larger supply vent or increased flow rate to provide more air changes. With the same occupancy levels the air will become polluted more rapidly than in a bed‐

It is also worth considering that the occupant slept with the door shut, magnifying the IAQ problem. There are no trickle vents between rooms and with the door shut there is no way of stale air leaving the room, this in turn inhibits supply flow.Bedroom 3's performance is of specific interest because it has the most potential to be used as a nursery. As previously mentioned in the IAQ of these spaces must be excellent as it is a time when childhood ill‐

room of a larger size, due to the smaller volume of air available for respiration.

range in SP2 shows that better ventilation must be taking place.

**Figure 7.** CP, House A & B, Bedroom 2

*4.3.4. Bedroom performance variation*

factors for varying bedroom performance.

ness/IAQ related health issues are most common.

Although a crude representation, looking at the mean time per day that bedrooms spend above 1000ppm clearly strengths the visible difference shown in the graphs, between the CO2 levels in both study period.


*2.2. Patient Consent, Ethical Committees, and Adhesion to Biosecurity and Institutional Safety Procedures*

The female Chinese patient herein described was hospitalized with recurrent kidney disease and multiple systemic dysfunctions at Jinling Hospital in Nanjing (China). One hundred healthy control adults were randomly recruited from a panel of unaffected, genetically unrelated Han Chinese individuals from the same geographic region. All methods were conducted in accordance with the Ethics Committee of the Jinling Hospital and in the respect of biosafety and public health. Written informed consents were obtained from the patient and her parents as well as the 100 healthy controls. The mother didn't consent for sequencing her mtDNA and medical examination.


4.2. Examples based on variations of the cellular automata model

Another important trend of research is to improve the properties of the pseudorandom sequence introducing more substantial modifications to the cellular automata model, in order to obtain a more complex structure and expected behavior. A first example, presented in [9], uses a homogenous programmable cellular automata of 256 cells, with a global feedback loop. Each cell supports any of the 256 possible rules, but the actual rule is selected by the feedback (depending on the global state, with a simple transformation like module 2 sum). Such an approach is now called self-programmable, since the rule is not changed externally, but here the global reaction acts like a central control. According to the author, the initial state influences the properties of the generator.

Another structural modification of the cellular automata model is presented in [25]. Here, an additional memory layer was used to improve the complexity of the evolution, and the next state is computed based on the present state of the neighborhood and the previous state of the cell (or the entire neighborhood). The local rules are 4-variable functions, in the simplest form.

In physical implementations, the actual dimension of the cellular automata is reduced. Several authors proposed randomization schemes that improve the quality of the generators (to compensate the limited dimension). In [26], the output of the hybrid cellular automata is combined with the output of a linear feedback shift register (LSFR) to produce a 1-bit random output using 32-bit cellular automata. A combination of rules 90 and 150 was applied in the cellular automata.

Sipper and Tomassini [21] included two innovative improvements: the selection of rules based on an evolutionary algorithm (four rules were selected, namely 90, 105, 150, and 165) and the so-called "time spacing" of the sequence, meaning that the output sequence skips some time steps. This technique improves the quality of the sequence, according to the authors, as comp-

In a later work, Tomassini and Sipper also proposed two-dimensional cellular automata [22] in order to improve the complexity of the evolution of the generator. Another two-dimensional cellular automata for pseudorandom sequence generation was presented in [23] and was applied by the authors in BIST and cryptography hardware implementations [6]. The difference is mainly quantitative in a two-dimensional topology, but also the neighborhood dimen-

One-dimensional topology is simpler from the perspective of hardware implementation. Still theoretical research is done on linear cellular automata in order to discover binary functions or class of functions that may be used in random sequence generation. In [24], the authors prove that uniform linear cellular automata with nearest neighbor's connections can meet cryptographic requirements for random sequences (The selection of rules and seeds was done empirically.)

The examples given so far illustrate the theoretical and experimental effort to prove the properties of cellular automata and to discover significant combinations of rules and initial states that lead to "good" pseudorandom sequences. All these examples are based on the basic cellular automata model. The only modification of the model is, for some of these examples, the heterogeneity, but this is restricted to the implementation of two/four rules in a static

Another important trend of research is to improve the properties of the pseudorandom sequence introducing more substantial modifications to the cellular automata model, in order to obtain a more complex structure and expected behavior. A first example, presented in [9], uses a homogenous programmable cellular automata of 256 cells, with a global feedback loop. Each cell supports any of the 256 possible rules, but the actual rule is selected by the feedback (depending on the global state, with a simple transformation like module 2 sum). Such an approach is now called self-programmable, since the rule is not changed externally, but here the global reaction acts like a central control. According to the author, the initial state influ-

Another structural modification of the cellular automata model is presented in [25]. Here, an additional memory layer was used to improve the complexity of the evolution, and the next state is computed based on the present state of the neighborhood and the previous state of the cell (or the entire neighborhood). The local rules are 4-variable functions, in the simplest form. In physical implementations, the actual dimension of the cellular automata is reduced. Several authors proposed randomization schemes that improve the quality of the generators (to compensate the limited dimension). In [26], the output of the hybrid cellular automata is combined

4.2. Examples based on variations of the cellular automata model

ared to classical random number generators.

176 From Natural to Artificial Intelligence - Algorithms and Applications

sion is higher (and the rules more complex).

manner to subsets of cells.

ences the properties of the generator.

The technique of self-programming, meaning that the local rules are changed during the evolution, was presented in [27]. The local rules are changed based on local conditions (in [9], the global configuration determines the rule). The result is a hierarchical structure of cellular automata. Each cell can switch between two rules (90 and 165, 150, and 105 were used), depending on a "super rule" based on the state of a larger neighborhood (up to 7 cells). The idea of the "super rule" is to interrupt static behavior (the patterns that appear in low-quality pseudorandom signals).

The idea of self-programmable cellular automata, with a hierarchical structure, and a behavior based on local conditions, was developed in several ways since its introduction in [27]. For instance, [28] presents a combination of this technique in hybrid cellular automata, with a linear feedback shift register. The global feedback is used in [29] in the generation of the super rule with the additional selection of the cell whose rule is changed. In [30], the self-programmable cellular automata are optimized for field programable gate arrays (FPGA) implementation, using the splittable look-up table concept and embedded carry logic of the FPGA.

Other variation of the model is the asynchronous update of the cells' states. There are several examples in the scientific literature, of which we mention [31] because it is one of the few examples that use two-dimensional cellular automata (for 1-bit output) and also because it is designed as a randomization algorithm, not targeting the hardware implementation.

In conclusion, several variations of the cellular automata model were developed. The validation of the models is often experimental, in absence of theoretical developments.