Are City Features Related to Obesity in Preschool Children? Evidence from Latin America

## **Effectiveness of Pharmaceutical and Non-Pharmaceutical Interventions Against Airborne Transmission: Computational Modeling Using CARA**

**Luis Aleixo1,2, André Henriques1, Gabriella Azzopardi1, James Devine1, Julian Tang3, Marco Andreini1 , Markus Rognlien4, Nicola Tarocco1, Nicolas Mounet1, Olivia Keiser5, and Philip Elson1**

1 CERN (European Organization for Nuclear Research), Geneva, Switzerland,

2 IST (Instituto Superior Técnico, University of Lisbon), Lisbon, Portugal,

3 Respiratory Sciences, University of Leicester, Leicester, UK,

4 NTNU (Norwegian University of Science and Technology), Torgarden, Norway,

5 Institute of Global Health, University of Geneva, Geneva, Switzerland

The COVID-19 pandemic has highlighted the need for a proper risk assessment of respiratory pathogens in indoor settings. We aimed to develop a COVID Airborne Risk Assessment (CARA) tool, to assess the potential exposure of airborne SARS-CoV-2 viruses in indoor settings. The tool enables decision-makers to assess the risk of exposure to the virus, comparing several pharmaceutical and non-pharmaceutical interventions. The CARA model exhibits five main aspects: 1) the emission rate of viruses coming from the infected host(s), which is a result of the respiratory activity combined with the possible use of masks; 2) the viral removal rate resulting from ventilation or air filtration, viability decay, and settlement; 3) the viral concentration profile over time resulting from the balance of the two previous quantities; 4) the accumulated viral dose absorbed by an exposed host, deposited in the respiratory tract; and 5) the probability of infection (transmission) resulting from such a dose, including epidemiological, virological, and immunological parameters. The use of masks provides a five-fold reduction in emissions. Many settings rely on natural ventilation strategies, which are very effective in decreasing the concentration of virions, and in combination with masks, yield up to a 12-fold decrease in cumulative dose. Vaccination is an effective measure against hospitalization; however, its effectiveness against transmission is not optimal; hence, non-pharmaceutical interventions (ventilation, filtration, masks) should be actively supported. Vaccine effectiveness will also be limited by the emergence of new variants, whereas non-pharmaceutical interventions are effective independently of the variant. Therefore, measures that reduce the viral density in the air should be actively supported and included early in the risk-assessment process. Vaccination and host immunity are very important protective measures; however, non-pharmaceutical interventions seem to be more effective in preventing transmission. CARA allows for such a quick and accurate assessment of various indoor settings to assess the effectiveness of several (combined) measures. In a post-COVID era, we will face a new paradigm with the inclusion of this novel occupational hazard, using tools such as CARA to endorse healthy buildings and protect their occupants against respiratory infections.

Architecture and Technology

Efficiency of Pharmaceutical and Non-Pharmaceutical Interventions

Baseline scenario: Regular working day, room volume of 160 m3, winter season (cold and dry), 20 occupants, 1 is infected.

1. Tang J W, et al. Covid-19 has redefined airborne transmission, *BMJ 2021; 373 :n913*, doi:10.1136/bmj.n913

#### Authors

Luis Aleixo1,2, Andre Henriques1, Gabriella Azzopardi1, James Devine1, Julian Tang3, Marco Andreini1, Markus Rognlien4, Nicola Tarocco1, Nicolas Mounet1, Olivia Keiser5, and Philip Elson1

1 CERN (European Organization for Nuclear Research), Geneva, Switzerland,

2 IST (Instituto Superior Técnico, University of Lisbon), Lisbon, Portugal,

3 Respiratory Sciences, University of Leicester, Leicester, UK,

4 NTNU (Norwegian University of Science and Technology), Torgarden, Norway,

5 Institute of Global Health, University of Geneva, Geneva, Switzerland

#### Airborne Transmission of SARS-CoV-2

#### What is CARA?


**5. Project of the Paraconsistent Expert System - PESPAL2v**

The project of PESPAL2v starts with the definition of the methods of acquisition of data through the evidence degree extracting algorithms with the goal of generating the paracon‐ sistent logical signals for the analysis network composed of algorithms based on the PAL2v.

#### **5.1. Acquisition of Measurements**

**1.** Pre-failure – consists in the analysis of the sub-transmission system in operation.

contingency.

46 Advances in Expert Systems

network. They are:

contingency.

analysis.

*4.1.1. Propositions Used in the Paraconsistent analysis*

Pp1: There is overcurrent in the electric power network

Pp2: There is sub-voltage in the electric power network

now related to the annotation of the object proposition:

the topology of the power network:

Po: There is the risk of drop by overload in the electric power network.

Po1: There are restrictions of loads in the electric power network.

Po2: The network topology is ideal for the current situation.

cy.

**2.** Post-failure – consists in the analysis of the sub-transmission system at the instant of the

**3.** Restoring – consists in the analysis of the sub-transmission system after the contingen‐

The paraconsistent analysis in the PESPAL2v is based on the configurations of the PANs where the paraconsistent logical signals are extracted from measured values of voltage and electric current. The PAL2v analysis is performed with applied paraconsistent logical signals with

The two first analyze the tension outage and overcurrent at the measurement points and generate evidence degrees related to the existence of overloading in the sub-transmission

Next, through the PANs algorithms, the paraconsistent analysis with the degrees of subvoltage and overcurrent generated by this initial analysis which result evidence degrees,

For the decision-making about the optimized restoring of the sub-transmission system after a contingency, PESPAL2v still analyzes other two propositions related to the restrictions and

That being so, the sequence of maneuvers which are offered to the operation will be condi‐ tioned directly to the configuration of topologies, technical norms and restrictions which in‐ volve the area of the sub-transmission system of the power network affected by the

The classification performed by the paraconsistent analysis network (PANet) generates a re‐ sulting evidence signal whose value will define the type of operation and sequences of re‐

Figure 10 shows the pre-failure analysis with its partial propositions which generate the evi‐ dence degrees for its object proposition and whose result will be used for the post-failure

storing closest to the ideal, given the conditions of the sub-transmission system.

annotations composed of evidence degrees related to 5 partial propositions.

The first task to be performed by the paraconsistent expert system PESPAL2v is the acquisition of values of measurements performed in the system so that the overload risk levels can be detected. For this purpose, we use the data available in the SCADA (Supervisory Control and Data Acquisition) system which, in this phase, has to receive several types of measure‐ ments from the field.

The SCADA system is responsible for the interface between the measurements of electric quantities and the communication network interconnected to the analysis systems.

#### *5.1.2. Block Diagrams of Primary Signals*

The measurements required by SCADA and stored into database are performed by the re‐ mote stations RTU (Remote Terminal Units) and / or by signal capturing devices IED (Intel‐ ligent Electronic Devices). In the practice due to the unbalancing of loads, errors in measurements performed by SCADA and other factors which influence the electric system, it is verified that the amplitude values of quantities of interest (voltage and current) are dif‐ ferent among the three phases of the transmission line.

This condition shows that the measured values bring contradiction levels among them right from the origin. So, in order to obtain reliable values in the signal treatment of the Paracon‐ sistent Expert System -PESPAL2v, the primary values receive an initial treatment of contradic‐ tion extraction.

two signals become input to a contradiction effect extracting algorithm outputting an evi‐

Electric Power System Operation Decision Support by Expert System Built with Paraconsistent Annotated Logic

http://dx.doi.org/10.5772/51379

49

Figure 12 shows the evidence degree of overload risk extracted from a measure point of the

The operating substation seen on the figure 12 is composed by two buses shown horizontal‐ ly where a total of 16 load control breakers are interconnected. Each breaker controls its cor‐ respondent load and are sources of extraction of evidence degrees for the paraconsistent analysis. In each one of the two buses there are 8 load breakers combined, however when a disconnector or electric key is open, it separates two buses with 4 breakers for each one.

Other breakers which control the feeding of the input transformers of the substation and the capacitor bank are also measurement points from where the evidence degrees of overload

The extracting module of resulting risk evidence degree uses the contradiction effect extract‐ ing algorithm so it can receive *n* evidence degrees from several breakers which are intercon‐ nected to the same bus. That being, the PAL2v analysis can offer a unique representing

**5.3. Extracting Module of Evidence Degrees from the Interconnection Buses**

dence degree of overload risk at the measurement point.

**Figure 12.** Overload Risk degree extraction from the breaker of load 16.

risk at the operating substation.

value of the evidence degree of the bus.

breaker (Load 16) of a typical substation of the sub-transmission system.

Considering this condition, the extracting block of primary signals uses algorithms capable of extracting evidence degrees and of extracting contradiction effects, as shown on Figure 11.

**Figure 11.** Block diagram which extracts primary evidence signals related to the sub-voltage and overcurrent on the measurement point.

The maximum and minimum values of the discourse universe of the evidence degree ex‐ tracting algorithm are particular to each load connected to the corresponding breaker at the substation of the electric power transmission line.

The evidence degree extracting system receives the three values of voltage (or current) cor‐ responding to the three phases of the line (RST) which are transformed in evidence degrees by a normalization defined by the interest interval or discourse universe. After this first process, the three resulting values pass through a contradiction effect extracting algorithm which outputs a unique resulting evidence degree.

#### *5.2.3. Evidence Degree Extraction at an Operating Substation*

PESPAL2v's project was carried out in order to perform analysis of overload risks through the applications of the algorithms of the PAL2v on monitoring essential points available at the operating substations of the sub-transmission electric system.

The buses that interconnect the several equipments installed at a substation such as trans‐ formers, electric keys and breakers of the sub-transmission system are points where voltage and current can be measured for each one of the loads interconnected by the breakers. In an operating substation a sub-voltage evidence degree and an overcurrent evidence degree are extracted from each breaker which activate loads at an operating substation.

Based on these values, the modules composed by the algorithms of the PAL2v verify the state of that point with respect to tension decreasing and excess of current intensity which together contributes to the increase of the overload risk at the point measured. After the ex‐ traction of the evidence degrees of sub-voltage and overcurrent from the load breaker, these two signals become input to a contradiction effect extracting algorithm outputting an evi‐ dence degree of overload risk at the measurement point.

Figure 12 shows the evidence degree of overload risk extracted from a measure point of the breaker (Load 16) of a typical substation of the sub-transmission system.

**Figure 12.** Overload Risk degree extraction from the breaker of load 16.

sistent Expert System -PESPAL2v, the primary values receive an initial treatment of contradic‐

Considering this condition, the extracting block of primary signals uses algorithms capable of extracting evidence degrees and of extracting contradiction effects, as shown on Figure 11.

**Figure 11.** Block diagram which extracts primary evidence signals related to the sub-voltage and overcurrent on the

The maximum and minimum values of the discourse universe of the evidence degree ex‐ tracting algorithm are particular to each load connected to the corresponding breaker at the

The evidence degree extracting system receives the three values of voltage (or current) cor‐ responding to the three phases of the line (RST) which are transformed in evidence degrees by a normalization defined by the interest interval or discourse universe. After this first process, the three resulting values pass through a contradiction effect extracting algorithm

PESPAL2v's project was carried out in order to perform analysis of overload risks through the applications of the algorithms of the PAL2v on monitoring essential points available at the

The buses that interconnect the several equipments installed at a substation such as trans‐ formers, electric keys and breakers of the sub-transmission system are points where voltage and current can be measured for each one of the loads interconnected by the breakers. In an operating substation a sub-voltage evidence degree and an overcurrent evidence degree are

Based on these values, the modules composed by the algorithms of the PAL2v verify the state of that point with respect to tension decreasing and excess of current intensity which together contributes to the increase of the overload risk at the point measured. After the ex‐ traction of the evidence degrees of sub-voltage and overcurrent from the load breaker, these

extracted from each breaker which activate loads at an operating substation.

tion extraction.

48 Advances in Expert Systems

measurement point.

substation of the electric power transmission line.

which outputs a unique resulting evidence degree.

*5.2.3. Evidence Degree Extraction at an Operating Substation*

operating substations of the sub-transmission electric system.

The operating substation seen on the figure 12 is composed by two buses shown horizontal‐ ly where a total of 16 load control breakers are interconnected. Each breaker controls its cor‐ respondent load and are sources of extraction of evidence degrees for the paraconsistent analysis. In each one of the two buses there are 8 load breakers combined, however when a disconnector or electric key is open, it separates two buses with 4 breakers for each one.

Other breakers which control the feeding of the input transformers of the substation and the capacitor bank are also measurement points from where the evidence degrees of overload risk at the operating substation.

#### **5.3. Extracting Module of Evidence Degrees from the Interconnection Buses**

The extracting module of resulting risk evidence degree uses the contradiction effect extract‐ ing algorithm so it can receive *n* evidence degrees from several breakers which are intercon‐ nected to the same bus. That being, the PAL2v analysis can offer a unique representing value of the evidence degree of the bus.

Figure 13 shows a general diagram of capture of the resulting evidence degree of a bus at a typical operating substation shown in the previous picture which is composed by four breakers.

**5.5. Computing the total resulting evidence degree of overload risk of the substation**

dures for the transmission of electric power after a contingency.

eration, where the load is attended and the operating limits are observed.

important items which must be in the knowledge base of the model are:

disconnection with the best precision possible.

will make the restoring process easier.

of the following situations:

*5.5.1. Restoring control actions*

ure.

studies.

**5.6. Restoring Plans**

After obtaining the evidence degree of overload risks the model for restoring control was developed. This model has as its goal to analyze and present optimized restoring proce‐

Electric Power System Operation Decision Support by Expert System Built with Paraconsistent Annotated Logic

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51

The restoring of an electric power system is a very complex procedure because it involves several activities which include: steps for previous study until steps of decision-making un‐ der intense emotional stress by the operators. The goal of the restoring control is to carry out the prompt restoration of the electric power system taking it to the condition of normal op‐

In the practice the reconnection procedure must be carried out by taking several precautions in order to suit all restrictions which take the new system state to a better level than that one which caused the disconnection. In order to start the actions of the restoring control, it is necessary to have full knowledge of the current situation of the electric system. The most

**b.** Knowledge about the parts of the electric power system which were affected by the fail‐

**c.** Knowledge of the source and cause of the disconnections, detecting what caused the

**d.** Knowledge of the existence of real conditions for the reconnection with the verification

If the source of the failure is a permanent failure that prevents from reconnecting.

If the disconnection happened from previously established emergency control actions. In this case the electric power system is known to have programmed "islands" which

If there are previously established control actions. This is a situation which happened when the emergency control takes the energy system to a condition known by previous

The restoring plans have detailed actions by means of operation instructions which must be carried out by the operator in order to reconnect the electric power system. The strategy of these plans is based on the division of the procedures in steps in order to obtain a larger de‐ centralization of the recomposition actions. In the development of the Paraconsistent Expert System - PESPAL2v, the model of restoring control was designed to present actions based on

**a.** Knowledge about the part of the electric power system which was disconnected.

Through amplitude signals of the quantities received by the extraction modules of primary signals, and the signal which represents the state of the breaker key, each breaker has its evi‐ dence degree of risk which will be treated by the final module, resulting in a unique value of evidence degree of overload risk on the buses.

#### **5.4. Paraconsistent Logical Model of Operating Substation**

Using contradiction effect extracting blocks a paraconsistent logical model of operating sub‐ station can be created encompassing the evidence degree of overload risk of all possible points to be monitored.

In the typical operating substation the goal is to obtain the degree of overload risk generated by the four buses. So, from the models of the main devices installed in a typical substation, a paraconsistent modeling for the whole substation was designed.

First the overload risks at a typical substation were classified in four types:

The resulting evidence degree of overload risk of the substation is then obtained by the par‐ aconsistent analysis performed among these four values extracted from the model.

**Figure 13.** Diagram of capture of evidence degree of risks from a typical bus of a substation.

#### **5.5. Computing the total resulting evidence degree of overload risk of the substation**

After obtaining the evidence degree of overload risks the model for restoring control was developed. This model has as its goal to analyze and present optimized restoring proce‐ dures for the transmission of electric power after a contingency.

#### *5.5.1. Restoring control actions*

Figure 13 shows a general diagram of capture of the resulting evidence degree of a bus at a typical operating substation shown in the previous picture which is composed by four

Through amplitude signals of the quantities received by the extraction modules of primary signals, and the signal which represents the state of the breaker key, each breaker has its evi‐ dence degree of risk which will be treated by the final module, resulting in a unique value of

Using contradiction effect extracting blocks a paraconsistent logical model of operating sub‐ station can be created encompassing the evidence degree of overload risk of all possible

In the typical operating substation the goal is to obtain the degree of overload risk generated by the four buses. So, from the models of the main devices installed in a typical substation, a

The resulting evidence degree of overload risk of the substation is then obtained by the par‐

aconsistent analysis performed among these four values extracted from the model.

**Figure 13.** Diagram of capture of evidence degree of risks from a typical bus of a substation.

breakers.

50 Advances in Expert Systems

points to be monitored.

evidence degree of overload risk on the buses.

**1.** Risks of overload on the buses (µEbus).

**2.** Risks of load transfer (µETRANS).

**5.4. Paraconsistent Logical Model of Operating Substation**

paraconsistent modeling for the whole substation was designed.

**3.** Risks of overload on the secondary windings (µESeC).

**4.** Risks of overload on the primary windings (µEPRIM).

First the overload risks at a typical substation were classified in four types:

The restoring of an electric power system is a very complex procedure because it involves several activities which include: steps for previous study until steps of decision-making un‐ der intense emotional stress by the operators. The goal of the restoring control is to carry out the prompt restoration of the electric power system taking it to the condition of normal op‐ eration, where the load is attended and the operating limits are observed.

In the practice the reconnection procedure must be carried out by taking several precautions in order to suit all restrictions which take the new system state to a better level than that one which caused the disconnection. In order to start the actions of the restoring control, it is necessary to have full knowledge of the current situation of the electric system. The most important items which must be in the knowledge base of the model are:

If the source of the failure is a permanent failure that prevents from reconnecting.

If the disconnection happened from previously established emergency control actions. In this case the electric power system is known to have programmed "islands" which will make the restoring process easier.

If there are previously established control actions. This is a situation which happened when the emergency control takes the energy system to a condition known by previous studies.

#### **5.6. Restoring Plans**

The restoring plans have detailed actions by means of operation instructions which must be carried out by the operator in order to reconnect the electric power system. The strategy of these plans is based on the division of the procedures in steps in order to obtain a larger de‐ centralization of the recomposition actions. In the development of the Paraconsistent Expert System - PESPAL2v, the model of restoring control was designed to present actions based on the evidence degrees of overload risks obtained by the paraconsistent analysis on several points of the electric power system.

The reconnecting maps were done based on operational norms and restrictions of each sub‐ station and sequences of optimized restoring were established taking into consideration the values of risk degrees of overload before and after the contingency.


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### **Index**

*Note*: *Italic* and **bold** pages refer to figures and tables respectively, and page numbers followed by "n" refer to notes.

Azerbaijan 18–19, 23, 36n48, 80, 86, 148, 176, 191, 194 Bafra 192 Baghdad 19–20, 86, 127–129, 141n102, 144, 153 Baghdadi, Baha*ʾ* al-Din 175–176 *Bahr al-haqa*ʾ*iq* 100 Baiju, Mongol general 17–19, 20 48, 51, 58, 123 Balkhi, Muhammad 157 Barbaro, Josaphat 151 *Barid al-saʿada* 26 Batu Khan, ruler of the Golden Horde 19 Baybars, Mamluk Sultan 21, 52, 54 Bayezid II, Ottoman Sultan 87, 109n103 Bektaş, Hacı 146 Bektashism 146 *beylerbey* 43, 48–49, 51, 54, 65n28, 202n134 Bishbaliq 148, 166n36 Bistami, Bayazid 154–155 Black Sea 6, 8, 15, 17, 24, 29–31, 38n85, 39n89, 40, 46–48, 55, 102–103, 123 Blochet, Edger 76, 78 Bosworth, Clifford E. 1 Byzantine empire (Byzantium) 5, 8–9, 28, 31, 50–51, 93, 99; *Alexiad* 29; Anatolia 29–30; armies 15–16, 41–42; Christian Byzantium 81, 98; Churches 100; civil war in 40; diplomacy 50; population 31; trade in the economy 6; Turkish relations 5 Cahen, Claude 1, 7 cannabis in Islam 150, 154 Çelebi, ʿArif 99–100

chancellery *(divān)* 10, 174

Chinggis Khan, ruler of the Mongol empire 18–19, 46, 141n103

Comnena, Anna, Byzantine princess 29 Comnenus, John, Byzantine emperor 29, 40 Comnenus, Manuel, Byzantine emperor 15–16 Constantinople 5, 17, 29–30, 40, 51, 54, 57, 59–60, 62, 63n5, 65n32, 168n65, 204 Crimea 1, 4, 17, 21, 31–32, 46–49, 54–55, 66n39, 123, 168n67, 205 Cumans 16, 18, 31, 48 Danishmandids 15–16, 29, 35n34, 40 *Dastur al-katib fī taʿyin al-maratib* 178, 198n50 *Destur-i dabiri* 175 Deveci Sultan Türbesi 114n175 Diyarbakır 15, 19, 55 Dorylaeum, Battle of 15 *Durrat al-Taj* 116 Egypt 21, 31, 97, 139n64, 148, 157, 163 *epistolographía* 174 Erzincan 17, 26–27, 36n46, 55, 108n92, 194

Euclid *(Tarjuma-yi Tahrir-iusul-i Uqlidis)* 27

*Fann al-rasāʾil wa tartīb mukhāṭabāt* 81 Farahi, Abu Nasr 83, 107n67, 187 Farghani, Muhammad b. Ahmad 27 Farisi, Khatib 145, 153–157 Fars 3, 22, 111n136 Faryabi, Zahir al-Din 26 *Fatihnama* 8, 57, 181, 185 *Fustat al-ʿadala* fi qawaʿid al-sultana 7, 68n91, 75–81, 91–92, 116–124, 127–131, 135, 145, 147, 149, 152–153, 155–156, 158, 160, 163–164, 211–217; added and omitted sections 124–127; chapters and structure 119–124; as mirror for princes 131–135; religious references in 127–131 *futuwwa* 30, 38n82, 102, 114n177 Gangras (modern Çankırı) 45 Geykhatu, Ilkhan 22, 56–60, 70n116, 70n122 Ghazali, Abu Hamid Muhammad 89, 91,

Ghiyath al-Din Kaykhusraw I, Sultan of Rum 16, 26, 43 Ghiyath al-Din Kaykhusraw II, Sultan of Rum 17–20 Ghiyath al-Din Kaykhusraw III, Sultan of Rum 21, 53 Ghiyath al-Din Masʿud II, Sultan of Rum 4, 21–22, 43, 57–58, 78, 87 Ghiyath al-Din Muhammad b. Malik Shah, Sultan of the Great Seljuqs 121 *Ghunyat al-talib wa munyat al-katib* 65n31, 83, 90, 179, 186, 192, 198n55, 200n80, 200n83, 203n152, **221** Golden Horde 2, 18–20, 31, 39n90, 48, 67n72, 124, 205 Great Seljuqs 3, 14–15, 73; domination of Anatolia 15–18; Seljuq structure of government 183 Gregoras, Nicephoros 5, 42, 58 Grigor of Akanc 34n16 *Gulistan* 180 Güyük, ruler of the Mongol empire 19 Habashi, Bilal 153–154 Hacı Bektaş Veli 146 *Hadayiq al-siyar* 26 hadiths 76, 81, 115, 127–128, 159–161, 164, 179, 191 Hafiz Shirazi, Shams al-Din 146 Harput 15, 55 Harran 19, 145, 165n11, 167n42 Hülegü, Ilkhan 19–21, 51, 67n72, 86, 133, 144–145 Husam al-Din Choban 3, 14, 17, 24, 30, 32, 42–48, 52, 54, 93, 95, 97, 121 Ibn al-Muqaffaʿ 115, 174 Ibn al-Yaman 160 Ibn al-Zaki 177, 179 Ibn al-ʿAlqami 141n102 Ibn Battuta 6, 30, 72n160, 92, 96–97, 102–103, 150–151, 154, 158 Ibn Bibi 3–4, 14, 34n17, 34n20, 43, 46–49, 54, 66n43 Ibn Fuwati 144, 165n12 İbn-i Neccar, mosque 97 Ibn Khaldun 118 Ibn Khallikan 140n76 Ibn Najjar 97 Ibn Saʿid 5, 29, 39n89, 41 Ibn Sina 26, 85, 108n85 Ibn ʿArabi, Muhyi al-Din 25, 140n76 *Ihyaʾ ʿulum al-din* 89

Islam under Chobanids 143–145; Antinomian sufis, 13th-century Anatolia 145–153; in the *Fustat al-ʿadala* 157–164

180–185; Atabey Gazi mosque 32; chancellery in medieval Kastamonu 185–189; crops in 29; economic and religious life 6, 30; geography 28; Husam al-Din Khuʾi and manuals for letter-writing 178–189; *Inshāʾ* (chancellery) literature 174–180; *iqṭāʿ* of 49; Islamisation 6, 32, 98; literary genre, evolution 174–178; personal letters 189–195; proto-beylik in 13th- century 204–209; revolt of 8, 56–62, 208; socio-cultural history of 13th-century northern Anatolia 189–195; socio-political transformation 173–174; Turkicisation 33; Turkmen control in 42, 178–189; Turkmen revolts 51 al-Katib, ʿAbd al-Hamid 174 Katip Çelebi 76, 83 Kayseri 19, 48, 176 Kerman region 3, 23 Khalaj 192, 202n132 Khaqani, Afzal al-Din Shirvani 176 *khāṭib* (preacher) 96, 135 al-Khatib, Muhammad 76 al-Khattab, ʿUmar b. 126 Khismatulin, Alexey A. 78, 117–118 Khurasan 14, 31, 46, 55, 74, 97, 116–117, 127–128, 146, 148, 212 Khuʾi, Hasan b. ʿAbd al-Muʾmin Husam al-Din 10, 13n32, 33, 56–57, 60, 65n31, 68n89. 69n95, 70n119, 90–91, 105n33, 105n37, 123, 158, 160, 174–175, 177–193, 195, 207; works of 80–85 al-Khuʾi, *ʿ*Amal-i *ʿ*Abd al-Muʾmin b. Muhammad al-Naqqash 105n34 Khwarazm region 17–18, 23, 134, 141n103 *Kitab al-tavassul ila al-tarassul* 175 *Kitab mahtutat al- Mawsil* 84 al-Kiya al-Harrasi 129–130 Konya 3–4, 15–21, 24, 27–28, 42, 50–52, 56–60, 70, 70n122, 78, 80, 86, 90,

Laskaris family 51 *al-Lataʾif al-ʿalaʾiyya* 26, 116 letter-writing 174–178, 197n25; teaching 180–185 literary legacy of Chobanid Kastamonu 73–75; Fustat al-ʿadala fi qawaʿid alsultana 75–80; Husam al-Din Khuʾi, work of 80–85; other works 89–92; Qutb al-Din Shirazi 85–89 Literature, in Turkish 74

mirrors for princes: Arabophone territories 115; Chobanid Kastamonu 127, 131; post-Mongol Anatolia 116; *Siyar al-muluk (Siyasatnama)* 117–124; *see also Fustat al-ʿadala* fi qawaʿid al-sultana

*Mirsad al-ʿibad min al-mabdaʾ ila almaʿad* 26, 155

Anatolia under 18–23; Kastamonu's economic activity 30; north-western Anatolia 48–53; Seljuq Anatolia 15–18; Sultan of Khwarazm 134; *see also* Chobanid dynasty mosques 32, 66n38, 93, 112n142–143, 150; Akçasu Camii 94; Atabey Gazi 32, 94–96, *95*; congregation 94; construction or restoration 92–93; distribution of 66n38 Muhammad, Prophet 82, 132–135, 150, 153, 160, 162 Muhammad al-Isfahani, Shams al-Din 19 Muhammad Beg 21 Muhammad b. Ghazi of Malatya 26 Muhammad II, Sultan of the Khwarazmshah 46 Mujir al-Din b. Muʿtazz 22 *al-Mukhtarat min al-rasaʾil* 175 al-Mulk, Nizam 10, 69n96, 76, 78, 92, 105n30, 111n125, 116–118, 121, 123, 129–130 *Multamasat* 85, **221** *munashaʾāt* 176, 197n26, **222** Murad II, Sultan of the Ottoman empire 41 al-Mushtahir, al-Hajj Nasir b. al-Murad 97 Mustawfi, Hamdallah 6, 12n21, 30, 57, 184 Mutʿazz, Taj al-Din 27, 49 al-Muwaffaq, Hibat Allah 117 Muzaffar al-Din b. Alp Yürek 10, 23, 32, 48, 52, 54–61, 68n89, 68n93, 78, 80, 93, 96, 102, 120, 152, 180, **219**–**220**, **222** Muʿawiyya I, caliphate 174 Muʿin al- Din Muhammad Bey 49 Muʿin al-Din Muhammad b. Sulayman*,* son of the *Parvāna* 62 Muʿin al-Din Sulayman*,* Parvāna 21, 26–27, 49, 52 Myriocephalum, Battle of 16 *Nafhat al-ruh wa tuhfat al-futuh* 89 al-Nakhjavani, Najm al-Din 26 al-Nakhjivani, Muhammad b. Hindushah 178 al-Nakidi, Muhammad b. Hajji Yaʿqub b. Musa 81 *namāz* 149 *Nasib al-fityan* 83, 85, 187, **219** *Nasihat al-muluk* 90–91, 110n118, 110n121, 111n125, 116, 188–190

Nasir al-Din b. Muzaffar al-Din b. Alp Yürek 57, 59 Nasr Allah b. Husam al-Din Khuʾi 83, 99, 186, 191–192 Nicaea, Empire of 5, 31, 43, 50, 65n32 *Nihayat al-idrak fi dirayat al-aflak* 86 Niksar 26, 30, 91, 192 Niksari, Abu Bakr 157 *Nisab al-sibyan* 83, 187 *niyābat-i shihr* 182 Nizam al-Mulk, Seljuq vizier 76, 78, 116–118, 129 Nizamiyya, madrasa 129–130 *Nuzhat al-kuttab wa tuhfat al-ahbab* 68n89, 80–82, 84, 106n46, 179–181, 186, 199n54, **220** Ocak, Ahmet Yaşar 75, 144 Ögetei Khan, ruler of the Mongol empire 17–19, 34n13 Öljeitü, Ilkhan 22, 171n106 Osman Bey, founder of the Ottoman empire 41 Ottoman Empire 1–2, 41, 84, 143, 146, 152 Pachymeres, George 5, 7–8, 42, 55, 58, 60–61 Palaeologus, Michael VIII 49–50 *Panchatantra* 115 *parhzgār* 132 *Partawnama* 26 Paul, Jürgen 174–175 Peacock, Andrew 8, 41, 47, 51, 57, 89, 119, 146, 177, 200 Plato 115 Plutarch 115 political history, Chobanid Kastamonu: al-Din Choban, Husam 42–48; al-Din Choban, Muzaffar *53*, 54–60; Bey, Mahmud and the end 60–63; Danishmandid domains 40; Islamisation process 42; north-western Anatolia and Mongol invasion 48–53 Ptolomean principles 87 *Qabusnama (Nasihatnama)* 116 Qadis 27, 86–87, 96, 129, 135, 163 qalandarism, origin of 152–157 *Qalandarnama* 145 Qaramanids 22

Qaraqorum 19

	- Shafiʿism 163
	- Shah Daʾi Shirazi 146
	- Shāhid- Bāzī 37n65
	- *Shahnama* 76
	- Shams-i Tabrizi 146
	- sharia 47, 146, 149, 151–152, 159, 207
	- Shaykh ʿUthman-i Rumi 153–156
	- Shia Islam 126, 143, 146, 163
	- Shirazi, Qutb al-Din 27, 56, 85–89, 116, 136n14, 180, 206
	- Shirvani, Afzal al-Din Khaqani 176
	- al-Sijistani, Abu Daʾud Sulayman b. al-Ashʿath 160
	- Sinop 6, 9, 13n37, 17, 28, 38n76, 40, 46, 48–49, 52, 55, 63, 67n64, 89–92, 97–100, 102, 110n117, 124, 150, 192, 202n133
	- *sipahdār* 54–55
	- Sivas 17, 19, 26–27, 36n57, 48, 55–56, 68n84, 86–87, 108n92
	- *Siyar al-muluk (Siyasatnama)* 10, 69n96, 76, 78, 91–92, 104n17, 105n27, 105n29, 111n125, 115–135, 139n52, 164, 211–217
	- slave trade 6, 31, 123–124
	- Sudak 24, 46–47, 66n43–44, 172n134
	- Sufism 6, 27–28, 89, 102, 144, 157; *see also* Antinomian Sufis
	- Suhrawardi, Yahya b. Habash 25
	- Sulayman, *parvāna* Muʿin al-Din 52
	- Sulayman b. Qutlumush 15
	- Sulayman Pasha 63, 89
	- Süleymaniye Library 80–82, 167n52, 199, 219–222
	- Sultanate of Rum (Seljuqs of Rum) 2–3, 9, 14–15, 17, 22, 24, 26, 42, 44, 48–49, 78, 92, 134, 152, 205; family tree *218*
	- *Sunan Abi Dawud* 160

*sunna* 146, 158 Sunni Islam 126, 128, 140, 143, 157–158, 173 *Surat-i fatihnama* 57 Syria 16–17, 21, 24, 63, 97, 116, 136, 158, 163, 213 al-Tabari, Shaykh ʿAli b. al-Hasan Kiyaharas 129 Tabaristan 128–130, 136n7, 140n73 Tabrizi, Muhammad b. Mahmud b. Abd al-Rahman 86 Taj al-Din Muʿtazz b. Tahir 27, 52, 86–87 *Tarikh-i al-i Saljuq* 90 *tashbīh* (anthropomorphism) 148 *al-Tawassul ila al-tarassul* 176 Tegüder Ahmed, *Ilkhan* 21, 55–56, 178 Temüjin *see* Chinggis Khan Terken Khatun 117 Theodore II, Byzantine emperor 49–51 al-Tirmidhi, Muhammad b. ʿIsa 160 Tokat 16, 19, 26, 50–52 Tor, Deborah 115 Töregene Khatun 19 Tughraʾi, Shams al-Din Baba 49–50 Tughril III, Sultan of the Great Seljuqs 24 *Tuhfat alshahiyya* 86 *al-Tuhfat al-shahiyya fi al-hayʾa* 86 *Tuhfa-yi Husami (Tuhfe-i Hüsami)* 84, **219** Turan, Osman 7–8, 75–76, 78, 159 Turco-Mongol dynasties 73, 93

Turkicisation 33 Turkmen dynasties 2, 11, 93, 98 Turkmen tribes in Anatolia 3, 41, 113n166, 148, 206 Tusi, Nasir al-Din 86, 118, 144 al-Tustari, Muhammad 87

al-ʿUmari, Shihab al-Din 6, 30 Umayyad Empire 174 Urmawi, Siraj al-Din 116 ʿUryani Baba Tahir 145 Uzunçarşılı, İsmail Hakkı 89 Uzun Hasan 151

Valad, Bahaʾ al-Din 27 *Varqah va Gulshah* 80 Vatvat, Rashid al-Din 175

*al-Walad al-shafiq* 150 Wali, Shaykh ʿAbd al-Fattah-i 99 *waqfnāma* 80

Yahya, Badr al-Din 177 Yahya b. Saʿidb. Ahmad 26 Yavtash, Shams al-Din 48–49, 51 Yazıcızade Ali 41, 62 Yücel, Yaşar 7–8, 48 Yusufi, Muhammad ʿAli 75

Zachariadou, Elizabeth 7–8 al-Zaki, Abu Bakr b. 177, 194 al-Zanjani, Aḥmad b. Saʿd al-ʿUthmani 26 *zāwiyas* 30, 97, 102–103


**14.6 Disease severity**

It is not clear whether it is the disease itself, its treatment or a combination of the two which might put IBD patients at increased risk of lymphoma. It is possible that the use of drugs such as thiopurines and biologics are a marker of more aggressive disease and it is disease severity which disposes to lymphoma development. However, modern management of IBD has led to earlier use of these drugs, often in patients who do not have severe disease but possess risk factors for complicated disease, in an attempt to alter the natural history of the condition.

Severity of disease as a risk factor for lymphoma has not been analysed in any depth in IBD patients but there is some evidence available from other autoimmune diseases. A study of 378 RA patients diagnosed with lymphoma found no significant association with individual drugs but a marked increased risk with high disease activity which conferred a 70-fold increased risk (Baecklund et al., 2006). The authors concluded that it was the disease activity, not its treatment that was important in lymphomagenesis. In the CESAME study, some data regarding disease activity was collated but was not linked to lymphoma risk (Beaugerie et al., 2009a).