**Author details**

*Monoclonal Antibodies*

Immune status modification as strategy of cancer therapy does hold a significant place. Although, the conventional cancer treatments such as surgery, chemotherapy, and radiotherapy are still being referred to as the prominent ones, for some cancer types, immunotherapies are considered as first-line of treatment. One of the most important discoveries in the last several years in immunotherapy has been the development of immune checkpoint inhibitor, monoclonal antibodies that promote antitumor activity. T cells are a form of lymphocyte which are produced within the thymus and performs a crucial function in stimulating body's immune reaction to combat most cancers. They apprehend the overseas particles (antigens) with the aid of using particularly variable T cell receptor. Unlike antibody, the TCR cannot bind antigen and as a substitute it wishes to have peptides of the antigen proven to it with the aid of using an antigen presenting cell (APC). The molecules at the APC that gift the antigen are called as major histocompatibility complexes (MHC). Many stimulatory alerts also are wanted at this time. B7 is a form of peripheral membrane protein observed on activated antigen-providing cells (APC). This B7 on an APC can bind to cytotoxic T-lymphocyte-related antigen 4 (CTLA-4) developing an inhibitory sign and TCR activation. Once the activated T-cell receptor is within the tumor surroundings it is able to apprehend the antigen supplied with the aid of APC within the tumor. At this time, the programmed cell death protein 1 (PD-1) receptor also sends an inhibitory signal to the T-cell when the receptor binds to programmed cell death 1 ligand 1 (PD-L1), that's regularly expressed on tumor cells. Monoclonal antibodies act by inhibiting the binding PD-1 to PD-L1 and thereby boost body's immune response against the tumor cells. Checkpoint inhibitors specifically goal PD-1/PD-L1) and CTLA4 Immune checkpoint efficacy is stricken by diverse factors, among which are tumor genomics, host germline genetics, PD-L1 levels, and intestine microbiome. Generally, in tumors, mutated or incorrectly expressed proteins are processed through the immunoproteasome into peptides which can be commonly loaded onto MHC molecules, which similarly now no longer usually are capable of eliciting CD8+ T cell reaction. This may also cause producing MHC-supplied immunogenic neoepitopes. It turned into proven, that after the intratumor heterogeneity rises, neoantigen-expressing clones emerge as greater homogenous with the differential expression of PD-L1. There are number of FDA approved monoclonal antibodies, that are considered

for the treatment of Urology oncology. These have been detailed in the **Table 1** and include FDA-approved PD-1 inhibitors such as nivolumab, pembrolizumab, cemiplimab, and FDA-approved PD-L1 such as atezolizumab, avelumab, and durvalumab. But all the open literatures do believe that combinatorial strategies with immune checkpoint therapies may provide a better survival benefit which have been demonstrated in various clinical trials. These can be in combination with radiation therapy, tyrosine kinase inhibitors and also many chemotherapeutic drugs. However, the response to immunotherapy with monoclonal antibodies varies subjectively and hence research into PD-L1 expression, gene signature expression, messenger RNA subtype, mutational and neoantigen load is essential to determine the varying response to monoclonal antibody immunotherapy. Although older modalities of treatment for cancer, has been extensively exploited, array of new drugs that offer hope of not only prolonging life but also curing significantly more

patients in the future bring a ray of hope to the scientific world.

The authors declare conflict of interest as None.

**8. Conclusion**

**134**

**Conflict of interest**

Maya Kulshekar1 , Shridhar C. Ghagane2 \*, Sridevi I. Puranik3 , Rajendra B. Nerli4 and Murigendra B. Hiremath5

1 Department of Biochemistry, JN Medical College, KLE Academy of Higher Education and Research (Deemed-to-be-University), JNMC Campus, Belagavi, Karnataka, India

2 Division of Urologic-Oncology, Department of Urology, KLES Dr. Prabhakar Kore Hospital and Medical Research Centre, Belagavi, Karnataka, India

3 Department of Zoology, KLES B.K. Arts, Science and Commerce College, Chikodi, Karnataka, India

4 Department of Urology, JN Medical College, KLE Academy of Higher Education and Research (Deemed-to-be-University), JNMC Campus, Belagavi, Karnataka, India

5 Department of Biotechnology and Microbiology, Karnatak University, Dharwad, Karnataka, India

\*Address all correspondence to<EMAIL_ADDRESS>
© 2021 The Author(s). Licensee IntechOpen. This chapter is distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/ by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.


*3.4. The Inhibition of HO-1 Activity by SnPP Controls the Gene Expression of Antioxidant Molecules*

To deeply analyze the relationship between HO-1 expression in peritoneal cells induced by *F. hepatica* infection with the production of ROS/RNS, the gene expression of different molecules involved in the oxidative and antioxidative responses was evaluated. PECs of SnPP-treated infected mice were characterized by a significant decrease in the mRNA levels of the transcription factor *nrf2* (Figure 5A). Moreover, the SnPP-induced decrease in *nrf2* gene expression levels was associated with decreased mRNA levels in the antioxidant enzymes *catalase,* glutathione peroxidase 2 (*gpx2*), and superoxide dismutase 2 (*sod2*) (Figure 5B). However, no differences were found in the gene expression of *gpx1*, while an increase in *sod1* expression was observed (Figure 5B). Finally, an unexpected decrease in the mRNA levels of the NADPH oxidase subunits *gp91phox* and *p47phox* was observed with SnPP treatment of infected mice (Figure 5C).


**Acknowledgements**

The authors acknowledge the International Science Programme of Uppsala University who funded the research and the Departments of Chemistry and Physics of The University Of Zambia for their laboratory facilities under the auspices of Materials Science and Solar Energy Network for Eastern and Southern Africa (MSSEESA).


**3. Quality of fresh cut fruits**

Quality is a combination of characteristics that determines the value of produce to the consumer. Fresh produce is expected by the consumer without any defects having optimum maturity, physicochemical characteristic and must be in fresh condition. The condition of fresh vegetable and fruits relates to their sensory quality, general appearance, nutritional quality and flavor [11]. Overall quality characteristics impacting the fresh cut fruits and vegetables are compiled in **Figure 1**. Consumers judge the quality of vegetables and fruits on the basis of their appearance and firmness (external attributes). Important aspect to be considered during the preservation of fresh cut produce is tissue color and control of surface browning and discoloration. Polyphenol oxidase (PPO) is the enzyme is responsible for the oxidative browning of the surfaces

**Figure 1.**

*Quality characteristics of fresh cut fruits and vegetables.*

#### *Fresh Cut Fruits and Vegetables Disinfection Pretreatment: A Novel Approach to Extend Fresh… DOI: http://dx.doi.org/10.5772/intechopen.107525*

of fresh cut produces. In this reaction, phenolic compounds present in the fruits and vegetables get converted into dark colored compound in the presence of oxygen after coming in contact with environmental conditions. Extent of browning varies by growing conditions, cultivator and commodity characteristics. Several researches have been done to reduce the PPO-mediated discoloration. Usually a simple visual, appraisal of appearance and quality, hopefully objective is desired. A detailed scale to analyze the color quality of both fresh cut and whole fruit is also followed [12]. Consumer often buys the product for the first time based on appearance or impulse. Apart from this, other intrinsic factors such as texture and flavor also plays role in determining the quality. Flavor is comprised of aroma and taste which mainly relates to sugar, acids, salts, bitter compounds and volatile components. Flavor in fruits and vegetables arises from numerous biosynthetic pathway and wide range of aldehydes, alcohols, esters, ketones, lactones, sulfur and nitrogen containing compounds.

Volatile compounds play major contribution to impact the aroma in fruits such as banana, apple, pear, guava, papaya, strawberry and melon. During the storage of fresh cut produces, flavor losses that act as the direct consequence of senescence. Flavor changes occur during the storage of fresh cut fruits and vegetables may affect the consumerpreferability towards buying of fresh cuts. Consumer generally considers flavor as the most important attribute for fruits and vegetables, but textural defect in interaction of flavor also responsible for the consumer rejection. Texture comprises of both mechanical and structural properties of the food, as well as sensory component in mouth and hand, all of which can be measured by several nondestructive and destructive instruments or objective method [13]. Subsequent purchases by consumers are, however, depends upon their eating experience (taste, aroma experience attribute or texture intrinsic).

Other quality parameters like nutritional and safety attributes also influence consumer decisions on making repeated purchases of the commodity. Fruits with highest quality should be used for the processing of fresh cut commodities. Fruits and vegetables act as the major source of vitamins, fibers, minerals and carbohydrate. There are certain metabolites such as anthocyanins, phenolic acids and flavonoids are obtained from fruits and vegetables which plays integral part in human metabolism and also assumed to beneficial for human health due to its biological properties including antiviral, anti-inflammatory, antibacterial, antioxidant function [14]. These above mentioned components are considered to be highly beneficial in curing certain diseases such as coronary heart diseases, osteoporosis, stroke, and hypertension. Daily intake of 400 g of fruits and vegetables is recommended by the world Health Organization in 2003 [15]. Processing of raw fruits and vegetables results in alteration of nutrients. After cutting of the fresh fruits and vegetable, the antioxidant capacity of the produce may increase (celery, carrot, parsnip, potato, white cabbage, sweet potato) [16] or decrease (potato, melon, zucchini, cabbage) during storage. Carotene and Vitamin C content decrease very little during short term storage of fresh cut fruits [17]. However, Vitamin C and ascorbic acid content usually decreases after cutting, especially during long term storage [18]. Safety is also the major component for maintaining the quality aspect of fresh produces. Safe food should be free from physical, chemical and biological hazards. Physical hazard include glass, stones, plastic, hair, jewelry and metals which may be intentionally or unintentionally get into the produce during the production process. Chemical hazards include natural substances for, e.g., mycotoxins, alkaloids, allergens and enzyme inhibitors, chemicals like pesticides and toxic elements like lead, arsenic, zinc, and cadmium. These elements affect the human health more rapidly as compared to biological hazards. This chemical

hazard enters into the fruits and vegetable during production process and postharvest handling. Biological hazards are caused by the pathogenic microorganisms. These microorganisms cause intoxication and food borne infections when consumed. Microbial population which may be insufficient to cause food spoilage but may be sufficient to cause illness in human beings on consumption of fresh produces. Thus, the fresh cut vegetable and fruits which seems perfect in appearance is not guaranteed to be microbiologically safe. These pathogenic microorganisms may contaminate the fruits and vegetables through irrigated water, soil, badly treated manure, sewage and poor worker hygiene or through poor postharvest handlings. There are many factors that impact on the quality of fresh cut produce is described below:


2.3.2. Cognitive Tests

The Paced Auditory Serial Addition Task (PASAT) [62] and the Memory for Intentions Screening Test (MIST) [63] were used.

The PASAT measures WM performance. The test consists of a list of 60 numbers between 1 and 9. Subjects are asked to add up each auditorily presented number in the list with the next one and to give the response within 1.8 milliseconds. For example, if the first two numbers presented are 5 and 6, the subject must answer 11 (6 + 5) [62].

The MIST assesses the ability to remember and perform planned activities at some point in the future. It consists of 8 everyday PM activities that involve the individual in an assigned task at a specific time (i.e., after 2 min or 15 min from the assignment) or when a specific cue is given (i.e., an event cue or a time cue). For instance, for event-based tasks: "When I show you a red pen, write your name in the chatroom"; for time-based tasks: "In 15 min, please ask me to take a break". Within the time frame in which subjects are required to remember to perform the assigned tasks, they are involved in an ongoing task (i.e., word search puzzle) [63].


*Role of Biotic and Abiotic Factors for Sustainable Cotton Production DOI: http://dx.doi.org/10.5772/intechopen.111914*

insect and mite pests in sub-continent [20]. The cotton crop is attacked by 96 different types of insect pests and mite species during its growth period. These insects and other pests normally observed to nourish on fiber crop in Pakistan [21].  Chewing insects generally chewed the different parts of cotton plant including cotton bollworms and leaf worms etc. e.g. pink bollworm, American bollworm, armyworm, spotted bollworm, cotton leaf worm etc. Mainly the immature stage of this group damages to cotton crop [14, 22]. **Table 1** includes the major chewing pests of cotton crop as given below.

 In Pakistan, India, China, Japan, Trinidad, Puerto Rico, Australia and Europe the key pests of cotton crop had sucking type of mouth parts are thrips, jassid, whitefly and they cause considerable loss in cotton production [35, 36]. Insects with sucking type of mouth parts became primary pests of cotton crop from the time transgenic cotton (Bt cotton) commercialized in the world [37]. **Table 2** includes the major piercing sucking pests of cotton crop as given below.


*3.4. Parallel Processing*

The optical flow method requires no encoding information and can be performed while other frames are being encoded. In a typical situation, while the first frame, which has to be Intra, is being encoded, there is enough time for the optical flow computation for the second frame, so if enough CPU cores are available, it can be computed before the need for it arises. If a single frame delay is acceptable, this method will allow saving a significant amount of time in the main encoding loop, which has to iterate over all blocks in order. Even in the case where this one frame delay would be unacceptable, the optical flow method used can be parallelized very well, and as it performs only basic mathematical operations, can easily run on a GPU or dedicated hardware.

### **4. Experimental Results**

### *4.1. Testing Conditions*

The HEVC reference encoder HM14 [16] is used as the anchor to estimate the Bjøntegaard Delta Bitrate (BD-R) [17] estimated bitrate savings and relative encoding time to compare the various methods.

We used the code from Tsutake et al. [10] to compare our proposed method with the existing state of the art. We also used their implementation of the gradient method from Reference [9] and a 3-parameter variant of the gradient approach that uses the same entropy coding as their method.

We used the same code for the entropy coding and transform calculations. We wrote the parameter estimation of the proposed method to replace theirs. This allows us to compare the parameter estimation process without other variables making the comparison difficult.

We compare our method to Reference [10], their implementation of Reference [9], and the 3-parameter variant of the gradient method.

For the encoding settings, the same settings as Reference [10] are used: The encoding mode is set to Low Delay P, and the quantization parameter (QP) values are 22, 27, 32, 37.

A total of seven sequences that show various motions were encoded with HM14 [16], Tsutake [10], Heithausen [9], the 3-parameter gradient and the proposed method. The sequences used are from two datasets, the ITE/ARIB Hi-Vision Test Sequence 2nd Edition [18] and Derf's collection [19]. Table 1 lists the sequences that were used, with the sequence number for the sequences from Reference [18]. To compute the encoding time, we used the following formula:

$$
\Delta T = \frac{T\_{\text{target}} - T\_{HM\_{14}}}{T\_{HM\_{14}}} \tag{14}
$$

**Table 1.** Video Sequences.

In the following tables, the encoding time shown is the average over all QP values.


3.3.10 Single-particle distribution function and the general form of the linearized Boltzmann equation

Kinetic equations are obtained if the occupation numbers nν of single-(quasi-) particle states ∣νi is taken as the set of relevant observables Bf gn . The single-particle state ν is described by a complete set of quantum numbers, e.g., the momentum, the spin and the species in the case of a homogeneous multi-component plasma. In thermal equilibrium, the averaged occupation numbers of the quasiparticle states are given by the Fermi or Bose distribution function, nh iν eq ¼ f 0 ν ¼ Tr ρeqnν n o. These equilibrium occupation numbers are changed under the influence of the external field. We consider the deviation Δnν ¼ nν � f 0 ν as relevant observables. They describe the fluctuations of the occupation numbers. The response equations, which eliminate the corresponding response parameters Fν, have the

structure of a linear system of coupled Boltzmann equations for the quasiparticles, see [11]

$$\frac{\mathcal{E}}{m}\mathbf{E} \cdot \left[ (\mathbf{P}|\mathbf{n}\_{\nu}) + \langle \mathbf{P}; \dot{\mathbf{n}}\_{\nu} \rangle\_{i\epsilon} \right] = \sum\_{\nu'} F\_{\nu'} P\_{\nu'\nu} \tag{187}$$

resistance leads to a vanishing result, and the correlation function of stochastic forces must be considered, in analogy to the corresponding term in the Langevin equation [6, 25]. A related projection operator technique was used by Mori [26] for

There are close relation to other approaches, such as kinetic theory or quantum master equations, where the response function of the bath is considered. Irreversibility is not inherent in the equilibrium correlation functions, but in the assumption that a nonequilibrium state is considered as a fluctuation in equilibrium with a prescribed value of the relevant quantity. Other degrees of freedom are forced to

The method of nonequilibrium statistical operator (NSO) to describe irreversible processes is based on a very general concept of entropy, the Shannon information entropy (10). This concept is not restricted to dynamical properties like energy, particle numbers, momentum, etc., occurring in physics, but may be applied also to other properties occurring, e.g., in economics, financial market, and game theory. The generalized Gibbs distributions (13) and (19) are obtained if the averages of a

given set of observables are known. Other statistical ensembles may be

constructed, where the values of some observables have a given distribution. For instance, the canonical ensemble follows if the particle numbers are fixed, and the microcanonical ensemble has in addition a fixed energy in the interval ΔE around E, see [1, 2]. There exist alternative concepts of entropy to valuate a probability

In physics, we have a dynamical evolution that forms the equilibrium distribution for ergodic systems, and any initial distribution that is compatible with the values of the conserved quantities can be used to produce the correct equilibrium distribution. The main problem is the microscopic approach to evaluate the dynamical averages, which can be done using quantum statistical methods such as Green function theory or path integral calculations, or, alternatively, numerical simulations of the microscopic equations of motion such as molecular dynamics. In more general, complex systems, we do not know the exact dynamics of the time evolution. However, we can observe time-dependent correlation functions which reflect the time evolution, and properties such as the fluctuation-dissipation theorem are not related to a specific dynamical model for the complex system. The most interesting issue of the NSO method is the selection of the set of relevant observables to describe a nonequilibrium process. The better the choice of the set of relevant observables is, for which a dynamical model for the time evolution can be found, the less influence is produced by the irrelevant observables which may be

An important application is the description of hydrodynamic processes and its relation to kinetic theory. The NSO method allows to treat this problem, selecting the single-particle distribution as well as the hydrodynamic variables as set of relevant observables. This approach has been worked out in [23]. A more general presentation is found in [4], and transport processes in multi-component fluids and superfluid systems are investigated. Until now, a rigorous theory of turbulence is

the memory-function approach.

Nonequilibrium Statistical Operator

DOI: http://dx.doi.org/10.5772/intechopen.84707

4. Concluding remarks

4.1 Information theory

adopt the distribution of thermal equilibrium.

distribution which are not discussed here.

described by time-dependent correlation functions.

4.2 Hydrodynamics

43

with Pν0 ν ¼ n\_ ν ð Þþ 0 jΔnν n\_ ν0 ; n\_ h iν iϵ. The response parameters Fν are related to the averaged occupation numbers as

$$f\_{\nu}(t) = \text{Tr}\left\{\rho(t)\mathbf{n}\_{\nu}\right\} = f\_{\nu}^{0} + \beta \sum\_{\nu'} F\_{\nu'}(\Delta \mathbf{n}\_{\nu'}|\Delta \mathbf{n}\_{\nu}).\tag{188}$$

The general form of the linear Boltzmann equation (187) can be compared with the expression obtained from kinetic theory. The left-hand side can be interpreted as the drift term, where self-energy effects are included in the correlation function P; n\_ h iν iϵ. Because the operators nν are commuting, from the Kubo identity follows n\_ ν ð Þ¼ 0 jnν ð Þ 1=ℏβ nν h i ½ � 0 ; nν ¼ 0. In the general form, the collision operator is expressed in terms of equilibrium correlation functions of fluctuations that can be evaluated by different many-body techniques. In particular, for the Lorentz model the result (186) with s ¼ 25=2 =π3=2 is obtained [5, 15, 16].


**2. Conclusions**

*Advances in Extracorporeal Membrane Oxygenation - Volume 3*

*ECMO "team". Adopted from reference intro chapter in Volume 1 [1].*

transfusion rates, etc.) must be tracked and compared to published benchmarks. Quality conferences in which cases are discussed can help a team and program formally recognize their successes while looking for opportunities for collective improvement. As discussed previously in the introductions (and various chapters) of the previous volumes, outcomes still remain less than ideal with survival rates that range from 60 to 70% for veno-venous respiratory support and 25–35% for venoarterial cardiopulmonary support and eCPR [8, 9]. These less-than-ideal success rates should improve over time as programs gain experience and implement guidelines and protocols, teams learn to function more effectively and efficiently, and patient selection and management improve. However, poor outcomes must also be tempered by the concerns that outcomes that are potentially "too good" might suggest that potentially salvageable but higher risk patients might not be offered therapy out of fear of experiencing a bad outcome. Nevertheless, it becomes the priority of a program to develop a "culture" of how aggressive they want to be with regard to offering therapy to high-risk (or low-risk) patients. Fortunately, scoring systems for venoarterial and veno-venous support indications can assist in patient selection. Again, outcomes and quality metrics must be benchmarked against similar programs, like institutions and established registries. Membership in the Extracorporeal Life Support Organization (ELSO: https://www.elso.org) is an important component of tracking outcomes and can play a key role in documenting program progress and success. In addition, membership can provide an opportunity to establish relationships with other programs to exchange ideas, share protocols, and have access to

important and timely developments and technological innovations.

While the advances in the field of ECMO are rapid and there has been an equally rapid worldwide growth in programs and the number of patients supported, a key aspect of ECMO therapy is the ethical component of a highly invasive, resourceintensive, and complex intervention. Because ECMO is still associated with less than ideal outcomes, relatively high complication rates (including neurologic complications), and high resource intensivity (not to mention expensive, depending on the reimbursement circumstances which can vary dramatically), a fundamental question remains regarding not on whether we can offer and continue support, but

**6**

**Figure 1.**

Experiences in the selection and management of patients with acute cardiac and respiratory failure who are treated with ECMO continue to grow. Recent trials continue to help demonstrate the effectiveness and role of ECMO as outcomes continue to slowly improve [10, 11]. Even though many patients treated with ECMO still die even in the best of circumstances, it remains important for everyone to continue to search for opportunities for improvement. Good outcomes must be embraced and shared with the entire team, as they can provide hope while also inspiring and motivating a team—even when there are concerns of futility (**Figure 2**). The goal of this volume is to offer further insights, experiences, and discussions of the current state of the art regarding many topics that challenge those who believed in the tremendous potential benefits of ECMO [12].

#### **Figure 2.**

*BH (center in wheelchair) with his parents after qualifying for the finals in the single-scull, arms and shoulder only, rowing competition in the 2016 Paralympics in Rio de Janeiro. BH, a five-time US national champion in the event, represented the USA in Rio as a member of the Olympic team. In 2016, he was selected US rowing "rower of the year." several years prior, BH lost both legs to complications of a necrotizing soft tissue infection and required cardiopulmonary support with venoarterial ECMO due to overwhelming septic shock. Picture used by permission by all represented [11, 12] and adopted from volume 2 [13].*

*Advances in Extracorporeal Membrane Oxygenation - Volume 3*


the high flux isotope rector (HFIR) at ORNL. While there is much overlap among the instrument specifications, each of these instruments has unique advantages for different types of experiments. Thus, there is considerable overlap in their user bases, who use multiple instruments for different experimental needs, such as specific performance or sample environment equipment needs. The SANS instrument suite has had very different instrument control and data acquisition systems (IC-DASs) over the past decade for historical reasons. EQ-SANS, located at the SNS, relied on PyDAS, a Python extension of the original home-developed SNS DAS [2]. Bio-SANS and GP-SANS, both located at HFIR, were served by a customized graphic user interface (GUI) extension based on the Spectrometer Instrument Control Environment (SPICE) software built on LabView [3].

At the SNS, the original SNS DAS played a critical role in the commissioning and operation of early instruments. It also laid the foundation of the data format for timestamped event mode data coming from the pulsed neutron source, which is required to record the time-of-flight of each neutron for further data process and reduction [4,5]. Later instruments with higher data throughput were commissioned at the SNS and exposed the data acquisition bandwidth limitations of the original SNS DAS. Years of operational experience also highlighted the need to improve the reliability and usability of the original SNS DAS. As a result, a series of significant software and hardware upgrade projects have taken place at the SNS [6–9], using the Experimental Physics and Industrial Control System (EPICS) toolkit and Control System Studio (CS-Studio) [10–12] that are used by the SNS accelerator control system. EQ-SANS at the SNS was the first among the SANS suite to be upgraded to the EPICS-based IC-DAS.

Over time, significant upgrades at Bio-SANS and GP-SANS, including upgrades allowing them to use the same type of 3He linear position-sensitive detectors used at EQ-SANS [13], neutron collimation systems, and additional sample environment equipment [1], have challenged the existing capabilities of SPICE and DAS. For example, the timestamped event mode data from the detector system have been converted to histograms to be handled by SPICE, losing valuable temporal information for neutron detection at Bio-SANS and GP-SANS. Moreover, given the growing suite of sample environment equipment shared among the SANS instruments and the need for a unified user experience, it is imperative that Bio-SANS and GP-SANS follow suit to upgrade to the EPICS-based system. This unification of the IC-DAS also will make system management and operation easier without splitting resources on what otherwise might be two separate development efforts.

This article shares our user needs and requirements analysis results, key methods we relied on, and new features of the EPICS-based SANS IC-DAS. Besides basic instrument control and data acquisition features, our new system is ready to be deployed for other similar SANS instruments, offering accurate and intuitive metadata handling, sophisticated experiment planning tools, and different user operation interfaces catering to different user communities.

#### **2. User Needs and Requirements Analysis**

#### *2.1. Diverse User Needs*

The users of the SANS IC-DAS are quite diverse. Categorized by different roles, they can be external facility users, instrument scientists, and supporting teams including sample environment teams, detector teams, and so forth. In addition to the rudimentary instrument control functionality, each role has more specific requirements of the system. For example, supporting teams require in-depth control of instrument components, clear status indication, and sufficient logs for monitoring and diagnostic troubleshooting. Instrument scientists have a good overall knowledge of the whole instrument system and are highly experienced in configuring an instrument for specific scientific requirements. However, they need to be freed from tedious hands-on operations to focus on scientific aspects of user experiments. The largest and most important group are external neutron scattering facility users. We want to empower them to successfully conduct experiments and collect useful measurement data with minimum effort expended on instrument oper-

ation. As the SANS instrument suite covers a large span of scientific communities with different skill sets; different experience levels with neutron scattering instruments; and, most crucially, different ways of conducting experiments suited for their sciences, our IC-DAS must make it easy for all of those communities to be productive during the short period of time they spend at an instrument. For example, users from the experimental condensed matter physics community usually have a single sample, but require various sample environment conditions in a particular experiment. They need to be able to easily visualize and interpret the reduced data to decide on the next condition to explore. They usually spend more time on a sample and require quick, flexible manipulation of many instrument and sample environment parameters. On the other hand, biochemists or biologists conducting solution SANS experiments usually use highly standardized instrument configurations for a series of samples with small variants. They need an effortless way of setting up their experiments, while they focus primarily on preparing fresh samples on-site. For these two extreme cases and others between them, a variety of user interfaces reflecting different workflows need to be designed and developed.

Our analysis of previous usage data further supports the above requirements. For example, the wizard-like GUI extension (Figure 1) previously developed within SPICE served 99.4% of Bio-SANS experiments and 7.8% of GP-SANS experiments over the 2 years since its deployment in 2016 (HFIR was in a long outage from late 2018 to late 2019). The rest of the experiments during that period were served by SPICE macros similar to scripting interfaces. Based on these findings, the new system implements and improves on both of these tools, offering wizard-like Panel Scans and Scripting Tool based on Python, in addition to the simple start/stop button-click GUI and Table Scan feature that were part of the previous EPICS/CS-Studio system.

**Figure 1.** The Spectrometer Instrument Control Environment (SPICE) graphic user interface (GUI) extension with a wizard-like tool to set up scans for small-angle neutron scattering (SANS); highlighted in purple is the currently selected function group.

#### *2.2. Complex Data and Metadata*

During a neutron scattering experiment, an extensive amount of data and metadata are collected. The data are mainly events detected by the large two-dimensional (2D) position-sensitive neutron detectors with timestamps of each neutron event referring to a reference time. Such event mode data provide convenience for post-collection time-slicing or synchronizing with sample environment parameters for time-resolved studies or a stroboscopic method. The metadata, also recorded with timestamps, include inherent instrument parameters such as hardware component positions, health conditions, and sample environment equipment readouts, and user-input supplementary information such as sample and background details. All of those parameters are critical for setting up the correct instrument measurement configuration, and many of them are required for correct data reduction and interpretation.

Most inherent parameters are motor positions and other equipment readouts (Figure 2). They can be grouped in relation to (1) beam characteristics (e.g., status of neutron guides, source aperture, attenuator, beam trap); (2) the Q-range (the momentum transfer, Q, is the most important factor in a small-angle scattering experiment, representing the size range to be probed in the reciprocal space within which SANS measures; e.g., velocity selector rotation speed and tilt angle, sample aperture size, detector motor positions); (3) sample environment devices and conditions (e.g., temperature, magnetic field, pressure, rotating cell speed); and (4) sample changer and slot number. Individual motor position and other readings are difficult for both expert and non-expert users to comprehend; therefore, it is necessary to associate and display them with more meaningful configuration descriptions. In addition, a higher-level collective setup can be used to configure an instrument without setting those parameters individually.

**Figure 2.** (**A**) An overview of a SANS instrument system. (**B**) The collimation system used at the biological small-angle neutron scattering instrument (Bio-SANS) and the general-purpose small-angle neutron scattering diffractometer (GP-SANS) with interchangeable neutron guide and aperture systems. There are eight sections of almost identical units with independent motor control.

> User-input supplementary information is needed for users to keep track of different parameters such as slight variations in composition, concentration, matching background, and so on. Some of this information can be pulled from the centralized sample management database, inventory tracking of equipment, material and sample (ITEMS), whereas some can be provided only by users manually during an experiment. Moreover, the system needs to provide an expandable pathway for adopting emerging standards for metadata, such as Information System for Protein crystallography Beamlines (ISPyB) used by the biological small-angle scattering community [14] and the collective action for nomadic small-angle scatterers (canSAS, www.cansas.org) community.

#### *2.3. Integrated Experiment Planning Tool*

Our instrument scientists and users have been using simple calculating tools such as Excel spreadsheets to plan experiments, including Q-range and measurement time, and manually convert that information into actual instrument parameters to use. Given the increasing complexity of instruments—e.g., multiple detectors, different sample changers, multiple beam stops, sample environment devices—an integrated experiment planning

tool that can take advantage of the known constraints of the parameters, and then easily save the planned configuration for use and reuse, is valuable. Clearly defined instrument configurations will also enable the implementation of automatic data reduction and provide coarse real-time data reduction, which is very helpful to guide users during measurements.

#### **3. Methods**

#### *3.1. Needs-Driven and User-Centered Design*

The developers initialized a thorough user experience–focused study on the existing SPICE and other relevant software environments. The study was guided by the principles from the latest user experience and design thinking research (e.g., references [15–17]), and based on effective practices from previous EPICS upgrade projects, such as using a beginner mindset, maintaining operational flexibility, and balancing between overall performance and individual process optimization. With help from database administrators, the previous metadata from SPICE (already ingested into the catalog database) were used to mine useful usage data to quantify the findings of our study. This effort not only helped clarify the required functionality but also helped prioritize the requirements based on evidence rather than impressions. The study further helped frame a shared vision of delivering an IC-DAS that is both functional and easy-to-use and helped build a relationship of trust between instrument scientists and control system developers.

Based on a goal of minimizing the physical, mental, and emotional efforts required of users in carrying out their tasks, the study identified four focus areas that could potentially be improved for a better user experience. These four focus areas are (1) the Q-range configuration representing the instrument configuration, including many component settings; (2) a user interface including both wizard-like Panel Scans and a Python-based Scripting Tool; (3) customizable detailed sample and buffer information tracking; and (4) detector geometry handling with various viable offsets and motor positions. With these in focus, we co-designed all main components of our high-level user-oriented tools; the details are provided in the results section.

#### *3.2. Automation Based on Process Knowledge*

Following the principle of "first make it work, then make it better," we built on the team's expertise and experience to create shared process knowledge regarding how the different parts of the SANS instruments are interwoven with one another. For example, as mentioned earlier, setting an instrument to a specific Q-range configuration requires coordinating the establishment of the beam characteristics with the settings for other hardware motors. For instance, the collimator guides and apertures used to define the incident beam are coupled with several pieces of distance metadata for Q-range calculation and other settings. This interwoven nature of the instrument parameters should be considered in aspects including instrument control, experiment planning, and metadata handling.

Another challenge is effectively managing instrument Q-range configurations with small variations that are sample- and proposal-specific. Improved understanding of the process knowledge in actual operation enabled us to differentiate between configurations that are standard or are changed infrequently, and those that are changed often. We did so by creating a sample- and proposal-dependent layer that allows a user to save Q-range configurations with only minor modifications, while still sharing the parameter files (e.g., flood, beam center, and dark current files) associated with the standard configurations. We designed our tools with as much automation as possible and set the order of development based on shared process knowledge.

#### **4. Results**

Within the user needs and requirements scope identified earlier, built on the EPICS system architecture (Figure 3), we developed and delivered a distributed IC-DAS customized for the SNS and HFIR SANS instruments. We added customizations and improvements to already developed features and building blocks, significantly reducing the user experience

gaps encountered among the three SANS instruments at ORNL. This system is built on the abundant base of EPICS drivers that interface with different items of physical hardware, such as motors, temperature controllers, and magnets. Among modular applications (also referred to as input–output controllers) and different user interfaces, a scan server application serves as the "brain" and controls the overall instrument state while maintaining a "command queue" for future measurements. Various user interfaces (including Panel Scans and Scripting Tool) cater to different needs and preferences and enable users to plan and conduct their experiments efficiently. This architecture ensures a sound and flexible system that can meet the requirements of complex instruments such as the SANS instrument suite.

In this section, we detail a few novel aspects of our system and discuss how they provide users with an improved instrument control and data handling experience. Additional screenshots of the new system are exhibited in the Supplementary Materials.

#### *4.1. Intuitive Motion Control with Reliable Metadata*

Following a good practice implemented in the previous SPICE software, we grouped the control of multiple devices into pseudo-motors. For example, for collimator motion control with eight different sections of guide motion control, a single "nguides (NGuides)" (number of guides) command can coordinate the control of guide motions in and out to keep a specific number of guides in the beam, along with apertures in the beam configuration. Note that the number of guides usually define the apparent source-to-sample distance (the flight path from the end of last guide to sample position). The distance needs to be coordinated with the sample-to-detector distance, various aperture sizes, and detector pixel resolution to provide optimal scattering geometry in SANS [18]. We included additional enhancements such as the newly defined 20 mm aperture in the beam configuration (aka, NGuides = "−1" in Figure 4); automated motor homing procedures; and logic embedded within the collimator motion control software to consistently compute and update the source aperture diameter and the metadata for the source-to-sample distance, based on motor positions. Similarly, detailed detector distance calculation logic is also integrated within the motion control to simplify or automate various offsets (e.g., sample-to–silicon window offset and detector motor position, see Figure 5) with a combined pseudo-motor total sample-to-detector distance. The latter matches the typical convention employed by SANS users in detector geometry and is critical in experiment planning and data

reduction. The pseudo-motor is controllable like any physical motor, and the corresponding actual motor position is calculated based on offset values that can be changed according to different experiment setups. Note that all individual values and combined pseudomotor values are captured in metadata redundantly in case they need to be cross-checked. The development and testing effort in motion control has been rewarded by a clean, simplified interface with enhanced functionalities, tighter integration with high-level tools, and more reliable metadata.

**Figure 4.** An example of motor grouping, the pseudo-motors for guide operation in the collimation system.

**Figure 5.** The diagram shows that the sample-to-detector distance combines the sample-to–silicon window offset and the actual detector motor position to form a controllable pseudo-motor.

#### *4.2. A Customized and Fully Integrated Q-Range Planner*

Instrument-specific experiment planning is important for the success of an experiment. For SANS experiments, Q-range is one of the most critical factors, as it determines the size range of a measurement. Previously, instrument scientists developed spreadsheets or other calculation tools independently to do their planning without much instrument-specific information or constraints. The integration of the Q-Range Planning tool within the IC-DAS enables the direct transfer of the Q-range configuration from the planning stage to the measurement stage. In addition to the benefit of imposing the physical constraints (e.g., motor limits) of a specific instrument, this implementation reduces the number of Q-range configurations that are due to small, unnecessary inconsistencies.

Once we understood these requirements, a customized Q-Range Planner was developed based on instrument scientists' spreadsheet calculators, as well as previous work on other instruments. The SANS Q-Range Planner helps users specify/update factors such as wavelength, attenuation factor, number of guides, aperture sizes, detector distance/rotation, distance offsets, and beam trap configuration for both scattering and transmission measurements. The factors then are converted into actual hardware settings such as motor positions. The planner then calculates the minimum Q at the beam stop rim, depending on the beam

stop chosen, maximum Q values at corners and edges on each detector, and direct beam size on the detector; and, when applicable, it calculates the overlapping ratios between different detectors. Users can easily save a Q-range configuration to use and reuse in actual measurements. The saved Q configuration is in a human-readable text file format.

The SANS Q-Range Planner is deployed among the instrument suite. At different instruments, the calculation incorporates different instrument component details (e.g., detector, collimator, and beam trap details) but with an almost identical high-level interface for users (Figure 6). We also added beam center enhancement so users could more accurately calculate Q-ranges based on the specified beam center on the 2D display, instead of always pretending the beam center is at the previous physical detector center. The beam center coordination and calculated Q-range details are captured in each Q configuration file, reused at the run time for live displays (see Section 4.4), and saved in each data file to be used by the data reduction software. To meet the different needs and access privileges of external users and instrument scientists, two instances of the Q-Range Planner are running on each instrument. One is dedicated to use by instrument scientists to establish new standard configurations. The other is embedded within Panel Scans (see Section 4.3) to flexibly deal with sample- or experiment-dependent minor modifications, as well as to collect data with more than one Q-range configuration. Q-range configurations can also be easily set like a simple variable by using other user operation interfaces such as Scripting Tool and the generic Table Scan. With Q-Range configurations generated by the planner, a soft matter simulator (Figure S7) has been developed for generating typical model scattering curves with realistic instrument factors such as flux at different neutron guide numbers, Q resolutions, etc., to further aid users to plan an experiment.

**Figure 6.** The Q-Range Planner.

#### *4.3. User Operation Interfaces: Panel Scans and Scripting Tool*

The previous EPICS/CS-Studio system included a generic Table Scan feature that used a spreadsheet to set up a list of scans for batching measurement. However, the intuitiveness and ease of use of the generic Table Scan was limited. To provide more functional and straightforward interfaces to different users, we implemented a wizard-like GUI-based tool, Panel Scans, and a Scripting Tool with an improved scripting middle-layer library. Both can use the Q-range configurations generated from the Q-Range Planner introduced in the previous section.

The SANS Panel Scans tool was designed based on earlier development of the SPICE GUI extension and other EPICS high-level scanning tools. It uses one instance of the Q-Range Planner and a six-step workflow (Figure 7) to guide users through the complex measurement planning process. This includes (1) checking or modifying individual Q-range configurations; (2) selecting up to four Q configurations in any order; (3) configuring sample environment variables; (4) keeping track of sample- and buffer-related variables for each sample changer slot; (5) specifying the exposure time for each combination of sample changer slot/Q configuration/sample environment variables; and (6) expanding the entire batch with all the details into a tabulated scan list. The scan list can then be simulated and tested before execution, submitted to the scan server to execute, and saved as a file for future reference.

**Figure 7.** The workflow laid out by different tabs in the Panel Scans interface (details on the workflow tabs are presented in the Supplemental Materials).

The guided workflow and clear steps of Panel Scans not only make the complex process more manageable and less overwhelming for even new users, but also encapsulate many thoughtful features and improvements within an appropriate sub-task context. These features include a user-friendly table view and visualization of Q-ranges (see Figure 8), generation of scripting snippets with Q-range configurations selected (Figure 8), support for both pre-configured and ad-hoc sample environment variables for routine and innovative experiments, standardized sample- and buffer-related metadata variables (still with customizable tags for automatic background association), and accommodation of flexible expanding orders. The system also provides a high degree of freedom for users to set up and change their measurements. For example, if users choose to submit each table row as a separate scan job, they can use the upgraded scan monitor to reorder or delete and resubmit queued scan jobs. In addition, the "load sample" step provides customizable sample information to obligate users to keep track of variants among different samples, which encourages good practices for accurate supplementary information and enables future automatic background matching in data reduction.

Similarly, in Scripting Tool, the Scan Tools helper library also incorporates lessons learned from the legacy PyDAS, SPICE macros, and several early generations of homedeveloped EPICS Python middle layer libraries. It supports all Python features and additional scan-specific commands to be executed by scan server. The scientists worked together with the developers to ensure that the scripting library is easy to use for users

with different levels of programming experience (see Figure 9). Other improvements that come with this implementation include converting other existing high-level scanning tools to use the same helper library, simplified and enhanced device configuration (including limits checking), and a simple screen for submitting scripts.

**Figure 8.** Friendly table view and visualization of Q-ranges (highlighted by the upper red rectangle box), scripting snippets generating with Q-range configurations selected (lower red box). The example shows up to four Q setups at the same time.

**Figure 9.** An example of the Python script used in Scripting Tool to control the magnet, cryostat temperature, and polarization for a highly flexible experiment.

#### *4.4. Event Data Mode and New Features Related to Live Displays*

One of the results of this upgrade is that it enables the production of event mode data for the whole DAS, especially the detectors at HFIR. Event mode imposes timestamps on neutron events detected and on other instrument systems, such as fast sample environment devices, enabling convenient post-data collection filtering. This is very useful in experiments that involve time-resolved kinetic studies, stroboscopic methods, setting up a time-of-flight source at HFIR with choppers, or simply detecting sample deterioration over time. At SNS, "event mode" refers to the pulsed accelerator timing signal; however, as a continuous neutron source, HFIR did not have an inherent timing signal as a reference. Therefore, a time server was set up for HFIR instruments; and Network Time Protocol [19] is used to synchronously timestamp events at an instrument, including neutron events and fast sample environment data, at a precision of within 1 ms. The system was made compatible with SNS event-based acquisition, which uses a timestamping rollover of 16.6667 ms, corresponding to the 60 Hz pulse rate. In other words, the events are grouped by their occurrence in the 16.6667 ms windows, and as such are saved in data files in Nexus format. For reactor-based instruments such as Bio-SANS and GP-SANS, the event timestamps are merely additional information about the neutron events and can be ignored if only the histogram of the data is used. No other changes or additions at HFIR SANS instruments are needed for event-based acquisition, as the relevant features had already been developed and tested on the SNS instruments. In Figure 10, an example of event mode data shows the rapid precipitation that occurs in a novel high strength, high ductility alloy that occurs as the sample was heated from room temperature to 700 ◦C from an experiment on GP-SANS [20]. The size and number density of the precipitates mediates the balance between strength and ductility in this family of alloys. The SANS data can detect such precipitates in-situ. This data was collected in the event mode in a single data set and was processed post-experimentally to split it up by 60-s interval during the temperature ramp and ten 300-s intervals for the annealing temperature. Moreover, the post-experiment process can be flexible depending on samples and sample conditions. The new capability also allows the visualization of sequential defined-time snapshots of the event mode data to aid real-time experimental evaluation and planning.

We have been using EPICS area detector driver for neutron event data (ADnED) and dynamicMapping (a Python tool for live data conversions) [9] to provide meaningful 2D and 1D live displays on other instruments. There are three related new features on SANS that may be worth mentioning. First, an ImageSnapshot Python tool has been deployed on the SANS instruments (and a few other instruments), which saves snapshots of histogrammed 2D detector images at the end of a run. These images are further parsed and ingested in the data catalog database for a quick view of measurement data. They can also be used for quick data reduction without reading the large HDF5 files if event mode data is not needed. Second, by updating dynamicMapping and saving and reusing beam center information in Q-range configurations, we were able to fully automate dynamicMapping to update mapping files for live d-spacing and Q conversions and for cursor display on the histogrammed 2D detector images. Third, a prototype Python tool, SANS-AutoRebin, has been developed to bin the data integrated by ADnED into log bins using default Q-range configurations, normalize log-binned data according to the number of pixels contributing to each log bin, and then scale the normalized data to beam monitor counts or run time. This prototype tool is part of our effort to provide users with live, coarsely reduced data for better steering of their experiments during the run time.

**Figure 10.** The SANS data processed over the time course of an experiment showing the rapid precipitation in a novel alloy under heating. On the left side, each slice along Q is a traditional Intensity vs. Q curve in SANS (the intensity is in cm<sup>−</sup>1). On the right side is the temperature of the sample environment over time, also recorded as the event mode data.

#### **5. Conclusions**

Our new IC-DAS is based on EPICS, an open source distributed control system environment that has been widely adopted by other large scientific facilities. The systems completed commissioning at all SANS instruments by early 2020; an example data using the event mode is shown in Figure 10. With carefully designed and developed features to meet the needs of the SANS user communities, our new system provides a uniform user experience across the ORNL SANS neutron scattering instrument suite. Collaboratively, we designed and delivered a system that reflects a deeper understanding of our diverse user needs, instrument configurations, and complex experiment processes. Easy-to-use tools and more automation result in less cognitive load, confusion, stress, and human error for novice users, and more efficient use of beam time with higher-quality measurement data. The flexible architecture of the new system can handle the ever-increasing complexity of modern instruments. It also provides a manageable solution for integrating existing and new sample environment equipment across all SANS instruments. We hope the enhanced capabilities of our new system and the improved user experience it enables can also benefit other, similar instruments by improving operation for more productive scientific discovery.

**Supplementary Materials:** The following are available online at https://www.mdpi.com/2076-3 417/11/3/1216/s1, Figure S1: Check Q Setups tab in the Panel Scans interface. The yellow outline highlights buttons are required to be clicked to ensure the output parameters to be calculated. Figure S2: Select Q Setups tab in the Panel Scans interface. Figure S3: Sample Environment Devices tab in the Panel Scans interface, for selecting specific sample environment for the current experiment. "Use other device combination:" will reveal the text input box to type a comma separated parameter

names or aliases. Figure S4: Load Samples tab in the Panel Scans interface, for more specific sample information. Figure S5: Specify Exposure Time tab in the Panel Scans interface to setup measurement time or detector count at different configurations and samples. Figure S6: Expand and Submit tab in the Panel Scans interface. It expands the scans in different ways with all conditions from previous setups (such as samples, sample environment, configurations, measurement type (transmission, scattering or both)), only part of the columns are shown in the screenshot. Figure S7: The soft matter simulator with instrument specific parameters

**Author Contributions:** Conceptualization, X.Y., L.D.-S., R.G., G.G., S.H., L.H. (Lilin He), R.K., K.L., S.V.P., S.Q., and V.U.; funding acquisition, G.T.; investigation, M.B., L.D.-S., X.G., R.G., G.G., M.H., S.H., L.H. (Lilin He), L.H. (Luke Heroux), K.K., J.K., C.L., K.L., M.P., S.V.P., C.P., S.Q., M.R.-R., V.S., and K.V.; methodology, B.A., M.H., R.K., J.K., K.L. and, M.R.-R.; project administration, X.Y. and R.K.; resources, L.H. (Luke Heroux) and R.K.; software, X.Y., B.A., M.B., X.G., R.G., G.G., K.K., M.P., M.R.- R., V.S., G.T., and K.V.; supervision, S.H.; writing—original draft, X.Y. and S.Q.; writing—review and editing, X.Y. and S.Q. All authors have read and agreed to the published version of the manuscript.

**Funding:** This research was funded by US DOE Office of Science.

**Institutional Review Board Statement:** Not applicable.

**Informed Consent Statement:** Not applicable.

**Data Availability Statement:** Not applicable.

**Acknowledgments:** The authors thank all beamline teams and external users with whom we worked, database administrators Jeff Patton and Peter Parker, beamline scientists Changwoo Do, William T. Heller, Mark Lumsden, and all SPICE developers, all EPICS and CS-Studio developers, and all previous and current developers in the SNS Instrument Data Acquisition and Controls group. This project used resources at HFIR and SNS, DOE Office of Science User Facilities operated by ORNL. The Bio-SANS instrument is supported by the DOE Office of Biological and Environmental Research.

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

#### **References**


**3. Biocompatibility**

*Oral Diseases*

of deciduous teeth, specification, and repair of surgically and non-surgically root perforations. In addition to these indications, MTA can be applied in other clinical situations such as the coronal plug after endodontic obturation; in the repair of vertical root fractures; prior to the internal bleaching of the dental element, as temporary restorative material; repair of root perforations. Its properties have been modified in order to obtain the excellent properties already well established of the MTA such as biocompatibility, high pH, no reabsorption, increase of root resistance, low cytotoxicity, non-contracting, and chemical stability in an endodontic sealant cement of root

Bioceramics are currently represented through restorative materials in the field of oral health, more precisely in endodontics. These bioceramic nanoparticulate cements have three presentations: the EndoSequence Root Putty Putty (ERRM Putty), in dense form; EndoSequence Root Repair Material Paste (ERRM Paste), which comes arranged in a syringe by having fluid constitution; and more recently,

For the closure of dentinal tubules, the use of bioceramic cements has been widely indicated. This material homogeneously seals the voids between the obturator material and the dentin walls. Its bioactivity favors bone repair and neoforma-

Among the clinical properties of endodontic cements are the endodontic repair capacity, and for this reason, it must be biocompatible, radiopaque, antibacterial, dimensionally stable, easy to handle, and should not be affected by blood contamination [11]. Some of the favorable properties of bioceramics in endodontics are their physical chemical properties, such as the release of Ca2+, pH, and radiopacity [12]. The bioceramic sealing ability is excellent, as it promotes satisfactory sealing [13, 14], as well as the capacity to increase the resistance of the sealed teeth [15]. It shows a greater adhesion to the root canals, which can be seen when the endodontic retreatments are present in the longer residues [16], requiring longer clinical work time [17]. According to manufacturers' specifications, bioceramics have antibacterial activity, alkaline pH, radiopacity, and excellent biocompatibility. Its physical, chemical, and biological properties are the main characteristics for its application in dentistry. It is a biocompatible material, non-toxic, and chemically stable in biological environment. This material also has the advantage of bioactivity, that is, it is capable of forming hydroxyapatite during the hardening or prepping process, exerting influence on the bond between the dentin and the obturator material. Besides, it hardens when exposed to a humid environment, making the local dentinal tubules ideal, since the water from inside the tubules causes the cement to hydrate, promoting the reduction of the solidification time that results in the

According to Trope et al. [8], the various forms of bioceramics are similar in composition (calcium silicates, zirconium oxide, tantalum oxide, and monobasic calcium phosphate), having excellent mechanical, biological, and manipulative properties. In addition to being hydrophilic, they are also insoluble, radiopaque, and aluminum free. The working time is over 30 min and the holding time is around 4 h under normal conditions, depending on the amount of moisture available. EndoSequence BC RRM Fast Set Putty cement has been recently launched presenting all the properties of the original product, but with a formula that promotes a

canal that is easy to work inside the root canals [5].

EndoSequence Root Repair Material [6–9].

tion by interacting with periapical tissues.

**2. Clinical properties**

formation of hydroxyapatite [1].

faster setting time (approximately 20 min).

**180**

During endodontic obturation, the cements come into contact with the periradicular tissues, which lead to the risk of a possible systemic toxicity [12], hence the great importance of biocompatibility. The materials EndoSequence Root Repair Material (ERRM) [11, 12], BioAggregate and iRoot [18], ProRoot MTA and MTA-Angelus [12], and EndoSequence BC sealer [19, 20] showed acceptable biocompatibility, not having induced critical cytotoxic effects [21].

Giacomino et al. [10] conducted a study to compare the biocompatibility and osteogenic potential of EndoSequence BC Sealer (Brasseler, Savannah, GA) and ProRoot ES (Johnson City Dental Specialties, Johnson City, TN) compared to Roth (Roth International, Chicago, IL) and AH Plus (Dentsply DeTrey). A precursor murine osteoblast lineage (IDG-SW3) was exposed to various concentrations of each of the cements for 7 days. Biocompatibility was determined by luminescence assay based on the quantification of adenosine triphosphate (Cell-Titer-Glo [Promega, Madison, Wisconsin]). The osteogenic potential was determined by fluorescence microscopy of the expression of DMP-1. Data were analyzed with bidirectional analysis of variance or univariate analysis of variance with the post hoc Bonferroni test. Both bioceramic cements have excellent biocompatibility even at high concentrations. On the other hand, cell death was detected when Roth and AH Plus were used in concentrations 100× lower than the bioceramic groups. It is important to note that both bioceramic cements significantly increased osteoblastic differentiation, although greater responses were observed with the EndoSequence BC Sealer. Concerning these results, DMP-1 expression, robust increase of osteogenic gene expression, and superior mineral deposition were shown. Osteoblastic differentiation and function were significantly impaired when Roth cement or AH Plus was used. Therefore, they concluded that the EndoSequence BC Sealer and ProRoot ES were significantly more biocompatible and promoted osteoblastic differentiation, a bioactivity not found in AH Plus and Roth cements.


*4.2. Sending Unit*

Figure 4 shows a more detailed sketch of the optical layout inside the sending unit. The cw-QCL (Alpes Laser, 40 mW) with emission wavelength around 4.85 μm is housed together with a collimator in a sealed laser mount. The laser is fixed on a home-made watercooled heat sink. A second cw-QCL (Hamamatsu L12004-2190H-C, 20 mW, Herrsching, Germany) with an emission wavelength centered around 4.56 μm is packaged in a sealed laser mount on a water-cooled heat sink (Hamamatsu A11709-02). An aspheric ZnSe lens (Hamamatsu, A11331-02) inside a precision zoom unit (Thorlabs, SM1ZM) can also be translated perpendicular to the laser beam path (*z* direction) with an *xy* translation mount (Thorlabs, ST1XY-D). The QCLs are thermoelectrically cooled with Peltier elements. To attain stable performance, the heat sinks are connected in series and water cooled with a chiller (Thermo Fisher Scientific, 200 W, Waltham, MA, USA). Two free-space adjustable narrowband optical isolators (Thorlabs, I4500W4 and I4730W5) are used to shield the lasers from back reflections, thus reducing intensity noise and mode hopping. The free beams from both lasers are collinearly combined on a narrowband filter (C1, Spectrogon, NB5040-155 nm, Täby, Sweden) that is used as a long-pass beam combiner transmitting the 4.85 μm beam and reflecting the 4.56 μm beam. In comparison with a 50:50 beam splitter, this filter achieves better overall optical efficiency of 75% for each beam. However, the filter may cause ghosting and beam distortion due to etaloning and limited surface optical quality, respectively.

**Figure 4.** Optical layout of the sending unit.

For the water transitions, two polarization-maintaining (PM) fiber-coupled DFB (distributed-feedback) lasers with emission wavelengths of 1.392 and 1.469 μm (NEL, 20 mW) are clamped into butterfly laser mounts (Arroyo, 203) and thermoelectrically cooled with Peltier elements. For a compact arrangement, they are stacked onto a platform mounted above other optical elements. To prevent damage of long sections of optical fibers, they are coiled into storage reels (Thorlabs, FSR1) separately fixed on the cover. With two 1 × 2 PM fiberoptic couplers (Thorlabs, PN1310R2A1 and PN1480R2A1), the two beams at 1.39 and 1.47 μm are separately split into two PM fractions of 90% and 10% each. To monitor the mode quality and to obtain frequency markers to convert scan time to relative wavelength, the 10% beams are combined using a single-mode (SM) fiber wavelength-division multiplexer (Thorlabs, WD202C-APC, WP9850A) and together propagate through a SM fiber-coupled interferometer (Micron Optics, FFP-I, 0.8 GHz). Its output is collimated and free-space recorded by an amplified InGaAs photodetector (Thorlabs, 0.8 mm2 active area, max. 11 MHz bandwidth). In front of the detector, two narrow bandpass filters (Thorlabs, FB1400-12 as N1, FB1480-12 as N2 in Figure 4) are placed in a dual-position filter holder slider with resonant piezoelectric motors (Thorlabs, ELL6K) such that either 1.39 or 1.47 μm can be selected for etalon analysis to determine the wavelength tuning characteristics. The motor of the filter slider is remotely controlled during the measurement.

The 90% beams are combined using another PM wavelength-division multiplexer (Thorlabs, WP9850A). The coupled beams with the two NIR wavelengths are collimated with an aspherical lens (Thorlabs, C280TMD-C) in a fiber-launch system (Thorlabs, KT110/M). They are subsequently combined with the two collinear propagating MIR beams by a second bandpass filter (Spectrogon, BP-4700-600 nm) operated as combiner C2, with most of the MIR beams being transmitted and the NIR beams reflected off the front surface. For wavelength and tuning characterization, a fraction of the incoming free-space MIR beams is reflected towards a solid germanium etalon and a reference absorption cell, respectively. The detectors record each laser beam through respective narrow bandpass filters (Laser Components, SNB-4860-001793 as M1 and Spectrogon, NB4560-135 nm as M2 in Figure 4) installed in another dual-position filter slider equipped with a resonant piezoelectric motor (Thorlabs, ELL6K). The filtered beams are split into two parts: One passes through the low-pressure reference cell to determine the spectral position of the narrow CO absorption line during a wavelength scan whose peak is identified with the absolute wavelength listed in the HITEMP database. The other part is guided to the 7.62-cm-long solid Ge etalon (free spectral range (FSR) 0.016 cm<sup>−</sup>1). The known fringe spacing enables conversion of tuning time into relative laser wavelength. Information from both channels can be combined to convert scan time into absolute wavelength.

All collinearly combined NIR and MIR beams are then steered to a parabolic mirror (Thorlabs) and are focused into a SM MIR InF3 patch fiber (Thorlabs, P3-32F-FC-1). Due to its small numerical aperture of 0.26, a six-fold reflective beam telescope (Thorlabs, BE06R/M) reduced the beam diameter to approximately 100 μm. This telescope significantly reduced the coupling losses in the fiber. By precisely adjusting the positions of the collimators in front of the PM fiber outputs of the NIR lasers (and the QCL at 4.56 μm), the difference in beam divergence of the four collimated beams can be slightly minimized to achieve better focus quality and reduce coupling losses.

All four lasers are driven by individual controllers (Arroyo, 6300 series) including temperature stabilization and injection current control. The optical setup is installed on a water-cooled aluminum optical board (450 × 600 mm2, Thorlabs), which rested on four aluminum profiles with passive vibration isolators (Figure 5). Below this plate, two fans provided air circulation within the unit. The remaining space accommodated controllers and power supplies for lasers and detectors.

**Figure 5.** Side view of the sending unit mounted on an optical breadboard with the laser controllers below.


**4.1.3 Clinical assessment of severity**

The timing of the click, the extent of splitting of the second sound, the intensity of the pulmonary component of the second sound, the length (duration) of the murmur, and timing of peaking of the systolic murmur are usually suggestive of the severity of pulmonary valve obstruction (Figure 2) (Rao 1991b, Vogelpoel & Schriere 1960).

The loudness of the ejection systolic murmur does not indicate the severity of obstruction but rather its duration and time of peaking; the longer the murmur and the later it peaks, the more severe is the PS. Similarly, the shorter the time interval between the first heart sound and ejection click, the wider the splitting of the second heart sound, and softer the pulmonary component, the more severe is the degree of pulmonary valve obstruction (Rao 2000).


**11. References**

Engrand, P. (1997). A multi-objective approach based on simulated annealing and its application to nuclear fuel management, *5th International Conference on Nuclear Engineering*, Nice, France, pp. 416-423.

<sup>\*</sup> Corresponding Author

Chu, C.L., Xue, X.Y., Zhu, J.C. & Yin, Z.D. (2006). In vivo study on biocompatibility and bonding strength of hydroxyapatite-20 vol%Ti composite with bone tissues in the rabbit. *Biomedical Materials and Engineering*, Vol.16, No. 3, pp. 203-213.

236 Simulated Annealing – Single and Multiple Objective Problems

This chapter presents some important findings for Functionally Graded Material (FGM) dental implant design in a power law configuration. This is vital for maintaining the overall health of the bone tissues. The research clearly suggests that, a better performance in bone turnover can be achieved by lowering the FGM dental implant material gradient. However, this will at the same time reduce the stiffness of implantation, consequently placing the

The problem may be solved by the multi-objective optimization method. The Pareto front was determined using the Suppapitnarm Multi-Objective Simulated Annealing (SMOSA) optimization procedure. The results obtained from the SMOSA confirm the results obtained by the Response Surface Methodology (RSM), in addition to offering further improvements. The SMOSA optimized the objective functions on a wider data range than RSM and offered better results with respect to the cortical density function (almost 33% more than RSM). SMOSA optimization in this case, gives more selection of material gradient (*m*) for designing FGM dental implant compared to RSM. The material gradient varies from 0.1 to 0.65 given

By considering the point that the scale in the FGM dental implant is in micro, the importance of accuracy in optimization of the FGM implant is understood. The design of FGM gradient parameter is expected to maximize the densities (cortical and cancellous) and minimize the displacement and plays a more important role in the design methodology. However, sacrifice may be made when the third criterion of displacement is introduced, which means that an optimal gradient *m* for bone remodeling may not be the best for stiffness. It is expected that the design methodology can produce more favorably patient specific implant, better improving the immediate and long-term restorative outcomes.

The authors would like to acknowledge for the Ministry of Higher Education of Malaysia and the University of Malaya, Kuala Lumpur, Malaysia for the financial support under

Engrand, P. (1997). A multi-objective approach based on simulated annealing and its application to nuclear fuel management, *5th International Conference on Nuclear*

bone-implant interface at higher risk of damage during the early healing stage.

**10. Conclusions**

by SMOSA.

**Author details**

**Acknowledgement**

**11. References**

Corresponding Author

 \*

Ali Sadollah and Ardeshir Bahreininejad\*

UM.TNC2/IPPP/UPGP/628/6/ER013/2011A grant.

*Engineering*, Nice, France, pp. 416-423.

*Faculty of Engineering, University of Malaya, Kuala Lumpur, Malaysia*

Suman, B. & Kumar, P. (2006). A survey of simulated annealing as a tool for single and multi-objective optimization. *Journal of the Operational Research Society, Vol. 57, pp. 1143- 1160.*

**Chapter 12**

© 2012 Sheng and Takahashi, licensee InTech. This is an open access chapter distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use, distribution, and reproduction in any medium, provided the original work is properly cited.

© 2012 Sheng and Takahashi, licensee InTech. This is a paper distributed under the terms of the Creative Commons Attribution License (http://creativecommons.org/licenses/by/3.0), which permits unrestricted use,

distribution, and reproduction in any medium, provided the original work is properly cited.

**A Simulated Annealing Based Approach**

The optimization techniques for integrated circuit (IC) layout design are important. Generally speaking, the basic process of modern hardware engineering includes designing, manufacturing and testing. IC layout is an inevitable stage of designing before manufacturing. There are many applications which are directly related with layout optimization in practice, such as floor plan for very-large-scale integration (VLSI) design, placement for printed circuit board (PCB) design, packing for logistics management, and so on. In this research, we mainly focus on the optimization for three layout problems, which are 2D packing, 3D packing and 2D placement. The 2D/3D packing is to position different modules into a fixed shape, normally rectangular one, with area or volume minimization. The placement can be regarded as the packing problem with interconnect optimization. Since a general placement problem is NP-hard, there are no practical exact algorithms so far to be sure to find optimal solutions. As an alternative to get the optima, heuristics [1-6] are

As product size keeps shrinking, product lifecycle keeps shortening and product complexity goes up, more electronic components will be integrated into a smaller IC chip or PCB with higher density and shorter time to market. At the same time, multi-objective optimization is common for IC/PCB layout in real product design, so another difficulty is the trade-off between conflicting objectives, such as low power and high performance. Pareto improvement for multiple objectives is one of the biggest challenges we have to face nowadays. The layout problem becomes much harder to find near-optimal or even acceptable solutions with high requirements. In order to improve the best cases and mitigate the worst cases of IC/PCB layout, it becomes increasingly critical and urgent to improve the

Simulated annealing based algorithm with a good representation for 2D/3D packing is one of the most popular ways to improve the quality of solution. On the one hand, many

**to Integrated Circuit Layout Design**

typically used to find near optimal solutions within a given runtime.

quality of solution and reduce runtime.

Yiqiang Sheng and Atsushi Takahashi

http://dx.doi.org/10.5772/51126

**1. Introduction**

Additional information is available at the end of the chapter

**Chapter 12**


*3.1.5 Modeling erosion using GIS*

The application of GIS in erosion models enables the storing of georeferenced data, computation of input parameters for multiple scenarios, geographical analysis of modeling outputs, and effective display. GIS is utilized for the statistical analysis and modeling of erosion processes found in remote sensing data. In the early 1990s, the Geographic Resource Analysis and Support System (GRASS) provided an environment for creative work on the integration of GIS with hydrologic and erosion modeling [72, 73]. The primary use of geospatial erosion models is found in the fields of agriculture, soil conservation, minimization of silt contamination, and sustainable military management as evidenced by various sources [64, 74]. One of the initial hillslope erosion models utilized in GIS was the Universal Computing Soil Loss Equation (USLE), which was applied to analyze the impact of recent wildfires on forests and hillslopes [75]. Furthermore, topographic parameters derived from digital elevation models (DEMs) are extracted to support the analysis. Moore and Wilson come next, then Moore and Birch [73, 76], set the stage for USLEAP-Applications to Landscapes with Complex Topography and the connection between unit flow power theory and USLE. The popularity of this strategy has led to multiple USLE installations supported by GIS for challenging topographic settings [77, 78]. Among the more recent GIS applications of USLE coverage area size, large watersheds with mapped land cover from remote sensing images are just a few examples [79–82].


*1.2. Stealth False Data Injection (FDI) Attack*

A stealth attack is a special type of attack that bypasses the PSSE technique test. The residual test is not able to detect a stealth attack. This attack is also known as an unobservable attack or undetectable attack. In a stealth attack, the Jacobian matrix **H** is fully known to the attacker. **H** is used for the construction of an undetectable attack. Stealth false data injection (FDI) is given as follows [17,19,22,28–30]:

$$\mathbf{z\_a} = \mathbf{z} + \mathbf{a} \tag{9}$$

where **a** represents the vector of false data that is added to the measurement vector **z**. The attacker hacks the data from the communication line and injects the attack vector **a** into it, where **a** = **Hc**.

The attack is done on the communication line by the attacker and all measurements of power are hacked. The Jacobian matrix **H** is determined with the help of those measurements of power. The whole power system topology can be understood with the help of **H**. The dependence of one power value on the other powers can be found using **H**. This leads the attacker to make an undetectable attack. In fact, it tells the attacker which specific values of power the attacker will have to change with one particular change in power. To understand the whole power network, the formation of the Jacobian matrix **H** is the most important component. The vector **c** is multiplied by matrix **H** and the resultant is added to the actual measurements when undertaking a stealth attack.

The stealth attack is executed against the PSSE in the power network and that is the attack of injecting false data into the system measurements. The state estimation technique is bypassed by the stealth attack [31]. In case of an attack, the estimated state becomes:

$$\hat{\mathbf{x}}\_{\mathbf{a}} = \left(\mathbf{H}^{\mathrm{T}}\mathbf{R}^{-1}\mathbf{H}\right)^{-1}\mathbf{H}^{\mathrm{T}}\mathbf{R}^{-1}\mathbf{z}\_{\mathbf{a}} \tag{10}$$

$$
\hat{\mathbf{x}}\_{\mathbf{a}} = \hat{\mathbf{x}} + \mathbf{c} \tag{11}
$$

The estimated state is changed in the case of a stealth attack. Now, the estimated state is equal to the original estimated state plus the addition of a constant vector **c**. It is assumed that **c***~N(*0,*σ*2 *c )*, where the false state variance is represented by *σ*2 *c* .

$$\mathbf{z\_{a}} = \mathbf{H}\mathbf{x} + \mathbf{e} + \mathbf{a} = \mathbf{H}\mathbf{x} + \mathbf{e} + \mathbf{H}\mathbf{c} \tag{12}$$

$$\mathbf{z\_a = Hx\_a + e} \tag{13}$$

Therefore, the attack changes the state of the power system. The technique used in the system for bad data detection is bypassed by the stealth false data injection attack in this way:

$$\mathbf{r\_a = z\_a - H\hat{x}\_a = r} \tag{14}$$

The attacked residual is represented by **ra**. In the attack, the attacked residue is the same as that of the normal residue. Therefore, the technique of bad data detection using residue is bypassed by this attack and the defender is not able to detect the stealth attack.


Section 3

Macular Degeneration and Diabetic Retinopathy: Visual Impairment, Pathophysiology, Clinical Features, Imaging/ Management and Treatment

**135**

**Chapter 8**

Action

**1. Introduction**

fied in AMD.

**2. The RPE: at the core of AMD disease**

*Giuseppe Lo Giudice*

Introductory Chapter: Macular

Degeneration: Mechanisms of

Macular degeneration refers as a progressive condition in which patients are suffering of a disease that is a leading cause of blindness in the elderly worldwide. In particular age-related macular degeneration (AMD) is characterized by two forms, wet and dry, that are classified on the presence or absence of new blood vessels (CNV) [1]. However, there is emerging evidence that significantly overlap which exists in the underlying pathogenetic mechanisms of these clinical conditions. Clarification of the overlapping process that lead to wet and dry diseases will be crucial for the future development in the prevention and treatment of AMD.

By definition, early AMD is characterized by confluent regions of drusen, which are multicomponent, heterogeneous aggregates that lie both external and internal to the retinal pigment epithelium (RPE) cells [2–3]. They are located primarily at the macular region of the retina with relative sparing of the surrounding peripheral retina. A slow growth of drusen occurs over years or decades with RPE cell death and synaptic dysfunction during the advanced stage of AMD, with the development of advanced AMD (CNV or geographic atrophy (GA)) [4]. All we know about pathogenetic mechanism underlying AMD is that it has the RPE as the fulcrum of AMD pathogenesis. However, whereas, the stepwise development of certain maladies is relatively well-defined, no such hallmarks of disease progression have been identi-

The RPE is the fulcrum of AMD pathogenesis. In general, in spite of interindividual heterogeneity, RPE dysfunction and atrophy precedes the final stages of AMD [5–6]. The RPE cells integrate numerous stimuli to regulate its own health, while also receiving and broadcasting signals to and from the retinal microenvironment. There are several human AMD samples displaying significant interindividual variation in RPE transcript expression, which supports the concept that heterogenic stress responses underlie a categorical AMD phenotype. The effect of specific AMD-associated stresses and AMD in retinal molecular composition have been cataloged by mean genome-wide stress-response transcriptome and proteome assays on whole-genome RPE gene. Such studies reveal common protective and deleterious RPE gene responses that could clarify the key molecular basis of the disease. One of the most important evidences involved in the AMD pathology is the crosstalk of RPE with immune and vascular system. Indeed there are numerous overlapping proangiogenic mechanisms that underlie AMD, many of which involve


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Color Detection http://dx.doi.org/10.5772/intechopen.74906 Edited by Ling-Wen Zeng and Shi-Lin Cao

## Contributors

Volkan Kılıç, Nesrin Horzum, Mehmet Ertugrul Solmaz, Mikhail Dolomatov, Natalia Yu. Grigoryeva, Ludmila Chistyakova, Amanda Santos, Luiz Antonio D'Avila, Gisele Borges, Kissya Da Silva, Lingwen Zeng, Shilin Cao

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