*1.2. Contributions*

In this work (an extension of [14]), we have proposed and implemented the Consumer to Fog to Cloud (C2F2C) based framework for efficient resource management in residential buildings. Our main contributions are described as below:

The remainder of the manuscript is summarized as follows. In Section 2, literature review is discussed along with the existing limitations. Section 3 describes the proposed system model and simulation results are elaborated in Section 4. Finally, the conclusion and future challenges are described in Section 5.

#### **2. Literature Review**

In order to subsist the intensive demand of the electricity, different methodologies have been studied from existing work. These methodologies are categorized based on their proposed architectures: (1) methodologies for cloud based architecture and (2) methodologies for fog based architecture. So, the techniques for energy management in SG using cloud computing have been discussed first then the techniques for energy management in SG using fog computing have been discussed.


**4. Discussion**

In patients with STS, [18F]FDG-PET/CTs are often acquired for staging. Next to the identification of metastatic lesions, these scans provide quantitative information on the metabolic activity of the tumor tissue. The results in this study show that this biological characteristic has a prognostic value and turned out to be an independent predictor of overall survival in the soft tissue sarcoma patient group with metastatic disease. This information on tumor biology adds to the already known prognostic clinical parameters reported in the literature by Billingsley et al., Italiano et al., and Lochner et al., such as patient age, disease-free interval, number of lesions, FNCLCC grade, and histologic subtype [4–6].

In a systematic search that was conducted in preparation of this study, no report was found on the value of [18F]FDG-PET features in metastatic STS patients (Appendix A), while prognosis is especially relevant in a cohort where cure might not be the primary goal of treatment. The prognostic value of [18F]FDG-PET features in non-metastatic STS is studied more extensively. Original investigations focusing on this topic in patients with localized disease have found [18F]FDG-PET features to be significantly predictive for progression-free and overall survival [20–23]. Nevertheless, in some of these studies, the added value of the features is not corrected for clinical parameters, such as resectability of the tumor, neoadjuvant treatment, etc., leaving the effect of [18F]FDG-PET features difficult to interpret on an individual level. In studies performing multivariate analyses, results are variable and partly clouded due to the limited statistical power caused by small cohort sizes [24–26].

In the current study, the overall survival of the whole cohort was comparable with survival in recent larger studies, suggesting the current study population is representative, and our findings might add to the ability to accurately predict survival in patient with metastases from STS [4,5]. Our results show both SUVmax and SUVpeak to have prognostic value, and therefore, are in line with the results in patients with localized disease. For SUVmean, TLG, and MTV, however, studies in localized STS patients typically find significant correlations with overall survival, while no predictive value was found in the metastatic cohort in our study [22,23,25]. Partially, this could be caused by the limited cohort size. Another plausible reason for this discrepancy is found in the composition of these features and the biological background they resemble. All [18F]FDG-PET features investigated in this study, i.e., SUVmax, SUVpeak, SUVmean, TLG, and MTV, quantify the metabolism in selected tumor tissue, but SUVmean and inherently TLG and MTV are strongly dependent on tumor size next to metabolism and thus altered after resection of the primary tumor. In contrast, SUVmax and SUVpeak are not dependent on lesion size and thus resemble the metabolic potential of tumor cells accurately, even after surgical volume reduction. Thus, the results suggest that the prognosis of a metastatic STS patient is determined by the most aggressive tumor clone in the body.

Research in other tumor types, such as breast, colorectal, and lung carcinoma, also shows added prognostic value of [18F]FDG-PET features next to clinical parameters in cohorts of patients with metastasized disease [16,27,28]. In contrast with the current results, TLG and MTV generally also show a correlation to survival in these cohorts. An explanation for this discrepancy is the relative heterogeneous population in our study, including both patients with synchronous diagnosis of the primary tumor and metastasis and patients with diagnosis of metastasis after resection of the primary tumor. In addition, differences in tumor biology, such as pattern and interval of spread, might cause deviation between results in different tumor types.

A strength of this study is the use of a multivariate analysis to determine the added value of the PET features in addition to prognostic clinical parameters that are readily available. This multiparametric analysis showed that both SUVmax and SUVpeak provide prognostic value, next to the two strongest predictive clinical characteristics. Furthermore, the [18F]FDG-PET scans are often performed in standard clinical practice for staging of disease, and therefore, the features can be determined without extra costs and distress for the patients [7]. There are some limitations to this study. Due to the retrospective nature, the performance status of some patients could not be determined accurately. Moreover, the limited cohort size and the heterogeneity in tumor subtypes prohibited definitive conclusions about [18F]FDG-PET features when correcting for all known clinical parameters. In this regard, especially the link with the wide variety of histological subtype remains unexplored to some extent.

In larger studies investigating prognosis in metastatic STS patients, correlations with subtype are typically found [4–6]. These results are, however, partly contradicting regarding which subtypes are causing poor survival rates. With metastatic leiomyosarcoma as a reference, both Italiano et al. and Lochner et al. concluded that patients with metastatic undifferentiated soft tissue sarcoma or malignant peripheral nerve sheath tumors have an impaired survival but reported conflicting results regarding liposarcoma and synovial sarcoma patients [4,5]. This leads to the conclusion that a correlation between histologic subtype and survival in metastatic patients exists but is difficult to define. Several reasons for this complexity are rarity of subtypes, heterogeneity within sarcoma subtypes, and shifts in histologic definitions of subtypes over the years. In the current study, the biological differences between histologic subtypes might have amplified the predictive value of quantitative [18F]FDG-PET features on survival. In literature, relatively aggressive subtypes, such as undifferentiated soft tissue sarcoma, are found to show high FDG avidity. Other specific subtypes, such as (myxoid) liposarcomas, tend to show relatively low avidity [29,30]. Nevertheless, these studies report varying and non-specific SUVmax values within subtypes, suggesting [18F]FDG-PET features could provide additional prognostic information. Figure 2 presents examples of differences in SUVmax between and within STS subtypes. Future studies validating the prognostic value of quantitative [18F]FDG-PET features in metastatic STS patients should aim to address the link with histologic subtypes.

Furthermore, the use of multimodality imaging should be considered in research aiming to identify more prognostic biomarkers in patients with metastatic STS. Magnetic resonance (MR) imaging is widely used for the characterization of localized soft tissue tumors. Quantitative diffusion-weighted imaging (DWI) and dynamic contrastenhanced (DCE) MR features are linked to tumor grade, response to treatment, and survival [31,32]. Multimodality imaging with [18F]FDG-PET/MR showed increased usefulness over [18F]FDG-PET alone in several studies on localized STS [33,34]. This raises the hypothesis that the addition of quantitative MR parameters to clinical and [18F]FDG-PET parameters could improve the characterization of tumor biology in patients with metastases from STS even further.

Personalized treatment in patients with metastases from STS is complex, and prognostic factors are important for multiple considerations during the development of treatment strategies. For example, factors linked to an impaired prognosis support the addition of chemotherapy to surgery in patients with resectable metastases. A high number of tumor lesions and a short recurrence-free interval are factors that are typically used for this purpose, as stated in the recent ESMO-EURACAN-GENTURIS guidelines [7]. The current study shows added value of [18F]FDG-PET features to these clinical factors. Moreover, in treatment strategies with a palliative intent specifically, periods without active treatment can be desirable to warrant the quality of life of patients. Prognostic factors are decisive in the timing of these treatment-free periods, as they are partly guided by the expected time to progression of disease [35].


*5.1.2 Event driven*

A controller only receives a message when a change of state occurs, so it is truly 'event driven' and it interprets each received event in the context of the source of the event message and the current state of other known information. The source of the message needs to know nothing about the functionality that lies behind the interface, allowing great flexibility for the controller designers to evolve and improve their design. This 'encapsulation' is a fundamental characteristic of the object orientated programming paradigm [35].

#### **5.2 Publication of a standard schema**

However, it was not until the introduction of functionality into Internet web pages, supported by tools and technologies based on XML, that it became practicable to publish a formal statement of the full set of terms and relationships necessary to define lift operation. The format of this publication is a single XML schema file written in XML Schema Definition (XSD) language that is at once readable by humans and a wide range of computer operating environments. It contains specifications of:

*Standard Elevator Information Schema: Its Origins, Features and Example Applications DOI: http://dx.doi.org/10.5772/intechopen.92552*

• aggregation relationships with optionality and multiplicity restriction rules, and every definition can be elaborated with textual documentation for the benefit of human readers.

So it was that in 2003 the knowledge from the earlier work was translated into an XSD – the Standard Elevator Information Schema – by Beebe and made publicly available [32] under the Collective Commons licence. It is hoped that through this mode of publication, discussion of the relative merits of any alternative schemas, should they be developed, would be limited to technical concerns thereby avoiding commercial and proprietorial considerations. The reference works of the day [36, 37] were analysed to identify significant nouns and their definitions that might form the atomic elements of the schema to ensure a wide-reaching scope. At the same time terms that were not directly relevant to lift operation were carefully excluded in order to avoid duplication or conflicts with schemas for other domains, currently available or potentially in the future. Based on the foundation elements and a more general survey of research across the field of lift traffic analysis, monitoring and simulation, further useful complex elements were identified and added to the schema e.g. demand profile, travel plan, door cycle, etc.

Two possible interaction modes were envisaged for SEIS at its inception (though a further mode was introduced with the application of REST transactions and more may be discovered as experience is gained):

This was the starting point for the root element types in the schema:2


**Premixed Combustion in Spark Ignition Engines and the Influence of Operating Variables**

Fabrizio Bonatesta

Additional information is available at the end of the chapter

http://dx.doi.org/10.5772/55495