Fractionated Stereotactic Radiotherapy in the Management of Dural Recurrence of Olfactory Neuroblastoma

PURPOSE/OBJECTIVE(S)

Treatment protocols for dural recurrence among esthesioneuroblastoma patients have not been standardized. We assess the outcomes of fractionated stereotactic radiotherapy (FSR) for patients with olfactory neuroblastoma (ONB) dura-based recurrences.

MATERIALS/METHODS

We identified ONB patients with dura-based recurrences treated with FSR after prior radiotherapy who were enrolled between 2013 and 2022 in our prospective head and neck reirradiation and skull base registries. In-field tumor control (within 2 cm of prescribed radiotherapy volume) and out-of-field tumor control (non-contiguous or contralateral dura, nodal, or distant metastases) were analyzed.

RESULTS

Thirteen patients with 28 dural lesions were included in this analysis. All patients were initially treated with surgery to their primary paranasal sinus disease; 69% with a craniofacial approach followed by adjuvant radiotherapy to a median dose of 63 Gy (range 60-72.4 Gy) prescribed to the resected tumor bed. Patients re-presented with dural recurrence at median 58.3 months (range 35.0 - 163.0 months) from completion of their initial treatment. Two patients underwent dural resections. On presentation of recurrence, 4 patients had 1 lesion treated, with a median of 2 lesions treated (range 1-4 lesions). All dural based tumors were treated with FSR to a median dose of 27 Gy in 3 fractions delivered QOD. 68Ga-DOTATATE PET/CT was utilized for FSR treatment planning in 31% of cases. The median follow up from FSR was 23.3 months (range: 13.1 - 51.6 months). The 1-year overall survival and progression free survival was 75% and 38%, respectively. The 1- and 2-year in-field control rate was 85% and 75%, respectively. Among treated lesions, 25 of 28 (89%) responded or remained stable following FSR. Two patients (3 lesions) had evidence of in-field radiographic progression at 17 and 9 months, respectively. Five patients (38%) experienced progression in the contralateral or non-contiguous dura, and 5 patients (38%) developed distant metastases. The overall out-of-field progression rate was 58% at 1 year. There was no grade 3 or higher toxicity observed. Three patients (23%) developed asymptomatic changes on MRI consistent with brain necrosis, all of which occurred in a previously irradiated region.

CONCLUSION

In the largest single institution study of FSR reirradiation for ONB dural recurrence to date, high local control rates with minimal toxicity are attainable. However, subsequent out-of-field dural recurrences and/or distant metastases remain problematic.

Novel domestic building energy consumption dataset: 1D timeseries and 2D Gramian Angular Fields representation

This data article describes a dataset collected in 2022 in a domestic household in the UK. The data provides appliance-level power consumption data and ambient environmental conditions as a timeseries and as a collection of 2D images created using Gramian Angular Fields (GAF). The importance of the dataset lies in (a) providing the research community with a dataset that combines appliance-level data coupled with important contextual information for the surrounding environment; (b) presents energy data summaries as 2D images to help obtain novel insights using data visualization and Machine Learning (ML). The methodology involves installing smart plugs to a number of domestic appliances, environmental and occupancy sensors, and connecting the plugs and the sensors to a High-Performance Edge Computing (HPEC) system to privately store, pre-process, and post-process data. The heterogenous data include several parameters, including power consumption (W), voltage (V), current (A), ambient indoor temperature (°C), relative indoor humidity (RH%), and occupancy (binary). The dataset also includes outdoor weather conditions based on data from The Norwegian Meteorological Institute (MET Norway) including temperature (°C), outdoor humidity (RH%), barometric pressure (hPA), wind bearing (deg), and windspeed (m/s). This dataset is valuable for energy efficiency researchers, electrical engineers, and computer scientists to develop, validate, and deploy and computer vision and data-driven energy efficiency systems.

Dual graph characteristics of water distribution networks-how optimal are design solutions?

Urban water infrastructures are an essential part of urban areas. For their construction and maintenance, major investments are required to ensure an efficient and reliable function. Vital parts of the urban water infrastructures are water distribution networks (WDNs), which transport water from the production (sources) to the spatially distributed consumers (sinks). To minimize the costs and at the same time maximize the resilience of such a system, multi-objective optimization procedures (e.g., meta-heuristic searches) are performed. Assessing the hydraulic behavior of WDNs in such an optimization procedure is no trivial task and is computationally demanding. Further, deciding how close to optimal design solutions the current solutions are, is difficult to assess and often results in an unnecessary extent of experiment. To tackle these challenges, an answer to the questions is sought: when is an optimization stage achieved from which no further improvements can be expected, and how can that be assessed? It was found that graph characteristics based on complex network theory (number of dual graph elements) converge towards a certain threshold with increasing number of generations. Furthermore, a novel method based on network topology and the demand distribution in WDNs, specifically based on changes in 'demand edge betweenness centrality', for identifying that threshold is developed and successfully tested. With the proposed novel approach, it is feasible, prior to the optimization, to determine characteristics that optimal design solutions should fulfill, and thereafter, test them during the optimization process. Therewith, numerous simulation runs of meta-heuristic search engines can be avoided.

Lightweight Gramian Angular Field classification for edge internet of energy applications

With adverse industrial effects on the global landscape, climate change is imploring the global economy to adopt sustainable solutions. The ongoing evolution of energy efficiency targets massive data collection and Artificial Intelligence (AI) for big data analytics. Besides, emerging on the Internet of Energy (IoE) paradigm, edge computing is playing a rising role in liberating private data from cloud centralization. In this direction, a creative visual approach to understanding energy data is introduced. Building upon micro-moments, which are timeseries of small contextual data points, the power of pictorial representations to encapsulate rich information in a small two-dimensional (2D) space is harnessed through a novel Gramian Angular Fields (GAF) classifier for energy micro-moments. Designed with edge computing efficiency in mind, current testing results on the ODROID-XU4 can classify up to 7 million GAF-converted datapoints with ~ 90% accuracy in less than 30 s, paving the path towards industrial adoption of edge IoE.

SUPPLEMENTARY INFORMATION

The online version contains supplementary material available at 10.1007/s10586-022-03704-1.

Modular interdependency analysis for water distribution systems

Complexity in water distribution systems (WDSs) poses a challenge for analysis and management of the systems. To reduce the complexity, the recent development of complex network science provides a system decomposition technique that converts a complex WDS with a large number of components into a simple system with a set of interconnected modules. Each module is a subsystem with stronger internal connections than external connections. Thus far, the topological features of the modular structure in WDS have been extensively studied but not the behavioural features, e.g. the hydraulic interdependencies among modules. Therefore, this paper aims to quantitatively measure and graphically visualize the module interdependency in WDSs, which helps understanding the behavioural complexity of WDSs and thus various WDS analyses, such as pipe maintenance, model calibration, rehabilitation, and District Metered Areas planning. Specifically, this study first identifies the WDS's modular structure then measures how changes in the state of one module (i.e. any single pipe failure or perturbed demand within each module) affect the state of another module. Modular interdependencies are summarized in an interdependency matrix and visualized by the digraph. Four real-world systems are analysed, and three of them shows low interdependencies among most of the modules and there are only a few critical modules whose status changes will substantially affect a number of other modules. Hence, highly interconnected topologies may not result in strong and complex module interdependency, which is a fact that simplifies several WDS analysis for practical applications as discussed in this paper.

Reliable, resilient and sustainable water management: the Safe & SuRe approach

Global threats such as climate change, population growth, and rapid urbanization pose a huge future challenge to water management, and, to ensure the ongoing reliability, resilience and sustainability of service provision, a paradigm shift is required. This paper presents an overarching framework that supports the development of strategies for reliable provision of services while explicitly addressing the need for greater resilience to emerging threats, leading to more sustainable solutions. The framework logically relates global threats, the water system (in its broadest sense), impacts on system performance, and social, economic, and environmental consequences. It identifies multiple opportunities for intervention, illustrating how mitigation, adaptation, coping, and learning each address different elements of the framework. This provides greater clarity to decision makers and will enable better informed choices to be made. The framework facilitates four types of analysis and evaluation to support the development of reliable, resilient, and sustainable solutions: "top-down," "bottom-up," "middle based," and "circular" and provides a clear, visual representation of how/when each may be used. In particular, the potential benefits of a middle-based analysis, which focuses on system failure modes and their impacts and enables the effects of unknown threats to be accounted for, are highlighted. The disparate themes of reliability, resilience and sustainability are also logically integrated and their relationships explored in terms of properties and performance. Although these latter two terms are often conflated in resilience and sustainability metrics, the argument is made in this work that the performance of a reliable, resilient, or sustainable system must be distinguished from the properties that enable this performance to be achieved.

Global resilience analysis of water distribution systems

Evaluating and enhancing resilience in water infrastructure is a crucial step towards more sustainable urban water management. As a prerequisite to enhancing resilience, a detailed understanding is required of the inherent resilience of the underlying system. Differing from traditional risk analysis, here we propose a global resilience analysis (GRA) approach that shifts the objective from analysing multiple and unknown threats to analysing the more identifiable and measurable system responses to extreme conditions, i.e. potential failure modes. GRA aims to evaluate a system's resilience to a possible failure mode regardless of the causal threat(s) (known or unknown, external or internal). The method is applied to test the resilience of four water distribution systems (WDSs) with various features to three typical failure modes (pipe failure, excess demand, and substance intrusion). The study reveals GRA provides an overview of a water system's resilience to various failure modes. For each failure mode, it identifies the range of corresponding failure impacts and reveals extreme scenarios (e.g. the complete loss of water supply with only 5% pipe failure, or still meeting 80% of demand despite over 70% of pipes failing). GRA also reveals that increased resilience to one failure mode may decrease resilience to another and increasing system capacity may delay the system's recovery in some situations. It is also shown that selecting an appropriate level of detail for hydraulic models is of great importance in resilience analysis. The method can be used as a comprehensive diagnostic framework to evaluate a range of interventions for improving system resilience in future studies.

Clustering analysis of water distribution systems: identifying critical components and community impacts

Large water distribution systems (WDSs) are networks with both topological and behavioural complexity. Thereby, it is usually difficult to identify the key features of the properties of the system, and subsequently all the critical components within the system for a given purpose of design or control. One way is, however, to more explicitly visualize the network structure and interactions between components by dividing a WDS into a number of clusters (subsystems). Accordingly, this paper introduces a clustering strategy that decomposes WDSs into clusters with stronger internal connections than external connections. The detected cluster layout is very similar to the community structure of the served urban area. As WDSs may expand along with urban development in a community-by-community manner, the correspondingly formed distribution clusters may reveal some crucial configurations of WDSs. For verification, the method is applied to identify all the critical links during firefighting for the vulnerability analysis of a real-world WDS. Moreover, both the most critical pipes and clusters are addressed, given the consequences of pipe failure. Compared with the enumeration method, the method used in this study identifies the same group of the most critical components, and provides similar criticality prioritizations of them in a more computationally efficient time.

Controllability analysis as a pre-selection method for sensor placement in water distribution systems

Detection of contamination events in water distribution systems is a crucial task for maintaining water security. Online monitoring is considered as the most cost-effective technology to protect against the impacts of contaminant intrusions. Optimization methods for sensor placement enable automated sensor layout design based on hydraulic and water quality simulation. However, this approach results in an excessive computational burden. In this paper we outline the application of controllability analysis as preprocessing method for sensor placement. Based on case studies we demonstrate that the method decreases the number of decision variables for subsequent optimization dramatically to app. 30 to 40 percent.

[Determianation of trace copper, iron and lead in purified AgNO3 with liquid membrane enrichment]

In this paper a new method of preconcentrating microimpurity Cu, Fe and Pb in high pure AgNO3 using emulsion liquid membrane technique was developed for FAAS determination. The enrichment multiple was 25, recoveries were over 95%. Matrix silver does not interfere enrichment and determination.