scholarly journals From CCTV Data to Strategic Planning: Deterioration Modelling for Large Sewer Networks in Germany and Colombia

10.29007/nbx2 ◽  
2018 ◽  
Author(s):  
Nicolas Caradot ◽  
Nathalie Hernandez ◽  
Hauke Sonnenberg ◽  
Andres Torres ◽  
Pascale Rouault
Keyword(s):  
Asset Management ◽  
Model Performance ◽  
Traffic Load ◽  
Investment Planning ◽  
Strategic Investment ◽  
Two Cities ◽  
Sewer Infrastructure ◽  
Inspection Data ◽  
Sewer Networks

Most cities are facing an aging sewer infrastructure in extensive and emerging need of repair, rehabilitation or renewal. Deterioration modelling can be a valued data mining tool to tackle this issue by supporting utilities in defining strategic investment planning. This study aims to demonstrate the benefits of deterioration modelling using sewer CCTV inspection data and GIS characteristics (material, age, depth, width, traffic load, etc.) of two different cities: Braunschweig in Germany and Bogota in Colombia. A probabilistic Markov-based model has been applied to identify and exploit relationships between sewer condition and characteristics in the extensive datasets of the two cities. The quality of prediction of the model has been evaluated by analyzing the deviation between model observations and model predictions. Results show relatively low deviations (< 15%) indicating a satisfying model performance in both cities and underlining the relevance of deterioration models to simulate the condition of sewer networks and to support strategic asset management.

ASPECTS OF THE ARCHITECTURAL AND URBAN HERITAGE: FROM REGISTERS TO CONSERVATION FOR ADAPTIVE AND MODERN USE AT THE HISTORIC CORES OF SALT AND IRBID, JORDAN

2017 ◽  
Vol 11 (2) ◽  
pp. 190
Author(s):  
Leen Adeeb Fakhoury ◽  
Naif Adel Haddad
Keyword(s):  
Local Community ◽  
Initial Assessment ◽  
Physical Factors ◽  
Cultural Significance ◽  
Community Quality Of Life ◽  
Urban Heritage ◽  
Two Cities ◽  
Conservation Actions ◽  
Historic Cities

This paper attempts to present and discuss the outcome of the results of the key different studies and projects carried out at Salt and at Irbid historic cores.  It focuses on the executed urban heritage projects undertaken mainly by the Ministry of Tourism and Antiquities (MoTA) of Jordan in the last two decades. It discusses their different aspects through initial assessment of the loss and degradation of the cultural heritage assets of the two cities; the fragmentation and lack of connectivity between the modern and historic cores; issues of sustainability of architectural and urban heritage projects i.e. tourism planning and conservation; and reuse projects at the historic cores in relation to cultural, physical factors and development needs. It also addresses the behaviour and characteristics of the urban regeneration process in those two historic cities, starting from their documentation to examination of the different aspects of the currently adopted urban practices and policies, and their impact on the existing urban heritage, depending on the specific identity of the respective historic cores. Finally, it aims to define the main constraints and challenges for the reuse of the existing heritage fabric including the local community quality of life, while building on sustainable heritage activities accommodating tourism opportunities. This will give, at least, some indications from which we can identify a use or combination of uses, and practical steps needed for successful heritage conservation actions in Jordan, in order to retain the cultural significance of the place.

scholarly journals Nodule Detection with Convolutional Neural Network Using Apache Spark and GPU Frameworks

2021 ◽  
Vol 11 (6) ◽  
pp. 2838
Author(s):  
Nikitha Johnsirani Venkatesan ◽  
Dong Ryeol Shin ◽  
Choon Sung Nam
Keyword(s):  
Neural Network ◽  
Radiation Dose ◽  
Convolutional Neural Network ◽  
Model Performance ◽  
Performance Comparison ◽  
Apache Spark ◽  
Training Time ◽  
Learning Framework ◽  
Proposed Model

In the pharmaceutical field, early detection of lung nodules is indispensable for increasing patient survival. We can enhance the quality of the medical images by intensifying the radiation dose. High radiation dose provokes cancer, which forces experts to use limited radiation. Using abrupt radiation generates noise in CT scans. We propose an optimal Convolutional Neural Network model in which Gaussian noise is removed for better classification and increased training accuracy. Experimental demonstration on the LUNA16 dataset of size 160 GB shows that our proposed method exhibit superior results. Classification accuracy, specificity, sensitivity, Precision, Recall, F1 measurement, and area under the ROC curve (AUC) of the model performance are taken as evaluation metrics. We conducted a performance comparison of our proposed model on numerous platforms, like Apache Spark, GPU, and CPU, to depreciate the training time without compromising the accuracy percentage. Our results show that Apache Spark, integrated with a deep learning framework, is suitable for parallel training computation with high accuracy.

scholarly journals Modelling Exposure by Spraying Activities—Status and Future Needs

2021 ◽  
Vol 18 (15) ◽  
pp. 7737
Author(s):  
Stefan Hahn ◽  
Jessica Meyer ◽  
Michael Roitzsch ◽  
Christiaan Delmaar ◽  
Wolfgang Koch ◽  
...  
Keyword(s):  
Current Knowledge ◽  
Model Performance ◽  
Relevant Information ◽  
Systematic Evaluation ◽  
Model Accuracy ◽  
Efficient Manner ◽  
User Perspective ◽  
Model Predictions ◽  
Spray Applications

Spray applications enable a uniform distribution of substances on surfaces in a highly efficient manner, and thus can be found at workplaces as well as in consumer environments. A systematic literature review on modelling exposure by spraying activities has been conducted and status and further needs have been discussed with experts at a symposium. This review summarizes the current knowledge about models and their level of conservatism and accuracy. We found that extraction of relevant information on model performance for spraying from published studies and interpretation of model accuracy proved to be challenging, as the studies often accounted for only a small part of potential spray applications. To achieve a better quality of exposure estimates in the future, more systematic evaluation of models is beneficial, taking into account a representative variety of spray equipment and application patterns. Model predictions could be improved by more accurate consideration of variation in spray equipment. Inter-model harmonization with regard to spray input parameters and appropriate grouping of spray exposure situations is recommended. From a user perspective, a platform or database with information on different spraying equipment and techniques and agreed standard parameters for specific spraying scenarios from different regulations may be useful.

Inline Inspection: Both Effective Data Collection and Interpretation Needed to Achieve High Quality Reporting Results

2010 ◽  
Author(s):  
James R. Walker ◽  
Paul Mallaburn ◽  
Derek Balmer
Keyword(s):  
Financial Risk ◽  
Service Providers ◽  
Holistic Approach ◽  
Poor Quality ◽  
International Standards ◽  
Quality Reporting ◽  
Certification Standard ◽  
Consensus Standard ◽  
Inspection Data

Historically, pipeline operators have tended to place more weight on inline inspection tool specifications than on the inherent design and reporting capabilities of the service providers themselves. While internal collection of integrity data is very important, it’s imperative that vendors, also, have high levels of expertise and effective quality control systems in place to successfully analyze exceedingly high volumes of inspection data. The quality of inspection information is vital to assessing if a pipeline is fit for purpose now and/or into the future. Integrity managers attempting to reduce overall operating risk by making decisions based on inaccurate or poor quality reporting are in fact exposing their networks to greater safety and financial risk. Recognizing these risks and that inline inspection (ILI) is an overall system that needs to be formally qualified, operators and ILI service providers have collaborated to develop several international standards. The most recent is the umbrella API-1163 industry consensus standard, which is now being widely adopted, primarily in USA. This standard provides requirements and recommended practices for qualification of the entire ILI process. Two companion standards: ASNT In-line Personnel Qualification and Certification Standard No. ILI-PQ and NACE Recommended Practice In-Line Inspection of Pipelines RP0102 combine to address specific requirements for personnel who operate and analyze the results of ILI systems. In Europe, the Pipeline Operators Forum (POF) has, also, established specific requirements for ILI reporting processes and data formats. However, these standards do not define how operators and vendors must meet these requirements. To follow will be a story about how an ILI service provider embraced a holistic approach to address these standards’ requirements, in particular in the areas of data analysis, reporting, and dig verification due to their significant importance in assuring the final quality of its deliverables. A key outcome desired will be to provide operators with greater insight into what best practices and technologies ILI service providers should have embraced and invested in to insure reliable service delivery.

scholarly journals Development of prognostic models for Health-Related Quality of Life following traumatic brain injury

2021 ◽  
Author(s):  
Isabel R. A. Retel Helmrich ◽  
David van Klaveren ◽  
Simone A. Dijkland ◽  
Hester F. Lingsma ◽  
Suzanne Polinder ◽  
...  
Keyword(s):  
Quality Of Life ◽  
Traumatic Brain Injury ◽  
Brain Injury ◽  
Model Performance ◽  
Prognostic Models ◽  
Health Related Quality ◽  
Mild Tbi ◽  
Related Quality ◽  
Health Related

Abstract Background Traumatic brain injury (TBI) is a leading cause of impairments affecting Health-Related Quality of Life (HRQoL). We aimed to identify predictors of and develop prognostic models for HRQoL following TBI. Methods We used data from the Collaborative European NeuroTrauma Effectiveness Research in Traumatic Brain Injury (CENTER-TBI) Core study, including patients with a clinical diagnosis of TBI and an indication for computed tomography presenting within 24 h of injury. The primary outcome measures were the SF-36v2 physical (PCS) and mental (MCS) health component summary scores and the Quality of Life after Traumatic Brain Injury (QOLIBRI) total score 6 months post injury. We considered 16 patient and injury characteristics in linear regression analyses. Model performance was expressed as proportion of variance explained (R2) and corrected for optimism with bootstrap procedures. Results 2666 Adult patients completed the HRQoL questionnaires. Most were mild TBI patients (74%). The strongest predictors for PCS were Glasgow Coma Scale, major extracranial injury, and pre-injury health status, while MCS and QOLIBRI were mainly related to pre-injury mental health problems, level of education, and type of employment. R2 of the full models was 19% for PCS, 9% for MCS, and 13% for the QOLIBRI. In a subset of patients following predominantly mild TBI (N = 436), including 2 week HRQoL assessment improved model performance substantially (R2 PCS 15% to 37%, MCS 12% to 36%, and QOLIBRI 10% to 48%). Conclusion Medical and injury-related characteristics are of greatest importance for the prediction of PCS, whereas patient-related characteristics are more important for the prediction of MCS and the QOLIBRI following TBI.

Prediction of quality of life in people with ALS

2021 ◽  
Vol 21 (2) ◽  
pp. 5-17
Author(s):  
Anna Markella Antoniadi ◽  
Miriam Galvin ◽  
Mark Heverin ◽  
Orla Hardiman ◽  
Catherine Mooney
Keyword(s):  
Quality Of Life ◽  
Disease Onset ◽  
Model Performance ◽  
Sampling Technique ◽  
Clinical Decision ◽  
Gradient Boosting ◽  
Rapid Decline ◽  
Extreme Gradient Boosting ◽  
Lateral Sclerosis

Amyotrophic Lateral Sclerosis (ALS) is a rare neurodegenerative disease that causes a rapid decline in motor functions and has a fatal trajectory. ALS is currently incurable, so the aim of the treatment is mostly to alleviate symptoms and improve quality of life (QoL) for the patients. The goal of this study is to develop a Clinical Decision Support System (CDSS) to alert clinicians when a patient is at risk of experiencing low QoL. The source of data was the Irish ALS Registry and interviews with the 90 patients and their primary informal caregiver at three time-points. In this dataset, there were two different scores to measure a person's overall QoL, based on the McGill QoL (MQoL) Questionnaire and we worked towards the prediction of both. We used Extreme Gradient Boosting (XGBoost) for the development of the predictive models, which was compared to a logistic regression baseline model. Additionally, we used Synthetic Minority Over-sampling Technique (SMOTE) to examine if that would increase model performance and SHAP (SHapley Additive explanations) as a technique to provide local and global explanations to the outputs as well as to select the most important features. The total calculated MQoL score was predicted accurately using three features - age at disease onset, ALSFRS-R score for orthopnoea and the caregiver's status pre-caregiving - with a F1-score on the test set equal to 0.81, recall of 0.78, and precision of 0.84. The addition of two extra features (caregiver's age and the ALSFRS-R score for speech) produced similar outcomes (F1-score 0.79, recall 0.70 and precision 0.90).

EHR-based machine learning modeling for preterm birth prediction: A systematic review (Preprint)

2021 ◽  
Author(s):  
Zahra Sharifiheris ◽  
Juho Laitala ◽  
Antti Airola ◽  
Amir M Rahmani ◽  
Miriam Bender
Keyword(s):  
Machine Learning ◽  
Systematic Review ◽  
Preterm Birth ◽  
Model Performance ◽  
Bibliographic Databases ◽  
Complementary Approach ◽  
Prisma Statement ◽  
Comprehensive Search ◽  
First Time

BACKGROUND Preterm birth (PTB) as a common pregnancy complication is responsible for 35% of the 3.1 million pregnancy-related deaths each year and significantly impacts around 15 million children annually across the world. Conventional approaches to predict PTB may neither be applicable for first-time mothers nor possess reliable predictive power. Recently, machine learning (ML) models have shown the potential as an appropriate complementary approach for PTB prediction. OBJECTIVE In this article we systematically reviewed the literature concerned with PTB prediction using ML modeling. METHODS This systematic review was conducted in accordance with the PRISMA statement. A comprehensive search was performed in seven bibliographic databases up until 15 May 2021. The quality of studies was assessed, and the descriptive information including socio-demographic characteristics, ML modeling processes, and model performance were extracted and reported. RESULTS A total of 732 papers were screened through title and abstract. Of these, 23 studies were screened by full text resulting in 13 papers that met the inclusion criteria. CONCLUSIONS We identified various ML models used for different EHR data resulting in a desirable performance for PTB prediction. However, evaluation metrics, software/package used, data size and type, and selected features, and importantly data management method often varied from study to study threatening the reliability and generalizability of the model. CLINICALTRIAL n.a.

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