Optimizing resilience decision-support for natural gas networks under uncertainty

2019 Summer.Includes bibliographical references.Community resilience in the aftermath of a hazard requires the functionality of complex, interdependent infrastructure systems become operational in a timely manner to support social and economic institutions. In the context of risk management and community resilience, critical decisions should be made not only in the aftermath of a disaster in order to immediately respond to the destructive event and properly repair the damage, but preventive decisions should to be made in order to mitigate the adverse impacts of hazards prior to their occurrence. This involves significant uncertainty about the basic notion of the hazard itself, and usually involves mitigation strategies such as strengthening components or preparing required resources for post-event repairs. In essence, instances of risk management problems that encourage a framework for coupled decisions before and after events include modeling how to allocate resources before the disruptive event so as to maximize the efficiency for their distribution to repair in the aftermath of the event, and how to determine which network components require preventive investments in order to enhance their performance in case of an event. In this dissertation, a methodology is presented for optimal decision making for resilience assessment, seismic risk mitigation, and recovery of natural gas networks, taking into account their interdependency with some of the other systems within the community. In this regard, the natural gas and electric power networks of a virtual community were modeled with enough detail such that it enables assessment of natural gas network supply at the community level. The effect of the industrial makeup of a community on its natural gas recovery following an earthquake, as well as the effect of replacing conventional steel pipes with ductile HDPE pipelines as an effective mitigation strategy against seismic hazard are investigated. In addition, a multi objective optimization framework that integrates probabilistic seismic risk assessment of coupled infrastructure systems and evolutionary algorithms is proposed in order to determine cost-optimal decisions before and after a seismic event, with the objective of making the natural gas network recover more rapidly, and thus the community more resilient. Including bi-directional interdependencies between the natural gas and electric power network, strategic decisions are pursued regarding which distribution pipelines in the gas network should be retrofitted under budget constraints, with the objectives to minimizing the number of people without natural gas in the residential sector and business losses due to the lack of natural gas in non-residential sectors. Monte Carlo Simulation (MCS) is used in order to propagate uncertainties and Probabilistic Seismic Hazard Assessment (PSHA) is adopted in order to capture uncertainties in the seismic hazard with an approach to preserve spatial correlation. A non-dominated sorting genetic algorithm (NSGA-II) approach is utilized to solve the multi-objective optimization problem under study. The results prove the potential of the developed methodology to provide risk-informed decision support, while being able to deal with large-scale, interdependent complex infrastructure considering probabilistic seismic hazard scenarios

Evaluating Properties of Asphalt Mixtures Containing polymers of Styrene Butadiene Rubber (SBR) and recycled Polyethylene Terephthalate (rPET) against Failures Caused by Rutting, Moisture and Fatigue

Properties of asphalt mixture play a vital role in structural integrity and performance of flexible pavements structure. In flexible pavements asphalt concrete surface layer consists of asphalt binder, aggregates and in some cased additives. In this research study styrene butadiene rubber (SBR) and recycled polyethylene terephthalate (rPET) used to evacuate their individual and also their combination effects upon moisture susceptibly, rutting and low temperature cracking of asphalt concrete mixture. Three combination of SBR and rPET along with water were vulcanized to from thermoplastic elastomer polymers as bitumen modifier. Then conventional bitumen tests including penetration grade, softening point and rotational viscosity (RV) as well as asphalt mixture tests including resilient modulus, dynamic creep, IDT fatigue and moisture susceptibility tests were performed on binders and asphalt mixture specimens. The test results indicated that SBR and rPET increase viscosity and softening point and stiffen the binders by reducing their penetration grade. Test results of spearmens prepared with modified binders showed higher tensile strength and higher rutting resistance than that of control specimen within the contest of this study it is conclude that modification of bitumen with SBR reduces low temperature stiffness of binder and hence reduces failure of thermal cracking and modification with rPET increases rutting resistance of the mixture at high temperatures

Spotting Keywords in Offline Handwritten Documents Using Hausdorff Edit Distance

Keyword spotting has become a crucial topic in handwritten document recognition, by enabling content-based retrieval of scanned documents using search terms. With a query keyword, one can search and index the digitized handwriting which in turn facilitates understanding of manuscripts. Common automated techniques address the keyword spotting problem through statistical representations. Structural representations such as graphs apprehend the complex structure of handwriting. However, they are rarely used, particularly for keyword spotting techniques, due to high computational costs. The graph edit distance, a powerful and versatile method for matching any type of labeled graph, has exponential time complexity to calculate the similarities of graphs. Hence, the use of graph edit distance is constrained to small size graphs. The recently developed Hausdorff edit distance algorithm approximates the graph edit distance with quadratic time complexity by efficiently matching local substructures. This dissertation speculates using Hausdorff edit distance could be a promising alternative to other template-based keyword spotting approaches in term of computational time and accuracy. Accordingly, the core contribution of this thesis is investigation and development of a graph-based keyword spotting technique based on the Hausdorff edit distance algorithm. The high representational power of graphs combined with the efficiency of the Hausdorff edit distance for graph matching achieves remarkable speedup as well as accuracy. In a comprehensive experimental evaluation, we demonstrate the solid performance of the proposed graph-based method when compared with state of the art, both, concerning precision and speed. The second contribution of this thesis is a keyword spotting technique which incorporates dynamic time warping and Hausdorff edit distance approaches. The structural representation of graph-based approach combined with statistical geometric features representation compliments each other in order to provide a more accurate system. The proposed system has been extensively evaluated with four types of handwriting graphs and geometric features vectors on benchmark datasets. The experiments demonstrate a performance boost in which outperforms individual systems

Some properties of residual mapping and convexity in ∧-hyperlattices

The aime of this paper is the study of residual mappings and convexity in hyperlattices. To get this point, we study principal down set in hyperlattices and we give some conditions for a mapping between two hyperlattices to be equivalent with a residual maping. Also, we investigate convex subsets in ∧-hyperlattices

Protein Enrichment of Olive Cake Substrate by Solid State Fermentation of Lentinus edodes

Solid-state fermentation technique can be used for protein enrichment of the olive cake substrate (OCS). Among microorganisms, mushrooms, in particular, white-rot fungi belonging to the genus Lentinus is known for its ability to digest the lignin and also the most effective producers of lignocellulosic enzymes. Hence, the objective of this work is to evaluate the effect of Lentinus edodes on protein content of agro by-product namely, olive cake substrate. To do so, solid state fermentation was performed at 25ºC in different conditions including various nitrogen sources, inoculum size, fermentation time, and moisture content using glass bottle as bioreactor. Protein extraction was carried out at 4ºC. The results obtained show significantly increasing protein content of OCS. HIGHLIGHTS•Solid-state fermentation technique can be used for protein enrichment of the olive cake substrate (OCS).•The nutritional value of olive cake substrate (OCS) was improved upon fungal treatment.•Lentinus edodes fungi enhanced the protein content in experimental OCS

Effects of Copigmentation on the Stability of Phycocyanin Pigments Extracted from Spirulina platensis Using Spray Dryer

Abstract   Background and Objective: Phycocyanin is a blue pigment extracted from Spirulina platensis algae as an excellent alternative for the comparison of synthetic dyes in various industries, including food industries. The aim of the present study was to assess effects of copigmentation on the stability of phycocyanin pigments using spray drying method. Material and Methods: An aqueous solution of phycocyanin (500 mg l-1) was prepared at three pH values of 3, 5 and 7. Then, polyphenolic compounds containing rosmarinic acid, tannic acid and digallic acid (0, 75, 150, 225 and 300 mgl-1) were separately added to the solution as copolymers. Pigment solutions were transferred into cylindrical containers with similar sizes under a light source at an intensity of 7000 l mm-2 and ambient temperature. Color changes of the solutions were assessed for 14 d. Phycocyanin pigment solution was copigmented with tannic acid (the best copolymer) and mixed with a combination of maltodextrin and Arabic gum (100:0, 75:25, 50:50, 25:75 and 0:100). Ratio of the core to the wall was 1:10. Spray dryer was used for drying and stability of the dried coated pigment powder was assessed for 14 d by investigating the absorption reduction ratio at the maximum absorption wavelength of phycocyanin (620 nm) using spectrophotometer. Results and Conclusion: Based on the results, using tannic acid (300 mgl-1) as the best copigmenting compound induced higher resistance to phycocyanin. In addition, the most stable pigment treatment was seen with maltodextrin and Arabic gum coating (ratio: 100:0). In particle size, findings showed that the powder samples containing maltodextrin were larger than the samples with Arabic gum (350.2 and 40.1 nm, respectively). Moreover, results showed that phycocyanin copigmented with tannic acid included higher resistance to environmental changes and encapsulation using spray dryer was further effective in increasing stability of phycocyanin. Conflict of interest: The authors declare no conflict of interest

A framework for investigating pet owners’ health information behaviour intervention

Objective: This study is a part of a research aiming to determine whether an information prescription given by veterinarians in a general pet clinic would change the behaviours of pet owners about using pet health information resources on the internet. For this purpose, we develop a model to intervene and evaluate pet owner’s online health information seeking behaviour (HISB). Methods: The framework emerges from a systematic literature review and qualitative content analysis. NVivo 10 was used in this paper as an analysis tool for coding text and for supporting framework generation through identifying patterns. Results: We indicate the most influencing factors on online HISB of pet owners, including human-pet relationship, veterinary-client interactions, and pet owner’s health literacy. Discussion: We strengthen our findings further by learning from health behaviour models which lead to a better pet health promotion. Based on adaption of the Interaction model of client health behaviour (IMCHB), we developed our initial model. Conclusion: this model serves as an initial step to engage information scientists and veterinarians for planning on pet health information outreach. However, future research needs to test the proposed model in various case studies and populations

Skin Cancer Detection Based on Deep Learning

Background: The conventional procedure of skin-related disease detection is a visual inspection by a dermatologist or a primary care clinician, using a dermatoscope. The suspected patients with early signs of skin cancer are referred for biopsy and histopathological examination to ensure the correct diagnosis and the best treatment. Recent advancements in deep convolutional neural networks (CNNs) have achieved excellent performance in automated skin cancer classification with accuracy similar to that of dermatologists. However, such improvements are yet to bring about a clinically trusted and popular system for skin cancer detection. Objective: This study aimed to propose viable deep learning (DL) based method for the detection of skin cancer in lesion images, to help physicians in diagnosis.Material and Methods: In this analytical study, a novel DL based model was proposed, in which other than the lesion image, the patient’s data, including the anatomical site of the lesion, age, and gender were used as the model input to predict the type of the lesion. An Inception-ResNet-v2 CNN pretrained for object recognition was employed in the proposed model. Results: Based on the results, the proposed method achieved promising performance for various skin conditions, and also using the patient’s metadata in addition to the lesion image for classification improved the classification accuracy by at least 5% in all cases investigated. On a dataset of 57536 dermoscopic images, the proposed approach achieved an accuracy of 89.3%±1.1% in the discrimination of 4 major skin conditions and 94.5%±0.9% in the classification of benign vs. malignant lesions.  Conclusion: The promising results highlight the efficacy of the proposed approach and indicate that the inclusion of the patient’s metadata with the lesion image can enhance the skin cancer detection performance

Depression and its Main Determinants Among Iranian Operating Room Personnel: A Systematic Review and Meta-Analysis

Background: Most nurses, especially operating room personnel, seems to be more likely to be affected by mood disorders than other social strata. The present study attempted to systematically review the prevalence of depression and its main determinants among operating room personnel in Iran.Methods: The method of this systematic review is documenting in a published protocol in the International Prospective Register of Systematic Reviews (PROSPERO) and the Preferred Reporting Items for Systematic Reviews and Meta-Analyses (PRISMA) checklist. After this massive search, titles and abstracts of retrieved documents have screened and all irrelevant articles excluded. Two reviewers screened the documents and selected all relevant studies and assessed included articles separately.Results: Totally, 12 citations found in the initial literature search where four citations excluded, as they did not meet the inclusion criteria. The final number of studies available for analysis was 12 including a total of 373 operating room personnel (86 men and 287 women, mean the age of 27.71 years ranged from 20 to 36 years). The pooled prevalence of depression among operating room personnel was estimated to be 45.3%. In this regard, 27.0% of personnel suffered from severe depression. A significant heterogeneity found in the overall analysis of the overall prevalence of depression and its severe pattern.Conclusion: A notable number of operating room personnel in Iran suffer from depression even in its severe condition emphasizing the importance of the managerial approach to minimize its adverse effects on their performance as well as to improve their quality of life

Designing Iranian Customer Expectations Model for Platform Growth and Development with Agent-Based Model Approach (Case Study: Instagram)

One of the most important factors in understanding customer behavior is identifying their expectations. Therefore, this study in order to design customer expectations model for platform with agent-based model approach. At first, for finding customers and servants expectations was used of semi-structure interview and then was done automatic clustering with using meta-heuristic algorithms in order to find different factors. Then, with the factor-based simulation approach, the model was designed in Any Logic software. After designing the model, using design method of Taguchi experiments (Qualitek-4 software), scenarios for the growth and development of the platform based on effective factors (liquidity the quality of communication and trust) designed on four levels and finally simulation was performed and scenarios were examined in the simulation environment. the research results showed that the appropriate level of platform growth and development indicators in the fourth level of liquidity, the fourth level of communication quality and the fourth level of trust. In addition, after implementing the optimal scenario in the simulation environment was determined that the percentage of value created on the Instagram platform due to the implementation of the desired scenario is equal to 0.934