Developing a Chaotic-Simulation Based Model for Ranking High Selected Network Links in Hazardous Material Transportation

Hazardous material transportation is one of the main concerns due to the nature of hazardous materials and their incident impacts. In general, transport risk is a main attribute to develop mathematical models for hazardous material routing problem as well as network designing or improving road safety. This paper presents a chaotic-simulation based model to determine the high selected links to improve road network quality for hazardous material transportation, in which risk is considered as a chaotic variable over the network whereas a simulation technique has been applied to cover a wide range of selecting paths. A real road network, consists of fifty-nine nodes and eighty two-way edges, is used for running the mathematical model and checking validation. Due to the large amounts of hazardous material transported by trucks, the proposed methodology is focused on fuel transportation, and high selected list of edges (links) has been obtained to improve road safety. Sensitivity analysis revealed that using different seeds for generating transport risk has no significant effects on finding the most frequent paths and high selected edges

Does Internet of Things Affect on Sustainable Development? Investigation through Intermediate Applications

Since, the technology of Internet of Things (IoT) is utilized to facilitate new and improve existing applications in a large variety of domains, such as manufacturing, healthcare and energy, the main aim of this research work is to evaluate the role of IoT applications on Sustainable Development (SD). To conduct this research work, a conceptual model has been proposed by considering intermediate applications to connect internet of things attributes to the main aspects of sustainable development. Sustainability is divided into three main components of environment, economy and society as well as IoT has been also divided into information dissemination, communication and information technology and information transmission. The proposed conceptual model has been validated using a purpose designed questionnaire to gather experts’ opinions in Likert scale where each application connects IoT attributes to SD components. Analysing filled out questionnaires using the well-known statistical method of T-Test revealed that there are significant relations between IoT attributes and sustainable development component. It can be also concluded that the applications of IoT would improve sustainablity over development process. Therefore, IoT applications would be improved and renewed over the next years because sustainability is getting to be a serious concern all over the world

Statistical Similarity of Mortality and Recovery Ratios for Covid-19 Patients based on Gender and Age

Background: Studying the behavior of patients infected with Covid-19 is an essential issue for health authorities during the global pandemic, so the aim of this study is to investigate the statistical similarity between the recovery and mortality ratios based on the patients’ age and gender. For this purpose, the well-known statistical testing method of Kolmogorov-Smirnov has been utilized to investigate the similarity of distribution functions for mortality and recovery rates for patients infected with Covid-19. Results: Data for 1015 patients resulting in death, recovery, and transfer has been collected and analyzed. The age is cross-classified by gender where the rates’ cumulative distribution functions are independently calculated and depicted for females and males. The results revealed that there is no significant difference between the distribution functions of mortality and recovery rates by gender, but there is by age. Conclusion: The research results would support the health authorities in managing the admission and discharge procedures of the Covid-19 patients where the hospitality services are traditionally provided differently by gender. Doi: 10.28991/HIJ-2021-02-04-05 Full Text: PD

Indium-Gallium-Zinc Oxide Thin-Film Transistors for Active-Matrix Flat-Panel Displays

Amorphous oxide semiconductors (AOSs) including amorphous InGaZnO (a-IGZO) areexpected to be used as the thin-film semiconducting materials for TFTs in the next-generation ultra-high definition (UHD) active-matrix flat-panel displays (AM-FPDs). a-IGZO TFTs satisfy almost all the requirements for organic light-emitting-diode displays (OLEDs), large and fast liquid crystal displays (LCDs) as well as three-dimensional (3D) displays, which cannot be satisfied using conventional amorphous silicon (a-Si) or polysilicon (poly-Si) TFTs. In particular, a-IGZO TFTs satisfy two significant requirements of the backplane technology: high field-effect mobility and large-area uniformity.In this work, a robust process for fabrication of bottom-gate and top-gate a-IGZO TFTs is presented. An analytical drain current model for a-IGZO TFTs is proposed and its validation is demonstrated through experimental results. The instability mechanisms in a-IGZO TFTs under high current stress is investigated through low-frequency noise measurements. For the first time, the effect of engineered glass surface on the performance and reliability of bottom-gate a-IGZO TFTs is reported. The effect of source and drain metal contacts on electrical properties of a-IGZO TFTs including their effective channel lengths is studied. In particular, a-IGZO TFTs with Molybdenum versus Titanium source and drain electrodes are investigated. Finally, the potential of aluminum substrates for use in flexible display applications is demonstrated by fabrication of high performance a-IGZO TFTs on aluminum substrates and investigation of their stability under high current electrical stress as well as tensile and compressive strain

A Metamaterial Path Towards Optical Integrated Nanocircuits

Metamaterials are known to demonstrate exotic electromagnetic and optical properties. The extra control over manipulation of waves and fields afforded by metamaterials can be exploited towards exploring various platforms, e.g., optical integrated circuits. Nanophotonic integrated circuits have been the topic of past and ongoing research in multiple fields including, but not limited to, electrical engineering, optics and materials science. In the present work, we theoretically study and analyze metamaterial properties that can be potentially utilized in the future design of optical integrated circuits. On this path, we seek inspiration from electronics to tackle multiple issues in developing such layered nanocircuitry. We identify modularity, directionality/isolation and tunability as three useful features of electronics and we theoretically explore mimicking them in nanoscale optics. Using epsilon-near-zero (ENZ) and mu-near-zero (MNZ) properties we propose concepts to transplant some aspects of modular design of electronic passive circuits and filters into nanophotonics. We also exploit ENZ materials to develop “transformer-like” functionality in optical nanocircuits. To bring directional selectivity and isolation to this domain we develop concepts for both spatial filtering of light using ENZ layered structures as well as identifying new regimes of nonreciprocal one-way surface wave propagation on the surface of magneto-optical materials. In order to have tunability in some of the proposed concepts in this work, we numerically study a wire-medium metamaterial whose permittivity can be tuned at will. All the proposed structures have simple geometries and layered structures wherever possible, which are more convenient for analysis, design and future implementation

Coupled Hydrological-Geotechnical Model for Determinine Bearing Capacity and Elastic Settlement of Foundations

This dissertation presents a coupled hydrological-geotechnical framework to investigate the performance of shallow and deep foundations under hydrological events such as heavy rainfall and drought. The variation in performance of foundation, interface between the structure and ground surface, is caused by the uncertainties associated with not only the geotechnical parameters but also the hydrological parameters that include intensity and duration of hydrological events and water table depth. The impact of such hydrological events significantly alters the performance of foundations by changing the soil strength and stiffness parameters of subsurface soil which may lead to foundation failures. Such failures can cause damage to the supporting structure. Therefore, to better understand the performance of geotechnical systems under different hydrological events and also to build sustainable and resilient infrastructure systems, the design of geotechnical systems should be carried out in a coupled hydrological-geotechnical manner considering the site-specific geotechnical and hydrological parameters. To this end, a numerical framework is developed based on the partially saturated soil mechanics principles and applied to a number of sites in the United States to show the impacts of hydrological events in the performance of shallow and deep foundations. In this framework, the one-dimensional Richards’ equation is numerically solved to compute the spatial and temporal variation of the degree of saturation and matric suction in subsurface soil due to the site-specific rainfall, evapotranspiration, and water table depth as model boundary conditions. Then, the critical settlement and bearing capacity of foundations (as critical design values) are calculated using the average degree of saturation and matric suction within the foundation influence zone. It is worth mentioning that two different design methodologies based on the probabilistic analysis and single extreme hydrological cycle are considered in the proposed framework to have a better assessment of foundation performance. The results show that the hydrological parameters have a significant impact on the performance of shallow and deep foundations, and in general, they improve the predicted foundation design values obtained from conventional methods in terms of the settlement and bearing capacity. The proposed method can be used as a decision-making tool for selecting the suitable design values of foundations in engineering practice

Distributed Supervised Statistical Learning

We live in the era of big data, nowadays, many companies face data of massive size that, in most cases, cannot be stored and processed on a single computer. Often such data has to be distributed over multiple computers which then makes the storage, pre-processing, and data analysis possible in practice. In the age of big data, distributed learning has gained popularity as a method to manage enormous datasets. In this thesis, we focus on distributed supervised statistical learning where sparse linear regression analysis is performed in a distributed framework. These methods are frequently applied in a variety of disciplines tackling large scale datasets analysis, including engineering, economics, and finance. In distributed learning, one key question is, for example, how to efficiently aggregate multiple estimators that are obtained based on data subsets stored on multiple computers. We investigate recent studies on distributed statistical inferences. There have been many efforts to propose efficient ways of aggregating local estimates, most popular methods are discussed in this thesis. Recently, an important question about the number of machines to deploy is addressed for several estimation methods, notable answers to the question are reviewed in this literature. We have considered a specific class of Liu-type shrinkage estimation methods for distributed statistical inference. We also conduct a Monte Carlo simulation study to assess performance of the Liu-type shrinkage estimation methods in a distributed framework

Research: A comprehensive and quantitative exploration of thousands of viral genomes

The complete assembly of viral genomes from metagenomic datasets (short genomic sequences gathered from environmental samples) has proven to be challenging, so there are significant blind spots when we view viral genomes through the lens of metagenomics. One approach to overcoming this problem is to leverage the thousands of complete viral genomes that are publicly available. Here we describe our efforts to assemble a comprehensive resource that provides a quantitative snapshot of viral genomic trends – such as gene density, noncoding percentage, and abundances of functional gene categories – across thousands of viral genomes. We have also developed a coarse-grained method for visualizing viral genome organization for hundreds of genomes at once, and have explored the extent of the overlap between bacterial and bacteriophage gene pools. Existing viral classification systems were developed prior to the sequencing era, so we present our analysis in a way that allows us to assess the utility of the different classification systems for capturing genomic trends

How do the Risk Equity Techniques Affect on Intercity Road Network Accessibility? An Empirical Study

Due to existing risk on hazardous materials transportation, it is essential to avoid risk agglomeration over the specific edges which are frequently used on the intercity road network. Therefore, local and/or national authorities are dealing with distributing risk over the network while risk distribution may affect on the network accessibility. The aim of this study is to propose a procedure and develop mathematical models to distribute Hazmat transport risk, named risk equity, on the intercity road network and investigate the effects on the network accessibility. Accessibility is defined as dividing transport demand by distance, where the Min (Max) risk distribution technique is utilized for risk equity over the network. The effects have been investigated on a medium size of intercity road network in Guilan province, at the north of Iran. The proposed procedure and mathematical models have been run using experimental data including 46 nodes and 126 two-way edges including Hazmat Origin-Destination matrix. The results revealed that risk distribution technique has significant effects on network accessibility in which nodes’ accessibilities are statistically affected by risk equity models

Estimating the Reliability of Travel Time on Railway Networks for Freight Transportation

Railway freight transportation is an important transport system that its reliability causes economic issues. Freight carriers require predictable travel times to schedule their programs in competitive environment, so the estimation of reliability of travel time is very important. The present study proposes a travel time index that estimates the reliability of railway freight transportation and evaluates performance as well. Travel time reliability is estimated based on the shortest path between O-D pairs. Statistical measures of travel time, defining as the ratio of the 95th percentile travel time and the shortest path mean travel time as an ideal travel time, for each obtained route are calculated according to their selected links. Experimental data on Iranian rail network has been used as case study and results revealed that the routes less than 400 kilometers should be improved in terms of their reliabilities, because they are less reliable than long distance routes