Search results for: structural modeling

Authors: Fares Khalfallah, Zakaria Boumerzoug, Selvarajan Rajakumar, Elhadj Raouache

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The ceramic-aluminum bond is strongly present in industrial tools, due to the need to combine the properties of metals, such as ductility, thermal and electrical conductivity, with ceramic properties like high hardness, corrosion and wear resistance. In recent years, some joining techniques have been developed to achieve a good bonding between these materials such as brazing, diffusion bonding, ultrasonic joining and friction welding. In this work, AA1100 aluminum alloy rods were welded with Alumina 99.9 wt% ceramic rods, by friction welding. The effect of friction pressure on mechanical and structural properties of welded joints was studied. The welding was performed by direct friction welding machine. The welding samples were rotated at a constant rotational speed of 900 rpm, friction time of 4 sec, forging strength of 18 MPa, and forging time of 3 sec. Three different friction pressures were applied to 20, 34 and 45 MPa. The three-point bending test and Vickers microhardness measurements were used to evaluate the strength of the joints and investigate the mechanical properties of the welding area. The microstructure of joints was examined by optical microscopy (OM), scanning electron microscopy (SEM) and energy dispersive spectroscopy (EDS). The results show that bending strength increased, and then decreased after reaching a maximum value, with increasing friction pressure. The SEM observation shows that the increase in friction pressure led to the appearance of cracks in the microstructure of the interface area, which is decreasing the bending strength of joints.

Keywords: welding of ceramic to aluminum, friction welding, alumina, AA1100 aluminum alloy

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Authors: J. Ramirez, A. Maldonado, M. de la L. Olvera

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The effect of both the molar concentration of the starting solution and the substrate temperature on the electrical, morphological, structural and optical properties of chemically sprayed fluorine-doped zinc oxide (ZnO:F) thin films deposited on glass substrates, is analyzed in this work. All the starting solutions employed were aged for ten days before the deposition. The results show that as the molar concentration increases, a decrease in the electrical resistivity values is obtained, reaching the minimum in films deposited from a 0.4 M solution at 500°C. A further increase in the molar concentration leads to a very slight increase in the resistivity. On the other hand, as the substrate temperature is increased, the resistivity decreases and a tendency towards to minimum value is evidenced; taking the molar concentration as parameter, minimum values are reached at 500°C. The attain of ZnO:F thin films, with a resistivity as low as 7.8×10-3 Ώcm (sheet resistance of 130 Ώ/☐ and film thickness of 600 nm) measured in as-deposited films is reported here for the first time. The concurrent effect of the high molar concentration of the starting solution, the substrate temperature values used, and the ageing of the starting solution, which might cause polymerization of the zinc ions with the fluorine species, enhance the electrical properties. The structure of the films is polycrystalline, with a (002) preferential growth. Molar concentration rules the surface morphology as at low concentration an hexagonal and porous structure is developed changing to a uniform compact and small grain size surface in the films deposited with the high molar concentrations.

Keywords: zinc oxide, chemical spray, thin films, TCO

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Authors: Kangujam Monika, Kiranbala Devi Thokchom, Soibam Sandhyarani Devi

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Earthquake is a major natural hazard in India. Manipur, located in the North-Eastern Region of India, is one of the most affected location in the region prone to earthquakes since it lies in an area where Indian and Eurasian tectonic plates meet and is in seismic Zone V which is the most severe intensity zone, according to IS Code. Some recent earthquakes recorded in Manipur are M 6.7 epicenter at Tamenglong (January 4, 2016), M 5.2 epicenter at Churachandpur (February 24, 2017) and most recent M 4.4 epicenter at Thoubal (June 19, 2017). In these recent earthquakes, some houses and buildings were damaged, landslides were also occurred. A field study was carried out. An overview of the various causal factors involved in triggering of earthquake in Manipur has been discussed. It is found that improper planning, poor design, negligence, structural irregularities, poor quality materials, construction of foundation without proper site soil investigation and non-implementation of remedial measures, etc., are possibly the main causal factors for damage in Manipur during earthquake. The study also suggests, though the proper design of structure and foundation along with soil investigation, ground improvement methods, use of modern techniques of construction, counseling with engineer, mass awareness, etc., might be effective solution to control the hazard in many locations. An overview on the analysis pertaining to earthquake in Manipur together with on-going detailed site specific geotechnical investigation were presented.

Keywords: Manipur, earthquake, hazard, structure, soil

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Authors: Mohsen Farhadloo, Majid Farhadloo

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Aspect-level opinion mining that aims at discovering aspects (aspect identification) and their corresponding ratings (sentiment identification) from customer reviews have increasingly attracted attention of researchers and practitioners as it provides valuable insights about products/services from customer's points of view. Instead of addressing aspect identification and sentiment identification in two separate steps, it is possible to simultaneously identify both aspects and sentiments. In recent years many graphical models based on Latent Dirichlet Allocation (LDA) have been proposed to solve both aspect and sentiment identifications in a single step. Although LDA models have been effective tools for the statistical analysis of document collections, they also have shortcomings in addressing some unique characteristics of opinion mining. Our goal in this paper is to address one of the limitations of topic models to date; that is, they fail to directly model the associations among topics. Indeed in many text corpora, it is natural to expect that subsets of the latent topics have higher probabilities. We propose a probabilistic graphical model called focus-LDA, to better capture the associations among topics when applied to aspect-level opinion mining. Our experiments on real-life data sets demonstrate the improved effectiveness of the focus-LDA model in terms of the accuracy of the predictive distributions over held out documents. Furthermore, we demonstrate qualitatively that the focus-LDA topic model provides a natural way of visualizing and exploring unstructured collection of textual data.

Keywords: aspect-level opinion mining, document modeling, Latent Dirichlet Allocation, LDA, sentiment analysis

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Authors: Pedro F. D. Oliveira, Rangel S. Maia, Aline S. Paula

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As the electric energy consumption grows, the necessity of energy transmission lines increases. One of the problems caused by an oscillatory response to dynamical loads (such as wind effects) in transmission lines is the cable fatigue. Thus, the dynamical behavior of transmission cables understanding and its control is extremely important. The socioeconomic damage caused by a failure in these cables can be quite significant, from large economic losses to energy supply interruption in large regions. Dynamic Vibration Absorbers (DVA) are oscillatory elements used to mitigate the vibration of a primary system subjected to harmonic excitation. The positioning of Stockbridge (DVA for overhead transmission lines) plays an important role in mitigating oscillations of transmission lines caused by airflows. Nowadays, the positioning is defined by technical standards or commercial software. The aim of this paper is to conduct an analysis of multiple DVAs performances in cable conductors of overhead transmission lines. The cable is analyzed by a finite element method and the model is calibrated by experimental results. DVAs performance is analyzed by evaluating total cable energy, and a study of multiple DVAs positioning is conducted. The results are compared to the existing regulations showing situations where proper positioning, different from the standard, can lead to better performance of the DVA. Results also show situations where the use of multiple DVAs is appropriate.

Keywords: dynamical vibration absorber, finite element method, overhead transmission lines, structural dynamics

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Authors: Maedeh Pourmajidian, Joseph R. McDermid

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Advanced High Strength Steels are revolutionizing both the steel and automotive industries due to their high specific strength and ability to absorb energy during crash events. This allows manufacturers to design vehicles with significantly increased fuel efficiency without compromising passenger safety. To maintain the structural integrity of the fabricated parts, they must be protected from corrosion damage through continuous hot-dip galvanizing process, which is challenging due to selective oxidation of Mn and Si on the surface of this AHSSs. The effects of process atmosphere oxygen partial pressure and small additions of Sn on the selective oxidation of a medium-Mn C-6Mn-2Si advanced high strength steel was investigated. Intercritical annealing heat treatments were carried out at 690˚C in an N2-5%H2 process atmosphere under dew points ranging from –50˚C to +5˚C. Surface oxide chemistries, morphologies, and thicknesses were determined at a variety of length scales by several techniques, including SEM, TEM+EELS, and XPS. TEM observations of the sample cross-sections revealed the transition to internal oxidation at the +5˚C dew point. EELS results suggested that the internal oxides network was composed of a multi-layer oxide structure with varying chemistry from oxide core towards the outer part. The combined effect of employing a known surface active element as a function of process atmosphere on the surface structure development and the possible impact on reactive wetting of the steel substrates by the continuous galvanizing zinc bath will be discussed.

Keywords: 3G AHSS, hot-dip galvanizing, oxygen partial pressure, selective oxidation

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Authors: Vorya Shabrandi

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The study factors of poverty and inequality causes in countries is the subject of many scholars and economists in the last century, theorists in various areas of economic science know different factors as the roots of poverty and inequality in Iran after the 1979 revolution. Economists have emphasized political elements and political scientists on political elements. This research reviews the political economy of poverty and corruption in Iran after the revolution. The findings of this research, based on AcemOgluand Robinson theory, show how the institutional structural dependence of Iran's economy to raw has led to the growth of its non-economic economic institutions and its consequence of the continuity of the release and looting economy and poverty and inequality in Iran's political economy Is. This research was carried out using descriptive-analytical and comparative methods. Many economists try to justify the conditions of the country based on war, sanctions; And the external factors, and ... knows. In this study, we tried to examine the roots of poverty and the looting economy of Iran by implementing Research AcemOgluand Robinson on the institutions and roots of poverty. Looking for a framework for understanding why countries, such as Iran, the reason for the difference in revenue in different countries, as well as the poor or wealth of countries, regardless of the non-effective and non-professional institutions, and why inefficient institutions in some countries, such as Iran, such as Iran It remains and does not have a voluntary political powers to change these institutions. Findings The research shows that institutions are broadly the main reason for the roots of the robust and looting economy (poverty and inequality) in Iran.

Keywords: Iran, plunderable (Loot) economy, raw shopping, poverty and inequality, acem oglu and robinson, non-inclusive institutions

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Authors: Husham Bayazed

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Immune responses following surgical trauma play a pivotal role in predicting postoperative outcomes from healing and recovery to postoperative complications. Postoperative complications, including infections and protracted recovery, occur in a significant number of about 300 million surgeries performed annually worldwide. Complications cause personal suffering along with a significant economic burden on the healthcare system in any community. The accurate prediction of postoperative complications and patient-targeted interventions for their prevention remain major clinical provocations. Recent Findings: Recent studies are focusing on immune dysregulation mechanisms that occur in response to surgical trauma as a key determinant of postoperative complications. Antecedent studies mainly were plunging into the detection of inflammatory plasma markers, which facilitate in providing important clues regarding their pathogenesis. However, recent Single-cell technologies, such as mass cytometry or single-cell RNA sequencing, have markedly enhanced our ability to understand the immunological basis of postoperative immunological trauma complications and to identify their prognostic biological signatures. Summary: The advent of proteomic technologies has significantly advanced our ability to predict the risk of postoperative complications. Multiomic modeling of patients' immune states holds promise for the discovery of preoperative predictive biomarkers and providing patients and surgeons with information to improve surgical outcomes. However, more studies are required to accurately predict the risk of postoperative complications in individual patients.

Keywords: immune dysregulation, postoperative complications, surgical trauma, flow cytometry

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Authors: Majid Samiee Zenoozian

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The Persian Gulf is a bordering sea with an normal depth of 35 m and a supreme depth of 100 m near its narrow appearance. Its lengthen bathymetric axis divorces two main geological shires — the steady Arabian Foreland and the unbalanced Iranian Fold Belt — which are imitated in the conflicting shore and bathymetric morphologies of Arabia and Iran. The sediments were experimented with from 72 offshore positions through an oceanographic cruise in the winter of 2018. Throughout the observation era, several storms and river discharge actions happened, as well as the major flood on record since 1982. Suspended-sediment focus at all three sites varied in reaction to both wave resuspension and advection of river-derived sediments. We used hydrological models to evaluation and associate the wave height and inundation distance required to carriage the rocks inland. Our results establish that no known or possible storm happening on the Makran coast is accomplished of detaching and transporting the boulders. The fluid mud consequently is conveyed seaward due to gravitational forcing. The measured sediment focus and velocity profiles on the shelf provide a strong indication to provision this assumption. The sediment model is joined with a 3D hydrodynamic module in the Environmental Fluid Dynamics Code (EFDC) model that offers data on estuarine rotation and salinity transport under normal temperature conditions. 3-D sediment transport from model simulations specify dynamic sediment resuspension and transport near zones of highly industrious oyster beds.

Keywords: sediment transport, storm, coast, fluid dynamics

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Authors: Ozgu Hafizoglu

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Analogy is an essential tool of human cognition that enables connecting diffuse and diverse systems with attributional, deep structural, casual relations that are essential to learning, to innovation in artificial worlds, and to discovery in science. Cognitive Model of Analogy (CMA) leads and creates information pattern transfer within and between domains and disciplines in science. This paper demonstrates the Cognitive Model of Analogy (CMA) as an evolutionary approach to scientific research. The model puts forward the challenges of deep uncertainty about the future, emphasizing the need for flexibility of the system in order to enable reasoning methodology to adapt to changing conditions. In this paper, the model of analogical reasoning is created based on brain cells, their fractal, and operational forms within the system itself. Visualization techniques are used to show correspondences. Distinct phases of the problem-solving processes are divided thusly: encoding, mapping, inference, and response. The system is revealed relevant to brain activation considering each of these phases with an emphasis on achieving a better visualization of the brain cells: glial cells, axons, axon terminals, and neurons, relative to matching conditions of analogical reasoning and relational information. It’s found that encoding, mapping, inference, and response processes in four-term analogical reasoning are corresponding with the fractal and operational forms of brain cells: glial, axons, and neurons.

Keywords: analogy, analogical reasoning, cognitive model, brain and glials

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Authors: Ayse Cobanoglu

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Working memory can be defined as a workspace that holds and regulates active information in mind. This study investigates individual changes in children's working memory from kindergarten to first grade. The main purpose of the study is whether parental discipline methods and child impulsive/overactive behaviors affect children's working memory initial status and growth rate, controlling for gender, minority status, and socioeconomic status (SES). A linear growth curve model with the first four waves of the Early Childhood Longitudinal Study-Kindergarten Cohort of 2011 (ECLS-K:2011) is performed to analyze the individual growth of children's working memory longitudinally (N=3915). Results revealed that there is a significant variation among students' initial status in the kindergarten fall semester as well as the growth rate during the first two years of schooling. While minority status, SES, and children's overactive/impulsive behaviors influenced children's initial status, only SES and minority status were significantly associated with the growth rate of working memory. For parental discipline methods, such as giving a warning and ignoring the child's negative behavior, are also negatively associated with initial working memory scores. Following that, students' working memory growth rate is examined, and students with lower SES as well as minorities showed a faster growth pattern during the first two years of schooling. However, the findings of parental disciplinary methods on working memory growth rates were mixed. It can be concluded that schooling helps low-SES minority students to develop their working memory.

Keywords: growth curve modeling, impulsive/overactive behaviors, parenting, working memory

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Authors: Kassahun Birhanu Tadesse, Megersa Olumana Dinka

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Reliable future river flow information is basic for planning and management of any river systems. For data scarce river system having only a river flow records like the Waterval River, a univariate time series models are appropriate for river flow forecasting. In this study, a univariate Seasonal Autoregressive Integrated Moving Average (SARIMA) model was applied for forecasting Waterval River flow using GRETL statistical software. Mean monthly river flows from 1960 to 2016 were used for modeling. Different unit root tests and Mann-Kendall trend analysis were performed to test the stationarity of the observed flow time series. The time series was differenced to remove the seasonality. Using the correlogram of seasonally differenced time series, different SARIMA models were identified, their parameters were estimated, and diagnostic check-up of model forecasts was performed using white noise and heteroscedasticity tests. Finally, based on minimum Akaike Information (AIc) and Hannan-Quinn (HQc) criteria, SARIMA (3, 0, 2) x (3, 1, 3)12 was selected as the best model for Waterval River flow forecasting. Therefore, this model can be used to generate future river information for water resources development and management in Waterval River system. SARIMA model can also be used for forecasting other similar univariate time series with seasonality characteristics.

Keywords: heteroscedasticity, stationarity test, trend analysis, validation, white noise

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Authors: L. Freeborn

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Recent years have seen an increasing number of neuroimaging studies related to language learning as imaging techniques such as fMRI and EEG have become more widely accessible to researchers. By using a variety of structural and functional neuroimaging techniques, these studies have already made considerable progress in terms of our understanding of neural networks and processing related to first and second language acquisition. However, the methodological designs employed in neuroimaging studies to test language learning have been questioned by applied linguists working within the field of second language acquisition (SLA). One of the major criticisms is that tasks designed to measure language learning gains rarely have a communicative function, and seldom assess learners’ ability to use the language in authentic situations. This brings the validity of many neuroimaging tasks into question. The fundamental reason why people learn a language is to communicate, and it is well-known that both first and second language proficiency are developed through meaningful social interaction. With this in mind, the SLA field is in agreement that second language acquisition and proficiency should be measured through learners’ ability to communicate in authentic real-life situations. Whilst authenticity is not always possible to achieve in a classroom environment, the importance of task authenticity should be reflected in the design of language assessments, teaching materials, and curricula. Tasks that bear little relation to how language is used in real-life situations can be considered to lack construct validity. This paper first describes the typical tasks used in neuroimaging studies to measure language gains and proficiency, then analyses to what extent these tasks can validly assess these constructs.

Keywords: neuroimaging studies, research design, second language acquisition, task validity

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Authors: Rao Fu, Jie Chen, Shutian Zeng, Yiping Zhuang, Agus Sudjianto

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Generative Adversarial Net (GAN) has proved to be a powerful machine learning tool in image data analysis and generation. In this paper, we propose to use Conditional Generative Adversarial Net (CGAN) to learn and simulate time series data. The conditions include both categorical and continuous variables with different auxiliary information. Our simulation studies show that CGAN has the capability to learn different types of normal and heavy-tailed distributions, as well as dependent structures of different time series. It also has the capability to generate conditional predictive distributions consistent with training data distributions. We also provide an in-depth discussion on the rationale behind GAN and the neural networks as hierarchical splines to establish a clear connection with existing statistical methods of distribution generation. In practice, CGAN has a wide range of applications in market risk and counterparty risk analysis: it can be applied to learn historical data and generate scenarios for the calculation of Value-at-Risk (VaR) and Expected Shortfall (ES), and it can also predict the movement of the market risk factors. We present a real data analysis including a backtesting to demonstrate that CGAN can outperform Historical Simulation (HS), a popular method in market risk analysis to calculate VaR. CGAN can also be applied in economic time series modeling and forecasting. In this regard, we have included an example of hypothetical shock analysis for economic models and the generation of potential CCAR scenarios by CGAN at the end of the paper.

Keywords: conditional generative adversarial net, market and credit risk management, neural network, time series

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Authors: Tasneem Halawani, Yamen Khateeb

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With customers’ ever-increasing expectations for fast services provisioning for all their business needs, information technology (IT) organizations, as business partners, have to cope with this demanding environment and deliver their services in the most effective and efficient way. The purpose of this paper is to identify optimization and automation opportunities for the top requested IT services in a heterogenic digital environment and widely spread customer base. In collaboration with systems, processes, and subject matter experts (SMEs), the processes in scope were approached by analyzing four-year related historical data, identifying and surveying stakeholders, modeling the as-is processes, and studying systems integration/automation capabilities. This effort resulted in identifying several pain areas, including standardization, unnecessary customer and IT involvement, manual steps, systems integration, and performance measurement. These pain areas were addressed by standardizing the top five requested IT services, eliminating/automating 43 steps, and utilizing a single platform for end-to-end process execution. In conclusion, the optimization of IT service request processes in a heterogenic digital environment and widely spread customer base is challenging, yet achievable without compromising the service quality and customers’ added value. Further studies can focus on measuring the value of the eliminated/automated process steps to quantify the enhancement impact. Moreover, a similar approach can be utilized to optimize other IT service requests, with a focus on business criticality.

Keywords: automation, customer value, heterogenic, integration, IT services, optimization, processes

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Authors: Joao Garretto, R. J. Yarwood, Vamsi Borra, Frank Li

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Bluetooth Low Energy (BLE) has emerged as one of the main wireless communication technologies used in low-power electronics, such as wearables, beacons, and Internet of Things (IoT) devices. BLE’s energy efficiency characteristic, smart mobiles interoperability, and Over the Air (OTA) capabilities are essential features for ultralow-power devices, which are usually designed with size and cost constraints. Most current research regarding the power analysis of BLE devices focuses on the theoretical aspects of the advertising and scanning cycles, with most results being presented in the form of mathematical models and computer software simulations. Such computer modeling and simulations are important for the comprehension of the technology, but hardware measurement is essential for the understanding of how BLE devices behave in real operation. In addition, recent literature focuses mostly on the BLE technology, leaving possible applications and its analysis out of scope. In this paper, a coin cell battery-powered BLE Data Acquisition Device, with a 4-in-1 sensor and one accelerometer, is proposed and evaluated with respect to its Power Consumption. First, evaluations of the device in advertising mode with the sensors turned off completely, followed by the power analysis when each of the sensors is individually turned on and data is being transmitted, and concluding with the power consumption evaluation when both sensors are on and respectively broadcasting the data to a mobile phone. The results presented in this paper are real-time measurements of the electrical current consumption of the BLE device, where the energy levels that are demonstrated are matched to the BLE behavior and sensor activity.

Keywords: bluetooth low energy, power analysis, BLE advertising cycle, wireless sensor node

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Authors: Penrug Pengsombut, Worawut Hamarn, Teerawuth Suwannasri, Kittiphong Songrukkiat, Kanatip Ratanachoo

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PTT Public Company Limited or simply PTT has played an important role in strengthening national energy security of the Kingdom of Thailand by transporting natural gas to customers in power, industrial and commercial sectors since 1981. PTT has been constructing and operating natural gas pipeline system of over 4,500-km network length both onshore and offshore laid through different area classifications i.e., marine, forest, agriculture, rural, urban, and city areas. During project development phase, an Environmental Impact Assessment (EIA) is conducted and submitted to the Office of Natural Resources and Environmental Policy and Planning (ONEP) for approval before project construction commencement. Knowledge and experiences gained and revealed from EIA in the past projects definitely are developed to further advance EIA study process for newly 5th Transmission Natural Gas Pipeline Project (5TP) with approximately 415 kilometers length. The preferred pipeline route is selected and justified by SMARTi map, an advance digital one-map platform with consists of multiple layers geographic and environmental information. Sensitive area impact focus (SAIF) is a practicable impact assessment methodology which appropriate for a particular long distance infrastructure project such as 5TP. An environmental modeling simulation is adopted into SAIF methodology for impact quantified in all sensitive areas whereas other area along pipeline right-of-ways is typically assessed as an impact representative. Resulting time and cost deduction is beneficial to project for early start.

Keywords: environmental impact assessment, EIA, natural gas pipeline, sensitive area impact focus, SAIF

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Authors: Seon Yeong Byeon, Hwa Sung Shin

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Nanofibrous sheets are of interest in the beauty industries due to the properties of moisturizing, adhesion to skin and delivery of nutrient materials. The benefit and function of the cosmetic products should not be considered without safety thus a non-toxic manufacturing process is ideal when fabricating the products. In this study, we have developed cosmetic patches consisting of alginate and Spirulina extract, a marine resource which has antibacterial and antioxidant effects, without addition of harmful cross-linkers. The patches obtained their structural stabilities by layer-upon-layer electrospinning of an alginate layer on a formerly spread polycaprolactone (PCL) layer instead of crosslinking method. The morphological characteristics, release of Spirulina extract, water absorption, skin adhesiveness and cytotoxicity of the double-layered patches were assessed. The image of scanning electron microscopy (SEM) showed that the addition of Spirulina extract has made the fiber diameter of alginate layers thinner. Impregnation of Spirulina extract increased their hydrophilicity, moisture absorption ability and skin adhesive ability. In addition, wetting the pre-dried patches resulted in releasing the Spirulina extract within 30 min. The patches were detected to have no cytotoxicity in the human keratinocyte cell-based MTT assay, but rather showed increased cell viability. All the results indicate the bioactive and hydro-adhesive double-layered patches have an excellent applicability to bioproducts for personal skin care in the trend of ‘A mask pack a day’.

Keywords: alginate, cosmetic patch, electrospun nanofiber, polycaprolactone, Spirulina extract

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Authors: Manavjit Singh Dhindsa, Ursula Das, Kshirasagar Naik, Marzia Zaman, Richard Purcell, Srinivas Sampalli, Abdul Mutakabbir, Chung-Horng Lung, Thambirajah Ravichandran

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These days, wildland fires, also known as forest fires, are more prevalent than ever. Wildfires have major repercussions that affect ecosystems, communities, and the environment in several ways. Wildfires lead to habitat destruction and biodiversity loss, affecting ecosystems and causing soil erosion. They also contribute to poor air quality by releasing smoke and pollutants that pose health risks, especially for individuals with respiratory conditions. Wildfires can damage infrastructure, disrupt communities, and cause economic losses. The economic impact of firefighting efforts, combined with their direct effects on forestry and agriculture, causes significant financial difficulties for the areas impacted. This research explores different forest fire spread models and presents a comprehensive review of various techniques and methodologies used in the field. A forest fire spread model is a computational or mathematical representation that is used to simulate and predict the behavior of a forest fire. By applying scientific concepts and data from empirical studies, these models attempt to capture the intricate dynamics of how a fire spreads, taking into consideration a variety of factors like weather patterns, topography, fuel types, and environmental conditions. These models assist authorities in understanding and forecasting the potential trajectory and intensity of a wildfire. Emphasizing the need for a comprehensive understanding of wildfire dynamics, this research explores the approaches, assumptions, and findings derived from various models. By using a comparison approach, a critical analysis is provided by identifying patterns, strengths, and weaknesses among these models. The purpose of the survey is to further wildfire research and management techniques. Decision-makers, researchers, and practitioners can benefit from the useful insights that are provided by synthesizing established information. Fire spread models provide insights into potential fire behavior, facilitating authorities to make informed decisions about evacuation activities, allocating resources for fire-fighting efforts, and planning for preventive actions. Wildfire spread models are also useful in post-wildfire mitigation strategies as they help in assessing the fire's severity, determining high-risk regions for post-fire dangers, and forecasting soil erosion trends. The analysis highlights the importance of customized modeling approaches for various circumstances and promotes our understanding of the way forest fires spread. Some of the known models in this field are Rothermel’s wildland fuel model, FARSITE, WRF-SFIRE, FIRETEC, FlamMap, FSPro, cellular automata model, and others. The key characteristics that these models consider include weather (includes factors such as wind speed and direction), topography (includes factors like landscape elevation), and fuel availability (includes factors like types of vegetation) among other factors. The models discussed are physics-based, data-driven, or hybrid models, also utilizing ML techniques like attention-based neural networks to enhance the performance of the model. In order to lessen the destructive effects of forest fires, this initiative aims to promote the development of more precise prediction tools and effective management techniques. The survey expands its scope to address the practical needs of numerous stakeholders. Access to enhanced early warning systems enables decision-makers to take prompt action. Emergency responders benefit from improved resource allocation strategies, strengthening the efficacy of firefighting efforts.

Keywords: artificial intelligence, deep learning, forest fire management, fire risk assessment, fire simulation, machine learning, remote sensing, wildfire modeling

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Authors: Tumelo W. P. Seadira, Thabang Ntho, Cornelius M. Masuku, Michael S. Scurrell

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A ternary Cu/TiO2/rGO photocatalysts was prepared using solvothermal method. Firstly, pure anatase TiO2 hollow spheres were prepared with titanium butoxide, ethanol, ammonium sulphate, and urea via hydrothermal method; and Cu nanoparticles were subsequently loaded on the surface of the hollow spheres by wet impregnation. During the solvothermal process, the deposition and well dispersion of Cu-TiO2 hollow spheres composites onto the graphene oxide surface, as well as the reduction of graphene oxide to graphene were achieved. The morphological and structural properties of the prepared samples were characterized by Brunauer-Emmett-Tellet (BET), X-ray Diffraction (XRD), Scanning Electron Microscope (SEM), Transmission Electron Microscopy (TEM), and UV-vis DRS, and photoelectrochemical. The activities of the prepared catalysts were tested for hydrogen production via simultaneous photocatalytic water-splitting and glycerol reforming under visible light irradiation. The excellent photocatalytic activity of the Cu-TiO2-hollow-spheres/rGO catalyst was attributed the rGO which acts as both storage and transferor of electrons generated at the Cu and TiO2 heterojunction, thus increasing the electron-hole pairs separation. This paper reports the preparation of photocatalyst which is highly active by coupling reduced graphene oxide with nano-structured TiO2 with high surface area that can efficiently harvest the visible light for effective water-splitting and glycerol photocatalytic reforming in order to achieve efficient hydrogen evolution.

Keywords: glycerol reforming, hydrogen evolution, graphene oxide, Cu/TiO2-hollow-spheres/rGO

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Authors: Emery A. Coppola Jr., Suna Cinar, Ferenc Szidarovszky

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Over-pumping of groundwater resources is a serious problem world-wide. In addition to depleting this valuable resource, hydraulically connected sensitive ecological resources like wetlands and surface water bodies are often impacted and even destroyed by over-pumping. Effectively managing groundwater in a way that satisfy human demand while preserving natural resources is a daunting challenge that will only worsen with growing human populations and climate change. As presented in this paper, a numerical flow model developed for a hypothetical but realistic groundwater/surface water system was combined with formal optimization. Response coefficients were used in an optimization management model to maximize groundwater pumping in a complex, multi-layered aquifer system while protecting against groundwater over-draft, streamflow depletion, and wetland impacts. Pumping optimization was performed for different constraint sets that reflect different resource protection preferences, yielding significantly different optimal pumping solutions. A sensitivity analysis on the optimal solutions was performed on select response coefficients to identify differences between wet and dry periods. Stochastic optimization was also performed, where uncertainty associated with changing irrigation demand due to changing weather conditions are accounted for. One of the strengths of this optimization approach is that it can efficiently and accurately identify superior management strategies that minimize risk and adverse environmental impacts associated with groundwater pumping under different hydrologic conditions.

Keywords: numerical groundwater flow modeling, water management optimization, groundwater overdraft, streamflow depletion

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Authors: Sanghamitra Acharya, Suwarna Datar

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Extensive use of digital and smart communication createsprolong expose of unwanted electromagnetic (EM) radiations. This harmful radiation creates not only malfunctioning of nearby electronic gadgets but also severely affects a human being. So, a suitable microwave absorbing material (MAM) becomes a necessary urge in the field of stealth and radar technology. Initially, Aluminum based hexa ferrite was prepared by sol-gel technique and for carbon derived composite was prepared by the simple one port chemical reduction method. Finally, composite films of Poly (Vinylidene) Fluoride (PVDF) are prepared by simple gel casting technique. Present work demands that aluminum-based hexaferrite phase conjugated with graphene in PVDF matrix becomes a suitable candidate both in commercially important X and Ku band. The structural and morphological nature was characterized by X-Ray diffraction (XRD), Field emission-scanning electron microscope (FESEM) and Raman spectra which conforms that 30-40 nm particles are well decorated over graphene sheet. Magnetic force microscopy (MFM) and conducting force microscopy (CFM) study further conforms the magnetic and conducting nature of composite. Finally, shielding effectiveness (SE) of the composite film was studied by using Vector network analyzer (VNA) both in X band and Ku band frequency range and found to be more than 30 dB and 40 dB, respectively. As prepared composite films are excellent microwave absorbers.

Keywords: carbon nanocomposite, microwave absorbing material, electromagnetic shielding, hexaferrite

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Authors: M. Ibrahim Ali, M. El Dawi, M. A. Mohamed Ali

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In this study, the use of magnetic gradient survey, and the geoelectrical ground methods used together to explore archaeological features in Nuri’s pyramids area. Research methods used and the procedures and methodologies have taken full right during the study. The magnetic survey method was used to search for archaeological features using (Geoscan Fluxgate Gradiometer (FM36)). The study area was divided into a number of squares (networks) exactly equal (20 * 20 meters). These squares were collected at the end of the study to give a major network for each region. Networks also divided to take the sample using nets typically equal to (0.25 * 0.50 meter), in order to give a more specific archaeological features with some small bipolar anomalies that caused by buildings built from fired bricks. This definition is important to monitor many of the archaeological features such as rooms and others. This main network gives us an integrated map displayed for easy presentation, and it also allows for all the operations required using (Geoscan Geoplot software). The parallel traverse is the main way to take readings of the magnetic survey, to get out the high-quality data. The study area is very rich in old buildings that vary from small to very large. According to the proportion of the sand dunes and the loose soil, most of these buildings are not visible from the surface. Because of the proportion of the sandy dry soil, there is no connection between the ground surface and the electrodes. We tried to get electrical readings by adding salty water to the soil, but, unfortunately, we failed to confirm the magnetic readings with electrical readings as previously planned.

Keywords: archaeological features, independent grids, magnetic gradient, Nuri pyramid

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Authors: J. Constanzo, B. Paquette, G. Charest, L. Masson-Côté, M. Guillot

Abstract:

Targeted and whole-brain irradiation in humans can result in significant side effects causing decreased patient quality of life. To adequately investigate structural and functional alterations after stereotactic radiosurgery, preclinical studies are needed. The first step is to establish a robust standardized method of targeted irradiation on small regions of the rat brain. Eleven euthanized male Fischer rats were imaged in a stereotactic bed, by computed tomographic (CT), to estimate positioning variations regarding to the bregma skull reference point. Using a rat brain atlas and the stereotactic bregma coordinates assessed from CT images, various regions of the brain were delimited and a treatment plan was generated. A dose of 37 Gy at 30% isodose which corresponds to 100 Gy in 100% of the target volume (X = 98.1; Y = 109.1; Z = 100.0) was set by Leksell Gamma Plan using sectors number 4, 5, 7, and 8 of the Gamma Knife unit with the 4-mm diameter collimators. Effects of positioning accuracy of the rat brain on the dose deposition were simulated by Gamma Plan and validated with dosimetric measurements. Our results showed that 90% of the target volume received 110 ± 4.7 Gy and the maximum of deposited dose was 124 ± 0.6 Gy, which corresponds to an excellent relative standard deviation of 0.5%. This dose deposition calculated with the Gamma Plan was validated with the dosimetric films resulting in a dose-profile agreement within 2%, both in X- and Z-axis,. Our results demonstrate the feasibility to standardize the irradiation procedure of a small volume in the rat brain using a Gamma Knife.

Keywords: brain irradiation, dosimetry, gamma knife, small-animal irradiation, stereotactic radiosurgery (SRS)

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Authors: Sadaf Karkoodi, Hassan Karampour

Abstract:

There is a growing interest in the construction industry towards hybrid systems. The hybrid systems use construction materials such as timber, steel, and concrete smartly, can be prefabricated, and are cost-effective and sustainable solutions to an industry targeting reduced carbon footprint. Moreover, in case of periodical shortage in timber resources, reusable and waste wood such as fibres can be used in the hybrid modules, which facilitates the circular economy. In this research, a hybrid frame is proposed and experimentally validated by introducing dried wood fibre products inside cold-formed steel square hollow sections without using any adhesives. As such, fibre-filled steel tubular (FFST) columns, beams, and 2D frames are manufactured and tested. The results show that the FFST columns have stiffness and strength 44% and 55% higher than cold-formed steel columns, respectively. The bearing strength of the FFST beams shows an increase of 39.5% compared to steel only. The flexural stiffness and strength of the FFST beams are 8.5% and 28% higher than the bare steel beams, respectively. The FFST frame depicted an 18.4% higher ultimate load capacity than the steel-only frame under a mid-point concentrated load. Moreover, the FFST beam-to-column bolted connection showed high ductile performance. The initial results and the proposed simple manufacturing process suggest that the proposed FFST concept can be upscaled and used in real structures.

Keywords: wood fibre, reusing wood, fibre-filled steel, hybrid construction

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Authors: Ka C. Lam, Oluwafunmibi S. Idowu

Abstract:

Uncertainty in cost is a major factor affecting performance of construction projects. To our knowledge, several conceptual cost models have been developed with varying degrees of accuracy. Incorporating conceptual quantities into conceptual cost models could improve the accuracy of early predesign cost estimates. Hence, the development of quantity models for estimating conceptual quantities of framed reinforced concrete structures using supervised machine learning is the aim of the current research. Using measured quantities of structural elements and design variables such as live loads and soil bearing pressures, response and predictor variables were defined and used for constructing conceptual quantities models. Twenty-four models were developed for comparison using a combination of non-parametric support vector regression, linear regression, and bootstrap resampling techniques. R programming language was used for data analysis and model implementation. Gross soil bearing pressure and gross floor loading were discovered to have a major influence on the quantities of concrete and reinforcement used for foundations. Building footprint and gross floor loading had a similar influence on beams and slabs. Future research could explore the modelling of other conceptual quantities for walls, finishes, and services using machine learning techniques. Estimation of conceptual quantities would assist construction planners in early resource planning and enable detailed performance evaluation of early cost predictions.

Keywords: bootstrapping, conceptual quantities, modelling, reinforced concrete, support vector regression

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Authors: Latha Nataraj, Todd Henry, Micheal Wallock, Asha Hall, Christine Hatter, Babak Anasori, Yury Gogotsi

Abstract:

Multifunctional composites with enhanced strength and toughness for superior damage tolerance are essential for advanced aerospace and military applications. Detection of structural changes prior to visible damage may be achieved by incorporating fillers with tunable properties such as two-dimensional (2D) nanomaterials with high aspect ratios and more surface-active sites. While 2D graphene with large surface areas, good mechanical properties, and high electrical conductivity seems ideal as a filler, the single-atomic thickness can lead to bending and rolling during processing, requiring post-processing to bond to polymer matrices. Lately, an emerging family of 2D transition metal carbides and nitrides, MXenes, has attracted much attention since their discovery in 2011. Metallic electronic conductivity and good mechanical properties, even with increased polymer content, coupled with hydrophilicity make MXenes a good candidate as a filler material in polymer composites and exceptional as multifunctional damage indicators in composites. Here, we systematically study MXene-based (Ti₃C₂) coated on glass fibers for fiber reinforced polymer composite for self-sensing using microscopy and micromechanical testing. Further testing is in progress through the investigation of local variations in optical, acoustic, and thermal properties within the damage sites in response to strain caused by mechanical loading.

Keywords: damage sensing, fiber composites, MXene, self-sensing

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Authors: Lizhi Ma, Dan Liu

Abstract:

Pipeline parallelism has been widely used to accelerate distributed deep learning to alleviate GPU memory bottlenecks and to ensure that models can be trained and deployed smoothly under limited graphics memory conditions. However, in highly heterogeneous distributed clusters, traditional model partitioning methods are not able to achieve load balancing. The overlap of communication and computation is also a big challenge. In this paper, HePipe is proposed, an efficient pipeline parallel training method for highly heterogeneous clusters. According to the characteristics of the neural network model pipeline training task, oriented to the 2-level heterogeneous cluster computing topology, a training method based on the 2-level stage division of neural network modeling and partitioning is designed to improve the parallelism. Additionally, a multi-forward 1F1B scheduling strategy is designed to accelerate the training time of each stage by executing the computation units in advance to maximize the overlap between the forward propagation communication and backward propagation computation. Finally, a dynamic recomputation strategy based on task memory requirement prediction is proposed to improve the fitness ratio of task and memory, which improves the throughput of the cluster and solves the memory shortfall problem caused by memory differences in heterogeneous clusters. The empirical results show that HePipe improves the training speed by 1.6×−2.2× over the existing asynchronous pipeline baselines.

Keywords: pipeline parallelism, heterogeneous cluster, model training, 2-level stage partitioning

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Authors: Mustapha Kamel Mihoubi, Mohamed Essadik Kerkar

Abstract:

Probabilistic risk analysis models are used to provide a better understanding of the reliability and structural failure of works, including when calculating the stability of large structures to a major risk in the event of an accident or breakdown. This work is interested in the study of the probability of failure of concrete dams through the application of reliability analysis methods including the methods used in engineering. It is in our case, the use of level 2 methods via the study limit state. Hence, the probability of product failures is estimated by analytical methods of the type first order risk method (FORM) and the second order risk method (SORM). By way of comparison, a level three method was used which generates a full analysis of the problem and involves an integration of the probability density function of random variables extended to the field of security using the Monte Carlo simulation method. Taking into account the change in stress following load combinations: normal, exceptional and extreme acting on the dam, calculation of the results obtained have provided acceptable failure probability values which largely corroborate the theory, in fact, the probability of failure tends to increase with increasing load intensities, thus causing a significant decrease in strength, shear forces then induce a shift that threatens the reliability of the structure by intolerable values of the probability of product failures. Especially, in case the increase of uplift in a hypothetical default of the drainage system.

Keywords: dam, failure, limit-state, monte-carlo, reliability, probability, simulation, sliding, taylor

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Authors: T. Nishido, S. Fukumoto

Abstract:

The functions of movable bearings decline due to corrosion and sediments. As the result, they cannot move or rotate according to the behaviors of girders. Because of the constraints, the bending moments are generated by the horizontal reaction forces and the heights of girders. Under these conditions, the authors obtained the following results by analysis and experiment. Tensile stresses due to the moments occurred at temperature fluctuations. The large tensile stresses on concrete slabs around the bearings caused cracks. Even if concrete slabs are newly replaced, cracks will come out again with function declined bearings. The functional declines of bearings are generally found by using displacement gauges. However the method is not suitable for long-term measurements. We focused on the change in the strains at the bearings and the lower flanges near them at temperature fluctuations. It was found that their strains were particularly large when the movements of the bearings were constrained. Therefore, we developed a long-term health monitoring wireless system with FBG (Fiber Bragg Grating) sensors which were attached to bearings and lower flanges. The FBG sensors have the characteristics such as non-electrical influence, resistance to weather, and high strain sensitivity. Such characteristics are suitable for long-term measurements. The monitoring system was inexpensive because it was limited to the purpose of measuring strains and temperature. Engineers can monitor the behaviors of bearings in real time with the wireless system. If an office is away from bridge sites, the system will save traveling time and cost.

Keywords: bridge bearing, concrete slab, 　FBG sensor, health monitoring

Procedia PDF Downloads 218