Search results for: crack growth prediction

Authors: Avinash Nayak, Debopam Das

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The current work implements the variational principle to find the optimum initial perturbation that provides maximum growth in an impulsively blocked channel flow. The conventional method for studying temporal stability has always been through modal analysis. In most of the transient flows, this modal analysis is still followed with the quasi-steady assumption, i.e. change in base flow is much slower compared to perturbation growth rate. There are other studies where transient analysis on time dependent flows is done by formulating the growth of perturbation as an initial value problem. But the perturbation growth is sensitive to the initial condition. This study intends to find the initial perturbation that provides the maximum growth at a later time. Here, the expression of base flow for blocked channel is derived and the formulation is based on the two dimensional perturbation with stream function representing the perturbation quantity. Hence, the governing equation becomes the Orr-Sommerfeld equation. In the current context, the cost functional is defined as the ratio of disturbance energy at a terminal time 'T' to the initial energy, i.e. G(T) = ||q(T)||2/||q(0)||2 where q is the perturbation and ||.|| defines the norm chosen. The above cost functional needs to be maximized against the initial perturbation distribution. It is achieved with the constraint that perturbation follows the basic governing equation, i.e. Orr-Sommerfeld equation. The corresponding adjoint equation is derived and is solved along with the basic governing equation in an iterative manner to provide the initial spatial shape of the perturbation that provides the maximum growth G (T). The growth rate is plotted against time showing the development of perturbation which achieves an asymptotic shape. The effects of various parameters, e.g. Reynolds number, are studied in the process. Thus, the study emphasizes on the usage of optimal perturbation and its growth to understand the stability characteristics of time dependent flows. The assumption of quasi-steady analysis can be verified against these results for the transient flows like impulsive blocked channel flow.

Keywords: blocked channel flow, calculus of variation, hydrodynamic stability, optimal perturbation

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Authors: Morgan Johansson, Joosef Leppanen, Mathias Flansbjer, Fabio Lozano, Josef Makdesi

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Concrete is commonly used for protective structures and how impact loading affects different types of concrete structures is an important issue. Often the knowledge gained from static loading is also used in the design of impulse loaded structures. A large plastic deformation capacity is essential to obtain a large energy absorption in an impulse loaded structure. However, the structural response of an impact loaded concrete beam may be very different compared to a statically loaded beam. Consequently, the plastic deformation capacity and failure modes of the concrete structure can be different when subjected to dynamic loads; and hence it is not sure that the observations obtained from static loading are also valid for dynamic loading. The aim of this paper is to investigate the residual plastic deformation capacity in reinforced concrete beams subjected to drop weight impact tests. A test-series consisting of 18 simply supported beams (0.1 x 0.1 x 1.18 m, ρs = 0.7%) with a span length of 1.0 m and subjected to a point load in the beam mid-point, was carried out. 2x6 beams were first subjected to drop weight impact tests, and thereafter statically tested until failure. The drop in weight had a mass of 10 kg and was dropped from 2.5 m or 5.0 m. During the impact tests, a high-speed camera was used with 5 000 fps and for the static tests, a camera was used with 0.5 fps. Digital image correlation (DIC) analyses were conducted and from these the velocities of the beam and the drop weight, as well as the deformations and crack propagation of the beam, were effectively measured. Additionally, for the static tests, the applied load and midspan deformation were measured. The load-deformation relations for the beams subjected to an impact load were compared with 6 reference beams that were subjected to static loading only. The crack pattern obtained were compared using DIC, and it was concluded that the resulting crack formation depended much on the test method used. For the static tests, only bending cracks occurred. For the impact loaded beams, though, distinctive diagonal shear cracks also formed below the zone of impact and less wide shear cracks were observed in the region half-way to the support. Furthermore, due to wave propagation effects, bending cracks developed in the upper part of the beam during initial loading. The results showed that the plastic deformation capacity increased for beams subjected to drop weight impact tests from a high drop height of 5.0 m. For beams subjected to an impact from a low drop height of 2.5 m, though, the plastic deformation capacity was in the same order of magnitude as for the statically loaded reference beams. The beams tested were designed to fail due to bending when subjected to a static load. However, for the impact tested beams, one beam exhibited a shear failure at a significantly reduced load level when it was tested statically; indicating that there might be a risk of reduced residual load capacity for impact loaded structures.

Keywords: digital image correlation (DIC), drop weight impact, experiments, plastic deformation capacity, reinforced concrete

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Authors: Rabaya Basori, Arup K. Raychaudhuri

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In recent years, a variety of semiconductor nanowires (NWs) has been synthesized and used as basic building blocks for the development of electronic and optoelectronic nanodevices. Dielectrophoresis (DEP) has been widely investigated as a scalable technique to trap and manipulate polarizable objects. This includes biological cells, nanoparticles, DNA molecules, organic or inorganic NWs and proteins using electric field gradients. In this article, we have used DEP force to localize nanowire growth by physical vapor deposition (PVD) method as well as control of NW diameter on field assisted growth of the NWs of CuTCNQ (Cu-tetracyanoquinodimethane); a metal-organic charge transfer complex material which is well known of resistive switching. We report a versatile analysis platform, based on a set of nanogap electrodes, for the controlled growth of nanowire. Non-uniform electric field and dielectrophoretic force is created in between two metal electrodes, patterned by electron beam lithography process. Suspended CuTCNQ nanowires have been grown laterally between two electrodes in the vicinity of electric field and dielectric force by applying external bias. Growth and diameter dependence of the nanowires on external bias has been investigated in the framework of these two forces by COMSOL Multiphysics simulation. This report will help successful in-situ nanodevice fabrication with constrained number of NW and diameter without any post treatment.

Keywords: nanowire, dielectrophoretic force, confined growth, controlled diameter, comsol multiphysics simulation

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Authors: Olena Kavats, Dmitry Khramov, Kateryna Sergieieva, Vladimir Vasyliev, Iurii Kavats

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Geoinformation technologies of space agromonitoring are a means of operative decision making support in the tasks of managing the agricultural sector of the economy. Existing technologies use satellite images in the optical range of electromagnetic spectrum. Time series of optical images often contain gaps due to the presence of clouds and haze. A geoinformation technology is created. It allows to fill gaps in time series of optical images (Sentinel-2, Landsat-8, PROBA-V, MODIS) with radar survey data (Sentinel-1) and use information about agrometeorological conditions of the growing season for individual monitoring years. The technology allows to perform crop classification and mapping for spring-summer (winter and spring crops) and autumn-winter (winter crops) periods of vegetation, monitoring the dynamics of crop state seasonal changes, crop yield forecasting. Crop classification is based on supervised classification algorithms, takes into account the peculiarities of crop growth at different vegetation stages (dates of sowing, emergence, active vegetation, and harvesting) and agriculture land state characteristics (row spacing, seedling density, etc.). A catalog of samples of the main agricultural crops (Ukraine) is created and crop spectral signatures are calculated with the preliminary removal of row spacing, cloud cover, and cloud shadows in order to construct time series of crop growth characteristics. The obtained data is used in grain crop growth tracking and in timely detection of growth trends deviations from reference samples of a given crop for a selected date. Statistical models of crop yield forecast are created in the forms of linear and nonlinear interconnections between crop yield indicators and crop state characteristics (temperature, precipitation, vegetation indices, etc.). Predicted values of grain crop yield are evaluated with an accuracy up to 95%. The developed technology was used for agricultural areas monitoring in a number of Great Britain and Ukraine regions using EOS Crop Monitoring Platform (https://crop-monitoring.eos.com). The obtained results allow to conclude that joint use of Sentinel-1 and Sentinel-2 images improve separation of winter crops (rapeseed, wheat, barley) in the early stages of vegetation (October-December). It allows to separate successfully the soybean, corn, and sunflower sowing areas that are quite similar in their spectral characteristics.

Keywords: geoinformation technology, crop classification, crop yield prediction, agricultural monitoring, EOS Crop Monitoring Platform

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Authors: Amit Kumar Verma

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The present study deals with fire prediction model in Terai Arc Landscape, one of the most dramatic ecosystems in Asia where large, wide-ranging species such as tiger, rhinos, and elephant will thrive while bringing economic benefits to the local people. Forest fires cause huge economic and ecological losses and release considerable quantities of carbon into the air and is an important factor inflating the global burden of carbon emissions. Forest fire is an important factor of behavioral cum ecological habit of tiger in wild. Post fire changes i.e. micro and macro habitat directly affect the tiger habitat or land. Vulnerability of fire depicts the changes in microhabitat (humus, soil profile, litter, vegetation, grassland ecosystem). Microorganism like spider, annelids, arthropods and other favorable microorganism directly affect by the forest fire and indirectly these entire microorganisms are responsible for the development of tiger (Panthera tigris) habitat. On the other hand, fire brings depletion in prey species and negative movement of tiger from wild to human- dominated areas, which may leads the conflict i.e. dangerous for both tiger & human beings. Early forest fire prediction through mapping the risk zones can help minimize the fire frequency and manage forest fires thereby minimizing losses. Satellite data plays a vital role in identifying and mapping forest fire and recording the frequency with which different vegetation types are affected. Thematic hazard maps have been generated by using IDW technique. A prediction model for fire occurrence is developed for TAL. The fire occurrence records were collected from state forest department from 2000 to 2014. Disciminant function models was used for developing a prediction model for forest fires in TAL, random points for non-occurrence of fire have been generated. Based on the attributes of points of occurrence and non-occurrence, the model developed predicts the fire occurrence. The map of predicted probabilities classified the study area into five classes very high (12.94%), high (23.63%), moderate (25.87%), low(27.46%) and no fire (10.1%) based upon the intensity of hazard. model is able to classify 78.73 percent of points correctly and hence can be used for the purpose with confidence. Overall, also the model works correctly with almost 69% of points. This study exemplifies the usefulness of prediction model of forest fire and offers a more effective way for management of forest fire. Overall, this study depicts the model for conservation of tiger’s natural habitat and forest conservation which is beneficial for the wild and human beings for future prospective.

Keywords: fire prediction model, forest fire hazard, GIS, landsat, MODIS, TAL

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Authors: Maha Soltan, Amal Z. Hassan, Howaida I. Abd-Alla, Atef G. Hanna

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Two naturally known cardenolides; acovenoside A and acobioside A were isolated from the Egyptian cultivar; Acokanthera spectabilis leaves. It is an ornamental and poisonous plant that has been traditionally claimed for their medicinal properties against infectious microbes, killing worms and curing some inflammations at little amounts. We examined the growth inhibition effects of both cardenolides against four types of human cancer cell lines using Sulphorhodamine B assay. In addition, the clonogenic assay was also performed for testing the growth inhibiting power of the isolated compounds. An in vitro mechanistic investigation was further accomplished against hepatocellular carcinoma HepG2 cell line. Microscopic examination, colorimetric ELISA and flow cytometry techniques were our tools of proving at least part of the anticancer pathway of the tested compounds. Both compounds were able to inhibit the growth of 4 human cancer cell lines at less than 100 nM. In addition, they were able to activate the executioner Caspase-3 and apoptosis was then induced as a consequence of cell growth arrest at G2/M. An attention must be payed to those bioactive agents particularly when giving their activity against cancer cells at considerable small values while presenting safe therapeutic margins as indicated by literature.

Keywords: anticancer, cardenolides, Caspase-3, apoptosis

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Authors: Waleed S. Alwaneen

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Dairy industry is flourishing in world to provide milk and milk products to local population. Besides milk products, dairy industries also generate a substantial amount of cow manure that significantly affects the environment. Moreover, heat produced during the decomposition of the cow manure adversely affects the crop germination. Different companies are producing vermicompost using different species of worms/larvae to overcome the harmful effects using fresh manure. Tiger worm treatment enhanced plant growth, especially in the compost-manure ratio (75% compost, 25% cow manure), followed by a ratio of 50% compost, 50% cow manure.  Results also indicated that plant growth in Waxworm treated manure was weak as compared to plant growth in compost treated with Fruit Beetle (FB), Waxworms (WW), and Control (C) especially in the compost (25% compost, 75% cow manure) and 100% cow manure where there was no growth at all. Freshplant weight, fresh leaf weight and fresh root weight were significantly higher in the compost treated with Tiger worms in (75% compost, 25% cow manure); no evidence was seen for any significant differences in the dry root weight measurement between FB, Tiger worms (TW), WW, Control (C) in all composts. TW produced the best product, especially at the compost ratio of 75% compost, 25% cow manure followed by 50% compost, 50% cow manure.

Keywords: fruit beetle, tiger worms, waxworms, control

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Authors: M. Pena, J. Shin, H. Park

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The main objective of any engineering activity is the development of a system that fulfills the specific economic, social or environmental needs. Green growth policies, as a system, targets to satisfy two main needs: economic and environmental growth. Cities are complex systems composed of varied characteristics such as differences in socio-environmental conditions and local affordability, among others. Thus, commissioned policies are required to address these differences and to ensure green development. A more maintainable and justifiable, resource-efficient green growth can be obtained in urban areas if multi-criteria framework of policies relevant to green buildings is designed. Reason is that, this approach fits to target the differences and unique conditions of urban areas. By following the principles of axiomatic design, this paper urges to derive a framework for the application of green buildings policies in urban areas with distinctive socio-economic and environmental characteristics. Functional requirements defined as principles to ensure green growth and design parameters are identified in each set of conditions. Design matrices are constructed for each group of urban areas. Thus, the understanding of the needs and differences for each group of urban areas and the methodology to ensure green buildings is achieved.

Keywords: axiomatic design, green growth, sustainable development, urban planning

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Authors: Abdullahi Jibrin, Aishetu Abdulkadir

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The development of allometric models is crucial to accurate forest biomass/carbon stock assessment. The aim of this study was to develop a set of biomass prediction models that will enable the determination of total tree aboveground biomass for savannah woodland area in Niger State, Nigeria. Based on the data collected through biometric measurements of 1816 trees and destructive sampling of 36 trees, five species specific and one site specific models were developed. The sample size was distributed equally between the five most dominant species in the study site (Vitellaria paradoxa, Irvingia gabonensis, Parkia biglobosa, Anogeissus leiocarpus, Pterocarpus erinaceous). Firstly, the equations were developed for five individual species. Secondly these five species were mixed and were used to develop an allometric equation of mixed species. Overall, there was a strong positive relationship between total tree biomass and the stem diameter. The coefficient of determination (R2 values) ranging from 0.93 to 0.99 P < 0.001 were realised for the models; with considerable low standard error of the estimates (SEE) which confirms that the total tree above ground biomass has a significant relationship with the dbh. The F-test value for the biomass prediction models were also significant at p < 0.001 which indicates that the biomass prediction models are valid. This study recommends that for improved biomass estimates in the study site, the site specific biomass models should preferably be used instead of using generic models.

Keywords: allometriy, biomass, carbon stock , model, regression equation, woodland, inventory

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Authors: Rebekah Volgin, Jane Shakespeare-Finch, Ian Shochet

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Commercial sexual exploitation (CSE) and sex trafficking affect between 5000-7000 girls and women in Nepal each year and can have devastating physical and psychological consequences. Much research has documented these effects, however, there is no published longitudinal research that focuses on whether healing and growth outcomes are possible for survivors of CSE and sex trafficking. The narratives of 27 girls and women (13-22 years) were taken at two-time points during participation in a six-week group psychoeducation and art therapy program which was delivered across three NGO’s in Kathmandu, Nepal. These narratives form part of a larger ethnographic project. Thematic analysis of the data was undertaken. Themes emerging from time point 1 were: psychological distress in the form of anxiety and grief over loss of family, psychosomatic symptoms, empathy and compassion, and posttraumatic growth (PTG) in the form of new possibilities, relating to others and personal strength. Posttraumatic growth refers to positive changes in the aftermath of trauma. The themes emerging from time point 2, were: empathy and compassion and PTG (cognitive restructuring, new possibilities, relating to others and personal strength). Alongside the distress that these participants experienced, they also experienced positive outcomes such as empathy and compassion and psychological growth. Future research would advance knowledge by further examining the process of PTG in this population, if the changes observed were lasting, and if so, ways in which PTG can be facilitated or promoted.

Keywords: commercial sexual exploitation, human trafficking, posttraumatic growth, sexual trauma

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Authors: Vongsathorn Ngampuak, Yutachai Chookaew, Wipawee Dejtisakdi

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Dunaliella salina has great potential as a system for generating commercially valuable products, including beta-carotene, pharmaceuticals, and biofuels. Our goal is to improve this potential by enhancing growth rate and other properties of D. salina under optimal growth conditions. We used ethyl methane sulfonate (EMS) to generate random mutants in D. salina KU11, a strain classified in Thailand. In a preliminary experiment, we first treated D. salina cells with 0%, 0.8%, 1.0%, 1.2%, 1.44% and 1.66% EMS to generate a killing curve. After that, we randomly picked 30 candidates from approximately 300 isolated survivor colonies from the 1.44% EMS treatment (which permitted 30% survival) as an initial test of the mutant screen. Among the 30 survivor lines, we found that 2 strains (mutant #17 and #24) had significantly improved growth rates and cell number accumulation at stationary phase approximately up to 1.8 and 1.45 fold, respectively, 2 strains (mutant #6 and #23) had significantly decreased growth rates and cell number accumulation at stationary phase approximately down to 1.4 and 1.35 fold, respectively, while 26 of 30 lines had similar growth rates compared with the wild type control. We also analyzed cell size for each strain and found there was no significant difference comparing all mutants with the wild type. In addition, mutant #24 had shown an increase of biomass accumulation approximately 1.65 fold compared with the wild type strain on day 5 that was entering early stationary phase. From these preliminary results, it could be feasible to identify D. salina mutants with significant improved growth rate, cell accumulation and biomass production compared to the wild type for the further study; this makes it possible to improve this microorganism as a platform for biotechnology application.

Keywords: Dunaliella salina, ethyl methyl sulfonate, growth rate, biomass

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Authors: Aysha Zamir

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This study examines to what extent foreign private investment (FPI) affects the clean industrial environment and sustainable economic growth through developed countries investment in China. Moreover, this study investiage an association among FPI, CO2 emission, energy consumption, and sustainable economic growth. This study uses random effects and generalized least squares (GLS) and panel VAR estimators for data analysis. The results indicate that the Chinese economy has a vastly positive influenced regarding the location and choice of emerging and developed countries’ investment in the domestic market. Furthermore, emerging and developed economies investment increases the contribution among domestic firms, environment sustainability toward the national economy. The further results show that foreign private investment and gross domestic investment have a positive impact on sustainable economic growth.

Keywords: clean industrial environment, energy consumption, CO2 emmission, foreign private investment, developed and emerging economies

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Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: David Percy

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Landslides are a geologic phenomenon that affects a large number of inhabited places and are constantly being monitored and studied for the prediction of future occurrences. Reconstructability analysis (RA) is a methodology for extracting informative models from large volumes of data that work exclusively with discrete data. While RA has been used in medical applications and social science extensively, we are introducing it to the spatial sciences through applications like landslide prediction. Since RA works exclusively with discrete data, such as soil classification or bedrock type, working with continuous data, such as porosity, requires that these data are binned for inclusion in the model. RA constructs models of the data which pick out the most informative elements, independent variables (IVs), from each layer that predict the dependent variable (DV), landslide occurrence. Each layer included in the model retains its classification data as a primary encoding of the data. Unlike other machine learning algorithms that force the data into one-hot encoding type of schemes, RA works directly with the data as it is encoded, with the exception of continuous data, which must be binned. The usual physical and derived layers are included in the model, and testing our results against other published methodologies, such as neural networks, yields accuracy that is similar but with the advantage of a completely transparent model. The results of an RA session with a data set are a report on every combination of variables and their probability of landslide events occurring. In this way, every combination of informative state combinations can be examined.

Keywords: reconstructability analysis, machine learning, landslides, raster analysis

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Authors: Shobha U. Jadhav

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Onion is most important vegetable crop grown throughout the world. Onion suffers from pest and fungal diseases but these fungicides cause pollution and disturb microbial balance of soil. Under integrated fungal disease management programme cost effective and eco- friendly component like plant extract are used to control plant pathogens. Alternaria porri, Fusarium oxysporium, Stemphylium vesicarium are soil-borne pathogens of onion. Effect of three different plant extracts (Ocimum sanctum L., Xanthium strumarium B. and H. Withania somnifera Dunal)at five different concentration Viz, 10, 25, 50, 75, and 100 percentage on these pathogens was studied by food poisoning technique. Ocimum sanctum gave 84.21% growth of Alternaria porri at 10% extract concentration and 10.52% growth in 100% extract concentration. As compared to Fusarium oxysporium and Stemphylium vesicarium, Alternaria porri give good inhibitory response. In Xanthium strumarium B. and H. at 10% extract concentration 46.42% growth and at 100% extract concentration 28.57% growth of Fusarium oxysporum was observed. Fusarium oxysporum give good inhibitory response as compared to Alternaria porri and Stemphylium vesicarium. In Withania somnifera Dunal in 10% extract concentration 84.21% growth and in 100% extract concentration 21.05% growth of Stemphylium vesicarium was recorded. Stemphylium vesicarium give good inhibitory response as compared to Alternaria porri and Fusarium oxysporum.

Keywords: pathogen, onion, plant, extract

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Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

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Authors: Sarika Bahadure

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Sustainability starts with conserving resources for future generations. Since human’s existence on this earth, he has been consuming natural resources. The resource consumption pace in the past was very slow, but industrialization in 18^th century brought a change in the human lifestyle. New inventions and discoveries upgraded the human workforce to machines. The mass manufacture of goods provided easy access to products. In the last few decades, the globalization and change in technologies brought consumer oriented market. The consumption of resources has increased at a very high scale. This overconsumption pattern brought economic boom and provided multiple opportunities, but it also put stress on the natural resources. This paper tries to put forth the facts and figures of the population growth and consumption of resources with examples. This is explained with the help of the mathematical expression of doubling known as exponential growth. It compares the carrying capacity of the earth and resource consumption of humans’ i.e. ecological footprint and bio-capacity. Further, it presents the need to conserve natural resources and re-examine sustainable resource use approach for sustainability.

Keywords: consumption, exponential growth, population, resources, sustainability

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Authors: Antoni Wibowo, Harry Pujianto, Dewi Retno Sari Saputro

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The stock market can provide huge profits in a relatively short time in financial sector; however, it also has a high risk for investors and traders if they are not careful to look the factors that affect the stock market. Therefore, they should give attention to the dynamic fluctuations and movements of the stock market to optimize profits from their investment. In this paper, we present a nonlinear autoregressive exogenous model (NARX) to predict the movements of stock market; especially, the movements of the closing price index. As case study, we consider to predict the movement of the closing price in Indonesia composite index (IHSG) and choose the best structures of NARX for IHSG’s prediction.

Keywords: NARX (Nonlinear Autoregressive Exogenous Model), prediction, stock market, time series

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Authors: Rajat Kapoor

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India is a developing country and has experienced a rapid and tumultuous urban growth in the 20th century. The total urban population of the city increased ten-fold between 1901 and 2001. The share of urban population to the total population increased from less than 11 percent to over 28 percent in the same period. Except few examples, most of the Indian cities have grown in a haphazard manner; concentration of population followed by the planning exercises. In this era of global competitiveness and rapid urbanization there is no scope for malpractices in development strategies. It is expected that the Indian cities shall be planned comprehensively and holistically. The study reveals the land transformations the city of Faridabad is witnessing due to development which is largely boosted by the virtue of its location in the Delhi NCR.

Keywords: Delhi NCR, Faridabad, urban growth patterns, India

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Authors: Aboussalih Amira, Zarza Tahar, Fedaoui Kamel, Hammoudi Saleh

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This work focuses on the simulation of the prediction of the behaviour of austenitic stainless steel (SS) 304L under complex loading in stress and imposed strain. The Chaboche model is a cable to describe the response of the material by the combination of two isotropic and nonlinear kinematic work hardening, the model is implemented in the ZébuLon computer code. First, we represent the evolution of the axial stress as a function of the plastic strain through hysteresis loops revealing a hardening behaviour caused by the increase in stress by stress in the direction of tension/compression. In a second step, the study of the ratcheting phenomenon takes a key place in this work by the appearance of the average stress. In addition to the solicitation of the material in the biaxial direction in traction / torsion.

Keywords: damage, 304L, Ratcheting, plastic strain

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Authors: Jon Cobb, Nasir

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Due to higher switching frequencies, the conducted Electromagnetic interference (EMI) noise is generated in a converter. It degrades the performance of a switching converter. Therefore, it is an essential requirement to mitigate EMI noise of high performance converter. Moreover, it includes two types of emission such as common mode (CM) and differential mode (DM) noise. CM noise is due to parasitic capacitance present in a converter and DM noise is caused by switching current. However, there is dire need to understand the main cause of EMI noise. Hence, we propose a novel method to predict conducted EMI noise of different converter topologies during early stage. This paper also presents the comparison of conducted electromagnetic interference (EMI) noise due to different SMPS topologies. We also make an attempt to develop an EMI noise model for a converter which allows detailed performance analysis. The proposed method is applied to different converter, as an example, and experimental results are verified the novel prediction technique.

Keywords: EMI, electromagnetic interference, SMPS, switch-mode power supply, common mode, CM, differential mode, DM, noise

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Authors: Moses Obenade, Henry U. Okeke, Francis I. Okpiliya, Eugene J. Aniah

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Population growth in Eleme has been rapid over the past 75 years with its attendant pressure on the natural resources of the area. Between 1937 and 2006 the population of Eleme grew from 2,528 to 190,194 and is projected to be above 265,707 in 2016 based on an annual growth rate of 3.4%. Using the combined technologies of Geographic Information Systems (GIS), remote sensing (RS) and Demography techniques as its methodology, this paper examines the environmental implications of rapid population growth on land use dynamics in Eleme between 1986 and 2015. The study reveals that between 1986 and 2006, Built-up area and Farmland increased by 72.67 and 12.77% respectively, while light and thick vegetation recorded a decrease of -6.92 and -61.64% respectively. Water body remains fairly constant with minimal changes. Also, between 2006 and 2015 covering a period of 9 years, Built-up area further increased by 53% with an annual growth rate of 2.32 km2 gaining more land area on the detriment of other land uses. Built-up area has an annual growth rate of 2.32km2 and is expected to increase from 18.67km2 in 2006 to 41.87km2 in 2016.The observed Land used/Land cover dynamics is derived by the demographic characteristics of the Study area. Eleme has a total area of 138km2 out of which the Federal Government of Nigeria compulsorily acquired an estimated area of 59.34km2 for industrial purposes excluding acquisitions by the Rivers State Government. It is evident from the findings of this study that the carrying capacity of Eleme ecosystem is under threat due to the current population growth and land consumption rates. Therefore, measures such as use of appropriate technologies in farming techniques, waste management; investment in family planning and female empowerment, maternal health and education, afforestation programs; and amendment of Land Use Act of 1978 are recommended.

Keywords: population growth, Eleme, land use, GIS and remote sensing

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Authors: Martin Botz, Michael Kraus, Geralt Siebert

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The use of laminated glass gains increasing importance in structural engineering. For safety reasons, at least two glass panes are laminated together with a polymeric interlayer. In case of breakage of one or all of the glass panes, the glass fragments are still connected to the interlayer due to adhesion forces and a certain residual load-bearing capacity is left in the system. Polymer interlayers used in the laminated glass show a viscoelastic material behavior, e.g. stresses and strains in the interlayer are dependent on load duration and temperature. In the intact stage only small strains appear in the interlayer, thus the material can be described in a linear way. In the broken stage, large strains can appear and a non-linear viscoelasticity material theory is necessary. Relaxation tests on two different types of polymeric interlayers are performed at different temperatures and strain amplitudes to determine the border to the non-linear material regime. Based on the small-scale specimen results further tests on broken laminated glass panes are conducted. So-called ‘through-crack-bending’ (TCB) tests are performed, in which the laminated glass has a defined crack pattern. The test set-up is realized in a way that one glass layer is still able to transfer compressive stresses but tensile stresses have to be transferred by the interlayer solely. The TCB-tests are also conducted under different temperatures but constant force (creep test). Aims of these experiments are to elaborate if the results of small-scale tests on the interlayer are transferable to a laminated glass system in the broken stage. In this study, limits of the applicability of linear-viscoelasticity are established in the context of two commercially available polymer-interlayers. Furthermore, it is shown that the results of small-scale tests agree to a certain degree to the results of the TCB large-scale experiments. In a future step, the results can be used to develop material models for the post breakage performance of laminated glass.

Keywords: glass breakage, laminated glass, relaxation test, viscoelasticity

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Authors: Wang Yuan-hao, Habtamu Ayalew, Jing Wang, Shugeng Wu, Kai Qiu, Guanghai Qi, Haijun Zhang

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N-carbamylglutamate (NCG) has emerged as a promising candidate for regulating endogenous arginine synthesis, thereby promoting desirable growth, carcass traits, and muscle development in broilers. Thus, this study aimed to investigate the effects of NCG in ovo feeding on the growth performance, growth hormones, and meat quality of Ross 308 breeder broilers. A total of 1680 embryo eggs were equally allocated into three treatment groups: non punctured control (NC), saline-injected control (SC; 100μL/egg), and N-carbamylglutamate injected group (NCG; 2 mg/egg). The treatment solution was injected into the amniotic cavity of the embryo at 17.5 days of incubation (DOI). For the subsequent 42 days of post hatch experimental sampling, a total of 360 broiler chicks with 6 replications per treatment and 15 chicks per replication were used. Chickens in the NCG group showed significantly higher (P<0.05) body weight gain (BWG) and final body weight (FBW) at both 21 and 42 days after hatch (DAH), while feed conversion efficiency (FCE) was significantly improved (P<0.05) at 42 DAH. The weight and percentage of drums at 21 DAH and the weight and percentage of breast muscle at 42 DAH were significantly higher (P<0.05) in the NCG group. In addition, insulin (INS), growth hormone (GH), and testosterone (T) levels were significantly higher (P<0.05) in the NCG groups at 21 and 42 DAH. Furthermore, triiodothyronine (T3) and tetraiodothyronine (T4) levels were significantly higher (P<0.05) in the NCG treatment group. Interestingly, meat color values were also significantly higher (P<0.05) in the NCG group at 24 hrs postmortem. Collectively, these findings show that 2 mg NCG in ovo injection improves the growth performance and meat quality of broilers; even the effects extend into the market age of the chickens.

Keywords: N-carbamylglutamate, broiler, in ovo injection, growth performance, meat quality

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Authors: Mohammad R. Bhuyan, Mohammad J. Khattak

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Deterioration of flexible pavements due to crack reflection from its soil-cement base layer is a major concern around the globe. The service life of flexible pavement diminishes significantly because of the reflective cracks. Highway agencies are struggling for decades to prevent or mitigate these cracks in order to increase pavement service lives. The root cause of reflective cracks is the shrinkage crack which occurs in the soil-cement bases during the cement hydration process. The primary factor that causes the shrinkage is the cement content of the soil-cement mixture. With the increase of cement content, the soil-cement base gains strength and durability, which is necessary to withstand the traffic loads. But at the same time, higher cement content creates more shrinkage resulting in more reflective cracks in pavements. Historically, various states of USA have used the soil-cement bases for constructing flexile pavements. State of Louisiana (USA) had been using 8 to 10 percent of cement content to manufacture the soil-cement bases. Such traditional soil-cement bases yield 2.0 MPa (300 psi) 7-day compressive strength and are termed as cement stabilized design (CSD). As these CSD bases generate significant reflective cracks, another design of soil-cement base has been utilized by adding 4 to 6 percent of cement content called cement treated design (CTD), which yields 1.0 MPa (150 psi) 7-day compressive strength. The reduction of cement content in the CTD base is expected to minimize shrinkage cracks thus increasing pavement service lives. Hence, this research study evaluates the long-term field performance of CTD bases with respect to CSD bases used in flexible pavements. Pavement Management System of the state of Louisiana was utilized to select flexible pavement projects with CSD and CTD bases that had good historical record and time-series distress performance data. It should be noted that the state collects roughness and distress data for 1/10th mile section every 2-year period. In total, 120 CSD and CTD projects were analyzed in this research, where more than 145 miles (CTD) and 175 miles (CSD) of roadways data were accepted for performance evaluation and benefit-cost analyses. Here, the service life extension and area based on distress performance were considered as benefits. It was found that CTD bases increased 1 to 5 years of pavement service lives based on transverse cracking as compared to CSD bases. On the other hand, the service lives based on longitudinal and alligator cracking, rutting and roughness index remain the same. Hence, CTD bases provide some service life extension (2.6 years, on average) to the controlling distress; transverse cracking, but it was inexpensive due to its lesser cement content. Consequently, CTD bases become 20% more cost-effective than the traditional CSD bases, when both bases were compared by net benefit-cost ratio obtained from all distress types.

Keywords: cement treated base, cement stabilized base, reflective cracking , service life, flexible pavement

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Authors: Shayla He

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Background and Purpose: According to Chamie (2017), it’s estimated that no less than 150 million people, or about 2 percent of the world’s population, are homeless. The homeless population in the United States has grown rapidly in the past four decades. In New York City, the sheltered homeless population has increased from 12,830 in 1983 to 62,679 in 2020. Knowing the trend on the homeless population is crucial at helping the states and the cities make affordable housing plans, and other community service plans ahead of time to better prepare for the situation. This study utilized the data from New York City, examined the key factors associated with the homelessness, and developed systematic modeling to predict homeless populations of the future. Using the best model developed, named HP-RNN, an analysis on the homeless population change during the months of 2020 and 2021, which were impacted by the COVID-19 pandemic, was conducted. Moreover, HP-RNN was tested on the data from Seattle. Methods: The methodology involves four phases in developing robust prediction methods. Phase 1 gathered and analyzed raw data of homeless population and demographic conditions from five urban centers. Phase 2 identified the key factors that contribute to the rate of homelessness. In Phase 3, three models were built using Linear Regression, Random Forest, and Recurrent Neural Network (RNN), respectively, to predict the future trend of society's homeless population. Each model was trained and tuned based on the dataset from New York City for its accuracy measured by Mean Squared Error (MSE). In Phase 4, the final phase, the best model from Phase 3 was evaluated using the data from Seattle that was not part of the model training and tuning process in Phase 3. Results: Compared to the Linear Regression based model used by HUD et al (2019), HP-RNN significantly improved the prediction metrics of Coefficient of Determination (R2) from -11.73 to 0.88 and MSE by 99%. HP-RNN was then validated on the data from Seattle, WA, which showed a peak %error of 14.5% between the actual and the predicted count. Finally, the modeling results were collected to predict the trend during the COVID-19 pandemic. It shows a good correlation between the actual and the predicted homeless population, with the peak %error less than 8.6%. Conclusions and Implications: This work is the first work to apply RNN to model the time series of the homeless related data. The Model shows a close correlation between the actual and the predicted homeless population. There are two major implications of this result. First, the model can be used to predict the homeless population for the next several years, and the prediction can help the states and the cities plan ahead on affordable housing allocation and other community service to better prepare for the future. Moreover, this prediction can serve as a reference to policy makers and legislators as they seek to make changes that may impact the factors closely associated with the future homeless population trend.

Keywords: homeless, prediction, model, RNN

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Authors: Nita Rukminasari

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The aim of this study is to select microalgae strains, which were isolated from agriculture and municipal wastewater drain, Reno, Nevada that has highest growth rate and lipid contents. The experiments in this study were carried out in two consecutive stages. The first stage is aimed at testing the survival capability of all isolated microalgae strains and determining the best candidates to grow in centrate cultivation system. The second stage was targeted at determination the highest growth rate and highest lipid content of the selected top performing algae strain when cultivated on centrate wastewater. 26 microalgae strains, which were isolated from municipal and agriculture waste water, were analyzed using Flow cytometer for FACS of lipid with BODIPY and Nile Red as a lipid dyes and they grew on 96 wells plate for 31 days to determine growth rate as a based line data for growth rate. The result showed that microalgae strains which showed a high mean of fluorescence for BODIPY and Nile Red were F3.BP.1, F3.LV.1, T1.3.1, and T1.3.3. Five microalgae strains which have high growth rate were T1.3.3, T2.4.1. F3.LV.1, T2.12.1 and T3.3.1. In conclusion, microalgae strain which showed the highest starch content was F3.LV.1. T1.3.1 had the highest mean of fluorescence for Nile Red and BODIPY. Microalgae strains were potential for biofuel feedstock such as F3.LV.1 and T1.3.1, those microalgae strains showed a positive correlation between growth rate at stationary phase, biomass and meant of fluorescence for Nile Red and BODIPY.

Keywords: agriculture and municipal wastewater, biofuel, centrate, microalgae

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Authors: M. Ben Slimene, M.-S. Ouali

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Asset management of urban infrastructures faces a multitude of challenges that need to be overcome to obtain a reliable measurement of performances. Predicting the performance of slowly aging systems is one of those challenges, which helps the asset manager to investigate specific failure modes and to undertake the appropriate maintenance and rehabilitation interventions to avoid catastrophic failures as well as to optimize the maintenance costs. This article presents a methodology for modeling the deterioration of slowly degrading assets based on an operating history. It consists of extracting degradation profiles by grouping together assets that exhibit similar degradation sequences using an unsupervised classification technique derived from artificial intelligence. The obtained clusters are used to build the performance prediction models. This methodology is applied to a sample of a stormwater drainage culvert dataset.

Keywords: artificial Intelligence, clustering, culvert, regression model, slow degradation

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Authors: Osama Moustafa Zayed

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Aim of the work: To evaluate the effectiveness of venous glucose, levels of serum lactate and base deficit in polytraumatized patients as simple parameters to predict the mortality in these patients. Compared to the predictive value of Trauma and injury severity (TRISS) and Acute Physiology And Chronic Health Evaluation IV (APACHE IV). Introduction: Trauma is a serious global health problem, accounting for approximately one in 10 deaths worldwide. Trauma accounts for 5 million deaths per year. Prediction of mortality in trauma patients is an important part of trauma care. Several trauma scores have been devised to predict injury severity and risk of mortality. The trauma and injury severity score (TRISS) was most common used. Regardless of the accuracy of trauma scores, is based on an anatomical description of every injury and cannot be assigned to the patients until a full diagnostic procedure has been performed. So we hypothesized that alterations in admission glucose, lactate levels and base deficit would be an early and easy rapid predictor of mortality. Patient and Method: a comparative cross-sectional study. 282 Polytraumatized patients attended to the Emergency Department(ED) of the Suez Canal university Hospital constituted. The period from 1/1/2012 to 1/4/2013 was included. Results: We found that the best cut off value of TRISS probability of survival score for prediction of mortality among poly-traumatized patients is = 90, with 77% sensitivity and 89% specificity using area under the ROC curve (0.89) at (95%CI). APACHE IV demonstrated 67% sensitivity and 95% specificity at 95% CI at cut off point 99. The best cutoff value of Random Blood Sugar (RBS) for prediction of mortality was>140 mg/dl, with 89%, sensitivity, 49% specificity. The best cut off value of base deficit for prediction of mortality was less than -5.6 with 64% sensitivity, 93% specificity. The best cutoff point of lactate for prediction of mortality was > 2.6 mmol/L with 92%, sensitivity, 42% specificity. Conclusion: According to our results from all evaluated predictors of mortality (laboratory and scores) and mortality based on the estimated cutoff values using ROC curves analysis, the highest risk of mortality was found using a cutoff value of 90 in TRISS score while with laboratory parameters the highest risk of mortality was with serum lactate > 2.6 . Although that all of the three parameter are accurate in predicting mortality in poly-traumatized patients and near with each other, as in serum lactate the area under the curve 0.82, in BD 0.79 and 0.77 in RBS.

Keywords: APACHE IV, emergency department, polytraumatized patients, serum lactate

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Authors: Naci Büyükkaracığan

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Today, the need for water sources is swiftly increasing due to population growth. At the same time, it is known that some regions will face with shortage of water and drought because of the global warming and climate change. In this context, evaluation and analysis of hydrological data such as the observed trends, drought and flood prediction of short term flow has great deal of importance. The most accurate selection probability distribution is important to describe the low flow statistics for the studies related to drought analysis. As in many basins In Turkey, Gediz River basin will be affected enough by the drought and will decrease the amount of used water. The aim of this study is to derive appropriate probability distributions for frequency analysis of annual minimum flows at 6 gauging stations of the Gediz Basin. After applying 10 different probability distributions, six different parameter estimation methods and 3 fitness test, the Pearson 3 distribution and general extreme values distributions were found to give optimal results.

Keywords: Gediz Basin, goodness-of-fit tests, minimum flows, probability distribution

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