Search results for: equivalent linear

Authors: Lucas Nascimento, Ana Rita, Margarida Soares, André Ribeiro, Zlatina Genisheva, Hugo Silva, Joana Carvalho

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The footwear industry is an essential fashion industry, focusing on producing various types of footwear, such as shoes, boots, sandals, sneakers, and slippers. Global footwear consumption has doubled every 20 years since the 1950s. It is estimated that in 1950, each person consumed one new pair of shoes yearly; by 2005, over 20 billion pairs of shoes were consumed. To meet global footwear demand, production reached $24.2 billion, equivalent to about $74 per person in the United States. This means three new pairs of shoes per person worldwide. The issue of footwear waste is related to the fact that shoe production can generate a large amount of waste, much of which is difficult to recycle or reuse. This waste includes scraps of leather, fabric, rubber, plastics, toxic chemicals, and other materials. The search for alternative solutions for waste treatment and valorization is increasingly relevant in the current context, mainly when focused on utilizing waste as a source of substitute materials. From the perspective of the new circular economy paradigm, this approach is of utmost importance as it aims to preserve natural resources and minimize the environmental impact associated with sending waste to landfills. In this sense, the incorporation of waste into industrial sectors that allow for the recovery of large volumes, such as road construction, becomes an urgent and necessary solution from an environmental standpoint. This study explores the use of plastic waste from the footwear industry as a substitute for virgin polymers in bitumen modification, a solution that presents a more sustainable future. Replacing conventional polymers with plastic waste in asphalt composition reduces the amount of waste sent to landfills and offers an opportunity to extend the lifespan of road infrastructures. By incorporating waste into construction materials, reducing the consumption of natural resources and the emission of pollutants is possible, promoting a more circular and efficient economy. In the initial phase of this study, waste materials from end-of-life footwear were selected, and plastic waste with the highest potential for application was separated. Based on a literature review, EVA (ethylene vinyl acetate) and PU (polyurethane) were identified as the polymers suitable for modifying 50/70 classification bitumen. Each polymer was analysed at concentrations of 3% and 5%. The production process involved the polymer's fragmentation to a size of 4 millimetres after heating the materials to 180 ºC and mixing for 10 minutes at low speed. After was mixed for 30 minutes in a high-speed mixer. The tests included penetration, softening point, viscosity, and rheological assessments. With the results obtained from the tests, the mixtures with EVA demonstrated better results than those with PU, as EVA had more resistance to temperature, a better viscosity curve and a greater elastic recovery in rheology.

Keywords: footwear waste, hot asphalt pavement, modified bitumen, polymers

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Authors: Sy, Melecio Jr

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The decision to adopt vertical integration (VI) is firm-specific, but there is a common practice among businesses in an industry to maximize the massive potential benefits of VI. This study aims to determine VI adoption in the restaurant industry in Davao City. Using a two-step sampling process, the study used a validated survey questionnaire among 264 restaurant owners and managers randomly selected and geographically classified. It is a quantitative study where the data were subjected to a structural equation model (SEM). The results revealed that VI is present but limited to procurement, production, restaurant services, and online marketing. Raw materials were outsourced while delivery to customers through third-party delivery services. VI slowly increased over ten years except for online marketing, which has grown significantly in a few years. The endogenous and exogenous variables were correlated and established the linear regression model. The SEM's best fit model revealed that strategic motives (SMOT) and market factors (MFAC) influenced VI adoption while MFAC is the best predictor. Favorable market factors may lead restaurants to adopt VI. It is, thus, recommended for restaurants to institutionalize strategic management, quantify the impact of double marginalization in future studies as a reason for VI and conduct this study during the new normal to see the influence of business efficiencies and firm economies on VI adoption.

Keywords: business efficiencies, business management, davao city, firm economies, market factors, philippines, strategic motives, structural equation model, supply chain, vertical integration adoption

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Authors: Rong Yuan, Zhao Tao

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Only a handful of literature have used to non-linear model to investigate the influencing factors of greenhouse gas (GHG) emissions in China’s manufacturing sectors. And there is a limit in investigating quantitatively and systematically the mechanism of correlation between economic development and GHG emissions considering inherent differences among manufacturing sub-sectors. Considering the sectorial characteristics, the manufacturing sub-sectors with various impacts of output on GHG emissions may be explained by different development modes in each manufacturing sub-sector, such as investment scale, technology level and the level of international competition. In order to assess the environmental impact associated with any specific level of economic development and explore the factors that affect GHG emissions in China’s manufacturing industry during the process of economic growth, using the threshold Stochastic Impacts by Regression on Population, Affluence and Technology (STIRPAT) model, this paper investigated the influence impacts of GHG emissions for China’s manufacturing sectors of different stages of economic development. A data set from 28 manufacturing sectors covering an 18-year period was used. Results demonstrate that output per capita and investment scale contribute to increasing GHG emissions while energy efficiency, R&D intensity and FDI mitigate GHG emissions. Results also verify the nonlinear effect of output per capita on emissions as: (1) the Environmental Kuznets Curve (EKC) hypothesis is supported when threshold point RMB 31.19 million is surpassed; (2) the driving strength of output per capita on GHG emissions becomes stronger as increasing investment scale; (3) the threshold exists for energy efficiency with the positive coefficient first and negative coefficient later; (4) the coefficient of output per capita on GHG emissions decreases as R&D intensity increases. (5) FDI shows a reduction in elasticity when the threshold is compassed.

Keywords: China, GHG emissions, manufacturing industry, threshold STIRPAT model

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Authors: Jose G. Tshikuka, Goabaone Rankgoane-Pono, Mgaywa G. M. D. Magafu, Julius C. Mwita, Tiny Masupe, Fortunat M. Kandanda, Shimeles G. Hamda, Roy Tapera, Mooketsi Molefi, John T. Tlhakanelo

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Background: Factors associated with overweight and obesity among antiretroviral therapy (ART) recipients have not been sufficiently studied in Botswana. We aimed to study (i) the prevalence and trends in overweight/obesity by duration of exposure to ART among recipients, (ii) changes in body mass index (BMI) categories among recipients before ART initiation (BMI-1) and after ART initiation (BMI-2), (iii) associations between ART and overweight/obesity and (iv) factors associated with BMI changes among ART recipients. Methods: A 12 years retrospective record-based review was conducted. Factors potentially associated with BMI change among patients after at least three years of ART exposure were examined using multiple regression model. Adjusted odds ratios (AOR) and their 95% confidence intervals (CIs) were computed. ART regimens, duration of exposure to ART, and recipients’ demographic and biomedical characteristics including the presence or absence of diabetes mellitus related comorbidities (DRC) were investigated as potential factors associated with overweight/obesity. Results: Twenty-nine percent of recipients were overweight, 16.6% had obesity of whom 2.4% were morbidly-obese at the last clinic visit. Overweight/obesity recipients were more likely to be female, to have DRC and less likely to have nadir CD4 count or CD4 count between 201 – 249 cells/mm³. Neither the first-line nor the second-, third-line ART regimens predicted overweight/obesity more than the other and neither did the duration of exposure to ART. No significant linear trends were observed in the prevalence of overweight/obesity by the duration of exposure to ART. Conclusions: These results indicate that overweight/obesity seen among ART recipients is not directly induced by ART. ART used CD4 and/or DRC pathway to induce overweight/obesity seen among recipients; suggesting that, weight gain documented herein is likely a reflection of improved health status that mirrors trends in the general population or a DRC related effect. Weight management programs may be important components of HIV care.

Keywords: overweight/obesity, recipients of antiretroviral therapy, HIV/AIDS, Botswana

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Authors: Mustafa Sertçelik, Turan Çalban, Hacali Necefoğlu, Sabri Çolak

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In this study, Chevreul’s salt solubility and its dissolution kinetics in ammonium chloride solutions were investigated. Chevreul’s salt that we used in the studies was obtained by using the optimum conditions (ammonium sulphide concentration; 0,4 M, copper sulphate concentration; 0,25 M, temperature; 60°C, stirring speed; 600 rev/min, pH; 4 and reaction time; 15 mins) determined by T. Çalban et al. Chevreul’s salt solubility in ammonium chloride solutions and the kinetics of dissolution were investigated. The selected parameters that affect solubility were reaction temperature, concentration of ammonium chloride, stirring speed, and solid/liquid ratio. Correlation of experimental results had been achieved using linear regression implemented in the statistical package program statistica. The effect of parameters on Chevreul’s salt solubility was examined and integrated rate expression of dissolution rate was found using kinetic models in solid-liquid heterogeneous reactions. The results revealed that the dissolution rate of Chevreul’s salt was decreasing while temperature, concentration of ammonium chloride and stirring speed were increasing. On the other hand, dissolution rate was found to be decreasing with the increase of solid/liquid ratio. Based on result of the applications of the obtained experimental results to the kinetic models, we can deduce that Chevreul’s salt dissolution rate is controlled by diffusion through the ash (or product layer). Activation energy of the reaction of dissolution was found as 74.83 kJ/mol. The integrated rate expression along with the effects of parameters on Chevreul's salt solubility was found to be as follows: 1-3(1-X)2/3+2(1-X)= [2,96.1013.(CA)3,08 .(S/L)-038.(W)1,23 e-9001,2/T].t

Keywords: Chevreul's salt, copper, ammonium chloride, ammonium sulphide, dissolution kinetics

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Authors: Elena Petersen, Inna Kornienko, Svetlana Guryeva, Sergey Dobdin, Anatoly Skripal, Andrey Usanov, Dmitry Usanov

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One of the most important unsolved problems in modern medicine is the increase of chronic diseases that lead to organ dysfunction or even complete loss of function. Current methods of treatment do not result in decreased mortality and disability statistics. Currently, the best treatment for many patients is still transplantation of organs and/or tissues. Therefore, finding a way of correct artificial matrix biofabrication in case of limited number of natural organs for transplantation is a critical task. One important problem that needs to be solved is development of a nondestructive and noninvasive method to analyze dynamic changes of mechanical characteristics of a matrix with minimal side effects on the growing cells. This research was focused on investigating the properties of matrix as a marker of graft condition. In this study, the collagen gel with human primary dermal fibroblasts in suspension (60, 120, 240*103 cells/mL) and collagen gel with cell spheroids were used as model objects. The stiffness and elasticity characteristics were evaluated by a semiconductor laser autodyne. The time and cell concentration dependency of the stiffness and elasticity were investigated. It was shown that these properties changed in a non-linear manner with respect to cell concentration. The maximum matrix stiffness was observed in the collagen gel with the cell concentration of 120*103 cells/mL. This study proved the opportunity to use the mechanical properties of matrix as a marker of graft condition, which can be measured by noninvasive semiconductor laser autodyne technique.

Keywords: graft, matrix, noninvasive method, regenerative medicine, semiconductor laser autodyne

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Authors: M. Regina Carreira-Lopez

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Lightweight ontologies seek to concrete union relationships between a parent node, and a secondary node, also called "child node". This logic relation (L) can be formally defined as a triple ontological relation (LO) equivalent to LO in ⟨LN, LE, LC⟩, and where LN represents a finite set of nodes (N); LE is a set of entities (E), each of which represents a relationship between nodes to form a rooted tree of ⟨LN, LE⟩; and LC is a finite set of concepts (C), encoded in a formal language (FL). Mondoc enables more refined searches on semantic and classified facets for retrieving specialized knowledge about Atlantic migrations, from the Declaration of Independence of the United States of America (1776) and to the end of the Spanish Civil War (1939). The model looks forward to increasing documentary relevance by applying an inverse frequency of co-ocurrent hypernymy phenomena for a concrete dataset of textual corpora, with RMySQL package. Mondoc profiles archival utilities implementing SQL programming code, and allows data export to XML schemas, for achieving semantic and faceted analysis of speech by analyzing keywords in context (KWIC). The methodology applies random and unrestricted sampling techniques with RMySQL to verify the resonance phenomena of inverse documentary relevance between the number of co-occurrences of the same term (t) in more than two documents of a set of texts (D). Secondly, the research also evidences co-associations between (t) and their corresponding synonyms and antonyms (synsets) are also inverse. The results from grouping facets or polysemic words with synsets in more than two textual corpora within their syntagmatic context (nouns, verbs, adjectives, etc.) state how to proceed with semantic indexing of hypernymy phenomena for subject-heading lists and for authority lists for documentary and archival purposes. Mondoc contributes to the development of web directories and seems to achieve a proper and more selective search of e-documents (classification ontology). It can also foster on-line catalogs production for semantic authorities, or concepts, through XML schemas, because its applications could be used for implementing data models, by a prior adaptation of the based-ontology to structured meta-languages, such as OWL, RDF (descriptive ontology). Mondoc serves to the classification of concepts and applies a semantic indexing approach of facets. It enables information retrieval, as well as quantitative and qualitative data interpretation. The model reproduces a triple tuple ⟨LN, LE, LT, LCF L, BKF⟩ where LN is a set of entities that connect with other nodes to concrete a rooted tree in ⟨LN, LE⟩. LT specifies a set of terms, and LCF acts as a finite set of concepts, encoded in a formal language, L. Mondoc only resolves partial problems of linguistic ambiguity (in case of synonymy and antonymy), but neither the pragmatic dimension of natural language nor the cognitive perspective is addressed. To achieve this goal, forthcoming programming developments should target at oriented meta-languages with structured documents in XML.

Keywords: hypernymy, information retrieval, lightweight ontology, resonance

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Authors: Sema Kayapinar, Ismail Karaoglan, Turan Paksoy, Hadi Gokcen

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Green Supply Chain Management is gaining growing interest among researchers and supply chain management. The concept of Green Supply Chain Management is to integrate environmental thinking into the Supply Chain Management. It is the systematic concept emphasis on environmental problems such as reduction of greenhouse gas emissions, energy efficiency, recycling end of life products, generation of solid and hazardous waste. This study is to present a green supply chain network model integrated Internet of Things applications. Internet of Things provides to get precise and accurate information of end-of-life product with sensors and systems devices. The forward direction consists of suppliers, plants, distributions centres and sales and collect centres while, the reverse flow includes the sales and collects centres, disassembled centre, recycling and disposal centre. The sales and collection centre sells the new products are transhipped from factory via distribution centre and also receive the end-of life product according their value level. We describe green logistics activities by presenting specific examples including “recycling of the returned products and “reduction of CO2 gas emissions”. The different transportation choices are illustrated between echelons according to their CO2 gas emissions. This problem is formulated as a mixed integer linear programming model to solve the green supply chain problems which are emerged from the environmental awareness and responsibilities. This model is solved by using Gams package program. Numerical examples are suggested to illustrate the efficiency of the proposed model.

Keywords: green supply chain optimization, internet of things, greenhouse gas emission, recycling

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Authors: Agada N. Ihuoma, Nagata Yasunori

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Artificial intelligence (AI) techniques play a crucial role in predicting the expected energy outcome and its performance, analysis, modeling, and control of renewable energy. Renewable energy is becoming more popular for economic and environmental reasons. In the face of global energy consumption and increased depletion of most fossil fuels, the world is faced with the challenges of meeting the ever-increasing energy demands. Therefore, incorporating artificial intelligence to predict solar radiation outcomes from the intermittent sunlight is crucial to enable a balance between supply and demand of energy on loads, predict the performance and outcome of solar energy, enhance production planning and energy management, and ensure proper sizing of parameters when generating clean energy. However, one of the major problems of forecasting is the algorithms used to control, model, and predict performances of the energy systems, which are complicated and involves large computer power, differential equations, and time series. Also, having unreliable data (poor quality) for solar radiation over a geographical location as well as insufficient long series can be a bottleneck to actualization. To overcome these problems, this study employs the anaconda Navigator (Jupyter Notebook) for machine learning which can combine larger amounts of data with fast, iterative processing and intelligent algorithms allowing the software to learn automatically from patterns or features to predict the performance and outcome of Solar Energy which in turns enables the balance of supply and demand on loads as well as enhance production planning and energy management.

Keywords: artificial Intelligence, backward elimination, linear regression, solar energy

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Authors: Ali panahi-Kosar Khosravi

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Earthquake resilience is one of the most important theoretical and practical concepts in crisis management. Over the past few decades, the rapid growth of urban areas and developing lower urban areas (especially in developing countries) have made them more vulnerable to human and natural crises. Therefore, the resilience of urban communities, especially low-income and unhealthy neighborhoods, is of particular importance. The present study seeks to assess and evaluate the resilience of neighborhoods in the center of district 6 of Tabriz in terms of awareness, knowledge and personal skills, social and psychological capital, managerial-institutional, and the ability to return to appropriate and sustainable conditions. The research method in this research is descriptive-analytical. The authors used library and survey methods to collect information and a questionnaire to assess resilience. The statistical population of this study is the total households living in the four neighborhoods of Shanb Ghazan, Khatib, Gharamalek, and Abuzar alley. Three hundred eighty-four families from four neighborhoods were selected based on the Cochran formula using a simple random sampling method. A one-sample t-test, simple linear regression, and structural equations were used to test the research hypotheses. Findings showed that only two social and psychological awareness and capital indicators in district 6 of Tabriz had a favorable and approved status. Therefore, considering the multidimensional concept of resilience, district 6 of Tabriz is in an unfavorable resilience situation. Also, the findings based on the analysis of variance indicated no significant difference between the neighborhoods of district 6 in terms of resilience, and most neighborhoods are in an unfavorable situation.

Keywords: resilience, statistical analysis, earthquake, district 6 of tabriz

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Authors: Md Ruhul Amin

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This study was conducted in the experimental field of the Department of Entomology, Bangabandhu Sheikh Mujibur Rahman Agricultural University, Gazipur, Bangladesh during November 2017 to May 2018 with a view to understanding the seasonal abundance of the major sucking pests namely thrips, aphid and red spider mite on rose. The findings showed that the thrips started to build up their population from the middle of January with abundance 1.0 leaf⁻¹, increased continuously, reached to the peak level (2.6 leaf⁻¹) in the middle of February and then declined. Aphid started to build up their population from the second week of November with abundance 6.0 leaf⁻¹, increased continuously, reached to the peak level (8.4 leaf⁻¹) in the last week of December and then declined. Mite started to build up their population from the first week of December with abundance 0.8 leaf⁻¹, increased continuously, reached to the peak level (8.2 leaf⁻¹) in the second week of March and then declined. Thrips and mite prevailed until the last week of April, and aphid showed their abundance till last week of May. The daily mean temperature, relative humidity, and rainfall had an insignificant negative correlation with thrips and significant negative correlation with aphid abundance. The daily mean temperature had significant positive, relative humidity had an insignificant positive, and rainfall had an insignificant negative correlation with mite abundance. The multiple linear regression analysis showed that the weather parameters together contributed 38.1, 41.0 and 8.9% abundance on thrips, aphid and mite on rose, respectively and the equations were insignificant.

Keywords: aphid, mite, thrips, weather factors

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Authors: Omar Concepcion, Osvaldo De Melo, Arturo Escobosa

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Topological insulator (TI) materials are insulating in the bulk and conducting in the surface. The unique electronic properties associated with these surface states make them strong candidates for exploring innovative quantum phenomena and as practical applications for quantum computing, spintronic and nanodevices. Many materials, including Bi₂Te₃, have been proposed as TIs and, in some cases, it has been demonstrated experimentally by angle-resolved photoemission spectroscopy (ARPES), scanning tunneling spectroscopy (STM) and/or magnetotransport measurements. A clean surface is necessary in order to make any of this measurements. Several techniques have been used to produce films and different kinds of nanostructures. Growth and characterization in situ is usually the best option although cleaving the films can be an alternative to have a suitable surface. In the present work, we report a comparison of Bi₂Te₃ grown by physical vapor transport (PVT) and molecular beam epitaxy (MBE). The samples were characterized by X-ray diffraction (XRD), Scanning electron microscopy (SEM), Atomic force microscopy (AFM), X-ray photoelectron spectroscopy (XPS) and ARPES. The Bi₂Te₃ samples grown by PVT, were cleaved in the ultra-high vacuum in order to obtain a surface free of contaminants. In both cases, the XRD shows a c-axis orientation and the pole diagrams proved the epitaxial relationship between film and substrate. The ARPES image shows the linear dispersion characteristic of the surface states of the TI materials. The samples grown by PVT, a relatively simple and cost-effective technique shows the same high quality and TI properties than the grown by MBE.

Keywords: Bismuth telluride, molecular beam epitaxy, physical vapor transport, topological insulator

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Authors: Akeel A. Shah, Tong Zhang

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Computational approaches to learning the properties of materials are commonplace, motivated by the need to screen or design materials for a given application, e.g., semiconductors and energy storage. Experimental approaches can be both time consuming and costly. Unfortunately, computational approaches such as ab-initio electronic structure calculations and classical or ab-initio molecular dynamics are themselves can be too slow for the rapid evaluation of materials, often involving thousands to hundreds of thousands of candidates. Machine learning assisted approaches have been developed to overcome the time limitations of purely physics-based approaches. These approaches, on the other hand, require large volumes of data for training (hundreds of thousands on many standard data sets such as QM7b). This means that they are limited by how quickly such a large data set of physics-based simulations can be established. At high fidelity, such as configuration interaction, composite methods such as G4, and coupled cluster theory, gathering such a large data set can become infeasible, which can compromise the accuracy of the predictions - many applications require high accuracy, for example band structures and energy levels in semiconductor materials and the energetics of charge transfer in energy storage materials. In order to circumvent this problem, multi-fidelity approaches can be adopted, for example the Δ-ML method, which learns a high-fidelity output from a low-fidelity result such as Hartree-Fock or density functional theory (DFT). The general strategy is to learn a map between the low and high fidelity outputs, so that the high-fidelity output is obtained a simple sum of the physics-based low-fidelity and correction, Although this requires a low-fidelity calculation, it typically requires far fewer high-fidelity results to learn the correction map, and furthermore, the low-fidelity result, such as Hartree-Fock or semi-empirical ZINDO, is typically quick to obtain, For high-fidelity outputs the result can be an order of magnitude or more in speed up. In this work, a new multi-fidelity approach is developed, based on a graph convolutional network (GCN) combined with semi-supervised learning. The GCN allows for the material or molecule to be represented as a graph, which is known to improve accuracy, for example SchNet and MEGNET. The graph incorporates information regarding the numbers of, types and properties of atoms; the types of bonds; and bond angles. They key to the accuracy in multi-fidelity methods, however, is the incorporation of low-fidelity output to learn the high-fidelity equivalent, in this case by learning their difference. Semi-supervised learning is employed to allow for different numbers of low and high-fidelity training points, by using an additional GCN-based low-fidelity map to predict high fidelity outputs. It is shown on 4 different data sets that a significant (at least one order of magnitude) increase in accuracy is obtained, using one to two orders of magnitude fewer low and high fidelity training points. One of the data sets is developed in this work, pertaining to 1000 simulations of quinone molecules (up to 24 atoms) at 5 different levels of fidelity, furnishing the energy, dipole moment and HOMO/LUMO.

Keywords: .materials screening, computational materials, machine learning, multi-fidelity, graph convolutional network, semi-supervised learning

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Authors: Vigen Barkhudaryan

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The influence of external effects (γ-, UV–radiations, high temperature) in presence of air oxygen on structural transformations of low-density polyethylene (LDPE) have been investigated dependent on the polymers’ thickness, the intensity and the dose of external actions. The methods of viscosimetry, light scattering, turbidimetry and gelation measuring were used for this purpose. The comparison of influence of external effects on LDPE shows, that the destruction and cross-linking processes of macromolecules proceed simultaneously with all kinds of external effects. A remarkable growth of average molecular mass of LDPE along with the irradiation doses and heat treatment exposure growth was established. It was linear for the mass average molecular mass and at the initial doses is mainly the result of the increase of the macromolecular branching. As a result, the macromolecular hydrodynamic volumes have been changed, and therefore the dependence of viscosity average molecular mass on the doses was going through the minimum at initial doses. A significant change of molecular mass, sizes and shape of macromolecules of LDPE occurs under the influence of external effects. The influence is limited only by diffusion of oxygen during -irradiation and heat treatment. At UV–irradiation the influence is limited both by diffusion of oxygen and penetration of radiation. Consequently, the molecular transformations are deeper and evident in case of -irradiation, as soon as the polymer is transformed in a whole volume. It was also established, that the mechanism of molecular transformations in polymers from the surface layer distinctly differs from those of the sample deeper layer. A comparison of the results of these investigations allows us to conclude, that the mechanisms of influence of investigated external effects on polyethylene are similar.

Keywords: cross-linking, destruction, high temperature, LDPE, γ-radiations, UV-radiations

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Authors: Thomas D. Collin, Rachel Cunningham, Bruce Jefferson, Raffaella Villa

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Fats, oils and grease (FOG) are by-products of food preparation and cooking processes. FOG enters wastewater systems through a variety of sources such as households, food service establishments, and industrial food facilities. Over time, if no source control is in place, FOG builds up on pipe walls, leading to blockages, and potentially to sewer overflows which are a major risk to the Environment and Human Health. UK water utilities spend millions of pounds annually trying to control FOG. Despite UK legislation specifying that discharge of such material is against the law, it is often complicated for water companies to identify and prosecute offenders. Hence, it leads to uncertainties regarding the attitude to take in terms of FOG management. Research is needed to seize the full potential of implementing current practices. The aim of this research was to undertake a comprehensive study to document the extent of FOG problems in sewer lines and reinforce existing knowledge. Data were collected to develop a model estimating quantities of FOG available for recovery within Thames Water wastewater catchments. Geographical Information System (GIS) software was used in conjunction to integrate data with a geographical component. FOG was responsible for at least 1/3 of sewer blockages in Thames Water waste area. A waste-based approach was developed through an extensive review to estimate the potential for FOG collection and recovery. Three main sources were identified: residential, commercial and industrial. Commercial properties were identified as one of the major FOG producers. The total potential FOG generated was estimated for the 354 wastewater catchments. Additionally, raw and settled sewage were sampled and analysed for FOG (as hexane extractable material) monthly at 20 sewage treatment works (STW) for three years. A good correlation was found with the sampled FOG and population equivalent (PE). On average, a difference of 43.03% was found between the estimated FOG (waste-based approach) and sampled FOG (raw sewage sampling). It was suggested that the approach undertaken could overestimate the FOG available, the sampling could only capture a fraction of FOG arriving at STW, and/or the difference could account for FOG accumulating in sewer lines. Furthermore, it was estimated that on average FOG could contribute up to 12.99% of the primary sludge removed. The model was further used to investigate the relationship between estimated FOG and number of blockages. The higher the FOG potential, the higher the number of FOG-related blockages is. The GIS-based tool was used to identify critical areas (i.e. high FOG potential and high number of FOG blockages). As reported in the literature, FOG was one of the main causes of sewer blockages. By identifying critical areas (i.e. high FOG potential and high number of FOG blockages) the model further explored the potential for source-control in terms of ‘sewer relief’ and waste recovery. Hence, it helped targeting where benefits from implementation of management strategies could be the highest. However, FOG is still likely to persist throughout the networks, and further research is needed to assess downstream impacts (i.e. at STW).

Keywords: fat, FOG, GIS, grease, oil, sewer blockages, sewer networks

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Authors: Hu Zhenxing, Gao Jianxin

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Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach.

Keywords: distortion, stereo-based digital image correlation, b-spline, 3D, 2D

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Authors: Yingjeng James Li, Yun Jyun Ou, Chih Chi Hsu, Chiao-Chih Hu

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A fuel cell is a device that produces electric power by reacting fuel and oxidant electrochemically. There is no pollution produced from a fuel cell if hydrogen is employed as the fuel. Therefore, a fuel cell is considered as a zero emission device and is a source of green power. A membrane electrode assembly (MEA) is the key component of a fuel cell. It is, therefore, beneficial to develop MEAs with high performance. In this study, an MEA for proton exchange membrane fuel cell (PEMFC) was prepared from a 15-micron thick reinforced PEM. The active area of such MEA is 25 cm2. Carbon supported platinum (Pt/C) was employed as the catalyst for both anode and cathode. The platinum loading is 0.6 mg/cm2 based on the sum of anode and cathode. Commercially available carbon papers coated with a micro porous layer (MPL) serve as gas diffusion layers (GDLs). The original thickness of the GDL is 250 μm. It was compressed down to 163 μm when assembled into the single cell test fixture. Polarization curves were taken by using eight different test conditions. At our standard test condition (cell: 70 °C; anode: pure hydrogen, 100%RH, 1.2 stoic, ambient pressure; cathode: air, 100%RH, 3.0 stoic, ambient pressure), the cell current density is 1250 mA/cm2 at 0.6 V, and 2400 mA/cm2 at 0.4 V. At self-humidified condition and cell temperature of 55 °C, the cell current density is 1050 mA/cm2 at 0.6 V, and 2250 mA/cm2 at 0.4 V. Hydrogen crossover rate of the MEA is 0.0108 mL/min*cm2 according to linear sweep voltammetry experiments. According to the MEA’s Pt loading and the cyclic voltammetry experiments, the Pt electrochemical surface area is 60 m2/g. The ohmic part of the impedance spectroscopy results shows that the membrane resistance is about 60 mΩ*cm2 when the MEA is operated at 0.6 V.

Keywords: fuel cell, membrane electrode assembly, proton exchange membrane, reinforced

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Authors: Chuan Yang, Jing Bie, Zhong Wang, Panagiotis Psimoulis

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Contemporary studies investigating the association between the spatial configuration of the urban network and economic activities at the street level were mostly conducted within space syntax conceptual framework. These findings supported the theory of 'movement economy' and demonstrated the impact of street configuration on the distribution of pedestrian movement and land-use shaping, especially retail activities. However, the effects varied between different urban contexts. In this paper, the relationship between economic activity distribution and the urban configurational characters was examined at the segment level. In the study area, three kinds of neighbourhood types, urban, suburban, and rural neighbourhood, were included. And among all neighbourhoods, three kinds of urban network form, 'tree-like', grid, and organic pattern, were recognised. To investigate the nested effects of urban configuration measured by space syntax approach and urban context, multilevel zero-inflated negative binomial (ZINB) regression models were constructed. Additionally, considering the spatial autocorrelation, spatial lag was also concluded in the model as an independent variable. The random effect ZINB model shows superiority over the ZINB model or multilevel linear (ML) model in the explanation of economic activities pattern shaping over the urban environment. And after adjusting for the neighbourhood type and network form effects, connectivity and syntax centrality significantly affect economic activities clustering. The comparison between accumulative and new established economic activities illustrated the different preferences for economic activity location choice.

Keywords: space syntax, economic activities, multilevel model, Chinese city

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

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Authors: Usha Loganathan, Haresh Pandya

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Analysis and correlation of soil properties represent an important outset for precision agriculture and is currently promoted and implemented in the developed world. Establishing relationships among indices of soil salinity has always been a challenging task in salt affected soils necessitating unique approaches for their reclamation and management to sustain long term productivity of Soil. Soil salinity indices like Electrical Conductivity (EC) and Sodium Adsorption Ratio (SAR) are normally used to characterize soils as either sodic or saline sodic. Currently, Determination of Soil sodium adsorption ratio is a more accepted and reliable measure of soil salinity. However, it involves arduous and protracted laboratory investigations which demand evolving new and economical methods to determine SAR based on simple soil salinity index. A linear regression model to predict soil SAR from soil electrical conductivity has been developed and presented in this paper as per which, soil SAR could very well be worked out as a pedotransfer function of soil EC. The present study was carried out in Orathupalayam (11.09-11.11 N latitude and 74.54-77.59 E longitude) in the vicinity of Orathupalayam Reservoir of Noyyal River Basin, India, over a period of 3 consecutive years from September 2013 through February 2016 in different locations chosen randomly through different seasons. The research findings are discussed in the light of micro level farming practices in India and recommend determination of SAR as a low cost technology aiding in the effective management of salt affected agricultural land.

Keywords: electrical conductivity, orathupalayam, pedotranfer function, sodium adsorption ratio

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Authors: Luis C. Parra

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The significant wave height prediction is an issue of great interest in the field of coastal activities because of the non-linear behavior of the wave height and its complexity of prediction. This study aims to present a machine learning model to forecast the significant wave height of the oceanographic wave measuring buoys anchored at Mooloolaba of the Queensland Government Data. Modeling was performed by a multilayer perceptron neural network-genetic algorithm (GA-MLP), considering Relu(x) as the activation function of the MLPNN. The GA is in charge of optimized the MLPNN hyperparameters (learning rate, hidden layers, neurons, and activation functions) and wrapper feature selection for the window width size. Results are assessed using Mean Square Error (MSE), Root Mean Square Error (RMSE), and Mean Absolute Error (MAE). The GAMLPNN algorithm was performed with a population size of thirty individuals for eight generations for the prediction optimization of 5 steps forward, obtaining a performance evaluation of 0.00104 MSE, 0.03222 RMSE, 0.02338 MAE, and 0.71163% of MAPE. The results of the analysis suggest that the MLPNNGA model is effective in predicting significant wave height in a one-step forecast with distant time windows, presenting 0.00014 MSE, 0.01180 RMSE, 0.00912 MAE, and 0.52500% of MAPE with 0.99940 of correlation factor. The GA-MLP algorithm was compared with the ARIMA forecasting model, presenting better performance criteria in all performance criteria, validating the potential of this algorithm.

Keywords: significant wave height, machine learning optimization, multilayer perceptron neural networks, evolutionary algorithms

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Authors: Hamdi Beji, Toufik Kanit, Tanguy Messager

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This study aims to construct a predictive model proficient in foreseeing the linear elastic and thermal characteristics of composite materials, drawing on a multitude of influencing parameters. These parameters encompass the shape of inclusions (circular, elliptical, square, triangle), their spatial coordinates within the matrix, orientation, volume fraction (ranging from 0.05 to 0.4), and variations in contrast (spanning from 10 to 200). A variety of machine learning techniques are deployed, including decision trees, random forests, support vector machines, k-nearest neighbors, and an artificial neural network (ANN), to facilitate this predictive model. Moreover, this research goes beyond the predictive aspect by delving into an inverse analysis using genetic algorithms. The intent is to unveil the intrinsic characteristics of composite materials by evaluating their thermomechanical responses. The foundation of this research lies in the establishment of a comprehensive database that accounts for the array of input parameters mentioned earlier. This database, enriched with this diversity of input variables, serves as a bedrock for the creation of machine learning and genetic algorithm-based models. These models are meticulously trained to not only predict but also elucidate the mechanical and thermal conduct of composite materials. Remarkably, the coupling of machine learning and genetic algorithms has proven highly effective, yielding predictions with remarkable accuracy, boasting scores ranging between 0.97 and 0.99. This achievement marks a significant breakthrough, demonstrating the potential of this innovative approach in the field of materials engineering.

Keywords: machine learning, composite materials, genetic algorithms, mechanical and thermal proprieties

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Authors: B. Ashraf, A. M. Malik

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The study was accomplished at the Department of Economics and Agriculture Economics, Pir Mahar Ali Shah ARID Agriculture University, Punjab, Pakistan during 2013-16 with a purpose to discover the effect of Islamic finance/banking on employment in Punjab, Pakistan. Islamic banking system is sub-component of conventional banking system in various countries of the world; however, in Pakistan, it has been established as a separate Islamic banking system. The Islamic banking operates under the doctrine of Shariah. It is claimed that the referred banking is free of interest (Riba) and addresses the philosophy and basic values of Islam in finance that reduces the factors of uncertainty, risk and others speculative activities. Two Islamic bank’s; Meezan Bank Limited (Pakistan) and Al-Baraka Bank Limited (Pakistan) from North Punjab (Bahawalnagar) and central Punjab (Lahore) west Punjab (Gujrat), Pakistan were randomly selected for the conduct of research. A total of 206 samples were collected from the define areas and banks through questionnaire. The data was analyzed by using the Statistical Package for Social Sciences (SPSS) version 21.0. Multiple linear regressions were applied to prove the hypothesis. The results revealed that the assets formation had significant positive; whereas, the technology, length of business (experience) and bossiness size had significant negative impact with employment generation in Islamic finance/banking in Punjab, Pakistan. This concludes that the employment opportunities may be created in the country by extending the finance to business/firms to start new business and increase the Public awareness by the Islamic banks through intensive publicity. However; Islamic financial institutions may be encouraged by Government as it enhances the employment in the country.

Keywords: assets formation, borrowers, employment generation, Islamic banks, Islamic finance

Procedia PDF Downloads 319

Authors: Ayodeji Sowale, Athanasios Kolios, Beatriz Fidalgo, Tosin Somorin, Aikaterini Anastasopoulou, Alison Parker, Leon Williams, Ewan McAdam, Sean Tyrrel

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Recently, energy recovery systems are thriving and raising attention in the power generation sector, due to the request for cleaner forms of energy that are friendly and safe for the environment. This has created an avenue for cogeneration, where Combined Heat and Power (CHP) technologies have been recognised for their feasibility, and use in homes and small-scale businesses. The efficiency of combustors and the advantages of the free piston Stirling engines over other conventional engines in terms of output power and efficiency, have been observed and considered. This study presents the numerical analysis of a micro-combustor with a free piston Stirling engine in an integrated model of a Nano Membrane Toilet (NMT) unit. The NMT unit will use the micro-combustor to produce waste heat of high energy content from the combustion of human waste and the heat generated will power the free piston Stirling engine which will be connected to a linear alternator for electricity production. The thermodynamic influence of the combustor on the free piston Stirling engine was observed, based on the heat transfer from the flue gas to working gas of the free piston Stirling engine. The results showed that with an input of 25 MJ/kg of faecal matter, and flue gas temperature of 773 K from the micro-combustor, the free piston Stirling engine generates a daily output power of 428 W, at thermal efficiency of 10.7% with engine speed of 1800 rpm. An experimental investigation into the integration of the micro-combustor and free piston Stirling engine with the NMT unit is currently underway.

Keywords: free piston stirling engine, micro-combustor, nano membrane toilet, thermodynamics

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Authors: Juan Huang, Hugo Ninanya

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Oil has been used as a source of energy and supply to make materials, such as asphalt or rubber for many years. This is the reason why new technologies have been implemented through time. However, research still needs to continue increasing due to new challenges engineers face every day, just like unconventional reservoirs. Various numerical methodologies have been applied in petroleum engineering as tools in order to optimize the production of reservoirs before drilling a wellbore, although not all of these have the same efficiency when talking about studying fracture propagation. Analytical methods like those based on linear elastic fractures mechanics fail to give a reasonable prediction when simulating fracture propagation in ductile materials whereas numerical methods based on the cohesive zone method (CZM) allow to represent the elastoplastic behavior in a reservoir based on a constitutive model; therefore, predictions in terms of displacements and pressure will be more reliable. In this work, a hydro-geomechanical coupled model of horizontal wells in fractured rock was developed using ABAQUS; both extended element method and cohesive elements were used to represent predefined fractures in a model (2-D). A power law for representing the rheological behavior of fluid (shear-thinning, power index <1) through fractures and leak-off rate permeating to the matrix was considered. Results have been showed in terms of aperture and length of the fracture, pressure within fracture and fluid loss. It was showed a high infiltration rate to the matrix as power index decreases. A sensitivity analysis is conclusively performed to identify the most influential factor of fluid loss.

Keywords: fracture, hydro-geomechanical model, non-Newtonian fluid, numerical analysis, sensitivity analysis

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Authors: Luh Eka Suryani, Purhadi

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Poisson regression is a non-linear regression model with response variable in the form of count data that follows Poisson distribution. Modeling for a pair of count data that show high correlation can be analyzed by Poisson Bivariate Regression. Data, the number of infant mortality and maternal mortality, are count data that can be analyzed by Poisson Bivariate Regression. The Poisson regression assumption is an equidispersion where the mean and variance values are equal. However, the actual count data has a variance value which can be greater or less than the mean value (overdispersion and underdispersion). Violations of this assumption can be overcome by applying Generalized Poisson Regression. Characteristics of each regency can affect the number of cases occurred. This issue can be overcome by spatial analysis called geographically weighted regression. This study analyzes the number of infant mortality and maternal mortality based on conditions in East Java in 2016 using Geographically Weighted Bivariate Generalized Poisson Regression (GWBGPR) method. Modeling is done with adaptive bisquare Kernel weighting which produces 3 regency groups based on infant mortality rate and 5 regency groups based on maternal mortality rate. Variables that significantly influence the number of infant and maternal mortality are the percentages of pregnant women visit health workers at least 4 times during pregnancy, pregnant women get Fe3 tablets, obstetric complication handled, clean household and healthy behavior, and married women with the first marriage age under 18 years.

Keywords: adaptive bisquare kernel, GWBGPR, infant mortality, maternal mortality, overdispersion

Procedia PDF Downloads 157

Authors: Ramin Mansouri

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Due to the contrast of rivers discharge regime with water demands, one of the best ways to use water resources is to regulate the natural flow of the rivers and supplying water needs to construct dams. Optimal utilization of reservoirs, consideration of multiple important goals together at the same is of very high importance. To study about analyzing this method, statistical data of Bakhtiari and Roudbar dam over 46 years (1955 until 2001) is used. Initially an appropriate objective function was specified and using DE algorithm, the rule curve was developed. In continue, operation policy using rule curves was compared to standard comparative operation policy. The proposed method distributed the lack to the whole year and lowest damage was inflicted to the system. The standard deviation of monthly shortfall of each year with the proposed algorithm was less deviated than the other two methods. The Results show that median values for the coefficients of F and Cr provide the optimum situation and cause DE algorithm not to be trapped in local optimum. The most optimal answer for coefficients are 0.6 and 0.5 for F and Cr coefficients, respectively. After finding the best combination of coefficients values F and CR, algorithms for solving the independent populations were examined. For this purpose, the population of 4, 25, 50, 100, 500 and 1000 members were studied in two generations (G=50 and 100). result indicates that the generation number 200 is suitable for optimizing. The increase in time per the number of population has almost a linear trend, which indicates the effect of population in the runtime algorithm. Hence specifying suitable population to obtain an optimal results is very important. Standard operation policy had better reversibility percentage, but inflicts severe vulnerability to the system. The results obtained in years of low rainfall had very good results compared to other comparative methods.

Keywords: reservoirs, differential evolution, dam, Optimal operation

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Authors: Domagoj Nakic, Drazen Vouk, Nina Stirmer, Mario Siljeg, Ana Baricevic

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Wastewater treatment processes generate significant quantities of sewage sludge that need to be adequately treated and disposed. In many EU countries, the problem of adequate disposal of sewage sludge has not been solved, nor is determined by the unique rules, instructions or guidelines. Disposal of sewage sludge is important not only in terms of satisfying the regulations, but the aspect of choosing the optimal wastewater and sludge treatment technology. Among the solutions that seem reasonable, recycling of sewage sludge and its byproducts reaches the top recommendation. Within the framework of sustainable development, recycling of sludge almost completely closes the cycle of wastewater treatment in which only negligible amounts of waste that requires landfilling are being generated. In many EU countries, significant amounts of sewage sludge are incinerated, resulting in a new byproduct in the form of ash. Sewage sludge ash is three to five times less in volume compared to stabilized and dehydrated sludge, but it also requires further management. The combustion process also destroys hazardous organic components in the sludge and minimizes unpleasant odors. The basic objective of the presented research is to explore the possibilities of recycling of the sewage sludge ash as a supplementary cementitious material. This is because of the main oxides present in the sewage sludge ash (SiO2, Al2O3 and Cao, which is similar to cement), so it can be considered as latent hydraulic and pozzolanic material. Physical and chemical characteristics of ashes, generated by sludge collected from different wastewater treatment plants, and incinerated in laboratory conditions at different temperatures, are investigated since it is a prerequisite of its subsequent recycling and the eventual use in other industries. Research was carried out by replacing up to 20% of cement by mass in cement mortar mixes with different obtained ashes and examining characteristics of created mixes in fresh and hardened condition. The mixtures with the highest ash content (20%) showed an average drop in workability of about 15% which is attributed to the increased water requirements when ash was used. Although some mixes containing added ash showed compressive and flexural strengths equivalent to those of reference mixes, generally slight decrease in strength was observed. However, it is important to point out that the compressive strengths always remained above 85% compared to the reference mix, while flexural strengths remained above 75%. Ecological impact of innovative construction products containing sewage sludge ash was determined by analyzing leaching concentrations of heavy metals. Results demonstrate that sewage sludge ash can satisfy technical and environmental criteria for use in cementitious materials which represents a new recycling application for an increasingly important waste material that is normally landfilled. Particular emphasis is placed on linking the composition of generated ashes depending on its origin and applied treatment processes (stage of wastewater treatment, sludge treatment technology, incineration temperature) with the characteristics of the final products. Acknowledgement: This work has been fully supported by Croatian Science Foundation under the project '7927 - Reuse of sewage sludge in concrete industry – from infrastructure to innovative construction products'.

Keywords: cement mortar, recycling, sewage sludge ash, sludge disposal

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Authors: Chui-Hsin Chen, Yu-Ting Chen

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Special concentrically braced frames is one of the seismic load resisting systems, which dissipates seismic energy when bracing members within the frames undergo yielding and buckling while sustaining their axial tension and compression load capacities. Most of the inelastic deformation of a buckling bracing member concentrates in the mid-length region. While experiencing cyclic loading, the region dissipates most of the seismic energy being input into the frame. Such a concentration makes the braces vulnerable to failure modes associated with low-cycle fatigue. In this research, a strategy to improve the cyclic behavior of the conventional steel bracing member is proposed by filling the Hollow Structural Shape (HSS) member with reinforcement. It prevents the local section from concentrating large plastic deformation caused by cyclic loading. The infill helps spread over the plastic hinge region into a wider area hence postpone the initiation of local buckling or even the rupture of the braces. The finite element method is introduced to simulate the complicated bracing member behavior and member-versus-infill interaction under cyclic loading. Fifteen 3-D-element-based models are built by ABAQUS software. The verification of the FEM model is done with unreinforced (UR) HSS bracing members’ cyclic test data and aluminum honeycomb plates’ bending test data. Numerical models include UR and filled HSS bracing members with various compactness ratios based on the specification of AISC-2016 and AISC-1989. The primary variables to be investigated include the relative bending stiffness and the material of the filling reinforcement. The distributions of von Mises stress and equivalent plastic strain (PEEQ) are used as indices to tell the strengths and shortcomings of each model. The result indicates that the change of relative bending stiffness of the infill is much more influential than the change of material in use to increase the energy dissipation capacity. Strengthen the relative bending stiffness of the reinforcement results in additional energy dissipation capacity to the extent of 24% and 46% in model based on AISC-2016 (16-series) and AISC-1989 (89-series), respectively. HSS members with infill show growth in 𝜂Local Buckling, normalized energy cumulated until the happening of local buckling, comparing to UR bracing members. The 89-series infill-reinforced members have more energy dissipation capacity than unreinforced 16-series members by 117% to 166%. The flexural rigidity of infills should be less than 29% and 13% of the member section itself for 16-series and 89-series bracing members accordingly, thereby guaranteeing the spread over of the plastic hinge and the happening of it within the reinforced section. If the parameters are properly configured, the ductility, energy dissipation capacity, and fatigue-life of HSS SCBF bracing members can be improved prominently by the infill-reinforced method.

Keywords: special concentrically braced frames, HSS, cyclic loading, infill reinforcement, finite element analysis, PEEQ

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Authors: Said Belaaouad

Abstract:

This study aimed to explore the quantitative structure-activity relationship (QSAR) of 1,2,4-Triazine-3(2H)-one derivative as a potential anticancer agent against breast cancer. The electronic descriptors were obtained using the Density Functional Theory (DFT) method, and a multiple linear regression techniques was employed to construct the QSAR model. The model exhibited favorable statistical parameters, including R2=0.849, R2adj=0.656, MSE=0.056, R2test=0.710, and Q2cv=0.542, indicating its reliability. Among the descriptors analyzed, absolute electronegativity (χ), total energy (TE), number of hydrogen bond donors (NHD), water solubility (LogS), and shape coefficient (I) were identified as influential factors. Furthermore, leveraging the validated QSAR model, new derivatives of 1,2,4-Triazine-3(2H)-one were designed, and their activity and pharmacokinetic properties were estimated. Subsequently, molecular docking (MD) and molecular dynamics (MD) simulations were employed to assess the binding affinity of the designed molecules. The Tubulin colchicine binding site, which plays a crucial role in cancer treatment, was chosen as the target protein. Through the simulation trajectory spanning 100 ns, the binding affinity was calculated using the MMPBSA script. As a result, fourteen novel Tubulin-colchicine inhibitors with promising pharmacokinetic characteristics were identified. Overall, this study provides valuable insights into the QSAR of 1,2,4-Triazine-3(2H)-one derivative as potential anticancer agent, along with the design of new compounds and their assessment through molecular docking and dynamics simulations targeting the Tubulin-colchicine binding site.

Keywords: QSAR, molecular docking, ADMET, 1, 2, 4-triazin-3(2H)-ones, breast cancer, anticancer, molecular dynamic simulations, MMPBSA calculation

Procedia PDF Downloads 89