Search results for: models synthesis

Authors: Suraj Mehrotra

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The stock market is a volatile, bustling marketplace that is a cornerstone of economics. It defines whether companies are successful or in spiral. A thorough understanding of it is important - many companies have whole divisions dedicated to analysis of both their stock and of rivaling companies. Linking the world of finance and artificial intelligence (AI), especially the stock market, has been a relatively recent development. Predicting how stocks will do considering all external factors and previous data has always been a human task. With the help of AI, however, machine learning models can help us make more complete predictions in financial trends. Taking a look at the stock market specifically, predicting the open, closing, high, and low prices for the next day is very hard to do. Machine learning makes this task a lot easier. A model that builds upon itself that takes in external factors as weights can predict trends far into the future. When used effectively, new doors can be opened up in the business and finance world, and companies can make better and more complete decisions. This paper explores the various techniques used in the prediction of stock prices, from traditional statistical methods to deep learning and neural networks based approaches, among other methods. It provides a detailed analysis of the techniques and also explores the challenges in predictive analysis. For the accuracy of the testing set, taking a look at four different models - linear regression, neural network, decision tree, and naïve Bayes - on the different stocks, Apple, Google, Tesla, Amazon, United Healthcare, Exxon Mobil, J.P. Morgan & Chase, and Johnson & Johnson, the naïve Bayes model and linear regression models worked best. For the testing set, the naïve Bayes model had the highest accuracy along with the linear regression model, followed by the neural network model and then the decision tree model. The training set had similar results except for the fact that the decision tree model was perfect with complete accuracy in its predictions, which makes sense. This means that the decision tree model likely overfitted the training set when used for the testing set.

Keywords: machine learning, testing set, artificial intelligence, stock analysis

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Authors: G. S. Sudha, Smita Mohanty, S. K. Nayak

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Epoxidized oils can replace petroleum derived materials in numerous industrial applications, because of their respectable oxirane oxygen content and high reactivity of oxirane ring. Epoxidized castor oil (ECO) has synthesized in the presence of a sulphonated polystyrene type cation exchange resin. The formation of the oxirane ring was confirmed by Fourier Transform Infrared Spectroscopy (FTIR) analysis. The epoxidation reaction was evaluated by Nuclear Magnetic Resonance (NMR) studies. ECO is used as a toughening phase to increase the toughness of petroleum-based epoxy resin.

Keywords: epoxy resin, epoxidized castor oil, sulphonated polystyrene type cation exchange resin, petroleum derived materials

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Authors: John Lamb, Robert Epstein, Vasundhara L. Bhupathi, Sanjeev Kumar Marimekala

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As we enter the new era of Artificial Intelligence (AI) and Cloud Computing, we mostly rely on the Machine and Natural Language Processing capabilities of AI, and Energy Efficient Hardware and Software Devices in almost every industry sector. In these industry sectors, much emphasis is on developing new and innovative methods for producing and conserving energy and sustaining the depletion of natural resources. The core pillars of sustainability are economic, environmental, and social, which is also informally referred to as the 3 P's (People, Planet and Profits). The 3 P's play a vital role in creating a core Sustainability Model in the Enterprise. Natural resources are continually being depleted, so there is more focus and growing demand for renewable energy. With this growing demand, there is also a growing concern in many industries on how to reduce carbon emissions and conserve natural resources while adopting sustainability in corporate business models and policies. In our paper, we would like to discuss the driving forces such as Climate changes, Natural Disasters, Pandemic, Disruptive Technologies, Corporate Policies, Scaled Business Models and Emerging social media and AI platforms that influence the 3 main pillars of Sustainability (3P’s). Through this paper, we would like to bring an overall perspective on enterprise strategies and the primary focus on bringing cultural shifts in adapting energy-efficient operational models. Overall, many industries across the globe are incorporating core sustainability principles such as reducing energy costs, reducing greenhouse gas (GHG) emissions, reducing waste and increasing recycling, adopting advanced monitoring and metering infrastructure, reducing server footprint and compute resources (Shared IT services, Cloud computing, and Application Modernization) with the vision for a sustainable environment.

Keywords: climate change, pandemic, disruptive technology, government policies, business model, machine learning and natural language processing, AI, social media platform, cloud computing, advanced monitoring, metering infrastructure

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Authors: Gabouze Nourredine, Saloua Merazga

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Due to their exceptional durability, low-cost, high-power density, and reliability, cathodes based on titanium dioxide, and more specifically spinel LTO (Li4Ti5O12), present an attractive alternative to conventional lithium cathode materials for multiple applications. The aim of this work is to synthesize and characterize the nanopowders of titanium dioxide (TiO₂) and lithium titanate (Li₄Ti5O₁₂) by the hydrothermal method and to use them as a cathode in a lithium-ion battery. The structural and morphological characterizations of the synthesized powders were performed by XRD, SEM, EDS, and FTIR-ATR. Nevertheless, the study of the electrochemical performances of the elaborated electrode materials was carried out by: cyclic voltametry (CV) and galvanostatic charge/discharge (CDG). The prepared electrode by the powder annealed at 800 °C has a good specific capacity of about 173 mAh/g and a good cyclic stability

Keywords: lithuim-ion, battery, LTO, tio2, capacity

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Authors: Vidhu Mitha Goutham, Ruma Mukherjee

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The low availability of well-trained, unlimited, dynamic-access models for specific languages makes it hard for corporate users to adopt quick translation techniques and incorporate them into product solutions. As translation tasks increasingly require a dynamic sequence learning curve; stable, cost-free opensource models are scarce. We survey and compare current translation techniques and propose a modified sequence to sequence model repurposed with attention techniques. Sequence learning using an encoder-decoder model is now paving the path for higher precision levels in translation. Using a Convolutional Neural Network (CNN) encoder and a Recurrent Neural Network (RNN) decoder background, we use Fairseq tools to produce an end-to-end bilingually trained Spanish-English machine translation model including source language detection. We acquire competitive results using a duo-lingo-corpus trained model to provide for prospective, ready-made plug-in use for compound sentences and document translations. Our model serves a decent system for large, organizational data translation needs. While acknowledging its shortcomings and future scope, it also identifies itself as a well-optimized deep neural network model and solution.

Keywords: attention, encoder-decoder, Fairseq, Seq2Seq, Spanish, translation

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Authors: Adeyemi Adebayo, Barry Ackers

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This paper comparatively analyses the corporate governance of SOEs in South Africa and Singapore in the context of the World Bank’s framework for corporate governance of SOEs. This framework ensured that the analysis holistically covered key aspects of corporate governance of SOEs in these states. In order to ground our understanding of the paths taken by SOEs in the states, the paper presents the evolution and reforms of SOEs in the states before analyzing key aspects of their corporate governance. The analysis shows that even though SOEs in South Africa and Singapore are comparable in a number of ways, there are notable differences. In this context, this paper finds that the main difference between corporate governance of SOEs in South Africa and Singapore is their organizing model. Further, the analysis, among other findings, shows that SOEs Boards in Singapore are better remunerated. Further finding reveals that, even though some board members are politically connected, Singaporean SOEs boards are better constituted based on skills and experience compared to SOEs boards in South Africa. Overall, the analysis opens up new debates and as such concludes by providing avenues for further research.

Keywords: corporate governance, comparative corporate governance, corporate governance framework, government business enterprises, government linked companies, organizing models, ownership models, state-owned companies, state-owned enterprises

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Authors: Abdulfattah M. Alkherraz, Zaineb I. Lusta, Ahmed E. Zubi

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The environmental pollution by heavy metals became more problematic nowadays. To solve the problem of Cadmium accumulation in human organs which lead to dangerous effects on human health, and to determine its concentration, the organic legand 1-phenyl-3-benzoyl-2-thiourea was used to extract the cadmium ions from its solution. This legand as one of thiourea derivatives was successfully synthesized. The legand was characterized by NMR and CHN elemental analysis, and used to extract the cadmium from its solutions by formation of a stable complex at neutral pH. The complex was characterized by elemental analysis and melting point. The concentrations of cadmium ions before and after the extraction were determined by Atomic Absorption Spectrophotometer (AAS). The data show the percentage of the extract was more than 98.7% of the concentration of cadmium used in the study.

Keywords: thiourea derivatives, cadmium extraction, water, environment

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Authors: Akinnubi Rufus Temidayo

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Coastal West Africa is highly sensitive to climate variability, driven by complex ocean-atmosphere interactions that shape temperature, precipitation, and extreme weather. Radiative feedback mechanisms—such as water vapor feedback, cloud-radiation interactions, and surface albedo—play a critical role in modulating these patterns. Yet, limited research addresses these feedbacks in climate models specific to West Africa’s coastal zones, creating challenges for accurate climate projections and adaptive planning. This study aims to evaluate the influence of radiative feedbacks on the coastal climate of West Africa by quantifying the effects of water vapor, cloud cover, and sea surface temperature (SST) on the region’s radiative balance. The study uses a regional climate model (RCM) to simulate feedbacks over a 20-year period (2005-2025) with high-resolution data from CORDEX and satellite observations. Key mechanisms investigated include (1) Water Vapor Feedback—the amplifying effect of humidity on warming, (2) Cloud-Radiation Interactions—the impact of cloud cover on radiation balance, especially during the West African Monsoon, and (3) Surface Albedo and Land-Use Changes—effects of urbanization and vegetation on the radiation budget. Preliminary results indicate that radiative feedbacks strongly influence seasonal climate variability in coastal West Africa. Water vapor feedback amplifies dry-season warming, cloud-radiation interactions moderate surface temperatures during monsoon seasons, and SST variations in the Atlantic affect the frequency and intensity of extreme rainfall events. The findings suggest that incorporating these feedbacks into climate planning can strengthen resilience to climate impacts in West African coastal communities. Further research should refine regional models to capture anthropogenic influences like greenhouse gas emissions, guiding sustainable urban and resource planning to mitigate climate risks.

Keywords: west africa, radiative, climate, resilence, anthropogenic

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Authors: John T. Leman, James Gibson, Peter J. Bonitatibus

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There have been no potential clinically translatable developments of intravenous CT contrast materials over decades, and iodinated contrast agents (ICA) remain the only FDA-approved media for CT. Small molecule ICA used to highlight vascular anatomy have weak CT signals in large-to-obese patients due to their rapid redistribution from plasma into interstitial fluid, thereby diluting their intravascular concentration, and because of a mismatch of iodine’s K-edge and the high kVp settings needed to image this patient population. The use of ICA is also contraindicated in a growing population of renally impaired patients who are hypersensitive to these contrast agents; a transformative intravenous contrast agent with improved capabilities is urgently needed. Tantalum oxide nanoparticles (TaO NPs) with zwitterionic siloxane polymer coatings have high potential as clinically translatable general-purpose CT contrast agents because of (1) substantially improved imaging efficacy compared to ICA in swine/phantoms emulating medium-sized and larger adult abdomens and superior thoracic vascular contrast enhancement of thoracic arteries and veins in rabbit, (2) promising biological safety profiles showing near-complete renal clearance and low tissue retention at 3x anticipated clinical dose (ACD), and (3) clinically acceptable physiochemical parameters as concentrated bulk solutions(250-300 mgTa/mL). Here, we review requirements for general-purpose intravenous CT contrast agents in terms of patient safety, X-ray attenuating properties and contrast-producing capabilities, and physicochemical and pharmacokinetic properties. We report the current status of a TaO NP-based contrast agent, including chemical process technology developments and results of newly defined scaled-up processes for NP synthesis and purification, yielding reproducible formulations with appropriate size and concentration specifications. We discuss recent results of recent pre-clinical in vitro immunology, non-GLP high dose tolerability in rats (10x ACD), non-GLP long-term biodistribution in rats at 3x ACD, and non-GLP repeat dose in rats at ACD. We also include a discussion of NP characterization, in particular size-stability testing results under accelerated conditions (37C), and insights into TaO NP purity, surface structure, and bonding of the zwitterionic siloxane polymer coating by multinuclear (1H, 13C, 29Si) and multidimensional (2D) solution NMR spectroscopy.

Keywords: nanoparticle, imaging, diagnostic, process technology, nanoparticle characterization

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Authors: A. Fettahoglu

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Theory of viscoelasticity is used by many researchers to represent behavior of many materials such as pavements on roads or bridges. Several researches used analytical methods and rheology to predict the material behaviors of simple models. Today, more complex engineering structures are analyzed using Finite Element Method, in which material behavior is embedded by means of three dimensional viscoelastic material laws. As a result, structures of unordinary geometry and domain like pavements of bridges can be analyzed by means of Finite Element Method and three dimensional viscoelastic equations. In the scope of this study, rheological models embedded in ANSYS, namely, generalized Maxwell elements and Prony series, which are two methods used by ANSYS to represent viscoelastic material behavior, are presented explicitly. Subsequently, a practical problem, which has an analytical solution given in literature, is used to verify the applicability of viscoelasticity tool embedded in ANSYS. Finally, amount of error in the results of ANSYS is compared with the analytical results to indicate the influence of used method and time step size.

Keywords: generalized Maxwell model, finite element method, prony series, time step size, viscoelasticity

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Authors: Simon Ingelsten, Anton Lundberg, Vijay Shankar, Lars-Olof Landström, Örjan Johansson

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The flow in a low consistency disc refiner is simulated with the aim of identifying flow structures possibly being of importance for a future study to optimise the energy efficiency in refining processes. A simplified flow geometry is used, where a single groove of a refiner disc is modelled. Two different fibre models are used to simulate turbulent fibre suspension flow in the groove. The first model is a Bingham viscoplastic fluid model where the fibre suspension is treated as a non-Newtonian fluid with a yield stress. The second model is a new model proposed in a recent study where the suspended fibres effect on flow is accounted for through a modelled orientation distribution function (ODF). Both models yielded similar results with small differences. Certain flow characteristics that were expected and that was found in the literature were identified. Some of these flow characteristics may be of importance in a future process to optimise the refiner geometry to increase the energy efficiency. Further study and a more detailed flow model is; however, needed in order for the simulations to yield results valid for quantitative use in such an optimisation study. An outline of the next steps in such a study is proposed.

Keywords: disc refiner, fibre flow, sustainability, turbulence modelling

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Authors: M. Hoseinnezhad, K. Gharanjig

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Two organic dyes comprising carbazole as the electron donors and cyanoacetic acid moieties as the electron acceptors were synthesized. The organic dye was prepared by standard reaction from carbazole as the starting material. To this end, carbazole was reacted with bromobenzene and further oxidation and reacted with cyanoacetic acid. The obtained organic dye was purified and characterized using differential scanning calorimetry (DSC), Fourier transform infrared spectroscopy (FT-IR), proton nuclear magnetic resonance (¹HNMR), carbon nuclear magnetic resonance (¹³CNMR) and elemental analysis. The influence of heteroatom on carbazole donors and cyno substitution on the acid acceptor is evidenced by spectral and electrochemical photovoltaic experiments. Finally, light fastness properties for organic dye were investigated.

Keywords: dye-sensitized solar cells, indoline dye, nanostructure, oxidation potential, solar energy

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Authors: Yijun Shao, Yan Cheng, Rashmee U. Shah, Charlene R. Weir, Bruce E. Bray, Qing Zeng-Treitler

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Deep neural network (DNN) models are being explored in the clinical domain, following the recent success in other domains such as image recognition. For clinical adoption, outcome prediction models require explanation, but due to the multiple non-linear inner transformations, DNN models are viewed by many as a black box. In this study, we developed a deep neural network model for predicting 1-year mortality of patients who underwent major cardio vascular procedures (MCVPs), using temporal image representation of past medical history as input. The dataset was obtained from the electronic medical data warehouse administered by Veteran Affairs Information and Computing Infrastructure (VINCI). We identified 21,355 veterans who had their first MCVP in 2014. Features for prediction included demographics, diagnoses, procedures, medication orders, hospitalizations, and frailty measures extracted from clinical notes. Temporal variables were created based on the patient history data in the 2-year window prior to the index MCVP. A temporal image was created based on these variables for each individual patient. To generate the explanation for the DNN model, we defined a new concept called impact score, based on the presence/value of clinical conditions’ impact on the predicted outcome. Like (log) odds ratio reported by the logistic regression (LR) model, impact scores are continuous variables intended to shed light on the black box model. For comparison, a logistic regression model was fitted on the same dataset. In our cohort, about 6.8% of patients died within one year. The prediction of the DNN model achieved an area under the curve (AUC) of 78.5% while the LR model achieved an AUC of 74.6%. A strong but not perfect correlation was found between the aggregated impact scores and the log odds ratios (Spearman’s rho = 0.74), which helped validate our explanation.

Keywords: deep neural network, temporal data, prediction, frailty, logistic regression model

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Authors: Levi Corallo

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Typed language made by humans on computers and cell phones has made a significant distinction from previous modes of written language exchanges. While acronyms remain one of the most predominant markings of typed language, another and perhaps more recent revolution in the way humans communicate has been with the use of symbols or glyphs, primarily Emojis—globally introduced on the iPhone keyboard by Apple in 2008. This paper seeks to analyze the use of symbols in typed communication from both a linguistic and machine learning perspective. The Unicode system will be explored and methods of encoding will be juxtaposed with the current machine and human perception. Topics in how typed symbol usage exists in conversation will be explored as well as topics across current research methods dealing with Emojis like sentiment analysis, predictive text models, and so on. This study proposes that sequential analysis is a significant feature for analyzing unicode characters in a corpus with machine learning. Current models that are trying to learn or translate the meaning of Emojis should be starting to learn using bi- and tri-grams of Emoji, as well as observing the relationship between combinations of different Emoji in tandem. The sociolinguistics of an entire new vernacular of language referred to here as ‘typed language’ will also be delineated across my analysis with unicode glyphs from both a semantic and technical perspective.

Keywords: unicode, text symbols, emojis, glyphs, communication

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Authors: Madhusudan Purohit, Stephen Philip, Bharathkumar Inturi

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In this paper we report the quantitative structure activity relationship of novel bis-triazole derivatives for predicting the activity profile. The full model encompassed a dataset of 46 Bis- triazoles. Tripos Sybyl X 2.0 program was used to conduct CoMSIA QSAR modeling. The Partial Least-Squares (PLS) analysis method was used to conduct statistical analysis and to derive a QSAR model based on the field values of CoMSIA descriptor. The compounds were divided into test and training set. The compounds were evaluated by various CoMSIA parameters to predict the best QSAR model. An optimum numbers of components were first determined separately by cross-validation regression for CoMSIA model, which were then applied in the final analysis. A series of parameters were used for the study and the best fit model was obtained using donor, partition coefficient and steric parameters. The CoMSIA models demonstrated good statistical results with regression coefficient (r2) and the cross-validated coefficient (q2) of 0.575 and 0.830 respectively. The standard error for the predicted model was 0.16322. In the CoMSIA model, the steric descriptors make a marginally larger contribution than the electrostatic descriptors. The finding that the steric descriptor is the largest contributor for the CoMSIA QSAR models is consistent with the observation that more than half of the binding site area is occupied by steric regions.

Keywords: 3D QSAR, CoMSIA, triazoles, novel heterocycles

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Authors: Gilad Padva, Sigal Barak Brandes

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This research centers on an unexpected Israeli advertising campaign for Elemento, a local furniture company, which eroticizes male nudity. The discussed campaign includes a series of printed ads that depict naked male models in effeminate positions. This campaign included a series of ads published in Haaretz, a small-scaled yet highly prestigious daily newspaper which is typically read by urban middle-upper-class left-winged Israelis. Apparently, this campaign embodies an alternative masculinity that challenges the prevalent machismo in Israeli society and advertising. Although some of the ads focus on young men in effeminate positions, they never expose their genitals and anuses, and their bodies are never permeable. The 2010s Elemento male models are seemingly contrasted to conventional representation of manhood in contemporary mainstream advertising. They display a somewhat inactive, passive and self-indulgent masculinity which involves 'conspicuous leisure'. In the process of commodity fetishism, the advertised furniture are emptied of the original meaning of their production, and then filled with new meanings in ways that both mystify the product and turn it into a fetish object. Yet, our research critically reconsiders this sensational campaign as sophisticated patriarchal parody that does not subvert but rather reconfirms and even fetishizes patriarchal premises; it parodizes effeminacy rather than the prevalent (Israeli) machismo. Following Pierre Bourdieu's politics of cultural taste, our research reconsiders and criticizes the male models' domesticated masculinity in a fantasized and cosmopolitan hedonistic habitus. Notwithstanding, we suggest that the Elemento campaign, despite its conformity, does question some Israeli and global axioms about gender roles, corporeal ideologies, idealized bodies, and domesticated phalluses and anuses. Although the naked truth is uncovered by this campaign, it does erect a vibrant discussion of contemporary masculinities and their exploitation in current mass consumption.

Keywords: male body, campaign, advertising, gender studies, men's studies, Israeli culture, masculinity, parody, effeminacy

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Authors: Sergo Tsiramua, Sulkhan Sulkhanishvili, Elisabed Asabashvili, Lazare Kvirtia

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The paper presents logical-probabilistic methods, models and algorithms for reliability assessment of complex systems, based on which a web application for structural analysis and reliability assessment of systems was created. The reliability assessment process included the following stages, which were reflected in the application: 1) Construction of a graphical scheme of the structural reliability of the system; 2) Transformation of the graphic scheme into a logical representation and modeling of the shortest ways of successful functioning of the system; 3) Description of system operability condition with logical function in the form of disjunctive normal form (DNF); 4) Transformation of DNF into orthogonal disjunction normal form (ODNF) using the orthogonalization algorithm; 5) Replacing logical elements with probabilistic elements in ODNF, obtaining a reliability estimation polynomial and quantifying reliability; 6) Calculation of weights of elements. Using the logical-probabilistic methods, models and algorithms discussed in the paper, a special software was created, by means of which a quantitative assessment of the reliability of systems of a complex structure is produced. As a result, structural analysis of systems, research and designing of optimal structure systems are carried out.

Keywords: Complex systems, logical-probabilistic methods, orthogonalization algorithm, reliability, weight of element

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Authors: Sungwon Jung

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The percentage of 20 years or older apartment houses in South Korea is approximately 20% (1.55 million houses), and the explosive increase of aged houses is expected around the first planned new towns. Accordingly, we should prepare for social issues such as difficulty of housing lease and degradation of housing performance. The improvement of performance of aged houses is essential for achieving the national energy and carbon reduction goals, and we should develop techniques to respond to the changing construction environment. Furthermore, we should develop a performance evaluation system that is appropriate for the demands of residents such as the improvement of remodeling floor plan by performance improvement in line with the residence type of the housing vulnerable groups such as low-income group and elderly people living alone. For this purpose, remodeling techniques and business models optimized for the target complexes must be spread through the development of various business models. In addition, it is necessary to improve the remodeling business by improving the laws and systems related to the improvement of the residential performance and to prepare techniques to respond to the increasing business demands. In other words, performance improvement and evaluation and knowledge systems need to be researched as new issues related to remodeling that has not been addressed in the existing research.

Keywords: remodelling, performance evaluation, web-based system, big data

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Authors: Azadeh Farzaneh, Mohammad Reza Abdi, A. Quaranta, Matteo Dalla Palma, Seyedshahram Mortazavi

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We report on the synthesis of cesium-iodide nanoparticles using sol-gel technique. The structural properties of CsI nanoparticles were characterized by X-ray diffraction and Scanning Electron Microscope (SEM) Also, optical properties were followed by optical absorption and UV–vis fluorescence. Intense photoluminescence is also observed, with some spectral tuning possible with ripening time getting a range of emission photon wavelength approximately from 366 to 350 nm. The size effect on CsI luminescence leads to an increase in scintillation light yield, a redshift of the emission bands of the on_center and off_center self_trapped excitons (STEs) and an increase in the contribution of the off_center STEs to the net intrinsic emission yield. The energy transfer from the matrix to CsI nanoparticles is a key characteristic for scintillation detectors. So the scintillation spectra to alpha particles of sample were monitored.

Keywords: nanoparticles, luminescence, sol gel, scintillator

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Authors: Otto Kakhidze, Hoda Alkhzaimi, Adam Ramey, Nasir Memon

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This paper provides an alternative, cyber security based a conceptual framework for the ethics of automated warfare. The large body of work produced on fully or partially autonomous warfare systems tends to overlook malicious security factors as in the possibility of technical attacks on these systems when it comes to the moral and legal decision-making. The argument provides a risk-oriented justification to why technical malicious risks cannot be dismissed in legal, ethical and policy considerations when warfare models are being implemented and deployed. The assumptions of the paper are supported by providing a broader model that contains the perspective of technological vulnerabilities through the lenses of the Game Theory, Just War Theory as well as standard and non-standard defense ethics. The paper argues that a conventional risk-benefit analysis without considering ethical factors is insufficient for making legal and policy decisions on automated warfare. This approach will provide the substructure for security and defense experts as well as legal scholars, ethicists and decision theorists to work towards common justificatory grounds that will accommodate the technical security concerns that have been overlooked in the current legal and policy models.

Keywords: automated warfare, ethics of automation, inherent hijacking, security vulnerabilities, risk, uncertainty

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Authors: M. N. Abu Seman, L. M. Kei, M. A. Yusoff

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Forward Osmosis (FO) polyamide thin-film composite membranes have been prepared by inter facial polymerization using commercial UF polyethersulfone as membrane support. Different inter facial polymerization times (10s, 30s and 60s) in the organic solution containing trimesoyl chloride (TMC) at constant m-phenylenediamine (MPD) concentration (2% w/v) were studied. The synthesized polyamide membranes then tested for treatment of natural organic matter (NOM) and compared to commercial Cellulose TriAcetate (CTA) membrane. It was found that membrane prepared with higher reaction time (30 s and 60 s) exhibited better membrane performance (flux and humic acid removal) over commercial CTA membrane.

Keywords: cellulose triacetate, forward osmosis, humic acid, polyamide

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Authors: Muhammad A. Nadeem, Shaheed Ullah

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The article reports the synthesis of Cr2O3/C nanocomposites obtained by the direct carbonization of PFA/MIL-101(Cr) bulk composite. The nanocomposites were characterized by various instrumental techniques like powder X-ray diffraction (PXRD), X-ray photoelectron spectroscopy (XPS), scanning electron microscopy (SEM), transmission electron microscopy (TEM), selected area electron diffraction (SAED) and the surface characterized were investigated via N2 adsorption/desorption analysis. TEM and SAED analysis shows that turbostatic graphitic carbon was obtained with high crystallinity. The nanocomposites were tested for electrochemical supercapacitor and the faradic and non-Faradic processes were checked through cyclic voltammetry (CV). The maximum specific capacitance calculated for Cr2O3/C 900 sample from CV measurement is 301 F g-1 at 2 mV s-1 due to its maximum charge storing capacity as confirm by frequency response analysis.

Keywords: nanocomposites, transmission electron microscopy, non-faradic process

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Authors: Bing Li, Zhi Li, Yilong Yang

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Web classification can promote the quality of service discovery and management in the service repository. It is widely used to locate developers desired services. Although traditional classification methods based on supervised learning models can achieve classification tasks, developers need to manually mark web services, and the quality of these tags may not be enough to establish an accurate classifier for service classification. With the doubling of the number of web services, the manual tagging method has become unrealistic. In recent years, the attention mechanism has made remarkable progress in the field of deep learning, and its huge potential has been fully demonstrated in various fields. This paper designs a multi-head attention graph convolutional network (MHAGCN) service classification method, which can assign different weights to the neighborhood nodes without complicated matrix operations or relying on understanding the entire graph structure. The framework combines the advantages of the attention mechanism and graph convolutional neural network. It can classify web services through automatic feature extraction. The comprehensive experimental results on a real dataset not only show the superior performance of the proposed model over the existing models but also demonstrate its potentially good interpretability for graph analysis.

Keywords: attention mechanism, graph convolutional network, interpretability, service classification, service discovery

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Authors: Katawut Kaewbanjong

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We analyzed a volume of data and found significant user satisfaction in software project factors. A statistical significance analysis (logistic regression) and collinearity analysis determined the significance factors from a group of 71 pre-defined factors from 191 software projects in ISBSG Release 12. The eight prediction models used for testing the prediction potential of these factors were Neural network, k-NN, Naïve Bayes, Random forest, Decision tree, Gradient boosted tree, linear regression and logistic regression prediction model. Fifteen pre-defined factors were truly significant in predicting user satisfaction, and they provided 82.71% prediction accuracy when used with a neural network prediction model. These factors were client-server, personnel changes, total defects delivered, project inactive time, industry sector, application type, development type, how methodology was acquired, development techniques, decision making process, intended market, size estimate approach, size estimate method, cost recording method, and effort estimate method. These findings may benefit software development managers considerably.

Keywords: prediction model, statistical analysis, software project, user satisfaction factor

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Authors: Suneet Jagdev

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17 Sustainable Development Goals were adopted by the world leaders in September 2015 at the United Nations Sustainable Development Summit. These goals were adopted by UN member states to promote prosperity, health and human rights while protecting the planet. Around the same time, the Government of India launched the Smart City Initiative to speed up development of state of the art infrastructure and services in 100 cities with a focus on sustainable and inclusive development. These cities are meant to become role models for other cities in India and promote sustainable regional development. This paper examines goals set under the Smart City Initiative and evaluates them in terms of the Sustainable Development Goals, using case studies of selected Smart Cities in India. The study concludes that most Smart City projects at present actually consist of individual solutions to individual problems identified in a community rather than comprehensive models for complex issues in cities across India. Systematic, logical and comparative analysis of important literature and data has been done, collected from government sources, government papers, research papers by various experts on the topic, and results from some online surveys. Case studies have been used for a graphical analysis highlighting the issues of migration, ecology, economy and social equity in these Smart Cities.

Keywords: housing, migration, smart cities, sustainable development goals, urban infrastructure

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Authors: Manit Pollar

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Identifying the dengue epidemic periods early would be helpful to take necessary actions to prevent the dengue outbreaks. Providing an accurate prediction on dengue epidemic seasons will allow sufficient time to take the necessary decisions and actions to safeguard the situation for local authorities. This study aimed to develop a forecasting model on number of dengue incidences in Nakhon Si Thammarat Province, Southern Thailand using time series analysis. We develop Seasonal Autoregressive Moving Average (SARIMA) models on the monthly data collected between 2003-2011 and validated the models using data collected between January-September 2012. The result of this study revealed that the SARIMA(1,1,0)(1,2,1)12 model closely described the trends and seasons of dengue incidence and confirmed the existence of dengue fever cases in Nakhon Si Thammarat for the years between 2003-2011. The study showed that the one-step approach for predicting dengue incidences provided significantly more accurate predictions than the twelve-step approach. The model, even if based purely on statistical data analysis, can provide a useful basis for allocation of resources for disease prevention.

Keywords: SARIMA, time series model, dengue cases, Thailand

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Authors: Prateek Goyal, Archini Paruthi, Superb K. Misra

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Contamination of water resources has been a major concern, which has drawn attention to the need to develop new material models for treatment of effluents. Existing conventional waste water treatment methods remain ineffective sometimes and uneconomical in terms of remediating contaminants like heavy metal ions (mercury, arsenic, lead, cadmium and chromium); organic matter (dyes, chlorinated solvents) and high salt concentration, which makes water unfit for consumption. We believe that nanotechnology based strategy, where we use nanoparticles as a tool to remediate a class of pollutants would prove to be effective due to its property of high surface area to volume ratio, higher selectivity, sensitivity and affinity. In recent years, scientific advancement has been made to study the application of photocatalytic (ZnO, TiO2 etc.) nanomaterials and magnetic nanomaterials in remediating contaminants (like heavy metals and organic dyes) from water/wastewater. Our study focuses on the synthesis and monitoring remediation efficiency of ZnO, Fe3O4 and Fe3O4 coated ZnO nanoparticulate system for the removal of heavy metals and dyes simultaneously. Multitude of ZnO nanostructures (spheres, rods and flowers) using multiple routes (microwave & hydrothermal approach) offers a wide range of light active photo catalytic property. The phase purity, morphology, size distribution, zeta potential, surface area and porosity in addition to the magnetic susceptibility of the particles were characterized by XRD, TEM, CPS, DLS, BET and VSM measurements respectively. Further on, the introduction of crystalline defects into ZnO nanostructures can also assist in light activation for improved dye degradation. Band gap of a material and its absorbance is a concrete indicator for photocatalytic activity of the material. Due to high surface area, high porosity and affinity towards metal ions and availability of active surface sites, iron oxide nanoparticles show promising application in adsorption of heavy metal ions. An additional advantage of having magnetic based nanocomposite is, it offers magnetic field responsive separation and recovery of the catalyst. Therefore, we believe that ZnO linked Fe3O4 nanosystem would be efficient and reusable. Improved photocatalytic efficiency in addition to adsorption for environmental remediation has been a long standing challenge, and the nano-composite system offers the best of features which the two individual metal oxides provide for nanoremediation.

Keywords: adsorption, nanocomposite, nanoremediation, photocatalysis

Procedia PDF Downloads 237

Authors: Julia Kapr, Andrea Rossi, Haribaskar Ramachandran, Marius Pollet, Ilka Egger, Selina Dangeleit, Katharina Koch, Jean Krutmann, Ellen Fritsche

Abstract:

It is widely acknowledged that animal models do not always represent human disease. Especially human brain development is difficult to model in animals due to a variety of structural and functional species-specificities. This causes significant discrepancies between predicted and apparent drug efficacies in clinical trials and their subsequent failure. Emerging alternatives based on 3D in vitro approaches, such as human brain spheres or organoids, may in the future reduce and ultimately replace animal models. Here, we present a human induced pluripotent stem cell (hiPSC)-based 3D neural in a vitro disease model for the Cockayne Syndrome B (CSB). CSB is a rare hereditary disease and is accompanied by severe neurologic defects, such as microcephaly, ataxia and intellectual disability, with currently no treatment options. Therefore, the aim of this study is to investigate the molecular and cellular defects found in neural hiPSC-derived CSB models. Understanding the underlying pathology of CSB enables the development of treatment options. The two CSB models used in this study comprise a patient-derived hiPSC line and its isogenic control as well as a CSB-deficient cell line based on a healthy hiPSC line (IMR90-4) background thereby excluding genetic background-related effects. Neurally induced and differentiated brain sphere cultures were characterized via RNA Sequencing, western blot (WB), immunocytochemistry (ICC) and multielectrode arrays (MEAs). CSB-deficiency leads to an altered gene expression of markers for autophagy, focal adhesion and neural network formation. Cell migration was significantly reduced and electrical activity was significantly increased in the disease cell lines. These data hint that the cellular pathologies is possibly underlying CSB. By induction of autophagy, the migration phenotype could be partially rescued, suggesting a crucial role of disturbed autophagy in defective neural migration of the disease lines. Altered autophagy may also lead to inefficient mitophagy. Accordingly, disease cell lines were shown to have a lower mitochondrial base activity and a higher susceptibility to mitochondrial stress induced by rotenone. Since mitochondria play an important role in neurotransmitter cycling, we suggest that defective mitochondria may lead to altered electrical activity in the disease cell lines. Failure to clear the defective mitochondria by mitophagy and thus missing initiation cues for new mitochondrial production could potentiate this problem. With our data, we aim at establishing a disease adverse outcome pathway (AOP), thereby adding to the in-depth understanding of this multi-faced disorder and subsequently contributing to alternative drug development.

Keywords: autophagy, disease modeling, in vitro, pluripotent stem cells

Procedia PDF Downloads 120

Authors: Ruchika Malhotra, Megha Khanna

Abstract:

The development of change prediction models can help the software practitioners in planning testing and inspection resources at early phases of software development. However, a major challenge faced during the training process of any classification model is the imbalanced nature of the software quality data. A data with very few minority outcome categories leads to inefficient learning process and a classification model developed from the imbalanced data generally does not predict these minority categories correctly. Thus, for a given dataset, a minority of classes may be change prone whereas a majority of classes may be non-change prone. This study explores various alternatives for adeptly handling the imbalanced software quality data using different sampling methods and effective MetaCost learners. The study also analyzes and justifies the use of different performance metrics while dealing with the imbalanced data. In order to empirically validate different alternatives, the study uses change data from three application packages of open-source Android data set and evaluates the performance of six different machine learning techniques. The results of the study indicate extensive improvement in the performance of the classification models when using resampling method and robust performance measures.

Keywords: change proneness, empirical validation, imbalanced learning, machine learning techniques, object-oriented metrics

Procedia PDF Downloads 418

Authors: Ewelina Nowak, Anna Wisla-Swider

Abstract:

Deoxyribonucleic acid (DNA) is a biomolecule which exhibits an electro-optic properties. These features are related with structure of double-stranded helix. Modification of DNA with ionic liquids allows intensify these properties. The aim of our study was synthesis of ionic liquids that are used the formation of DNA-surfactant complexes in order to obtain new materials with potential application for nonlinear optics. Complexes were achieved through the ion exchange reactions of carbazole-based and imidazole-based ionic liquids with H+ ions from salmon DNA. To examination the properties of obtained complexes DNA-ionic liquids there were investigated using circular dichroism (CD), UV-Vis spectra and infrared spectroscopy (IR). Additionally, the resulting DNA-surfactant complexes were characterized in terms of solubility in common organic solvents and water.

Keywords: deoxyribonucleic acid, biomolecule, carbazole, imidazole, ionic liquids, ion exchange reactions

Procedia PDF Downloads 465