Search results for: models synthesis

Authors: Priyesh Kumar Singh, S. K. Sharma, Sanjay Verma, C. Samuel

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Supplier Relationship Management (SRM), is strategic planning and managing of all interactions with suppliers to maximize its value. Its application varies from construction industries to healthcare system and investment banks to aviation industries. Several buyer-supplier relationship models, as well as supplier selection and evaluation strategies, have been documented by many academicians and researchers. In this paper, through a comprehensive literature review of over 30 published papers, different theoretical models, empirical data and conclusions were analysed relating to SRM to find its role in establishing better supplier relationships. These journal articles were searched by using the keyword “supplier relationship management,” in databases of Mendeley Library, ProQuest, EBSCO and Google Scholar. This paper reviews the academic literature on different relationship models, supplier evaluation, and selection strategies to discuss its implications in different situations. It also describes the dominant factors responsible for buyer-supplier relationships such trust and power. Finally, conclusions have been drawn which can be validated by various researchers and can help practitioners in industries.

Keywords: supplier relationship management, supplier performance, supplier evaluation, supplier selection strategies

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Authors: Kourosh Modarresi

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The Netflix problem has brought the topic of “Recommendation Systems” into the mainstream of computer science, mathematics, and statistics. Though much progress has been made, the available algorithms do not obtain satisfactory results. The success of these algorithms is rarely above 5%. This work is based on the belief that the main challenge is to come up with “scalable personalization” models. This paper uses an adaptive regularization of inverse singular value decomposition (SVD) that applies adaptive penalization on the singular vectors. The results show far better matching for recommender systems when compared to the ones from the state of the art models in the industry.

Keywords: convex optimization, LASSO, regression, recommender systems, singular value decomposition, low rank approximation

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Authors: Abderahman Efhialelbum

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The period since the fall of the Berlin Wall on November 9, 1989, and the collapse of the former Soviet Union in December 1985 has seen a major change in the economies and labour markets of Eastern Europe. The events also had reverberating effects across Asia and South America and parts of Africa, including Libya. This article examines the typologies and the models of transition economies. Also, it sheds light on the Libyan transition in particular and the impact of Qadhafi’s regime on the transition process. Finally, it illustrates how the Libyan transition process followed the trajectory of other countries using economic indicators such as free trade, property rights, and inflation.

Keywords: transition, economy, typology, model, Libya

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Authors: J. Marx, D. Smazna, R. Adelung, B. Fiedler

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Aerographite is a 3D interconnected carbon foam. Its tetrapodal morphology is based on the zinc oxide (ZnO) template structure, which is replicated in the chemical vapour deposition (CVD) into a hollow carbon structure. This replication process is analyzed in ex-situ studies via interrupted synthesis and the observation of the reaction progress by using scanning electron (SEM), transmission electron microscopy (TEM) and Raman spectroscopy techniques. Based on the epitaxial growth process, with a layer-by-layer growth behaviour of the wall thickness or number of layers and the catalytical graphitization of the deposited amorphous carbon into graphitic carbon by zinc, a growth model is created. The properties of aerographite, such as the electrical conductivity is dependent on the graphitization and number of layer (wall thickness). Wall thicknesses between 3 nm and 22 nm are achieved by a controlled stepwise reduction of the synthesis time on the basis of the developed growth model, and by a further thermal treatment at 1800 °C the graphitization of the presented carbon foam is modified. The variation of the wall thickness leads to an optimum defect density (ID/IG ratio) and the graphitization to an improvement in the electrical conductivity. Furthermore, a metallic conducting behaviour of untreated and 1800 °C treated aerographite can be observed. Due to these structural and defective modifications, a fundamental structural-property equation for the description of their influences on the electrical conductivity is developed.

Keywords: electrical conductivity, electron microscopy (SEM/TEM), graphitization, wall thickness

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Authors: Mujeeb Mohd, Aqil Mohd, A. K. Najmi, Akhtar MMohd, Vasim Mohd

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Dioscorea deltoidea belongs to the Dioscoreaceae family, is usually found in the north-western Himalayas and some other parts of the world up to an altitude of 1000–3000 m. D. deltoidea commonly known as yam and is an extensively used medicinal plant in the indigenous system of medicine. It has been reported to contain dioscine a steroidal glycoside in higher concentration. In the present investigation, silver nanoparticles (AgNPs) have been synthesized by a simple, efficient, environmentally benevolent and economic microwave-assisted method. Callus culture of D. deltoidea was developed and maintained on Murashige and skooge basal medium supplemented with different combination and concentration of plant growth regulators. Aqueous extract of callus culture was used as the reducing and stabilizing agent. The synthesized nanoparticles have been characterized by UV–Vis spectroscopy, Fourier transform infrared spectroscopy (FT-IR), transmission electron microscopy (TEM), scanning electron microscopy (SEM) and X-ray diffraction (XRD analysis. The presence of a characteristic surface plasmon resonance (SPR) absorption band at 430 nm in UV–Vis reveals the reduction of silver metal ions into silver nanoparticles. Whereas FTIR analysis was performed to probe the possible functional group involved in the synthesis of AgNPs. Further extract and AgNPs were evaluated for antimicrobial activity against different pathogenic microorganisms.

Keywords: antimicrobial, Dioscorea deltoidea, microwave, silver, nanoparticles

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Authors: N. Didiş Körhasan, D. Kaltakçı Gürel

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Many students have difficulty in learning physics from elementary to university level. In addition, students' expectancy, attitude, and motivation may be influenced negatively with their experience (failure) and prejudice about physics learning. For this reason, physics educators, who are also physics teachers, search for the best ways to make students' learning of physics easier by considering cognitive, affective, and psychomotor issues in learning. This research critically discusses the history of physics education, fundamental pedagogical approaches, and models to teach physics, and transformation of physics education with recent research.

Keywords: pedagogy, physics, physics education, science education

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Authors: Siamack A. Shirazi, Farzin Darihaki

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Solid particles could be found in many multiphase flows, including transport pipelines and pipe fittings. Such particles interact with the pipe material and cause erosion which threats the integrity of the system. Therefore, predicting the erosion rate is an important factor in the design and the monitor of such systems. Mechanistic models can provide reliable predictions for many conditions while demanding only relatively low computational cost. Mechanistic models utilize a representative particle trajectory to predict the impact characteristics of the majority of the particle impacts that cause maximum erosion rate in the domain. The erosion caused by particle impacts is not only due to the direct impacts but also random impingements. In the present study, an alternative model has been introduced to describe the erosion due to random impingement of particles. The present model provides a realistic trend for erosion with changes in the particle size and particle Stokes number. The present model is examined against the experimental data and CFD simulation results and indicates better agreement with the data incomparison to the available models in the literature.

Keywords: erosion, mechanistic modeling, particles, multiphase flow, gas-liquid-solid

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Authors: Srijitra Khanpakdee, Teera Butburee, Jung-Ho Yun, Miaoqiang Lyu, Supphasin Thaweesak, Piangjai Peerakiatkhajohn

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The synthesis of titanium dioxide (TiO₂) nanorods (NRs) on fluorine-doped tin oxide (FTO) glass via hydrothermal methods was investigated to determine the optimal reaction time for enhanced photocatalytic and optical performance. Reaction times of 4, 6, and 8 hours were studied. Characterization through SEM, UV-vis, XRD, FTIR, Raman spectroscopy and photoelectrochemical (PEC) techniques revealed significant differences in the properties of the TiO₂ NRs based on the reaction duration. XRD and Raman spectroscopy analysis confirmed the formation of the rutile phase of TiO₂. As photoanodes in PEC cells, TiO₂ NRs synthesized for 4 hours exhibited the best photocatalytic activity, with the highest photocurrent density and superior charge transport properties, attributed to their densely packed vertical structure. Longer reaction times resulted in less optimal morphological and photoelectrochemical characteristics. The bandgap of the TiO₂ NRs remained consistent around 3.06 eV, with only slight variations observed. This study highlights the critical role of reaction time in hydrothermal synthesis, identifying 4 hours as the optimal duration for producing TiO₂ NRs with superior photoelectrochemical performance. These findings provide valuable insights for optimizing TiO₂-based materials for solar energy conversion and renewable energy applications.

Keywords: titanium dioxide, nanorods, hydrothermal, photocatalytic, photoelectrochemical

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Authors: Petr Gurný

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One of the most important tasks in the risk management is the correct determination of probability of default (PD) of particular financial subjects. In this paper a possibility of determination of financial institution’s PD according to the credit-scoring models is discussed. The paper is divided into the two parts. The first part is devoted to the estimation of the three different models (based on the linear discriminant analysis, logit regression and probit regression) from the sample of almost three hundred US commercial banks. Afterwards these models are compared and verified on the control sample with the view to choose the best one. The second part of the paper is aimed at the application of the chosen model on the portfolio of three key Czech banks to estimate their present financial stability. However, it is not less important to be able to estimate the evolution of PD in the future. For this reason, the second task in this paper is to estimate the probability distribution of the future PD for the Czech banks. So, there are sampled randomly the values of particular indicators and estimated the PDs’ distribution, while it’s assumed that the indicators are distributed according to the multidimensional subordinated Lévy model (Variance Gamma model and Normal Inverse Gaussian model, particularly). Although the obtained results show that all banks are relatively healthy, there is still high chance that “a financial crisis” will occur, at least in terms of probability. This is indicated by estimation of the various quantiles in the estimated distributions. Finally, it should be noted that the applicability of the estimated model (with respect to the used data) is limited to the recessionary phase of the financial market.

Keywords: credit-scoring models, multidimensional subordinated Lévy model, probability of default

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Authors: Sophia Liang Zhou

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For investors, businesses, consumers, and governments, an accurate assessment of future housing prices is crucial to critical decisions in resource allocation, policy formation, and investment strategies. Previous studies are contradictory about macroeconomic determinants of housing price and largely focused on one or two areas using point prediction. This study aims to develop data-driven models to accurately predict future housing market trends in different markets. This work studied five different metropolitan areas representing different market trends and compared three-time lagging situations: no lag, 6-month lag, and 12-month lag. Linear regression (LR), random forest (RF), and artificial neural network (ANN) were employed to model the real estate price using datasets with S&P/Case-Shiller home price index and 12 demographic and macroeconomic features, such as gross domestic product (GDP), resident population, personal income, etc. in five metropolitan areas: Boston, Dallas, New York, Chicago, and San Francisco. The data from March 2005 to December 2018 were collected from the Federal Reserve Bank, FBI, and Freddie Mac. In the original data, some factors are monthly, some quarterly, and some yearly. Thus, two methods to compensate missing values, backfill or interpolation, were compared. The models were evaluated by accuracy, mean absolute error, and root mean square error. The LR and ANN models outperformed the RF model due to RF’s inherent limitations. Both ANN and LR methods generated predictive models with high accuracy ( > 95%). It was found that personal income, GDP, population, and measures of debt consistently appeared as the most important factors. It also showed that technique to compensate missing values in the dataset and implementation of time lag can have a significant influence on the model performance and require further investigation. The best performing models varied for each area, but the backfilled 12-month lag LR models and the interpolated no lag ANN models showed the best stable performance overall, with accuracies > 95% for each city. This study reveals the influence of input variables in different markets. It also provides evidence to support future studies to identify the optimal time lag and data imputing methods for establishing accurate predictive models.

Keywords: linear regression, random forest, artificial neural network, real estate price prediction

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Authors: Mastaneh Safarnejad Shad, Wim Dehaen, Steven De Jonghe

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Since CXCR4 is the main coreceptor of HIV-1 and plays an important role in human immunodeficiency virus (HIV) entry, numerous efforts were directed towards the discovery of new classes of small molecules that act as CXCR4 antagonists. In addition, CXCR4 antagonists are potentially useful in the treatment of several other disorders, such as cancer cell metastasis, leukemia cell proliferation, rheumatoid arthritis, and pulmonary fibrosis. Since AMD3100 (plerixafor) is the only CXCR4 antagonist which obtained approval by the Food and Drug Administration (FDA), we were motivated to investigate a new category of molecules as CXCR4 antagonists. Most of the scaffolds which have been studied so far as CXCR4 antagonists are based on the tetrahydroquinoline (THQ) moiety in which AMD11070 (mavorixafor), GSK-812394, and TIQ15 displayed the most potent CXCR4 antagonism. Due to the high potency of these scaffolds, two different series of compounds were prepared in this work. In the first set, the THQ moiety is coupled to an amine chain and various isoquinoline derivatives (prepared by an in-house developed triazolization strategy), of which the upper part of molecules is identical to AMD11070 and TIQ15. In the second category of compounds, the THQ moiety was simplified by the synthesis of a substituted pyridine moiety. In order to investigate if CXCR4 antagonism requires the presence of an isoquinoline moiety, the corresponding pyridine analogues were also prepared. In both series of compounds, potent CXCR4 antagonism was noticed.

Keywords: CXCR4 coreceptor, CXCR4 antagonists, HIV inhibitor, tetrahydroquinoline

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Authors: Srutirekha Giri, Manoranjan Sahoo, Anuradha Das, Pravanjan Mallick, Biswajit Mallick

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Ion Beam (IB) technique is a very powerful experimental technique for both material synthesis and material modifications. In this work, 3MeV proton beam was generated using the 3MV Tandem machine of the Institute of Physics, Bhubaneswar and extracted into air for the irradiation-induced modification purpose[1]. The polymeric material can be modeled for a three-phase system viz. crystalline(I), amorphous(II) and mesomorphic(III). So far, our knowledge is concerned. There are only few techniques reported for the synthesis of this third-phase(III) of polymer. The IB induced technique is one of them and has been reported very recently [2-4]. It was observed that by irradiating polyethylene terephthalate (PET) fiber at very low proton fluence, 10¹⁰ - 10¹² p/s, possess 2D mesophase structure. This was confirmed using X-ray diffraction technique. A low-intensity broad peak was observed at small angle of about 2θ =6º, when the fiber axis was mounted parallel to the X-ray direction. Such peak vanished in the diffraction spectrum when the fiber axis was mounted perpendicular to the beam direction. The appearance of this extra peak in a particular orientation confirms that the phase is 2-dimensionally oriented (mesophase). It is well known that the mesophase is a 2-dimensionally ordered structure but 3-dimensionally disordered. Again, the crystallite of the mesophase peak particle was measured about 3nm. The MeV proton-induced 2D mesophase patterning of nanocrystallites (3nm) of PET due to irradiation was observed within the above low fluence range and failed in high proton fluence. This is mainly due to the breaking of crystallites, radiation-induced thermal degradation, etc.

Keywords: Ion irradiation, mesophase, nanocrystallites, polymer

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Authors: Evgeniya Y. Yuzbasheva, Tigran V. Yuzbashev, Natalia I. Perkovskaya, Elizaveta B. Mostova

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Cell-surface display of lipase is of great interest as it has many applications in the field of biotechnology owing to its unique advantages: simplified product purification, and cost-effective downstream processing. One promising area of application for whole-cell biocatalysts with surface displayed lipase is biodiesel synthesis. Biodiesel is biodegradable, renewable, and nontoxic alternative fuel for diesel engines. Although the alkaline catalysis method has been widely used for biodiesel production, it has a number of limitations, such as rigorous feedstock specifications, complicated downstream processes, including removal of inorganic salts from the product, recovery of the salt-containing by-product glycerol, and treatment of alkaline wastewater. Enzymatic synthesis of biodiesel can overcome these drawbacks. In this study, Lip2p lipase was displayed on Yarrowia lipolytica cells via C- and N-terminal fusion variant. The active site of lipase is located near the C-terminus, therefore to prevent the activity loosing the insertion of glycine-serine linker between Lip2p and C-domains was performed. The hydrolytic activity of the displayed lipase reached 12,000–18,000 U/g of dry weight. However, leakage of enzyme from the cell wall was observed. In case of C-terminal fusion variant, the leakage was occurred due to the proteolytic cleavage within the linker peptide. In case of N-terminal fusion variant, the leaking enzyme was presented as three proteins, one of which corresponded to the whole hybrid protein. The calculated number of recombinant enzyme displayed on the cell surface is approximately 6–9 × 105 molecules per cell, which is close to the theoretical maximum (2 × 106 molecules/cell). Thus, we attribute the enzyme leakage to the limited space available on the cell surface. Nevertheless, cell-bound lipase exhibited greater stability to short-term and long-term temperature treatment than the native enzyme. It retained 74% of original activity at 60°C for 5 min of incubation, and 83% of original activity after incubation at 50°C during 5 h. Cell-bound lipase had also higher stability in organic solvents and detergents. The developed whole-cell biocatalyst was used for recycling biodiesel synthesis. Two repeated cycles of methanolysis yielded 84.1–% and 71.0–% methyl esters after 33–h and 45–h reactions, respectively.

Keywords: biodiesel, cell-surface display, lipase, whole-cell biocatalyst

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Authors: L. M. Ombaka, R. S. Oosthuizen, P. G. Ndungu, V. O. Nyamori

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Understanding the role of nitrogen species on the catalytic properties of nitrogen-doped carbon nanotubes (N-CNTs) as catalysts supports is critical as nitrogen species influence the support’s properties. To evaluate the influence of pyrrolic nitrogen on the physicochemical properties and catalytic activity of N-CNTs supported Pd (Pd/N-CNTs); N-CNTs containing varying pyrrolic contents were synthesized. The catalysts were characterised by the use of transmission electron microscope (TEM), scanning electron microscope, X-ray photoelectron spectroscopy (XPS), X-ray diffraction, Fourier transform infrared spectroscopy, and temperature programmed reduction. TEM analysis showed that the Pd nanoparticles were mainly located along the defect sites on N-CNTs. XPS analysis revealed that the abundance of Pd0 decreased while that of Pd2+ increased as the quantity of pyrrolic nitrogen increased. The increase of Pd2+ species was accredited to the formation of stable Pd-N coordination complexes which prevented further reduction of Pd2+ to Pd0 during synthesis. The formed Pd-N complexes increased the stability and dispersion of Pd2+ nanoparticles. The selective hydrogenation of nitrobenzophenone to aminobenzophenone over Pd/N-CNTs was compared to that of Pd on carbon nanotubes (Pd/CNTs). Pd/N-CNTs showed a higher catalytic activity and selectivity compared with Pd/CNTs. Pyrrolic nitrogen functional groups significantly promoted the selectivity towards aminobenzophenone formation.

Keywords: pyrrolic N-CNTs, hydrogenation reactions, chemical vapour deposition technique

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Authors: Nabila Jaman, K. E. Hoque, S. Sawall, M. Ferdows

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Coronary artery disease (CAD) is one of the most lethal diseases of the cardiovascular diseases. Coronary arteries stenosis and bifurcation angles closely interact for myocardial infarction. We want to use computer-aided design model coupled with computational hemodynamics (CHD) simulation for detecting several types of coronary artery stenosis with different locations in an idealized model for identifying virtual fractional flow reserve (vFFR). The vFFR provides us the information about the severity of stenosis in the computational models. Another goal is that we want to imitate patient-specific computed tomography coronary artery angiography model for constructing our idealized models with different left anterior descending (LAD) and left circumflex (LCx) bifurcation angles. Further, we want to analyze whether the bifurcation angles has an impact on the creation of narrowness in coronary arteries or not. The numerical simulation provides the CHD parameters such as wall shear stress (WSS), velocity magnitude and pressure gradient (PGD) that allow us the information of stenosis condition in the computational domain.

Keywords: CAD, CHD, vFFR, bifurcation angles, coronary stenosis

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Authors: M. Rilliard

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Based on a Multiliteracies-inspired and sociolinguistically-informed advanced French composition class, this study employed autobiographical narratives from speakers traditionally considered non-legitimate models for L2 teaching purposes of inspiring students to develop an authentic L2 voice and to see themselves as legitimate L2 speakers. Students explored their L2 identities in French through a self-inspired fictional character. Two autobiographical narratives of identity quest by non-traditional French speakers provided them guidance through this process: the novel Le Bleu des Abeilles (2013) and the film Qu’Allah Bénisse la France (2014). Written and French oral productions for different genres, as well as metalinguistic reflections in English, were collected and analyzed. Results indicate that ideas and materials that were relatable to students, namely relatable experiences and relatable language, were most useful to them in developing their L2 voices and achieving authentic and legitimate L2 speakership. These results point towards the benefits of using non-traditional speakers as pedagogical models, as they serve to legitimize students’ sense of their own L2-speakership, which ultimately leads them towards a better, more informed, mastery of the language.

Keywords: foreign language classroom, L2 identity, L2 learning and teaching, L2 writing, sociolinguistics

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Authors: Sepideh Hassanpour Khodaei

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Background/Objectives: The purpose of this study is to evaluate the effect of mummy substances on two issues of proliferation and production of matrix protein synthesis in wound healing. Methods: The methodology used for this aim involves isolating mesenchymal stem cells and human fibroblasts procured at Pastor Institute, Iran. The cells were treated with mummy substances separately and co-cultured between ASCs and WJSCs, and fibroblasts. Proliferation was assessed by Ki67 method in monolayer conditions. Synthesis of components of extracellular matrix (ECM) such as collagen type I, type III, and fibronectin 1 (FN1) was determined by qPCR. Results: The effects of adipocyte stem cells (ASCs), Wharton Jelly Stem Cells (WJSCs), and Mummy material on fibroblast proliferation and migration were evaluated. The present finding underlined the importance of Mummy material, ASCs, and WJSCs in the proliferation and migration of fibroblast cells. Furthermore, the expression of collagen I, III, and FN1 was increased in the presence of the above material and cells. Conclusion: This study presented an effective in vitro method for the healing process. Hence, the prospect of utilizing Mummy material and stem cell-based therapies in wound healing as a therapeutic approach is promising.

Keywords: mummy material, wound healing, adipose tissue, Wharton’s jelly

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Authors: Akinbo Razak Yinka, Adesanya Kehinde Kazeem, Oladokun Oluwagbenga Peter

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Time series data are sequences of observations collected over a period of time. Such data can be used to predict health outcomes, such as disease progression, mortality, hospitalization, etc. The Statistical approach is based on mathematical models that capture the patterns and trends of the data, such as autocorrelation, seasonality, and noise, while Neural methods are based on artificial neural networks, which are computational models that mimic the structure and function of biological neurons. This paper compared both parametric and non-parametric time series models of patients treated for malaria in Maternal and Child Health Centres in Lagos State, Nigeria. The forecast methods considered linear regression, Integrated Moving Average, ARIMA and SARIMA Modeling for the parametric approach, while Multilayer Perceptron (MLP) and Long Short-Term Memory (LSTM) Network were used for the non-parametric model. The performance of each method is evaluated using the Mean Absolute Error (MAE), R-squared (R2) and Root Mean Square Error (RMSE) as criteria to determine the accuracy of each model. The study revealed that the best performance in terms of error was found in MLP, followed by the LSTM and ARIMA models. In addition, the Bootstrap Aggregating technique was used to make robust forecasts when there are uncertainties in the data.

Keywords: ARIMA, bootstrap aggregation, MLP, LSTM, SARIMA, time-series analysis

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Authors: F. Jafari, S. GharehzadehShirazi, S. Khodabakhshi

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An efficient, simple, and environmentally benign procedure for the one-pot synthesis of dicoumarols was reported. The reaction entails the condensation of aryl glyoxals and 4-hydroxyxoumarin in the presence of catalytic amount of zinc oxide nanoparticles (ZnO NPs) as recyclable catalyst in aqueous media. High product yields and use of clean conditions are important factors of green chemistry.Part of our continued interest to achieve high atom economic reactions by the use safe catalysts. The reaction mixture was refluxed with catalytic amount (3 mol%) of zinc oxide nanoparticles.Reducing the amount of toxic waste and byproducts arising from chemical reactions is an important issue in the context of green chemistry. In comparison with commonly organic solvents, the aqueous media is cheaper and more environmentally friendly. Avoiding the use of organic solvents is an important way to prevent waste in chemical processes. In the context of green and sustainable chemistry, one ofthe most promising approaches is the use of water as the reaction media. In recent years, there has been increasing recognition that water is an attractive media for manyorganic reactions. Using water continues to attract wide attention among synthetic chemists in the design of new synthetic methods.

Keywords: zinc oxide, dicoumarol, aryl glyoxal, green chemistry, catalyst

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Authors: Yan Lyu, Yiqun Pan, Zhizhong Huang

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In the design stage of a new building, the energy model of this building is often required for the analysis of the performance on energy efficiency. In practice, a certain degree of geometric simplification should be done in the establishment of building energy models, since the detailed geometric features of a real building are hard to be described perfectly in most energy simulation engine, such as ESP-r, eQuest or EnergyPlus. Actually, the detailed description is not necessary when the result with extremely high accuracy is not demanded. Therefore, this paper analyzed the relationship between the error of the simulation result from building energy models and the geometric simplification of the models. Finally, the following two parameters are selected as the indices to characterize the geometric feature of in building energy simulation: the southward projected area and total side surface area of the building, Based on the parameterization method, the simplification from an arbitrary column building to a typical shape (a cuboid) building can be made for energy modeling. The result in this study indicates that this simplification would only lead to the error that is less than 7% for those buildings with the ratio of southward projection length to total perimeter of the bottom of 0.25~0.35, which can cover most situations.

Keywords: building energy model, simulation, geometric simplification, design, regression

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Authors: Temsiri Suwan, Penpicha Wanachantararak, Sakornrat Khongkhunthian, Siriporn Okonogi

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In the present study, silver nanoparticles (AgNPs) were synthesized by green synthesis approach using Moringa oleifera aqueous extract (ME) as a reducing agent and silver nitrate as a precursor. The obtained AgNPs were characterized using UV-Vis spectroscopy (UV-Vis), dynamic light scattering (DLS), scanning electron microscopy (SEM), energy-dispersive X-ray spectroscopy (EDX), and X-ray diffractometry (XRD). The results from UV-Vis revealed that the maximum absorption of AgNPs was at 430 nm and the EDX spectrum confirmed Ag element. The results from DLS indicated that the amount of ME played an important role in particle size, size distribution, and zeta potential of the obtained AgNPs. The smallest size (62.4 ± 1.8 nm) with narrow distribution (0.18 ± 0.02) of AgNPs was obtained after using 1% w/v of ME. This system gave high negative zeta potential of -36.5 ± 2.8 mV. SEM results indicated that the obtained AgNPs were spherical in shape. Antibacterial activity using dilution method revealed that the minimum inhibitory and minimum bactericidal concentrations of the obtained AgNPs against Streptococcus mutans were 0.025 and 0.1 mg/mL, respectively. Cytotoxicity test of AgNPs on adenocarcinomic human alveolar basal epithelial cells (A549) indicated that the particles impacted against A549 cells. The percentage of cell growth inhibition was 87.5 ± 3.6 % when only 0.1 mg/mL AgNPs was used. These results suggest that ME is the potential reducing agent for green synthesis of AgNPs.

Keywords: antibacterial activity, Moringa oleifera extract, reducing agent, silver nanoparticles

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Authors: S. O. Oyamakin, A. U. Chukwu,

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We proposed a Hyperbolic Gompertz Growth Model (HGGM), which was developed by introducing a stabilizing parameter called θ using hyperbolic sine function into the classical gompertz growth equation. The resulting integral solution obtained deterministically was reprogrammed into a statistical model and used in modeling the height and diameter of Pines (Pinus caribaea). Its ability in model prediction was compared with the classical gompertz growth model, an approach which mimicked the natural variability of height/diameter increment with respect to age and therefore provides a more realistic height/diameter predictions using goodness of fit tests and model selection criteria. The Kolmogorov-Smirnov test and Shapiro-Wilk test was also used to test the compliance of the error term to normality assumptions while using testing the independence of the error term using the runs test. The mean function of top height/Dbh over age using the two models under study predicted closely the observed values of top height/Dbh in the hyperbolic gompertz growth models better than the source model (classical gompertz growth model) while the results of R2, Adj. R2, MSE, and AIC confirmed the predictive power of the Hyperbolic Monomolecular growth models over its source model.

Keywords: height, Dbh, forest, Pinus caribaea, hyperbolic, gompertz

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Authors: Babak Azari, Afshin Pourahmad, Babak Sadeghi, Masuod Mokhtari

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In this work, Equisetnm arvense plant extract was used for preparing amorphous SiO₂. For preparing of SiO₂/zeolitic imidazolate framework-8 (ZIF-8) nanocomposite by solvothermal method, the synthesized SiO₂ was added to the synthesis mixture ZIF-8. The nanocomposite was characterized using a range of techniques. The photocatalytic activity of SiO₂/ZIF-8 was investigated systematically by degrading crystal violet as a cationic dye under Ultraviolet light irradiation. Among synthesized samples (SiO₂, ZIF-8 and SiO₂/ZIF-8), the SiO₂/ZIF-8 exhibited the highest photocatalytic activity and improved stability compared to pure SiO₂ and ZIF-8. As evidenced by Scanning Electron Microscopy and Transmission electron microscopy images, ZIF-8 particles without aggregation are located over SiO₂. The SiO₂ not only provides structured support for ZIF-8 but also prevents the aggregation of ZIF-8 Metal-organic framework in comparison to the isolated ZIF-8. The superior activity of this photocatalyst was attributed to the synergistic effects from SiO₂ owing to (I) an electron acceptor (from ZIF-8) and an electron donor (to O₂ molecules), (II) preventing recombination of electron-hole in ZIF-8, and (III) maximum interfacial contact ZIF-8 with the SiO₂ surface without aggregation or prevent the accumulation of ZIF-8. The results demonstrate that holes (h+) and •O₂- are primary reactive species involved in the photocatalytic oxidation process. Moreover, the SiO₂/ZIF-8 photocatalyst did not show any obvious loss of photocatalytic activity during five-cycle tests, which indicates that the heterostructured photocatalyst was highly stable and could be used repeatedly.

Keywords: nano, zeolit, potocatalist, nanocomposite

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Authors: Timothy Kayode Samson, Adedoyin Isola Lawal

Abstract:

The past five years have shown a sharp increase in public interest in the crypto market, with its market capitalization growing from $100 billion in June 2017 to $2158.42 billion on April 5, 2022. Despite the outrageous nature of the volatility of cryptocurrencies, the use of skewed error innovation distributions in modelling the volatility behaviour of these digital currencies has not been given much research attention. Hence, this study models the volatility of 5 largest cryptocurrencies by market capitalization (Bitcoin, Ethereum, Tether, Binance coin, and USD Coin) using four variants of GARCH models (GJR-GARCH, sGARCH, EGARCH, and APARCH) estimated using three skewed error innovation distributions (skewed normal, skewed student- t and skewed generalized error innovation distributions). Daily closing prices of these currencies were obtained from Yahoo Finance website. Finding reveals that the Binance coin reported higher mean returns compared to other digital currencies, while the skewness indicates that the Binance coin, Tether, and USD coin increased more than they decreased in values within the period of study. For both Bitcoin and Ethereum, negative skewness was obtained, meaning that within the period of study, the returns of these currencies decreased more than they increased in value. Returns from these cryptocurrencies were found to be stationary but not normality distributed with evidence of the ARCH effect. The skewness parameters in all best forecasting models were all significant (p<.05), justifying of use of skewed error innovation distributions with a fatter tail than normal, Student-t, and generalized error innovation distributions. For Binance coin, EGARCH-sstd outperformed other volatility models, while for Bitcoin, Ethereum, Tether, and USD coin, the best forecasting models were EGARCH-sstd, APARCH-sstd, EGARCH-sged, and GJR-GARCH-sstd, respectively. This suggests the superiority of skewed Student t- distribution and skewed generalized error distribution over the skewed normal distribution.

Keywords: skewed generalized error distribution, skewed normal distribution, skewed student t- distribution, APARCH, EGARCH, sGARCH, GJR-GARCH

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Authors: Jonas Colin

Abstract:

Standing at the vanguard of AI development, Alpha represents an unprecedented synthesis of logical rigor and human abstraction, meticulously crafted to mirror the user's unique persona and personality, a feat previously unattainable in AI development. Alpha, an avant-garde artefact in the realm of artificial intelligence, epitomizes a paradigmatic shift in personalized digital interaction, amalgamating user-specific dialogic patterns with the sophisticated algorithmic prowess of OpenAI's GPT-3.5 to engender a platform for enhanced metacognitive engagement and individualized user experience. Underpinned by a sophisticated algorithmic framework, Alpha integrates vast datasets through a complex interplay of neural network models and symbolic AI, facilitating a dynamic, adaptive learning process. This integration enables the system to construct a detailed user profile, encompassing linguistic preferences, emotional tendencies, and cognitive styles, tailoring interactions to align with individual characteristics and conversational contexts. Furthermore, Alpha incorporates advanced metacognitive elements, enabling real-time reflection and adaptation in communication strategies. This self-reflective capability ensures continuous refinement of its interaction model, positioning Alpha not just as a technological marvel but as a harbinger of a new era in human-computer interaction, where machines engage with us on a deeply personal and cognitive level, transforming our interaction with the digital world.

Keywords: chatbot, GPT 3.5, metacognition, symbiose

Procedia PDF Downloads 52

Authors: Tesfay Gebremicheal Reda, K. Samatha, Paul Douglas Sanasi, D. Parajuli

Abstract:

Nanoparticle technology is fast progressing and is being employed in innumerable medical applications. At this time, the public's health is seriously threatened by the rise of bacterial strains resistant to several medications. Metal nanoparticles are a potential alternate approach for tackling this global concern, and this is the main focus of this study. The citrate precursor sol-gel synthesis method was used to synthesize the Niₓ Co₁₋ₓ Fe₂ O₄, (where x = 0.0:0.2:1.0) nanoparticle. XRD identified the development of the cubic crystal structure to have a preferential orientation along (311), and the average particle size was found to be 29-38 nm. The average crystallizes assessed with ImageJ software and origin 22 of the SEM are nearly identical to the XRD results. In the created NCF NPs, the FT-IR spectroscopy reveals structural examinations and the redistribution of cations between octahedral (505-428 cm⁻¹) and tetrahedral (653-603 cm⁻¹) locales. As the Co²⁺ cation is substituted with Ni²⁺, the coercive fields HC decrease from 2384 Oe to 241.93 Oe. Band gap energy rises as Ni concentration increases, which may be attributed to the fact that the ionic radii of Ni²⁺ ions are smaller than that of Co²⁺ ions, which results in a strong electrostatic interaction. On the contrary, except at x = 0.4, the dielectric constant decreases as the nickel concentration increases. According to the findings of this research work, nanoparticles are composed of Ni₀.₄ Co₀.₆ Fe₂ O₄ have demonstrated a promising value against S. aureus and E. coli, and it suggests a proposed model for their potential use as a source of antibacterial agent.

Keywords: antimicrobial, band gap, citrate precursor, dielectric, nanoparticle

Procedia PDF Downloads 54

Authors: Sean Paulsen, Michael Casey

Abstract:

In this work we present a self-supervised pretraining framework for transformers on functional Magnetic Resonance Imaging (fMRI) data. First, we pretrain our architecture on two self-supervised tasks simultaneously to teach the model a general understanding of the temporal and spatial dynamics of human auditory cortex during music listening. Our pretraining results are the first to suggest a synergistic effect of multitask training on fMRI data. Second, we finetune the pretrained models and train additional fresh models on a supervised fMRI classification task. We observe significantly improved accuracy on held-out runs with the finetuned models, which demonstrates the ability of our pretraining tasks to facilitate transfer learning. This work contributes to the growing body of literature on transformer architectures for pretraining and transfer learning with fMRI data, and serves as a proof of concept for our pretraining tasks and multitask pretraining on fMRI data.

Keywords: transfer learning, fMRI, self-supervised, brain decoding, transformer, multitask training

Procedia PDF Downloads 79

Authors: Panagiotis Karadimos, Leonidas Anthopoulos

Abstract:

Predicting the actual cost and duration in construction projects concern a continuous and existing problem for the construction sector. This paper addresses this problem with modern methods and data available from past public construction projects. 39 bridge projects, constructed in Greece, with a similar type of available data were examined. Considering each project’s attributes with the actual cost and the actual duration, correlation analysis is performed and the most appropriate predictive project variables are defined. Additionally, the most efficient subgroup of variables is selected with the use of the WEKA application, through its attribute selection function. The selected variables are used as input neurons for neural network models through correlation analysis. For constructing neural network models, the application FANN Tool is used. The optimum neural network model, for predicting the actual cost, produced a mean squared error with a value of 3.84886e-05 and it was based on the budgeted cost and the quantity of deck concrete. The optimum neural network model, for predicting the actual duration, produced a mean squared error with a value of 5.89463e-05 and it also was based on the budgeted cost and the amount of deck concrete.

Keywords: actual cost and duration, attribute selection, bridge construction, neural networks, predicting models, FANN TOOL, WEKA

Procedia PDF Downloads 122

Authors: Yasaman Tohidi, Rouzbeh Riazi, Shidvash Vakilipour, Masoud Mohammadi

Abstract:

The objective of the present study is to numerically investigate the effect of CO₂ replacement of N₂ in air stream on the flame characteristics of the CH₄ turbulent diffusion flame. The Open source Field Operation and Manipulation (OpenFOAM) has been used as the computational tool. In this regard, laminar flamelet and modified k-ε models have been utilized as combustion and turbulence models, respectively. Results reveal that the presence of CO₂ in air stream changes the flame shape and maximum flame temperature. Also, CO₂ dilution causes an increment in CO mass fraction.

Keywords: CH4 diffusion flame, CO2 dilution, OpenFOAM, turbulent flame

Procedia PDF Downloads 266

Authors: Saima Ali, Pathamanathan Rajeev, Imteaz A. Monzur

Abstract:

In this paper, a brief review of the corrosion mechanism in buried pipe and modes of failure is provided together with the available corrosion models. Moreover, the sensitivity analysis is performed to understand the influence of corrosion model parameters on the remaining life estimation. Further, the probabilistic analysis is performed to propagate the uncertainty in the corrosion model on the estimation of the renaming life of the pipe. Finally, the comparison among the corrosion models on the basis of the remaining life estimation will be provided to improve the renewal plan.

Keywords: corrosion, pit depth, sensitivity analysis, exposure period

Procedia PDF Downloads 512