Search results for: energy models

Authors: Tuba Kizilirmak

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Distinct properties of porous metamaterials have been largely processed for biomedicine requiring a three-dimensional (3D) porous structure engaged with fine mechanical features, biodegradation ability, and biocompatibility. Applications of metamaterials are (i) porous orthopedic and dental implants; (ii) in vitro cell culture of metamaterials and bone regeneration of metamaterials in vivo; (iii) macro-, micro, and nano-level porous metamaterials for sensors, diagnosis, and drug delivery. There are some specific properties to design metamaterials for tissue engineering. These are surface to volume ratio, pore size, and interconnection degrees are selected to control cell behavior and bone ingrowth. In this study, additive manufacturing technique selective laser melting will be used to print the scaffolds. Selective Laser Melting prints the 3D components according to designed 3D CAD models and manufactured materials, adding layers progressively by layer. This study aims to design metamaterials with Ti6Al4V material, which gives benefit in respect of mechanical and biological properties. Ti6Al4V scaffolds will support cell attachment by conferring a suitable area for cell adhesion. This study will control the osteoblast cell attachment on Ti6Al4V scaffolds after the determination of optimum stiffness and other mechanical properties which are close to mechanical properties of bone. Before we produce the samples, we will use a modeling technique to simulate the mechanical behavior of samples. These samples include different lattice models with varying amounts of porosity and density.

Keywords: additive manufacturing, titanium lattices, metamaterials, porous metals

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Authors: Sarmistha Baruah, Akshai Kumar, Nageswara Rao Peela

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The global energy crisis due to the dependence on fossil fuels and its limited reserves as well as environmental pollution are key concerns to the research communities. However, the implementation of alcohol-based fuel cells such as methanol is anticipated as a reliable source of future energy technology due to their high energy density, environment friendliness, ease of storage, transportation, etc. To drive the anodic methanol oxidation reaction (MOR) in direct methanol fuel cells (DMFCs), an active and long-lasting catalyst is necessary for efficient energy conversion from methanol. Recently, transition metal-zeolite-based materials have been considered versatile catalysts for a variety of industrial and lab-scale processes. Large specific surface area, well-organized micropores, and adjustable acidity/basicity are characteristics of zeolites that make them excellent supports for immobilizing small-sized and highly dispersed metal species. Significant advancement in the production and characterization of well-defined metal clusters encapsulated within zeolite matrix has substantially expanded the library of materials available, and consequently, their catalytic efficacy. In this context, we developed bimetallic Ni-Co catalysts encapsulated within LTA (also known as 4A) zeolite via a method combined with the in-situ encapsulation of metal species using hydrothermal treatment followed by a chemical reduction process. The prepared catalyst was characterized using advanced characterization techniques, such as X-ray diffraction (XRD), field emission transmission electron microscope (FETEM), field emission scanning electron microscope (FESEM), energy dispersive X-ray (EDX), and X-ray photoelectron spectroscopy (XPS). The electrocatalytic activity of the catalyst for MOR was carried out in an alkaline medium at room temperature using techniques such as cyclic voltammetry (CV), and chronoamperometry (CA). The resulting catalyst exhibited better catalytic activity of 12.1 mA cm-2 at 1.12 V vs Ag/AgCl and retained remarkable stability (~77%) even after 1000 cycles CV test for the electro-oxidation of methanol in alkaline media without any significant microstructural changes. The high surface area, better Ni-Co species integration in the zeolite, and the ample amount of surface hydroxyl groups contribute to highly dispersed active sites and quick analyte diffusion, which provide notable MOR kinetics. Thus, this study will open up new possibilities to develop a noble metal-free zeolite-based electrocatalyst due to its simple synthesis steps, large-scale fabrication, improved stability, and efficient activity for DMFC application.

Keywords: alkaline media, bimetallic, encapsulation, methanol oxidation reaction, LTA zeolite.

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Authors: Jun Yuan

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Shipping is a major transportation mode all over the world, and it has a significant contribution to global carbon emissions. Electrification of ships is one of the main strategies to reduce shipping carbon emissions. The number of electric ships has continued to grow in recent years. However, charging infrastructure is still scarce, which severely restricts the development of electric ships. Therefore, it is very important to design ship charging stations reasonably by comprehensively considering charging demand and investment costs. This study aims to minimize the full life cycle cost of charging stations, considering the uncertainty of charging demand. A mixed integer programming model is developed for this optimization problem. Based on the characteristics of the mathematical model, a simulation based optimization method is proposed to find the optimal number and rated power of chargers. In addition, the impact of renewable energy and storage systems is analyzed. The results can provide decision support and a reference basis for the design of ship charging stations.

Keywords: shipping emission, electricity ship, charging station, optimal design

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Authors: A. Rezaei, M. Huisman, W. Van Paepegem

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Atomic interactions in molecular systems are mainly studied by particle mechanics. Nevertheless, researches have also put on considerable effort to simulate them using continuum methods. In early 2000, simple equivalent finite element models have been developed to study the mechanical properties of carbon nanotubes and graphene in composite materials. Afterward, many researchers have employed similar structural simulation approaches to obtain mechanical properties of nanostructured materials, to simplify interface behavior of fiber-reinforced composites, and to simulate defects in carbon nanotubes or graphene sheets, etc. These structural approaches, however, are limited to small deformations due to complicated local rotational coordinates. This article proposes a method for the finite element simulation of molecular mechanics. For ease in addressing the approach, here it is called Structural Finite Element Molecular Modeling (SFEMM). SFEMM method improves the available structural approaches for large deformations, without using any rotational degrees of freedom. Moreover, the method simulates molecular conformation, which is a big advantage over the previous approaches. Technically, this method uses nonlinear multipoint constraints to simulate kinematics of the atomic multibody interactions. Only truss elements are employed, and the bond potentials are implemented through constitutive material models. Because the equilibrium bond- length, bond angles, and bond-torsion potential energies are intrinsic material parameters, the model is independent of initial strains or stresses. In this paper, the SFEMM method has been implemented in ABAQUS finite element software. The constraints and material behaviors are modeled through two Fortran subroutines. The method is verified for the bond-stretch, bond-angle and bond-torsion of carbon atoms. Furthermore, the capability of the method in the conformation simulation of molecular structures is demonstrated via a case study of a graphene sheet. Briefly, SFEMM builds up a framework that offers more flexible features over the conventional molecular finite element models, serving the structural relaxation modeling and large deformations without incorporating local rotational degrees of freedom. Potentially, the method is a big step towards comprehensive molecular modeling with finite element technique, and thereby concurrently coupling an atomistic domain to a solid continuum domain within a single finite element platform.

Keywords: finite element, large deformation, molecular mechanics, structural method

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Authors: Peter M. Schneider, Batyr Garlyyev, Sebastian A. Watzele, Aliaksandr S. Bandarenka

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Functional nanostructures such as nanoparticles are a promising class of materials for energy applications due to their unique properties. Bottom-up synthetic routes for nanostructured materials often involve multiple synthesis steps and the use of surfactants, reducing agents, or stabilizers. This results in complex and extensive synthesis protocols. In recent years, a novel top-down synthesis approach to form metal nanoparticles has been established, in which bulk metal wires are immersed in an electrolyte (primarily alkali earth metal based) and subsequently subjected to a high alternating potential. This leads to the generation of nanoparticles dispersed in the electrolyte. The main advantage of this facile top-down approach is that there are no reducing agents, surfactants, or precursor solutions. The complete synthesis can be performed in one pot involving one main step with consequent washing and drying of the nanoparticles. More recent studies investigated the effect of synthesis parameters such as potential amplitude, frequency, electrolyte composition, and concentration on the size and shape of the nanoparticles. Here, we investigate the electrochemical erosion of various metal wires such as Ti, Pt, Pd, and Sn in various electrolyte compositions via this facile top-down technique and its experimental optimization to successfully synthesize nanostructured materials for various energy applications. As an example, for Pt and Pd, homogeneously distributed nanoparticles on carbon support can be obtained. These materials can be used as electrocatalyst materials for the oxygen reduction reaction (ORR) and hydrogen evolution reaction (HER), respectively. In comparison, the top-down erosion of Sn wires leads to the formation of nanoparticles, which have great potential as oxygen evolution reaction (OER) support materials. The application of the technique on Ti wires surprisingly leads to the formation of nanowires, which show a high surface area and demonstrate great potential as an alternative support material to carbon.

Keywords: ORR, electrochemistry, electrocatalyst, synthesis

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Authors: Sawsan Al-Saaidi, Ghayth K. Shaker Al-Shaibani

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This paper investigates certain linguistics properties of four selected speeches by Al-Qaeda’s former leader Osama bin Laden who legitimated the use of jihad by Muslims in various countries when he was alive. The researchers adopt van Dijk’s (2009; 1998) Socio-Cognitive approach and Ideological Square theory respectively. Socio-Cognitive approach revolves around various cognitive, socio-political, and discursive aspects that can be found in political discourse as in Osama bin Laden’s one. The political discourse can be defined in terms of textual properties and contextual models. Pertaining to the ideological square, it refers to positive self-presentation and negative other-presentation which help to enhance the textual and contextual analyses. Therefore, among the most significant properties in Osama bin Laden’s discourse are the use of presuppositions and implicatures which are based on background knowledge and contextual models as well. Thus, the paper concludes that Osama bin Laden used a number of manipulative strategies which augmented and embellished the use of ‘jihad’ in order to develop a more effective discourse for his audience. In addition, the findings have revealed that bin Laden used different implicit and embedded interpretations of different topics which have been accepted as taken-for-granted truths for him to legitimate Jihad against his enemies. There are many presuppositions in the speeches analysed that result in particular common-sense assumptions and a world-view about the selected speeches. More importantly, the assumptions in the analysed speeches help consolidate the ideological analysis in terms of in-group and out-group members.

Keywords: Al-Qaeda, cognition, critical discourse analysis, Osama Bin Laden, jihad, implicature, legitimisation, presupposition, political discourse

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Authors: Hichem Sassi

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Pneumonia, being a respiratory infection, has garnered global attention due to its rapid transmission and relatively high mortality rates. Timely detection and treatment play a crucial role in significantly reducing mortality associated with pneumonia. Presently, X-ray diagnosis stands out as a reasonably effective method. However, the manual scrutiny of a patient's X-ray chest radiograph by a proficient practitioner usually requires 5 to 15 minutes. In situations where cases are concentrated, this places immense pressure on clinicians for timely diagnosis. Relying solely on the visual acumen of imaging doctors proves to be inefficient, particularly given the low speed of manual analysis. Therefore, the integration of artificial intelligence into the clinical image diagnosis of pneumonia becomes imperative. Additionally, AI recognition is notably rapid, with convolutional neural networks (CNNs) demonstrating superior performance compared to human counterparts in image identification tasks. To conduct our study, we utilized a dataset comprising chest X-ray images obtained from Kaggle, encompassing a total of 5216 training images and 624 test images, categorized into two classes: normal and pneumonia. Employing five mainstream network algorithms, we undertook a comprehensive analysis to classify these diseases within the dataset, subsequently comparing the results. The integration of artificial intelligence, particularly through improved network architectures, stands as a transformative step towards more efficient and accurate clinical diagnoses across various medical domains.

Keywords: deep learning, computer vision, pneumonia, models, comparative study

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Authors: Jinkoo Kim, Hyungoo Kang, Hyungjun Shin

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In this study, a hybrid energy dissipation device is developed by combining a steel slit plate and friction pads to be used for seismic retrofit of structures, and its effectiveness is investigated by comparing the life cycle costs of the structure before and after the retrofit. The seismic energy dissipation capability of the dampers is confirmed by cyclic loading tests. The probabilities of reaching various damage states are obtained by fragility analysis, and the life cycle costs of the model structures are computed using the PACT (Performance Assessment Calculation Tool) program based on FEMA P-58 methodology. The fragility analysis shows that the probabilities of reaching limit states are minimized by the seismic retrofit with hybrid dampers and increasing column size. The seismic retrofit with increasing column size and hybrid dampers results in the lowest repair cost and shortest repair time.

Keywords: slit dampers, friction dampers, seismic retrofit, life cycle cost, FEMA P-58, PACT

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Authors: Jian Li

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The uncertainty quantification and inversion problems of subsurface oil-water phase flow usually require extensive repeated forward calculations for new runs with changed conditions. To reduce the computational time, various forms of surrogate models have been built. Related research shows that deep learning has emerged as an effective surrogate model, while most surrogate models with deep learning are purely data-driven, which always leads to poor robustness and abnormal results. To guarantee the model more consistent with the physical laws, a coupled theory-guided convolutional neural network (TgCNN) based surrogate model is built to facilitate computation efficiency under the premise of satisfactory accuracy. The model is a convolutional neural network based on multi-well reservoir simulation. The core notion of this proposed method is to bridge two separate blocks on top of an overall network. They underlie the TgCNN model in a coupled form, which reflects the coupling nature of pressure and water saturation in the two-phase flow equation. The model is driven by not only labeled data but also scientific theories, including governing equations, stochastic parameterization, boundary, and initial conditions, well conditions, and expert knowledge. The results show that the TgCNN-based surrogate model exhibits satisfactory accuracy and efficiency in subsurface oil-water phase flow under multi-well conditions.

Keywords: coupled theory-guided convolutional neural network, multi-well conditions, surrogate model, subsurface oil-water phase

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Authors: Mia I. Allen, Bernard N. Johnson, Gagan Deep, Yixin Su, Sangeeta Singth, Ashish Kumar, , Michael A. Nader

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With no FDA-approved treatments for cocaine use disorders (CUD), research has focused on the behavioral and neuropharmacological effects of cocaine in animal models, with the goal of identifying novel interventions. While the majority of people with CUD also use tobacco/nicotine, the majority of preclinical cocaine research does not include the co-use of nicotine. The present study examined nicotine and cocaine co-use under several conditions of intravenous drug self-administration in monkeys. In Experiment 1, male rhesus monkeys (N=3) self-administered cocaine (0.001-0.1 mg/kg/injection) alone and cocaine+nicotine (0.01-0.03 mg/kg/injection) under a progressive-ratio schedule of reinforcement. When nicotine was added to cocaine, there was a significant leftward shift and significant increase in peak break point. In Experiment 2, socially housed female and male cynomolgus monkeys (N=14) self-administered cocaine under a concurrent drug-vs-food choice schedule. Combining nicotine significantly decreased cocaine choice ED50 values (i.e., shifted the cocaine dose-response curve to the left) in females but not in males. There was no evidence of social rank differences. In delay discounting studies, the co-use of nicotine and cocaine required significantly larger delays to the preferred drug reinforcer to reallocate choice compared with cocaine alone. Overall, these results suggest drug interactions of nicotine and cocaine co-use is not simply a function of potency but rather a fundamentally distinctive condition that should be utilized to better understand the neuropharmacology of CUD and the evaluation of potential treatments.

Keywords: polydrug use, animal models, nonhuman primates, behavioral pharmacology, drug self-administration

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Authors: Ghada Abo-Zaid

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The main objective of this study was to examine the association between the elevated level of CRP and incidence of hypertension before and after adjusting by age, BMI, gender, SES, smoking, diabetes, cholesterol LDL and cholesterol HDL and to determine whether the association were differ by race. Method: Cross sectional data for participations from age 17 to age 74 years who included in The National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010 were analysed. CRP level was classified into three categories ( > 3mg/L, between 1mg/LL and 3mg/L, and < 3 mg/L). Blood pressure categorization was done using JNC 7 algorithm Hypertension defined as either systolic blood pressure (SBP) of 140 mmHg or more and disystolic blood pressure (DBP) of 90mmHg or greater, otherwise a self-reported prior diagnosis by a physician. Pre-hypertension was defined as (139 > SBP > 120 or 89 > DPB > 80). Multinominal regression model was undertaken to measure the association between CRP level and hypertension. Results: In univariable models, CRP concentrations > 3 mg/L were associated with a 73% greater risk of incident hypertension compared with CRP concentrations < 1 mg/L (Hypertension: odds ratio [OR] = 1.73; 95% confidence interval [CI], 1.50-1.99). Ethnic comparisons showed that American Mexican had the highest risk of incident hypertension (odds ratio [OR] = 2.39; 95% confidence interval [CI], 2.21-2.58).This risk was statistically insignificant, however, either after controlling by other variables (Hypertension: OR = 0.75; 95% CI, 0.52-1.08,), or categorized by race [American Mexican: odds ratio [OR] = 1.58; 95% confidence interval [CI], 0,58-4.26, Other Hispanic: odds ratio [OR] = 0.87; 95% confidence interval [CI], 0.19-4.42, Non-Hispanic white: odds ratio [OR] = 0.90; 95% confidence interval [CI], 0.50-1.59, Non-Hispanic Black: odds ratio [OR] = 0.44; 95% confidence interval [CI], 0.22-0,87]. The same results were found for pre-hypertension, and the Non-Hispanic black showed the highest significant risk for Pre-Hypertension (odds ratio [OR] = 1.60; 95% confidence interval [CI], 1.26-2.03). When CRP concentrations were between 1.0-3.0 mg/L, in an unadjusted models prehypertension was associated with higher likelihood of elevated CRP (OR = 1.37; 95% CI, 1.15-1.62). The same relationship was maintained in Non-Hispanic white, Non-Hispanic black, and other race (Non-Hispanic white: OR = 1.24; 95% CI, 1.03-1.48, Non-Hispanic black: OR = 1.60; 95% CI, 1.27-2.03, other race: OR = 2.50; 95% CI, 1.32-4.74) while the association was insignificant with American Mexican and other Hispanic. In the adjusted model, the relationship between CRP and prehypertension were no longer available. In contrary, Hypertension was not independently associated with elevated CRP, and the results were the same after grouped by race or adjusted by the confounder variables. The same results were obtained when SBP or DBP were on a continuous measure. Conclusions: This study confirmed the existence of an association between hypertension, prehypertension and elevated level of CRP, however this association was no longer available after adjusting by other variables. Ethic group differences were statistically significant at the univariable models, while it disappeared after controlling by other variables.

Keywords: CRP, hypertension, ethnicity, NHANES, blood pressure

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Authors: Hyeon Mi Noh, Ju Hyun Oh, Jung Hyun Jeong, Haeyoung Choi, Jung Hwan Kim

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The Er³⁺/Yb³⁺/Tm³⁺ tri-doped Y₂O₃ nanophosphors were synthesized by solvothermal method and its temperature dependence of the white upconversion emission has been studied by using 975 nm laser diode. The upconversion emission spectra in 1 mol% Er³⁺/5 mol% Yb³⁺/xTm³ tri-doped Y₂O₃ nanophosphors sintered at 1000 °C with x from 0 to 0.5 mol%. The blue emission intensity increase with Tm³⁺ concentration from 0 to 0.5 mol%, it is due to the 2F7/2→2F5/2 transition of Yb³⁺ around 10,000 cm-1 could easily reach the Tm³⁺ sates. The white light is composed with the blue (1G4→3H6 of Tm³⁺), green (2H11/2, 4S3/2→4I15/2 of Er³⁺), and red (4F9/2→4I15/2 of Er³⁺) upconversion radiations. The Y₂O₃: Er³⁺/Yb³⁺/Tm³⁺ nanophosphors show from white to green upconversion emission at power of 600 mW/cm² as sintering temperature increased. The calculated Commission Internationale de l’Eclairage (CIE) coordinates can be located in the white area with various sintering temperatures, in sintered at 1000 °C, and their color coordinates are very close to the standard white-light emission (0.33, 0.33). Their upconversion processes were explained by measuring the upconversion luminescence spectra and pump power dependence and energy level diagram.

Keywords: white upconversion emission, nanophosphors, energy transfer, solvothermal method

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Authors: Ghofrane Rehaiem, Hamza Gharsellaoui, Samir Benahmed

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In this study, Artificial Neural Networks (ANNs) were used for modeling the parameters that allow the real-time scheduling of embedded systems under resources constraints designed for real-time applications running. The objective of this work is to implement a neural networks based approach for real-time scheduling of embedded systems in order to handle real-time constraints in execution scenarios. In our proposed approach, many techniques have been proposed for both the planning of tasks and reducing energy consumption. In fact, a combination of Dynamic Voltage Scaling (DVS) and time feedback can be used to scale the frequency dynamically adjusting the operating voltage. Indeed, we present in this paper a hybrid contribution that handles the real-time scheduling of embedded systems, low power consumption depending on the combination of DVS and Neural Feedback Scheduling (NFS) with the energy Priority Earlier Deadline First (PEDF) algorithm. Experimental results illustrate the efficiency of our original proposed approach.

Keywords: optimization, neural networks, real-time scheduling, low-power consumption

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Authors: Dina Tamazin, Nicoleta Tipi, Sahar Validi

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The oil refining industry plays vital role in the world economy. Oil refining companies operate in a more complex and dynamic environment than ever before. In addition, oil refining companies and the public are becoming more conscious of crude oil scarcity and climate changes. Hence, sustainability in the oil refining industry is becoming increasingly critical to the industry's long-term viability and to the environmental sustainability. Mainly, it is relevant to the measurement and evaluation of the company's sustainable performance to support the company in understanding their performance and its implication more objectively and establishing sustainability development plans. Consequently, the oil refining companies attempt to re-engineer their supply chain to meet the sustainable goals and standards. On the other hand, this research realized that previous research in oil refining sustainable supply chain performance measurements reveals that there is a lack of studies that consider the integration of sustainability in the supply chain performance measurement practices in the oil refining industry. Therefore, there is a need for research that provides performance guidance, which can be used to measure sustainability and assist in setting sustainable goals for oil refining supply chains. Accordingly, this paper aims to present a comprehensive oil refining sustainable supply chain performance measurement theoretical framework. In development of this theoretical framework, the main characteristics of oil refining industry have been identified. For this purpose, a thorough review of relevant literature on performance measurement models and sustainable supply chain performance measurement models has been conducted. The comprehensive oil refining sustainable supply chain performance measurement theoretical framework introduced in this paper aims to assist oil refining companies in measuring and evaluating their performance from a sustainability aspect to achieve sustainable operational excellence.

Keywords: oil refining industry, oil refining sustainable supply chain, performance measurement, sustainability

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Authors: Darshana Thomas, Edward Wilkie, James Irvine

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The Internet of things (IoT) is currently a highly researched topic, especially within the context of the smart home. These are small sensors that are capable of gathering data and transmitting it to a server. The majority of smart home products use protocols such as ZigBee or Bluetooth Low Energy (BLE). As these small sensors are increasing in number, the need to implement these with much more capable and ubiquitous transmission technology is necessary. The high power consumption is the reason that holds these small sensors back from using other protocols such as the most ubiquitous form of communication, WiFi. Comparing the power consumption of existing transmission technologies to one with WiFi inbuilt, would provide a better understanding for choosing between these technologies. We have developed a small IoT device with WiFi capability and proven that it is much more efficient than the first protocol, 433 MHz. We extend our work in this paper and compare WiFi power consumption with the other most widely used protocol BLE. The experimental results in this paper would conclude whether the developed prototype is capable in terms of power consumption to replace the existing protocol BLE with WiFi.

Keywords: bluetooth, internet of things (IoT), power consumption, WiFi

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Authors: Zeytu G. Asfaw, Serkalem K. Abrha, Demisew G. Degefu

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Background: HIV/AIDS remains a major public health problem in Ethiopia and heavily affecting people of productive and reproductive age. We aimed to compare the performance of Parametric Survival Analysis and Bayesian Survival Analysis using simulations and in a real dataset application focused on determining predictors of HIV patient survival. Methods: A Parametric Survival Models - Exponential, Weibull, Log-normal, Log-logistic, Gompertz and Generalized gamma distributions were considered. Simulation study was carried out with two different algorithms that were informative and noninformative priors. A retrospective cohort study was implemented for HIV infected patients under Highly Active Antiretroviral Therapy in Alamata General Hospital, North Ethiopia. Results: A total of 320 HIV patients were included in the study where 52.19% females and 47.81% males. According to Kaplan-Meier survival estimates for the two sex groups, females has shown better survival time in comparison with their male counterparts. The median survival time of HIV patients was 79 months. During the follow-up period 89 (27.81%) deaths and 231 (72.19%) censored individuals registered. The average baseline cluster of differentiation 4 (CD4) cells count for HIV/AIDS patients were 126.01 but after a three-year antiretroviral therapy follow-up the average cluster of differentiation 4 (CD4) cells counts were 305.74, which was quite encouraging. Age, functional status, tuberculosis screen, past opportunistic infection, baseline cluster of differentiation 4 (CD4) cells, World Health Organization clinical stage, sex, marital status, employment status, occupation type, baseline weight were found statistically significant factors for longer survival of HIV patients. The standard error of all covariate in Bayesian log-normal survival model is less than the classical one. Hence, Bayesian survival analysis showed better performance than classical parametric survival analysis, when subjective data analysis was performed by considering expert opinions and historical knowledge about the parameters. Conclusions: Thus, HIV/AIDS patient mortality rate could be reduced through timely antiretroviral therapy with special care on the potential factors. Moreover, Bayesian log-normal survival model was preferable than the classical log-normal survival model for determining predictors of HIV patients survival.

Keywords: antiretroviral therapy (ART), Bayesian analysis, HIV, log-normal, parametric survival models

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Authors: Chunyang Miao, Bingxin Li, Shanglong Ning, Christopher J. B. Ford

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While it is challenging to fabricate electronic devices close to atomic dimensions in conventional top-down lithography, molecular electronics is promising to help maintain the exponential increase in component densities via using molecular building blocks to fabricate electronic components from the bottom up. It offers smaller, faster, and more energy-efficient electronic and photonic systems. A self-assembled monolayer (SAM) of molecules is a layer of molecules that self-assembles on a substrate. They are mechanically flexible, optically transparent, low-cost, and easy to fabricate. A large-area multi-layer structure has been designed and investigated by the team, where a SAM of designed molecules is sandwiched between graphene and gold electrodes. Each molecule can act as a quantum dot, with all molecules conducting in parallel. When a source-drain bias is applied, significant current flows only if a molecular orbital (HOMO or LUMO) lies within the source-drain energy window. If electrons tunnel sequentially on and off the molecule, the charge on the molecule is well-defined and the finite charging energy causes Coulomb blockade of transport until the molecular orbital comes within the energy window. This produces ‘Coulomb diamonds’ in the conductance vs source-drain and gate voltages. For different tunnel barriers at either end of the molecule, it is harder for electrons to tunnel out of the dot than in (or vice versa), resulting in the accumulation of two or more charges and a ‘Coulomb staircase’ in the current vs voltage. This nanostructure exhibits highly reproducible Coulomb-staircase patterns, together with additional oscillations, which are believed to be attributed to molecular vibrations. Molecules are more isolated than semiconductor dots, and so have a discrete phonon spectrum. When tunnelling into or out of a molecule, one or more vibronic states can be excited in the molecule, providing additional transport channels and resulting in additional peaks in the conductance. For useful molecular electronic devices, achieving the optimum orbital alignment of molecules to the Fermi energy in the leads is essential. To explore it, a drop of ionic liquid is employed on top of the graphene to establish an electric field at the graphene, which screens poorly, gating the molecules underneath. Results for various molecules with different alignments of Fermi energy to HOMO have shown highly reproducible Coulomb-diamond patterns, which agree reasonably with DFT calculations. In summary, this large-area SAM molecular junction is a promising candidate for future electronic circuits. (1) The small size (1-10nm) of the molecules and good flexibility of the SAM lead to the scalable assembly of ultra-high densities of functional molecules, with advantages in cost, efficiency, and power dissipation. (2) The contacting technique using graphene enables mass fabrication. (3) Its well-observed Coulomb blockade behaviour, narrow molecular resonances, and well-resolved vibronic states offer good tuneability for various functionalities, such as switches, thermoelectric generators, and memristors, etc.

Keywords: molecular electronics, Coulomb blokade, electron-phonon coupling, self-assembled monolayer

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Authors: Rizwan Ullah, Abdar Ali, Zahid Ullah

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This paper illustrates the study of three isolated zero voltage switched (ZVS) PWM full bridge (FB) converters to charge the high voltage battery in the charger of electric vehicle (EV). EV battery chargers have several challenges such as high efficiency, high reliability, low cost, isolation, and high power density. The cost of magnetic and filter components in the battery charger is reduced when switching frequency is increased. The increase in the switching frequency increases switching losses. ZVS is used to reduce switching losses and to operate the converter in the battery charger at high frequency. The performance of each of the three converters is evaluated on the basis of ZVS range, dead times of the switches, conduction losses of switches, circulating current stress, circulating energy, duty cycle loss, and efficiency. The limitations and merits of each PWM FB converter are reviewed. The converter with broader ZVS range, high efficiency and low switch stresses is selected for battery charger applications in EV.

Keywords: electric vehicle, PWM FB converter, zero voltage switching, circulating energy

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Authors: Ira Joseph

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The LNG paradox addresses the issue of how the most expensive form of gas supply, which is LNG, will grow in an end user market where demand is most competitive, which is power generation. In this case, LNG demand growth is under siege from two entirely different directions. At one end is price; it will be extremely difficult for gas to replace coal in Asia due to the low price of coal and the age of the generation plants. Asia's coal fleet, on average, is less than two decades old and will need significant financial incentives to retire before its state lifespan. While gas would cut emissions in half relative to coal, it would also more than double the price of the fuel source for power generation, which puts it in a precarious position. In most countries in Asia other than China, this cost increase, particularly from imports, is simply not realistic when it is also necessary to focus on economic growth and social welfare. On the other end, renewables are growing at an exponential rate for three reasons. One is that prices are dropping. Two is that policy incentives are driving deployment, and three is that China is forcing renewables infrastructure into the market to take a political seat at the global energy table with Saudi Arabia, the US, and Russia. Plus, more renewables will lower import growth of oil and gas in China, if not end it altogether. Renewables are the predator at the gate of gas demand in power generation and in every year that passes, renewables cut into demand growth projections for gas; in particular, the type of gas that is most expensive, which is LNG. Gas does have a role in the future, particularly within a domestic market. Once it crosses borders in the form of LNG or even pipeline gas, it quickly becomes a premium fuel and must be marketed and used this way. Our research shows that gas will be able to compete with batteries as an intermittency and storage tool and does offer a method to harmonize with renewables as part of the energy transition. As a baseload fuel, however, the role of gas, particularly, will be limited by cost once it needs to cross a border. Gas converted into blue or green hydrogen or ammonia is also an option for storage depending on the location. While this role is much reduced from the primary baseload role that gas once aspired to land, it still offers a credible option for decades to come.

Keywords: natural gas, LNG, demand, price, intermittency, storage, renewables

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Authors: Suresh Kumar Verma, Jugal Kishore Das, Ealisha Jha, Mrutyunjay Suar, SKS Parashar

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In recent years nanoforms of inorganic metallic oxides has attracted a lot of interest due to their small size and significantly improved physical, chemical and biological properties compared to their molecular precursor. Some of the inorganic materials such as TiO2, ZnO, MgO, CaO, Al2O3 have been extensively used in biological applications. Zinc Oxide is a Wurtzite-type semiconductor and piezo-electric material exhibiting excellent electrical, optical and chemical properties with a band energy gap of 3.1-3.4 eV. Nanoforms of Zinc Oxide (ZnO) are increasingly recognised for their utility in biological application. The significant physical parameters such as surface area, particle size, surface charge and Zeta potential of Zinc Oxide (ZnO) nanoparticles makes it suitable for the uptake, persistance, biological, and chemical activities inside the living cells. The present study shows the effect of intrinsic defects of ZnO nanocrystals synthesized by high energy ball milling (HEBM) technique in their antibacterial activities. Bulk Zinc oxide purchased from market were ball milled for 7 h, 10 h, and 15 h respectively to produce nanosized Zinc Oxide. The structural and optical modification of such synthesized particles were determined by X-ray diffraction (XRD), Scanning Electron Microscopy and Electron Paramagnetic Resonance (EPR). The antibacterial property of synthesized Zinc Oxide nanoparticles was tested using well diffusion, minimum inhibitory Concentration, minimum bacteriocidal concentration, reactive oxygen species (ROS) estimation and membrane potential determination methods. In this study we observed that antibacterial activity of ZnO nanoparticles is because of the intrinsic defects that exist as a function of difference in size and milling time.

Keywords: high energy ball milling, ZnO nanoparticles, EPR, Antibacterial properties

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Authors: Jan Luxa, Vlastimil Mazanek, Petr Malinsky, Alexander Romanenko, Mariapompea Cutroneo, Vladimir Havranek, Josef Novak, Eva Stepanovska, Anna Mackova, Zdenek Sofer

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Using accelerated energetic ions is an interesting method for the introduction of structural changes in various carbon-based materials. This way, the properties can be altered in two ways: a) the ions lead to the formation of conductive pathways in graphene oxide structures due to the elimination of oxygen functionalities and b) doping with selected ions to form metal nanoclusters, thus increasing the conductivity. In this work, energetic beams were employed in two ways to prepare capacitor structures in graphene oxide (GO), reduced graphene oxide (rGO) and polyimide (PI) on a micro-scale. The first method revolved around using ion beam writing with a focused ion beam, and the method involved ion implantation via a polymeric mask. To prepare the polymeric mask, a direct spin-coating of PMMA on top of the foils was used. Subsequently, proton beam writing and development in isopropyl alcohol were employed. Finally, the mask was removed using acetone solvent. All three materials were exposed to ion beams with an energy of 2.5-5 MeV and an ion fluence of 3.75x10¹⁴ cm-² (1800 nC.mm-²). Thus, prepared microstructures were thoroughly characterized by various analytical methods, including Scanning electron microscopy (SEM) with Energy-Dispersive X-ray spectroscopy (EDS), X-ray Photoelectron spectroscopy (XPS), micro-Raman spectroscopy, Rutherford Back-scattering Spectroscopy (RBS) and Elastic Recoil Detection Analysis (ERDA) spectroscopy. Finally, these materials were employed and tested as sensors for humidity using electrical conductivity measurements. The results clearly demonstrate that the type of ions, their energy and fluence all have a significant influence on the sensory properties of thus prepared sensors.

Keywords: graphene, graphene oxide, polyimide, ion implantation, sensors

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Authors: Milica Karadžić, Lidija Jevrić, Sanja Podunavac-Kuzmanović, Strahinja Kovačević, Anamarija Mandić, Aleksandar Oklješa, Andrea Nikolić, Marija Sakač, Katarina Penov Gaši

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Using chemometric approach, the relationships between the chromatographic lipophilicity and in silico molecular descriptors for twenty-nine selected steroid derivatives were studied. The chromatographic lipophilicity was predicted using artificial neural networks (ANNs) method. The most important in silico molecular descriptors were selected applying stepwise selection (SS) paired with partial least squares (PLS) method. Molecular descriptors with satisfactory variable importance in projection (VIP) values were selected for ANN modeling. The usefulness of generated models was confirmed by detailed statistical validation. High agreement between experimental and predicted values indicated that obtained models have good quality and high predictive ability. Global sensitivity analysis (GSA) confirmed the importance of each molecular descriptor used as an input variable. High-quality networks indicate a strong non-linear relationship between chromatographic lipophilicity and used in silico molecular descriptors. Applying selected molecular descriptors and generated ANNs the good prediction of chromatographic lipophilicity of the studied steroid derivatives can be obtained. This article is based upon work from COST Actions (CM1306 and CA15222), supported by COST (European Cooperation and Science and Technology).

Keywords: artificial neural networks, chemometrics, global sensitivity analysis, liquid chromatography, steroids

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Authors: Piotr A. Kowalski, Kasper Sapala, Wiktor Warchalowski

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PM10 is a suspended dust that primarily has a negative effect on the respiratory system. PM10 is responsible for attacks of coughing and wheezing, asthma or acute, violent bronchitis. Indirectly, PM10 also negatively affects the rest of the body, including increasing the risk of heart attack and stroke. Unfortunately, Poland is a country that cannot boast of good air quality, in particular, due to large PM concentration levels. Therefore, based on the dense network of Airly sensors, it was decided to deal with the problem of prediction of suspended particulate matter concentration. Due to the very complicated nature of this issue, the Machine Learning approach was used. For this purpose, Convolution Neural Network (CNN) neural networks have been adopted, these currently being the leading information processing methods in the field of computational intelligence. The aim of this research is to show the influence of particular CNN network parameters on the quality of the obtained forecast. The forecast itself is made on the basis of parameters measured by Airly sensors and is carried out for the subsequent day, hour after hour. The evaluation of learning process for the investigated models was mostly based upon the mean square error criterion; however, during the model validation, a number of other methods of quantitative evaluation were taken into account. The presented model of pollution prediction has been verified by way of real weather and air pollution data taken from the Airly sensor network. The dense and distributed network of Airly measurement devices enables access to current and archival data on air pollution, temperature, suspended particulate matter PM1.0, PM2.5, and PM10, CAQI levels, as well as atmospheric pressure and air humidity. In this investigation, PM2.5, and PM10, temperature and wind information, as well as external forecasts of temperature and wind for next 24h served as inputted data. Due to the specificity of the CNN type network, this data is transformed into tensors and then processed. This network consists of an input layer, an output layer, and many hidden layers. In the hidden layers, convolutional and pooling operations are performed. The output of this system is a vector containing 24 elements that contain prediction of PM10 concentration for the upcoming 24 hour period. Over 1000 models based on CNN methodology were tested during the study. During the research, several were selected out that give the best results, and then a comparison was made with the other models based on linear regression. The numerical tests carried out fully confirmed the positive properties of the presented method. These were carried out using real ‘big’ data. Models based on the CNN technique allow prediction of PM10 dust concentration with a much smaller mean square error than currently used methods based on linear regression. What's more, the use of neural networks increased Pearson's correlation coefficient (R²) by about 5 percent compared to the linear model. During the simulation, the R² coefficient was 0.92, 0.76, 0.75, 0.73, and 0.73 for 1st, 6th, 12th, 18th, and 24th hour of prediction respectively.

Keywords: air pollution prediction (forecasting), machine learning, regression task, convolution neural networks

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Authors: Eleni Tsangouri, Xander Hillewaere, David Garoz Gómez, Dimitrios Aggelis, Filip Du Prez, Danny Van Hemelrijck

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Tapered Double Cantilever Beam (TDCB) is the most commonly used test set-up to evaluate the self-healing feature of thermoset polymers autonomously activated in the presence of crack. TDCB is a modification of the established fracture mechanics set-up of Double Cantilever Beam and is designed to provide constant strain energy release rate with crack length under stable load evolution (mode-I). In this study, the damage of virgin and autonomously healed TDCB polymer samples is evaluated considering the load-crack opening diagram, the strain maps provided by Digital Image Correlation technique and the fractography maps given by optical microscopy. It is shown that the pre-crack introduced prior to testing (razor blade tapping), the loading rate and the length of the side groove are the features that dominate the crack propagation and lead to inconstant fracture energy release rate.

Keywords: polymers, autonomous healing, fracture, tapered double cantilever beam

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Authors: R. Rajabpour, N. Talebbeydokhti, M. H. Ahmadi

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Carefully scheduling the operations of pumps can be resulted to significant energy savings. Schedules can be defined either implicit, in terms of other elements of the network such as tank levels, or explicit by specifying the time during which each pump is on/off. In this study, two new explicit representations based on time-controlled triggers were analyzed, where the maximum number of pump switches was established beforehand, and the schedule may contain fewer switches than the maximum. The optimal operation of pumping stations was determined using a Jumping Particle Swarm Optimization (JPSO) algorithm to achieve the minimum energy cost. The model integrates JPSO optimizer and EPANET hydraulic network solver. The optimal pump operation schedule of VanZyl water distribution system was determined using the proposed model and compared with those from Genetic and Ant Colony algorithms. The results indicate that the proposed model utilizing the JPSP algorithm outperformed the others and is a versatile management model for the operation of real-world water distribution system.

Keywords: JPSO, operation, optimization, water distribution system

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Authors: Van-Thuan Tran, Minh-Khai Nguyen, Geum-Bae Cho

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Recently, multilevel inverters have become more attractive for researchers due to low total harmonic distortion (THD) in the output voltage and low electromagnetic interference (EMI). This paper proposes a single-phase cascaded H-bridge quasi switched boost inverter (CHB-qSBI) for renewable energy sources applications. The proposed inverter has the advantage over the cascaded H-bridge quasi-Z-source inverter (CHB-qZSI) in reducing two capacitors and two inductors. As a result, cost, weight, and size are reduced. Furthermore, the dc-link voltage of each module is controlled by individual shoot-through duty cycle to get the same values. Therefore, the proposed inverter solves the imbalance problem of dc-link voltage in traditional CHB inverter. This paper shows the operating principles and analysis of the single-phase cascaded H-bridge quasi switched boost inverter. Also, a control strategy for the proposed inverter is shown. Experimental and simulation results are shown to verify the operating principle of the proposed inverter.

Keywords: renewable energy sources, cascaded h-bridge inverter, quasi switched boost inverter, quasi z-source inverter, multilevel inverter

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Authors: Ryunhye Kim

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Despite its relatively short history as an independent state, Qatar has emerged as a highly regarded Gulf state and global power. Since its independence in September 1971, the state has employed deliberate policy initiatives designed to put Qatar on the map and distinguish it from other Gulf states. Because Qatar and its neighbors are resource-poor apart from energy, whoever is first to introduce a unique aspect of branding not only takes the lead but assumes what is often an insurmountable advantage. This study examines three specific modes of branding undertaken by Qatar: (1) energy policies to utilize its natural gas to become a dominant supplier; (2) the deliberate construction of a distinct cultural brand utilizing sports, architecture, museums, and media; and (3) ‘niche diplomacy’ to serve as a mediator in regional and intra-national conflicts, especially as interlocutor between the United States and Arab regimes and Muslim groups. Gleaning data from a range of sources, this study analyzes the effectiveness and significance of Qatar’s place branding on the global stage, as well as potential disadvantages and limits in this branding, including problems encountered before and after the ‘Qatar crisis.’

Keywords: national branding, national-identity, Qatar, soft-power

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Authors: Sotirios Longinos, Anna Loskutova, Assel Tolegenova, Assem Imanzhussip, Lei Wang

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Hot, dry rock (HDR) has substantial potential as a thermal energy source. It has been exploited by hydraulic fracturing to extract heat and generate electricity, which is a well-developed technique known for creating the enhanced geothermal systems (EGS). These days, LN2 is being tested as an environmental friendly fracturing fluid to generate densely interconnected crevices to augment heat exchange efficiency and production. This study examines experimentally the efficacy of LN2 cryogenic fracturing for granite samples in Kazakhstan with immersion method. A comparison of two different experimental models is carried out. The first mode is rock heating along with liquid nitrogen treatment (heating with freezing time), and the second mode is multiple times of heating along with liquid nitrogen treatment (heating with LN2 freezing-thawing cycles). The experimental results indicated that with multiple heating and LN2-treatment cycles, the permeability of granite first ameliorates with increasing number of cycles and later reaches a plateau after a certain number of cycles. On the other hand, density, P-wave velocity, uniaxial compressive strength, elastic modulus, and tensile strength indicate a downward trend with increasing heating and treatment cycles. The thermal treatment cycles do not seem to have an obvious effect on the Poisson’s ratio. The changing rate of granite rock properties decreases as the number of cycles increases. The deterioration of granite primarily happens within the early few cycles. The heating temperature during the cycles shows an important influence on the deterioration of granite. More specifically, mechanical deterioration and permeability amelioration become more remarkable as the heating temperature increases.LN2 fracturing generates many positives compared to conventional fracturing methods such as little water consumption, requirement of zero chemical additives, lessening of reservoir damage, and so forth. Based on the experimental observations, LN2 can work as a promising waterless fracturing fluid to stimulate hot, dry rock reservoirs.

Keywords: granite, hydraulic fracturing, liquid nitrogen, Kazakhstan

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Authors: Orsolya Horvath, Laszlo Deres, Krisztian Eros, Katalin Ordog, Tamas Habon, Balazs Sumegi, Kalman Toth, Robert Halmosi

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Introduction: Oxidative stress induces an imbalance in mitochondrial fusion and fission processes, finally leading to cell death. The two antioxidant molecules, BGP-15 and L2286 have beneficial effects on mitochondrial functions and on cellular oxidative stress response. In this work, we studied the effects of these compounds on the processes of mitochondrial quality control. Methods: We used H9c2 cardiomyoblast and isolated neonatal rat cardiomyocytes (NRCM) for the experiments. The concentration of stressors and antioxidants was beforehand determined with MTT test. We applied 1-Methyl-3-nitro-1-nitrosoguanidine (MNNG) in 125 µM, 400 µM and 800 µM concentrations for 4 and 8 hours on H9c2 cells. H₂O₂ was applied in 150 µM and 300 µM concentration for 0.5 and 4 hours on both models. L2286 was administered in 10 µM, while BGP-15 in 50 µM doses. Cellular levels of the key proteins playing role in mitochondrial dynamics were measured in Western blot samples. For the analysis of mitochondrial network dynamics, we applied electron microscopy and immunocytochemistry. Results: Due to MNNG treatment the level of fusion proteins (OPA1, MFN2) decreased, while the level of fission protein DRP1 elevated markedly. The levels of fusion proteins OPA1 and MNF2 increased in the L2286 and BGP-15 treated groups. During the 8 hour treatment period, the level of DRP1 also increased in the treated cells (p < 0.05). In the H₂O₂ stressed cells, administration of L2286 increased the level of OPA1 in both H9c2 and NRCM models. MFN2 levels in isolated neonatal rat cardiomyocytes raised considerably due to BGP-15 treatment (p < 0.05). L2286 administration decreased the DRP1 level in H9c2 cells (p < 0.05). We observed that the H₂O₂-induced mitochondrial fragmentation could be decreased by L2286 treatment. Conclusion: Our results indicated that the PARP-inhibitor L2286 has beneficial effect on mitochondrial dynamics during oxidative stress scenario, and also in the case of directly induced DNA damage. We could make the similar conclusions in case of BGP-15 administration, which, via reducing ROS accumulation, propagates fusion processes, this way aids preserving cellular viability. Funding: GINOP-2.3.2-15-2016-00049; GINOP-2.3.2-15-2016-00048; GINOP-2.3.3-15-2016-00025; EFOP-3.6.1-16-2016-00004; ÚNKP-17-4-I-PTE-209

Keywords: H9c2, mitochondrial dynamics, neonatal rat cardiomyocytes, oxidative stress

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Authors: E. G. Zaki, M. A. Migahed, A. M. Al-Sabagh, E. A. Khamis

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Inhibition effect of four novel nonionic surfactants based on sulphonamide, of linear alkyl benzene sulphonic acid (LABS), was reacted with 1 mole triethylenetetramine, tetraethylenepentamine then Ethoxylation of amide X 65 type carbon steel in oil wells formation water under H2S environment was investigated by electrochemical measurements. Scanning electron microscopy (SEM) and energy dispersion X-ray (EDX) were used to characterize the steel surface. The results showed that these surfactants act as a corrosion inhibitor in and their inhibition efficiencies depend on the ethylene oxide content in the system. The obtained results showed that the percentage inhibition efficiency (η%) was increased by increasing the inhibitor concentration until the critical micelle concentration (CMC) reached The quantum chemistry calculations were carried out to study the molecular geometry and electronic structure of obtained derivatives. The energy gap between the highest occupied molecular orbital and lowest unoccupied molecular orbital has been calculated using the theoretical computations to reflect the chemical reactivity and kinetic stability of compounds.

Keywords: corrosion, surfactants, steel surface, quantum

Procedia PDF Downloads 355