Search results for: molecular dynamics simulation.

Authors: Vun Yee Thien, Kenneth Francis Rodrigues, Clemente Michael Vui Ling Wong, Wilson Thau Lym Yong

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Transcriptomes associated with the process of photosynthesis have offered insights into the mechanism of gene regulation in terrestrial plants; however, limited information is available as far as macroalgae are concerned. This investigation aims to decipher the underlying mechanisms associated with photosynthesis in the red alga, Kappaphycus alvarezii, by performing a differential expression analysis on a de novo assembled transcriptomes. Comparative analysis of gene expression was designed to examine the alteration of light qualities and its effect on physiological mechanisms in the red alga. High-throughput paired-end RNA-sequencing was applied to profile the transcriptome of K. alvarezii irradiated with different wavelengths of light (blue 492-455 nm, green 577-492 nm and red 780-622 nm) as compared to the full light spectrum, resulted in more than 60 million reads individually and assembled using Trinity and SOAPdenovo-Trans. The transcripts were annotated in the NCBI non-redundant (nr) protein, SwissProt, KEGG and COG databases with a cutoff E-value of 1e-5 and nearly 30% of transcripts were assigned to functional annotation by Blast searches. Differential expression analysis was performed using edgeR. The DEGs were designated to six categories: BL (blue light) regulated, GL (green light) regulated, RL (red light) regulated, BL or GL regulated, BL or RL regulated, GL or RL regulated, and either BL, GL or RL regulated. These DEGs were mapped to terms in KEGG database and compared with the whole transcriptome background to search for genes that regulated by light quality. The outcomes of this study will enhance our understanding of molecular mechanisms underlying light-induced responses in red algae.

Keywords: de novo transcriptome sequencing, differential gene expression, Kappaphycus alvareziired, red alga

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Authors: François Vallée, Jean-François Toubeau, Zacharie De Grève, Jacques Lobry

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When evaluating the capacity of a generation park to cover the load in transmission systems, traditional Loss of Load Expectation (LOLE) and Expected Energy not Served (EENS) indices can be used. If those indices allow computing the annual duration and severity of load non-covering situations, they do not take into account the fact that the load excess is generally shifted from one penury state (hour or quarter of an hour) to the following one. In this paper, a sequential Monte Carlo framework is introduced in order to compute adjusted LOLE and EENS indices. Practically, those adapted indices permit to consider the effect of load excess transfer on the global adequacy of a generation park, providing thus a more accurate evaluation of this quantity.

Keywords: expected energy not served, loss of load expectation, Monte Carlo simulation, reliability, wind generation

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Authors: A. A. A. Aboalnour, Ahmed M. Amasaib, Mohammed-Almujtaba A. Mohammed-Farah, Abdelhakam, A. Noreldien

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This paper presents a design and study of Parabolic Trough Solar Collector (PTC). Mathematical models were used in this work to find the direct and reflected solar radiation from the air layer on the surface of the earth per hour based on the total daily solar radiation on a horizontal surface. Also mathematical models had been used to calculate the radiation of the tilted surfaces. Most of the ingredients used in this project as previews data required on several solar energy applications, thermal simulation, and solar power systems. In addition, mathematical models had been used to study the flow of the fluid inside the tube (receiver), and study the effect of direct and reflected solar radiation on the pressure, temperature, speed, kinetic energy and forces of fluid inside the tube. Finally, the mathematical models had been used to study the (PTC) performances and estimate its thermal efficiency.

Keywords: CFD, experimental, mathematical models, parabolic trough, radiation

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Authors: Andrés I. Ávila, Patricia Aros, César San Martín, Elizabeth Kehr, Yovana Leal

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Planning models for fresh products is a very useful tool for improving the net profits. To get an efficient supply chain model, several functions should be considered to get a complete simulation of several operational units. We consider a linear programming model to help farmers to decide if it is convenient to choose what area should be planted for three kinds of export fruits considering their future investment. We consider area, investment, water, productivity minimal unit, and harvest restrictions to develop a monthly based model to compute the average income in five years. Also, conditions on the field as area, water availability, and initial investment are required. Using the Chilean costs and dollar-peso exchange rate, we can simulate several scenarios to understand the possible risks associated to this market. Also, this tool help to support decisions for government and individual farmers.

Keywords: mixed integer problem, fresh fruit production, support decision model, agricultural and biosystems engineering

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Authors: Salina Saddick, Mohamed Afifi, Osama Abuznadah

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This study was carried out to determine the median lethal concentrations (LC50) of Zinc nanoparticles (ZnNPs) on Oreochromis niloticus and Tilapia zillii. The biochemical and molecular potential effects of ZnNPs (500 and 2000 μg L−1) on the antioxidant system in the brain tissue of O. niloticus and T. zillii were investigated. Four hundred fish were used for acute and sub-acute studies. ZnNP LC50 concentrations were investigated in O. niloticus and T. zillii. The effect of 500 and 2000 μg L−1 ZnNPs on brain antioxidants of O. niloticus and T. zillii was investigated. The result indicated that 69 h LC50 was 5.5 ± 0.6 and 5.6 ± 0.4 for O. nilotica and T. zillii, respectively. Fish exposed to 500 μg L−1 ZnNPs showed a significant increase in reduced glutathione (GSH), total glutathione (tGSH) levels, superoxide dismutase (SOD), catalase (CAT), glutathione reductase (GR), glutathione peroxidase (GPx) and glutathione-S-transferase (GST) activity and gene expression. On the contrary, malondialdehyde (MDA) levels significantly decreased. Meanwhile, fish exposed to 2000 μg L−1 ZnNPs showed a significant decrease of GSH, tGSH levels, SOD, CAT, GR, GPx and GST activity and gene expression. On the contrary, MDA levels significantly increased. It was concluded that, the 96 h LC50 of ZnNPs was 5.5 ± 0.6 and 5.6 ± 0.4 for O. nilotica and T. zillii, respectively. ZnNPs in exposure concentrations of 2000 μg/L induced a deleterious effect on the brain antioxidant system of O. nilotica and T. zillii. In contrast, ZnNPs in exposure concentrations of 500 μg L−1 produced an inductive effect on the brain antioxidant system of O. nilotica and T. zillii.

Keywords: ZnNPs, LC50, antioxidants, O. nilotica

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Authors: Zoran Šverko, Saša Vlahinić, Nino Stojković, Ivan Markovinović

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In this article, the classification of target letters is performed using data from the EEG P300 Speller paradigm. Neural networks trained with the results of dynamic connectivity analysis between different brain regions are used for classification. Dynamic connectivity analysis is based on the adaptive window size and the imaginary part of the complex Pearson correlation coefficient. Brain dynamics are analysed using the relative intersection of confidence intervals for the imaginary component of the complex Pearson correlation coefficient method (RICI-imCPCC). The RICI-imCPCC method overcomes the shortcomings of currently used dynamical connectivity analysis methods, such as the low reliability and low temporal precision for short connectivity intervals encountered in constant sliding window analysis with wide window size and the high susceptibility to noise encountered in constant sliding window analysis with narrow window size. This method overcomes these shortcomings by dynamically adjusting the window size using the RICI rule. This method extracts information about brain connections for each time sample. Seventy percent of the extracted brain connectivity information is used for training and thirty percent for validation. Classification of the target word is also done and based on the same analysis method. As far as we know, through this research, we have shown for the first time that dynamic connectivity can be used as a parameter for classifying EEG signals.

Keywords: dynamic connectivity analysis, EEG, neural networks, Pearson correlation coefficients

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Authors: Somayeh Komeylian

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The antenna arrays for the entire 3D spherical coverage have been developed for their potential use in variety of applications such as radars and body-worn devices of the body area networks. In this study, we have rigorously revamped the hybrid antenna array using the optimum geometry of bowtie elements for achieving a significant improvement in the angular discrimination capability as well as in separating two adjacent targets. In this scenario, we have analogously investigated the effectiveness of increasing the virtual array length in fostering and enhancing the directivity and angular resolution in the 10 GHz frequency. The simulation results have extensively verified that the proposed antenna array represents a drastic enhancement in terms of size, directivity, side lobe level (SLL) and, especially resolution compared with the other available geometries. We have also verified that the maximum directivities of the proposed hybrid antenna array represent the robustness to the all  variations, which is accompanied by the uniform 3D scanning characteristic.

Keywords: bowtie antenna, hybrid antenna array, array signal processing, body area networks

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Authors: A. Bouloufa, F. Khaled, K. Djessas

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This paper presents a new structure of solar cell based on p-type microcrystalline silicon as an absorber and n-type aluminum doped zinc oxide (ZnO:Al) transparent conductive oxide as an optical window. The ZnO:Al layer deposited by rf-magnetron sputtering at room temperature yields a low resistivity about 7,64.10-2Ω.cm and more than 85% mean optical transmittance in the VIS–NIR range, with an optical band gap of 3.3 eV. These excellent optical properties of this layer in combination with an optimal contact at the front surface result in a superior light trapping yielding to efficiencies about 20%. In order to improve efficiency, we have used a p+-µc-Si thin layer highly doped as a back surface field which minimizes significantly the impact of rear surface recombination velocity on voltage and current leading to a high efficiency of 24%. Optoelectronic parameters were determined using the current density-voltage (J-V) curve by means of a numerical simulation with Analysis of Microelectronic and Photonic Structures (AMPS-1D) device simulator.

Keywords: optical window, thin film, solar cell, efficiency

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Authors: Glaoui Hachemi

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In this paper, a scheme based on multi-input multi output Fuzzy Sliding Mode control (MIMO-FSMC) for linear speed regulation of winding system is proposed. Once the uncoupled model of the winding system was obtained, a smooth control function with a threshold was selected to indicate how far away the case was from the sliding surface. nevertheless, this control function depends closely on the higher bound of the uncertainties, which generates overlap. So, this size has to be chosen with broad care to obtain high performances. Usually, the upper bound of uncertainties is difficult to know before motor operation, so, a Fuzzy Sliding Mode controller is investigated to resolve this problem, a simple Fuzzy inference mechanism is used to decrease the chattering phenomenon by simple adjustments. A simulation study is achieved and that the indicate fuzzy sliding mode controllers have great potential for use as an alternative to the conventional sliding mode control.

Keywords: Winding system, induction machine, Mechanical tension, Proportional-integral (PI), sliding mode control, Fuzzy logic

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Authors: Elin Linder

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Ethnographic endeavors feature a long history of creative modes of engagements, and anthropology an equally long critique of its disciplinary attention to worded representations of beyond worded experiences. Curious and critical as our research comes about, takes place, unfolds, and develops, processes of documenting, exploring, experiencing, and producing knowledge commonly evolve as intrinsic parts of our situated wishes to make sense of the worlds we study. We may imagine to do one thing and to use a specific mode of fieldnoting, only to end up doing something else, such as to capture dynamics and dimensions otherwise not attentively engaged or even lost. This paper builds on such an experience, and it acts window to open the conversation for doing and representing ethnographic work as creatively as it was undertaken. Expressively and actively undertaken by means of sensuous scholarship, fieldworking in the world of olivicoltura in Apulia intriguingly advanced into resourcefully embodied research using a drone. While the drone first and foremost allowed perspectives that one as a human is largely and physically incapable of exploring, it rapidly emerged into a mode of engagement that probed critical question how one comes to learn how to see that which one watches, listen to that which one hears, smell that which one scents, feel that which one touch, and gather that which one experience. This paper develops how the drone incorporated a transition of a particularly situated ethnographic sense of attention, all while visualizing how imaginative conceptualizations enable unexpected modes of multimodal knowing in much multisensorial worlds of being.

Keywords: drone, multimodality, sensuous scholarship, critical creativity, ethnographic practice

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Authors: N. Tadrisi Parsa, A. R. Vali, R. Ghasemi

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Diabetes is a growing health problem in worldwide. Especially, the patients with Type 1 diabetes need strict glycemic control because they have deficiency of insulin production. This paper attempts to control blood glucose based on body mathematical body model. The Bergman minimal mathematical model is used to develop the nonlinear controller. A novel back-stepping based sliding mode control (B-SMC) strategy is proposed as a solution that guarantees practical tracking of a desired glucose concentration. In order to show the performance of the proposed design, it is compared with conventional linear and fuzzy controllers which have been done in previous researches. The numerical simulation result shows the advantages of sliding mode back stepping controller design to linear and fuzzy controllers.

Keywords: bergman model, nonlinear control, back stepping, sliding mode control

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Authors: Xun Li, Haojie Wang

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Federated learning is a kind of distributed training and centralized training mode, which is of great value in the protection of user privacy. In order to solve the problem that the model is vulnerable to backdoor attacks in federated learning, a backdoor attack detection method based on a weighted k-medoids algorithm is proposed. First of all, this paper collates the update parameters of the client to construct a vector group, then uses the principal components analysis (PCA) algorithm to extract the corresponding feature information from the vector group, and finally uses the improved k-medoids clustering algorithm to identify the normal and backdoor update parameters. In this paper, the backdoor is implanted in the federation learning model through the model replacement attack method in the simulation experiment, and the update parameters from the attacker are effectively detected and removed by the defense method proposed in this paper.

Keywords: federated learning, backdoor attack, PCA, k-medoids, backdoor defense

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Authors: Mahwish Ghulam Rasool

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The design of a space is a complex process that involves multiple stages, from conceptualization, identifying design problems to understanding the context, materiality, and functionality of the space. Out of all the design elements, color is one of the most dominant and expressive factors that affect the spatial dynamics of the interior space. Color affects aesthetic comfort in space and has a lasting impact on human perception and psychology. Using color as a tool for creating spatial experiences is a new paradigm. Color semantics in spaces are not only used for surface treatment or aesthetics, but it also has more powerful functional characteristics. As interior spaces are evolving and becoming experiential with each decade, designers are looking for new processes to enhance the spatial and experiential quality of interior spaces. The relationship between color and interior typologies is a relatively new paradigm. This paper discusses the role of colors in interior spaces from various perspectives, exploring their impact on the formation of interior typologies and the use of colors in space design. The paper analyzes interior typologies worldwide, from residential to commercial interior spaces, where color semantics plays a prominent role in the design. The paper also emphasizes the design process and the creation of design language, unveiling the possibilities of applying colors in interior spaces that can be in harmony with the building context, space functionality, or in opposition to the existing building envelope or environment. The paper aims to contribute to the field of interior design education and practices. By using experimental and various research methodologies for investigation, it aims to fill the gap in the literature regarding color semantics and the relationship between interior typologies.

Keywords: color psychology, color semantics, interior environments, interior typologies

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Authors: Anum Ali, Kashaf ad Dooja, Asif Saleem

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The future smart cities trend will be towards Internet of Things (IoT); IoT creates dynamic connections in a ubiquitous manner. Smart cities offer ease and flexibility for daily life matters. By using small devices that are connected to cloud servers based on IoT, network traffic between these devices is growing exponentially, whose security is a concerned issue, since ratio of cyber attack may make the network traffic vulnerable. This paper discusses the latest machine learning approaches in related work further to tackle the increasing rate of cyber attacks, machine learning algorithm is applied to IoT-based network traffic data. The proposed algorithm train itself on data and identify different sections of devices interaction by using supervised learning which is considered as a classifier related to a specific IoT device class. The simulation results clearly identify the attacks and produce fewer false detections.

Keywords: IoT, traffic security, deep learning, classification

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Authors: Nadya Patel, Jawn Lim

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Foresight 3.0 is a synergistic methodology that encompasses systems analysis, future studies, capacity building, and forward planning. These components are interconnected, fostering a collective anticipatory intelligence that promotes societal resilience (Ravetz, 2020). However, traditional applications of these strands can often fall short, leading to missed opportunities and narrow perspectives. Therefore, Foresight 3.0 champions a holistic approach to tackling complex issues, focusing on systemic transformations and power dynamics. Businesses are pivotal in preparing the workforce for an increasingly uncertain and complex world. This necessitates the adoption of innovative tools and methodologies, such as Foresight 3.0, that can better equip young employees to anticipate and navigate future challenges. Firstly, the incorporation of its methodology into workplace training can foster a holistic perspective among employees. This approach encourages employees to think beyond the present and consider wider social, economic, and environmental contexts, thereby enhancing their problem-solving skills and resilience. This paper discusses our research on integrating Foresight 3.0's transformative principles with a newly developed Critical Design Futures (CDF) framework to equip organisations with the ability to innovate for the world's most complex social problems. This approach is grounded in 'collective forward intelligence,' enabling mutual learning, co-innovation, and co-production among a diverse stakeholder community, where business transformation and innovation are achieved.

Keywords: business transformation, innovation, foresight, critical design

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Authors: Akbarov Elbek Elmurodovich

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Introduction: We are analyzing the topic of hemolytic anemia observed in patients after COVID-19 infection. The purpose of this research is to investigate the development of hemolytic anemia, identify its causes, and study treatment methods. Objective and Task: The goal of our research is to analyze the changes in blood occurring after COVID-19 infection and study the development of hemolytic anemia. Our main task is to analyze the results and assess subsequent changes in patients. Materials and Methods: The study was conducted among patients treated with a diagnosis of COVID-19 in the Department of Infectious Diseases at the TTA 1-Multiprofile Clinic from March to August 2023. Out of the 32 patients included, 16 were female, and 16 were male. Monitoring Blood Coagulation in Patients: The hemoglobin level of patients upon admission was initially measured using the URITEST-150 analyzer. The average for women was 110 g/l, and for men was 120 g/l. Over the course of 3 months, a decrease was observed: an average of 72 g/l in women (a decrease of up to 35%) and 84 g/l in men (a decrease of up to 30%). In the next 2 months, the positive dynamics of hemoglobin levels were observed, with an average increase to 93 g/l in women (>28%) and 112 g/l in men (>25%). Research Results: Hemolytic anemia developed in men within 5 months, reaching up to 112 g/l. In women, this process required a longer period, with the last month of observation (6 months) showing that women reached levels of up to 112 g/l, similar to men. Conclusion: Hemolytic anemia observed in patients after COVID-19 infection was monitored for 6 months (5 months in men, 6 months in women), reaching up to 112 g/l. The first 3 months after contracting COVID showed the period of development of anemia, and the subsequent 3 months indicated a stabilization period in patients.

Keywords: COVID, anemia, hemoglobin, tma, virus, viral infrection

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Authors: Hyojin Lim, Cuong Nguyen Khac, Ho-Youl Jung

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In this paper, we propose moving object detection method which is helpful for driver to safely take his/her car out of parking lot. When moving objects such as motorbikes, pedestrians, the other cars and some obstacles are detected at the rear-side of host vehicle, the proposed algorithm can provide to driver warning. We assume that the host vehicle is just before departure. Gaussian Mixture Model (GMM) based background subtraction is basically applied. Pre-processing such as smoothing and post-processing as morphological filtering are added.We examine “which color space has better performance for detection of moving objects?” Three color spaces including RGB, YCbCr, and Y are applied and compared, in terms of detection rate. Through simulation, we prove that RGB space is more suitable for moving object detection based on background subtraction.

Keywords: gaussian mixture model, background subtraction, moving object detection, color space, morphological filtering

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Authors: Mohammad Reza Taherkhani‎

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Wireless sensor networks that are used to monitor a special environment, are formed from a large number of sensor nodes. The role of these sensors is to sense special parameters from ambient and to make a connection. In these networks, the most important challenge is the management of energy usage. Clustering is one of the methods that are broadly used to face this challenge. In this paper, a distributed clustering protocol based on learning automata is proposed for underwater wireless sensor networks. The proposed algorithm that is called LA-Clustering forms clusters in the same energy level, based on the energy level of nodes and the connection radius regardless of size and the structure of sensor network. The proposed approach is simulated and is compared with some other protocols with considering some metrics such as network lifetime, number of alive nodes, and number of transmitted data. The simulation results demonstrate the efficiency of the proposed approach.

Keywords: underwater sensor networks, clustering, learning automata, energy consumption

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Authors: Hamidreza Namazi

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Fractal analysis is assessing fractal characteristics of data. It consists of several methods to assign fractal characteristics to a dataset which may be a theoretical dataset or a pattern or signal extracted from phenomena including natural geometric objects, sound, market fluctuations, heart rates, digital images, molecular motion, networks, etc. Fractal analysis is now widely used in all areas of science. An important limitation of fractal analysis is that arriving at an empirically determined fractal dimension does not necessarily prove that a pattern is fractal; rather, other essential characteristics have to be considered. For this purpose a Visual C++ based software called FRATSAN (FRActal Time Series ANalyser) was developed which extract information from signals through three measures. These measures are Fractal Dimensions, Jeffrey’s Measure and Hurst Exponent. After computing these measures, the software plots the graphs for each measure. Besides computing three measures the software can classify whether the signal is fractal or no. In fact, the software uses a dynamic method of analysis for all the measures. A sliding window is selected with a value equal to 10% of the total number of data entries. This sliding window is moved one data entry at a time to obtain all the measures. This makes the computation very sensitive to slight changes in data, thereby giving the user an acute analysis of the data. In order to test the performance of this software a set of EEG signals was given as input and the results were computed and plotted. This software is useful not only for fundamental fractal analysis of signals but can be used for other purposes. For instance by analyzing the Hurst exponent plot of a given EEG signal in patients with epilepsy the onset of seizure can be predicted by noticing the sudden changes in the plot.

Keywords: EEG signals, fractal analysis, fractal dimension, hurst exponent, Jeffrey’s measure

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Authors: Andy Zhang, Awnik Roy, Trevor B. Chen, Bibik Oleksandr, Subodh Adhikari, Paul S. Hsu

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The durability of a combustor in gas-turbine enginesrequiresa good control of its component temperatures. Since the temperature of combustion gases frequently exceeds the melting point of the combustion liner walls, an efficient air-cooling system is significantly important to elongatethe lifetime of liner walls. To determine the effectiveness of the air-cooling system, accurate 2D surface temperature measurement of combustor liner walls is crucial for advanced engine development. Traditional diagnostic techniques for temperature measurement, such as thermocouples, thermal wall paints, pyrometry, and phosphors, have shown disadvantages, including being intrusive and affecting local flame/flow dynamics, potential flame quenching, and physical damages to instrumentation due to harsh environments inside the combustor and strong optical interference from strong combustion emission in UV-Mid IR wavelength. To overcome these drawbacks, a compact and small borescope long-wave-infrared (LWIR) sensor is developed to achieve two-dimensional high-spatial resolution, high-fidelity thermal imaging of 2D surface temperature in gas-turbine engines, providing the desired engine component temperature distribution. The compactLWIRborescope sensor makes it feasible to promote the durability of combustor in gas-turbine engines.

Keywords: borescope, engine, long-wave-infrared, sensor

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Authors: Dua K. S. Y. Klaas, Monzur A. Imteaz, Ika Sudiayem, Elkan M. E. Klaas, Eldav C. M. Klaas

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Properly selecting the most suitable and effective geostatistics method in the parameterization step of groundwater modeling is critical to attain a satisfactory model. In this paper, two geostatistics methods, i.e., Grid-Based Pilot Point (GB-PP) and Head-Zonation Based Pilot Point (HZB-PP) methods, were applied in an eogenetic karst catchment and compared using as model performances and computation time the criteria. Overall, the results show that appropriate selection of method is substantial in the parameterization of physically-based groundwater models, as it influences both the accuracy and simulation times. It was found that GB-PP method performed comparably superior to HZB-PP method. However, reflecting its model performances, HZB-PP method is promising for further application in groundwater modeling.

Keywords: groundwater model, geostatistics, pilot point, parameterization step

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Authors: Fatih Tarlak

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Predictive microbiology can be considered as an important field in food microbiology in which it uses predictive models to describe the microbial growth in different food products. Predictive models estimate the growth of microorganisms quickly, efficiently, and in a cost-effective way as compared to traditional methods of enumeration, which are long-lasting, expensive, and time-consuming. The mathematical models used in predictive microbiology are mainly categorised as primary and secondary models. The primary models are the mathematical equations that define the growth data as a function of time under a constant environmental condition. The secondary models describe the effects of environmental factors, such as temperature, pH, and water activity (aw) on the parameters of the primary models, including the maximum specific growth rate and lag phase duration, which are the most critical growth kinetic parameters. The combination of primary and secondary models provides valuable information to set limits for the quantitative detection of the microbial spoilage and assess product shelf-life.

Keywords: shelf-life, growth model, predictive microbiology, simulation

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Authors: L. Spitaleri, C. M. A. Gangemi, R. Purrello, G. Nicotra, G. Trusso Sfrazzetto, G. Casella, M. Casarin, A. Gulino

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Hybrid molecular–nanoparticle materials, obtained with a bottom-up approach, are suitable for the fabrication of functional nanostructures showing structural control and well-defined properties, i.e., optical, electronic or catalytic properties, in the perspective of applications in different fields of nanotechnology. Gold nanoparticles (Au NPs) exhibit important chemical, electronic and optical properties due to their size, shape and electronic structures. In fact, Au NPs containing no more than 30-40 atoms are only luminescent because they can be considered as large molecules with discrete energy levels, while nano-sized Au NPs only show the surface plasmon resonance. Hence, it appears that gold nanoparticles can alternatively be luminescent or plasmonic, and this represents a severe constraint for their use as an optical material. The aim of this work was the fabrication of nanoscale assembly of Au NPs covalently anchored to each other by means of novel bi-functional porphyrin molecules that work as bridges between different gold nanoparticles. This functional architecture shows a strong surface plasmon due to the Au nanoparticles and a strong luminescence signal coming from porphyrin molecules, thus, behaving like an artificial organized plasmonic and fluorescent network. The self-assembly geometry of this porphyrin on the Au NPs was studied by investigation of the conformational properties of the porphyrin derivative at the DFT level. The morphology, electronic structure and optical properties of the conjugated Au NPs – porphyrin system were investigated by TEM, XPS, UV–vis and Luminescence. The present nanostructures can be used for plasmon-enhanced fluorescence, photocatalysis, nonlinear optics, etc., under atmospheric conditions since our system is not reactive to air nor water and does not need to be stored in a vacuum or inert gas.

Keywords: gold nanoparticle, porphyrin, surface plasmon resonance, luminescence, nanostructures

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Authors: Greg Fetzer

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One of the fundamental tensions of creative work is between the need to be passionate and persistent in advancing novel and risky ideas and the need to be flexible, revising, or even abandoning ideas in favor of others. The tension becomes fraught in part because of the attachment that creators have toward their ideas. Idea attachment is defined here as a multifaceted concept referring to affection, passion, and connection toward a target—in this case, one’s projects or ideas. Yet feeling attached can make creators resistant to feedback, making them less flexible and leading them to escalate commitment. Despite a growing understanding of how attachment develops and evolves in response to project changes, feedback, and creative jolts, we still know relatively little about the organizational dynamics that may shape idea attachment. Through a qualitative, inductive study of early-stage R&D scientists in the pharmaceutical industry, this research finds that scientists develop bounded attachment, a mindset that limits emotional attachment to ideas while still fostering engagement in idea development. This research develops a process model of how bounded attachment is developed and enacted across three stages of the creative process, idea generation, idea evaluation, and outcome assessment, as well as the role that organizational practices and professional identity play in shaping this process: these collective practices provided structures to ensure ideas were evaluated in a rational (i.e. non-emotional way) while also providing socioemotional support in the face of setbacks. Together, this process led to continued creative engagement across ideas in a portfolio and helped scientists construct a sense of meaningful work despite a high likelihood (and frequency) of failure.

Keywords: creativity, innovation, organizational practices, qualitative, attachment

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Authors: Sangeeta Singh, Pankaj Kumar, P. N. Kondekar

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In this paper, the transient device performance analysis of n-type Gate Inside Junctionless Transistor (GIJLT)has been evaluated. 3-D Bohm Quantum Potential (BQP)transport device simulation has been used to evaluate the delay and power dissipation performance. GI-JLT has a number of desirable device parameters such as reduced propagation delay, dynamic power dissipation, power and delay product, intrinsic gate delay and energy delay product as compared to Gate-all-around transistors GAA-JLT. In addition to this, various other device performance parameters namely, on/off current ratio, short channel effects (SCE), transconductance Generation Factor(TGF) and unity gain cut-off frequency (fT) and subthreshold slope (SS) of the GI-JLT and Gate-all-around junctionless transistor(GAA-JLT) have been analyzed and compared. GI-JLT shows better device performance characteristics than GAA-JLT for low power and high frequency applications, because of its larger gate electrostatic control on the device operation.

Keywords: gate-inside junctionless transistor GI-JLT, gate-all-around junctionless transistor GAA-JLT, propagation delay, power delay product

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Authors: Yaacoub Tina, Youssef Roua, Radoi Emanuel, Burel Gilles

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Recently, low-complexity sub-Nyquist sampling schemes based on the Finite Rate of Innovation (FRI) theory have been introduced to sample parametric signals at minimum rates. The multichannel modulating waveforms (MCMW) is such an efficient scheme, where the received signal is mixed with an appropriate set of arbitrary waveforms, integrated and sampled at rates far below the Nyquist rate. In this paper, the MCMW scheme is adapted to the special case of ultra wideband (UWB) channel estimation, characterized by dense multipaths. First, an appropriate structure, which accounts for the bandpass spectrum feature of UWB signals, is defined. Then, a novel approach to decrease the number of processing channels and reduce the complexity of this sampling scheme is presented. Finally, the proposed concepts are validated by simulation results, obtained with real filters, in the framework of a coherent Rake receiver.

Keywords: coherent rake receiver, finite rate of innovation, sub-nyquist sampling, ultra wideband

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Authors: Aliasghar Baziar, Hassan Masoumi, Alireza Ale Saadi

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The control of the response of electric motors plays a significant role in the damping of transient responses. In this regard, this paper presents a static VAR compensator (SVC) based on a fuzzy logic which is applied to an industrial power network consisting of three phase synchronous, asynchronous and DC motor loads. The speed and acceleration variations of a specific machine are the inputs of the proposed fuzzy logic controller (FLC). In order to verify the effectiveness and proficiency of the proposed Fuzzy Logic based SVC (FLSVC), several non-linear time-domain digital simulation tests are performed. The proposed fuzzy model can properly control the response of electric motors. The results show that the FLSVC is successful to improve the voltage profile significantly over a wide range of operating conditions and disturbances thus improving the overall dynamic performance of the network.

Keywords: fuzzy logic controller, VAR compensator, single cage asynchronous motor, DC motor

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Authors: Badr M. Alshammari, Mohamed A. Khlifi

Abstract:

This paper presents the analysis of DC/DC converter including a comparative study of control methods to extract the maximum power and to track the maximum power point (MPP) from photovoltaic (PV) systems under changeable environmental conditions. This paper proposes two methods of maximum power point tracking algorithm for photovoltaic systems, based on the first hand on P&O control and the other hand on the first order IC. The MPPT system ensures that solar cells can deliver the maximum power possible to the load. Different algorithms are used to design it. Here we compare them and simulate the photovoltaic system with two algorithms. The algorithms are used to control the duty cycle of a DC-DC converter in order to boost the output voltage of the PV generator and guarantee the operation of the solar panels in the Maximum Power Point (MPP). Simulation and experimental results show that the proposed algorithms can effectively improve the efficiency of a photovoltaic array output.

Keywords: solar cell, DC/DC boost converter, MPPT, photovoltaic system

Procedia PDF Downloads 175

Authors: Karthika Durairaj, Buvaneswari G., Gowdham M., Gilles M., Velmurugan G.

Abstract:

Background: Hyperglycaemia is the primary cause of metabolic illness. Recently, researchers focused on the possibility that chemical exposure could promote metabolic disease. Hyperglycaemia causes a variety of metabolic diseases dependent on its etiologic conditions. According to animal and population-based research, individual chemical exposure causes health problems through alteration of endocrine function with the influence of microbial influence. We were intrigued by the function of gut microbiota variation in high fat and chemically induced hyperglycaemia. Methodology: C57/Bl6 mice were subjected to two different treatments to generate the etiologic-based diabetes model: I – a high-fat diet with a 45 kcal diet, and II - endocrine disrupting chemicals (EDCs) cocktail. The mice were monitored periodically for changes in body weight and fasting glucose. After 120 days of the experiment, blood anthropometry, faecal metagenomics and metabolomics were performed and analyzed through statistical analysis using one-way ANOVA and student’s t-test. Results: After 120 days of exposure, we found hyperglycaemic changes in both experimental models. The treatment groups also differed in terms of plasma lipid levels, creatinine, and hepatic markers. To determine the influence on glucose metabolism, microbial profiling and metabolite levels were significantly different between groups. The gene expression studies associated with glucose metabolism vary between hosts and their treatments. Conclusion: This research will result in the identification of biomarkers and molecular targets for better diabetes control and treatment.

Keywords: hyperglycaemia, endocrine-disrupting chemicals, gut microbiota, host metabolism

Procedia PDF Downloads 27

Authors: Zhou BingHai, Fradet Victor

Abstract:

Lean philosophy follows the simple principle of “creating more value with fewer resources”. In accordance with this policy, material handling can be managed by the mean of Kanban which by triggering every feeding tour only when needed regulates the flow of material in one of the most efficient way. This paper focuses on Kanban Supermarket’s parameters and their optimization on a purely cost-based point of view. Number and size of forklifts, as well as size of the containers they carry, will be variables of the cost function which includes handling costs, inventory costs but also shortage costs. With an innovative computational approach encoded into industrial engineering software Tecnomatix and reproducing real-life conditions, a fictive assembly line is established and produces a random list of orders. Multi-scenarios are then run to study the impact of each change of parameter and the variation of costs it implies. Lastly, best-case scenarios financially speaking are selected.

Keywords: Kanban, supermarket, parts-feeding policies, multi-scenario simulation, assembly line

Procedia PDF Downloads 180