Search results for: network transformation

Authors: Nadia Chagtmi, Nejla Rejab, Noureddine Boulejfen

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This paper presents a calibration approach based on artificial neural networks (ANN) to determine the envelop signal (I+jQ) of a six-port based receiver (SPR). The memory effects called also dynamic behavior and the nonlinearity brought by diode based power detector have been taken into consideration by the ANN. Experimental set-up has been performed to validate the efficiency of this method. The efficiency of this approach has been confirmed by the obtained results in terms of waveforms. Moreover, the obtained error vector magnitude (EVM) and the mean absolute error (MAE) have been calculated in order to confirm and to test the ANN’s performance to achieve I/Q recovery using the output voltage detected by the power based detector. The baseband signal has been recovered using ANN with EVMs no higher than 1 % and an MAE no higher than 17, 26 for the SPR excited different type of signals such QAM (quadrature amplitude modulation) and LTE (Long Term Evolution).

Keywords: six-port based receiver; calibration, nonlinearity, memory effect, artificial neural network

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Authors: A. P. Mikhailovich, V. V. Fomin, E. M. Agapitov, V. E. Rogachev, E. A. Kostousova, E. S. Perekhodova

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Woody vegetation among the upper limit of its habitat is a sensitive indicator of biota reaction to regional climate changes. Quantitative assessment of temporal and spatial changes in the distribution of trees and plant biocenoses calls for the development of new modeling approaches based upon selected data from measurements on the ground level and ultra-resolution aerial photography. Statistical models were developed for the study area located in the Polar Urals. These models allow obtaining probabilistic estimates for placing Siberian Larch trees into one of the three age intervals, namely 1-10, 11-40 and over 40 years, based on the Weilbull distribution of the maximum horizontal crown projection. Authors developed the distribution map for larch trees with crown diameters exceeding twenty centimeters by deciphering aerial photographs made by a UAV from an altitude equal to fifty meters. The total number of larches was equal to 88608, forming the following distribution row across the abovementioned intervals: 16980, 51740, and 19889 trees. The results demonstrate that two processes can be observed in the course of recent decades: first is the intensive forestation of previously barren or lightly wooded fragments of the study area located within the patches of wood, woodlands, and sparse stand, and second, expansion into mountain tundra. The current expansion of the Siberian Larch in the region replaced the depopulation process that occurred in the course of the Little Ice Age from the late 13ᵗʰ to the end of the 20ᵗʰ century. Using data from field measurements of Siberian larch specimen biometric parameters (including height, diameter at root collar and at 1.3 meters, and maximum projection of the crown in two orthogonal directions) and data on tree ages obtained at nine circular test sites, authors developed a model for artificial neural network including two layers with three and two neurons, respectively. The model allows quantitative assessment of a specimen's age based on height and maximum crone projection values. Tree height and crown diameters can be quantitatively assessed using data from aerial photographs and lidar scans. The resulting model can be used to assess the age of all Siberian larch trees. The proposed approach, after validation, can be applied to assessing the age of other tree species growing near the upper tree boundaries in other mountainous regions. This research was collaboratively funded by the Russian Ministry for Science and Education (project No. FEUG-2023-0002) and Russian Science Foundation (project No. 24-24-00235) in the field of data modeling on the basis of artificial neural network.

Keywords: treeline, dynamic, climate, modeling

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Authors: Erika Takidah, Salina Kassim

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Digital transformation has accelerated in the new millennium. It is reshaping the financial services industry from a traditional system to financial technology. Moreover, the number of financial inclusion rates in Indonesia is less than 60%. An innovative model needed to elucidate this national problem. On the other hand, the Islamic financial service industry and financial technology grow fast as a new aspire in economic development. An Islamic bank, takaful, Islamic microfinance, Islamic financial technology and Islamic social finance institution could collaborate to intensify the financial inclusion number in Indonesia. The primary motive of this paper is to examine the strategy of collaboration-based Islamic financial services to enhance financial inclusion in Indonesia, particularly facing the digital era. The fundamental findings for the main problems are the foundations and key ecosystems aspect involved in the development of collaboration-based Islamic financial services. By using the Interpretive Structural Model (ISM) approach, the core problems faced in the development of the models have lacked policy instruments guarding the collaboration-based Islamic financial services with fintech work process and availability of human resources for fintech. The core strategies or foundations that are needed in the framework of collaboration-based Islamic financial services are the ability to manage and analyze data in the big data era. For the aspects of the Ecosystem or actors involved in the development of this model, the important actor is government or regulator, educational institutions, and also existing industries (Islamic financial services). The outcome of the study designates that strategy collaboration of Islamic financial services institution supported by robust technology, a legal and regulatory commitment of the regulators and policymakers of the Islamic financial institutions, extensive public awareness of financial inclusion in Indonesia. The study limited itself to realize financial inclusion, particularly in Islamic finance development in Indonesia. The study will have an inference for the concerned professional bodies, regulators, policymakers, stakeholders, and practitioners of Islamic financial service institutions.

Keywords: collaboration, financial inclusion, Islamic financial services, Islamic fintech

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Authors: Hassan Nour, Nouh Mounadi, Oussama Abchir, Belaidi Salah, Samir Chtita

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Cholinesterase enzymes are biological catalysts essential for the transformation of acetylcholine, which is a neurotransmitter implicated in memory and learning, into acetic acid and choline, altering the neurotransmission process in Alzheimer’s disease patients. Therefore, inhibition of cholinesterase enzymes is a relevant strategy for the symptomatic treatment of Alzheimer’s disease. The current investigation aims to explore potential Cholinesterase (ChE) inhibitors through a comprehensive computational approach. Forty-nine phytoconstituents extracted from Cannabis sativa L were in-silico screened using molecular docking, pharmacokinetic and toxicological analysis to evaluate their possible inhibitory effect towards the cholinesterase enzymes. Two phytoconstituents belonging to cannabinoid derivatives were revealed to be promising candidates for Alzheimer therapy by acting as cholinesterase inhibitors. They have exhibited high binding affinities towards the cholinesterase enzymes and showed their ability to interact with key residues involved in cholinesterase enzymatic activity. In addition, they presented good ADMET profiles allowing them to be promising oral drug candidates. Furthermore, molecular dynamics (MD) simulations were executed to explore their interactions stability under mimetic biological conditions and thus support our findings. To corroborate the docking results, the binding free energy corresponding to the more stable ligand-ChE complexes was re-estimated by applying the MM-PBSA method. MD and MM-PBSA studies affirmed that the ligand-ChE recognition is spontaneous reaction leading to stable complexes. The conducted investigations have led to great findings that would strongly guide the pharmaceutical industries towards the rational development of potent anti-Alzheimer agents.

Keywords: alzheimer’s disease, molecular docking, cannabis sativa l, cholinesterase inhibitors

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Authors: Ruichong Zhang, Fadi Sawaged, Lotfi Gargab

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This paper introduces a wave-based approach for system identification of high-rise building structures with a pair of seismic recordings, which can be used to evaluate structural integrity and detect damage in post-earthquake structural condition assessment. The fundamental of the approach is based on wave features of generalized impulse and frequency response functions (GIRF and GFRF), i.e., wave responses at one structural location to an impulsive motion at another reference location in time and frequency domains respectively. With a pair of seismic recordings at the two locations, GFRF is obtainable as Fourier spectral ratio of the two recordings, and GIRF is then found with the inverse Fourier transformation of GFRF. With an appropriate continuous model for the structure, a closed-form solution of GFRF, and subsequent GIRF, can also be found in terms of wave transmission and reflection coefficients, which are related to structural physical properties above the impulse location. Matching the two sets of GFRF and/or GIRF from recordings and the model helps identify structural parameters such as wave velocity or shear modulus. For illustration, this study examines ten-story Millikan Library in Pasadena, California with recordings of Yorba Linda earthquake of September 3, 2002. The building is modelled as piecewise continuous layers, with which GFRF is derived as function of such building parameters as impedance, cross-sectional area, and damping. GIRF can then be found in closed form for some special cases and numerically in general. Not only does this study reveal the influential factors of building parameters in wave features of GIRF and GRFR, it also shows some system-identification results, which are consistent with other vibration- and wave-based results. Finally, this paper discusses the effectiveness of the proposed model in system identification.

Keywords: wave-based approach, seismic responses of buildings, wave propagation in structures, construction

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Authors: Hayder Shanbara, Felicite Ruddock, William Atherton, Ali Al-Rifaie

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In highways construction, Hot Mix Asphalt (HMA) is used predominantly as a paving material from many years. Around 90 percent of the world road network is laid by flexible pavements. However, there are some restrictions on paving hot mix asphalt such as immoderate greenhouse gas emission, rainy season difficulties, fuel and energy consumption and cost. Therefore, Cold Bitumen Emulsion Mixture (CBEM) is considered an alternative mix to the HMA. CBEM is the popular type of Cold Mix Asphalt (CMA). It is unheated emulsion, aggregate and filler mixtures, which can be prepared and mixed at ambient temperature. This research presents a simple and more practicable design procedure of CBEM and discusses limitations of this design. CBEM is a mixture of bitumen emulsion and aggregates that mixed and produced at ambient temperature. It is relatively easy to produce, but the design procedure that provided by Asphalt Institute (Manual Series 14 (1989)) pose some issues in its practical application.

Keywords: cold bitumen, emulsion mixture, design procedure, pavement

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Authors: Mohammad Shams Esfand Abadi, Mohammad Ranjbar, Reza Ebrahimpour

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This work presents the distributed processing solution problem in a diffusion network based on the adapt then combine (ATC) and combine then adapt (CTA)selective partial update normalized least mean squares (SPU-NLMS) algorithms. Also, we extend this approach to dynamic selection affine projection algorithm (DS-APA) and ATC-DS-APA and CTA-DS-APA are established. The purpose of ATC-SPU-NLMS and CTA-SPU-NLMS algorithm is to reduce the computational complexity by updating the selected blocks of weight coefficients at every iteration. In CTA-DS-APA and ATC-DS-APA, the number of the input vectors is selected dynamically. Diffusion cooperation strategies have been shown to provide good performance based on these algorithms. The good performance of introduced algorithm is illustrated with various experimental results.

Keywords: selective partial update, affine projection, dynamic selection, diffusion, adaptive distributed networks

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Authors: William Anderson, Kyle Kobold, Oleg Yakimenko

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Green microgrids using mostly renewable energy (RE) for generation, are complex systems with inherent nonlinear dynamics. Among a variety of different optimization tools there are only a few ones that adequately consider this complexity. This paper evaluates applicability of two somewhat similar optimization tools tailored for standalone RE microgrids and also assesses a machine learning tool for performance prediction that can enhance the reliability of any chosen optimization tool. It shows that one of these microgrid optimization tools has certain advantages over another and presents a detailed routine of preparing input data to simulate RE microgrid behavior. The paper also shows how neural-network-based predictive modeling can be used to validate and forecast solar power generation based on weather time series data, which improves the overall quality of standalone RE microgrid analysis.

Keywords: microgrid, renewable energy, complex systems, optimization, predictive modeling, neural networks

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Authors: Minh Quang Le, Dane Taylor

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Convection is a well-studied topic in fluid dynamics, yet it is less understood in the context of networks flows. Here, we incorporate techniques from topological data analysis (namely, persistent homology) to automate the detection and characterization of convective/cyclic/chiral flows over networks, particularly those that arise for irreversible Markov chains (MCs). As two applications, we study convection cycles arising under the PageRank algorithm, and we investigate chiral edges flows for a stochastic model of a bi-monomer's configuration dynamics. Our experiments highlight how system parameters---e.g., the teleportation rate for PageRank and the transition rates of external and internal state changes for a monomer---can act as homology regularizers of convection, which we summarize with persistence barcodes and homological bifurcation diagrams. Our approach establishes a new connection between the study of convection cycles and homology, the branch of mathematics that formally studies cycles, which has diverse potential applications throughout the sciences and engineering.

Keywords: homology, persistent homolgy, markov chains, convection cycles, filtration

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Authors: L. A. Ostrovsky, Yu. A. Stepanyants

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Effect of Earth rotation on nonlinear waves is a practically important and theoretically challenging problem of fluid mechanics and geophysics. Whereas the large-scale, geostrophic processes such as Rossby waves are a classical object of oceanic and atmospheric physics, rotation effects on mesoscale waves are not well studied. In particular, the Coriolis force can radically modify the behavior of nonlinear internal gravity waves in the ocean having spatial scales of 1-10 kilometers and time durations of few hours. In the last decade, such a non-trivial behavior was observed more than once. Similar effects are possible for magnetic sound in the ionosphere. Here we outline the main physical peculiarities in the behavior of nonlinear internal waves due to the rotation effect and present some results of our recent studies. The consideration is based on the fourth-order equation derived by one of the authors as a rotation-modified Korteweg–de Vries (rKdV) equation which includes two types of dispersion: one is responsible for the finiteness of depth as in the classical KdV equation; another is due to the Coriolis effect. This equation is, in general, non-integrable; moreover, under the conditions typical of oceanic waves (positive dispersion parameter), it does not allow solitary solutions at all. In the opposite case (negative dispersion) which is possible for, e.g., magnetic sound, solitary solutions do exist and can form complex bound states (multisoliton). Another non-trivial properties of nonlinear internal waves with rotation include, to name a few, the ‘terminal’ damping of the initial KdV soliton disappearing in a finite time due to radiation losses caused by Earth’s rotation, and eventual transformation of a KdV soliton into a wave packet (an envelope soliton). The new results to be discussed refer to the interaction of a soliton with a long background wave. It is shown, in particular, that in this case internal solitons can exist since the radiation losses are compensated by energy pumping from the background wave. Finally, the relevant oceanic observations of rotation effect on internal waves are briefly described.

Keywords: Earth rotation, internal waves, nonlinear waves, solitons

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Authors: Jasraj Meena, Malay Kumar, Manu Vardhan

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Cloud computing is a new technology in industry and academia. The technology has grown and matured in last half decade and proven their significant role in changing environment of IT infrastructure where cloud services and resources are offered over the network. Cloud technology enables users to use services and resources without being concerned about the technical implications of technology. There are substantial research work has been performed for the usage of cloud computing in educational institutes and majority of them provides cloud services over high-end blade servers or other high-end CPUs. However, this paper proposes a new stack called “CiCKAStack” which provide cloud services over unutilized computing resources, named as commodity computers. “CiCKAStack” provides IaaS and PaaS using underlying commodity computers. This will not only increasing the utilization of existing computing resources but also provide organize file system, on demand computing resource and design and development environment.

Keywords: commodity computers, cloud-computing, KVM, CloudStack, AppScale

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Authors: Emad A. Mohammed

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Utilizing the capabilities of Data Mining and Artificial Intelligence in the prediction of the minimum miscibility pressure (MMP) required for multi-contact miscible (MCM) displacement of reservoir petroleum by hydrocarbon gas flooding using Fuzzy Logic models and Artificial Neural Network models will help a lot in giving accurate results. The factors affecting the (MMP) as it is proved from the literature and from the dataset are as follows: XC2-6: Intermediate composition in the oil-containing C2-6, CO2 and H2S, in mole %, XC1: Amount of methane in the oil (%),T: Temperature (°C), MwC7+: Molecular weight of C7+ (g/mol), YC2+: Mole percent of C2+ composition in injected gas (%), MwC2+: Molecular weight of C2+ in injected gas. Fuzzy Logic and Neural Networks have been used widely in prediction and classification, with relatively high accuracy, in different fields of study. It is well known that the Fuzzy Inference system can handle uncertainty within the inputs such as in our case. The results of this work showed that our proposed models perform better with higher performance indices than other emprical correlations.

Keywords: MMP, gas flooding, artificial intelligence, correlation

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Authors: S. Fares, K. Mellakh, A. Hmouri

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The StorMer project aims to set up a network of researchers to study traditional hydraulic rainwater harvesting systems in the Mediterranean basin, a region suffering from the major impacts of climate change and limited natural water resources. The arid and semi-arid Mediterranean basin has a long history of pioneering water management practices. The region has developed various ancient traditional water management systems, such as cisterns and qanats, to sustainably manage water resources under historical conditions of scarcity. Therefore, the StorMer project brings together Spain, France, Italy, Greece, Jordan and Morocco to explore traditional rainwater harvesting practices and systems in the Mediterranean region and to develop accurate modeling to simulate the performance and sustainability of these technologies under present-day climatic conditions. The ultimate goal of this project was to resuscitate and valorize these practices in the context of contemporary challenges. This project was intended to establish a Mediterranean network to serve as a basis for a more ambitious project. The ultimate objective was to analyze traditional hydraulic systems and create a prototype hydraulic ecosystem using a coupled environmental approach and traditional and ancient know-how, with the aim of reinterpreting them in the light of current techniques. The combination of ‘traditional’ and ‘modern knowledge/techniques’ is expected to lead to proposals for innovative hydraulic systems. The pandemic initially slowed our progress, but in the end it forced us to carry out the fieldwork in Morocco and Saudi Arabia, and so restart the project. With the participation of colleagues from chronologically distant fields (archaeology, sociology), we are now prepared to share our observations and propose the next steps. This interdisciplinary approach should give us a global vision of the project's objectives and challenges. A diachronic approach is needed to tackle the question of the long-term adaptation of societies in a Mediterranean context that has experienced several periods of water stress. The next stage of the StorMer project is the implementation of pilots in non-urbanized regions. These pilots will test the implementation of traditional systems and will be maintained and evaluated in terms of effectiveness, cost and acceptance. Based on these experiences, larger projects will be proposed and could provide information for regional water management policies. One of the most important lessons learned from this project is the highly social nature of managing traditional rainwater harvesting systems. Unlike modern, centralized water infrastructures, these systems often require the involvement of communities, which assume ownership and responsibility for them. This kind of community engagement leads to greater maintenance and, therefore, sustainability of the systems. Knowledge of the socio-cultural characteristics of these communities means that the systems can be adapted to the needs of each location, ensuring greater acceptance and efficiency.

Keywords: oasis, rainfall harvesting, arid regions, Mediterranean

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Authors: Lim Pey Huan

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Yi-Kuan-Tao has been developing in Malaysia for nearly 60 years. It was initially introduced from mainland China and later from Taiwan starting from the 1970s. Yi-Kuan-Tao was considered a 'new religion' for the local Chinese community in Malaysia in its early stages, as Chinese immigrants primarily practiced Taoism, Buddhism, Christianity, or Catholicism upon settling in the region. The overseas propagation and development of Yi-Kuan-Tao today primarily occur through Taiwanese temples, which began spreading abroad as early as 1949. Particularly since the 1970s, with the rapid economic growth of Taiwan, various branches of Taiwanese Yi-Kuan-Tao have gained economic strength to propagate abroad, further expanding the influence of Yi-Kuan-Tao overseas. Southeast Asia is the region out from Taiwan where the propagation and development of Yi-Kuan-Tao are fastest and most concentrated. With approximately over 6 million Chinese inhabitants, Malaysia's pursuit of traditional Chinese culture has led to a flourishing interest in Yi-Kuan-Tao, particularly its advocacy of the unity of Confucianism, Buddhism, and Taoism, with an emphasis on promoting Confucian thought. Moreover, Taiwan's rapid economic development since the 1970s has enabled Yi-Kuan-Tao to allocate significant human and financial resources for external propagation efforts. Additionally, Malaysia's government has adopted a relatively tolerant policy towards religion since that time, further fostering the flourishing development of Yi-Kuan-Tao in Malaysia. Furthermore, this thesis aims to strengthen the lineage and continuity of the Yi-Kuan-Tao tradition, particularly the branch of Fa Yi Chong De, through the perspective of Heavenly Mandate (天命). By examining the different origins and ethnic backgrounds, it investigates how the Malaysian Chinese community has experienced different changes through the cultural baptism of religion, thus delving into the religious influence of Yi-Kuan-Tao. Given that the Fa Yi Chong De Academy in Taiwan is currently in an active development and construction phase, academic works related to Yi-Kuan-Tao will lay a more solid academic foundation for the future establishment of the academy.

Keywords: initial exploration, cultural shifts, Yi-Kuan-Tao, Malaysian Chinese community

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Authors: A. Šapkauskienė, A. Mačerinskienė, R. Andrulienė, R. Bruzgė, S. Masteika, K. Driaunys

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The COVID-19 pandemic has affected not only world markets and economies but also the daily lives of customers and their payment habits. The pandemic has accelerated the digital transformation, so the role of technology will become even more important post-COVID. Although the popularity of cryptocurrencies has reached unprecedented heights, there are still obstacles, such as a lack of consumer experience and distrust of these technologies, so exploring the role of cryptocurrency and blockchain in the context of international travel becomes extremely important. Research on tourists’ intentions to use cryptocurrencies for payment purposes is limited due to the small number of research studies. To fill this research gap, an exploratory study based on the analysis of survey data was conducted. The purpose of the research is to explore how the behavior of tourists has changed making their financial transactions when paying for the tourism services in order to determine the intention to pay in cryptocurrencies. Behavioral intention can be examined as a dependent variable that is useful for the study of the acceptance of blockchain as cutting-edge technology. Therefore, this study examines the intention of travelers to use cryptocurrencies in electronic payments for tourism services. Several studies have shown that the intention to accept payments in a cryptocurrency is affected by the perceived usefulness of these payments and the perceived ease of use. The findings deepen our understanding of the readiness of service users to apply for blockchain-based payment in the tourism sector. The tourism industry has to focus not only on the technology but on consumers who can use cryptocurrencies, creating new possibilities and increasing business competitiveness. Based on research results, suggestions are made to guide future research on the use of cryptocurrencies by tourists in the tourism industry. Therefore, in line with the rapid expansion of virtual currency users, market capitalization, and payment in cryptographic currencies, it is necessary to explore the possibilities of implementing a blockchain-based system aiming to promote the use of services in the tourism sector as the most affected by the pandemic.

Keywords: behavioral intention, blockchain-based payment, cryptocurrency, tourism

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Authors: Tugce Caliskan

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The gentrification literature in Turkey provided important insights regarding the analysis of the socio-spatial change in İstanbul mostly through the existing gentrification theories which were produced in Anglo-American literature. Yet early researches focused on the classical gentrification while failing to notice other place-specific forms of the phenomena. It was only after the mid-2000s that scholarly attention shifted to the recent discussions in the mainstream such as the neoliberal urban policies, government involvement, and resistance. Although these studies have considerable potential to contribute to the geography of gentrification, it seems that copying the linear timeline of Anglo-American conceptualization limited the space to introduce contextually nuanced way of process in Turkey. More specifically, the gentrification literature in Turkey acknowledged the linear timeline of the process drawing on the mainstream studies, and, made the spontaneous classical gentrification as the starting point in İstanbul at the expense of contextually specific forms of the phenomenon that took place in the same years. This paper is an attempt to understand place-specific forms of gentrification through the abandonment of the linear understanding of time. In this vein, this paper approaches the process as moving both linear and cyclical rather than the waves succeeded each other. Maintaining a dialectical relationship between the cyclical and the linear time, this paper investigates how the components of gentrification have been taken place in the cyclical timeline while becoming bolder in the linear timeline. This paper argues that taking the (re)investment in the secondary circuit of capital and class transformation as the core characteristics of gentrification, and accordingly, searching for these components beyond the linear timeline provide strategic value to decenter the perspectives, not merely for Turkish studies. In this vein, this strategy revealed that Western experience of gentrification did not travel, adopted or copied in Turkey but gentrification -as an abstract and general concept- has emerged as a product of different contextual, historical and temporal forces which must be considered within the framework of state-led urbanization as early as 1980 differing from the Global North trajectories.

Keywords: comparative urbanism, geography of gentrification, linear and cyclical timeline, state-led gentrification

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Authors: Kanika Kalra, Harmeet Kaur, Dinesh Goyal

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Total phenolics were extracted from waste orange peels by solvent extraction and alkali hydrolysis method. The most efficient solvents for extracting phenolic compounds from waste biomass were methanol (60%) > dimethyl sulfoxide > ethanol (60%) > distilled water. The extraction yields were significantly impacted by solvents (ethanol, methanol, and dimethyl sulfoxide) due to varying polarity and concentrations. Extraction of phenolics using 60% methanol yielded the highest phenolics (in terms of gallic acid equivalent (GAE) per gram of biomass) in orange peels. Alkali hydrolyzed extract from orange peels contained 7.58±0.33 mg GAE g⁻¹. By using the solvent extraction technique, it was observed that 60% methanol is comparatively the best-suited solvent for extracting polyphenolic compounds and gave the maximum yield of 4.68 ± 0.47 mg GAE g⁻¹ in orange peel extracts. DPPH radical scavenging activity and reducing the power of orange peel extract were checked, where 60% methanolic extract showed the highest antioxidant activity, 85.50±0.009% for DPPH, and dimethyl sulfoxide (DMSO) extract gave the highest yield of 1.75±0.01% for reducing power ability of the orange peels extract. Characterization of the polyphenolic compounds was done by using Fourier transformation infrared (FTIR) spectroscopy. Solvent and alkali hydrolysed extracts were evaluated for antibacterial activity using the agar well diffusion method against Gram-positive Bacillus subtilis MTCC441 and Gram-negative Escherichia coli MTCC729. Methanolic extract at 300µl concentration showed an inhibition zone of around 16.33±0.47 mm against Bacillus subtilis, whereas, for Escherichia coli, it was comparatively less. Broth-based turbidimetric assay revealed the antibacterial effect of different volumes of orange peel extracts against both organisms.

Keywords: orange peels, total phenolic content, antioxidant, antibacterial

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Authors: Jingpeng Gao, Xinrui Mao, Zhibin Deng

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Aiming at the low recognition rate on the composite signal modulation in low signal to noise ratio (SNR), this paper proposes a modulation recognition algorithm based on ICanny-CNN. Firstly, the radar signal is transformed into the time-frequency image by Choi-Williams Distribution (CWD). Secondly, we propose an image processing algorithm using the Guided Filter and the threshold selection method, which is combined with the hole filling and the mask operation. Finally, the shallow convolutional neural network (CNN) is combined with the idea of the depth-wise convolution (Dw Conv) and the point-wise convolution (Pw Conv). The proposed CNN is designed to complete image classification and realize modulation recognition of radar signal. The simulation results show that the proposed algorithm can reach 90.83% at 0dB and 71.52% at -8dB. Therefore, the proposed algorithm has a good classification and anti-noise performance in radar signal modulation recognition and other fields.

Keywords: modulation recognition, image processing, composite signal, improved Canny algorithm

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Authors: Pegah Eshraghi, Zahra Sadat Zomorodian, Mohammad Tahsildoost

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Over the past decade, energy consumption in educational buildings has steadily increased. The purpose of this research is to provide a method to quickly predict the energy consumption of buildings using separate evaluation of zones and decomposing the building to eliminate the complexity of geometry at the early design stage. To produce this framework, machine learning algorithms such as Support vector regression (SVR) and Artificial neural network (ANN) are used to predict energy consumption and thermal comfort metrics in a school as a case. The database consists of more than 55000 samples in three climates of Iran. Cross-validation evaluation and unseen data have been used for validation. In a specific label, cooling energy, it can be said the accuracy of prediction is at least 84% and 89% in SVR and ANN, respectively. The results show that the SVR performed much better than the ANN.

Keywords: early stage of design, energy, thermal comfort, validation, machine learning

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Authors: Ramin Karimi

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One of common highly mobile wireless ad hoc networks is Vehicular Ad Hoc Networks. Hence routing in vehicular ad hoc network (VANET) has attracted much attention during the last few years. VANET is characterized by its high mobility of nodes and specific topology patterns. Moreover these networks encounter a significant loss rate and a very short duration of communication. In vehicular ad hoc networks, one of challenging is routing of data due to high speed mobility and changing topology of vehicles. Geographic routing protocols are becoming popular due to advancement and availability of GPS devices. Delay Tolerant Networks (DTNs) are a class of networks that enable communication where connectivity issues like sparse connectivity, intermittent connectivity; high latency, long delay, high error rates, asymmetric data rate, and even no end-to-end connectivity exist. In this paper, we review the existing Geographic Routing Protocols for VANETs and also provide a qualitative comparison of them.

Keywords: vehicular ad hoc networks, mobility, geographic routing, delay tolerant networks

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Authors: S. A. Naeini, A. Khalili

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Nowadays, tunnels with different applications are developed, and most of them are related to subway tunnels. The excavation of shallow tunnels that pass under municipal utilities is very important, and the surface settlement control is an important factor in the design. The study sought to analyze the settlement and also to find an appropriate model in order to predict the behavior of the tunnel in Tehran subway line-3. The displacement in these sections is also determined by using numerical analyses and numerical modeling. In addition, the Adaptive Neuro-Fuzzy Inference System (ANFIS) method is utilized by Hybrid training algorithm. The database pertinent to the optimum network was obtained from 46 subway tunnels in Iran and Turkey which have been constructed by the new Austrian tunneling method (NATM) with similar parameters based on type of their soil. The surface settlement was measured, and the acquired results were compared to the predicted values. The results disclosed that computing intelligence is a good substitute for numerical modeling.

Keywords: settlement, Subway Line, FLAC3D, ANFIS Method

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Authors: Dylan Santos De Pinho, Nabil Ouerhani

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Energy consumption of machine-tools is becoming critical for machine-tool builders and end-users because of economic, ecological and legislation-related reasons. Many machine-tool builders are seeking for solutions that allow the reduction of energy consumption of machine-tools while preserving the same productivity rate and the same quality of machined parts. In this paper, we present the first results of a project conducted jointly by academic and industrial partners to reduce the energy consumption of a Swiss-Type lathe. We employ genetic algorithms to find optimal machining parameters – the set of parameters that lead to the best trade-off between energy consumption, part quality and tool lifetime. Three main machining process parameters are considered in our optimization technique, namely depth of cut, spindle rotation speed and material feed rate. These machining process parameters have been identified as the most influential ones in the configuration of the Swiss-type machining process. A state-of-the-art multi-objective genetic algorithm has been used. The algorithm combines three fitness functions, which are objective functions that permit to evaluate a set of parameters against the three objectives: energy consumption, quality of the machined parts, and tool lifetime. In this paper, we focus on the investigation of the fitness function related to energy consumption. Four different energy consumption related fitness functions have been investigated and compared. The first fitness function refers to the Kienzle cutting force model. The second fitness function uses the Material Removal Rate (RMM) as an indicator of energy consumption. The two other fitness functions are non-deterministic, learning-based functions. One fitness function uses a simple Neural Network to learn the relation between the process parameters and the energy consumption from experimental data. Another fitness function uses Lasso regression to determine the same relation. The goal is, then, to find out which fitness functions predict best the energy consumption of a Swiss-Type machining process for the given set of machining process parameters. Once determined, these functions may be used for optimization purposes – determine the optimal machining process parameters leading to minimum energy consumption. The performance of the four fitness functions has been evaluated. The Tornos DT13 Swiss-Type Lathe has been used to carry out the experiments. A mechanical part including various Swiss-Type machining operations has been selected for the experiments. The evaluation process starts with generating a set of CNC (Computer Numerical Control) programs for machining the part at hand. Each CNC program considers a different set of machining process parameters. During the machining process, the power consumption of the spindle is measured. All collected data are assigned to the appropriate CNC program and thus to the set of machining process parameters. The evaluation approach consists in calculating the correlation between the normalized measured power consumption and the normalized power consumption prediction for each of the four fitness functions. The evaluation shows that the Lasso and Neural Network fitness functions have the highest correlation coefficient with 97%. The fitness function “Material Removal Rate” (MRR) has a correlation coefficient of 90%, whereas the Kienzle-based fitness function has a correlation coefficient of 80%.

Keywords: adaptive machining, genetic algorithms, smart manufacturing, parameters optimization

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Authors: Devansh Jain, Shu Ting Goh

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With the aim of improving the quality of life of people of Singapore with provision of better transport services, Land and Transport Authority Singapore recently published its Master Plan 2013. The major objectives mentioned in the plan were to make a comprehensive public transport network with better quality Mass Rapid Transit, bus services along with cycling and walking. MRT is the backbone of the transport system in Singapore, and to promote and increase the MRT ridership, good accessibility to access the MRT stations is a necessity. The aim of this paper is to investigate the relationship between MRT ridership and the provision of feeder bus services in Singapore planning areas and also to understand the hub and spoke model adopted by Singapore for provision of transport services. The findings of the study will lead to conclusions made from the Regression model developed by the various factors affecting MRT ridership, and hence will benefit to enhance the services provided by the system.

Keywords: quality of life, public transport, mass rapid transit, ridership

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Authors: Galina Portnova, Kseniya Gladun

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In our EEG experiment participated 27 children with ASD with the average age of 6.13 years and the average score for CARS 32.41 and 25 healthy children (of 6.35 years). Six types of musical stimulation were presented, included Gluck, Javier-Naida, Kenny G, Chopin and other classic musical compositions. Children with autism showed orientation reaction to the music and give behavioral responses to different types of music, some of them might assess stimulation by scales. The participants were instructed to remain calm. Brain electrical activity was recorded using a 19-channel EEG recording device, 'Encephalan' (Russia, Taganrog). EEG epochs lasting 150 s were analyzed using EEGLab plugin for MatLab (Mathwork Inc.). For EEG analysis we used Fast Fourier Transform (FFT), analyzed Peak alpha frequency (PAF), correlation dimension D2 and Stability of rhythms. To express the dynamics of desynchronizing of different rhythms we've calculated the envelope of the EEG signal, using the whole frequency range and a set of small narrowband filters using Hilbert transformation. Our data showed that healthy children showed similar EEG spectral changes during musical stimulation as well as described the feelings induced by musical fragments. The exception was the ‘Chopin. Prelude’ fragment (no.6). This musical fragment induced different subjective feeling, behavioral reactions and EEG spectral changes in children with ASD and healthy children. The correlation dimension D2 was significantly lower in autists compared to healthy children during musical stimulation. Hilbert envelope frequency was reduced in all group of subjects during musical compositions 1,3,5,6 compositions compared to the background. During musical fragments 2 and 4 (terrible) lower Hilbert envelope frequency was observed only in children with ASD and correlated with the severity of the disease. Alfa peak frequency was lower compared to the background during this musical composition in healthy children and conversely higher in children with ASD.

Keywords: electroencephalogram (EEG), emotional perception, ASD, musical perception, childhood Autism rating scale (CARS)

Procedia PDF Downloads 266

Authors: Amir Mabhout, Toktam Ghafarian, Amirhossein Farzin, Zahra Makki, Sajjad Alizadeh, Amirhossein Ghavi

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The high number of Netflix subscribers makes it attractive for data scientists to extract valuable knowledge from the viewers' behavioural analyses. This paper presents a set of statistical insights into viewers' viewing history. After that, a deep learning model is used to predict the future watching behaviour of the users based on previous watching history within the Netflixlatte data pool. Netflixlatte in an aggregated and anonymized data pool of 320 Netflix viewers with a length 250 000 data points recorded between 2008-2022. We observe insightful correlations between the distribution of viewing time and the COVID-19 pandemic outbreak. The presented deep learning model predicts future movie and TV series viewing habits with an average loss of 0.175.

Keywords: data analysis, deep learning, LSTM neural network, netflix

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Authors: Sahar Khedr, Dina Sayed, Ayman Hanafy

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Arabic is one of the most ancient and critical languages in the world. It has over than 250 million Arabic native speakers and more than twenty countries having Arabic as one of its official languages. In the past decade, we have witnessed a rapid evolution in smart devices, social network and technology sector which led to the need to provide tools and libraries that properly tackle the Arabic language in different domains. Stemming is one of the most crucial linguistic fundamentals. It is used in many applications especially in information extraction and text mining fields. The motivation behind this work is to enhance the Arabic light stemmer to serve the data mining industry and leverage it in an open source community. The presented implementation works on enhancing the Arabic light stemmer by utilizing and enhancing an algorithm that provides an extension for a new set of rules and patterns accompanied by adjusted procedure. This study has proven a significant enhancement for better search accuracy with an average 10% improvement in comparison with previous works.

Keywords: Arabic data mining, Arabic Information extraction, Arabic Light stemmer, Arabic stemmer

Procedia PDF Downloads 290

Authors: Cristian Matti, Valerie Fowles, Eva Enyedi, Piotr Pogorzelski

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The system mapping process can be defined as a knowledge service where a team of facilitators, experts and practitioners facilitate a guided conversation, enable the exchange of information and support an iterative curation process. System mapping processes rely on science-based tools to introduce and simplify a variety of components and concepts of socio-technical systems through metaphors while facilitating an interactive dialogue process to enable the design of co-created maps. System maps work then as “artifacts” to provide information and focus the conversation into specific areas around the defined challenge and related decision-making process. Knowledge management facilitates the curation of that data gathered during the system mapping sessions through practices of documentation and subsequent knowledge co-production for which common practices from data science are applied to identify new patterns, hidden insights, recurrent loops and unexpected elements. This study presents empirical evidence on the application of these techniques to explore mechanisms by which visual tools provide guiding principles to portray system components, key variables and types of data through the lens of climate change. In addition, data science facilitates the structuring of elements that allow the analysis of layers of information through affinity and clustering analysis and, therefore, develop simple indicators for supporting the decision-making process. This paper addresses methodological and empirical elements on the horizontal learning process that integrate system mapping through visual tools, interpretation, cognitive transformation and analysis. The process is designed to introduce practitioners to simple iterative and inclusive processes that create actionable knowledge and enable a shared understanding of the system in which they are embedded.

Keywords: indicators, knowledge management, system mapping, visual tools

Procedia PDF Downloads 178

Authors: Rabail Shafique Satti, Sidra Ejaz, Madiha Arshad, Marwa Khalid, Sadia Majeed

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The paper presents the development of an automated security system to automate the entry of visitors, providing more flexibility of managing their record and securing homes or workplaces. Face recognition is part of this system to authenticate the visitors. A cost effective and SMS based door security module has been developed and integrated with the GSM network and made part of this system to allow communication between system and owner. This system functions in real time as when the visitor’s arrived it will detect and recognizes his face and on the result of face recognition process it will open the door for authorized visitors or notifies and allows the owner’s to take further action in case of unauthorized visitor. The proposed system is developed and it is successfully ensuring security, managing records and operating gate without physical interaction of owner.

Keywords: SMS, e-mail, GSM modem, authenticate, face recognition, authorized

Procedia PDF Downloads 771

Authors: Yinan Cao, Francine Herrmann

Abstract:

Adversary emulation is a very effective concrete way to evaluate the defense of an information system or network. It is about building an emulator, which depending on the vulnerability of a target system, will allow to detect and execute a set of identified attacks. However, emulating an adversary is very costly in terms of time and resources. Verifying the information of each technique and building up the countermeasures in the middle of the test is also needed to be accomplished manually. In this article, a synthesis of previous MITRE research on the creation of the ATT&CK matrix will be as the knowledge base of the known techniques and a well-designed adversary emulation software CALDERA based on ATT&CK Matrix will be used as our platform. Inspired and guided by the previous study, a plugin in CALDERA called Tinker will be implemented, which is aiming to help the tester to get more information and also the mitigation of each technique used in the previous operation. Furthermore, the optional countermeasures for some techniques are also implemented and preset in Tinker in order to facilitate and fasten the process of the defense improvement of the tested system.

Keywords: automation, adversary emulation, CALDERA, countermeasures, MITRE ATT&CK

Procedia PDF Downloads 185

Authors: Rafid A. Al-Khannak, Fawzi M. Al-Naima

Abstract:

Power load is one of the most important controlling keys which decide power demands and illustrate power usage to shape power market. Hence, power load forecasting is the parameter which facilitates understanding and analyzing all these aspects. In this paper, power load forecasting is solved under MATLAB environment by constructing a neural network for the power load to find an accurate simulated solution with the minimum error. A developed algorithm to achieve load forecasting application with faster technique is the aim for this paper. The algorithm is used to enable MATLAB power application to be implemented by multi machines in the Grid computing system, and to accomplish it within much less time, cost and with high accuracy and quality. Grid Computing, the modern computational distributing technology, has been used to enhance the performance of power applications by utilizing idle and desired Grid contributor(s) by sharing computational power resources.

Keywords: DeskGrid, Grid Server, idle contributor(s), grid computing, load forecasting

Procedia PDF Downloads 460