Search results for: electrical network frequency stability

Authors: George C. Yao, Chao-Yu Tu, Wei-Chung Chen, Fung-Wen Kuo, Yu-Shan Chang

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Building structures are subjected to both horizontal and vertical ground motions during earthquakes, but only the horizontal ground motion has been extensively studied and considered in design. Most of the prevailing seismic codes assume the vertical component to be 1/2 to 2/3 of the horizontal one. In order to understand the building responses from vertical ground motions, many earthquakes records are studied in this paper. System identification methods (ARX Model) are used to analyze the strong motions and to find out the characteristics of the vertical amplification factors and the natural frequencies of buildings. Analysis results show that the vertical amplification factors for high-rise buildings and low-rise building are 1.78 and 2.52 respectively, and the average vertical amplification factor of all buildings is about 2. The relationship between the vertical natural frequency and building height was regressed to a suggested formula in this study. The result points out an important message; the taller the building is, the greater chance of resonance of vertical vibration on the building will be.

Keywords: vertical ground motion, vertical amplification factor, natural frequency, component

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Authors: Qaisar Fraz, H. M. Jafar, Mojeeb Bin Ihsan

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This paper presents simulation and experimen-tal results for wide band U-shaped side slots loaded linearly polarized rectangular microstrip antenna with broad side radiation characteristics suitable for onboard applications. The structure has been evolved in rugged and compact form to make it suitable for on-board applications. In addition to U-shaped central slot, pair of parallel narrow slots has been embedded close to non-radiating edges. The size and shape of these side slots have been optimized to improve the matching at upper frequency of the band. The impedance bandwidth of 34.8% as compared to 2-5% bandwidth of conventional microstrip antenna has been achieved. The frequency ratio of the two well-matched operating sections is found to be f2 / f1=1.33. The experimental results are in good agreement with the numerical results.

Keywords: low profile antennas, u-slot antennas, broad band antennas, broad-side radiation pattern, high gain antennas

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Authors: Jaiprakash Dargad

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CdMnSe dilute semiconductor or semimagnetic semiconductors have become the focus of intense research due to their interesting combination of magnetic and semiconducting properties, and are employed in a variety of devices including solar cells, gas sensors etc. A series of thin films of this material, Cd1−xMnxSe (0 ≤ x ≤ 0.5), were therefore synthesized onto precleaned amorphous glass substrates using a solution growth technique. The sources of cadmium (Cd2+) and manganese (Mn2+) were aqueous solutions of cadmium sulphate and manganese sulphate, and selenium (Se2−) was extracted from a reflux of sodium selenosulphite. The different deposition parameters such as temperature, time of deposition, speed of mechanical churning, pH of the reaction mixture etc were optimized to yield good quality deposits. The as-grown samples were thin, relatively uniform, smooth and tightly adherent to the substrate support. The colour of the deposits changed from deep red-orange to yellowish-orange as the composition parameter, x, was varied from 0 to 0.5. The terminal layer thickness decreased with increasing value of, x. The optical energy gap decreased from 1.84 eV to 1.34 eV for the change of x from 0 to 0.5. The coefficient of optical absorption is of the order of 10-4 - 10-5 cm−1 and the type of transition (m = 0.5) is of the band-to-band direct type. The dc electrical conductivities were measured at room temperature and in the temperature range 300 K - 500 K. It was observed that the room temperature electrical conductivity increased with the composition parameter x up to 0.1, gradually decreasing thereafter. The thermo power measurements showed n-type conduction in these films.

Keywords: dilute semiconductor, reflux, CBD, thin film

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Authors: Jaiden Xuan Schraut, Leon Liu, Yiqiao Yin

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Computer vision in medical diagnosis has achieved a high level of success in diagnosing diseases with high accuracy. However, conventional classifiers that produce an image to-label result provides insufficient information for medical professionals to judge and raise concerns over the trust and reliability of a model with results that cannot be explained. In order to gain local insight into cancerous regions, separate tasks such as imaging segmentation need to be implemented to aid the doctors in treating patients, which doubles the training time and costs which renders the diagnosis system inefficient and difficult to be accepted by the public. To tackle this issue and drive AI-first medical solutions further, this paper proposes a multi-output network that follows a U-Net architecture for image segmentation output and features an additional convolutional neural networks (CNN) module for auxiliary classification output. Class activation maps are a method of providing insight into a convolutional neural network’s feature maps that leads to its classification but in the case of lung diseases, the region of interest is enhanced by U-net-assisted Class Activation Map (CAM) visualization. Therefore, our proposed model combines image segmentation models and classifiers to crop out only the lung region of a chest X-ray’s class activation map to provide a visualization that improves the explainability and is able to generate classification results simultaneously which builds trust for AI-led diagnosis systems. The proposed U-Net model achieves 97.61% accuracy and a dice coefficient of 0.97 on testing data from the COVID-QU-Ex Dataset which includes both diseased and healthy lungs.

Keywords: multi-output network model, U-net, class activation map, image classification, medical imaging analysis

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Authors: Natalia Efremova, Sergey Tarasenko

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We propose a novel neural network architecture for visual saliency detections, which utilizes neuro physiologically plausible mechanisms for extraction of salient regions. The model has been significantly inspired by recent findings from neuro physiology and aimed to simulate the bottom-up processes of human selective attention. Two types of features were analyzed: color and direction of maximum variance. The mechanism we employ for processing those features is PCA, implemented by means of normalized Hebbian learning and the waves of spikes. To evaluate performance of our model we have conducted psychological experiment. Comparison of simulation results with those of experiment indicates good performance of our model.

Keywords: neural network models, visual saliency detection, normalized Hebbian learning, Oja's rule, psychological experiment

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Authors: Mohammed S. Al-Rawi, J. Bastos, J. Rodriguez

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Object detection and object recognition are essential components of every computer vision system. Despite the high computational complexity and other problems related to numerical stability and accuracy, Zernike moments of 2D images (ZMs) have shown resilience when used in object recognition and have been used in various image analysis applications. In this work, we propose a novel method for computing ZMs via Fast Fourier Transform (FFT). Notably, this is the first algorithm that can generate ZMs up to extremely high orders accurately, e.g., it can be used to generate ZMs for orders up to 1000 or even higher. Furthermore, the proposed method is also simpler and faster than the other methods due to the availability of FFT software and/or hardware. The accuracies and numerical stability of ZMs computed via FFT have been confirmed using the orthogonality property. We also introduce normalizing ZMs with Neumann factor when the image is embedded in a larger grid, and color image reconstruction based on RGB normalization of the reconstructed images. Astonishingly, higher-order image reconstruction experiments show that the proposed methods are superior, both quantitatively and subjectively, compared to the q-recursive method.

Keywords: Chebyshev polynomial, fourier transform, fast algorithms, image recognition, pseudo Zernike moments, Zernike moments

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Authors: Ali Shareef, Aliha Shareef, Yifeng Zhu

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Energy minimization is of great importance in wireless sensor networks in extending the battery lifetime. One of the key activities of nodes in a WSN is communication and the routing of their data to a centralized base-station or sink. Routing using the shortest path to the sink is not the best solution since it will cause nodes along this path to fail prematurely. We propose a cross-layer energy efficient routing protocol Optrix that utilizes a convex formulation to maximize the lifetime of the network as a whole. We further propose, Optrix-BW, a novel convex formulation with bandwidth constraint that allows the channel conditions to be accounted for in routing. By considering this key channel parameter we demonstrate that Optrix-BW is capable of congestion control. Optrix is implemented in TinyOS, and we demonstrate that a relatively large topology of 40 nodes can converge to within 91% of the optimal routing solution. We describe the pitfalls and issues related with utilizing a continuous form technique such as convex optimization with discrete packet based communication systems as found in WSNs. We propose a routing controller mechanism that allows for this transformation. We compare Optrix against the Collection Tree Protocol (CTP) and we found that Optrix performs better in terms of convergence to an optimal routing solution, for load balancing and network lifetime maximization than CTP.

Keywords: wireless sensor network, Energy Efficient Routing

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Authors: Md. Aminul Islam, Adel Merabet, Rachid Beguenane, Hussein Ibrahim

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This paper presents a simulation and mathematical model of stand-alone solar-wind-diesel based hybrid energy system (HES). A power management system is designed for multiple energy resources in a stand-alone hybrid energy system. Both Solar photovoltaic and wind energy conversion system consists of maximum power point tracking (MPPT), voltage regulation, and basic power electronic interfaces. An additional diesel generator is included to support and improve the reliability of stand-alone system when renewable energy sources are not available. A power management strategy is introduced to distribute the generated power among resistive load banks. The frequency regulation is developed with conventional phase locked loop (PLL) system. The power management algorithm was applied in Matlab®/Simulink® to simulate the results.

Keywords: solar photovoltaic, wind energy, diesel engine, hybrid energy system, power management, frequency and voltage regulation

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Authors: Wabinyai Fidel Raja, Gideon Lubisa

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Air pollution is becoming a growing concern in Africa due to rapid industrialization and urbanization, leading to implications for public health and the environment. Establishing a comprehensive air quality monitoring network is crucial to combat this issue. However, conventional methods of monitoring are insufficient in African cities due to the high cost of setup and maintenance. To address this, low-cost sensors (LCS) can be deployed in various urban areas through the use of participatory air quality network siting (PAQNS). PAQNS involves stakeholders from the community, local government, and private sector working together to determine the most appropriate locations for air quality monitoring stations. This approach improves the accuracy and representativeness of air quality monitoring data, engages and empowers community members, and reflects the actual exposure of the population. Implementing PAQNS in African cities can build trust, promote accountability, and increase transparency in the air quality management process. However, challenges to implementing this approach must be addressed. Nonetheless, improving air quality is essential for protecting public health and promoting a sustainable environment. Implementing participatory and data-informed air quality monitoring can take a significant step toward achieving these important goals in African cities and beyond.

Keywords: low-cost sensors, participatory air quality network siting, air pollution, air quality management

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Authors: Md. Samsuzzaman, M. T. Islam, J. S. Mandeep, N. Misran

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In this article, a compact simple structure modified Swastika shape patch multiband antenna on a substrate of available low cost polymer resin composite material is designed for Wi-Fi and WiMAX applications. The substrate material consists of an epoxy matrix reinforced by woven glass. The designed micro-strip line fed compact antenna comprises of a planar wide square slot ground with four slits and Swastika shape radiation patch with a rectangular slot. The effect of the different substrate materials on the reflection coefficients of the proposed antennas was also analyzed. It can be clearly seen that the proposed antenna provides a wider bandwidth and acceptable return loss value compared to other reported materials. The simulation results exhibits that the antenna has an impedance bandwidth with -10 dB return loss at 3.01-3.89 GHz and 4.88-6.10 GHz which can cover both the WLAN, WiMAX and public safety WLAN bands. The proposed swastika shape antenna was designed and analyzed by using a finite element method based simulator HFSS and designed on a low cost FR4 (polymer resin composite material) printed circuit board. The electrical performances and superior frequency characteristics make the proposed material antenna desirable for wireless communications.

Keywords: epoxy resin polymer, multiband, swastika shaped, wide slot, WLAN/WiMAX

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Authors: Zhaocheng Shang

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In order to study the topic of cities crossing rivers, a Four-Dimensional Analysis Method consisting of timeline, X-axis, Y-axis, and Z-axis is proposed. Policies, plans, and their implications are summarized and researched along with the timeline. The X-axis is the direction which is parallel to the river. The research area was chosen because of its important connection function. It is proposed that more surface water network should be built because of the ecological orientation of the research area. And the analysis of groundwater makes it for sure that the proposal is feasible. After the blue water network is settled, the green landscape network which is surrounded by it could be planned. The direction which is transversal to the river (Y-axis) should run through the transportation axis so that the urban texture could stretch in an ecological way. Therefore, it is suggested that the work of the planning bureau and river bureau should be coordinated. The Z-axis research is on the section view of the river, especially on the Yellow River’s special feature of being a perched river. Based on water control safety demands, river parks could be constructed on the embankment buffer zone, whereas many kinds of ornamental trees could be used to build the buffer zone. City Crossing River is a typical case where we make use of landscaping to build a symbiotic relationship between the urban landscape architecture and the environment. The local environment should be respected in the process of city expansion. The planning order of "Benefit- Flood Control Safety" should be replaced by "Flood Control Safety - Landscape Architecture- People - Benefit".

Keywords: blue-green landscape network, city crossing river, four-dimensional analysis method, planning order

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Authors: Danladi Ali, Abdullahi Mukaila

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In this work, GSM signal strength (power) was monitored in an indoor environment. Samples of the GSM signal strength was measured on mobile equipment (ME). One-dimensional multilevel wavelet is used to predict the fading phenomenon of the GSM signal measured and neural network clustering to determine the average power received in the study area. The wavelet prediction revealed that the GSM signal is attenuated due to the fast fading phenomenon which fades about 7 times faster than the radio wavelength while the neural network clustering determined that -75dBm appeared more frequently followed by -85dBm. The work revealed that significant part of the signal measured is dominated by weak signal and the signal followed more of Rayleigh than Gaussian distribution. This confirmed the wavelet prediction.

Keywords: decomposition, clustering, propagation, model, wavelet, signal strength and spectral efficiency

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Authors: El Mehdi Haily, Lahcen Bih, Mohammed Azrour, Bouchaib Manoun

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The effects of xBi₂O₃-yBaO-zP₂O₅ (BBP) glass addition on the sintering, structural, and dielectric properties of BaTiO₃ ceramic (BT) are studied. The BT ceramic was synthesized by the conventional solid-state reaction method while the glasses BaO-Bi₂O₃-P₂O₅ (BBP) were elaborated by melting and quenching process. Different composites BT-xBBP were formed by mixing the BBP glasses with BT ceramic. For each glass composition, where the ratio (x:y:z) is maintained constant, we have developed three composites with different glass weight percentage (x = 2.5, 5, and 7.5 wt %). Addition of the glass helps in better sintering at lower temperatures with the presence of liquid phase at the respective sintering temperatures. The results showed that the sintering temperature decreased from more than 1300°C to 900°C. Density measurements of the composites are performed using the standard Archimedean method with water as medium liquid. It is found that their density and molar volume decrease and increase with glass content, respectively. Raman spectroscopy is used to characterize their structural approach. This technique has allowed the identification of different structural units of phosphate and the characteristic vibration modes of the BT. The electrical properties of the composite samples are carried out by impedance spectroscopy in the frequency range of 10 Hz to 1 MHz under various temperatures from 300 to 473 K. The obtained results show that their dielectric properties depend both on the content of the glass in the composite and the Bi/P ratio in the glasses.

Keywords: phosphate, glasses, composite, Raman spectroscopy, dielectric properties

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Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: Albert W. K. Tan

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This research consolidates a comprehensive array of publications from peer-reviewed journals, case studies, and seminar reports focused on reverse logistics and network design. By synthesizing this secondary knowledge, our objective is to identify and articulate key decision factors crucial to reverse logistics network design for e-commerce. Through this exploration, we aim to present a refined mathematical model that offers valuable insights for companies seeking to optimize their reverse logistics operations. The primary goal of this research endeavor is to develop a comprehensive framework tailored to advising organizations and companies on crafting effective networks for their reverse logistics operations, thereby facilitating the achievement of their organizational goals. This involves a thorough examination of various network configurations, weighing their advantages and disadvantages to ensure alignment with specific business objectives. The key objectives of this research include: (i) Identifying pivotal factors pertinent to network design decisions within the realm of reverse logistics across diverse supply chains. (ii) Formulating a structured framework designed to offer informed recommendations for sound network design decisions applicable to relevant industries and scenarios. (iii) Propose a mathematical model to optimize its reverse logistics network. A conceptual framework for designing a reverse logistics network has been developed through a combination of insights from the literature review and information gathered from company websites. This framework encompasses four key stages in the selection of reverse logistics operations modes: (1) Collection, (2) Sorting and testing, (3) Processing, and (4) Storage. Key factors to consider in reverse logistics network design: I) Centralized vs. decentralized processing: Centralized processing, a long-standing practice in reverse logistics, has recently gained greater attention from manufacturing companies. In this system, all products within the reverse logistics pipeline are brought to a central facility for sorting, processing, and subsequent shipment to their next destinations. Centralization offers the advantage of efficiently managing the reverse logistics flow, potentially leading to increased revenues from returned items. Moreover, it aids in determining the most appropriate reverse channel for handling returns. On the contrary, a decentralized system is more suitable when products are returned directly from consumers to retailers. In this scenario, individual sales outlets serve as gatekeepers for processing returns. Considerations encompass the product lifecycle, product value and cost, return volume, and the geographic distribution of returns. II) In-house vs. third-party logistics providers: The decision between insourcing and outsourcing in reverse logistics network design is pivotal. In insourcing, a company handles the entire reverse logistics process, including material reuse. In contrast, outsourcing involves third-party providers taking on various aspects of reverse logistics. Companies may choose outsourcing due to resource constraints or lack of expertise, with the extent of outsourcing varying based on factors such as personnel skills and cost considerations. Based on the conceptual framework, the authors have constructed a mathematical model that optimizes reverse logistics network design decisions. The model will consider key factors identified in the framework, such as transportation costs, facility capacities, and lead times. The authors have employed mixed LP to find the optimal solutions that minimize costs while meeting organizational objectives.

Keywords: reverse logistics, supply chain management, optimization, e-commerce

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Authors: Hamed Zolfaghari, Mojtaba Kord

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After feasibility study the design phase is started and the rest of other phases are highly dependent on this phase. forecasting the duration of design phase could do a miracle and would save a lot of time. This study provides a fast and accurate Machine learning (ML) and optimization framework, which allows a quick duration estimation of project design phase, hence improving operational efficiency and competitiveness of a design construction company. 3 data sets of three years composed of daily time spent for different design projects are used to train and validate the ML models to perform multiple projects. Our study concluded that Artificial Neural Network (ANN) performed an accuracy of 0.94.

Keywords: time estimation, machine learning, Artificial neural network, project design phase

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Authors: Muhammad Umer, Aishwariya Giri, Umaiyma Rakha

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Mimicked from the humpback whale flippers, the application of tubercle technology is seen to be particularly advantageous at high angles of attack. This particular advantage is of paramount importance when it comes to structures producing lift at high angles of attack. This characteristic of the technology makes it ideal for horizontal stabilizers and selecting the same as the subject of study to identify and exploit the advantage highlighted by researchers on airfoils, this project aims in establishing a foundation for the application of the bio-mimicked technology on an existing aircraft. Using a baseline and 2 tubercle configuration integrated models, the project targets to achieve the twin aim of highlighting the possibility and merits over the base model and also choosing the right configuration in providing the best characteristic suitable for high angles of attack. To facilitate this study, the required models are generated using Solidworks followed by trials in a virtual aerodynamic environment using Fluent in Ansys for resolving the project objectives. Following a structured plan, the aim is to initially identify the advantages mathematically and then selecting the optimal configuration, simulate the end configuration at angles mimicking the actual operation envelope for the particular structure. Upon simulating the baseline configuration at various angles of attack, the stall angle was determined to be 22 degrees. Thus, the tubercle configurations will be simulated and compared at 4 different angles of attacks: 0, 10, 20, and 24. Further, after providing the optimum configuration of horizontal stabilizers, this study aims at the integration of aircraft structure so that the results better imply the end deliverables of real life application. This draws the project scope closer at this point into longitudinal static stability considerations and improvements in the manoeuvrability characteristics. The objective of the study is to achieve a complete overview ready for real life application with marked benefits obtainable from bio morphing of the tubercle technology.

Keywords: flow simulation, horizontal stabilizer, stability enhancement, tubercle

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Authors: Muhammad Hassan, Kemal Celebi

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Developing a highly efficient electrochromic energy storage device with sufficient color fluctuation and significant electrochemical performance is highly desirable for practical energy-saving applications. Here, to achieve a highly stable material with a large electrochemical storage capacity, a W₁₈O₄₉ NW/Ti₃C₂Tₓ composite has been fabricated and deposited on a pre-assembled Ag and W₁₈O₄₉ NW conductive network by Langmuir-Blodgett technique. The resulting hybrid electrode composed of 15 layers of W₁₈O₄₉ NW/Ti₃C₂Tₓ exhibits an areal capacitance of 125 mF/cm², with a fast and reversible switching response. An optical modulation of 98.2% can be maintained at a current density of 5 mAcm⁻². Using this electrode, we fabricated a bifunctional symmetric electrochromic supercapacitor device having an energy density of 10.26 μWh/cm² and a power density of 0.605 mW/cm², with high capacity retention and full columbic efficiency over 4000 charge-discharge cycles. Meanwhile, the device displays remarkable electrochromic characteristics, including fast switching time (5 s for coloring and 7 s for bleaching) and a significant coloration efficiency of 116 cm²/C with good optical modulation stability. In addition, the device exhibits remarkable mechanical flexibility and fast switching while being stable over 100 bending cycles, which is promising for real-world applications.

Keywords: MXene, nanowires, supercapacitor, ion diffusion, electrochromic, coloration efficiency

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Authors: Dhaksha Vivekanandan

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DeFi is a network that avoids reliance on financial intermediaries through its peer-to-peer financial network. DeFi operates outside of government control; hence it is important for us to understand its impacts. This study employs a literature review to understand DeFi and its emergence, as well as its implications on transparency, social impact, and regulation. Further, 3 case studies are analysed within the context of these categories. DeFi’s provision of increased transparency poses environmental and storage costs and can lead to user privacy being endangered. DeFi allows for the provision of entrepreneurial incentives and protection against monetary censorship and capital control. Despite DeFi's transparency issues and volatility costs, it has huge potential to reduce poverty; however, regulation surrounding DeFi still requires further tightening by governments.

Keywords: DeFi, transparency, regulation, social impact

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Authors: Sanjeev Kumar, Vikas Kumar

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Inconel 718 (IN718) is a high strength nickel-based superalloy designed for high-temperature applications up to 650 °C. It is widely used in gas turbines of jet engines and related aerospace applications because of its good mechanical properties and structural stability at elevated temperatures. Because of good performance ratio and excellent process capability, this alloy has been used predominantly for aeronautic engine components like compressor disc and compressor blade. The main precipitates that contribute to high-temperature strength of IN718 are γʹ Ni₃(Al, Ti) and mainly γʹʹ (Ni₃ Nb). Various processes have been used for modification of the surface of components, such as Laser Shock Peening (LSP), Conventional Shot Peening (SP) and Ultrasonic Shot Peening (USP) to induce compressive residual stress (CRS) and development of fine-grained structure in the surface region. Surface nanostructure by ultrasonic shot peening is a novel methodology of surface modification to improve the overall performance of structural components. Surface nanostructure was developed on the peak aged IN718 superalloy using USP and its effect was studied on low cycle fatigue (LCF) life. Nanostructure of ~ 49 to 73 nm was developed in the surface region of the alloy by USP. The gage section of LCF samples was USPed for 5 minutes at a constant frequency of 20 kHz using StressVoyager to modify the surface. Strain controlled cyclic tests were performed for non-USPed and USPed samples at ±Δεt/2 from ±0.50% to ±1.0% at strain rate (ė) 1×10⁻³ s⁻¹ under reversal loading (R=‒1) at room temperature. The fatigue life of the USPed specimens was found to be more than that of the non-USPed ones. LCF life of the USPed specimen at Δεt/2=±0.50% was enhanced by more than twice of the non-USPed specimen.

Keywords: IN718 superalloy, nanostructure, USP, LCF life

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Authors: N. Hoosain, S. Nene, B. Pearce, C. Jacobs, M. Du Plessis, M. Benjeddou

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Organic Cation Transporters play a vital role in the absorption, tissue distribution and elimination of various substrates. Numerous studies have suggested that variations in non-synonymous single nucleotide polymorphisms (SNPs) of SLC22A2 could influence an individual’s response to various treatments, including clinically important drugs. This study is the first to determine the baseline frequency distribution for twenty SNPs of SLC22A2in the Zulu population. DNA was collected from 101 unrelated “healthy” Zulu participants. Genotypes of all samples were determined using a multiplex PCR and SNaPshot assay followed by the generation of the haplotype structure. This is the first time that the baseline frequency distribution of SNPs is reported for the Zulu population. Data from this study could be used in in vitro and in vivo pharmacogenetic and pharmacokinetic studies to evaluate the potential role the studied SNPs play in the therapeutic efficacy of clinically important drugs.

Keywords: SLC22A2 gene, SNaPshot assay, PCR, Zulu population

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Authors: Oleg Ye Sysoyev, Artem Yu Dobryshkin, Nyein Sitt Naing

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Cylindrical shells are widely used in the construction of buildings and structures, as well as in the air structure. Thin-walled casings made of aluminum alloys are an effective substitute for reinforced concrete and steel structures in construction. The correspondence of theoretical calculations and the actual behavior of aluminum alloy structures is to ensure their trouble-free operation. In the laboratory of our university, "Building Constructions" conducted an experimental study to determine the effect of the system of attached masses on the natural oscillations of shallow cylindrical shells of aluminum alloys, the results of which were compared with theoretical calculations. The purpose of the experiment is to measure the free oscillations of an open, sloping cylindrical shell for various variations of the attached masses. Oscillations of an open, slender, thin-walled cylindrical shell, rectangular in plan, were measured using induction accelerometers. The theoretical calculation of the shell was carried out on the basis of the equations of motion of the theory of shallow shells, using the Bubnov-Galerkin method. A significant splitting of the flexural frequency spectrum is found, influenced not only by the systems of attached маsses but also by the values of the wave formation parameters, which depend on the relative geometric dimensions of the shell. The correspondence of analytical and experimental data is found, using the example of an open shell of alloy D19, which allows us to speak about the high quality of the study. A qualitative new analytical solution of the problem of determining the value of the oscillation frequency of the shell, carrying a system of attached masses is shown.

Keywords: open hollow shell, nonlinear oscillations, associated mass, frequency

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Authors: Kashyap L. Mokariya, Varsha A. Shah, Makarand M. Lokhande

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As the current status and growth of Indian automobile industry is remarkable, transportation sectors are the main concern in terms of Energy security and climate change. Rising demand of fuel and its dependency on other countries affects the GDP of nation. So in this context if the 10 percent of vehicle got operated in Electrical mode how much saving in terms of Rs and in terms of liters is achieved has been analyzed which is also a part of Nations Electric mobility mission plan. Analysis is also done for converting unit consumption of Electricity of Electric vehicle into equivalent fuel consumption in liters which shows that at present tariff rate Electrical operated vehicles are far more beneficial. It also gives benchmark to the authorities to set the tariff rate for Electrical vehicles. Current situation of Indian grid is shown and how the Gap between Generation and Demand can be reduced is analyzed in terms of increasing generation capacity and Energy Conservation measures. As the certain regions of country is facing serious deficit than how to take energy conservation measures in Industry and especially in rural areas where generally Energy Auditing is not carried out that is analyzed in context of Electric vehicle penetration in near future. Author was a part of Vishvakarma yojna where in 255 villages of Gujarat Energy losses were measured and solutions were given to mitigate them and corresponding report to the authorities of villages was delivered.

Keywords: vehiclepenetration, feasibility, Energyconservation, future grid, Energy security, pf controller

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Authors: Andrew Kurochkin, Kostiantyn Bokhan

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In this study, a new meme dataset of ~650K meme instances was created, a technology of meme generation based on the state of the art deep learning technique - GPT-2 model was researched, a comparative analysis of machine-generated memes and human-created was conducted. We justified that Amazon Mechanical Turk workers can be used for the approximate estimating of users' behavior in a social network, more precisely to measure engagement. It was shown that generated memes cause the same engagement as human memes that produced low engagement in the social network (historically). Thus, generated memes are less engaging than random memes created by humans.

Keywords: content generation, computational social science, memes generation, Reddit, social networks, social media interaction

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Authors: P. Karthick, K. Mahesh

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Video has become an increasingly significant component of our digital everyday contact. With the advancement of greater contents and shows of the resolution, its significant volume poses serious obstacles to the objective of receiving, distributing, compressing, and revealing video content of high quality. In this paper, we propose the primary beginning to complete a deep video compression model that jointly upgrades all video compression components. The video compression method involves splitting the video into frames, comparing the images using convolutional neural networks (CNN) to remove duplicates, repeating the single image instead of the duplicate images by recognizing and detecting minute changes using generative adversarial network (GAN) and recorded with long short-term memory (LSTM). Instead of the complete image, the small changes generated using GAN are substituted, which helps in frame level compression. Pixel wise comparison is performed using K-nearest neighbours (KNN) over the frame, clustered with K-means, and singular value decomposition (SVD) is applied for each and every frame in the video for all three color channels [Red, Green, Blue] to decrease the dimension of the utility matrix [R, G, B] by extracting its latent factors. Video frames are packed with parameters with the aid of a codec and converted to video format, and the results are compared with the original video. Repeated experiments on several videos with different sizes, duration, frames per second (FPS), and quality results demonstrate a significant resampling rate. On average, the result produced had approximately a 10% deviation in quality and more than 50% in size when compared with the original video.

Keywords: video compression, K-means clustering, convolutional neural network, generative adversarial network, singular value decomposition, pixel visualization, stochastic gradient descent, frame per second extraction, RGB channel extraction, self-detection and deciding system

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Authors: Xiaolin Zeng, Lei Cheng, Zhirong Li, Xueping Liu

Abstract:

Aiming at the problem of low accuracy and susceptibility to cultural relic diseases in the generation of high-resolution archaeology drawings by current image generation algorithms, an archaeology drawings generation algorithm based on a conditional generative adversarial network is proposed. An attention mechanism is added into the high-resolution image generation network as the backbone network, which enhances the line feature extraction capability and improves the accuracy of line drawing generation. A dual-branch parallel architecture consisting of two backbone networks is implemented, where the semantic translation branch extracts semantic features from orthophotographs of cultural relics, and the gradient screening branch extracts effective gradient features. Finally, the fusion fine-tuning module combines these two types of features to achieve the generation of high-quality and high-resolution archaeology drawings. Experimental results on the self-constructed archaeology drawings dataset of grotto temple statues show that the proposed algorithm outperforms current mainstream image generation algorithms in terms of pixel accuracy (PA), structural similarity (SSIM), and peak signal-to-noise ratio (PSNR) and can be used to assist in drawing archaeology drawings.

Keywords: archaeology drawings, digital heritage, image generation, deep learning

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Authors: Reenu Yadav, Abhay Asthana, S. K. Yadav

Abstract:

Purpose: The aim of the present work was to develop an oral gel for brushing with an antimicrobial activity which will cure/protect from various periodontal diseases such as periodontitis, gingivitis, and pyorrhea. Methods: Plant materials procured from local suppliers, extracted and standardized. Screening of antimicrobial activity was carried out with the help of disk diffusion method. The gel was formulated by dried extracts of Beautea monosperma and Cordia obliquus. Gels were evaluated on various parameters and standardization of the formulation was performed. The release of drugs was studied in pH 6.8 using a mastication device.Total phenolic and flavonoid contents were estimated by folin-Ciocalteu and aluminium chloride method, and stability studies were performed (40°C and RH 75% ± 5% for 90 days) to assess the effect of temperature and humidity on the concentration of phenolic and flavonoid contents. The results of accelerated stability conditions were compared with that of samples kept at controlled conditions (RT). The control samples were kept at room temperature (25°C, 35% RH for 180 days). Results: Results are encouraging; extracts possess significant antimicrobial activity at very low concentration (15µg/disc, 20µg/disc and 15 µg/ disc) on oral pathogenic bacteria. The formulation has optimal characteristics, as well as has a pleasant appearance, fragrance, texture, and taste, is highly acceptable by the volunteers. The diffusion coefficient values ranged from 0.6655 to 0.9164. Since the R values of korsmayer papas were close to 1, Drug release from formulation follows matrix diffusion kinetics. Hence, diffusion was the mechanism of the drug release. Formulation follows non-Fickian transport mechanism. Most Formulations released 50 % of their contents within 25-30 minutes. Results obtained from the accelerated stability studies are indicative of a slight reduction in flavonoids and phenolic contents with time on long time storage. When measured degradation under ambient conditions, degradation was significantly lower than in accelerated stability study. Conclusion: Plant extracts possess compounds with antimicrobial properties can be used as. Developed formulation will cure/protect from various periodontal diseases. Further development and evaluations oral gel including the isolated compounds on the commercial scale and their clinical and toxicological studies are the future challenges.

Keywords: herbal gel, dental care, ambient conditions, commercial scale

Procedia PDF Downloads 429

Authors: Hafizah Che Hasan, Fateen Nazwa Yusof, Maslina Daud

Abstract:

Internet of Things (IoT) is a powerful industry system, which end-devices are interconnected and automated, allowing the devices to analyze data and execute actions based on the analysis. The IoT technology leverages the technology of Radio-Frequency Identification (RFID) and Wireless Sensor Network (WSN), including mobile and sensor. These technologies contribute to the evolution of IoT. However, due to more devices are connected each other in the Internet, and data from various sources exchanged between things, confidentiality of the data becomes a major concern. This paper focuses on one of the major challenges in IoT; authentication, in order to preserve data integrity and confidentiality are in place. A few solutions are reviewed based on papers from the last few years. One of the proposed solutions is securing the communication between IoT devices and cloud servers with Elliptic Curve Cryptograhpy (ECC) based mutual authentication protocol. This solution focuses on Hyper Text Transfer Protocol (HTTP) cookies as security parameter.  Next proposed solution is using keyed-hash scheme protocol to enable IoT devices to authenticate each other without the presence of a central control server. Another proposed solution uses Physical Unclonable Function (PUF) based mutual authentication protocol. It emphasizes on tamper resistant and resource-efficient technology, which equals a 3-way handshake security protocol.

Keywords: Internet of Things (IoT), authentication, PUF ECC, keyed-hash scheme protocol

Procedia PDF Downloads 246

Authors: Li Yu, Chenlu Jiao, Hongrun Xin, Yan Wang, Kaiyang Wang

Abstract:

In order to study the impact of various factors on the housing price, we propose to build different prediction models based on deep learning to determine the existing data of the real estate in order to more accurately predict the housing price or its changing trend in the future. Considering that the factors which affect the housing price vary widely, the proposed prediction models include two categories. The first one is based on multiple characteristic factors of the real estate. We built Convolution Neural Network (CNN) prediction model and Long Short-Term Memory (LSTM) neural network prediction model based on deep learning, and logical regression model was implemented to make a comparison between these three models. Another prediction model is time series model. Based on deep learning, we proposed an LSTM-1 model purely regard to time series, then implementing and comparing the LSTM model and the Auto-Regressive and Moving Average (ARMA) model. In this paper, comprehensive study of the second-hand housing price in Beijing has been conducted from three aspects: crawling and analyzing, housing price predicting, and the result comparing. Ultimately the best model program was produced, which is of great significance to evaluation and prediction of the housing price in the real estate industry.

Keywords: deep learning, convolutional neural network, LSTM, housing prediction

Procedia PDF Downloads 292

Authors: Mohammad Moridzadeh, Mohammad Djavid, Barry Doyle

Abstract:

An assessment of the long-term stability of a cavern in Chagrin shale excavated by the sequential excavation method was performed during and after construction. During the excavation of the cavern, deformations of rock mass were measured at the surface of excavation and within the rock mass by surface and deep measurement instruments. Rock deformations were measured during construction which appeared to result from the as-built excavation sequence that had potentially disturbed the rock and its behavior. Also some additional time dependent rock deformations were observed during and post excavation. Several opinions have been expressed to explain this time dependent deformation including stress changes induced by excavation, strain softening (or creep) in the beddings with and without clay and creep of the shaley rock under compressive stresses. In order to analyze and replicate rock behavior observed during excavation, including current and post excavation elastic, plastic, and time dependent deformation, Finite Element Analysis (FEA) was performed. The analysis was also intended to estimate long term deformation of the rock mass around the excavation. Rock mass behavior including time dependent deformation was measured by means of rock surface convergence points, MPBXs, extended creep testing on the long anchors, and load history data from load cells attached to several long anchors. Direct creep testing of Chagrin Shale was performed on core samples from the wall of the Pump Room. Results of these measurements were used to calibrate the FEA of the excavation. These analyses incorporate time dependent constitutive modeling for the rock to evaluate the potential long term movement in the roof, walls, and invert of the cavern. The modeling was performed due to the concerns regarding the unanticipated behavior of the rock mass as well as the forecast of long term deformation and stability of rock around the excavation.

Keywords: Cavern, Chagrin shale, creep, finite element.

Procedia PDF Downloads 335