Search results for: hybrid PLD

Authors: Joshua Paul Steimel, Michael Pappas, Ethan Hall

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Particle-particle interactions are critical in determining the state of an active matter system. Unique and ubiquitous non-equilibrium behavior like swarming, vortexing, spiraling, and much more is governed by interactions between active units or particles. In hybrid active-passive matter systems, the attraction between spinning active units in a 2D monolayer of passive particles is controlled by the mechanical behavior of the passive monolayer. We demonstrate here that the range and dynamics of this attraction can be controlled by changing the composition of the passive monolayer by adding dopant passive particles. These dopant passive particles effectively pin the movement of dislocation motion in the passive media and reduce the probability of defect motion required to erode the bridge of passive particles between active spinners, thus reducing the range of attraction. Additionally, by adding an out of plane component to the magnetic moment and creating a top-like motion a short range repulsion emerges between the top-like particle. At inter-top distances less than four particle diameters apart, the tops repel but beyond that, distance attract up to 13 particle diameters apart. The tops were also able to locally and transiently anneal the passive monolayer. Thus we demonstrate that by tuning several parameters of the hybrid active matter system, one can observe very different emergent behavior.

Keywords: active matter, colloids, ferromagnetic, annealing

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Authors: Foad Nazari, Seyed Mahmood Hosseini, Mohammad Hossein Abolbashari, Mohammad Hassan Abolbashari

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The present study is concerned with the optimal design of functionally graded plates using particle swarm optimization (PSO) algorithm. In this study, meshless local Petrov-Galerkin (MLPG) method is employed to obtain the functionally graded (FG) plate’s natural frequencies. Effects of two parameters including thickness to height ratio and volume fraction index on the natural frequencies and total mass of plate are studied by using the MLPG results. Then the first natural frequency of the plate, for different conditions where MLPG data are not available, is predicted by an artificial neural network (ANN) approach which is trained by back-error propagation (BEP) technique. The ANN results show that the predicted data are in good agreement with the actual one. To maximize the first natural frequency and minimize the mass of FG plate simultaneously, the weighted sum optimization approach and PSO algorithm are used. However, the proposed optimization process of this study can provide the designers of FG plates with useful data.

Keywords: optimal design, natural frequency, FG plate, hybrid meshless method, MLPG method, ANN approach, particle swarm optimization

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Authors: Roberto Cabezas H

Abstract:

The current work shows the methodology developed to create narrative lighting spaces for the multimedia performance piece 'cluster: the vanished paradise.' This empirical research is focused on exploring unconventional roles for machines in subjective creative processes, by delving into the semantics of data and machine intelligence algorithms in hybrid technological, creative contexts to expand epistemic domains trough human-machine cooperation. The creative process in scenic and performing arts is guided mostly by intuition; from that idea, we developed an approach to embed collective intuition in computational creative systems, by joining the properties of Generative Adversarial Networks (GAN’s) and Fuzzy Clustering based on a semi-supervised data creation and analysis pipeline. The model makes use of GAN’s to learn from phenomenological data (data generated from experience with lighting scenography) and algorithmic design data (augmented data by procedural design methods), fuzzy logic clustering is then applied to artificially created data from GAN’s to define narrative transitions built on membership index; this process allowed for the creation of simple and complex spaces with expressive capabilities based on position and light intensity as the parameters to guide the narrative. Hybridization comes not only from the human-machine symbiosis but also on the integration of different techniques for the implementation of the aided design system. Machine intelligence tools as proposed in this work are well suited to redefine collaborative creation by learning to express and expand a conglomerate of ideas and a wide range of opinions for the creation of sensory experiences. We found in GAN’s and Fuzzy Logic an ideal tool to develop new computational models based on interaction, learning, emotion and imagination to expand the traditional algorithmic model of computation.

Keywords: fuzzy clustering, generative adversarial networks, human-machine cooperation, hybrid collective data, multimedia performance

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Authors: Manisha A. Hira, Arup Rakshit

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Textile substrates are endowed with flexibility and ease of making–up, but are non-conductors of electricity. Conductive materials like carbon can be incorporated into textile structures to make flexible conductive materials. Such conductive textiles find applications as electrostatic discharge materials, electromagnetic shielding materials and flexible materials to carry current or signals. This work focuses on use of carbon fiber as conductor of electricity. Carbon fibers in staple or tow form can be incorporated in textile yarn structure to conduct electricity. The paper highlights the process for development of these conductive yarns of polyester/carbon using Friction spinning (DREF) as well as ring spinning. The optimized process parameters for processing hybrid structure of polyester with carbon tow on DREF spinning and polyester with carbon staple fiber using ring spinning have been presented. The studies have been linked to highlight the electrical conductivity of the developed yarns. Further, the developed yarns have been incorporated as weft in fabric and their electrical conductivity has been evaluated. The paper demonstrates the structure and properties of fabrics developed from such polyester/carbon blend yarns and their suitability as electrically dissipative fabrics.

Keywords: carbon fiber, conductive textiles, electrostatic dissipative materials, hybrid yarns

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Authors: Anwar U. Haque, Waqar Asrar, Ashraf Ali Omar, Erwin Sulaeman, Jaffer Sayed Mohamed Ali

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Due to the interference effects, the intrinsic aerodynamic parameters obtained from the individual component testing are always fundamentally different than those obtained for complete model testing. Consideration and limitation for such testing need to be taken into account in any design work related to the component buildup method. In this paper, the scaled model of a straight rectangular canard of a hybrid buoyant aircraft is tested at 50 m/s in IIUM-LSWT (Low-Speed Wind Tunnel). Model and its attachment with the balance are kept rigid to have results free from the aeroelastic distortion. Based on the velocity profile of the test section’s floor; the height of the model is kept equal to the corresponding boundary layer displacement. Balance measurements provide valuable but limited information of the overall aerodynamic behavior of the model. Zero lift coefficient is obtained at -2.2o and the corresponding drag coefficient was found to be less than that at zero angles of attack. As a part of the validation of low fidelity tool, the plot of lift coefficient plot was verified by the experimental data and except the value of zero lift coefficient, the overall trend has under-predicted the lift coefficient. Based on this comparative study, a correction factor of 1.36 is proposed for lift curve slope obtained from the panel method.

Keywords: wind tunnel testing, boundary layer displacement, lift curve slope, canard, aerodynamics

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Authors: Mimoun Melliti

Abstract:

In the wake of the global pandemic, educational systems worldwide faced unprecedented challenges and had to swiftly adapt to new conditions. This necessitated a fundamental shift in assessment processes, as traditional in-person exams became impractical. The present paper aims to investigate how educational systems have adapted to the new conditions imposed by the outbreak of the pandemic. This paper serves as a case study documenting the various decisions, conditions, experiments, and outcomes associated with transitioning the assessment processes of a higher education institution to a fully online format. The participants of this study consisted of 4666 students from health, engineering, science, and humanities disciplines, who were enrolled in general English (Eng101/104) and English for specific purposes (Eng102/113) courses at a preparatory year institution in Saudi Arabia. The findings of this study indicate that online assessment can be effectively implemented given the fulfillment of specific requirements. These prerequisites encompass the presence of competent staff, administrative flexibility, and the availability of necessary infrastructure and technological support. The significance of this case study lies in its comprehensive description of the various steps and measures undertaken to adapt to the "new normal" situation. Furthermore, it evaluates the impact of these measures and offers detailed recommendations for potential similar future scenarios.

Keywords: hybrid learning, testing, adaptive teaching, EFL

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Authors: Dimitrios Ntardas, Alexandros Tzanetos, Georgios Dounias

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High Voltage Substations (HVS) are the intermediate step between production of power and successfully transmitting it to clients, making them one of the most important checkpoints in power grids. Nowadays - renewable resources and consequently distributed generation are growing fast, the construction of HVS is of high importance both in terms of quality and time completion so that new energy producers can quickly and safely intergrade in power grids. The resources needed, such as machines and workers, should be carefully allocated so that the construction of a HVS is completed on time, with the lowest possible cost (e.g. not spending additional cost that were not taken into consideration, because of project delays), but in the highest quality. In addition, there are milestones and several checkpoints to be precisely achieved during construction to ensure the cost and timeline control and to ensure that the percentage of governmental funding will be granted. The management of such a demanding project is a NP-hard problem that consists of prerequisite constraints and resource limits for each task of the project. In this work, a hybrid meta-heuristic method is implemented to solve this problem. Meta-heuristics have been proven to be quite useful when dealing with high-dimensional constraint optimization problems. Hybridization of them results in boost of their performance.

Keywords: hybrid meta-heuristic methods, substation construction, resource allocation, time-cost efficiency

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo Ferraioli

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In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring. SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, polarimetric SAR image, convolutional neural network, deep learnig, deep neural network

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Authors: Bhaumik Tyagi, Mandeep Kaur, Kanika Singla

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The advancement of blockchain has facilitated scholars to remodel e-voting systems for future generations. Server-side attacks like SQL injection attacks and DOS attacks are the most common attacks nowadays, where malicious codes are injected into the system through user input fields by illicit users, which leads to data leakage in the worst scenarios. Besides, quantum attacks are also there which manipulate the transactional data. In order to deal with all the above-mentioned attacks, integration of blockchain, convolutional neural network (CNN), and Quantum Key Distribution is done in this very research. The utilization of blockchain technology in e-voting applications is not a novel concept. But privacy and security issues are still there in a public and private blockchains. To solve this, the use of a hybrid blockchain is done in this research. This research proposed cryptographic signatures and blockchain algorithms to validate the origin and integrity of the votes. The convolutional neural network (CNN), a normalized version of the multilayer perceptron, is also applied in the system to analyze visual descriptions upon registration in a direction to enhance the privacy of voters and the e-voting system. Quantum Key Distribution is being implemented in order to secure a blockchain-based e-voting system from quantum attacks using quantum algorithms. Implementation of e-voting blockchain D-app and providing a proposed solution for the privacy of voters in e-voting using Blockchain, CNN, and Quantum Key Distribution is done.

Keywords: hybrid blockchain, secure e-voting system, convolutional neural networks, quantum key distribution, one-time pad

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Authors: Hossein Aghababaei, Sergio Vitale, Giampaolo ferraioli

Abstract:

In the context of Synthetic Aperture Radar (SAR) data, polarization is an important source of information for Earth's surface monitoring . SAR Systems are often considered to transmit only one polarization. This constraint leads to either single or dual polarimetric SAR imaging modalities. Single polarimetric systems operate with a fixed single polarization of both transmitted and received electromagnetic (EM) waves, resulting in a single acquisition channel. Dual polarimetric systems, on the other hand, transmit in one fixed polarization and receive in two orthogonal polarizations, resulting in two acquisition channels. Dual polarimetric systems are obviously more informative than single polarimetric systems and are increasingly being used for a variety of remote sensing applications. In dual polarimetric systems, the choice of polarizations for the transmitter and the receiver is open. The choice of circular transmit polarization and coherent dual linear receive polarizations forms a special dual polarimetric system called hybrid polarimetry, which brings the properties of rotational invariance to geometrical orientations of features in the scene and optimizes the design of the radar in terms of reliability, mass, and power constraints. The complete characterization of target scattering, however, requires fully polarimetric data, which can be acquired with systems that transmit two orthogonal polarizations. This adds further complexity to data acquisition and shortens the coverage area or swath of fully polarimetric images compared to the swath of dual or hybrid polarimetric images. The search for solutions to augment dual polarimetric data to full polarimetric data will therefore take advantage of full characterization and exploitation of the backscattered field over a wider coverage with less system complexity. Several methods for reconstructing fully polarimetric images using hybrid polarimetric data can be found in the literature. Although the improvements achieved by the newly investigated and experimented reconstruction techniques are undeniable, the existing methods are, however, mostly based upon model assumptions (especially the assumption of reflectance symmetry), which may limit their reliability and applicability to vegetation and forest scenarios. To overcome the problems of these techniques, this paper proposes a new framework for reconstructing fully polarimetric information from hybrid polarimetric data. The framework uses Deep Learning solutions to augment hybrid polarimetric data without relying on model assumptions. A convolutional neural network (CNN) with a specific architecture and loss function is defined for this augmentation problem by focusing on different scattering properties of the polarimetric data. In particular, the method controls the CNN training process with respect to several characteristic features of polarimetric images defined by the combination of different terms in the cost or loss function. The proposed method is experimentally validated with real data sets and compared with a well-known and standard approach from the literature. From the experiments, the reconstruction performance of the proposed framework is superior to conventional reconstruction methods. The pseudo fully polarimetric data reconstructed by the proposed method also agree well with the actual fully polarimetric images acquired by radar systems, confirming the reliability and efficiency of the proposed method.

Keywords: SAR image, deep learning, convolutional neural network, deep neural network, SAR polarimetry

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Authors: Sumbul Farhat, Pratyay Vaibhav, Sarah Jain, Kapinder Kumar, Archna Kumar

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The Diamondback Moth, Plutella xylostella (Linnaeus), is a major pest of cole crops that causes approximately 50% loss in global production. The utilization of inorganic pesticides is reflected in the development of resistance to this pest. Thus, there is a great need for an eco-friendly, sustainable strategy for the control of this pest. Although this pest, several natural enemies are reported worldwide, none of them can control it efficiently. Therefore, a proposed study is planned to understand the Volatile Organic Compounds (VOCs) mediated signaling interaction mechanism of the plant, pest, and natural enemy. For VOCs collection during different deployment stages of Cabbage POI, Green Ball, Pusa Cabbage, Cabbage Local, Snowball 16, Kanchan Plus, Pusa Meghna, Farm Sona Hybrid F1, and Samridhi F1 Hybrid, the Solid-phase microextraction (SPME) method was employed. Characterization of VOCs was conducted by Gas Chromatography-Mass Spectrometry (GC-MS). The impact of collected VOCs was assessed through Y-Tube Bioassays. The results indicate that the Cabbage variety Green Ball shows maximum repellency for P. xylostella (-100%). The cues present in this variety may be exploited for efficient management of P. xylostella in the cole crop ecosystem.

Keywords: Plutella xylostella, cole crops, volatile organic compounds, GC-MS, Green Ball

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Authors: A. Siva, K. Bala Subramanian, Kinson Prabu

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Concrete is widely used construction materials all over the world. Now a day’s fibers are used in this construction due to its advantages like increase in stiffness, energy absorption, ductility and load carrying capacity. The fiber used in the concrete to increases the structural integrity of the member. It is one of the emerging techniques used in the construction industry. In this paper, the effective utilization of high-performance fiber reinforced concrete (HPFRC) beams has been experimental investigated. The experimental investigation has been conducted on different steel fibers (Hooked, Crimpled, and Hybrid) under cyclic loading. The behaviour of HPFRC beams is compared with the conventional beams. Totally four numbers of specimens were cast with different content of fiber concrete and compared conventional concrete. The fibers are added to the concrete by base volume replacement of concrete. The silica fume and superplasticizers were used to modify the properties of concrete. Single point loading was carried out for all the specimens, and the beam specimens were subjected to cyclic loading. The load-deflection behaviour of fibers is compared with the conventional concrete. The ultimate load carrying capacity, energy absorption and ductility of hybrid fiber reinforced concrete is higher than the conventional concrete by 5% to 10%.

Keywords: cyclic loading, ductility, high performance fiber reinforced concrete, structural integrity

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Authors: Juan Pablo Bertuzzi, Mauro Chiarella

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Employing a communication studies perspective and a socio-technological approach, this paper introduces a theoretical framework for understanding the concept of hybrid world; the avatarization phenomena; and the communicational archetype of co-hybridization. This analysis intends to make a contribution to future design of virtual reality experimental applications. Ultimately, this paper presents an ongoing research project that proposes the study of human-avatar interactions in digital educational environments, as well as an innovative reflection on inner digital communication. The aforementioned project presents the analysis of human-avatar interactions, through the development of an interactive experience in virtual reality. The goal is to generate an innovative communicational dimension that could reinforce the hypotheses presented throughout this paper. Being thought for its initial application in educational environments, the analysis and results of this research are dependent and have been prepared in regard of a meticulous planning of: the conception of a 3D digital platform; the interactive game objects; the AI or computer avatars; the human representation as hybrid avatars; and lastly, the potential of immersion, ergonomics and control diversity that can provide the virtual reality system and the game engine that were chosen. The project is divided in two main axes: The first part is the structural one, as it is mandatory for the construction of an original prototype. The 3D model is inspired by the physical space that belongs to an academic institution. The incorporation of smart objects, avatars, game mechanics, game objects, and a dialogue system will be part of the prototype. These elements have all the objective of gamifying the educational environment. To generate a continuous participation and a large amount of interactions, the digital world will be navigable both, in a conventional device and in a virtual reality system. This decision is made, practically, to facilitate the communication between students and teachers; and strategically, because it will help to a faster population of the digital environment. The second part is concentrated to content production and further data analysis. The challenge is to offer a scenario’s diversity that compels users to interact and to question their digital embodiment. The multipath narrative content that is being applied is focused on the subjects covered in this paper. Furthermore, the experience with virtual reality devices proposes users to experiment in a mixture of a seemingly infinite digital world and a small physical area of movement. This combination will lead the narrative content and it will be crucial in order to restrict user’s interactions. The main point is to stimulate and to grow in the user the need of his hybrid avatar’s help. By building an inner communication between user’s physicality and user’s digital extension, the interactions will serve as a self-guide through the gameworld. This is the first attempt to make explicit the avatarization phenomena and to further analyze the communicational archetype of co-hybridization. The challenge of the upcoming years will be to take advantage from these forms of generalized avatarization, in order to create awareness and establish innovative forms of hybridization.

Keywords: avatar, hybrid worlds, socio-technology, virtual reality

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Authors: Ruobing Liang, Jili Zhang, Chao Zhou

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A solar-heating-and-cooling (SHC) system, consisting of a hybrid photovoltaic/ thermal collector array, a hot water storage tank, and an absorption chiller unit is designed and modeled to satisfy thermal loads (space heating, domestic hot water, and space cooling). The system is applied for Dalian, China, a location with cold climate conditions, where cooling demand is moderate, while space heating demand is slightly high. The study investigates the potential of a solar system installed and operated onsite in a detached single-family household to satisfy all necessary thermal loads. The hot water storage tank is also connected to an auxiliary heater (electric boiler) to supplement solar heating, when needed. The main purpose of the study is to model the overall system and contact a parametric study that will determine the optimum economic system performance in terms of design parameters. The system is compared, through a cost analysis, to an electric heat pump (EHP) system. This paper will give the optimum system combination of solar collector area and volumetric capacity of the hot water storage tank, respectively.

Keywords: absorption chiller, solar PVT collector, solar heating and cooling, solar air-conditioning, parametric study, cost analysis

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Authors: J. F. Gonzalez-Rojo, Federico Lluesma-Rodriguez, Temoatzin Gonzalez

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Hyperloop, a new mode of transport, is gaining significance. It consists of the use of a ground-based transport system which includes a levitation system, that avoids rolling friction forces, and which has been covered with a tube, controlling the inner atmosphere lowering the aerodynamic drag forces. Thus, hyperloop is proposed as a solution to the current limitation on ground transportation. Rolling and aerodynamic problems, that limit large speeds for traditional high-speed rail or even maglev systems, are overcome using a hyperloop solution. Zeleros is one of the companies developing technology for hyperloop application worldwide. It is working on a concept that reduces the infrastructure cost and minimizes the power consumption as well as the losses associated with magnetic drag forces. For this purpose, Zeleros proposes a Hybrid ElectroMagnetic Suspension (EMS) for its prototype. In the present manuscript an active and optimal electromagnetic suspension levitation method based on nearly zero power consumption individual modules is presented. This system consists of several hybrid permanent magnet-coil levitation units that can be arranged along the vehicle. The proposed unit manages to redirect the magnetic field along a defined direction forming a magnetic circuit and minimizing the loses due to field dispersion. This is achieved using an electrical steel core. Each module can stabilize the gap distance using the coil current and either linear or non-linear control methods. The ratio between weight and levitation force for each unit is 1/10. In addition, the quotient between the lifted weight and power consumption at the target gap distance is 1/3 [kg/W]. One degree of freedom (DoF) (along the gap direction) is controlled by a single unit. However, when several units are present, a 5 DoF control (2 translational and 3 rotational) can be achieved, leading to the full attitude control of the vehicle. The proposed system has been successfully tested reaching TRL-4 in a laboratory test bench and is currently in TRL-5 state development if the module association in order to control 5 DoF is considered.

Keywords: active optimal control, electromagnetic levitation, HEMS, high-speed transport, hyperloop

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Authors: Oshin Anand, Atanu Rakshit

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The vast increment in the e-commerce ventures makes this area a prominent research stream. Besides several quantified parameters, the textual content of reviews is a storehouse of many information that can educate companies and help them earn profit. This study is an attempt in this direction. The article attempts to categorize data based on a computed metric that quantifies the influencing capacity of reviews rendering two categories of high and low influential reviews. Further, each of these document is studied to conclude several product feature categories. Each of these categories along with the computed metric is converted to linguistic identifiers and are used in an association mining model. The article makes a novel attempt to combine feature attraction with quantified metric to categorize review text and finally provide frequent patterns that depict customer preferences. Frequent mentions in a highly influential score depict customer likes or preferred features in the product whereas prominent pattern in low influencing reviews highlights what is not important for customers. This is achieved using a hybrid approach of text mining for feature and term extraction, sentiment analysis, multicriteria decision-making technique and association mining model.

Keywords: association mining, customer preference, frequent pattern, online reviews, text mining

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Authors: Claire Xu, Welton Wang, Manasvi Pinnaka, Anqi Pan, Michael Han

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Hospitalizations account for one-third of the total health care spending in the US. Early risk detection and intervention can reduce this high cost and increase the satisfaction of both patients and physicians. Due to the lack of awareness of the potential arising risks in home environment, the opportunities for patients to seek early actions of clinical visits are dramatically reduced. This research aims to offer a highly personalized remote patients monitoring and risk assessment AI framework to identify the potentially preventable hospitalization for both acute as well as chronic diseases. A hybrid-AI framework is trained with data from clinical setting, patients surveys, as well as online databases. 20+ risk factors are analyzed ranging from activities, biometric info, demographic info, socio-economic info, hospitalization history, medication info, lifestyle info, etc. The AI model yields high performance of 87% accuracy and 88 sensitivity with 20+ features. This hybrid-AI framework is proven to be effective in identifying the potentially preventable hospitalization. Further, the high indicative features are identified by the models which guide us to a healthy lifestyle and early intervention suggestions.

Keywords: hospitalization prevention, machine learning, remote patient monitoring, risk prediction

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Authors: Mohammad Tawhid Kawser, Ishrak Kabir, Sadia Sultana, Tanjim Ahmad

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A promising service category of 5G, popularly known as Ultra-Reliable Low-Latency Communications (URLLC), is devoted to providing users with the staunchest fail-safe connections in the splits of a second. The reliability of data transfer, as offered by Hybrid ARQ (HARQ), should be employed as URLLC applications are highly error-sensitive. However, the delay added by HARQ ACK/NACK and retransmissions can degrade performance as URLLC applications are highly delay-sensitive too. To improve latency while maintaining reliability, this paper proposes the use of blind transmissions of redundancy versions exploiting the frequency diversity of wide bandwidth of 5G. The blind HARQ retransmissions proposed so far consider narrow bandwidth cases, for example, dedicated short range communication (DSRC), shared channels for device-to-device (D2D) communication, etc., and thus, do not gain much from the frequency diversity. The proposal also combines blind and ACK/NACK based retransmissions for different multiplexing options between time and frequency depending on the current radio channel quality and stringency of latency requirements. The wide bandwidth of 5G justifies that the proposed blind retransmission, without waiting for ACK/NACK, is not palpably extravagant. A simulation is performed to demonstrate the improvement in latency of the proposed scheme.

Keywords: 5G, URLLC, HARQ, latency, frequency diversity

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Authors: Moulana Mohammed

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Topic models are widely used in building clusters of documents for more than a decade, yet problems occurring in choosing optimal number of topics. The main problem is the lack of a stable metric of the quality of topics obtained during the construction of topic models. The authors analyzed from previous works, most of the models used in determining the number of topics are non-parametric and quality of topics determined by using perplexity and coherence measures and concluded that they are not applicable in solving this problem. In this paper, we used the parametric method, which is an extension of the traditional topic model with visual access tendency for visualization of the number of topics (clusters) to complement clustering and to choose optimal number of topics based on results of cluster validity indices. Developed hybrid topic models are demonstrated with different Twitter datasets on various topics in obtaining the optimal number of topics and in measuring the quality of clusters. The experimental results showed that the Visual Non-negative Matrix Factorization (VNMF) topic model performs well in determining the optimal number of topics with interactive visualization and in performance measure of the quality of clusters with validity indices.

Keywords: interactive visualization, visual mon-negative matrix factorization model, optimal number of topics, cluster validity indices, Twitter data clustering

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Authors: Imen Boudali, Marwa Ragmoun

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The Vehicle Routing Problem with Hard Time Windows (VRPHTW) is a basic distribution management problem that models many real-world problems. The objective of the problem is to deliver a set of customers with known demands on minimum-cost vehicle routes while satisfying vehicle capacity and hard time windows for customers. In this paper, we propose to deal with our optimization problem by using a new hybrid stochastic algorithm based on two metaheuristics: Chemical Reaction Optimization (CRO) and Greedy Randomized Adaptive Search Procedure (GRASP). The first method is inspired by the natural process of chemical reactions enabling the transformation of unstable substances with excessive energy to stable ones. During this process, the molecules interact with each other through a series of elementary reactions to reach minimum energy for their existence. This property is embedded in CRO to solve the VRPHTW. In order to enhance the population diversity throughout the search process, we integrated the GRASP in our method. Simulation results on the base of Solomon’s benchmark instances show the very satisfactory performances of the proposed approach.

Keywords: Benchmark Problems, Combinatorial Optimization, Vehicle Routing Problem with Hard Time Windows, Meta-heuristics, Hybridization, GRASP, CRO

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Authors: A.Assariotakis, P. Vahamidis, P. Tarantilis, G. Economou

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Greece, due to its geographical location and the particular climatic conditions, presents high biodiversity of Medicinal and Aromatic Plants. Among them, the genus Origanum not only presents a wide distribution, but it also has great economic importance. After extensive surveys in Ikaria Island (Greece), 3 species of the genus Origanum were identified, namely, Origanum vulgare ssp. hirtum (Greek oregano), Origanum onites (Turkish oregano) and Origanum x intercedens (hybrid), a naturally occurring hybrid between O. hirtum and O. onites. The purpose of this study was to determine their morphological as well as their temporal variability in essential oil yield and composition under field conditions. For this reason, a plantation of each species was created using vegetative propagation and was established at the experimental field of the Agricultural University of Athens (A.U.A.). From the establishment year and for the following two years (3 years of observations), several observations were taken during each growing season with the purpose of identifying the morphological differences among the studied species. Each year collected plant (at bloom stage) material was air-dried at room temperature in the shade. The essential oil content was determined by hydrodistillation using a Clevenger-type apparatus. The chemical composition of essential oils was investigated by Gas Chromatography-Mass Spectrometry (GC – MS). Significant differences were observed among the three oregano species in terms of plant height, leaf size, inflorescence features, as well as concerning their biological cycle. O. intercedens inflorescence presented more similarities with O. hirtum than with O. onites. It was found that calyx morphology could serve as a clear distinction feature between O. intercedens and O. hirtum. The calyx in O. hirtum presents five isometric teeth whereas in O. intercedens two high and three shorter. Essential oil content was significantly affected by genotype and year. O. hirtum presented higher essential oil content than the other two species during the first year of cultivation, however during the second year the hybrid (O. intercedens) recorded the highest values. Carvacrol, p-cymene and γ-terpinene were the main essential oil constituents of the three studied species. In O. hirtum carvacrol content varied from 84,28 - 93,35%, in O. onites from 86,97 - 91,89%, whereas in O. intercedens it was recorded the highest carvacrol content, namely from 89,25 - 97,23%.

Keywords: variability, oregano biotypes, essential oil, carvacrol

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Authors: I. Belyamani, M. K. Hassan, J. U. Otaigbe, W. R. Fielding, K. A. Mauritz, J. S. Wiggins, W. L. Jarrett

Abstract:

We have investigated the flame-retardant efficiency of special new phosphate glass (P-glass) compositions having different glass transition temperatures (Tg) on the processing conditions of polyamide 6,6 (PA6,6) and the final hybrid flame retardancy (FR). We have showed that the low Tg P glass composition (i.e., ILT 1) is a promising flame retardant for PA6,6 at a concentration of up to 15 wt. % compared to intermediate (IIT 3) and high (IHT 1) Tg P glasses. Cone calorimetry data showed that the ILT 1 decreased both the peak heat release rate and the total heat amount released from the PA6,6/ILT 1 hybrids, resulting in an efficient formation of a glassy char layer. These intriguing findings prompted to address several questions concerning the mechanism of action of the different P glasses studied. The general mechanism of action of phosphorous based FR additives occurs during the combustion stage by enhancing the morphology of the char and the thermal shielding effect. However, the present work shows that P glass based FR additives act during melt processing of PA6,6/P glass hybrids. Dynamic mechanical analysis (DMA) revealed that the Tg of PA6,6/ILT 1 was significantly shifted to a lower Tg (~65 oC) and another transition appeared at high temperature (~ 166 oC), thus indicating a strong interaction between PA6,6 and ILT 1. This was supported by a drop in the melting point and crystallinity of the PA6,6/ILT 1 hybrid material as detected by differential scanning calorimetry (DSC). The dielectric spectroscopic investigation of the networks’ molecular level structural variations (i.e. hybrids chain motion, Tg and sub-Tg relaxations) agreed very well with the DMA and DSC findings; it was found that the three different P glass compositions did not show any effect on the PA6,6 sub-Tg relaxations (related to the NH2 and OH chain end groups motions). Nevertheless, contrary to IIT 3 and IHT 1 based hybrids, the PA6,6/ILT 1 hybrid material showed an evidence of splitting the PA6,6 Tg relaxations into two peaks. Finally, the CPMAS 31P-NMR data confirmed the miscibility between ILT 1 and PA6,6 at the molecular level, as a much larger enhancement in cross-polarization for the PA6,6/15%ILT 1 hybrids was observed. It can be concluded that compounding low Tg P-glass (i.e. ILT 1) with PA6,6 facilitates hydrolytic chain scission of the PA6,6 macromolecules through a potential chemical interaction between phosphate and the alpha-Carbon of the amide bonds of the PA6,6, leading to better flame retardant properties.

Keywords: broadband dielectric spectroscopy, composites, flame retardant, polyamide, phosphate glass, sustainable

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Authors: S. Sehar, S. Khan, N. Ali, S. Ahmed

Abstract:

In the last decade, the hunt for cost-effective, eco-friendly and energy sustainable technologies for waste water treatment are gaining much attention due to emerging water crisis and rapidly depleting existing water reservoirs all over the world. In this scenario, constructed wetland being a “green technology” could be a reliable mean for waste water treatment especially in small communities due to cost-effectiveness, ease in management, less energy consumption and sludge production. Therefore, a low cost, lab-scale sub-surface flow hybrid constructed wetland (SS-HCW) was established for domestic waste water treatment.It was observed that not only the presence but also choice of suitable vegetation along with hydraulic retention time (HRT) are key intervening ingredients which directly influence pollutant removals in constructed wetlands. Another important aspect of vegetation is that it may facilitate microbial attachment in rhizosphere, thus promote biofilm formation via microbial interactions. The major factors that influence initial aggregation and subsequent biofilm formation i.e. divalent cations (Ca2+) and extra cellular DNA (eDNA) were also studied in detail. The presence of Ca2+ in constructed wetland demonstrate superior performances in terms of effluent quality, i.e BOD5, COD, TDS, TSS, and PO4- than in absence of Ca2+. Finally, light and scanning electron microscopies coupled with EDS were carried out to get more insights into the mechanics of biofilm formation with or without Ca addition. Therefore, the same strategy can be implemented in other waste water treatment technologies.

Keywords: hybrid constructed wetland, biofilm formation, waste water treatment, waste water

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Authors: Xinhuang Wu, Yousef Sardahi

Abstract:

A hybrid and optimal multi-loop control structure combining linear and nonlinear control algorithms are introduced in this paper to regulate the position of a quadcopter unmanned aerial vehicle (UAV) driven by four brushless DC motors. To this end, a nonlinear mathematical model of the UAV is derived and then linearized around one of its operating points. Using the nonlinear version of the model, a sliding mode control is used to derive the control laws of the motor thrust forces required to drive the UAV to a certain position. The linear model is used to design two controllers, XG-controller and YG-controller, responsible for calculating the required roll and pitch to maneuver the vehicle to the desired X and Y position. Three attitude controllers are designed to calculate the desired angular rates of rotors, assuming that the Euler angles are minimal. After that, a many-objective optimization problem involving 20 design parameters and ten objective functions is formulated and solved by HypE (Hypervolume estimation algorithm), one of the widely used many-objective optimization algorithms approaches. Both stability and performance constraints are imposed on the optimization problem. The optimization results in terms of Pareto sets and fronts are obtained and show that some of the design objectives are competing. That is, when one objective goes down, the other goes up. Also, Numerical simulations conducted on the nonlinear UAV model show that the proposed optimization method is quite effective.

Keywords: optimal control, many-objective optimization, sliding mode control, linear control, cascade controllers, UAV, drones

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Authors: Shiri Elisheva Decktor, Sharon Hornstein

Abstract:

Highway merges and unmarked junctions are key components in any urban road network, which can act as bottlenecks and create traffic disruption. Inefficient highway merges may trigger traffic instabilities such as stop-and-go waves, pose safety conditions and lead to longer journey times. These phenomena occur spontaneously if the average vehicle density exceeds a certain critical value. This study focuses on modeling the traffic using a microscopic traffic flow model. A hybrid traffic model, which combines human-driven and controlled vehicles is assumed. The controlled vehicles obey different driving policies when approaching the merge, or in the vicinity of other vehicles. We developed a co-simulation model in SUMO (Simulation of Urban Mobility), in which the human-driven cars are modeled using the IDM model, and the controlled cars are modeled using a dedicated controller. The scenario chosen for this study is a closed track with one merge and one exit, which could be later implemented using a scaled infrastructure on our lab setup. This will enable us to benchmark the results of this study obtained in simulation, to comparable results in similar conditions in the lab. The metrics chosen for the comparison of the performance of our algorithm on the overall traffic conditions include the average speed, wait time near the merge, and throughput after the merge, measured under different travel demand conditions (low, medium, and heavy traffic).

Keywords: highway merges, traffic modeling, SUMO, driving policy

Procedia PDF Downloads 106

Authors: Raoudane Bouziyan, Kawser Mohammad Tawhid

Abstract:

Microstrip antennas are low in profile, light in weight, conformable in structure and are now developed for many applications. The main difficulty of the microstrip antenna is its narrow bandwidth. Several modern applications like satellite communications, remote sensing, and multi-function radar systems will find it useful if there is dual-band antenna operating from a single aperture. Some applications require covering both transmitting and receiving frequency bands which are spaced apart. Providing multiple antennas to handle multiple frequencies and polarizations becomes especially difficult if the available space is limited as with airborne platforms and submarine periscopes. Dual band operation can be realized from a single feed using slot loaded or stacked microstrip antenna or two separately fed antennas sharing a common aperture. The former design, when used in arrays, has certain limitations like complicated beam forming or diplexing network and difficulty to realize good radiation patterns at both the bands. The second technique provides more flexibility with separate feed system as beams in each frequency band can be controlled independently. Another desirable feature of a dual band antenna is easy adjustability of upper and lower frequency bands. This thesis presents investigation of a new dual-band antenna, which is a hybrid of microstrip and waveguide radiating elements. The low band radiator is a Shorted Annular Ring (SAR) microstrip antenna and the high band radiator is an aperture antenna. The hybrid antenna is realized by forming a waveguide radiator in the shorted region of the SAR microstrip antenna. It is shown that the upper to lower frequency ratio can be controlled by the proper choice of various dimensions and dielectric material. Operation in both linear and circular polarization is possible in either band. Moreover, both broadside and conical beams can be generated in either band from this antenna element. Finite Element Method based software, HFSS and Method of Moments based software, FEKO were employed to perform parametric studies of the proposed dual-band antenna. The antenna was not tested physically. Therefore, in most cases, both HFSS and FEKO were employed to corroborate the simulation results.

Keywords: FEKO, HFSS, dual band, shorted annular ring patch

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Authors: Gabriel S. Adesina, Ruixue Cheng, Geetika Aggarwal, Michael Short

Abstract:

With the global shift towards sustainability and technological advancements, electric Hybrid vehicles (EHVs) are increasingly being seen as viable alternatives to traditional internal combustion (IC) engine vehicles, which also require efficient cooling systems. The electric Automotive Water Pump (AWP) has been introduced as an alternative to IC engine belt-driven pump systems. However, current control methods for AWPs typically employ fixed gain settings, which are not ideal for the varying conditions of dynamic vehicle environments, potentially leading to overheating issues. To overcome the limitations of fixed gain control, this paper proposes implementing an artificial neural network (ANN) for managing the AWP in EHVs. The proposed ANN provides an intelligent, adaptive control strategy that enhances the AWP's performance, supported through MATLAB simulation work illustrated in this paper. Comparative analysis demonstrates that the ANN-based controller surpasses conventional PID and fuzzy logic-based controllers (FLC), exhibiting no overshoot, 0.1secs rapid response, and 0.0696 IAE performance. Consequently, the findings suggest that ANNs can be effectively utilized in EHVs.

Keywords: automotive water pump, cooling system, electric hybrid vehicles, artificial neural networks, PID control, fuzzy logic control, IAE, MATLAB

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Authors: M. Akhlaque Ahmed, Sidra A. Shaikh, Maliha A. Siddiqui

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Global and diffuse solar radiation on horizontal surface of Lower Sindh, namely Karachi, Hyderabad, Nawabshah were carried out using sunshine hour data of the area to assess the feasibility of solar energy utilization for power generation in Sindh province. The results obtained show a large variation in the direct and diffuse component of solar radiation in summer and winter months in Lower Sindh (50% direct and 50% diffuse for Karachi and Hyderabad). In Nawabshah area, the contribution of diffuse solar radiation is low during the monsoon months, July and August. The K_T value of Nawabshah indicates a clear sky throughout almost the entire year. The percentage of diffuse radiation does not exceed more than 20%. In Nawabshah, the appearance of cloud is rare even during the monsoon months. The estimated values indicate that Nawabshah has high solar potential, whereas Karachi and Hyderabad have low solar potential. During the monsoon months the Lower part of Sindh can utilize the hybrid system with wind power. Near Karachi and Hyderabad, the wind speed ranges between 6.2 m/sec to 6.9 m/sec. A wind corridor exists near Karachi, Hyderabad, Gharo, Keti Bander and Shah Bander. The short fall of solar can be compensated by wind because in the monsoon months of July and August, wind speeds are higher in the Lower region of Sindh.

Keywords: hybrid power system, lower Sindh, power generation, solar and wind energy potential

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Authors: Klaudia Gołębiowska

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This article analyzes the actions of Poland's ruling party facing two refugee crises. These crises emerged almost one after the other within a few months. The first concerned irregular migrants from various countries, including the Middle East, seeking to cross the Polish border from the territory of Belarus. The second was caused by Russia's full-scale invasion of Ukraine. I aim to show the evolution of the discourse and law towards immigrants and refugees by the party Prawo i Sprawiedliwość (PiS, ang. Law and Justice), which has been in power in Poland since 2015. The authorities, in power since 2015, have radically changed its anti-immigrant discourse towards the exodus of civilians from Ukraine. Research questions are the following: What were the roots of the refugee crises in Poland in 2021 and 2022? What legal or illegal measures were taken in Poland to deal with the refugee crises? The methods of qualitative source analysis and process tracing. From the first days of the war in Ukraine, not only was aid organised for Ukrainians, but they were also given access to public services and education. All refugees were granted temporary international protection. At the same time, the basic physiological needs of those on the Polish-Belarusian border were ignored. Moreover, illegal pushbacks were used against those coming mainly from the Middle East, pushing them into the territory of Belarus, where they were often subjected to torture and inhumane treatment. The Polish government justified such treatment on the grounds that these people were part of a 'hybrid war' waged by Russia and Belarus using migrants. Only Ukrainians were treated as 'real' refugees in the analyzed crises at the Polish borders.

Keywords: refugee, irregular migrants, hybrid war, migrants

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Authors: Abdul Mannan Akhtar

Abstract:

In this paper a novel concept of a self learning smart personalized computer assistant (ACPCA) is established which is a context aware system. Based on user habits, moods, and other routines/situational reactions the system will manage various services and suggestions at appropriate times including what schedule to follow, what to watch, what software to be used, what should be deleted etc. This system will utilize a hybrid fuzzyNeural model to predict what the user will do next and support his actions. This will be done by establishing fuzzy sets of user activities, choices, preferences etc. and utilizing their combinations to predict his moods and immediate preferences. Various application of context aware systems exist separately e.g. on certain websites for music or multimedia suggestions but a personalized autonomous system that could adapt to user’s personality does not exist at present. Due to the novelty and massiveness of this concept, this paper will primarily focus on the problem establishment, product features and its functionality; however a small mini case is also implemented on MATLAB to demonstrate some of the aspects of ACPCA. The mini case involves prediction of user moods, activity, routine and food preference using a hybrid fuzzy-Neural soft computing technique.

Keywords: context aware systems, APCPCA, soft computing techniques, artificial intelligence, fuzzy logic, neural network, mood detection, face detection, activity detection

Procedia PDF Downloads 464