Search results for: Dynamic Mode Decomposition (DMD)

Authors: Prescilla Lambert, Christophe Roos, Mounim Lebrini

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For several years, the Caribbean islands and West Africa have had to deal with the massive arrival of the brown seaweed Sargassum. Overall, this macroalgae, which constitutes a habitat for a great diversity of marine organisms, is also an additional stress factor for the marine environment (e.g., coral reefs). In addition, the accumulation followed by the significant decomposition of the Sargassum spp. biomass on the coast leads to the release of toxic gases (H₂S and NH₃), which calls into question the functioning of the economic, health and tourist life of the island and the other interested territories. Originally, these algae are formed by the eutrophication of the oceans accentuated by global warming. Unfortunately, scientists predict a significant recurrence of these Sargassum strandings for years to come. It is therefore more than necessary to find solutions by putting in place a sustainable management plan for this phenomenon. Martinique, a small island in the Caribbean arc, is one of the many areas impacted by Sargassum seaweed strandings. Since 2011, there has been a constant increase in the degradation of the materials present in this region, largely due to toxic/corrosive gases released by the algae decomposition. In order to protect the structures and the vulnerable building materials while limiting the use of synthetic/petroleum based molecules as much as possible, research is being conducted on molecules of natural origin. Thus, thanks to the chemical composition, which comprise molecules with interesting properties, algae such as Sargassum could potentially help to solve many issues. Therefore, this study focuses on the green extraction and characterization of molecules from the species Sargassum fluitans and Sargassum natans present in Martinique. The secondary metabolites found in these extracts showed variability in yield rates due to local climatic conditions. The tests carried out shed light on the anticorrosive and antibacterial potential of the algae. These extracts can thus be described as natural inhibitors. The effect of variation in inhibitor concentrations was tested in electrochemistry using electrochemical impedance spectroscopy and polarization curves. The analysis of electrochemical results obtained by direct immersion in the extracts and self-assembled molecular layers (SAMs) for Sargassum fluitans III, Sargassum natans I and VIII species was conclusive in acid and alkaline environments. The excellent results obtained reveal an inhibitory efficacy of 88% at 50mg/L for the crude extract of Sargassum fluitans III and efficacies greater than 97% for the chemical families of Sargassum fluitans III. Similarly, microbiological tests also suggest a bactericidal character. Results for Sargassum fluitans III crude extract show a minimum inhibitory concentration (MIC) of 0.005 mg/mL on Gram-negative bacteria and a MIC greater than 0.6 mg/mL on Gram-positive bacteria. These results make it possible to consider the management of local and international issues while valuing a biomass rich in biodegradable molecules. The next step in this study will therefore be the evaluation of the toxicity of Sargassum spp..

Keywords: Sargassum, secondary metabolites, anticorrosive, antibacterial, natural inhibitors

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Authors: T. Zitoun, M. Bouhadef

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When a partially or completely immersed solid moves in a liquid such as water, it undergoes a force called hydrodynamic drag. Reducing this force has always been the objective of hydrodynamic engineers to make water slide better on submerged bodies. This paper deals with the examination of the different terms composing the analytical solution of the flow over an obstacle embedded at the bottom of a hydraulic channel. We have chosen to use a linear method to study a two-dimensional flow over an obstacle, in order to understand the evolution of the drag. We set the following assumptions: incompressible inviscid fluid, irrotational flow, low obstacle height compared to the water height. Those assumptions allow overcoming the difficulties associated with modelling these waves. We will mathematically formulate the equations that allow the determination of the stream function, and then the free surface equation. A similar method is used to determine the exact analytical solution for an obstacle in the shape of a sinusoidal arch.

Keywords: analytical solution, free-surface wave, hydraulic channel, inviscid fluid

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Authors: Assem M. F. Sallam, Ah. El-S. Makled

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This paper presents a study on the trajectory of a two stage launch vehicle. The study includes dynamic responses of motion parameters as well as the variation of angles affecting the orientation of the launch vehicle (LV). LV dynamic characteristics including state vector variation with corresponding altitude and velocity for the different LV stages separation, as well as the angle of attack and flight path angles are also discussed. A flight trajectory study for the drop zone of first stage and the jettisoning of fairing are introduced in the mathematical modeling to study their effect. To increase the accuracy of the LV model, atmospheric model is used taking into consideration geographical location and the values of solar flux related to the date and time of launch, accurate atmospheric model leads to enhancement of the calculation of Mach number, which affects the drag force over the LV. The mathematical model is implemented on MATLAB based software (Simulink). The real available experimental data are compared with results obtained from the theoretical computation model. The comparison shows good agreement, which proves the validity of the developed simulation model; the maximum error noticed was generally less than 10%, which is a result that can lead to future works and enhancement to decrease this level of error.

Keywords: launch vehicle modeling, launch vehicle trajectory, mathematical modeling, Matlab- Simulink

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Authors: O. Adjoul, A. Feugier, K. Benfriha, A. Aoussat

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In general, issues related to design and maintenance are considered in an independent manner. However, the decisions made in these two sets influence each other. The design for maintenance is considered an opportunity to optimize the life cycle cost of a product, particularly in the nuclear or aeronautical field, where maintenance expenses represent more than 60% of life cycle costs. The design of large-scale systems starts with product architecture, a choice of components in terms of cost, reliability, weight and other attributes, corresponding to the specifications. On the other hand, the design must take into account maintenance by improving, in particular, real-time monitoring of equipment through the integration of new technologies such as connected sensors and intelligent actuators. We noticed that different approaches used in the Design For Maintenance (DFM) methods are limited to the simultaneous characterization of the reliability and maintainability of a multi-component system. This article proposes a method of DFM that assists designers to propose dynamic maintenance for multi-component industrial systems. The term "dynamic" refers to the ability to integrate available monitoring data to adapt the maintenance decision in real time. The goal is to maximize the availability of the system at a given life cycle cost. This paper presents an approach for simultaneous optimization of the design and maintenance of multi-component systems. Here the design is characterized by four decision variables for each component (reliability level, maintainability level, redundancy level, and level of monitoring data). The maintenance is characterized by two decision variables (the dates of the maintenance stops and the maintenance operations to be performed on the system during these stops). The DFM model helps the designers choose technical solutions for the large-scale industrial products. Large-scale refers to the complex multi-component industrial systems and long life-cycle, such as trains, aircraft, etc. The method is based on a two-level hybrid algorithm for simultaneous optimization of design and maintenance, using genetic algorithms. The first level is to select a design solution for a given system that considers the life cycle cost and the reliability. The second level consists of determining a dynamic and optimal maintenance plan to be deployed for a design solution. This level is based on the Maintenance Free Operating Period (MFOP) concept, which takes into account the decision criteria such as, total reliability, maintenance cost and maintenance time. Depending on the life cycle duration, the desired availability, and the desired business model (sales or rental), this tool provides visibility of overall costs and optimal product architecture.

Keywords: availability, design for maintenance (DFM), dynamic maintenance, life cycle cost (LCC), maintenance free operating period (MFOP), simultaneous optimization

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Authors: Zabeehullah, Fahim Arif, Yawar Abbas

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Software Defined Network (SDN) is a next genera-tion networking model which simplifies the traditional network complexities and improve the utilization of constrained resources. Currently, most of the SDN based Internet of Things(IoT) environments use traditional network routing strategies which work on the basis of max or min metric value. However, IoT network heterogeneity, dynamic traffic flow and complexity demands intelligent and self-adaptive routing algorithms because traditional routing algorithms lack the self-adaptions, intelligence and efficient utilization of resources. To some extent, SDN, due its flexibility, and centralized control has managed the IoT complexity and heterogeneity but still Software Defined IoT (SDIoT) lacks intelligence. To address this challenge, we proposed a model called Deep Routing Strategy (DRS) which uses Deep Learning algorithm to perform routing in SDIoT intelligently and efficiently. Our model uses real-time traffic for training and learning. Results demonstrate that proposed model has achieved high accuracy and low packet loss rate during path selection. Proposed model has also outperformed benchmark routing algorithm (OSPF). Moreover, proposed model provided encouraging results during high dynamic traffic flow.

Keywords: SDN, IoT, DL, ML, DRS

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Authors: Milad Vahidi, Mahmod R. Sahebi, Mehrnoosh Omati, Reza Mohammadi

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Forest management organizations need information to perform their work effectively. Remote sensing is an effective method to acquire information from the Earth. Two datasets of remote sensing images were used to classify forested regions. Firstly, all of extractable features from hyperspectral and PolSAR images were extracted. The optical features were spectral indexes related to the chemical, water contents, structural indexes, effective bands and absorption features. Also, PolSAR features were the original data, target decomposition components, and SAR discriminators features. Secondly, the particle swarm optimization (PSO) and the genetic algorithms (GA) were applied to select optimization features. Furthermore, the support vector machine (SVM) classifier was used to classify the image. The results showed that the combination of PSO and SVM had higher overall accuracy than the other cases. This combination provided overall accuracy about 90.56%. The effective features were the spectral index, the bands in shortwave infrared (SWIR) and the visible ranges and certain PolSAR features.

Keywords: hyperspectral, PolSAR, feature selection, SVM

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Authors: Ting Kai Chia, Ruifeng Yan, Feifei Bai, Tapan Saha

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This research presents a real-world power system oscillation incident in 2022 originated by a hybrid solar photovoltaic (PV) and wind renewable energy farm with a rated capacity of approximately 300MW in Australia. The voltage and reactive power outputs recorded at the point of common coupling (PCC) oscillated at a sub-synchronous frequency region, which sustained for approximately five hours in the network. The reactive power oscillation gradually increased over time and reached a recorded maximum of approximately 250MVar peak-to-peak (from inductive to capacitive). The network service provider was not able to quickly identify the location of the oscillation source because the issue was widespread across the network. After the incident, the original equipment manufacturer (OEM) concluded that the oscillation problem was caused by the incorrect setting recovery of the hybrid power plant controller (HPPC) in the voltage and reactive power control loop after a loss of communication event. The voltage controller normally outputs a reactive (Q) reference value to the Q controller which controls the Q dispatch setpoint of PV and wind plants in the hybrid farm. Meanwhile, a feed-forward (FF) configuration is used to bypass the Q controller in case there is a loss of communication. Further study found that the FF control mode was still engaged when communication was re-established, which ultimately resulted in the oscillation event. However, there was no detailed explanation of why the FF control mode can cause instability in the hybrid farm. Also, there was no duplication of the event in the simulation to analyze the root cause of the oscillation. Therefore, this research aims to model and replicate the oscillation event in a simulation environment and investigate the underlying behavior of the HPPC and the consequent oscillation mechanism during the incident. The outcome of this research will provide significant benefits to the safe operation of large-scale renewable energy generators and power networks.

Keywords: PV, oscillation, modelling, wind

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Authors: Eli Buchbinder

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Introduction. Language is the basis to our experience as human being. We use language in describing our experiences and construct meaning and narratives from experiences. Metaphors are a valuable linguistic tool commonly use. Metaphors link two domains that are ordinarily not related. Metaphors achieve simultaneously multi-level integration: abstract and concrete, rational and imaginative, familiar and the unfamiliar, conscious and preconscious/unconscious. As such, metaphors epistemological and ontological tool that are important in social work in every field and domain. Goals and Methods The presentation’s aim is to validate the value of metaphors through the first psychiatric breakdown is a traumatic for families. The presentation is based on two pooled qualitative studies. The first study focused on 12 spouses: 7 women and 5 men, between the ages of 22 and 57, regarding their experiences and meanings of the first psychiatric hospitalization of their partners diagnosed with affective disorders. The second study focused on 10 parents, between the ages of 47 and 62, regarding their experiences and meanings following their child's first psychotic breakdown during young adulthood. Results Two types of major metaphors evolved from the interviews in farming the trauma of the first mental breakdown. The first mode - orientation (spatial) metaphors, reflect symbolic expression of the loss of a secure base, represented in the physical environment, e.g., describing hospitalization as "falling into an abyss." The second mode- ontological metaphors, reflect how parents and spouses present their traumatic experiences of hospitalization in terms of discrete, powerful and coherent entities, e.g., describing the first hospitalization as "swimming against the tide." The two metaphors modes reflect the embodiment of the unpredictability, being mired in distress, shock, intense pain and the experience the collapse of continuity on the life course and cuts off the experience of control. Conclusions Metaphors are important and powerful guide in assessing individuals and families’ phenomenological reality. As such, metaphors are useful for understanding and orientated therapeutic intervening, in the studies above, with the first psychiatric hospitalization experienced, as well as in others social workers’ interventions.

Keywords: first mental breakdown, metaphors, family perspective, qualitative research

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Authors: Fawaz A. Binsarra, Halim Boussabaine

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The notion of complexity science has been attracting the interest of researchers and professionals due to the need of enhancing the efficiency of understanding complex systems dynamic and structure of interactions. In addition, complexity analysis has been used as an approach to investigate complex systems that contains a large number of components interacts with each other to accomplish specific outcomes and emerges specific behavior. The design process is considered as a complex action that involves large number interacted components, which are ranked as design tasks, design team, and the components of the design process. Those three main aspects of the building design process consist of several components that interact with each other as a dynamic system with complex information flow. In this paper, the goal is to uncover the complex structure of information interactions in building design process. The Investigating of Royal Institute of British Architects Plan Of Work 2013 information interactions as a case study to uncover the structure and building design process complexity using network analysis software to model the information interaction will significantly enhance the efficiency of the building design process outcomes.

Keywords: complexity, process, building desgin, Riba, design complexity, network, network analysis

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Authors: Minhyuk Heo, Jihwan Yun, Seonghun Park

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It has been reported that intervertebral fusion surgery, which removes most of the ligaments and muscles of the spine, increases the degenerative disease in adjacent spinal segments. Therefore, it is required to develop a lumbar interspinous process fixation device that minimizes the risks and side effects from the surgery. The objective of the current study is to design an interspinous process fixation device with simple structures in order to minimize soft tissue removal and operation time during intervertebral fusion surgery. For the design concepts of a lumbar fixation device, the principle of the ratchet was first applied on the joining parts of the device in order to shorten the operation time. The coil spring structure was selected for connecting parts between the spinous processes so that a normal range of motion in spinal segments is preserved and degenerative spinal diseases are not developed in the adjacent spinal segments. The stiffness of the spring was determined not to interrupt the motion of a lumbar spine. The designed value of the spring stiffness allows the upper part of the spring to move ~10° which is higher than the range of flexion and extension for normal lumbar spine (6°-8°), when a moment of 10Nm is applied on the upper face of L1. A finite element (FE) model composed of L1 to L5 lumbar spines was generated to verify the mechanical integrity and the dynamic stability of the designed lumbar fixation device and to further optimize the lumbar fixation device. The FE model generated above produced the same pressure value on intervertebral disc and dynamic behavior as the normal intact model reported in the literature. The consistent results from this comparison validates the accuracy in the modeling of the current FE model. Currently, we are trying to generate an abnormal model with defects in one or more components of the normal FE model above. Then, the mechanical integrity and the dynamic stability of the designed lumbar fixation device will be analyzed after being installed in the abnormal model and then the lumbar fixation device will be further optimized.

Keywords: lumbar interspinous process fixation device, finite element method, lumbar spine, kinematics

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Authors: Nizar Tayem, AbuMuhammad Moinuddeen, Ahmed A. Hussain, Redha M. Radaydeh

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This research paper introduces an approach for estimating the direction of arrival (DOA) of multiple RF noncoherent sources in a uniform linear array (ULA). The proposed method utilizes a Capon-like estimation algorithm and incorporates LU decomposition to enhance the accuracy of DOA estimation while significantly reducing computational complexity compared to existing methods like the Capon method. Notably, the proposed method does not require prior knowledge of the number of sources. To validate its effectiveness, the proposed method undergoes validation through both software simulations and practical experimentation on a prototype testbed constructed using a software-defined radio (SDR) platform and GNU Radio software. The results obtained from MATLAB simulations and real-time experiments provide compelling evidence of the proposed method's efficacy.

Keywords: DOA estimation, real-time validation, software defined radio, computational complexity, Capon's method, GNU radio

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Authors: Walid Moudani

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The Crew Reserve Assignment Problem (CRAP) considers the assignment of the crew members to a set of reserve activities covering all the scheduled flights in order to ensure a continuous plan so that operations costs are minimized while its solution must meet hard constraints resulting from the safety regulations of Civil Aviation as well as from the airlines internal agreements. The problem considered in this study is of highest interest for airlines and may have important consequences on the service quality and on the economic return of the operations. In this communication, a new mathematical formulation for the CRAP is proposed which takes into account the regulations and the internal agreements. While current solutions make use of Artificial Intelligence techniques run on main frame computers, a low cost approach is proposed to provide on-line efficient solutions to face perturbed operating conditions. The proposed solution method uses a dynamic programming approach for the duties scheduling problem and when applied to the case of a medium airline while providing efficient solutions, shows good potential acceptability by the operations staff. This optimization scheme can then be considered as the core of an on-line Decision Support System for crew reserve assignment operations management.

Keywords: airlines operations management, combinatorial optimization, dynamic programming, crew scheduling

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Authors: Rossella Arcucci, Luisa D'Amore, Simone Celestino, Giuseppe Scotti, Giuliano Laccetti

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This work is the first dowel in a rather wide research activity in collaboration with Euro Mediterranean Center for Climate Changes, aimed at introducing scalable approaches in Ocean Circulation Models. We discuss designing and implementation of a parallel algorithm for solving the Variational Data Assimilation (DA) problem on Graphics Processing Units (GPUs). The algorithm is based on the fully scalable 3DVar DA model, previously proposed by the authors, which uses a Domain Decomposition approach (we refer to this model as the DD-DA model). We proceed with an incremental porting process consisting of 3 distinct stages: requirements and source code analysis, incremental development of CUDA kernels, testing and optimization. Experiments confirm the theoretic performance analysis based on the so-called scale up factor demonstrating that the DD-DA model can be suitably mapped on GPU architectures.

Keywords: data assimilation, GPU architectures, ocean models, parallel algorithm

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Authors: Bruk Gebregziabher

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The safe and efficient operation of Autonomous Mobile Robots (AMRs) in complex environments, such as manufacturing, logistics, and agriculture, necessitates accurate multiobject tracking and predictive collision avoidance. This paper presents algorithms and techniques for addressing these challenges using Lidar sensor data, emphasizing ensemble Kalman filter. The developed predictive collision avoidance algorithm employs the data provided by lidar sensors to track multiple objects and predict their velocities and future positions, enabling the AMR to navigate safely and effectively. A modification to the dynamic windowing approach is introduced to enhance the performance of the collision avoidance system. The overall system architecture encompasses object detection, multi-object tracking, and predictive collision avoidance control. The experimental results, obtained from both simulation and real-world data, demonstrate the effectiveness of the proposed methods in various scenarios, which lays the foundation for future research on global planners, other controllers, and the integration of additional sensors. This thesis contributes to the ongoing development of safe and efficient autonomous systems in complex and dynamic environments.

Keywords: autonomous mobile robots, multi-object tracking, predictive collision avoidance, ensemble Kalman filter, lidar sensors

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Authors: Ruchi Makani, B. V. R. Reddy

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Mobile Adhoc Networks (MANETs) are gaining popularity due to their potential of providing low-cost mobile connectivity solutions to real-world communication problems. Integrating Intrusion Detection Systems (IDS) in MANETs is a tedious task by reason of its distinctive features such as dynamic topology, de-centralized authority and highly controlled/limited resource environment. IDS primarily use automated soft-computing techniques to monitor the inflow/outflow of traffic packets in a given network to detect intrusion. Use of machine learning techniques in IDS enables system to make decisions on intrusion while continuous keep learning about their dynamic environment. An appropriate IDS model is essential to be selected to expedite this application challenges. Thus, this paper focused on fuzzy-logic based machine learning IDS technique for MANETs and presented their applicability for achieving effectiveness in identifying the intrusions. Further, the selection of appropriate protocol attributes and fuzzy rules generation plays significant role for accuracy of the fuzzy-logic based IDS, have been discussed. This paper also presents the critical attributes of MANET’s routing protocol and its applicability in fuzzy logic based IDS.

Keywords: AODV, mobile adhoc networks, intrusion detection, anomaly detection, fuzzy logic, fuzzy membership function, fuzzy inference system

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Authors: Pierre Tize Mha, Mohammad Jahazi, Amèvi Togne, Olivier Pantalé

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Large-size forged blocks made of medium carbon high-strength steels are extensively used in the automotive industry as dies for the production of bumpers and dashboards through the plastic injection process. The manufacturing process of the large blocks starts with ingot casting, followed by open die forging and a quench and temper heat treatment process to achieve the desired mechanical properties and numerical simulation is widely used nowadays to predict these properties before the experiment. But the temperature gradient inside the specimen remains challenging in the sense that the temperature before loading inside the material is not the same, but during the simulation, constant temperature is used to simulate the experiment because it is assumed that temperature is homogenized after some holding time. Therefore to be close to the experiment, real distribution of the temperature through the specimen is needed before the mechanical loading. Thus, We present here a robust algorithm that allows the calculation of the temperature gradient within the specimen, thus representing a real temperature distribution within the specimen before deformation. Indeed, most numerical simulations consider a uniform temperature gradient which is not really the case because the surface and core temperatures of the specimen are not identical. Another feature that influences the mechanical properties of the specimen is recrystallization which strongly depends on the deformation conditions and the type of deformation like Upsetting, Cogging...etc. Indeed, Upsetting and Cogging are the stages where the greatest deformations are observed, and a lot of microstructural phenomena can be observed, like recrystallization, which requires in-depth characterization. Complete dynamic recrystallization plays an important role in the final grain size during the process and therefore helps to increase the mechanical properties of the final product. Thus, the identification of the conditions for the initiation of dynamic recrystallization is still relevant. Also, the temperature distribution within the sample and strain rate influence the recrystallization initiation. So the development of a technique allowing to predict the initiation of this recrystallization remains challenging. In this perspective, we propose here, in addition to the algorithm allowing to get the temperature distribution before the loading stage, an analytical model leading to determine the initiation of this recrystallization. These two techniques are implemented into the Abaqus finite element software via the UAMP and VUHARD subroutines for comparison with a simulation where an isothermal temperature is imposed. The Artificial Neural Network (ANN) model to describe the plastic behavior of the material is also implemented via the VUHARD subroutine. From the simulation, the temperature distribution inside the material and recrystallization initiation is properly predicted and compared to the literature models.

Keywords: dynamic recrystallization, finite element modeling, artificial neural network, numerical implementation

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Authors: Orathai Pornsunthorntawee, Wareerom Polrut, Nopphawan Phonthammachai

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In the present work, the effects of additives, including contents of the added antioxidants and type of the selected metallic stearates (either calcium stearate (CaSt) or zinc stearate (ZnSt)), on the thermal stabilities of carbon black (CB)/high density polyethylene (HDPE) compounds were studied. The results showed that the AO contents played a key role in the thermal stabilities of the CB/HDPE compounds—the higher the AO content, the higher the thermal stabilities. Although the CaSt-containing compounds were slightly superior to those with ZnSt in terms of the thermal stabilities, the remaining solid residue of CaSt after heated to the temperature of 600 °C (mainly calcium carbonate (CaCO3) as characterized by the X-ray diffraction (XRD) technique) seemed to catalyze the decomposition of CB in the HDPE-based compounds. Hence, the quantification of CB in the CaSt-containing compounds with a muffle furnace gave an inaccurate CB content—much lower than actual value. However, this phenomenon was negligible in the ZnSt-containing system.

Keywords: antioxidant, stearate, carbon black, polyethylene

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Authors: Muhammad Bilal Mustafa, Javed Hussain, Simeon Babatunde

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The Covid-19 pandemic has exposed the vulnerabilities of countless organisations beyond their size, type, and location. However, some groups and sectors are disproportionally get impacted by the pandemic. In the context of the UK, ethnic Small and Medium Enterprises (SMEs) turn out to be the most precarious group among all private sectors. Many ethnic SMEs shut down their business operations during a pandemic. A large portion of Black, Asian and minority ethnic (BAME) owners have huge concerns regarding their business’ survival and resilience. The current UK-centric studies have focused on the large business population, and there is a gap in ethnic SMEs and how they get affected by the Covid-19 pandemic. Moreover, there is a need to further knowledge and academic research to investigate the fundamental factors that could strengthen the resilience of ethnic SMEs as well as contribute to long-term sustainability. Therefore, this study aims to capture the effect of the Covid-19 pandemic on ethnic SMEs in the UK and assess the survival measures taken by ethnic SMEs during Covid-19. Besides, this study adopts a dynamic capabilities perspective that how firms' specific capabilities enable ethnic SMEs to exploit entrepreneurial opportunities during the Covid-19 pandemic. Finally, this research will help ethnic SMEs to develop vigorous resilience to address future external shocks and market uncertainties.

Keywords: COVID-19 pandemic, ethnic minority SMEs, entrepreneurial resilience, dynamic capabilities, sustainability

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Authors: Shahrokh Barati

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In this paper, we introduced AIDS disease at first, then proposed dynamic model illustrate its progress, after expression of a short history of nonlinear modeling by polynomial phasing systems, we considered the stability conditions of the systems, which contained a huge amount of researches in order to modeling and control of AIDS in dynamic nonlinear form, in this approach using a frame work of control any polynomial phasing modeling system which have been generalized by part of phasing model of T-S, in order to control the system in better way, the stability conditions were achieved based on polynomial functions, then we focused to design the appropriate controller, firstly we considered the equilibrium points of system and their conditions and in order to examine changes in the parameters, we presented polynomial phase model that was the generalized approach rather than previous Takagi Sugeno models, then with using case we evaluated the equations in both open loop and close loop and with helping the controlling feedback, the close loop equations of system were calculated, to simulate nonlinear model of AIDS disease, we used polynomial phasing controller output that was capable to make the parameters of a nonlinear system to follow a sustainable reference model properly.

Keywords: polynomial fuzzy, AIDS, nonlinear AIDS model, fuzzy control systems

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Authors: Alpana Tiwari, N. K. Gaur

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We have incorporated the translational rotational (TR) coupling effects in the framework of three body force shell model (TSM) to develop an extended TSM (ETSM). The dynamical matrix of ETSM has been applied to compute the phonon frequencies of orientationally disordered mixed crystal (ND4Br)x(KBr)1-x in (q00), (qq0) and (qqq) symmetry directions for compositions 0.10≤x≤0.50 at T=300K.These frequencies are plotted as a function of wave vector k. An unusual acoustic mode softening is found along symmetry directions (q00) and (qq0) as a result of translation-rotation coupling.

Keywords: orientational glass, phonons, TR-coupling, lattice dynamics

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Authors: Rajasekar Elangopandian, Anand Shanmugam

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In order to reduce the cost of launching satellite and payloads to the orbit this project envisages some immense combined technology. This new technology in space odyssey contains literally four concepts. The first mode in this innovation is flight mission characteristics which, says how the mission will induct. The conventional technique of magnetic levitation will help us to produce the initial thrust. The name states reusable launch vehicle shows its viability of reuseness. The flight consists miniature rocket which produces the required thrust and the two JATO (jet assisted takeoff) boosters which gives the initial boost for the vehicle. The vehicle ostensibly looks like an airplane design and will be located on the super conducting rail track. When the high power electric current given to the rail track, the vehicle starts floating as per the principle of magnetic levitation. If the flight reaches the particular takeoff distance the two boosters gets starts and will give the 48KN thrust each. Obviously it`ll follow the vertical path up to the atmosphere end/start to space. As soon as it gets its speed the two boosters will cutoff. Once it reaches the space the inbuilt spacecraft keep the satellite in the desired orbit. When the work finishes, the apogee motors gives the initial kick to the vehicle to come in to the earth’s atmosphere with 22N thrust and automatically comes to the ground by following the free fall, the help of gravitational force. After the flying region it makes the spiral flight mode then gets landing where the super conducting levitated rail track located. It will catch up the vehicle and keep it by changing the poles of magnets and varying the current. Initial cost for making this vehicle might be high but for the frequent usage this will reduce the launch cost exactly half than the now-a-days technology. The incorporation of such a mechanism gives `hybrid` and the reusability gives `reusable launch vehicle` and ultimately Hybrid reusable launch vehicle.

Keywords: the two JATO (jet assisted takeoff) boosters, magnetic levitation, 48KN thrust each, 22N thrust and automatically comes to the ground

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Authors: Mokhtar Arazpour

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Objectives: Recently a new kind of stance control knee joint has been developed called the 'sensor lock.' This study aimed to develop and evaluate 'sensor lock', which could potentially solve the problems of walking parameters and gait symmetry in subjects with quadriceps weakness. Methods: Nine subjects with quadriceps weakness were enrolled in this study. A custom-made knee ankle foot orthosis (KAFO) with the same set of components was constructed for each participant. Testing began after orthotic gait training was completed with each of the KAFOs and subjects demonstrated that they could safely walk with crutches. Subjects rested 30 minutes between each trial. The 10 meters walking test is used to assess walking speed in meters/second (m/s). The total time taken to ambulate 6 meters (m) is recorded to the nearest hundredth of a second. 6 m is then divided by the total time (in seconds) taken to ambulate and recorded in m/s. The 6 Minutes Walking Test was used to assess walking endurance in this study. Participants walked around the perimeter of a set circuit for a total of six minutes. To evaluate Physiological cost index (PCI), the subjects were asked to walk using each type of KAFOs along a pre-determined 40 m rectangular walkway at their comfortable self-selected speed. A stopwatch was used to calculate the speed of walking by measuring the time between starting and stopping time and the distance walked. Results: The use of a KAFO fitted with the “sensor lock” knee joint resulted in improvements to walking speed, distance walked and physiological cost index when compared with the knee joint in lock mode. Conclusions: This study demonstrated that the use of a KAFO with the “sensor lock” knee joint could provide significant benefits for subjects with a quadriceps weakness when compared to a KAFO with the knee joint in lock mode.

Keywords: stance control knee joint, knee ankle foot orthosis, quadriceps weakness, walking, energy consumption

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Authors: Noura Al Akkari, Aurélie Foucquier, Sylvain Lespinats

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Load monitoring is a management process for energy consumption towards energy savings and energy efficiency. Non Intrusive Load Monitoring (NILM) is one method of load monitoring used for disaggregation purposes. NILM is a technique for identifying individual appliances based on the analysis of the whole residence data retrieved from the main power meter of the house. Our NILM framework starts with data acquisition, followed by data preprocessing, then event detection, feature extraction, then general appliance modeling and identification at the final stage. The event detection stage is a core component of NILM process since event detection techniques lead to the extraction of appliance features. Appliance features are required for the accurate identification of the household devices. In this research work, we aim at developing a new event detection methodology with accurate load disaggregation to extract appliance features. Time-domain features extracted are used for tuning general appliance models for appliance identification and classification steps. We use unsupervised algorithms such as Dynamic Time Warping (DTW). The proposed method relies on detecting areas of operation of each residential appliance based on the power demand. Then, detecting the time at which each selected appliance changes its states. In order to fit with practical existing smart meters capabilities, we work on low sampling data with a frequency of (1/60) Hz. The data is simulated on Load Profile Generator software (LPG), which was not previously taken into consideration for NILM purposes in the literature. LPG is a numerical software that uses behaviour simulation of people inside the house to generate residential energy consumption data. The proposed event detection method targets low consumption loads that are difficult to detect. Also, it facilitates the extraction of specific features used for general appliance modeling. In addition to this, the identification process includes unsupervised techniques such as DTW. To our best knowledge, there exist few unsupervised techniques employed with low sampling data in comparison to the many supervised techniques used for such cases. We extract a power interval at which falls the operation of the selected appliance along with a time vector for the values delimiting the state transitions of the appliance. After this, appliance signatures are formed from extracted power, geometrical and statistical features. Afterwards, those formed signatures are used to tune general model types for appliances identification using unsupervised algorithms. This method is evaluated using both simulated data on LPG and real-time Reference Energy Disaggregation Dataset (REDD). For that, we compute performance metrics using confusion matrix based metrics, considering accuracy, precision, recall and error-rate. The performance analysis of our methodology is then compared with other detection techniques previously used in the literature review, such as detection techniques based on statistical variations and abrupt changes (Variance Sliding Window and Cumulative Sum).

Keywords: electrical disaggregation, DTW, general appliance modeling, event detection

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Authors: E. Ghasemi Ardi, K. J. Dong, A. B. Yu, R. Y. Yang

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In current work, a numerical model based on the discrete element method (DEM) was developed which provided information about particle dynamic and impact event condition inside a laboratory scale impact mill (Fritsch). It showed that each particle mostly experiences three impacts inside the mill. While the first impact frequently happens at front surface of the rotor’s rib, the frequent location of the second impact is side surfaces of the rotor’s rib. It was also showed that while the first impact happens at small impact angle mostly varying around 35º, the second impact happens at around 70º which is close to normal impact condition. Also analyzing impact energy revealed that varying mill speed from 6000 to 14000 rpm, the ratio of first impact’s average impact energy and minimum required energy to break particle (Wₘᵢₙ) increased from 0.30 to 0.85. Moreover, it was seen that second impact poses intense impact energy on particle which can be considered as the main cause of particle splitting. Finally, obtained information from DEM simulation along with obtained data from conducted experiments was implemented in semi-empirical equations in order to find selection and breakage functions. Then, using a back-calculation approach, those parameters were used to predict the PSDs of ground particles under different impact energies. Results were compared with experiment results and showed reasonable accuracy and prediction ability.

Keywords: single particle breakage, particle dynamic, population balance model, particle size distribution, discrete element method

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Authors: Benjamin Newman

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The picture of an adverse contest between two parties has often been used as an archetypal description of the Anglo-American adversarial criminal trial. However, in actuality, guilty pleas and plea-bargains have been dominating the procedure for over the last half-a-century. Characterised by two adverse parties, the court adjudicative system in the Anglo-American world adhere to the adversarial procedure, and while further features have been attributed and the values that are embedded within the procedure vary, it is a system that we have no adequate theory. Damaska had argued that the adversarial conflict-resolution mode of administration of justice stems from a liberal laissez-faire concept of a value neutral liberal state. Having said that, the court’s neutrality has been additionally rationalised in light of its liberal end as a safeguard from the state’s coercive force. Both conceptions of the court’s neutrality conflict in cases where the by-standing role disposes of its liberal duty in safeguarding the individual. Such is noticeable in plea bargains, where the defendant has the liberty to plead guilty, despite concerns over wrongful convictions and deprivation of liberty. It is an inner liberal tension within the notion of criminal adversarialism, between the laissez-faire mode which grants autonomy to the parties and the safeguarding liberal end of the trial. Langbein had asserted that the adversarial system is a criminal procedure for which we have no adequate theory, and it is by reference to political and moral theories that the research aims to articulate a normative account. The paper contemplates on the above liberal-tension, and by reference to Duff’s ‘calling-to-account’ theory, argues that autonomy is of inherent value to the criminal process, being considered a constitutive element in the process of being called to account. While the aspiration is that the defendant’s guilty plea should be genuine, the guilty-plea decision must be voluntary if it is to be considered a performative act of accountability. Thus, by valuing procedural autonomy as a necessary element within the criminal adjudicative process, it assimilates a liberal procedure, whilst maintaining the liberal end by holding the defendant to account.

Keywords: liberal theory, adversarial criminal procedure, criminal law theory, liberal perfectionism, political liberalism

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Authors: Supriya Mahida, Sangita, Yogesh U. Shah, Shanta Kumar

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The global production of thermocol increasing day by day, due to vast applications of the use of thermocole in many sectors. Thermocol being non-biodegradable and more toxic than plastic leads towards a number of problems like its management into value-added products, environmental damage and landfill problems due to weight to volume ratio. Utilization of waste thermocol for modification of bitumen binders resulted in waste thermocol modified bitumen (WTMB) used in road construction and maintenance technology. Modification of bituminous mixes through incorporating thermocol into bituminous mixes through a dry process is one of the new options besides recycling process which consumes lots of waste thermocol. This process leads towards waste management and remedies against thermocol waste disposal. The present challenge is to dispose the thermocol waste under different forms in road infrastructure, either through the dry process or wet process to be developed in future. This paper focuses on the use of thermocol wastes which is mixed with VG 10 bitumen in proportions of 0.5%, 1%, 1.5%, and 2% by weight of bitumen. The physical properties of neat bitumen are evaluated and compared with modified VG 10 bitumen having thermocol. Empirical characterization like penetration, softening, and viscosity of bitumen has been carried out. Thermocol and waste thermocol modified bitumen (WTMB) were further analyzed by Fourier Transform Infrared Spectroscopy (FT-IR), field emission scanning electron microscopy (FESEM), and Dynamic Shear Rheometer (DSR).

Keywords: DSR, FESEM, FT-IR, thermocol wastes

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Authors: Ljerka Kratofil Krehula, Vanja Gilja, Andrea Husak, Sniježana Šuka, Zlata Hrnjak-Murgić

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Zinc oxide (ZnO) can be used as photocatalysts for water purification. However, one particular interest is given on the integration of inorganic ZnO nanoclusters with conducting polymers because the resulting nanocomposites may possess unique properties and enhanced photocatalytic activity in comparison to pure ZnO, using UV and also visible light. It is needed to explore the appropriate structure of polypyrrole that can induce activation of ZnO photocatalyst since the synthesis of organic/inorganic hybrid materials can result in a synergistic and complementary feature, increasing ZnO photocatalytic efficiency. In this paper several different composites of polypyrrole/zinc oxide (ZnO) were studied. Composite samples were characterized by X-ray powder diffraction (XRD), Fourier transform infrared spectroscopy (FTIR), cyclic voltammetry (CV) and scanning electron microscopy (SEM). The photocatalytic efficiency of prepared samples was studied as a decomposition of Reactive Red 45 (RR 45) dye, which was monitored by UV-Vis spectroscopy as a change in absorbance of characteristic wavelength at 542 nm. Results show good photocatalytic efficiency of all nanocomposite samples.

Keywords: photocatalysis, polypyrrole, wastewater, zinc oxide

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Authors: V. Rashtchi, H. Bizhani, F. R. Tatari

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This paper presents a new online loss minimization for an induction motor drive. Among the many loss minimization algorithms (LMAs) for an induction motor, a particle swarm optimization (PSO) has the advantages of fast response and high accuracy. However, the performance of the PSO and other optimization algorithms depend on the accuracy of the modeling of the motor drive and losses. In the development of the loss model, there is always a trade off between accuracy and complexity. This paper presents a new online optimization to determine an optimum flux level for the efficiency optimization of the vector-controlled induction motor drive. An induction motor (IM) model in d-q coordinates is referenced to the rotor magnetizing current. This transformation results in no leakage inductance on the rotor side, thus the decomposition into d-q components in the steady-state motor model can be utilized in deriving the motor loss model. The suggested algorithm is simple for implementation.

Keywords: induction machine, loss minimization, magnetizing current, particle swarm optimization

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Authors: Majid Habib Khan, Jinwei Zhao, Xinhong Hei, Liu Jiedong, Rana Shahzad Noor, Muhammad Imran

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Deep learning is increasingly employed in image classification, yet the scarcity and high cost of labeled data for training remain a challenge. Limited samples often lead to overfitting due to biased sample distribution. This paper introduces a dynamic distribution calibration method for few-shot learning. Initially, base and new class samples undergo normalization to mitigate disparate feature magnitudes. A pre-trained model then extracts feature vectors from both classes. The method dynamically selects distribution characteristics from base classes (both adjacent and remote) in the embedding space, using a threshold value approach for new class samples. Given the propensity of similar classes to share feature distributions like mean and variance, this research assumes a Gaussian distribution for feature vectors. Subsequently, distributional features of new class samples are calibrated using a corrected hyperparameter, derived from the distribution features of both adjacent and distant base classes. This calibration augments the new class sample set. The technique demonstrates significant improvements, with up to 4% accuracy gains in few-shot classification challenges, as evidenced by tests on miniImagenet and CUB datasets.

Keywords: deep learning, computer vision, image classification, few-shot learning, threshold

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Authors: Pervaiz Ahmad, Mayeen Uddin Khandaker, Yusoff Mohd Amin

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A unique cone-like morphologies of boron nitride microtubes with larger internal space and thin walls structure are synthesized in a dual zone quartz tube furnace at 1200 ° C with ammonia as a reaction atmosphere. The synthesized microtubes are found to have diameter in the range of 1 to ̴ 2 μm with walls thickness estimated from 10 – 100 nm. XPS survey shows N 1s and B 1s peaks at 398.7 eV and 191 eV that represent h-BN in the sample. Raman spectroscopy indicates a high intensity peak at 1372.53 (cm-1) that corresponds to the E2g mode of h-BN.

Keywords: BNMTs, synthesis, reaction atmosphere, growth

Procedia PDF Downloads 380