Search results for: transmission optimization

Authors: Ghasem Sharifi, Hamed Shahmohamadi Ousaloo, Milad Azimi, Mehran Mirshams

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The ever-increasing use of satellites demands a search for increasingly accurate and reliable pointing systems. Reaction wheels are rotating devices used commonly for the attitude control of the spacecraft since provide a wide range of torque magnitude and high reliability. The numerical modeling of this device can significantly enhance the accuracy of the satellite control in space. Modeling the wheel rotation in the presence of the various frictions is one of the critical parts of this approach. This paper presents a Dynamic Model Control of a Reaction Wheel (DMCR) in the current control mode. In current-mode, the required current is delivered to the coils in order to achieve the desired torque. During this research, all the friction parameters as viscous and coulomb, motor coefficient, resistance and voltage constant are identified. In order to model identification of a reaction wheel, numerous varying current commands apply on the particular wheel to verify the estimated model. All the parameters of DMCR are identified by classical Levenberg-Marquardt (CLM) optimization method. The experimental results demonstrate that the developed model has an appropriate precise and can be used in the satellite control simulation.

Keywords: experimental modeling, friction parameters, model identification, reaction wheel

Procedia PDF Downloads 233

Authors: Kevin V. Bui, Richard A. Claytor, Elizabeth M. Priolo, Weihui Li

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Oxygen deprivation masks operate through the use of restricting valves as a means to reduce respiratory flow where flow is inversely proportional to the resistance applied. This produces the same effect as higher altitudes where lower pressure leads to reduced respiratory flow. Both increased resistance with restricting valves and reduce the pressure of higher altitudes make breathing difficultier and force breathing muscles (diaphragm and intercostal muscles) working harder. The process exercises these muscles, improves their strength and results in overall better breathing efficiency. Currently, these oxygen deprivation masks are purely mechanical devices without any electronic sensor to monitor the breathing condition, thus not be able to provide feedback on the breathing effort nor to evaluate the lung function. That is part of the reason that these masks are mainly used for high-level athletes to mimic training in higher altitude conditions, not suitable for patients or customers. The design aims to improve the current method of oxygen deprivation mask to include a larger scope of patients and customers while providing quantitative biometric data that the current design lacks. This will be accomplished by integrating sensors into the mask’s breathing valves along with data acquisition and Bluetooth modules for signal processing and transmission. Early stages of the sensor mask will measure breathing rate as a function of changing the air pressure in the mask, with later iterations providing feedback on flow rate. Data regarding breathing rate will be prudent in determining whether training or therapy is improving breathing function and quantify this improvement.

Keywords: oxygen deprivation mask, lung function, spirometer, Bluetooth

Procedia PDF Downloads 218

Authors: Mohammed Elsayed Lotfy Elsayed Abouzeid, Tomonobu Senjyu

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A novel multi-objective park and ride control approach is presented in this research. Park and ride will encourage the owners of the vehicles to leave their cars in the nearest points (on the edges of the crowded cities) and use public transportation facilities (train, bus, metro, or mon-rail) to reach their work inside the crowded city. The proposed control scheme is used to design electric vehicle charging stations (EVCS) to charge 1000 electric vehicles (EV) during their owners' work time. Cairo, Egypt is used as a case study. Photovoltaic (PV) and battery energy storage system (BESS) are used to meet the EVCS demand. Two multi-objective optimization techniques (MOGA and epsilon-MOGA) are utilized to get the optimal sizes of PV and BESS so as to meet the load demand and minimize the total life cycle cost. Detailed analysis and comparison are held to investigate the performance of the proposed control scheme using MATLAB.

Keywords: Battery Energy Storage System, Electric Vehicle, Park and Ride, Photovoltaic, Multi-objective

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Authors: Violeta Damjanovic-Behrendt

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This paper presents an approach for optimal cyber security decisions to protect instances of a federated Internet of Things (IoT) platform in the cloud. The presented solution implements the repeated Stackelberg Security Game (SSG) and a model called Stochastic Human behaviour model with AttRactiveness and Probability weighting (SHARP). SHARP employs the Subjective Utility Quantal Response (SUQR) for formulating a subjective utility function, which is based on the evaluations of alternative solutions during decision-making. We augment the repeated SSG (including SHARP and SUQR) with a reinforced learning algorithm called Naïve Q-Learning. Naïve Q-Learning belongs to the category of active and model-free Machine Learning (ML) techniques in which the agent (either the defender or the attacker) attempts to find an optimal security solution. In this way, we combine GT and ML algorithms for discovering optimal cyber security policies. The proposed security optimization components will be validated in a collaborative cloud platform that is based on the Industrial Internet Reference Architecture (IIRA) and its recently published security model.

Keywords: security, internet of things, cloud computing, stackelberg game, machine learning, naive q-learning

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Authors: Zhibo Zhao, Horst Hahn, Robert Kruk, Abhisheck Sarkar

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High entropy oxides (HEOs), based on the incorporation of multiple-principal cations into the crystal lattice, offer the possibility to explore previously inaccessible oxide compositions and unconventional properties. Here it is demonstrated that despite the chemical complexity of HEOs external stimuli, such as epitaxial strain, can selectively stabilize certain magneto-electronic states. Epitaxial (Co₀.₂Cr₀.₂Fe₀.₂Mn₀.₂Ni₀.₂)₃O₄-HEO thin films are grown in three different strain states: tensile, compressive, and relaxed. A unique coexistence of rocksalt and spinel-HEO phases, which are fully coherent with no detectable chemical segregation, is revealed by transmission electron microscopy. This dual-phase coexistence appears as a universal phenomenon in (Co₀.₂Cr₀.₂Fe₀.₂Mn₀.₂Ni₀.₂)₃O₄ epitaxial films. Prominent changes in the magnetic anisotropy and domain structure highlight the strain-induced bidirectional control of magnetic properties in HEOs. When the films are relaxed, their magnetization behavior is isotropic, similar to that of bulk materials. However, under tensile strain, the hardness of the out-of-plane (OOP) axis increases significantly. On the other hand, compressive straining results in an easy OOP magnetization and a maze-like magnetic domain structure, indicating perpendicular magnetic anisotropy. Generally, this study emphasizes the adaptability of the high entropy design strategy, which, when combined with coherent strain engineering, opens additional prospects for fine-tuning properties in oxides.

Keywords: high entropy oxides, thin film, strain tuning, perpendicular magnetic anistropy

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Authors: Taufik Rizkiandi, Teuku Yuri M. Zagloel, Andri Dwi Setiawan

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Distribution and growth in the transportation and warehousing business sector decreased by 15,04%. There was a decrease in Gross Domestic Product (GDP) contribution level from rank 7 of 4,41% in 2019 to 3,81% in rank 8 in 2020. A decline in the transportation and warehousing business sector contributes to GDP, resulting in oil and gas companies implementing an efficient supply chain strategy to ensure the availability of goods, especially lubricants. Fluctuating demand for lubricants and warehouse service time limits are essential things that are taken into account in determining an efficient route. Add depots points as a solution so that demand for lubricants is fulfilled (not stock out). However, adding a depot will increase operating costs and storage costs. Therefore, it is necessary to optimize the addition of depots using the Capacitated Vehicle Routing Problem with Time Windows (CVRPTW). This research case study was conducted at an oil and gas company that produces lubricants from 2019 to 2021. The study results obtained the optimal route and the addition of a depot with a minimum additional cost. The total cost remains efficient with the addition of a depot when compared to one depot from Jakarta.

Keywords: CVRPTW, optimal route, depot, tabu search algorithm

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Authors: Gamze Karanfil Celep, Kevser Dincer

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The effects of polymer concentration and electrospinning process parameters on the average diameters of electrospun polyacrylonitrile (PAN) nanofibers were experimentally investigated. Besides, mechanical and thermal properties of PAN nanofibers were examined by tensile test and thermogravimetric analysis (TGA), respectively. For this purpose, the polymer concentration, solution feed rate, supply voltage and tip-to-collector distance were determined as the control factors. To succeed these aims, Taguchi’s L16 orthogonal design (4 parameters, 4 level) was employed for the experimental design. Optimal electrospinning conditions were defined using the signal-to-noise (S/N) ratio that was calculated from diameters of the electrospun PAN nanofibers according to "the-smaller-the-better" approachment. In addition, analysis of variance (ANOVA) was evaluated to conclude the statistical significance of the process parameters. The smallest diameter of PAN nanofibers was observed. According to the S/N ratio response results, the most effective parameter on finding out of nanofiber diameter was determined. Finally, the Taguchi design of experiments method has been found to be an effective method to statistically optimize the critical electrospinning parameters used in nanofiber production. After determining the optimum process parameters of nanofiber production, electrical conductivity and fuel cell performance of electrospun PAN nanofibers on the carbon papers will be evaluated.

Keywords: nanofiber, electrospinning, polyacrylonitrile, Taguchi method

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Authors: Seyyed Masoud Seyyedoshohadaei, Borhanuddin Mohd Ali

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To support group mobility, the NEMO Basic Support Protocol has been standardized as an extension of Mobile IP that enables an entire network to change its point of attachment to the Internet. Using NEMO in IEEE 802.16e (WiMax) networks causes latency in handover procedure and affects seamless communication of real-time applications. To decrease handover latency and service disruption time, an integrated scheme named Optimized Fast NEMO (OFNEMO) was introduced by authors of this paper. In OFNEMO a pre-establish multi tunnels concept, cross function optimization and cross layer design are used. In this paper, an analytical model is developed to evaluate total cost consisting of signaling and packet delivery costs of the OFNEMO compared with RFC3963. Results show that OFNEMO increases probability of predictive mode compared with RFC3963 due to smaller handover latency. Even though OFNEMO needs extra signalling to pre-establish multi tunnel, it has less total cost thanks to its optimized algorithm. OFNEMO can minimize handover latency for supporting real time application in moving networks.

Keywords: fast mobile IPv6, handover latency, IEEE802.16e, network mobility

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Authors: Ammara Mehmood, Aneela Zameer, Muhammad Asif Zahoor Raja, Muhammad Faisal Fateh

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In this work, the strength of bio-inspired computational intelligence based technique is exploited for parameter estimation for the periodic signals using Continuous Differential Evolution (CDE) by defining an error function in the mean square sense. Multidimensional and nonlinear nature of the problem emerging in sinusoidal signal models along with noise makes it a challenging optimization task, which is dealt with robustness and effectiveness of CDE to ensure convergence and avoid trapping in local minima. In the proposed scheme of Continuous Differential Evolution based Signal Parameter Estimation (CDESPE), unknown adjustable weights of the signal system identification model are optimized utilizing CDE algorithm. The performance of CDESPE model is validated through statistics based various performance indices on a sufficiently large number of runs in terms of estimation error, mean squared error and Thiel’s inequality coefficient. Efficacy of CDESPE is examined by comparison with the actual parameters of the system, Genetic Algorithm based outcomes and from various deterministic approaches at different signal-to-noise ratio (SNR) levels.

Keywords: parameter estimation, bio-inspired computing, continuous differential evolution (CDE), periodic signals

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Authors: Yogita Patil-Sen

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Magnetic nanoparticles such as those made of iron oxide have been widely explored as biocatalysts, contrast agents, and drug delivery systems. However, some of the challenges associated with these particles are agglomeration and biocompatibility, which lead to concern of toxicity of the particles, especially for drug delivery applications. Coating the particles with biocompatible materials such as lipids and peptides have shown to improve the mentioned issues. Thus, these core-shell type nanoparticles are emerging as the new class of nanomaterials for targeted drug delivery applications. In this study, various types of core-shell magnetic nanoparticles are prepared and characterized using techniques, such as Fourier Transform Infrared Spectroscopy (FTIR), X-ray Diffraction (XRD), Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), Vibrating Sample Magnetometer (VSM) and Thermogravimetric Analysis (TGA). The heating ability of nanoparticles is tested under oscillating magnetic field. The efficacy of the nanoparticles as drug carrier is also investigated. The loading of an anticancer drug, Doxorubicin at 18 °C is measured up to 48 hours using UV-visible spectrophotometer. The drug release profile is obtained under thermal incubation condition at 37 °C and compared with that under the influence of oscillating field. The results suggest that the core-shell nanoparticles exhibit superparamagnetic behaviour, although, coating reduces the magnetic properties of the particles. Both the uncoated and coated particles show good heating ability, again it is observed that coating decreases the heating behaviour of the particles. However, coated particles show higher drug loading efficiency than the uncoated particles and the drug release is much more controlled under the oscillating magnetic field. Thus, the results strongly indicate the suitability of the prepared core-shell type nanoparticles as drug delivery vehicles and their potential in magnetic hyperthermia applications and for hyperthermia cancer therapy.

Keywords: core-shell, hyperthermia, magnetic nanoparticles, targeted drug delivery

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Authors: Disha Gupta

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The aim of this research is to conduct X-ray and e-beam characterization techniques on lithium-ion battery materials for the improvement of battery performance. The key characterization techniques employed are the synchrotron X-ray Absorption Spectroscopy (XAS) combined with X-ray diffraction (XRD), scanning electron microscopy (SEM) and transmission electron microscopy (TEM) to obtain a more holistic approach to understanding material properties. This research effort provides additional battery characterization knowledge that promotes the development of new cathodes, anodes, electrolyte and separator materials for batteries, hence, leading to better and more efficient battery performance. Both ex-situ and in-situ synchrotron experiments were performed on LiFePO₄, one of the most common cathode material, from different commercial sources and their structural analysis, were conducted using Athena/Artemis software. This analysis technique was then further extended to study other cathode materials like LiMnxFe(₁₋ₓ)PO₄ and even some sulphate systems like Li₂Mn(SO₄)₂ and Li₂Co0.5Mn₀.₅ (SO₄)₂. XAS data were collected for Fe and P K-edge for LiFePO4, and Fe, Mn and P-K-edge for LiMnxFe(₁₋ₓ)PO₄ to conduct an exhaustive study of the structure. For the sulphate system, Li₂Mn(SO₄)₂, XAS data was collected at both Mn and S K-edge. Finite Difference Method for Near Edge Structure (FDMNES) simulations were also conducted for various iron, manganese and phosphate model compounds and compared with the experimental XANES data to understand mainly the pre-edge structural information of the absorbing atoms. The Fe K-edge XAS results showed a charge compensation occurring on the Fe atom for all the differently synthesized LiFePO₄ materials as well as the LiMnxFe(₁₋ₓ)PO₄ systems. However, the Mn K-edge showed a difference in results as the Mn concentration changed in the materials. For the sulphate-based system Li₂Mn(SO₄)₂, however, no change in the Mn K-edge was observed, even though electrochemical studies showed Mn redox reactions.

Keywords: li-ion batteries, electrochemistry, X-ray absorption spectroscopy, XRD

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Authors: Nneka Augustina Akwu, Yougasphree Naidoo

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Molecular advancement in technology has created a means whereby the atoms and molecules (solid forms) of certain materials such as plants, can now be reduced to a range of 1-100 nanometres. Green synthesis of silver nanoparticles (AgNPs) was carried out at room temperature (RT) 25 ± 2°C and 80°C, using the metabolites in the aqueous extracts of the leaves and stem bark of Grewia lasiocarpa as reductants and stabilizing agents. The biosynthesized AgNPs were characterized by UV-Vis spectrophotometry, attenuated total reflectance - Fourier transforms infrared (ATR-FTIR) spectroscopy, nanoparticle tracking analysis (NTA), Energy Dispersive X-ray fluorescence scanning electron microscope (SEM-EDXRF) and high-resolution transmission electron microscopy (HRTEM). The AgNPs were biologically evaluated for antioxidant, antibacterial and cytotoxicity activities. The phytochemical and FTIR analyses revealed the presence of metabolites that act as reducing and capping agents, while the UV-Vis spectroscopy of the biosynthesized NPs showed absorption between 380-460 nm, confirming AgNP synthesis. The Zeta potential values were between -9.1 and -20.6 mV with a hydrodynamics diameter ranging from 38.3 to 46.7 nm. SEM and HRTEM analyses revealed that AgNPs were predominately spherical with an average particle size of 2- 31 nm for the leaves and 5-27 nm for the stem bark. The cytotoxicity IC50 values of the AgNPs against HeLa, Caco-2 and MCF-7 were >1 mg/mL. The AgNPs were sensitive to all strains of bacteria used, with methicillin-resistant Staphylococcus aureus (MRSA), Staphylococcus aureus (ATCC 25923) and Escherichia coli (ATCC 25922) being more sensitive to the AgNPs. Our findings propose that antibacterial and anticancer agents could be derived from these AgNPs of G. lasiocarpa, and warrant their further investigation.

Keywords: antioxidant, cytotoxicity, Grewia lasiocarpa, silver nanoparticles, Zeta potentials

Procedia PDF Downloads 143

Authors: Gautam Kumar Saharia, Sagardeep Talukdar, Riki Dutta, Sudipta Nandy

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The physics informed neural network (PINN) method opens up an approach for numerically solving nonlinear partial differential equations leveraging fast calculating speed and high precession of modern computing systems. We construct the PINN based on strong universal approximation theorem and apply the initial-boundary value data and residual collocation points to weekly impose initial and boundary condition to the neural network and choose the optimization algorithms adaptive moment estimation (ADAM) and Limited-memory Broyden-Fletcher-Golfard-Shanno (L-BFGS) algorithm to optimize learnable parameter of the neural network. Next, we improve the PINN with a weighted loss function to obtain both the bright and dark soliton solutions of Fokas-Lenells equation (FLE). We find the proposed scheme of adjustable weight coefficients into PINN has a better convergence rate and generalizability than the basic PINN algorithm. We believe that the PINN approach to solve the partial differential equation appearing in nonlinear optics would be useful to study various optical phenomena.

Keywords: deep learning, optical Soliton, neural network, partial differential equation

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Authors: Ajay Kumar Choubey, Geeta Agnihotri, C. Sasikumar, Rashmi Dwivedi

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Deep-drawing operations are performed widely in industrial applications. It is very important for efficiency to achieve parts with no or minimum defects. Deep drawn parts are used in high performance, high strength and high reliability applications where tension, stress, load and human safety are critical considerations. Wrinkling is a kind of defect caused by stresses in the flange part of the blank during metal forming operations. To avoid wrinkling appropriate blank-holder pressure/force or drawbead can be applied. Now-a-day computer simulation plays a vital role in the field of manufacturing process. So computer simulation of manufacturing has much advantage over previous conventional process i.e. mass production, good quality of product, fast working etc. In this study, a two dimensional elasto-plastic Finite Element (F.E.) model for Mild Steel material blank has been developed to study the behavior of the flange wrinkling and deep drawing parameters under different Blank-Holder Pressure (B.H.P.). For this, commercially available Finite Element software ANSYS 14 has been used in this study. Simulation results are critically studied and salient conclusions have been drawn.

Keywords: ANSYS, deep drawing, BHP, finite element simulation, wrinkling

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Authors: Halison da Silva Pereira, Ciro Sobrinho Campolina Martins, Vitor Mainenti Leal Lopes

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Long range performance is a goal for aircraft configuration optimization. Blended Wing Body (BWB) is presented in many works of literature as the most aerodynamically efficient design for a fixed-wing aircraft. Because of its high weight to thrust ratio, BWB is the ideal configuration for many Unmanned Aerial Vehicle (UAV) missions on geomatics applications. In this work, a BWB aerodynamic design for typical light geomatics payload is presented. Aerodynamic non-dimensional coefficients are predicted using low Reynolds number computational techniques (3D Panel Method) and wing parameters like aspect ratio, taper ratio, wing twist and sweep are optimized for high cruise performance and flight quality. The methodology of this work is a summary of tailless aircraft wing design and its application, with appropriate computational schemes, to light UAV subjected to low Reynolds number flows leads to conclusions like the higher performance and flight quality of thicker airfoils in the airframe body and the benefits of using aerodynamic twist rather than just geometric.

Keywords: blended wing body, low Reynolds number, panel method, UAV

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Authors: Jinyan Li, Simon Fong, Raymond Wong, Mohammed Sabah, Fiaidhi Jinan

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Clinical data analysis and forecasting have make great contributions to disease control, prevention and detection. However, such data usually suffer from highly unbalanced samples in class distributions. In this paper, we target at the binary imbalanced dataset, where the positive samples take up only the minority. We investigate two different meta-heuristic algorithms, particle swarm optimization and bat-inspired algorithm, and combine both of them with the synthetic minority over-sampling technique (SMOTE) for processing the datasets. One approach is to process the full dataset as a whole. The other is to split up the dataset and adaptively process it one segment at a time. The experimental results reveal that while the performance improvements obtained by the former methods are not scalable to larger data scales, the later one, which we call Adaptive Swarm Balancing Algorithms, leads to significant efficiency and effectiveness improvements on large datasets. We also find it more consistent with the practice of the typical large imbalanced medical datasets. We further use the meta-heuristic algorithms to optimize two key parameters of SMOTE. Leading to more credible performances of the classifier, and shortening the running time compared with the brute-force method.

Keywords: Imbalanced dataset, meta-heuristic algorithm, SMOTE, big data

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Authors: Aminu Ololade Matilda

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Child abuse is a significant issue worldwide, affecting the socio-development and mental and physical health of young individuals. It is the maltreatment of a child by an adult or a child. This paper focuses on child abuse in Communities in Lagos State. The aim of this study is to investigate the extent of child abuse and its impact on the mood, social activities, self-worth, concentration, and academic performance of children in Communities in Lagos State. The primary research instrument used in this study was the interview (Forensic), which consisted of two sections. The first section gathered data on the details of the child and the forms and impacts of abuse experienced, while the second section focused on parental style. The study found that children who experienced various forms of abuse, such as emotional, neglect, physical, or sexual abuse, were hesitant to report it out of fear of threats or even death from the abuser. These abused children displayed withdrawn behaviour, depression, and low self-worth and underperformed academically compared to their peers who did not experience abuse. The findings align with socio-learning and intergenerational transmission of violence theories, which suggest that parents and caregivers who engage in child abuse often do so because they themselves experienced or witnessed abuse as children, thereby normalizing violence. The study highlights the prevalent issue of child abuse in Lagos State and emphasizes the need for advocacy programs and capacity building to raise awareness about child abuse and prevention. The distribution of the Child’s Rights Act in various sectors is also recommended to underscore the importance of protecting the rights of children. Additionally, the inclusion of courses on child abuse in the school curriculum is proposed to ensure children are educated on recognizing and reporting abuse.

Keywords: abuse, child, awareness, effects, emotional, neglect, physical, psychological, sexual, recognize, reporting, right

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Authors: S. B. Mayil Vealan, C. Sekar

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Bisphenol A (BIS A) and 4 Nitrophenol (4N) are the most prevalent environmental endocrine-disrupting chemicals which mimic hormones and have a direct relationship to the development and growth of animal and human reproductive systems. Moreover, intensive exposure to the compound is related to prostate and breast cancer, infertility, obesity, and diabetes. Hence, accurate and reliable determination techniques are crucial for preventing human exposure to these harmful chemicals. Lanthanum Copper Oxide (La₂Cu₂O₅) nanoparticles were synthesized and investigated through various techniques such as scanning electron microscopy, high-resolution transmission electron microscopy, X-ray diffraction, X-ray photoelectron spectroscopy, and electrochemical impedance spectroscopy. Cyclic voltammetry and square wave voltammetry techniques are employed to evaluate the electrochemical behavior of as-synthesized samples toward the electrochemical detection of Bisphenol A and 4-Nitrophenol. Under the optimal conditions, the oxidation current increased linearly with increasing the concentration of BIS A and 4-N in the range of 0.01 to 600 μM with a detection limit of 2.44 nM and 3.8 nM. These are the lowest limits of detection and the widest linear ranges in the literature for this determination. The method was applied to the simultaneous determination of BIS A and 4-N in real samples (food packing materials and river water) with excellent recovery values ranging from 95% to 99%. Better stability, sensitivity, selectivity and reproducibility, fast response, and ease of preparation made the sensor well-suitable for the simultaneous determination of bisphenol and 4 Nitrophenol. To the best of our knowledge, this is the first report in which La₂Cu₂O₅ nano particles were used as efficient electron mediators for the fabrication of endocrine disruptor (BIS A and 4N) chemical sensors.

Keywords: endocrine disruptors, electrochemical sensor, Food contacting materials, lanthanum cuprates, nanomaterials

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Authors: D. S. Naumann, B. J. Evans, O. Hassan

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This paper outlines the development of an automated aerodynamic optimisation algorithm using a novel method of parameterising a computational mesh by employing user–defined control nodes. The shape boundary movement is coupled to the movement of the novel concept of the control nodes via a quasi-1D-linear deformation. Additionally, a second order smoothing step has been integrated to act on the boundary during the mesh movement based on the change in its second derivative. This allows for both linear and non-linear shape transformations dependent on the preference of the user. The domain mesh movement is then coupled to the shape boundary movement via a Delaunay graph mapping. A Modified Cuckoo Search (MCS) algorithm is used for optimisation within the prescribed design space defined by the allowed range of control node displacement. A finite volume compressible NavierStokes solver is used for aerodynamic modelling to predict aerodynamic design fitness. The resulting coupled algorithm is applied to a range of test cases in two dimensions including the design of a subsonic, transonic and supersonic intake and the optimisation approach is compared with more conventional optimisation strategies. Ultimately, the algorithm is tested on a three dimensional wing optimisation case.

Keywords: mesh movement, aerodynamic shape optimization, cuckoo search, shape parameterisation

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Authors: Kaushik Sathupadi, Sandesh Achar

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Human action recognition modeling is a critical task in machine learning. These systems require better techniques for recognizing body parts and selecting optimal features based on vision sensors to identify complex action patterns efficiently. Still, there is a considerable gap and challenges between images and videos, such as brightness, motion variation, and random clutters. This paper proposes a robust approach for classifying human actions over cloud-based image data. First, we apply pre-processing and detection, human and outer shape detection techniques. Next, we extract valuable information in terms of cues. We extract two distinct features: fuzzy local binary patterns and sequence representation. Then, we applied a greedy, randomized adaptive search procedure for data optimization and dimension reduction, and for classification, we used a random forest. We tested our model on two benchmark datasets, AAMAZ and the KTH Multi-view football datasets. Our HMR framework significantly outperforms the other state-of-the-art approaches and achieves a better recognition rate of 91% and 89.6% over the AAMAZ and KTH multi-view football datasets, respectively.

Keywords: computer vision, human motion analysis, random forest, machine learning

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Authors: Masoud Soltanialvar, Ali Bagherpour

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Documented cases of human infection with H9N2 avian influenza viruses, first detected in 1999 in Hong Kong and China, indicate that these viruses can be directly transmitted from birds to humans. In this study, we characterized the mutation in the Hemagglutinin (HA) genes and proteins that correlates with a shift in affinity of the Hemagglutinin (HA) protein from the “avian” type sialic receptors to the “human” type in 10 Iranian isolates. We delineated the genomes and receptor binding profile of HA gene of some field isolates and established their phylogenetic relationship to the other Asian H9N2 sub lineages. A total of 1200 tissue samples collected from 40 farms located in various states of Iran during 2008 – 2010 as part of a program to monitor Avian Influenza Viruses (AIV) infection. To determine the genetic relationship of Iranian viruses, the Hemagglutinin (HA) genes from ten isolates were amplified and sequenced (by RT-PCR method). Nucleotide sequences (orf) of the (HA) genes were used for phylogenetic tree construction. Deduced amino acid sequences showed the presence of L226 (234 in H9 numbering) in all ten Iranian isolates which indicates a preference to binding of α (2–6) sialic acid receptors, so these Iranian H9N2 viruses have the potential to infect human beings. These isolates showed high degree of homology with 2 human H9N2 isolates A/HK/1073/99, A/HK/1074/99. Phylogenetic analysis of showed that all the HA genes of the Iranian H9N2 viruses fall into a single group within a G1-like sublineage which had contributed as donor of six internal genes to H5N1 highly pathogenic avian influenza. The results of this study indicated that all Iranian viruses have the potential to emerge as highly pathogenic influenza virus, and considering the homology of these isolates with human H9N2 strains, it seems that the potential of these avian influenza isolates to infect human should not be overlooked.

Keywords: influenza virus, hemagglutinin, neuraminidase, Iran

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Authors: Muhammad Habib, Lin Tang, Guoliang Xue, Attaur Rahman, Myong-Ho Kim, Soonil Lee, Xuefan Zhou, Yan Zhang, Dou Zhang

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Designing a high-performance, lead-free ceramic has become a cutting-edge research topic due to growing concerns about the toxic nature of lead-based materials. In this work, a convenient strategy of compositional design and domain engineering is applied to the lead-fee BiFeO₃-BaTiO₃ ceramics, which provides a flexible polarization-free-energy profile for domain switching. Here, simultaneously enhanced dynamic piezoelectric constant (d33* = 772 pm/V) and a good thermal-stability (d33* = 26% over the temperature of 20-180 ᵒC) are achieved with a high Curie temperature (TC) of 432 ᵒC. This high piezoelectric strain performance is collectively attributed to multiple effects such as thermal quenching, suppression of defect charges by donor doping, chemically induced local structure heterogeneity, and electric field-induced phase transition. Furthermore, the addition of BT content decreased octahedral tilting, reduced anisotropy for domain switching and increased tetragonality (cₜ/aₜ), providing a wider polar length for B-site cation displacement, leading to high piezoelectric strain performance. Atomic-resolution transmission electron microscopy and piezoelectric force microscopy combined with X-ray diffraction results strongly support the origin of high piezoelectricity. The high and temperature-stable piezoelectric strain response of this work is superior to those of other lead-free ceramics. The synergistic approach of composition design and the concept present here for the origin of high strain response provides a paradigm for the development of materials for high-temperature piezoelectric actuator applications.

Keywords: Piezoelectric, BiFeO3-BaTiO3, Quenching, Temperature-insensitive

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Authors: Felix Boehnisch, Alexander Lutz

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Based on the number of users and the amount of content posted daily, Facebook is considered the largest social network in the world. This content includes images or text posts from companies but also private persons, which are also commented on by other users. However, it can sometimes be difficult for companies to keep track of all the posts and the reactions to them, especially when there are several posts a day that contain hundreds to thousands of comments. To facilitate this, the following paper deals with the possible applications of sentiment analysis to social media comments in order to be able to support the work in social media marketing. In a first step, post comments were divided into positive and negative by a subjective rating, then the same comments were checked for their polarity value by the two german python libraries TextBlobDE and SentiWS and also grouped into positive, negative, or even neutral. As a control, the subjective classifications were compared with the machine-generated ones by a confusion matrix, and relevant quality criteria were determined. The accuracy of both libraries was not really meaningful, with 60% to 66%. However, many words or sentences were not evaluated at all, so there seems to be room for optimization to possibly get more accurate results. In future studies, the use of these specific German libraries can be optimized to gain better insights by either applying them to stricter cleaned data or by adding a sentiment value to emojis, which have been removed from the comments in advance, as they are not contained in the libraries.

Keywords: Facebook, German libraries, polarity, sentiment analysis, social media comments

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Authors: Sena Su, Burak Ozbek, Yesim M. Sahin, Sevil Yucel, Dilek Kazan, Faik N. Oktar, Nazmi Ekren, Oguzhan Gunduz

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In recent years, protein, lipid or polysaccharide-based polymers have been used in order to develop biodegradable materials and their chemical nature determines the physical properties of the resulting films. Among these polymers, proteins from different sources have been extensively employed because of their relative abundance, film forming ability, and nutritional qualities. In this study, the biodegradable composite nanofiber films based on fish sarcoplasmic protein (FSP) were prepared via electrospinning technique. Biodegradable polycaprolactone (PCL) was blended with the FSP to obtain hybrid FSP/PCL nanofiber mats with desirable physical properties. Mixture solutions of FSP and PCL were produced at different concentrations and their density, viscosity, electrical conductivity and surface tension were measured. Mechanical properties of electrospun nanofibers were evaluated. Morphology of composite nanofibers was observed using scanning electron microscopy (SEM). Moreover, Fourier transform infrared spectrometer (FTIR) studies were used for analysis chemical composition of composite nanofibers. This study revealed that the FSP based nanofibers have the potential to be used for different applications such as biodegradable packaging, drug delivery, and wound dressing, etc.

Keywords: edible film, electrospinning, fish sarcoplasmic protein, nanofiber

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Authors: M. Kouyate, M. Sangare, S. Samake, S. Keita, H. G. Kim, D. H. Geschwind

Abstract:

Background & Objectives: Human genetic studies can be expensive, even unaffordable, in developing countries, partly due to the sequencing costs. Our aim is to pilot the use of bioinformatics tools to guide scientifically valid, locally relevant, and economically sound autism genetic research in Mali. Methods: The following databases, NCBI, HGMD, and LSDB, were used to identify hot point mutations. Phenotype, transmission pattern, theoretical protein expression in the brain, the impact of the mutation on the 3D structure of the protein) were used to prioritize selected autism genes. We used the protein database, Modeller, and clustal W. Results: We found Mef2c (Gly27Ala/Leu38Gln), Pten (Thr131IIle), Prodh (Leu289Met), Nme1 (Ser120Gly), and Dhcr7 (Pro227Thr/Glu224Lys). These mutations were associated with endonucleases BseRI, NspI, PfrJS2IV, BspGI, BsaBI, and SpoDI, respectively. Gly27Ala/Leu38Gln mutations impacted the 3D structure of the Mef2c protein. Mef2c protein sequences across species showed a high percentage of similarity with a highly conserved MADS domain. Discussion: Mef2c, Pten, Prodh, Nme1, and Dhcr 7 gene mutation frequencies in the Malian population will be very informative. PCR coupled with restriction enzyme digestion can be used to screen the targeted gene mutations. Sanger sequencing will be used for confirmation only. This will cut down considerably the sequencing cost for gene-to-gene mutation screening. The knowledge of the 3D structure and potential impact of the mutations on Mef2c protein informed the protein family and altered function (ex. Leu38Gln). Conclusion & Future Work: Bio-informatics will positively impact autism research in Mali. Our approach can be applied to another neuropsychiatric disorder.

Keywords: bioinformatics, endonucleases, autism, Sanger sequencing, point mutations

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Authors: Wei-Jing Li, Ren-Xuan Yang, Kui-Hao Chuang, Ming-Yen Wey

Abstract:

Nowadays, plastic product and utilization are extensive and have greatly improved our life. Yet, plastic wastes are stable and non-biodegradable challenging issues to the environment. Waste-to-energy strategies emerge a promising way for waste management. This work investigated the co-production of hydrogen and carbon nanotubes from the syngas which was from the gasification of polypropylene. A nickel-silica core-shell catalyst was applied for syngas reaction from plastic waste gasification in a fixed-bed reactor. SiO2 were prepared through various synthesis solvents by Stöber process. Ni plays a role as modified SiO2 support, which were synthesized by deposition-precipitation method. Core-shell catalysts have strong interaction between active phase and support, in order to avoid catalyst sintering. Moreover, Fe or Co metal acts as promoter to enhance catalytic activity. The effects of calcined atmosphere, second metal addition, and reaction temperature on hydrogen production and carbon yield were examined. In this study, the catalytic activity and carbon yield results revealed that the Ni/SiO2 catalyst calcined under H2 atmosphere exhibited the best performance. Furthermore, Co promoted Ni/SiO2 catalyst produced 3 times more than Ni/SiO2 on carbon yield at long-term operation. The structure and morphological nature of the calcined and spent catalysts were examined using different characterization techniques including scanning electron microscopy, transmission electron microscopy, X-ray diffraction. In addition, the quality and thermal stability of the nano-carbon materials were also evaluated by Raman spectroscopy and thermogravimetric analysis.

Keywords: plastic wastes, hydrogen, carbon nanotube, core-shell catalysts

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Authors: Olivia Borgue, Massimo Panarotto, Ola Isaksson

Abstract:

This article presents a neural networks-based strategy for reducing the need for support structures when designing for additive manufacturing (AM). Additive manufacturing is a relatively new and immature industrial technology, and the information to make confident decisions when designing for AM is limited. This lack of information impacts especially the early stages of engineering design, for instance, it is difficult to actively consider the support structures needed for manufacturing a part. This difficulty is related to the challenge of designing a product geometry accounting for customer requirements, manufacturing constraints and minimization of support structure. The approach presented in this article proposes an automatized geometry modification technique for reducing the use of the support structures while designing for AM. This strategy starts with a neural network-based strategy for shape recognition to achieve product classification, using an STL file of the product as input. Based on the classification, an automatic part geometry modification based on MATLAB© is implemented. At the end of the process, the strategy presents different geometry modification alternatives depending on the type of product to be designed. The geometry alternatives are then evaluated adopting a QFD-like decision support tool.

Keywords: additive manufacturing, engineering design, geometry modification optimization, neural networks

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Authors: Xiao-Yin Liu, Liang-Xue Zhou

Abstract:

Traumatic brain injury (TBI), as a kind of nerve trauma caused by an external force, affects people all over the world and is a global public health problem. Although there are various clinical treatments for brain injury, including surgery, drug therapy, and rehabilitation therapy, the therapeutic effect is very limited. To improve the therapeutic effect of TBI, scaffolds combined with exosomes are a promising but challenging method for TBI repair. In this study, we examined whether a novel 3D-printed collagen/chitosan scaffold/exosomes derived from neural stem cells (NSCs) pretreated with insulin growth factor-1 (IGF-I) scaffolds (3D-CC-INExos) could be used to improve TBI repair and functional recovery after TBI. Our results showed that composite scaffolds of collagen-, chitosan- and exosomes derived from NSCs pretreated with IGF-I (INExos) could continuously release the exosomes for two weeks. In the rat TBI model, 3D-CC-INExos scaffold transplantation significantly improved motor and cognitive function after TBI, as assessed by the Morris water maze test and modified neurological severity scores. In addition, immunofluorescence staining and transmission electron microscopy showed that the recovery of damaged nerve tissue in the injured area was significantly improved by 3D-CC-INExos implantation. In conclusion, our data suggest that 3D-CC-INExos might provide a potential strategy for the treatment of TBI and lay a solid foundation for clinical translation.

Keywords: traumatic brain injury, exosomes, insulin growth factor-1, neural stem cells, collagen, chitosan, 3D printing, neural regeneration, angiogenesis, functional recovery

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Authors: Felix O. Omosele

Abstract:

Several studies have established that electronic voting machines are prone to multi-faceted challenges. One of which is their capacity to lose votes after the ballots might have been cast. Therefore, the international consensus appears to favour the use of electronic voting machines that are accompanied with verifiable audit paper audit trail (VVPAT). At present, there is no known study that has evaluated the impacts (or otherwise) of this verification and auditing on the authentication, admissibility and evidential weight of electronically-obtained electoral data. This legal inquiry is important as elections are sometimes won or lost in courts and on the basis of such data. This gap will be filled by the present research work. Using the United States of America as a case study, this paper employed a qualitative legal analysis of several of its appellate courts’ judicial opinions. This analysis equally unearths the necessary statutory rules and regulations that are important to the research problem. The objective of the research is to highlight the roles played by VVPAT on electoral evidence- as seen from the eyes of the court. The preliminary outcome of this qualitative analysis shows that the admissibility and weight attached to ‘Computer Evidence from e-voting machines (CEEM)’ are often treated with general standards applied to other computer-stored evidence. These standards sometimes fail to embrace the peculiar challenges faced by CEEM, particularly with respect to their tabulation and transmission. This paper, therefore, argues that CEEM should be accorded unique consideration by courts. It proposes the development of a legal standard which recognises verification and auditing as ‘weight enhancers’ for electronically-obtained electoral data.

Keywords: admissibility of computer evidence, electronic voting, qualitative legal analysis, voting machines in the USA

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Authors: Linhang Zhu, Hongyu Zhu, Jiahe Liu

Abstract:

This paper investigated a method of indoor real-time positioning and mapping based on the Manhattan world assumption. In indoor environments, relying solely on feature matching techniques or other geometric algorithms for sensor pose estimation inevitably resulted in cumulative errors, posing a significant challenge to indoor positioning. To address this issue, we adopt the Manhattan world hypothesis to optimize the camera pose algorithm based on feature matching, which improves the accuracy of camera pose estimation. A special processing method was applied to image data frames that conformed to the Manhattan world assumption. When similar data frames appeared subsequently, this could be used to eliminate drift in sensor pose estimation, thereby reducing cumulative errors in estimation and optimizing mapping and positioning. Through experimental verification, it is found that our method achieves high-precision real-time positioning in indoor environments and successfully generates maps of indoor environments. This provides effective technical support for applications such as indoor navigation and robot control.

Keywords: Manhattan world hypothesis, real-time positioning and mapping, feature matching, loopback detection

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