Search results for: dimensional accuracy of holes drilled in composites

Authors: Zeinab Jafari A., Ali Sharifnezhad B., Mohammad Razi C., Mohammad Haghpanahi D., Arash Maghsoudi

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Background and Objective: Sports-related rupture of the anterior cruciate ligament (ACL) and following injuries have been associated with various disorders, such as long-lasting changes in muscle activation patterns in athletes, which might last after ACL reconstruction (ACLR). The rupture of the ACL might result in abnormal patterns of movement execution, extending the treatment period and delaying athletes’ return to sports (RTS). As ACL injury is especially prevalent among athletes, the lengthy treatment process and athletes’ absence from sports are of great concern to athletes and coaches. Thus, estimating safe time of RTS is of crucial importance. Therefore, using a deep neural network (DNN) to classify the health levels of ACL in injured athletes, this study aimed to estimate the safe time for athletes to return to competitions. Methods: Ten athletes with ACLR and fourteen healthy controls participated in this study. Three health levels of ACL were defined: healthy, six-month post-ACLR surgery and nine-month post-ACLR surgery. Athletes with ACLR were tested six and nine months after the ACLR surgery. During the course of this study, surface electromyography (sEMG) signals were recorded from five knee muscles, namely Rectus Femoris (RF), Vastus Lateralis (VL), Vastus Medialis (VM), Biceps Femoris (BF), Semitendinosus (ST), during single-leg drop landing (SLDL) and forward hopping (SLFH) tasks. The Pseudo-Wigner-Ville distribution (PWVD) was used to produce three-dimensional (3-D) images of the energy distribution patterns of sEMG signals. Then, these 3-D images were converted to two-dimensional (2-D) images implementing the heat mapping technique, which were then fed to a deep convolutional neural network (DCNN). Results: In this study, we estimated the safe time of RTS by designing a DCNN classifier with an accuracy of 90 %, which could classify ACL into three health levels. Discussion: The findings of this study demonstrate the potential of the DCNN classification technique using sEMG signals in estimating RTS time, which will assist in evaluating the recovery process of ACLR in athletes.

Keywords: anterior cruciate ligament reconstruction, return to sports, surface electromyography, deep convolutional neural network

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Authors: Pablo Martin, Jorge Olivares, Fernando Maass

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A method to obtain analytic approximations for special function of interest in engineering and physics is described here. Each approximate function will be valid for every positive value of the variable and accuracy will be high and increasing with the number of parameters to determine. The general technique will be shown through an application to the modified Bessel function of order zero, I₀(x). The form and the calculation of the parameters are performed with the simultaneous use of the power series and asymptotic expansion. As in Padé method rational functions are used, but now they are combined with other elementary functions as; fractional powers, hyperbolic, trigonometric and exponential functions, and others. The elementary function is determined, considering that the approximate function should be a bridge between the power series and the asymptotic expansion. In the case of the I₀(x) function two analytic approximations have been already determined. The simplest one is (1+x²/4)⁻¹/⁴(1+0.24273x²) cosh(x)/(1+0.43023x²). The parameters of I₀(x) were determined using the leading term of the asymptotic expansion and two coefficients of the power series, and the maximum relative error is 0.05. In a second case, two terms of the asymptotic expansion were used and 4 of the power series and the maximum relative error is 0.001 at x≈9.5. Approximations with much higher accuracy will be also shown. In conclusion a new technique is described to obtain analytic approximations to some functions of interest in sciences, such that they have a high accuracy, they are valid for every positive value of the variable, they can be integrated and differentiated as the usual, functions, and furthermore they can be calculated easily even with a regular pocket calculator.

Keywords: analytic approximations, mathematical-physics applications, quasi-rational functions, special functions

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Authors: Kaustubh Chakradeo, Michael Delves, Sofya Titarenko

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Malaria is a serious disease which affects hundreds of millions of people around the world, each year. If not treated in time, it can be fatal. Despite recent developments in malaria diagnostics, the microscopy method to detect malaria remains the most common. Unfortunately, the accuracy of microscopic diagnostics is dependent on the skill of the microscopist and limits the throughput of malaria diagnosis. With the development of Artificial Intelligence tools and Deep Learning techniques in particular, it is possible to lower the cost, while achieving an overall higher accuracy. In this paper, we present a VGG-based model and compare it with previously developed models for identifying infected cells. Our model surpasses most previously developed models in a range of the accuracy metrics. The model has an advantage of being constructed from a relatively small number of layers. This reduces the computer resources and computational time. Moreover, we test our model on two types of datasets and argue that the currently developed deep-learning-based methods cannot efficiently distinguish between infected and contaminated cells. A more precise study of suspicious regions is required.

Keywords: convolution neural network, deep learning, malaria, thin blood smears

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Authors: Joyraj Chakraborty, Geoffrey Ottoy, Jean-Pierre Goemaere, Lieven De Strycker

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Sensor swarms can be a cost-effectieve and more user-friendly alternative for location based service systems in different application like health-care. To increase the lifetime of such swarm networks, the energy consumption should be scaled to the required localization accuracy. In this paper we have investigated some parameter for energy model that couples localization accuracy to energy-related sensor parameters such as signal length,Bandwidth and sample frequency. The goal is to use the model for the localization of undetermined environmental sounds, by means of wireless acoustic sensors. we first give an overview of TDOA-based localization together with the primary sources of TDOA error (including reverberation effects, Noise). Then we show that in localization, the signal sample rate can be under the Nyquist frequency, provided that enough frequency components remain present in the undersampled signal. The resulting localization error is comparable with that of similar localization systems.

Keywords: sensor swarms, localization, wireless sensor swarms, scalable energy

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Authors: S. Ghosh, L. Ramos, A. N. Kouamé, A.-L. Teillout, H. Remita

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Catalytic materials have attracted continuous attention due to their promising applications in a variety of energy and environmental applications including clean energy, energy conversion and storage, purification and separation, degradation of pollutants and electrochemical reactions etc. With the advanced synthetic technologies, polymer nanostructures and nanocomposites can be directly synthesized through soft template mediated approach using swollen hexagonal mesophases and modulate the size, morphology, and structure of polymer nanostructures. As an alternative to conventional catalytic materials, one-dimensional PDPB polymer nanostructures shows high photocatalytic activity under visible light for the degradation of pollutants. These photocatalysts are very stable with cycling. Transmission electron microscopy (TEM), and AFM-IR characterizations reveal that the morphology and structure of the polymer nanostructures do not change after photocatalysis. These stable and cheap polymer nanofibers and metal polymer nanocomposites are easy to process and can be reused without appreciable loss of activity. The polymer nanocomposites formed via one pot chemical redox reaction with 3.4 nm Pd nanoparticles on poly(diphenylbutadiyne) (PDPB) nanofibers (30 nm). The reduction of Pd (II) ions is accompanied by oxidative polymerization leading to composites materials. Hybrid Pd/PDPB nanocomposites used as electrode materials for the electrocatalytic oxidation of ethanol without using support of proton exchange Nafion membrane. Hence, these conducting polymer nanofibers and nanocomposites offer the perspective of developing a new generation of efficient photocatalysts for environmental protection and in electrocatalysis for fuel cell applications.

Keywords: conducting polymer, swollen hexagonal mesophases, solar photocatalysis, electrocatalysis, water depollution

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Authors: Kamil Dydek, Szymon Demski, Kamil Majchrowicz, Paulina Kozera, Bogna Sztorch, Dariusz Brząkalski, Zuzanna Krawczyk, Robert Przekop, Anna Boczkowska

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Carbon fibre reinforced polymers (CFRPs) are characterized by very high strength and stiffness in relation to their weight. In addition, properties such as corrosion resistance and low thermal expansion allow them to replace traditional materials, i.e., wood or metals, in many industries such as aerospace, automotive, marine, and sports goods. However, CFRPs, have some disadvantages -they have relatively low electrical conductivity and break brittle, which significantly limits their application possibilities. Moreover, conventional CFRPs are usually manufactured based on thermosets, which makes them difficult to recycle. The solution to these drawbacks is the use of the innovative thermoplastic resin (ELIUM from ARKEMA) as a matrix of composites and the modification by introducing into their structure thermoplastic nonwovens based on PET-G with the addition of multi-wall carbon nanotubes (MWCNTs). The acrylic-carbon composites, which were produced by the infusion technique, were tested for mechanical, thermo-mechanical, and electrical properties, and the effect of modifications on their microstructure was studied. Acknowledgment: This study was carried out with funding from grant no. LIDER/46/0185/L-11/19/NCBR/2020, financed by The National Centre for Research and Development.

Keywords: CFRP, MWCNT, ELIUM, electrical properties, infusion

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Authors: Laura Dembovska, Ina Pundiene, Diana Bajare

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Alkali-activated aluminium-silicate composites (AASC) can be used in the production of innovative materials with a wide range of properties and applications. AASC are associated with low CO₂ emissions; in the production process, it is possible to use industrial by-products and waste, thereby minimizing the use of a non-renewable natural resource. This study deals with the preparation of heat-resistant porous AASC based on chamotte for high-temperature applications up to 1200°C. Different fillers, aluminium scrap recycling waste as pores forming agent and alkali activation with 6M sodium hydroxide (NaOH) and potassium hydroxide (KOH) solution were used. Sodium hydroxide (NaOH) is widely used for the synthesis of AASC compared to potassium hydroxide (KOH), but comparison of using different activator for geopolymer synthesis is not well established. Changes in chemical composition of AASC during heating were identified and quantitatively analyzed by using DTA, dimension changes during the heating process were determined by using HTOM, pore microstructure was examined by SEM, and mineralogical composition of AASC was determined by XRD. Lightweight porous AASC activated with NaOH have been obtained with density in range from 600 to 880 kg/m³ and compressive strength from 0.8 to 2.7 MPa, but for AAM activated with KOH density was in range from 750 to 850 kg/m³ and compressive strength from 0.7 to 2.1 MPa.

Keywords: alkali activation, alkali activated materials, elevated temperature application, heat resistance

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Authors: Vishal Mahale, Jayashree Bijwe, Sujeet K. Sinha

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Range of thermal conductivity (TC) of Friction Materials (FMs) is a critical issue since lower TC leads to accumulation of frictional heat on the working surface, which results in excessive fade while higher TC leads to excessive heat flow towards back-plate resulting in boiling of brake-fluid leading to ‘spongy brakes’. This phenomenon prohibits braking action, which is most undesirable. Therefore, TC of the FMs across the brake pads should not be high while along the brake pad, it should be high. To enhance TC, metals in the forms of powder and fibers are used in the FMs. Apart from TC improvement, metals provide strength and structural integrity to the composites. Due to higher TC Copper (Cu) powder/fiber is a most preferred metallic ingredient in FM industry. However, Cu powders/fibers are responsible for metallic wear debris generation, which has harmful effects on aquatic organisms. Hence to get rid of a problem of metallic wear debris generation and to keep the positive effect of TC improvement, incorporation of Cu fabric in NAO brake-pads can be an innovative solution. Keeping this in view, two realistic multi-ingredient FM composites with identical formulations were developed in the form of brake-pads. Out of which one composite series consisted of a single layer of Cu fabric in the body of brake-pad and designated as C1 while double layer of Cu fabric was incorporated in another brake-pad series with designation of C2. Distance of Cu fabric layer from the back-plate was kept constant for C1 and C2. One more composite (C0) was developed without Cu fabric for the sake of comparison. Developed composites were characterized for physical properties. Tribological performance was evaluated on full scale inertia dynamometer by following JASO C 406 testing standard. It was concluded that Cu fabric successfully improved fade resistance by increasing conductivity of the composite and also showed slight improvement in wear resistance. Worn surfaces of pads and disc were analyzed by SEM and EDAX to study wear mechanism.

Keywords: brake inertia dynamometer, copper fabric, non-asbestos organic (NAO) friction materials, thermal conductivity enhancement

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Authors: Muthukumarasamy Govindarajan

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Detection of unwanted, unsolicited mails called spam from email is an interesting area of research. It is necessary to evaluate the performance of any new spam classifier using standard data sets. Recently, ensemble-based classifiers have gained popularity in this domain. In this research work, an efficient email filtering approach based on ensemble methods is addressed for developing an accurate and sensitive spam classifier. The proposed approach employs Naive Bayes (NB), Support Vector Machine (SVM) and Genetic Algorithm (GA) as base classifiers along with different ensemble methods. The experimental results show that the ensemble classifier was performing with accuracy greater than individual classifiers, and also hybrid model results are found to be better than the combined models for the e-mail dataset. The proposed ensemble-based classifiers turn out to be good in terms of classification accuracy, which is considered to be an important criterion for building a robust spam classifier.

Keywords: accuracy, arcing, bagging, genetic algorithm, Naive Bayes, sentiment mining, support vector machine

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Authors: Mohammad Zavid Parvez, Manoranjan Paul

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A seizure prediction method is proposed by extracting global features using phase correlation between adjacent epochs for detecting relative changes and local features using fluctuation/deviation within an epoch for determining fine changes of different EEG signals. A classifier and a regularization technique are applied for the reduction of false alarms and improvement of the overall prediction accuracy. The experiments show that the proposed method outperforms the state-of-the-art methods and provides high prediction accuracy (i.e., 97.70%) with low false alarm using EEG signals in different brain locations from a benchmark data set.

Keywords: Epilepsy, seizure, phase correlation, fluctuation, deviation.

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Authors: Jae Ni Jang, Young Uk Kim

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Background: Chat Generative Pre-training Transformer-3 (ChatGPT; San Francisco, California, Open Artificial Intelligence) is an artificial intelligence chatbot based on a large language model designed to generate human-like text. As the usage of ChatGPT is increasing among less knowledgeable patients, medical students, and anesthesia and pain medicine residents or trainees, we aimed to evaluate the accuracy of ChatGPT-3 responses to questions about the American Society of Anesthesiologists (ASA) classification based on patients’ underlying diseases and assess the quality of the generated responses. Methods: A total of 47 questions were submitted to ChatGPT using textual prompts. The questions were designed for ChatGPT-3 to provide answers regarding ASA classification in response to common underlying diseases frequently observed in adult patients. In addition, we created 18 questions regarding the ASA classification for pediatric patients and pregnant women. The accuracy of ChatGPT’s responses was evaluated by cross-referencing with Miller’s Anesthesia, Morgan & Mikhail’s Clinical Anesthesiology, and the American Society of Anesthesiologists’ ASA Physical Status Classification System (2020). Results: Out of the 47 questions pertaining to adults, ChatGPT -3 provided correct answers for only 23, resulting in an accuracy rate of 48.9%. Furthermore, the responses provided by ChatGPT-3 regarding children and pregnant women were mostly inaccurate, as indicated by a 28% accuracy rate (5 out of 18). Conclusions: ChatGPT provided correct responses to questions relevant to the daily clinical routine of anesthesiologists in approximately half of the cases, while the remaining responses contained errors. Therefore, caution is advised when using ChatGPT to retrieve anesthesia-related information. Although ChatGPT may not yet be suitable for clinical settings, we anticipate significant improvements in ChatGPT and other large language models in the near future. Regular assessments of ChatGPT's ASA classification accuracy are essential due to the evolving nature of ChatGPT as an artificial intelligence entity. This is especially important because ChatGPT has a clinically unacceptable rate of error and hallucination, particularly in pediatric patients and pregnant women. The methodology established in this study may be used to continue evaluating ChatGPT.

Keywords: American Society of Anesthesiologists, artificial intelligence, Chat Generative Pre-training Transformer-3, ChatGPT

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Authors: Hatim Laalej, Jon Stammers

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In machining, monitoring of tool wear is essential for achieving the desired dimensional accuracy and surface finish of a machined workpiece. Currently, the task of monitoring the wear on the cutting tool is carried out by the operator who performs manual inspections of the cutting tool, causing undesirable stoppages of machine tools and consequently resulting in costs incurred from loss of productivity. The cutting tool consumable costs may also be higher than necessary when tools are changed before the end of their useful life. Furthermore, damage can be caused to the workpiece when tools are not changed soon enough leading to a significant increase in the costs of manufacturing. The present study is concerned with the development of break sensor printed on the flank surface of poly-crystalline diamond (PCD) cutting to perform on-line condition monitoring of the cutting tool used to machine Titanium Ti-6al-4v bar. The results clearly show that there is a strong correlation between the break sensor measurements and the amount of wear in the cutting tool. These findings are significant in that they help the user/operator of the machine tool to determine the condition of the cutting tool without the need of performing manual inspection, thereby reducing the manufacturing costs such as the machine down time.

Keywords: machining, manufacturing, tool wear, signal processing

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Authors: Laura Pytel, Georg Baumann, Gregor Gstrein, Corina Klug

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Premium cars often coat the instrument panels with a bilaminate consisting of a soft foam and a plastic skin. The coating is torn open during the passenger airbag deployment under high strain rates. Characterizing and simulating the top coat layer is crucial for predicting the attenuation that delays the airbag deployment, effecting the design of the restrain system and to reduce the demand of simulation adjustments through expensive physical component testing.Up to now, bilaminates used within cars either have been modelled by using a two-dimensional shell formulation for the whole coating system as one which misses out the interaction of the two layers or by combining a three-dimensional formulation foam layer with a two-dimensional skin layer but omitting the foam in the significant parts like the expected tear line area and the hinge where high compression is expected. In both cases, the properties of the coating causing the attenuation are not considered. Further, at present, the availability of material information, as there are failure dependencies of the two layers, as well as the strain rate of up to 200 1/s, are insufficient. The velocity of the passenger airbag flap during an airbag shot has been measured with about 11.5 m/s during first ripping; the digital image correlation evaluation showed resulting strain rates of above 1500 1/s. This paper provides a high strain rate material characterization of a bilaminate consisting of a thin polypropylene foam and a thermoplasctic olefins (TPO) skin and the creation of validated material models. With the help of a Split Hopkinson tension bar, strain rates of 1500 1/s were within reach. The experimental data was used to calibrate and validate a more physical modelling approach of the forced ripping of the bilaminate. In the presented model, the three-dimensional foam layer is continuously tied to the two-dimensional skin layer, allowing failure in both layers at any possible position. The simulation results show a higher agreement in terms of the trajectory of the flaps and its velocity during ripping. The resulting attenuation of the airbag deployment measured by the contact force between airbag and flaps increases and serves usable data for dimensioning modules of an airbag system.

Keywords: bilaminate ripping behavior, High strain rate material characterization and modelling, induced material failure, TPO and foam

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Authors: Abu Harera Nadeem, Kingsley Donkor

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Tea is a popular beverage due to its flavour, aroma and antioxidant properties—with the most consumed varieties being green and black tea. Antioxidants in tea can lower the risk of Alzheimer’s and heart disease and obesity. However, these teas contain heavy metals such as Hg, Cd, or Pb, which can cause autoimmune diseases like Graves disease. In this study, 11 heavy metals in various commercial green, black, and oolong tea samples were determined using inductively coupled plasma-mass spectrometry (ICP-MS). Two methods of sample preparation were compared for accuracy and precision, which were microwave-assisted digestion and ultrasonic-assisted extraction. The developed method was further validated by detection limit, precision, and accuracy. Results showed that the proposed method was highly sensitive with detection limits within parts-per-billion levels. Reasonable method accuracy was obtained by spiked experiments. The findings of this study can be used to delve into the link between tea consumption and disease and to provide information for future studies on metal determination in tea.

Keywords: ICP-MS, green tea, black tea, microwave-assisted acid digestion, ultrasound-assisted extraction

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Authors: Ibrahim Alsendid, Rob Sturman, Benjamin Sharp

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In this paper, we consider a family of 2-dimensional nonlinear area-preserving transformations on the torus. A single parameter η varies between 0 and 1, taking the transformation from a hyperbolic toral automorphism to the “Cerbelli-Giona” map, a system known to exhibit multifractal properties. Here we study the multifractal properties of the family of maps. We apply a box-counting method by defining a grid of boxes Bi(δ), where i is the index and δ is the size of the boxes, to quantify the distribution of stable and unstable manifolds of the map. When the parameter is in the range 0.51< η <0.58 and 0.68< η <1 the map is ergodic; i.e., the unstable and stable manifolds eventually cover the whole torus, although not in a uniform distribution. For accurate numerical results, we require correspondingly accurate construction of the stable and unstable manifolds. Here we use the piecewise linearity of the map to achieve this, by computing the endpoints of line segments that define the global stable and unstable manifolds. This allows the generalized fractal dimension Dq, and spectrum of dimensions f(α), to be computed with accuracy. Finally, the intersection of the unstable and stable manifold of the map will be investigated and compared with the distribution of periodic points of the system.

Keywords: Discrete-time dynamical systems, Fractal geometry, Multifractal behaviour of the Perturbed map, Multifractal of Dynamical systems

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Authors: Mohanad Alfach, Amjad Al Helwani

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Post-seismic observations of recent devastating earthquakes have shown that the behavior of the soil-pile-structure shows strong nonlinearity of soil and concrete under intensive seismic loading. Many of pile ruptures recently observed after the strong earthquake due to structural reasons (development of plastic hinges in the piles). The most likely reason for this rupture is the exceeding of maximum bending moment supported by the pile at several points. An analysis of these problems is necessary to take into account the nonlinearity of concrete, the strategy of strengthening the damaged piles and the interaction of these piles with the proposed strengthening by using micropiles. This study aims to investigate the interaction aspects for soil-piles- micropiles-structure using a global approach with a three dimensional finite difference code Flac 3D (Fast lagrangian analysis of continua in 3 dimensions).

Keywords: interaction, piles, micropiles, concrete, seismic, nonlinear, three-dimensional

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Authors: Z. Derikvand, M. Dusek, V. Eigner

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One dimensional coordination polymer of Cdᴵᴵ based on pyrazine (pz) and 3-nitrophthalic acid (3-nphaH₂), namely poly[[diaqua bis(3-nitro-2-carboxylato-1-carboxylic acid)(µ₂-pyrazine) cadmium(II)]dihydrate], {[Cd(3-nphaH)2(pz)(H₂O)₂]. 2H₂O}ₙ was prepared and characterized. The asymmetric unit consists of one Cdᴵᴵ center, two (3-nphaH)– anions, two halves of two crystallographically distinct pz ligands, two coordinated and two uncoordinated water molecules. The Cdᴵᴵ cation is surrounded by four oxygen atoms from two (3-nphaH)– and two water molecules as well as two nitrogen atoms from two pz ligands in distorted octahedral geometry. Complicated hydrogen bonding network accompanied with N–O···π and C–O···π stacking interactions leads to formation of a 3D supramolecular network. Commonly, this kind of C–O–π and N–O···π interaction is detected in electron-rich CO/NO groups of (3-nphaH)– ligand and electron-deficient π-system of pyrazine.

Keywords: supramolecular chemistry, Cd coordination polymer, crystal structure, 3-nithrophethalic acid

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Authors: C. W. Kwak, I. J. Park, D. I. Jang

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The importance of cost-wise effective application and construction is getting increase due to the surge of traffic volume in the metropolitan cities. Accordingly, the necessity of the tunnel has large section becomes more critical. Double deck tunnel can be one of the most appropriate solutions to the necessity. The dynamic stability of double deck tunnel is essential against seismic load since it has large section and connection between perimeter lining and interim slab. In this study, 3-dimensional dynamic numerical analysis was conducted based on the Finite Difference Method to investigate the seismic behavior of double deck tunnel. Seismic joint for dynamic stability and the mitigation of seismic impact on the lining was considered in the modeling and analysis. Consequently, the mitigation of acceleration, lining displacement and stress were verified successfully.

Keywords: double deck tunnel, interim slab, 3-dimensional dynamic numerical analysis, seismic joint

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Authors: Aamir Mubashar, Ibrahim Fiaz

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This research presents a three-dimensional finite element modelling strategy to simulate damage in a quasi-static three-point bending analysis of woven twill 2/2 type carbon fibre reinforced plastic (CFRP) composite on a micro-meso level using cohesive zone modelling technique. A meso scale finite element model comprised of a number of plies was developed in the commercial finite element code Abaqus/explicit. The interfaces between the plies were explicitly modelled using cohesive zone elements to allow for debonding by crack initiation and propagation. Load-deflection response of the CRFP within the quasi-static range was obtained and compared with the data existing in the literature. This provided validation of the model at the global scale. The outputs resulting from the global model were then used to develop a simulation model capturing the micro-meso scale material features. The sub-model consisted of a refined mesh representative volume element (RVE) modelled in texgen software, which was later embedded with cohesive elements in the finite element software environment. The results obtained from the developed strategy were successful in predicting the overall load-deflection response and the damage in global and sub-model at the flexure limit of the specimen. Detailed analysis of the effects of the micro-scale features was carried out.

Keywords: woven composites, multi-scale modelling, cohesive zone, finite element model

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Authors: Abe D. Desta

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This study suggests a deep learning-based method for identifying and following moving objects in surveillance video. The proposed method uses a fast regional convolution neural network (F-RCNN) trained on a substantial dataset of vehicle images to first detect vehicles. A Kalman filter and a data association technique based on a Hungarian algorithm are then used to monitor the observed vehicles throughout time. However, in general, F-RCNN algorithms have been shown to be effective in achieving high detection accuracy and robustness in this research study. For example, in one study The study has shown that the vehicle detection and tracking, the system was able to achieve an accuracy of 97.4%. In this study, the F-RCNN algorithm was compared to other popular object detection algorithms and was found to outperform them in terms of both detection accuracy and speed. The presented system, which has application potential in actual surveillance systems, shows the usefulness of deep learning approaches in vehicle detection and tracking.

Keywords: artificial intelligence, computer vision, deep learning, fast-regional convolutional neural networks, feature extraction, vehicle tracking

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Authors: Yawen Dai, Ping Cheng, Jian Ru Gong

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Photoelctrochemical (PEC) water splitting using semiconductors (SCs) provides a convenient way to convert sustainable but intermittent solar energy into clean hydrogen energy, and it has been regarded as one of most promising technology to solve the energy crisis and environmental pollution in modern society. However, the record energy conversion efficiency of a PEC cell (~3%) is still far lower than the commercialization requirement (~10%). The sluggish kinetics of oxygen evolution reaction (OER) half reaction on photoanodes is a significant limiting factor of the PEC device efficiency, and electrocatalysts (ECs) are always deposited on SCs to accelerate the hole injection for OER. However, an active EC cannot guarantee enhanced PEC performance, since the newly emerged SC-EC interface complicates the interfacial charge behavior. Herein, α-Fe2O3 photoanodes coated with Co3O4 and CoO ECs are taken as the model system to glean fundamental understanding on the EC-dependent interfacial charge behavior. Intensity modulated photocurrent spectroscopy and electrochemical impedance spectroscopy were used to investigate the competition between interfacial charge transfer and recombination, which was found to be dominated by the charge storage capacities of ECs. The combined results indicate that both ECs can store holes and increase the hole density on photoanode surface. It is like a double-edged sword that benefit the multi-hole participated OER, as well as aggravate the SC-EC interfacial charge recombination due to the Coulomb attraction, thus leading to a nonmonotonic PEC performance variation trend with the increasing surface hole density. Co3O4 has low hole storage capacity which brings limited interfacial charge recombination, and thus the increased surface holes can be efficiently utilized for OER to generate enhanced photocurrent. In contrast, CoO has overlarge hole storage capacity that causes severe interfacial charge recombination, which hinders hole transfer to electrolyte for OER. Therefore, the PEC performance of α-Fe2O3 is improved by Co3O4 but decreased by CoO despite the similar electrocatalytic activity of the two ECs. First-principle calculation was conducted to further reveal how the charge storage capacity depends on the EC’s intrinsic property, demonstrating that the larger hole storage capacity of CoO than that of Co3O4 is determined by their Co valence states and original Fermi levels. This study raises up a new strategy to manipulate interfacial charge behavior and the resultant PEC performance by the charge storage capacity of ECs, providing insightful guidance for the interface design in PEC devices.

Keywords: charge storage capacity, electrocatalyst, interfacial charge behavior, photoelectrochemistry, water-splitting

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Authors: Linghui Meng, James Atlas, Deborah Munro

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There is an increasing demand for prosthetics capable of mimicking natural limb movements and hand gestures, but precise movement control of prosthetics using only electrode signals continues to be challenging. This study considers the implementation of machine learning as a means of improving accuracy and presents an initial investigation into hand gesture recognition using models based on electromyographic (EMG) signals. EMG signals, which capture muscle activity, are used as inputs to machine learning algorithms to improve prosthetic control accuracy, functionality and adaptivity. Using logistic regression, a machine learning classifier, this study evaluates the accuracy of classifying two hand gestures from the publicly available Ninapro dataset using two-time series feature extraction algorithms: Time Series Feature Extraction (TSFE) and Convolutional Neural Networks (CNNs). Trials were conducted using varying numbers of EMG channels from one to eight to determine the impact of channel quantity on classification accuracy. The results suggest that although both algorithms can successfully distinguish between hand gesture EMG signals, CNNs outperform TSFE in extracting useful information for both accuracy and computational efficiency. In addition, although more channels of EMG signals provide more useful information, they also require more complex and computationally intensive feature extractors and consequently do not perform as well as lower numbers of channels. The findings also underscore the potential of machine learning techniques in developing more effective and adaptive prosthetic control systems.

Keywords: EMG, machine learning, prosthetic control, electromyographic prosthetics, hand gesture classification, CNN, computational neural networks, TSFE, time series feature extraction, channel count, logistic regression, ninapro, classifiers

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Authors: Farhad Aalizadeh, Ali Moosavi

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This paper presents a two-dimensional Computational Fluid Dynamics (CFDs) simulation for the steady, particle tracking. The purpose of this paper is applied magnetic field effect on Magnetic Nanoparticles velocities distribution. It is shown that the permeability of the particles determines the effect of the magnetic field on the deposition of the particles and the deposition of the particles is inversely proportional to the Reynolds number. Using MHD and its property it is possible to control the flow velocity, remove the fouling on the walls and return the system to its original form. we consider a channel 2D geometry and solve for the resulting spatial distribution of particles. According to obtained results when only magnetic fields are applied perpendicular to the flow, local particles velocity is decreased due to the direct effect of the magnetic field return the system to its original fom. In the method first, in order to avoid mixing with blood, the ferromagnetic particles are covered with a gel-like chemical composition and are injected into the blood vessels. Then, a magnetic field source with a specified distance from the vessel is used and the particles are guided to the affected area. This paper presents a two-dimensional Computational Fluid Dynamics (CFDs) simulation for the steady, laminar flow of an incompressible magnetorheological (MR) fluid between two fixed parallel plates in the presence of a uniform magnetic field. The purpose of this study is to develop a numerical tool that is able to simulate MR fluids flow in valve mode and determineB0, applied magnetic field effect on flow velocities and pressure distributions.

Keywords: MHD, channel clots, magnetic nanoparticles, simulations

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Authors: Arya Pirooz

Abstract:

Air and fuel must be mixed correctly so that there is perfect combustion, which calls for fuel atomization by injection. In this study, the effects of different parameters such as number of orifices, length and diameter of orifices, diameter of nozzle sac and the angle of needle seat in injectors were investigated with the use of rate of injection and sac pressure. The unit pump of the OM-457 diesel engine was modelled on Avl-Hydsim. The results illustrate that the sac pressure decreased by 46% when the number of holes were doubled, although the rate of injection had an immense change. Also, the sac pressure increased up to 60% when the diameter of orifices decreased by 40% in spite of the semi-constant injection rate.

Keywords: injection, OM-457 engine, nozzle geometry, atomization

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Authors: A. S. Ibrahim Mamane, S. Giljean, M.-J. Pac, G. L’Hostis

Abstract:

Fiber reinforced composite laminates are particularly subject to residual stresses due to their heterogeneity and the complex chemical, mechanical and thermal mechanisms that occur during their processing. Residual stresses are now well known to cause damage accumulation, shape instability, and behavior disturbance in composite parts. Many works exist in the literature on techniques for minimizing residual stresses in thermosetting and thermoplastic composites mainly. To study in-depth the influence of processing mechanisms on the formation of residual stresses and to minimize them by establishing a reliable correlation, it is essential to be able to measure very precisely the profile of residual stresses in the composite. Residual stresses are important data to consider when sizing composite parts and predicting their behavior. The incremental hole drilling is very effective in measuring the gradient of residual stresses in composite laminates. This method is semi-destructive and consists of drilling incrementally a hole through the thickness of the material and measuring relaxation strains around the hole for each increment using three strain gauges. These strains are then converted into residual stresses using a matrix of coefficients. These coefficients, called calibration coefficients, depending on the diameter of the hole and the dimensions of the gauges used. The reliability of the incremental hole drilling depends on the accuracy with which the calibration coefficients are determined. These coefficients are calculated using a finite element model. The samples’ features and the experimental conditions must be considered in the simulation. Any mismatch can lead to inadequate calibration coefficients, thus introducing errors on residual stresses. Several calibration coefficient correction methods exist for isotropic material, but there is a lack of information on this subject concerning composite laminates. In this work, a Python program was developed to automatically generate the adequate finite element model. This model allowed us to perform a parametric study to assess the influence of experimental errors on the calibration coefficients. The results highlighted the sensitivity of the calibration coefficients to the considered errors and gave an order of magnitude of the precisions required on the experimental device to have reliable measurements. On the basis of these results, improvements were proposed on the experimental device. Furthermore, a numerical method was proposed to correct the calibration coefficients for different types of materials, including thick composite parts for which the analytical approach is too complex. This method consists of taking into account the experimental errors in the simulation. Accurate measurement of the experimental errors (such as eccentricity of the hole, angular deviation of the gauges from their theoretical position, or errors on increment depth) is therefore necessary. The aim is to determine more precisely the residual stresses and to expand the validity domain of the incremental hole drilling technique.

Keywords: fiber reinforced composites, finite element simulation, incremental hole drilling method, numerical correction of the calibration coefficients, residual stresses

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Authors: R. Dangtungee, A. Rattanapan, S. Siengchin

Abstract:

Waste silicon carbide (waste SiC) filled high-density polyethylene (HDPE) with and without surface modifiers were studied. Two types of surface modifiers namely; high-density polyethylene-grafted-maleic anhydride (HDPE-g-MA) and 3-aminopropyltriethoxysilane have been used in this study. The composites were produced using a two roll mill, extruder and shaped in a hydraulic compression molding machine. The mechanical properties of polymer composites such as flexural strength and modulus, impact strength, tensile strength, stiffness and hardness were investigated over a range of compositions. It was found that, flexural strength and modulus, tensile modulus and hardness increased, whereas impact strength and tensile strength decreased with the increasing in filler contents, compared to the neat HDPE. At similar filler content, the effect of both surface modifiers increased flexural modulus, impact strength, tensile strength and stiffness but reduced the flexural strength. Morphological investigation using SEM revealed that the improvement in mechanical properties was due to enhancement of the interfacial adhesion between waste SiC and HDPE.

Keywords: high-density polyethylene, HDPE-g-MA, mechanical properties, morphological properties, silicon carbide, waste silicon carbide

Procedia PDF Downloads 361

Authors: Xin-Hua Zhao

Abstract:

In recent years, the growth rate of social responsibility reports disclosed by Chinese corporations has grown rapidly. The economic effects of the growing corporate social responsibility reports have become a hot topic. The article takes the chemical listed engineering corporations that disclose social responsibility reports in China as a sample, and based on the information asymmetry theory, examines the economic effect generated by corporate social responsibility disclosure with the method of ordinary least squares. The research is conducted from the perspective of analysts’ earnings forecasts and studies the impact of corporate social responsibility information disclosure on improving the accuracy of analysts' earnings forecasts. The results show that there is a statistically significant negative correlation between corporate social responsibility disclosure index and analysts’ earnings forecast error. The conclusions confirm that enterprises can reduce the asymmetry of social and environmental information by disclosing social responsibility reports, and thus improve the accuracy of analysts’ earnings forecasts. It can promote the effective allocation of resources in the market.

Keywords: analysts' earnings forecasts, corporate social responsibility disclosure, economic effect, information asymmetry

Procedia PDF Downloads 156

Authors: Katsuya Takasaki, Manabu Takao, Toshiaki Setoguchi

Abstract:

Effect of 3-dimensional (3D) blade on the turbine characteristics of Wells turbine for wave energy conversion has been investigated experimentally by model testing under steady flow conditions in the study, in order to improve the peak efficiency and the stall characteristics. The aim of the use of 3D blade is to prevent flow separation on the suction surface near the tip. The chord length is constant with radius and the blade profile changes gradually from mean radius to tip. The proposed blade profiles in the study are NACA0015 from hub to mean radius and NACA0025 at the tip. The performances of Wells turbine with 3D blades has been compared with those of the original Wells turbine, i.e. the turbine with 2-dimensional (2D) blades. As a result, it was concluded that although the peak efficiency of Wells turbine can be improved by the use of the proposed 3D blade, its blade does not overcome the weakness of stalling.

Keywords: fluid machinery, ocean engineering, stall, wave energy conversion, wells turbine

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Authors: Saziye Ugur

Abstract:

In this study, optical, film formation, morphological and the magnetic properties of a nanocomposite system, composed of polystyrene (PS) latex polymer and core-shell magnetic nanoparticles (MNPs) is presented. Nine different mixtures were prepared by mixing of PS latex dispersion with different amount of MNPs in the range of (0- 100 wt%). PS/MNPs films were prepared from these mixtures on glass substrates by drop casting method. After drying at room temperature, each film sample was separately annealed at temperatures from 100 to 250 °C for 10 min. In order to monitor film formation process, the transmittance of these composites was measured after each annealing step as a function of MNPs content. Below a critical MNPs content (30 wt%), it was found that PS percolates into the MNPs hard phase and forms an interconnected network upon annealing. The transmission results showed above this critical value, PS latexes were no longer film forming at all temperatures. Besides, the PS/MNPs composite films also showed excellent magnetic properties. All composite films showed superparamagnetic behaviors. The saturation magnetisation (Ms) first increased up to 0.014 emu in the range of (0-50) wt% MNPs content and then decreased to 0.010 emu with increasing MNPs content. The highest value of Ms was approximately 0.020 emu and was obtained for the film filled with 85 wt% MNPs content. These results indicated that the optical, film formation and magnetic properties of PS/MNPs composite films can be readily tuned by varying loading content of MNPs nanoparticles.

Keywords: composite film, film formation, magnetic nanoparticles, ps latex, transmission

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Authors: T. Bielewicz, S. Dogan, C. Klinke

Abstract:

Two-dimensional, solution-processable semiconductor materials are interesting for low-cost electronic applications [1]. We demonstrate the synthesis of lead sulfide nanosheets and how their size, shape and height can be tuned by varying concentrations of pre-cursors, ligands and by varying the reaction temperature. Especially, the charge carrier confinement in the nanosheets’ height adjustable from 2 to 20 nm has a decisive impact on their electronic properties. This is demonstrated by their use as conduction channel in a field effect transistor [2]. Recently we also showed that especially thin nanosheets show a high carrier multiplication (CM) efficiency [3] which could make them, through the confinement induced band gap and high photoconductivity, very attractive for application in photovoltaic devices. We are already able to manufacture photovoltaic devices out of single nanosheets which show promising results.

Keywords: physical sciences, chemistry, materials, chemistry, colloids, physics, condensed-matter physics, semiconductors, two-dimensional materials

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