Search results for: fast Fourier algorithms

Authors: Chaca Nugraha Zaid, Hikmat Ronaldo, Emerald Falah Brayoga, Azizah Eddy Setiawati, Soviandini Dwiki Kartika Putri, Novita Wijayanti

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In the tofu stick production, the manual cutting process takes a half of working day or 4 hours for 21 kg of tofu. This issue has hampered the small and medium enterprises (SMEs) to increase the capacity of production to fulfill the market demand. In order to address the issue, the cutting process should be automized to create fast, efficient, and effective tools. This innovation to tackle this problem is an automatic cutter tool that is able to move continuously to cut the tofu into stick size. The tool uses the 78,5-watt electric motor and automatic sensors to drive the cutting tool automatically, resulting faster process time with more uniform size compared to the manual cutter. The component of this tool, i.e., cutting knife and the driver, electric motor, limit switch sensors, riley, Arduino nano, and power supply. The cutting speed cutting speed of this tool is 101,25 mm/s producing 64 tofu sticks. Benefits that can be obtained from the use of automatic tofu stick cutter, i.e. (1) Faster process (2) More uniform cutting result; (3) The quality of the tofu stick is maintained due to minimal contact with humans so that contamination can be suppressed; (4) The cutting knife can be modified to the desired size of the owner.

Keywords: automatic, cutter, small and medium enterprise, tofu stick

Procedia PDF Downloads 173

Authors: Mo-Se Lee, Cheol-Hwi Ahn, Kee-Young Kwahk, Hyunchul Ahn

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Over the past decade, deep learning has been in spotlight among various machine learning algorithms. In particular, CNN (Convolutional Neural Network), which is known as effective solution for recognizing and classifying images, has been popularly applied to classification and prediction problems in various fields. In this study, we try to apply CNN to stock market prediction, one of the most challenging tasks in the machine learning research. In specific, we propose to apply CNN as the binary classifier that predicts stock market direction (up or down) by using a graph as its input. That is, our proposal is to build a machine learning algorithm that mimics a person who looks at the graph and predicts whether the trend will go up or down. Our proposed model consists of four steps. In the first step, it divides the dataset into 5 days, 10 days, 15 days, and 20 days. And then, it creates graphs for each interval in step 2. In the next step, CNN classifiers are trained using the graphs generated in the previous step. In step 4, it optimizes the hyper parameters of the trained model by using the validation dataset. To validate our model, we will apply it to the prediction of KOSPI200 for 1,986 days in eight years (from 2009 to 2016). The experimental dataset will include 14 technical indicators such as CCI, Momentum, ROC and daily closing price of KOSPI200 of Korean stock market.

Keywords: convolutional neural network, deep learning, Korean stock market, stock market prediction

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Authors: Osaretin Rosemary Uyi, A. O. Ogungbemi

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Female involvement in policy making and partnership in a man-driven-world is fast gaining international recognition. The Nigeria commercial real estate is one of the sectors of the economy that has a significant number of the male in the business. This study was conducted to assess the participation of females in estate management in Lagos state, Nigeria. Lagos is the commercial nerve center of Nigeria having the highest number of real estate practitioners and investors. The population due to the daily influx of people has made real estate business to continue to grow in this part of Nigeria. A structured questionnaire duly pre-tested and validated was used to elicit information from the respondents. The data collected were presented using tables and charts and were analyzed using descriptive statistical tools such as frequency counts, percentages, were used to test the hypothesis. The results also indicated that most females that participated in commercial real estate business are educated (80%), fell within 31-40 years of age (75%) and of high income status (88%) earn above ₦800,000 per year, while 10% are real estate investors and 82% of the female in the sector are employee. The study concluded that the number of female participating in various aspect of commercial real estate business in the study area was moderate while the numbers of female investors are low when compared to male. This might be due to the problems associated with rent collection, land disputes and other issues that are associated with property management in Nigeria. It is therefore recommended that females in real estate should be empowered and encouraged to match with their male counterpart.

Keywords: commercial real estate, empowerment, female, participation, property management

Procedia PDF Downloads 336

Authors: Cheng-Jhe Lee, Chiun-Chieh Hsu

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Due to the rapid development of the Internet and the increased availability of digital documents, the excessive information on the Internet has led to information overflow problem. In order to solve these problems for effective information retrieval, document clustering in text mining becomes a popular research topic. Clustering is the unsupervised classification of data items into groups without the need of training data. Many conventional document clustering methods perform inefficiently for large document collections because they were originally designed for relational database. Therefore they are impractical in real-world document clustering and require special handling for high dimensionality and high volume. We propose the FIHC (Frequent Itemset-based Hierarchical Clustering) method, which is a hierarchical clustering method developed for document clustering, where the intuition of FIHC is that there exist some common words for each cluster. FIHC uses such words to cluster documents and builds hierarchical topic tree. In this paper, we combine FIHC algorithm with ontology to solve the semantic problem and mine the meaning behind the words in documents. Furthermore, we use the closed frequent itemsets instead of only use frequent itemsets, which increases efficiency and scalability. The experimental results show that our method is more accurate than those of well-known document clustering algorithms.

Keywords: FIHC, documents clustering, ontology, closed frequent itemset

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Authors: Ben Clapperton, Daniel Stahl, Kimberley Goldsmith, Trudie Chalder

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Chronic fatigue syndrome (CFS) is a condition characterised by chronic disabling fatigue and other symptoms that currently can't be explained by any underlying medical condition. Although clinical trials support the effectiveness of cognitive behaviour therapy (CBT), the success rate for individual patients is modest. Patients vary in their response and little is known which factors predict or moderate treatment outcomes. The aim of the project is to develop a prediction model from baseline characteristics of patients, such as demographics, clinical and psychological variables, which may predict likely treatment outcome and provide guidance for clinical decision making and help clinicians to recommend the best treatment. The project is aimed at identifying subgroups of patients with similar baseline characteristics that are predictive of treatment effects using modern cluster analyses and data mining machine learning algorithms. The characteristics of these groups will then be used to inform the types of individuals who benefit from a specific treatment. In addition, results will provide a better understanding of for whom the treatment works. The suitability of different clustering methods to identify subgroups and their response to different treatments of CFS patients is compared.

Keywords: chronic fatigue syndrome, latent class analysis, prediction modelling, self-organizing maps

Procedia PDF Downloads 229

Authors: Martin Elstner

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Processing of information by means of handling chemicals is a ubiquitous phenomenon in nature. Technical implementations of chemical information processing lack of low integration densities compared to electronic devices. Stimuli responsive hydrogels are promising candidates for materials with information processing capabilities. These hydrogels are sensitive toward chemical stimuli like metal ions or amino acids. The binding of an analyte molecule induces conformational changes inside the polymer network and subsequently the water content and volume of the hydrogel varies. This volume change can control material flows, and concurrently information flows, in microfluidic devices. The combination of this technology with powerful chemical logic gates yields in a platform for highly integrated chemical circuits. The manufacturing process of such devices is very challenging and rapid prototyping is a key technology used in the study. 3D printing allows generating three-dimensional defined structures of high complexity in a single and fast process step. This thermoplastic master is molded into PDMS and the master is removed by dissolution in an organic solvent. A variety of hydrogel materials is prepared by dispenser printing of pre-polymer solutions. By a variation of functional groups or cross-linking units, the functionality of the hole circuit can be programmed. Finally, applications in the field of bio-molecular analytics were demonstrated with an autonomously operating microfluidic chip.

Keywords: bioanalytics, hydrogels, information processing, microvalve

Procedia PDF Downloads 312

Authors: Mohamed K. Metwally, Sherif H. El-Gohary, Kyung Min Byun, Seung Moo Han, Soo Yeol Lee, Min Hyoung Cho, Gon Khang, Jinsung Cho, Tae-Seong Kim

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Photoacoustic imaging (PAI) is a non-invasive and non-ionizing imaging modality that combines the absorption contrast of light with ultrasound resolution. Laser is used to deposit optical energy into a target (i.e., optical fluence). Consequently, the target temperature rises, and then thermal expansion occurs that leads to generating a PA signal. In general, most image reconstruction algorithms for PAI assume uniform fluence within an imaging object. However, it is known that optical fluence distribution within the object is non-uniform. This could affect the reconstruction of PA images. In this study, we have investigated the influence of optical fluence distribution on PA back-propagation imaging using finite element method. The uniform fluence was simulated as a triangular waveform within the object of interest. The non-uniform fluence distribution was estimated by solving light propagation within a tissue model via Monte Carlo method. The results show that the PA signal in the case of non-uniform fluence is wider than the uniform case by 23%. The frequency spectrum of the PA signal due to the non-uniform fluence has missed some high frequency components in comparison to the uniform case. Consequently, the reconstructed image with the non-uniform fluence exhibits a strong smoothing effect.

Keywords: finite element method, fluence distribution, Monte Carlo method, photoacoustic imaging

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Authors: Marek Dlapa

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The algebraic approach is applied to the control of the HiMAT (Highly Maneuverable Aircraft Technology). The objective is to find a robust controller which guarantees robust stability and decoupled control of longitudinal model of a scaled remotely controlled vehicle version of the advanced fighter HiMAT. Control design is performed by decoupling the nominal MIMO (multi-input multi-output) system into two identical SISO (single-input single-output) plants which are approximated by a 4th order transfer function. The algebraic approach is then used for pole placement design, and the nominal closed-loop poles are tuned so that the peak of the µ-function is minimal. As an optimization tool, evolutionary algorithm Differential Migration is used in order to overcome the multimodality of the cost function yielding simple controller with decoupling for nominal plant which is compared with the D-K iteration through simulations of standard longitudinal manoeuvres documenting decoupled control obtained from algebraic approach for nominal plant as well as worst case perturbation.

Keywords: algebraic approach, evolutionary computation, genetic algorithms, HiMAT, robust control, structured singular value

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Authors: Elmineh Tsegahun Gedif

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Zinc oxide nanoparticles (ZnO NPs) have attracted huge attention due to catalytic, optical, photonic, and antibacterial activity. Zinc oxide nanoparticles were successfully synthesized via a fast, non-toxic, cost-effective, and eco-friendly method by biologically reducing Zn(NO3)2.6H2O solution with Neem (Azadirachta indica) leaf extract under optimum conditions (pH = 9). The presence of active flavonoids, phenolic groups, alkaloids, terpenoids, and tannins, which were in the biomass of the Neem leaf extract before and after reduction, was identified using qualitative screening methods (observing the color changes) and FT-IR Spectroscopy. The formation of ZnO NPs was visually indicated by the color changes from colorless to light yellow color. Biosynthesized nanoparticles were also characterized by UV-visible, FT-IR, and XRD spectroscopies. The reduction process was simple and convenient to handle and was monitored by UV-visible spectroscopy that showed surface plasmon resonance (SPR) of the ZnO NPs at 321 nm. This result clearly revealed the formation of ZnO NPs. X-ray diffraction was used to investigate the crystal structure. The average particle size of ZnO powder and around 20 nm using the line width of the plane, and the refraction peak using Scherrer’s equation. The synthesized zinc oxide nanoparticles were evaluated for antimicrobial activities against Gram-positive and Gram-negative bacteria. Zinc nanoparticles exhibited the maximum zone of inhibition against Escherichia coli (15 mm), while the least activity was seen against Staphylococcus aureus.

Keywords: antimicrobial activity, azadirachta indica, green synthesis, ZnO NPs

Procedia PDF Downloads 119

Authors: Shimaa Nagro, Russell Campion

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Mobile devices are becoming ever more widely available, with growing functionality, and they are increasingly used as enabling technology to give students access to educational material anytime and anywhere. However, the design of educational material's user interfaces for mobile devices is beset by many unresolved research problems such as those arising from constraints associated with mobile devices or from issues linked to effective learning. The proposed research aims to produce: (i) a method framework for the design and evaluation of educational material’s interfaces to be delivered on mobile devices, in multimedia form based on Human Computer Interaction strategies; and (ii) a software tool implemented as a fast-track alternative to use the method framework in full. The investigation will combine qualitative and quantitative methods, including interviews and questionnaires for data collection and three case studies for validating the method framework. The method framework is a framework to enable an educational designer to effectively and efficiently create educational multimedia interfaces to be used on mobile devices by following a particular methodology that contains practical and usable tools and techniques. It is a method framework that accepts any educational material in its final lesson plan and deals with this plan as a static element, it will not suggest any changes in any information given in the lesson plan but it will help the instructor to design his final lesson plan in a multimedia format to be presented in mobile devices.

Keywords: mobile learning, M-Learn, HCI, educational multimedia, interface design

Procedia PDF Downloads 378

Authors: Eman M. Sarhan, Doaa A. Ghareeb, Gabriella Ortore, Amr A. Amara, Mohamed M. El-Sayed

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Drug salting and nanoparticle-based drug delivery formulations are considered to be an effective means for rendering the hydrophobic drugs’ nano-scale dispersion in aqueous media, and thus circumventing the pitfalls of their poor solubility as well as enhancing their membrane permeability. The current study aims to increase the bioavailability of quaternary ammonium berberine through acid salting and biodegradable bovine serum albumin (BSA)-based nanoparticulate drug formulation. Berberine hydroxide (BBR-OH) that was chemically synthesized by alkalization of the commercially available berberine hydrochloride (BBR-HCl) was then acidified to get Di-berberine sulfate (BBR)₂SO₄. The purified crystals were spectrally characterized. The desolvation technique was optimized for the preparation of size-controlled BSA-BBR-HCl, BSA-BBR-OH, and BSA-(BBR)₂SO₄ nanoparticles. Particle size, zeta potential, drug release, encapsulation efficiency, Fourier transform infrared spectroscopy (FTIR), tandem MS-MS spectroscopy, energy-dispersive X-ray spectroscopy (EDX), scanning and transmitting electron microscopic examination (SEM, TEM), in vitro bioactivity, and in silico drug-polymer interaction were determined. BSA (PDB ID; 4OR0) protonation state at different pH values was predicted using Amber12 molecular dynamic simulation. Then blind docking was performed using Lamarkian genetic algorithm (LGA) through AutoDock4.2 software. Results proved the purity and the size-controlled synthesis of berberine-BSA-nanoparticles. The possible binding poses, hydrophobic and hydrophilic interactions of berberine on BSA at different pH values were predicted. Antioxidant, anti-hemolytic, and cell differentiated ability of tested drugs and their nano-formulations were evaluated. Thus, drug salting and the potentially effective albumin berberine nanoparticle formulations can be successfully developed using a well-optimized desolvation technique and exhibiting better in vitro cellular bioavailability.

Keywords: berberine, BSA, BBR-OH, BBR-HCl, BSA-BBR-HCl, BSA-BBR-OH, (BBR)₂SO₄, BSA-(BBR)₂SO₄, FTIR, AutoDock4.2 Software, Lamarkian genetic algorithm, SEM, TEM, EDX

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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: Awatef Chniguir, Tarek Farah, Zouhair Ben Jemaa, Safya Belguith

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Optimal routing allows minimizing energy consumption in wireless sensor networks (WSN). Clustering has proven its effectiveness in organizing WSN by reducing channel contention and packet collision and enhancing network throughput under heavy load. Therefore, nowadays, with the emergence of the Internet of Things, heterogeneity is essential. Stable election protocol (SEP) that has increased the network stability period and lifetime is the first clustering protocol for heterogeneous WSN. SEP and its descendants, namely SEP, Threshold Sensitive SEP (TSEP), Enhanced TSEP (ETSSEP) and Current Energy Allotted TSEP (CEATSEP), were studied. These algorithms’ performance was evaluated based on different metrics, especially first node death (FND), to compare their stability. Simulations were conducted on the MATLAB tool considering two scenarios: The first one demonstrates the fraction variation of advanced nodes by setting the number of total nodes. The second considers the interpretation of the number of nodes while keeping the number of advanced nodes permanent. CEATSEP outperforms its antecedents by increasing stability and, at the same time, keeping a low throughput. It also operates very well in a large-scale network. Consequently, CEATSEP has a useful lifespan and energy efficiency compared to the other routing protocol for heterogeneous WSN.

Keywords: clustering, heterogeneous, stability, scalability, IoT, WSN

Procedia PDF Downloads 136

Authors: Sanda-Maria Ardeleanu, Ana Crăciunescu

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This paper is sustained by our previous research on discursive strategies, whichdemonstrated that tourismhas developed and employed apromotional languageper se. We have studied this concept within the framework of audio-visual advertising by analyzing its discursive structures at the level of three main strategies (textual, visual, and both textual and visual) and confirmed the applicability of the promotional language per se within the field. Tourism, at large, represents a largely potential interdisplinary field, which allowed us to use qualitative methods of research such as Discourse Analysis (DA). Due to further research which showed that in the third phase of qualitative research methodologies, scholars in tourism recognized semiotics and DA as potential paths to follow, but which were insufficiently explored at the time, we soon realized that the natural next step to take is to bring together common qualitative methodologies for both fields, such as the method of observation, the triangulation, Discourse Analysis, etc. Therefore and in the light of fast transformations of the medium that intermediates the message, in this paper, we are going to focus on the manifestations of the promotional language in social media texts, which advertise for the urban industry of accommodation in Romania. We shall constitute a corpus of study as the basis for our research methodology and, through the empirical method of observation and DA, we propose to recognize or discover new patterns developed at textual (mainly) and visual level or the mix of the two, known as strategies of the promotional language of tourism.

Keywords: discourse analysis, promotional language of tourism, social media, urban accommodation industry, tourism

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Authors: Hassan Farhan Alfugha

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the cenozoic volcanism in westt central jordan which show homohgenous lava from upper mantle.es represented by basaltic scoria cones and flows and covers approximately 10 km. fourtten nephelinitic rock samples were collected at jurf ed-darawish volcanism to analyze major minor and trace elements by using XRF.. geochemical parameters of these samp;es such as MG/MG+FE+2, the ratio range from 0.41 to 0.45 and high ti contents 3.09-3.28wt % indicate that the corresponding magmas are nearly of primary origin . this magma show low variable abundances of compatible and incompatible trace elements reflecting a homogenous source. the studied volcanic rocks, which are mainly nephlinites, belong to the alkaline rocks series containing 4.38-5.95wt% alkali oxides they are usually undersaturated in regard it the silica content, which ranges between 39.88-41.50wt.%.value compared to other jordanien basaltic rocks majorminor and trace elementes data as well as mantel xenoliths entrained in the volcanic rocks are spinel iherzolites that suggest the lithospheric mantle as the source for the pleistocene volcanism these xenoliths resided at shallow mantle depths (45 km ) because a geothermobarometric analysis yielded p-t conditions close to 15 kbar and 1100c the mantle nodules did not equilibrate with the melts indicating a fast transport from the mantle to the surface and a mgma >65 km deeper source area of the melts.

Keywords: nephelinite plestocene western central jordan, western central jordan, volcano in western central jordan, central jordan

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Authors: Cao Jiayu, Lan Ximing, Huang Jingjia, Burra Venkata Durga Kumar

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The first virus to attack personal computers was born in early 1986, called C-Brain, written by a pair of Pakistani brothers. In those days, people still used dos systems, manipulating computers with the most basic command lines. In the 21st century today, computer performance has grown geometrically. But computer viruses are also evolving and escalating. We never stop fighting against security problems. Stack overflow is one of the most common security vulnerabilities in operating systems. It may result in serious security issues for an operating system if a program in it has a vulnerability with administrator privileges. Certain viruses change the value of specific memory through a stack overflow, allowing computers to run harmful programs. This study developed a mechanism to detect and respond to time whenever a stack overflow occurs. We demonstrate the effectiveness of standard machine learning algorithms and control flow enforcement techniques in predicting computer OS security using generating suspicious vulnerability functions (SVFS) and associated suspect areas (SAS). The method can minimize the possibility of stack overflow attacks occurring.

Keywords: operating system, security, stack overflow, buffer overflow, machine learning, control-flow enforcement technology

Procedia PDF Downloads 119

Authors: Kritiyaporn Kunsook

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Machine learning algorithms (MLAs) such us artificial neural networks (ANNs), decision tree, support vector machines (SVMs), Naïve Bayes, and ensemble classifier by voting are powerful data driven methods that are relatively less widely used in the mapping of technique of system, and thus have not been comparatively evaluated together thoroughly in this field. The performances of a series of MLAs, ANNs, decision tree, SVMs, Naïve Bayes, and ensemble classifier by voting in technique of hydroponic systems prospectively modeling are compared based on the accuracy of each model. Classification of hydroponic systems only covers the test samples from vegetables grown with Nutrient film technique (NFT) and Deep flow technique (DFT). The feature, which are the characteristics of vegetables compose harvesting height width, temperature, require light and color. The results indicate that the classification performance of the ANNs is 98%, decision tree is 98%, SVMs is 97.33%, Naïve Bayes is 96.67%, and ensemble classifier by voting is 98.96% algorithm respectively.

Keywords: artificial neural networks, decision tree, support vector machines, naïve Bayes, ensemble classifier by voting

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Authors: Ahmed Fradi

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Nowadays, recent technological advances in the acquisition, modeling, and processing of three-dimensional (3D) objects data lead to the creation of models stored in huge databases, which are used in various domains such as computer vision, augmented reality, game industry, medicine, CAD (Computer-aided design), 3D printing etc. On the other hand, the industry is currently benefiting from powerful modeling tools enabling designers to easily and quickly produce 3D models. The great ease of acquisition and modeling of 3D objects make possible to create large 3D models databases, then, it becomes difficult to navigate them. Therefore, the indexing of 3D objects appears as a necessary and promising solution to manage this type of data, to extract model information, retrieve an existing model or calculate similarity between 3D objects. The objective of the proposed research is to develop a framework allowing easy and fast access to 3D objects in a CAD models database with specific indexing algorithm to find objects similar to a reference model. Our main objectives are to study existing methods of similarity calculation of 3D objects (essentially shape-based methods) by specifying the characteristics of each method as well as the difference between them, and then we will propose a new approach for indexing and comparing 3D models, which is suitable for our case study and which is based on some previously studied methods. Our proposed approach is finally illustrated by an implementation, and evaluated in a professional context.

Keywords: CAD, 3D object retrieval, shape based retrieval, similarity calculation

Procedia PDF Downloads 263

Authors: Kholik

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Nowadays, the demand of renewable energy in general is increasing due to the crisis of fossil fuels. Biohydrogen is an alternative fuel with zero emission derived from renewable resources such as banana pseudo stem of agricultural residues. Banana plant can be easily found in tropical and subtropical areas, so the resource is abundant and readily available as a biohydrogen substrate. Banana pseudo stem has not been utilised as a resource or substrate of biohydrogen production and it mainly contains 45-65% cellulose (α-cellulose), 5-15% hemicellulose and 20-30% Lignin, which indicates that banana pseudo stem will be renewable, sustainable and promising resource as lignocellulosic biomass. In this research, biohydrogen is derived from banana pseudo stem by dark fermentation. Dark fermentation is the most suitable approach for practical biohydrogen production from organic solid wastes. The process has several advantages including a fast reaction rate, no need of light, and a smaller footprint. 321 million metric tonnes banana pseudo stem of 428 million metric tonnes banana plantation residues in worldwide for 2013 and 22.5 million metric tonnes banana pseudo stem of 30 million metric tonnes banana plantation residues in Indonesia for 2015 will be able to generate 810.60 million tonne mol H2 and 56.819 million tonne mol H2, respectively. In this paper, we will show that the banana pseudo stem is the renewable, sustainable and promising resource to be utilised and to produce biohydrogen as energy generation with high yield and high contain of cellulose in comparison with the other substrates.

Keywords: banana pseudo stem, biohydrogen, dark fermentation, lignocellulosic

Procedia PDF Downloads 356

Authors: Ahmad Umar, Sheikh Akbar, Ahmed A. Ibrahim, Mohsen A. Alhamami

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The development of advanced materials for energy storage and gas sensing applications has gained significant attention in recent years. In this study, we synthesized and characterized PANI@MnO2@rGO ternary nanocomposites (NCs) to explore their potential in supercapacitors and gas sensing devices. The ternary NCs were synthesized through a multi-step process involving the hydrothermal synthesis of MnO2 nanoparticles, preparation of PANI@rGO composites and the assembly to the ternary PANI@MnO2@rGO ternary NCs. The structural, morphological, and compositional characteristics of the materials were thoroughly analyzed using techniques such as XRD, FESEM, TEM, FTIR, and Raman spectroscopy. In the realm of gas sensing, the ternary NCs exhibited excellent performance as NH3 gas sensors. The optimized operating temperature of 100 °C yielded a peak response of 15.56 towards 50 ppm NH3. The nanocomposites demonstrated fast response and recovery times of 6 s and 10 s, respectively, and displayed remarkable selectivity for NH3 gas over other tested gases. For supercapacitor applications, the electrochemical performance of the ternary NCs was evaluated using cyclic voltammetry and galvanostatic charge-discharge techniques. The composites exhibited pseudocapacitive behavior, with the capacitance reaching up to 185 F/g at 1 A/g and excellent capacitance retention of approximately 88.54% over 4000 charge-discharge cycles. The unique combination of rGO, PANI, and MnO2 nanoparticles in these ternary NCs offer synergistic advantages, showcasing their potential to address challenges in energy storage and gas sensing technologies.

Keywords: paniI@mnO2@rGO ternary NCs, synergistic effects, supercapacitors, gas sensing, energy storage

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Authors: Hafiz Mehtab Gull Nasir

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Industrial development and urbanization have significant impacts on air emissions, and their relationship diverges at different stages of economic progress. The revolution further propelled these activities as principal paths to economic and social transformation; nevertheless, the paths also promoted environmental degradation. Resultantly, both developed and developing countries undergone through fast-paced development; in which developed countries implemented legislation towards environmental pollution control however developing countries took the advantage of technology without caring about the environment. In this study, meta-analysis is performed on production of particulate matter (i.e., PM10 and PM2.5) from urbanized cities of first, second and third world countries to assess the air quality. The cities were selected based on ranked set principles. In case of PM10, third world countries showed highest PM level (~95% confidence interval of 0.74-1.86) followed by second world countries but with managed situation. Besides, first, world countries indicated the lowest pollution (~95% confidence interval of 0.12-0.2). Similarly, highest level of PM2.5 was produced by third world countries followed by the second and first world countries. Hereby, level of PM2.5 was not significantly different for both second and third world countries; however, first world countries showed minimum PM load. Finally, the study revealed different that levels of pollution status exist among different countries; whereas developed countries also devised better strategies towards pollution control while developing countries are least caring about their environmental resources. It is suggested that although industrialization and urbanization are directly involved with interference in natural elements, however, production of nature appears to be more societal rather hermetical.

Keywords: meta-analysis, particulate matter, developing countries, urbanization

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Authors: Emna Benmohamed, Hela Ltifi, Mounir Ben Ayed

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Bayesian Network (BN) is one of the most efficient classification methods. It is widely used in several fields (i.e., medical diagnostics, risk analysis, bioinformatics research). The BN is defined as a probabilistic graphical model that represents a formalism for reasoning under uncertainty. This classification method has a high-performance rate in the extraction of new knowledge from data. The construction of this model consists of two phases for structure learning and parameter learning. For solving this problem, the K2 algorithm is one of the representative data-driven algorithms, which is based on score and search approach. In addition, the integration of the expert's knowledge in the structure learning process allows the obtainment of the highest accuracy. In this paper, we propose a hybrid approach combining the improvement of the K2 algorithm called K2 algorithm for Parents and Children search (K2PC) and the expert-driven method for learning the structure of BN. The evaluation of the experimental results, using the well-known benchmarks, proves that our K2PC algorithm has better performance in terms of correct structure detection. The real application of our model shows its efficiency in the analysis of the phosphate laundry effluents' impact on the watershed in the Gafsa area (southwestern Tunisia).

Keywords: Bayesian network, classification, expert knowledge, structure learning, surface water analysis

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Authors: Theresa N. Uzor

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The word cardiovascular fitness is a topic of much interest to people of Nigeria, especially during this time, some heart diseases run in families. Cardiovascular fitness is the ability of the heart and lungs to supply-rich blood to the working muscle tissues. This type of fitness is a health-related component of physical fitness that is brought about by sustained physical activity such as plyometric training. Plyometric is a form of advanced fitness training that uses fast muscular contractions to improve power and speed in the sports performance by coaches and athletes. Plyometric training involves a rapid stretching of muscle (eccentric phase) immediately followed by a concentric or shortening action of the same muscle and connective tissue. However, the most basic example of true plyometric training is running and can be safe for a wide variety of populations. This paper focused on building cardiovascular health through Plyometric Training. The centre focus of the article is cardiovascular fitness and plyometric training with factors of cardiovascular fitness. Plyometric training at any age provides multiple benefits even beyond weight control and weight loss, decrease the risk of cardiovascular diseases, stroke, high blood pressure, diabetes, and other diseases, among other benefits of plyometric training to cardiovascular fitness. Participation in plyometric training will increase metabolism of an individual, thereby burning more calories even when at rest and reduces weight is also among the benefits of plyometric training. Some guidelines were recommended for planning plyometric training programme to minimise the chance of injury. With plyometric training in Nigeria, fortune can change for good, especially now that there has been an increase in cardiovascular diseases within the society for great savings would be saved.

Keywords: aerobic, cardiovascular, concentric, stretch-shortening cycle, plyometric

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Authors: Maisyatus Suadaa Irfana, Nove Eka Variant Anna, Dyah Puspitasari Sri Rahayu

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Technological developments provide convenience to all the people. Nowadays, the communication of human with the computer is done via text. With the development of technology, human and computer communications have been conducted with a voice like communication between human beings. It provides an easy facility for many people, especially those who have special needs. Voice search technology is applied in the search of book collections in the OPAC (Online Public Access Catalog), so library visitors will find it faster and easier to find books that they need. Integration with Google is needed to convert the voice into text. To optimize the time and the results of searching, Server will download all the book data that is available in the server database. Then, the data will be converted into JSON format. In addition, the incorporation of some algorithms is conducted including Decomposition (parse) in the form of array of JSON format, the index making, analyzer to the result. It aims to make the process of searching much faster than the usual searching in OPAC because the data are directly taken to the database for every search warrant. Data Update Menu is provided with the purpose to enable users perform their own data updates and get the latest data information.

Keywords: OPAC, voice, searching, faster

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Authors: Gerard Van Es

Abstract:

In this study, the use of a high resolution surface laser scanner to assess the micro texture of runway surfaces was investigated experimentally. Micro texture is one of the important surface components that helps to provide high braking friction between aircraft tires and a wet runway surface. Algorithms to derive different parameters that characterise micro texture was developed. Surface scans with a high resolution laser scanner were conducted on 40 different runway (like) surfaces. For each surface micro texture parameters were calculated from the laser scan data. These results were correlated with results obtained from a British pendulum tester that was used on the same surface. Results obtained with the British pendulum tester are generally considered to be indicative for the micro texture related friction characteristics. The results show that a meaningful correlation can be found between different parameters that characterise micro texture obtained with the laser scanner and the British pendulum tester results. Surface laser scanners are easier to operate and give more consistent results than a British pendulum tester. Therefore for airport operators surface laser scanners can be a useful tool to determine if their runway becomes slippery when wet due to a smooth micro texture.

Keywords: runway friction, micro texture, aircraft braking performance, slippery runways

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Authors: J. Herrero, D. Puigserver, I. Nijenhuis, K. Kuntze, J. M. Carmona

Abstract:

In the transition zone between aquifers and basal aquitards, the perchloroethene (PCE)-pools are more recalcitrant than those elsewhere in the aquifer. Although biodegradation of chloroethenes occur in this zone, it is a slow process and a remediation strategy is needed. The aim of this study is to demonstrate that combined strategy of biostimulation and in situ chemical reduction (ISCR) is more efficient than the two separated strategies. Four different microcosm experiments with sediment and groundwater of a selected field site where an aged pool exists at the bottom of a transition zone were designed under i) natural conditions, ii) biostimulation with lactic acid, iii) ISCR with zero-value iron (ZVI) and under iv) a combined strategy with lactic acid and ZVI. Biotic and abiotic dehalogenation, terminal electron acceptor processes and evolution of microbial communities were determined for each experiment. The main results were: i) reductive dehalogenation of PCE-pools occurs under sulfate-reducing conditions; ii) biostimulation with lactic acid supports more pronounced reductive dehalogenation of PCE and trichloroethene (TCE), but results in an accumulation of 1,2-cis-dichloroethene (cDCE); iii) ISCR with ZVI produces a sustained dehalogenation of PCE and its metabolites iv) combined strategy of biostimulation and ISCR results in a fast dehalogenation of PCE and TCE and a sustained dehalogenation of cisDCE. These findings suggest that biostimulation and ISCR with ZVI are the most suitable strategies for a complete reductive dehalogenation of PCE-pools in the transition zone and further to enable the dissolution of dense non-aqueous phase liquids.

Keywords: aged PCE-pool, anaerobic microcosm experiment, biostimulation, in situ chemical reduction, natural attenuation

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Authors: Welawat Tienpratarn, Chaiyaporn Yuksen, Rungrawin Promkul, Chetsadakon Jenpanitpong, Pajit Bunta, Suthap Jaiboon

Abstract:

Supraventricular tachycardia (SVT) is an abnormally fast atrial tachycardia characterized by narrow (≤ 120 ms) and constant QRS. Adenosine was the drug of choice; the first dose was 6 mg. It can be repeated with the second and third doses of 12 mg, with greater than 90% success. The study found that patients observed at 4 hours after normal sinus rhythm was no recurrence within 24 hours. The objective of this study was to investigate the factors that influence the recurrence of SVT after adenosine in the emergency department (ED). The study was conducted retrospectively exploratory model, prognostic study at the Emergency Department (ED) in Faculty of Medicine, Ramathibodi Hospital, a university-affiliated super tertiary care hospital in Bangkok, Thailand. The study was conducted for ten years period between 2010 and 2020. The inclusion criteria were age > 15 years, visiting the ED with SVT, and treating with adenosine. Those patients were recorded with the recurrence SVT in ED. The multivariable logistic regression model developed the predictive model and prediction score for recurrence PSVT. 264 patients met the study criteria. Of those, 24 patients (10%) had recurrence PSVT. Five independent factors were predictive of recurrence PSVT. There was age>65 years, heart rate (after adenosine) > 100 per min, structural heart disease, and dose of adenosine. The clinical risk score to predict recurrence PSVT is developed accuracy 74.41%. The score of >6 had the likelihood ratio of recurrence PSVT by 5.71 times. The clinical predictive score of > 6 was associated with recurrence PSVT in ED.

Keywords: supraventricular tachycardia, recurrance, emergency department, adenosine

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Authors: Han-Bin Park, Taesam Kang, SunKi Hong, Jeong Hoi Gu

Abstract:

The piezoelectric material is being used widely for actuators due to its large power density with simple structure. It can generate a larger force than the conventional actuators with the same size. Furthermore, the response time of piezoelectric actuators is very short, and thus, it can be used for very fast system applications with compact size. To control the piezoelectric actuator, we need analog signal conditioning circuits as well as digital microcontrollers. Conventional microcontrollers are not equipped with analog parts and thus the control system becomes bulky compared with the small size of the piezoelectric devices. To overcome these weaknesses, we are developing one-chip micro controller that can handle analog and digital signals simultaneously using mixed signal FPGA technology. We used the SmartFusion™ FPGA device that integrates ARM®Cortex-M3, analog interface and FPGA fabric in a single chip and offering full customization. It gives more flexibility than traditional fixed-function microcontrollers with the excessive cost of soft processor cores on traditional FPGAs. In this paper we introduce the design of single chip controller using mixed signal FPGA, SmartFusion™[1] device. To demonstrate its performance, we implemented a PI controller for power driving circuit and a 5th order H-infinity controller for the system with piezoelectric actuator in the FPGA fabric. We also demonstrated the regulation of a power output and the operation speed of a 5th order H-infinity controller.

Keywords: mixed signal FPGA, PI control, piezoelectric actuator, SmartFusion™

Procedia PDF Downloads 522

Authors: Jaishree Ranganathan, Poonam Rajurkar, Angelina A. Tzacheva, Zbigniew W. Ras

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In today’s world, business often depends on the customer feedback and reviews. Sentiment analysis helps identify and extract information about the sentiment or emotion of the of the topic or document. Attribute selection is a challenging problem, especially with large datasets in actionable pattern mining algorithms. Action Rule Mining is one of the methods to discover actionable patterns from data. Action Rules are rules that help describe specific actions to be made in the form of conditions that help achieve the desired outcome. The rules help to change from any undesirable or negative state to a more desirable or positive state. In this paper, we present a Lexicon based weighted scheme approach to identify emotions from customer feedback data in the area of manufacturing business. Also, we use Rough sets and explore the attribute selection method for large scale datasets. Then we apply Actionable pattern mining to extract possible emotion change recommendations. This kind of recommendations help business analyst to improve their customer service which leads to customer satisfaction and increase sales revenue.

Keywords: actionable pattern discovery, attribute selection, business data, data mining, emotion

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Authors: Willi Zschiebsch, Alrik Dargel, Sebastian Spitzer, Philipp Johst, Robert Böhm, Niels Modler

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In this article, we investigate an approach that digitally links individual development process steps by using the drive shaft of an aircraft engine as a representative example of a fiber polymer composite. Such high-performance, lightweight composite structures have many adjustable parameters that influence the mechanical properties. Only a combination of optimal parameter values can lead to energy efficient lightweight structures. The development tools required for the Engineering Design Process (EDP) are often isolated solutions, and their compatibility with each other is limited. A digital framework is presented in this study, which allows individual specialised tools to be linked via the generated data in such a way that automated optimization across programs becomes possible. This is demonstrated using the example of linking geometry generation with numerical structural analysis. The proposed digital framework for automated design optimization demonstrates the feasibility of developing a complete digital approach to design optimization. The methodology shows promising potential for achieving optimal solutions in terms of mass, material utilization, eigenfrequency, and deformation under lateral load with less development effort. The development of such a framework is an important step towards promoting a more efficient design approach that can lead to stable and balanced results.

Keywords: digital linked process, composite, CFRP, multi-objective, EDP, NSGA-2, NSGA-3, TPE

Procedia PDF Downloads 79