Search results for: modified simplex algorithm

Authors: Annalakshmi G., Sakthivel Murugan S.

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This paper presents a local directional encoded derivative binary pattern (LDEDBP) feature extraction method that can be applied for the classification of submarine coral reef images. The classification of coral reef images using texture features is difficult due to the dissimilarities in class samples. In coral reef image classification, texture features are extracted using the proposed method called local directional encoded derivative binary pattern (LDEDBP). The proposed approach extracts the complete structural arrangement of the local region using local binary batten (LBP) and also extracts the edge information using local directional pattern (LDP) from the edge response available in a particular region, thereby achieving extra discriminative feature value. Typically the LDP extracts the edge details in all eight directions. The process of integrating edge responses along with the local binary pattern achieves a more robust texture descriptor than the other descriptors used in texture feature extraction methods. Finally, the proposed technique is applied to an extreme learning machine (ELM) method with a meta-heuristic algorithm known as weighted distance grey wolf optimizer (GWO) to optimize the input weight and biases of single-hidden-layer feed-forward neural networks (SLFN). In the empirical results, ELM-WDGWO demonstrated their better performance in terms of accuracy on all coral datasets, namely RSMAS, EILAT, EILAT2, and MLC, compared with other state-of-the-art algorithms. The proposed method achieves the highest overall classification accuracy of 94% compared to the other state of art methods.

Keywords: feature extraction, local directional pattern, ELM classifier, GWO optimization

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Authors: Manel Langar, Riad Bourguiba, Jaouhar Mouine

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In this paper, we propose the design and implementation of an enhanced wormhole virtual channel on chip router. It is a heart of a mesh NoC using the XY deterministic routing algorithm. It is characterized by its simple virtual channel allocation strategy which allows reducing area and complexity of connections without affecting the performance. We implemented our router on a Tezzaron process to validate its performances. This router is a basic element that will be used later to design a 3D mesh NoC.

Keywords: NoC, mesh, router, 3D NoC

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Authors: Adam Majczak, Michał Bialy, Zbigniew Czyż, Zdzislaw Kaminski

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Increasingly stringent emission standards for passenger cars require us to find alternative drives. The share of electric vehicles in the sale of new cars increases every year. However, their performance and, above all, range cannot be today successfully compared to those of cars with a traditional internal combustion engine. Battery recharging lasts hours, which can be hardly accepted due to the time needed to refill a fuel tank. Therefore, the ways to reduce the adverse features of cars equipped with electric motors only are searched for. One of the methods is a combination of an electric engine as a main source of power and a small internal combustion engine as an electricity generator. This type of drive enables an electric vehicle to achieve a radically increased range and low emissions of toxic substances. For several years, the leading automotive manufacturers like the Mazda and the Audi together with the best companies in the automotive industry, e.g., AVL have developed some electric drive systems capable of recharging themselves while driving, known as a range extender. An electricity generator is powered by a Wankel engine that has seemed to pass into history. This low weight and small engine with a rotating piston and a very low vibration level turned out to be an excellent source in such applications. Its operation as an energy source for a generator almost entirely eliminates its disadvantages like high fuel consumption, high emission of toxic substances, or short lifetime typical of its traditional application. The operation of the engine at a constant rotational speed enables a significant increase in its lifetime, and its small external dimensions enable us to make compact modules to drive even small urban cars like the Audi A1 or the Mazda 2. The algorithm to maintain a constant speed was investigated on the engine dynamometer with an eddy current brake and the necessary measuring apparatus. The research object was the Aixro XR50 rotary engine with the electronic power supply developed at the Lublin University of Technology. The load torque of the engine was altered during the research by means of the eddy current brake capable of giving any number of load cycles. The parameters recorded included speed and torque as well as a position of a throttle in an inlet system. Increasing and decreasing load did not significantly change engine speed, which means that control algorithm parameters are correctly selected. This work has been financed by the Polish Ministry of Science and Higher Education.

Keywords: electric vehicle, power generator, range extender, Wankel engine

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Authors: Atbin Mahabbati, Jason Beringer, Matthias Leopold

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To address the global challenges of climate and environmental changes, there is a need for quantifying and reducing uncertainties in environmental data, including observations of carbon, water, and energy. Global eddy covariance flux tower networks (FLUXNET), and their regional counterparts (i.e., OzFlux, AmeriFlux, China Flux, etc.) were established in the late 1990s and early 2000s to address the demand. Despite the capability of eddy covariance in validating process modelling analyses, field surveys and remote sensing assessments, there are some serious concerns regarding the challenges associated with the technique, e.g. data gaps and uncertainties. To address these concerns, this research has developed an ensemble model to fill the data gaps of CO₂ flux to avoid the limitations of using a single algorithm, and therefore, provide less error and decline the uncertainties associated with the gap-filling process. In this study, the data of five towers in the OzFlux Network (Alice Springs Mulga, Calperum, Gingin, Howard Springs and Tumbarumba) during 2013 were used to develop an ensemble machine learning model, using five feedforward neural networks (FFNN) with different structures combined with an eXtreme Gradient Boosting (XGB) algorithm. The former methods, FFNN, provided the primary estimations in the first layer, while the later, XGB, used the outputs of the first layer as its input to provide the final estimations of CO₂ flux. The introduced model showed slight superiority over each single FFNN and the XGB, while each of these two methods was used individually, overall RMSE: 2.64, 2.91, and 3.54 g C m⁻² yr⁻¹ respectively (3.54 provided by the best FFNN). The most significant improvement happened to the estimation of the extreme diurnal values (during midday and sunrise), as well as nocturnal estimations, which is generally considered as one of the most challenging parts of CO₂ flux gap-filling. The towers, as well as seasonality, showed different levels of sensitivity to improvements provided by the ensemble model. For instance, Tumbarumba showed more sensitivity compared to Calperum, where the differences between the Ensemble model on the one hand and the FFNNs and XGB, on the other hand, were the least of all 5 sites. Besides, the performance difference between the ensemble model and its components individually were more significant during the warm season (Jan, Feb, Mar, Oct, Nov, and Dec) compared to the cold season (Apr, May, Jun, Jul, Aug, and Sep) due to the higher amount of photosynthesis of plants, which led to a larger range of CO₂ exchange. In conclusion, the introduced ensemble model slightly improved the accuracy of CO₂ flux gap-filling and robustness of the model. Therefore, using ensemble machine learning models is potentially capable of improving data estimation and regression outcome when it seems to be no more room for improvement while using a single algorithm.

Keywords: carbon flux, Eddy covariance, extreme gradient boosting, gap-filling comparison, hybrid model, OzFlux network

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Authors: Jaehong Yu, Seoung Bum Kim

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Clustering analysis can facilitate the extraction of intrinsic patterns in a dataset and reveal its natural groupings without requiring class information. For effective clustering analysis in high dimensional datasets, unsupervised dimensionality reduction is an important task. Unsupervised dimensionality reduction can generally be achieved by feature extraction or feature selection. In many situations, feature selection methods are more appropriate than feature extraction methods because of their clear interpretation with respect to the original features. The unsupervised feature selection can be categorized as feature subset selection and feature ranking method, and we focused on unsupervised feature ranking methods which evaluate the features based on their importance scores. Recently, several unsupervised feature ranking methods were developed based on ensemble approaches to achieve their higher accuracy and stability. However, most of the ensemble-based feature ranking methods require the true number of clusters. Furthermore, these algorithms evaluate the feature importance depending on the ensemble clustering solution, and they produce undesirable evaluation results if the clustering solutions are inaccurate. To address these limitations, we proposed an ensemble-based feature ranking method with random subspace and multiple-k ensemble (FRRM). The proposed FRRM algorithm evaluates the importance of each feature with the random subspace ensemble, and all evaluation results are combined with the ensemble importance scores. Moreover, FRRM does not require the determination of the true number of clusters in advance through the use of the multiple-k ensemble idea. Experiments on various benchmark datasets were conducted to examine the properties of the proposed FRRM algorithm and to compare its performance with that of existing feature ranking methods. The experimental results demonstrated that the proposed FRRM outperformed the competitors.

Keywords: clustering analysis, multiple-k ensemble, random subspace-based feature evaluation, unsupervised feature ranking

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Authors: Nimisha Jadon, Rajeev Jain, Swati Sharma

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A chemically modified carbon paste sensor has been developed for the simultaneous determination of amlodipine (AML) and nebivolol (NBV). Carbon paste electrode (CPE) was fabricated by the addition of Gr/PANI-CeO2. Gr/PANI-CeO2/CPE has achieved excellent electrocatalytic activity and sensitivity. AML and NBV exhibited oxidation peaks at 0.70 and 0.90 V respectively on Gr/ PANI-CeO2/CPE. The linearity range of AML and NBV was 0.1 to 1.6 μgmL-1 in BR buffer (pH 8.0). The Limit of detection (LOD) was 20.0 ngmL-1 for AML and 30.0 ngmL-1 for NBV and limit of quantification (LOQ) was 80.0 ngmL-1 for AML and 100 ngmL-1 for NBV respectively. These analyses were also determined in pharmaceutical formulation and human serum and good recovery was obtained for the developed method.

Keywords: amlodipine, nebivolol, square wave voltammetry, carbon paste electrode, simultaneous quantification

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Authors: Prince Amartey

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Bees are clever creatures, thus, capturing bees implies that the hives are intelligent in the sense that they have all of the required circumstances to attract and trap the bees. If the hive goes above and beyond to keep the bees in the hive and to keep the activities of in-hive pests to a minimal in order for the bees to develop to their maximum potential, the hive is becoming or is more 'intelligent'. Some bee pests, such as tiny beehive beetles, are endemic to Africa; however, the way we now extract honey by cutting off the combs and pressing for honey prevents the spread of these bees' insect enemies. However, when we explore entering the commercialization. When freshly collected combs are returned to the hives following the adoption of the frame and other systems, there is a need to consider putting in strategies to manage the accompanying pest concerns that arise with unprotected combs.The techniques for making hives more'intelligent' are thus more important presently, given that the African apicultural business does not wish to encourage the use of pesticides in the hives. This include changing the hive's entrance holes in order to improve the bees' own mechanism for defending the entry sites, as well as collecting pests by setting exterior and in-hive traps to prevent pest infiltration into hives by any means feasible. Material and Methods: The following five (5) mechanisms are proposed to make the hives more 'intelligent.' i. The usage of modified frames with five (5) beetle traps positioned horizontally on the vertical 'legs' to catch the beetle along the combs' surfaces-multiple bee ii. Baited bioelectric frame traps, which has both vertical sections of frame covered with a 3mm mesh that allows pest entry but not bees. The pest is attracted by strips of combs of honey, open brood, pollen on metal plates inserted horizontally on the vertical ‘legs’ of the frames. An electrical ‘mine’ system in place that electrocutes the pests as they step on the wires in the trap to enter the frame trap iii. The ten rounded hive entry holes are adapted as the bees are able to police the entrance to prevent entry of pest. The holes are arranged in two rows, with one on top of the other What Are the Main Contributions of Your Research?-Results Discussions and Conclusions The techniques implemented decrease pest ingress, while in-hive traps capture those that escape entry into the hives. Furthermore, the stand alteration traps larvae and stops their growth into adults. As beekeeping commercialization grows throughout Africa, these initiatives will minimize insect infestation in hives and necessarily enhance honey output.

Keywords: bee pests, modified frames, multiple beetle trap, Baited bioelectric frame traps

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Authors: Min Wang, Sergey Utev

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The lessons from the 2007-09 global financial crisis have driven scientific research, which considers the design of new methodologies and financial models in the global market. The quantum mechanics approach was introduced in the unpredictable stock market modeling. One famous quantum tool is Feynman path integral method, which was used to model insurance risk by Tamturk and Utev and adapted to formalize the path-dependent option pricing by Hao and Utev. The research is based on the path-dependent calculation method, which is motivated by the Feynman path integral method. The path calculation can be studied in two ways, one way is to label, and the other is computational. Labeling is a part of the representation of objects, and generating functions can provide many different ways of representing share price paths. In this paper, the recent works on graphical theoretical construction of individual share price path via matroid is presented. Firstly, a study is done on the knowledge of matroid, relationship between lattice path matroid and Tutte polynomials and ways to connect points in the lattice path matroid and Tutte polynomials is suggested. Secondly, It is found that a general binary tree can be validly constructed from a connected lattice path matroid rather than general lattice path matroid. Lastly, it is suggested that there is a way to represent share price paths via a general binary tree, and an algorithm is developed to construct share price paths from general binary trees. A relationship is also provided between lattice integer points and Tutte polynomials of a transversal matroid. Use this way of connection together with the algorithm, a share price path can be constructed from a given connected lattice path matroid.

Keywords: combinatorial construction, graphical representation, matroid, path calculation, share price, Tutte polynomial

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Authors: Parveen Kumar, J. K. Juneja, Chandra Prakash, K. K. Raina

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A series of polycrystalline ferroelectric ceramics with compositional formula Ba0.80-xSmxPb0.20Ti0.90Zr0.10O3 with x varying from 0 to 0.01 in the steps of 0.0025 has been prepared by solid state reaction method. The dielectric constant and tangent loss was measured as a function of frequency from 100Hz to 1MHz at different temperatures (200-500oC). The electrical behavior was then investigated using complex impedance spectroscopy (CIS) technique. From the CIS study, it has been found that there is a contribution of both grain and grain boundary in the electrical behavior of such ceramics. Grain and grain boundary resistivity and capacitance were calculated at different temperature using CIS technique. The present paper is about the discussion of grain and grain boundary contribution towards the electrical properties of Sm modified BaTiO3 based ceramics at high temperature.

Keywords: grain, grain boundary, impedance, dielectric

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Authors: Kittipong Tripetch

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This paper proposes for the first time symbolic formula of the power spectrum of cross couple oscillator and its modified circuit. Many principle existed to derived power spectrum in microwave textbook such as impedance, admittance parameters, ABCD, H parameters, etc. It can be compared by graph of power spectrum which methodology is the best from the point of view of practical measurement setup such as condition of impedance parameter which used superposition of current to derived (its current injection of the other port of the circuit is zero, which is impossible in reality). Four Graphs of impedance parameters of cross couple oscillator is proposed. After that four graphs of Scattering parameters of cross couple oscillator will be shown.

Keywords: optimization, power spectrum, impedance parameters, scattering parameter

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Authors: Rida Ahmad, Areej S. Habib, Sohail A. Dogar, Saqib H. Qazi

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Transverse testicular ectopia is a rare congenital disorder involving mal descent and mal-positioning of the testes, reported in the medical literature about 300 times. Many theories attempt to explain the failure of the testes to migrate to their correct location. While the age at presentation can vary; most cases present in early adolescents or late adulthood. It is often an incidental discovery made during an operative intervention, most commonly during hernia exploration. It can be isolated or present with a plethora of anomalies. We present the case of a 2-year-old male with transverse testicular ectopia who presented with vague abdominal pain. He was managed successfully with the Modified Ombredanne procedure and good outcome 6 months after the procedure.

Keywords: cryptorchidism, persistent Mullerian duct syndrome, transverse testicular ectopia, testicular mal-descent

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Authors: Sulalit Bandyopadhyay, Muhammad Awais Ashfaq Alvi, Anuvansh Sharma, Wilhelm R. Glomm

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Release of drugs from nanogels and nanogel-based systems can occur under the influence of external stimuli like temperature, pH, magnetic fields and so on. pNIPAm-AAc nanogels respond to the combined action of both temperature and pH, the former being mostly determined by hydrophilic-to-hydrophobic transitions above the volume phase transition temperature (VPTT), while the latter is controlled by the degree of protonation of the carboxylic acid groups. These nanogels based systems are promising candidates in the field of drug delivery. Combining nanogels with magneto-plasmonic nanoparticles (NPs) introduce imaging and targeting modalities along with stimuli-response in one hybrid system, thereby incorporating multifunctionality. Fe@Au core-shell NPs possess optical signature in the visible spectrum owing to localized surface plasmon resonance (LSPR) of the Au shell, and superparamagnetic properties stemming from the Fe core. Although there exist several synthesis methods to control the size and physico-chemical properties of pNIPAm-AAc nanogels, yet, there is no comprehensive study that highlights the dependence of incorporation of one or more layers of NPs to these nanogels. In addition, effective determination of volume phase transition temperature (VPTT) of the nanogels is a challenge which complicates their uses in biological applications. Here, we have modified the swelling-collapse properties of pNIPAm-AAc nanogels, by combining with Fe@Au NPs using different solution based methods. The hydrophilic-hydrophobic transition of the nanogels above the VPTT has been confirmed to be reversible. Further, an analytical method has been developed to deduce the average VPTT which is found to be 37.3°C for the nanogels and 39.3°C for nanogel coated Fe@Au NPs. An opposite swelling –collapse behaviour is observed for the latter where the Fe@Au NPs act as bridge molecules pulling together the gelling units. Thereafter, Cyt C, a model protein drug and L-Dopa, a drug used in the clinical treatment of Parkinson’s disease were loaded separately into the nanogels and nanogel coated Fe@Au NPs, using a modified breathing-in mechanism. This gave high loading and encapsulation efficiencies (L Dopa: ~9% and 70µg/mg of nanogels, Cyt C: ~30% and 10µg/mg of nanogels respectively for both the drugs. The release kinetics of L-Dopa, monitored using UV-vis spectrophotometry was observed to be rather slow (over several hours) with highest release happening under a combination of high temperature (above VPTT) and acidic conditions. However, the release of L-Dopa from nanogel coated Fe@Au NPs was the fastest, accounting for release of almost 87% of the initially loaded drug in ~30 hours. The chemical structure of the drug, drug incorporation method, location of the drug and presence of Fe@Au NPs largely alter the drug release mechanism and the kinetics of these nanogels and Fe@Au NPs coated with nanogels.

Keywords: controlled release, nanogels, volume phase transition temperature, l-dopa

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Authors: Sangita Pokhrel, Nalinda Somasiri, Rebecca Jeyavadhanam, Swathi Ganesan

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Tourism is a booming industry with huge future potential for global wealth and employment. There are countless data generated over social media sites every day, creating numerous opportunities to bring more insights to decision-makers. The integration of Big Data Technology into the tourism industry will allow companies to conclude where their customers have been and what they like. This information can then be used by businesses, such as those in charge of managing visitor centers or hotels, etc., and the tourist can get a clear idea of places before visiting. The technical perspective of natural language is processed by analysing the sentiment features of online reviews from tourists, and we then supply an enhanced long short-term memory (LSTM) framework for sentiment feature extraction of travel reviews. We have constructed a web review database using a crawler and web scraping technique for experimental validation to evaluate the effectiveness of our methodology. The text form of sentences was first classified through Vader and Roberta model to get the polarity of the reviews. In this paper, we have conducted study methods for feature extraction, such as Count Vectorization and TFIDF Vectorization, and implemented Convolutional Neural Network (CNN) classifier algorithm for the sentiment analysis to decide the tourist’s attitude towards the destinations is positive, negative, or simply neutral based on the review text that they posted online. The results demonstrated that from the CNN algorithm, after pre-processing and cleaning the dataset, we received an accuracy of 96.12% for the positive and negative sentiment analysis.

Keywords: counter vectorization, convolutional neural network, crawler, data technology, long short-term memory, web scraping, sentiment analysis

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Authors: Kyuwon Lee, Young-Wook Chang

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Superhydrophobic surfaces have been paid great attentions over the years due to their various applications. In this study, superhydrophobic coatings based on the hybrids of hydrophobically modified silica nanoparticles and waterborne polyolefin were fabricated onto a cotton fabric by spraying a mixture of surface dodecylated silica nanoparticles with aqueous dispersion of polyolefin onto the fabric and a subsequent drying at 80℃. The coated fabrics were characterized using water-contact angle measurement, SEM, and AFM analysis. The coated fabrics exhibit superhydrophobicity with a water contact angle of 155° along with excellent self-cleaning and water/oil separation ability. It was also revealed that such superhydrophobicity was maintained after repeated mechanical abrasion using a sandpaper.

Keywords: superhydrophobic coating, waterborne polyolefin, dodecylated silica nanoparticle, durability

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Authors: Ni Made Dian Swandewi, Wustari L. Mangundjaya

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Employees’ commitment to change are required for the success of organizational change in the company. The objective of this study is to identify the correlation between psychological hardiness and psychological resilience on commitment to change. The respondents of current research are permanent employees and employees that have worked for at least two years in a company that has been experiencing organizational change. Data was collected using Commitment to Change Inventory, Dispositional Resilience Scale (DRS), and Modified CD-RISC. The data were analyzed using regression. The results of the research show that both Psychological Hardiness and Psychological Resilience have positive and significant correlation and contribution on Commitment to Change. This research is important for companies who undergo organizational change in order plan and implement change more effectively.

Keywords: commitment to change, organizational change, psychological hardiness, psychological resilience

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Authors: M. Nekri, A. Khelladi

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The world wide web network is a network with a complex topology, the main properties of which are the distribution of degrees in power law, A low clustering coefficient and a weak average distance. Modeling the web as a graph allows locating the information in little time and consequently offering a help in the construction of the research engine. Here, we present a model based on the already existing probabilistic graphs with all the aforesaid characteristics. This work will consist in studying the web in order to know its structuring thus it will enable us to modelize it more easily and propose a possible algorithm for its exploration.

Keywords: clustering coefficient, preferential attachment, small world, web community

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Authors: Brayden Mi

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Reverse time migration has been widely used in the Petroleum exploration industry to reveal subsurface images and to detect rock and fluid properties since the early 1980s. The seismic technology involves the construction of a velocity model through interpretive model construction, seismic tomography, or full waveform inversion, and the application of the reverse-time propagation of acquired seismic data and the original wavelet used in the acquisition. The methodology has matured from 2D, simple media to present-day to handle full 3D imaging challenges in extremely complex geological conditions. Conventional Ultrasound computed tomography (USCT) utilize travel-time-inversion to reconstruct the velocity structure of an organ. With the velocity structure, USCT data can be migrated with the “bend-ray” method, also known as migration. Its seismic application counterpart is called Kirchhoff depth migration, in which the source of reflective energy is traced by ray-tracing and summed to produce a subsurface image. It is well known that ray-tracing-based migration has severe limitations in strongly heterogeneous media and irregular acquisition geometries. Reverse time migration (RTM), on the other hand, fully accounts for the wave phenomena, including multiple arrives and turning rays due to complex velocity structure. It has the capability to fully reconstruct the image detectable in its acquisition aperture. The RTM algorithms typically require a rather accurate velocity model and demand high computing powers, and may not be applicable to real-time imaging as normally required in day-to-day medical operations. However, with the improvement of computing technology, such a computational bottleneck may not present a challenge in the near future. The present-day (RTM) algorithms are typically implemented from a flat datum for the seismic industry. It can be modified to accommodate any acquisition geometry and aperture, as long as sufficient illumination is provided. Such flexibility of RTM can be conveniently implemented for the application in USCT imaging if the spatial coordinates of the transmitters and receivers are known and enough data is collected to provide full illumination. This paper proposes an implementation of a full 3D RTM algorithm for USCT imaging to produce an accurate 3D acoustic image based on the Phase-shift-plus-interpolation (PSPI) method for wavefield extrapolation. In this method, each acquired data set (shot) is propagated back in time, and a known ultrasound wavelet is propagated forward in time, with PSPI wavefield extrapolation and a piece-wise constant velocity model of the organ (breast). The imaging condition is then applied to produce a partial image. Although each image is subject to the limitation of its own illumination aperture, the stack of multiple partial images will produce a full image of the organ, with a much-reduced noise level if compared with individual partial images.

Keywords: illumination, reverse time migration (RTM), ultrasound computed tomography (USCT), wavefield extrapolation

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Authors: Sergio A. Navarro Tuch, Martin Rogelio Bustamante Bello, Leopoldo Julian Lechuga Lopez

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In recent years, Mexico’s population has seen a rise of different physiological and mental negative states. Two main consequences of this problematic are deficient work performance and high levels of stress generating and important impact on a person’s physical, mental and emotional health. Several approaches, such as the use of audiovisual stimulus to induce emotions and modify a person’s emotional state, can be applied in an effort to decreases these negative effects. With the use of different non-invasive physiological sensors such as EEG, luminosity and face recognition we gather information of the subject’s current emotional state. In a controlled environment, a subject is shown a series of selected images from the International Affective Picture System (IAPS) in order to induce a specific set of emotions and obtain information from the sensors. The raw data obtained is statistically analyzed in order to filter only the specific groups of information that relate to a subject’s emotions and current values of the physical variables in the controlled environment such as, luminosity, RGB light color, temperature, oxygen level and noise. Finally, a neural network based control algorithm is given the data obtained in order to feedback the system and automate the modification of the environment variables and audiovisual content shown in an effort that these changes can positively alter the subject’s emotional state. During the research, it was found that the light color was directly related to the type of impact generated by the audiovisual content on the subject’s emotional state. Red illumination increased the impact of violent images and green illumination along with relaxing images decreased the subject’s levels of anxiety. Specific differences between men and women were found as to which type of images generated a greater impact in either gender. The population sample was mainly constituted by college students whose data analysis showed a decreased sensibility to violence towards humans. Despite the early stage of the control algorithm, the results obtained from the population sample give us a better insight into the possibilities of emotional domotics and the applications that can be created towards the improvement of performance in people’s lives. The objective of this research is to create a positive impact with the application of technology to everyday activities; nonetheless, an ethical problem arises since this can also be applied to control a person’s emotions and shift their decision making.

Keywords: data analysis, emotional domotics, performance improvement, neural network

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Authors: Jung Hyun Kim, Gyo Woo Lee

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In this study, the enhancement of the heat release performance of an extruded-type heat sink to prepare the large-capacity solar inverter thru the flow holes in the base plate near the heat sources was investigated. Optimal location and number of the holes in the baseplate were determined by using a commercial computation program. The heat release performance of the shape-modified heat sink was measured experimentally and compared with that of the simulation. The heat sink with 12 flow holes in the 18-mm-thick base plate has a 8.1% wider heat transfer area, a 2.5% more mass flow of air, and a 2.7% higher heat release rate than those of the original heat sink. Also, the surface temperature of the base plate was lowered 1.5°C by the holes.

Keywords: heat sink, forced convection, heat transfer, performance evaluation, flow holes

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Authors: Anne M. Denton, Rahul Gomes, David W. Franzen

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High-resolution elevation data are becoming increasingly available, but typical approaches for computing topographic features, like slope and curvature, still assume small sliding windows, for example, of size 3x3. That means that the digital elevation model (DEM) has to be resampled to the scale of the landform features that are of interest. Any higher resolution is lost in this resampling. When the topographic features are computed through regression that is performed at the resolution of the original data, the accuracy can be much higher, and the reported result can be adjusted to the length scale that is relevant locally. Slope and variance are calculated for overlapping windows, meaning that one regression result is computed per raster point. The number of window centers per area is the same for the output as for the original DEM. Slope and variance are computed by performing regression on the points in the surrounding window. Such an approach is computationally feasible because of the additive nature of regression parameters and variance. Any doubling of window size in each direction only takes a single pass over the data, corresponding to a logarithmic scaling of the resulting algorithm as a function of the window size. Slope and variance are stored for each aggregation step, allowing the reported slope to be selected to minimize variance. The approach thereby adjusts the effective window size to the landform features that are characteristic to the area within the DEM. Starting with a window size of 2x2, each iteration aggregates 2x2 non-overlapping windows from the previous iteration. Regression results are stored for each iteration, and the slope at minimal variance is reported in the final result. As such, the reported slope is adjusted to the length scale that is characteristic of the landform locally. The length scale itself and the variance at that length scale are also visualized to aid in interpreting the results for slope. The relevant length scale is taken to be half of the window size of the window over which the minimum variance was achieved. The resulting process was evaluated for 1-meter DEM data and for artificial data that was constructed to have defined length scales and added noise. A comparison with ESRI ArcMap was performed and showed the potential of the proposed algorithm. The resolution of the resulting output is much higher and the slope and aspect much less affected by noise. Additionally, the algorithm adjusts to the scale of interest within the region of the image. These benefits are gained without additional computational cost in comparison with resampling the DEM and computing the slope over 3x3 images in ESRI ArcMap for each resolution. In summary, the proposed approach extracts slope and aspect of DEMs at the lengths scales that are characteristic locally. The result is of higher resolution and less affected by noise than existing techniques.

Keywords: high resolution digital elevation models, multi-scale analysis, slope calculation, window-based regression

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Authors: Tarek Aly

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The shoulder is the most mobile joint whose stability requires the interaction of both dynamic and static stabilizers. Its wide range of movement predisposes to a high susceptibility to dislocation, accounting for nearly 50% of all dislocations. This trauma typically results in ligament injury (e.g., labral tear, capsular strain) or bony fracture (e.g., loss of glenoid or humeral head bone), which frequently causes recurrent instability. Patients with significant glenoid defects may require Latarjet procedure, which involves transferring the coracoid to the antero-inferior glenoid rim. In spite of outstanding results, 15 to 30% of cases suffer complications. In this article, we discuss the diagnosis of recurrent shoulder instability, the surgical technique and various complications of Latarjet procedure.

Keywords: recurrent, anterior gleno-humeral instability, latarjet, unstable shoulder

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Authors: Animish Jain

Abstract:

This study deals with using Multiplicative Weight Update within artificial intelligence and machine learning to create models that can diagnose skin cancer using microscopic images of cancer samples. In this study, the multiplicative weight update method is used to take the predictions of multiple models to try and acquire more accurate results. Logistic Regression, Convolutional Neural Network (CNN), and Support Vector Machine Classifier (SVMC) models are employed within the Multiplicative Weight Update system. These models are trained on pictures of skin cancer from the ISIC-Archive, to look for patterns to label unseen scans as either benign or malignant. These models are utilized in a multiplicative weight update algorithm which takes into account the precision and accuracy of each model through each successive guess to apply weights to their guess. These guesses and weights are then analyzed together to try and obtain the correct predictions. The research hypothesis for this study stated that there would be a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The SVMC model had an accuracy of 77.88%. The CNN model had an accuracy of 85.30%. The Logistic Regression model had an accuracy of 79.09%. Using Multiplicative Weight Update, the algorithm received an accuracy of 72.27%. The final conclusion that was drawn was that there was a significant difference in the accuracy of the three models and the Multiplicative Weight Update system. The conclusion was made that using a CNN model would be the best option for this problem rather than a Multiplicative Weight Update system. This is due to the possibility that Multiplicative Weight Update is not effective in a binary setting where there are only two possible classifications. In a categorical setting with multiple classes and groupings, a Multiplicative Weight Update system might become more proficient as it takes into account the strengths of multiple different models to classify images into multiple categories rather than only two categories, as shown in this study. This experimentation and computer science project can help to create better algorithms and models for the future of artificial intelligence in the medical imaging field.

Keywords: artificial intelligence, machine learning, multiplicative weight update, skin cancer

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Authors: Reza Ziaie Moayed, Hamidreza Rahmani

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Today, with developments in science and technology, there is an excessive potential for the use of nanomaterials in various fields of geotechnical project such as soil stabilization. This study investigates the effect of Nano-SiO₂ solution on the unconfined compression strength and Young's elastic modulus of Kaolinite. For this purpose, nano-SiO₂ was mixed with kaolinite in five different contents: 1, 2, 3, 4 and 5% by weight of the dry soil and a series of the unconfined compression test with curing time of one-day was selected as laboratory test. Analyses of the tests results show that stabilization of kaolinite with Nano-SiO₂ solution can improve effectively the unconfined compression strength of modified soil up to 1.43 times compared to  the pure soil.

Keywords: kaolinite, Nano-SiO2, stabilization, unconfined compression test, Young's modulus

Procedia PDF Downloads 394

Authors: Hao Min Chuah, Tatsuya Yamazaki, Ryosui Iwasawa, Tatsumi Suto

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In recent years, with the advancement of digital technology, the movement to introduce so-called ICT (Information and Communication Technology), such as computer technology and network technology, to civil engineering construction sites and construction sites is accelerating. As part of this movement, attempts are being made in various situations to reproduce actual sites inside computers and use them for designing and construction planning, as well as for training inexperienced engineers. The installation of wave-dissipating blocks on coasts, etc., is a type of work that has been carried out by skilled workers based on their years of experience and is one of the tasks that is difficult for inexperienced workers to carry out on site. Wave-dissipating blocks are structures that are designed to protect coasts, beaches, and so on from erosion by reducing the energy of ocean waves. Wave-dissipating blocks usually weigh more than 1 t and are installed by being suspended by a crane, so it would be time-consuming and costly for inexperienced workers to train on-site. In this paper, therefore, a block installation simulator is developed based on Unity 3D, a game development engine. The simulator computes porosity. Porosity is defined as the ratio of the total volume of the wave breaker blocks inside the structure to the final shape of the ideal structure. Using the evaluation of porosity, the simulator can determine how well the user is able to install the blocks. The voxelization technique is used to calculate the porosity of the structure, simplifying the calculations. Other techniques, such as raycasting and box overlapping, are employed for accurate simulation. In the near future, the simulator will install an automatic block installation algorithm based on combinatorial optimization solutions and compare the user-demonstrated block installation and the appropriate installation solved by the algorithm.

Keywords: 3D simulator, porosity, user interface, voxelization, wave-dissipating blocks

Procedia PDF Downloads 105

Authors: P. Vit, J. Uddin, V. Zuccato, F. Maza, E. Schievano

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In Ecuador honeys are produced by Apis mellifera and stingless bees (Meliponini). We studied honey produced in beeswax combs by Apis mellifera, and honey produced in pots by Geotrigona and Scaptotrigona bees. Chloroform extracts of honey were obtained for fast NMR spectra. The 1D spectra were acquired at 298 K, with a 600 MHz NMR Bruker instrument, using a modified double pulsed field gradient spin echoes (DPFGSE) sequence. Signals of 1H NMR spectra were integrated and used as inputs for PCA, PLS-DA analysis, and labelled sets of classes were successfully identified, enhancing the separation between the three groups of honey according to the entomological origin: A. mellifera, Geotrigona and Scaptotrigona. This procedure is therefore recommended for authenticity test of honey in Ecuador.

Keywords: Apis mellifera, honey, 1H NMR, entomological origin, meliponini

Procedia PDF Downloads 404

Authors: Khin Thandar Soe, Mark Stephen Pulham

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Ballistic building materials play a crucial role in ensuring the safety of the occupants within protective structures. Traditional options like Ordinary Portland Cement (OPC)-based walls, including reinforced concrete walls, precast concrete walls, masonry walls, and concrete blocks, are frequently employed for ballistic protection, but they have several drawbacks such as being thick, heavy, costly, and challenging to construct. On the other hand, glass and composite materials offer lightweight and easier construction alternatives, but they come with a high price tag. There has been no reported test data on magnesium-based ballistic wall panels or modular wall systems so far. This paper presents groundbreaking small arms test data related to the development of the world’s first magnesia cement ballistic wall panels and modular wall system. Non-hydraulic magnesia cement exhibits several superior properties, such as lighter weight, flexibility, acoustics, and fire performance, compared to the traditional Portland Cement. However, magnesia cement is hydrophilic and may degrade in prolonged contact with water. In this research, modified magnesia cement for water resistant and durability from UBIQ Technology is applied. The specimens are made of a modified magnesia cement formula and prepared in the Laboratory of UBIQ Technology Pty Ltd. The specimens vary in thickness, and the tests cover various small arms threats in compliance with standards AS/NZS2343 and UL752 and are performed up to the maximum threat level of Classification R2 (NATO) and UL-Level 8(NATO) by the Accredited Test Centre, BMT (Ballistic and Mechanical Testing, VIC, Australia). In addition, the results of the test conducted on the specimens subjected to the small 12mm diameter steel ball projectile impact generated by a gas gun are also presented and discussed in this paper. Gas gun tests were performed in UNSW@ADFA, Canberra, Australia. The tested results of the magnesia panels and wall systems are compared with one of concrete and other wall panels documented in the literature. The conclusion drawn is that magnesia panels and wall systems exhibit several advantages over traditional OPC-based wall systems, and they include being lighter, thinner, and easier to construct, all while providing equivalent protection against threats. This makes magnesia cement-based materials a compelling choice of application where efficiency and performance are critical to create a protective environment.

Keywords: ballistics, small arms, gas gun, projectile, impact, wall panels, modular, magnesia cement

Procedia PDF Downloads 77

Authors: Beatriz Lafuente Alcázar, Yash Wani, Amit J. Nimunkar

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Cardiovascular Diseases (CVDs) are the leading cause of death globally, and around 80% of sudden cardiac deaths are due to arrhythmias or irregular heartbeats. The majority of these pathologies are revealed by either short-term or long-term alterations in the electrocardiogram (ECG) morphology. The ECG is the main diagnostic tool in cardiology. It is a non-invasive, pain free procedure that measures the heart’s electrical activity and that allows the detecting of abnormal rhythms and underlying conditions. A cardiologist can diagnose a wide range of pathologies based on ECG’s form alterations, but the human interpretation is subjective and it is contingent to error. Moreover, ECG records can be quite prolonged in time, which can further complicate visual diagnosis, and deeply retard disease detection. In this context, deep learning methods have risen as a promising strategy to extract relevant features and eliminate individual subjectivity in ECG analysis. They facilitate the computation of large sets of data and can provide early and precise diagnoses. Therefore, the cardiology field is one of the areas that can most benefit from the implementation of deep learning algorithms. In the present study, a deep learning algorithm is trained following a novel approach, using a combination of different databases as the training set. The goal of the algorithm is to achieve the detection of R-peaks in ECG signals. Its performance is further evaluated in ECG signals with different origins and features to test the model’s ability to generalize its outcomes. Performance of the model for detection of R-peaks for clean and noisy ECGs is presented. The model is able to detect R-peaks in the presence of various types of noise, and when presented with data, it has not been trained. It is expected that this approach will increase the effectiveness and capacity of cardiologists to detect divergences in the normal cardiac activity of their patients.

Keywords: arrhythmia, deep learning, electrocardiogram, machine learning, R-peaks

Procedia PDF Downloads 189

Authors: Rabia Saleem

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This research has a recently proposed strategy to find the exact inflationary solution of the Friedman equations in the context of the Rastall theory of gravity (RTG), known as constant-roll warm inflation, including dissipation effects. We establish the model to evaluate the effective potential of inflation and entropy. We develop the inflationary observable like scalar-tensor power spectra, scalar-tensor spectral indices, tensor-to-scalar ratio, and running of spectral-index. The theory parameter $\lambda$ is constrained to observe the compatibility of our model with Planck 2013, Planck TT, TE, EE+lowP (2015), and Planck 2018 bounds. The results are feasible and interesting up to the 2$\sigma$ confidence level.

Keywords: modified gravity, warm inflation, constant-roll limit, dissipation

Procedia PDF Downloads 101

Authors: Misagh Ghobadi, Rich Crane, Karen Hudson-Edwards, Clemens Vinzenz Ullmann

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Lithium is recognized as the critical energy metal of the 21st century, comparable in importance to coal in the 19th century and oil in the 20th century, often termed 'white gold'. Current global demand for lithium, estimated at 0.95-0.98 million metric tons (Mt) of lithium carbonate equivalent (LCE) annually in 2024, is projected to rise to 1.87 Mt by 2027 and 3.06 Mt by 2030. Despite anticipated short-term stability in supply and demand, meeting the forecasted 2030 demand will require the lithium industry to develop an additional capacity of 1.42 Mt of LCE annually, exceeding current planned and ongoing efforts. Brine resources constitute nearly 65% of global lithium reserves, underscoring the importance of exploring lithium recovery from underutilized sources, especially geothermal brines. However, conventional lithium extraction from brine deposits faces challenges due to its time-intensive process, low efficiency (30-50% lithium recovery), unsuitability for low lithium concentrations (<300 mg/l), and notable environmental impacts. Addressing these challenges, direct lithium extraction (DLE) methods have emerged as promising technologies capable of economically extracting lithium even from low-concentration brines (>50 mg/l) with high recovery rates (75-98%). However, most studies (70%) have predominantly focused on synthetic brines instead of native (natural/real), with limited application of these approaches in real-world case studies or industrial settings. This study aims to bridge this gap by investigating a geothermal brine sample collected from a real case study site in the UK. A Mn-based lithium-ion sieve (LIS) adsorbent was synthesized and employed to selectively extract lithium from the sample brine. Adsorbents with a Li:Mn molar ratio of 1:1 demonstrated superior lithium selectivity and adsorption capacity. Furthermore, the pristine Mn-based adsorbent was modified through transition metals doping, resulting in enhanced lithium selectivity and adsorption capacity. The modified adsorbent exhibited a higher separation factor for lithium over major co-existing cations such as Ca, Mg, Na, and K, with separation factors exceeding 200. The adsorption behaviour was well-described by the Langmuir model, indicating monolayer adsorption, and the kinetics followed a pseudo-second-order mechanism, suggesting chemisorption at the solid surface. Thermodynamically, negative ΔG° values and positive ΔH° and ΔS° values were observed, indicating the spontaneity and endothermic nature of the adsorption process.

Keywords: adsorption, critical minerals, DLE, geothermal brines, geochemistry, lithium, lithium-ion sieves

Procedia PDF Downloads 49

Authors: Davit Mirzoyan, Ararat Khachatryan

Abstract:

A process corner monitoring circuit (PCMC) is presented in this work. The circuit generates a signal, the logical value of which depends on the process corner only. The signal can be used in both digital and analog circuits for testing and compensation of process variations (PV). The presented circuit uses only metal-oxide-semiconductor (MOS) transistors, which allow increasing its detection accuracy, decrease power consumption and area. Due to its simplicity the presented circuit can be easily modified to monitor parametrical variations of only n-type and p-type MOS (NMOS and PMOS, respectively) transistors, resistors, as well as their combinations. Post-layout simulation results prove correct functionality of the proposed circuit, i.e. ability to monitor the process corner (equivalently die-to-die variations) even in the presence of within-die variations.

Keywords: detection, monitoring, process corner, process variation

Procedia PDF Downloads 526