Search results for: axial flux applications

Authors: Anita Sadat Sadati Rostami, Reza Almasi Ghaleh

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Surface electromyography (sEMG) offers important insights into muscle activation and has applications in fields including rehabilitation and human-computer interaction. The purpose of this work is to predict the degree of activation of two joints in the index finger using an LSTM-Transformer architecture trained on sEMG data from the Ninapro DB8 dataset. We apply advanced preprocessing techniques, such as multi-band filtering and customizable rectification methods, to enhance the encoding of sEMG data into features that are beneficial for regression tasks. The processed data is converted into spike patterns and simulated using Leaky Integrate-and-Fire (LIF) neuron models, allowing for neuromorphic-inspired processing. Our findings demonstrate that adjusting filtering parameters and neuron dynamics and employing the LSTM-Transformer model improves joint angle prediction performance. This study contributes to the ongoing development of deep learning frameworks for sEMG analysis, which could lead to improvements in motor control systems.

Keywords: surface electromyography, LSTM-transformer, spiking neural networks, hand kinematics, leaky integrate-and-fire neuron, band-pass filtering, muscle activity decoding

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Authors: Endalamaw Ewnu Kassa, Ade Kurniawan, Ya-Fen Wu, Sajal Biring

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Perovskite-based gas sensors represent a highly promising materials within the realm of gas sensing technology, with a particular focus on detecting ammonia (NH3) due to its potential hazards. Our work conducted thorough comparison of various solvents, including dimethylformamide (DMF), DMF-dimethyl sulfoxide (DMSO), dimethylacetamide (DMAC), and DMAC-DMSO, for the preparation of our perovskite solution (MAPbI3). Significantly, we achieved an exceptional response at 10 ppm of ammonia gas by employing a binary solvent mixture of DMAC-DMSO. In contrast to prior reports that relied on single solvents for MAPbI3 precursor preparation, our approach using mixed solvents demonstrated a marked improvement in gas sensing performance. We attained enhanced surface coverage, a reduction in pinhole occurrences, and precise control over grain size in our perovskite films through the careful selection and mixtures of appropriate solvents. This study shows a promising potential of employing binary and multi-solvent mixture strategies as a means to propel advancements in gas sensor technology, opening up new opportunities for practical applications in environmental monitoring and industrial safety.

Keywords: sensors, binary solvents, ammonia, sensitivity, grain size, pinholes, surface coverage

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Authors: Nieto Bernal Wilson, Carmona Suarez Edgar

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The organizations have structured and unstructured information in different formats, sources, and systems. Part of these come from ERP under OLTP processing that support the information system, however these organizations in OLAP processing level, presented some deficiencies, part of this problematic lies in that does not exist interesting into extract knowledge from their data sources, as also the absence of operational capabilities to tackle with these kind of projects.  Data Warehouse and its applications are considered as non-proprietary tools, which are of great interest to business intelligence, since they are repositories basis for creating models or patterns (behavior of customers, suppliers, products, social networks and genomics) and facilitate corporate decision making and research. The following paper present a structured methodology, simple, inspired from the agile development models as Scrum, XP and AUP. Also the models object relational, spatial data models, and the base line of data modeling under UML and Big data, from this way sought to deliver an agile methodology for the developing of data warehouses, simple and of easy application. The methodology naturally take into account the application of process for the respectively information analysis, visualization and data mining, particularly for patterns generation and derived models from the objects facts structured.

Keywords: data warehouse, model data, big data, object fact, object relational fact, process developed data warehouse

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Authors: Min Kyung An

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In Wireless Sensor Networks which consist of tiny wireless sensor nodes with limited battery power, one of the most fundamental applications is data aggregation which collects nearby environmental conditions and aggregates the data to a designated destination, called a sink node. Important issues concerning the data aggregation are time efficiency and energy consumption due to its limited energy, and therefore, the related problem, named Minimum Latency Aggregation Scheduling (MLAS), has been the focus of many researchers. Its objective is to compute the minimum latency schedule, that is, to compute a schedule with the minimum number of timeslots, such that the sink node can receive the aggregated data from all the other nodes without any collision or interference. For the problem, the two interference models, the graph model and the more realistic physical interference model known as Signal-to-Interference-Noise-Ratio (SINR), have been adopted with different power models, uniform-power and non-uniform power (with power control or without power control), and different antenna models, omni-directional antenna and directional antenna models. In this survey article, as the problem has proven to be NP-hard, we present and compare several state-of-the-art approximation algorithms in various models on the basis of latency as its performance measure.

Keywords: data aggregation, convergecast, gathering, approximation, interference, omni-directional, directional

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Authors: Raymond Feng, Shadi Ghiasi

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An electroencephalogram (EEG) is a non-invasive technique that records electrical activity in the brain using scalp electrodes. Researchers have studied the use of EEG to detect emotions and moods by collecting signals from participants and analyzing how those signals correlate with their activities. In this study, researchers investigated the relationship between EEG signals and music enjoyment. Participants listened to music while data was collected. During the signal-processing phase, power spectral densities (PSDs) were computed from the signals, and dominant brainwave frequencies were extracted from the PSDs to form a comprehensive feature matrix. A machine learning approach was then taken to find correlations between the processed data and the music enjoyment level indicated by the participants. To improve on previous research, multiple machine learning models were employed, including K-Nearest Neighbors Classifier, Support Vector Classifier, and Decision Tree Classifier. Hyperparameters were used to fine-tune each model to further increase its performance. The experiments showed that a strong correlation exists, with the Decision Tree Classifier with hyperparameters yielding 85% accuracy. This study proves that EEG is a reliable means to detect music enjoyment and has future applications, including personalized music recommendation, mood adjustment, and mental health therapy.

Keywords: EEG, electroencephalogram, machine learning, mood, music enjoyment, physiological signals

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Authors: Chih-Hsing Lin, Wen-Ching Chen, Chih-Ting Kuo, Gang-Neng Sung, Chih-Chyau Yang, Chien-Ming Wu, Chun-Ming Huang

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This paper proposes a cellular-based structure health monitoring device (HMD) for temporary bridge monitoring without the requirement of power line and internet service. The proposed HMD includes sensor node, power module, cellular gateway, and rechargeable batteries. The purpose of HMD focuses on short-term collection of civil infrastructure information. It achieves the features of low cost by using three 1-axis accelerometers with data synchronization problem being solved. Furthermore, instead of using data acquisition system (DAQ) sensed data is transmitted to Host through cellular gateway. Compared with 3-axis accelerometer, our proposed 1-axis accelerometers based device achieves 50.5% cost saving with high sensitivity 2000mv/g. In addition to fit different monitoring environments, the proposed system can be easily replaced and/or extended with different PCB boards, such as communication interfaces and sensors, to adapt to various applications. Therefore, with using the proposed device, the real-time diagnosis system for civil infrastructure damage monitoring can be conducted effectively.

Keywords: cellular-based structural health monitoring, cost-effective 1-axis accelerometers, short-term monitoring, structural engineering

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Authors: Eiman Kattan

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This study aims to evaluate the impact of classifying labelled remote sensing images conventional neural network (CNN) architecture, i.e., AlexNet on different land cover scenarios based on two remotely sensed datasets from different point of views such as the computational time and performance. Thus, a set of experiments were conducted to specify the effectiveness of the selected convolutional neural network using two implementing approaches, named fully trained and fine-tuned. For validation purposes, two remote sensing datasets, AID, and RSSCN7 which are publicly available and have different land covers features were used in the experiments. These datasets have a wide diversity of input data, number of classes, amount of labelled data, and texture patterns. A specifically designed interactive deep learning GPU training platform for image classification (Nvidia Digit) was employed in the experiments. It has shown efficiency in training, validation, and testing. As a result, the fully trained approach has achieved a trivial result for both of the two data sets, AID and RSSCN7 by 73.346% and 71.857% within 24 min, 1 sec and 8 min, 3 sec respectively. However, dramatic improvement of the classification performance using the fine-tuning approach has been recorded by 92.5% and 91% respectively within 24min, 44 secs and 8 min 41 sec respectively. The represented conclusion opens the opportunities for a better classification performance in various applications such as agriculture and crops remote sensing.

Keywords: conventional neural network, remote sensing, land cover, land use

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Authors: Mervet A. El-sherif, Ginat M El-sherif, Kadry H Tolba

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The objective of this study was to determine, identify and investigate the effects of natural antioxidants of licorice and carob. Besides, their effects as powder and antioxidant extracts addition on refined sunflower oil stability as natural antioxidants were evaluated. Total polyphenol contents as total phenols, total carotenoids and total tannins were 353.93mg/100g (gallic acid), 10.62mg/100g (carotenoids) and 83.33mg/100g (tannic acid), respectively in licorice, while in carob, it was 186.07, 18.66 and 106.67, respectively. Polyphenol compounds of the studied licorice and carob extracts were determined and identified by HPLC. The stability of refined sunflower oil (which determined by peroxide value and Rancimat) was increased with increasing the level of polyphenols extracts addition. Also, our study shows the effect of addition of these polyphenols extracts to El-mewled El-nabawy sweets fortified by full cream milk powder (sesames and folia). We found that, licorice and carob as powder and polyphenols extracts were delayed the rancidity of sesame and peanut significantly. That encourages using licorice and carob as powder and polyphenols extracts as a good natural antioxidants source instead of synthetic antioxidants.

Keywords: licorice, carob, natural antioxidants, antioxidant activity, applications

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Authors: Farmesk Abubaker, Francesco Tortorici, Marco Capogni, Concetta Sutera, Vincenzo Bellini

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This project concerns with the detection efficiency of the portable triple-to-double coincidence ratio (TDCR) at the National Institute of Metrology of Ionizing Radiation (INMRI-ENEA) which allows direct activity measurement and radionuclide standardization for pure-beta emitter or pure electron capture radionuclides. The dependency of the simulated detection efficiency of the TDCR, by using Monte Carlo simulation Geant4 code, on the Birks factor (kB) and defocusing parameter has been examined especially for low energy beta-emitter radionuclides such as 3H and 14C, for which this dependency is relevant. The results achieved in this analysis can be used for selecting the best kB factor and the defocusing parameter for computing theoretical TDCR parameter value. The theoretical results were compared with the available ones, measured by the ENEA TDCR portable detector, for some pure-beta emitter radionuclides. This analysis allowed to improve the knowledge of the characteristics of the ENEA TDCR detector that can be used as a traveling instrument for in-situ measurements with particular benefits in many applications in the field of nuclear medicine and in the nuclear energy industry.

Keywords: Birks constant, defocusing parameter, GEANT4 code, TDCR parameter

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Authors: Uc-Cayetano E. G., Ake-Uh O. E., Villanueva-Mena I. E., Ordonez L. C.

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Carbon nanotubes (CNTs) and other graphitic nanostructures are materials with extraordinary physical, physicochemical and electrochemical properties which are being aggressively investigated for a variety of sensing applications. Thus, sensing of biological molecules such as proteins, DNA, glucose and other enzymes using either single wall or multiwall carbon nanotubes (MWCNTs) has been widely reported. Despite the current progress in this area, the electrochemical response of CNTs used in a variety of sensing arrangements still needs to be improved. An alternative towards the enhancement of this CNTs' electrochemical response is to chemically (or physically) modify its surface. The influence of the decoration with iron oxide nanoparticles in different types of MWCNTs on the amperometric sensing of glucose, urea, and cholesterol in solution is investigated. Commercial MWCNTs were oxidized in acid media and subsequently decorated with iron oxide nanoparticles; finally, the enzymes glucose oxidase, urease, and cholesterol oxidase are chemically immobilized to oxidized and decorated MWCNTs for glucose, urease, and cholesterol electrochemical sensing. The results of the electrochemical characterizations consistently show that the presence of iron oxide nanoparticles decorating the surface of MWCNTs enhance the amperometric response and the sensitivity to increments in glucose, urease, and cholesterol concentration when compared to non-decorated MWCNTs.

Keywords: WCNTs, enzymes, oxidation, decoration

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Authors: Devesh Motwani, Amey Kashyap

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Interest in cost effective drilling has increased substantially in the past years. Economics associated with drilling fluids is needed to be considered seriously for lesser cost per foot in planning and drilling of a wellbore and the various environmental concerns imposed by international communities related with the constituents of the drilling fluid. Viscofier such as Guar Gum is a high molecular weight polysaccharide from Guar plants, is used to increase viscosity in water-based and brine-based drilling fluids thus enabling more efficient cleaning of the bore. Other applications of this Viscofier are to reduce fluid loss by giving a better colloidal solution, decrease fluid friction and so minimising power requirements and used in hydraulic fracturing to increase the recovery of oil and gas. Guar gum is also used as a surfactant, synthetic polymer and defoamer. This paper presents experimental results to verifying the properties of guar gum as a viscofier and filtrate retainer as well as observing the impact of different quantities of guar gum and Carboxymethyl cellulose (CMC) in a standard sample of water based bentonite mud solution. This is in attempt to make a drilling fluid which contains half of the quantity of drilling mud used and yet is equally viscous to the standardised mud sample. Thus we can see that mud economics will be greatly affected by this approach. However guar gum is thermally stable till 60-65°C thus limited to be used in drilling shallow wells and for a wider thermal range, suitable chrome free additives are required.

Keywords: economics, guargum, viscofier, CMC, thermal stability

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Authors: Mona Francesca B. Dela Cruz, Katrina Marie R. Mamaril, Mariah Hannah Kassandra Salazar, Emerald Jay D. Ilac

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One key factor that should be considered when determining sustainable solutions to various environmental problems is the potential impact of individual human beings. In order to understand an individual, there is a need to examine cognitive, emotional, dispositional, and behavioral factors which are all indicative of one’s environmental consciousness. This quantitative study explored the moderated mediation between environmental consciousness, socio-economic status, social norms as a mediator, and the perceived role of government as a moderator for 381 Filipinos, aged 25 to 65, in urban and suburban settings. Results showed social norms do not have a mediating effect between socio-economic status and environmental consciousness. This may be influenced by the collectivist culture of the Philippines and the tendency for people to copy behaviors according to the descriptive norm effect. Meanwhile, there exists a moderating effect of the perceived role of government between the relationship of social norms and environmental consciousness which can be explained by the government’s ability to impose social norms that can induce a person to think and act pro-environmentally. Practical applications of this study can be used to tap the ability of the government to strengthen their influence and control over environmental protection and to provide a basis for the development of class-specific environmental solutions that can be done by individuals depending on their socioeconomic status.

Keywords: environmental consciousness, role of government, social norms, socio-economic status

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Authors: Sudarshan Kalsulkar, Sunil S. Bhagwat

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Biosorption is a physiochemical process that occurs naturally in certain biomass which allows it to passively concentrate and bind contaminants onto its cellular structure. Activated carbons (AC) are one such efficient biosorbents made by utilizing lignocellulosic materials from agricultural waste. Steam activated carbon (AC) was synthesized from Barley husk. Its synthesis parameters of time and temperature were optimized. Its physico-chemical properties like density, surface area, pore volume, Methylene blue and Iodine values were characterized. BET surface area was found to be 42 m²/g. Batch Adsorption tests were carried out to determine the maximum adsorption capacity (qmax) for various metal ions. Cd+2 48.74 mg/g, Pb+2 19.28 mg/g, Hg+2 39.1mg/g were the respective qmax values. pH and time were optimized for adsorption of each ion. Column Adsorptions were carried for each to obtain breakthrough data. Microbial adsorption was carried using E. coli K12 strain. 78% reduction in cell count was observed at operating conditions. Thus the synthesized Barley husk AC can be an economically feasible replacement for commercially available AC prepared from the costlier coconut shells. Breweries and malting industries where barley husk is a primary waste generated on a large scale can be a good source for bulk raw material.

Keywords: activated carbon, Barley husk, biosorption, decontamination, heavy metal removal, water treatment

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Authors: Nur Mohammad Ali, Md. Shafiqul Alam, Jayanta Bhusan Deb, Nowrin Sharmin

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The past ten years have seen significant expansion in the aviation sector, which during the previous five years has steadily pushed emerging countries closer to economic independence. It is crucial to accurately forecast the potential demand for air travel to make long-term financial plans. To forecast market demand for low-cost passenger carriers, this study suggests working with low-cost airlines, airports, consultancies, and governmental institutions' strategic planning divisions. The study aims to develop an artificial intelligence-based methods, notably fuzzy inference systems (FIS), to determine the most accurate forecasting technique for domestic low-cost carrier demand in Bangladesh. To give end users real-world applications, the study includes nine variables, two sub-FIS, and one final Mamdani Fuzzy Inference System utilizing a graphical user interface (GUI) made with the app designer tool. The evaluation criteria used in this inquiry included mean square error (MSE), accuracy, precision, sensitivity, and specificity. The effectiveness of the developed air passenger demand prediction FIS is assessed using 240 data sets, and the accuracy, precision, sensitivity, specificity, and MSE values are 90.83%, 91.09%, 90.77%, and 2.09%, respectively.

Keywords: aviation industry, fuzzy inference system, membership function, graphical user interference

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Authors: R. Ruslee, H. Gollee

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Functional electrical stimulation (FES) is a commonly used technique in rehabilitation and often associated with rapid muscle fatigue which becomes the limiting factor in its applications. The objective of this study is to investigate the effects on the onset of fatigue of conventional synchronous stimulation, as well as asynchronous stimulation that mimic voluntary muscle activation targeting different motor units which are activated sequentially or randomly via multiple pairs of stimulation electrodes. We investigate three different approaches with various electrode configurations, as well as different patterns of stimulation applied to the gastrocnemius muscle: Conventional Synchronous Stimulation (CSS), Asynchronous Sequential Stimulation (ASS) and Asynchronous Random Stimulation (ARS). Stimulation was applied repeatedly for 300 ms followed by 700 ms of no-stimulation with 40 Hz effective frequency for all protocols. Ten able-bodied volunteers (28±3 years old) participated in this study. As fatigue indicators, we focused on the analysis of Normalized Fatigue Index (NFI), Fatigue Time Interval (FTI) and pre-post Twitch-Tetanus Ratio (ΔTTR). The results demonstrated that ASS and ARS give higher NFI and longer FTI confirming less fatigue for asynchronous stimulation. In addition, ASS and ARS resulted in higher ΔTTR than conventional CSS. In this study, we proposed a randomly distributed stimulation method for the application of FES and investigated its suitability for reducing muscle fatigue compared to previously applied methods. The results validated that asynchronous stimulation reduces fatigue, and indicates that random stimulation may improve fatigue resistance in some conditions.

Keywords: asynchronous stimulation, electrode configuration, functional electrical stimulation (FES), muscle fatigue, pattern stimulation, random stimulation, sequential stimulation, synchronous stimulation

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Authors: Hamid Sharif, Nazish Saleem Abbas, Muhammad Haris Jamil

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An adaptive filter is a filter that self-adjusts its transfer function according to an optimization algorithm driven by an error signal. Because of the complexity of the optimization algorithms, most adaptive filters are digital filters. Adaptive filtering constitutes one of the core technologies in digital signal processing and finds numerous application areas in science as well as in industry. Adaptive filtering techniques are used in a wide range of applications, including adaptive noise cancellation and echo cancellation. Acoustic echo cancellation is a common occurrence in today’s telecommunication systems. The signal interference caused by acoustic echo is distracting to both users and causes a reduction in the quality of the communication. In this paper, we review different techniques of adaptive filtering to reduce this unwanted echo. In this paper, we see the behavior of techniques and algorithms of adaptive filtering like Least Mean Square (LMS), Normalized Least Mean Square (NLMS), Variable Step-Size Least Mean Square (VSLMS), Variable Step-Size Normalized Least Mean Square (VSNLMS), New Varying Step Size LMS Algorithm (NVSSLMS) and Recursive Least Square (RLS) algorithms to reduce this unwanted echo, to increase communication quality.

Keywords: adaptive acoustic, echo cancellation, LMS algorithm, adaptive filter, normalized least mean square (NLMS), variable step-size least mean square (VSLMS)

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Authors: Fernanda M. Assef, Maria Teresinha A. Steiner, David Gabriel F. Barros

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Review researches are useful in terms of analysis of research problems. Between the types of review documents, we commonly find bibliometric studies. This type of application often helps the global visualization of a research problem and helps academics worldwide to understand the context of a research area better. In this document, a bibliometric view surrounding clustering techniques and sustainability problems is presented. The authors aimed at which issues mostly use clustering techniques, and, even which sustainability issue would be more impactful on today’s moment of research. During the bibliometric analysis, we found ten different groups of research in clustering applications for sustainability issues: Energy; Environmental; Non-urban planning; Sustainable Development; Sustainable Supply Chain; Transport; Urban Planning; Water; Waste Disposal; and, Others. And, by analyzing the citations of each group, we discovered that the Environmental group could be classified as the most impactful research cluster in the area mentioned. Now, after the content analysis of each paper classified in the environmental group, we found that the k-means technique is preferred for solving sustainability problems with clustering methods since it appeared the most amongst the documents. The authors finally conclude that a bibliometric assessment could help indicate a gap of researches on waste disposal – which was the group with the least amount of publications – and the most impactful research on environmental problems.

Keywords: bibliometric assessment, clustering, sustainability, territorial partitioning

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Authors: Glemarie C. Hermosa, Sheng-Jie You, Chien Chih Hu

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Efficient separation technologies are required for the removal of carbon dioxide from natural gas streams. Membrane-based natural gas separation has emerged as one of the fastest growing technologies, due to the compactness, higher energy efficiency and economic advantages which can be reaped. The removal of Carbon dioxide from gas streams using membrane technology will also give the advantage like environmental friendly process compared to the other technologies used in gas separation. In this study, Polyimide membranes, which are mostly used in the separation of gases, are blended with a new kind of solvent: Deep Eutectic Solvents or simply DES. The three types of DES are used are choline chloride based mixed with three different hydrogen bond donors: Lactic acid, N-methylurea and Urea. The blending of the DESs to Polyimide gave out high permeability performance. The Gas Separation performance for all the membranes involving CO2/CH4 showed low performance while for CO2/N2 surpassed the performance of some studies. Among the three types of DES used the solvent Choline Chloride/Lactic acid exhibited the highest performance for both Gas Separation applications. The values are 10.5 for CO2/CH4 selectivity and 60.5 for CO2/N2. The separation results for CO2/CH4 may be due to the viscosity of the DESs affecting the morphology of the fabricated membrane thus also impacts the performance. DES/blended Polyimide membranes fabricated are novel and have the potential of a low-cost and environmental friendly application for gas separation.

Keywords: deep eutectic solvents, gas separation, polyimide blends, polyimide membranes

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Authors: Nagri Sateesh, Kundavarapu Vengalrao, Kopparthi Phaneendra Kumar

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Vacuum assisted resin transfer molding (VARTM) is one of the manufacturing technique that is viable for production of fiber reinforced polymer composite components suitable for aerospace, marine and commercial applications. However, the repeatable quality of the product can be achieved by critically fixing the process parameters such as Vacuum Pressure (VP) and permeability of the preform. The present investigation is aimed at studying the effect of permeability for production of Glass Fiber Reinforced Plastic (GFRP) components with consistent quality. The VARTM mould is made with an acrylic transparent top cover to observe and record the resin flow pattern. Six layers of randomly placed glass fiber under five different vacuum pressures VP1 = 0.013, VP2 = 0.026, VP3 = 0.039, VP4 = 0.053 and VP5 = 0.066 MPa were studied. The laminates produced by this process under the above mentioned conditions were characterized with ASTM D procedures so as to study the effect of these process parameters on the quality of the laminate. Moreover, as mentioned there is a considerable effect of permeability on the impact strength and the void content in the laminates under different vacuum pressures. SEM analysis of the impact tested fractured GFRP composites showed the bonding of fiber and matrix.

Keywords: permeability, vacuum assisted resin transfer molding (VARTM), ASTM D standards, SEM

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Authors: Zahra Alipour, Amirreza Moheb Afzali

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In the field of human-computer interaction (HCI), hand gestures play a crucial role in facilitating communication by expressing emotions and intentions. The precise tracking of the index finger and the estimation of joint positions are essential for developing effective gesture recognition systems. However, various challenges, such as anatomical variations, occlusions, and environmental influences, hinder optimal functionality. This study investigates the performance of the YOLOv8m model for hand detection using the EgoHands dataset, which comprises diverse hand gesture images captured in various environments. Over three training processes, the model demonstrated significant improvements in precision (from 88.8% to 96.1%) and recall (from 83.5% to 93.5%), achieving a mean average precision (mAP) of 97.3% at an IoU threshold of 0.7. We also compared YOLOv8m with MediaPipe and an integrated YOLOv8 + MediaPipe approach. The combined method outperformed the individual models, achieving an accuracy of 99% and a recall of 99%. These findings underscore the benefits of model integration in enhancing gesture recognition accuracy and localization for real-time applications. The results suggest promising avenues for future research in HCI, particularly in augmented reality and assistive technologies, where improved gesture recognition can significantly enhance user experience.

Keywords: YOLOv8, mediapipe, finger tracking, joint estimation, human-computer interaction (HCI)

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Authors: Saji George, Eng Khuan Seng, Christof Luda

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Nanotechnology has been recognized as an important tool for modern agriculture and has the potential to overcome some of the pressing challenges faced by aquaculture industry. A strategy for optimizing nanotechnology-based therapeutic delivery platform for immunizing farmed fish was developed. Accordingly, a compositional library of nanomaterials of natural chemistry (Halloysite (clay), Chitosan, Hydroxyapatite, Mesoporous Silica and a composite material of clay-chitosan) was screened for their toxicity and efficiency in delivering models antigens in cellular and zebrafish embryo models using high throughput screening platforms. Through multi-parametric optimization, chitosan modified halloysite (clay) nanomaterial was identified as an optimal vaccine delivery platform. Further, studies conducted in juvenile seabass showed the potential of clay-chitosan in delivering outer membrane protein of Tenacibaculum maritimum- TIMA (pathogenic bacteria) to and its efficiency in eliciting immune responses in fish. In short, as exemplified by this work, the strategy of using compositional nanomaterial libraries and their biological profiling using high-throughput screening platform could fasten the discovery process of nanomaterials with potential applications in food and agriculture.

Keywords: nanotechnology, fish-vaccine, drug-delivery, halloysite-chitosan

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Authors: Ming-Han Tsai, Chihpin Huang

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Electrochemical oxidation of ammonia (AEO) is one of the highly efficient and environmentally friendly methods for NH₃ removal from wastewater. Recently, researchers have focused on non-Pt-based electrodes (n-PtE) for AEO, aiming to evaluate the feasibility of these low-cost electrodes for future practical applications. However, for most n-PtE, NH₃ is oxidized mainly to nitrate ion NO₃⁻ instead of the desired nitrogen gas N₂, which requires further treatment to remove excess NO₃⁻. Therefore, developing a high N₂ conversion electrode for AEO is highly urgent. In this study, we fabricated various Cu₂O/Ni foam (NF) electrodes by electrodeposition of Cu on NF. The Cu plating bath contained different additives, including cetyltrimethylammonium chloride (CTAC), sodium dodecyl sulfate (SDS), polyamide acid (PAA), and sodium alginate (SA). All the prepared electrodes were physically and electrochemically investigated. Batch AEO experiments were conducted for 3 h to clarify the relation between electrode structures and N₂ selectivity. The SEM and XRD results showed that crystalline platelets-like Cu₂O, particles-like Cu₂O, cracks-like Cu₂O, and sheets-like Cu₂O were formed in the Cu plating bath by adding CTAC, SDS, PAA, and SA, respectively. For electrochemical analysis, all Cu₂O/NF electrodes revealed a higher current density (2.5-3.2 mA/cm²) compared to that without additives modification (1.6 mA/cm²). At a constant applied potential of 0.95 V (vs Hg/HgO), the Cu₂O sheet (51%) showed the highest N₂ selectivity, followed by Cu₂O cracks (38%), Cu₂O particles (30%), and Cu₂O platelet (18%) after 3 h reaction. Our result demonstrated that the selectivity of N₂ during AEO was surface structural dependent.

Keywords: ammonia, electrooxidation, selectivity, cuprous oxide, Ni foam

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Authors: Dehong Li, Yuchen Chen, Alireza Kaboorani, Denis Rodrigue, Xiaodong (Alice) Wang

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Utilizing solar energy for thermal energy storage has emerged as an appealing option for lowering the amount of energy that is consumed by buildings. Due to their high heat storage density, and non-corrosive and non-polluting properties, alcohols can be a good alternative to petroleum-derived paraffin phase change materials (PCMs). In this paper, ternary eutectic PCMs with suitable phase change temperatures were designed and prepared using lauryl alcohol (LA), cetyl alcohol (CA), stearyl alcohol (SA), and xylitol (X). The differential scanning calorimetry (DSC) results revealed that the phase change temperatures of LA-CA-SA, LA-CA-X, and LA-SA-X were 20.52°C, 20.37°C, and 22.18°C, respectively. The latent heat of phase change of the ternary eutectic PCMs was all stronger than that of the paraffinic PCMs at roughly the same temperature. The highest latent heat was 195 J/g. It had good thermal energy storage capacity. The preparation mechanism was investigated using Fourier-transform Infrared Spectroscopy (FTIR), and it was found that the ternary eutectic PCMs were only physically mixed among the components. Ternary eutectic PCMs had a simple preparation process, suitable phase change temperature, and high energy storage density. They are suitable for low-temperature architectural packaging applications.

Keywords: thermal energy storage, buildings, phase change materials, alcohols

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Authors: Luís Pina

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The conservation of marine biodiversity keeps ecosystems in balance and ensures the sustainable use of resources. In this context, technological resources have been used for monitoring marine species to allow biologists to obtain data in real-time. There are different mobile applications developed for data collection for monitoring purposes, but these systems are designed to be utilized only on third-generation (3G) phones or smartphones with Internet access and in rural parts of the developing countries, Internet services and smartphones are scarce. Thus, the objective of this work is to develop a system to monitor marine turtles using Unstructured Supplementary Service Data (USSD), which users can access through basic mobile phones. The system aims to improve the data collection mechanism and enhance the effectiveness of current systems in monitoring sea turtles using any type of mobile device without Internet access. The system will be able to report information related to the biological activities of marine turtles. Also, it will be used as a platform to assist marine conservation entities to receive reports of illegal sales of sea turtles. The system can also be utilized as an educational tool for communities, providing knowledge and allowing the inclusion of communities in the process of monitoring marine turtles. Therefore, this work may contribute with information to decision-making and implementation of contingency plans for marine conservation programs.

Keywords: GSM, marine biology, marine turtles, unstructured supplementary service data (USSD)

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Authors: Gizem Buldum, Alexander Bismarck, Athanasios Mantalaris

Abstract:

Cellulose is the most abundant biopolymer on earth and it is currently being utilised in a multitude of industrial applications. Over the last 30 years, attention has been paid to the bacterial cellulose (BC), since BC exhibits unique physical, chemical and mechanical properties when compared to plant-based cellulose, including high purity and biocompatibility. Although Acetobacter xylinum is the most efficient producer of BC, it’s long doubling time results in insufficient yields of the cellulose production. This limits widespread and continued use of BC. In this study, E. coli BL21 (DE3) or E. coli HMS cells are selected as host organisms for the expression of bacterial cellulose synthase operon (bcs) of A.xylinum. The expression system is created based on pET-Duet1 and pCDF plasmid vectors, which carry bcs operon. The results showed that all bcs genes were successfully transferred and expressed in E.coli strains. The expressions of bcs proteins were shown by SDS and Native page analyses. The functionality of the bcs operon was proved by congo red binding assay. The effect of culturing temperature and the inducer concentration (IPTG) on cell growth and plasmid stability were monitored. The percentage of plasmid harboring cells induced with 0.025 mM IPTG was obtained as 85% at 22˚C in the end of 10-hr culturing period. It was confirmed that the high output cellulose production machinery of A.xylinum can be transferred into other organisms.

Keywords: bacterial cellulose, biopolymer, recombinant expression system, production

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Authors: Babatunde Oluwole Ogunsile, Omosola Monisola Fasoranti

Abstract:

High costs and toxicological hazards associated with the physicochemical methods of producing nanoparticles have limited their widespread use in clinical and biomedical applications. An ethically sound alternative is the utilization of plant bioresources as a low cost and eco–friendly biological approach. Silver nanoparticles (AgNPs) were synthesized from aqueous leaf extract of Tithonia diversifolia plant. The UV-Vis Spectrophotometer was used to monitor the formation of the AgNPs at different time intervals and different ratios of plant extract to the AgNO₃ solution. The biosynthesized AgNPs were characterized by FTIR, X-ray Diffraction (XRD) and Scanning Electron Microscope (SEM). Antimicrobial activities of the AgNPs were investigated against ten human pathogens using agar well diffusion method. The AgNPs yields were modeled using a second-order factorial design. The result showed that the rate of formation of the AgNPs increased with respect to time while the optimum ratio of plant extract to the AgNO₃ solution was 1:1. The hydroxyl group was strongly involved in the bioreduction of the silver salt as indicated by the FTIR spectra. The synthesized AgNPs were crystalline in nature, with a uniformly distributed network of the web-like structure. The factorial model predicted the nanoparticles yields with minimal errors. The nanoparticles were active against all the tested pathogens and thus have great potentials as antimicrobial agents.

Keywords: antimicrobial activities, green synthesis, silver nanoparticles, Tithonia diversifolia

Procedia PDF Downloads 146

Authors: Natcha Laethongkham, Gayan Ramanayake, Philip Charlesworth, Leshan Uggalla

Abstract:

The throttling process in hybrid rocket engines (HREs) poses challenges due to inherent instability, impacting the engine’s reliability and robustness. Identifying and advancing existing technology is crucial to meet the demands of complex mission profiles required for next-generation launch vehicles. This paper reviews the current literature, focusing on oxidiser flow control for throttling purposes in HREs. Covered areas include oxidiser choices, commonly used throttle valves, and literature trends. Common oxidisers for throttling are hydrogen peroxide, nitrous oxide, and liquid oxygen. Two frequently chosen valves for throttling are the ball and variation pintle valves. The review identifies two primary research focuses: flow control valve studies and control system design. The current research stage is highlighted, and suggestions for future directions are proposed to advance thrust control systems in HREs. This includes further studies in existing research focuses and exploring new approaches such as system scheme design, numerical modelling, and applications.

Keywords: hybrid rocket engines, oxidiser flow control, thrust control, throttle valve, review

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Authors: K. Chethana, A. S. Guru Prasad, H. N. Vikranth, H. Varun, S. N. Omkar, S. Asokan

Abstract:

This paper describes a novel application of Fiber Braggs Grating (FBG) sensors on an unstable platform to assess human postural stability and balance. The FBG sensor based Stability Analyzing Device (FBGSAD) developed demonstrates the applicability of FBG sensors in the measurement of plantar strain to assess the postural stability of subjects on unstable platforms during different stances in eyes open and eyes closed conditions on a rocker board. Comparing the Centre of Gravity (CG) variations measured on the lumbar vertebra of subjects using a commercial accelerometer along with FBGSAD validates the study. The results obtained depict qualitative similarities between the data recorded by both FBGSAD and accelerometer, illustrating the reliability and consistency of FBG sensors in biomechanical applications for both young and geriatric population. The developed FBGSAD simultaneously measures plantar strain distribution and postural stability and can serve as a tool/yardstick to mitigate space motion sickness, identify individuals who are susceptible to falls and to qualify subjects for balance and stability, which are important factors in the selection of certain unique professionals such as aircraft pilots, astronauts, cosmonauts etc.

Keywords: biomechanics, fiber bragg gratings, plantar strain measurement, postural stability analysis

Procedia PDF Downloads 571

Authors: Saleh Gareh, B. C. Kok, H. H. Goh

Abstract:

Piezoelectric energy harvesting has advantages over other alternative sources due to its large power density, ease of applications, and capability to be fabricated at different scales: macro, micro, and nano. This paper presents an electromechanical-traffic model for roadway compression-based piezoelectric energy harvesting system. A two-degree-of-freedom (2-DOF) electromechanical model has been developed for the piezoelectric energy harvesting unit to define its performance in power generation under a number of external excitations on road surface. Lead Zirconate Titanate (PZT-5H) is selected as the piezoelectric material to be used in this paper due to its high Piezoelectric Charge Constant (d) and Piezoelectric Voltage Constant (g) values. The main source of vibration energy that has been considered in this paper is the moving vehicle on the road. The effect of various frequencies on possible generated power caused by different vibration characteristics of moving vehicle has been studied. A single unit of circle-shape Piezoelectric Cymbal Transducer (PCT) with diameter of 32 mm and thickness of 0.3 mm be able to generate about 0.8 mW and 3 mW of electric power under 4 Hz and 20 Hz of excitation, respectively. The estimated power to be generated for multiple arrays of PCT is approximately 150 kW/ km. Thus, the developed electromechanical-traffic model has enormous potential to be used in estimating the macro scale of roadway power generation system.

Keywords: piezoelectric energy harvesting, cymbal transducer, PZT (lead zirconate titanate), 2-DOF

Procedia PDF Downloads 353

Authors: Shalini Tripathi, Pardeep Kumar

Abstract:

The method of translating observed symptoms into disease names is known as disease diagnosis. The ability to solve clinical problems in a complex manner is critical to a doctor's effectiveness in providing health care. The accuracy of his or her expertise is crucial to the survival and well-being of his or her patients. Artificial Intelligence (AI) has a huge economic influence depending on how well it is applied. In the medical sector, human brain-simulated intellect can help not only with classification accuracy, but also with reducing diagnostic time, cost and pain associated with pathologies tests. In light of AI's present and prospective applications in the biomedical, we will identify them in the paper based on potential benefits and risks, social and ethical consequences and issues that might be contentious but have not been thoroughly discussed in publications and literature. Current apps, personal tracking tools, genetic tests and editing programmes, customizable models, web environments, virtual reality (VR) technologies and surgical robotics will all be investigated in this study. While AI holds a lot of potential in medical diagnostics, it is still a very new method, and many clinicians are uncertain about its reliability, specificity and how it can be integrated into clinical practice without jeopardising clinical expertise. To validate their effectiveness, more systemic refinement of these implementations, as well as training of physicians and healthcare facilities on how to effectively incorporate these strategies into clinical practice, will be needed.

Keywords: Artificial Intelligence, medical diagnosis, virtual reality, healthcare ethical implications 

Procedia PDF Downloads 131