Search results for: artificial potential approach

Authors: Shuafeng Yuan, Bojian Zheng

Abstract:

The RNA-dependent RNA polymerase of influenza a virus comprises conserved and independently folded subdomains with defined functionalities. The N-terminal domain of the PA subunit (PAN) harbors the endonuclease function so that it can serve as a desired target for drug discovery. To identify a class of anti-influenza inhibitors that impedes PAN endonuclease activity, a screening approach that integrated the fluorescence resonance energy transfer based endonuclease inhibitor assay with the DNA gel-based endonuclease inhibitor assay was conducted, followed by the evaluation of antiviral efficacies and potential cytotoxicity of the primary hits in vitro and in vivo. A small-molecule compound ANA-0 was identified as a potent inhibitor against the replication of multiple subtypes of influenza A virus, including H1N1, H3N2, H5N1, H7N7, H7N9 and H9N2, in cell cultures. Combinational treatment of zanamivir and ANA-0 exerted synergistic anti-influenza effect in vitro. Intranasal administration of ANA-0 protected mice from lethal challenge and reduced lung viral loads in H1N1 virus infected BALB/c mice. Docking analyses predicted ANA-0 bound the endonuclease cavity of PAN by interacting with the metal-binding and catalytic residues. In summary, ANA-0 shows potential to be developed to novel anti-influenza agents.

Keywords: anti-influenza, novel compound, inhibition of endonuclease, PA

Procedia PDF Downloads 234

Authors: Waleed A. Basuliman, Khalid S. AlSaleh, Mohamed Z. Ramadan

Abstract:

Applying the anthropometric dimensions is considered one of the important factors when designing any human-machine system. In this study, the estimation of anthropometric dimensions has been improved by developing artificial neural network that aims to predict the anthropometric measurements of the male in Saudi Arabia. A total of 1427 Saudi males from age 6 to 60 participated in measuring twenty anthropometric dimensions. These anthropometric measurements are important for designing the majority of work and life applications in Saudi Arabia. The data were collected during 8 months from different locations in Riyadh City. Five of these dimensions were used as predictors variables (inputs) of the model, and the remaining fifteen dimensions were set to be the measured variables (outcomes). The hidden layers have been varied during the structuring stage, and the best performance was achieved with the network structure 6-25-15. The results showed that the developed Neural Network model was significantly able to predict the body dimensions for the population of Saudi Arabia. The network mean absolute percentage error (MAPE) and the root mean squared error (RMSE) were found 0.0348 and 3.225 respectively. The accuracy of the developed neural network was evaluated by compare the predicted outcomes with a multiple regression model. The ANN model performed better and resulted excellent correlation coefficients between the predicted and actual dimensions.

Keywords: artificial neural network, anthropometric measurements, backpropagation, real anthropometric database

Procedia PDF Downloads 560

Authors: Anurakti Shukla, Sudhakar Srivastava

Abstract:

Cyanobacteria are known for rapid growth rates, high biomass, and the ability to accumulate potentially toxic elements and contaminants. The present work was planned to develop a low-cost, feasible prototype, CyanoClean, for the growth of a cyanobacterial consortium for the removal of arsenic (As) from water. The cyanobacterial consortium consisting of Oscillatoria, Phormidiumand Gloeotrichiawas used, and the conditions for optimal growth of the consortium were standardized. A pH of 7.6, initial cyanobacterial biomass of 10 g/L, and arsenite [As(III)] and arsenate [As(V)] concentration of 400 μΜand 600 μM, respectively, were found to be suitable. The CyanoClean prototype was designed with acrylic sheet and had arrangements for optimal cyanobacterial growth in natural sunlight and also in artificial light. The As removal experiments in concentration- and duration-dependent manner demonstrated removal of up to 39-69% and 9-33% As respectively from As(III) and As(V)-contaminated water. In field testing of CyanoClean, natural As-contaminated groundwater was used, and As reduction was monitored when a flow rate of 3 L/h was maintained. In a field experiment, As concentration in groundwater was found to reduce from 102.43 μg L⁻¹ to <10 μg L⁻¹ after 6 h in natural sunlight. However, in shaded conditions under artificial light, the same result was achieved after 9 h. The CyanoClean prototype is of simple design and can be easily up-scaled for application at a small- to medium-size land and shall be affordable even for a low- to middle-income group farmer.

Keywords: cyanoclean, gloeotrichia, oscillatoria, phormidium, phycoremediation

Procedia PDF Downloads 127

Authors: Hamed Jafargholi

Abstract:

Global warming presents an immense environmental risk, causing detrimental impacts on ecological systems and putting coastal areas at risk. Implementing efficient measures to minimize greenhouse gas emissions and the use of fossil fuels is essential to reducing global warming. Vapor compression heat pumps provide a practical method for harnessing energy from waste heat sources and reducing energy consumption. However, traditional working fluids used in these heat pumps generally contain a significant global warming potential (GWP), which might cause severe greenhouse effects if they are released. The goal of the emphasis on low-GWP (below 150) refrigerants is to further the vapor compression heat pumps. A classification system for vapor compression heat pumps is offered, with different boundaries based on the needed heat temperature and advancements in heat pump technology. A heat pump could be classified as a low temperature heat pump (LTHP), medium temperature heat pump (MTHP), high temperature heat pump (HTHP), or ultra-high temperature heat pump (UHTHP). The HTHP/UHTHP border is 160 °C, the MTHP/HTHP and LTHP/MTHP limits are 100 and 60 °C, respectively. The refrigerant is one of the most important parts of a vapor compression heat pump system. Presently, the main ways to choose a refrigerant are based on ozone depletion potential (ODP) and GWP, with GWP being the lowest possible value and ODP being zero. Pure low-GWP refrigerants, such as natural refrigerants (R718 and R744), hydrocarbons (R290, R600), hydrofluorocarbons (R152a and R161), hydrofluoroolefins (R1234yf, R1234ze(E)), and hydrochlorofluoroolefin (R1233zd(E)), were selected as candidates for vapor compression heat pump systems based on these selection principles. The performance, characteristics, and potential uses of these low-GWP refrigerants in heat pump systems are investigated in this paper. As vapor compression heat pumps with pure low-GWP refrigerants become more common, more and more low-grade heat can be recovered. This means that energy consumption would decrease. The research outputs showed that the refrigerants R718 for UHTHP application, R1233zd(E) for HTHP application, R600, R152a, R161, R1234ze(E) for MTHP, and R744, R290, and R1234yf for LTHP application are appropriate. The selection of an appropriate refrigerant should, in fact, take into consideration two different environmental and thermodynamic points of view. It might be argued that, depending on the situation, a trade-off between these two groups should constantly be considered. The environmental approach is now far stronger than it was previously, according to the European Union regulations. This will promote sustainable energy consumption and social development in addition to assisting in the reduction of greenhouse gas emissions and the management of global warming.

Keywords: vapor compression, global warming potential, heat pumps, greenhouse

Procedia PDF Downloads 8

Authors: Dalin Si, Azizan Aziz, Bertrand Lasternas

Abstract:

To encourage building owners to purchase electricity at the wholesale market and reduce building peak demand, this study aims to develop models that predict day-ahead hourly electricity consumption and demand using artificial neural network (ANN) and support vector machine (SVM). All prediction models are built in Python, with tool Scikit-learn and Pybrain. The input data for both consumption and demand prediction are time stamp, outdoor dry bulb temperature, relative humidity, air handling unit (AHU), supply air temperature and solar radiation. Solar radiation, which is unavailable a day-ahead, is predicted at first, and then this estimation is used as an input to predict consumption and demand. Models to predict consumption and demand are trained in both SVM and ANN, and depend on cooling or heating, weekdays or weekends. The results show that ANN is the better option for both consumption and demand prediction. It can achieve 15.50% to 20.03% coefficient of variance of root mean square error (CVRMSE) for consumption prediction and 22.89% to 32.42% CVRMSE for demand prediction, respectively. To conclude, the presented models have potential to help building owners to purchase electricity at the wholesale market, but they are not robust when used in demand response control.

Keywords: building energy prediction, data mining, demand response, electricity market

Procedia PDF Downloads 308

Authors: Viera Zatkalíková, Lenka Markovičová, Aneta Tor-Swiatek

Abstract:

The effect of molybdate addition to chloride environment on resistance of AISI 316Ti stainless steel to pitting corrosion was studied. Potentiodynamic polarisation tests were performed in 1 M and 0.1 M chloride acidified solutions with various additions of sodium molybdate at room temperature. The presented results compare the effect of molybdate anions on quality of passive film (expressed by the pitting potential) in both chloride solutions. The pitting potential increases with the increase inhibitor concentration. The inhibitive effect of molybdate ions is stronger in chloride solution of lower aggressiveness (0.1M).

Keywords: AISI 316Ti steel, molybdate inhibitor, pitting corrosion, pitting potential, potentiodynamic polarisation

Procedia PDF Downloads 378

Authors: Mourad Makhlouf Sabrina Bouriche, Zoubir Benmaamar, Didier Villemin

Abstract:

Background: The increasing presence of electromagnetic interference (EMI) requires the development of effective protection solutions. Intelligent textiles offer a promising approach due to their wear ability and the possibility of integration into everyday clothing. In this study, the use of graphene and polyaniline for EMI shielding on cotton fabrics was examined. Methods: In this study, the continuous dyeing of recycled graphite-derived graphene and polyaniline was examined. Bottom-reforming technology was adopted to improve adhesion and achieve uniform distribution of conductive material on the fiber surface. The effect of material weight ratio on fabric performance and X-band EMI shielding effectiveness (SE) was evaluated. Significant Findings: The dyed cotton fabrics incorporating graphene, polyaniline, and their combination exhibited improved conductivity. Notably, these fabrics achieved EMI SE values ranging from 9 to 16 dB within the X-band frequency range (8-9 GHz). These findings demonstrate the potential of this approach for developing intelligent textiles with effective EMI shielding capabilities. Additionally, the utilization of recycled materials contributes to a more sustainable shielding solution.

Keywords: Intelligent textiles, graphene, polyaniline, electromagnetic shielding, conductivity, recycling

Procedia PDF Downloads 19

Authors: A. Luft, S. Bremen, N. Balc

Abstract:

The task of flexibility planning and design, just like factory planning, for example, is to create the long-term systemic framework that constitutes the restriction for short-term operational management. This is a strategic challenge since, due to the decision defect character of the underlying flexibility problem, multiple types of flexibility need to be considered over the course of various scenarios, production programs, and production system configurations. In this context, an evaluation model has been developed that integrates both conventional and additive resources on a basic task level and allows the quantification of flexibility enhancement in terms of mix and volume flexibility, complexity reduction, and machine capacity. The model helps companies to decide in early decision-making processes about the potential gains of implementing additive manufacturing technologies on a strategic level. For companies, it is essential to consider both additive and conventional manufacturing beyond pure unit costs. It is necessary to achieve an integrative view of manufacturing that incorporates both additive and conventional manufacturing resources and quantifies their potential with regard to flexibility and manufacturing complexity. This also requires a structured process for the strategic production systems design that spans the design of various scenarios and allows for multi-dimensional and comparative analysis. A respective guideline for the planning of additive resources on a strategic level is being laid out in this paper.

Keywords: additive manufacturing, production system design, flexibility enhancement, strategic guideline

Procedia PDF Downloads 112

Authors: BenedictI Ita, Etido P. Inyang

Abstract:

In this work, the resolutions of the N-dimensional Schrödinger equation with the screened modified Kratzerplus inversely quadratic Yukawa potential (SMKIQYP) have been obtained with the Greene-Aldrich approximation scheme using the Nikiforov-Uvarov method. The eigenvalues and the normalized eigenfunctions are obtained. We then apply the energy spectrum to study four (HCl, N₂, NO, and CO) diatomic molecules. The results show that the energy spectra of these diatomic molecules increase as quantum numbers increase. The energy equation was also used to calculate the partition function and other thermodynamic properties. We predicted the partition function of CO and NO. To check the accuracy of our work, the special case (Modified Kratzer and screened Modified Kratzer potentials) of the collective potential energy eigenvalues agrees excellently with the existing literature.

Keywords: Schrödinger equation, Nikiforov-Uvarov method, modified screened Kratzer, inversely quadratic Yukawa potential, diatomic molecules

Procedia PDF Downloads 76

Authors: Xingyu Gao, Qiang Wu

Abstract:

Patents play a crucial role in protecting innovation and intellectual property. Early prediction of the impact of artificial intelligence (AI) patents helps researchers and companies allocate resources and make better decisions. Understanding the key factors that influence patent impact can assist researchers in gaining a better understanding of the evolution of AI technology and innovation trends. Therefore, identifying highly impactful patents early and providing support for them holds immeasurable value in accelerating technological progress, reducing research and development costs, and mitigating market positioning risks. Despite the extensive research on AI patents, accurately predicting their early impact remains a challenge. Traditional methods often consider only single factors or simple combinations, failing to comprehensively and accurately reflect the actual impact of patents. This paper utilized the artificial intelligence patent database from the United States Patent and Trademark Office and the Len.org patent retrieval platform to obtain specific information on 35,708 AI patents. Using six machine learning models, namely Multiple Linear Regression, Random Forest Regression, XGBoost Regression, LightGBM Regression, Support Vector Machine Regression, and K-Nearest Neighbors Regression, and using early indicators of patents as features, the paper comprehensively predicted the impact of patents from three aspects: technical, social, and economic. These aspects include the technical leadership of patents, the number of citations they receive, and their shared value. The SHAP (Shapley Additive exPlanations) metric was used to explain the predictions of the best model, quantifying the contribution of each feature to the model's predictions. The experimental results on the AI patent dataset indicate that, for all three target variables, LightGBM regression shows the best predictive performance. Specifically, patent novelty has the greatest impact on predicting the technical impact of patents and has a positive effect. Additionally, the number of owners, the number of backward citations, and the number of independent claims are all crucial and have a positive influence on predicting technical impact. In predicting the social impact of patents, the number of applicants is considered the most critical input variable, but it has a negative impact on social impact. At the same time, the number of independent claims, the number of owners, and the number of backward citations are also important predictive factors, and they have a positive effect on social impact. For predicting the economic impact of patents, the number of independent claims is considered the most important factor and has a positive impact on economic impact. The number of owners, the number of sibling countries or regions, and the size of the extended patent family also have a positive influence on economic impact. The study primarily relies on data from the United States Patent and Trademark Office for artificial intelligence patents. Future research could consider more comprehensive data sources, including artificial intelligence patent data, from a global perspective. While the study takes into account various factors, there may still be other important features not considered. In the future, factors such as patent implementation and market applications may be considered as they could have an impact on the influence of patents.

Keywords: patent influence, interpretable machine learning, predictive models, SHAP

Procedia PDF Downloads 34

Authors: Yana Snegireva

Abstract:

Carbonate reservoirs are known for their heterogeneity, resulting from various geological processes such as diagenesis and fracturing. These complexities may cause great challenges in understanding fluid flow behavior and predicting the production performance of naturally fractured reservoirs. The investigation of carbonate reservoirs is crucial, as many petroleum reservoirs are naturally fractured, which can be difficult due to the complexity of their fracture networks. This can lead to geological uncertainties, which are important for global petroleum reserves. The problem outlines the key challenges in carbonate reservoir modeling, including the accurate representation of fractures and their connectivity, as well as capturing the impact of fractures on fluid flow and production. Traditional reservoir modeling techniques often oversimplify fracture networks, leading to inaccurate predictions. Therefore, there is a need for a modern approach that can capture the complexities of carbonate reservoirs and provide reliable predictions for effective reservoir management and production optimization. The modern approach to carbonate reservoir modeling involves the utilization of the hybrid fracture modeling approach, including the discrete fracture network (DFN) method and implicit fracture network, which offer enhanced accuracy and reliability in characterizing complex fracture systems within these reservoirs. This study focuses on the application of the hybrid method in the Nepsko-Botuobinskaya anticline of the Eastern Siberia field, aiming to prove the appropriateness of this method in these geological conditions. The DFN method is adopted to model the fracture network within the carbonate reservoir. This method considers fractures as discrete entities, capturing their geometry, orientation, and connectivity. But the method has significant disadvantages since the number of fractures in the field can be very high. Due to limitations in the amount of main memory, it is very difficult to represent these fractures explicitly. By integrating data from image logs (formation micro imager), core data, and fracture density logs, a discrete fracture network (DFN) model can be constructed to represent fracture characteristics for hydraulically relevant fractures. The results obtained from the DFN modeling approaches provide valuable insights into the East Siberia field's carbonate reservoir behavior. The DFN model accurately captures the fracture system, allowing for a better understanding of fluid flow pathways, connectivity, and potential production zones. The analysis of simulation results enables the identification of zones of increased fracturing and optimization opportunities for reservoir development with the potential application of enhanced oil recovery techniques, which were considered in further simulations on the dual porosity and dual permeability models. This approach considers fractures as separate, interconnected flow paths within the reservoir matrix, allowing for the characterization of dual-porosity media. The case study of the East Siberia field demonstrates the effectiveness of the hybrid model method in accurately representing fracture systems and predicting reservoir behavior. The findings from this study contribute to improved reservoir management and production optimization in carbonate reservoirs with the use of enhanced and improved oil recovery methods.

Keywords: carbonate reservoir, discrete fracture network, fracture modeling, dual porosity, enhanced oil recovery, implicit fracture model, hybrid fracture model

Procedia PDF Downloads 66

Authors: Shizra Waris, Bilal Shoaib, Munib Ahmad

Abstract:

Background; The using of clinical decision support systems (CDSSs) may recover unceasing disease organization, which requires regular visits to multiple health professionals, treatment monitoring, disease control, and patient behavior modification. The objective of this survey is to determine if these CDSSs improve the processes of unceasing care including diagnosis, treatment, and monitoring of diseases. Though artificial intelligence is not a new idea it has been widely documented as a new technology in computer science. Numerous areas such as education business, medical and developed have made use of artificial intelligence Methods: The survey covers articles extracted from relevant databases. It uses search terms related to information technology and viral hepatitis which are published between 2000 and 2016. Results: Overall, 80% of studies asserted the profit provided by information technology (IT); 75% of learning asserted the benefits concerned with medical domain;25% of studies do not clearly define the added benefits due IT. The CDSS current state requires many improvements to hold up the management of liver diseases such as HCV, liver fibrosis, and cirrhosis. Conclusion: We concluded that the planned model gives earlier and more correct calculation of hepatitis B and it works as promising tool for calculating of custom hepatitis B from the clinical laboratory data.

Keywords: detection, hapataties, observation, disesese

Procedia PDF Downloads 142

Authors: Muhammad Zaheer Awan, Adnan Qadir, Zeeshan Anjum

Abstract:

Housefly, Musca domestica Linnaeus is ubiquitous and hazardous for Homo sapiens and livestock in sundry venerations. Musca domestica cart 100 different pathogens, such as typhoid, salmonella, bacillary dysentery, tuberculosis, anthrax and parasitic worms. The flies in rural areas usually carry more pathogens. Houseflies feed on liquid or semi-liquid substances besides solid materials which are softened by saliva. Neem botanically known as Azadirachta indica belongs to the family Meliaceae and is an indigenous tree to Pakistan. The neem tree is also one such tree which has been revered by the Pakistanis and Kashmiris for its medicinal properties. Present study showed neem seed extract has potentially toxic ability that affect Total Haemocyte Count (THC) and Differential Haemocytes Count (DHC) in insect’s blood cells, of the housefly. A significant variation in haemolymph density was observed just after application, 30 minutes and 60 minutes post treatment in term of THC and DHC in comparison with novastar. The study strappingly acclaim use of neem seed extract as insecticide as compare to artificial insecticides.

Keywords: neem, Azadirachta indica, Musca domestica, differential haemocyte count (DHC), total haemocytes count (DHC), novastar

Procedia PDF Downloads 194

Authors: Rakhyun Kim, Sungho Tae

Abstract:

Global environmental issues such as abnormal weathers due to global warming, resource depletion, and ecosystem distortions have been escalating due to rapid increase of population growth, and expansion of industrial and economic development. Accordingly, initiatives have been implemented by many countries to protect the environment through indirect regulation methods such as Environmental Product Declaration (EPD), in addition to direct regulations such as various emission standards. Following this trend, life cycle assessment (LCA) techniques that provide quantitative environmental information, such as Human Toxicity Potential (HTP), for buildings are being developed in the construction industry. However, at present, the studies on the environmental database of building materials are not sufficient to provide this support adequately. The purpose of this study is to analysis human toxicity potential of major building material production stage using life cycle assessment. For this purpose, the theoretical consideration of the life cycle assessment and environmental impact category was performed and the direction of the study was set up. That is, the major material in the global warming potential view was drawn against the building and life cycle inventory database was selected. The classification was performed about 17 kinds of substance and impact index, such as human toxicity potential, that it specifies in CML2001. The environmental impact of analysis human toxicity potential for the building material production stage was calculated through the characterization. Meanwhile, the environmental impact of building material in the same category was analyze based on the characterization impact which was calculated in this study. In this study, establishment of environmental impact coefficients of major building material by complying with ISO 14040. Through this, it is believed to effectively support the decisions of stakeholders to improve the environmental performance of buildings and provide a basis for voluntary participation of architects in environment consideration activities.

Keywords: human toxicity potential, major building material, life cycle assessment, production stage

Procedia PDF Downloads 124

Authors: Syed Bilawal Ali Shah

Abstract:

The investigation of the hydrocarbon source rock potential of Eocene-Paleocene formations of Potwar Basin, part of Upper Indus Basin Pakistan, was done using geochemical and petrological techniques. Analysis was performed on forty-five core-cutting samples from two wells. The sequences analysed are Sakesar, Lockhart and Patala formations of Potwar Basin. Patala Formation is one of Potwar Basin's major petroleum-bearing source rocks. The Lockhart Formation samples VR (%Ro) and Tmax data indicate that the formation is early mature to immature for petroleum generation for hydrocarbon generation; samples from the Patala and Sakesar formations, however, have a peak oil generation window and an early maturity (oil window). With 3.37 weight percent mean TOC and HI levels up to 498 mg HC/g TOC, the source rock characteristics of the Sakesar and Patala formations generally exhibit good to very strong petroleum generative potential. The majority of sediments representing Lockhart Formation have 1.5 wt.% mean TOC having fair to good potential with HI values ranging between 203-498 mg HC/g TOC. 1. The analysed sediments of all formations possess primarily mixed Type II/III and Type III kerogen. Analysed sediments indicate that both the Sakesar and Patala formations can possess good oil-generation potential and may act as an oil source rock in the Potwar Basin.

Keywords: Potwar Basin, Patala Shale, Rock-Eval pyrolysis, Indus Basin, VR %Ro

Procedia PDF Downloads 64

Authors: Dilusha Rajapakse

Abstract:

The main reason for printing coloured imageries, pattern or motif onto textiles is to enhance the visual appearance of the surface so that the final textile product would get the required attention from potential customers. Such colours and patterns are permanently applied onto the textiles using conventional static colourants, and we expect such decorations to be last for the entire lifecycle of the textile product. The focus of this research presentation is to discuss the ability to integrate multicolour and pattern changing aesthetics onto textiles with the application of water based photochromic colourants. By adopting a research through design approach, a number of iterative flatbed screen printing experiments were conducted to explore the process of printing water based photochromic colours on textile surfaces. The research resulted in several technical parameters that have to be considered during the process of screen printing. Moreover, a modified printing technique that could be used to apply decorative photographic imagery onto textile with multicolour changing effects was also identified. A number of product applications for such dynamic printed textiles were revealed, and appropriate visual evidence was referred to justify the finding.

Keywords: dynamic aesthetics, multicolour changing textiles, non-emissive colours, printed textile design

Procedia PDF Downloads 381

Authors: Mylena McCoggle, Shyra Wilson, Andrea Rivera, Rocio Alba-Flores

Abstract:

This work introduces the use of EMGs (electromyography) from muscle sensors to develop an Artificial Neural Network (ANN) for pattern recognition to control a small unmanned aerial vehicle. The objective of this endeavor exhibits interfacing drone applications beyond manual control directly. MyoWare Muscle sensor contains three EMG electrodes (dual and single type) used to collect signals from the posterior (extensor) and anterior (flexor) forearm and the bicep. Collection of raw voltages from each sensor were connected to an Arduino Uno and a data processing algorithm was developed with the purpose of interpreting the voltage signals given when performing flexing, resting, and motion of the arm. Each sensor collected eight values over a two-second period for the duration of one minute, per assessment. During each two-second interval, the movements were alternating between a resting reference class and an active motion class, resulting in controlling the motion of the drone with left and right movements. This paper further investigated adding up to three sensors to differentiate between hand gestures to control the principal motions of the drone (left, right, up, and land). The hand gestures chosen to execute these movements were: a resting position, a thumbs up, a hand swipe right motion, and a flexing position. The MATLAB software was utilized to collect, process, and analyze the signals from the sensors. The protocol (machine learning tool) was used to classify the hand gestures. To generate the input vector to the ANN, the mean, root means squared, and standard deviation was processed for every two-second interval of the hand gestures. The neuromuscular information was then trained using an artificial neural network with one hidden layer of 10 neurons to categorize the four targets, one for each hand gesture. Once the machine learning training was completed, the resulting network interpreted the processed inputs and returned the probabilities of each class. Based on the resultant probability of the application process, once an output was greater or equal to 80% of matching a specific target class, the drone would perform the motion expected. Afterward, each movement was sent from the computer to the drone through a Wi-Fi network connection. These procedures have been successfully tested and integrated into trial flights, where the drone has responded successfully in real-time to predefined command inputs with the machine learning algorithm through the MyoWare sensor interface. The full paper will describe in detail the database of the hand gestures, the details of the ANN architecture, and confusion matrices results.

Keywords: artificial neural network, biosensors, electromyography, machine learning, MyoWare muscle sensors, Arduino

Procedia PDF Downloads 165

Authors: T. Taşköprü, M. Zor, E. Turan

Abstract:

The advantages of nickel oxide as an electrochromic material are its good contrast of transmittance and its suitable use as a secondary electrochromic film with WO3 for electrochromic devices. Electrochromic nickel oxide film was prepared by using a simple and inexpensive chemical deposition bath (CBD) technique onto fluorine-doped tin oxide (FTO) coated glass substrates from nickel nitrate solution. The films were ace centered cubic NiO with preferred orientation in the (2 0 0) direction. The electrochromic (EC) properties of the films were studied as a function of pH (8, 9, 10 and 11) in an aqueous alkaline electrolyte (0.3 M KOH) using cyclic voltammetry (CV). The EC cell was formed with the following configuration; FTO/nickel oxide film/0.3 M KOH/Pt The potential was cycled from 0.1 to 0.6V at diffferent potential sweep rates in the range 10- 50 mV/s. The films exhibit anodic electrochromism, changing colour from transparent to black.CV results of a nickel oxide film showed well-resolved anodic current peak at potential; 45 mV and cathodic peak at potential 28 mV. The structural, morphological, and optical changes in NiO film following the CV were investigated by means of X-ray diffractometer (XRD), field emission electron microscopy (FESEM) and UV-Vis- NIR spectrophotometry. No change was observed in XRD, besides surface morphology undergoes change due to the electrical discharge. The change in tansmittance between the bleached and colored state is 68% for the film deposited with pH=11 precursor.

Keywords: nickel oxide, XRD, SEM, cyclic voltammetry

Procedia PDF Downloads 294

Authors: Lorico D. S. Lapitan Jr., Yihan Xu, Yuan Guo, Dejian Zhou

Abstract:

The ability of ultrasensitive detection of specific genes and discrimination of single nucleotide polymorphisms is important for clinical diagnosis and biomedical research. Herein, we report the development of a new ultrasensitive approach for label-free DNA detection using magnetic nanoparticle (MNP) assisted rapid target capture/separation in combination with signal amplification using poly-enzyme tagged polymer nanobead. The sensor uses an MNP linked capture DNA and a biotin modified signal DNA to sandwich bind the target followed by ligation to provide high single-nucleotide polymorphism discrimination. Only the presence of a perfect match target DNA yields a covalent linkage between the capture and signal DNAs for subsequent conjugation of a neutravidin-modified horseradish peroxidase (HRP) enzyme through the strong biotin-nuetravidin interaction. This converts each captured DNA target into an HRP which can convert millions of copies of a non-fluorescent substrate (amplex red) to a highly fluorescent product (resorufin), for great signal amplification. The use of polymer nanobead each tagged with thousands of copies of HRPs as the signal amplifier greatly improves the signal amplification power, leading to greatly improved sensitivity. We show our biosensing approach can specifically detect an unlabeled DNA target down to 10 aM with a wide dynamic range of 5 orders of magnitude (from 0.001 fM to 100.0 fM). Furthermore, our approach has a high discrimination between a perfectly matched gene and its cancer-related single-base mismatch targets (SNPs): It can positively detect the perfect match DNA target even in the presence of 100 fold excess of co-existing SNPs. This sensing approach also works robustly in clinical relevant media (e.g. 10% human serum) and gives almost the same SNP discrimination ratio as that in clean buffers. Therefore, this ultrasensitive SNP biosensor appears to be well-suited for potential diagnostic applications of genetic diseases.

Keywords: DNA detection, polymer beads, signal amplification, single nucleotide polymorphisms

Procedia PDF Downloads 242

Authors: Siyanda S. Biyela, Willie A. Cronje

Abstract:

This paper presents a desktop study of comparing two different wave energy to electricity technologies (WECs) using a techno-economic approach. This techno-economic approach forms basis of a framework for rapid comparison of current and future technologies. The approach also seeks to assist in investment and strategic decision making expediting future deployment of wave energy harvesting in South Africa.

Keywords: cost of energy (COE) tool, sea state, wave energy converter (WEC), WEC-Sim

Procedia PDF Downloads 282

Authors: Lindani Ncube, Baojin Zhao, Ken Liu, Helen Johanna Van Niekerk

Abstract:

Acid Base Accounting (ABA) is a tool used to assess the total amount of acidity or alkalinity contained in a specific rock sample, and is based on the total S concentration and the carbonate content of a sample. A preliminary ABA test was conducted on 14 sandstone and 5 coal samples taken from coalfields representing the Main Karoo Basin (Highveld, Vryheid and Molteno/Indwe Coalfields) and the Sub-basins (Witbank and Waterberg Coalfields). The results indicate that sandstone and coal from the Main Karoo Basin have the potential of generating Acid Mine Drainage (AMD) as they contain sufficient pyrite to generate acid, with the final pH of samples relatively low upon complete oxidation of pyrite. Sandstone from collieries representing the Main Karoo Basin are characterised by elevated contents of reactive S%. All the studied samples were characterised by an Acid Potential (AP) that is less than the Neutralizing Potential (NP) except for two samples. The results further indicate that the sandstone from the Main Karoo Basin is prone to acid generation as compared to the sandstone from the Sub-basins. However, the coal has a relatively low potential of generating any acid. The application of ABA in this study contributes to an understanding of the complexities governing water-rock interactions. In general, the coalfields from the Main Karoo Basin have much higher potential to produce AMD during mining processes than the coalfields in the Sub-basins.

Keywords: Main Karoo Basin, sub-basin, coal, sandstone, acid base accounting (ABA)

Procedia PDF Downloads 424

Authors: Yinghui Shang, Ziqing Zhou, Rong Han, Hang Wang, Xiaodi Liu, Yong Yang

Abstract:

The design of advanced amorphous solids, such as metallic glasses, with targeted properties through artificial intelligence signifies a paradigmatic shift in physical metallurgy and materials technology. Here, we developed a machine-learning architecture that facilitates the generation of metallic glasses with targeted multifunctional properties. Our architecture integrates the state-of-the-art unsupervised generative adversarial network model with supervised models, allowing the incorporation of general prior knowledge derived from thousands of data points across a vast range of alloy compositions, into the creation of data points for a specific type of composition, which overcame the common issue of data scarcity typically encountered in the design of a given type of metallic glasses. Using our generative model, we have successfully designed copper-based metallic glasses, which display exceptionally high hardness or a remarkably low modulus. Notably, our architecture can not only explore uncharted regions in the targeted compositional space but also permits self-improvement after experimentally validated data points are added to the initial dataset for subsequent cycles of data generation, hence paving the way for the customized design of amorphous solids without human intervention.

Keywords: metallic glass, artificial intelligence, mechanical property, automated generation

Procedia PDF Downloads 37

Authors: Dike H. Ogbuagu, Etsede J. Oritsematosan

Abstract:

This work investigated possible inductions of CaC₂, often misused by fruit vendors to stimulate artificial ripening, on mammalian sperm morphology and viability. Thirty isogenic strains of male albino mice, Mus musculus (age≈ 8weeks; weight= 32.5±2.0g) were acclimatized (ambient temperature 28.0±1.0°C) for 2 weeks and fed standard growers mash and water ad libutum. They were later exposed to graded toxicant concentrations (w/w) of 2.5000, 1.2500, 0.6250, and 0.3125% in 4 cages. A control cage was also established. After 5 weeks, 3 animals from each cage were sacrificed by cervical dislocation and the cauda epididymis excised. Sperm morphology and viability were determined by microscopic procedures. The ANOVA, means plots, Student’s t-test and variation plots were used to analyze data. The common abnormalities observed included Double Head, Pin Head, Knobbed Head, No Tail and With Hook. The higher toxicant concentrations induced significantly lower body weights [F(829.899) ˃ Fcrit(4.19)] and more abnormalities [F(26.52) ˃ Fcrit(4.00)] at P˂0.05. Sperm cells in the control setup were significantly more viable than those in the 0.625% (t=0.005) and 2.500% toxicant doses (t=0.018) at the 95% confidence limit. CaC₂ appeared to induced morphological abnormalities and reduced viability in sperm cells of M. musculus.

Keywords: artificial ripening, calcium carbide, fruit vendors, sperm morphology, sperm viability

Procedia PDF Downloads 210

Authors: Wighens Ngoie Ilunga, Pamela J. Welz, Olewaseun O. Oyekola, Daniel Ikhu-Omoregbe

Abstract:

Currently, most sludge from the wastewater treatment plants of edible oil factories is disposed to landfills, but landfill sites are finite and potential sources of environmental pollution. Production of biodiesel from wastewater sludge can contribute to energy production and waste minimization. However, conventional biodiesel production is energy and waste intensive. Generally, biodiesel is produced from the transesterification reaction of oils with alcohol (i.e., Methanol, ethanol) in the presence of a catalyst. Homogeneously catalysed transesterification is the conventional approach for large-scale production of biodiesel as reaction times are relatively short. Nevertheless, homogenous catalysis presents several challenges such as high probability of soap. The current study aimed to reuse wastewater sludge from the edible oil industry as a novel feedstock for both monounsaturated fats and bioethanol for the production of biodiesel. Preliminary results have shown that the fatty acid profile of the oilseed wastewater sludge is favourable for biodiesel production with 48% (w/w) monounsaturated fats and that the residue left after the extraction of fats from the sludge contains sufficient fermentable sugars after steam explosion followed by an enzymatic hydrolysis for the successful production of bioethanol [29% (w/w)] using a commercial strain of Saccharomyces cerevisiae. A novel nano-magnetic catalyst was synthesised from mineral processing alkaline tailings, mainly containing dolomite originating from cupriferous ores using a modified sol-gel. The catalyst elemental chemical compositions and structural properties were characterised by X-ray diffraction (XRD), scanning electron microscopy (SEM), Fourier transform infra-red (FTIR) and the BET for the surface area with 14.3 m²/g and 34.1 nm average pore diameter. The mass magnetization of the nano-magnetic catalyst was 170 emu/g. Both the catalytic properties and reusability of the catalyst were investigated. A maximum biodiesel yield of 78% was obtained, which dropped to 52% after the fourth transesterification reaction cycle. The proposed approach has the potential to reduce material costs, energy consumption and water usage associated with conventional biodiesel production technologies. It may also mitigate the impact of conventional biodiesel production on food and land security, while simultaneously reducing waste.

Keywords: biodiesel, bioethanol, edible oil wastewater sludge, nano-magnetism

Procedia PDF Downloads 131

Authors: Syed Zameer, Mohamed Haneef

Abstract:

Hip joint plays very important role in human beings as it takes up the whole body forces generated due to various activities. These loads are repetitive and fluctuating depending on the activities such as standing, sitting, jogging, stair casing, climbing, etc. which may lead to failure of Hip joint. Hip joint modification and replacement are common in old aged persons as well as younger persons. In this research study static and Fatigue analysis of Hip joint model was carried out using finite element software ANSYS. Stress distribution obtained from result of static analysis, material properties and S-N curve data of fabricated Ultra High molecular weight polyethylene / 50 wt% short E glass fibres + 40 wt% TiO2 Polymer matrix composites specimens were used to estimate fatigue life of Hip joint using stiffness Degradation model for polymer matrix composites. The stress distribution obtained from static analysis was found to be within the acceptable range.The factor of safety calculated from linear Palmgren linear damage rule is less than one, which indicates the component is safe under the design.

Keywords: hip joint, polymer matrix composite, static analysis, fatigue analysis, stress life approach

Procedia PDF Downloads 346

Authors: Rony Tawk, Boutros Ghannam, Maroun Nemer

Abstract:

Topology optimization technique presents a potential tool for the design and optimization of structures involved in mass and heat transfer. The method starts with an initial intermediate domain and should be able to progressively distribute the solid and the two fluids exchanging heat. The multi-objective function of the problem takes into account minimization of total pressure loss and maximization of heat transfer between solid and fluid subdomains. Existing methods account for the presence of only one fluid, while the actual work extends optimization distribution of solid and two different fluids. This requires to separate the channels of both fluids and to ensure a minimum solid thickness between them. This is done by adding a third objective function to the multi-objective optimization problem. This article uses density approach where each cell holds two local design parameters ranging from 0 to 1, where the combination of their extremums defines the presence of solid, cold fluid or hot fluid in this cell. Finite volume method is used for direct solver coupled with a discrete adjoint approach for sensitivity analysis and method of moving asymptotes for numerical optimization. Several examples are presented to show the ability of the method to find a trade-off between minimization of power dissipation and maximization of heat transfer while ensuring the separation and continuity of the channel of each fluid without crossing or mixing the fluids. The main conclusion is the possibility to find an optimal bi-fluid domain using topology optimization, defining a fluid to fluid heat exchanger device.

Keywords: topology optimization, density approach, bi-fluid domain, laminar steady state regime, fluid-to-fluid heat exchanger

Procedia PDF Downloads 388

Authors: Muhammad Mukhtar Aliyu

Abstract:

This study investigated tutors’ experiences and perceptions of a problem-based learning approach (PBL) in a writing classroom. The study involved two Nigerian lecturers who facilitated an intact class of second-year students in an English composition course for the period of 12 weeks. Semi-structured interviews were employed to collect data of the study. The lecturers were interviewed before and after the implementation of the PBL process. The overall findings of the study show that the lecturers had positive perceptions of the use of PBL in a writing classroom. Specifically, the findings reveal the lecturers’ positive experiences and perception of the group activities. Finally, the paper gives some pedagogical implications which would give insight for better implementation of the PBL approach.

Keywords: experiences and perception, Nigeria, problem-based learning approach, writing classroom

Procedia PDF Downloads 155

Authors: George Kyriakopoulos, Photini Pazartzis, Anthi Koskina, Crystalie Bourcha

Abstract:

The need for securing safe access to and return from outer space, as well as ensuring the viability of outer space operations, maintains vivid the debate over the promotion of organization of space traffic through a Space Traffic Management System (STM). The proliferation of outer space activities in recent years as well as the dynamic emergence of the private sector has gradually resulted in a diverse universe of actors operating in outer space. The said developments created an increased adverse impact on outer space sustainability as the case of the growing number of space debris clearly demonstrates. The above landscape sustains considerable threats to outer space environment and its operators that need to be addressed by a combination of scientific-technological measures and regulatory interventions. In this context, recourse to recent technological advancements and, in particular, to Artificial Intelligence (AI) and machine learning systems, could achieve exponential results in promoting space traffic management with respect to collision avoidance as well as launch and re-entry procedures/phases. New technologies can support the prospects of a successful space traffic management system at an international scale by enabling, inter alia, timely, accurate and analytical processing of large data sets and rapid decision-making, more precise space debris identification and tracking and overall minimization of collision risks and reduction of operational costs. What is more, a significant part of space activities (i.e. launch and/or re-entry phase) takes place in airspace rather than in outer space, hence the overall discussion also involves the highly developed, both technically and legally, international (and national) Air Traffic Management System (ATM). Nonetheless, from a regulatory perspective, the use of AI for the purposes of space traffic management puts forward implications that merit particular attention. Key issues in this regard include the delimitation of AI-based activities as space activities, the designation of the applicable legal regime (international space or air law, national law), the assessment of the nature and extent of international legal obligations regarding space traffic coordination, as well as the appropriate liability regime applicable to AI-based technologies when operating for space traffic coordination, taking into particular consideration the dense regulatory developments at EU level. In addition, the prospects of institutionalizing international cooperation and promoting an international governance system, together with the challenges of establishment of a comprehensive international STM regime are revisited in the light of intervention of AI technologies. This paper aims at examining regulatory implications advanced by the use of AI technology in the context of space traffic management operations and its key correlating concepts (SSA, space debris mitigation) drawing in particular on international and regional considerations in the field of STM (e.g. UNCOPUOS, International Academy of Astronautics, European Space Agency, among other actors), the promising advancements of the EU approach to AI regulation and, last but not least, national approaches regarding the use of AI in the context of space traffic management, in toto. Acknowledgment: The present work was co-funded by the European Union and Greek national funds through the Operational Program "Human Resources Development, Education and Lifelong Learning " (NSRF 2014-2020), under the call "Supporting Researchers with an Emphasis on Young Researchers – Cycle B" (MIS: 5048145).

Keywords: artificial intelligence, space traffic management, space situational awareness, space debris

Procedia PDF Downloads 238

Authors: Mamta Kumari, Shashi Jain

Abstract:

Tannins are a unique category of plant phytochemicals especially in terms of their vast potential health-benefiting properties. Researchers have described the capacity of tannins to enhance glucose uptake and inhibit adipogenesis, thus being potential drugs for the treatment of non-insulin dependent diabetes mellitus. Thus, the present research was conducted to find out tannin content of food products. The percentage of tannin in various analyzed sources ranged from 0.0 to 108.53%; highest in kathaa and lowest in ker and mango bark. The percentage of tannins present in the plants, however, varies. Numerous studies have confirmed that the naturally occurring polyphenols are key factor for the beneficial effects of the herbal medicines. Isolation and identification of active constituents from plants, preparation of standardized dose & dosage regimen can play a significant role in improving the hypoglycaemic action.

Keywords: tannins, diabetes, polyphenols, antioxidant, hypoglycemia

Procedia PDF Downloads 372

Authors: Edward B. Ssekulima, Amir Etemadi

Abstract:

This paper presents a Systems Thinking and System of Systems (SoS) viewpoint for managing requirements complexity in the grid integration of Variable Renewable Energy (VRE). To achieve a SoS approach, it is often necessary to inculcate a Systems Thinking (ST) perspective in the planning and design of the attendant system. We show how this approach can support the enhanced integration of VRE (wind, solar small hydro) for which intermittency is a key inhibiting factor to their sustainable grid integration. The results indicate that a ST and SoS approach are a critical tool for decision makers in the planning, design and deployment of VRE Sources for their sustainable grid-integration in accordance with relevant techno-economic, social and environmental requirements.

Keywords: sustainable grid-integration, system of systems, systems thinking, variable energy resources

Procedia PDF Downloads 106