Search results for: invasive weed optimization algorithm
842 Magnetic Chloromethylated Polymer Nanocomposite for Selective Pollutant Removal
Authors: Fabio T. Costa, Sergio E. Moya, Marcelo H. Sousa
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Nanocomposites designed by embedding magnetic nanoparticles into a polymeric matrix stand out as ideal magnetic-hybrid and magneto-responsive materials as sorbents for removal of pollutants in environmental applications. Covalent coupling is often desired for the immobilization of species on these nanocomposites, in order to keep them permanently bounded, not desorbing or leaching over time. Moreover, unwanted adsorbates can be separated by successive washes/magnetic separations, and it is also possible to recover the adsorbate covalently bound to the nanocomposite surface through detaching/cleavage protocols. Thus, in this work, we describe the preparation and characterization of highly-magnetizable chloromethylated polystyrene-based nanocomposite beads for selective covalent coupling in environmental applications. For synthesis optimization, acid resistant core-shelled maghemite (γ-Fe₂O₃) nanoparticles were coated with oleate molecules and directly incorporated into the organic medium during a suspension polymerization process. Moreover, the cross-linking agent ethylene glycol dimethacrylate (EGDMA) was utilized for co-polymerization with the 4-vinyl benzyl chloride (VBC) to increase the resistance of microbeads against leaching. After characterizing samples with XRD, ICP-OES, TGA, optical, SEM and TEM microscopes, a magnetic composite consisting of ~500 nm-sized cross-linked polymeric microspheres embedding ~8 nm γ-Fe₂O₃ nanoparticles was verified. This nanocomposite showed large room temperature magnetization (~24 emu/g) due to the high content in maghemite (~45 wt%) and resistance against leaching even in acidic media. Moreover, the presence of superficial chloromethyl groups, probed by FTIR and XPS spectroscopies and confirmed by an amination test can selectively adsorb molecules through the covalent coupling and be used in molecular separations as shown for the selective removal of 4-aminobenzoic acid from a mixture with benzoic acid.Keywords: nanocomposite, magnetic nanoparticle, covalent separation, pollutant removal
Procedia PDF Downloads 105841 Error Detection and Correction for Onboard Satellite Computers Using Hamming Code
Authors: Rafsan Al Mamun, Md. Motaharul Islam, Rabana Tajrin, Nabiha Noor, Shafinaz Qader
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In an attempt to enrich the lives of billions of people by providing proper information, security and a way of communicating with others, the need for efficient and improved satellites is constantly growing. Thus, there is an increasing demand for better error detection and correction (EDAC) schemes, which are capable of protecting the data onboard the satellites. The paper is aimed towards detecting and correcting such errors using a special algorithm called the Hamming Code, which uses the concept of parity and parity bits to prevent single-bit errors onboard a satellite in Low Earth Orbit. This paper focuses on the study of Low Earth Orbit satellites and the process of generating the Hamming Code matrix to be used for EDAC using computer programs. The most effective version of Hamming Code generated was the Hamming (16, 11, 4) version using MATLAB, and the paper compares this particular scheme with other EDAC mechanisms, including other versions of Hamming Codes and Cyclic Redundancy Check (CRC), and the limitations of this scheme. This particular version of the Hamming Code guarantees single-bit error corrections as well as double-bit error detections. Furthermore, this version of Hamming Code has proved to be fast with a checking time of 5.669 nanoseconds, that has a relatively higher code rate and lower bit overhead compared to the other versions and can detect a greater percentage of errors per length of code than other EDAC schemes with similar capabilities. In conclusion, with the proper implementation of the system, it is quite possible to ensure a relatively uncorrupted satellite storage system.Keywords: bit-flips, Hamming code, low earth orbit, parity bits, satellite, single error upset
Procedia PDF Downloads 123840 Development and Characterization of Multiphase Hydrogel Systems for Wound Healing
Authors: Rajendra Jangde, Deependra Singh
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Present work was based with objective to release of the antimicrobial and debriding agent in sustained manner at the wound surface. In order to provide a long-lasting antimicrobial action and moist environment on wound space, Biocompatible moist system was developed for complete healing. In the present study, a biocompatible moist system of PVA-gelatin hydrogel was developed capable of carrying multiple drugs- Quercetin and Cabopol in controlled manner for effective and complete wound healing. Carbopol and Quercetin were prepared by thin film hydration techniques and optimized system was incorporated in PVA-Gelatin slurry. PVA-Gelatin hydrogels were prepared by freeze thaw method. The prepared dispersion was casted into films to prepare multiphase hydrogel system and characterized by in vitro and in vivo studies. Results revealed the uniform dispersion of microspheres in a three-dimensional matrix of the PVA-Gelatin hydrogel observed at different magnifications. The in vitro release data showed typical biphasic release pattern, i.e., a burst release followed by a slower sustained release for 5 days. Prepared system was found to be stable under both normal and accelerated conditions. Histopathological study showed significant (p<0.05) increase in fibroblast cells, collagen fibres and blood vessels formation. All parameters such as wound contraction, tensile strength, histopathological and biochemical parameters- hydroxyproline content, protein level, etc. were observed significant (p<0.05) in comparison to control group. Present results suggest an accelerated re-epithelialization under moist wound environment with delivery of multiple drugs effective at different stages of wound healing cascade with minimum disturbance of wound bed.Keywords: multiphase hydrogel, optimization quercetin, wound healing
Procedia PDF Downloads 237839 Response Surface Methodology to Supercritical Carbon Dioxide Extraction of Microalgal Lipids
Authors: Yen-Hui Chen, Terry Walker
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As the world experiences an energy crisis, investing in sustainable energy resources is a pressing mission for many countries. Microalgae-derived biodiesel has attracted intensive attention as an important biofuel, and microalgae Chlorella protothecoides lipid is recognized as a renewable source for microalgae-derived biodiesel production. Supercritical carbon dioxide (SC-CO₂) is a promising green solvent that may potentially substitute the use of organic solvents for lipid extraction; however, the efficiency of SC-CO₂ extraction may be affected by many variables, including temperature, pressure and extraction time individually or in combination. In this study, response surface methodology (RSM) was used to optimize the process parameters, including temperature, pressure and extraction time, on C. protothecoides lipid yield by SC-CO₂ extraction. A second order polynomial model provided a good fit (R-square value of 0.94) for the C. protothecoides lipid yield. The linear and quadratic terms of temperature, pressure and extraction time—as well as the interaction between temperature and pressure—showed significant effects on lipid yield during extraction. The optimal lipid yield from the model was predicted as the temperature of 59 °C, the pressure of 350.7 bar and the extraction time 2.8 hours. Under these conditions, the experimental lipid yield (25%) was close to the predicted value. The principal fatty acid methyl esters (FAME) of C. protothecoides lipid-derived biodiesel were oleic acid methyl ester (60.1%), linoleic acid methyl ester (18.6%) and palmitic acid methyl ester (11.4%), which made up more than 90% of the total FAMEs. In summary, this study indicated that RSM was useful to characterize the optimization the SC-CO₂ extraction process of C. protothecoides lipid yield, and the second-order polynomial model could be used for predicting and describing the lipid yield very well. In addition, C. protothecoides lipid, extracted by SC-CO₂, was suggested as a potential candidate for microalgae-derived biodiesel production.Keywords: Chlorella protothecoides, microalgal lipids, response surface methodology, supercritical carbon dioxide extraction
Procedia PDF Downloads 434838 Pilot-free Image Transmission System of Joint Source Channel Based on Multi-Level Semantic Information
Authors: Linyu Wang, Liguo Qiao, Jianhong Xiang, Hao Xu
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In semantic communication, the existing joint Source Channel coding (JSCC) wireless communication system without pilot has unstable transmission performance and can not effectively capture the global information and location information of images. In this paper, a pilot-free image transmission system of joint source channel based on multi-level semantic information (Multi-level JSCC) is proposed. The transmitter of the system is composed of two networks. The feature extraction network is used to extract the high-level semantic features of the image, compress the information transmitted by the image, and improve the bandwidth utilization. Feature retention network is used to preserve low-level semantic features and image details to improve communication quality. The receiver also is composed of two networks. The received high-level semantic features are fused with the low-level semantic features after feature enhancement network in the same dimension, and then the image dimension is restored through feature recovery network, and the image location information is effectively used for image reconstruction. This paper verifies that the proposed multi-level JSCC algorithm can effectively transmit and recover image information in both AWGN channel and Rayleigh fading channel, and the peak signal-to-noise ratio (PSNR) is improved by 1~2dB compared with other algorithms under the same simulation conditions.Keywords: deep learning, JSCC, pilot-free picture transmission, multilevel semantic information, robustness
Procedia PDF Downloads 113837 Application of Functionalized Magnetic Particles as Demulsifier for Oil‐in‐Water Emulsions
Authors: Hamideh Hamedi, Nima Rezaei, Sohrab Zendehboudi
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Separating emulsified oil contaminations from waste- or produced water is of interest to various industries. Magnetic particles (MPs) application for separating dispersed and emulsified oil from wastewater is becoming more popular. Stabilization of MPs is required through developing a coating layer on their surfaces to prevent their agglomeration and enhance their dispersibility. In this research, we study the effects of coating material, size, and concentration of iron oxide MPs on oil separation efficiency, using oil adsorption capacity measurements. We functionalize both micro-and nanoparticles of Fe3O4 using sodium dodecyl sulfate (SDS) as an anionic surfactant, cetyltrimethylammonium bromide (CTAB) as a cationic surfactant, and stearic acid (SA). The chemical structures and morphologies of these particles are characterized using Scanning Electron Microscopy (SEM), Transmission Electron Microscopy (TEM), and Energy Dispersive X-ray (EDX). The oil-water separation results indicate that a low dosage of the coated magnetic nanoparticle with CTAB (0.5 g/L MNP-CTAB) results the highest oil adsorption capacity (nearly 100%) for 1000 ppm dodecane-in-water emulsion, containing ultra-small droplets (250–300 nm). While separation efficiency of the same dosage of bare MNPs is around 57.5%. Demulsification results of magnetic microparticles (MMPs) also reveal that the functionalizing particles with CTAB increase oil removal efficiency from 86.3% for bare MMP to 92% for MMP-CTAB. Comparing the results of different coating materials implies that the major interaction reaction is an electrostatic attraction between negatively charged oil droplets and positively charged MNP-CTAB and MMP-CTAB. Furthermore, the synthesized nanoparticles could be recycled and reused; after ten cycles the oil adsorption capacity slightly decreases to near 95%. In conclusion, functionalized magnetic particles with high oil separation efficiency could be used effectively in treatment of oily wastewater. Finally, optimization of the adsorption process is required by considering the effective system variables, and fluid properties.Keywords: oily wastewater treatment, emulsions, oil-water separation, adsorption, magnetic nanoparticles
Procedia PDF Downloads 100836 Comparison of Fuel Properties from Species of Microalgae and Selected Second-Generation Oil Feedstocks
Authors: Andrew C. Eloka Eboka, Freddie L. Inambao
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Comparative investigation and assessment of microalgal technology as a biodiesel production option was studied alongside other second generation feedstocks. This was carried out by comparing the fuel properties of species of Chlorella vulgaris, Duneliella spp, Synechococus spp and Senedesmus spp with the feedstock of Jatropha (ex-basirika variety), Hura crepitans, rubber and Natal mahogany seed oils. The micro-algae were cultivated in an open pond using a photobioreactor (New Brunsink set-up model BF-115 Bioflo/CelliGen made in the US) with operating parameters: 14L capacity, working volume of 7.5L media, including 10% inoculum, at optical density of 3.144 @540nm and light intensity of 200 lux, for 23 and 16 days respectively. Various produced/accumulated biomasses were harvested by draining, flocculation, centrifugation, drying and then subjected to lipid extraction processes. The oils extracted from the algae and feedstocks were characterised and used to produce biodiesel fuels, by the transesterification method, using modified optimization protocol. Fuel properties of the final biodiesel products were evaluated for chemo-physical and fuel properties. Results revealed Chlorella vulgaris as the best strain for biomass cultivation, having the highest lipid productivity (5.2mgL-1h-1), the highest rate of CO2 absorption (17.85mgL-1min-1) and the average carbon sequestration in the form of CO2 was 76.6%. The highest biomass productivity was 35.1mgL-1h-1 (Chlorella), while Senedesmus had the least output (3.75mgL-1h-1, 11.73mgL-1min-1). All species had good pH value adaptation, ranging from 6.5 to 8.5. The fuel properties of the micro-algal biodiesel in comparison with Jatropha, rubber, Hura and Natal mahogany were within ASTM specification and AGO used as the control. Fuel cultivation from microalgae is feasible and will revolutionise the biodiesel industry.Keywords: biodiesel, fuel properties, microalgae, second generation, seed oils, feedstock, photo-bioreactor, open pond
Procedia PDF Downloads 360835 Using Geo-Statistical Techniques and Machine Learning Algorithms to Model the Spatiotemporal Heterogeneity of Land Surface Temperature and its Relationship with Land Use Land Cover
Authors: Javed Mallick
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In metropolitan areas, rapid changes in land use and land cover (LULC) have ecological and environmental consequences. Saudi Arabia's cities have experienced tremendous urban growth since the 1990s, resulting in urban heat islands, groundwater depletion, air pollution, loss of ecosystem services, and so on. From 1990 to 2020, this study examines the variance and heterogeneity in land surface temperature (LST) caused by LULC changes in Abha-Khamis Mushyet, Saudi Arabia. LULC was mapped using the support vector machine (SVM). The mono-window algorithm was used to calculate the land surface temperature (LST). To identify LST clusters, the local indicator of spatial associations (LISA) model was applied to spatiotemporal LST maps. In addition, the parallel coordinate (PCP) method was used to investigate the relationship between LST clusters and urban biophysical variables as a proxy for LULC. According to LULC maps, urban areas increased by more than 330% between 1990 and 2018. Between 1990 and 2018, built-up areas had an 83.6% transitional probability. Furthermore, between 1990 and 2020, vegetation and agricultural land were converted into built-up areas at a rate of 17.9% and 21.8%, respectively. Uneven LULC changes in built-up areas result in more LST hotspots. LST hotspots were associated with high NDBI but not NDWI or NDVI. This study could assist policymakers in developing mitigation strategies for urban heat islandsKeywords: land use land cover mapping, land surface temperature, support vector machine, LISA model, parallel coordinate plot
Procedia PDF Downloads 70834 Generation of Ultra-Broadband Supercontinuum Ultrashort Laser Pulses with High Energy
Authors: Walid Tawfik
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The interaction of intense short nano- and picosecond laser pulses with plasma leads to reach variety of important applications, including time-resolved laser induced breakdown spectroscopy (LIBS), soft x-ray lasers, and laser-driven accelerators. The progress in generating of femtosecond down to sub-10 fs optical pulses has opened a door for scientists with an essential tool in many ultrafast phenomena, such as femto-chemistry, high field physics, and high harmonic generation (HHG). The advent of high-energy laser pulses with durations of few optical cycles provided scientists with very high electric fields, and produce coherent intense UV to NIR radiation with high energy which allows for the investigation of ultrafast molecular dynamics with femtosecond resolution. In this work, we could experimentally achieve the generation of a two-octave-wide supercontinuum ultrafast pulses extending from ultraviolet at 3.5 eV to the near-infrared at 1.3 eV in neon-filled capillary fiber. These pulses are created due to nonlinear self-phase modulation (SPM) in neon as a nonlinear medium. The measurements of the generated pulses were performed using spectral phase interferometry for direct electric-field reconstruction. A full characterization of the output pulses was studied. The output pulse characterization includes the pulse width, the beam profile, and the spectral bandwidth. Under optimization conditions, the reconstructed pulse intensity autocorrelation function was exposed for the shorts possible pulse duration to achieve transform-limited pulses with energies up to 600µJ. Furthermore, the effect of variation of neon pressure on the pulse-width was studied. The nonlinear SPM found to be increased with the neon pressure. The obtained results may give an opportunity to monitor and control ultrafast transit interaction in femtosecond chemistry.Keywords: femtosecond laser, ultrafast, supercontinuum, ultra-broadband
Procedia PDF Downloads 200833 Model-Based Fault Diagnosis in Carbon Fiber Reinforced Composites Using Particle Filtering
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Carbon fiber reinforced composites (CFRP) used as aircraft structure are subject to lightning strike, putting structural integrity under risk. Indirect damage may occur after a lightning strike where the internal structure can be damaged due to excessive heat induced by lightning current, while the surface of the structures remains intact. Three damage modes may be observed after a lightning strike: fiber breakage, inter-ply delamination and intra-ply cracks. The assessment of internal damage states in composite is challenging due to complicated microstructure, inherent uncertainties, and existence of multiple damage modes. In this work, a model based approach is adopted to diagnose faults in carbon composites after lighting strikes. A resistor network model is implemented to relate the overall electrical and thermal conduction behavior under simulated lightning current waveform to the intrinsic temperature dependent material properties, microstructure and degradation of materials. A fault detection and identification (FDI) module utilizes the physics based model and a particle filtering algorithm to identify damage mode as well as calculate the probability of structural failure. Extensive simulation results are provided to substantiate the proposed fault diagnosis methodology with both single fault and multiple faults cases. The approach is also demonstrated on transient resistance data collected from a IM7/Epoxy laminate under simulated lightning strike.Keywords: carbon composite, fault detection, fault identification, particle filter
Procedia PDF Downloads 188832 Healthy Feeding and Drinking Troughs for Profitable Intensive Deep-Litter Poultry Farming
Authors: Godwin Ojochogu Adejo, Evelyn UnekwuOjo Adejo, Sunday UnenwOjo Adejo
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The mainstream contemporary approach to controlling the impact of diseases among poultry birds rely largely on curative measures through the administration of drugs to infected birds. Most times as observed in the deep liter poultry farming system, entire flocks including uninfected birds receive the treatment they do not need. As such, unguarded use of chemical drugs and antibiotics has led to wastage and accumulation of chemical residues in poultry products with associated health hazards to humans. However, wanton and frequent drug usage in poultry is avoidable if feeding and drinking equipment are designed to curb infection transmission among birds. Using toxicological assays as guide and with efficiency and simplicity in view, two newly field-tested and recently patented equipments called 'healthy liquid drinking trough (HDT)' and 'healthy feeding trough (HFT)' that systematically eliminate contamination of the feeding and drinking channels, thereby, curbing wide-spread infection and transmission of diseases in the (intensive) deep litter poultry farming system were designed. Upon combined usage, they automatically and drastically reduced both the amount and frequency of antibiotics use in poultry by over > 50%. Additionally, they conferred optimization of feed and water utilization/elimination of wastage by > 80%, reduced labour by > 70%, reduced production cost by about 15%, and reduced chemical residues in poultry meat or eggs by > 85%. These new and cheap technologies which require no energy input are likely to elevate safety of poultry products for consumers' health, increase marketability locally and for export, and increase output and profit especially among poultry farmers and poor people who keep poultry or inevitably utilize poultry products in developing countries.Keywords: healthy, trough, toxicological, assay-guided, poultry
Procedia PDF Downloads 149831 Pattern of Adverse Drug Reactions with Platinum Compounds in Cancer Chemotherapy at a Tertiary Care Hospital in South India
Authors: Meena Kumari, Ajitha Sharma, Mohan Babu Amberkar, Hasitha Manohar, Joseph Thomas, K. L. Bairy
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Aim: To evaluate the pattern of occurrence of adverse drug reactions (ADRs) with platinum compounds in cancer chemotherapy at a tertiary care hospital. Methods: It was a retrospective, descriptive case record study done on patients admitted to the medical oncology ward of Kasturba Hospital, Manipal from July to November 2012. Inclusion criteria comprised of patients of both sexes and all ages diagnosed with cancer and were on platinum compounds, who developed at least one adverse drug reaction during or after the treatment period. CDSCO proforma was used for reporting ADRs. Causality was assessed using Naranjo Algorithm. Results: A total of 65 patients was included in the study. Females comprised of 67.69% and rest males. Around 49.23% of the ADRs were seen in the age group of 41-60 years, followed by 20 % in 21-40 years, 18.46% in patients over 60 years and 12.31% in 1-20 years age group. The anticancer agents which caused adverse drug reactions in our study were carboplatin (41.54%), cisplatin (36.92%) and oxaliplatin (21.54%). Most common adverse drug reactions observed were oral candidiasis (21.53%), vomiting (16.92%), anaemia (12.3%), diarrhoea (12.3%) and febrile neutropenia (0.08%). The results of the causality assessment of most of the cases were probable. Conclusion: The adverse effect of chemotherapeutic agents is a matter of concern in the pharmacological management of cancer as it affects the quality of life of patients. This information would be useful in identifying and minimizing preventable adverse drug reactions while generally enhancing the knowledge of the prescribers to deal with these adverse drug reactions more efficiently.Keywords: adverse drug reactions, platinum compounds, cancer, chemotherapy
Procedia PDF Downloads 422830 Applying Kinect on the Development of a Customized 3D Mannequin
Authors: Shih-Wen Hsiao, Rong-Qi Chen
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In the field of fashion design, 3D Mannequin is a kind of assisting tool which could rapidly realize the design concepts. While the concept of 3D Mannequin is applied to the computer added fashion design, it will connect with the development and the application of design platform and system. Thus, the situation mentioned above revealed a truth that it is very critical to develop a module of 3D Mannequin which would correspond with the necessity of fashion design. This research proposes a concrete plan that developing and constructing a system of 3D Mannequin with Kinect. In the content, ergonomic measurements of objective human features could be attained real-time through the implement with depth camera of Kinect, and then the mesh morphing can be implemented through transformed the locations of the control-points on the model by inputting those ergonomic data to get an exclusive 3D mannequin model. In the proposed methodology, after the scanned points from the Kinect are revised for accuracy and smoothening, a complete human feature would be reconstructed by the ICP algorithm with the method of image processing. Also, the objective human feature could be recognized to analyze and get real measurements. Furthermore, the data of ergonomic measurements could be applied to shape morphing for the division of 3D Mannequin reconstructed by feature curves. Due to a standardized and customer-oriented 3D Mannequin would be generated by the implement of subdivision, the research could be applied to the fashion design or the presentation and display of 3D virtual clothes. In order to examine the practicality of research structure, a system of 3D Mannequin would be constructed with JAVA program in this study. Through the revision of experiments the practicability-contained research result would come out.Keywords: 3D mannequin, kinect scanner, interactive closest point, shape morphing, subdivision
Procedia PDF Downloads 301829 Prediction Modeling of Alzheimer’s Disease and Its Prodromal Stages from Multimodal Data with Missing Values
Authors: M. Aghili, S. Tabarestani, C. Freytes, M. Shojaie, M. Cabrerizo, A. Barreto, N. Rishe, R. E. Curiel, D. Loewenstein, R. Duara, M. Adjouadi
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A major challenge in medical studies, especially those that are longitudinal, is the problem of missing measurements which hinders the effective application of many machine learning algorithms. Furthermore, recent Alzheimer's Disease studies have focused on the delineation of Early Mild Cognitive Impairment (EMCI) and Late Mild Cognitive Impairment (LMCI) from cognitively normal controls (CN) which is essential for developing effective and early treatment methods. To address the aforementioned challenges, this paper explores the potential of using the eXtreme Gradient Boosting (XGBoost) algorithm in handling missing values in multiclass classification. We seek a generalized classification scheme where all prodromal stages of the disease are considered simultaneously in the classification and decision-making processes. Given the large number of subjects (1631) included in this study and in the presence of almost 28% missing values, we investigated the performance of XGBoost on the classification of the four classes of AD, NC, EMCI, and LMCI. Using 10-fold cross validation technique, XGBoost is shown to outperform other state-of-the-art classification algorithms by 3% in terms of accuracy and F-score. Our model achieved an accuracy of 80.52%, a precision of 80.62% and recall of 80.51%, supporting the more natural and promising multiclass classification.Keywords: eXtreme gradient boosting, missing data, Alzheimer disease, early mild cognitive impairment, late mild cognitive impair, multiclass classification, ADNI, support vector machine, random forest
Procedia PDF Downloads 182828 Optimizing The Residential Design Process Using Automated Technologies
Authors: Martin Georgiev, Milena Nanova, Damyan Damov
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Architects, engineers, and developers need to analyse and implement a wide spectrum of data in different formats, if they want to produce viable residential developments. Usually, this data comes from a number of different sources and is not well structured. The main objective of this research project is to provide parametric tools working with real geodesic data that can generate residential solutions. Various codes, regulations and design constraints are described by variables and prioritized. In this way, we establish a common workflow for architects, geodesists, and other professionals involved in the building and investment process. This collaborative medium ensures that the generated design variants conform to various requirements, contributing to a more streamlined and informed decision-making process. The quantification of distinctive characteristics inherent to typical residential structures allows a systematic evaluation of the generated variants, focusing on factors crucial to designers, such as daylight simulation, circulation analysis, space utilization, view orientation, etc. Integrating real geodesic data offers a holistic view of the built environment, enhancing the accuracy and relevance of the design solutions. The use of generative algorithms and parametric models offers high productivity and flexibility of the design variants. It can be implemented in more conventional CAD and BIM workflow. Experts from different specialties can join their efforts, sharing a common digital workspace. In conclusion, our research demonstrates that a generative parametric approach based on real geodesic data and collaborative decision-making could be introduced in the early phases of the design process. This gives the designers powerful tools to explore diverse design possibilities, significantly improving the qualities of the building investment during its entire lifecycle.Keywords: architectural design, residential buildings, urban development, geodesic data, generative design, parametric models, workflow optimization
Procedia PDF Downloads 44827 NiFe-Type Catalysts for Anion Exchange Membrane (AEM) Electrolyzers
Authors: Boldin Roman, Liliana Analía Diaz
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As the hydrogen economy continues to expand, reducing energy consumption and emissions while stimulating economic growth, the development of efficient and cost-effective hydrogen production technologies is critical. Among various methods, anion exchange membrane (AEM) water electrolysis stands out due to its potential for using non-noble metal catalysts. The exploration and enhancement of non-noble metal catalysts, such as NiFe-type catalysts, are pivotal for the advancement of AEM technology, ensuring its commercial viability and environmental sustainability. NiFe-type catalysts were synthesized through electrodeposition and characterized both electrochemically and physico-chemically. Various supports, including Ni foam and Ni mesh, were used as porous transport layers (PTLs) to evaluate the effective catalyst thickness and the influence of the PTL in a 5 cm² AEM electrolyzer. This methodological approach allows for a detailed assessment of catalyst performance under operational conditions typical of industrial hydrogen production. The study revealed that electrodeposited non-noble multi-metallic catalysts maintain stable performance as anodes in AEM water electrolysis. NiFe-type catalysts demonstrated superior activity, with the NiFeCoP alloy outperforming others by delivering the lowest overpotential and the highest current density. Furthermore, the use of different PTLs showed significant effects on the electrochemical behavior of the catalysts, indicating that PTL selection is crucial for optimizing performance and efficiency in AEM electrolyzers. Conclusion: The research underscores the potential of non-noble metal catalysts in enhancing efficiency and reducing the costs of AEM electrolysers. The findings highlight the importance of catalyst and PTL optimization in developing scalable and economically viable hydrogen production technologies. Continued innovation in this area is essential for supporting the growth of the hydrogen economy and achieving sustainable energy solutions.Keywords: AEMWE, electrocatalyst, hydrogen production, water electrolysis.
Procedia PDF Downloads 11826 Artificial Intelligence-Generated Previews of Hyaluronic Acid-Based Treatments
Authors: Ciro Cursio, Giulia Cursio, Pio Luigi Cursio, Luigi Cursio
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Communication between practitioner and patient is of the utmost importance in aesthetic medicine: as of today, images of previous treatments are the most common tool used by doctors to describe and anticipate future results for their patients. However, using photos of other people often reduces the engagement of the prospective patient and is further limited by the number and quality of pictures available to the practitioner. Pre-existing work solves this issue in two ways: 3D scanning of the area with manual editing of the 3D model by the doctor or automatic prediction of the treatment by warping the image with hand-written parameters. The first approach requires the manual intervention of the doctor, while the second approach always generates results that aren’t always realistic. Thus, in one case, there is significant manual work required by the doctor, and in the other case, the prediction looks artificial. We propose an AI-based algorithm that autonomously generates a realistic prediction of treatment results. For the purpose of this study, we focus on hyaluronic acid treatments in the facial area. Our approach takes into account the individual characteristics of each face, and furthermore, the prediction system allows the patient to decide which area of the face she wants to modify. We show that the predictions generated by our system are realistic: first, the quality of the generated images is on par with real images; second, the prediction matches the actual results obtained after the treatment is completed. In conclusion, the proposed approach provides a valid tool for doctors to show patients what they will look like before deciding on the treatment.Keywords: prediction, hyaluronic acid, treatment, artificial intelligence
Procedia PDF Downloads 109825 Optimizing Detection Methods for THz Bio-imaging Applications
Authors: C. Bolakis, I. S. Karanasiou, D. Grbovic, G. Karunasiri, N. Uzunoglu
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A new approach for efficient detection of THz radiation in biomedical imaging applications is proposed. A double-layered absorber consisting of a 32 nm thick aluminum (Al) metallic layer, located on a glass medium (SiO2) of 1 mm thickness, was fabricated and used to design a fine-tuned absorber through a theoretical and finite element modeling process. The results indicate that the proposed low-cost, double-layered absorber can be tuned based on the metal layer sheet resistance and the thickness of various glass media taking advantage of the diversity of the absorption of the metal films in the desired THz domain (6 to 10 THz). It was found that the composite absorber could absorb up to 86% (a percentage exceeding the 50%, previously shown to be the highest achievable when using single thin metal layer) and reflect less than 1% of the incident THz power. This approach will enable monitoring of the transmission coefficient (THz transmission ‘’fingerprint’’) of the biosample with high accuracy, while also making the proposed double-layered absorber a good candidate for a microbolometer pixel’s active element. Based on the aforementioned promising results, a more sophisticated and effective double-layered absorber is under development. The glass medium has been substituted by diluted poly-si and the results were twofold: An absorption factor of 96% was reached and high TCR properties acquired. In addition, a generalization of these results and properties over the active frequency spectrum was achieved. Specifically, through the development of a theoretical equation having as input any arbitrary frequency in the IR spectrum (0.3 to 405.4 THz) and as output the appropriate thickness of the poly-si medium, the double-layered absorber retains the ability to absorb the 96% and reflects less than 1% of the incident power. As a result, through that post-optimization process and the spread spectrum frequency adjustment, the microbolometer detector efficiency could be further improved.Keywords: bio-imaging, fine-tuned absorber, fingerprint, microbolometer
Procedia PDF Downloads 341824 Evaluation of the Impact of Reducing the Traffic Light Cycle for Cars to Improve Non-Vehicular Transportation: A Case of Study in Lima
Authors: Gheyder Concha Bendezu, Rodrigo Lescano Loli, Aldo Bravo Lizano
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In big urbanized cities of Latin America, motor vehicles have priority over non-motor vehicles and pedestrians. There is an important problem that affects people's health and quality of life; lack of inclusion towards pedestrians makes it difficult for them to move smoothly and safely since the city has been planned for the transit of motor vehicles. Faced with the new trend for sustainable and economical transport, the city is forced to develop infrastructure in order to incorporate pedestrians and users with non-motorized vehicles in the transport system. The present research aims to study the influence of non-motorized vehicles on an avenue, the optimization of a cycle using traffic lights based on simulation in Synchro software, to improve the flow of non-motor vehicles. The evaluation is of the microscopic type; for this reason, field data was collected, such as vehicular, pedestrian, and non-motor vehicle user demand. With the values of speed and travel time, it is represented in the current scenario that contains the existing problem. These data allow to create a microsimulation model in Vissim software, later to be calibrated and validated so that it has a behavior similar to reality. The results of this model are compared with the efficiency parameters of the proposed model; these parameters are the queue length, the travel speed, and mainly the travel times of the users at this intersection. The results reflect a reduction of 27% in travel time, that is, an improvement between the proposed model and the current one for this great avenue. The tail length of motor vehicles is also reduced by 12.5%, a considerable improvement. All this represents an improvement in the level of service and in the quality of life of users.Keywords: bikeway, microsimulation, pedestrians, queue length, traffic light cycle, travel time
Procedia PDF Downloads 158823 Establishment of a Classifier Model for Early Prediction of Acute Delirium in Adult Intensive Care Unit Using Machine Learning
Authors: Pei Yi Lin
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Objective: The objective of this study is to use machine learning methods to build an early prediction classifier model for acute delirium to improve the quality of medical care for intensive care patients. Background: Delirium is a common acute and sudden disturbance of consciousness in critically ill patients. After the occurrence, it is easy to prolong the length of hospital stay and increase medical costs and mortality. In 2021, the incidence of delirium in the intensive care unit of internal medicine was as high as 59.78%, which indirectly prolonged the average length of hospital stay by 8.28 days, and the mortality rate is about 2.22% in the past three years. Therefore, it is expected to build a delirium prediction classifier through big data analysis and machine learning methods to detect delirium early. Method: This study is a retrospective study, using the artificial intelligence big data database to extract the characteristic factors related to delirium in intensive care unit patients and let the machine learn. The study included patients aged over 20 years old who were admitted to the intensive care unit between May 1, 2022, and December 31, 2022, excluding GCS assessment <4 points, admission to ICU for less than 24 hours, and CAM-ICU evaluation. The CAMICU delirium assessment results every 8 hours within 30 days of hospitalization are regarded as an event, and the cumulative data from ICU admission to the prediction time point are extracted to predict the possibility of delirium occurring in the next 8 hours, and collect a total of 63,754 research case data, extract 12 feature selections to train the model, including age, sex, average ICU stay hours, visual and auditory abnormalities, RASS assessment score, APACHE-II Score score, number of invasive catheters indwelling, restraint and sedative and hypnotic drugs. Through feature data cleaning, processing and KNN interpolation method supplementation, a total of 54595 research case events were extracted to provide machine learning model analysis, using the research events from May 01 to November 30, 2022, as the model training data, 80% of which is the training set for model training, and 20% for the internal verification of the verification set, and then from December 01 to December 2022 The CU research event on the 31st is an external verification set data, and finally the model inference and performance evaluation are performed, and then the model has trained again by adjusting the model parameters. Results: In this study, XG Boost, Random Forest, Logistic Regression, and Decision Tree were used to analyze and compare four machine learning models. The average accuracy rate of internal verification was highest in Random Forest (AUC=0.86), and the average accuracy rate of external verification was in Random Forest and XG Boost was the highest, AUC was 0.86, and the average accuracy of cross-validation was the highest in Random Forest (ACC=0.77). Conclusion: Clinically, medical staff usually conduct CAM-ICU assessments at the bedside of critically ill patients in clinical practice, but there is a lack of machine learning classification methods to assist ICU patients in real-time assessment, resulting in the inability to provide more objective and continuous monitoring data to assist Clinical staff can more accurately identify and predict the occurrence of delirium in patients. It is hoped that the development and construction of predictive models through machine learning can predict delirium early and immediately, make clinical decisions at the best time, and cooperate with PADIS delirium care measures to provide individualized non-drug interventional care measures to maintain patient safety, and then Improve the quality of care.Keywords: critically ill patients, machine learning methods, delirium prediction, classifier model
Procedia PDF Downloads 63822 Drought Risk Analysis Using Neural Networks for Agri-Businesses and Projects in Lejweleputswa District Municipality, South Africa
Authors: Bernard Moeketsi Hlalele
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Drought is a complicated natural phenomenon that creates significant economic, social, and environmental problems. An analysis of paleoclimatic data indicates that severe and extended droughts are inevitable part of natural climatic circle. This study characterised drought in Lejweleputswa using both Standardised Precipitation Index (SPI) and neural networks (NN) to quantify and predict respectively. Monthly 37-year long time series precipitation data were obtained from online NASA database. Prior to the final analysis, this dataset was checked for outliers using SPSS. Outliers were removed and replaced by Expectation Maximum algorithm from SPSS. This was followed by both homogeneity and stationarity tests to ensure non-spurious results. A non-parametric Mann Kendall's test was used to detect monotonic trends present in the dataset. Two temporal scales SPI-3 and SPI-12 corresponding to agricultural and hydrological drought events showed statistically decreasing trends with p-value = 0.0006 and 4.9 x 10⁻⁷, respectively. The study area has been plagued with severe drought events on SPI-3, while on SPI-12, it showed approximately a 20-year circle. The concluded the analyses with a seasonal analysis that showed no significant trend patterns, and as such NN was used to predict possible SPI-3 for the last season of 2018/2019 and four seasons for 2020. The predicted drought intensities ranged from mild to extreme drought events to come. It is therefore recommended that farmers, agri-business owners, and other relevant stakeholders' resort to drought resistant crops as means of adaption.Keywords: drought, risk, neural networks, agri-businesses, project, Lejweleputswa
Procedia PDF Downloads 119821 Secure Automatic Key SMS Encryption Scheme Using Hybrid Cryptosystem: An Approach for One Time Password Security Enhancement
Authors: Pratama R. Yunia, Firmansyah, I., Ariani, Ulfa R. Maharani, Fikri M. Al
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Nowadays, notwithstanding that the role of SMS as a means of communication has been largely replaced by online applications such as WhatsApp, Telegram, and others, the fact that SMS is still used for certain and important communication needs is indisputable. Among them is for sending one time password (OTP) as an authentication media for various online applications ranging from chatting, shopping to online banking applications. However, the usage of SMS does not pretty much guarantee the security of transmitted messages. As a matter of fact, the transmitted messages between BTS is still in the form of plaintext, making it extremely vulnerable to eavesdropping, especially if the message is confidential, for instance, the OTP. One solution to overcome this problem is to use an SMS application which provides security services for each transmitted message. Responding to this problem, in this study, an automatic key SMS encryption scheme was designed as a means to secure SMS communication. The proposed scheme allows SMS sending, which is automatically encrypted with keys that are constantly changing (automatic key update), automatic key exchange, and automatic key generation. In terms of the security method, the proposed scheme applies cryptographic techniques with a hybrid cryptosystem mechanism. Proofing the proposed scheme, a client to client SMS encryption application was developed using Java platform with AES-256 as encryption algorithm, RSA-768 as public and private key generator and SHA-256 for message hashing function. The result of this study is a secure automatic key SMS encryption scheme using hybrid cryptosystem which can guarantee the security of every transmitted message, so as to become a reliable solution in sending confidential messages through SMS although it still has weaknesses in terms of processing time.Keywords: encryption scheme, hybrid cryptosystem, one time password, SMS security
Procedia PDF Downloads 123820 Analysis of Cycling Accessibility on Chengdu Tianfu Greenway Based on Improved Two-Step Floating Catchment Area Method: A Case Study of Jincheng Greenway
Authors: Qin Zhu
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Under the background of accelerating the construction of Beautiful and Livable Park City in Chengdu, the Tianfu greenway system, as an important support system for the construction of parks in the whole region, its accessibility is one of the key indicators to measure the effectiveness of the greenway construction. In recent years, cycling has become an important transportation mode for residents to go to the greenways because of its low-carbon, healthy and convenient characteristics, and the study of greenway accessibility under cycling mode can provide reference suggestions for the optimization and improvement of greenways. Taking Jincheng Greenway in Chengdu City as an example, the Baidu Map Application Programming Interface (API) and questionnaire survey was used to improve the two-step floating catchment area (2SFCA) method from the three dimensions of search threshold, supply side and demand side, to calculate the cycling accessibility of the greenway and to explore the spatial matching relationship with the population density, the number of entrances and the comprehensive attractiveness. The results show that: 1) the distribution of greenway accessibility in Jincheng shows a pattern of "high in the south and low in the north, high in the west and low in the east", 2) the spatial match between greenway accessibility and population density of the residential area is imbalanced, and there is a significant positive correlation between accessibility and the number of selectable greenway access points in residential areas, as well as the overall attractiveness of greenways, with a high degree of match. On this basis, it is proposed to give priority to the mismatch area to alleviate the contradiction between supply and demand, optimize the greenway access points to improve the traffic connection, enhance the comprehensive quality of the greenway and strengthen the service capacity, to further improve the cycling accessibility of the Jincheng Greenway and improve the spatial allocation of greenway resources.Keywords: accessibility, Baidu maps API, cycling, greenway, 2SFCA
Procedia PDF Downloads 71819 Real-Time Generative Architecture for Mesh and Texture
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In the evolving landscape of physics-based machine learning (PBML), particularly within fluid dynamics and its applications in electromechanical engineering, robot vision, and robot learning, achieving precision and alignment with researchers' specific needs presents a formidable challenge. In response, this work proposes a methodology that integrates neural transformation with a modified smoothed particle hydrodynamics model for generating transformed 3D fluid simulations. This approach is useful for nanoscale science, where the unique and complex behaviors of viscoelastic medium demand accurate neurally-transformed simulations for materials understanding and manipulation. In electromechanical engineering, the method enhances the design and functionality of fluid-operated systems, particularly microfluidic devices, contributing to advancements in nanomaterial design, drug delivery systems, and more. The proposed approach also aligns with the principles of PBML, offering advantages such as multi-fluid stylization and consistent particle attribute transfer. This capability is valuable in various fields where the interaction of multiple fluid components is significant. Moreover, the application of neurally-transformed hydrodynamical models extends to manufacturing processes, such as the production of microelectromechanical systems, enhancing efficiency and cost-effectiveness. The system's ability to perform neural transfer on 3D fluid scenes using a deep learning algorithm alongside physical models further adds a layer of flexibility, allowing researchers to tailor simulations to specific needs across scientific and engineering disciplines.Keywords: physics-based machine learning, robot vision, robot learning, hydrodynamics
Procedia PDF Downloads 62818 Modeling of Sediment Yield and Streamflow of Watershed Basin in the Philippines Using the Soil Water Assessment Tool Model for Watershed Sustainability
Authors: Warda L. Panondi, Norihiro Izumi
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Sedimentation is a significant threat to the sustainability of reservoirs and their watershed. In the Philippines, the Pulangi watershed experienced a high sediment loss mainly due to land conversions and plantations that showed critical erosion rates beyond the tolerable limit of -10 ton/ha/yr in all of its sub-basin. From this event, the prediction of runoff volume and sediment yield is essential to examine using the country's soil conservation techniques realistically. In this research, the Pulangi watershed was modeled using the soil water assessment tool (SWAT) to predict its watershed basin's annual runoff and sediment yield. For the calibration and validation of the model, the SWAT-CUP was utilized. The model was calibrated with monthly discharge data for 1990-1993 and validated for 1994-1997. Simultaneously, the sediment yield was calibrated in 2014 and validated in 2015 because of limited observed datasets. Uncertainty analysis and calculation of efficiency indexes were accomplished through the SUFI-2 algorithm. According to the coefficient of determination (R2), Nash Sutcliffe efficiency (NSE), King-Gupta efficiency (KGE), and PBIAS, the calculation of streamflow indicates a good performance for both calibration and validation periods while the sediment yield resulted in a satisfactory performance for both calibration and validation. Therefore, this study was able to identify the most critical sub-basin and severe needs of soil conservation. Furthermore, this study will provide baseline information to prevent floods and landslides and serve as a useful reference for land-use policies and watershed management and sustainability in the Pulangi watershed.Keywords: Pulangi watershed, sediment yield, streamflow, SWAT model
Procedia PDF Downloads 195817 La₀.₈Ba₀.₂FeO₃ Perovskite as an Additive in the Three-Way Catalyst (TWCs) for Reduction of PGMs Loading
Authors: Mahshid Davoodpoor, Zahra Shamohammadi Ghahsareh, Saeid Razfar, Alaleh Dabbaghi
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Nowadays, air pollution has become a topic of great concern all over the world. One of the main sources of air pollution is automobile exhaust gas, which introduces a large number of toxic gases, including CO, unburned hydrocarbons (HCs), NOx, and non-methane hydrocarbons (NMHCs), into the air. The application of three-way catalysts (TWCs) is still the most effective strategy to mitigate the emission of these pollutants. Due to the stringent environmental regulations which continuously become stricter, studies on the TWCs are ongoing despite several years of research and development. This arises from the washcoat complexity and the several numbers of parameters involved in the redox reactions. The main objectives of these studies are the optimization of washcoat formulation and the investigation of different coating modes. Perovskite (ABO₃), as a promising class of materials, has unique features that make it versatile to use as an alternative to commonly mixed oxides in washcoats. High catalytic activity for oxidation reactions and its relatively high oxygen storage capacity are important properties of perovskites in catalytic applications. Herein, La₀.₈Ba₀.₂FeO₃ perovskite material was synthesized using the co-precipitation method and characterized by XRD, ICP, and BET analysis. The effect of synthesis conditions, including B site metal (Fe and Co), metal precursor concentration, and dopant (Ba), were examined on the phase purity of the products. The selected perovskite sample was used as one of the components in the TWC formulation to evaluate its catalytic performance through Light-off, oxygen storage capacity, and emission analysis. Results showed a remarkable increment in oxygen storage capacity and also revealed that T50 and emission of CO, HC, and NOx reduced in the presence of perovskite structure which approves the enhancement of catalytic performance for the new washcoat formulation. This study shows the brilliant future of advanced oxide structures in the TWCs.Keywords: Perovskite, three-way catalyst, PGMs, PGMs reduction
Procedia PDF Downloads 59816 Optimization of Marine Waste Collection Considering Dynamic Transport and Ship’s Wake Impact
Authors: Guillaume Richard, Sarra Zaied
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Marine waste quantities increase more and more, 5 million tons of plastic waste enter the ocean every year. Their spatiotemporal distribution is never homogeneous and depends mainly on the hydrodynamic characteristics of the environment, as well as the size and location of the waste. As part of optimizing collect of marine plastic wastes, it is important to measure and monitor their evolution over time. In this context, diverse studies have been dedicated to describing waste behavior in order to identify its accumulation in ocean areas. None of the existing tools which track objects at sea had the objective of tracking down a slick of waste. Moreover, the applications related to marine waste are in the minority compared to rescue applications or oil slicks tracking applications. These approaches are able to accurately simulate an object's behavior over time but not during the collection mission of a waste sheet. This paper presents numerical modeling of a boat’s wake impact on the floating marine waste behavior during a collection mission. The aim is to predict the trajectory of a marine waste slick to optimize its collection using meteorological data of ocean currents, wind, and possibly waves. We have made the choice to use Ocean Parcels which is a Python library suitable for trajectoring particles in the ocean. The modeling results showed the important role of advection and diffusion processes in the spatiotemporal distribution of floating plastic litter. The performance of the proposed method was evaluated on real data collected from the Copernicus Marine Environment Monitoring Service (CMEMS). The results of the evaluation in Cape of Good Hope (South Africa) prove that the proposed approach can effectively predict the position and velocity of marine litter during collection, which allowed for optimizing time and more than $90\%$ of the amount of collected waste.Keywords: marine litter, advection-diffusion equation, sea current, numerical model
Procedia PDF Downloads 83815 Machine Learning for Exoplanetary Habitability Assessment
Authors: King Kumire, Amos Kubeka
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The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.Keywords: machine-learning, habitability, exoplanets, supercomputing
Procedia PDF Downloads 84814 Machine Learning for Exoplanetary Habitability Assessment
Authors: King Kumire, Amos Kubeka
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The synergy of machine learning and astronomical technology advancement is giving rise to the new space age, which is pronounced by better habitability assessments. To initiate this discussion, it should be recorded for definition purposes that the symbiotic relationship between astronomy and improved computing has been code-named the Cis-Astro gateway concept. The cosmological fate of this phrase has been unashamedly plagiarized from the cis-lunar gateway template and its associated LaGrange points which act as an orbital bridge to the moon from our planet Earth. However, for this study, the scientific audience is invited to bridge toward the discovery of new habitable planets. It is imperative to state that cosmic probes of this magnitude can be utilized as the starting nodes of the astrobiological search for galactic life. This research can also assist by acting as the navigation system for future space telescope launches through the delimitation of target exoplanets. The findings and the associated platforms can be harnessed as building blocks for the modeling of climate change on planet earth. The notion that if the human genus exhausts the resources of the planet earth or there is a bug of some sort that makes the earth inhabitable for humans explains the need to find an alternative planet to inhabit. The scientific community, through interdisciplinary discussions of the International Astronautical Federation so far, has the common position that engineers can reduce space mission costs by constructing a stable cis-lunar orbit infrastructure for refilling and carrying out other associated in-orbit servicing activities. Similarly, the Cis-Astro gateway can be envisaged as a budget optimization technique that models extra-solar bodies and can facilitate the scoping of future mission rendezvous. It should be registered as well that this broad and voluminous catalog of exoplanets shall be narrowed along the way using machine learning filters. The gist of this topic revolves around the indirect economic rationale of establishing a habitability scoping platform.Keywords: exoplanets, habitability, machine-learning, supercomputing
Procedia PDF Downloads 107813 Evaluation of Bucket Utility Truck In-Use Driving Performance and Electrified Power Take-Off Operation
Authors: Robert Prohaska, Arnaud Konan, Kenneth Kelly, Adam Ragatz, Adam Duran
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In an effort to evaluate the in-use performance of electrified Power Take-off (PTO) usage on bucket utility trucks operating under real-world conditions, data from 20 medium- and heavy-duty vehicles operating in California, USA were collected, compiled, and analyzed by the National Renewable Energy Laboratory's (NREL) Fleet Test and Evaluation team. In this paper, duty-cycle statistical analyses of class 5, medium-duty quick response trucks and class 8, heavy-duty material handler trucks are performed to examine and characterize vehicle dynamics trends and relationships based on collected in-use field data. With more than 100,000 kilometers of driving data collected over 880+ operating days, researchers have developed a robust methodology for identifying PTO operation from in-field vehicle data. Researchers apply this unique methodology to evaluate the performance and utilization of the conventional and electric PTO systems. Researchers also created custom representative drive-cycles for each vehicle configuration and performed modeling and simulation activities to evaluate the potential fuel and emissions savings for hybridization of the tractive driveline on these vehicles. The results of these analyses statistically and objectively define the vehicle dynamic and kinematic requirements for each vehicle configuration as well as show the potential for further system optimization through driveline hybridization. Results are presented in both graphical and tabular formats illustrating a number of key relationships between parameters observed within the data set that relates specifically to medium- and heavy-duty utility vehicles operating under real-world conditions.Keywords: drive cycle, heavy-duty (HD), hybrid, medium-duty (MD), PTO, utility
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