Search results for: artificial Bee colony algorithm

Authors: Oscar E. Cariceo, Claudia V. Casal

Abstract:

Machine learning offers a set of techniques to promote social work interventions and can lead to support decisions of practitioners in order to predict new behaviors based on data produced by the organizations, services agencies, users, clients or individuals. Machine learning techniques include a set of generalizable algorithms that are data-driven, which means that rules and solutions are derived by examining data, based on the patterns that are present within any data set. In other words, the goal of machine learning is teaching computers through 'examples', by training data to test specifics hypothesis and predict what would be a certain outcome, based on a current scenario and improve that experience. Machine learning can be classified into two general categories depending on the nature of the problem that this technique needs to tackle. First, supervised learning involves a dataset that is already known in terms of their output. Supervising learning problems are categorized, into regression problems, which involve a prediction from quantitative variables, using a continuous function; and classification problems, which seek predict results from discrete qualitative variables. For social work research, machine learning generates predictions as a key element to improving social interventions on complex social issues by providing better inference from data and establishing more precise estimated effects, for example in services that seek to improve their outcomes. This paper exposes the results of a classification algorithm to predict cyberbullying among adolescents. Data were retrieved from the National Polyvictimization Survey conducted by the government of Chile in 2017. A logistic regression model was created to predict if an adolescent would experience cyberbullying based on the interaction and behavior of gender, age, grade, type of school, and self-esteem sentiments. The model can predict with an accuracy of 59.8% if an adolescent will suffer cyberbullying. These results can help to promote programs to avoid cyberbullying at schools and improve evidence based practice.

Keywords: cyberbullying, evidence based practice, machine learning, social work research

Procedia PDF Downloads 153

Authors: Nasser Koleini Mamaghani, Sara Mostowfi

Abstract:

The complexity of public space design makes it difficult for designers to simultaneously consider all issues for thorough decision-making. Among public spaces, the public space around people’s house is the most prominent space that affects and impacts people’s daily life. Considering recreational public spaces in cities, their main purpose would be to design for experiences that enable a deep feeling of peace and a moment of being away from the hectic daily life. Respecting human emotions and restoring natural environments, although difficult and to some extent out of reach, are key issues for designing such spaces. In this paper we propose to analyse the structure of recreational public spaces and the related emotional impressions. Furthermore, we suggest investigating how these structures influence people’s choice for public spaces by using differential semantics. According to Kansei methodology, in order to evaluate a situation appropriately, the assessment variables must be adapted to the user’s mental scheme. This means that the first step would have to be the identification of a space’s conceptual scheme. In our case study, 32 Kansei words and 4 different locations, each with a different sensual experience, were selected. The 4 locations were all parks in the city of Tehran (Iran), each with a unique structure and artifacts such as a fountain, lighting, sculptures, and music. It should be noted that each of these parks has different combination and structure of environmental and artificial elements like: fountain, lightning, sculpture, music (sound) and so forth. The first one was park No.1, a park with natural environment, the selected space was a fountain with motion light and sculpture. The second park was park No.2, in which there are different styles of park construction: ways from different countries, the selected space was traditional Iranian architecture with a fountain and trees. The third one was park No.3, the park with modern environment and spaces, and included a fountain that moved according to music and lighting. The fourth park was park No.4, the park with combination of four elements: water, fire, earth, wind, the selected space was fountains squirting water from the ground up. 80 participant (55 males and 25 females) aged from 20-60 years participated in this experiment. Each person filled the questionnaire in the park he/she was in. Five-point semantic differential scale was considered to determine the relation between space details and adjectives (kansei words). Received data were analyzed by multivariate statistical technique (factor analysis using SPSS statics). Finally the results of this analysis are criteria as inspiration which can be used in future space designing for creating pleasant feeling in users.

Keywords: environmental design, differential semantics, Kansei engineering, subjective preferences, space

Procedia PDF Downloads 389

Authors: Roberta Pecoraro

Abstract:

The focus of this work was to investigate the potential toxic effect of titanium dioxide-reduced graphene oxide (TiO₂-rGO) nanocomposites on nauplii of microcrustacean Artemia sp. In order to assess the nanocomposite’s toxicity, a short-term test was performed by exposing nauplii to solutions containing TiO₂-rGO. To prepare titanium dioxide-reduced graphene oxide (TiO₂-rGO) nanocomposites, a green procedure based on solar photoreduction was proposed; it allows to obtain the photocatalysts by exploiting the photocatalytic properties of titania activated by the solar irradiation in order to avoid the high temperatures and pressures required for the standard hydrothermal synthesis. Powders of TiO₂-rGO supplied by the Department of Chemical Sciences (University of Catania) are indicated as TiO₂-rGO at 1% and TiO₂-rGO at 2%. Starting from a stock solution (1mg rGO-TiO₂/10 ml ASPM water) of each type, we tested four different concentrations (serial dilutions ranging from 10⁻¹ to 10⁻⁴ mg/ml). All the solutions have been sonicated for 12 min prior to use. Artificial seawater (called ASPM water) was prepared to guarantee the hatching of the cysts and to maintain nauplii; the durable cysts used in this study, marketed by JBL (JBL GmbH & Co. KG, Germany), were hydrated with ASPM water to obtain nauplii (instar II-III larvae). The hatching of the cysts was carried out in the laboratory by immersing them in ASPM water inside a 500 ml beaker and keeping them constantly oxygenated thanks to an aerator for the insufflation of microbubble air: after 24-48 hours, the cysts hatched, and the nauplii appeared. The nauplii in the second and third stages of development were collected one-to-one, using stereomicroscopes, and transferred into 96-well microplates where one nauplius per well was added. The wells quickly have been filled with 300 µl of each specific concentration of the solution used, and control samples were incubated only with ASPM water. Replication was performed for each concentration. Finally, the microplates were placed on an orbital shaker, and the tests were read after 24 and 48 hours from inoculating the solutions to assess the endpoint (immobility/death) for the larvae. Nauplii that appeared motionless were counted as dead, and the percentages of mortality were calculated for each treatment. The results showed a low percentage of immobilization both for TiO₂-rGO at 1% and TiO₂-rGO at 2% for all concentrations tested: for TiO₂-rGO at 1% was below 12% after 24h and below 15% after 48h; for TiO₂-rGO at 2% was below 8% after 24h and below 12% after 48h. According to other studies in the literature, the results have not shown mortality nor toxic effects on the development of larvae after exposure to rGO. Finally, it is important to highlight that the TiO₂-rGO catalysts were tested in the solar photodegradation of a toxic herbicide (2,4-Dichlorophenoxyacetic acid, 2,4-D), obtaining a high percentage of degradation; therefore, this alternative approach could be considered a good strategy to obtain performing photocatalysts.

Keywords: Nauplii, photocatalytic properties, reduced GO, short-term toxicity test, titanium dioxide

Procedia PDF Downloads 167

Authors: Kathryn Rooney, Kaitlyn Eddy, Evan Landers, Weihui Li

Abstract:

The international mortality rate for neonates and infants has been declining at a disproportionally low rate when compared to the overall decline in child mortality in recent decades. A significant portion of infant deaths could be prevented with the implementation of low-cost and easy to use physiological monitoring devices, by enabling early identification of symptoms before they progress into life-threatening illnesses. The oral diagnostic device discussed in this paper serves to continuously monitor the key vital signs of body temperature, respiratory rate, heart rate, and oxygen saturation. The device mimics an infant pacifier, designed to be easily tolerated by infants as well as orthodontically inert. The fundamental measurements are gathered via thermistors and a pulse oximeter, each encapsulated in medical-grade silicone and wired internally to a microcontroller chip. The chip then translates the raw measurements into physiological values via an internal algorithm, before outputting the data to a liquid crystal display screen and an Android application. Additionally, a biological sample collection chamber is incorporated into the internal portion of the device. The movement within the oral chamber created by sucking on the pacifier-like device pushes saliva through a small check valve in the distal end, where it is accumulated and stored. The collection chamber can be easily removed, making the sample readily available to be tested for various diseases and analytes. With the vital sign monitoring and sample collection offered by this device, abnormal fluctuations in physiological parameters can be identified and appropriate medical care can be sought. This device enables preventative diagnosis for infants who may otherwise have gone undiagnosed, due to the inaccessibility of healthcare that plagues vast numbers of underprivileged populations.

Keywords: neonate mortality, infant mortality, low-cost diagnostics, vital signs, saliva testing, preventative care

Procedia PDF Downloads 140

Authors: Kana Matsuyanagi

Abstract:

Anorexia Nervosa (AN) is a psychiatric disorder characterized by a relentless pursuit of thinness and strict restriction of food. The current therapeutic approaches for AN predominantly revolve around outpatient psychotherapies, which create significant financial barriers for the majority of affected patients, hindering their access to treatment. Nonetheless, AN exhibit one of the highest mortality and relapse rates among psychological disorders, underscoring the urgent need to provide patients with an affordable self-treatment tool, enabling those unable to access conventional medical intervention to address their condition autonomously. To this end, a neurofeedback software, termed RelaxNeuron, was developed with the objective of providing an economical and portable means to aid individuals in self-managing AN. Electroencephalography (EEG) was chosen as the preferred modality for RelaxNeuron, as it aligns with the study's goal of supplying a cost-effective and convenient solution for addressing AN. The primary aim of the software is to ameliorate the negative emotional responses towards food stimuli and the accompanying aberrant eye-tracking patterns observed in AN patient, ultimately alleviating the profound fear towards food an elemental symptom and, conceivably, the fundamental etiology of AN. The core functionality of RelaxNeuron hinges on the acquisition and analysis of EEG signals, alongside an electrocardiogram (ECG) signal, to infer the user's emotional state while viewing dynamic food-related imagery on the screen. Moreover, the software quantifies the user's performance in accurately tracking the moving food image. Subsequently, these two parameters undergo further processing in the subsequent algorithm, informing the delivery of either negative or positive feedback to the user. Preliminary test results have exhibited promising outcomes, suggesting the potential advantages of employing RelaxNeuron in the treatment of AN, as evidenced by its capacity to enhance emotional regulation and attentional processing through repetitive and persistent therapeutic interventions.

Keywords: Anorexia Nervosa, fear conditioning, neurofeedback, BCI

Procedia PDF Downloads 20

Authors: Xiaowei Zhang, Min Xu, Saeid Habibi, Fengjun Yan, Ryan Ahmed

Abstract:

Recently, Electric Vehicles (EVs) have received extensive consideration since they offer a more sustainable and greener transportation alternative compared to fossil-fuel propelled vehicles. Lithium-Ion (Li-ion) batteries are increasingly being deployed in EVs because of their high energy density, high cell-level voltage, and low rate of self-discharge. Since Li-ion batteries represent the most expensive component in the EV powertrain, accurate monitoring and control strategies must be executed to ensure their prolonged lifespan. The Battery Management System (BMS) has to accurately estimate parameters such as the battery State-of-Charge (SOC), State-of-Health (SOH), and Remaining Useful Life (RUL). In order for the BMS to estimate these parameters, an accurate and control-oriented battery model has to work collaboratively with a robust state and parameter estimation strategy. Since battery physical parameters, such as the internal resistance and diffusion coefficient change depending on the battery state-of-life (SOL), the BMS has to be adaptive to accommodate for this change. In this paper, an extensive battery aging study has been conducted over 12-months period on 5.4 Ah, 3.7 V Lithium polymer cells. Instead of using fixed charging/discharging aging cycles at fixed C-rate, a set of real-world driving scenarios have been used to age the cells. The test has been interrupted every 5% capacity degradation by a set of reference performance tests to assess the battery degradation and track model parameters. As battery ages, the combined model parameters are optimized and tracked in an offline mode over the entire batteries lifespan. Based on the optimized model, a state and parameter estimation strategy based on the Extended Kalman Filter (EKF) and the relatively new Smooth Variable Structure Filter (SVSF) have been applied to estimate the SOC at various states of life.

Keywords: lithium-ion batteries, genetic algorithm optimization, battery aging test, parameter identification

Procedia PDF Downloads 252

Authors: A. Kachmazova, A. Shadiev, Y. Teterin, P. Yartcev

Abstract:

Introduction: Biliary calculi disease (LCS) still occupies the leading position among urgent diseases of the abdominal cavity, manifesting itself from asymptomatic course to life-threatening states. Nowadays arsenal of diagnostic methods for choledocholithiasis is quite wide: ultrasound, hepatobiliscintigraphy (HBSG), magnetic resonance imaging (MRI), endoscopic retrograde cholangiography (ERCP). Among them, transabdominal ultrasound (TA ultrasound) is the most accessible and routine diagnostic method. Nowadays ERCG is the "gold" standard in diagnosis and one-stage treatment of biliary tract obstruction. However, transpapillary techniques are accompanied by serious postoperative complications (postmanipulative pancreatitis (3-5%), endoscopic papillosphincterotomy bleeding (2%), cholangitis (1%)), the lethality being 0.4%. GBSG and MRI are also quite informative methods in the diagnosis of choledocholithiasis. Small size of concrements, their localization in intrapancreatic and retroduodenal part of common bile duct significantly reduces informativity of all diagnostic methods described above, that demands additional studying of this problem. Materials and Methods: 890 patients with the diagnosis of cholelithiasis (calculous cholecystitis) were admitted to the Sklifosovsky Scientific Research Institute of Hospital Medicine in the period from August, 2020 to June, 2021. Of them 115 people with mechanical jaundice caused by concrements in bile ducts. Results: Final EUS diagnosis was made in all patients (100,0%). In all patients in whom choledocholithiasis diagnosis was revealed or confirmed after EUS, ERCP was performed urgently (within two days from the moment of its detection) as the X-ray operation room was provided; it confirmed the presence of concrements. All stones were removed by lithoextraction using Dormia basket. The postoperative period in these patients had no complications. Conclusions: EUS is the most informative and safe diagnostic method, which allows to detect choledocholithiasis in patients with discrepancies between clinical-laboratory and instrumental methods of diagnosis in shortest time, that in its turn will help to decide promptly on the further tactics of patient treatment. We consider it reasonable to include EUS in the diagnostic algorithm for choledocholithiasis. Disclosure: Nothing to disclose.

Keywords: endoscopic ultrasonography, choledocholithiasis, common bile duct, concrement, ERCP

Procedia PDF Downloads 70

Authors: Ravi Ande, Mousumi Hazari

Abstract:

One of the common geophysical techniques for mapping subsurface geo-electrical structures, extensive hydro-geological research, and engineering and environmental geophysics applications is the use of time domain electromagnetic (TDEM)/transient electromagnetic (TEM) soundings. A large transmitter loop for energising the ground and a small receiver loop or magnetometer for recording the transient voltage or magnetic field in the air or on the surface of the earth, with the receiver at the center of the loop or at any random point inside or outside the source loop, make up a large loop TEM system. In general, one can acquire data using one of the configurations with a large loop source, namely, with the receiver at the center point of the loop (central loop method), at an arbitrary in-loop point (in-loop method), coincident with the transmitter loop (coincidence-loop method), and at an arbitrary offset loop point (offset-loop method), respectively. Because of the mathematical simplicity associated with the expressions of EM fields, as compared to the in-loop and offset-loop systems, the central loop system (for ground surveys) and coincident loop system (for ground as well as airborne surveys) have been developed and used extensively for the exploration of mineral and geothermal resources, for mapping contaminated groundwater caused by hazardous waste and thickness of permafrost layer. Because a proper analytical expression for the TEM response over the layered earth model for the large loop TEM system does not exist, the forward problem used in this inversion scheme is first formulated in the frequency domain and then it is transformed in the time domain using Fourier cosine or sine transforms. Using the EMLCLLER algorithm, the forward computation is initially carried out in the frequency domain. As a result, the EMLCLLER modified the forward calculation scheme in NLSTCI to compute frequency domain answers before converting them to the time domain using Fourier Cosine and/or Sine transforms.

Keywords: time domain electromagnetic (TDEM), TEM system, geoelectrical sounding structure, Fourier cosine

Procedia PDF Downloads 75

Authors: Neha Devi, P. K. Joshi

Abstract:

Fire fuel map is one of the most critical factors for planning and managing the fire hazard and risk. One of the most significant forms of global disturbance, impacting community dynamics, biogeochemical cycles and local and regional climate across a wide range of ecosystems ranging from boreal forests to tropical rainforest is wildfire Assessment of fire danger is a function of forest type, fuelwood stock volume, moisture content, degree of senescence and fire management strategy adopted in the ground. Remote sensing has potential of reduction the uncertainty in mapping fuels. Hyperspectral remote sensing is emerging to be a very promising technology for wildfire fuels characterization. Fine spectral information also facilitates mapping of biophysical and chemical information that is directly related to the quality of forest fire fuels including above ground live biomass, canopy moisture, etc. We used Hyperion imagery acquired in February, 2016 and analysed four fuel characteristics using Hyperion sensor data on-board EO-1 satellite, acquired over the Shiwalik Himalayas covering the area of Champawat, Uttarakhand state. The main objective of this study was to present an overview of methodologies for mapping fuel properties using hyperspectral remote sensing data. Fuel characteristics analysed include fuel biomass, fuel moisture, and fuel condition and fuel type. Fuel moisture and fuel biomass were assessed through the expression of the liquid water bands. Fuel condition and type was assessed using green vegetation, non-photosynthetic vegetation and soil as Endmember for spectral mixture analysis. Linear Spectral Unmixing, a partial spectral unmixing algorithm, was used to identify the spectral abundance of green vegetation, non-photosynthetic vegetation and soil.

Keywords: forest fire fuel, Hyperion, hyperspectral, linear spectral unmixing, spectral mixture analysis

Procedia PDF Downloads 144

Authors: Ahmad S. Darwish, Tarek Lemaoui, Hanifa Taher, Inas M. AlNashef, Fawzi Banat

Abstract:

This research reports a systematic top-down approach for designing neoteric hydrophobic solvents –particularly, deep eutectic solvents (DES) and ionic liquids (IL)– as furfural extractants from aqueous media for the application of sustainable biomass conversion. The first stage of the framework entailed screening 32 neoteric solvents to determine their efficacy against toluene as the application’s conventional benchmark for comparison. The selection criteria for the best solvents encompassed not only their efficiency in extracting furfural but also low viscosity and minimal toxicity levels. Additionally, for the DESs, their natural origins, availability, and biodegradability were also taken into account. From the screening pool, two neoteric solvents were selected: thymol:decanoic acid 1:1 (Thy:DecA) and trihexyltetradecyl phosphonium bis(trifluoromethylsulfonyl) imide [P₁₄,₆,₆,₆][NTf₂]. These solvents outperformed the toluene benchmark, achieving efficiencies of 94.1% and 97.1% respectively, compared to toluene’s 81.2%, while also possessing the desired properties. These solvents were then characterized thoroughly in terms of their physical properties, thermal properties, critical properties, and cross-contamination solubilities. The selected neoteric solvents were then extensively tested under various operating conditions, and an exceptional stable performance was exhibited, maintaining high efficiency across a broad range of temperatures (15–100 °C), pH levels (1–13), and furfural concentrations (0.1–2.0 wt%) with a remarkable equilibrium time of only 2 minutes, and most notably, demonstrated high efficiencies even at low solvent-to-feed ratios. The durability of the neoteric solvents was also validated to be stable over multiple extraction-regeneration cycles, with limited leachability to the aqueous phase (≈0.1%). Moreover, the extraction performance of the solvents was then modeled through machine learning, specifically multiple non-linear regression (MNLR) and artificial neural networks (ANN). The models demonstrated high accuracy, indicated by their low absolute average relative deviations with values of 2.74% and 2.28% for Thy:DecA and [P₁₄,₆,₆,₆][NTf₂], respectively, using MNLR, and 0.10% for Thy:DecA and 0.41% for [P₁₄,₆,₆,₆][NTf₂] using ANN, highlighting the significantly enhanced predictive accuracy of the ANN. The neoteric solvents presented herein offer noteworthy advantages over traditional organic solvents, including their high efficiency in both extraction and regeneration processes, their stability and minimal leachability, making them particularly suitable for applications involving aqueous media. Moreover, these solvents are more environmentally friendly, incorporating renewable and sustainable components like thymol and decanoic acid. This exceptional efficacy of the newly developed neoteric solvents signifies a significant advancement, providing a green and sustainable alternative for furfural production from biowaste.

Keywords: sustainable biomass conversion, furfural extraction, ionic liquids, deep eutectic solvents

Procedia PDF Downloads 50

Authors: Nathenal Thomas

Abstract:

In the food chain and in many other agricultural products, corn, also known as maize, which goes by the scientific name Zea mays subsp, is a widely produced agricultural product. Corn has the highest adaptability. It comes in many different types, is employed in many different industrial processes, and is more adaptable to different agro-climatic situations. In Ethiopia, maize is among the most widely grown crop. Small-scale corn farming may be a household's only source of food in developing nations like Ethiopia. The aforementioned data demonstrates that the country's requirement for this crop is excessively high, and conversely, the crop's productivity is very low for a variety of reasons. The most damaging disease that greatly contributes to this imbalance between the crop's supply and demand is the corn disease. The failure to diagnose diseases in maize plant until they are too late is one of the most important factors influencing crop output in Ethiopia. This study will aid in the early detection of such diseases and support farmers during the cultivation process, directly affecting the amount of maize produced. The diseases in maize plants, such as northern leaf blight and cercospora leaf spot, have distinct symptoms that are visible. This study aims to detect the most frequent and degrading maize diseases using the most efficiently used subset of machine learning technology, deep learning so, called Image Processing. Deep learning uses networks that can be trained from unlabeled data without supervision (unsupervised). It is a feature that simulates the exercises the human brain goes through when digesting data. Its applications include speech recognition, language translation, object classification, and decision-making. Convolutional Neural Network (CNN) for Image Processing, also known as convent, is a deep learning class that is widely used for image classification, image detection, face recognition, and other problems. it will also use this algorithm as the state-of-the-art for my research to detect maize diseases by photographing maize leaves using a mobile phone.

Keywords: CNN, zea mays subsp, leaf blight, cercospora leaf spot

Procedia PDF Downloads 59

Authors: Wenyan Xu, Rundong Wang, Chen Li, Yonghong Hu, Zhonghua Lu

Abstract:

In the field of quantitative trading, it is common to find patterns in short-term volatile trends of the market. These patterns are known as High-Frequency (HF) risk factors, serving as effective indicators of future stock price volatility. However, in the past, these risk factors were usually generated by traditional financial models, and the validity of these risk factors is heavily based on domain-specific knowledge manually added instead of extensive market data. Inspired by symbolic regression (SR), the task of inferring mathematical laws from existing data, we take the extraction of formulaic risk factors from high-frequency trading (HFT) market data as an SR task. In this paper, we challenge the procedure of manually constructing risk factors and propose an end-to-end methodology, Intraday Risk Factor Transformer (IRFT) to directly predict the full formulaic factors, constants included. Specifically, we utilize a hybrid symbolic-numeric vocabulary where symbolic tokens denote operators/stock features and numeric tokens denote constants. Then, we train a Transformer model on the HFT dataset to directly generate complete formulaic HF risk factors without relying on the skeleton, which is a parametric function using a pre-defined list of operators – typically, the math operations (+, ×, /) and functions(√x, log x, cos x). It determines the general shape of the stock volatility law up to a choice of constants, e.g., f(x) = tan(ax+b) (x is the stock price). We further refine predicted constants(a,b) using the Broyden–Fletcher–Goldfarb–Shanno algorithm (BFGS) as informed guesses to mitigate non-linear issues. Compared to the 10 approaches in SRBench, which is a living benchmark for SR, IRFT gains a 30% excess investment return on the HS300 and S&P500 datasets, with inference times orders of magnitude faster than theirs in HF risk factor mining tasks.

Keywords: transformer, factor-mining language model, highfrequency risk factor collections

Procedia PDF Downloads 11

Authors: Shohreh Ghasemi

Abstract:

Introduction: Managing oral and maxillofacial trauma is a multifaceted challenge, as it can have life-threatening consequences and significant functional and aesthetic impact. Navigation techniques have been introduced to improve surgical precision to meet this challenge. A machine learning algorithm was also developed to support clinical decision-making regarding treating oral and maxillofacial trauma. Given these advances, this systematic meta-analysis aims to assess the efficacy of navigational techniques in treating oral and maxillofacial trauma and explore the impact of machine learning on their management. Methods: A detailed and comprehensive analysis of studies published between January 2010 and September 2021 was conducted through a systematic meta-analysis. This included performing a thorough search of Web of Science, Embase, and PubMed databases to identify studies evaluating the efficacy of navigational techniques and the impact of machine learning in managing oral and maxillofacial trauma. Studies that did not meet established entry criteria were excluded. In addition, the overall quality of studies included was evaluated using Cochrane risk of bias tool and the Newcastle-Ottawa scale. Results: Total of 12 studies, including 869 patients with oral and maxillofacial trauma, met the inclusion criteria. An analysis of studies revealed that navigation techniques effectively improve surgical accuracy and minimize the risk of complications. Additionally, machine learning algorithms have proven effective in predicting treatment outcomes and identifying patients at high risk for complications. Conclusion: The introduction of navigational technology has great potential to improve surgical precision in oral and maxillofacial trauma treatment. Furthermore, developing machine learning algorithms offers opportunities to improve clinical decision-making and patient outcomes. Still, further studies are necessary to corroborate these results and establish the optimal use of these technologies in managing oral and maxillofacial trauma

Keywords: trauma, machine learning, navigation, maxillofacial, management

Procedia PDF Downloads 44

Authors: Rasha Waheeb, Bjorn Andersen, Rafa Shakir

Abstract:

Purpose The purpose of this study is to avoid delays that occur in emergency reconstruction projects especially in post disaster circumstances whether if they were natural or manmade due to their particular national and humanitarian importance. We presented a theoretical and practical concepts for projects management in the field of construction industry that deal with a range of global and local trails. This study aimed to identify the factors of effective delay in construction projects in Iraq that affect the time and the specific quality cost, and find the best solutions to address delays and solve the problem by setting parameters to restore balance in this study. 30 projects were selected in different areas of construction were selected as a sample for this study. Design/methodology/approach This study discusses the reconstruction strategies and delay in time and cost caused by different delay factors in some selected projects in Iraq (Baghdad as a case study).A case study approach was adopted, with thirty construction projects selected from the Baghdad region, of different types and sizes. Project participants from the case projects provided data about the projects through a data collection instrument distributed through a survey. Mixed approach and methods were applied in this study. Mathematical data analysis was used to construct models to predict delay in time and cost of projects before they started. The artificial neural networks analysis was selected as a mathematical approach. These models were mainly to help decision makers in construction project to find solutions to these delays before they cause any inefficiency in the project being implemented and to strike the obstacles thoroughly to develop this industry in Iraq. This approach was practiced using the data collected through survey and questionnaire data collection as information form. Findings The most important delay factors identified leading to schedule overruns were contractor failure, redesigning of designs/plans and change orders, security issues, selection of low-price bids, weather factors, and owner failures. Some of these are quite in line with findings from similar studies in other countries/regions, but some are unique to the Iraqi project sample, such as security issues and low-price bid selection. Originality/value we selected ANN’s analysis first because ANN’s was rarely used in project management , and never been used in Iraq to finding solutions for problems in construction industry. Also, this methodology can be used in complicated problems when there is no interpretation or solution for a problem. In some cases statistical analysis was conducted and in some cases the problem is not following a linear equation or there was a weak correlation, thus we suggested using the ANN’s because it is used for nonlinear problems to find the relationship between input and output data and that was really supportive.

Keywords: construction projects, delay factors, emergency reconstruction, innovation ANN, post disasters, project management

Procedia PDF Downloads 149

Authors: Benjamin K. Ghansah, Richard K. Appoh, Iliya Nababa, Eric K. Forkuo

Abstract:

The Nasia River is an important source of water for domestic and agricultural purposes to the inhabitants of its catchment. Major farming activities takes place within the floodplain of the river and its network of tributaries. The actual inundation extent of the river system is; however, unknown. Reasons for this lack of information include financial constraints and inadequate human resources as flood modelling is becoming increasingly complex by the day. Knowledge of the inundation extent will help in the assessment of risk posed by the annual flooding of the river, and help in the planning of flood recession agricultural activities. This study used a simple terrain based algorithm, Height Above Nearest Drainage (HAND), to delineate the floodplain of the Nasia River and its tributaries. The HAND model is a drainage normalized digital elevation model, which has its height reference based on the local drainage systems rather than the average mean sea level (AMSL). The underlying principle guiding the development of the HAND model is that hillslope flow paths behave differently when the reference gradient is to the local drainage network as compared to the seaward gradient. The new terrain model of the catchment was created using the NASA’s SRTM Digital Elevation Model (DEM) 30m as the only data input. Contours (HAND Contour) were then generated from the normalized DEM. Based on field flood inundation survey, historical information of flooding of the area as well as satellite images, a HAND Contour of 2m was found to best correlates with the flood inundation extent of the river and its tributaries. A percentage accuracy of 75% was obtained when the surface area created by the 2m contour was compared with surface area of the floodplain computed from a satellite image captured during the peak flooding season in September 2016. It was estimated that the flooding of the Nasia River and its tributaries created a floodplain area of 1011 km².

Keywords: digital elevation model, floodplain, HAND contour, inundation extent, Nasia River

Procedia PDF Downloads 434

Authors: Insaf Ajili, Malik Mallem, Jean-Yves Didier

Abstract:

Human motion recognition has been extensively increased in recent years due to its importance in a wide range of applications, such as human-computer interaction, intelligent surveillance, augmented reality, content-based video compression and retrieval, etc. However, it is still regarded as a challenging task especially in realistic scenarios. It can be seen as a general machine learning problem which requires an effective human motion representation and an efficient learning method. In this work, we introduce a descriptor based on Laban Movement Analysis technique, a formal and universal language for human movement, to capture both quantitative and qualitative aspects of movement. We use Discrete Hidden Markov Model (DHMM) for training and classification motions. We improve the classification algorithm by proposing two DHMMs for each motion class to process the motion sequence in two different directions, forward and backward. Such modification allows avoiding the misclassification that can happen when recognizing similar motions. Two experiments are conducted. In the first one, we evaluate our method on a public dataset, the Microsoft Research Cambridge-12 Kinect gesture data set (MSRC-12) which is a widely used dataset for evaluating action/gesture recognition methods. In the second experiment, we build a dataset composed of 10 gestures(Introduce yourself, waving, Dance, move, turn left, turn right, stop, sit down, increase velocity, decrease velocity) performed by 20 persons. The evaluation of the system includes testing the efficiency of our descriptor vector based on LMA with basic DHMM method and comparing the recognition results of the modified DHMM with the original one. Experiment results demonstrate that our method outperforms most of existing methods that used the MSRC-12 dataset, and a near perfect classification rate in our dataset.

Keywords: human motion recognition, motion representation, Laban Movement Analysis, Discrete Hidden Markov Model

Procedia PDF Downloads 187

Authors: Éva Pintér, Péter Bagó, Nikolett Deutsch, Miklós Hetényi

Abstract:

The process of digital transformation has a profound influence on all sectors of the worldwide economy and the business environment. The influence of blockchain technology can be observed in the digital economy and e-government, rendering it an essential element of a nation's growth strategy. The banking industry is experiencing significant expansion and development of financial technology firms. Utilizing developing technologies such as artificial intelligence (AI), machine learning (ML), and big data (BD), these entrants are offering more streamlined financial solutions, promptly addressing client demands, and presenting a challenge to incumbent institutions. The advantages of digital transformation are evident in the corporate realm, and firms that resist its adoption put their survival at risk. The advent of digital technologies has revolutionized the business environment, streamlining processes and creating opportunities for enhanced communication and collaboration. Thanks to the aid of digital technologies, businesses can now swiftly and effortlessly retrieve vast quantities of information, all the while accelerating the process of creating new and improved products and services. Big data analytics is generally recognized as a transformative force in business, considered the fourth paradigm of science, and seen as the next frontier for innovation, competition, and productivity. Big data, an emerging technology that is shaping the future of the banking sector, offers numerous advantages to banks. It enables them to effectively track consumer behavior and make informed decisions, thereby enhancing their operational efficiency. Banks may embrace big data technologies to promptly and efficiently identify fraud, as well as gain insights into client preferences, which can then be leveraged to create better-tailored products and services. Moreover, the utilization of big data technology empowers banks to develop more intelligent and streamlined models for accurately recognizing and focusing on the suitable clientele with pertinent offers. There is a scarcity of research on big data analytics in the banking industry, with the majority of existing studies only examining the advantages and prospects associated with big data. Although big data technologies are crucial, there is a dearth of empirical evidence about the role of big data analytics (BDA) capabilities in bank performance. This research addresses a gap in the existing literature by introducing a model that combines the resource-based view (RBV), the technical organization environment framework (TOE), and dynamic capability theory (DC). This study investigates the influence of Big Data Analytics (BDA) utilization on the performance of market and risk management. This is supported by a comparative examination of Hungarian mobile banking services.

Keywords: big data, digital transformation, dynamic capabilities, mobile banking

Procedia PDF Downloads 35

Authors: Pardis Moslemzadeh Tehrani

Abstract:

Due to the emergence of blockchain technology and the benefits of cryptocurrencies, intelligent or smart contracts are gaining traction. Artificial intelligence (AI) is transforming our lives, and it is being embraced by a wide range of sectors. Smart contracts, which are at the heart of blockchains, incorporate AI characteristics. Such contracts are referred to as "smart" contracts because of the underlying technology that allows contracting parties to agree on terms expressed in computer code that defines machine-readable instructions for computers to follow under specific situations. The transmission happens automatically if the conditions are met. Initially utilised for financial transactions, blockchain applications have since expanded to include the financial, insurance, and medical sectors, as well as supply networks. Raw material acquisition by suppliers, design, and fabrication by manufacturers, delivery of final products to consumers, and even post-sales logistics assistance are all part of supply chains. Many issues are linked with managing supply chains from the planning and coordination stages, which can be implemented in a smart contract in a blockchain due to their complexity. Manufacturing delays and limited third-party amounts of product components have raised concerns about the integrity and accountability of supply chains for food and pharmaceutical items. Other concerns include regulatory compliance in multiple jurisdictions and transportation circumstances (for instance, many products must be kept in temperature-controlled environments to ensure their effectiveness). Products are handled by several providers before reaching customers in modern economic systems. Information is sent between suppliers, shippers, distributors, and retailers at every stage of the production and distribution process. Information travels more effectively when individuals are eliminated from the equation. The usage of blockchain technology could be a viable solution to these coordination issues. In blockchains, smart contracts allow for the rapid transmission of production data, logistical data, inventory levels, and sales data. This research investigates the legal and technical advantages and disadvantages of AI-blockchain technology in the supply chain business. It aims to uncover the applicable legal problems and barriers to the use of AI-blockchain technology to supply chains, particularly in the food industry. It also discusses the essential legal and technological issues and impediments to supply chain implementation for stakeholders, as well as methods for overcoming them before releasing the technology to clients. Because there has been little research done on this topic, it is difficult for industrial stakeholders to grasp how blockchain technology could be used in their respective operations. As a result, the focus of this research will be on building advanced and complex contractual terms in supply chain smart contracts on blockchains to cover all unforeseen supply chain challenges.

Keywords: blockchain, supply chain, IoT, smart contract

Procedia PDF Downloads 103

Authors: María Dolores Caro, Eugenio M. Fedriani, Ángel F. Tenorio

Abstract:

The transportation of dangerous goods by lorries throughout Europe must be done by using the roads conforming the European Road Transport Network. In this network, there are several parking areas where lorry drivers can park to rest according to the regulations. According to the "European Agreement concerning the International Carriage of Dangerous Goods by Road", parking areas where lorries transporting dangerous goods can park to rest, must follow several security stipulations to keep safe the rest of road users. At this respect, these lorries must be parked in adapted areas with strict and permanent surveillance measures. Moreover, drivers must satisfy several restrictions about resting and driving time. Under these facts, one may expect that there exist enough parking areas for the transport of this type of goods in order to obey the regulations prescribed by the European Union and its member countries. However, the already-existing parking areas are not sufficient to cover all the stops required by drivers transporting dangerous goods. Our main goal is, starting from the already-existing parking areas and the loading-and-unloading location, to provide an optimal answer to the following question: how many additional parking areas must be built and where must they be located to assure that lorry drivers can transport dangerous goods following all the stipulations about security and safety for their stops? The sense of the word “optimal” is due to the fact that we give a global solution for the location of parking areas throughout the whole European Road Transport Network, adjusting the number of additional areas to be as lower as possible. To do so, we have modeled the problem using graph theory since we are working with a road network. As nodes, we have considered the locations of each already-existing parking area, each loading-and-unloading area each road bifurcation. Each road connecting two nodes is considered as an edge in the graph whose weight corresponds to the distance between both nodes in the edge. By applying a new efficient algorithm, we have found the additional nodes for the network representing the new parking areas adapted for dangerous goods, under the fact that the distance between two parking areas must be less than or equal to 400 km.

Keywords: trans-european transport network, dangerous goods, parking areas, graph-based modeling

Procedia PDF Downloads 265

Authors: Sungho Kim, Jaechoul Shin, Yujin Baek, Nakseok Kim, Kyungnam Kim, Shinhaeng Jo

Abstract:

The climate change and increase in heavy traffic have been accelerating damages that cause the problems such as pothole on asphalt pavement. Pothole causes traffic accidents, vehicle damages, road casualties and traffic congestion. A quick and efficient maintenance method is needed because pothole is caused by stripping and accelerates pavement distress. In this study, we propose a rapid and systematic pothole management by developing a pothole automated repairing equipment including a volume measurement system of pothole. Three kinds of cold mix asphalt mixture were investigated to select repair materials. The materials were evaluated for satisfaction with quality standard and applicability to automated equipment. The volume measurement system of potholes was composed of multi-sensor that are combined with laser sensor and ultrasonic sensor and installed in front and side of the automated repair equipment. An algorithm was proposed to calculate the amount of repair material according to the measured pothole volume, and the system for releasing the correct amount of material was developed. Field test results showed that the loss of repair material amount could be reduced from approximately 20% to 6% per one point of pothole. Pothole rapid automated repair equipment will contribute to improvement on quality and efficient and economical maintenance by not only reducing materials and resources but also calculating appropriate materials. Through field application, it is possible to improve the accuracy of pothole volume measurement, to correct the calculation of material amount, and to manage the pothole data of roads, thereby enabling more efficient pavement maintenance management. Acknowledgment: The author would like to thank the MOLIT(Ministry of Land, Infrastructure, and Transport). This work was carried out through the project funded by the MOLIT. The project name is 'development of 20mm grade for road surface detecting roadway condition and rapid detection automation system for removal of pothole'.

Keywords: automated equipment, management, multi-beam sensor, pothole

Procedia PDF Downloads 213

Authors: Juan Bernal-Martinez, Zoe Quinones-Jurado, Miguel Waldo-Mendoza, Elias Perez

Abstract:

Introduction and Objective: Ag-TiO2 nanoparticles (NP) have been characterized as effective antibacterial compounds against E. aureous, E. coli, Salmonella and others. Because these nanoparticles have been used in plastic-food containers, there is a concern about the toxicity of Ag-TiO2 NP for higher organisms from protozoan, invertebrates, and mammals. The objective of this study is to evaluate the cytotoxic effect of Ag-TiO2 NP on the survival and swimming behavior of the unicellular organism Paramecium tetraurelia. Material and Methods: Preparation of metallic silver on TiO2 surface was based on chemical reduction route of AgNO3. Aqueous suspension of TiO2 nanoparticles was preparing by adding 5 g of TiO2 to 250 ml of deionized water and followed by sonication for 10 min. The required amount of AgNO3 solutions was added to TiO2 suspension, maintaining heating and stirring. Silver concentration was 0.5, 1.5, 5.0, 25, 35 and 45 % w/w versus TiO2. Paramecium tetraurelia (Carolina Biological, Cat. # 131560) was used as a biological preparation. It was cultured in artificial culture media made as follows: Stigmasterol 5 mg/ml of ethanol, Caseaminoacids 0.3 gr/lt.; KCl 4mM; CaCl2 1mM; MgCl2 100uM and MOPS 1mM, pH 7.3. This media was inoculated with Enterobacter-sp. Paramecium was concentrated after 24 hours of incubation by centrifugation. The pellet of cells was resuspended in 4.1.1 solution prepared as follows (in mM): KCl, 4 mM; CaCl2, 1mM and Trizma, 1mM; pH 7.3. Transmission electron microscopy (TEM) studies were performed to evaluate the appropriate dispersion and topographic distribution AgNPs deposited on TiO2. The experimental solutions were prepared as follows: 50 mg of Polyvinyhlpirolidone were added to 5 ml of 4.1.1. solution. Then, 50 mg of powder 25-Ag-TiO2 was added, mixing for 10 min and sonicated for 60 min. Survival of Paramecium and possible toxic effects after 25-Ag-TiO2 treatment was observed through an inverted microscope. The Paramecium swimming behavior and possible dead cells were recorded for periods of approximately 20-50 seconds by using a digital USB camera adapted to the microscope. Results and Discussion: TEM micrographs demonstrated the topographic distribution of AgNPs deposited on TiO2. 25Ag-TiO2 NP was efficiently dissolved and dispersed in 4.1.1 solution at concentrations from 0.1, 1 and 10 mg/ml. When Paramecium were treated with 25Ag-TiO2 NP at 100 ug/ml, it was observed that cells started swimming backwards. This backward swimming behavior is the typical avoiding reaction of the ciliate in response to a noxious stimulus. After 10 min of incubation, it was observed that Paramecium stopped swimming backwards and exploited. We can argue that this toxic effect of 25Ag-TiO2 NP is probably due to the calcium influx and calcium accumulation during the long-lasting swimming backwards. Conclusions: Here we have demonstrated that 25Ag-TiO2 NP has a specific toxic effect on an organism higher than bacteria such as the protozoan Paremecium. Probably these toxic phenomena could be expected to be observed in a higher organism such as invertebrates and mammals.

Keywords: Ag-TiO2, calcium permeability, cytotoxicity, paramecium

Procedia PDF Downloads 276

Authors: Dewi Retno Sari Saputro, Purnami Widyaningsih, Hendrika Handayani

Abstract:

Extreme data on an observation can occur due to unusual circumstances in the observation. The data can provide important information that can’t be provided by other data so that its existence needs to be further investigated. The method for obtaining extreme data is one of them using maxima block method. The distribution of extreme data sets taken with the maxima block method is called the distribution of extreme values. Distribution of extreme values is Gumbel distribution with two parameters. The parameter estimation of Gumbel distribution with maximum likelihood method (ML) is difficult to determine its exact value so that it is necessary to solve the approach. The purpose of this study was to determine the parameter estimation of Gumbel distribution with quasi-Newton BFGS method. The quasi-Newton BFGS method is a numerical method used for nonlinear function optimization without constraint so that the method can be used for parameter estimation from Gumbel distribution whose distribution function is in the form of exponential doubel function. The quasi-New BFGS method is a development of the Newton method. The Newton method uses the second derivative to calculate the parameter value changes on each iteration. Newton's method is then modified with the addition of a step length to provide a guarantee of convergence when the second derivative requires complex calculations. In the quasi-Newton BFGS method, Newton's method is modified by updating both derivatives on each iteration. The parameter estimation of the Gumbel distribution by a numerical approach using the quasi-Newton BFGS method is done by calculating the parameter values that make the distribution function maximum. In this method, we need gradient vector and hessian matrix. This research is a theory research and application by studying several journals and textbooks. The results of this study obtained the quasi-Newton BFGS algorithm and estimation of Gumbel distribution parameters. The estimation method is then applied to daily rainfall data in Purworejo District to estimate the distribution parameters. This indicates that the high rainfall that occurred in Purworejo District decreased its intensity and the range of rainfall that occurred decreased.

Keywords: parameter estimation, Gumbel distribution, maximum likelihood, broyden fletcher goldfarb shanno (BFGS)quasi newton

Procedia PDF Downloads 308

Authors: Weixing Tan

Abstract:

Production of nutraceuticals from microalgae—a virtually untapped natural phyto-based source of which there are 200,000 to 1,000,000 species—offers a sustainable and healthy alternative to conventionally sourced nutraceuticals for the market. Microalgae can be grown organically using only natural sunlight, water and nutrients at an extremely fast rate, e.g. 10-100 times more efficiently than crops or trees. However, the commercial success of microalgae products at scale remains limited largely due to the lack of economically viable technologies. There are two major microalgae production systems or technologies currently available: 1) the open system as represented by open pond technology and 2) the closed system such as photobioreactors (PBR). Each carries its own unique features and challenges. Although an open system requires a lower initial capital investment relative to a PBR, it conveys many unavoidable drawbacks; for example, much lower productivity, difficulty in contamination control/cleaning, inconsistent product quality, inconvenience in automation, restriction in location selection, and unsuitability for cold areas – all directly linked to the system openness and flat underground design. On the other hand, a PBR system has characteristics almost entirely opposite to the open system, such as higher initial capital investment, better productivity, better contamination and environmental control, wider suitability in different climates, ease in automation, higher and consistent product quality, higher energy demand (particularly if using artificial lights), and variable operational expenses if not automated. Although closed systems like PBRs are not highly competitive yet in current nutraceutical supply market, technological advances can be made, in particular for the PBR technology, to narrow the gap significantly. One example is a readily scalable P2P Microalgae PBR Technology at Grande Prairie Regional College, Canada, developed over 11 years considering return on investment (ROI) for key production processes. The P2P PBR system is approaching economic viability at a pre-commercial stage due to five ROI-integrated major components. They include: (1) optimum use of free sunlight through attenuation (patented); (2) simple, economical, and chemical-free harvesting (patent ready to file); (3) optimum pH- and nutrient-balanced culture medium (published), (4) reliable water and nutrient recycling system (trade secret); and (5) low-cost automated system design (trade secret). These innovations have allowed P2P Microalgae Technology to increase daily yield to 106 g/m2/day of Chlorella vulgaris, which contains 50% proteins and 2-3% omega-3. Based on the current market prices and scale-up factors, this P2P PBR system presents as a promising microalgae technology for market competitive nutraceutical supply.

Keywords: microalgae technology, nutraceuticals, open pond, photobioreactor PBR, return on investment ROI, technological advances

Procedia PDF Downloads 142

Authors: Xiaobin Liang, Wei Liang, Laibin Zhang, Xiaoyan Guo

Abstract:

Pipelines pass through coal mined gobs inevitably in the mining area, the stability of which has great influence on the safety of pipelines. After extensive literature study and field research, it was found that there are a few risk assessment methods for coal mined gob pipelines, and there is a lack of data on the gob sites. Therefore, the fuzzy comprehensive evaluation method is widely used based on expert opinions. However, the subjective opinions or lack of experience of individual experts may lead to inaccurate evaluation results. Hence the accuracy of the results needs to be further improved. This paper presents a comprehensive approach to achieve this purpose by combining bow-tie model and cloud inference. The specific evaluation process is as follows: First, a bow-tie model composed of a fault tree and an event tree is established to graphically illustrate the probability and consequence indicators of pipeline failure. Second, the interval estimation method can be scored in the form of intervals to improve the accuracy of the results, and the censored mean algorithm is used to remove the maximum and minimum values of the score to improve the stability of the results. The golden section method is used to determine the weight of the indicators and reduce the subjectivity of index weights. Third, the failure probability and failure consequence scores of the pipeline are converted into three numerical features by using cloud inference. The cloud inference can better describe the ambiguity and volatility of the results which can better describe the volatility of the risk level. Finally, the cloud drop graphs of failure probability and failure consequences can be expressed, which intuitively and accurately illustrate the ambiguity and randomness of the results. A case study of a coal mine gob pipeline carrying natural gas has been investigated to validate the utility of the proposed method. The evaluation results of this case show that the probability of failure of the pipeline is very low, the consequences of failure are more serious, which is consistent with the reality.

Keywords: bow-tie model, natural gas pipeline, coal mine gob, cloud inference

Procedia PDF Downloads 235

Authors: Diego F. Camacho, Cristian J. Mejia, Carlos Duque-Daza

Abstract:

The recent widespread use of Unmanned Aerial Vehicles (UAVs) has fueled a renewed interest in efficiency and performance of airfoils, particularly for applications at low and moderate Reynolds numbers, typical of this kind of vehicles. Most of previous efforts in the aeronautical industry, regarding aerodynamic efficiency, had been focused on high Reynolds numbers applications, typical of commercial airliners and large size aircrafts. However, in order to increase the levels of efficiency and to boost the performance of these UAV, it is necessary to explore new alternatives in terms of airfoil design and application of drag reduction techniques. The objective of the present work is to carry out the analysis and comparison of performance levels between a standard NACA0012 profile against another one featuring a wall protuberance or surface perturbation. A computational model, based on the finite volume method, is employed to evaluate the effect of the presence of geometrical distortions on the wall. The performance evaluation is achieved in terms of variations of drag and lift coefficients for the given profile. In particular, the aerodynamic performance of the new design, i.e. the airfoil with a surface perturbation, is examined under conditions of incompressible and subsonic flow in transient state. The perturbation considered is a shaped protrusion prescribed as a small surface deformation on the top wall of the aerodynamic profile. The ultimate goal by including such a controlled smooth artificial roughness was to alter the turbulent boundary layer. It is shown in the present work that such a modification has a dramatic impact on the aerodynamic characteristics of the airfoil, and if properly adjusted, in a positive way. The computational model was implemented using the unstructured, FVM-based open source C++ platform OpenFOAM. A number of numerical experiments were carried out at Reynolds number 5x104, based on the length of the chord and the free-stream velocity, and angles of attack 6° and 12°. A Large Eddy Simulation (LES) approach was used, together with the dynamic Smagorinsky approach as subgrid scale (SGS) model, in order to account for the effect of the small turbulent scales. The impact of the surface perturbation on the performance of the airfoil is judged in terms of changes in the drag and lift coefficients, as well as in terms of alterations of the main characteristics of the turbulent boundary layer on the upper wall. A dramatic change in the whole performance can be appreciated, including an arguably large level of lift-to-drag coefficient ratio increase for all angles and a size reduction of laminar separation bubble (LSB) for a twelve-angle-of-attack.

Keywords: CFD, LES, Lift-to-drag ratio, LSB, NACA 0012 airfoil

Procedia PDF Downloads 369

Authors: Keda Li, Hong Hu

Abstract:

Auxetic materials, as an emerging artificial designed metamaterial has attracted growing attention due to their promising negative Poisson’s ratio behaviors and tunable properties. The conventional auxetic lattice structures for which the deformation process is governed by a bending-dominated mechanism have faced the limitation of poor mechanical performance for many potential engineering applications. Recently, both load-bearing and energy absorption capabilities have become a crucial consideration in auxetic structure design. This study reports the finite element analysis of a class of hexagonal double-arrowhead auxetic structures with enhanced stiffness and energy absorption performance. The structure design was developed by extending the traditional double-arrowhead honeycomb to a hexagon frame, the stretching-dominated deformation mechanism was determined according to Maxwell’s stability criterion. The finite element (FE) models of 2D lattice structures established with stainless steel material were analyzed in ABAQUS/Standard for predicting in-plane structural deformation mechanism, failure process, and compressive elastic properties. Based on the computational simulation, the parametric analysis was studied to investigate the effect of the structural parameters on Poisson’s ratio and mechanical properties. The geometrical optimization was then implemented to achieve the optimal Poisson’s ratio for the maximum specific energy absorption. In addition, the optimized 2D lattice structure was correspondingly converted into a 3D geometry configuration by using the orthogonally splicing method. The numerical results of 2D and 3D structures under compressive quasi-static loading conditions were compared separately with the traditional double-arrowhead re-entrant honeycomb in terms of specific Young's moduli, Poisson's ratios, and specified energy absorption. As a result, the energy absorption capability and stiffness are significantly reinforced with a wide range of Poisson’s ratio compared to traditional double-arrowhead re-entrant honeycomb. The auxetic behaviors, energy absorption capability, and yield strength of the proposed structure are adjustable with different combinations of joint angle, struts thickness, and the length-width ratio of the representative unit cell. The numerical prediction in this study suggests the proposed concept of hexagonal double-arrowhead structure could be a suitable candidate for the energy absorption applications with a constant request of load-bearing capacity. For future research, experimental analysis is required for the validation of the numerical simulation.

Keywords: auxetic, energy absorption capacity, finite element analysis, negative Poisson's ratio, re-entrant hexagonal honeycomb

Procedia PDF Downloads 74

Authors: Iris Xiaohong Quan

Abstract:

The global digital health market reached 175 billion U.S. dollars in 2019, and is expected to grow at about 25% CAGR to over 650 billion USD by 2025. Different terms such as digital health, e-health, mHealth, telehealth have been used in the field, which can sometimes cause confusion. The term digital health was originally introduced to refer specifically to the use of interactive media, tools, platforms, applications, and solutions that are connected to the Internet to address health concerns of providers as well as consumers. While mHealth emphasizes the use of mobile phones in healthcare, telehealth means using technology to remotely deliver clinical health services to patients. According to FDA, “the broad scope of digital health includes categories such as mobile health (mHealth), health information technology (IT), wearable devices, telehealth and telemedicine, and personalized medicine.” Some researchers believe that digital health is nothing else but the cultural transformation healthcare has been going through in the 21st century because of digital health technologies that provide data to both patients and medical professionals. As digital health is burgeoning, but research in the area is still inadequate, our paper aims to clear the definition confusion and provide an overall picture of digital health companies. We further investigate how business models are designed and differentiated in the emerging digital health sector. Both quantitative and qualitative methods are adopted in the research. For the quantitative analysis, our research data came from two databases Crunchbase and CBInsights, which are well-recognized information sources for researchers, entrepreneurs, managers, and investors. We searched a few keywords in the Crunchbase database based on companies’ self-description: digital health, e-health, and telehealth. A search of “digital health” returned 941 unique results, “e-health” returned 167 companies, while “telehealth” 427. We also searched the CBInsights database for similar information. After merging and removing duplicate ones and cleaning up the database, we came up with a list of 1464 companies as digital health companies. A qualitative method will be used to complement the quantitative analysis. We will do an in-depth case analysis of three successful unicorn digital health companies to understand how business models evolve and discuss the challenges faced in this sector. Our research returned some interesting findings. For instance, we found that 86% of the digital health startups were founded in the recent decade since 2010. 75% of the digital health companies have less than 50 employees, and almost 50% with less than 10 employees. This shows that digital health companies are relatively young and small in scale. On the business model analysis, while traditional healthcare businesses emphasize the so-called “3P”—patient, physicians, and payer, digital health companies extend to “5p” by adding patents, which is the result of technology requirements (such as the development of artificial intelligence models), and platform, which is an effective value creation approach to bring the stakeholders together. Our case analysis will detail the 5p framework and contribute to the extant knowledge on business models in the healthcare industry.

Keywords: digital health, business models, entrepreneurship opportunities, healthcare

Procedia PDF Downloads 168

Authors: Bartosz Kedra, Robert Malkowski

Abstract:

This paper provides description and presentation of laboratory test unit built basing on 150 kVA power frequency converter and Simulink RealTime platform. Assumptions, based on criteria which load and generator types may be simulated using discussed device, are presented, as well as control algorithm structure. As laboratory setup contains transformer with thyristor controlled tap changer, a wider scope of setup capabilities is presented. Information about used communication interface, data maintenance, and storage solution as well as used Simulink real-time features is presented. List and description of all measurements are provided. Potential of laboratory setup modifications is evaluated. For purposes of Rapid Control Prototyping, a dedicated environment was used Simulink RealTime. Therefore, load model Functional Unit Controller is based on a PC computer with I/O cards and Simulink RealTime software. Simulink RealTime was used to create real-time applications directly from Simulink models. In the next step, applications were loaded on a target computer connected to physical devices that provided opportunity to perform Hardware in the Loop (HIL) tests, as well as the mentioned Rapid Control Prototyping process. With Simulink RealTime, Simulink models were extended with I/O cards driver blocks that made automatic generation of real-time applications and performing interactive or automated runs on a dedicated target computer equipped with a real-time kernel, multicore CPU, and I/O cards possible. Results of performed laboratory tests are presented. Different load configurations are described and experimental results are presented. This includes simulation of under frequency load shedding, frequency and voltage dependent characteristics of groups of load units, time characteristics of group of different load units in a chosen area and arbitrary active and reactive power regulation basing on defined schedule.

Keywords: MATLAB, power converter, Simulink Real-Time, thyristor-controlled tap changer

Procedia PDF Downloads 305

Authors: Shaikah Alsubayae, Gianluigi Casse, Carlos Chavez, Jon Taylor, Alan Taylor, Mohammad Alsulimane

Abstract:

Achieving precise radiotherapy through carbon therapy necessitates the accurate monitoring of radiation dose distribution within the patient's body. This process is pivotal for targeted tumor treatment, minimizing harm to healthy tissues, and enhancing overall treatment effectiveness while reducing the risk of side effects. In our investigation, we adopted a methodological approach to monitor secondary proton doses in carbon therapy using Monte Carlo (MC) simulations. Initially, Geant4 simulations were employed to extract the initial positions of secondary particles generated during interactions between carbon ions and water, including protons, gamma rays, alpha particles, neutrons, and tritons. Subsequently, we explored the relationship between the carbon ion beam and these secondary particles. Interaction vertex imaging (IVI) proves valuable for monitoring dose distribution during carbon therapy, providing information about secondary particle locations and abundances, particularly protons. The IVI method relies on charged particles produced during ion fragmentation to gather range information by reconstructing particle trajectories back to their point of origin, known as the vertex. In the context of carbon ion therapy, our simulation results indicated a strong correlation between some secondary particles and the range of carbon ions. However, challenges arose due to the unique elongated geometry of the target, hindering the straightforward transmission of forward-generated protons. Consequently, the limited protons that did emerge predominantly originated from points close to the target entrance. Fragment (protons) trajectories were approximated as straight lines, and a beam back-projection algorithm, utilizing interaction positions recorded in Si detectors, was developed to reconstruct vertices. The analysis revealed a correlation between the reconstructed and actual positions.

Keywords: radiotherapy, carbon therapy, monitor secondary proton doses, interaction vertex imaging

Procedia PDF Downloads 61

Authors: Soumava Boral, Sanjay K. Chaturvedi, Ian Howard, Kristoffer McKee, V. N. A. Naikan

Abstract:

Due to strict regulations from government to enhance the possibilities of sustainability practices in industries, and noting the advances in sustainable manufacturing practices, it is necessary that the associated processes are also sustainable. Maintenance of large scale and complex machines is a pivotal task to maintain the uninterrupted flow of manufacturing processes. Appropriate maintenance practices can prolong the lifetime of machines, and prevent associated breakdowns, which subsequently reduces different cost heads. Selection of the best maintenance strategies for such machines are considered as a burdensome task, as they require the consideration of multiple technical criteria, complex mathematical calculations, previous fault data, maintenance records, etc. In the era of the fourth industrial revolution, organizations are rapidly changing their way of business, and they are giving their utmost importance to sensor technologies, artificial intelligence, data analytics, automations, etc. In this work, the effectiveness of several maintenance strategies (e.g., preventive, failure-based, reliability centered, condition based, total productive maintenance, etc.) related to a large scale and complex gearbox, operating in a steel processing plant is evaluated in terms of economic, social, environmental and technical criteria. As it is not possible to obtain/describe some criteria by exact numerical values, these criteria are evaluated linguistically by cross-functional experts. Fuzzy sets are potential soft-computing technique, which has been useful to deal with linguistic data and to provide inferences in many complex situations. To prioritize different maintenance practices based on the identified sustainable criteria, multi-criteria decision making (MCDM) approaches can be considered as potential tools. Multi-Attributive Ideal Real Comparative Analysis (MAIRCA) is a recent addition in the MCDM family and has proven its superiority over some well-known MCDM approaches, like TOPSIS (Technique for Order Preference by Similarity to Ideal Solution) and ELECTRE (ELimination Et Choix Traduisant la REalité). It has a simple but robust mathematical approach, which is easy to comprehend. On the other side, due to some inherent drawbacks of Intuitionistic Fuzzy Sets (IFS) and Pythagorean Fuzzy Sets (PFS), recently, the use of Fermatean Fuzzy Sets (FFSs) has been proposed. In this work, we propose the novel concept of FF-MAIRCA. We obtain the weights of the criteria by experts’ evaluation and use them to prioritize the different maintenance practices according to their suitability by FF-MAIRCA approach. Finally, a sensitivity analysis is carried out to highlight the robustness of the approach.

Keywords: Fermatean fuzzy sets, Fermatean fuzzy MAIRCA, maintenance strategy selection, sustainable manufacturing, MCDM

Procedia PDF Downloads 130