Search results for: neural machine translation

Authors: Aschalew C. Workneh, K. S. Hari Prasad, C. S. P. Ojha

Abstract:

The crop water stress index (CWSI) is a cost-effective, non-destructive, and simple technique for tracking the start of crop water stress. This study investigated the feasibility of CWSI derived from canopy temperature to detect the water status of wheat crops. Artificial intelligence (AI) techniques have become increasingly popular in recent years for determining CWSI. In this study, the performance of two AI techniques, adaptive neuro-fuzzy inference system (ANFIS) and self-organizing maps (SOM), are compared while determining the CWSI of paddy crops. Field experiments were conducted for varying irrigation water applications during two seasons in 2022 and 2023 at the irrigation field laboratory at the Civil Engineering Department, Indian Institute of Technology Roorkee, India. The ANFIS and SOM-simulated CWSI values were compared with the experimentally calculated CWSI (EP-CWSI). Multiple regression analysis was used to determine the upper and lower CWSI baselines. The upper CWSI baseline was found to be a function of crop height and wind speed, while the lower CWSI baseline was a function of crop height, air vapor pressure deficit, and wind speed. The performance of ANFIS and SOM were compared based on mean absolute error (MAE), mean bias error (MBE), root mean squared error (RMSE), index of agreement (d), Nash-Sutcliffe efficiency (NSE), and coefficient of correlation (R²). Both models successfully estimated the CWSI of the paddy crop with higher correlation coefficients and lower statistical errors. However, the ANFIS (R²=0.81, NSE=0.73, d=0.94, RMSE=0.04, MAE= 0.00-1.76 and MBE=-2.13-1.32) outperformed the SOM model (R²=0.77, NSE=0.68, d=0.90, RMSE=0.05, MAE= 0.00-2.13 and MBE=-2.29-1.45). Overall, the results suggest that ANFIS is a reliable tool for accurately determining CWSI in wheat crops compared to SOM.

Keywords: adaptive neuro-fuzzy inference system, canopy temperature, crop water stress index, self-organizing map, wheat

Procedia PDF Downloads 28

Authors: Uday Shanker Goel, Vinay Vasant Mahashabde, Biswajit Ghosh, Arvind Jha, Amit Kumar, Sanjay Kumar Patel, Uma Shanker Pattanaik, Vinit Kumar Shah, Chaitanya Bhanu

Abstract:

Pinch rolls of the Hot Mills must possess resistance to wear, thermal stability, high thermal conductivity and through hardness. Conventionally, pinch rolls have been procured either as new ones or refurbished ones. Discarded Work Rolls from the Cold Mill were taken and machined inhouse at Tata Steel to be used subsequently as the bottom pinch rolls of the Hot Mill. The hardness of the scrapped work rolls from CRM is close to 55HRC and the typical composition is ( C - 0.8% , Mn - 0.40 % , Si - 0.40% , Cr - 3.5% , Mo - 0.5% & V - 0.1% ).The Innovation was the use of a roll which would otherwise have been otherwise discarded as scrap. Also, the innovation helped in using the scrapped roll which had better wear and heat resistance. In a conventional Pinch roil (Hardness 50 HRC and typical chemistry - C - 10% , Mo+Co+V+Nb ~ 5 % ) , Pick-up is a condition whereby foreign material becomes adhered to the surface of the pinch roll during service. The foreign material is usually adhered metal from the actual product being rolled. The main attributes of the weld overlay rolls are wear resistance and crack resistance. However, the weld overlay roll has a strong tendency for strip pick-up particularly in the area of bead overlap. However, the greatest disadvantage is the depth of weld deposit, which is less than half of the usable shell thickness in most mills. Because of this, the stainless rolls require re-welding on a routine basis. By providing a significantly cheaper in house and more robust alternative of the existing bottom pinch rolls , this innovation results in significant lower worries for the roll shop. Pinch rolls now don't have to be sent outside Jamshedpur for refurbishment or for procuring new ones. Scrapped rolls from adjacent Cold Mill are procured and sent for machining to our Machine Shop inside Tata Steel works in Jamshedpur. This is far more convenient than the older methodology. The idea is also being deployed to the other hot mills of Tata Steel. Multiple campaigns have been tried out at both down coilers of Hot Strip with significantly lower wear.

Keywords: hot rolling flat, cold mill work roll, hot strip pinch roll, strip surface

Procedia PDF Downloads 81

Authors: Wedad Albalawi

Abstract:

The mathematical inequalities have been the core of mathematical study and used in almost all branches of mathematics as well in various areas of science and engineering. The inequalities by Hardy, Littlewood and Polya were the first significant composition of several science. This work presents fundamental ideas, results and techniques, and it has had much influence on research in various branches of analysis. Since 1934, various inequalities have been produced and studied in the literature. Furthermore, some inequalities have been formulated by some operators; in 1989, weighted Hardy inequalities have been obtained for integration operators. Then, they obtained weighted estimates for Steklov operators that were used in the solution of the Cauchy problem for the wave equation. They were improved upon in 2011 to include the boundedness of integral operators from the weighted Sobolev space to the weighted Lebesgue space. Some inequalities have been demonstrated and improved using the Hardy–Steklov operator. Recently, a lot of integral inequalities have been improved by differential operators. Hardy inequality has been one of the tools that is used to consider integrity solutions of differential equations. Then, dynamic inequalities of Hardy and Coposon have been extended and improved by various integral operators. These inequalities would be interesting to apply in different fields of mathematics (functional spaces, partial differential equations, mathematical modeling). Some inequalities have been appeared involving Copson and Hardy inequalities on time scales to obtain new special version of them. A time scale is an arbitrary nonempty closed subset of the real numbers. Then, the dynamic inequalities on time scales have received a lot of attention in the literature and has become a major field in pure and applied mathematics. There are many applications of dynamic equations on time scales to quantum mechanics, electrical engineering, neural networks, heat transfer, combinatorics, and population dynamics. This study focuses on Hardy and Coposon inequalities, using Steklov operator on time scale in double integrals to obtain special cases of time-scale inequalities of Hardy and Copson on high dimensions. The advantage of this study is that it uses the one-dimensional classical Hardy inequality to obtain higher dimensional on time scale versions that will be applied in the solution of the Cauchy problem for the wave equation. In addition, the obtained inequalities have various applications involving discontinuous domains such as bug populations, phytoremediation of metals, wound healing, maximization problems. The proof can be done by introducing restriction on the operator in several cases. The concepts in time scale version such as time scales calculus will be used that allows to unify and extend many problems from the theories of differential and of difference equations. In addition, using chain rule, and some properties of multiple integrals on time scales, some theorems of Fubini and the inequality of H¨older.

Keywords: time scales, inequality of hardy, inequality of coposon, steklov operator

Procedia PDF Downloads 59

Authors: Mohammad M. Khan, Gajendra Dixit

Abstract:

Partially lubricated sliding wear behaviour of a zinc-based alloy reinforced with 10wt% SiC particles has been studied as a function of applied load and solid lubricant particle size and has been compared with that of matrix alloy and conventionally used grey cast iron. The wear tests were conducted at the sliding velocities of 2.1m/sec in various partial lubricated conditions using pin on disc machine as per ASTM G-99-05. Base oil (SAE 20W-40) or mixture of the base oil with 5wt% graphite of particle sizes (7-10 µm) and (100 µm) were used for creating lubricated conditions. The matrix alloy revealed primary dendrites of a and eutectoid a + h and Î phases in the Inter dendritic regions. Similar microstructure has been depicted by the composite with an additional presence of the dispersoid SiC particles. In the case of cast iron, flakes of graphite were observed in the matrix; the latter comprised of (majority of) pearlite and (limited quantity of) ferrite. Results show a large improvement in wear resistance of the zinc-based alloy after reinforcement with SiC particles. The cast iron shows intermediate response between the matrix alloy and composite. The solid lubrication improved the wear resistance and friction behaviour of both the reinforced and base alloy. Moreover, minimum wear rate is obtained in oil+ 5wt % graphite (7-10 µm) lubricated environment for the matrix alloy and composite while for cast iron addition of solid lubricant increases the wear rate and minimum wear rate is obtained in case of oil lubricated environment. The cast iron experienced higher frictional heating than the matrix alloy and composite in all the cases especially at higher load condition. As far as friction coefficient is concerned, a mixed trend of behaviour was noted. The wear rate and frictional heating increased with load while friction coefficient was affected in an opposite manner. Test duration influenced the frictional heating and friction coefficient of the samples in a mixed manner.

Keywords: solid lubricant, sliding wear, grey cast iron, zinc based metal matrix composites

Procedia PDF Downloads 288

Authors: Mikhail O. Eremin

Abstract:

Present work is devoted to experimental study and numerical modelling of failure of rocks typical for Kuzbass coal basin (Russia). The main goal was to define strength and deformation characteristics of rocks on the base of uniaxial compression and three-point bending loadings and then to build a mathematical model of failure process for both types of loading. Depending on particular physical-mechanical characteristics typical rocks of Kuzbass coal basin (sandstones, siltstones, mudstones, etc. of different series – Kolchuginsk, Tarbagansk, Balohonsk) manifest brittle and quasi-brittle character of failure. The strength characteristics for both tension and compression are found. Other characteristics are also found from the experiment or taken from literature reviews. On the base of obtained characteristics and structure (obtained from microscopy) the mathematical and structural models are built and numerical modelling of failure under different types of loading is carried out. Effective characteristics obtained from modelling and character of failure correspond to experiment and thus, the mathematical model was verified. An Instron 1185 machine was used to carry out the experiments. Mathematical model includes fundamental conservation laws of solid mechanics – mass, impulse, energy. Each rock has a sufficiently anisotropic structure, however, each crystallite might be considered as isotropic and then a whole rock model has a quasi-isotropic structure. This idea gives an opportunity to use the Hooke’s law inside of each crystallite and thus explicitly accounting for the anisotropy of rocks and the stress-strain state at loading. Inelastic behavior is described in frameworks of two different models: von Mises yield criterion and modified Drucker-Prager yield criterion. The damage accumulation theory is also implemented in order to describe a failure process. Obtained effective characteristics of rocks are used then for modelling of rock mass evolution when mining is carried out both by an open-pit or underground opening.

Keywords: damage accumulation, Drucker-Prager yield criterion, failure, mathematical modelling, three-point bending, uniaxial compression

Procedia PDF Downloads 145

Authors: Shaukat Ali Shahee, Usha Ananthakumar

Abstract:

A data set exhibits class imbalance problem when one class has very few examples compared to the other class, and this is also referred to as between class imbalance. The traditional classifiers fail to classify the minority class examples correctly due to its bias towards the majority class. Apart from between-class imbalance, imbalance within classes where classes are composed of a different number of sub-clusters with these sub-clusters containing different number of examples also deteriorates the performance of the classifier. Previously, many methods have been proposed for handling imbalanced dataset problem. These methods can be classified into four categories: data preprocessing, algorithmic based, cost-based methods and ensemble of classifier. Data preprocessing techniques have shown great potential as they attempt to improve data distribution rather than the classifier. Data preprocessing technique handles class imbalance either by increasing the minority class examples or by decreasing the majority class examples. Decreasing the majority class examples lead to loss of information and also when minority class has an absolute rarity, removing the majority class examples is generally not recommended. Existing methods available for handling class imbalance do not address both between-class imbalance and within-class imbalance simultaneously. In this paper, we propose a method that handles between class imbalance and within class imbalance simultaneously for binary classification problem. Removing between class imbalance and within class imbalance simultaneously eliminates the biases of the classifier towards bigger sub-clusters by minimizing the error domination of bigger sub-clusters in total error. The proposed method uses model-based clustering to find the presence of sub-clusters or sub-concepts in the dataset. The number of examples oversampled among the sub-clusters is determined based on the complexity of sub-clusters. The method also takes into consideration the scatter of the data in the feature space and also adaptively copes up with unseen test data using Lowner-John ellipsoid for increasing the accuracy of the classifier. In this study, neural network is being used as this is one such classifier where the total error is minimized and removing the between-class imbalance and within class imbalance simultaneously help the classifier in giving equal weight to all the sub-clusters irrespective of the classes. The proposed method is validated on 9 publicly available data sets and compared with three existing oversampling techniques that rely on the spatial location of minority class examples in the euclidean feature space. The experimental results show the proposed method to be statistically significantly superior to other methods in terms of various accuracy measures. Thus the proposed method can serve as a good alternative to handle various problem domains like credit scoring, customer churn prediction, financial distress, etc., that typically involve imbalanced data sets.

Keywords: classification, imbalanced dataset, Lowner-John ellipsoid, model based clustering, oversampling

Procedia PDF Downloads 391

Authors: Martin Pellon Consunji

Abstract:

Due to the rise in popularity of Bitcoin and other crypto assets as a store of wealth and speculative investment, there is an ever-growing demand for automated trading tools, such as bots, in order to gain an advantage over the market. Traditionally, trading in the stock market was done by professionals with years of training who understood patterns and exploited market opportunities in order to gain a profit. However, nowadays a larger portion of market participants are at minimum aided by market-data processing bots, which can generally generate more stable signals than the average human trader. The rise in trading bot usage can be accredited to the inherent advantages that bots have over humans in terms of processing large amounts of data, lack of emotions of fear or greed, and predicting market prices using past data and artificial intelligence, hence a growing number of approaches have been brought forward to tackle this task. However, the general limitation of these approaches can still be broken down to the fact that limited historical data doesn’t always determine the future, and that a lot of market participants are still human emotion-driven traders. Moreover, developing markets such as those of the cryptocurrency space have even less historical data to interpret than most other well-established markets. Due to this, some human traders have gone back to the tried-and-tested traditional technical analysis tools for exploiting market patterns and simplifying the broader spectrum of data that is involved in making market predictions. This paper proposes a method which uses neuro evolution techniques on both sentimental data and, the more traditionally human-consumed, technical analysis data in order to gain a more accurate forecast of future market behavior and account for the way both automated bots and human traders affect the market prices of Bitcoin and other cryptocurrencies. This study’s approach uses evolutionary algorithms to automatically develop increasingly improved populations of bots which, by using the latest inflows of market analysis and sentimental data, evolve to efficiently predict future market price movements. The effectiveness of the approach is validated by testing the system in a simulated historical trading scenario, a real Bitcoin market live trading scenario, and testing its robustness in other cryptocurrency and stock market scenarios. Experimental results during a 30-day period show that this method outperformed the buy and hold strategy by over 260% in terms of net profits, even when taking into consideration standard trading fees.

Keywords: neuro-evolution, Bitcoin, trading bots, artificial neural networks, technical analysis, evolutionary algorithms

Procedia PDF Downloads 94

Authors: Rafael do Amaral Teodoro, Leandro Augusto da Silva

Abstract:

Originated from graphite, graphene is a two-dimensional (2D) material that promises to revolutionize technology in many different areas, such as energy, telecommunications, civil construction, aviation, textile, and medicine. This is possible because its structure, formed by carbon bonds, provides desirable optical, thermal, and mechanical characteristics that are interesting to multiple areas of the market. Thus, several research and development centers are studying different manufacturing methods and material applications of graphene, which are often compromised by the scarcity of more agile and accurate methodologies to characterize the material – that is to determine its composition, shape, size, and the number of layers and crystals. To engage in this search, this study proposes a computational methodology that applies deep learning to identify graphene oxide crystals in order to characterize samples by crystal sizes. To achieve this, a fully convolutional neural network called U-net has been trained to segment SEM graphene oxide images. The segmentation generated by the U-net is fine-tuned with a standard deviation technique by classes, which allows crystals to be distinguished with different labels through an object delimitation algorithm. As a next step, the characteristics of the position, area, perimeter, and lateral measures of each detected crystal are extracted from the images. This information generates a database with the dimensions of the crystals that compose the samples. Finally, graphs are automatically created showing the frequency distributions by area size and perimeter of the crystals. This methodological process resulted in a high capacity of segmentation of graphene oxide crystals, presenting accuracy and F-score equal to 95% and 94%, respectively, over the test set. Such performance demonstrates a high generalization capacity of the method in crystal segmentation, since its performance considers significant changes in image extraction quality. The measurement of non-overlapping crystals presented an average error of 6% for the different measurement metrics, thus suggesting that the model provides a high-performance measurement for non-overlapping segmentations. For overlapping crystals, however, a limitation of the model was identified. To overcome this limitation, it is important to ensure that the samples to be analyzed are properly prepared. This will minimize crystal overlap in the SEM image acquisition and guarantee a lower error in the measurements without greater efforts for data handling. All in all, the method developed is a time optimizer with a high measurement value, considering that it is capable of measuring hundreds of graphene oxide crystals in seconds, saving weeks of manual work.

Keywords: characterization, graphene oxide, nanomaterials, U-net, deep learning

Procedia PDF Downloads 137

Authors: Mohammad Tarek Al Muallim, Ozhan Duzenli, Ceyhun Ilguy

Abstract:

Harbor protection systems are so important. The need for automatic protection systems has increased over the last years. Diver detection active sonar has great significance. It used to detect underwater threats such as divers and autonomous underwater vehicle. To automatically detect such threats the sonar image is processed by algorithms. These algorithms used to detect, track and classify of underwater objects. In this work, divers tracking and classification algorithm is improved be proposing a robust wake detection method. To detect objects the sonar images is normalized then segmented based on fixed threshold. Next, the centroids of the segments are found and clustered based on distance metric. Then to track the objects linear Kalman filter is applied. To reduce effect of noise and creation of false tracks, the Kalman tracker is fine tuned. The tuning is done based on our active sonar specifications. After the tracks are initialed and updated they are subjected to a filtering stage to eliminate the noisy and unstable tracks. Also to eliminate object with a speed out of the diver speed range such as buoys and fast boats. Afterwards the result tracks are subjected to a classification stage to deiced the type of the object been tracked. Here the classification stage is to deice wither if the tracked object is an open circuit diver or a close circuit diver. At the classification stage, a small area around the object is extracted and a novel wake detection method is applied. The morphological features of the object with his wake is extracted. We used support vector machine to find the best classifier. The sonar training images and the test images are collected by ARMELSAN Defense Technologies Company using the portable diver detection sonar ARAS-2023. After applying the algorithm to the test sonar data, we get fine and stable tracks of the divers. The total classification accuracy achieved with the diver type is 97%.

Keywords: harbor protection, diver detection, active sonar, wake detection, diver classification

Procedia PDF Downloads 211

Authors: Sevde Altuntas, Fatih Buyukserin

Abstract:

Alzheimer’s disease is responsible for irreversible neural damage of brain parts. One of the disease markers is Amyloid-β 1-42 protein that accumulates in the brain in the form plaques. The basic problem for detection of the protein is the low amount of protein that cannot be detected properly in body liquids such as blood, saliva or urine. To solve this problem, tests like ELISA or PCR are proposed which are expensive, require specialized personnel and can contain complex protocols. Therefore, Surface-enhanced Raman Spectroscopy (SERS) a good candidate for detection of Amyloid-β 1-42 protein. Because the spectroscopic technique can potentially allow even single molecule detection from liquid and solid surfaces. Besides SERS signal can be improved by using nanopattern surface and also is specific to molecules. In this context, our study proposes to fabricate diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin - T to detect low concentrations of Amyloid-β 1-42 protein in water and artificial saliva medium by the enhancement of protein SERS signal. The nanopatterned PC surface that was used to enhance SERS signal was fabricated by using Anodic Alumina Membranes (AAM) as a template. It is possible to produce AAMs with different column structures and varying thicknesses depending on voltage and anodization time. After fabrication process, the pore diameter of AAMs can be arranged with dilute acid solution treatment. In this study, two different columns structures were prepared. After a surface modification to decrease their surface energy, AAMs were treated with PC solution. Following the solvent evaporation, nanopatterned PC films with tunable pillared structures were peeled off from the membrane surface. The PC film was then modified with Au and Thioflavin-T for the detection of Amyloid-β 1-42 protein. The protein detection studies were conducted first in water via this biosensor platform. Same measurements were conducted in artificial saliva to detect the presence of Amyloid Amyloid-β 1-42 protein. SEM, SERS and contact angle measurements were carried out for the characterization of different surfaces and further demonstration of the protein attachment. SERS enhancement factor calculations were also completed via experimental results. As a result, our research group fabricated diagnostic test models that utilize Au-coated nanopatterned polycarbonate (PC) surfaces modified with Thioflavin-T to detect low concentrations of Alzheimer’s Amiloid – β protein in water and artificial saliva medium. This work was supported by The Scientific and Technological Research Council of Turkey (TUBITAK) Grant No: 214Z167.

Keywords: alzheimer, anodic aluminum oxide, nanotopography, surface enhanced Raman spectroscopy

Procedia PDF Downloads 266

Authors: Haniyeh Parhizkar

Abstract:

When Sarris came up with the "auteur theory" in 1962, he emphasized that the utmost premise of auteur theory is the inner meanings and concepts of a film and that a film is purely an art form. Today's mainstream movies are conceptually closer to what the Frankfurt School scholars regarded as "reproduced" and "mass culture" years ago. Hollywood goes on to be a huge movie-making machine that leads the dominant paradigms of films throughout the world and cinema is far from art. Although there are still movies, directors, and audiences who favor art cinema over Hollywood and mainstream movies, it's an almost undeniable fact that, for the most part, people's perception of movies is widely influenced by their American depiction and Hollywood's legacy of mass culture. With the uprising of Hollywood studios as the forerunners of the movie industry and cinema being largely dependent on economics rather than artistic values, this distinctive role of cinema has diminished and is replaced with a global standard. The Blockbuster 2022 film, 'Everything, Everywhere, All at Once' is now the most-awarded movie of all time, winning seven Oscars at the 95th Academy Awards. Despite its main cast being Asian, the movie is produced by American incorporation and is heavily influenced by Hollywood's dominant themes of superheroes, fantasy, action, and adventure. The New Yorker film critic, Richard Brody, called the movie "a pitch for a Marvel" and critiqued the film for being "universalized" and "empty of history and culture". Other critics of Variety pinpointed the movie's similarities to Marvel, particularly in their storylines of multi-universe which manifest traces of American legacy. As argued by these critics, 'Everything, Everywhere, All at Once' might appear as a unique and authentic film at first glance, but it can be argued that it is yet another version of a Marvel movie. While the movie's universal acclaim was regarded as recognition and an acknowledgment of its Asian cast, the issue that arises here is when the Hollywood influences and American themes are so robust in the film, is the movie industry honoring another culture or is it yet another celebration of Hollywood's dominant paradigm. This essay will employ a critical approach to Hollywood's dominance and mass-produced culture, which has deprived authenticity of non-American movies and is constantly reproducing the same formula of success.

Keywords: hollywoodization, universalization, blockbuster, dominant paradigm, marvel, authenticity, diversity

Procedia PDF Downloads 58

Authors: Ayesha Sanjana Kawser Parsha

Abstract:

S-acylation is an irreversible bond in which cysteine residues are linked to fatty acids palmitate (74%) or stearate (22%), either at the COOH or NH2 terminal, via a thioester linkage. There are several experimental methods that can be used to identify the S-palmitoylation site; however, since they require a lot of time, computational methods are becoming increasingly necessary. There aren't many predictors, however, that can locate S- palmitoylation sites in Arabidopsis Thaliana with sufficient accuracy. This research is based on the importance of building a better prediction tool. To identify the type of machine learning algorithm that predicts this site more accurately for the experimental dataset, several prediction tools were examined in this research, including the GPS PALM 6.0, pCysMod, GPS LIPID 1.0, CSS PALM 4.0, and NBA PALM. These analyses were conducted by constructing the receiver operating characteristics plot and the area under the curve score. An AI-driven deep learning-based prediction tool has been developed utilizing the analysis and three sequence-based input data, such as the amino acid composition, binary encoding profile, and autocorrelation features. The model was developed using five layers, two activation functions, associated parameters, and hyperparameters. The model was built using various combinations of features, and after training and validation, it performed better when all the features were present while using the experimental dataset for 8 and 10-fold cross-validations. While testing the model with unseen and new data, such as the GPS PALM 6.0 plant and pCysMod mouse, the model performed better, and the area under the curve score was near 1. It can be demonstrated that this model outperforms the prior tools in predicting the S- palmitoylation site in the experimental data set by comparing the area under curve score of 10-fold cross-validation of the new model with the established tools' area under curve score with their respective training sets. The objective of this study is to develop a prediction tool for Arabidopsis Thaliana that is more accurate than current tools, as measured by the area under the curve score. Plant food production and immunological treatment targets can both be managed by utilizing this method to forecast S- palmitoylation sites.

Keywords: S- palmitoylation, ROC PLOT, area under the curve, cross- validation score

Procedia PDF Downloads 48

Authors: Bahareh Sarmadi, Paul A. Kilmartin, Leandro D. Araújo, Brandt P. Bastow

Abstract:

Aim: Sauvignon blanc is the most substantial variety cultivated in almost 62% of all producing vineyards of New Zealand. The popularity of New Zealand Sauvignon blanc is due to its unique taste. It is the most famous wine characterized by its aroma profile derived from mercaptans. 3-mercaptohexan-1-ol (3MH) and 3-mercaptohexyl acetate (3MHA) are two of the most important volatile mercaptans found in Sauvignon blanc wines. “Viticultural” and “Enological” factors such as machine-harvesting, the most common harvesting practice used in New Zealand, can be among the reasons for this distinct flavor. Elemental sulfur is commonly sprayed in the fields to protect berries against powdery mildew. Although it is not the only source of sulfur, this practice creates a source of elemental sulfur that can be transferred into the must and eventually into wines. Despite the clear effects of residual elemental sulfur present in the must on the quality and aroma of the final wines, its measurement before harvest or fermentation is not a regular practice in the wineries. This can be due to the lack of accessible and applicable methods for the equipment at most commercial wineries. This study aims to establish a relationship between the number and frequency of elemental sulfur applications and the concentration of polyfunctional mercaptans in the final wines. Methods: An apparatus was designed to reduce elemental sulfur to sulfide, then an ion-selective electrode to measure sulfide concentration. During harvest 2022, we explored a wider range of residual elemental sulfur levels than what typically applies in the vineyards. This has been done through later manual elemental sulfur applications in the vineyard. Additional sulfur applications were made 20, 10 and 5 days prior to harvesting the treated grapes, covering long and short pre-harvest intervals (PHI). The grapes were processed into juice and fermented into wine; then, they were analyzed to find the correlation between polyfunctional mercaptans concentrations in the wines and residual elemental sulfur in the juice samples. Results: The research showed that higher 3MH/3MHA was formed when elemental sulfur was applied more frequent in the vineyards and supported the proposed pathway in which elemental sulfur is a source of 3MH formation in wines.

Keywords: sauvignon blanc, elemental sulfur, polyfunctional mercaptans, varietal thiols

Procedia PDF Downloads 72

Authors: Ali Al-Shehri, Abdulaziz Al-Qasim, Abdulkarim Sofi, Ali Yousef

Abstract:

The oil and gas industry has played a major role in improving the prosperity of mankind and driving the world economy. According to the International Energy Agency (IEA) and Integrated Environmental Assessment (EIA) estimates, the world will continue to rely heavily on hydrocarbons for decades to come. This growing energy demand mandates taking sustainability measures to prolong the availability of reliable and affordable energy sources, and ensure lowering its environmental impact. Unlike any other industry, the oil and gas upstream operations are energy-intensive and scattered over large zonal areas. These challenging conditions require unique sustainability solutions. In recent years there has been a concerted effort by the oil and gas industry to develop and deploy innovative technologies to: maximize efficiency, reduce carbon footprint, reduce CO2 emissions, and optimize resources and material consumption. In the past, the main driver for research and development (R&D) in the exploration and production sector was primarily driven by maximizing profit through higher hydrocarbon recovery and new discoveries. Environmental-friendly and sustainable technologies are increasingly being deployed to balance sustainability and profitability. Analyzing technology and its sustainability impact is increasingly being used in corporate decision-making for improved portfolio management and allocating valuable resources toward technology R&D.This paper articulates and discusses a novel workflow to identify strategic sustainable technologies for improved portfolio management by addressing existing and future upstream challenges. It uses a systematic approach that relies on sustainability key performance indicators (KPI’s) including energy efficiency quotient, carbon footprint, and CO2 emissions. The paper provides examples of various technologies including CCS, reducing water cuts, automation, using renewables, energy efficiency, etc. The use of 4IR technologies such as Artificial Intelligence, Machine Learning, and Data Analytics are also discussed. Overlapping technologies, areas of collaboration and synergistic relationships are identified. The unique sustainability analyses provide improved decision-making on technology portfolio management.

Keywords: sustainability, oil& gas, technology portfolio, key performance indicator

Procedia PDF Downloads 163

Authors: M. Hossain, A. Abdkader, C. Cherif, A. Berger, M. Sparing, R. Hühne, L. Schultz, K. Nielsch

Abstract:

Due to the best quality of yarn and the flexibility of the machine, the ring spinning process is the most widely used spinning method for short staple yarn production. However, the productivity of these machines is still much lower in comparison to other spinning systems such as rotor or air-jet spinning process. The main reason for this limitation lies on the twisting mechanism of the ring spinning process. In the ring/traveler twisting system, each rotation of the traveler along with the ring inserts twist in the yarn. The rotation of the traveler at higher speed includes strong frictional forces, which in turn generates heat. Different ring/traveler systems concerning with its geometries, material combinations and coatings have already been implemented to solve the frictional problem. However, such developments can neither completely solve the frictional problem nor increase the productivity. The friction free superconducting magnetic bearing (SMB) system can be a right alternative replacing the existing ring/traveler system. The unique concept of SMB bearings is that they possess a self-stabilizing behavior, i.e. they remain fully passive without any necessity for expensive position sensing and control. Within the framework of a research project funded by German research foundation (DFG), suitable concepts of the SMB-system have been designed, developed, and integrated as a twisting device of ring spinning replacing the existing ring/traveler system. With the help of the developed mathematical model and experimental investigation, the physical limitations of this innovative twisting device in the spinning process have been determined. The interaction among the parameters of the spinning process and the superconducting twisting element has been further evaluated, which derives the concrete information regarding the new spinning process. Moreover, the influence of the implemented SMB twisting system on the yarn quality has been analyzed with respect to different process parameters. The presented work reveals the enormous potential of the innovative twisting mechanism, so that the productivity of the ring spinning process especially in case of thermoplastic materials can be at least doubled for the first time in a hundred years. The SMB ring spinning tester has also been presented in the international fair “International Textile Machinery Association (ITMA) 2015”.

Keywords: ring spinning, superconducting magnetic bearing, yarn properties, productivity

Procedia PDF Downloads 209

Authors: Mardin Abdalqadir, Paul Adzakro, Tannaz Pak, Sina Rezaei Gomari

Abstract:

Climate change and global warming recently became significant concerns due to the massive emissions of greenhouse gases into the atmosphere, predominantly CO2 gases. Therefore, it is necessary to find sustainable and inexpensive methods to capture the greenhouse gasses and protect the environment for live species. The application of naturally available and cheap adsorbents of carbon such as clay minerals became a great interest. However, the minerals prone to low storage capacity despite their high affinity to adsorb carbon. This paper aims to explore ways to improve the pore volume and surface area of two selected clay minerals, ‘montmorillonite and kaolinite’ by acid treatment to overcome their low storage capacity. Montmorillonite and kaolinite samples were treated with different sulfuric acid concentrations (0.5, 1.2 and 2.5 M) at 40 °C for 8 hours to achieve the above aim. The grain size distribution and morphology of clay minerals before and after acid treatment were explored with Scanning Electron Microscope to evaluate surface area improvement. The ImageJ software was used to find the porosity and pore volume of treated and untreated clay samples. The structure of the clay minerals was also analyzed using an X-ray Diffraction machine. The results showed that the pore volume and surface area were increased substantially through acid treatment, which speeded up the rate of carbon dioxide adsorption. XRD pattern of kaolinite did not change after sulfuric acid treatment, which indicates that acid treatment would not affect the structure of kaolinite. It was also discovered that kaolinite had a higher pore volume and porosity than montmorillonite before and after acid treatment. For example, the pore volume of untreated kaolinite was equal to 30.498 um3 with a porosity of 23.49%. Raising the concentration of acid from 0.5 M to 2.5 M in 8 hours’ time reaction led to increased pore volume from 30.498 um3 to 34.73 um3. The pore volume of raw montmorillonite was equal to 15.610 um3 with a porosity of 12.7%. When the acid concentration was raised from 0.5 M to 2.5 M for the same reaction time, pore volume also increased from 15.610 um3 to 20.538 um3. However, montmorillonite had a higher specific surface area than kaolinite. This study concludes that clay minerals are inexpensive and available material sources to model the realistic conditions and apply the results of carbon capture to prevent global warming, which is one of the most critical and urgent problems in the world.

Keywords: acid treatment, kaolinite, montmorillonite, pore volume, porosity, surface area

Procedia PDF Downloads 140

Authors: Giuliana Murfet, Heidi Behrens

Abstract:

Chronic disease is Australia’s biggest health concern and obesity the leading risk factor for many. Obesity and chronic disease have a higher representation in rural Tasmania, where levels of socio-disadvantage are also higher. People living outside major cities have less access to health services and poorer health outcomes. To help primary healthcare professionals manage obesity, the Australian NHMRC evidence-based clinical practice guidelines for management of overweight and obesity in adults were developed. They include recommendations for practice and models for obesity management. To our knowledge there has been no research conducted that investigates translation of these guidelines into practice in rural-regional areas; where implementation can be complicated by limited financial and staffing resources. Also, the systematic review that informed the guidelines revealed a lack of evidence for chronic disease models of obesity care. The aim was to establish and evaluate a multidisciplinary model for obesity management in a group of adult people with type 2 diabetes in a dispersed rural population in Australia. Extensive stakeholder engagement was undertaken to both garner support for an obesity clinic and develop a sustainable model of care. A comprehensive nurse practitioner-led outpatient model for obesity care was designed. Multidisciplinary obesity clinics for adults with type 2 diabetes including a dietitian, psychologist, physiotherapist and nurse practitioner were set up in the north-west of Tasmania at two geographically-rural towns. Implementation was underpinned by the NHMRC guidelines and recommendations focused on: assessment approaches; promotion of health benefits of weight loss; identification of relevant programs for individualising care; medication and bariatric surgery options for obesity management; and, the importance of long-term weight management. A clinical pathway for adult weight management is delivered by the multidisciplinary team with recognition of the impact of and adjustments needed for other comorbidities. The model allowed for intensification of intervention such as bariatric surgery according to recommendations, patient desires and suitability. A randomised controlled trial is ongoing, with the aim to evaluate standard care (diabetes-focused management) compared with an obesity-related approach with additional dietetic, physiotherapy, psychology and lifestyle advice. Key barriers and enablers to guideline implementation were identified that fall under the following themes: 1) health care delivery changes and the project framework development; 2) capacity and team-building; 3) stakeholder engagement; and, 4) the research project and partnerships. Engagement of not only local hospital but also state-wide health executives and surgical services committee were paramount to the success of the project. Staff training and collective development of the framework allowed for shared understanding. Staff capacity was increased with most taking on other activities (e.g., surgery coordination). Barriers were often related to differences of opinions in focus of the project; a desire to remain evidenced based (e.g., exercise prescription) without adjusting the model to allow for consideration of comorbidities. While barriers did exist and challenges overcome; the development of critical partnerships did enable the capacity for a potential model of obesity care for rural regional areas. Importantly, the findings contribute to the evidence base for models of diabetes and obesity care that coordinate limited resources.

Keywords: diabetes, interdisciplinary, model of care, obesity, rural regional

Procedia PDF Downloads 205

Authors: Ahmad Haidar

Abstract:

This research paper investigates the concept of modern management, concentrating on the development of managerial practices and the adoption of innovative strategies in response to the fast-changing business landscape caused by Artificial Intelligence (AI). The study begins by examining the historical context of management theories, tracing the progression from classical to contemporary models, and identifying key drivers of change. Through a comprehensive review of existing literature and case studies, this paper provides valuable insights into the principles and practices of modern management, offering a roadmap for organizations aiming to navigate the complexities of the contemporary business world. The paper examines the growing role of digital technology in modern management, focusing on incorporating AI, machine learning, and data analytics to streamline operations and facilitate informed decision-making. Moreover, the research highlights the emergence of new principles, such as adaptability, flexibility, public participation, trust, transparency, and digital mindset, as crucial components of modern management. Also, the role of business leaders is investigated by studying contemporary leadership styles, such as transformational, situational, and servant leadership, emphasizing the significance of emotional intelligence, empathy, and collaboration in fostering a healthy organizational culture. Furthermore, the research delves into the crucial role of environmental sustainability, corporate social responsibility (CSR), and corporate digital responsibility (CDR). Organizations strive to balance economic growth with ethical considerations and long-term viability. The primary research question for this study is: "What are the key principles, practices, and innovations that define modern management, and how can organizations effectively implement these strategies to thrive in the rapidly changing business landscape?." The research contributes to a comprehensive understanding of modern management by examining its historical context, the impact of digital technologies, the importance of contemporary leadership styles, and the role of CSR and CDR in today's business landscape.

Keywords: modern management, digital technology, leadership styles, adaptability, innovation, corporate social responsibility, organizational success, corporate digital responsibility

Procedia PDF Downloads 43

Authors: Mohd Javaid, Shahbaz Khan, Abid Haleem

Abstract:

Purpose: Large numbers of 3D printing technologies are now available for sophisticated applications in different fields. Additive manufacturing has established its dominance in design, development, and customisation of the product. In the era of developing technologies, there is a need to identify the appropriate technology for different application. In order to fulfil this need, two widely used printing technologies such as Fused Deposition Modeling (FDM), and Binding-Jet 3D Printing are compared for effective utilisation in the current scenario for different applications. Methodology: Systematic literature review conducted for both technologies with applications and associated factors enabling for the same. Appropriate MCDM tool is used to compare critical factors for both the technologies. Findings: Both technologies have their potential and capabilities to provide better direction to the industry. Additionally, this paper is helpful to develop a decision support system for the proper selection of technologies according to their continuum of applications and associated research and development capability. The vital issue is raw materials, and research-based material development is key to the sustainability of the developed technologies. FDM is a low-cost technology which provides high strength product as compared to binding jet technology. Researcher and companies can take benefits of this study to achieve the required applications in lesser resources. Limitations: Study has undertaken the comparison with the opinion of experts, which may not always be free from bias, and some own limitations of each technology. Originality: Comparison between these technologies will help to identify best-suited technology as per the customer requirements. It also provides development in this different field as per their extensive capability where these technologies can be successfully adopted. Conclusion: FDM and binding jet technology play an active role in industrial development. These help to assist the customisation and production of personalised parts cost-effectively. So, there is a need to understand how these technologies can provide these developments rapidly. These technologies help in easy changes or in making revised versions of the product, which is not easily possible in the conventional manufacturing system. High machine cost, the requirement of skilled human resources, low surface finish, and mechanical strength of product and material changing option is the main limitation of this technology. However, these limitations vary from technology to technology. In the future, these technologies are to be commercially viable for efficient usage in direct manufacturing of varied parts.

Keywords: 3D printing, comparison, fused deposition modeling, FDM, binding jet technology

Procedia PDF Downloads 84

Authors: Renu Saini, Deepak Agrawal

Abstract:

Background: Patients with head and spinal cord injury frequently have deranged coagulation profiles and require blood products transfusion perioperatively. Thromboelastography (TEG) is a ‘bedside’ global test of coagulation which may have role in deciding the need of transfusion in such patients. Aim: To assess the usefulness of TEG in department of neurosurgery in decreasing transfusion rates and coagulation-related mortality in traumatic head and spinal cord injury. Method and Methodology: A retrospective comparative study was carried out in the department of neurosurgery over a period of 1 year. There are two groups in this study. ‘Control’ group constitutes the patients in whom data was collected over 6 months (1/6/2009-31/12/2009) prior to installation of TEG machine. ‘Test’ group includes patients in whom data was collected over 6months (1/1/2013-30/6/2013) post TEG installation. Total no. of platelet, FFP, and cryoprecipitate transfusions were noted in both groups along with in hospital mortality and length of stay. Result: Both groups were matched in age and sex of patients, number of head and spinal cord injury cases, number of patients with thrombocytopenia and number of patients who underwent operation. Total 178 patients (135 head injury and 43 spinal cord injury patents) were admitted in neurosurgery department during time period June 2009 to December 2009 i.e. prior to TEG installation and after TEG installation a total of 243 patients(197 head injury and 46 spinal cord injury patents) were admitted. After TEG introduction platelet transfusion significantly reduced (p=0.000) compare to control group (67 units to 34 units). Mortality rate was found significantly reduced after installation (77 patients to 57 patients, P=0.000). Length of stay was reduced significantly (Prior installation 1-211days and after installation 1-115days, p=0.02). Conclusion: Bedside TEG can dramatically reduce platelet transfusion components requirement in department of neurosurgery. TEG also lead to a drastic decrease in mortality rate and length of stay in patients with traumatic head and spinal cord injuries. We recommend its use as a standard of care in the patients with traumatic head and spinal cord injuries.

Keywords: blood component transfusion, mortality, neurosurgery ICU, thromboelastography

Procedia PDF Downloads 304

Authors: Nihar Bheda

Abstract:

Protecting digital assets such as networks, systems, and data from advanced cyber threats is the aim of Digital Immunity Systems (DIS), which are a subset of cybersecurity. With features like continuous monitoring, coordinated reactions, and long-term adaptation, DIS seeks to mimic biological immunity. This minimizes downtime by automatically identifying and eliminating threats. Traditional security measures, such as firewalls and antivirus software, are insufficient for enterprises, such as healthcare providers, given the rapid evolution of cyber threats. The number of medical record breaches that have occurred in recent years is proof that attackers are finding healthcare data to be an increasingly valuable target. However, obstacles to enhancing security include outdated systems, financial limitations, and a lack of knowledge. DIS is an advancement in cyber defenses designed specifically for healthcare settings. Protection akin to an "immune system" is produced by core capabilities such as anomaly detection, access controls, and policy enforcement. Coordination of responses across IT infrastructure to contain attacks is made possible by automation and orchestration. Massive amounts of data are analyzed by AI and machine learning to find new threats. After an incident, self-healing enables services to resume quickly. The implementation of DIS is consistent with the healthcare industry's urgent requirement for resilient data security in light of evolving risks and strict guidelines. With resilient systems, it can help organizations lower business risk, minimize the effects of breaches, and preserve patient care continuity. DIS will be essential for protecting a variety of environments, including cloud computing and the Internet of medical devices, as healthcare providers quickly adopt new technologies. DIS lowers traditional security overhead for IT departments and offers automated protection, even though it requires an initial investment. In the near future, DIS may prove to be essential for small clinics, blood banks, imaging centers, large hospitals, and other healthcare organizations. Cyber resilience can become attainable for the whole healthcare ecosystem with customized DIS implementations.

Keywords: digital immunity system, cybersecurity, healthcare data, emerging technology

Procedia PDF Downloads 36

Authors: V. Jaime Contreras, S. Iliana Nunez, S. Mario Sanchez

Abstract:

Inside a manufacturing facility, we can find innumerable automatic and manual operations, all of which are relevant to the production process. Some of these processes add more value to the products more than others. Manual operations tend to add value to the product since they can be found in the final assembly area o final operations of the process. In this areas, where a mistake or accident can increase the cost of waste exponentially. To reduce or mitigate these costly mistakes, one approach is to rely on automation to eliminate the operator from the production line - requires a hefty investment and development of specialized machinery. In our approach, the center of the solution is the operator through sufficient and adequate instrumentation, real-time reporting and ergonomics. Efficiency and reduced cycle time can be achieved thorough the integration of Internet-of-Things (IoT) ready technologies into assembly operations to enhance the ergonomics of the workstations. Augmented reality visual aids, RFID triggered personalized workstation dimensions and real-time data transfer and reporting can help achieve these goals. In this case study, a standard work cell will be used for real-life data acquisition and a simulation software to extend the data points beyond the test cycle. Three comparison scenarios will run in the work cell. Each scenario will introduce a dimension of the ergonomics to measure its impact independently. Furthermore, the separate test will determine the limitations of the technology and provide a reference for operating costs and investment required. With the ability, to monitor costs, productivity, cycle time and scrap/waste in real-time the ROI (return on investment) can be determined at the different levels to integration. This case study will help to show that ergonomics in the assembly lines can make significant impact when IoT technologies are introduced. Ergonomics can effectively reduce waste and increase productivity with minimal investment if compared with setting up to custom machine.

Keywords: augmented reality visual aids, ergonomics, real-time data acquisition and reporting, RFID triggered workstation dimensions

Procedia PDF Downloads 190

Authors: Shahnam Behnam Malekzadeh, Ian Kerr, Tyson Kaempffer, Teague Harper, Andrew Watson

Abstract:

The Site C Hydroelectric dam is currently being constructed in north-eastern British Columbia on sub-horizontal sedimentary strata that dip approximately 15 meters from one bank of the Peace River to the other. More than 615 pressure sensors (Vibrating Wire Piezometers) have been installed on bedding planes (BPs) since construction began, with over 80 more planned before project completion. These pressure measurements are essential to monitor the stability of the rock foundation during and after construction and for dam safety purposes. BPs are identified by their clay gouge infilling, which varies in thickness from less than 1 to 20 mm and can be challenging to identify as the core drilling process often disturbs or washes away the gouge material. Without the use of depth predictions from nearby boreholes, stratigraphic markers, and downhole geophysical data, it is difficult to confidently identify BP targets for the sensors. In this paper, a Case-Based Reasoning (CBR) method was used to develop an empirical model called the Bedding Plane Elevation Prediction (BPEP) to help geologists and geotechnical engineers to predict geological features and bedding planes at new locations in a fast and accurate manner. To develop CBR, a database was developed based on 64 pressure sensors already installed on key bedding planes BP25, BP28, and BP31 on the Right Bank, including bedding plane elevations and coordinates. Thirteen (20%) of the most recent cases were selected to validate and evaluate the accuracy of the developed model, while the similarity was defined as the distance between previous cases and recent cases to predict the depth of significant BPs. The average difference between actual BP elevations and predicted elevations for above BPs was ±55cm, while the actual results showed that 69% of predicted elevations were within ±79 cm of actual BP elevations while 100% of predicted elevations for new cases were within ±99cm range. Eventually, the actual results will be used to develop the database and improve BPEP to perform as a learning machine to predict more accurate BP elevations for future sensor installations.

Keywords: case-based reasoning, geological feature, geology, piezometer, pressure sensor, core logging, dam construction

Procedia PDF Downloads 56

Authors: Tomasz Dubiel, Tadeusz Balawender, Miroslaw Osetek

Abstract:

The paper presents the influence of non-metallic inclusions on the multi-stage forging process and the mechanical properties of the dodecagon socket bolt used in the automotive industry. The detected metallurgical defect was so large that it directly influenced the mechanical properties of the bolt and resulted in failure to meet the requirements of the mechanical property class. In order to assess the defect, an X-ray examination and metallographic examination of the defective bolt were performed, showing exogenous non-metallic inclusion. The size of the defect on the cross-section was 0.531 [mm] in width and 1.523 [mm] in length; the defect was continuous along the entire axis of the bolt. In analysis, a FEM simulation of the multi-stage forging process was designed, taking into account a non-metallic inclusion parallel to the sample axis, reflecting the studied case. The process of defect propagation due to material upset in the head area was analyzed. The final forging stage in shaping the dodecagonal socket and filling the flange area was particularly studied. The effect of the defect was observed to significantly reduce the effective cross-section as a result of the expansion of the defect perpendicular to the axis of the bolt. The mechanical properties of products with and without the defect were analyzed. In the first step, the hardness test confirmed that the required value for the mechanical class 8.8 of both bolt types was obtained. In the second step, the bolts were subjected to a static tensile test. The bolts without the defect gave a positive result, while all 10 bolts with the defect gave a negative result, achieving a tensile strength below the requirements. Tensile strength tests were confirmed by metallographic tests and FEM simulation with perpendicular inclusion spread in the area of the head. The bolts were damaged directly under the bolt head, which is inconsistent with the requirements of ISO 898-1. It has been shown that non-metallic inclusions with orientation in accordance with the axis of the bolt can directly cause loss of functionality and these defects should be detected even before assembling in the machine element.

Keywords: continuous casting, multi-stage forging, non-metallic inclusion, upset bolt head

Procedia PDF Downloads 135

Authors: Jaydip M. Desai, Antonio Valdevit, Arthur Ritter

Abstract:

Robotic surgery is used to enhance minimally invasive surgical procedure. It provides greater degree of freedom for surgical tools but lacks of haptic feedback system to provide sense of touch to the surgeon. Surgical robots work on master-slave operation, where user is a master and robotic arms are the slaves. Current, surgical robots provide precise control of the surgical tools, but heavily rely on visual feedback, which sometimes cause damage to the inner organs. The goal of this research was to design and develop a real-time simulink based robotic system to study force feedback mechanism during instrument-object interaction. Setup includes three Velmex XSlide assembly (XYZ Stage) for three dimensional movement, an end effector assembly for forceps, electronic circuit for four strain gages, two Novint Falcon 3D gaming controllers, microcontroller board with linear actuators, MATLAB and Simulink toolboxes. Strain gages were calibrated using Imada Digital Force Gauge device and tested with a hard-core wire to measure instrument-object interaction in the range of 0-35N. Designed simulink model successfully acquires 3D coordinates from two Novint Falcon controllers and transfer coordinates to the XYZ stage and forceps. Simulink model also reads strain gages signal through 10-bit analog to digital converter resolution of a microcontroller assembly in real time, converts voltage into force and feedback the output signals to the Novint Falcon controller for force feedback mechanism. Experimental setup allows user to change forward kinematics algorithms to achieve the best-desired movement of the XYZ stage and forceps. This project combines haptic technology with surgical robot to provide sense of touch to the user controlling forceps through machine-computer interface.

Keywords: surgical robot, haptic feedback, MATLAB, strain gage, simulink

Procedia PDF Downloads 510

Authors: Angelis P. Barlampas

Abstract:

The purpose of this study is to inform the reader, about the various applications of artificial intelligence (AI), in cardiac imaging. AI grows fast and its role is crucial in medical specialties, which use large amounts of digital data, that are very difficult or even impossible to be managed by human beings and especially doctors.Artificial intelligence (AI) refers to the ability of computers to mimic human cognitive function, performing tasks such as learning, problem-solving, and autonomous decision making based on digital data. Whereas AI describes the concept of using computers to mimic human cognitive tasks, machine learning (ML) describes the category of algorithms that enable most current applications described as AI. Some of the current applications of AI in cardiac imaging are the follows: Ultrasound: Automated segmentation of cardiac chambers across five common views and consequently quantify chamber volumes/mass, ascertain ejection fraction and determine longitudinal strain through speckle tracking. Determine the severity of mitral regurgitation (accuracy > 99% for every degree of severity). Identify myocardial infarction. Distinguish between Athlete’s heart and hypertrophic cardiomyopathy, as well as restrictive cardiomyopathy and constrictive pericarditis. Predict all-cause mortality. CT Reduce radiation doses. Calculate the calcium score. Diagnose coronary artery disease (CAD). Predict all-cause 5-year mortality. Predict major cardiovascular events in patients with suspected CAD. MRI Segment of cardiac structures and infarct tissue. Calculate cardiac mass and function parameters. Distinguish between patients with myocardial infarction and control subjects. It could potentially reduce costs since it would preclude the need for gadolinium-enhanced CMR. Predict 4-year survival in patients with pulmonary hypertension. Nuclear Imaging Classify normal and abnormal myocardium in CAD. Detect locations with abnormal myocardium. Predict cardiac death. ML was comparable to or better than two experienced readers in predicting the need for revascularization. AI emerge as a helpful tool in cardiac imaging and for the doctors who can not manage the overall increasing demand, in examinations such as ultrasound, computed tomography, MRI, or nuclear imaging studies.

Keywords: artificial intelligence, cardiac imaging, ultrasound, MRI, CT, nuclear medicine

Procedia PDF Downloads 47

Authors: Fabiana Franceschi, Martha Cobo, Manuel Figueredo

Abstract:

Bogotá, the capital of Colombia, is its largest city and one of the most polluted in Latin America due to the fast economic growth over the last ten years. Bogotá has been affected by high pollution events which led to the high concentration of PM10 and NO2, exceeding the local 24-hour legal limits (100 and 150 g/m3 each). The most important pollutants in the city are PM10 and PM2.5 (which are associated with respiratory and cardiovascular problems) and it is known that their concentrations in the atmosphere depend on the local meteorological factors. Therefore, it is necessary to establish a relationship between the meteorological variables and the concentrations of the atmospheric pollutants such as PM10, PM2.5, CO, SO2, NO2 and O3. This study aims to determine the interrelations between meteorological variables and air pollutants in Bogotá, using data mining techniques. Data from 13 monitoring stations were collected from the Bogotá Air Quality Monitoring Network within the period 2010-2015. The Principal Component Analysis (PCA) algorithm was applied to obtain primary relations between all the parameters, and afterwards, the K-means clustering technique was implemented to corroborate those relations found previously and to find patterns in the data. PCA was also used on a per shift basis (morning, afternoon, night and early morning) to validate possible variation of the previous trends and a per year basis to verify that the identified trends have remained throughout the study time. Results demonstrated that wind speed, wind direction, temperature, and NO2 are the most influencing factors on PM10 concentrations. Furthermore, it was confirmed that high humidity episodes increased PM2,5 levels. It was also found that there are direct proportional relationships between O3 levels and wind speed and radiation, while there is an inverse relationship between O3 levels and humidity. Concentrations of SO2 increases with the presence of PM10 and decreases with the wind speed and wind direction. They proved as well that there is a decreasing trend of pollutant concentrations over the last five years. Also, in rainy periods (March-June and September-December) some trends regarding precipitations were stronger. Results obtained with K-means demonstrated that it was possible to find patterns on the data, and they also showed similar conditions and data distribution among Carvajal, Tunal and Puente Aranda stations, and also between Parque Simon Bolivar and las Ferias. It was verified that the aforementioned trends prevailed during the study period by applying the same technique per year. It was concluded that PCA algorithm is useful to establish preliminary relationships among variables, and K-means clustering to find patterns in the data and understanding its distribution. The discovery of patterns in the data allows using these clusters as an input to an Artificial Neural Network prediction model.

Keywords: air pollution, air quality modelling, data mining, particulate matter

Procedia PDF Downloads 235

Authors: Raphael C. Zero, Thiago A. S. S. Rocha, Marita V. Cardozo, Caio C. C. Santos, Alisson D. S. Fechis, Antonio C. Shimano, FabríCio S. Oliveira

Abstract:

Biomechanical analysis is an important factor in tissue studies. The objective of this study was to determine the feasibility of a new anatomical technique and quantify the changes in skin and the jejunum resistance of cats’ corpses throughout the process. Eight adult cat cadavers were used. For every kilogram of weight, 120ml of fixative solution (95% 96GL ethyl alcohol and 5% pure glycerin) was applied via the external common carotid artery. Next, the carcasses were placed in a container with 96 GL ethyl alcohol for 60 days. After fixing, all carcasses were preserved in a 30% sodium chloride solution for 60 days. Before fixation, control samples were collected from fresh cadavers and after fixation, three skin and jejunum fragments from each cadaver were tested monthly for strength and displacement until complete rupture in a universal testing machine. All results were analyzed by F-test (P <0.05). In the jejunum, the force required to rupture the fresh samples and the samples fixed in alcohol for 60 days was 31.27±19.14N and 29.25±11.69N, respectively. For the samples preserved in the sodium chloride solution for 30 and 60 days, the strength was 26.17±16.18N and 30.57±13.77N, respectively. In relation to the displacement required for the rupture of the samples, the values of fresh specimens and those fixed in alcohol for 60 days was 2.79±0.73mm and 2.80±1.13mm, respectively. For the samples preserved for 30 and 60 days with sodium chloride solution, the displacement was 2.53±1.03mm and 2.83±1.27mm, respectively. There was no statistical difference between the samples (P=0.68 with respect to strength, and P=0.75 with respect to displacement). In the skin, the force needed to rupture the fresh samples and the samples fixed for 60 days in alcohol was 223.86±131.5N and 211.86±137.53N respectively. For the samples preserved in sodium chloride solution for 30 and 60 days, the force was 227.73±129.06 and 224.78±143.83N, respectively. In relation to the displacement required for the rupture of the samples, the values of fresh specimens and those fixed in alcohol for 60 days were 3.67±1.03mm and 4.11±0.87mm, respectively. For the samples preserved for 30 and 60 days with sodium chloride solution, the displacement was 4.21±0.93mm and 3.93±0.71mm, respectively. There was no statistical difference between the samples (P=0.65 with respect to strength, and P=0.98 with respect to displacement). The resistance of the skin and intestines of the cat carcasses suffered little change when subjected to alcohol fixation and preservation in sodium chloride solution, each for 60 days, which is promising for use in surgery training. All experimental procedures were approved by the Municipal Legal Department (protocol 02.2014.000027-1). The project was funded by FAPESP (protocol 2015-08259-9).

Keywords: anatomy, conservation, fixation, small animal

Procedia PDF Downloads 268

Authors: Sreejith Jayachandran, Mojtaba Ghods, Morteza Mohammadzaheri

Abstract:

The modern busy world is running behind new embedded technologies based on computers and software; meanwhile, some people forget to do their health condition and regular medical check-ups. Some of them postpone medical check-ups due to a lack of time and convenience, while others skip these regular evaluations and medical examinations due to huge medical bills and hospital expenses. Engineers and medical experts have come together to give birth to a new device in the telemonitoring system capable of monitoring, checking, and evaluating the health status of the human body remotely through the internet for the needs of all kinds of people. The remote health monitoring device is a microcontroller-based embedded unit. Various types of sensors in this device are connected to the human body, and with the help of an Arduino UNO board, the required analogue data is collected from the sensors. The microcontroller on the Arduino board processes the analogue data collected in this way into digital data and transfers that information to the cloud, and stores it there, and the processed digital data is instantly displayed through the LCD attached to the machine. By accessing the cloud storage with a username and password, the concerned person’s health care teams/doctors and other health staff can collect this data for the assessment and follow-up of that patient. Besides that, the family members/guardians can use and evaluate this data for awareness of the patient's current health status. Moreover, the system is connected to a Global Positioning System (GPS) module. In emergencies, the concerned team can position the patient or the person with this device. The setup continuously evaluates and transfers the data to the cloud, and also the user can prefix a normal value range for the evaluation. For example, the blood pressure normal value is universally prefixed between 80/120 mmHg. Similarly, the RHMS is also allowed to fix the range of values referred to as normal coefficients. This IoT-based miniature system (11×10×10) cm³ with a low weight of 500 gr only consumes 10 mW. This smart monitoring system is manufactured with 100 GBP, which can be used not only for health systems, it can be used for numerous other uses including aerospace and transportation sections.

Keywords: embedded technology, telemonitoring system, microcontroller, Arduino UNO, cloud storage, global positioning system, remote health monitoring system, alert system

Procedia PDF Downloads 62

Authors: Benjamin Beltzung, Marie Pelé, Julien P. Renoult, Cédric Sueur

Abstract:

To this day, it is not known if drawing is specifically human behavior or if this behavior finds its origins in ancestor species. An interesting window to enlighten this question is to analyze the drawing behavior in genetically close to human species, such as non-human primate species. A good candidate for this approach is the orangutan, who shares 97% of our genes and exhibits multiple human-like behaviors. Focusing on figurative aspects may not be suitable for orangutans’ drawings, which may appear as scribbles but may have meaning. A manual feature selection would lead to an anthropocentric bias, as the features selected by humans may not match with those relevant for orangutans. In the present study, we used deep learning to analyze the drawings of a female orangutan named Molly († in 2011), who has produced 1,299 drawings in her last five years as part of a behavioral enrichment program at the Tama Zoo in Japan. We investigate multiple ways to decipher Molly’s drawings. First, we demonstrate the existence of differences between seasons by training a deep learning model to classify Molly’s drawings according to the seasons. Then, to understand and interpret these seasonal differences, we analyze how the information spreads within the network, from shallow to deep layers, where early layers encode simple local features and deep layers encode more complex and global information. More precisely, we investigate the impact of feature complexity on classification accuracy through features extraction fed to a Support Vector Machine. Last, we leverage style transfer to dissociate features associated with drawing style from those describing the representational content and analyze the relative importance of these two types of features in explaining seasonal variation. Content features were relevant for the classification, showing the presence of meaning in these non-figurative drawings and the ability of deep learning to decipher these differences. The style of the drawings was also relevant, as style features encoded enough information to have a classification better than random. The accuracy of style features was higher for deeper layers, demonstrating and highlighting the variation of style between seasons in Molly’s drawings. Through this study, we demonstrate how deep learning can help at finding meanings in non-figurative drawings and interpret these differences.

Keywords: cognition, deep learning, drawing behavior, interpretability

Procedia PDF Downloads 130