Search results for: classification algorithm

Authors: El Harraj Abdeslam, Raissouni Naoufal

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The detection of moving objects from a video image sequences is very important for object tracking, activity recognition, and behavior understanding in video surveillance. The most used approach for moving objects detection / tracking is background subtraction algorithms. Many approaches have been suggested for background subtraction. But, these are illumination change sensitive and the solutions proposed to bypass this problem are time consuming. In this paper, we propose a robust yet computationally efficient background subtraction approach and, mainly, focus on the ability to detect moving objects on dynamic scenes, for possible applications in complex and restricted access areas monitoring, where moving and motionless persons must be reliably detected. It consists of three main phases, establishing illumination changes in variance, background/foreground modeling and morphological analysis for noise removing. We handle illumination changes using Contrast Limited Histogram Equalization (CLAHE), which limits the intensity of each pixel to user determined maximum. Thus, it mitigates the degradation due to scene illumination changes and improves the visibility of the video signal. Initially, the background and foreground images are extracted from the video sequence. Then, the background and foreground images are separately enhanced by applying CLAHE. In order to form multi-modal backgrounds we model each channel of a pixel as a mixture of K Gaussians (K=5) using Gaussian Mixture Model (GMM). Finally, we post process the resulting binary foreground mask using morphological erosion and dilation transformations to remove possible noise. For experimental test, we used a standard dataset to challenge the efficiency and accuracy of the proposed method on a diverse set of dynamic scenes.

Keywords: video surveillance, background subtraction, contrast limited histogram equalization, illumination invariance, object tracking, object detection, behavior understanding, dynamic scenes

Procedia PDF Downloads 248

Authors: Yong Min You

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The study on the torque ripple of Permanent Magnet Synchronous Motors (PMSMs) has been rapidly progressed, which effects on the noise and vibration of the electric vehicle. There are several ways to reduce torque ripple, which are the increase in the number of slots and poles, the notch of the rotor and stator teeth, and the skew of the rotor and stator. However, the conventional methods have the disadvantage in terms of material cost and productivity. The demagnetization characteristic of PMSMs must be attained for electric vehicle application. Due to rare earth supply issue, the demand for Dy-free permanent magnet has been increasing, which can be applied to PMSMs for the electric vehicle. Dy-free permanent magnet has lower the coercivity; the demagnetization characteristic has become more significant. To improve the torque ripple as well as the demagnetization characteristics, which are significant parameters for electric vehicle application, an unequal air-gap model is proposed for a PMSM. A shape optimization is performed to optimize the design variables of an unequal air-gap model. Optimal design variables are the shape of an unequal air-gap and the angle between V-shape magnets. An optimization process is performed by Latin Hypercube Sampling (LHS), Kriging Method, and Genetic Algorithm (GA). Finite element analysis (FEA) is also utilized to analyze the torque and demagnetization characteristics. The torque ripple and the demagnetization temperature of the initial model of 45kW PMSM with unequal air-gap are 10 % and 146.8 degrees, respectively, which are reaching a critical level for electric vehicle application. Therefore, the unequal air-gap model is proposed, and then an optimization process is conducted. Compared to the initial model, the torque ripple of the optimized unequal air-gap model was reduced by 7.7 %. In addition, the demagnetization temperature of the optimized model was also increased by 1.8 % while maintaining the efficiency. From these results, a shape optimized unequal air-gap PMSM has shown the usefulness of an improvement in the torque ripple and demagnetization temperature for the electric vehicle.

Keywords: permanent magnet synchronous motor, optimal design, finite element method, torque ripple

Procedia PDF Downloads 267

Authors: Mahmoud Huleihel, George Abu-Aqil, Manal Suleiman, Klaris Riesenberg, Itshak Lapidot, Ahmad Salman

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Urinary tract infections (UTIs) are considered to be the most common bacterial infections worldwide, which are caused mainly by Escherichia (E.) coli (about 80%). Klebsiella pneumoniae (about 10%) and Pseudomonas aeruginosa (about 6%). Although antibiotics are considered as the most effective treatment for bacterial infectious diseases, unfortunately, most of the bacteria already have developed resistance to the majority of the commonly available antibiotics. Therefore, it is crucial to identify the infecting bacteria and to determine its susceptibility to antibiotics for prescribing effective treatment. Classical methods are time consuming, require ~48 hours for determining bacterial susceptibility. Thus, it is highly urgent to develop a new method that can significantly reduce the time required for determining both infecting bacterium at the species level and diagnose its susceptibility to antibiotics. Fourier-Transform Infrared (FTIR) spectroscopy is well known as a sensitive and rapid method, which can detect minor molecular changes in bacterial genome associated with the development of resistance to antibiotics. The main goal of this study is to examine the potential of FTIR spectroscopy, in tandem with machine learning algorithms, to identify the infected bacteria at the species level and to determine E. coli susceptibility to different antibiotics directly from patients' urine in about 30minutes. For this goal, 1600 different E. coli isolates were isolated for different patients' urine sample, measured by FTIR, and analyzed using different machine learning algorithm like Random Forest, XGBoost, and CNN. We achieved 98% success in isolate level identification and 89% accuracy in susceptibility determination.

Keywords: urinary tract infections (UTIs), E. coli, Klebsiella pneumonia, Pseudomonas aeruginosa, bacterial, susceptibility to antibiotics, infrared microscopy, machine learning

Procedia PDF Downloads 156

Authors: Emiel Caron

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Patent databases contain patent related data, organized in a relational data model, and are used to produce various patent statistics. These databases store raw data about scientific references cited by patents. For example, Patstat holds references to tens of millions of scientific journal publications and conference proceedings. These references might be used to connect patent databases with bibliographic databases, e.g. to study to the relation between science, technology, and innovation in various domains. Problematic in such studies is the low data quality of the references, i.e. they are often ambiguous, unstructured, and incomplete. Moreover, a complete bibliographic reference is stored in only one attribute. Therefore, a computerized cleaning and disambiguation method for large patent databases is developed in this work. The method uses rule-based scoring and clustering. The rules are based on bibliographic metadata, retrieved from the raw data by regular expressions, and are transparent and adaptable. The rules in combination with string similarity measures are used to detect pairs of records that are potential duplicates. Due to the scoring, different rules can be combined, to join scientific references, i.e. the rules reinforce each other. The scores are based on expert knowledge and initial method evaluation. After the scoring, pairs of scientific references that are above a certain threshold, are clustered by means of single-linkage clustering algorithm to form connected components. The method is designed to disambiguate all the scientific references in the Patstat database. The performance evaluation of the clustering method, on a large golden set with highly cited papers, shows on average a 99% precision and a 95% recall. The method is therefore accurate but careful, i.e. it weighs precision over recall. Consequently, separate clusters of high precision are sometimes formed, when there is not enough evidence for connecting scientific references, e.g. in the case of missing year and journal information for a reference. The clusters produced by the method can be used to directly link the Patstat database with bibliographic databases as the Web of Science or Scopus.

Keywords: clustering, data cleaning, data disambiguation, data mining, patent analysis, scientometrics

Procedia PDF Downloads 181

Authors: Daniele Calvi, Loris Franchi, Sabrina Corpino

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Thanks to the latest Mars mission, the planetary exploration has made enormous strides over the past ten years increasing the interest of the scientific community and beyond. These missions aim to fulfill many complex operations which are of paramount importance to mission success. Among these, a special mention goes to the Entry, Descent and Landing (EDL) functions which require a dedicated system to overcome all the obstacles of these critical phases. The general objective of the system is to safely bring the spacecraft from orbital conditions to rest on the planet surface, following the designed mission profile. For this reason, this work aims to develop a simulation tool integrating the re-entry trajectory algorithm in order to support the EDL design during the preliminary phase of the mission. This tool was used on a reference unmanned mission, whose objective is finding bio-evidence and bio-hazards on Martian (sub)surface in order to support the future manned mission. Regarding the concept of operations (CONOPS) of the mission, it concerns the use of Space Penetrator Systems (SPS) that will descend on Mars surface following a ballistic fall and will penetrate the ground after the impact with the surface (around 50 and 300 cm of depth). Each SPS shall contain all the instrumentation required to sample and make the required analyses. Respecting the low-cost and low-mass requirements, as result of the tool, an Entry Descent and Impact (EDI) system based on inflatable structure has been designed. Hence, a solution could be the one chosen by Finnish Meteorological Institute in the Mars Met-Net mission, using an inflatable Thermal Protection System (TPS) called Inflatable Braking Unit (IBU) and an additional inflatable decelerator. Consequently, there are three configurations during the EDI: at altitude of 125 km the IBU is inflated at speed 5.5 km/s; at altitude of 16 km the IBU is jettisoned and an Additional Inflatable Braking Unit (AIBU) is inflated; Lastly at about 13 km, the SPS is ejected from AIBU and it impacts on the Martian surface. Since all parameters are evaluated, it is possible to confirm that the chosen EDI system and strategy verify the requirements of the mission.

Keywords: EDL, Mars, mission, SPS, TPS

Procedia PDF Downloads 157

Authors: Skyler Kim

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An early diagnosis of leukemia has always been a challenge to doctors and hematologists. On a worldwide basis, it was reported that there were approximately 350,000 new cases in 2012, and diagnosing leukemia was time-consuming and inefficient because of an endemic shortage of flow cytometry equipment in current clinical practice. As the number of medical diagnosis tools increased and a large volume of high-quality data was produced, there was an urgent need for more advanced data analysis methods. One of these methods was the AI approach. This approach has become a major trend in recent years, and several research groups have been working on developing these diagnostic models. However, designing and implementing a leukemia diagnostic system in real clinical environments based on a deep learning approach with larger sets remains complex. Leukemia is a major hematological malignancy that results in mortality and morbidity throughout different ages. We decided to select acute lymphocytic leukemia to develop our diagnostic system since acute lymphocytic leukemia is the most common type of leukemia, accounting for 74% of all children diagnosed with leukemia. The results from this development work can be applied to all other types of leukemia. To develop our model, the Kaggle dataset was used, which consists of 15135 total images, 8491 of these are images of abnormal cells, and 5398 images are normal. In this paper, we design and implement a leukemia diagnostic system in a real clinical environment based on deep learning approaches with larger sets. The proposed diagnostic system has the function of detecting and classifying leukemia. Different from other AI approaches, we explore hybrid architectures to improve the current performance. First, we developed two independent convolutional neural network models: VGG19 and ResNet50. Then, using both VGG19 and ResNet50, we developed a hybrid deep learning architecture employing transfer learning techniques to extract features from each input image. In our approach, fusing the features from specific abstraction layers can be deemed as auxiliary features and lead to further improvement of the classification accuracy. In this approach, features extracted from the lower levels are combined into higher dimension feature maps to help improve the discriminative capability of intermediate features and also overcome the problem of network gradient vanishing or exploding. By comparing VGG19 and ResNet50 and the proposed hybrid model, we concluded that the hybrid model had a significant advantage in accuracy. The detailed results of each model’s performance and their pros and cons will be presented in the conference.

Keywords: acute lymphoblastic leukemia, hybrid model, leukemia diagnostic system, machine learning

Procedia PDF Downloads 179

Authors: Samir Chawdhury, Guido Morgenthal

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The paper presents a novel extension of Vortex Particle Methods (VPM) where the study aims to reproduce a template simulation of complex flow field that is generated from impulsively started flow past an upstream bluff body at certain Reynolds number Re-Vibration of a structural system under upstream wake flow is often considered its governing design criteria. Therefore, the attention is given in this study especially for the reproduction of wake flow simulation. The basic methodology for the implementation of the flow reproduction requires the downstream velocity sampling from the template flow simulation; therefore, at particular distances from the upstream section the instantaneous velocity components are sampled using a series of square sampling-cells arranged vertically where each of the cell contains four velocity sampling points at its corner. Since the grid free Lagrangian VPM algorithm discretises vorticity on particle elements, the method requires transformation of the velocity components into vortex circulation, and finally the simulation of the reproduction of the template flow field by seeding these vortex circulations or particles into a free stream flow. It is noteworthy that the vortex particles have to be released into the free stream exactly at same rate of velocity sampling. Studies have been done, specifically, in terms of different sampling rates and velocity sampling positions to find their effects on flow reproduction quality. The quality assessments are mainly done, using a downstream flow monitoring profile, by comparing the characteristic wind flow profiles using several statistical turbulence measures. Additionally, the comparisons are performed using velocity time histories, snapshots of the flow fields, and the vibration of a downstream bluff section by performing wake buffeting analyses of the section under the original and reproduced wake flows. Convergence study is performed for the validation of the method. The study also describes the possibilities how to achieve flow reproductions with less computational effort.

Keywords: vortex particle method, wake flow, flow reproduction, wake buffeting analysis

Procedia PDF Downloads 301

Authors: Rohini Hariharan, Yazhini R, Bhamidipati Naga Shrikarti

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In the realm of cricket, particularly within the context of the Indian Premier League (IPL), the ability to predict team scores accurately holds significant importance for both cricket enthusiasts and stakeholders alike. This paper presents a comprehensive study on IPL score prediction utilizing various machine learning algorithms, including Support Vector Machines (SVM), XGBoost, Multiple Regression, Linear Regression, K-nearest neighbors (KNN), and Random Forest. Through meticulous data preprocessing, feature engineering, and model selection, we aimed to develop a robust predictive framework capable of forecasting team scores with high precision. Our experimentation involved the analysis of historical IPL match data encompassing diverse match and player statistics. Leveraging this data, we employed state-of-the-art machine learning techniques to train and evaluate the performance of each model. Notably, Multiple Regression emerged as the top-performing algorithm, achieving an impressive accuracy of 77.19% and a precision of 54.05% (within a threshold of +/- 10 runs). This research contributes to the advancement of sports analytics by demonstrating the efficacy of machine learning in predicting IPL team scores. The findings underscore the potential of advanced predictive modeling techniques to provide valuable insights for cricket enthusiasts, team management, and betting agencies. Additionally, this study serves as a benchmark for future research endeavors aimed at enhancing the accuracy and interpretability of IPL score prediction models.

Keywords: indian premier league (IPL), cricket, score prediction, machine learning, support vector machines (SVM), xgboost, multiple regression, linear regression, k-nearest neighbors (KNN), random forest, sports analytics

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Authors: Zhuanfang Fred Zhang, Tim C. Johnson, Yilin Fang, Chris E. Strickland

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A variety of geophysical techniques are available to image the opaque subsurface with little or no contact with the soil. It is common to conduct time-lapse surveys of different types for a given site for improved results of subsurface imaging. Regardless of the chosen survey methods, it is often a challenge to process the massive amount of survey data. The currently available software applications are generally based on the one-dimensional assumption for a desktop personal computer. Hence, they are usually incapable of imaging the three-dimensional (3D) processes/variables in the subsurface of reasonable spatial scales; the maximum amount of data that can be inverted simultaneously is often very small due to the capability limitation of personal computers. Presently, high-performance or integrating software that enables real-time integration of multi-process geophysical methods is needed. E4D-MP enables the integration and inversion of time-lapsed large-scale data surveys from geophysical methods. Using the supercomputing capability and parallel computation algorithm, E4D-MP is capable of processing data across vast spatiotemporal scales and in near real time. The main code and the modules of E4D-MP for inverting individual or combined data sets of time-lapse 3D electrical resistivity, spectral induced polarization, and gravity surveys have been developed and demonstrated for sub-surface imaging. E4D-MP provides capability of imaging the processes (e.g., liquid or gas flow, solute transport, cavity development) and subsurface properties (e.g., rock/soil density, conductivity) critical for successful control of environmental engineering related efforts such as environmental remediation, carbon sequestration, geothermal exploration, and mine land reclamation, among others.

Keywords: gravity survey, high-performance computing, sub-surface monitoring, electrical resistivity tomography

Procedia PDF Downloads 145

Authors: Srinaath Anbu Durai, Wang Zhaoxia

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Twitter sentiment has been used as a predictor to predict price values or trends in both the stock market and housing market. The pioneering works in this stream of research drew upon works in behavioural economics to show that sentiment or emotions impact economic decisions. Latest works in this stream focus on the algorithm used as opposed to the data used. A literature review of works in this stream through the lens of data used shows that there is a paucity of work that considers the impact of sentiments caused due to an external factor on either the stock or the housing market. This is despite an abundance of works in behavioural economics that show that sentiment or emotions caused due to an external factor impact economic decisions. To address this gap, this research studies the impact of Twitter sentiment pertaining to the Covid-19 pandemic on resale Housing Development Board (HDB) apartment prices in Singapore. It leverages SNSCRAPE to collect tweets pertaining to Covid-19 for sentiment analysis, lexicon based tools VADER and TextBlob are used for sentiment analysis, Granger Causality is used to examine the relationship between Covid-19 cases and the sentiment score, and neural networks are leveraged as prediction models. Twitter sentiment pertaining to Covid-19 as a predictor of HDB price in Singapore is studied in comparison with the traditional predictors of housing prices i.e., the structural and neighbourhood characteristics. The results indicate that using Twitter sentiment pertaining to Covid19 leads to better prediction than using only the traditional predictors and performs better as a predictor compared to two of the traditional predictors. Hence, Twitter sentiment pertaining to an external factor should be considered as important as traditional predictors. This paper demonstrates the real world economic applications of sentiment analysis of Twitter data.

Keywords: sentiment analysis, Covid-19, housing price prediction, tweets, social media, Singapore HDB, behavioral economics, neural networks

Procedia PDF Downloads 98

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

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Ensuring accurate radiotherapy with carbon therapy requires precise monitoring of radiation dose distribution within the patient's body. This monitoring is essential for targeted tumor treatment, minimizing harm to healthy tissues, and improving treatment effectiveness while lowering side effects. In our investigation, we employed a methodological approach to monitor secondary proton doses in carbon therapy using Monte Carlo simulations. Initially, Geant4 simulations were utilized to extract the initial positions of secondary particles formed during interactions between carbon ions and water. These particles included protons, gamma rays, alpha particles, neutrons, and tritons. Subsequently, we studied the relationship between the carbon ion beam and these secondary particles. Interaction Vertex Imaging (IVI) is valuable for monitoring dose distribution in carbon therapy. It provides details about the positions and amounts of secondary particles, particularly protons. The IVI method depends on charged particles produced during ion fragmentation to gather information about the range by reconstructing particle trajectories back to their point of origin, referred to as the vertex. In our simulations regarding carbon ion therapy, we observed a strong correlation between some secondary particles and the range of carbon ions. However, challenges arose due to the target's unique elongated geometry, which hindered the straightforward transmission of forward-generated protons. Consequently, the limited protons that emerged mostly originated from points close to the target entrance. The trajectories of fragments (protons) were approximated as straight lines, and a beam back-projection algorithm, using recorded interaction positions in Si detectors, was developed to reconstruct vertices. The analysis revealed a correlation between the reconstructed and actual positions.

Keywords: radiotherapy, carbon therapy, monitoring of radiation dose, interaction vertex imaging

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Authors: Naum Simanovsky, Vladimir Goldman, Michael Zaidman

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Background: The optimal algorithm for the management of symptomatic tarsal coalition is still under discussion in pediatric literature. It's debatable what surgical steps are essential to achieve the best outcome. Method: The investigators retrospectively reviewed the records of twelve patients with symptomatic tarsal coalition that were treated operatively between 2017 and 2019. Only painful flat feet were operated. Two patients were excluded from the study due to lack of sufficient follow-up. Ten of eleven feet were treated with the combination of calcaneal lengthening osteotomy (CLO) and resection of coalition (RC). Only one foot was operated with CLO alone. In half of our patients, Achilles lengthening was performed. For two children, medial plication was added. Short leg cast was applied to all children for 6-8 weeks, and soft shoe insoles for medial arch support were prescribed after. Demographic, clinical, and radiographic records were reviewed. The outcome was evaluated using American Orthopedic Foot and Ankle Society (AOFAS) Ankle Hindfoot Score. Results: There were seven boys and three girls. The mean age at the time of surgery was 13.9 (range 12 to 17) years, and the mean follow-up was 18 (range 8 to 34) months. The early complications included one superficial wound infection and dehiscence. Late complication includes two children with residual forefoot supination. None of our patients required additional operations during the follow-up period. All feet achieved complete deformity correction or dramatic improvement. In the last follow-up, seven feet were painless, and four children had some mild pain after intensive activities. All feet achieved excellent and good scoring on AOFAS. Conclusions: Many patients with talocalcaneal coalition also have rigid or stiff, painful, flat feet. For these patients, the resection of coalition with concomitant CLO can be safely recommended.

Keywords: Tarsal coalition, calcaneal lengthening osteotomy., flat foot, coalition resection

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Authors: Zayd Khayi

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This paper deals with the construction and analysis of the Tamazight text corpus. The grammatical structure of the Tamazight remains poorly understood, and a lack of comparative grammar leads to linguistic issues. In order to fill this gap, even though it is small, by constructed the diachronic corpus of the Tamazight language, and elaborated the program tool. In addition, this work is devoted to constructing that tool to analyze the different aspects of the Tamazight, with its different dialects used in the north of Africa, specifically in Morocco. It also focused on three Moroccan dialects: Tamazight, Tarifiyt, and Tachlhit. The Latin version was good choice because of the many sources it has. The corpus is based on the grammatical parameters and features of that language. The text collection contains more than 500 texts that cover a long historical period. It is free, and it will be useful for further investigations. The texts were transformed into an XML-format standardization goal. The corpus counts more than 200,000 words. Based on the linguistic rules and statistical methods, the original user interface and software prototype were developed by combining the technologies of web design and Python. The corpus presents more details and features about how this corpus provides users with the ability to distinguish easily between feminine/masculine nouns and verbs. The interface used has three languages: TMZ, FR, and EN. Selected texts were not initially categorized. This work was done in a manual way. Within corpus linguistics, there is currently no commonly accepted approach to the classification of texts. Texts are distinguished into ten categories. To describe and represent the texts in the corpus, we elaborated the XML structure according to the TEI recommendations. Using the search function may provide us with the types of words we would search for, like feminine/masculine nouns and verbs. Nouns are divided into two parts. The gender in the corpus has two forms. The neutral form of the word corresponds to masculine, while feminine is indicated by a double t-t affix (the prefix t- and the suffix -t), ex: Tarbat (girl), Tamtut (woman), Taxamt (tent), and Tislit (bride). However, there are some words whose feminine form contains only the prefix t- and the suffix –a, ex: Tasa (liver), tawja (family), and tarwa (progenitors). Generally, Tamazight masculine words have prefixes that distinguish them from other words. For instance, 'a', 'u', 'i', ex: Asklu (tree), udi (cheese), ighef (head). Verbs in the corpus are for the first person singular and plural that have suffixes 'agh','ex', 'egh', ex: 'ghrex' (I study), 'fegh' (I go out), 'nadagh' (I call). The program tool permits the following characteristics of this corpus: list of all tokens; list of unique words; lexical diversity; realize different grammatical requests. To conclude, this corpus has only focused on a small group of parts of speech in Tamazight language verbs, nouns. Work is still on the adjectives, prounouns, adverbs and others.

Keywords: Tamazight (Berber) language, corpus linguistic, grammar rules, statistical methods

Procedia PDF Downloads 54

Authors: Matthias Steffan, Franz Haas

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The production process grinding is one of the latest steps in a value-added manufacturing chain. Within this step, workpiece geometry and surface roughness are determined. Up to this process stage, considerable costs and energy have already been spent on components. According to the current state of the art, therefore, large safety reserves are calculated in order to guarantee a process capability. Especially for non-circular grinding, this fact leads to considerable losses of process efficiency. With present technology, various non-circular geometries on a workpiece must be grinded subsequently in an oscillating process where X- and Q-axis of the machine are coupled. With the approach of RPM-Synchronous Noncircular Grinding, such workpieces can be machined in an ordinary plung grinding process. Therefore, the workpieces and the grinding wheels revolutionary rate are in a fixed ratio. A non-circular grinding wheel is used to transfer its geometry onto the workpiece. The authors use a worldwide unique machine tool that was especially designed for this technology. Highest revolution rates on the workpiece spindle (up to 4500 rpm) are mandatory for the success of this grinding process. This grinding approach is performed in a two-step process. For roughing, a highly porous vitrified bonded grinding wheel with medium grain size is used. It ensures high specific material removal rates for efficiently producing the non-circular geometry on the workpiece. This process step is adapted by a force control algorithm, which uses acquired data from a three-component force sensor located in the dead centre of the tailstock. For finishing, a grinding wheel with a fine grain size is used. Roughing and finishing are performed consecutively among the same clamping of the workpiece with two locally separated grinding spindles. The approach of RPM-Synchronous Noncircular Grinding shows great efficiency enhancement in non-circular grinding. For the first time, three-dimensional non-circular shapes can be grinded that opens up various fields of application. Especially automotive industries show big interest in the emerging trend in finishing machining.

Keywords: efficiency enhancement, finishing machining, non-circular grinding, rpm-synchronous grinding

Procedia PDF Downloads 270

Authors: Gianpaolo Ghiani, Emanuela Guerriero, Emanuele Manni, Alessandro Romano

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In this article, we deal with a variant of the classical course timetabling problem that has a practical application in many areas of education. In particular, in this paper we are interested in high schools remedial courses. The purpose of such courses is to provide under-prepared students with the skills necessary to succeed in their studies. In particular, a student might be under prepared in an entire course, or only in a part of it. The limited availability of funds, as well as the limited amount of time and teachers at disposal, often requires schools to choose which courses and/or which teaching units to activate. Thus, schools need to model the training offer and the related timetabling, with the goal of ensuring the highest possible teaching quality, by meeting the above-mentioned financial, time and resources constraints. Moreover, there are some prerequisites between the teaching units that must be satisfied. We first present a Mixed-Integer Programming (MIP) model to solve this problem to optimality. However, the presence of many peculiar constraints contributes inevitably in increasing the complexity of the mathematical model. Thus, solving it through a general purpose solver may be performed for small instances only, while solving real-life-sized instances of such model requires specific techniques or heuristic approaches. For this purpose, we also propose a heuristic approach, in which we make use of a fast constructive procedure to obtain a feasible solution. To assess our exact and heuristic approaches we perform extensive computational results on both real-life instances (obtained from a high school in Lecce, Italy) and randomly generated instances. Our tests show that the MIP model is never solved to optimality, with an average optimality gap of 57%. On the other hand, the heuristic algorithm is much faster (in about the 50% of the considered instances it converges in approximately half of the time limit) and in many cases allows achieving an improvement on the objective function value obtained by the MIP model. Such an improvement ranges between 18% and 66%.

Keywords: heuristic, MIP model, remedial course, school, timetabling

Procedia PDF Downloads 591

Authors: Ben Levenger

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Social life revolves around a central meeting place or gathering space. It is where the community integrates, earns social skills, and ultimately becomes part of the community. Following this premise, public spaces are one of the most important spaces that downtowns offer, providing locations for people to be witnessed, heard, and most importantly, seamlessly integrate into the downtown as part of the community. To facilitate this, these local spaces must be envisioned and designed to meet the changing needs of a downtown, offering a space and purpose for everyone. This paper will dive deep into analyzing, designing, and implementing public space design for small plazas or gathering spaces. These spaces often require a detailed level of study, followed by a broad stroke of design implementation, allowing for adaptability. This paper will highlight how to assess needs, define needed types of spaces, outline a program for spaces, detail elements of design to meet the needs, assess your new space, and plan for change. This study will provide participants with the necessary framework for conducting a grass-roots-level assessment of public space and programming, including short-term and long-term improvements. Participants will also receive assessment tools, sheets, and visual representation diagrams. Urbanism, for the sake of urbanism, is an exercise in aesthetic beauty. An economic improvement or benefit must be attained to solidify these efforts' purpose further and justify the infrastructure or construction costs. We will deep dive into case studies highlighting economic impacts to ground this work in quantitative impacts. These case studies will highlight the financial impact on an area, measuring the following metrics: rental rates (per sq meter), tax revenue generation (sales and property), foot traffic generation, increased property valuations, currency expenditure by tenure, clustered development improvements, cost/valuation benefits of increased density in housing. The economic impact results will be targeted by community size, measuring in three tiers: Sub 10,000 in population, 10,001 to 75,000 in population, and 75,000+ in population. Through this classification breakdown, the participants can gauge the impact in communities similar to their work or for which they are responsible. Finally, a detailed analysis of specific urbanism enhancements, such as plazas, on-street dining, pedestrian malls, etc., will be discussed. Metrics that document the economic impact of each enhancement will be presented, aiding in the prioritization of improvements for each community. All materials, documents, and information will be available to participants via Google Drive. They are welcome to download the data and use it for their purposes.

Keywords: downtown, economic development, planning, strategic

Procedia PDF Downloads 66

Authors: Richard Ren

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Air pollution is a serious danger to international well-being and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.Air pollution is a serious danger to international wellbeing and economies - it will kill an estimated 7 million people every year, costing world economies $2.6 trillion by 2060 due to sick days, healthcare costs, and reduced productivity. In the United States alone, 60,000 premature deaths are caused by poor air quality. For this reason, there is a crucial need to develop effective methods to forecast air quality, which can mitigate air pollution’s detrimental public health effects and associated costs by helping people plan ahead and avoid exposure. The goal of this study is to propose an artificial intelligence framework for predicting future air quality based on timing variables (i.e. season, weekday/weekend), future weather forecasts, as well as past pollutant and air quality measurements. The proposed framework utilizes multiple machine learning algorithms (logistic regression, random forest, neural network) with different specifications and averages the results of the three top-performing models to eliminate inaccuracies, weaknesses, and biases from any one individual model. Over time, the proposed framework uses new data to self-adjust model parameters and increase prediction accuracy. To demonstrate its applicability, a prototype of this framework was created to forecast air quality in Los Angeles, California using datasets from the RP4 weather data repository and EPA pollutant measurement data. The results showed good agreement between the framework’s predictions and real-life observations, with an overall 92% model accuracy. The combined model is able to predict more accurately than any of the individual models, and it is able to reliably forecast season-based variations in air quality levels. Top air quality predictor variables were identified through the measurement of mean decrease in accuracy. This study proposed and demonstrated the efficacy of a comprehensive air quality prediction framework leveraging multiple machine learning algorithms to overcome individual algorithm shortcomings. Future enhancements should focus on expanding and testing a greater variety of modeling techniques within the proposed framework, testing the framework in different locations, and developing a platform to automatically publish future predictions in the form of a web or mobile application. Accurate predictions from this artificial intelligence framework can in turn be used to save and improve lives by allowing individuals to protect their health and allowing governments to implement effective pollution control measures.

Keywords: air quality prediction, air pollution, artificial intelligence, machine learning algorithms

Procedia PDF Downloads 108

Authors: Anderson S. Fonseca, Italo F. S. Da Silva, Robert D. A. Santos, Mayara G. Da Silva, Pedro H. C. Vieira, Antonio M. S. Sobrinho, Victor H. B. Lemos, Petterson S. Diniz, Anselmo C. Paiva, Eliana M. G. Monteiro

Abstract:

In Brazil, the National Electric Energy Agency (ANEEL) establishes that electrical energy companies are responsible for measuring and billing their customers. Among these regulations, it’s defined that a company must bill your customers within 27-33 days. If a relocation or a change of period is required, the consumer must be notified in writing, in advance of a billing period. To make it easier to organize a workday’s measurements, these companies create a reading plan. These plans consist of grouping customers into reading groups, which are visited by an employee responsible for measuring consumption and billing. The creation process of a plan efficiently and optimally is a capacitated clustering problem with constraints related to homogeneity and compactness, that is, the employee’s working load and the geographical position of the consuming unit. This process is a work done manually by several experts who have experience in the geographic formation of the region, which takes a large number of days to complete the final planning, and because it’s human activity, there is no guarantee of finding the best optimization for planning. In this paper, the GBKMeans method presents a technique based on K-Means and genetic algorithms for creating a capacitated cluster that respects the constraints established in an efficient and balanced manner, that minimizes the cost of relocating consumer units and the time required for final planning creation. The results obtained by the presented method are compared with the current planning of a real city, showing an improvement of 54.71% in the standard deviation of working load and 11.97% in the compactness of the groups.

Keywords: capacitated clustering, k-means, genetic algorithm, districting problems

Procedia PDF Downloads 184

Authors: Alberto Sanchidrián, José A. Parra, Jesús Alonso, Julián Pecharromán, Antonia Pacios, Consuelo Huerta

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Glass is a commonly used material in building; there is not a unique design solution as plates with a different number of layers and interlayers may be used. In most façades, a security glazing have to be used according to its performance in the impact pendulum. The European Standard EN 12600 establishes an impact test procedure for classification under the point of view of the human security, of flat plates with different thickness, using a pendulum of two tires and 50 kg mass that impacts against the plate from different heights. However, this test does not replicate the actual dimensions and border conditions used in building configurations and so the real stress distribution is not determined with this test. The influence of different boundary conditions, as the ones employed in construction sites, is not well taking into account when testing the behaviour of safety glazing and there is not a detailed procedure and criteria to determinate the glass resistance against human impact. To reproduce the actual boundary conditions on site, when needed, the pendulum test is arranged to be used "in situ", with no account for load control, stiffness, and without a standard procedure. Fracture stress of small and large glass plates fit a Weibull distribution with quite a big dispersion so conservative values are adopted for admissible fracture stress under static loads. In fact, test performed for human impact gives a fracture strength two or three times higher, and many times without a total fracture of the glass plate. Newest standards, as for example DIN 18008-4, states for an admissible fracture stress 2.5 times higher than the ones used for static and wing loads. Now two working areas are open: a) to define a standard for the ‘in situ’ test; b) to prepare a laboratory procedure that allows testing with more real stress distribution. To work on both research lines a laboratory that allows to test medium size specimens with different border conditions, has been developed. A special steel frame allows reproducing the stiffness of the glass support substructure, including a rigid condition used as reference. The dynamic behaviour of the glass plate and its support substructure have been characterized with finite elements models updated with modal tests results. In addition, a new portable impact machine is being used to get enough force and direction control during the impact test. Impact based on 100 J is used. To avoid problems with broken glass plates, the test have been done using an aluminium plate of 1000 mm x 700 mm size and 10 mm thickness supported on four sides; three different substructure stiffness conditions are used. A detailed control of the dynamic stiffness and the behaviour of the plate is done with modal tests. Repeatability of the test and reproducibility of results prove that procedure to control both, stiffness of the plate and the impact level, is necessary.

Keywords: glass plates, human impact test, modal test, plate boundary conditions

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Authors: Monika Rani, Claudio Marchesi, Stefania Federici, Laura E. Depero

Abstract:

Degradation of the aquatic environment by plastic litter, especially microplastics (MPs), i.e., any water-insoluble solid plastic particle with the longest dimension in the range 1µm and 1000 µm (=1 mm) size, is an unfortunate indication of the advancement of the Anthropocene age on Earth. Microplastics formed due to natural weathering processes are termed as secondary microplastics, while when these are synthesized in industries, they are called primary microplastics. Their presence from the highest peaks to the deepest points in oceans explored and their resistance to biological and chemical decay has adversely affected the environment, especially marine life. Even though the presence of MPs in the marine environment is well-reported, a legitimate and authentic analytical technique to sample, analyze, and quantify the MPs is still under progress and testing stages. Among the characterization techniques, vibrational spectroscopic techniques are largely adopted in the field of polymers. And the ongoing miniaturization of these methods is on the way to revolutionize the plastic recycling industry. In this scenario, the capability and the feasibility of a miniaturized near-infrared (MicroNIR) spectroscopy combined with chemometrics tools for qualitative and quantitative analysis of urban plastic waste collected from a recycling plant and microplastic mixture fragmented in the lab were investigated. Based on the Resin Identification Code, 250 plastic samples were used for macroplastic analysis and to set up a library of polymers. Subsequently, MicroNIR spectra were analysed through the application of multivariate modelling. Principal Components Analysis (PCA) was used as an unsupervised tool to find trends within the data. After the exploratory PCA analysis, a supervised classification tool was applied in order to distinguish the different plastic classes, and a database containing the NIR spectra of polymers was made. For the microplastic analysis, the three most abundant polymers in the plastic litter, PE, PP, PS, were mechanically fragmented in the laboratory to micron size. The distinctive arrangement of blends of these three microplastics was prepared in line with a designed ternary composition plot. After the PCA exploratory analysis, a quantitative model Partial Least Squares Regression (PLSR) allowed to predict the percentage of microplastics in the mixtures. With a complete dataset of 63 compositions, PLS was calibrated with 42 data-points. The model was used to predict the composition of 21 unknown mixtures of the test set. The advantage of the consolidated NIR Chemometric approach lies in the quick evaluation of whether the sample is macro or micro, contaminated, coloured or not, and with no sample pre-treatment. The technique can be utilized with bigger example volumes and even considers an on-site evaluation and in this manner satisfies the need for a high-throughput strategy.

Keywords: chemometrics, microNIR, microplastics, urban plastic waste

Procedia PDF Downloads 147

Authors: Mustafa Metin Donma, Orkide Donma

Abstract:

Vitamin deficiencies are common in obese individuals. Particularly, the status of vitamin B12 and its association with vitamin B9 (folate) and vitamin D is under investigation in recent time. Vitamin B12 is closely related to many vital processes in the body. In clinical studies, its involvement in fat metabolism draws attention from the obesity point of view. Obesity, in its advanced stages and in combination with metabolic syndrome (MetS) findings, may be a life-threatening health problem. Pediatric obesity is particularly important because it may be a predictor of severe chronic diseases during the adulthood period of the child. Due to its role in fat metabolism, vitamin B12 deficiency may disrupt metabolic pathways of the lipid and energy metabolisms in the body. The association of low B12 levels with obesity degree may be an interesting topic to be investigated. Obesity indices may be helpful at this point. Weight- and fat-based indices are available. Of them, body mass index (BMI) is in the first group. Fat mass index (FMI), fat-free mass index (FFMI) and diagnostic obesity notation model assessment-II (D2I) index lie in the latter group. The aim of this study is to clarify possible associations between vitamin B12 status and obesity indices in the pediatric population. The study comprises a total of one hundred and twenty-two children. Thirty-two children were included in the normal body mass index (N-BMI) group. Forty-six and forty-four children constitute groups with morbid obese children without MetS and with MetS, respectively. Informed consent forms and the approval of the institutional ethics committee were obtained. Tables prepared for obesity classification by World Health Organization were used. Metabolic syndrome criteria were defined. Anthropometric and blood pressure measurements were taken. Body mass index, FMI, FFMI, D2I were calculated. Routine laboratory tests were performed. Vitamin B9, B12, D concentrations were determined. Statistical evaluation of the study data was performed. Vitamin B9 and vitamin D levels were reduced in MetS group compared to children with N-BMI (p>0.05). Significantly lower values were observed in vitamin B12 concentrations of MetS group (p<0.01). Upon evaluation of blood pressure as well as triglyceride levels, there exist significant increases in morbid obese children. Significantly decreased concentrations of high density lipoprotein cholesterol were observed. All of the obesity indices and insulin resistance index exhibit increasing tendency with the severity of obesity. Inverse correlations were calculated between vitamin D and insulin resistance index as well as vitamin B12 and D2I in morbid obese groups. In conclusion, a fat-based index, D2I, was the most prominent body index, which shows a strong correlation with vitamin B12 concentrations in the late stage of obesity in children. A negative correlation between these two parameters was a confirmative finding related to the association between vitamin B12 and obesity degree.

Keywords: body mass index, children, D2I index, fat mass index, obesity

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Authors: Monish Sengupta, Benjamin Heydecker, Daniel Woodland

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New train control systems rely mainly on Automatic Train Protection (ATP) and Automatic Train Operation (ATO) dynamically to control the speed and hence performance. The ATP and the ATO form the vital element within the CBTC (Communication Based Train Control) and within the ERTMS (European Rail Traffic Management System) system architectures. Reliable and accurate measurement of train location, speed and acceleration are vital to the operation of train control systems. In the past, all CBTC and ERTMS system have deployed a balise or equivalent to correct the uncertainty element of the train location. Typically a CBTC train is allowed to miss only one balise on the track, after which the Automatic Train Protection (ATP) system applies emergency brake to halt the service. This is because the location uncertainty, which grows within the train control system, cannot tolerate missing more than one balise. Balises contribute a significant amount towards wayside maintenance and studies have shown that balises on the track also forms a constraint for future track layout change and change in speed profile.This paper investigates the causes of the location uncertainty that is currently experienced and considers whether it is possible to identify an effective filter to ascertain, in conjunction with appropriate sensors, more accurate speed, distance and location for a CBTC driven train without the need of any external balises. An appropriate sensor fusion algorithm and intelligent sensor selection methodology will be deployed to ascertain the railway location and speed measurement at its highest precision. Similar techniques are already in use in aviation, satellite, submarine and other navigation systems. Developing a model for the speed control and the use of Kalman filter is a key element in this research. This paper will summarize the research undertaken and its significant findings, highlighting the potential for introducing alternative approaches to train positioning that would enable removal of all trackside location correction balises, leading to huge reduction in maintenances and more flexibility in future track design.

Keywords: ERTMS, CBTC, ATP, ATO

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Authors: Luis Amador Jimenez, Md. Shohel Amin

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Pavement performance models are based on projections of observed traffic loads, which makes uncertain to study funding strategies in the long run if history does not repeat. Neural networks can be used to estimate deterioration rates but the learning rate and momentum have not been properly investigated, in addition, economic evolvement could change traffic flows. This study addresses both issues through a case study for roads of Montreal that simulates traffic for a period of 50 years and deals with the measurement error of the pavement deterioration model. Travel demand models are applied to simulate annual average daily traffic (AADT) every 5 years. Accumulated equivalent single axle loads (ESALs) are calculated from the predicted AADT and locally observed truck distributions combined with truck factors. A back propagation Neural Network (BPN) method with a Generalized Delta Rule (GDR) learning algorithm is applied to estimate pavement deterioration models capable of overcoming measurement errors. Linear programming of lifecycle optimization is applied to identify M&R strategies that ensure good pavement condition while minimizing the budget. It was found that CAD 150 million is the minimum annual budget to good condition for arterial and local roads in Montreal. Montreal drivers prefer the use of public transportation for work and education purposes. Vehicle traffic is expected to double within 50 years, ESALS are expected to double the number of ESALs every 15 years. Roads in the island of Montreal need to undergo a stabilization period for about 25 years, a steady state seems to be reached after.

Keywords: pavement management system, traffic simulation, backpropagation neural network, performance modeling, measurement errors, linear programming, lifecycle optimization

Procedia PDF Downloads 450

Authors: Daria Zabolotnaya, Svetlana Degtyareva, Veronika Kanestri, Danila Konnov

Abstract:

An adequate choice of the initial antiretroviral treatment determines the treatment efficacy. In the clinical guidelines in Russia non-nucleoside reverse transcriptase inhibitors (NNRTIs) are still considered to be an option for first-line treatment while pretreatment drug resistance (PDR) testing is not routinely performed. We conducted a cohort retrospective study in HIV-positive treatment naïve patients of the H-clinic (Moscow, Russia) who performed PDR testing from July 2017 to November 2021. All the information was obtained from the medical records anonymously. We analyzed the mutations in reverse transcriptase and protease genes. RT-sequences were obtained by AmpliSens HIV-Resist-Seq kit. Drug resistance was defined using the HIVdb Program v. 8.9-1. PDR was estimated using the Stanford algorithm. Descriptive statistics were performed in Excel (Microsoft Office, 2019). A total of 261 HIV-1 infected patients were enrolled in the study including 197 (75.5%) male and 64 (24.5%) female. The mean age was 34.6±8.3 years. The median CD4 count – 521 cells/µl (IQR 367-687 cells/µl). Data on risk factors of HIV-infection were scarce. The total quantity of strains containing mutations in the reverse transcriptase gene was 75 (28.7%). From these 5 (1.9%) mutations were associated with PDR to nucleoside reverse transcriptase inhibitors (NRTIs) and 30 (11.5%) – with PDR to NNRTIs. The number of strains with mutations in protease gene was 43 (16.5%), from these only 3 (1.1%) mutations were associated with resistance to protease inhibitors. For NNRTIs the most prevalent PDR mutations were E138A, V106I. Most of the HIV variants exhibited a single PDR mutation, 2 were found in 3 samples. Most of HIV variants with PDR mutation displayed a single drug class resistance mutation. 2/37 (5.4%) strains had both NRTIs and NNRTIs mutations. There were no strains identified with PDR mutations to all three drug classes. Though earlier data demonstrated a lower level of PDR in HIV treatment naïve population in Russia and our cohort can be not fully representative as it is taken from the private clinic, it reflects the trend of increasing PDR especially to NNRTIs. Therefore, we consider either pretreatment testing or giving the priority to other drugs as first-line treatment necessary.

Keywords: HIV, resistance, mutations, treatment

Procedia PDF Downloads 83

Authors: Waseem Akram, Hafiz Azhar Ali Khan

Abstract:

Unplanned urbanization together with change in life from simple to more technologically advanced style with flow of rural masses to urban areas has played a vital role in pilling loads of solid wastes in our environment. The cities and towns have expanded beyond boundaries. Even the uncontrolled population expansion has caused the overall environmental burden. Thus, today the indifference remains as one of the biggest trash that has come up due to the non-responsive behavior of the people. Everyday huge amount of solid waste is thrown in the streets, on the roads, parks, and in all those places that are frequently and often visited by the human beings. This behavior based response in many countries of the world has led to serious health concerns and environmental issues. Over 80% of our products that are sold in the market are packed in plastic bags. None of the bags are later recycled but simply become a permanent environment concern that flies, choke lines or are burnt and release toxic gases in the environment or form dumps of heaps. Lack of classification of the daily waste generated from houses and other places lead to worst clogging of the sewerage lines and formation of ponding areas which ultimately favor vector borne disease and sometimes become a cause of transmission of polio virus. Solid waste heaps were checked at different places of the cities. All of the wastes on visual assessments were classified into plastic bags, papers, broken plastic pots, clay pots, steel boxes, wrappers etc. All solid waste dumping sites in the cities and wastes that were thrown outside of the trash containers usually contained wrappers, plastic bags, and unconsumed food products. Insect populations seen in these sites included the house flies, bugs, cockroaches and mosquito larvae breeding in water filled wrappers, containers or plastic bags. The population of the mosquitoes, cockroaches and houseflies were relatively very high in dumping sites close to human population. This population has been associated with cases like dengue, malaria, dysentery, gastro and also to skin allergies during the monsoon and summer season. Thus, dumping of the huge amount of solid wastes in and near the residential areas results into serious environmental concerns, bad smell circulation, and health related issues. In some places, the same waste is burnt to get rid of mosquitoes through smoke which ultimately releases toxic material in the atmosphere. Therefore, a proper environmental strategy is needed to minimize environmental burden and promote concepts of recycled products and thus, reduce the disease burden.

Keywords: solid waste accumulation, disease burden, mosquitoes, vector borne diseases

Procedia PDF Downloads 266

Authors: Daniel Thompson, Muhammad Peerbux, Sophie Cerutti, Hansraj Bookun

Abstract:

Introduction: Dialysis access is a key component of the care of patients with end stage renal failure. In our institution, a combined service of vascular surgeons and nephrologists are responsible for the creation and maintenance of arteriovenous fisultas (AVF), tenckhoff cathethers and Hickman/permcath lines. This poster investigates the last 10 years of dialysis access surgery conducted at St. Vincent’s Hospital Melbourne. Method: A cross-sectional retrospective analysis was conducted of patients of St. Vincent’s Hospital Melbourne (Victoria, Australia) utilising data collection from the Australasian Vascular Audit (Australian and New Zealand Society for Vascular Surgery). Descriptive demographic analysis was carried out as well as operation type, length of hospital stays, postoperative deaths and need for reoperation. Results: 2085 patients with renal failure were operated on between the years of 2011 and 2020. 1315 were male (63.1%) and 770 were female (36.9%). The mean age was 58 (SD 13.8). 92% of patients scored three or greater on the American Society of Anesthiologiests classification system. Almost half had a history of ischaemic heart disease (48.4%), more than half had a history of diabetes (64%), and a majority had hypertension (88.4%). 1784 patients had a creatinine over 150mmol/L (85.6%), the rest were on dialysis (14.4%). The most common access procedure was AVF creation, with 474 autologous AVFs and 64 prosthetic AVFs. There were 263 Tenckhoff insertions. We performed 160 cadeveric renal transplants. The most common location for AVF formation was brachiocephalic (43.88%) followed by radiocephalic (36.7%) and brachiobasilic (16.67%). Fistulas that required re-intervention were most commonly angioplastied (n=163), followed by thrombectomy (n=136). There were 107 local fistula repairs. Average length of stay was 7.6 days, (SD 12). There were 106 unplanned returns to theatre, most commonly for fistula creation, insertion of tenckhoff or permacath removal (71.7%). There were 8 deaths in the immediately postoperative period. Discussion: Access to dialysis is vital for patients with end stage kidney disease, and requires a multidisciplinary approach from both nephrologists, vascular surgeons, and allied health practitioners. Our service provides a variety of dialysis access methods, predominately fistula creation and tenckhoff insertion. Patients with renal failure are heavily comorbid, and prolonged hospital admission following surgery is a source of significant healthcare expenditure. AVFs require careful monitoring and maintenance for ongoing utility, and our data reflects a multitude of operations required to maintain usable access. The requirement for dialysis is growing worldwide and our data demonstrates a local experience in access, with preferred methods, common complications and the associated surgical interventions.

Keywords: dialysis, fistula, nephrology, vascular surgery

Procedia PDF Downloads 102

Authors: Young-Tae Lee, Hee-Chang Lim

Abstract:

This paper presents the performance characteristics of Darrieus-type vertical axis wind turbine (VAWT) with NACA airfoil blades. The performance of Darrieus-type VAWT can be characterized by torque and power. There are various parameters affecting the performance such as chord length, helical angle, pitch angle and rotor diameter. To estimate the optimum shape of Darrieustype wind turbine in accordance with various design parameters, we examined aerodynamic characteristics and separated flow occurring in the vicinity of blade, interaction between flow and blade, and torque and power characteristics derived from it. For flow analysis, flow variations were investigated based on the unsteady RANS (Reynolds-averaged Navier-Stokes) equation. Sliding mesh algorithm was employed in order to consider rotational effect of blade. To obtain more realistic results we conducted experiment and numerical analysis at the same time for three-dimensional shape. In addition, several parameters (chord length, rotor diameter, pitch angle, and helical angle) were considered to find out optimum shape design and characteristics of interaction with ambient flow. Since the NACA airfoil used in this study showed significant changes in magnitude of lift and drag depending on an angle of attack, the rotor with low drag, long cord length and short diameter shows high power coefficient in low tip speed ratio (TSR) range. On the contrary, in high TSR range, drag becomes high. Hence, the short-chord and long-diameter rotor produces high power coefficient. When a pitch angle at which airfoil directs toward inside equals to -2° and helical angle equals to 0°, Darrieus-type VAWT generates maximum power.

Keywords: darrieus wind turbine, VAWT, NACA airfoil, performance

Procedia PDF Downloads 358

Authors: K. R. Roopesh Bharatwaj, Avinash Maharana, Favour Tobi Aborisade, Roger Young

Abstract:

Presently, there are few cases of complete automation of drones and its allied intelligence capabilities. In essence, the potential of the drone has not yet been fully utilized. This paper presents feasible methods to build an intelligent drone with smart capabilities such as self-driving, and obstacle avoidance. It does this through advanced Reinforcement Learning Techniques and performs object detection using latest advanced algorithms, which are capable of processing light weight models with fast training in real time instances. For the scope of this paper, after researching on the various algorithms and comparing them, we finally implemented the Deep-Q-Networks (DQN) algorithm in the AirSim Simulator. In future works, we plan to implement further advanced self-driving and object detection algorithms, we also plan to implement voice-based speech recognition for the entire drone operation which would provide an option of speech communication between users (People) and the drone in the time of unavoidable circumstances. Thus, making drones an interactive intelligent Robotic Voice Enabled Service Assistant. This proposed drone has a wide scope of usability and is applicable in scenarios such as Disaster management, Air Transport of essentials, Agriculture, Manufacturing, Monitoring people movements in public area, and Defense. Also discussed, is the entire drone communication based on the satellite broadband Internet technology for faster computation and seamless communication service for uninterrupted network during disasters and remote location operations. This paper will explain the feasible algorithms required to go about achieving this goal and is more of a reference paper for future researchers going down this path.

Keywords: convolution neural network, natural language processing, obstacle avoidance, satellite broadband technology, self-driving

Procedia PDF Downloads 235

Authors: Gunta Resevica, Nikita Zrelovs, Ivars Silamikelis, Ieva Kalnciema, Helvijs Niedra, Gunārs Lācis, Toms Bartulsons, Inga Moročko-Bičevska, Arturs Stalažs, Kristīne Drevinska, Andris Zeltins, Ina Balke

Abstract:

Newly discovered viruses provide novel knowledge for basic phytovirus research, serve as tools for biotechnology and can be helpful in identification of epidemic outbreaks. Blackcurrant-associated rhabdovirus (BCaRV) have been discovered in USA germplasm collection samples from Russia and France. As it was reported in one accession originating from France it is unclear whether the material was already infected when it entered in the USA or it became infected while in collection in the USA. Due to that BCaRV was definite as non-EU viruses. According to ICTV classification BCaRV is representative of Blackcurrant betanucleorhabdovirus specie in genus Betanucleorhabdovirus (family Rhabdoviridae). Nevertheless, BCaRV impact on the host, transmission mechanisms and vectors are still unknown. In RNA-seq data pool from Ribes plants resistance gene study by high throughput sequencing (HTS) we observed differences between sample group gene transcript heat maps. Additional analysis of the whole data pool (total 393660492 of 150 bp long read pairs) by rnaSPAdes v 3.13.1 resulted into 14424 bases long contig with an average coverage of 684x with shared 99.5% identity to the previously reported first complete genome of BCaRV (MF543022.1) using EMBOSS Needle. This finding proved BCaRV presence in EU and indicated that it might be relevant pathogen. In this study leaf tissue from twelve asymptomatic blackcurrant cv. Mara Eglite plants (negatively tested for blackcurrant reversion virus (BRV)) from Dobele, Latvia (56°36'31.9"N, 23°18'13.6"E) was collected and used for total RNA isolation with RNeasy Plant Mini Kit with minor modifications, followed by plant rRNA removal by a RiboMinus Plant Kit for RNA-Seq. HTS libraries were prepared using MGI Easy RNA Directional Library Prep Set for 16 reactions to obtain 150 bp pair-end reads. Libraries were pooled, circularized and cleaned and sequenced on DNBSEQ-G400 using PE150 flow cell. Additionally, all samples were tested by RT-PCR, and amplicons were directly sequenced by Sanger-based method. The contig representing the genome of BCaRV isolate Mara Eglite was deposited at European Nucleotide Archive under accession number OU015520. Those findings indicate a second evidence on the presence of this particular virus in the EU and further research on BCaRV prevalence in Ribes from other geographical areas should be performed. As there are no information on BCaRV impact on the host this should be investigated, regarding the fact that mixed infections with BRV and nucleorhabdoviruses are reported.

Keywords: BCaRV, Betanucleorhabdovirus, Ribes, RNA-seq

Procedia PDF Downloads 175

Authors: Ana Londral, Burcu Demiray, Marcus Cheetham

Abstract:

Speech recording is a methodology used in many different studies related to cognitive and behaviour research. Modern advances in digital equipment brought the possibility of continuously recording hours of speech in naturalistic environments and building rich sets of sound files. Speech analysis can then extract from these files multiple features for different scopes of research in Language and Communication. However, tools for analysing a large set of sound files and automatically extract relevant features from these files are often inaccessible to researchers that are not familiar with programming languages. Manual analysis is a common alternative, with a high time and efficiency cost. In the analysis of long sound files, the first step is the voice segmentation, i.e. to detect and label segments containing speech. We present a comprehensive methodology aiming to support researchers on voice segmentation, as the first step for data analysis of a big set of sound files. Praat, an open source software, is suggested as a tool to run a voice detection algorithm, label segments and files and extract other quantitative features on a structure of folders containing a large number of sound files. We present the validation of our methodology with a set of 5000 sound files that were collected in the daily life of a group of voluntary participants with age over 65. A smartphone device was used to collect sound using the Electronically Activated Recorder (EAR): an app programmed to record 30-second sound samples that were randomly distributed throughout the day. Results demonstrated that automatic segmentation and labelling of files containing speech segments was 74% faster when compared to a manual analysis performed with two independent coders. Furthermore, the methodology presented allows manual adjustments of voiced segments with visualisation of the sound signal and the automatic extraction of quantitative information on speech. In conclusion, we propose a comprehensive methodology for voice segmentation, to be used by researchers that have to work with large sets of sound files and are not familiar with programming tools.

Keywords: automatic speech analysis, behavior analysis, naturalistic environments, voice segmentation

Procedia PDF Downloads 274