Search results for: modular wound rotor synchronous machine

Authors: M. Pappa, C. Efstathiou, G. Livanos, P. Xidas, D. Vakondios, E. Maravelakis, M. Zervakis, A. Antoniadis

Abstract:

The choice of cutting tools in manufacturing processes is an essential parameter on which the required manufacturing time, the consumed energy and the cost effort all depend. If the number of tool changing times could be minimized or even eliminated by using a single convex tool providing multiple profiles, then a significant benefit of time and energy saving, as well as tool cost, would be achieved. A typical machine contains a variety of tools in order to deal with different curvatures and material removal rates. In order to minimize the required cutting tool changes, Actively Deformable micro-Cutters (ADmC) will be developed. The design of the Actively Deformable micro-Cutters will be based on the same cutting technique and mounting method as that in typical cutters.

Keywords: deformable cutters, cutting tool, milling, turning, manufacturing

Procedia PDF Downloads 443

Authors: Zixuan Xu, Yichun Lou, Yang Song, Zihuai Lin

Abstract:

MediaPipe is an open-source machine learning computer vision framework that can be ported into a multi-platform environment, which makes it easier to use it to recognize the human activity. Based on this framework, many human recognition systems have been created, but the fundamental issue is the recognition of human behavior and posture. In this paper, two methods are proposed to recognize human gestures based on MediaPipe, the first one uses the Adaptive Boosting algorithm to recognize a series of fitness gestures, and the second one uses the Fast Dynamic Time Warping algorithm to recognize 413 continuous fitness actions. These two methods are also applicable to any human posture movement recognition.

Keywords: computer vision, MediaPipe, adaptive boosting, fast dynamic time warping

Procedia PDF Downloads 98

Authors: Alonggot Limcharoen Kaeochotchuangkul, Pathomporn Sawatchai

Abstract:

This research aims to design and build a wet etching system, which is suitable for anisotropic wet etching, in order to reduce etching time, to reduce hillocks on the etched surface (to reduce roughness), and to create a 45-degree wall angle (micro-mirror). This study would start by designing a wet etching system. There are four main components in this system: an ultrasonic cleaning, a condenser, a motor and a substrate holder. After that, an ultrasonic machine was modified by applying a condenser to maintain the consistency of the solution concentration during the etching process and installing a motor for improving the roughness. This effect on the etch rate and the roughness showed that the etch rate increased and the roughness was reduced.

Keywords: anisotropic wet etching, wet etching system, hillocks, ultrasonic cleaning

Procedia PDF Downloads 109

Authors: Alain Kusmoko, Druce Dunne, Huijun Li

Abstract:

Stellite 6 was deposited by laser cladding on a chromium bearing substrate (P91) with energy inputs of 1 kW (P91-1) and 1.8 kW (P91-1.8). The chemical compositions and microstructures of these coatings were characterized by atomic absorption spectroscopy, optical microscopy and scanning electron microscopy. The microhardness of the coatings was measured and the wear mechanism of the coatings was assessed using a pin-on-plate (reciprocating) wear testing machine. The results showed less cracking and pore development for Stellite 6 coatings applied to the P91 steel substrate with the lower heat input (P91-1). Further, the Stellite coating for P91-1 was significantly harder than that obtained for P91-1.8. The wear test results indicated that the weight loss for P91-1 was much lower than for P91-1.8. It is concluded that the lower hardness of the coating for P91-1.8, together with the softer underlying substrate structure, markedly reduced the wear resistance of the Stellite 6 coating.

Keywords: friction and wear, laser cladding, P91 steel, Stellite 6 coating

Procedia PDF Downloads 427

Authors: K. Kaouther, D. Hafedh, A. Ben Cheikh Larbi

Abstract:

Various structures and compositions thin films were deposited on 100C6 (AISI 52100) steel substrate by PVD magnetron sputtering system. The morphological proprieties were evaluated using an atomic force microscopy (AFM). Vickers microindentation tests were performed with a Shimadzu HMV-2000 hardness testing machine. Hardness measurement was carried out using Jonsson and Hogmark model. The results show that the coatings topography was dominated by domes and craters. Mechanical behavior and failure modes under microindentation were depending of coatings structure and composition. TiAlN multilayer showed exception in the microindentation resistance compared to TiN single layer and TiAlN/TiAlN nanolayer. Piled structure provides an increase of failure resistance and a decrease in cracks propagation.

Keywords: PVD thin films, multilayer, microindentation, cracking, damage mechanisms

Procedia PDF Downloads 394

Authors: Edward Hage, Pieter Lietaert, Gabriel Abedrabbo

Abstract:

Musculoskeletal disorders are an important category of work-related diseases. They are often caused by working in non-ergonomic postures and are preventable with proper workplace design, possibly including human-machine collaboration. This paper presents a methodology and a supporting software prototype to design a simple assembly cell with minimal ergonomic risk. The methodology helps to determine the optimal position and orientation of workpieces and workplace resources for specific operator assembly actions. The methodology is tested on an industrial use case: a collaborative robot (cobot) assisted assembly of a clamping device. It is shown that the automated methodology results in a workplace design with significantly reduced ergonomic risk to the operator compared to a manual design of the cell.

Keywords: ergonomics optimization, design for ergonomics, workplace design, pose generation

Procedia PDF Downloads 114

Authors: Saqib Rasool, Adnan N. Mian

Abstract:

Web components are introduced as the latest standard of HTML5 for writing modular web interfaces for ensuring maintainability through the isolated scope of web components. Reusability can also be achieved by sharing plug-and-play web components that can be used as off-the-shelf components by other developers. A web component encapsulates all the required HTML, CSS and JavaScript code as a standalone package which must be imported for integrating a web component within an existing web interface. It is then followed by the integration of web component with the web services for dynamically populating its content. Since web components are reusable as off-the-shelf components, these must be equipped with some mechanism for ensuring their proper integration with web services. The consistency of a service behavior can be verified through type-checking. This is one of the popular solutions for improving the quality of code in many programming languages. However, HTML does not provide type checking as it is a markup language and not a programming language. The contribution of this work is to introduce a new extension of HTML called Service Type-checking Markup Language (STML) for adding support of type checking in HTML for JSON based REST services. STML can be used for defining the expected data types of response from JSON based REST services which will be used for populating the content within HTML elements of a web component. Although JSON has five data types viz. string, number, boolean, object and array but STML is made to supports only string, number and object. This is because of the fact that both object and array are considered as string, when populated in HTML elements. In order to define the data type of any HTML element, developer just needs to add the custom STML attributes of st-string, st-number and st-boolean for string, number and boolean respectively. These all annotations of STML are used by the developer who is writing a web component and it enables the other developers to use automated type-checking for ensuring the proper integration of their REST services with the same web component. Two utilities have been written for developers who are using STML based web components. One of these utilities is used for automated type-checking during the development phase. It uses the browser console for showing the error description if integrated web service is not returning the response with expected data type. The other utility is a Gulp based command line utility for removing the STML attributes before going in production. This ensures the delivery of STML free web pages in the production environment. Both of these utilities have been tested to perform type checking of REST services through STML based web components and results have confirmed the feasibility of evaluating service behavior only through HTML. Currently, STML is designed for automated type-checking of integrated REST services but it can be extended to introduce a complete service testing suite based on HTML only, and it will transform STML from Service Type-checking Markup Language to Service Testing Markup Language.

Keywords: REST, STML, type checking, web component

Procedia PDF Downloads 239

Authors: Jamal Choudhry, Roland Larsson, Andreas Almqvist

Abstract:

A multi-scale flash temperature model has been developed and validated against existing work. The core strength of the proposed model is that it can be adapted to predict flash contact temperatures occurring in various types of sliding systems. In this paper, it is used to investigate how different surface roughness parameters affect the flash temperatures. The results show that for decreasing Hurst exponents as well as increasing values of the high-frequency cut-off, the maximum flash temperature increases. It was also shown that the effect of surface roughness does not influence the average interface temperature. The model predictions were validated against data from an experiment conducted in a pin-on-disc machine. This also showed the importance of including a wear model when simulating flash temperature development in a sliding system.

Keywords: multiscale, pin-on-disc, finite element method, flash temperature, surface roughness

Procedia PDF Downloads 105

Authors: Jurijs Salijevs, Katrina Bolocko

Abstract:

The integration of artificial intelligence and neural networks has significantly changed full-body imaging and high-resolution 3D mapping systems, and this paper reviews research in these areas. With an emphasis on their use in the early identification of melanoma and other disorders, the goal is to give a wide perspective on the current status and potential future of these medical imaging technologies. Authors also examine methodologies such as machine learning and deep learning, seeking to identify efficient procedures that enhance diagnostic capabilities through the analysis of 3D body scans. This work aims to encourage further research and technological development to harness the full potential of AI in disease diagnosis.

Keywords: artificial intelligence, neural networks, 3D scan, body scan, 3D mapping system, healthcare

Procedia PDF Downloads 84

Authors: Mahesh Chudasama, Harit Raval

Abstract:

Conical sections and shells of metal plates manufactured by 3-roller conical bending process are widely used in the industries. The process is completed by first bending the metal plates statically and then dynamic roller bending sequentially. It is required to have an analytical model to get maximum bending force, for optimum design of the machine, for static bending stage. Analytical models assuming various stress conditions are considered and these analytical models are compared considering various parameters and reported in this paper. It is concluded from the study that for higher bottom roller inclination, the shear stress affects greatly to the static bending force whereas for lower bottom roller inclination it can be neglected.

Keywords: roller-bending, static-bending, stress-conditions, analytical-modeling

Procedia PDF Downloads 239

Authors: R. K. Saket, K. S. Anand Kumar

Abstract:

This paper presents an innovative 3-wire capacitor circuit system used to increase the efficiency and comfort improvement of permanent split-capacitor ceiling fan. In this innovative circuit, current has been reduced to save electrical power. The system could be used to replace standard single phase motor 2-wire capacitor configuration by cost effective split value X rated of optimized AC capacitors with the auxiliary winding to provide reliable ceiling fan operation and improved machine performance to save power. In basic system operations, comparisons with conventional ceiling fan are described.

Keywords: permanent split-capacitor motor, innovative 3-wire capacitor circuit system, standard 2-wire capacitor circuit system, metalized film X-rated capacitor

Procedia PDF Downloads 512

Authors: Chih-Fong Tsai, Ya-Han Hu

Abstract:

The aim of feature selection (or dimensionality reduction) is to filter out unrepresentative features (or variables) making the classifier perform better than the one without feature selection. Since there are many well-known feature selection algorithms, and different classifiers based on different selection results may perform differently, very few studies consider examining the effect of performing different feature selection algorithms on the classification performances by different classifiers over different types of datasets. In this paper, two widely used algorithms, which are the genetic algorithm (GA) and information gain (IG), are used to perform feature selection. On the other hand, three well-known classifiers are constructed, which are the CART decision tree (DT), multi-layer perceptron (MLP) neural network, and support vector machine (SVM). Based on 14 different types of datasets, the experimental results show that in most cases IG is a better feature selection algorithm than GA. In addition, the combinations of IG with DT and IG with SVM perform best and second best for small and large scale datasets.

Keywords: data mining, feature selection, pattern classification, dimensionality reduction

Procedia PDF Downloads 656

Authors: A. H. Taghdisi, M. Mirmohammadi, S. Kamali

Abstract:

Evening primrose (Oenothera biennis L.) is a biennial and herbaceous and one of the most important species of medicinal plants in the world. due to the production of unsaturated fatty acids such as linoleic acid, alpha-linolenic acid, etc. in its seeds and roots, and compounds such as kaempferol in its leaves, Evening primrose has important medicinal efficiency such as reducing premenstrual problems, acceleration of wound healing, inhibiting platelet aggregation, sedation of cardiovascular diseases, and treatment of viral infections. The sap of the plant is used to treat warts, and the plant itself is used as a charm against mental and spiritual diseases and poisonous animals. Its leaves have significant antibacterial activity against yellow staphylococci. It is also used in the treatment of poisoning, especially the toxication caused by the consumption of alcoholic beverages, in the treatment of arteriosclerosis and diseases caused by liver cell insufficiency. Low germination and production speed are the problems of evening primrose growth and propagation. In the present study, extracts were obtained from four components (flowers, stems, seeds, leaves) of the evening primrose plant using the Soxhlet apparatus. To measure the antibacterial properties against MDR bacteria, microbial methods, including dilution, cultivation on a plate containing nutrient agar culture medium, and disc diffusion in agar, were performed using Staphylococcus aureus and Escherichia coli bacteria on all four extracts. The maximum antibacterial activity related to the dilution method was obtained in all extracts. In the plate culture method, antibacterial activity was obtained for all extracts in the nutrient agar medium. The maximum diameter of the non-growth halo was obtained in the disc diffusion method in agar in the leaf extract. The statistical analysis of the microbial part was done by one-way ANOVA test (SPSS). By comparing the amount of omega-3 in extracts of Iranian and foreign oils available in the market and the extracts extracted from evening primrose plant samples with gas chromatography, it is shown that the stem extract had the most omega-3 (oleic acid) and compared to the extract of the mentioned oils, it had the highest amount of omega-3 overall. Also, the amount of omega-3 in the extract of Iranian oils was much higher than in the extract of foreign oils. It should be noted that the extract of foreign oils had a more complete composition of omega-3 than the extract of Iranian oils.

Keywords: antibacterial activity, MDR bacteria, evening primrose, omega-3

Procedia PDF Downloads 87

Authors: Troels Bo Jørgensen, Preben Hagh Strunge Holm, Henrik Gordon Petersen, Norbert Kruger

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This paper presents a simulation framework for using machine learning techniques to determine robust robotic motions for handling deformable objects. The main focus is on applications in the meat sector, which mainly handle three-dimensional objects. In order to optimize the robotic handling, the robot motions have been parameterized in terms of grasp points, robot trajectory and robot speed. The motions are evaluated based on a dynamic simulation environment for robotic control of deformable objects. The evaluation indicates certain parameter setups, which produce robust motions in the simulated environment, and based on a visual analysis indicate satisfactory solutions for a real world system.

Keywords: deformable objects, robotic manipulation, simulation, real world system

Procedia PDF Downloads 271

Authors: Mohamed Ahmed Abdelmawla

Abstract:

Calling to achieve Millennium Development Goals (MDGs) engaged with agriculture, i.e. poverty alleviation targets, human resources involved in agricultural sectors with special emphasis on irrigation must receive wealth of practical experience and training. Increased food production, including staple food, is needed to overcome the present and future threats to food security. This should happen within a framework of sustainable management of natural resources, elimination of unsustainable methods of production and poverty reduction (i.e. axes of modernization). A didactic tool to confirm the task of wise and maximum utility is the best management and accurate measurement, as major requisites for modernization process. The key component to modernization as a warranted goal is adhering great attention to management and measurement issues via capacity building. As such, this paper stressed the issues of discharge management and measurement by Field Outlet Pipes (FOP) for selected ones within the Gezira Scheme, where randomly nine FOPs were selected as representative locations. These FOPs extended along the Gezira Main Canal at Kilo 57 areas in the South up to Kilo 194 in the North. The following steps were followed during the field data collection and measurements: For each selected FOP, a 90 v- notch thin plate weir was placed in such away that the water was directed to pass only through the notch. An optical survey level was used to measure the water head of the notch and FOP. Both calculated discharge rates as measured by the v – notch, denoted as [Qc], and the adopted discharges given by (MOIWR), denoted as [Qa], are tackled for the average of three replicated readings undertaken at each location. The study revealed that the FOP overestimates and sometimes underestimates the discharges. This is attributed to the fact that the original design specifications were not fulfilled or met at present conditions where water is allowed to flow day and night with high head fluctuation, knowing that the FOP is non modular structure, i.e. the flow depends on both levels upstream and downstream and confirmed by the results of this study. It is convenient and formative to quantify the discharge in FOP with weirs or Parshall flumes. Cropping calendar should be clearly determined and agreed upon before the beginning of the season in accordance and consistency with the Sudan Gezira Board (SGB) and Ministry of Irrigation and Water Resources. As such, the water indenting should be based on actual Crop Water Requirements (CWRs), not on rules of thumb (420 m3/feddan, irrespective of crop or time of season).

Keywords: management, measurement, MDGs, modernization

Procedia PDF Downloads 242

Authors: Kwei Huan Liw, Sashi B. Darshan

Abstract:

Background: Double eyelid surgery has become one of the most sought-after aesthetic procedures among Asians. Many surgeons perform surgical blepharoplasty and various other methods of non-incisional blepharoplasty. Face-Q is a validated method of measuring patient satisfaction for facial aesthetic procedures. Here we have analyzed the overall eye satisfaction score, the upper eyelid appraisal score and the adverse effect on eyes score Methods: 274 patients (548 eyes), aged between 18 to 40 years old, were recruited from 2015-2018. Each patient underwent a non-incisional double-eyelid blepharoplasty using a single-knotted continuous buried suture. 3 – 5 stab incisions were made depending on the upper eyelid size. A needle loaded with 7-0 nylon is passed from the lateral most wound through the dermis and the conjunctiva in an alternate fashion into the remaining stab wounds. The suture is then tunneled back laterally in the deeper dermis and knotted securely with the suture end. The knot is then buried within the orbicularis oculi muscle. Each patient was required to fill the Face-Q questionnaire before the procedure and 2 weeks post procedure. The results are described based on the percentage of the maximum achievable score. Patients were reviewed after 12 to 18 months to assess the long-term outcome. Results: The overall eye satisfaction score demonstrated a high level of post-operative satisfaction (97.85%), compared to 27.32% pre-operatively. The appraisal of upper eyelid scores showed drastic improvement in perception post-operatively (95.31%) compared to 21.44% pre-operatively. Adverse effect on eyes score showed a very low post-operative complication rate (0.4%) The long-term follow-up showed 6 cases that had developed asymmetrical folds. Only 1 patient agreed for revision surgery. The other 5 patients were still satisfied with the outcome and were not keen for revision surgery. None of the cases had loosening of knots. Conclusion: Modified single-knot continuous buried suture technique is a simple and non-invasive method to create aesthetically pleasing non-surgical double-eyelids, which has long-term effects. Proper patient selection is crucial and good surgical technique is required to achieve a desirable outcome.

Keywords: blepharoplasty, double-eyelid, face-Q, non-incisional

Procedia PDF Downloads 107

Authors: Qiuyu Dai, Haochong Zhang, Xiangrong Liu

Abstract:

Diagonal sparse matrix-vector multiplication is a well-studied topic in the fields of scientific computing and big data processing. However, when diagonal sparse matrices are stored in DIA format, there can be a significant number of padded zero elements and scattered points, which can lead to a degradation in the performance of the current DIA kernel. This can also lead to excessive consumption of computational and memory resources. In order to address these issues, the authors propose the DIA-Adaptive scheme and its kernel, which leverages the parallel instruction sets on MLU. The researchers analyze the effect of allocating a varying number of threads, clusters, and hardware architectures on the performance of SpMV using different formats. The experimental results indicate that the proposed DIA-Adaptive scheme performs well and offers excellent parallelism.

Keywords: adaptive method, DIA, diagonal sparse matrices, MLU, sparse matrix-vector multiplication

Procedia PDF Downloads 112

Authors: Manish Kumar Thakur, Subrata Kumar Ghosh

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Various types of particles found in lubricant may be characterized by their fractal dimension. Some of the available methods are: yard-stick method or structured walk method, box-counting method. This paper presents a review of the developments and progress in fractal dimension computing methods as applied to characteristics the surface of wear particles. An overview of these methods, their implementation, their advantages and their limits is also present here. It has been accepted that wear particles contain major information about wear and friction of materials. Morphological analysis of wear particles from a lubricant is a very effective way for machine condition monitoring. Fractal dimension methods are used to characterize the morphology of the found particles. It is very useful in the analysis of complexity of irregular substance. The aim of this review is to bring together the fractal methods applicable for wear particles.

Keywords: fractal dimension, morphological analysis, wear, wear particles

Procedia PDF Downloads 470

Authors: Endrick Barnacin, Jean-Luc Henry, Jimmy Nagau, Jack Molinie

Abstract:

Palynology is a field of interest in many disciplines due to its multiple applications: chronological dating, climatology, allergy treatment, and honey characterization. Unfortunately, the analysis of a pollen slide is a complicated and time consuming task that requires the intervention of experts in the field, which are becoming increasingly rare due to economic and social conditions. That is why the need for automation of this task is urgent. A lot of studies have investigated the subject using different standard image processing descriptors and sometimes hand-crafted ones.In this work, we make a comparative study between classical feature extraction methods (Shape, GLCM, LBP, and others) and Deep Learning (CNN, Autoencoders, Transfer Learning) to perform a recognition task over 80 regional pollen species. It has been found that the use of Transfer Learning seems to be more precise than the other approaches

Keywords: pollens identification, features extraction, pollens classification, automated palynology

Procedia PDF Downloads 123

Authors: Fahem Belkacemi, Lyes Ouali

Abstract:

Maintenance strategies and assessments continue to be a major concern for companies today. The socio-economic bets of their competitiveness are closely linked to the activities and quality of maintenance. This work deals with a study of a preventive maintenance plan to improve the availability of the production line within SAMSUNG HOME APPLIANCE “SAMHA”, Setif, Algeria. First, we applied the method of analysis of failure modes, their impact, and criticality to reduce downtime and identification of the most critical elements. Finally, to improve the availability of the production line, we carried out a study of the current preventive maintenance plan in the production line workshop at the company level and according to the history sheet of machine failures. We proposed a preventive maintenance plan to improve the availability of the production line.

Keywords: preventive maintenance, DRUM line, AMDEC, availability

Procedia PDF Downloads 49

Authors: Muhammet Baldan, Emel Timuçin

Abstract:

Generally, absorption and bioavailability increase if solubility increases; therefore, it is crucial to predict them in drug discovery applications. Molecular descriptors and Molecular properties are traditionally used for the prediction of water solubility. There are various key descriptors that are used for this purpose, namely Drogan Descriptors, Morgan Descriptors, Maccs keys, etc., and each has different prediction capabilities with differentiating successes between different data sets. Another source for the prediction of solubility is structural features; they are commonly used for the prediction of solubility. However, there are little to no studies that combine three or more properties or descriptors for prediction to produce a more powerful prediction model. Unlike available models, we used a combination of those features in a random forest machine learning model for improved solubility prediction to better predict and, therefore, contribute to drug discovery systems.

Keywords: solubility, random forest, molecular descriptors, maccs keys

Procedia PDF Downloads 27

Authors: Ron Cottam, Nils Langloh, Willy Ranson, Roger Vounckx

Abstract:

We start by formally describing the requirements for environmental-reaction survival computation in a natural temporally-demanding medium, and develop this into a more general model of the evolutionary context as a computational machine. The effect of this development is to replace deterministic logic by a modified form which exhibits a continuous range of dimensional fractal diffuseness between the isolation of perfectly ordered localization and the extended communication associated with nonlocality as represented by pure causal chaos. We investigate the appearance of life and consciousness in the derived general model, and propose a representation of Nature within which all localizations have the character of quasi-quantal entities. We compare our conclusions with Heisenberg’s uncertainty principle and nonlocal teleportation, and maintain that computability is the principal influence on evolution in the model we propose.

Keywords: computability, evolution, life, localization, modeling, nonlocality

Procedia PDF Downloads 389

Authors: Nouha Khediri, Mohammed Ben Ammar, Monji Kherallah

Abstract:

Recently, facial emotion recognition (FER) has become increasingly essential to understand the state of the human mind. Accurately classifying emotion from the face is a challenging task. In this paper, we present a facial emotion recognition approach named CV-FER, benefiting from deep learning, especially CNN and VGG16. First, the data is pre-processed with data cleaning and data rotation. Then, we augment the data and proceed to our FER model, which contains five convolutions layers and five pooling layers. Finally, a softmax classifier is used in the output layer to recognize emotions. Based on the above contents, this paper reviews the works of facial emotion recognition based on deep learning. Experiments show that our model outperforms the other methods using the same FER2013 database and yields a recognition rate of 92%. We also put forward some suggestions for future work.

Keywords: CNN, deep-learning, facial emotion recognition, machine learning

Procedia PDF Downloads 80

Authors: Yang Li, Xiaorong Guan, Cheng Xu

Abstract:

In order to design a suitable control scheme for human extremity exoskeleton, the interaction force control scheme with traditional PI controller was presented, and the simulation study of the electromechanical system of the human extremity exoskeleton was carried out by using a MATLAB/Simulink module. By analyzing the simulation calculation results, it was shown that the traditional PI controller is not very suitable for every movement speed of human body. So, at last the fuzzy self-adaptive PI controller was presented to solve this problem. Eventually, the superiority and feasibility of the fuzzy self-adaptive PI controller was proved by the simulation results and experimental results.

Keywords: human extremity exoskeleton, interaction force control scheme, simulation study, fuzzy self-adaptive pi controller, man-machine coordinated walking, bear payload

Procedia PDF Downloads 352

Authors: Madeleine Elder, Aye Htun

Abstract:

Background: Myiasis is the parasitic infestation of body tissues by fly larvae. It predominantly occurs in poor socioeconomic regions of tropical and subtropical countries where it is associated with poor hygiene and sanitation. Cutaneous and wound myiasis are the most common presentations whereas urogenital myiasis is rare, with few reported cases. Case: a 26-year-old primiparous woman with a low-risk pregnancy presented to the emergency department at 37+3-weeks’ gestation after passing a 2cm black larva during micturition, with 2 weeks of mild vulvar pruritus and dysuria. She had travelled to India 9-months prior. Examination of the external genitalia showed small white larvae over the vulva and anus and a mildly inflamed introitus. Speculum examination showed infiltration into the vagina and heavy white discharge. High vaginal swab reported Candida albicans. Urine microscopy reported bacteriuria with Enterobacter cloacae. Urine parasite examination showed myiasis caused by Clogmia albipunctata species of fly larvae from the family Psychodidae. Renal tract ultrasound and inflammatory markers were normal. Infectious diseases, urology and paediatric teams were consulted. The woman received treatment for her urinary tract infection (which was likely precipitated by bladder irritation from local parasite infestation) and vaginal candidiasis. She underwent daily physical removal of parasites with cleaning, speculum examination and removal, and hydration to promote bladder emptying. Due to the risk of neonatal exposure, aspiration pneumonitis and facial infestation, the woman was steroid covered and proceeded to have an elective caesarean section at 38+3-weeks’ gestation, with delivery of a healthy infant. She then proceeded to have a rigid cystoscopy and washout, which was unremarkable. Placenta histopathology revealed focal eosinophilia in keeping with the history of maternal parasites. Conclusion: Urogenital myiasis is very rare, especially in the developed world where it is seen in returned travellers. Treatment may include systemic therapy with ivermectin and physical removal of parasites. During pregnancy, physical removal is considered the safest treatment option, and discussion around the timing and mode of delivery should consider the risk of harm to the foetus.

Keywords: urogenital myiasis, parasitic infection, infection in pregnancy, returned traveller

Procedia PDF Downloads 118

Authors: Richard Ren

Abstract:

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 107

Authors: Uzu-Kuei Hsu, Chang-Hsien Tai, Kai-Wun Jin

Abstract:

When sinters are filled into the cooler from the sintering machine, and the non-uniform distribution of the sinters leads to uneven cooling. This causes the temperature difference of the sinters leaving the cooler to be so large that it results in the conveyors being deformed by the heat. The present work applies CFD method to investigate the thermo flowfield phenomena in a sinter cooler by the Porous Media Model. Using the obtained experimental data to simulate porosity (Ε), permeability (κ), inertial coefficient (F), specific heat (Cp) and effective thermal conductivity (keff) of the sinter packed beds. The physical model is a similar geometry whose Darcy numbers (Da) are similar to the sinter cooler. Using the Cooling Index (CI) and Uniformity Index (UI) to analyze the thermo flowfield in the sinter packed bed obtains the cooling performance of the sinter cooler.

Keywords: porous media, sinter, cooling index (CI), uniformity index (UI), CFD

Procedia PDF Downloads 385

Authors: Doaa H. Elgohary, Tamer F. Khalifa, Mona M. Salem, M. A. Saad, Ehab Haider Sherazy

Abstract:

Textiles have conquered new areas over the past three decades, including agriculture, transportation, filtration, military, and medicine. The use of textiles in the medical field has increased significantly in recent years and covers almost everything. Medical textiles represent a huge market as they are widely used not only in hospitals, hygiene, and healthcare but also in hotels and other environments where hygiene is required. However, not all fibers are suitable for the manufacture of medical textile products. Some special properties are required for the manufactured materials, e.g. Strength, elasticity, spinnability, etc. In addition to the usual properties of medical fibers, non-toxicity, sterilizability, biocompatibility, biodegradability, good absorbability, softness, and freedom from additives, etc., desirable properties include impurities. Stitching is one of the most common practices in the medical field. as it is a biomaterial device, either natural or synthetic, used to connect blood vessels and connect tissues. In addition to being very strong, suture material should easily dissolve in bodily fluids and lose strength as the tissue gains strength. In this work, a study to select the most used materials for sutures, it was found that silk, VICRYL and polypropylene were the most used materials in varying numbers. The research involved the analysis of 36 samples from three different materials (mostly commonly used), the tests were carried out on 36 imported samples for four different companies. Each company supplied three different materials (silk, VICRYL and polypropylene) with three different gauges (4, 3.5 and 3 metric). The results of the study were tabulated, presented, and discussed. Practical statistical science serves to support the practical analysis of experimental work products and the various relationships between variables to achieve the best sampling performance with the functional purpose generated for it. Analysis of the imported sutures shows that VICRYL sutures had the highest tensile strength, toughness, knot tensile strength and knot toughness, followed by polypropylene and silk. As yarn counts, weight and diameter increase, its tensile strength and toughness increase while its elongation and knot tension decrease. The multifilament yarn construction (silk and VICRYL) scores higher compared to the monofilament construction (polypropylene), resulting in increases in tenacity, toughness, knot tensile strength and knot toughness.

Keywords: biodegradable yarns, braided sutures, irritation, knot tying, medical textiles, surgical sutures, wound healing

Procedia PDF Downloads 49

Authors: Murat Sarı, Emin Gundogar, Mumtaz Ipek

Abstract:

Nowadays, in a furniture sector, competition of market share (portion) and production variety and changeability enforce the firm to reengineer operations on manufacturing line to increase the productivity. In this study, spring mattress manufacturing line of the furniture manufacturing firm is analyzed analytically. It’s intended to search and find the bottlenecks of production to balance the semi-finished material flow. There are four base points required to investigate in bottleneck elimination process. These are bottlenecks of Method, Material, Machine and Man (work force) resources, respectively. Mentioned bottlenecks are investigated and varied scenarios are created for recruitment of manufacturing system. Probable near optimal alternatives are determined by system models built in Arena simulation software.

Keywords: bottleneck search, buffer stock, furniture sector, simulation

Procedia PDF Downloads 348

Authors: Nebi Gedik

Abstract:

One of the significant and continual public health problems in the world is breast cancer. Early detection is very important to fight the disease, and mammography has been one of the most common and reliable methods to detect the disease in the early stages. However, it is a difficult task, and computer-aided diagnosis (CAD) systems are needed to assist radiologists in providing both accurate and uniform evaluation for mass in mammograms. In this study, a multiresolution statistical method to classify mammograms as normal and abnormal in digitized mammograms is used to construct a CAD system. The mammogram images are represented by wave atom transform, and this representation is made by certain groups of coefficients, independently. The CAD system is designed by calculating some statistical features using each group of coefficients. The classification is performed by using support vector machine (SVM).

Keywords: wave atom transform, statistical features, multi-resolution representation, mammogram

Procedia PDF Downloads 210