Search results for: automated remanufacturing

Authors: Carol Yue-En Ong, Angelina Hui-Min Tan, Quan Liu, Paul Chi-Lui Ho

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A public general hospital in Singapore has recently implemented an automated unit-dose machine in their inpatient dispensary, Swisslog, with the objective of reducing human error and improving patient safety. However, a concern in stability arises as tablets are removed from their original packaging (bottled loose tablets/capsules) and are repackaged into individual, clear plastic wrappers as unit doses in the system. Drugs that are light-sensitive and hygroscopic would be more susceptible to degradation as the wrapper does not offer full protection. Hence, this study was carried out to study the stability of haloperidol tablets and phenytoin capsules that are light-sensitive and hygroscopic respectively. Validated HPLC-UV assays were first established for quantification of these two compounds. The medications involved were put in the Swisslog and sampled every week for one month. The collected data was analysed and showed no degradation over time. This study also explored an alternative approach for drug stability determination-Raman spectroscopy. The advantage of Raman spectroscopy is its high time efficiency and non-destructive nature. The results suggest that drug degradation can indeed be detected using Raman microscopy, but further research is needed to establish this approach for quantification or qualification of compounds. NanoRam®, a portable Raman spectrocope was also used alongside Raman microscopy but was unsuccessful in detecting degradation in this study.

Keywords: drug stability, haloperidol, HPLC, phenytoin, raman spectroscopy, Swisslog

Procedia PDF Downloads 317

Authors: Virender Singh, Mathew Rees, Simon Hampton, Sivaram Annadurai

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Plant identification is a challenging task that aims to identify the family, genus, and species according to plant morphological features. Automated deep learning-based computer vision algorithms are widely used for identifying plants and can help users narrow down the possibilities. However, numerous morphological similarities between and within species render correct classification difficult. In this paper, we tested custom convolution neural network (CNN) and vision transformer (ViT) based models using the PyTorch framework to classify plants. We used a large dataset of 88,000 provided by the Royal Horticultural Society (RHS) and a smaller dataset of 16,000 images from the PlantClef 2015 dataset for classifying plants at genus and species levels, respectively. Our results show that for classifying plants at the genus level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420 and other state-of-the-art CNN-based models suggested in previous studies on a similar dataset. ViT model achieved top accuracy of 83.3% for classifying plants at the genus level. For classifying plants at the species level, ViT models perform better compared to CNN-based models ResNet50 and ResNet-RS-420, with a top accuracy of 92.5%. We show that the correct set of augmentation techniques plays an important role in classification success. In conclusion, these results could help end users, professionals and the general public alike in identifying plants quicker and with improved accuracy.

Keywords: plant identification, CNN, image processing, vision transformer, classification

Procedia PDF Downloads 67

Authors: Jocelyn B. Barbosa, Angeli L. Magbaril, Mariel T. Sabanal, John Paul T. Galario, Mikka P. Baldovino

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The use of technology has become ubiquitous in different areas of business today. With the advent of digital imaging and database technology, business owners have been motivated to integrate technology to their business operation ranging from small, medium to large enterprises. Technology has been found to have brought many benefits that can make a business grow. Hog or swine raising, for example, is a very popular enterprise in the Philippines, whose challenges in production monitoring can be addressed through technology integration. Swine production monitoring can become a tedious task as the enterprise goes larger. Specifically, problems like delayed and inconsistent reports are most likely to happen if counting of swine per pen of which building is done manually. In this study, we present iCount, which aims to ensure efficient swine detection and counting that hastens the swine production monitoring task. We develop a system that automatically detects and counts swine based on Sobel filter and ellipse fitting model, given the still photos of the group of swine captured in a pen. We improve the Sobel filter detection result through 8-neigbhorhood rule implementation. Ellipse fitting technique is then employed for proper swine detection. Furthermore, the system can generate periodic production reports and can identify the specific consumables to be served to the swine according to schedules. Experiments reveal that our algorithm provides an efficient way for detecting swine, thereby providing a significant amount of accuracy in production monitoring.

Keywords: automatic swine counting, swine detection, swine production monitoring, ellipse fitting model, sobel filter

Procedia PDF Downloads 290

Authors: Yiping Meng, Yiming Sun

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The traditional construction industry is unable to meet the requirements for novel fabrication and construction. Automated construction and digital design have emerged as industry development trends that compensate for this shortcoming under the backdrop of Industrial Revolution 4.0. Benefitting from more flexible working space and more various end-effector tools compared to CNC methods, robot fabrication and construction techniques have been used in irregular architectural design. However, there is a lack of a systematic and comprehensive design and optimisation workflow considering geometric form, material, and fabrication methods. This paper aims to propose a design optimisation workflow for improving the rationality of a free-form timber structure fabricated by the robotic arm. Firstly, the free-form surface is described by NURBS, while its structure is calculated using the finite element analysis method. Then, by considering the characteristics and limiting factors of robotic timber fabrication, strain energy and robustness are set as optimisation objectives to optimise structural morphology by gradient descent method. As a result, an optimised structure with axial force as the main force and uniform stress distribution is generated after the structure morphology optimisation process. With the decreased strain energy and the improved robustness, the generated structure's bearing capacity and mechanical properties have been enhanced. The results prove the feasibility and effectiveness of the proposed optimisation workflow for free-form timber structure morphology design.

Keywords: robotic fabrication, free-form timber structure, Multi-objective optimisation, Structural morphology, rational design

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Authors: Ada Garus

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This paper presents the outcomes of the sustainability assessment of food delivery with a last-mile delivery service introduced in a real-world case study. The methodology used in the sustainability assessment integrates multi-criteria decision-making analysis, sustainability pillars, and scenario analysis to best reflect the conflicting needs of stakeholders involved in the last mile delivery system. The case study provides an application of the framework to the food delivery system of the Joint Research Centre of the European Commission where three alternative solutions were analyzed I) the existent state in which individuals frequent the local cantine or pick up their food, using their preferred mode of transport II) the hypothetical scenario in which individuals can only order their food using the delivery droid system III) a scenario in which the food delivery droid based system is introduced as a supplement to the current system. The environmental indices are calculated using a simulation study in which decision regarding the food delivery is predicted using a multinomial logit model. The vehicle dynamics model is used to predict the fuel consumption of the regular combustion engines vehicles used by the cantine goers and the electricity consumption of the droid. The sustainability assessment allows for the evaluation of the economic, environmental, and social aspects of food delivery, making it an apt input for policymakers. Moreover, the assessment is one of the first studies to investigate automated delivery droids, which could become a frequent addition to the urban landscape in the near future.

Keywords: innovations in transportation technologies, behavioural change and mobility, urban freight logistics, innovative transportation systems

Procedia PDF Downloads 172

Authors: Daudi S. Simbeye, Mbazingwa E. Mkiramweni

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Plant diseases have the potential to drastically diminish the quantity and quality of agricultural products. Guava (Psidium guajava), sometimes known as the apple of the tropics, is one of the most widely cultivated fruits in tropical regions. Monitoring plant health and diagnosing illnesses is an essential matter for sustainable agriculture, requiring the inspection of visually evident patterns on plant leaves and fruits. Due to minor variations in the symptoms of various guava illnesses, a professional opinion is required for disease diagnosis. Due to improper pesticide application by farmers, erroneous diagnoses may result in economic losses. This study proposes a method that uses artificial intelligence (AI) to detect and classify the most widespread guava plant by comparing images of its leaves and fruits to datasets. ESP32 CAM is responsible for data collection, which includes images of guava leaves and fruits. By comparing the datasets, these image formats are used as datasets to help in the diagnosis of plant diseases through the leaves and fruits, which is vital for the development of an effective automated agricultural system. The system test yielded the most accurate identification findings (99 percent accuracy in differentiating four guava fruit diseases (Canker, Mummification, Dot, and Rust) from healthy fruit). The proposed model has been interfaced with a mobile application to be used by smartphones to make a quick and responsible judgment, which can help the farmers instantly detect and prevent future production losses by enabling them to take precautions beforehand.

Keywords: early identification, guava plants, fruit diseases, deep learning

Procedia PDF Downloads 47

Authors: John Gibbs, Benjamin Flanders, Dylan Pozorski, Weixuan Liu

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Weight Painting–adjusting the influence a skeletal joint has on a given vertex in a character mesh–is an arduous and time con- suming part of the 3D animation pipeline. This process generally requires a trained technical animator and many hours of work to complete. Our skiNNer plug-in, which works within Autodesk’s Maya 3D animation software, uses Machine Learning and data pro- cessing techniques to create a deep neural network model that can accomplish the weight painting task in seconds rather than hours for bipedal quasi-humanoid character meshes. In order to create a properly trained network, a number of challenges were overcome, including curating an appropriately large data library, managing an arbitrary 3D mesh size, handling arbitrary skeletal architectures, accounting for extreme numeric values (most data points are near 0 or 1 for weight maps), and constructing an appropriate neural network model that can properly capture the high frequency alter- ation between high weight values (near 1.0) and low weight values (near 0.0). The arrived at neural network model is a cross between a traditional CNN, deep residual network, and fully dense network. The resultant network captures the unusually hard-edged features of a weight map matrix, and produces excellent results on many bipedal models.

Keywords: 3d animation, animation, character, rigging, skinning, weight painting, machine learning, artificial intelligence, neural network, deep neural network

Procedia PDF Downloads 243

Authors: Walaa Mohammed Saeed

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Studies suggest a crosstalk between bone and metabolism through Osteocalcin (OC), a bone-derived protein that plays an important role in regulating glucose and fat metabolism. Studies relate type II Diabetes Mellitus (DMII) with Homocysteine (Hcy) and cardiovascular diseases (CVD). This study investigates the relationship between levels of OC, Hcy, and DMII in 85 subjects of which 50 were diabetic female patients (29–65 years) and 35 healthy controls. OC and Hcy levels were measured in fasting blood samples using immunoassay analyzer. Fasting serum glucose, glycated hemoglobin, lipid profile, were estimated by automated Siemens Dimension XP auto-analyzer. A significant increase in the frequency of low OC levels (p < 0.001) and high Hcy levels (p < 0.001) was detected in diabetic patients compared to controls (chi-squared test). Using ANOVA test, patients were divided into tertiles based on plasma OC and Hcy levels; fasting serum glucose varied inversely with OC but directly with Hcy tertiles (p=0.049, p=0.033 respectively). Atherogenic Index of Plasma (AIP=Log TG/HDL) predicts that diabetic patients with 36% high and 15% intermediate cardiovascular risk had increased frequency of low OC levels compared to low-risk patients (p=0.047). Another group of diabetic patients with 39% high and 11% intermediate CVD risk had increased frequency of high Hcy levels (p=0.033). A significant negative correlation existed between OC and glucose (r = -0.318; p = 0.035) while correlation between glucose level and Hcy (r = 0.851 p=0.022) was positive. Hence, low serum OC levels and high Hcy levels were associated with impaired glucose metabolism that may increase cardiovascular risk in DMII.

Keywords: osteocalcin, homocysteine, type 2 diabetes, cardiovascular

Procedia PDF Downloads 124

Authors: Oluibukun G. Ajayi, Joseph O. Odumosu, Oladimeji T. Babafemi, Azeez Z. Opeyemi, Asaleye O. Samuel

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The need for the complete automation of every procedure of surveying and most especially, its engineering applications cannot be overemphasized due to the many demerits of the conventional manual or analogue approach. This paper presents the summarized details of the development of a Route Information Management (RIM) software. The software, codenamed ‘AutoROUTE’, was encoded using Microsoft visual studio-visual basic package, and it offers complete automation of the computational procedures and plan production involved in route surveying. It was experimented using a route survey data (longitudinal profile and cross sections) of a 2.7 km road which stretches from Dama to Lunko village in Minna, Niger State, acquired with the aid of a Hi-Target DGPS receiver. The developed software (AutoROUTE) is capable of computing the various simple curve parameters, horizontal curve, and vertical curve, and it can also plot road alignment, longitudinal profile, and cross-section with a capability to store this on the SQL incorporated into the Microsoft visual basic software. The plotted plans with AutoROUTE were compared with the plans produced with the conventional AutoCAD Civil 3D software, and AutoROUTE proved to be more user-friendly and accurate because it plots in three decimal places whereas AutoCAD plots in two decimal places. Also, it was discovered that AutoROUTE software is faster in plotting and the stages involved is less cumbersome compared to AutoCAD Civil 3D software.

Keywords: automated systems, cross sections, curves, engineering construction, longitudinal profile, route surveying

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Authors: Tasneem Halawani, Yamen Khateeb

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With customers’ ever-increasing expectations for fast services provisioning for all their business needs, information technology (IT) organizations, as business partners, have to cope with this demanding environment and deliver their services in the most effective and efficient way. The purpose of this paper is to identify optimization and automation opportunities for the top requested IT services in a heterogenic digital environment and widely spread customer base. In collaboration with systems, processes, and subject matter experts (SMEs), the processes in scope were approached by analyzing four-year related historical data, identifying and surveying stakeholders, modeling the as-is processes, and studying systems integration/automation capabilities. This effort resulted in identifying several pain areas, including standardization, unnecessary customer and IT involvement, manual steps, systems integration, and performance measurement. These pain areas were addressed by standardizing the top five requested IT services, eliminating/automating 43 steps, and utilizing a single platform for end-to-end process execution. In conclusion, the optimization of IT service request processes in a heterogenic digital environment and widely spread customer base is challenging, yet achievable without compromising the service quality and customers’ added value. Further studies can focus on measuring the value of the eliminated/automated process steps to quantify the enhancement impact. Moreover, a similar approach can be utilized to optimize other IT service requests, with a focus on business criticality.

Keywords: automation, customer value, heterogenic, integration, IT services, optimization, processes

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Authors: Belgherbi Aicha, Hadjidj Ismahen, Bessaid Abdelhafid

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The interpretation of medical images benefits from anatomical and physiological priors to optimize computer- aided diagnosis applications. Segmentation of liver and liver lesion is regarded as a major primary step in computer aided diagnosis of liver diseases. Precise liver segmentation in abdominal CT images is one of the most important steps for the computer-aided diagnosis of liver pathology. In this papers, a semi- automated method for medical image data is presented for the liver and liver lesion segmentation data using mathematical morphology. Our algorithm is currency in two parts. In the first, we seek to determine the region of interest by applying the morphological filters to extract the liver. The second step consists to detect the liver lesion. In this task; we proposed a new method developed for the semi-automatic segmentation of the liver and hepatic lesions. Our proposed method is based on the anatomical information and mathematical morphology tools used in the image processing field. At first, we try to improve the quality of the original image and image gradient by applying the spatial filter followed by the morphological filters. The second step consists to calculate the internal and external markers of the liver and hepatic lesions. Thereafter we proceed to the liver and hepatic lesions segmentation by the watershed transform controlled by markers. The validation of the developed algorithm is done using several images. Obtained results show the good performances of our proposed algorithm

Keywords: anisotropic diffusion filter, CT images, hepatic lesion segmentation, Liver segmentation, morphological filter, the watershed algorithm

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Authors: Erika Duggan

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A Major Hospital had identified that there was no standard process for handling a patient’s medication that they brought with them to the hospital. It was also identified that each floor was handling the patient’s medication differently and storing it in multiple locations. Based on this disconnect many patients were leaving the hospital without their medication. The project team was tasked with creating a cohesive process to send a patient’s unneeded medication home on admission, storing any of the patient’s medication that could not be sent home, storing any of the patient’s medication for inpatient administration, and sending all of the patient’s medication home on discharge. The project team consisted of pharmacists, RNs, LPNs, members from nursing informatics and a project engineer and followed a DMAIC framework. Working together observations were performed to identify what was working and not working on the different floors which resulted in process maps. Using the multidisciplinary team, brainstorming, including affinity diagramming and other lean six sigma techniques, the best process for receiving, storing, and returning the medication was created. It was highlighted that being able to track the medication throughout the patient’s stay would be beneficial and would help make sure the medication left with the patient on discharge. Using an automated medications dispensing system would help store, and track patient’s medications. Also, the use of a specific order that would show up on the discharge instructions would assist the front line staff in retrieving the medication from a set location and sending it home with the patient. This new process will effectively streamline the admission and discharge process for patients who brought their medication with them as well as effectively tracking the medication during the patient’s stay. As well as increasing patient safety as it relates to medication administration.

Keywords: lean six sigma, medication dispensing, process improvement, process mapping

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Authors: Zonglin Yang, Tatsuya Akiyama, Kerry S. Williamson, Michael J. Franklin, Thiruvarangan Ramaraj

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Pseudomonas aeruginosa is an opportunistic pathogen that forms surface-associated microbial communities (biofilms) on artificial implant devices and on human tissue. Biofilm infections are difficult to treat with antibiotics, in part, because the bacteria in biofilms are physiologically heterogeneous. One measure of biological heterogeneity in a population of cells is to quantify the cellular concentrations of ribosomes, which can be probed with fluorescently labeled nucleic acids. The fluorescent signal intensity following fluorescence in situ hybridization (FISH) analysis correlates to the cellular level of ribosomes. The goals here are to provide computationally and statistically robust approaches to automatically quantify cellular heterogeneity in biofilms from a large library of epifluorescent microscopy FISH images. In this work, the initial steps were developed toward these goals by developing an automated biofilm detection approach for use with FISH images. The approach allows rapid identification of biofilm regions from FISH images that are counterstained with fluorescent dyes. This methodology provides advances over other computational methods, allowing subtraction of spurious signals and non-biological fluorescent substrata. This method will be a robust and user-friendly approach which will enable users to semi-automatically detect biofilm boundaries and extract intensity values from fluorescent images for quantitative analysis of biofilm heterogeneity.

Keywords: image informatics, Pseudomonas aeruginosa, biofilm, FISH, computer vision, data visualization

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Authors: Seyedeh Fatemeh Nabavi, Hamid Dalir, Anooshiravan Farshidianfar

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Laser welding is pivotal in modern manufacturing, offering unmatched precision, speed, and efficiency. Its versatility in minimizing heat-affected zones, seamlessly joining dissimilar materials, and working with various metals makes it indispensable for crafting intricate automotive components. Integration into automated systems ensures consistent delivery of high-quality welds, thereby enhancing overall production efficiency. Noteworthy are the safety benefits of laser welding, including reduced fumes and consumable materials, which align with industry standards and environmental sustainability goals. As the automotive sector increasingly demands advanced materials and stringent safety and quality standards, laser welding emerges as a cornerstone technology. A comprehensive model encompassing thermal dynamic and characteristics models accurately predicts geometrical, metallurgical, and mechanical aspects of the laser beam welding process. Notably, Model 2 showcases exceptional accuracy, achieving remarkably low error rates in predicting primary and secondary dendrite arm spacing (PDAS and SDAS). These findings underscore the model's reliability and effectiveness, providing invaluable insights and predictive capabilities crucial for optimizing welding processes and ensuring superior productivity, efficiency, and quality in the automotive industry.

Keywords: laser welding process, geometrical characteristics, mechanical characteristics, metallurgical characteristics, comprehensive model, thermal dynamic

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Authors: Toshifumi Hiramatsu, Satoshi Morita, Manuel Pencelli, Marta Niccolini, Matteo Ragaglia, Alfredo Argiolas

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Automation technologies for agriculture field are needed to promote labor-saving. One of the most relevant problems in automated agriculture is represented by controlling the robot along a predetermined path in presence of rough terrain or incline ground. Unfortunately, disturbances originating from interaction with the ground, such as slipping, make it quite difficult to achieve the required accuracy. In general, it is required to move within 5-10 cm accuracy with respect to the predetermined path. Moreover, lateral velocity caused by gravity on the incline field also affects slipping. In this paper, a path-tracking controller for tracked mobile robots moving on rough terrains of incline field such as vineyard is presented. The controller is composed of a disturbance observer and an adaptive controller based on the kinematic model of the robot. The disturbance observer measures the difference between the measured and the reference yaw rate and linear velocity in order to estimate slip. Then, the adaptive controller adapts “virtual” parameter of the kinematics model: Instantaneous Centers of Rotation (ICRs). Finally, target angular velocity reference is computed according to the adapted parameter. This solution allows estimating the effects of slip without making the model too complex. Finally, the effectiveness of the proposed solution is tested in a simulation environment.

Keywords: the agricultural robot, autonomous control, path-tracking control, tracked mobile robot

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Authors: Nabila Mohamed, Santiago Aparicio, Bahman Tohidi, Mert Atilhan

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Qatar's one of the biggest problem in processing its natural resource, which is natural gas, is the often occurring blockage in the pipelines caused due to uncontrolled gas hydrate formation in the pipelines. Several millions of dollars are being spent at the process site to dehydrate the blockage safely by using chemical inhibitors. We aim to establish national database, which addresses the physical conditions that promotes Qatari natural gas to form gas hydrates in the pipelines. Moreover, we aim to design and test novel hydrate inhibitors that are suitable for Qatari natural gas and its processing facilities. From these perspectives we are aiming to provide more effective and sustainable reservoir utilization and processing of Qatari natural gas. In this work, we present the initial findings of a QNRF funded project, which deals with the natural gas hydrate formation characteristics of Qatari type gas in both experimental (PVTx) and computational (molecular simulations) methods. We present the data from the two fully automated apparatus: a gas hydrate autoclave and a rocking cell. Hydrate equilibrium curves including growth/dissociation conditions for multi-component systems for several gas mixtures that represent Qatari type natural gas with and without the presence of well known kinetic and thermodynamic hydrate inhibitors. Ionic liquids were designed and used for testing their inhibition performance and their DFT and molecular modeling simulation results were also obtained and compared with the experimental results. Results showed significant performance of ionic liquids with up to 0.5 % in volume with up to 2 to 4 0C inhibition at high pressures.

Keywords: gas hydrates, natural gas, ionic liquids, inhibition, thermodynamic inhibitors, kinetic inhibitors

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Authors: Devadrita Dey Sarkar

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Regardless of the many technologic advances in the past decade, increased training and experience, and the obvious benefits of uniform standards, the false-negative rate in screening mammography remains unacceptably high .A computer aided neural network classification of regions of suspicion (ROS) on digitized mammograms is presented in this abstract which employs features extracted by a new technique based on independent component analysis. CAD is a concept established by taking into account equally the roles of physicians and computers, whereas automated computer diagnosis is a concept based on computer algorithms only. With CAD, the performance by computers does not have to be comparable to or better than that by physicians, but needs to be complementary to that by physicians. In fact, a large number of CAD systems have been employed for assisting physicians in the early detection of breast cancers on mammograms. A CAD scheme that makes use of lateral breast images has the potential to improve the overall performance in the detection of breast lumps. Because breast lumps can be detected reliably by computer on lateral breast mammographs, radiologists’ accuracy in the detection of breast lumps would be improved by the use of CAD, and thus early diagnosis of breast cancer would become possible. In the future, many CAD schemes could be assembled as packages and implemented as a part of PACS. For example, the package for breast CAD may include the computerized detection of breast nodules, as well as the computerized classification of benign and malignant nodules. In order to assist in the differential diagnosis, it would be possible to search for and retrieve images (or lesions) with these CAD systems, which would be reliable and useful method for quantifying the similarity of a pair of images for visual comparison by radiologists.

Keywords: CAD(computer-aided design), lesions, neural network, ROS(region of suspicion)

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Authors: M. Gerasimou, S. Mantzoukis, P. Christodoulou, N. Varsamis, G. Kolliopoulou, N. Zotos

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Purpose: Microbial infection of the blood is a serious condition where bacteria invade the bloodstream and cause systemic disease. In such cases, blood cultures are performed. Blood cultures are a key diagnostic test for intensive care unit (ICU) patients. Material and method: The BacT/Alert system, which measures the production of carbon dioxide with metabolic organisms, is used. The positive result in the BacT/Alert system is followed by culture in the following selective media: Blood, Mac Conkey No 2, Chocolate, Mueller Hinton, Chapman and Sabaureaud agar. Gram staining method was used to differentiate bacterial species. The microorganisms were identified by biochemical techniques in the automated Microscan (Siemens) system and followed by a sensitivity test on the same system using the minimum inhibitory concentration MIC technique. The sensitivity test is verified by a Kirby Bauer-based test. Results: In 2017 the Laboratory of Microbiology received 3347 blood cultures. Of these, 170 came from the ICU. 116 found positive. Of these S. epidermidis was identified in 42, A. baumannii in 27, K. pneumoniae in 12 (4 of these KPC ‘Klebsiella pneumoniae carbapenemase’), S. hominis in 8, E. faecium in 7, E. faecalis in 5, P. aeruginosa in 3, C. albicans in 3, S. capitis in 2, K. oxytoca in 2, P. mirabilis in 2, E. coli in 1, S. intermidius in 1 and S. lugdunensis in 1. Conclusions: The study of epidemiological data and microbial resistance phenotypes is essential for the choice of therapeutic regimen for the early treatment and limitation of multivalent strains, while it is a crucial factor to solve diagnostic problems.

Keywords: blood culture, bloodstream, infection, intensive care unit

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Authors: Jun-Ki Choi

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The usage of valuable resources and the potential for waste generation at the end of the life cycle of photovoltaic (PV) technologies necessitate a proactive planning for a PV recycling infrastructure. To ensure the sustainability of PV in large scales of deployment, it is vital to develop and institute low-cost recycling technologies and infrastructure for the emerging PV industry in parallel with the rapid commercialization of these new technologies. There are various issues involved in the economics of PV recycling and this research examine those at macro and micro levels, developing a holistic interpretation of the economic viability of the PV recycling systems. This study developed mathematical models to analyze the profitability of recycling technologies and to guide tactical decisions for allocating optimal location of PV take-back centers (PVTBC), necessary for the collection of end of life products. The economic decision is usually based on the level of the marginal capital cost of each PVTBC, cost of reverse logistics, distance traveled, and the amount of PV waste collected from various locations. Results illustrated that the reverse logistics costs comprise a major portion of the cost of PVTBC; PV recycling centers can be constructed in the optimally selected locations to minimize the total reverse logistics cost for transporting the PV wastes from various collection facilities to the recycling center. In the micro- process level, automated recycling processes should be developed to handle the large amount of growing PV wastes economically. The market price of the reclaimed materials are important factors for deciding the profitability of the recycling process and this illustrates the importance of the recovering the glass and expensive metals from PV modules.

Keywords: photovoltaic, recycling, mathematical models, sustainability

Procedia PDF Downloads 225

Authors: Siobhan O’Shea, Sangeetha Vijaysri Nair, Hee Cheol Kim, Charles Thomas Nugent, Cheuk Yan William Tong, Sam Douthwaite, Andrew Worlock

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The Aptima® HIV-1 Quant Dx Assay is a fully automated assay on the Panther system. It is based on Transcription-Mediated Amplification and real time detection technologies. This assay is intended for monitoring HIV-1 viral load in plasma specimens and for the detection of HIV-1 in plasma and serum specimens. Nine-hundred and seventy nine specimens selected at random from routine testing at St Thomas’ Hospital, London were anonymised and used to compare the performance of the Aptima HIV-1 Quant Dx assay and Roche COBAS® AmpliPrep/COBAS® TaqMan® HIV-1 Test, v2.0. Two-hundred and thirty four specimens gave quantitative HIV-1 viral load results in both assays. The quantitative results reported by the Aptima Assay were comparable those reported by the Roche COBAS AmpliPrep/COBAS TaqMan HIV-1 Test, v2.0 with a linear regression slope of 1.04 and an intercept on -0.097. The Aptima assay detected HIV-1 in more samples than the Roche assay. This was not due to lack of specificity of the Aptima assay because this assay gave 99.83% specificity on testing plasma specimens from 600 HIV-1 negative individuals. To understand the reason for this higher detection rate a side-by-side comparison of low level panels made from the HIV-1 3rd international standard (NIBSC10/152) and clinical samples of various subtypes were tested in both assays. The Aptima assay was more sensitive than the Roche assay. The good sensitivity, specificity and agreement with other commercial assays make the HIV-1 Quant Dx Assay appropriate for both viral load monitoring and detection of HIV-1 infections.

Keywords: HIV viral load, Aptima, Roche, Panther system

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Authors: Hamilton V. Niculescu

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Following a three year study conducted on eighteen Irish people that are involved in growing vegetables in various community gardens around Dublin, Republic of Ireland, it was revealed that addition of some automated features aimed at improving agricultural practices represented a process which was regarded as potentially beneficial, and as a great tool to closely monitor climate conditions inside the greenhouses. The participants were provided with a free custom-built mobile app through which they could remotely monitor and control features such as irrigation, air ventilation, and windows to ensure optimal growing conditions for vegetables growing inside purpose-built greenhouses. While the initial interest was generally high, within weeks, the participants' level of interaction with the enclosures slowly declined. By employing a constructivist grounded theory methodology, following focus group discussions, in-depth semi-structured interviews, and observations, it was revealed that participants' trust in newer technologies, and renewables, in particular, was low. There are various reasons for this, but because the participants in this study consist of mainly working-class people, it can be argued that lack of education and knowledge are the main barriers acting against the adoption of innovations. Consequently, it was revealed that most participants eventually decided to "set and forget" the systems in automatic working mode, indicating that the immediate effect of introducing people to assisting technologies also introduced some unintended consequences into their lifestyle. It is argued that this occurrence also indicates the fact that people initially "read" newer technologies and only adopt those features that they find useful and less intrusive in regards to their current lifestyle.

Keywords: automation, communication, greenhouse, sustainable

Procedia PDF Downloads 101

Authors: Manendra Singh Parihar, Sandip Ghosh Chowdhury

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Austenitic stainless steels are widely used and give a good combination of properties. When this steel is plastically deformed, a phase transformation of the metastable Face Centred Cubic Austenite to the stable Body Centred Cubic (α’) or to the Hexagonal close packed (ԑ) martensite may occur, leading to the enhancement in the mechanical properties like strength. The work was based on variant selection and corresponding texture analysis for the strain induced martensitic transformation during deformation of the parent austenite FCC phase to form the product HCP and the BCC martensite phases separately, obeying their respective orientation relationships. The automated method for reconstruction of the parent phase orientation using the EBSD data of the product phase orientation is done using the MATLAB and TSL-OIM software. The method of triplets was used which involves the formation of a triplet of neighboring product grains having a common variant and linking them using a misorientation-based criterion. This led to the proper reconstruction of the pre-transformation phase orientation data and thus to its microstructure and texture. The computational speed of current method is better compared to the previously used methods of reconstruction. The reconstruction of austenite from ԑ and α’ martensite was carried out for multiple samples and their IPF images, pole figures, inverse pole figures and ODFs were compared. Similar type of results was observed for all samples. The comparison gives the idea for estimating the correct sequence of the transformation i.e. γ → ε → α’ or γ → α’, during deformation of AISI 304 austenitic stainless steel.

Keywords: variant selection, reconstruction, EBSD, austenitic stainless steel, martensitic transformation

Procedia PDF Downloads 465

Authors: Parthiban Srinivasan

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With recent advances in computational power and access to enough data in biosciences, artificial intelligence methods are increasingly being used in drug discovery research. These methods are essentially a series of advanced statistics based exercises that review the past to indicate the likely future. Our goal is to develop a model that accurately predicts biological activity and toxicity parameters for novel compounds. We have compiled a robust library of over 150,000 chemical compounds with different pharmacological properties from literature and public domain databases. The compounds are stored in simplified molecular-input line-entry system (SMILES), a commonly used text encoding for organic molecules. We utilize an automated process to generate an array of numerical descriptors (features) for each molecule. Redundant and irrelevant descriptors are eliminated iteratively. Our prediction engine is based on a portfolio of machine learning algorithms. We found Random Forest algorithm to be a better choice for this analysis. We captured non-linear relationship in the data and formed a prediction model with reasonable accuracy by averaging across a large number of randomized decision trees. Our next step is to apply deep neural network (DNN) algorithm to predict the biological activity and toxicity properties. We expect the DNN algorithm to give better results and improve the accuracy of the prediction. This presentation will review all these prominent machine learning and deep learning methods, our implementation protocols and discuss these techniques for their usefulness in biomedical and health informatics.

Keywords: deep learning, drug discovery, health informatics, machine learning, toxicity prediction

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Authors: Apichet Boonsoong

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In the Chanthaburi-Trat basalts, xenoliths are composed of essentially ultramafic xenoliths (particularly spinel lherzolite) with a few of an aggregate of feldspar. Some 19 ultramafic xenoliths were collected from 13 different locations. They range in size from 3.5 to 60mm across. Most are weathered and oxidized on the surface but fresh samples are obtained from cut surfaces. Chemical analyses were performed on carbon-coated polished thin sections using a fully automated CAMECA SX-50 electron microprobe (EMPA) in wavelength-dispersive mode. In thin section, they are seen to consist of variable amounts of olivine, clinopyroxene, orthopyroxene with minor spinel and plagioclase, and are classed as lherzolite. Modal compositions of the ultramafic nodules vary with olivine (60-75%), clinopyroxene (20-30%), orthopyroxene (0-15%), minor spinel (1-3%) and plagioclase (<1%). The essential minerals form an equigranular, medium- to coarse-grained, granoblastic texture, and all are in mutual contact indicating attainment of equilibrium. Reaction rims are common along the nodule margins and in some are also present along grain boundaries. Zoning occurs in clinopyroxene, and to a lesser extent in orthopyroxene. The homogeneity of mineral compositions in lherzolite xenoliths suggests the attainment of equilibrium. The equilibration temperatures of these xenoliths are estimated to be in the range of 973 to 1063°C. Pressure estimates are not so easily obtained because no suitable barometer exists for garnet-free lherzolites and so an indirect method was used. The general mineral assemblage of the lherzolite xenoliths and the absence of garnet indicate a pressure range of approximately 12–19kbar, which is equivalent to depths approximately of 38 to 60km.

Keywords: chanthaburi-trat basalts, spinel lherzolite, xenoliths, 973 to 1063°C, 38 to 60km

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Authors: Joseph George, Anne Kotteswara Roa

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Skin disease is one of the most common and popular kinds of health issues faced by people nowadays. Skin cancer (SC) is one among them, and its detection relies on the skin biopsy outputs and the expertise of the doctors, but it consumes more time and some inaccurate results. At the early stage, skin cancer detection is a challenging task, and it easily spreads to the whole body and leads to an increase in the mortality rate. Skin cancer is curable when it is detected at an early stage. In order to classify correct and accurate skin cancer, the critical task is skin cancer identification and classification, and it is more based on the cancer disease features such as shape, size, color, symmetry and etc. More similar characteristics are present in many skin diseases; hence it makes it a challenging issue to select important features from a skin cancer dataset images. Hence, the skin cancer diagnostic accuracy is improved by requiring an automated skin cancer detection and classification framework; thereby, the human expert’s scarcity is handled. Recently, the deep learning techniques like Convolutional neural network (CNN), Deep belief neural network (DBN), Artificial neural network (ANN), Recurrent neural network (RNN), and Long and short term memory (LSTM) have been widely used for the identification and classification of skin cancers. This survey reviews different DL techniques for skin cancer identification and classification. The performance metrics such as precision, recall, accuracy, sensitivity, specificity, and F-measures are used to evaluate the effectiveness of SC identification using DL techniques. By using these DL techniques, the classification accuracy increases along with the mitigation of computational complexities and time consumption.

Keywords: skin cancer, deep learning, performance measures, accuracy, datasets

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Authors: Prodipta Bhowmik, Poulami Saha, Preety Mehra, Yogesh Soni, Triloki Nath Jha

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In India, the smart city concept is growing day by day. So, for smart city development, a better traffic management and monitoring system is a very important requirement. Nowadays, road accidents increase due to more vehicles on the road. Reckless driving is mainly responsible for a huge number of accidents. So, an efficient traffic management system is required for all kinds of roads to control the traffic speed. The speed limit varies from road to road basis. Previously, there was a radar system but due to high cost and less precision, the radar system is unable to become favorable in a traffic management system. Traffic management system faces different types of problems every day and it has become a researchable topic on how to solve this problem. This paper proposed a computer vision and machine learning-based automated system for multiple vehicle detection, tracking, and speed estimation of vehicles using image processing. Detection of vehicles and estimating their speed from a real-time video is tough work to do. The objective of this paper is to detect vehicles and estimate their speed as accurately as possible. So for this, a real-time video is first captured, then the frames are extracted from that video, then from that frames, the vehicles are detected, and thereafter, the tracking of vehicles starts, and finally, the speed of the moving vehicles is estimated. The goal of this method is to develop a cost-friendly system that can able to detect multiple types of vehicles at the same time.

Keywords: OpenCV, Haar Cascade classifier, DLIB, YOLOV3, centroid tracker, vehicle detection, vehicle tracking, vehicle speed estimation, computer vision

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Authors: Serin Darwish, Hakim Saibi, Amir Gabr

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The United Arab Emirates (UAE) is situated within an arid zone where the climate is arid and the recharge of the groundwater is very low. Groundwater is the primary source of water in the United Arab Emirates. However, rapid expansion, population growth, agriculture, and industrial activities have negatively affected these limited water resources. The shortage of water resources has become a serious concern due to the over-pumping of groundwater to meet demand. In addition to the deficit of groundwater, the UAE has one of the highest per capita water consumption rates in the world. In this study, a combination of time-lapse measurements of microgravity and depth to groundwater level in selected wells in Al Ain city was used to estimate the variations in groundwater storage. Al-Ain is the second largest city in Abu Dhabi Emirates and the third largest city in the UAE. The groundwater in this region has been overexploited. Relative gravity measurements were acquired using the Scintrex CG-6 Autograv. This latest generation gravimeter from Scintrex Ltd provides fast, precise gravity measurements and automated corrections for temperature, tide, instrument tilt and rejection of data noise. The CG-6 gravimeter has a resolution of 0.1μGal. The purpose of this study is to measure the groundwater storage changes in the shallow aquifers based on the application of microgravity method. The gravity method is a nondestructive technique that allows collection of data at almost any location over the aquifer. Preliminary results indicate a possible relationship between microgravity and water levels, but more work needs to be done to confirm this. The results will help to develop the relationship between monthly microgravity changes with hydrological and hydrogeological changes of shallow phreatic. The study will be useful in water management considerations and additional future investigations.

Keywords: Al-Ain, arid region, groundwater, microgravity

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Authors: Daniel Mueller, Melanie Breitbach, Stefan Cornelius, Sarah Pakulla-Dickel, Margaretha Koenig, Anke Prochnow, Mario Scherer

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The European STR standard set (ESS) of loci as well as the new expanded CODIS core loci set as recommended by the CODIS Core Loci Working Group, has led to a higher standardization and harmonization in STR analysis across borders. Various multiplex PCRs assays have since been developed for the analysis of these 17 ESS or 23 CODIS expansion STR markers that all meet high technical demands. However, forensic analysts are often faced with difficult STR results and the questions thereupon. What is the reason that no peaks are visible in the electropherogram? Did the PCR fail? Was the DNA concentration too low? QIAGEN’s newest Investigator STR kits contain a novel Quality Sensor (QS) that acts as internal performance control and gives useful information for evaluating the amplification efficiency of the PCR. QS indicates if the reaction has worked in general and furthermore allows discriminating between the presence of inhibitors or DNA degradation as a cause for the typical ski slope effect observed in STR profiles of such challenging samples. This information can be used to choose the most appropriate rework strategy.Based on the latest PCR chemistry called FRM 2.0, QIAGEN now provides the next technological generation for STR analysis, the Investigator ESSplex SE QS and Investigator 24plex QS Kits. The new PCR chemistry ensures robust and fast PCR amplification with improved inhibitor resistance and easy handling for a manual or automated setup. The short cycling time of 60 min reduces the duration of the total PCR analysis to make a whole workflow analysis in one day more likely. To facilitate the interpretation of STR results a smart primer design was applied for best possible marker distribution, highest concordance rates and a robust gender typing.

Keywords: PCR, QIAGEN, quality sensor, STR

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Authors: Ankit Sharma, Mudit Sachan, Anurag Prakash

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Rapid growth in data acquisition technologies through drones, have led to advances and interests in collecting high-resolution images of geological fields. Being advantageous in capturing high volume of data in short flights, a number of challenges have to overcome for efficient analysis of this data, especially while data acquisition, image interpretation and processing. We introduce a method that allows effective mapping of geological fields using photogrammetric data of surfaces, drainage area, water bodies etc, which will be captured by airborne vehicles like UAVs, we are not taking satellite images because of problems in adequate resolution, time when it is captured may be 1 yr back, availability problem, difficult to capture exact image, then night vision etc. This method includes advanced automated image interpretation technology and human data interaction to model structures and. First Geological structures will be detected from the primary photographic dataset and the equivalent three dimensional structures would then be identified by digital elevation model. We can calculate dip and its direction by using the above information. The structural map will be generated by adopting a specified methodology starting from choosing the appropriate camera, camera’s mounting system, UAVs design ( based on the area and application), Challenge in air borne systems like Errors in image orientation, payload problem, mosaicing and geo referencing and registering of different images to applying DEM. The paper shows the potential of using our method for accurate and efficient modeling of geological structures, capture particularly from remote, of inaccessible and hazardous sites.

Keywords: digital elevation model, mapping, photogrammetric data analysis, geological structures

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Authors: Suvarna Kumar Gogula, Sandhya Devi Gogula, P. Chanakya

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Augmented reality is defined as the collection of the digital (or) computer generated information like images, audio, video, 3d models, etc. and overlay them over the real time environment. Augmented reality can be thought as a blend between completely synthetic and completely real. Augmented reality provides scope in a wide range of industries like manufacturing, retail, gaming, advertisement, tourism, etc. and brings out new dimensions in the modern digital world. As it overlays the content, it makes the users enhance the knowledge by providing the content blended with real world. In this application, we integrated augmented reality with data analytics and integrated with cloud so the virtual content will be generated on the basis of the data present in the database and we used marker based augmented reality where every marker will be stored in the database with corresponding unique ID. This application can be used in wide range of industries for different business processes, but in this paper, we mainly focus on the marketing industry which helps the customer in gaining the knowledge about the products in the market which mainly focus on their prices, customer feedback, quality, and other benefits. This application also focuses on providing better market strategy information for marketing managers who obtain the data about the stocks, sales, customer response about the product, etc. In this paper, we also included the reports from the feedback got from different people after the demonstration, and finally, we presented the future scope of Augmented Reality in different business processes by integrating with new technologies like cloud, big data, artificial intelligence, etc.

Keywords: augmented reality, data analytics, catch room, marketing and sales

Procedia PDF Downloads 199