Search results for: edge detection algorithm

Authors: Amit Bhunia, Mohit Kumar Singh, Maryam Al Huwayz, Mohamed Henini, Shouvik Datta

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We report [https://arxiv.org/abs/2107.13518] experimental detection and control of Schrodinger’s Cat like macroscopically large, quantum coherent state of a two-component Bose-Einstein condensate of spatially indirect electron-hole pairs or excitons using a resonant tunneling diode of III-V Semiconductors. This provides access to millions of excitons as qubits to allow efficient, fault-tolerant quantum computation. In this work, we measure phase-coherent periodic oscillations in photo-generated capacitance as a function of an applied voltage bias and light intensity over a macroscopically large area. Periodic presence and absence of splitting of excitonic peaks in the optical spectra measured by photocapacitance point towards tunneling induced variations in capacitive coupling between the quantum well and quantum dots. Observation of negative ‘quantum capacitance’ due to a screening of charge carriers by the quantum well indicates Coulomb correlations of interacting excitons in the plane of the sample. We also establish that coherent resonant tunneling in this well-dot heterostructure restricts the available momentum space of the charge carriers within this quantum well. Consequently, the electric polarization vector of the associated indirect excitons collective orients along the direction of applied bias and these excitons undergo Bose-Einstein condensation below ~100 K. Generation of interference beats in photocapacitance oscillation even with incoherent white light further confirm the presence of stable, long-range spatial correlation among these indirect excitons. We finally demonstrate collective Rabi oscillations of these macroscopically large, ‘multipartite’, two-level, coupled and uncoupled quantum states of excitonic condensate as qubits. Therefore, our study not only brings the physics and technology of Bose-Einstein condensation within the reaches of semiconductor chips but also opens up experimental investigations of the fundamentals of quantum physics using similar techniques. Operational temperatures of such two-component excitonic BEC can be raised further with a more densely packed, ordered array of QDs and/or using materials having larger excitonic binding energies. However, fabrications of single crystals of 0D-2D heterostructures using 2D materials (e.g. transition metal di-chalcogenides, oxides, perovskites etc.) having higher excitonic binding energies are still an open challenge for semiconductor optoelectronics. As of now, these 0D-2D heterostructures can already be scaled up for mass production of miniaturized, portable quantum optoelectronic devices using the existing III-V and/or Nitride based semiconductor fabrication technologies.

Keywords: exciton, Bose-Einstein condensation, quantum computation, heterostructures, semiconductor Physics, quantum fluids, Schrodinger's Cat

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Authors: Hamdan Hamood Aldumaini, Amjad Hussein Meqdam, Shamsaldeen kassim Ali, Hamed Mohammed Al-Yousefi, Haron Ahmed Al-Badawi

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Background: Autism Spectrum Disorder (ASD) is a complex developmental challenge characterized by significant impairments in social interaction, communication, and behavioral patterns. Diagnosing ASD is challenging due to the lack of definitive medical tests, making early identification crucial. Therefore, increasing people's awareness about autism leads to early diagnosis and better prognosis. Objective: Our study aims to identify the initial symptoms prompting families to seek medical advice, determine the timeline between symptom onset and formal diagnosis, and explore methods for assessing the severity of ASD. Subjects and Methods: The study design employed was a descriptive cross-sectional design, which was suitable for the nature of the research. The data collection took place from March 5, 2022, to April 5, 2022, in Autism Rehabilitation Centers in Sana'a, Yemen. The study population consisted of all children who were diagnosed with autism and visited Autism rehabilitation centers in Sana'a city. The sample size was determined using Epi info version 7, and a total population of 587 autistic children attending the treatment was calculated, but only 250 children were included in this study (176 were male vs. 74 female). Result: In terms of sociability problems, it was found that a significant proportion of Yemeni children with autism experienced difficulties in this area. Specifically, 39.6% were classified as having severe sociability problems, while 28.4% were classified as having moderate issues. Sensory-cognitive awareness problems were also prevalent among the respondents, with 29.6% exhibiting severe difficulties in this domain. Health and physical problems were identified as significant concerns for Yemeni children with autism. The results indicated that 38.4% of the participants experienced severe health and physical issues. Identifying the first symptoms of autism is crucial for early detection and intervention. According to the study, speech delay was the most commonly observed first abnormality, reported by 71.3% of parents. Communication difficulties with others were the second most noticed abnormality, reported by 54.9% of parents. Repetitive movements were the third most commonly observed abnormality, reported by 18% of parents. Regarding the awareness among parents of ASD, our study showed that a significant portion (62%) of parents lack awareness about Autism Spectrum Disorder (ASD) and its causes. Surprisingly, a majority of these parents (over 80%) believe that autism is a curable condition. Additionally, more than half (51.2%) of the parents surveyed reported insufficient knowledge about medication options available to support therapy and rehabilitation for their autistic children.

Keywords: autism characteristics, rehabilitation centres, yemen, children

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Authors: Garry Gorman, Nigel McKelvey, James Connolly

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This study will evaluate a growth mindset intervention with Junior Cycle Coding and Senior Cycle Computer Science students in Ireland, where gamification will be used to incentivise growth mindset behaviour. An artificial intelligence (AI) driven personalised learning system will be developed to present computer programming learning tasks in a manner that is best suited to the individuals’ own learning preferences while incentivising and rewarding growth mindset behaviour of persistence, mastery response to challenge, and challenge seeking. This research endeavours to measure mindset with before and after surveys (conducted nationally) and by recording growth mindset behaviour whilst playing a digital game. This study will harness the capabilities of AI and aims to determine how a personalised learning (PL) experience can impact the mindset of a broad range of students. The focus of this study will be to determine how personalising the learning experience influences female and disadvantaged students' sense of belonging in the computer science classroom when tasks are presented in a manner that is best suited to the individual. Whole Brain Learning will underpin this research and will be used as a framework to guide the research in identifying key areas such as thinking and learning styles, cognitive potential, motivators and fears, and emotional intelligence. This research will be conducted in multiple school types over one academic year. Digital games will be played multiple times over this period, and the data gathered will be used to inform the AI algorithm. The three data sets are described as follows: (i) Before and after survey data to determine the grit scores and mindsets of the participants, (ii) The Growth Mind-Set data from the game, which will measure multiple growth mindset behaviours, such as persistence, response to challenge and use of strategy, (iii) The AI data to guide PL. This study will highlight the effectiveness of an AI-driven personalised learning experience. The data will position AI within the Irish educational landscape, with a specific focus on the teaching of CS. These findings will benefit coding and computer science teachers by providing a clear pedagogy for the effective delivery of personalised learning strategies for computer science education. This pedagogy will help prevent students from developing a fixed mindset while helping pupils to exhibit persistence of effort, use of strategy, and a mastery response to challenges.

Keywords: computer science education, artificial intelligence, growth mindset, pedagogy

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Authors: Syed Asif Hassan, Tabrej Khan

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Tuberculosis (TB) caused by bacillus Mycobacterium tuberculosis (Mtb) continues to take a disturbing toll on human life and healthcare facility worldwide. The global burden of TB remains enormous. The alarming rise of multi-drug resistant strains of Mycobacterium tuberculosis calls for an increase in research efforts towards the development of new target specific therapeutics against diverse strains of M. tuberculosis. Therefore, the discovery of new molecular scaffolds targeting new drug sites should be a priority for a workable plan for fighting resistance in Mycobacterium tuberculosis (Mtb). Mtb non-acylated lipoprotein LprG (Rv1411c) has a Toll-like receptor 2 (TLR2) agonist actions that depend on its association with triacylated glycolipids binding specifically with the hydrophobic pocket of Mtb LprG lipoprotein. The detection of a glycolipid carrier function has important implications for the role of LprG in Mycobacterial physiology and virulence. Therefore, considering the pivotal role of glycolipids in mycobacterial physiology and host-pathogen interactions, designing competitive antagonist (chemotherapeutics) ligands that competitively bind to glycolipid binding domain in LprG lipoprotein, will lead to inhibition of tuberculosis infection in humans. In this study, a unified approach involving ligand-based virtual screening protocol USRCAT (Ultra Shape Recognition) software and molecular docking studies using Auto Dock Vina 1.1.2 using the X-ray crystal structure of Mtb LprG protein was implemented. The docking results were further confirmed by DSX (DrugScore eXtented), a robust program to evaluate the binding energy of ligands bound to the Ligand binding domain of the Mtb LprG lipoprotein. The ligand, which has the higher hypothetical affinity, also has greater negative value. Based on the USRCAT, Lipinski’s values and molecular docking results, [(2R)-2,3-di(hexadecanoyl oxy)propyl][(2S,3S,5S,6R)-3,4,5-trihydroxy-2,6-bis[[(2R,3S,4S,5R,6S)-3,4,5-trihydroxy-6 (hydroxymethyl)tetrahydropyran-2-yl]oxy]cyclohexyl] phosphate (XPX) was confirmed as a promising drug-like lead compound (antagonist) binding specifically to the hydrophobic domain of LprG protein with affinity greater than that of PIM2 (agonist of LprG protein) with a free binding energy of -9.98e+006 Kcal/mol and binding affinity of -132 Kcal/mol, respectively. A further, in vitro assay of this compound is required to establish its potency in inhibiting molecular evasion mechanism of MTB within the infected host macrophages. These results will certainly be helpful in future anti-TB drug discovery efforts against Multidrug-Resistance Tuberculosis (MDR-TB).

Keywords: antagonist, agonist, binding affinity, chemotherapeutics, drug-like, multi drug resistance tuberculosis (MDR-TB), RV1411c protein, toll-like receptor (TLR2)

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Authors: Imed Feki, Faouzi Msahli

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Selection of relevant parameters from a high dimensional process operation setting space is a problem frequently encountered in industrial process modelling. This paper presents a method for selecting the most relevant fabric physical parameters for each sensory quality feature. The proposed relevancy criterion has been developed using two approaches. The first utilizes a fuzzy sensitivity criterion by exploiting from experimental data the relationship between physical parameters and all the sensory quality features for each evaluator. Next an OWA aggregation procedure is applied to aggregate the ranking lists provided by different evaluators. In the second approach, another panel of experts provides their ranking lists of physical features according to their professional knowledge. Also by applying OWA and a fuzzy aggregation model, the data sensitivity-based ranking list and the knowledge-based ranking list are combined using our proposed percolation technique, to determine the final ranking list. The key issue of the proposed percolation technique is to filter automatically and objectively the relevant features by creating a gap between scores of relevant and irrelevant parameters. It permits to automatically generate threshold that can effectively reduce human subjectivity and arbitrariness when manually choosing thresholds. For a specific sensory descriptor, the threshold is defined systematically by iteratively aggregating (n times) the ranking lists generated by OWA and fuzzy models, according to a specific algorithm. Having applied the percolation technique on a real example, of a well known finished textile product especially the stonewashed denims, usually considered as the most important quality criteria in jeans’ evaluation, we separate the relevant physical features from irrelevant ones for each sensory descriptor. The originality and performance of the proposed relevant feature selection method can be shown by the variability in the number of physical features in the set of selected relevant parameters. Instead of selecting identical numbers of features with a predefined threshold, the proposed method can be adapted to the specific natures of the complex relations between sensory descriptors and physical features, in order to propose lists of relevant features of different sizes for different descriptors. In order to obtain more reliable results for selection of relevant physical features, the percolation technique has been applied for combining the fuzzy global relevancy and OWA global relevancy criteria in order to clearly distinguish scores of the relevant physical features from those of irrelevant ones.

Keywords: data sensitivity, feature selection, fuzzy logic, OWA operators, percolation technique

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Authors: Wu Liching

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Aim This case aims to discuss the pathogenesis, clinical manifestations and medical treatment of strokes caused by mitochondrial gene mutations. Methods Diagnosis of ischemic stroke caused by mitochondrial gene defect by means of "next-generation sequencing mitochondrial DNA gene variation detection", imaging examination, neurological examination, and medical history; this study took samples from the neurology ward of a medical center in northern Taiwan cases diagnosed with acute cerebral infarction as the research objects. Result This case is a 49-year-old married woman with a rare disease, mitochondrial gene mutation inducing ischemic stroke. She has severe hearing impairment and needs to use hearing aids, and has a history of diabetes. During the patient’s hospitalization, the blood test showed that serum Lactate: 7.72 mmol/L, Lactate (CSF) 5.9 mmol/L. Through the collection of relevant medical history, neurological evaluation showed changes in consciousness and cognition, slow response in language expression, and brain magnetic resonance imaging examination showed subacute bilateral temporal lobe infarction, which was an atypical type of stroke. The lineage DNA gene has m.3243A>G known pathogenic mutation point, and its heteroplasmic level is 24.6%. This pathogenic point is located in MITOMAP and recorded as Mitochondrial Encephalopathy, Lactic Acidosis, and Stroke-like episodes (MELAS) , Leigh Syndrome and other disease-related pathogenic loci, this mutation is located in ClinVar and recorded as Pathogenic (dbSNP: rs199474657), so it is diagnosed as a case of stroke caused by a rare disease mitochondrial gene mutation. After medical treatment, there was no more seizure during hospitalization. After interventional rehabilitation, the patient's limb weakness, poor language function, and cognitive impairment have all improved significantly. Conclusion Mitochondrial disorders can also be associated with abnormalities in psychological, neurological, cerebral cortical function, and autonomic functions, as well as problems with internal medical diseases. Therefore, the differential diagnoses cover a wide range and are not easy to be diagnosed. After neurological evaluation, medical history collection, imaging and rare disease serological examination, atypical ischemic stroke caused by rare mitochondrial gene mutation was diagnosed. We hope that through this case, the diagnosis of rare disease mitochondrial gene variation leading to cerebral infarction will be more familiar to clinical medical staff, and this case report may help to improve the clinical diagnosis and treatment for patients with similar clinical symptoms in the future.

Keywords: acute stroke, MELAS, lactic acidosis, mitochondrial disorders

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Authors: Ghada Abo-Zaid

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The main objective of this study was to examine the association between the elevated level of CRP and incidence of hypertension before and after adjusting by age, BMI, gender, SES, smoking, diabetes, cholesterol LDL and cholesterol HDL and to determine whether the association were differ by race. Method: Cross sectional data for participations from age 17 to age 74 years who included in The National Health and Nutrition Examination Survey (NHANES) from 1999 to 2010 were analysed. CRP level was classified into three categories ( > 3mg/L, between 1mg/LL and 3mg/L, and < 3 mg/L). Blood pressure categorization was done using JNC 7 algorithm Hypertension defined as either systolic blood pressure (SBP) of 140 mmHg or more and disystolic blood pressure (DBP) of 90mmHg or greater, otherwise a self-reported prior diagnosis by a physician. Pre-hypertension was defined as (139 > SBP > 120 or 89 > DPB > 80). Multinominal regression model was undertaken to measure the association between CRP level and hypertension. Results: In univariable models, CRP concentrations > 3 mg/L were associated with a 73% greater risk of incident hypertension compared with CRP concentrations < 1 mg/L (Hypertension: odds ratio [OR] = 1.73; 95% confidence interval [CI], 1.50-1.99). Ethnic comparisons showed that American Mexican had the highest risk of incident hypertension (odds ratio [OR] = 2.39; 95% confidence interval [CI], 2.21-2.58).This risk was statistically insignificant, however, either after controlling by other variables (Hypertension: OR = 0.75; 95% CI, 0.52-1.08,), or categorized by race [American Mexican: odds ratio [OR] = 1.58; 95% confidence interval [CI], 0,58-4.26, Other Hispanic: odds ratio [OR] = 0.87; 95% confidence interval [CI], 0.19-4.42, Non-Hispanic white: odds ratio [OR] = 0.90; 95% confidence interval [CI], 0.50-1.59, Non-Hispanic Black: odds ratio [OR] = 0.44; 95% confidence interval [CI], 0.22-0,87]. The same results were found for pre-hypertension, and the Non-Hispanic black showed the highest significant risk for Pre-Hypertension (odds ratio [OR] = 1.60; 95% confidence interval [CI], 1.26-2.03). When CRP concentrations were between 1.0-3.0 mg/L, in an unadjusted models prehypertension was associated with higher likelihood of elevated CRP (OR = 1.37; 95% CI, 1.15-1.62). The same relationship was maintained in Non-Hispanic white, Non-Hispanic black, and other race (Non-Hispanic white: OR = 1.24; 95% CI, 1.03-1.48, Non-Hispanic black: OR = 1.60; 95% CI, 1.27-2.03, other race: OR = 2.50; 95% CI, 1.32-4.74) while the association was insignificant with American Mexican and other Hispanic. In the adjusted model, the relationship between CRP and prehypertension were no longer available. In contrary, Hypertension was not independently associated with elevated CRP, and the results were the same after grouped by race or adjusted by the confounder variables. The same results were obtained when SBP or DBP were on a continuous measure. Conclusions: This study confirmed the existence of an association between hypertension, prehypertension and elevated level of CRP, however this association was no longer available after adjusting by other variables. Ethic group differences were statistically significant at the univariable models, while it disappeared after controlling by other variables.

Keywords: CRP, hypertension, ethnicity, NHANES, blood pressure

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Authors: Gulfam Haider, sana danish

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Convolutional Neural Networks (CNNs) have emerged as powerful tools for image classification, and the choice of optimizers profoundly affects their performance. The study of optimizers and their adaptations remains a topic of significant importance in machine learning research. While numerous studies have explored and advocated for various optimizers, the efficacy of these optimization techniques is still subject to scrutiny. This work aims to address the challenges surrounding the effectiveness of optimizers by conducting a comprehensive analysis and evaluation. The primary focus of this investigation lies in examining the performance of different optimizers when employed in conjunction with the popular activation function, Rectified Linear Unit (ReLU). By incorporating ReLU, known for its favorable properties in prior research, the aim is to bolster the effectiveness of the optimizers under scrutiny. Specifically, we evaluate the adjustment of these optimizers with both the original Softmax activation function and the modified ReLU activation function, carefully assessing their impact on overall performance. To achieve this, a series of experiments are conducted using a well-established benchmark dataset for image classification tasks, namely the Canadian Institute for Advanced Research dataset (CIFAR-10). The selected optimizers for investigation encompass a range of prominent algorithms, including Adam, Root Mean Squared Propagation (RMSprop), Adaptive Learning Rate Method (Adadelta), Adaptive Gradient Algorithm (Adagrad), and Stochastic Gradient Descent (SGD). The performance analysis encompasses a comprehensive evaluation of the classification accuracy, convergence speed, and robustness of the CNN models trained with each optimizer. Through rigorous experimentation and meticulous assessment, we discern the strengths and weaknesses of the different optimization techniques, providing valuable insights into their suitability for image classification tasks. By conducting this in-depth study, we contribute to the existing body of knowledge surrounding optimizers in CNNs, shedding light on their performance characteristics for image classification. The findings gleaned from this research serve to guide researchers and practitioners in making informed decisions when selecting optimizers and activation functions, thus advancing the state-of-the-art in the field of image classification with convolutional neural networks.

Keywords: deep neural network, optimizers, RMsprop, ReLU, stochastic gradient descent

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Authors: Frank Dorrian, Aniket Adhikari

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The placenta accreta spectrum (PAS), which includes placenta accreta, increta, and percreta, is characterized by the abnormal implantation of placental chorionic villi beyond the decidua basalis. Key risk factors include placenta previa, prior cesarean sections, advanced maternal age, uterine surgeries, multiparity, pelvic radiation, and in vitro fertilization (IVF). The incidence of PAS has increased tenfold over the past 50 years, largely due to rising cesarean rates. PAS is associated with significant peripartum and postpartum hemorrhage. Magnetic resonance imaging (MRI) and ultrasound assist in the evaluation of PAS, enabling a multidisciplinary approach to mitigate morbidity and mortality. This study retrospectively analyzed PAS cases at Royal Prince Alfred Hospital, Sydney, Australia. Using the SAR-ESUR joint consensus statement, seven imaging signs were reassessed for their sensitivity and specificity in predicting PAS, with histopathological correlation. The standardized MRI protocols for PAS at the institution were also reviewed. Data were collected from the picture archiving and communication system (PACS) records from 2010 to July 2024, focusing on cases where MR imaging and confirmed histopathology or operative notes were available. This single-center, observational study provides insights into the reliability of MRI for PAS detection and the optimization of imaging protocols for accurate diagnosis. The findings demonstrate that intraplacental dark bands serve as highly sensitive markers for diagnosing PAS, achieving sensitivities of 88.9%, 85.7%, and 100% for placenta accreta, increta, and percreta, respectively, with a combined specificity of 42.9%. Sensitivity for abnormal vascularization was lower (33.3%, 28.6%, and 50%), with a specificity of 57.1%. The placenta bulge exhibited sensitivities of 55.5%, 57.1%, and 100%, with a specificity of 57.1%. Loss of the T2 hypointense interface had sensitivities of 66.6%, 85.7%, and 100%, with 42.9% specificity. Myometrial thinning showed high sensitivity across PAS conditions (88.9%, 71.4%, and 100%) and a specificity of 57.1%. Bladder wall thinning was sensitive only for placenta percreta (50%) but had a specificity of 100%. Focal exophytic mass displayed variable sensitivity (22.9%, 42.9%, and 100%) with a specificity of 85.7%. These results highlight the diagnostic variability among markers, with intraplacental dark bands and myometrial thinning being useful in detecting abnormal placentation, though they lack high specificity. The literature and the results of our study highlight that while no single feature can definitively diagnose PAS, the presence of multiple features -especially when combined with elevated clinical risk- significantly increases the likelihood of an underlying PAS. A thorough understanding of the range of MRI findings associated with PAS, along with awareness of the clinical significance of each sign, helps the radiologist more accurately diagnose the condition and assist in surgical planning, ultimately improving patient care.

Keywords: placenta, accreta, spectrum, MRI

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Authors: Swapnil Kumar Singh

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Gamma-ray bursts (GRB's), short and intense pulses of low-energy γ rays, have fascinated astronomers and astrophysicists since their unexpected discovery in the late sixties. GRB'sare accompanied by long-lasting afterglows, and they are associated with core-collapse supernovae. The detection of delayed emission in X-ray, optical, and radio wavelength, or "afterglow," following a γ-ray burst can be described as the emission of a relativistic shell decelerating upon collision with the interstellar medium. While it is fair to say that there is strong diversity amongst the afterglow population, probably reflecting diversity in the energy, luminosity, shock efficiency, baryon loading, progenitor properties, circumstellar medium, and more, the afterglows of GRBs do appear more similar than the bursts themselves, and it is possible to identify common features within afterglows that lead to some canonical expectations. After an initial flash of gamma rays, a longer-lived "afterglow" is usually emitted at longer wavelengths (X-ray, ultraviolet, optical, infrared, microwave, and radio). It is a slowly fading emission at longer wavelengths created by collisions between the burst ejecta and interstellar gas. In X-ray wavelengths, the GRB afterglow fades quickly at first, then transitions to a less-steep drop-off (it does other stuff after that, but we'll ignore that for now). During these early phases, the X-ray afterglow has a spectrum that looks like a power law: flux F∝ E^β, where E is energy and beta is some number called the spectral index. This kind of spectrum is characteristic of synchrotron emission, which is produced when charged particles spiral around magnetic field lines at close to the speed of light. In addition to the outgoing forward shock that ploughs into the interstellar medium, there is also a so-called reverse shock, which propagates backward through the ejecta. In many ways," reverse" shock can be misleading; this shock is still moving outward from the restframe of the star at relativistic velocity but is ploughing backward through the ejecta in their frame and is slowing the expansion. This reverse shock can be dynamically important, as it can carry comparable energy to the forward shock. The early phases of the GRB afterglow still provide a good description even if the GRB is highly collimated since the individual emitting regions of the outflow are not in causal contact at large angles and so behave as though they are expanding isotropically. The majority of afterglows, at times typically observed, fall in the slow cooling regime, and the cooling break lies between the optical and the X-ray. Numerous observations support this broad picture for afterglows in the spectral energy distribution of the afterglow of the very bright GRB. The bluer light (optical and X-ray) appears to follow a typical synchrotron forward shock expectation (note that the apparent features in the X-ray and optical spectrum are due to the presence of dust within the host galaxy). We need more research in GRB and Particle Physics in order to unfold the mysteries of afterglow.

Keywords: GRB, synchrotron, X-ray, isotropic energy

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Authors: Damtew Samson Zerihun

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This paper primarily describes the concept of a hierarchical safety control (HSC) in discrete manufacturing to up-hold productivity with human intervention and machine failures using a systematic approach, through increasing the system availability and using additional knowledge on machines so as to improve the human machine collaboration (HMC). It also highlights the implemented PLC safety algorithm, in applying this generic concept to a concrete pro-duction line using a lab demonstrator called FATIE (Factory Automation Test and Integration Environment). Furthermore, the paper describes a model and provide a systematic representation of human-machine collabora-tion in discrete manufacturing and to this end, the Hierarchical Safety Control concept is proposed. This offers a ge-neric description of human-machine collaboration based on Finite State Machines (FSM) that can be applied to vari-ous discrete manufacturing lines instead of using ad-hoc solutions for each line. With its reusability, flexibility, and extendibility, the Hierarchical Safety Control scheme allows upholding productivity while maintaining safety with reduced engineering effort compared to existing solutions. The approach to the solution begins with a successful partitioning of different zones around the Integrated Manufacturing System (IMS), which are defined by operator tasks and the risk assessment, used to describe the location of the human operator and thus to identify the related po-tential hazards and trigger the corresponding safety functions to mitigate it. This includes selective reduced speed zones and stop zones, and in addition with the hierarchical safety control scheme and advanced safety functions such as safe standstill and safe reduced speed are used to achieve the main goals in improving the safe Human Ma-chine Collaboration and increasing the productivity. In a sample scenarios, It is shown that an increase of productivity in the order of 2.5% is already possible with a hi-erarchical safety control, which consequently under a given assumptions, a total sum of 213 € could be saved for each intervention, compared to a protective stop reaction. Thereby the loss is reduced by 22.8%, if occasional haz-ard can be refined in a hierarchical way. Furthermore, production downtime due to temporary unavailability of safety devices can be avoided with safety failover that can save millions per year. Moreover, the paper highlights the proof of the development, implementation and application of the concept on the lab demonstrator (FATIE), where it is realized on the new safety PLCs, Drive Units, HMI as well as Safety devices in addition to the main components of the IMS.

Keywords: discrete automation, hierarchical safety controller, human machine collaboration, programmable logical controller

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Authors: Rabbia Idrees, Ananda Maiti, Saurabh Garg, Muhammad Bilal Amin

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IoT devices are the basic building blocks of IoT network that generate enormous volume of real-time and high-speed data to help organizations and companies to take intelligent decisions. To integrate this enormous data from multisource and transfer it to the appropriate client is the fundamental of IoT development. The handling of this huge quantity of devices along with the huge volume of data is very challenging. The IoT devices are battery-powered and resource-constrained and to provide energy efficient communication, these IoT devices go sleep or online/wakeup periodically and a-periodically depending on the traffic loads to reduce energy consumption. Sometime these devices get disconnected due to device battery depletion. If the node is not available in the network, then the IoT network provides incomplete, missing, and inaccurate data. Moreover, many IoT applications, like vehicle tracking and patient tracking require the IoT devices to be mobile. Due to this mobility, If the distance of the device from the sink node become greater than required, the connection is lost. Due to this disconnection other devices join the network for replacing the broken-down and left devices. This make IoT devices dynamic in nature which brings uncertainty and unreliability in the IoT network and hence produce bad quality of data. Due to this dynamic nature of IoT devices we do not know the actual reason of abnormal data. If data are of poor-quality decisions are likely to be unsound. It is highly important to process data and estimate data quality before bringing it to use in IoT applications. In the past many researchers tried to estimate data quality and provided several Machine Learning (ML), stochastic and statistical methods to perform analysis on stored data in the data processing layer, without focusing the challenges and issues arises from the dynamic nature of IoT devices and how it is impacting data quality. A comprehensive review on determining the impact of dynamic nature of IoT devices on data quality is done in this research and presented a data quality model that can deal with this challenge and produce good quality of data. This research presents the data quality model for the sensors monitoring water quality. DBSCAN clustering and weather sensors are used in this research to make data quality model for the sensors monitoring water quality. An extensive study has been done in this research on finding the relationship between the data of weather sensors and sensors monitoring water quality of the lakes and beaches. The detailed theoretical analysis has been presented in this research mentioning correlation between independent data streams of the two sets of sensors. With the help of the analysis and DBSCAN, a data quality model is prepared. This model encompasses five dimensions of data quality: outliers’ detection and removal, completeness, patterns of missing values and checks the accuracy of the data with the help of cluster’s position. At the end, the statistical analysis has been done on the clusters formed as the result of DBSCAN, and consistency is evaluated through Coefficient of Variation (CoV).

Keywords: clustering, data quality, DBSCAN, and Internet of things (IoT)

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Authors: Sakshi Gupta, Anupam Agrawal, Ekta Singla

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An exoskeleton design for rehabilitation purpose encounters many challenges, including ergonomically acceptable wearing technology, architectural design human-motion compatibility, actuation type, human-robot interaction, etc. In this paper, a passive architecture for upper limb exoskeleton is proposed for assisting in rehabilitation tasks. Kinematic modelling is detailed for task-based kinematic synthesis of the wearable exoskeleton for self-feeding tasks. The exoskeleton architecture possesses expansion and torsional springs which are able to store and redistribute energy over the human arm joints. The elastic characteristics of the springs have been optimized to minimize the mechanical work of the human arm joints. The concept of hybrid combination of a 4-bar parallelogram linkage and a serial linkage were chosen, where the 4-bar parallelogram linkage with expansion spring acts as a rigid structure which is used to provide the rotational degree-of-freedom (DOF) required for lowering and raising of the arm. The single linkage with torsional spring allows for the rotational DOF required for elbow movement. The focus of the paper is kinematic modelling, analysis and task-based synthesis framework for the proposed architecture, keeping in considerations the essential tasks of self-feeding and self-exercising during rehabilitation of partially healthy person. Rehabilitation of primary functional movements (activities of daily life, i.e., ADL) is routine activities that people tend to every day such as cleaning, dressing, feeding. We are focusing on the feeding process to make people independent in respect of the feeding tasks. The tasks are focused to post-surgery patients under rehabilitation with less than 40% weakness. The challenges addressed in work are ensuring to emulate the natural movement of the human arm. Human motion data is extracted through motion-sensors for targeted tasks of feeding and specific exercises. Task-based synthesis procedure framework will be discussed for the proposed architecture. The results include the simulation of the architectural concept for tracking the human-arm movements while displaying the kinematic and static study parameters for standard human weight. D-H parameters are used for kinematic modelling of the hybrid-mechanism, and the model is used while performing task-based optimal synthesis utilizing evolutionary algorithm.

Keywords: passive mechanism, task-based synthesis, emulating human-motion, exoskeleton

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Authors: Ipsita Biswas, Arnab Sarkar, Ashikur Rahaman, Gopeswar Mukherjee, Subhrangsu Chatterjee, Shamee Bhattacharjee, Deba Prasad Mandal

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One of the major reasons for the high mortality rate of lung cancer is the substantial delays in disease detection at late metastatic stages. It is of utmost importance to understand the detailed molecular signaling and detect the molecular markers that can be used for the early diagnosis of cancer. Several studies explored the emerging roles of long noncoding RNAs (lncRNAs) in various cancers as well as lung cancer. A long non-coding RNA LINC00273 was recently discovered to promote cancer cell migration and invasion, and its positive correlation with the pathological stages of metastasis may prove it to be a potential target for inhibiting cancer cell metastasis. Comparing real-time expression of LINC00273 in various human clinical cancer tissue samples with normal tissue samples revealed significantly higher expression in cancer tissues. This long intergenic noncoding RNA was found to be highly expressed in human liver tumor-initiating cells, human gastric adenocarcinoma AGS cell line, as well as human non-small cell lung cancer A549 cell line. SiRNA and shRNA-induced knockdown of LINC00273 in both in vitro and in vivo nude mice significantly subsided AGS and A549 cancer cell migration and invasion. LINC00273 knockdown also reduced TGF-β induced SNAIL, SLUG, VIMENTIN, ZEB1 expression, and metastasis in A549 cells. Plenty of reports have suggested the role of microRNAs of the miR200 family in reversing epithelial to mesenchymal transition (EMT) by inhibiting ZEB transcription factors. In this study, hsa-miR-200a-3p was predicted via IntaRNA-Freiburg RNA tools to be a potential target of LINC00273 with a negative free binding energy of −8.793 kcal/mol, and this interaction was verified as a confirmed target of LINC00273 by RNA pulldown, real-time PCR and luciferase assay. Mechanistically, LINC00273 accelerated TGF-β induced EMT by sponging hsa-miR-200a-3p which in turn liberated ZEB1 and promoted prometastatic functions in A549 cells in vitro as verified by real-time PCR and western blotting. The similar expression patterns of these EMT regulatory pathway molecules, viz. LINC00273, hsa-miR-200a-3p, ZEB1 and TGF-β, were also detected in various clinical samples like breast cancer tissues, oral cancer tissues, lung cancer tissues, etc. Overall, this LINC00273 mediated EMT regulatory signaling can serve as a potential therapeutic target for the prevention of lung cancer metastasis.

Keywords: epithelial to mesenchymal transition, long noncoding RNA, microRNA, non-small-cell lung carcinoma

Procedia PDF Downloads 156

Authors: B. S. Muddukrishna, Karthik Aithal, Aravind Pai

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Introduction: Preparation of binary cocrystals of Etraverine (ETR) by using Tartaric Acid (TAR) as a conformer was the main focus of this study. Etravirine is a Class IV drug, as per the BCS classification system. Methods: Cocrystals were prepared by slow evaporation technique. A mixture of total 500mg of ETR: TAR was weighed in molar ratios of 1:1 (371.72mg of ETR and 128.27mg of TAR). Saturated solution of Etravirine was prepared in Acetone: Methanol (50:50) mixture in which tartaric acid is dissolved by sonication and then this solution was stirred using a magnetic stirrer until the solvent got evaporated. Shimadzu FTIR – 8300 system was used to acquire the FTIR spectra of the cocrystals prepared. Shimadzu thermal analyzer was used to achieve DSC measurements. X-ray diffractometer was used to obtain the X-ray powder diffraction pattern. Shake flask method was used to determine the equilibrium dynamic solubility of pure, physical mixture and cocrystals of ETR. USP buffer (pH 6.8) containing 1% of Tween 80 was used as the medium. The pure, physical mixture and the optimized cocrystal of ETR were accurately weighed sufficient to maintain the sink condition and were filled in hard gelatine capsules (size 4). Electrolab-Tablet Dissolution tester using basket apparatus at a rotational speed of 50 rpm and USP phosphate buffer (900 mL, pH = 6.8, 37 ˚C) + 1% Tween80 as a media, was used to carry out dissolution. Shimadzu LC-10 series chromatographic system was used to perform the analysis with PDA detector. An Hypersil BDS C18 (150mm ×4.6 mm ×5 µm) column was used for separation with mobile phase comprising of a mixture of ace¬tonitrile and phosphate buffer 20mM, pH 3.2 in the ratio 60:40 v/v. The flow rate was 1.0mL/min and column temperature was set to 30°C. The detection was carried out at 304 nm for ETR. Results and discussions: The cocrystals were subjected to various solid state characterization and the results confirmed the formation of cocrystals. The C=O stretching vibration (1741cm-1) in tartaric acid was disappeared in the cocrystal and the peak broadening of primary amine indicates hydrogen bond formation. The difference in the melting point of cocrystals when compared to pure Etravirine (265 °C) indicates interaction between the drug and the coformer which proves that first ordered transformation i.e. melting endotherm has disappeared. The difference in 2θ values of pure drug and cocrystals indicates the interaction between the drug and the coformer. Dynamic solubility and dissolution studies were also conducted by shake flask method and USP apparatus one respectively and 3.6 fold increase in the dynamic solubility were observed and in-vitro dissolution study shows four fold increase in the solubility for the ETR: TAR (1:1) cocrystals. The ETR: TAR (1:1) cocrystals shows improved solubility and dissolution as compared to the pure drug which was clearly showed by solid state characterization and dissolution studies.

Keywords: dynamic solubility, Etraverine, in vitro dissolution, slurry method

Procedia PDF Downloads 356

Authors: K. Nazan Turhan, Tuğçe Ersan

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The population of the world is approximately 8 billion, and it increases uncontrollably and irrepressibly, leading to an increase in consumption. This situation causes crucial problems, and food waste is one of these. The Food and Agriculture Organization of the United Nations (FAO) defines food waste as the discarding or alternative utilization of food that is safe and nutritious for the consumption of humans along the entire food supply chain, from primary production to end household consumer level. In addition, according to the estimation of FAO, one-third of all food produced for human consumption is lost or wasted worldwide every year. Wasting food endangers natural resources and causes hunger. For instance, excessive amounts of food waste cause greenhouse gas emissions, contributing to global warming. Therefore, waste management has been gaining significance in the last few decades at both local and global levels due to the expected scarcity of resources for the increasing population of the world. There are several ways to recover food waste. According to the United States Environmental Protection Agency’s Food Recovery Hierarchy, food waste recovery ways are source reduction, feeding hungry people, feeding animals, industrial uses, composting, and landfill/incineration from the most preferred to the least preferred, respectively. Bioethanol, biodiesel, biogas, agricultural fertilizer and animal feed can be obtained from food waste that is generated by different food industries. In this project, feeding animals was selected as a food waste recovery method and food waste of a plant was used to provide ingredient uniformity. Grasshoppers were used as a protein source. In other words, the project was performed to develop a dog food product by recovery of the plant’s food waste after following some steps. The collected food waste and purchased grasshoppers were sterilized, dried and pulverized. Then, they were all mixed with 60 g agar-agar solution (4%w/v). 3 different aromas were added, separately to the samples to enhance flavour quality. Since there are differences in the required amounts of different species of dogs, fulfilling all nutritional needs is one of the problems. In other words, there is a wide range of nutritional needs in terms of carbohydrates, protein, fat, sodium, calcium, and so on. Furthermore, the requirements differ depending on age, gender, weight, height, and species. Therefore, the product that was developed contains average amounts of each substance so as not to cause any deficiency or surplus. On the other hand, it contains more protein than similar products in the market. The product was evaluated in terms of contamination and nutritional content. For contamination risk, detection of E. coli and Salmonella experiments were performed, and the results were negative. For the nutritional value test, protein content analysis was done. The protein contents of different samples vary between 33.68% and 26.07%. In addition, water activity analysis was performed, and the water activity (aw) values of different samples ranged between 0.2456 and 0.4145.

Keywords: food waste, dog food, animal nutrition, food waste recovery

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Authors: Justin Fu, Thomas Mazzuchi, Shahram Sarkani

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A key factor in technological immaturity in defense weapons acquisition is lack of understanding critical integrations at the subsystem and component level. To address this shortfall, recent research in integration readiness level (IRL) combines with technology readiness level (TRL) to form a system readiness level (SRL). SRL can be enriched with more robust quantitative methods to provide the program manager a useful tool prior to committing to major weapons acquisition programs. This research harnesses previous mathematical models based on graph theory, Petri nets, and tropical algebra and proposes a modification of the desirable SRL mathematical properties such that a tightly integrated (multitude of interfaces) subsystem can display a lower SRL than an inherently less coupled subsystem. The synthesis of these methods informs an improved decision tool for the program manager to commit to expensive technology development. This research ties the separately developed manufacturing readiness level (MRL) into the network representation of the system and addresses shortfalls in previous frameworks, including the lack of integration weighting and the over-importance of a single extremely immature component. Tropical algebra (based on the minimum of a set of TRLs or IRLs) allows one low IRL or TRL value to diminish the SRL of the entire system, which may not be reflective of actuality if that component is not critical or tightly coupled. Integration connections can be weighted according to importance and readiness levels are modified to be a cardinal scale (based on an analytic hierarchy process). Integration arcs’ importance are dependent on the connected nodes and the additional integrations arcs connected to those nodes. Lack of integration is not represented by zero, but by a perfect integration maturity value. Naturally, the importance (or weight) of such an arc would be zero. To further explore the impact of grouping subsystems, a multi-objective genetic algorithm is then used to find various clusters or communities that can be optimized for the most representative subsystem SRL. This novel calculation is then benchmarked through simulation and using past defense acquisition program data, focusing on the newly introduced Middle Tier of Acquisition (rapidly field prototypes). The model remains a relatively simple, accessible tool, but at higher fidelity and validated with past data for the program manager to decide major defense acquisition program milestones.

Keywords: readiness, maturity, system, integration

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Authors: Changdong Oh

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With the arrival of post-industrial society, social scientists have been giving attention to issues of which factors shape cleavage of political parties. Especially, there is a heated controversy over whether and how social and cultural values influence the identities of parties and voting behavior. Drawing from Moral Foundations Theory (MFT), which approached similar issues by considering the effect of five moral foundations on political decision-making of people, this study investigates the role of moral rhetoric in the evolution of Korean political parties. Researcher collected official announcements released by the major two parties (Democratic Party of Korea, Saenuri Party) from 2007 to 2016, and analyzed the data by using Word2Vec algorithm and Moral Foundations Dictionary. Five moral decision modules of MFT, composed of care, fairness (individualistic morality), loyalty, authority and sanctity (group-based, Durkheimian morality), can be represented in vector spaces consisted of party announcements data. By comparing the party vector and the five morality vectors, researcher can see how the political parties have actively used each of the five moral foundations to express themselves and the opposition. Results report that the conservative party tends to actively draw on collective morality such as loyalty, authority, purity to differentiate itself. Notably, such moral differentiation strategy is prevalent when they criticize an opposition party. In contrast, the liberal party tends to concern with individualistic morality such as fairness. This result indicates that moral cleavage does exist between parties in South Korea. Furthermore, individualistic moral gaps of the two political parties are eased over time, which seems to be due to the discussion of economic democratization of conservative party that emerged after 2012, but the community-related moral gaps widened. These results imply that past political cleavages related to economic interests are diminishing and replaced by cultural and social values associated with communitarian morality. However, since the conservative party’s differentiation strategy is largely related to negative campaigns, it is doubtful whether such moral differentiation among political parties can contribute to the long-term party identification of the voters, thus further research is needed to determine it is sustainable. Despite the limitations, this study makes it possible to track and identify the moral changes of party system through automated text analysis. More generally, this study could contribute to the analysis of various texts associated with the moral foundation and finding a distributed representation of moral, ethical values.

Keywords: moral foundations theory, moral politics, party system, Word2Vec

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Authors: Joanna Grzybowska, Magdalena Igras, Mariusz Ziółko

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Association rule learning is an approach for discovering interesting relationships in large databases. The analysis of relations, invisible at first glance, is a source of new knowledge which can be subsequently used for prediction. We used this data mining technique (which is an automatic and objective method) to learn about interesting affects associations in a corpus of emergency phone calls. We also made an attempt to match revealed rules with their possible situational context. The corpus was collected and subjectively annotated by two researchers. Each of 3306 recordings contains information on emotion: (1) type (sadness, weariness, anxiety, surprise, stress, anger, frustration, calm, relief, compassion, contentment, amusement, joy) (2) valence (negative, neutral, or positive) (3) intensity (low, typical, alternating, high). Also, additional information, that is a clue to speaker’s emotional state, was annotated: speech rate (slow, normal, fast), characteristic vocabulary (filled pauses, repeated words) and conversation style (normal, chaotic). Exponentially many rules can be extracted from a set of items (an item is a previously annotated single information). To generate the rules in the form of an implication X → Y (where X and Y are frequent k-itemsets) the Apriori algorithm was used - it avoids performing needless computations. Then, two basic measures (Support and Confidence) and several additional symmetric and asymmetric objective measures (e.g. Laplace, Conviction, Interest Factor, Cosine, correlation coefficient) were calculated for each rule. Each applied interestingness measure revealed different rules - we selected some top rules for each measure. Owing to the specificity of the corpus (emergency situations), most of the strong rules contain only negative emotions. There are though strong rules including neutral or even positive emotions. Three examples of the strongest rules are: {sadness} → {anxiety}; {sadness, weariness, stress, frustration} → {anger}; {compassion} → {sadness}. Association rule learning revealed the strongest configurations of affects (as well as configurations of affects with affect-related information) in our emergency phone calls corpus. The acquired knowledge can be used for prediction to fulfill the emotional profile of a new caller. Furthermore, a rule-related possible context analysis may be a clue to the situation a caller is in.

Keywords: data mining, emergency phone calls, emotional profiles, rules

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Authors: Taylan Kabbani, Ekrem Duman

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The design of adaptive systems that take advantage of financial markets while reducing the risk can bring more stagnant wealth into the global market. However, most efforts made to generate successful deals in trading financial assets rely on Supervised Learning (SL), which suffered from various limitations. Deep Reinforcement Learning (DRL) offers to solve these drawbacks of SL approaches by combining the financial assets price "prediction" step and the "allocation" step of the portfolio in one unified process to produce fully autonomous systems capable of interacting with its environment to make optimal decisions through trial and error. In this paper, a continuous action space approach is adopted to give the trading agent the ability to gradually adjust the portfolio's positions with each time step (dynamically re-allocate investments), resulting in better agent-environment interaction and faster convergence of the learning process. In addition, the approach supports the managing of a portfolio with several assets instead of a single one. This work represents a novel DRL model to generate profitable trades in the stock market, effectively overcoming the limitations of supervised learning approaches. We formulate the trading problem, or what is referred to as The Agent Environment as Partially observed Markov Decision Process (POMDP) model, considering the constraints imposed by the stock market, such as liquidity and transaction costs. More specifically, we design an environment that simulates the real-world trading process by augmenting the state representation with ten different technical indicators and sentiment analysis of news articles for each stock. We then solve the formulated POMDP problem using the Twin Delayed Deep Deterministic Policy Gradient (TD3) algorithm, which can learn policies in high-dimensional and continuous action spaces like those typically found in the stock market environment. From the point of view of stock market forecasting and the intelligent decision-making mechanism, this paper demonstrates the superiority of deep reinforcement learning in financial markets over other types of machine learning such as supervised learning and proves its credibility and advantages of strategic decision-making.

Keywords: the stock market, deep reinforcement learning, MDP, twin delayed deep deterministic policy gradient, sentiment analysis, technical indicators, autonomous agent

Procedia PDF Downloads 178

Authors: Rajan Goyal, S. Lamba, S. Annapoorni

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The indirect exchange coupled system consists of two ferromagnetic layers separated by non-magnetic spacer layer. The type of exchange coupling may be either ferro or anti-ferro depending on the thickness of the spacer layer. In the present work, the strength of exchange coupling in FePt/Al₂O₃/Fe₃Pt has been investigated by varying the thickness of the spacer layer Al₂O₃. The FePt/Al₂O₃/Fe₃Pt trilayer structure is fabricated on Si <100> single crystal substrate using sputtering technique. The thickness of FePt and Fe₃Pt is fixed at 60 nm and 2 nm respectively. The thickness of spacer layer Al₂O₃ was varied from 0 to 16 nm. The normalized hysteresis loops recorded at room temperature both in the in-plane and out of plane configuration reveals that the orientation of easy axis lies along the plane of the film. It is observed that the hysteresis loop for ts=0 nm does not exhibit any knee around H=0 indicating that the hard FePt layer and soft Fe₃Pt layer are strongly exchange coupled. However, the insertion of Al₂O₃ spacer layer of thickness ts = 0.7 nm results in appearance of a minor knee around H=0 suggesting the weakening of exchange coupling between FePt and Fe₃Pt. The disappearance of knee in hysteresis loop with further increase in thickness of the spacer layer up to 8 nm predicts the co-existence of ferromagnetic (FM) and antiferromagnetic (AFM) exchange interaction between FePt and Fe₃Pt. In addition to this, the out of plane hysteresis loop also shows an asymmetry around H=0. The exchange field Hex = (Hc↑-HC↓)/2, where Hc↑ and Hc↓ are the coercivity estimated from lower and upper branch of hysteresis loop, increases from ~ 150 Oe to ~ 700 Oe respectively. This behavior may be attributed to the uncompensated moments in the hard FePt layer and soft Fe₃Pt layer at the interface. A better insight into the variation in indirect exchange coupling has been investigated using recoil curves. It is observed that the almost closed recoil curves are obtained for ts= 0 nm up to a reverse field of ~ 5 kOe. On the other hand, the appearance of appreciable open recoil curves at lower reverse field ~ 4 kOe for ts = 0.7 nm indicates that uncoupled soft phase undergoes irreversible magnetization reversal at lower reverse field suggesting the weakening of exchange coupling. The openness of recoil curves decreases with increase in thickness of the spacer layer up to 8 nm. This behavior may be attributed to the competition between FM and AFM exchange interactions. The FM exchange coupling between FePt and Fe₃Pt due to porous nature of Al₂O₃ decreases much slower than the weak AFM coupling due to interaction between Fe ions of FePt and Fe₃Pt via O ions of Al₂O₃. The hysteresis loop has been simulated using Monte Carlo based on Metropolis algorithm to investigate the variation in strength of exchange coupling in FePt/Al₂O₃/Fe₃Pt trilayer system.

Keywords: indirect exchange coupling, MH loop, Monte Carlo simulation, recoil curve

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Authors: Zélia Maria Da Costa Ludwig, Maria José Valenzuela Bell, Geraldo Henriques Da Silva, Thales Alves Faraco, Victor Rocha Da Silva, Daniel Rotmeister Teixeira, Vírgilio De Carvalho Dos Anjos, Valdemir Ludwig

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Advances in telecommunications grow with the development of optical amplifiers based on rare earth ions. The focus has been concentrated in silicate glasses although their amplified spontaneous emission is limited to a few tens of nanometers (~ 40nm). Recently, phosphate glasses have received great attention due to their potential application in optical data transmission, detection, sensors and laser detector, waveguide and optical fibers, besides its excellent physical properties such as high thermal expansion coefficients and low melting temperature. Compared with the silica glasses, phosphate glasses provide different optical properties such as, large transmission window of infrared, and good density. Research on the improvement of physical and chemical durability of phosphate glass by addition of heavy metals oxides in P2O5 has been performed. The addition of Na2O further improves the solubility of rare earths, while increasing the Al2O3 links in the P2O5 tetrahedral results in increased durability and aqueous transition temperature and a decrease of the coefficient of thermal expansion. This work describes the structural and spectroscopic characterization of a phosphate glass matrix doped with different Er (Erbium) concentrations. The phosphate glasses containing Er3+ ions have been prepared by melt technique. A study of the optical absorption, luminescence and lifetime was conducted in order to characterize the infrared emission of Er3+ ions at 1540 nm, due to the radiative transition 4I13/2 → 4I15/2. Our results indicate that the present glass is a quite good matrix for Er3+ ions, and the quantum efficiency of the 1540 nm emission was high. A quenching mechanism for the mentioned luminescence was not observed up to 2,0 mol% of Er concentration. The Judd-Ofelt parameters, radiative lifetime and quantum efficiency have been determined in order to evaluate the potential of Er3+ ions in new phosphate glass. The parameters follow the trend as Ω2 > Ω4 > Ω6. It is well known that the parameter Ω2 is an indication of the dominant covalent nature and/or structural changes in the vicinity of the ion (short range effects), while Ω4 and Ω6 intensity parameters are long range parameters that can be related to the bulk properties such as viscosity and rigidity of the glass. From the PL measurements, no red or green upconversion was measured when pumping the samples with laser excitation at 980 nm. As future prospects: Synthesize this glass system with silver in order to determine the influence of silver nanoparticles on the Er3+ ions.

Keywords: phosphate glass, erbium, luminescence, glass system

Procedia PDF Downloads 510

Authors: Maria Sciammarella, Uttam M. Shrestha, Youngho Seo, Grant T. Gullberg, Elias H. Botvinick

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Background: Myocardial perfusion imaging (MPI) is typically performed with static imaging protocols and visually assessed for perfusion defects based on the relative intensity distribution. Dynamic cardiac SPECT, on the other hand, is a new imaging technique that is based on time varying information of radiotracer distribution, which permits quantification of myocardial blood flow (MBF). In this abstract, we report a progress and current status of dynamic cardiac SPECT using conventional gamma camera (Infinia Hawkeye 4, GE Healthcare) for estimation of myocardial blood flow and coronary flow reserve. Methods: A group of patients who had high risk of coronary artery disease was enrolled to evaluate our methodology. A low-dose/high-dose rest/pharmacologic-induced-stress protocol was implemented. A standard rest and a standard stress radionuclide dose of ⁹⁹ᵐTc-tetrofosmin (140 keV) was administered. The dynamic SPECT data for each patient were reconstructed using the standard 4-dimensional maximum likelihood expectation maximization (ML-EM) algorithm. Acquired data were used to estimate the myocardial blood flow (MBF). The correspondence between flow values in the main coronary vasculature with myocardial segments defined by the standardized myocardial segmentation and nomenclature were derived. The coronary flow reserve, CFR, was defined as the ratio of stress to rest MBF values. CFR values estimated with SPECT were also validated with dynamic PET. Results: The range of territorial MBF in LAD, RCA, and LCX was 0.44 ml/min/g to 3.81 ml/min/g. The MBF between estimated with PET and SPECT in the group of independent cohort of 7 patients showed statistically significant correlation, r = 0.71 (p < 0.001). But the corresponding CFR correlation was moderate r = 0.39 yet statistically significant (p = 0.037). The mean stress MBF value was significantly lower for angiographically abnormal than that for the normal (Normal Mean MBF = 2.49 ± 0.61, Abnormal Mean MBF = 1.43 ± 0. 0.62, P < .001). Conclusions: The visually assessed image findings in clinical SPECT are subjective, and may not reflect direct physiologic measures of coronary lesion. The MBF and CFR measured with dynamic SPECT are fully objective and available only with the data generated from the dynamic SPECT method. A quantitative approach such as measuring CFR using dynamic SPECT imaging is a better mode of diagnosing CAD than visual assessment of stress and rest images from static SPECT images Coronary Flow Reserve.

Keywords: dynamic SPECT, clinical SPECT/CT, selective coronary angiograph, ⁹⁹ᵐTc-Tetrofosmin

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Authors: Massimiliano Panella, Sara Bortoluzzi, Sophia Russotto, Daniele Nicolini, Carmela Rinaldi

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Osteoporosis affects more than 200 million people worldwide. About 75% of osteoporosis cases are undiagnosed or diagnosed only when a bone fracture occurs. Since osteoporosis related fractures are significant determinants of the burden of disease and health and social costs of aging populations, we believe that this is the early identification and treatment of high-risk patients should be a priority in actual healthcare systems. Screening for osteoporosis by dual energy x-ray absorptiometry (DEXA) is not cost-effective for general population. An alternative is pulse-echo ultrasound (PEUS) because of the minor costs. To this end, we developed an early detection program for osteoporosis with PEUS, and we evaluated is possible impact and sustainability. We conducted a cross-sectional study including 1,050 people in Italy. Subjects with >1 major or >2 minor risk factors for osteoporosis were invited to PEUS bone mass density (BMD) measurement at the proximal tibia. Based on BMD values, subjects were classified as healthy subjects (BMD>0.783 g/cm²) and pathological including subjects with suspected osteopenia (0.783≤BMD>0.719 g/cm²) or osteoporosis (BMD ≤ 0.719 g/cm²). The responder rate was 60.4% (634/1050). According to the risk, PEUS scan was recommended to 436 people, of whom 300 (mean age 45.2, 81% women) accepted to participate. We identified 240 (80%) healthy and 60 (20%) pathological subjects (47 osteopenic and 13 osteoporotic). We observed a significant association between high risk people and reduced bone density (p=0.043) with increased risks for female gender, older ages, and menopause (p<0.01). The yearly cost of the screening program was 8,242 euros. With actual Italian fracture incidence rates in osteoporotic patients, we can reasonably expect in 20 years that at least 6 fractures will occur in our sample. If we consider that the mean costs per fracture in Italy is today 16,785 euros, we can estimate a theoretical cost of 100,710 euros. According to literature, we can assume that the early treatment of osteoporosis could avoid 24,170 euros of such costs. If we add the actual yearly cost of the treatments to the cost of our program and we compare this final amount of 11,682 euros to the avoidable costs of fractures (24,170 euros) we can measure a possible positive benefits/costs ratio of 2.07. As a major outcome, our study let us to early identify 60 people with a significant bone loss that were not aware of their condition. This diagnostic anticipation constitutes an important element of value for the project, both for the patients, for the preventable negative outcomes caused by the fractures, and for the society in general, because of the related avoidable costs. Therefore, based on our finding, we believe that the PEUS based screening performed could be a cost-effective approach to early identify osteoporosis. However, our study has some major limitations. In fact, in our study the economic analysis is based on theoretical scenarios, thus specific studies are needed for a better estimation of the possible benefits and costs of our program.

Keywords: osteoporosis, prevention, public health, screening

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Authors: Golnush Masghati-Amoli, Paul Chin

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Over the past recent years, with the rapid increases in data availability and computing power, Machine Learning (ML) techniques have been called on in a range of different industries for their strong predictive capability. However, the use of Machine Learning in commercial banking has been limited due to a special challenge imposed by numerous regulations that require lenders to be able to explain their analytic models, not only to regulators but often to consumers. In other words, although Machine Leaning techniques enable better prediction with a higher level of accuracy, in comparison with other industries, they are adopted less frequently in commercial banking especially for scoring purposes. This is due to the fact that Machine Learning techniques are often considered as a black box and fail to provide information on why a certain risk score is given to a customer. In order to bridge this gap between the explain-ability and performance of Machine Learning techniques, a Hybrid Model is developed at Dun and Bradstreet that is focused on blending Machine Learning algorithms with traditional approaches such as scorecards. The Hybrid Model maximizes efficiency of traditional scorecards by merging its practical benefits, such as explain-ability and the ability to input domain knowledge, with the deep insights of Machine Learning techniques which can uncover patterns scorecard approaches cannot. First, through development of Machine Learning models, engineered features and latent variables and feature interactions that demonstrate high information value in the prediction of customer risk are identified. Then, these features are employed to introduce observed non-linear relationships between the explanatory and dependent variables into traditional scorecards. Moreover, instead of directly computing the Weight of Evidence (WoE) from good and bad data points, the Hybrid Model tries to match the score distribution generated by a Machine Learning algorithm, which ends up providing an estimate of the WoE for each bin. This capability helps to build powerful scorecards with sparse cases that cannot be achieved with traditional approaches. The proposed Hybrid Model is tested on different portfolios where a significant gap is observed between the performance of traditional scorecards and Machine Learning models. The result of analysis shows that Hybrid Model can improve the performance of traditional scorecards by introducing non-linear relationships between explanatory and target variables from Machine Learning models into traditional scorecards. Also, it is observed that in some scenarios the Hybrid Model can be almost as predictive as the Machine Learning techniques while being as transparent as traditional scorecards. Therefore, it is concluded that, with the use of Hybrid Model, Machine Learning algorithms can be used in the commercial banking industry without being concerned with difficulties in explaining the models for regulatory purposes.

Keywords: machine learning algorithms, scorecard, commercial banking, consumer risk, feature engineering

Procedia PDF Downloads 134

Authors: Anders Schou Simonsen, Kim Sorensen, Thomas Condra

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Wet scrubbers are used for flue gas desulfurization by injecting water directly into the flue gas stream from a set of sprayers. The water droplets will flow freely inside the scrubber, and flow down along the scrubber walls as a thin wall film while reacting with the gas phase to remove SO₂. This complex multiphase phenomenon can be divided into three main contributions: the continuous gas phase, the liquid droplet phase, and the liquid wall film phase. This study proposes a complete model, where all three main contributions are taken into account and resolved using OpenFOAM for the continuous gas phase, and MATLAB for the liquid droplet and wall film phases. The 3D continuous gas phase is composed of five species: CO₂, H₂O, O₂, SO₂, and N₂, which are resolved along with momentum, energy, and turbulence. Source terms are present for four species, energy and momentum, which are affecting the steady-state solution. The liquid droplet phase experiences breakup, collisions, dynamics, internal chemistry, evaporation and condensation, species mass transfer, energy transfer and wall film interactions. Numerous sub-models have been implemented and coupled to realise the above-mentioned phenomena. The liquid wall film experiences impingement, acceleration, atomization, separation, internal chemistry, evaporation and condensation, species mass transfer, and energy transfer, which have all been resolved using numerous sub-models as well. The continuous gas phase has been coupled with the liquid phases using source terms by an approach, where the two software packages are couples using a link-structure. The complete CFD model has been verified using 16 experimental tests from an existing scrubber installation, where a gradient-based pattern search optimization algorithm has been used to tune numerous model parameters to match the experimental results. The CFD model needed to be fast for evaluation in order to apply this optimization routine, where approximately 1000 simulations were needed. The results show that the complex multiphase phenomena governing wet scrubbers can be resolved in a single model. The optimization routine was able to tune the model to accurately predict the performance of an existing installation. Furthermore, the study shows that a coupling between OpenFOAM and MATLAB is realizable, where the data and source term exchange increases the computational requirements by approximately 5%. This allows for exploiting the benefits of both software programs.

Keywords: desulfurization, discrete phase, scrubber, wall film

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Authors: Mohamed El Morsy, Gabriela Achtenová

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Condition monitoring is used to increase machinery availability and machinery performance, whilst reducing consequential damage, increasing machine life, reducing spare parts inventories, and reducing breakdown maintenance. An efficient condition monitoring system provides early warning of faults by predicting them at an early stage. When a localized fault occurs in gears, the vibration signals always exhibit non-stationary behavior. The periodic impulsive feature of the vibration signal appears in the time domain and the corresponding gear mesh frequency (GMF) emerges in the frequency domain. However, one limitation of frequency-domain analysis is its inability to handle non-stationary waveform signals, which are very common when machinery faults occur. Particularly at the early stage of gear failure, the GMF contains very little energy and is often overwhelmed by noise and higher-level macro-structural vibrations. An effective signal processing method would be necessary to remove such corrupting noise and interference. In this paper, a new hybrid method based on optimal Morlet wavelet filter and autocorrelation enhancement is presented. First, to eliminate the frequency associated with interferential vibrations, the vibration signal is filtered with a band-pass filter determined by a Morlet wavelet whose parameters are selected or optimized based on maximum Kurtosis. Then, to further reduce the residual in-band noise and highlight the periodic impulsive feature, an autocorrelation enhancement algorithm is applied to the filtered signal. The test stand is equipped with three dynamometers; the input dynamometer serves as the internal combustion engine, the output dynamometers induce a load on the output joint shaft flanges. The pitting defect is manufactured on the tooth side of a gear of the fifth speed on the secondary shaft. The gearbox used for experimental measurements is of the type most commonly used in modern small to mid-sized passenger cars with transversely mounted powertrain and front wheel drive: a five-speed gearbox with final drive gear and front wheel differential. The results obtained from practical experiments prove that the proposed method is very effective for gear fault diagnosis.

Keywords: wavelet analysis, pitted gear, autocorrelation, gear fault diagnosis

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Authors: Antònio Inês, Davide Silva, Filipa Carvalho, Luís Filipe-Riberiro, Fernando M. Nunes, Luís Abrunhosa, Fernanda Cosme

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The presence of mycotoxins in foodstuff is a matter of concern for food safety. Mycotoxins are toxic secondary metabolites produced by certain molds, being ochratoxin A (OTA) one of the most relevant. Wines can also be contaminated with these toxicants. Several authors have demonstrated the presence of mycotoxins in wine, especially ochratoxin A. Its chemical structure is a dihydro-isocoumarin connected at the 7-carboxy group to a molecule of L-β-phenylalanine via an amide bond. As these toxicants can never be completely removed from the food chain, many countries have defined levels in food in order to attend health concerns. OTA contamination of wines might be a risk to consumer health, thus requiring treatments to achieve acceptable standards for human consumption. The maximum acceptable level of OTA in wines is 2.0 μg/kg according to the Commission regulation No. 1881/2006. Therefore, the aim of this work was to reduce OTA to safer levels using different fining agents, as well as their impact on white wine physicochemical characteristics. To evaluate their efficiency, 11 commercial fining agents (mineral, synthetic, animal and vegetable proteins) were used to get new approaches on OTA removal from white wine. Trials (including a control without addition of a fining agent) were performed in white wine artificially supplemented with OTA (10 µg/L). OTA analyses were performed after wine fining. Wine was centrifuged at 4000 rpm for 10 min and 1 mL of the supernatant was collected and added of an equal volume of acetonitrile/methanol/acetic acid (78:20:2 v/v/v). Also, the solid fractions obtained after fining, were centrifuged (4000 rpm, 15 min), the resulting supernatant discarded, and the pellet extracted with 1 mL of the above solution and 1 mL of H2O. OTA analysis was performed by HPLC with fluorescence detection. The most effective fining agent in removing OTA (80%) from white wine was a commercial formulation that contains gelatin, bentonite and activated carbon. Removals between 10-30% were obtained with potassium caseinate, yeast cell walls and pea protein. With bentonites, carboxymethylcellulose, polyvinylpolypyrrolidone and chitosan no considerable OTA removal was verified. Following, the effectiveness of seven commercial activated carbons was also evaluated and compared with the commercial formulation that contains gelatin, bentonite and activated carbon. The different activated carbons were applied at the concentration recommended by the manufacturer in order to evaluate their efficiency in reducing OTA levels. Trial and OTA analysis were performed as explained previously. The results showed that in white wine all activated carbons except one reduced 100% of OTA. The commercial formulation that contains gelatin, bentonite and activated carbon reduced only 73% of OTA concentration. These results may provide useful information for winemakers, namely for the selection of the most appropriate oenological product for OTA removal, reducing wine toxicity and simultaneously enhancing food safety and wine quality.

Keywords: wine, ota removal, food safety, fining

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Authors: Denisa Denglerova, Radim Sip

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Academic sources as well as international agreements and national documents define inclusion in terms of several criteria: equal opportunities, fulfilling individual needs, development of human resources, community participation. In order for these criteria to be met, the community must be cohesive. Community cohesion, which is a relatively new concept, is not determined by homogeneity, but by the acceptance of diversity among the community members and utilisation of its positive potential. This brings us to a central category of inclusion - appreciating diversity and using it to a positive effect. However, school diversity is a real phenomenon, which schools need to tackle more and more often. This is also indicated by the number of publications focused on diversity in schools. These sources present recent analyses of using identity as a tool of coping with the demands of a diversified society. The aim of this study is to identify and describe in detail the processes taking place in selected schools, which contribute to their pro-inclusive character. The research is designed around a multiple case study of three pro-inclusive schools. Paradigmatically speaking, the research is rooted in situational epistemology. This is also related to the overall framework of interpretation, for which we are going to use innovative methods of situational analysis. In terms of specific research outcomes this will manifest itself in replacing the idea of “objective theory” by the idea of “detailed cartography of a social world”. The cartographic approach directs both the logic of data collection and the choice of methods of their analysis and interpretation. The research results include detection of the following sensitive concepts: Key persons. All participants can contribute to promoting an inclusion-friendly environment; however, some do so with greater motivation than others. These could include school management, teachers with a strong vision of equality, or school counsellors. They have a significant effect on the transformation of the school, and are themselves deeply convinced that inclusion is necessary. Accordingly, they select suitable co-workers; they also inspire some of the other co-workers to make changes, leading by example. Employees with strongly opposing views gradually leave the school, and new members of staff are introduced to the concept of inclusion and openness from the beginning. Manifestations of school openness in working with diversity on all important levels. By this we mean positive manipulation with diversity both in the relationships between “traditional” school participants (directors, teachers, pupils) and school-parent relationships, or relationships between schools and the broader community, in terms of teaching methods as well as ways how the school culture affects the school environment. Other important detected concepts significantly helping to form a pro-inclusive environment in the school are individual and parallel classes; freedom and responsibility of both pupils and teachers, manifested on the didactic level by tendencies towards an open curriculum; ways of asserting discipline in the school environment.

Keywords: inclusion, diversity, education, sensitive concept, situational analysis

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Authors: Nisa Mohan

Abstract:

Clinical coding is a feasible method for estimating the national prevalence of adverse drug event (ADE) admissions. However, under-coding of ADE admissions is a limitation of this method. Whilst the under-coding will impact the accurate estimation of the actual burden of ADEs, the feasibility of the coded data in estimating the adverse drug event admissions goes much further compared to the other methods. Therefore, it is necessary to know the reasons for the under-coding in order to improve the clinical coding of ADE admissions. The ability to identify the reasons for the under-coding of ADE admissions rests on understanding the decision-making process of coding ADE admissions. Hence, the current study aimed to explore the decision-making process of clinical coders when coding cases of ADE admissions. Clinical coders from different levels of coding job such as trainee, intermediate and advanced level coders were purposefully selected for the interviews. Thirteen clinical coders were recruited from two Auckland region District Health Board hospitals for the interview study. Semi-structured, one-on-one, face-to-face interviews using open-ended questions were conducted with the selected clinical coders. Interviews were about 20 to 30 minutes long and were audio-recorded with the approval of the participants. The interview data were analysed using a general inductive approach. The interviews with the clinical coders revealed that the coders have targets to meet, and they sometimes hesitate to adhere to the coding standards. Coders deviate from the standard coding processes to make a decision. Coders avoid contacting the doctors for clarifying small doubts such as ADEs and the name of the medications because of the delay in getting a reply from the doctors. They prefer to do some research themselves or take help from their seniors and colleagues for making a decision because they can avoid a long wait to get a reply from the doctors. Coders think of ADE as a small thing. Lack of time for searching for information to confirm an ADE admission, inadequate communication with clinicians, along with coders’ belief that an ADE is a small thing may contribute to the under-coding of the ADE admissions. These findings suggest that further work is needed on interventions to improve the clinical coding of ADE admissions. Providing education to coders about the importance of ADEs, educating clinicians about the importance of clear and confirmed medical records entries, availing pharmacists’ services to improve the detection and clear documentation of ADE admissions, and including a mandatory field in the discharge summary about external causes of diseases may be useful for improving the clinical coding of ADE admissions. The findings of the research will help the policymakers to make informed decisions about the improvements. This study urges the coding policymakers, auditors, and trainers to engage with the unconscious cognitive biases and short-cuts of the clinical coders. This country-specific research conducted in New Zealand may also benefit other countries by providing insight into the clinical coding of ADE admissions and will offer guidance about where to focus changes and improvement initiatives.

Keywords: adverse drug events, clinical coders, decision making, hospital admissions

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