Search results for: quantum genetic algorithm
219 Evaluation of Antimicrobial Properties of Lactic Acid Bacteria of Enterococcus Genus
Authors: Kristina Karapetyan, Flora Tkhruni, Tsovinar Balabekyan, Arevik Israyelyan, Tatyana Khachatryan
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The ability of the lactic acid bacteria (LAB) to prevent and cure a variety of diseases, their protective role against infections and colonization of pathogenic microorganisms in the digestive tract, has lead to the coining of the term probiotics or pro-life. LAB inhibiting the growth of pathogenic and food spoilage microorganisms, maintaining the nutritive quality and improving the shelf life of foods. They have also been used as flavor and texture producers. Enterococcus strains have been used for treatment of diseases such as diarrhea or antibiotic associated diarrhea, inflammatory pathologies that affect colon such as irritable bowel syndrome, or immune regulation, diarrhea caused by antibiotic treatments. The obtaining and investigation of biological properties of proteinoceous antibiotics, on the basis of probiotic LAB shown, that bacteriocins, metabiotics, and peptides of LAB represent bactericides have a broad range of activity and are excellent candidates for development of new prophylactic and therapeutic substances to complement or replace conventional antibiotics. The genotyping by 16S rRNA sequencing for LAB were used. Cell free culture broth (CFC) broth was purified by the Gel filtration method on the Sephadex Superfine G 25 resin. Antimicrobial activity was determined by spot-on-lawn method and expressed in arbitrary units (AU/ml). The diversity of multidrug-resistance (MDR) of pathogenic strains to antibiotics, most widely used for treatment of human diseases in the Republics of Armenia and Nagorno Karabakh were examined. It was shown, that difference of resistance of pathogens to antibiotics depends on their isolation sources. The influences of partially purified antimicrobial preparations (AMP), obtained from the different strains of Enterococcus genus on the growth of MDR pathogenic bacteria were investigated. It was shown, that bacteriocin containing partially purified preparations, obtained from different strains of Enterococcus faecium and durans species, possess bactericidal or bacteriostatic activity against antibiotic resistant intestinal, spoilage and food-borne pathogens such as Listeria monocytogenes, Staphylococcus aureus, E. coli, and Salmonella. Endemic strains of LAB, isolated from Matsoni made from donkey, buffalo and goat milk, shown broad spectrum of activity against food spoiling microorganisms, moulds and fungi, such as Salmonella sp., Esherichia coli, Aspergillus and Penicillium species. Highest activity against MDR pathogens shown bacteria, isolated from goat milk products. High stability of the investigated strains of the genus Enerococcus, isolated from samples of matsun from different regions of Nagorno-Karabakh (NKR) to the antibiotics was shown. The obtained data show high stability of the investigated different strains of the genus Enerococcus. The high genetic diversity in Enterococcus group suggests adaptations for specific mutations in different environments. Thus, endemic strains of LAB are able to produce bacteriocins with high and different inhibitory activity against broad spectrum of microorganisms isolated from different sources and belong to different taxonomic group. Prospect of the use of certain antimicrobial preparations against pathogenic strains is obvious. These AMP can be applied for long term use against different etiology antibiotic resistant pathogens for prevention or treatment of infectional diseases as an alternative to antibiotics.Keywords: antimicrobial biopreparation, endemic lactic acid bacteria, intra-species diversity, multidrug resistance of pathogens
Procedia PDF Downloads 308218 Extracting Opinions from Big Data of Indonesian Customer Reviews Using Hadoop MapReduce
Authors: Veronica S. Moertini, Vinsensius Kevin, Gede Karya
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Customer reviews have been collected by many kinds of e-commerce websites selling products, services, hotel rooms, tickets and so on. Each website collects its own customer reviews. The reviews can be crawled, collected from those websites and stored as big data. Text analysis techniques can be used to analyze that data to produce summarized information, such as customer opinions. Then, these opinions can be published by independent service provider websites and used to help customers in choosing the most suitable products or services. As the opinions are analyzed from big data of reviews originated from many websites, it is expected that the results are more trusted and accurate. Indonesian customers write reviews in Indonesian language, which comes with its own structures and uniqueness. We found that most of the reviews are expressed with “daily language”, which is informal, do not follow the correct grammar, have many abbreviations and slangs or non-formal words. Hadoop is an emerging platform aimed for storing and analyzing big data in distributed systems. A Hadoop cluster consists of master and slave nodes/computers operated in a network. Hadoop comes with distributed file system (HDFS) and MapReduce framework for supporting parallel computation. However, MapReduce has weakness (i.e. inefficient) for iterative computations, specifically, the cost of reading/writing data (I/O cost) is high. Given this fact, we conclude that MapReduce function is best adapted for “one-pass” computation. In this research, we develop an efficient technique for extracting or mining opinions from big data of Indonesian reviews, which is based on MapReduce with one-pass computation. In designing the algorithm, we avoid iterative computation and instead adopt a “look up table” technique. The stages of the proposed technique are: (1) Crawling the data reviews from websites; (2) cleaning and finding root words from the raw reviews; (3) computing the frequency of the meaningful opinion words; (4) analyzing customers sentiments towards defined objects. The experiments for evaluating the performance of the technique were conducted on a Hadoop cluster with 14 slave nodes. The results show that the proposed technique (stage 2 to 4) discovers useful opinions, is capable of processing big data efficiently and scalable.Keywords: big data analysis, Hadoop MapReduce, analyzing text data, mining Indonesian reviews
Procedia PDF Downloads 197217 Measurement System for Human Arm Muscle Magnetic Field and Grip Strength
Authors: Shuai Yuan, Minxia Shi, Xu Zhang, Jianzhi Yang, Kangqi Tian, Yuzheng Ma
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The precise measurement of muscle activities is essential for understanding the function of various body movements. This work aims to develop a muscle magnetic field signal detection system based on mathematical analysis. Medical research has underscored that early detection of muscle atrophy, coupled with lifestyle adjustments such as dietary control and increased exercise, can significantly enhance muscle-related diseases. Currently, surface electromyography (sEMG) is widely employed in research as an early predictor of muscle atrophy. Nonetheless, the primary limitation of using sEMG to forecast muscle strength is its inability to directly measure the signals generated by muscles. Challenges arise from potential skin-electrode contact issues due to perspiration, leading to inaccurate signals or even signal loss. Additionally, resistance and phase are significantly impacted by adipose layers. The recent emergence of optically pumped magnetometers introduces a fresh avenue for bio-magnetic field measurement techniques. These magnetometers possess high sensitivity and obviate the need for a cryogenic environment unlike superconducting quantum interference devices (SQUIDs). They detect muscle magnetic field signals in the range of tens to thousands of femtoteslas (fT). The utilization of magnetometers for capturing muscle magnetic field signals remains unaffected by issues of perspiration and adipose layers. Since their introduction, optically pumped atomic magnetometers have found extensive application in exploring the magnetic fields of organs such as cardiac and brain magnetism. The optimal operation of these magnetometers necessitates an environment with an ultra-weak magnetic field. To achieve such an environment, researchers usually utilize a combination of active magnetic compensation technology with passive magnetic shielding technology. Passive magnetic shielding technology uses a magnetic shielding device built with high permeability materials to attenuate the external magnetic field to a few nT. Compared with more layers, the coils that can generate a reverse magnetic field to precisely compensate for the residual magnetic fields are cheaper and more flexible. To attain even lower magnetic fields, compensation coils designed by Biot-Savart law are involved to generate a counteractive magnetic field to eliminate residual magnetic fields. By solving the magnetic field expression of discrete points in the target region, the parameters that determine the current density distribution on the plane can be obtained through the conventional target field method. The current density is obtained from the partial derivative of the stream function, which can be represented by the combination of trigonometric functions. Optimization algorithms in mathematics are introduced into coil design to obtain the optimal current density distribution. A one-dimensional linear regression analysis was performed on the collected data, obtaining a coefficient of determination R2 of 0.9349 with a p-value of 0. This statistical result indicates a stable relationship between the peak-to-peak value (PPV) of the muscle magnetic field signal and the magnitude of grip strength. This system is expected to be a widely used tool for healthcare professionals to gain deeper insights into the muscle health of their patients.Keywords: muscle magnetic signal, magnetic shielding, compensation coils, trigonometric functions.
Procedia PDF Downloads 54216 Computer-Aided Drug Repurposing for Mycobacterium Tuberculosis by Targeting Tryptophanyl-tRNA Synthetase
Authors: Neslihan Demirci, Serdar Durdağı
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Mycobacterium tuberculosis is still a worldwide disease-causing agent that, according to WHO, led to the death of 1.5 million people from tuberculosis (TB) in 2020. The bacteria reside in macrophages located specifically in the lung. There is a known quadruple drug therapy regimen for TB consisting of isoniazid (INH), rifampin (RIF), pyrazinamide (PZA), and ethambutol (EMB). Over the past 60 years, there have been great contributions to treatment options, such as recently approved delamanid (OPC67683) and bedaquiline (TMC207/R207910), targeting mycolic acid and ATP synthesis, respectively. Also, there are natural compounds that can block the tryptophanyl-tRNA synthetase (TrpRS) enzyme, chuangxinmycin, and indolmycin. Yet, already the drug resistance is reported for those agents. In this study, the newly released TrpRS enzyme structure is investigated for potential inhibitor drugs from already synthesized molecules to help the treatment of resistant cases and to propose an alternative drug for the quadruple drug therapy of tuberculosis. Maestro, Schrodinger is used for docking and molecular dynamic simulations. In-house library containing ~8000 compounds among FDA-approved indole-containing compounds, a total of 57 obtained from the ChemBL were used for both ATP and tryptophan binding pocket docking. Best of indole-containing 57 compounds were subjected to hit expansion and compared later with virtual screening workflow (VSW) results. After docking, VSW was done. Glide-XP docking algorithm was chosen. When compared, VSW alone performed better than the hit expansion module. Best scored compounds were kept for ten ns molecular dynamic simulations by Desmond. Further, 100 ns molecular dynamic simulation was performed for elected molecules according to Z-score. The top three MMGBSA-scored compounds were subjected to steered molecular dynamic (SMD) simulations by Gromacs. While SMD simulations are still being conducted, ponesimod (for multiple sclerosis), vilanterol (β₂ adrenoreceptor agonist), and silodosin (for benign prostatic hyperplasia) were found to have a significant affinity for tuberculosis TrpRS, which is the propulsive force for the urge to expand the research with in vitro studies. Interestingly, top-scored ponesimod has been reported to have a side effect that makes the patient prone to upper respiratory tract infections.Keywords: drug repurposing, molecular dynamics, tryptophanyl-tRNA synthetase, tuberculosis
Procedia PDF Downloads 112215 Artificial Neural Network Based Parameter Prediction of Miniaturized Solid Rocket Motor
Authors: Hao Yan, Xiaobing Zhang
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The working mechanism of miniaturized solid rocket motors (SRMs) is not yet fully understood. It is imperative to explore its unique features. However, there are many disadvantages to using common multi-objective evolutionary algorithms (MOEAs) in predicting the parameters of the miniaturized SRM during its conceptual design phase. Initially, the design variables and objectives are constrained in a lumped parameter model (LPM) of this SRM, which leads to local optima in MOEAs. In addition, MOEAs require a large number of calculations due to their population strategy. Although the calculation time for simulating an LPM just once is usually less than that of a CFD simulation, the number of function evaluations (NFEs) is usually large in MOEAs, which makes the total time cost unacceptably long. Moreover, the accuracy of the LPM is relatively low compared to that of a CFD model due to its assumptions. CFD simulations or experiments are required for comparison and verification of the optimal results obtained by MOEAs with an LPM. The conceptual design phase based on MOEAs is a lengthy process, and its results are not precise enough due to the above shortcomings. An artificial neural network (ANN) based parameter prediction is proposed as a way to reduce time costs and improve prediction accuracy. In this method, an ANN is used to build a surrogate model that is trained with a 3D numerical simulation. In design, the original LPM is replaced by a surrogate model. Each case uses the same MOEAs, in which the calculation time of the two models is compared, and their optimization results are compared with 3D simulation results. Using the surrogate model for the parameter prediction process of the miniaturized SRMs results in a significant increase in computational efficiency and an improvement in prediction accuracy. Thus, the ANN-based surrogate model does provide faster and more accurate parameter prediction for an initial design scheme. Moreover, even when the MOEAs converge to local optima, the time cost of the ANN-based surrogate model is much lower than that of the simplified physical model LPM. This means that designers can save a lot of time during code debugging and parameter tuning in a complex design process. Designers can reduce repeated calculation costs and obtain accurate optimal solutions by combining an ANN-based surrogate model with MOEAs.Keywords: artificial neural network, solid rocket motor, multi-objective evolutionary algorithm, surrogate model
Procedia PDF Downloads 82214 Early Outcomes and Lessons from the Implementation of a Geriatric Hip Fracture Protocol at a Level 1 Trauma Center
Authors: Peter Park, Alfonso Ayala, Douglas Saeks, Jordan Miller, Carmen Flores, Karen Nelson
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Introduction Hip fractures account for more than 300,000 hospital admissions every year. Many present as fragility fractures in geriatric patients with multiple medical comorbidities. Standardized protocols for the multidisciplinary management of this patient population have been shown to improve patient outcomes. A hip fracture protocol was implemented at a Level I Trauma center with a focus on pre-operative medical optimization and early surgical care. This study evaluates the efficacy of that protocol, including the early transition period. Methods A retrospective review was performed of all patients ages 60 and older with isolated hip fractures who were managed surgically between 2020 and 2022. This included patients 1 year prior and 1 year following the implementation of a hip fracture protocol at a Level I Trauma center. Results 530 patients were identified: 249 patients were treated before, and 281 patients were treated after the protocol was instituted. There was no difference in mean age (p=0.35), gender (p=0.3), or Charlson Comorbidity Index (p=0.38) between the cohorts. Following the implementation of the protocol, there were observed increases in time to surgery (27.5h vs. 33.8h, p=0.01), hospital length of stay (6.3d vs. 9.7d, p<0.001), and ED LOS (5.1h vs. 6.2h, p<0.001). There were no differences in in-hospital mortality (2.01% pre vs. 3.20% post, p=0.39) and complication rates (25% pre vs 26% post, p=0.76). A trend towards improved outcomes was seen after the early transition period but failed to yield statistical significance. Conclusion Early medical management and surgical intervention are key determining factors affecting outcomes following fragility hip fractures. The implementation of a hip fracture protocol at this institution has not yet significantly affected these parameters. This could in part be due to the restrictions placed at this institution during the COVID-19 pandemic. Despite this, the time to OR pre-and post-implementation was quicker than figures reported elsewhere in literature. Further longitudinal data will be collected to determine the final influence of this protocol. Significance/Clinical Relevance Given the increasing number of elderly people and the high morbidity and mortality associated with hip fractures in this population finding cost effective ways to improve outcomes in the management of these injuries has the potential to have enormous positive impact for both patients and hospital systems.Keywords: hip fracture, geriatric, treatment algorithm, preoperative optimization
Procedia PDF Downloads 69213 An Adaptive Conversational AI Approach for Self-Learning
Authors: Airy Huang, Fuji Foo, Aries Prasetya Wibowo
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In recent years, the focus of Natural Language Processing (NLP) development has been gradually shifting from the semantics-based approach to deep learning one, which performs faster with fewer resources. Although it performs well in many applications, the deep learning approach, due to the lack of semantics understanding, has difficulties in noticing and expressing a novel business case with a pre-defined scope. In order to meet the requirements of specific robotic services, deep learning approach is very labor-intensive and time consuming. It is very difficult to improve the capabilities of conversational AI in a short time, and it is even more difficult to self-learn from experiences to deliver the same service in a better way. In this paper, we present an adaptive conversational AI algorithm that combines both semantic knowledge and deep learning to address this issue by learning new business cases through conversations. After self-learning from experience, the robot adapts to the business cases originally out of scope. The idea is to build new or extended robotic services in a systematic and fast-training manner with self-configured programs and constructed dialog flows. For every cycle in which a chat bot (conversational AI) delivers a given set of business cases, it is trapped to self-measure its performance and rethink every unknown dialog flows to improve the service by retraining with those new business cases. If the training process reaches a bottleneck and incurs some difficulties, human personnel will be informed of further instructions. He or she may retrain the chat bot with newly configured programs, or new dialog flows for new services. One approach employs semantics analysis to learn the dialogues for new business cases and then establish the necessary ontology for the new service. With the newly learned programs, it completes the understanding of the reaction behavior and finally uses dialog flows to connect all the understanding results and programs, achieving the goal of self-learning process. We have developed a chat bot service mounted on a kiosk, with a camera for facial recognition and a directional microphone array for voice capture. The chat bot serves as a concierge with polite conversation for visitors. As a proof of concept. We have demonstrated to complete 90% of reception services with limited self-learning capability.Keywords: conversational AI, chatbot, dialog management, semantic analysis
Procedia PDF Downloads 130212 The Path to Ruthium: Insights into the Creation of a New Element
Authors: Goodluck Akaoma Ordu
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Ruthium (Rth) represents a theoretical superheavy element with an atomic number of 119, proposed within the context of advanced materials science and nuclear physics. The conceptualization of Rth involves theoretical frameworks that anticipate its atomic structure, including a hypothesized stable isotope, Rth-320, characterized by 119 protons and 201 neutrons. The synthesis of Ruthium (Rth) hinges on intricate nuclear fusion processes conducted in state-of-the-art particle accelerators, notably utilizing Calcium-48 (Ca-48) as a projectile nucleus and Einsteinium-253 (Es-253) as a target nucleus. These experiments aim to induce fusion reactions that yield Ruthium isotopes, such as Rth-301, accompanied by neutron emission. Theoretical predictions outline various physical and chemical properties attributed to Ruthium (Rth). It is envisaged to possess a high density, estimated at around 25 g/cm³, with melting and boiling points anticipated to be exceptionally high, approximately 4000 K and 6000 K, respectively. Chemical studies suggest potential oxidation states of +2, +3, and +4, indicating a versatile reactivity, particularly with halogens and chalcogens. The atomic structure of Ruthium (Rth) is postulated to feature an electron configuration of [Rn] 5f^14 6d^10 7s^2 7p^2, reflecting its position in the periodic table as a superheavy element. However, the creation and study of superheavy elements like Ruthium (Rth) pose significant challenges. These elements typically exhibit very short half-lives, posing difficulties in their stabilization and detection. Research efforts are focused on identifying the most stable isotopes of Ruthium (Rth) and developing advanced detection methodologies to confirm their existence and properties. Specialized detectors are essential in observing decay patterns unique to Ruthium (Rth), such as alpha decay or fission signatures, which serve as key indicators of its presence and characteristics. The potential applications of Ruthium (Rth) span across diverse technological domains, promising innovations in energy production, material strength enhancement, and sensor technology. Incorporating Ruthium (Rth) into advanced energy systems, such as the Arc Reactor concept, could potentially amplify energy output efficiencies. Similarly, integrating Ruthium (Rth) into structural materials, exemplified by projects like the NanoArc gauntlet, could bolster mechanical properties and resilience. Furthermore, Ruthium (Rth)--based sensors hold promise for achieving heightened sensitivity and performance in various sensing applications. Looking ahead, the study of Ruthium (Rth) represents a frontier in both fundamental science and applied research. It underscores the quest to expand the periodic table and explore the limits of atomic stability and reactivity. Future research directions aim to delve deeper into Ruthium (Rth)'s atomic properties under varying conditions, paving the way for innovations in nanotechnology, quantum materials, and beyond. The synthesis and characterization of Ruthium (Rth) stand as a testament to human ingenuity and technological advancement, pushing the boundaries of scientific understanding and engineering capabilities. In conclusion, Ruthium (Rth) embodies the intersection of theoretical speculation and experimental pursuit in the realm of superheavy elements. It symbolizes the relentless pursuit of scientific excellence and the potential for transformative technological breakthroughs. As research continues to unravel the mysteries of Ruthium (Rth), it holds the promise of reshaping materials science and opening new frontiers in technological innovation.Keywords: superheavy element, nuclear fusion, bombardment, particle accelerator, nuclear physics, particle physics
Procedia PDF Downloads 27211 Cluster Analysis and Benchmarking for Performance Optimization of a Pyrochlore Processing Unit
Authors: Ana C. R. P. Ferreira, Adriano H. P. Pereira
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Given the frequent variation of mineral properties throughout the Araxá pyrochlore deposit, even if a good homogenization work has been carried out before feeding the processing plants, an operation with quality and performance’s high variety standard is expected. These results could be improved and standardized if the blend composition parameters that most influence the processing route are determined, and then the types of raw materials are grouped by them, finally presenting a great reference with operational settings for each group. Associating the physical and chemical parameters of a unit operation through benchmarking or even an optimal reference of metallurgical recovery and product quality reflects in the reduction of the production costs, optimization of the mineral resource, and guarantee of greater stability in the subsequent processes of the production chain that uses the mineral of interest. Conducting a comprehensive exploratory data analysis to identify which characteristics of the ore are most relevant to the process route, associated with the use of Machine Learning algorithms for grouping the raw material (ore) and associating these with reference variables in the process’ benchmark is a reasonable alternative for the standardization and improvement of mineral processing units. Clustering methods through Decision Tree and K-Means were employed, associated with algorithms based on the theory of benchmarking, with criteria defined by the process team in order to reference the best adjustments for processing the ore piles of each cluster. A clean user interface was created to obtain the outputs of the created algorithm. The results were measured through the average time of adjustment and stabilization of the process after a new pile of homogenized ore enters the plant, as well as the average time needed to achieve the best processing result. Direct gains from the metallurgical recovery of the process were also measured. The results were promising, with a reduction in the adjustment time and stabilization when starting the processing of a new ore pile, as well as reaching the benchmark. Also noteworthy are the gains in metallurgical recovery, which reflect a significant saving in ore consumption and a consequent reduction in production costs, hence a more rational use of the tailings dams and life optimization of the mineral deposit.Keywords: mineral clustering, machine learning, process optimization, pyrochlore processing
Procedia PDF Downloads 140210 Psoriasis Diagnostic Test Development: Exploratory Study
Authors: Salam N. Abdo, Orien L. Tulp, George P. Einstein
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The purpose of this exploratory study was to gather the insights into psoriasis etiology, treatment, and patient experience, for developing psoriasis and psoriatic arthritis diagnostic test. Data collection methods consisted of a comprehensive meta-analysis of relevant studies and psoriasis patient survey. Established meta-analysis guidelines were used for the selection and qualitative comparative analysis of psoriasis and psoriatic arthritis research studies. Only studies that clearly discussed psoriasis etiology, treatment, and patient experience were reviewed and analyzed, to establish a qualitative data base for the study. Using the insights gained from meta-analysis, an existing psoriasis patient survey was modified and administered to collect additional data as well as triangulate the results. The hypothesis is that specific types of psoriatic disease have specific etiology and pathophysiologic pattern. The following etiology categories were identified: bacterial, environmental/microbial, genetic, immune, infectious, trauma/stress, and viral. Additional results, obtained from meta-analysis and confirmed by patient survey, were the common age of onset (early to mid-20s) and type of psoriasis (plaque; mild; symmetrical; scalp, chest, and extremities, specifically elbows and knees). Almost 70% of patients reported no prescription drug use due to severe side effects and prohibitive cost. These results will guide the development of psoriasis and psoriatic arthritis diagnostic test. The significant number of medical publications classified psoriatic arthritis disease as inflammatory of an unknown etiology. Thus numerous meta-analyses struggle to report any meaningful conclusions since no definitive results have been reported to date. Therefore, return to the basics is an essential step to any future meaningful results. To date, medical literature supports the fact that psoriatic disease in its current classification could be misidentifying subcategories, which in turn hinders the success of studies conducted to date. Moreover, there has been an enormous commercial support to pursue various immune-modulation therapies, thus following a narrow hypothesis/mechanism of action that is yet to yield resolution of disease state. Recurrence and complications may be considered unacceptable in a significant number of these studies. The aim of the ongoing study is to focus on a narrow subgroup of patient population, as identified by this exploratory study via meta-analysis and patient survey, and conduct an exhaustive work up, aiming at mechanism of action and causality before proposing a cure or therapeutic modality. Remission in psoriasis has been achieved and documented in medical literature, such as immune-modulation, phototherapy, various over-the-counter agents, including salts and tar. However, there is no psoriasis and psoriatic arthritis diagnostic test to date, to guide the diagnosis and treatment of this debilitating and, thus far, incurable disease. Because psoriasis affects approximately 2% of population, the results of this study may affect the treatment and improve the quality of life of a significant number of psoriasis patients, potentially millions of patients in the United States alone and many more millions worldwide.Keywords: biologics, early diagnosis, etiology, immune disease, immune modulation therapy, inflammation skin disorder, phototherapy, plaque psoriasis, psoriasis, psoriasis classification, psoriasis disease marker, psoriasis diagnostic test, psoriasis marker, psoriasis mechanism of action, psoriasis treatment, psoriatic arthritis, psoriatic disease, psoriatic disease marker, psoriatic patient experience, psoriatic patient quality of life, remission, salt therapy, targeted immune therapy
Procedia PDF Downloads 109209 Phenotype and Psychometric Characterization of Phelan-Mcdermid Syndrome Patients
Authors: C. Bel, J. Nevado, F. Ciceri, M. Ropacki, T. Hoffmann, P. Lapunzina, C. Buesa
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Background: The Phelan-McDermid syndrome (PMS) is a genetic disorder caused by the deletion of the terminal region of chromosome 22 or mutation of the SHANK3 gene. Shank3 disruption in mice leads to dysfunction of synaptic transmission, which can be restored by epigenetic regulation with both Lysine Specific Demethylase 1 (LSD1) inhibitors. PMS subjects result in a variable degree of intellectual disability, delay or absence of speech, autistic spectrum disorders symptoms, low muscle tone, motor delays and epilepsy. Vafidemstat is an LSD1 inhibitor in Phase II clinical development with a well-established and favorable safety profile, and data supporting the restoration of memory and cognition defects as well as reduction of agitation and aggression in several animal models and clinical studies. Therefore, vafidemstat has the potential to become a first-in-class precision medicine approach to treat PMS patients. Aims: The goal of this research is to perform an observational trial to psychometrically characterize individuals carrying deletions in SHANK3 and build a foundation for subsequent precision psychiatry clinical trials with vafidemstat. Methodology: This study is characterizing the clinical profile of 20 to 40 subjects, > 16-year-old, with genotypically confirmed PMS diagnosis. Subjects will complete a battery of neuropsychological scales, including the Repetitive Behavior Questionnaire (RBQ), Vineland Adaptive Behavior Scales, Escala de Observación para el Diagnostico del Autismo (Autism Diagnostic Observational Scale) (ADOS)-2, the Battelle Developmental Inventory and the Behavior Problems Inventory (BPI). Results: By March 2021, 19 patients have been enrolled. Unsupervised hierarchical clustering of the results obtained so far identifies 3 groups of patients, characterized by different profiles of cognitive and behavioral scores. The first cluster is characterized by low Battelle age, high ADOS and low Vineland, RBQ and BPI scores. Low Vineland, RBQ and BPI scores are also detected in the second cluster, which in contrast has high Battelle age and low ADOS scores. The third cluster is somewhat in the middle for the Battelle, Vineland and ADOS scores while displaying the highest levels of aggression (high BPI) and repeated behaviors (high RBQ). In line with the observation that female patients are generally affected by milder forms of autistic symptoms, no male patients are present in the second cluster. Dividing the results by gender highlights that male patients in the third cluster are characterized by a higher frequency of aggression, whereas female patients from the same cluster display a tendency toward higher repetitive behavior. Finally, statistically significant differences in deletion sizes are detected comparing the three clusters (also after correcting for gender), and deletion size appears to be positively correlated with ADOS and negatively correlated with Vineland A and C scores. No correlation is detected between deletion size and the BPI and RBQ scores. Conclusions: Precision medicine may open a new way to understand and treat Central Nervous System disorders. Epigenetic dysregulation has been proposed to be an important mechanism in the pathogenesis of schizophrenia and autism. Vafidemstat holds exciting therapeutic potential in PMS, and this study will provide data regarding the optimal endpoints for a future clinical study to explore vafidemstat ability to treat shank3-associated psychiatric disorders.Keywords: autism, epigenetics, LSD1, personalized medicine
Procedia PDF Downloads 160208 Development of a Feedback Control System for a Lab-Scale Biomass Combustion System Using Programmable Logic Controller
Authors: Samuel O. Alamu, Seong W. Lee, Blaise Kalmia, Marc J. Louise Caballes, Xuejun Qian
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The application of combustion technologies for thermal conversion of biomass and solid wastes to energy has been a major solution to the effective handling of wastes over a long period of time. Lab-scale biomass combustion systems have been observed to be economically viable and socially acceptable, but major concerns are the environmental impacts of the process and deviation of temperature distribution within the combustion chamber. Both high and low combustion chamber temperature may affect the overall combustion efficiency and gaseous emissions. Therefore, there is an urgent need to develop a control system which measures the deviations of chamber temperature from set target values, sends these deviations (which generates disturbances in the system) in the form of feedback signal (as input), and control operating conditions for correcting the errors. In this research study, major components of the feedback control system were determined, assembled, and tested. In addition, control algorithms were developed to actuate operating conditions (e.g., air velocity, fuel feeding rate) using ladder logic functions embedded in the Programmable Logic Controller (PLC). The developed control algorithm having chamber temperature as a feedback signal is integrated into the lab-scale swirling fluidized bed combustor (SFBC) to investigate the temperature distribution at different heights of the combustion chamber based on various operating conditions. The air blower rates and the fuel feeding rates obtained from automatic control operations were correlated with manual inputs. There was no observable difference in the correlated results, thus indicating that the written PLC program functions were adequate in designing the experimental study of the lab-scale SFBC. The experimental results were analyzed to study the effect of air velocity operating at 222-273 ft/min and fuel feeding rate of 60-90 rpm on the chamber temperature. The developed temperature-based feedback control system was shown to be adequate in controlling the airflow and the fuel feeding rate for the overall biomass combustion process as it helps to minimize the steady-state error.Keywords: air flow, biomass combustion, feedback control signal, fuel feeding, ladder logic, programmable logic controller, temperature
Procedia PDF Downloads 124207 AIR SAFE: an Internet of Things System for Air Quality Management Leveraging Artificial Intelligence Algorithms
Authors: Mariangela Viviani, Daniele Germano, Simone Colace, Agostino Forestiero, Giuseppe Papuzzo, Sara Laurita
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Nowadays, people spend most of their time in closed environments, in offices, or at home. Therefore, secure and highly livable environmental conditions are needed to reduce the probability of aerial viruses spreading. Also, to lower the human impact on the planet, it is important to reduce energy consumption. Heating, Ventilation, and Air Conditioning (HVAC) systems account for the major part of energy consumption in buildings [1]. Devising systems to control and regulate the airflow is, therefore, essential for energy efficiency. Moreover, an optimal setting for thermal comfort and air quality is essential for people’s well-being, at home or in offices, and increases productivity. Thanks to the features of Artificial Intelligence (AI) tools and techniques, it is possible to design innovative systems with: (i) Improved monitoring and prediction accuracy; (ii) Enhanced decision-making and mitigation strategies; (iii) Real-time air quality information; (iv) Increased efficiency in data analysis and processing; (v) Advanced early warning systems for air pollution events; (vi) Automated and cost-effective m onitoring network; and (vii) A better understanding of air quality patterns and trends. We propose AIR SAFE, an IoT-based infrastructure designed to optimize air quality and thermal comfort in indoor environments leveraging AI tools. AIR SAFE employs a network of smart sensors collecting indoor and outdoor data to be analyzed in order to take any corrective measures to ensure the occupants’ wellness. The data are analyzed through AI algorithms able to predict the future levels of temperature, relative humidity, and CO₂ concentration [2]. Based on these predictions, AIR SAFE takes actions, such as opening/closing the window or the air conditioner, to guarantee a high level of thermal comfort and air quality in the environment. In this contribution, we present the results from the AI algorithm we have implemented on the first s et o f d ata c ollected i n a real environment. The results were compared with other models from the literature to validate our approach.Keywords: air quality, internet of things, artificial intelligence, smart home
Procedia PDF Downloads 86206 Implementation of Chlorine Monitoring and Supply System for Drinking Water Tanks
Authors: Ugur Fidan, Naim Karasekreter
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Healthy and clean water should not contain disease-causing micro-organisms and toxic chemicals and must contain the necessary minerals in a balanced manner. Today, water resources have a limited and strategic importance, necessitating the management of water reserves. Water tanks meet the water needs of people and should be regularly chlorinated to prevent waterborne diseases. For this purpose, automatic chlorination systems placed in water tanks for killing bacteria. However, the regular operation of automatic chlorination systems depends on refilling the chlorine tank when it is empty. For this reason, there is a need for a stock control system, in which chlorine levels are regularly monitored and supplied. It has become imperative to take urgent measures against epidemics caused by the fact that most of our country is not aware of the end of chlorine. The aim of this work is to rehabilitate existing water tanks and to provide a method for a modern water storage system in which chlorination is digitally monitored by turning the newly established water tanks into a closed system. A sensor network structure using GSM/GPRS communication infrastructure has been developed in the study. The system consists of two basic units: hardware and software. The hardware includes a chlorine level sensor, an RFID interlock system for authorized personnel entry into water tank, a motion sensor for animals and other elements, and a camera system to ensure process safety. It transmits the data from the hardware sensors to the host server software via the TCP/IP protocol. The main server software processes the incoming data through the security algorithm and informs the relevant unit responsible (Security forces, Chlorine supply unit, Public health, Local Administrator) by e-mail and SMS. Since the software is developed base on the web, authorized personnel are also able to monitor drinking water tank and report data on the internet. When the findings and user feedback obtained as a result of the study are evaluated, it is shown that closed drinking water tanks are built with GRP type material, and continuous monitoring in digital environment is vital for sustainable health water supply for people.Keywords: wireless sensor networks (WSN), monitoring, chlorine, water tank, security
Procedia PDF Downloads 151205 Exploiting the Potential of Fabric Phase Sorptive Extraction for Forensic Food Safety: Analysis of Food Samples in Cases of Drug Facilitated Crimes
Authors: Bharti Jain, Rajeev Jain, Abuzar Kabir, Torki Zughaibi, Shweta Sharma
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Drug-facilitated crimes (DFCs) entail the use of a single drug or a mixture of drugs to render a victim unable. Traditionally, biological samples have been gathered from victims and conducted analysis to establish evidence of drug administration. Nevertheless, the rapid metabolism of various drugs and delays in analysis can impede the identification of such substances. For this, the present article describes a rapid, sustainable, highly efficient and miniaturized protocol for the identification and quantification of three sedative-hypnotic drugs, namely diazepam, chlordiazepoxide and ketamine in alcoholic beverages and complex food samples (cream of biscuit, flavored milk, juice, cake, tea, sweets and chocolate). The methodology involves utilizing fabric phase sorptive extraction (FPSE) to extract diazepam (DZ), chlordiazepoxide (CDP), and ketamine (KET). Subsequently, the extracted samples are subjected to analysis using gas chromatography-mass spectrometry (GC-MS). Several parameters, including the type of membrane, pH, agitation time and speed, ionic strength, sample volume, elution volume and time, and type of elution solvent, were screened and thoroughly optimized. Sol-gel Carbowax 20M (CW-20M) has demonstrated the most effective extraction efficiency for the target analytes among all evaluated membranes. Under optimal conditions, the method displayed linearity within the range of 0.3–10 µg mL–¹ (or µg g–¹), exhibiting a coefficient of determination (R2) ranging from 0.996–0.999. The limits of detection (LODs) and limits of quantification (LOQs) for liquid samples range between 0.020-0.069 µg mL-¹ and 0.066-0.22 µg mL-¹, respectively. Correspondingly, the LODs for solid samples ranged from 0.056-0.090 µg g-¹, while the LOQs ranged from 0.18-0.29 µg g-¹. Notably, the method showcased better precision, with repeatability and reproducibility both below 5% and 10%, respectively. Furthermore, the FPSE-GC-MS method proved effective in determining diazepam (DZ) in forensic food samples connected to drug-facilitated crimes (DFCs). Additionally, the proposed method underwent evaluation for its whiteness using the RGB12 algorithm.Keywords: drug facilitated crime, fabric phase sorptive extraction, food forensics, white analytical chemistry
Procedia PDF Downloads 60204 Roboweeder: A Robotic Weeds Killer Using Electromagnetic Waves
Authors: Yahoel Van Essen, Gordon Ho, Brett Russell, Hans-Georg Worms, Xiao Lin Long, Edward David Cooper, Avner Bachar
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Weeds reduce farm and forest productivity, invade crops, smother pastures and some can harm livestock. Farmers need to spend a significant amount of money to control weeds by means of biological, chemical, cultural, and physical methods. To solve the global agricultural labor shortage and remove poisonous chemicals, a fully autonomous, eco-friendly, and sustainable weeding technology is developed. This takes the form of a weeding robot, ‘Roboweeder’. Roboweeder includes a four-wheel-drive self-driving vehicle, a 4-DOF robotic arm which is mounted on top of the vehicle, an electromagnetic wave generator (magnetron) which is mounted on the “wrist” of the robotic arm, 48V battery packs, and a control/communication system. Cameras are mounted on the front and two sides of the vehicle. Using image processing and recognition, distinguish types of weeds are detected before being eliminated. The electromagnetic wave technology is applied to heat the individual weeds and clusters dielectrically causing them to wilt and die. The 4-DOF robotic arm was modeled mathematically based on its structure/mechanics, each joint’s load, brushless DC motor and worm gear’ characteristics, forward kinematics, and inverse kinematics. The Proportional-Integral-Differential control algorithm is used to control the robotic arm’s motion to ensure the waveguide aperture pointing to the detected weeds. GPS and machine vision are used to traverse the farm and avoid obstacles without the need of supervision. A Roboweeder prototype has been built. Multiple test trials show that Roboweeder is able to detect, point, and kill the pre-defined weeds successfully although further improvements are needed, such as reducing the “weeds killing” time and developing a new waveguide with a smaller waveguide aperture to avoid killing crops surrounded. This technology changes the tedious, time consuming and expensive weeding processes, and allows farmers to grow more, go organic, and eliminate operational headaches. A patent of this technology is pending.Keywords: autonomous navigation, machine vision, precision heating, sustainable and eco-friendly
Procedia PDF Downloads 238203 Aerosol Direct Radiative Forcing Over the Indian Subcontinent: A Comparative Analysis from the Satellite Observation and Radiative Transfer Model
Authors: Shreya Srivastava, Sagnik Dey
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Aerosol direct radiative forcing (ADRF) refers to the alteration of the Earth's energy balance from the scattering and absorption of solar radiation by aerosol particles. India experiences substantial ADRF due to high aerosol loading from various sources. These aerosols' radiative impact depends on their physical characteristics (such as size, shape, and composition) and atmospheric distribution. Quantifying ADRF is crucial for understanding aerosols’ impact on the regional climate and the Earth's radiative budget. In this study, we have taken radiation data from Clouds and the Earth’s Radiant Energy System (CERES, spatial resolution=1ox1o) for 22 years (2000-2021) over the Indian subcontinent. Except for a few locations, the short-wave DARF exhibits aerosol cooling at the TOA (values ranging from +2.5 W/m2 to -22.5W/m2). Cooling due to aerosols is more pronounced in the absence of clouds. Being an aerosol hotspot, higher negative ADRF is observed over the Indo-Gangetic Plain (IGP). Aerosol Forcing Efficiency (AFE) shows a decreasing seasonal trend in winter (DJF) over the entire study region while an increasing trend over IGP and western south India during the post-monsoon season (SON) in clear-sky conditions. Analysing atmospheric heating and AOD trends, we found that only the aerosol loading is not governing the change in atmospheric heating but also the aerosol composition and/or their vertical profile. We used a Multi-angle Imaging Spectro-Radiometer (MISR) Level-2 Version 23 aerosol products to look into aerosol composition. MISR incorporates 74 aerosol mixtures in its retrieval algorithm based on size, shape, and absorbing properties. This aerosol mixture information was used for analysing long-term changes in aerosol composition and dominating aerosol species corresponding to the aerosol forcing value. Further, ADRF derived from this method is compared with around 35 studies across India, where a plane parallel Radiative transfer model was used, and the model inputs were taken from the OPAC (Optical Properties of Aerosols and Clouds) utilizing only limited aerosol parameter measurements. The result shows a large overestimation of TOA warming by the latter (i.e., Model-based method).Keywords: aerosol radiative forcing (ARF), aerosol composition, MISR, CERES, SBDART
Procedia PDF Downloads 45202 Case Report: Peripartum Cardiomyopathy, a Rare but Fatal Condition in Pregnancy and Puerperium
Authors: Sadaf Abbas, HimGauri Sabnis
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Introduction: Peripartum cardiomyopathy is a rare but potentially life-threatening condition that presents as heart failure during the last month of pregnancy or within five months postpartum. The incidence of postpartum cardiomyopathy ranges from 1 in 1300 to 1 in 15,000 pregnancies. Risk factors include multiparty, advanced maternal age, multiple pregnancies, pre-eclampsia, and chronic hypertension. Study: A 30-year-old Para3+0 presented to the Emergency Department of St’Marry Hospital, Isle of Wight, on the seventh day postpartum, with acute shortness of breath (SOB), chest pain, cough, and a temperature of 38 degrees. The risk factors were smoking and class II obesity (BMI of 40.62). The patient had mild pre-eclampsia in the last pregnancy and was on labetalol and aspirin during an antenatal period, which was stopped postnatally. There was also a history of pre-eclampsia and haemolysis, elevated liver enzymes, low platelets (HELLP syndrome) in previous pregnancies, which led to preterm delivery at 35 weeks in the second pregnancy, and the first baby was stillborn at 24 weeks. On assessment, there was a national early warning score (NEWS score) of 3, persistent tachycardia, and mild crepitation in the lungs. Initial investigations revealed an enlarged heart on chest X-ray, and a CT pulmonary angiogram indicated bilateral basal pulmonary congestion without pulmonary embolism, suggesting fluid overload. Laboratory results showed elevated CRP and normal troponin levels initially, which later increased, indicating myocardial involvement. Echocardiography revealed a severely dilated left ventricle with an ejection fraction (EF) of 31%, consistent with severely impaired systolic function. The cardiology team reviewed the patient and admitted to the Coronary Care Unit. As sign and symptoms were suggestive of fluid overload and congestive cardiac failure, management was done with diuretics, beta-blockers, angiotensin-converting enzyme inhibitors (ACE inhibitors), proton pump inhibitors, and supportive care. During admission, there was complications such as acute kidney injury, but then recovered well. Chest pain had resolved following the treatment. After being admitted for eight days, there was an improvement in the symptoms, and the patient was discharged home with a further plan of cardiac MRI and genetic testing due to a family history of sudden cardiac death. Regular appointment has been made with the Cardiology team to follow-up on the symptoms. Since discharge, the patient made a good recovery. A cardiac MRI was done, which showed severely impaired left ventricular function, ejection fraction (EF) of 38% with mild left ventricular dilatation, and no evidence of previous infarction. Overall appearance is of non-ischemic dilated cardiomyopathy. The main challenge at the time of admission was the non-availability of a cardiac radiology team, so the definitive diagnosis was delayed. The long-term implications include risk of recurrence, chronic heart failure, and, consequently, an effect on quality of life. Therefore, regular follow-up is critical in patient’s management. Conclusions: Peripartum cardiomyopathy is one of the cardiovascular diseases whose causes are still unknown yet and, in some cases, are uncontrolled. By raising awareness about the symptoms and management of this complication it will reduce morbidity and mortality rates and also the length of stay in the hospital.Keywords: cardiomyopathy, cardiomegaly, pregnancy, puerperium
Procedia PDF Downloads 15201 The Proposal for a Framework to Face Opacity and Discrimination ‘Sins’ Caused by Consumer Creditworthiness Machines in the EU
Authors: Diogo José Morgado Rebelo, Francisco António Carneiro Pacheco de Andrade, Paulo Jorge Freitas de Oliveira Novais
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Not everything in AI-power consumer credit scoring turns out to be a wonder. When using AI in Creditworthiness Assessment (CWA), opacity and unfairness ‘sins’ must be considered to the task be deemed Responsible. AI software is not always 100% accurate, which can lead to misclassification. Discrimination of some groups can be exponentiated. A hetero personalized identity can be imposed on the individual(s) affected. Also, autonomous CWA sometimes lacks transparency when using black box models. However, for this intended purpose, human analysts ‘on-the-loop’ might not be the best remedy consumers are looking for in credit. This study seeks to explore the legality of implementing a Multi-Agent System (MAS) framework in consumer CWA to ensure compliance with the regulation outlined in Article 14(4) of the Proposal for an Artificial Intelligence Act (AIA), dated 21 April 2021 (as per the last corrigendum by the European Parliament on 19 April 2024), Especially with the adoption of Art. 18(8)(9) of the EU Directive 2023/2225, of 18 October, which will go into effect on 20 November 2026, there should be more emphasis on the need for hybrid oversight in AI-driven scoring to ensure fairness and transparency. In fact, the range of EU regulations on AI-based consumer credit will soon impact the AI lending industry locally and globally, as shown by the broad territorial scope of AIA’s Art. 2. Consequently, engineering the law of consumer’s CWA is imperative. Generally, the proposed MAS framework consists of several layers arranged in a specific sequence, as follows: firstly, the Data Layer gathers legitimate predictor sets from traditional sources; then, the Decision Support System Layer, whose Neural Network model is trained using k-fold Cross Validation, provides recommendations based on the feeder data; the eXplainability (XAI) multi-structure comprises Three-Step-Agents; and, lastly, the Oversight Layer has a 'Bottom Stop' for analysts to intervene in a timely manner. From the analysis, one can assure a vital component of this software is the XAY layer. It appears as a transparent curtain covering the AI’s decision-making process, enabling comprehension, reflection, and further feasible oversight. Local Interpretable Model-agnostic Explanations (LIME) might act as a pillar by offering counterfactual insights. SHapley Additive exPlanation (SHAP), another agent in the XAI layer, could address potential discrimination issues, identifying the contribution of each feature to the prediction. Alternatively, for thin or no file consumers, the Suggestion Agent can promote financial inclusion. It uses lawful alternative sources such as the share of wallet, among others, to search for more advantageous solutions to incomplete evaluation appraisals based on genetic programming. Overall, this research aspires to bring the concept of Machine-Centered Anthropocentrism to the table of EU policymaking. It acknowledges that, when put into service, credit analysts no longer exert full control over the data-driven entities programmers have given ‘birth’ to. With similar explanatory agents under supervision, AI itself can become self-accountable, prioritizing human concerns and values. AI decisions should not be vilified inherently. The issue lies in how they are integrated into decision-making and whether they align with non-discrimination principles and transparency rules.Keywords: creditworthiness assessment, hybrid oversight, machine-centered anthropocentrism, EU policymaking
Procedia PDF Downloads 28200 Experimental Study on Granulated Steel Slag as an Alternative to River Sand
Authors: K. Raghu, M. N. Vathhsala, Naveen Aradya, Sharth
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River sand is the most preferred fine aggregate for mortar and concrete. River sand is a product of natural weathering of rocks over a period of millions of years and is mined from river beds. Sand mining has disastrous environmental consequences. The excessive mining of river bed is creating an ecological imbalance. This has lead to have restrictions imposed by ministry of environment on sand mining. Driven by the acute need for sand, stone dust or manufactured sand prepared from the crushing and screening of coarse aggregate is being used as sand in the recent past. However manufactured sand is also a natural material and has quarrying and quality issues. To reduce the burden on the environment, alternative materials to be used as fine aggregates are being extensively investigated all over the world. Looking to the quantum of requirements, quality and properties there has been a global consensus on a material – Granulated slags. Granulated slag has been proven as a suitable material for replacing natural sand / crushed fine aggregates. In developed countries, the use of granulated slag as fine aggregate to replace natural sand is well established and is in regular practice. In the present paper Granulated slag has been experimented for usage in mortar. Slags are the main by-products generated during iron and steel production in the steel industry. Over the past decades, the steel production has increased and, consequently, the higher volumes of by-products and residues generated which have driven to the reuse of these materials in an increasingly efficient way. In recent years new technologies have been developed to improve the recovery rates of slags. Increase of slags recovery and use in different fields of applications like cement making, construction and fertilizers help in preserving natural resources. In addition to the environment protection, these practices produced economic benefits, by providing sustainable solutions that can allow the steel industry to achieve its ambitious targets of “zero waste” in coming years. Slags are generated at two different stages of steel production, iron making and steel making known as BF(Blast Furnace) slag and steel slag respectively. The slagging agent or fluxes, such as lime stone, dolomite and quartzite added into BF or steel making furnaces in order to remove impurities from ore, scrap and other ferrous charges during smelting. The slag formation is the result of a complex series of physical and chemical reactions between the non-metallic charge(lime stone, dolomite, fluxes), the energy sources(coal, coke, oxygen, etc.) and refractory materials. Because of the high temperatures (about 15000 C) during their generation, slags do not contain any organic substances. Due to the fact that slags are lighter than the liquid metal, they float and get easily removed. The slags protect the metal bath from atmosphere and maintain temperature through a kind of liquid formation. These slags are in liquid state and solidified in air after dumping in the pit or granulated by impinging water systems. Generally, BF slags are granulated and used in cement making due to its high cementious properties, and steel slags are mostly dumped due to unfavourable physio-chemical conditions. The increasing dump of steel slag not only occupies a plenty of land but also wastes resources and can potentially have an impact on the environment due to water pollution. Since BF slag contains little Fe and can be used directly. BF slag has found a wide application, such as cement production, road construction, Civil Engineering work, fertilizer production, landfill daily cover, soil reclamation, prior to its application outside the iron and steel making process.Keywords: steel slag, river sand, granulated slag, environmental
Procedia PDF Downloads 241199 Efficient Reuse of Exome Sequencing Data for Copy Number Variation Callings
Authors: Chen Wang, Jared Evans, Yan Asmann
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With the quick evolvement of next-generation sequencing techniques, whole-exome or exome-panel data have become a cost-effective way for detection of small exonic mutations, but there has been a growing desire to accurately detect copy number variations (CNVs) as well. In order to address this research and clinical needs, we developed a sequencing coverage pattern-based method not only for copy number detections, data integrity checks, CNV calling, and visualization reports. The developed methodologies include complete automation to increase usability, genome content-coverage bias correction, CNV segmentation, data quality reports, and publication quality images. Automatic identification and removal of poor quality outlier samples were made automatically. Multiple experimental batches were routinely detected and further reduced for a clean subset of samples before analysis. Algorithm improvements were also made to improve somatic CNV detection as well as germline CNV detection in trio family. Additionally, a set of utilities was included to facilitate users for producing CNV plots in focused genes of interest. We demonstrate the somatic CNV enhancements by accurately detecting CNVs in whole exome-wide data from the cancer genome atlas cancer samples and a lymphoma case study with paired tumor and normal samples. We also showed our efficient reuses of existing exome sequencing data, for improved germline CNV calling in a family of the trio from the phase-III study of 1000 Genome to detect CNVs with various modes of inheritance. The performance of the developed method is evaluated by comparing CNV calling results with results from other orthogonal copy number platforms. Through our case studies, reuses of exome sequencing data for calling CNVs have several noticeable functionalities, including a better quality control for exome sequencing data, improved joint analysis with single nucleotide variant calls, and novel genomic discovery of under-utilized existing whole exome and custom exome panel data.Keywords: bioinformatics, computational genetics, copy number variations, data reuse, exome sequencing, next generation sequencing
Procedia PDF Downloads 251198 A Modified Estimating Equations in Derivation of the Causal Effect on the Survival Time with Time-Varying Covariates
Authors: Yemane Hailu Fissuh, Zhongzhan Zhang
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a systematic observation from a defined time of origin up to certain failure or censor is known as survival data. Survival analysis is a major area of interest in biostatistics and biomedical researches. At the heart of understanding, the most scientific and medical research inquiries lie for a causality analysis. Thus, the main concern of this study is to investigate the causal effect of treatment on survival time conditional to the possibly time-varying covariates. The theory of causality often differs from the simple association between the response variable and predictors. A causal estimation is a scientific concept to compare a pragmatic effect between two or more experimental arms. To evaluate an average treatment effect on survival outcome, the estimating equation was adjusted for time-varying covariates under the semi-parametric transformation models. The proposed model intuitively obtained the consistent estimators for unknown parameters and unspecified monotone transformation functions. In this article, the proposed method estimated an unbiased average causal effect of treatment on survival time of interest. The modified estimating equations of semiparametric transformation models have the advantage to include the time-varying effect in the model. Finally, the finite sample performance characteristics of the estimators proved through the simulation and Stanford heart transplant real data. To this end, the average effect of a treatment on survival time estimated after adjusting for biases raised due to the high correlation of the left-truncation and possibly time-varying covariates. The bias in covariates was restored, by estimating density function for left-truncation. Besides, to relax the independence assumption between failure time and truncation time, the model incorporated the left-truncation variable as a covariate. Moreover, the expectation-maximization (EM) algorithm iteratively obtained unknown parameters and unspecified monotone transformation functions. To summarize idea, the ratio of cumulative hazards functions between the treated and untreated experimental group has a sense of the average causal effect for the entire population.Keywords: a modified estimation equation, causal effect, semiparametric transformation models, survival analysis, time-varying covariate
Procedia PDF Downloads 170197 Classification of Emotions in Emergency Call Center Conversations
Authors: Magdalena Igras, Joanna Grzybowska, Mariusz Ziółko
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The study of emotions expressed in emergency phone call is presented, covering both statistical analysis of emotions configurations and an attempt to automatically classify emotions. An emergency call is a situation usually accompanied by intense, authentic emotions. They influence (and may inhibit) the communication between caller and responder. In order to support responders in their responsible and psychically exhaustive work, we studied when and in which combinations emotions appeared in calls. A corpus of 45 hours of conversations (about 3300 calls) from emergency call center was collected. Each recording was manually tagged with labels of emotions valence (positive, negative or neutral), type (sadness, tiredness, anxiety, surprise, stress, anger, fury, calm, relief, compassion, satisfaction, amusement, joy) and arousal (weak, typical, varying, high) on the basis of perceptual judgment of two annotators. As we concluded, basic emotions tend to appear in specific configurations depending on the overall situational context and attitude of speaker. After performing statistical analysis we distinguished four main types of emotional behavior of callers: worry/helplessness (sadness, tiredness, compassion), alarm (anxiety, intense stress), mistake or neutral request for information (calm, surprise, sometimes with amusement) and pretension/insisting (anger, fury). The frequency of profiles was respectively: 51%, 21%, 18% and 8% of recordings. A model of presenting the complex emotional profiles on the two-dimensional (tension-insecurity) plane was introduced. In the stage of acoustic analysis, a set of prosodic parameters, as well as Mel-Frequency Cepstral Coefficients (MFCC) were used. Using these parameters, complex emotional states were modeled with machine learning techniques including Gaussian mixture models, decision trees and discriminant analysis. Results of classification with several methods will be presented and compared with the state of the art results obtained for classification of basic emotions. Future work will include optimization of the algorithm to perform in real time in order to track changes of emotions during a conversation.Keywords: acoustic analysis, complex emotions, emotion recognition, machine learning
Procedia PDF Downloads 390196 Detection of Abnormal Process Behavior in Copper Solvent Extraction by Principal Component Analysis
Authors: Kirill Filianin, Satu-Pia Reinikainen, Tuomo Sainio
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Frequent measurements of product steam quality create a data overload that becomes more and more difficult to handle. In the current study, plant history data with multiple variables was successfully treated by principal component analysis to detect abnormal process behavior, particularly, in copper solvent extraction. The multivariate model is based on the concentration levels of main process metals recorded by the industrial on-stream x-ray fluorescence analyzer. After mean-centering and normalization of concentration data set, two-dimensional multivariate model under principal component analysis algorithm was constructed. Normal operating conditions were defined through control limits that were assigned to squared score values on x-axis and to residual values on y-axis. 80 percent of the data set were taken as the training set and the multivariate model was tested with the remaining 20 percent of data. Model testing showed successful application of control limits to detect abnormal behavior of copper solvent extraction process as early warnings. Compared to the conventional techniques of analyzing one variable at a time, the proposed model allows to detect on-line a process failure using information from all process variables simultaneously. Complex industrial equipment combined with advanced mathematical tools may be used for on-line monitoring both of process streams’ composition and final product quality. Defining normal operating conditions of the process supports reliable decision making in a process control room. Thus, industrial x-ray fluorescence analyzers equipped with integrated data processing toolbox allows more flexibility in copper plant operation. The additional multivariate process control and monitoring procedures are recommended to apply separately for the major components and for the impurities. Principal component analysis may be utilized not only in control of major elements’ content in process streams, but also for continuous monitoring of plant feed. The proposed approach has a potential in on-line instrumentation providing fast, robust and cheap application with automation abilities.Keywords: abnormal process behavior, failure detection, principal component analysis, solvent extraction
Procedia PDF Downloads 303195 Reduction of the Risk of Secondary Cancer Induction Using VMAT for Head and Neck Cancer
Authors: Jalil ur Rehman, Ramesh C, Tailor, Isa Khan, Jahanzeeb Ashraf, Muhammad Afzal, Geofferry S. Ibbott
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The purpose of this analysis is to estimate secondary cancer risks after VMAT compared to other modalities of head and neck radiotherapy (IMRT, 3DCRT). Computer tomography (CT) scans of Radiological Physics Center (RPC) head and neck phantom were acquired with CT scanner and exported via DICOM to the treatment planning system (TPS). Treatment planning was done using four arc (182-178 and 180-184, clockwise and anticlockwise) for volumetric modulated arc therapy (VMAT) , Nine fields (200, 240, 280, 320,0,40,80,120 and 160), which has been commonly used at MD Anderson Cancer Center Houston for intensity modulated radiation therapy (IMRT) and four fields for three dimensional radiation therapy (3DCRT) were used. True beam linear accelerator of 6MV photon energy was used for dose delivery, and dose calculation was done with CC convolution algorithm with prescription dose of 6.6 Gy. Primary Target Volume (PTV) coverage, mean and maximal doses, DVHs and volumes receiving more than 2 Gy and 3.8 Gy of OARs were calculated and compared. Absolute point dose and planar dose were measured with thermoluminescent dosimeters (TLDs) and GafChromic EBT2 film, respectively. Quality Assurance of VMAT and IMRT were performed by using ArcCHECK method with gamma index criteria of 3%/3mm dose difference to distance to agreement (DD/DTA). PTV coverage was found 90.80 %, 95.80 % and 95.82 % for 3DCRT, IMRT and VMAT respectively. VMAT delivered the lowest maximal doses to esophagus (2.3 Gy), brain (4.0 Gy) and thyroid (2.3 Gy) compared to all other studied techniques. In comparison, maximal doses for 3DCRT were found higher than VMAT for all studied OARs. Whereas, IMRT delivered maximal higher doses 26%, 5% and 26% for esophagus, normal brain and thyroid, respectively, compared to VMAT. It was noted that esophagus volume receiving more than 2 Gy was 3.6 % for VMAT, 23.6 % for IMRT and up to 100 % for 3DCRT. Good agreement was observed between measured doses and those calculated with TPS. The averages relative standard errors (RSE) of three deliveries within eight TLD capsule locations were, 0.9%, 0.8% and 0.6% for 3DCRT, IMRT and VMAT, respectively. The gamma analysis for all plans met the ±5%/3 mm criteria (over 90% passed) and results of QA were greater than 98%. The calculations for maximal doses and volumes of OARs suggest that the estimated risk of secondary cancer induction after VMAT is considerably lower than IMRT and 3DCRT.Keywords: RPC, 3DCRT, IMRT, VMAT, EBT2 film, TLD
Procedia PDF Downloads 499194 The Yield of Neuroimaging in Patients Presenting to the Emergency Department with Isolated Neuro-Ophthalmological Conditions
Authors: Dalia El Hadi, Alaa Bou Ghannam, Hala Mostafa, Hana Mansour, Ibrahim Hashim, Soubhi Tahhan, Tharwat El Zahran
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Introduction: Neuro-ophthalmological emergencies require prompt assessment and management to avoid vision or life-threatening sequelae. Some would require neuroimaging. Most commonly used are the CT and MRI of the Brain. They can be over-used when not indicated. Their yield remains dependent on multiple factors relating to the clinical scenario. Methods: A retrospective cross-sectional study was conducted by reviewing the electronic medical records of patients presenting to the Emergency Department (ED) with isolated neuro-ophthalmologic complaints. For each patient, data were collected on the clinical presentation, whether neuroimaging was performed (and which type), and the result of neuroimaging. Analysis of the performed neuroimaging was made, and its yield was determined. Results: A total of 211 patients were reviewed. The complaints or symptoms at presentation were: blurry vision, change in the visual field, transient vision loss, floaters, double vision, eye pain, eyelid droop, headache, dizziness and others such as nausea or vomiting. In the ED, a total of 126 neuroimaging procedures were performed. Ninety-four imagings (74.6%) were normal, while 32 (25.4%) had relevant abnormal findings. Only 2 symptoms were significant for abnormal imaging: blurry vision (p-value= 0.038) and visual field change (p-value= 0.014). While 4 physical exam findings had significant abnormal imaging: visual field defect (p-value= 0.016), abnormal pupil reactivity (p-value= 0.028), afferent pupillary defect (p-value= 0.018), and abnormal optic disc exam (p-value= 0.009). Conclusion: Risk indicators for abnormal neuroimaging in the setting of neuro-ophthalmological emergencies are blurred vision or changes in the visual field on history taking. While visual field irregularities, abnormal pupil reactivity with or without afferent pupillary defect, or abnormal optic discs, are risk factors related to physical testing. These findings, when present, should sway the ED physician towards neuroimaging but still individualizing each case is of utmost importance to prevent time-consuming, resource-draining, and sometimes unnecessary workup. In the end, it suggests a well-structured patient-centered algorithm to be followed by ED physicians.Keywords: emergency department, neuro-ophthalmology, neuroimaging, risk indicators
Procedia PDF Downloads 173193 Simultaneous Measurement of Wave Pressure and Wind Speed with the Specific Instrument and the Unit of Measurement Description
Authors: Branimir Jurun, Elza Jurun
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The focus of this paper is the description of an instrument called 'Quattuor 45' and defining of wave pressure measurement. Special attention is given to measurement of wave pressure created by the wind speed increasing obtained with the instrument 'Quattuor 45' in the investigated area. The study begins with respect to theoretical attitudes and numerous up to date investigations related to the waves approaching the coast. The detailed schematic view of the instrument is enriched with pictures from ground plan and side view. Horizontal stability of the instrument is achieved by mooring which relies on two concrete blocks. Vertical wave peak monitoring is ensured by one float above the instrument. The synthesis of horizontal stability and vertical wave peak monitoring allows to create a representative database for wave pressure measuring. Instrument ‘Quattuor 45' is named according to the way the database is received. Namely, the electronic part of the instrument consists of the main chip ‘Arduino', its memory, four load cells with the appropriate modules and the wind speed sensor 'Anemometers'. The 'Arduino' chip is programmed to store two data from each load cell and two data from the anemometer on SD card each second. The next part of the research is dedicated to data processing. All measured results are stored automatically in the database and after that detailed processing is carried out in the MS Excel. The result of the wave pressure measurement is synthesized by the unit of measurement kN/m². This paper also suggests a graphical presentation of the results by multi-line graph. The wave pressure is presented on the left vertical axis, while the wind speed is shown on the right vertical axis. The time of measurement is displayed on the horizontal axis. The paper proposes an algorithm for wind speed measurements showing the results for two characteristic winds in the Adriatic Sea, called 'Bura' and 'Jugo'. The first of them is the northern wind that reaches high speeds, causing low and extremely steep waves, where the pressure of the wave is relatively weak. On the other hand, the southern wind 'Jugo' has a lower speed than the northern wind, but due to its constant duration and constant speed maintenance, it causes extremely long and high waves that cause extremely high wave pressure.Keywords: instrument, measuring unit, waves pressure metering, wind seed measurement
Procedia PDF Downloads 191192 Laminar Periodic Vortex Shedding over a Square Cylinder in Pseudoplastic Fluid Flow
Authors: Shubham Kumar, Chaitanya Goswami, Sudipto Sarkar
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Pseudoplastic (n < 1, n being the power index) fluid flow can be found in food, pharmaceutical and process industries and has very complex flow nature. To our knowledge, inadequate research work has been done in this kind of flow even at very low Reynolds numbers. Here, in the present computation, we have considered unsteady laminar flow over a square cylinder in pseudoplastic flow environment. For Newtonian fluid flow, this laminar vortex shedding range lies between Re = 47-180. In this problem, we consider Re = 100 (Re = U∞ a/ ν, U∞ is the free stream velocity of the flow, a is the side of the cylinder and ν is the kinematic viscosity of the fluid). The pseudoplastic fluid range has been chosen from close to the Newtonian fluid (n = 0.8) to very high pseudoplasticity (n = 0.1). The flow domain is constituted using Gambit 2.2.30 and this software is also used to generate mesh and to impose the boundary conditions. For all places, the domain size is considered as 36a × 16a with 280 ×192 grid point in the streamwise and flow normal directions respectively. The domain and the grid points are selected after a thorough grid independent study at n = 1.0. Fine and equal grid spacing is used close to the square cylinder to capture the upper and lower shear layers shed from the cylinder. Away from the cylinder the grid is unequal in size and stretched out in all direction. Velocity inlet (u = U∞), pressure outlet (Neumann condition), symmetry (free-slip boundary condition du/dy = 0, v = 0) at upper and lower domain boundary conditions are used for this simulation. Wall boundary (u = v = 0) is considered on the square cylinder surface. Fully conservative 2-D unsteady Navier-Stokes equations are discretized and then solved by Ansys Fluent 14.5 to understand the flow nature. SIMPLE algorithm written in finite volume method is selected for this purpose which is the default solver in scripted in Fluent. The result obtained for Newtonian fluid flow agrees well with previous work supporting Fluent’s usefulness in academic research. A minute analysis of instantaneous and time averaged flow field is obtained both for Newtonian and pseudoplastic fluid flow. It has been observed that drag coefficient increases continuously with the reduced value of n. Also, the vortex shedding phenomenon changes at n = 0.4 due to flow instability. These are some of the remarkable findings for laminar periodic vortex shedding regime in pseudoplastic flow environment.Keywords: Ansys Fluent, CFD, periodic vortex shedding, pseudoplastic fluid flow
Procedia PDF Downloads 190191 Developing Optical Sensors with Application of Cancer Detection by Elastic Light Scattering Spectroscopy
Authors: May Fadheel Estephan, Richard Perks
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Context: Cancer is a serious health concern that affects millions of people worldwide. Early detection and treatment are essential for improving patient outcomes. However, current methods for cancer detection have limitations, such as low sensitivity and specificity. Research Aim: The aim of this study was to develop an optical sensor for cancer detection using elastic light scattering spectroscopy (ELSS). ELSS is a noninvasive optical technique that can be used to characterize the size and concentration of particles in a solution. Methodology: An optical probe was fabricated with a 100-μm-diameter core and a 132-μm centre-to-centre separation. The probe was used to measure the ELSS spectra of polystyrene spheres with diameters of 2, 0.8, and 0.413 μm. The spectra were then analysed to determine the size and concentration of the spheres. Findings: The results showed that the optical probe was able to differentiate between the three different sizes of polystyrene spheres. The probe was also able to detect the presence of polystyrene spheres in suspension concentrations as low as 0.01%. Theoretical Importance: The results of this study demonstrate the potential of ELSS for cancer detection. ELSS is a noninvasive technique that can be used to characterize the size and concentration of cells in a tissue sample. This information can be used to identify cancer cells and assess the stage of the disease. Data Collection: The data for this study were collected by measuring the ELSS spectra of polystyrene spheres with different diameters. The spectra were collected using a spectrometer and a computer. Analysis Procedures: The ELSS spectra were analysed using a software program to determine the size and concentration of the spheres. The software program used a mathematical algorithm to fit the spectra to a theoretical model. Question Addressed: The question addressed by this study was whether ELSS could be used to detect cancer cells. The results of the study showed that ELSS could be used to differentiate between different sizes of cells, suggesting that it could be used to detect cancer cells. Conclusion: The findings of this research show the utility of ELSS in the early identification of cancer. ELSS is a noninvasive method for characterizing the number and size of cells in a tissue sample. To determine cancer cells and determine the disease's stage, this information can be employed. Further research is needed to evaluate the clinical performance of ELSS for cancer detection.Keywords: elastic light scattering spectroscopy, polystyrene spheres in suspension, optical probe, fibre optics
Procedia PDF Downloads 73190 Structuring Highly Iterative Product Development Projects by Using Agile-Indicators
Authors: Guenther Schuh, Michael Riesener, Frederic Diels
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Nowadays, manufacturing companies are faced with the challenge of meeting heterogeneous customer requirements in short product life cycles with a variety of product functions. So far, some of the functional requirements remain unknown until late stages of the product development. A way to handle these uncertainties is the highly iterative product development (HIP) approach. By structuring the development project as a highly iterative process, this method provides customer oriented and marketable products. There are first approaches for combined, hybrid models comprising deterministic-normative methods like the Stage-Gate process and empirical-adaptive development methods like SCRUM on a project management level. However, almost unconsidered is the question, which development scopes can preferably be realized with either empirical-adaptive or deterministic-normative approaches. In this context, a development scope constitutes a self-contained section of the overall development objective. Therefore, this paper focuses on a methodology that deals with the uncertainty of requirements within the early development stages and the corresponding selection of the most appropriate development approach. For this purpose, internal influencing factors like a company’s technology ability, the prototype manufacturability and the potential solution space as well as external factors like the market accuracy, relevance and volatility will be analyzed and combined into an Agile-Indicator. The Agile-Indicator is derived in three steps. First of all, it is necessary to rate each internal and external factor in terms of the importance for the overall development task. Secondly, each requirement has to be evaluated for every single internal and external factor appropriate to their suitability for empirical-adaptive development. Finally, the total sums of internal and external side are composed in the Agile-Indicator. Thus, the Agile-Indicator constitutes a company-specific and application-related criterion, on which the allocation of empirical-adaptive and deterministic-normative development scopes can be made. In a last step, this indicator will be used for a specific clustering of development scopes by application of the fuzzy c-means (FCM) clustering algorithm. The FCM-method determines sub-clusters within functional clusters based on the empirical-adaptive environmental impact of the Agile-Indicator. By means of the methodology presented in this paper, it is possible to classify requirements, which are uncertainly carried out by the market, into empirical-adaptive or deterministic-normative development scopes.Keywords: agile, highly iterative development, agile-indicator, product development
Procedia PDF Downloads 238