Search results for: edge detection algorithm

Authors: Yi-Ting Chen, Yu-Fang Huang, Pei-Wei Wang, Hai-Wei Liang, Chun-Hao Lai, Mei-Lien Chen

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Background: Bisphenol A (BPA) is commonly used in consumer products, such as inner coatings of cans and polycarbonated bottles. BPA is considered to be an endocrine disrupting substance (EDs) that affects normal human hormones and may cause adverse effects on human health. Pregnant women and fetuses are susceptible groups of endocrine disrupting substances. Prenatal exposure to BPA has been shown to affect the fetus through the placenta. Therefore, it is important to evaluate the potential health risk of fetal exposure to BPA during pregnancy. The aims of this study were (1) to determine the urinary concentration of BPA in pregnant women, and (2) to investigate the association between BPA exposure during pregnancy and birth outcomes. Methods: This study recruited 117 pregnant women and their fetuses from 2012 to 2014 from the Taiwan Maternal- Infant Cohort Study (TMICS). Maternal urine samples were collected in the third trimester and questionnaires were used to collect socio-demographic characteristics, eating habits and medical conditions of the participants. Information about birth outcomes of the fetus was obtained from medical records. As for chemicals analysis, BPA concentrations in urine were determined by off-line solid-phase extraction-ultra-performance liquid chromatography coupled with a Q-Tof mass spectrometer. The urinary concentrations were adjusted with creatinine. The association between maternal concentrations of BPA and birth outcomes was estimated using the logistic regression model. Results: The detection rate of BPA is 99%; the concentration ranges (μg/g) from 0.16 to 46.90. The mean (SD) BPA levels are 5.37(6.42) μg/g creatinine. The mean ±SD of the body weight, body length, head circumference, chest circumference and gestational age at birth are 3105.18 ± 339.53 g, 49.33 ± 1.90 cm, 34.16 ± 1.06 cm, 32.34 ± 1.37 cm and 38.58 ± 1.37 weeks, respectively. After stratifying the exposure levels into two groups by median, pregnant women in higher exposure group would have an increased risk of lower body weight (OR=0.57, 95%CI=0.271-1.193), smaller chest circumference (OR=0.70, 95%CI=0.335-1.47) and shorter gestational age at birth newborn (OR=0.46, 95%CI=0.191-1.114). However, there are no associations between BPA concentration and birth outcomes reach a significant level (p < 0.05) in statistics. Conclusions: This study presents prenatal BPA profiles and infants in northern Taiwan. Women who have higher BPA concentrations tend to give birth to lower body weight, smaller chest circumference or shorter gestational age at birth newborn. More data will be included to verify the results. This report will also present the predictors of BPA concentrations for pregnant women.

Keywords: bisphenol A, birth outcomes, biomonitoring, prenatal exposure

Procedia PDF Downloads 143

Authors: Dávid Kovács, Bálint Csanády, Dániel Boros, Iván Ivkovic, Lóránt Nagy, Dalma Tóth-Lakits, László Márkus, András Lukács

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The modeling practice of financial instruments has seen significant change over the last decade due to the recognition of time-dependent and stochastically changing correlations among the market prices or the prices and market characteristics. To represent this phenomenon, the Stochastic Correlation Process (SCP) has come to the fore in the joint modeling of prices, offering a more nuanced description of their interdependence. This approach has allowed for the attainment of realistic tail dependencies, highlighting that prices tend to synchronize more during intense or volatile trading periods, resulting in stronger correlations. Evidence in statistical literature suggests that, similarly to the volatility, the SCP of certain stock prices follows rough paths, which can be described using fractional differential equations. However, estimating parameters for these equations often involves complex and computation-intensive algorithms, creating a necessity for alternative solutions. In this regard, the Fractional Ornstein-Uhlenbeck (fOU) process from the family of fractional processes offers a promising path. We can effectively describe the rough SCP by utilizing certain transformations of the fOU. We employed neural networks to understand the behavior of these processes. We had to develop a fast algorithm to generate a valid and suitably large sample from the appropriate process to train the network. With an extensive training set, the neural network can estimate the process parameters accurately and efficiently. Although the initial focus was the fOU, the resulting model displayed broader applicability, thus paving the way for further investigation of other processes in the realm of financial mathematics. The utility of SCP extends beyond its immediate application. It also serves as a springboard for a deeper exploration of fractional processes and for extending existing models that use ordinary Wiener processes to fractional scenarios. In essence, deploying both SCP and fractional processes in financial models provides new, more accurate ways to depict market dynamics.

Keywords: fractional Ornstein-Uhlenbeck process, fractional stochastic processes, Heston model, neural networks, stochastic correlation, stochastic differential equations, stochastic volatility

Procedia PDF Downloads 118

Authors: Camille Van Eupen, Dirk Maes, Marc Herremans, Kristijn R. R. Swinnen, Ben Somers, Stijn Luca

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In ecology, species distribution models are commonly implemented to study species-environment relationships. These models increasingly rely on opportunistic citizen science data when high-quality species records collected through standardized recording protocols are unavailable. While these opportunistic data are abundant, uncertainty is usually high, e.g., due to observer effects or a lack of metadata. Data quality filtering is often used to reduce these types of uncertainty in an attempt to increase the value of studies relying on opportunistic data. However, filtering should not be performed blindly. In this study, recommendations are built for data quality filtering of opportunistic species occurrence data that are used as input for species distribution models. Using an extensive database of 5.7 million citizen science records from 255 species in Flanders, the impact on model performance was quantified by applying three data quality filters, and these results were linked to species traits. More specifically, presence records were filtered based on record attributes that provide information on the observation process or post-entry data validation, and changes in the area under the receiver operating characteristic (AUC), sensitivity, and specificity were analyzed using the Maxent algorithm with and without filtering. Controlling for sample size enabled us to study the combined impact of data quality filtering, i.e., the simultaneous impact of an increase in data quality and a decrease in sample size. Further, the variation among species in their response to data quality filtering was explored by clustering species based on four traits often related to data quality: commonness, popularity, difficulty, and body size. Findings show that model performance is affected by i) the quality of the filtered data, ii) the proportional reduction in sample size caused by filtering and the remaining absolute sample size, and iii) a species ‘quality profile’, resulting from a species classification based on the four traits related to data quality. The findings resulted in recommendations on when and how to filter volunteer generated and opportunistically collected data. This study confirms that correctly processed citizen science data can make a valuable contribution to ecological research and species conservation.

Keywords: citizen science, data quality filtering, species distribution models, trait profiles

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Authors: F. DelGaudio, H. Gill

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Resuscitation during cardiopulmonary (CPA) arrest is dynamic, high stress, high acuity situation, which can easily lead to communication breakdown, and errors. The care of these high acuity patients has also been shown to increase physiologic stress and task saturation of providers, which can negatively impact the care being provided. These difficulties are further complicated during a global pandemic and pose a significant safety risk to bedside providers. Nurse-led codes are a relatively new concept that may be a potential solution for alleviating some of these difficulties. An experienced nurse who has completed advanced cardiac life support (ACLS), and additional training, assumed the responsibility of directing the mechanics of the appropriate ACLS algorithm. This was done in conjunction with a physician who also acted as a physician leader. The additional nurse-led code training included a multi-disciplinary in situ simulation of a CPA on a suspected COVID-19 patient. During the CPA, the nurse leader’s responsibilities include: ensuring adequate compression depth and rate, minimizing interruptions in chest compressions, the timing of rhythm/pulse checks, and appropriate medication administration. In addition, the nurse leader also functions as a last line safety check for appropriate personal protective equipment and limiting exposure of staff. The use of nurse-led codes for CPA has shown to decrease the cognitive overload and task saturation for the physician, as well as limiting the number of staff being exposed to a potentially infectious patient. The real-world application has allowed physicians to perform and oversee high-risk procedures such as intubation, line placement, and point of care ultrasound, without sacrificing the integrity of the resuscitation. Nurse-led codes have also given the physician the bandwidth to review pertinent medical history, advanced directives, determine reversible causes, and have the end of life conversations with family. While there is a paucity of research on the effectiveness of nurse-led codes, there are many potentially significant benefits. In addition to its value during a pandemic, it may also be beneficial during complex circumstances such as extracorporeal cardiopulmonary resuscitation.

Keywords: cardiopulmonary arrest, COVID-19, nurse-led code, task saturation

Procedia PDF Downloads 155

Authors: Chao Li, Lei Zhou, Ruiqi Ren, Dan Li, Yali Wang, Daxin Ni, Zijian Feng, Qun Li

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Background: In 2016, yellow fever had been first ever discovered in China, soon after the yellow fever epidemic occurred in Angola. After the discovery, China had promptly made the national protocol of control and prevention and strengthened the surveillance on passenger and vector. In this study, a descriptive analysis was conducted to summarize China’s experiences of response towards this import epidemic, in the hope of providing experiences on prevention and control of yellow fever and other similar imported infectious diseases in the future. Methods: The imported cases were discovered and reported by General Administration of Quality Supervision, Inspection and Quarantine (AQSIQ) and several hospitals. Each clinically diagnosed yellow fever case was confirmed by real-time reverse transcriptase polymerase chain reaction (RT–PCR). The data of the imported yellow fever cases were collected by local Centers for Disease Control and Prevention (CDC) through field investigations soon after they received the reports. Results: A total of 11 imported cases from Angola were reported in China, during Angola’s yellow fever outbreak. Six cases were discovered by the AQSIQ, among which two with mild symptom were initiative declarations at the time of entry. Except for one death, the remaining 10 cases all had recovered after timely and proper treatment. All cases are Chinese, and lived in Luanda, the capital of Angola. 73% were retailers (8/11) from Fuqing city in Fujian province, and the other three were labors send by companies. 10 cases had experiences of medical treatment in Luanda after onset, among which 8 cases visited the same local Chinese medicine hospital (China Railway four Bureau Hospital). Among the 11 cases, only one case had an effective vaccination. The result of emergency surveillance for mosquito density showed that only 14 containers of water were found positive around places of three cases, and the Breteau Index is 15. Conclusions: Effective response was taken to control and prevent the outbreak of yellow fever in China after discovering the imported cases. However, though the similar origin of Chinese in Angola has provided an easy access for disease detection, information sharing, health education and vaccination on yellow fever; these conveniences were overlooked during previous disease prevention methods. Besides, only one case having effective vaccination revealed the inadequate capacity of immunization service in China. These findings will provide suggestions to improve China’s capacity to deal with not only yellow fever but also other similar imported diseases in China.

Keywords: yellow fever, first import, China, suggestion

Procedia PDF Downloads 187

Authors: Fazıl Gökgöz, Fahrettin Filiz

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Load forecasting has become crucial in recent years and become popular in forecasting area. Many different power forecasting models have been tried out for this purpose. Electricity load forecasting is necessary for energy policies, healthy and reliable grid systems. Effective power forecasting of renewable energy load leads the decision makers to minimize the costs of electric utilities and power plants. Forecasting tools are required that can be used to predict how much renewable energy can be utilized. The purpose of this study is to explore the effectiveness of LSTM-based neural networks for estimating renewable energy loads. In this study, we present models for predicting renewable energy loads based on deep neural networks, especially the Long Term Memory (LSTM) algorithms. Deep learning allows multiple layers of models to learn representation of data. LSTM algorithms are able to store information for long periods of time. Deep learning models have recently been used to forecast the renewable energy sources such as predicting wind and solar energy power. Historical load and weather information represent the most important variables for the inputs within the power forecasting models. The dataset contained power consumption measurements are gathered between January 2016 and December 2017 with one-hour resolution. Models use publicly available data from the Turkish Renewable Energy Resources Support Mechanism. Forecasting studies have been carried out with these data via deep neural networks approach including LSTM technique for Turkish electricity markets. 432 different models are created by changing layers cell count and dropout. The adaptive moment estimation (ADAM) algorithm is used for training as a gradient-based optimizer instead of SGD (stochastic gradient). ADAM performed better than SGD in terms of faster convergence and lower error rates. Models performance is compared according to MAE (Mean Absolute Error) and MSE (Mean Squared Error). Best five MAE results out of 432 tested models are 0.66, 0.74, 0.85 and 1.09. The forecasting performance of the proposed LSTM models gives successful results compared to literature searches.

Keywords: deep learning, long short term memory, energy, renewable energy load forecasting

Procedia PDF Downloads 266

Authors: Marjila Burhanzoi, Kenta Onohara, Tomoaki Ikegami

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Faults in photovoltaic (PV) modules should be detected to the greatest extent as early as possible. For that conventional fault detection methods such as electrical characterization, visual inspection, infrared (IR) imaging, ultraviolet fluorescence and electroluminescence (EL) imaging are used, but they either fail to detect the location or category of fault, or they require expensive equipment and are not convenient for onsite application. Hence, these methods are not convenient to use for monitoring small-scale PV systems. Therefore, low cost and efficient inspection techniques with the ability of onsite application are indispensable for PV modules. In this study in order to establish efficient inspection technique, correlation between faults and magnetic flux density on the surface is of crystalline PV modules are investigated. Magnetic flux on the surface of normal and faulted PV modules is measured under the short circuit and illuminated conditions using two different sensor devices. One device is made of small integrated sensors namely 9-axis motion tracking sensor with a 3-axis electronic compass embedded, an IR temperature sensor, an optical laser position sensor and a microcontroller. This device measures the X, Y and Z components of the magnetic flux density (Bx, By and Bz) few mm above the surface of a PV module and outputs the data as line graphs in LabVIEW program. The second device is made of a laser optical sensor and two magnetic line sensor modules consisting 16 pieces of magnetic sensors. This device scans the magnetic field on the surface of PV module and outputs the data as a 3D surface plot of the magnetic flux intensity in a LabVIEW program. A PC equipped with LabVIEW software is used for data acquisition and analysis for both devices. To show the effectiveness of this method, measured results are compared to those of a normal reference module and their EL images. Through the experiments it was confirmed that the magnetic field in the faulted areas have different profiles which can be clearly identified in the measured plots. Measurement results showed a perfect correlation with the EL images and using position sensors it identified the exact location of faults. This method was applied on different modules and various faults were detected using it. The proposed method owns the ability of on-site measurement and real-time diagnosis. Since simple sensors are used to make the device, it is low cost and convenient to be sued by small-scale or residential PV system owners.

Keywords: fault diagnosis, fault location, integrated sensors, PV modules

Procedia PDF Downloads 224

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 201

Authors: Johannes Mertl

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Our surrounding air contains various particles. Besides typical representatives of inorganic dust, such as soot and ash, also particles originating from animals, microorganisms or plants are floating through the air, so-called bioaerosols. The group of bioaerosols consists of a broad spectrum of particles of different size, including fungi, bacteria, viruses, spores, or tree, flower and grass pollen that are of high relevance for allergy sufferers. In dependence of the environmental climate and the actual season, these allergenic particles can be found in enormous numbers in the air and are inhaled by humans via the respiration tract, with a potential for inflammatory diseases of the airways, such as asthma or allergic rhinitis. As a consequence air filter systems of ventilation and air conditioning devices are required to meet very high standards to prevent, or at least lower the number of allergens and airborne germs entering the indoor air. Still, filter systems are merely classified for their separation rates using well-defined mineral test dust, while no appropriate sufficiently standardized test methods for bioaerosols exist. However, determined separation rates for mineral test particles of a certain size cannot simply be transferred to bioaerosols, as separation efficiency of particularly fine and respirable particles (< 10 microns) is dependent not only on their shape and particle diameter, but also defined by their density and physicochemical properties. For this reason, the OFI developed a test method, which directly enables a testing of filters and filter media for their separation rates on bioaerosols, as well as a classification of filters. Besides allergens from an intact or fractured tree or grass pollen, allergenic proteins bound to particulates, as well as allergenic fungal spores (e.g. Cladosporium cladosporioides), or bacteria can be used to classify filters regarding their separation rates. Allergens passing through the filter can then be detected by highly sensitive immunological assays (ELISA) or in the case of fungal spores by microbiological methods, which allow for the detection of even one single spore passing the filter. The test procedure, which is carried out in laboratory scale, was furthermore validated regarding its sufficiency to cover real life situations by upscaling using air conditioning devices showing great conformity in terms of separation rates. Additionally, a clinical study with allergy sufferers was performed to verify analytical results. Several different air conditioning filters from the car industry have been tested, showing significant differences in their separation rates.

Keywords: airborne germs, allergens, classification of filters, fine dust

Procedia PDF Downloads 253

Authors: Brock Liden, Eric Janowitz

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Introduction: Diabetic peripheral neuropathy (DPN) affects 70% of individuals with diabetes1. DPN causes a loss of protective sensation, which can lead to tissue damage and diabetic foot ulcer (DFU) formation2. These ulcers can result in infections and lower-extremity amputations of toes, the entire foot, and the lower leg. Even after a DFU is healed, recurrence is common, with 49% of DFU patients developing another ulcer within a year and 68% within 5 years3. This case series examines the use of sensory insoles and newly available plantar data (pressure, temperature, step count, adherence) and remote patient monitoring in patients at risk of DFU. Methods: Participants were provided with custom-made sensory insoles to monitor plantar pressure, temperature, step count, and daily use and were provided with real-time cues for pressure offloading as they went about their daily activities. The sensory insoles were used to track subject compliance, ulceration, and response to feedback from real-time alerts. Patients were remotely monitored by a qualified healthcare professional and were contacted when areas of concern were seen and provided coaching on reducing risk factors and overall support to improve foot health. Results: Of the 40 participants provided with the sensory insole system, 4 presented with a DFU. Based on flags generated from the available plantar data, patients were contacted by the remote monitor to address potential concerns. A standard clinical escalation protocol detailed when and how concerns should be escalated to the provider by the remote monitor. Upon escalation to the provider, patients were brought into the clinic as needed, allowing for any issues to be addressed before more serious complications might arise. Conclusion: This case series explores the use of innovative sensory technology to collect plantar data (pressure, temperature, step count, and adherence) for DFU detection and early intervention. The results from this case series suggest the importance of sensory technology and remote patient monitoring in providing proactive, preventative care for patients at risk of DFU. This robust plantar data, with the addition of remote patient monitoring, allow for patients to be seen in the clinic when concerns arise, giving providers the opportunity to intervene early and prevent more serious complications, such as wounds, from occurring.

Keywords: diabetic foot ulcer, DFU prevention, digital therapeutics, remote patient monitoring

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Authors: Umut Kokbas, Mustafa Nisari

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Immunoglobulins, also known as antibodies, are glycoprotein molecules produced by activated B cells that transform into plasma cells and result in them. Antibodies are critical molecules of the immune response to fight, which help the immune system specifically recognize and destroy antigens such as bacteria, viruses, and toxins. Immunoglobulin classes differ in their biological properties, structures, targets, functions, and distributions. Five major classes of antibodies have been identified in mammals: IgA, IgD, IgE, IgG, and IgM. Evaluation of the immunoglobulin isotype can provide a useful insight into the complex humoral immune response. Evaluation and knowledge of immunoglobulin structure and classes are also important for the selection and preparation of antibodies for immunoassays and other detection applications. The immunoglobulin test measures the level of certain immunoglobulins in the blood. IgA, IgG, and IgM are usually measured together. In this way, they can provide doctors with important information, especially regarding immune deficiency diseases. Hypogammaglobulinemia (HGG) is one of the main groups of primary immunodeficiency disorders. HGG is caused by various defects in B cell lineage or function that result in low levels of immunoglobulins in the bloodstream. This affects the body's immune response, causing a wide range of clinical features, from asymptomatic diseases to severe and recurrent infections, chronic inflammation and autoimmunity Transient infant hypogammaglobulinemia (THGI), IgM deficiency (IgMD), Bruton agammaglobulinemia, IgA deficiency (SIgAD) HGG samples are a few. Most patients can continue their normal lives by taking prophylactic antibiotics. However, patients with severe infections require intravenous immune serum globulin (IVIG) therapy. The IgE level may rise to fight off parasitic infections, as well as a sign that the body is overreacting to allergens. Also, since the immune response can vary with different antigens, measuring specific antibody levels also aids in the interpretation of the immune response after immunization or vaccination. Immune deficiencies usually occur in childhood. In Immunology and Allergy clinics, apart from the classical methods, it will be more useful in terms of diagnosis and follow-up of diseases, if it is fast, reliable and especially in childhood hypogammaglobulinemia, sampling from children with a method that is more convenient and uncomplicated. The antibodies were attached to the electrode surface via the poly hydroxyethyl methacrylamide cysteine nanopolymer. It was used to evaluate the anodic peak results obtained in the electrochemical study. According to the data obtained, immunoglobulin determination can be made with a biosensor. However, in further studies, it will be useful to develop a medical diagnostic kit with biomedical engineering and to increase its sensitivity.

Keywords: biosensor, immunosensor, immunoglobulin, infection

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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

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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

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Authors: Samson Munn, Virginia H. Kim, Huija Chen, Sean Maldonado, Michelle Kim, Paul Koscheski, Babak N. Kalantari, Gregory Eckel, Albert Lee

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In terms of screening mammography quality, neither the portion of reports that advise call-back imaging that should be bilateral versus unilateral nor how much the unilateral call-backs may appropriately diverge from 50–50 (left versus right) is known. Many factors may affect detection laterality: display arrangement, reflections preferentially striking one display location, hanging protocols, seating positions with respect to others and displays, visual field cuts, health, etc. The call-back bilateral fraction may reflect radiologist experience (not in our data) or confidence level. Thus, laterality and bilaterality of call-backs advised in screening mammography reports could be worthy quality metrics. Here, laterality data did not reveal a concern until drilling down to individuals. Bilateral screening mammogram report recommendations by five breast imaging, attending radiologists at Harbor-UCLA Medical Center (Torrance, California) 9/1/15--8/31/16 and 9/1/16--8/31/17 were retrospectively reviewed. Recommended call-backs for bilateral versus unilateral, and for left versus right, findings were counted. Chi-square (χ²) statistic was applied. Year 1: of 2,665 bilateral screening mammograms, reports of 556 (20.9%) recommended call-back, of which 99 (17.8% of the 556) were for bilateral findings. Of the 457 unilateral recommendations, 222 (48.6%) regarded the left breast. Year 2: of 2,106 bilateral screening mammograms, reports of 439 (20.8%) recommended call-back, of which 65 (14.8% of the 439) were for bilateral findings. Of the 374 unilateral recommendations, 182 (48.7%) regarded the left breast. Individual ranges of call-backs that were bilateral were 13.2–23.3%, 10.2–22.5%, and 13.6–17.9%, by year(s) 1, 2, and 1+2, respectively; these ranges were unrelated to experience level; the two-year mean was 15.8% (SD=1.9%). The lowest χ² p value of the group's sidedness disparities years 1, 2, and 1+2 was > 0.4. Regarding four individual radiologists, the lowest p value was 0.42. However, the fifth radiologist disfavored the left, with p values of 0.21, 0.19, and 0.07, respectively; that radiologist had the greatest number of years of experience. There was a concerning, 93% likelihood that bias against left breast findings evidenced by one of our radiologists was not random. Notably, very soon after the period under review, he retired, presented with leukemia, and died. We call for research to be done, particularly by large departments with many radiologists, of two possible, new, quality metrics in screening mammography: laterality and bilaterality. (Images, patient outcomes, report validity, and radiologist psychological confidence levels were not assessed. No intervention nor subsequent data collection was conducted. This uncomplicated collection of data and simple appraisal were not designed, nor had there been any intention to develop or contribute, to generalizable knowledge (per U.S. DHHS 45 CFR, part 46)).

Keywords: mammography, screening mammography, quality, quality metrics, laterality

Procedia PDF Downloads 162

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

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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

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Authors: M. Aboalazayem, Essam A. Gouda, Ahmed M. Moussa, Amr E. Flifl

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Field data collecting is considered one of the most difficult work due to the difficulty of accessing large zones such as large lakes. Also, it is well known that the cost of obtaining field data is very expensive. Remotely monitoring of lake water quality (WQ) provides an economically feasible approach comparing to field data collection. Researchers have shown that lake WQ can be properly monitored via Remote sensing (RS) analyses. Using satellite images as a method of WQ detection provides a realistic technique to measure quality parameters across huge areas. Landsat (LS) data provides full free access to often occurring and repeating satellite photos. This enables researchers to undertake large-scale temporal comparisons of parameters related to lake WQ. Satellite measurements have been extensively utilized to develop algorithms for predicting critical water quality parameters (WQPs). The goal of this paper is to use RS to derive WQ indicators in Aswan High Dam Reservoir (AHDR), which is considered Egypt's primary and strategic reservoir of freshwater. This study focuses on using Landsat8 (L-8) band surface reflectance (SR) observations to predict water-quality characteristics which are limited to Turbidity (TUR), total suspended solids (TSS), and chlorophyll-a (Chl-a). ArcGIS pro is used to retrieve L-8 SR data for the study region. Multiple linear regression analysis was used to derive new correlations between observed optical water-quality indicators in April and L-8 SR which were atmospherically corrected by values of various bands, band ratios, and or combinations. Field measurements taken in the month of May were used to validate WQP obtained from SR data of L-8 Operational Land Imager (OLI) satellite. The findings demonstrate a strong correlation between indicators of WQ and L-8 .For TUR, the best validation correlation with OLI SR bands blue, green, and red, were derived with high values of Coefficient of correlation (R2) and Root Mean Square Error (RMSE) equal 0.96 and 3.1 NTU, respectively. For TSS, Two equations were strongly correlated and verified with band ratios and combinations. A logarithm of the ratio of blue and green SR was determined to be the best performing model with values of R2 and RMSE equal to 0.9861 and 1.84 mg/l, respectively. For Chl-a, eight methods were presented for calculating its value within the study area. A mix of blue, red, shortwave infrared 1(SWR1) and panchromatic SR yielded the greatest validation results with values of R2 and RMSE equal 0.98 and 1.4 mg/l, respectively.

Keywords: remote sensing, landsat 8, nasser lake, water quality

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Authors: Milijana N. Miljkovic, Viktorija M. Dragojevic-Simic, Nemanja K. Rancic, Vesna M. Jacevic, Snezana B. Djordjevic, Momir M. Mikov, Aleksandra M. Kovacevic

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Itraconazole (ITZ) is a weak base and extremely lipophilic compound, with water solubility as a rate-limiting step in its absorption from the gastrointestinal tract. Its absolute bioavailability, about 55%, is maximal when its oral formulation, capsules, are taken immediately after a full meal. Peak plasma concentrations (Cmax) are reached within 2 to 5 hrs after their administration. ITZ undergoes extensive hepatic metabolism by human CYP3A4 isoenzyme and more than 30 different metabolites have been identified. One of the main ones is hydroxyitraconazole (HITZ), in which plasma concentrations are almost twice higher than those of ITZ. Gender differences in drug PK (Pharmacokinetics) have already been recognized, but variations in metabolism are believed to be their major cause. The aim of the study was to investigate the influence of gender on ITZ PK parameters after administration of oral capsule formulation, following 100 mg single dosing in healthy adult volunteers under fed conditions. The single-center, open-label PK study was performed. PK analyses included PK parameters obtained after a single 100 mg dose administration of itraconazole capsules to 48 females and 66 males. Blood samples were collected at pre-dose and up to 72.0 h after administration (1.0, 2.0, 3.0, 3.5, 4.0, 4.5, 5.0, 5.5, 6.0, 7.0, 9.0, 12.0, 24.0, 36.0 and 72.0 hrs). The calculated pharmacokinetic parameters, based on the plasma concentrations of itraconazole and hydroxyitraconazole, were Cmax, AUClast, and AUCtot. Plasma concentrations of ITZ and HITZ were determined using a validated liquid chromatographic method with mass spectrometric detection, while pharmacokinetic parameters were estimated using non-compartmental methods. The pharmacokinetic analyses were performed using Kinetica software version 5.0. The mean value of ITZ Cmaxmen was 74.79 ng/ml, and Cmaxwomen was 51.291 ng/ml (independent samples test; p = 0.005). Hydroxyitraconazole had a mean value of Cmaxmen 106.37 ng/ml, and the mean value Cmaxwomen was 70.05 ng/ml. Women had, on average, lower AUClast and Cmax than men. AUClastmen for ITZ was 736.02 ng/mL*h and AUClastwomen was 566.62 ng/mL*h, while AUClastmen for HITZ was 1154.80 was ng/mL*h and AUClastwomen for HITZ was 708.12 ng/mL*h (independent samples test; p = 0.033). The mean values of ITZ AUCtotmen were 884.73 ng/mL*h and AUCtotwomen was 685.10 ng/mL*h. AUCtotmen for HITZ was 1290.41 ng/mL*h, while AUCtotwomen for HIZT was 788.60 ng/mL*h (p < 0.001). The results could point out to lower oral bioavailability of ITZ in women, since values of Cmax, AUClast, and AUCtot of both ITZ and HITZ were significantly lower in women than in men, respectively. The reason may be higher expression and activity of CYP3A4 in women than in men, but there also may be differences in other PK parameters. High variability of both ITZ and HITZ concentrations in both genders confirmed that ITZ is a highly variable drug. Further examinations of its PK are needed to justify strategies for therapeutic drug monitoring in patients treated by this antifungal agent.

Keywords: itraconazole, gender, hydroxyitraconazole, pharmacokinetics

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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

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Authors: Diana M. Torres, Anngie K. Hernandez, Andrea Villareal, Magda R. Gomez, Sadao Kobayashi

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Colombian Garlic crops exhibited mild mosaic, yellow stripes, and deformation. This group of symptoms suggested a viral infection. Several viruses belonging to the genera Potyvirus, Carlavirus and Allexivirus are known to infect garlic and lower their yield worldwide, but in Colombia, there are no studies of viral infections in this crop, only leek yellow stripe virus (LYSV) has been reported to our best knowledge. In Colombia, there are no management strategies for viral diseases in garlic because of the lack of information about viral infections on this crop, which is reflected in (i) high prevalence of viral related symptoms in garlic fields and (ii) high dispersal rate. For these reasons, the purpose of the present study was to evaluate the viral status of garlic in Colombia, which can represent a major threat on garlic yield and quality for this country 55 symptomatic leaf samples were collected for virus detection by RT-PCR and mechanical inoculation. Total RNA isolated from infected samples were subjected to RT-PCR with primers 1-OYDV-G/2-OYDV-G for Onion yellow dwarf virus (OYDV) (expected size 774pb), 1LYSV/2LYSV for LYSV (expected size 1000pb), SLV 7044/SLV 8004 for Shallot latent virus (SLV) (expected size 960pb), GCL-N30/GCL-C40 for Garlic common latent virus (GCLV) (expected size 481pb) and EF1F/EF1R for internal control (expected size 358pb). GCLV, SLV, and LYSV were detected in infected samples; in 95.6% of the analyzed samples was detected at least one of the viruses. GCLV and SLV were detected in single infection with low prevalence (9.3% and 7.4%, respectively). Garlic generally becomes coinfected with several types of viruses. Four viral complexes were identified: three double infection (64% of analyzed samples) and one triple infection (15%). The most frequent viral complex was SLV + GCLV infecting 48.1% of the samples. The other double complexes identified had a prevalence of 7% (GCLV + LYSV and SLV + LYSV) and 5.6% of the samples were free from these viruses. Mechanical transmission experiments were set up using leaf tissues of collected samples from infected fields, different test plants were assessed to know the host range, but it was restricted to C. quinoa, confirming the presence of detected viruses which have limited host range and were detected in C. quinoa by RT-PCR. The results of molecular and biological tests confirm the presence of SLV, LYSV, and GCLV; this is the first report of SLV and LYSV in garlic plants in Colombia, which can represent a serious threat for this crop in this country.

Keywords: SLV, GCLV, LYSV, leek yellow stripe virus, Allium sativum

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Authors: Temmuz Gönç Şavran

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Migration is a multidimensional and health-related concept that has important consequences for both migrants and the host society. Due to past conflicts and poor living conditions that lead to migration, the dangerous and difficult journey, and the problems they face upon arrival in the destination country, migrants are at higher risk for poor health. Health is a human right, and all societies and communities, including migrant groups, must receive adequate health care. In addition, the health of migrants must be improved to protect the health of the host society and ensure social integration. The main determinants of health are employment, income, education, good housing, and adequate nutrition. It can be said that migrants are among the most vulnerable groups in society in these respects, and migrant health is negatively affected by this situation. Rigid immigration policies or financial constraints in destination countries, the complexity and bureaucracy of health systems, the low health literacy of migrant groups, and the inadequate provision of translation services in health facilities are among the other main factors affecting migrant health. Migrants are also at risk of stigma, exclusion, detection, and deportation when seeking medical care. Based on data from a qualitative study with a descriptive case study design, this paper aims to highlight and sociologically assess the health-related problems of international migrants in Eskisehir, Turkey. The sample consists of 30 international migrants living in Eskisehir, two-thirds of whom are from Syria, Iraq, Afghanistan, and Pakistan. Those who are citizens of the Republic of Turkey are excluded from the study; otherwise, the legal status of the participants is not considered in the selection of the sample. This makes it possible to distinguish the different needs and problems of subgroups and to consider migrant health as a comprehensive concept. The research is supported by Anadolu University in Eskisehir, and data will be collected through semi-structured interviews between November 2022 and February 2023. With holistic sociology of health approach, this study considers migrant health as a comprehensive sociological concept. It aims to reveal the health-related resources and needs of the international migrant groups living in the center of Eskisehir, the problems they encounter in meeting these needs, and the strategies they use to solve these problems. The results are expected to show that the health of migrants is not only influenced by legislation but is shaped by many processes, from housing conditions to cultural habits. It is expected that the results will also raise awareness of discrimination, exclusion, marginalization, and hate speech in migrants’ access to health services.

Keywords: migrant health, sociology of health, sociology of migration, Turkey, refugees

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Authors: Elene Zhuravliova, Tamar Barbakadze, Irina Kalandadze, Elnari Zaalishvili, Lali Shanshiashvili, David Mikeladze

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Background: Multiple sclerosis (MS) is an inflammatory, neurodegenerative disease, which is accompanied by demyelination and autoimmune response to myelin proteins. Among post-translational modifications, which mediate the modulation of inflammatory pathways during MS, methylation is the main one. The methylation of DNA, also amino acids lysine and arginine, occurs in the cell. It was found that decreased trans-methylation is associated with neuroinflammatory diseases. Therefore, abnormal regulation of the methyl cycle could induce demyelination through the action on PAD (peptidyl-arginine-deiminase) gene promoter. PAD takes part in protein citrullination and targets myelin basic protein (MBP), which is affected during demyelination. To determine whether MBP charge isomers are changing the methyl cycle, we have estimated the concentrations of methyl cycle metabolites in MBP-activated primary astrocytes and oligodendrocytes. For this purpose, the action of the citrullinated MBP- C8 and the most cationic MBP-C1 isomers on the primary cells were investigated. Methods: Primary oligodendrocyte and astrocyte cell cultures were prepared from whole brains of 2-day-old Wistar rats. The methyl cycle metabolites, including homocysteine, S-adenosylmethionine (SAM), and S-adenosylhomocysteine (SAH), were estimated by HPLC analysis using fluorescence detection and prior derivatization. Results: We found that the action of MBP-C8 and MBP-C1 induces a decrease in the concentration of both methyl cycle metabolites, S-adenosylmethionine (SAM) and S-adenosylhomocysteine (SAH), in astrocytes compared to the control cells. As for oligodendrocytes, the concentration of SAM was increased by the addition of MBP-C1, while MBP-C8 has no significant effect. As for SAH, its concentration was increased compared to the control cells by the action of both MBP-C1 and MBP-C8. A significant increase in homocysteine concentration was observed by the action of the MBP-C8 isomer in both oligodendrocytes and astrocytes. Conclusion: These data suggest that MBP charge isomers change the concentration of methyl cycle metabolites. MBP-C8 citrullinated isomer causes elevation of homocysteine in astrocytes and oligodendrocytes, which may be the reason for decreased astrocyte proliferation and increased oligodendrocyte cell death which takes place in neurodegenerative processes. Elevated homocysteine levels and subsequent abnormal regulation of methyl cycles in oligodendrocytes possibly change the methylation of DNA that activates PAD gene promoter and induces the synthesis of PAD, which in turn provokes the process of citrullination, which is the accompanying process of demyelination. Acknowledgment: This research was supported by the SRNSF Georgia RF17_534 grant.

Keywords: myelin basic protein, astrocytes, methyl cycle metabolites, homocysteine, oligodendrocytes

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Authors: Jeh Patel, Chandrahas Paidi, Ahmed Hambaba

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Traditional methods for analyzing athlete movement often lack the detail and immediacy required for optimal training. This project aims to address this limitation by developing a Real-time human pose estimation system specifically designed to enhance athlete training across various sports. This system leverages the power of convolutional neural networks (CNNs) to provide a comprehensive and immediate analysis of an athlete’s movement patterns during training sessions. The core architecture utilizes dilated convolutions to capture crucial long-range dependencies within video frames. Combining this with the robust encoder-decoder architecture to further refine pose estimation accuracy. This capability is essential for precise joint localization across the diverse range of athletic poses encountered in different sports. Furthermore, by quantifying movement efficiency, power output, and range of motion, the system provides data-driven insights that can be used to optimize training programs. Pose estimation data analysis can also be used to develop personalized training plans that target specific weaknesses identified in an athlete’s movement patterns. To overcome the limitations posed by outdoor environments, the project employs strategies such as multi-camera configurations or depth sensing techniques. These approaches can enhance pose estimation accuracy in challenging lighting and occlusion scenarios, where pose estimation accuracy in challenging lighting and occlusion scenarios. A dataset is collected From the labs of Martin Luther King at San Jose State University. The system is evaluated through a series of tests that measure its efficiency and accuracy in real-world scenarios. Results indicate a high level of precision in recognizing different poses, substantiating the potential of this technology in practical applications. Challenges such as enhancing the system’s ability to operate in varied environmental conditions and further expanding the dataset for training were identified and discussed. Future work will refine the model’s adaptability and incorporate haptic feedback to enhance the interactivity and richness of the user experience. This project demonstrates the feasibility of an advanced pose detection model and lays the groundwork for future innovations in assistive enhancement technologies.

Keywords: computer vision, deep learning, human pose estimation, U-NET, CNN

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Authors: Lakmini Wijesooriya, Vicki Chalker, Jessica Day, Priyantha Perera, N. P. Sunil-Chandra

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M. pneumoniae has been identified as an etiology for exacerbation of asthma (EQA), although viruses play a major role in EOA. M. pneumoniae infection is treated empirically with macrolides, and its antibiotic sensitivity is not detected routinely. Characterization of the organism by genotyping and determination of macrolide resistance is important epidemiologically as it guides the empiric antibiotic treatment. To date, there is no such characterization of M. pneumoniae performed in Sri Lanka. The present study describes the characterization of M. pneumoniae detected from a child with EOA following a screening of 100 children with EOA. Of the hundred children with EOA, M. pneumoniae was identified only in one child by Real-Time polymerase chain reaction (PCR) test for identifying the community-acquired respiratory distress syndrome (CARDS) toxin nucleotide sequences. The M. pneumoniae identified from this patient underwent detection of macrolide resistance via conventional PCR, amplifying and sequencing the region of the 23S rDNA gene that contains single nucleotide polymorphisms that confer resistance. Genotyping of the isolate was performed via nested Multilocus Sequence Typing (MLST) in which eight (8) housekeeping genes (ppa, pgm, gyrB, gmk, glyA, atpA, arcC, and adk) were amplified via nested PCR followed by gene sequencing and analysis. As per MLST analysis, the M. pneumoniae was identified as sequence type 14 (ST14), and no mutations that confer resistance were detected. Resistance to macrolides in M. pneumoniae is an increasing problem globally. Establishing surveillance systems is the key to informing local prescriptions. In the absence of local surveillance data, antibiotics are started empirically. If the relevant microbiological samples are not obtained before antibiotic therapy, as in most occasions in children, the course of antibiotic is completed without a microbiological diagnosis. This happens more frequently in therapy for M. pneumoniae which is treated with a macrolide in most patients. Hence, it is important to understand the macrolide sensitivity of M. pneumoniae in the setting. The M. pneumoniae detected in the present study was macrolide sensitive. Further studies are needed to examine a larger dataset in Sri Lanka to determine macrolide resistance levels to inform the use of macrolides in children with EOA. The MLST type varies in different geographical settings, and it also provides a clue to the existence of macrolide resistance. The present study enhances the database of the global distribution of different genotypes of M. pneumoniae as this is the first such characterization performed with the increased number of samples to determine macrolide resistance level in Sri Lanka. M. pneumoniae detected from a child with exacerbation of asthma in Sri Lanka was characterized as ST14 by MLST and no mutations that confer resistance were detected.

Keywords: mycoplasma pneumoniae, Sri Lanka, characterization, macrolide resistance

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Authors: Ibrahim Umar, Ashiru Rajab, Sumayya Chindo, Emmanuel Olashore

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INTRODUCTION: Kano is the most populous state in Nigeria and one of the two states with the highest TB burden in the country. The state notifies an average of 8,000+ TB cases quarterly and has the highest yearly notification of all the states in Nigeria from 2020 to 2022. The contribution of the state TB program to the National TB notification varies from 9% to 10% quarterly between the first quarter of 2022 and second quarter of 2023. The Kano State TB Situation Room is an innovative platform for timely data collection, collation and analysis for informed decision in health system. During the 2023 second National TB Testing week (NTBTW) Kano TB program aimed at early TB detection, prevention and treatment. The state TB Situation room provided avenue to the state for coordination and surveillance through real time data reporting, review, analysis and use during the NTBTW. OBJECTIVES: To assess the role of innovative information and communication technology platform for real-time TB reporting during second National TB Testing week in Nigeria 2023. To showcase the NTBTW data cascade analysis using TSR as innovative ICT platform. METHODOLOGY: The State TB deployed a real-time virtual dashboard for NTBTW reporting, analysis and feedback. A data room team was set up who received realtime data using google link. Data received was analyzed using power BI analytic tool with statistical alpha level of significance of <0.05. RESULTS: At the end of the week-long activity and using the real-time dashboard with onsite mentorship of the field workers, the state TB program was able to screen a total of 52,054 people were screened for TB from 72,112 individuals eligible for screening (72% screening rate). A total of 9,910 presumptive TB clients were identified and evaluated for TB leading to diagnosis of 445 TB patients with TB (5% yield from presumptives) and placement of 435 TB patients on treatment (98% percentage enrolment). CONCLUSION: The TB Situation Room (TBSR) has been a great asset to Kano State TB Control Program in meeting up with the growing demand for timely data reporting in TB and other global health responses. The use of real time surveillance data during the 2023 NTBTW has in no small measure improved the TB response and feedback in Kano State. Scaling up this intervention to other disease areas, states and nations is a positive step in the right direction towards global TB eradication.

Keywords: tuberculosis (tb), national tb testing week (ntbtw), tb situation rom (tsr), information communication technology (ict)

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Authors: Ciara Pienaar, J. W. Odendaal, J. Joubert, J. C. Smit

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Radar cross section (RCS) of dielectric objects play an important role in many applications, such as low observability technology development, drone detection, and monitoring as well as coastal surveillance. Various materials are used to construct the targets of interest such as metal, wood, composite materials, radar absorbent materials, and other dielectrics. Since simulated datasets are increasingly being used to supplement infield measurements, as it is more cost effective and a larger variety of targets can be simulated, it is important to have a high level of confidence in the predicted results. Confidence can be attained through validation. Various computational electromagnetic (CEM) methods are capable of predicting the RCS of dielectric targets. This study will extend previous studies by validating full-wave and asymptotic RCS simulations of dielectric targets with measured data. The paper will provide measured RCS data of a number of canonical dielectric targets exhibiting different material properties. As stated previously, these measurements are used to validate numerous CEM methods. The dielectric properties are accurately characterized to reduce the uncertainties in the simulations. Finally, an analysis of the sensitivity of oblique and normal incidence scattering predictions to material characteristics is also presented. In this paper, the ability of several CEM methods, including method of moments (MoM), and physical optics (PO), to calculate the RCS of dielectrics were validated with measured data. A few dielectrics, exhibiting different material properties, were selected and several canonical targets, such as flat plates and cylinders, were manufactured. The RCS of these dielectric targets were measured in a compact range at the University of Pretoria, South Africa, over a frequency range of 2 to 18 GHz and a 360° azimuth angle sweep. This study also investigated the effect of slight variations in the material properties on the calculated RCS results, by varying the material properties within a realistic tolerance range and comparing the calculated RCS results. Interesting measured and simulated results have been obtained. Large discrepancies were observed between the different methods as well as the measured data. It was also observed that the accuracy of the RCS data of the dielectrics can be frequency and angle dependent. The simulated RCS for some of these materials also exhibit high sensitivity to variations in the material properties. Comparison graphs between the measured and simulation RCS datasets will be presented and the validation thereof will be discussed. Finally, the effect that small tolerances in the material properties have on the calculated RCS results will be shown. Thus the importance of accurate dielectric material properties for validation purposes will be discussed.

Keywords: asymptotic, CEM, dielectric scattering, full-wave, measurements, radar cross section, validation

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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

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Authors: Rene Hellmuth

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Factory planning has the task of designing products, plants, processes, organization, areas, and the construction of a factory. The requirements for factory planning and the building of a factory have changed in recent years. Regular restructuring of the factory building is becoming more important in order to maintain the competitiveness of a factory. Restrictions in new areas, shorter life cycles of product and production technology as well as a VUCA world (Volatility, Uncertainty, Complexity & Ambiguity) lead to more frequent restructuring measures within a factory. A building information model (BIM) is the planning basis for rebuilding measures and becomes an indispensable data repository to be able to react quickly to changes. Use as a planning basis for restructuring measures in factories only succeeds if the BIM model has adequate data quality. Under this aspect and the industrial requirement, three data quality factors are particularly important for this paper regarding the BIM model: up-to-dateness, completeness, and correctness. The research question is: how can a BIM model be kept up to date with required data quality and which visualization techniques can be applied in a short period of time on the construction site during conversion measures? An as-is analysis is made of how BIM models and digital factory models (including laser scans) are currently being kept up to date. Industrial companies are interviewed, and expert interviews are conducted. Subsequently, the results are evaluated, and a procedure conceived how cost-effective and timesaving updating processes can be carried out. The availability of low-cost hardware and the simplicity of the process are of importance to enable service personnel from facility mnagement to keep digital factory models (BIM models and laser scans) up to date. The approach includes the detection of changes to the building, the recording of the changing area, and the insertion into the overall digital twin. Finally, an overview of the possibilities for visualizations suitable for construction sites is compiled. An augmented reality application is created based on an updated BIM model of a factory and installed on a tablet. Conversion scenarios with costs and time expenditure are displayed. A user interface is designed in such a way that all relevant conversion information is available at a glance for the respective conversion scenario. A total of three essential research results are achieved: As-is analysis of current update processes for BIM models and laser scans, development of a time-saving and cost-effective update process and the conception and implementation of an augmented reality solution for BIM models suitable for construction sites.

Keywords: building information modeling, digital factory model, factory planning, restructuring

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Authors: Bibiana Matilde Bernal Gómez, Manuel Salvador Rodríguez Susa, Mildred Fernanda Lemus Perez

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In chlorinated drinking water, Haloacetic acids have been identified and are classified as disinfection byproducts originating from reaction between natural organic matter and/or bromide ions in water sources. These byproducts can be generated through a variety of chemical and pharmaceutical processes. The term ‘Total Haloacetic Acids’ (THAAs) is used to describe the cumulative concentration of dichloroacetic acid, trichloroacetic acid, monochloroacetic acid, monobromoacetic acid, and dibromoacetic acid in water samples, which are usually measured to evaluate water quality. Chronic presence of these acids in drinking water has a risk of cancer in humans. The detection of THAAs for the first time in 15 municipalities of Boyacá was accomplished in 2023. Aim is to describe the correlation between the levels of THAAs and digestive cancer in Tunja, a city in Colombia with higher rates of digestive cancer and to compare the risk across 15 towns, taking into account factors such as water quality. A research project was conducted with the aim of comparing water sources based on the geographical features of the town, describing the disinfection process in 15 municipalities, and exploring physical properties such as water temperature and pH level. The project also involved a study of contact time based on habits documented through a survey, and a comparison of socioeconomic factors and lifestyle, in order to assess the personal risk of exposure. Data on the levels of THAAs were obtained after characterizing the water quality in urban sectors in eight months of 2022. This, based on the protocol described in the Stage 2 DBP of the United States Environmental Protection Agency (USEPA) from 2006, which takes into account the size of the population being supplied. A cancer risk assessment was conducted to evaluate the likelihood of an individual developing cancer due to exposure to pollutants THAAs. The assessment considered exposure methods like oral ingestion, skin absorption, and inhalation. The chronic daily intake (CDI) for these exposure routes was calculated using specific equations. The lifetime cancer risk (LCR) was then determined by adding the cancer risks from the three exposure routes for each HAA. The risk assessment process involved four phases: exposure assessment, toxicity evaluation, data gathering and analysis, and risk definition and management. The results conclude that there is a cumulative higher risk of digestive cancer due to THAAs exposure in drinking water.

Keywords: haloacetic acids, drinking water, water quality, cancer risk assessment

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Authors: Angela T. H. Kwan, Saman Arfaie, Joseph Therriault, Zahra Azizi, Firoza Z. Lussier, Cecile Tissot, Mira Chamoun, Gleb Bezgin, Stijn Servaes, Jenna Stevenon, Nesrine Rahmouni, Vanessa Pallen, Serge Gauthier, Pedro Rosa-Neto

Abstract:

Alzheimer’s disease (AD) can be detected in living people using in vivo biomarkers of amyloid-β (Aβ) and tau, even in the absence of cognitive impairment during the preclinical phase. [¹⁸F]-MK-6420 is a high affinity positron emission tomography (PET) tracer that quantifies tau neurofibrillary tangles, but its ability to predict cognitive changes associated with early AD symptoms, such as memory decline, is unclear. Here, we assess the prognostic accuracy of baseline [18F]-MK-6420 tau PET for predicting longitudinal memory decline in asymptomatic elderly individuals. In a longitudinal observational study, we evaluated a cohort of cognitively normal elderly participants (n = 111) from the Translational Biomarkers in Aging and Dementia (TRIAD) study (data collected between October 2017 and July 2020, with a follow-up period of 12 months). All participants underwent tau PET with [¹⁸F]-MK-6420 and Aβ PET with [¹⁸F]-AZD-4694. The exclusion criteria included the presence of head trauma, stroke, or other neurological disorders. There were 111 eligible participants who were chosen based on the availability of Aβ PET, tau PET, magnetic resonance imaging (MRI), and APOEε4 genotyping. Among these participants, the mean (SD) age was 70.1 (8.6) years; 20 (18%) were tau PET positive, and 71 of 111 (63.9%) were women. A significant association between baseline Braak I-II [¹⁸F]-MK-6240 SUVR positivity and change in composite memory score was observed at the 12-month follow-up, after correcting for age, sex, and years of education (Logical Memory and RAVLT, standardized beta = -0.52 (-0.82-0.21), p < 0.001, for dichotomized tau PET and -1.22 (-1.84-(-0.61)), p < 0.0001, for continuous tau PET). Moderate cognitive decline was observed for A+T+ over the follow-up period, whereas no significant change was observed for A-T+, A+T-, and A-T-, though it should be noted that the A-T+ group was small.Our results indicate that baseline tau neurofibrillary tangle pathology is associated with longitudinal changes in memory function, supporting the use of [¹⁸F]-MK-6420 PET to predict the likelihood of asymptomatic elderly individuals experiencing future memory decline. Overall, [¹⁸F]-MK-6420 PET is a promising tool for predicting memory decline in older adults without cognitive impairment at baseline. This is of critical relevance as the field is shifting towards a biological model of AD defined by the aggregation of pathologic tau. Therefore, early detection of tau pathology using [¹⁸F]-MK-6420 PET provides us with the hope that living patients with AD may be diagnosed during the preclinical phase before it is too late.

Keywords: alzheimer’s disease, braak I-II, in vivo biomarkers, memory, PET, tau

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