Search results for: accurate forecast

Authors: Hu Zhenxing, Gao Jianxin

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Stereo-based digital image correlation (also referred to as three-dimensional (3D) digital image correlation (DIC)) is a technique for both 3D shape and surface deformation measurement of a component, which has found increasing applications in academia and industries. The accuracy of the reconstructed coordinate depends on many factors such as configuration of the setup, stereo-matching, distortion, etc. Most of these factors have been investigated in literature. For instance, the configuration of a binocular vision system determines the systematic errors. The stereo-matching errors depend on the speckle quality and the matching algorithm, which can only be controlled in a limited range. And the distortion is non-linear particularly in a complex imaging acquisition system. Thus, the distortion correction should be carefully considered. Moreover, the distortion function is difficult to formulate in a complex imaging acquisition system using conventional models in such cases where microscopes and other complex lenses are involved. The errors of the distortion correction will propagate to the reconstructed 3D coordinates. To address the problem, an accurate mapping method based on 2D B-spline functions is proposed in this study. The mapping functions are used to convert the distorted coordinates into an ideal plane without distortions. This approach is suitable for any image acquisition distortion models. It is used as a prior process to convert the distorted coordinate to an ideal position, which enables the camera to conform to the pin-hole model. A procedure of this approach is presented for stereo-based DIC. Using 3D speckle image generation, numerical simulations were carried out to compare the accuracy of both the conventional method and the proposed approach.

Keywords: distortion, stereo-based digital image correlation, b-spline, 3D, 2D

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Authors: Ananya Ananya, Karthik Rao

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Accurate segmentation of knee cartilage in 3-D magnetic resonance (MR) images for quantitative assessment of volume is crucial for studying and diagnosing osteoarthritis (OA) of the knee, one of the major causes of disability in elderly people. Radiologists generally perform this task in slice-by-slice manner taking 15-20 minutes per 3D image, and lead to high inter and intra observer variability. Hence automatic methods for knee cartilage segmentation are desirable and are an active field of research. This paper presents design and experimental evaluation of 2D convolutional neural networks based fully automated methods for knee cartilage segmentation in 3D MRI. The architectures are validated based on 40 test images and 60 training images from SKI10 dataset. The proposed methods segment 2D slices one by one, which are then combined to give segmentation for whole 3D images. Proposed methods are modified versions of U-net and dilated convolutions, consisting of a single step that segments the given image to 5 labels: background, femoral cartilage, tibia cartilage, femoral bone and tibia bone; cartilages being the primary components of interest. U-net consists of a contracting path and an expanding path, to capture context and localization respectively. Dilated convolutions lead to an exponential expansion of receptive field with only a linear increase in a number of parameters. A combination of modified U-net and dilated convolutions has also been explored. These architectures segment one 3D image in 8 – 10 seconds giving average volumetric Dice Score Coefficients (DSC) of 0.950 - 0.962 for femoral cartilage and 0.951 - 0.966 for tibia cartilage, reference being the manual segmentation.

Keywords: convolutional neural networks, dilated convolutions, 3 dimensional, fully automated, knee cartilage, MRI, segmentation, U-net

Procedia PDF Downloads 253

Authors: Andy Zhang, Awnik Roy, Trevor B. Chen, Bibik Oleksandar, Subodh Adhikari, Paul S. Hsu

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The durability of a combustor in gas-turbine engines is a strong function of its component temperatures and requires good control of these temperatures. Since the temperature of combustion gases frequently exceeds the melting point of the combustion liner walls, an efficient air-cooling system with optimized flow rates of cooling air is significantly important to elongate the lifetime of liner walls. To determine the effectiveness of the air-cooling system, accurate two-dimensional (2D) surface temperature measurement of combustor liner walls is crucial for advanced engine development. Traditional diagnostic techniques for temperature measurement in this application include the rmocouples, thermal wall paints, pyrometry, and phosphors. They have shown some disadvantages, including being intrusive and affecting local flame/flow dynamics, potential flame quenching, and physical damages to instrumentation due to harsh environments inside the combustor and strong optical interference from strong combustion emission in UV-Mid IR wavelength. To overcome these drawbacks, a compact and small borescope long-wave-infrared (LWIR) sensor is developed to achieve 2D high-spatial resolution, high-fidelity thermal imaging of 2D surface temperature in gas-turbine engines, providing the desired engine component temperature distribution. The compactLWIRborescope sensor makes it feasible to promote the durability of a combustor in gas-turbine engines and, furthermore, to develop more advanced gas-turbine engines.

Keywords: borescope, engine, low-wave-infrared, sensor

Procedia PDF Downloads 124

Authors: A. Maciejak, M. Kiliszek, G. Opolski, D. Tulacz, A. Segiet, K. Matlak, S. Dobrzycki, G. Sygitowicz, B. Burzynska, M. Gora

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Introduction and aims: Acute myocardial infarction (AMI) is one of the most severe cardiovascular diseases affecting millions of patients each year worldwide. An early and accurate diagnosis of AMI is essential for optimal treatment. Therefore, new approaches that can complement and improve current strategies for AMI diagnosis are urgently needed. Recent studies have revealed the presence of stable circulating myocardial-derived microRNAs (miRNAs) in human peripheral blood, suggesting that such miRNAs could serve as potential biomarkers of infarction. The present study aimed to identify differentially expressed circulating miRNAs in ST-segment elevation myocardial infarction (STEMI) patients. Materials and methods: miRNA expression profile analysis was performed using Exiqon Serum/Plasma Focus microRNA PCR panel in plasma samples of n=16 patients on the first day of AMI (admission) and in samples from the same patients collected six months after AMI. Selected miRNAs were validated by RT-qPCR using serum samples from an independent set of n=14 AMI patients. Results: The profiling study identified 46 species of plasma miRNAs that were differentially expressed (p < 0.05) on admission compared to six months after AMI. The validation in the independent group of patients confirmed that miR-133b and miR-22-5p were significantly up-regulated upon AMI. Conclusions: Our results suggest that miRNA expression profiling provides better understanding of the changes that occur in the acute phase of MI in the myocardium and could be useful in determination of the potential role of extracellular miRNAs as paracrine signaling molecules. miR-22-5p represents a novel promising biomarker for the diagnosis of acute myocardial infarction.

Keywords: acute myocardial infarction, circulating microRNAs, microRNA expression profiling, miR-22-5p

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Authors: Harshit Mittal, Neeraj Garg

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Hand symbol recognition is a pivotal component in the domain of computer vision, with far-reaching applications spanning sign language interpretation, human-computer interaction, and accessibility. This research paper discusses the approach with the integration of the Canny Edge algorithm and convolutional neural network. The significance of this study lies in its potential to enhance communication and accessibility for individuals with hearing impairments or those engaged in gesture-based interactions with technology. In the experiment mentioned, the data is manually collected by the authors from the webcam using Python codes, to increase the dataset augmentation, is applied to original images, which makes the model more compatible and advanced. Further, the dataset of about 6000 coloured images distributed equally in 5 classes (i.e., 1, 2, 3, 4, 5) are pre-processed first to gray images and then by the Canny Edge algorithm with threshold 1 and 2 as 150 each. After successful data building, this data is trained on the Convolutional Neural Network model, giving accuracy: 0.97834, precision: 0.97841, recall: 0.9783, and F1 score: 0.97832. For user purposes, a block of codes is built in Python to enable a window for hand symbol recognition. This research, at its core, seeks to advance the field of computer vision by providing an advanced perspective on hand sign recognition. By leveraging the capabilities of the Canny Edge algorithm and convolutional neural network, this study contributes to the ongoing efforts to create more accurate, efficient, and accessible solutions for individuals with diverse communication needs.

Keywords: hand symbol recognition, computer vision, Canny edge algorithm, convolutional neural network

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Authors: Jigg L. Pelayo, Ricardo G. Villar, Einstine M. Opiso

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The advances of aerial laser scanning in the Philippines has open-up entire fields of research in remote sensing and machine vision aspire to provide accurate timely information for the government and the public. Rapid mapping of polygonal roads and roof boundaries is one of its utilization offering application to disaster risk reduction, mitigation and development. The study uses low density LiDAR data and high resolution aerial imagery through object-oriented approach considering the theoretical concept of data analysis subjected to machine learning algorithm in minimizing the constraints of feature extraction. Since separating one class from another in distinct regions of a multi-dimensional feature-space, non-trivial computing for fitting distribution were implemented to formulate the learned ideal hyperplane. Generating customized hybrid feature which were then used in improving the classifier findings. Supplemental algorithms for filtering and reshaping object features are develop in the rule set for enhancing the final product. Several advantages in terms of simplicity, applicability, and process transferability is noticeable in the methodology. The algorithm was tested in the different random locations of Misamis Oriental province in the Philippines demonstrating robust performance in the overall accuracy with greater than 89% and potential to semi-automation. The extracted results will become a vital requirement for decision makers, urban planners and even the commercial sector in various assessment processes.

Keywords: feature extraction, machine learning, OBIA, remote sensing

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Authors: Srinivas Peri, Siva Abhishek Sirivella, Tejaswini Kallakuri, Uzair Ahmad

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Effective traffic management and infrastructure planning are crucial for the development of smart cities and intelligent transportation systems. This study addresses the challenge of data scarcity by generating realistic synthetic traffic data using the PeMS-Bay dataset, improving the accuracy and reliability of predictive modeling. Advanced synthetic data generation techniques, including TimeGAN, GaussianCopula, and PAR Synthesizer, are employed to produce synthetic data that replicates the statistical and structural characteristics of real-world traffic. Future integration of Spatial-Temporal Generative Adversarial Networks (ST-GAN) is planned to capture both spatial and temporal correlations, further improving data quality and realism. The performance of each synthetic data generation model is evaluated against real-world data to identify the best models for accurately replicating traffic patterns. Long Short-Term Memory (LSTM) networks are utilized to model and predict complex temporal dependencies within traffic patterns. This comprehensive approach aims to pinpoint areas with low vehicle counts, uncover underlying traffic issues, and inform targeted infrastructure interventions. By combining GAN-based synthetic data generation with LSTM-based traffic modeling, this study supports data-driven decision-making that enhances urban mobility, safety, and the overall efficiency of city planning initiatives.

Keywords: GAN, long short-term memory, synthetic data generation, traffic management

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Authors: Vasile Caunii, Andreea D. Serban, Mihaela Ivancia

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The paper presents a new approach in terms of the circular economy of technologies for feeding and aeration of accumulations and water basins for fish farming and aquaculture. Because fish is and will be one of the main foods on the planet, the use of bio-eco-technologies is a priority for all producers. The technologies proposed in the paper want to reduce by a substantial percentage the costs of operation of ponds and water accumulation, using non-polluting technologies with minimal impact on the environment. The paper proposes two innovative, intelligent systems, fully automated that use a common platform, completely eco-friendly. One system is intended to aerate the water of the fish pond, and the second is intended to feed the fish by dispersing an optimal amount of fodder, depending on population size, age and habits. Both systems use a floating platform, regenerative energy sources, are equipped with intelligent and innovative systems, and in addition to fully automated operation, significantly reduce the costs of aerating water accumulations (natural or artificial) and feeding fish. The intelligent system used for feeding, in addition, to reduce operating costs, optimizes the amount of food, thus preventing water pollution and the development of bacteria, microorganisms. The advantages of the systems are: increasing the yield of fish production, these are green installations, with zero pollutant emissions, can be arranged anywhere on the water surface, depending on the user's needs, can operate autonomously or remotely controlled, if there is a component failure, the system provides the operator with accurate data on the issue, significantly reducing maintenance costs, transmit data about the water physical and chemical parameters.

Keywords: bio-eco-technologies, economy, environment, fish

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Authors: Xavier Bertrand, Alexandre Cayrel

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The early phase of an aircraft development is instrumental as it really drives the potential of a new concept. Any weakness in the high-level design (wing planform, moveable surfaces layout etc.) will be extremely difficult and expensive to recover later in the aircraft development process. Aerodynamic evaluation in this very early development phase is driven by two main criteria: a short lead-time to allow quick iterations of the geometrical design, and a high quality of the calculations to get an accurate & reliable assessment of the current status. These two criteria are usually quite contradictory. Actually, short lead time of a couple of hours from end-to-end can be obtained with very simple tools (semi-empirical methods for instance) although their accuracy is limited, whereas higher quality calculations require heavier/more complex tools, which obviously need more complex inputs as well, and a significantly longer lead time. At this point, the choice has to be done between accuracy and lead-time. A brand new approach has been developed within Airbus, aiming at obtaining quickly high quality evaluations of the aerodynamic of an aircraft. This methodology is based on a joint use of Surrogate Modelling and a lifting line code. The Surrogate Modelling is used to get the wing sections characteristics (e.g. lift coefficient vs. angle of attack), whatever the airfoil geometry, the status of the moveable surfaces (aileron/spoilers) or the high-lift devices deployment. From these characteristics, the lifting line code is used to get the 3D effects on the wing whatever the flow conditions (low/high Mach numbers etc.). This methodology has been applied successfully to a concept of medium range aircraft.

Keywords: aerodynamics, lifting line, surrogate model, CFD

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Authors: Yogesh Kumar Singh, T. S. Murugesh Prabhu, Upasana Dutta, Girishchandra Yendargaye, Rahul Yadav, Rohini Gopinath Kale, Binay Kumar, Manoj Khare

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The Indian subcontinent is severely affected by floods that cause intense irreversible devastation to crops and livelihoods. With increased incidences of floods and their related catastrophes, an Early Warning System for Flood Prediction and an efficient Flood Management System for the river basins of India is a must. Accurately modeled hydrological conditions and a web-based early warning system may significantly reduce economic losses incurred due to floods and enable end users to issue advisories with better lead time. This study describes the design and development of an EWS-FP using advanced computational tools/methods, viz. High-Performance Computing (HPC), Remote Sensing, GIS technologies, and open-source tools for the Mahanadi River Basin of India. The flood prediction is based on a robust 2D hydrodynamic model, which solves shallow water equations using the finite volume method. Considering the complexity of the hydrological modeling and the size of the basins in India, it is always a tug of war between better forecast lead time and optimal resolution at which the simulations are to be run. High-performance computing technology provides a good computational means to overcome this issue for the construction of national-level or basin-level flash flood warning systems having a high resolution at local-level warning analysis with a better lead time. High-performance computers with capacities at the order of teraflops and petaflops prove useful while running simulations on such big areas at optimum resolutions. In this study, a free and open-source, HPC-based 2-D hydrodynamic model, with the capability to simulate rainfall run-off, river routing, and tidal forcing, is used. The model was tested for a part of the Mahanadi River Basin (Mahanadi Delta) with actual and predicted discharge, rainfall, and tide data. The simulation time was reduced from 8 hrs to 3 hrs by increasing CPU nodes from 45 to 135, which shows good scalability and performance enhancement. The simulated flood inundation spread and stage were compared with SAR data and CWC Observed Gauge data, respectively. The system shows good accuracy and better lead time suitable for flood forecasting in near-real-time. To disseminate warning to the end user, a network-enabled solution is developed using open-source software. The system has query-based flood damage assessment modules with outputs in the form of spatial maps and statistical databases. System effectively facilitates the management of post-disaster activities caused due to floods, like displaying spatial maps of the area affected, inundated roads, etc., and maintains a steady flow of information at all levels with different access rights depending upon the criticality of the information. It is designed to facilitate users in managing information related to flooding during critical flood seasons and analyzing the extent of the damage.

Keywords: flood, modeling, HPC, FOSS

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Authors: Mohammadamin Esmaeilzadehazimi, Morteza Shayan Arani, Mohammad Toorani, Aouni Lakis

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This study uses a hybrid finite element method to predict the dynamic behavior of both fully and partially-filled truncated conical shells stiffened with ring stiffeners. The method combines classical shell theory and the finite element method, and employs displacement functions derived from exact solutions of Sanders' shell equilibrium equations for conical shells. The shell-fluid interface is analyzed by utilizing the velocity potential, Bernoulli's equation, and impermeability conditions to determine an explicit expression for fluid pressure. The equations of motion presented in this study apply to both conical and cylindrical shells. This study presents the first comparison of the method applied to ring-stiffened shells with other numerical and experimental findings. Vibration frequencies for conical shells with various boundary conditions and geometries in a vacuum and filled with water are compared with experimental and numerical investigations, achieving good agreement. The study thoroughly investigates the influence of geometric parameters, stiffener quantity, semi-vertex cone angle, level of water filled in the cone, and applied boundary conditions on the natural frequency of fluid-loaded ring-stiffened conical shells, and draws some useful conclusions. The primary advantage of the current method is its use of a minimal number of finite elements while achieving highly accurate results.

Keywords: finite element method, fluid–structure interaction, conical shell, natural frequency, ring-stiffener

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Authors: Sandhra M. Pillai

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This article examines the relevance, difficulties, and potential applications of DNA analysis in the criminal justice system. A potent tool for connecting suspects to crime sites, clearing the innocent of wrongdoing, and resolving cold cases, DNA analysis has transformed forensic investigations. The scientific foundations of DNA analysis, including DNA extraction, sequencing, and statistical analysis, are covered in the article. To guarantee accurate and trustworthy findings, it also discusses the significance of quality assurance procedures, chain of custody, and DNA sample storage. DNA analysis has significantly advanced science, but it also brings up substantial moral and legal issues. To safeguard individual rights and uphold public confidence, privacy concerns, possible discrimination, and abuse of DNA information must be properly addressed. The paper also emphasises the effects of the criminal justice system on people and communities while highlighting the necessity of equity, openness, and fair access to DNA testing. The essay describes the obstacles and future directions for DNA analysis. It looks at cutting-edge technology like next-generation sequencing, which promises to make DNA analysis quicker and more affordable. To secure the appropriate and informed use of DNA evidence, it also emphasises the significance of multidisciplinary collaboration among scientists, law enforcement organisations, legal experts, and policymakers. In conclusion, DNA analysis has enormous potential for improving the course of criminal justice. We can exploit the potential of DNA technology while respecting the ideals of justice, fairness, and individual rights by navigating the ethical, legal, and societal issues and encouraging discussion and collaboration.

Keywords: DNA analysis, DNA evidence, reliability, validity, legal frame, admissibility, ethical considerations, impact, future direction, challenges

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Authors: Walelgn Dilnesa Cherie, Fasikaw Atanaw Zimale, Bekalu W. Asres

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The most catastrophic condition for agricultural production is a drought event, which is also one of the most hydro-metrological-related hazards. According to the combined susceptibility of plants to meteorological and hydrological conditions, agricultural drought is defined as the magnitude, severity, and duration of a drought that affects crop production. The accurate and timely assessment of agricultural drought can lead to the development of risk management strategies, appropriate proactive mechanisms for the protection of farmers, and the improvement of food security. The evaluation of agricultural drought in the East Gojjam zone was the primary subject of this study. To identify the agricultural drought, soil moisture anomalies, soil moisture deficit indices, and Normalized Difference Vegetation Indices (NDVI) are used. The measured welting point, field capacity, and soil moisture were utilized to validate the soil water deficit indices computed from the satellite data. The soil moisture and soil water deficit indices in 2013 in all woredas were minimum; this makes vegetation stress also in all woredas. The soil moisture content decreased in 2013/2014/2019, and 2021 in Dejen, 2014, and 2019 in Awobel Woreda. The max/ min values of NDVI in 2013 are minimum; it dominantly shows vegetation stress and an observed agricultural drought that happened in all woredas. The validation process of satellite and in-situ soil moisture and soil water deficit indices shows a good agreement with a value of R²=0.87 and 0.56, respectively. The study area becomes drought detected region, so government officials, policymakers, and environmentalists pay attention to the protection of drought effects.

Keywords: NDVI, agricultural drought, SWDI, soil moisture

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Authors: Sanjai Sureshkumar, Sathish G. Kumar, P. V. V. Sathyanarayana

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For e-gear drivetrain, the transmission error and the resulting variation in mesh stiffness is one of the main source of excitation in High performance Electric Vehicle. These vibrations are transferred through the shaft to the bearings and then to the e-Gear drive housing eventually radiating noise. A parametrical model developed in 1-D system simulation by optimizing the micro and macro geometry along with bearing properties and oil filtration to achieve least transmission error and high contact ratio. Histogram analysis is performed to condense the actual road load data into condensed duty cycle to find the bearing forces. The structural vibration generated by these forces will be simulated in a nonlinear solver obtaining the normal surface velocity of the housing and the results will be carried forward to Acoustic software wherein a virtual environment of the surrounding (actual testing scenario) with accurate microphone position will be maintained to predict the sound pressure level of radiated noise and directivity plot of the e-Gear Drive. Order analysis will be carried out to find the root cause of the vibration and whine noise. Broadband spectrum will be checked to find the rattle noise source. Further, with the available results, the design will be optimized, and the next loop of simulation will be performed to build a best e-Gear Drive on NVH aspect. Structural analysis will be also carried out to check the robustness of the e-Gear Drive.

Keywords: 1-D system simulation, contact ratio, e-Gear, mesh stiffness, micro and macro geometry, transmission error, radiated noise, NVH

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Authors: K. Meera Saheb, K. Krishna Bhaskar

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Complex structures used in many fields of engineering are made up of simple structural elements like beams, plates etc. These structural elements, sometimes carry concentrated masses at discrete points, and when subjected to severe dynamic environment tend to vibrate with large amplitudes. The frequency amplitude relationship is very much essential in determining the response of these structural elements subjected to the dynamic loads. For Timoshenko beams, the effects of shear deformation and rotary inertia are to be considered to evaluate the fundamental linear and nonlinear frequencies. A commonly used method for solving vibration problem is energy method, or a finite element analogue of the same. In the present Coupled Displacement Field method the number of undetermined coefficients is reduced to half when compared to the famous Rayleigh Ritz method, which significantly simplifies the procedure to solve the vibration problem. This is accomplished by using a coupling equation derived from the static equilibrium of the shear flexible structural element. The prime objective of the present paper here is to study, in detail, the effect of a central concentrated mass on the large amplitude free vibrations of uniform shear flexible beams. Accurate closed form expressions for linear frequency parameter for uniform shear flexible beams with a central concentrated mass was developed and the results are presented in digital form.

Keywords: coupled displacement field, coupling equation, large amplitude vibrations, moderately thick plates

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Authors: Dinoo Kirthinanda, Sujani Wijeratne

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Pseudohyperkalaemia is a common benign in vitro phenomenon caused by the release of potassium ions (K+) from cells during specimen processing. Analysis of haemolysed blood samples for predominantly intracellular electrolytes may lead to re-investigation and potentially harmful interventions. We report a case of a 52-year male with myeloproliferative disease manifested as Polycythaemia Rubra Vera, Hypertension and hypertensive nephropathy with stage 3 chronic kidney disease admitted to Neuro-intensive care unit (NICU) with an intra-cerebral haemorrhage secondary to hypertensive bleed. His initial blood investigations showed hyperkalemia with serum K+ 6.2 mmol/L yet the bedside arterial blood gas analysis yielded K+ of 4.6 mmol/L. The patient was however given hyperkalemia regime twice based on venous electrolyte analysis. The discrepancy between the bedside electrolyte analysis using arterial blood and venous blood prompted further evaluation. The 12 lead Electrocardiogram showed U waves and sinus bradycardia corresponding to the serum K+ of 2.8 mmol/L on arterial blood gas analysis. Immediate K+ replacement ensured the patient did not develop life-threatening cardiac complications. Pseudohyperkalaemia may pose diagnostic challenges in the absence of detectable haemolysis and should be suspected in susceptible patients with normal Electrocardiogram and Glomerular Filtration Rate to avoid potentially life-threatening interventions. When in doubt, rapid analysis of arterial blood gas may be useful for accurate quantification of potassium.

Keywords: patient safety, pseudohyperkalaemia, haemolysis, myeloproliferative disorder

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Authors: Silvano Salvador, Maryam Zarrinderakht, Alan Martin

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Open pits, which are the result of mining, are filled by water over time until the water reaches the elevation of the local water table and generates mine pit lakes. There are several specific regulations about the water quality of pit lakes, and mining operations should keep the quality of groundwater above pre-defined standards. Therefore, an accurate, acceptable numerical model predicting pit lakes’ water balance and water quality is needed in advance of mine excavation. We carry on analyzing and developing the model introduced by Crusius, Dunbar, et al. (2002) for pit lakes. This model, called “PitMod”, simulates the physical and geochemical evolution of pit lakes over time scales ranging from a few months up to a century or more. Here, a lake is approximated as one-dimensional, horizontally averaged vertical layers. PitMod calculates the time-dependent vertical distribution of physical and geochemical pit lake properties, like temperature, salinity, conductivity, pH, trace metals, and dissolved oxygen, within each model layer. This model considers the effect of pit morphology, climate data, multiple surface and subsurface (groundwater) inflows/outflows, precipitation/evaporation, surface ice formation/melting, vertical mixing due to surface wind stress, convection, background turbulence and equilibrium geochemistry using PHREEQC and linking that to the geochemical reactions. PitMod, which is used and validated in over 50 mines projects since 2002, incorporates physical processes like those found in other lake models such as DYRESM (Imerito 2007). However, unlike DYRESM PitMod also includes geochemical processes, pit wall runoff, and other effects. In addition, PitMod is actively under development and can be customized as required for a particular site.

Keywords: pit lakes, mining, modeling, hydrology

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Authors: Surabhi Agrawal Kohli, Sunita Gupta, Esha Khanuja, Parul Garg, P. Gupta

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Background: Adenomyosis is a common gynaecological condition that affects the menstruating women. Uterine enlargement, dysmenorrhoea, and menorrhagia are regarded as the cardinal clinical symptoms of adenomyosis. Classically it was thought, compared with ultrasonography, when adenomyosis is suspected, MRI enables more accurate diagnosis of the disease. Materials and Methods: 172 subjects were enrolled after an informed consent that had complaints of HMB, dyspareunia, dysmenorrhea, and chronic pelvic pain. Detailed history of the enrolled subjects was taken, followed by a clinical examination. These patients were then subjected to TVS where myometrial echo texture, presence of myometrial cysts, blurring of endomyometrial junction was noted. MRI was followed which noted the presence of junctional zone thickness and myometrial cysts. After hysterectomy, histopathological diagnosis was obtained. Results: 78 participants were analysed. The mean age was 44.2 years. 43.5% had parity of 4 or more. heavy menstrual bleeding (HMB) was present in 97.8% and dysmenorrhea in 93.48 % of HPE positive patient. Transvaginal sonography (TVS) and MRI had a sensitivity of 89.13% and 80.43%, specificity of 90.62% and 84.37%, positive likelihood ratio of 9.51 and 5.15, negative likelihood ratio of 0.12 and 0.23, positive predictive value of 93.18% and 88.1%, negative predictive value of 85.29% and 75% and a diagnostic accuracy of 89.74% and 82.5%. Comparison of sensitivity (p=0.289) and specificity (p=0.625) showed no statistically significant difference between TVS and MRI. Conclusion: Prevalence of 30.23%. HMB with dysmenorrhoea and chronic pelvic pain helps in diagnosis. TVS (Endomyometrial junction blurring) is both sensitive and specific in diagnosing adenomyosis without need for additional diagnostic tool. Both TVS and MRI are equally efficient, however because of certain additional advantages of TVS over MRI, it may be used as the first choice of imaging. MRI may be used additionally in difficult cases as well as in patients with existing co-pathologies.

Keywords: adenomyosis, heavy menstrual bleeding, MRI, TVS

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Authors: Milca F. Coelho, K. Bousson, Kawser Ahmed

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To accurately track an aerospace vehicle in a time-critical situation and in a highly nonlinear environment, is one of the strongest interests within the aerospace community. The tracking is achieved by estimating accurately the state of a moving target, which is composed of a set of variables that can provide a complete status of the system at a given time. One of the main ingredients for a good estimation performance is the use of efficient estimation algorithms. A well-known framework is the Kalman filtering methods, designed for prediction and estimation problems. The success of the Kalman Filter (KF) in engineering applications is mostly due to the Extended Kalman Filter (EKF), which is based on local linearization. Besides its popularity, the EKF presents several limitations. To address these limitations and as a possible solution to tracking problems, this paper proposes the use of the Ensemble Kalman Filter (EnKF). Although the EnKF is being extensively used in the context of weather forecasting and it is being recognized for producing accurate and computationally effective estimation on systems with a very high dimension, it is almost unknown by the tracking community. The EnKF was initially proposed as an attempt to improve the error covariance calculation, which on the classic Kalman Filter is difficult to implement. Also, in the EnKF method the prediction and analysis error covariances have ensemble representations. These ensembles have sizes which limit the number of degrees of freedom, in a way that the filter error covariance calculations are a lot more practical for modest ensemble sizes. In this paper, a realistic simulation of a radar tracking was performed, where the EnKF was applied and compared with the Extended Kalman Filter. The results suggested that the EnKF is a promising tool for tracking applications, offering more advantages in terms of performance.

Keywords: Kalman filter, nonlinear state estimation, optimal tracking, stochastic environment

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Authors: Chhabi Nigam, S. Ramakrishnan

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This paper brings out the analysis of the airborne Synthetic Aperture Radar (SAR) data to study the impact of Doppler centroid on Image quality and geocoding accuracy from the perspective of Stripmap mode of data acquisition. Although in Stripmap mode of data acquisition radar beam points at 90 degrees broad side (side looking), shift in the Doppler centroid is invariable due to platform motion. In-accurate estimation of Doppler centroid leads to poor image quality and image miss-registration. The effect of Doppler centroid is analyzed in this paper using multiple sets of data collected from airborne platform. Occurrences of ghost (ambiguous) targets and their power levels have been analyzed that impacts appropriate choice of PRF. Effect of aircraft attitudes (roll, pitch and yaw) on the Doppler centroid is also analyzed with the collected data sets. Various stages of the RDA (Range Doppler Algorithm) algorithm used for image formation in Stripmap mode, range compression, Doppler centroid estimation, azimuth compression, range cell migration correction are analyzed to find the performance limits and the dependence of the imaging geometry on the final image. The ability of Doppler centroid estimation to enhance the imaging accuracy for registration are also illustrated in this paper. The paper also tries to bring out the processing of low squint SAR data, the challenges and the performance limits imposed by the imaging geometry and the platform dynamics on the final image quality metrics. Finally, the effect on various terrain types, including land, water and bright scatters is also presented.

Keywords: ambiguous target, Doppler Centroid, image registration, Airborne SAR

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Authors: Bi-Huei Tsai

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This work explores the inter-region investment behaviors of integrated circuit (IC) design industry from Taiwan to China using the amount of foreign direct investment (FDI). According to the mutual dependence among different IC design industrial locations, Lotka-Volterra model is utilized to explore the FDI interactions between South and East China. Effects of inter-regional collaborations on FDI flows into China are considered. Evolutions of FDIs into South China for IC design industry significantly inspire the subsequent FDIs into East China, while FDIs into East China for Taiwan’s IC design industry significantly hinder the subsequent FDIs into South China. The supply chain along IC industry includes IC design, manufacturing, packing and testing enterprises. I C manufacturing, packaging and testing industries depend on IC design industry to gain advanced business benefits. The FDI amount from Taiwan’s IC design industry into East China is the greatest among the four regions: North, East, Mid-West and South China. The FDI amount from Taiwan’s IC design industry into South China is the second largest. If IC design houses buy more equipment and bring more capitals in South China, those in East China will have pressure to undertake more FDIs into East China to maintain the leading position advantages of the supply chain in East China. On the other hand, as the FDIs in East China rise, the FDIs in South China will successively decline since capitals have concentrated in East China. Prediction of Lotka-Volterra model in FDI trends is accurate because the industrial interactions between the two regions are included. Finally, this work confirms that the FDI flows cannot reach a stable equilibrium point, so the FDI inflows into East and South China will expand in the future.

Keywords: Lotka-Volterra model, foreign direct investment, competitive, Equilibrium analysis

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Authors: Anju Joshi, C. N. Tharamani

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Diabetes is one of the leading cause of death at present and remains an important concern as the prevalence of the disease is increasing at an alarming rate. Therefore, it is crucial to diagnose the accurate levels of glucose for developing an efficient therapeutic for diabetes. Due to the availability of convenient and compact self-testing, continuous monitoring of glucose is feasible nowadays. Enzyme based electrochemical sensing of glucose is quite popular because of its high selectivity but suffers from drawbacks like complicated purification and immobilization procedures, denaturation, high cost, and low sensitivity due to indirect electron transfer. Hence, designing a robust enzyme free platform using transition metal oxides remains crucial for the efficient and sensitive determination of glucose. In the present work, manganese doped nickel oxide nanoparticles (Mn-NiO) has been synthesized onto the surface of multiwalled carbon nanotubes using a simple microwave assisted approach for non-enzymatic electrochemical sensing of glucose. The morphology and structure of the synthesized nanostructures were characterized using scanning electron microscopy (SEM) and X-Ray diffraction (XRD). We demonstrate that the synthesized nanostructures show enormous potential for electrocatalytic oxidation of glucose with high sensitivity and selectivity. Cyclic voltammetry and square wave voltammetry studies suggest superior sensitivity and selectivity of Mn-NiO decorated carbon nanotubes towards the non-enzymatic determination of glucose. A linear response between the peak current and the concentration of glucose has been found to be in the concentration range of 0.01 μM- 10000 μM which suggests the potential efficacy of Mn-NiO decorated carbon nanotubes for sensitive determination of glucose.

Keywords: diabetes, glucose, Mn-NiO decorated carbon nanotubes, non-enzymatic

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Authors: Manana Lobzhanidze

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The COVID-19 pandemic has made significant changes in migration processes, notably changes in the study migration process. The constraints caused by the COVID-19 pandemic led to changes in the studying process, which negatively affected its efficiency. The educational process has partially or completely shifted to distance learning; Both labor and study migration have increased significantly in the world. The employment and education market has become global and consequently, a number of challenges have arisen for employers, researchers, and businesses. The role of preparing qualified personnel in achieving high productivity is justified, the benefits for employers and employees are assessed on the one hand, and the role of study migration for the country’s development is examined on the other hand. Research methods. The research is based on methods of analysis and synthesis, quantitative and qualitative, groupings, relative and mean quantities, graphical representation, comparison, analysis and etc. In-depth interviews were conducted with experts to determine quantitative and qualitative indicators. Research findings. Factors affecting study migration are analysed in the paper and the environment that stimulates migration is explored. One of the driving forces of migration is considered to be the desire for receiving higher pay. Levels and indicators of study migration are studied by country. Comparative analysis has found that study migration rates are high in countries where the price of skilled labor is high. The productivity of individuals with low skills is low, which negatively affects the economic development of countries. It has been revealed that students leave the country to improve their skills during study migration. The process mentioned in the article is evaluated as a positive event for a developing country, as individuals are given the opportunity to share the technology of developed countries, gain knowledge, and then introduce it in their own country. The downside of study migration is the return of a small proportion of graduates from developed economies to their home countries. The article concludes that countries with emerging economies devote less resources to research and development, while this is a priority in developed countries, allowing highly skilled individuals to use their skills efficiently. The paper studies the national education system examines the level of competition in the education market and the indicators of educational migration. The level of competition in the education market and the indicators of educational migration are studied. The role of qualified personnel in achieving high productivity is substantiated, the benefits of employers and employees are assessed on the one hand, and the role of study migration in the development of the country is revealed on the other hand. The paper also analyzes the level of competition in the education and labor markets and identifies indicators of study migration. During the pandemic period, there was a great demand for the digital technologies. Open access to a variety of comprehensive platforms will significantly reduce study migration to other countries. As a forecast, it can be said that the intensity of the use of e-learning platforms will be increased significantly in the post-pandemic period. The paper analyzes the positive and negative effects of study migration on economic development, examines the challenges of study migration in light of the COVID-19 pandemic, suggests ways to avoid negative consequences, and develops recommendations for improving the study migration process in the post-pandemic period.

Keywords: study migration, COVID-19 pandemic, factors affecting migration, economic development, post-pandemic migration

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Authors: Tugba Bayoglu

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Aerodynamic parameter estimation is very crucial in missile design phase, since accurate high fidelity aerodynamic model is required for designing high performance and robust control system, developing high fidelity flight simulations and verification of computational and wind tunnel test results. However, in literature, there is not enough missile aerodynamic parameter identification study for three main reasons: (1) most air to air missiles cannot fly with constant speed, (2) missile flight test number and flight duration are much less than that of fixed wing aircraft, (3) variation of the missile aerodynamic parameters with respect to Mach number is higher than that of fixed wing aircraft. In addition to these challenges, identification of aerodynamic parameters for high wind angles by using classical estimation techniques brings another difficulty in the estimation process. The reason for this, most of the estimation techniques require employing polynomials or splines to model the behavior of the aerodynamics. However, for the missiles with a large variation of aerodynamic parameters with respect to flight variables, the order of the proposed model increases, which brings computational burden and complexity. Therefore, in this study, it is aimed to solve nonlinear aerodynamic parameter identification problem for a supersonic air to air missile by using Artificial Neural Networks. The method proposed will be tested by using simulated data which will be generated with a six degree of freedom missile model, involving a nonlinear aerodynamic database. The data will be corrupted by adding noise to the measurement model. Then, by using the flight variables and measurements, the parameters will be estimated. Finally, the prediction accuracy will be investigated.

Keywords: air to air missile, artificial neural networks, open loop simulation, parameter identification

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Authors: Michelle Nighswander

Abstract:

Suicide can be a tragic response to mental illness. It is difficult for people to disclose or discuss suicidal impulses. The stigma surrounding mental health can create a reluctance to seek help for mental illness. Patients may feel pressure to exhibit a socially desirable demeanor rather than reveal these issues, especially if they sense their healthcare provider is pressed for time or does not have an extensive history with their provider. Overcoming these barriers can be challenging. Although there are several validated depression and suicide risk instruments, varying processes used to administer these tools may impact the truthfulness of the responses. A literature review was conducted to find evidence of the impact of the environment on the accuracy of depression screening. Many investigations do not describe the environment and fewer studies use a comparison design. However, three studies demonstrated that computerized self-reporting might be more likely to elicit truthful and accurate responses due to increased privacy when responding compared to a face-to-face interview. These studies showed patients reported positive reactions to computerized screening for other stigmatizing health conditions such as alcohol use during pregnancy. Computerized self-screening for depression offers the possibility of more privacy and patient reflection, which could then send a targeted message of risk to the healthcare provider. This could potentially increase the accuracy while also increasing time efficiency for the clinic. Considering the persistent effects of mental health stigma, how these screening questions are posed can impact patients’ responses. This literature review analyzes trends in depression screening methodologies, the impact of setting on the results and how this may assist in overcoming one barrier caused by stigma.

Keywords: computerized self-report, depression, mental health stigma, suicide risk

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Authors: Mona Hassan Aburahma, Alaa Hamed Salama

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Ufasomes “unsaturated fatty acids liposomes” are unique nano-sized self-assembled bilayered vesicles that can be easily created from the readily available unsaturated fatty acid. Ufasomes are formed due to weak associative interaction of the fully ionized and unionized fatty acids into bilayers structures. In the ufasomes constructs, the fatty acid molecules are oriented with their hydrocarbon tails directed toward the membrane interior and the carboxyl groups are in contact with water. Although ufasomes can be employed as a safe vesicular carrier for drugs, the extreme instability of their aqueous dispersions hinders their effective use in drug delivery field. Accordingly, in our study, lyophilized gels containing ufasomes were prepared using a simple assembling technique form the readily available oleic acid to overcome the colloidal instability of the ufasomes dispersions and convert them into accurate unit dosage forms. The influence of changing cholesterol percentage relative to oleic acid on the ufasomes vesicles were investigated using factorial design. The optimized oleic acid ufasomes comprised nanoscaled spherical vesicles. Scanning electron micrographs of the lyophilized gels revealed that the included ufasomes were intact, non-aggregating, and preserved their spherical morphology. Rheological characterization (viscosity and shear stress versus shear rate) of reconstituted ufasomal lyophilized gel ensured the ease of application. The capability of the ufasomes, included in the gel, to penetrate deep through the mucosa layers was illustrated using ex-vivo confocal laser imaging, thereby, highlighting the feasibility of stabilizing ufasomes using lyophilized gel platforms.

Keywords: ufasomes, lyophilized gel, confocal scanning microscopy, rheological characterization, oleic acid

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Authors: Mitchell Stanton

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Background: Endovascular management is well established as a mainstay treatment option for cerebral aneurysms. It is also well established that immediate post procedural imaging can be difficult to interpret due to the presence of contrast material. However, through the use of Dual-Energy computed tomography, it has become possible to differentiate contrast extravasation and intracranial haemorrhage. This case illustrates the importance of this technology following endovascular treatment of an unruptured cerebral aneurysm. Case Presentation: A 79-year-old female was found to have an unruptured large intracavernous ICA fusiform aneurysm on CT Brain Angiogram after presenting with acute ophthalmoplegia. This ophthalmoplegia was caused by mass effect from the aneurysm and subsequently the aneurysm was treated with an endovascular flow diverting stent. CT brain was performed post operatively due to a reduced level of consciousness and this showed diffuse subarachnoid hyperdensity of the left hemisphere. The use of Dual-Energy CT allowed accurate differentiation and illustrated diffuse contrast material extravasation, allowing patient to continue on dual-antiplatelets and therapeutic anticoagulation to reduce the risk of ischaemic injury post endovascular stent. Conclusion: Endovascular treatment options for management of intracranial aneurysms are constantly evolving. The use of Dual-Energy CT therefore has an integral role in accurately diagnosing any post-operative complications. Specifically, differentiating between subarachnoid haemorrhage and contrast extravasation is vital in these patients due to the significant consequences to their ongoing management in regards to continuation or cessation of antiplatelets or anticoagulation. With increasing access to this technology, its use should become standard practice in the post-operative investigation of these patients undergoing endovascular treatment.

Keywords: aneurysm, computed tomography, contrast extravasation, dual-energy CT, endovascular, subarachnoid haemorrhage

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Authors: Vahid Nourani, Atefeh Ashrafi

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Prediction of treated wastewater quality is a matter of growing importance in water treatment procedure. In this way artificial neural network (ANN), as a robust data-driven approach, has been widely used for forecasting the effluent quality of wastewater treatment. However, developing ANN model based on appropriate input variables is a major concern due to the numerous parameters which are collected from treatment process and the number of them are increasing in the light of electronic sensors development. Various studies have been conducted, using different clustering methods, in order to classify most related and effective input variables. This issue has been overlooked in the selecting dominant input variables among wastewater treatment parameters which could effectively lead to more accurate prediction of water quality. In the presented study two ANN models were developed with the aim of forecasting effluent quality of Tabriz city’s wastewater treatment plant. Biochemical oxygen demand (BOD) was utilized to determine water quality as a target parameter. Model A used Principal Component Analysis (PCA) for input selection as a linear variance-based clustering method. Model B used those variables identified by the mutual information (MI) measure. Therefore, the optimal ANN structure when the result of model B compared with model A showed up to 15% percent increment in Determination Coefficient (DC). Thus, this study highlights the advantage of PCA method in selecting dominant input variables for ANN modeling of wastewater plant efficiency performance.

Keywords: Artificial Neural Networks, biochemical oxygen demand, principal component analysis, mutual information, Tabriz wastewater treatment plant, wastewater treatment plant

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Authors: Mohammad H. Yarmohammadian, Reza Safdari, Mahmoud Keyvanara, Nahid Tavakoli

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One of the greatest challenges in disaster and emergencies is patient tracking. The concept of tracking has different denotations. One of the meanings refers to tracking patients’ physical locations and the other meaning refers to tracking patients ‘medical needs during emergency services. The main goal of patient tracking is to provide patient safety during disaster and emergencies and manage the flow of patient and information in different locations. In most of cases, there are not sufficient and accurate data regarding the number of injuries, medical conditions and their accommodation and transference. The objective of the present study is to survey on patient tracking issue in natural disaster and emergencies. Methods: This was a narrative study in which the population was E-Journals and the electronic database such as PubMed, Proquest, Science direct, Elsevier, etc. Data was gathered by Extraction Form. All data were analyzed via content analysis. Results: In many countries there is no appropriate and rapid method for tracking patients and transferring victims after the occurrence of incidents. The absence of reliable data of patients’ transference and accommodation, even in the initial hours and days after the occurrence of disasters, and coordination for appropriate resource allocation, have faced challenges for evaluating needs and services challenges. Currently, most of emergency services are based on paper systems, while these systems do not act appropriately in great disasters and incidents and this issue causes information loss. Conclusion: Patient tracking system should update the location of patients or evacuees and information related to their states. Patients’ information should be accessible for authorized users to continue their treatment, accommodation and transference. Also it should include timely information of patients’ location as soon as they arrive somewhere and leave therein such a way that health care professionals can be able to provide patients’ proper medical treatment.

Keywords: patient tracking, challenges, disaster, emergency

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Authors: Marilena Ianculescu, Adriana Alexandru

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Internet of Health Things (IoHT) has already proved to be a persuasive means to support a proper assessment of the living conditions by collecting a huge variety of data. For a customized health management of a senior patient, IoHT provides the capacity to build a dynamic solution for sustaining the shift inside the patient-physician relationship by allowing a real-time and continuous remote monitoring of the health status, well-being, safety and activities of the senior, especially in a non-clinical environment. Thus, is created a win-win solution in which both the patient and the physician enhance their involvement and shared decision-making, with significant outcomes. Health monitoring systems in smart environments are becoming a viable alternative to traditional healthcare solutions. The ongoing “Non-invasive monitoring and health assessment of the elderly in a smart environment (RO-SmartAgeing)” project aims to demonstrate that the existence of complete and accurate information is critical for assessing the health condition of the seniors, improving wellbeing and quality of life in relation to health. The researches performed inside the project aim to highlight how the management of IoHT devices connected to the RO-SmartAgeing platform in a secure way by using a role-based access control system, can allow the physicians to provide health services at a high level of efficiency and accessibility, which were previously only available in hospitals. The project aims to identify deficient aspects in the provision of health services tailored to a senior patient’s specificity and to offer a more comprehensive perspective of proactive and preventive medical acts.

Keywords: health management, internet of health things, remote monitoring, senior patient

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