Search results for: numerical algorithms

Authors: Kaviyarasan D., Satish Kumar S. R.

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India being vast in area and population with great scope of international business, roadways and railways network connection within the country is expected to have a big growth. There are numerous rail-cum-road bridges constructed across many major rivers in India and few are getting very old. So there is more possibility of repairing or coming up with such new bridges in India. Analysis and design of such bridges are practiced through conventional procedure and end up with heavy and uneconomical sections. Such heavy class steel bridges when subjected to high seismic shaking has more chance to fail by stability because the members are too much rigid and stocky rather than being flexible to dissipate the energy. This work is the collective study of the researches done in the truss bridge and steel concrete composite truss bridges presenting the method of analysis, tools for numerical and analytical modeling which evaluates its seismic behaviour and collapse mechanisms. To ascertain the inelastic and nonlinear behaviour of the structure, generally at research level static pushover analysis is adopted. Though the static pushover analysis is now extensively used for the framed steel and concrete buildings to study its lateral action behaviour, those findings by pushover analysis done for the buildings cannot directly be used for the bridges as such, because the bridges have completely a different performance requirement, behaviour and typology as compared to that of the buildings. Long span steel bridges are mostly the truss bridges. Truss bridges being formed by many members and connections, the failure of the system does not happen suddenly with single event or failure of one member. Failure usually initiates from one member and progresses gradually to the next member and so on when subjected to further loading. This kind of progressive collapse of the truss bridge structure is dependent on many factors, in which the live load distribution and span to length ratio are most significant. The ultimate collapse is anyhow by the buckling of the compression members only. For regular bridges, single step pushover analysis gives results closer to that of the non-linear dynamic analysis. But for a complicated bridge like heavy class steel bridge or the skewed bridges or complicated dynamic behaviour bridges, nonlinear analysis capturing the progressive yielding and collapse pattern is mandatory. With the knowledge of the postelastic behaviour of the bridge and advancements in the computational facility, the current level of analysis and design of bridges has moved to state of ascertaining the performance levels of the bridges based on the damage caused by seismic shaking. This is because the buildings performance levels deals much with the life safety and collapse prevention levels, whereas the bridges mostly deal with the extent damages and how quick it can be repaired with or without disturbing the traffic after a strong earthquake event. The paper would compile the wide spectrum of modeling to analysis of the steel concrete composite truss bridges in general.

Keywords: bridge engineering, performance based design of steel truss bridge, seismic design of composite bridge, steel-concrete composite bridge

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Authors: Juan Pablo Botero Torres, Alba Avila, Luis Felipe Giraldo

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Independent navigation and mobility through unknown spaces pose a challenge for the autonomy of visually impaired people (VIP), who have relied on the use of traditional assistive tools like the white cane and trained dogs. However, emerging visually assistive technologies (VAT) have proposed several human-machine interfaces (HMIs) that could improve VIP’s ability for self-guidance. Hereby, we introduce the design and implementation of a visually assistive device, Anajaa – Visual Substitution System (AVSS). This system integrates ultrasonic sensors with custom electronics, and computer vision models (convolutional neural networks), in order to achieve a robust system that acquires information of the surrounding space and transmits it to the user in an intuitive and efficient manner. AVSS consists of two modules: the sensing and the actuation module, which are fitted to a chest mount and belt that communicate via Bluetooth. The sensing module was designed for the acquisition and processing of proximity signals provided by an array of ultrasonic sensors. The distribution of these within the chest mount allows an accurate representation of the surrounding space, discretized in three different levels of proximity, ranging from 0 to 6 meters. Additionally, this module is fitted with an RGB-D camera used to detect potentially threatening obstacles, like staircases, using a convolutional neural network specifically trained for this purpose. Posteriorly, the depth data is used to estimate the distance between the stairs and the user. The information gathered from this module is then sent to the actuation module that creates an HMI, by the means of a 3x2 array of vibration motors that make up the tactile display and allow the system to deliver haptic feedback. The actuation module uses vibrational messages (tactones); changing both in amplitude and frequency to deliver different awareness levels according to the proximity of the obstacle. This enables the system to deliver an intuitive interface. Both modules were tested under lab conditions, and the HMI was additionally tested with a focal group of VIP. The lab testing was conducted in order to establish the processing speed of the computer vision algorithms. This experimentation determined that the model can process 0.59 frames per second (FPS); this is considered as an adequate processing speed taking into account that the walking speed of VIP is 1.439 m/s. In order to test the HMI, we conducted a focal group composed of two females and two males between the ages of 35-65 years. The subject selection was aided by the Colombian Cooperative of Work and Services for the Sightless (COOTRASIN). We analyzed the learning process of the haptic messages throughout five experimentation sessions using two metrics: message discrimination and localization success. These correspond to the ability of the subjects to recognize different tactones and locate them within the tactile display. Both were calculated as the mean across all subjects. Results show that the focal group achieved message discrimination of 70% and a localization success of 80%, demonstrating how the proposed HMI leads to the appropriation and understanding of the feedback messages, enabling the user’s awareness of its surrounding space.

Keywords: computer vision on embedded systems, electronic trave aids, human-machine interface, haptic feedback, visual assistive technologies, vision substitution systems

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Authors: Anne Schattel, Mitja Echim, Christof Büskens

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Trajectory planning for deep space missions has become a recent topic of great interest. Flying to space objects like asteroids provides two main challenges. One is to find rare earth elements, the other to gain scientific knowledge of the origin of the world. Due to the enormous spatial distances such explorer missions have to be performed unmanned and autonomously. The mathematical field of optimization and optimal control can be used to realize autonomous missions while protecting recourses and making them safer. The resulting algorithms may be applied to other, earth-bound applications like e.g. deep sea navigation and autonomous driving as well. The project KaNaRiA ('Kognitionsbasierte, autonome Navigation am Beispiel des Ressourcenabbaus im All') investigates the possibilities of cognitive autonomous navigation on the example of an asteroid mining mission, including the cruise phase and approach as well as the asteroid rendezvous, landing and surface exploration. To verify and test all methods an interactive, real-time capable simulation using virtual reality is developed under KaNaRiA. This paper focuses on the specific challenge of the guidance during the cruise phase of the spacecraft, i.e. trajectory optimization and optimal control, including first solutions and results. In principle there exist two ways to solve optimal control problems (OCPs), the so called indirect and direct methods. The indirect methods are being studied since several decades and their usage needs advanced skills regarding optimal control theory. The main idea of direct approaches, also known as transcription techniques, is to transform the infinite-dimensional OCP into a finite-dimensional non-linear optimization problem (NLP) via discretization of states and controls. These direct methods are applied in this paper. The resulting high dimensional NLP with constraints can be solved efficiently by special NLP methods, e.g. sequential quadratic programming (SQP) or interior point methods (IP). The movement of the spacecraft due to gravitational influences of the sun and other planets, as well as the thrust commands, is described through ordinary differential equations (ODEs). The competitive mission aims like short flight times and low energy consumption are considered by using a multi-criteria objective function. The resulting non-linear high-dimensional optimization problems are solved by using the software package WORHP ('We Optimize Really Huge Problems'), a software routine combining SQP at an outer level and IP to solve underlying quadratic subproblems. An application-adapted model of impulsive thrusting, as well as a model of an electrically powered spacecraft propulsion system, is introduced. Different priorities and possibilities of a space mission regarding energy cost and flight time duration are investigated by choosing different weighting factors for the multi-criteria objective function. Varying mission trajectories are analyzed and compared, both aiming at different destination asteroids and using different propulsion systems. For the transcription, the robust method of full discretization is used. The results strengthen the need for trajectory optimization as a foundation for autonomous decision making during deep space missions. Simultaneously they show the enormous increase in possibilities for flight maneuvers by being able to consider different and opposite mission objectives.

Keywords: deep space navigation, guidance, multi-objective, non-linear optimization, optimal control, trajectory planning.

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Authors: Torsten Hermanns, You Wang, Stefan Janssen, Markus Niessen, Christoph Schoeler, Ulrich Thombansen, Wolfgang Schulz

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In long pulse laser drilling of metals, it can be demonstrated that the ablation shape approaches a so-called asymptotic shape such that it changes only slightly or not at all with further irradiation. These findings are already known from ultra short pulse (USP) ablation of dielectric and semiconducting materials. The explanation for the occurrence of an asymptotic shape in long pulse drilling of metals is identified, a model for the description of the asymptotic hole shape numerically implemented, tested and clearly confirmed by comparison with experimental data. The model assumes a robust process in that way that the characteristics of the melt flow inside the arising melt film does not change qualitatively by changing the laser or processing parameters. Only robust processes are technically controllable and thus of industrial interest. The condition for a robust process is identified by a threshold for the mass flow density of the assist gas at the hole entrance which has to be exceeded. Within a robust process regime the melt flow characteristics can be captured by only one model parameter, namely the intensity threshold. In analogy to USP ablation (where it is already known for a long time that the resulting hole shape results from a threshold for the absorbed laser fluency) it is demonstrated that in the case of robust long pulse ablation the asymptotic shape forms in that way that along the whole contour the absorbed heat flux density is equal to the intensity threshold. The intensity threshold depends on the special material and radiation properties and has to be calibrated be one reference experiment. The model is implemented in a numerical simulation which is called AsymptoticDrill and requires such a few amount of resources that it can run on common desktop PCs, laptops or even smart devices. Resulting hole shapes can be calculated within seconds what depicts a clear advantage over other simulations presented in literature in the context of industrial every day usage. Against this background the software additionally is equipped with a user-friendly GUI which allows an intuitive usage. Individual parameters can be adjusted using sliders while the simulation result appears immediately in an adjacent window. A platform independent development allow a flexible usage: the operator can use the tool to adjust the process in a very convenient manner on a tablet during the developer can execute the tool in his office in order to design new processes. Furthermore, at the best knowledge of the authors AsymptoticDrill is the first simulation which allows the import of measured real beam distributions and thus calculates the asymptotic hole shape on the basis of the real state of the specific manufacturing system. In this paper the emphasis is placed on the investigation of the effect of multiple reflections on the asymptotic hole shape which gain in importance when drilling holes with large aspect ratios.

Keywords: asymptotic hole shape, intensity threshold, long pulse laser drilling, robust process

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Authors: Regina A. Tayong, Reza Barati

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A stable, fast and robust three-phase, 2D IMPES simulator has been developed for assessing the influence of; breaker concentration on yield stress of filter cake and broken gel viscosity, varying polymer concentration/yield stress along the fracture face, fracture conductivity, fracture length, capillary pressure changes and formation damage on fracturing fluid cleanup in tight gas reservoirs. This model has been validated as against field data reported in the literature for the same reservoir. A 2-D, two-phase (gas/water) fracture propagation model is used to model our invasion zone and create the initial conditions for our clean-up model by distributing 200 bbls of water around the fracture. A 2-D, three-phase IMPES simulator, incorporating a yield-power-law-rheology has been developed in MATLAB to characterize fluid flow through a hydraulically fractured grid. The variation in polymer concentration along the fracture is computed from a material balance equation relating the initial polymer concentration to total volume of injected fluid and fracture volume. All governing equations and the methods employed have been adequately reported to permit easy replication of results. The effect of increasing capillary pressure in the formation simulated in this study resulted in a 10.4% decrease in cumulative production after 100 days of fluid recovery. Increasing the breaker concentration from 5-15 gal/Mgal on the yield stress and fluid viscosity of a 200 lb/Mgal guar fluid resulted in a 10.83% increase in cumulative gas production. For tight gas formations (k=0.05 md), fluid recovery increases with increasing shut-in time, increasing fracture conductivity and fracture length, irrespective of the yield stress of the fracturing fluid. Mechanical induced formation damage combined with hydraulic damage tends to be the most significant. Several correlations have been developed relating pressure distribution and polymer concentration to distance along the fracture face and average polymer concentration variation with injection time. The gradient in yield stress distribution along the fracture face becomes steeper with increasing polymer concentration. The rate at which the yield stress (τ_o) is increasing is found to be proportional to the square of the volume of fluid lost to the formation. Finally, an improvement on previous results was achieved through simulating yield stress variation along the fracture face rather than assuming constant values because fluid loss to the formation and the polymer concentration distribution along the fracture face decreases as we move away from the injection well. The novelty of this three-phase flow model lies in its ability to (i) Simulate yield stress variation with fluid loss volume along the fracture face for different initial guar concentrations. (ii) Simulate increasing breaker activity on yield stress and broken gel viscosity and the effect of (i) and (ii) on cumulative gas production within reasonable computational time.

Keywords: formation damage, hydraulic fracturing, polymer cleanup, multiphase flow numerical simulation

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Authors: Aleksandra Zieminska-Stolarska, Ireneusz Zbicinski

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Introduction: The objective of the present work was to develop and validate a 3D CFD numerical model for simulating flow through 17 kilometers long dam reservoir of a complex bathymetry. In contrast to flowing waters, dam reservoirs were not emphasized in the early years of water quality modeling, as this issue has never been the major focus of urban development. Starting in the 1970s, however, it was recognized that natural and man-made lakes are equal, if not more important than estuaries and rivers from a recreational standpoint. The Sulejow Reservoir (Central Poland) was selected as the study area as representative of many lowland dam reservoirs and due availability of a large database of the ecological, hydrological and morphological parameters of the lake. Method: 3D, 2-phase and 1-phase CFD models were analysed to determine hydrodynamics in the Sulejow Reservoir. Development of 3D, 2-phase CFD model of flow requires a construction of mesh with millions of elements and overcome serious convergence problems. As 1-phase CFD model of flow in relation to 2-phase CFD model excludes from the simulations the dynamics of waves only, which should not change significantly water flow pattern for the case of lowland, dam reservoirs. In 1-phase CFD model, the phases (water-air) are separated by a plate which allows calculations of one phase (water) flow only. As the wind affects velocity of flow, to take into account the effect of the wind on hydrodynamics in 1-phase CFD model, the plate must move with speed and direction equal to the speed and direction of the upper water layer. To determine the velocity at which the plate will move on the water surface and interacts with the underlying layers of water and apply this value in 1-phase CFD model, the 2D, 2-phase model was elaborated. Result: Model was verified on the basis of the extensive flow measurements (StreamPro ADCP, USA). Excellent agreement (an average error less than 10%) between computed and measured velocity profiles was found. As a result of work, the following main conclusions can be presented: •The results indicate that the flow field in the Sulejow Reservoir is transient in nature, with swirl flows in the lower part of the lake. Recirculating zones, with the size of even half kilometer, may increase water retention time in this region •The results of simulations confirm the pronounced effect of the wind on the development of the water circulation zones in the reservoir which might affect the accumulation of nutrients in the epilimnion layer and result e.g. in the algae bloom. Conclusion: The resulting model is accurate and the methodology develop in the frame of this work can be applied to all types of storage reservoir configurations, characteristics, and hydrodynamics conditions. Large recirculating zones in the lake which increase water retention time and might affect the accumulation of nutrients were detected. Accurate CFD model of hydrodynamics in large water body could help in the development of forecast of water quality, especially in terms of eutrophication and water management of the big water bodies.

Keywords: CFD, mathematical modelling, dam reservoirs, hydrodynamics

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Authors: Mark Kennedy, E. Bantugon, Noruane A. Daileg

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Leaks arising from aircraft fuel tanks is a protracted problem for the aircraft manufacturers, operators, and maintenance crews. It principally arises from stress, structural defects, or degraded sealants as the aircraft age. It can be ignited by different sources, which can result in catastrophic flight and consequences, exhibiting a major drain both on time and budget. In order to mitigate and eliminate this kind of problem, the researcher produced an experimental sealant having a base material of natural tree resin, the Pili Tree Resin. Aside from producing an experimental sealant, the main objective of this research is to analyze its physical, chemical, mechanical, thermal, and rheological properties, which is beneficial and effective for specific aircraft parts, particularly the integral fuel tank. The experimental method of research was utilized in this study since it is a product invention. This study comprises two parts, specifically the Optimization Process and the Characterization Process. In the Optimization Process, the experimental sealant was subjected to the Flammability Test, an important test and consideration according to 14 Code of Federal Regulation Appendix N, Part 25 - Fuel Tank Flammability Exposure and Reliability Analysis, to get the most suitable formulation. Followed by the Characterization Process, where the formulated experimental sealant has undergone thirty-eight (38) different standard testing including Organoleptic, Instrumental Color Measurement Test, Smoothness of Appearance Test, Miscibility Test, Boiling Point Test, Flash Point Test, Curing Time, Adhesive Test, Toxicity Test, Shore A Hardness Test, Compressive Strength, Shear Strength, Static Bending Strength, Tensile Strength, Peel Strength Test, Knife Test, Adhesion by Tape Test, Leakage Test), Drip Test, Thermogravimetry-Differential Thermal Analysis (TG-DTA), Differential Scanning Calorimetry, Calorific Value, Viscosity Test, Creep Test, and Anti-Sag Resistance Test to determine and analyze the five (5) material properties of the sealant. The numerical values of the mentioned tests are determined using product application, testing, and calculation. These values are then used to calculate the efficiency of the experimental sealant. Accordingly, this efficiency is the means of comparison between the experimental and commercial sealant. Based on the results of the different standard testing conducted, the experimental sealant exceeded all the data results of the commercial sealant. This result shows that the physicochemical-mechanical, thermal, and rheological properties of the experimental sealant are far more effective as an aircraft integral fuel tank sealant alternative in comparison to the commercial sealant. Therefore, Pili Tree possesses a new role and function: a source of ingredients in sealant production.

Keywords: Aircraft Integral Fuel Tank, Physicochemi-mechanical, Pili Tree Resin, Properties, Rheological, Sealant, Thermal

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Authors: Nan Xu

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In airlines, the crew scheduling problem is usually decomposed into two stages: crew pairing and crew rostering. In the crew pairing stage, pairings are generated such that each flight is covered by exactly one pairing and the overall cost is minimized. In the crew rostering stage, the pairings generated in the crew pairing stage are combined with off days, training and other breaks to create individual work schedules. The paper focuses on cabin crew rostering problem, which is challenging due to the extremely large size and the complex working rules involved. In our approach, the objective of rostering consists of two major components. The first is to minimize the number of unassigned pairings and the second is to ensure the fairness to crew members. There are two measures of fairness to crew members, the number of overnight duties and the total fly-hour over a given period. Pairings should be assigned to each crew member so that their actual overnight duties and fly hours are as close to the expected average as possible. Deviations from the expected average are penalized in the objective function. Since several small deviations are preferred than a large deviation, the penalization is quadratic. Our model of the airline crew rostering problem is based on column generation. The problem is decomposed into a master problem and subproblems. The mater problem is modeled as a set partition problem and exactly one roster for each crew is picked up such that the pairings are covered. The restricted linear master problem (RLMP) is considered. The current subproblem tries to find columns with negative reduced costs and add them to the RLMP for the next iteration. When no column with negative reduced cost can be found or a stop criteria is met, the procedure ends. The subproblem is to generate feasible crew rosters for each crew member. A separate acyclic weighted graph is constructed for each crew member and the subproblem is modeled as resource constrained shortest path problems in the graph. Labeling algorithm is used to solve it. Since the penalization is quadratic, a method to deal with non-additive shortest path problem using labeling algorithm is proposed and corresponding domination condition is defined. The major contribution of our model is: 1) We propose a method to deal with non-additive shortest path problem; 2) Operation to allow relaxing some soft rules is allowed in our algorithm, which can improve the coverage rate; 3) Multi-thread techniques are used to improve the efficiency of the algorithm when generating Line-of-Work for crew members. Here a column generation based algorithm for the airline cabin crew rostering problem is proposed. The objective is to assign a personalized roster to crew member which minimize the number of unassigned pairings and ensure the fairness to crew members. The algorithm we propose in this paper has been put into production in a major airline in China and numerical experiments show that it has a good performance.

Keywords: aircrew rostering, aircrew scheduling, column generation, SPPRC

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Authors: M. Cardenas-Garcia, P. P. Gonzalez-Perez

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Intercellular communication is a necessary condition for cellular functions and it allows a group of cells to survive as a population. Throughout this interaction, the cells work in a coordinated and collaborative way which facilitates their survival. In the case of cancerous cells, these take advantage of intercellular communication to preserve their malignancy, since through these physical unions they can send signs of malignancy. The Wnt/β-catenin signaling pathway plays an important role in the formation of intercellular communications, being also involved in a large number of cellular processes such as proliferation, differentiation, adhesion, cell survival, and cell death. The modeling and simulation of cellular signaling systems have found valuable support in a wide range of modeling approaches, which cover a wide spectrum ranging from mathematical models; e.g., ordinary differential equations, statistical methods, and numerical methods– to computational models; e.g., process algebra for modeling behavior and variation in molecular systems. Based on these models, different simulation tools have been developed from mathematical ones to computational ones. Regarding cellular and molecular processes in cancer, its study has also found a valuable support in different simulation tools that, covering a spectrum as mentioned above, have allowed the in silico experimentation of this phenomenon at the cellular and molecular level. In this work, we simulate and explore the complex interaction patterns of intercellular communication in cancer cells using the Cellulat bioinformatics tool, a computational simulation tool developed by us and motivated by two key elements: 1) a biochemically inspired model of self-organizing coordination in tuple spaces, and 2) the Gillespie’s algorithm, a stochastic simulation algorithm typically used to mimic systems of chemical/biochemical reactions in an efficient and accurate way. The main idea behind the Cellulat simulation tool is to provide an in silico experimentation environment that complements and guides in vitro experimentation in intra and intercellular signaling networks. Unlike most of the cell signaling simulation tools, such as E-Cell, BetaWB and Cell Illustrator which provides abstractions to model only intracellular behavior, Cellulat is appropriate for modeling both intracellular signaling and intercellular communication, providing the abstractions required to model –and as a result, simulate– the interaction mechanisms that involve two or more cells, that is essential in the scenario discussed in this work. During the development of this work we made evident the application of our computational simulation tool (Cellulat) for the modeling and simulation of intercellular communication between normal and cancerous cells, and in this way, propose key molecules that may prevent the arrival of malignant signals to the cells that surround the tumor cells. In this manner, we could identify the significant role that has the Wnt/β-catenin signaling pathway in cellular communication, and therefore, in the dissemination of cancer cells. We verified, using in silico experiments, how the inhibition of this signaling pathway prevents that the cells that surround a cancerous cell are transformed.

Keywords: cancer cells, in silico approach, intercellular communication, key molecules, modeling and simulation

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Authors: Joseph M. Rich, Vinay A. Duddalwar, Assad A. Oberai

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Prostate adenocarcinoma is the most common cancer in males, with osseous metastases as the commonest site of metastatic prostate carcinoma (mPC). Treatment monitoring is based on the evaluation and characterization of lesions on multiple imaging studies, including Computed Tomography (CT). Monitoring of the osseous disease burden, including follow-up of lesions and identification and characterization of new lesions, is a laborious task for radiologists. Deep learning algorithms are increasingly used to perform tasks such as identification and segmentation for osseous metastatic disease and provide accurate information regarding metastatic burden. Here, nnUNet was used to produce a model which can segment CT scan images of prostate adenocarcinoma vertebral bone metastatic lesions. nnUNet is an open-source Python package that adds optimizations to deep learning-based UNet architecture but has not been extensively combined with transfer learning techniques due to the absence of a readily available functionality of this method. The IRB-approved study data set includes imaging studies from patients with mPC who were enrolled in clinical trials at the University of Southern California (USC) Health Science Campus and Los Angeles County (LAC)/USC medical center. Manual segmentation of metastatic lesions was completed by an expert radiologist Dr. Vinay Duddalwar (20+ years in radiology and oncologic imaging), to serve as ground truths for the automated segmentation. Despite nnUNet’s success on some medical segmentation tasks, it only produced an average Dice Similarity Coefficient (DSC) of 0.31 on the USC dataset. DSC results fell in a bimodal distribution, with most scores falling either over 0.66 (reasonably accurate) or at 0 (no lesion detected). Applying more aggressive data augmentation techniques dropped the DSC to 0.15, and reducing the number of epochs reduced the DSC to below 0.1. Datasets have been identified for transfer learning, which involve balancing between size and similarity of the dataset. Identified datasets include the Pancreas data from the Medical Segmentation Decathlon, Pelvic Reference Data, and CT volumes with multiple organ segmentations (CT-ORG). Some of the challenges of producing an accurate model from the USC dataset include small dataset size (115 images), 2D data (as nnUNet generally performs better on 3D data), and the limited amount of public data capturing annotated CT images of bone lesions. Optimizations and improvements will be made by applying transfer learning and generative methods, including incorporating generative adversarial networks and diffusion models in order to augment the dataset. Performance with different libraries, including MONAI and custom architectures with Pytorch, will be compared. In the future, molecular correlations will be tracked with radiologic features for the purpose of multimodal composite biomarker identification. Once validated, these models will be incorporated into evaluation workflows to optimize radiologist evaluation. Our work demonstrates the challenges of applying automated image segmentation to small medical datasets and lays a foundation for techniques to improve performance. As machine learning models become increasingly incorporated into the workflow of radiologists, these findings will help improve the speed and accuracy of vertebral metastatic lesions detection.

Keywords: deep learning, image segmentation, medicine, nnUNet, prostate carcinoma, radiomics

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

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With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarms, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Getaneh Berie Tarekegn, Li-Chia Tai

Abstract:

With the rapid advancement of artificial intelligence, low-power built-in sensors on Internet of Things devices, and communication technologies, location-aware services have become increasingly popular and have permeated every aspect of people’s lives. Global navigation satellite systems (GNSSs) are the default method of providing continuous positioning services for ground and aerial vehicles, as well as consumer devices (smartphones, watches, notepads, etc.). However, the environment affects satellite positioning systems, particularly indoors, in dense urban and suburban cities enclosed by skyscrapers, or when deep shadows obscure satellite signals. This is because (1) indoor environments are more complicated due to the presence of many objects surrounding them; (2) reflection within the building is highly dependent on the surrounding environment, including the positions of objects and human activity; and (3) satellite signals cannot be reached in an indoor environment, and GNSS doesn't have enough power to penetrate building walls. GPS is also highly power-hungry, which poses a severe challenge for battery-powered IoT devices. Due to these challenges, IoT applications are limited. Consequently, precise, seamless, and ubiquitous Positioning, Navigation and Timing (PNT) systems are crucial for many artificial intelligence Internet of Things (AI-IoT) applications in the era of smart cities. Their applications include traffic monitoring, emergency alarming, environmental monitoring, location-based advertising, intelligent transportation, and smart health care. This paper proposes a generative AI-based positioning scheme for large-scale wireless settings using fingerprinting techniques. In this article, we presented a novel semi-supervised deep convolutional generative adversarial network (S-DCGAN)-based radio map construction method for real-time device localization. We also employed a reliable signal fingerprint feature extraction method with t-distributed stochastic neighbor embedding (t-SNE), which extracts dominant features while eliminating noise from hybrid WLAN and long-term evolution (LTE) fingerprints. The proposed scheme reduced the workload of site surveying required to build the fingerprint database by up to 78.5% and significantly improved positioning accuracy. The results show that the average positioning error of GAILoc is less than 0.39 m, and more than 90% of the errors are less than 0.82 m. According to numerical results, SRCLoc improves positioning performance and reduces radio map construction costs significantly compared to traditional methods.

Keywords: location-aware services, feature extraction technique, generative adversarial network, long short-term memory, support vector machine

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Authors: Neoclis Hadjigeorgiou, Spyridon Angelopoulos, Evangelos V. Hristoforou, Paul P. Sotiriadis

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The increasing demand for accurate and reliable magnetic measurements over the past decades has paved the way for the development of different types of magnetic sensing systems as well as of more advanced measurement techniques. Anisotropic Magneto Resistance (AMR) sensors have emerged as a promising solution for applications requiring high resolution, providing an ideal balance between performance and cost. However, certain issues of AMR sensors such as non-linear response and measurement noise are rarely discussed in the relevant literature. In this work, an analog closed loop compensation system is proposed, developed and tested as a means to eliminate the non-linearity of AMR response, reduce the 1/f noise and enhance the sensitivity of magnetic sensor. Additional performance aspects, such as cross-axis and hysteresis effects are also examined. This system was analyzed using an analytical model and a P-Spice model, considering both the sensor itself as well as the accompanying electronic circuitry. In addition, a commercial closed loop architecture Flux-Gate sensor (calibrated and certified), has been used for comparison purposes. Three different experimental setups have been constructed for the purposes of this work, each one utilized for DC magnetic field measurements, AC magnetic field measurements and Noise density measurements respectively. The DC magnetic field measurements have been conducted in laboratory environment employing a cubic Helmholtz coil setup in order to calibrate and characterize the system under consideration. A high-accuracy DC power supply has been used for providing the operating current to the Helmholtz coils. The results were recorded by a multichannel voltmeter The AC magnetic field measurements have been conducted in laboratory environment employing a cubic Helmholtz coil setup in order to examine the effective bandwidth not only of the proposed system but also for the Flux-Gate sensor. A voltage controlled current source driven by a function generator has been utilized for the Helmholtz coil excitation. The result was observed by the oscilloscope. The third experimental apparatus incorporated an AC magnetic shielding construction composed of several layers of electric steel that had been demagnetized prior to the experimental process. Each sensor was placed alone and the response was captured by the oscilloscope. The preliminary experimental results indicate that closed loop AMR response presented a maximum deviation of 0.36% with respect to the ideal linear response, while the corresponding values for the open loop AMR system and the Fluxgate sensor reached 2% and 0.01% respectively. Moreover, the noise density of the proposed close loop AMR sensor system remained almost as low as the noise density of the AMR sensor itself, yet considerably higher than that of the Flux-Gate sensor. All relevant numerical data are presented in the paper.

Keywords: AMR sensor, chopper, closed loop, electronic noise, magnetic noise, memory effects, flux-gate sensor, linearity improvement, sensitivity improvement

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Authors: Kantha Siddhanth Gujjari, Shaani Singhal, Robert Andrew Danks, Adrian Praeger

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Background: Trigeminal neuralgia (TN) is a heterogenous pain syndrome characterised by short paroxysms of lancinating facial pain in the distribution of the trigeminal nerve, often triggered by usually innocuous stimuli. TN has a low prevalence of less than 0.1%, of which 80% to 90% is caused by compression of the trigeminal nerve from an adjacent artery or vein. The root entry zone of the trigeminal nerve is most sensitive to neurovascular conflict (NVC), causing dysmyelination. Whilst microvascular decompression (MVD) is an effective treatment for TN with NVC, all patients do not achieve long-term pain relief. Pre-operative scoring systems by Panczykowski and Hardaway have been proposed but have not been externally validated. These pre-operative scoring systems are composite scores calculated according to a subtype of TN, presence and degree of neurovascular conflict, and response to medical treatments. There is discordance in the assessment of NVC identified on pre-operative magnetic resonance imaging (MRI) between neurosurgeons and radiologists. To our best knowledge, the prognostic impact for MVD of this difference of interpretation has not previously been investigated in the form of a composite scoring system such as those suggested by Panczykowski and Hardaway. Aims: This study aims to identify prognostic factors and externally validate the proposed scoring systems by Panczykowski and Hardaway for TN. A secondary aim is to investigate the prognostic difference between a neurosurgeon's interpretation of NVC on MRI compared with a radiologist’s. Methods: This retrospective cohort study included 95 patients who underwent de novo MVD in a single neurosurgical unit in Melbourne. Data was recorded from patients’ hospital records and neurosurgeon’s correspondence from perioperative clinic reviews. Patient demographics, type of TN, distribution of TN, response to carbamazepine, neurosurgeon, and radiologist interpretation of NVC on MRI, were clearly described prospectively and preoperatively in the correspondence. Scoring systems published by Panczykowski et al. and Hardaway et al. were used to determine composite scores, which were compared with the recurrence of TN recorded during follow-up over 1-year. Categorical data analysed using Pearson chi-square testing. Independent numerical and nominal data analysed with logistical regression. Results: Logistical regression showed that a Panczykowski composite score of greater than 3 points was associated with a higher likelihood of pain-free outcome 1-year post-MVD with an OR 1.81 (95%CI 1.41-2.61, p=0.032). The composite score using neurosurgeon’s impression of NVC had an OR 2.96 (95%CI 2.28-3.31, p=0.048). A Hardaway composite score of greater than 2 points was associated with a higher likelihood of pain-free outcome 1 year post-MVD with an OR 3.41 (95%CI 2.58-4.37, p=0.028). The composite score using neurosurgeon’s impression of NVC had an OR 3.96 (95%CI 3.01-4.65, p=0.042). Conclusion: Composite scores developed by Panczykowski and Hardaway were validated for the prediction of response to MVD in TN. A composite score based on the neurosurgeon’s interpretation of NVC on MRI, when compared with the radiologist’s had a greater correlation with pain-free outcomes 1 year post-MVD.

Keywords: de novo microvascular decompression, neurovascular conflict, prognosis, trigeminal neuralgia

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Authors: Mozhdeh Shirinzadeh, Richard Stroetmann

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Orthotropic steel deck bridge consists of a deck plate, longitudinal stiffeners under the deck plate, cross beams and the main longitudinal girders. Due to the several advantages, Orthotropic Steel Deck (OSD) systems have been utilized in many bridges worldwide. The significant feature of this structural system is its high load-bearing capacity while having relatively low dead weight. In addition, cost efficiency and the ability of rapid field erection have made the orthotropic steel deck a popular type of bridge worldwide. However, OSD bridges are highly susceptible to fatigue damage. A large number of welded joints can be regarded as the main weakness of this system. This problem is, in particular, evident in the bridges which were built before 1994 when the fatigue design criteria had not been introduced in the bridge design codes. Recently, an Orthotropic-composite slab (OCS) for road bridges has been experimentally and numerically evaluated and developed at Technische Universität Dresden as a part of AIF-FOSTA research project P1265. The results of the project have provided a solid foundation for the design and analysis of Orthotropic-composite decks with dowel strips as a durable alternative to conventional steel or reinforced concrete decks. In continuation, while using the achievements of that project, the application of a modified Ortho-composite deck for an existing typical OSD bridge is investigated. Composite action is obtained by using rows of dowel strips in a clothoid (CL) shape. Regarding Eurocode criteria for different fatigue detail categories of an OSD bridge, the effect of the proposed modification approach is assessed. Moreover, a numerical parametric study is carried out utilizing finite element software to determine the impact of different variables, such as the size and arrangement of dowel strips, the application of transverse or longitudinal rows of dowel strips, and local wheel loads. For the verification of the simulation technique, experimental results of a segment of an OCS deck are used conducted in project P1265. Fatigue assessment is performed based on the last draft of Eurocode 1993-2 (2024) for the most probable detail categories (Hot-Spots) that have been reported in the previous statistical studies. Then, an analytical comparison is provided between the typical orthotropic steel deck and the modified Ortho-composite deck bridge in terms of fatigue issues and durability. The load-bearing capacity of the bridge, the critical deflections, and the composite behavior are also evaluated and compared. Results give a comprehensive overview of the efficiency of the rehabilitation method considering the required design service life of the bridge. Moreover, the proposed approach is assessed with regard to the construction method, details and practical aspects, as well as the economic point of view.

Keywords: composite action, fatigue, finite element method, steel deck, bridge

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Authors: Anna Makuch, Grzegorz Kokot, Konstanty Skalski, Jakub Banczorowski

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Cancellous bone is a porous composite of a hierarchical structure and anisotropic properties. The biological tissue is considered to be a viscoelastic material, but many studies based on a nanoindentation method have focused on their elasticity and microhardness. However, the response of many organic materials depends not only on the load magnitude, but also on its duration and time course. Depth Sensing Indentation (DSI) technique has been used for examination of creep in polymers, metals and composites. In the indentation tests on biological samples, the mechanical properties are most frequently determined for animal tissues (of an ox, a monkey, a pig, a rat, a mouse, a bovine). However, there are rare reports of studies of the bone viscoelastic properties on microstructural level. Various rheological models were used to describe the viscoelastic behaviours of bone, identified in the indentation process (e. g Burgers model, linear model, two-dashpot Kelvin model, Maxwell-Voigt model). The goal of the study was to determine the influence of creep effect on the mechanical properties of human cancellous bone in indentation tests. The aim of this research was also the assessment of the material properties of bone structures, having in mind the energy aspects of the curve (penetrator loading-depth) obtained in the loading/unloading cycle. There was considered how the different holding times affected the results within trabecular bone.As a result, indentation creep (CIT), hardness (HM, HIT, HV) and elasticity are obtained. Human trabecular bone samples (n=21; mean age 63±15yrs) from the femoral heads replaced during hip alloplasty were removed and drained from alcohol of 1h before the experiment. The indentation process was conducted using CSM Microhardness Tester equipped with Vickers indenter. Each sample was indented 35 times (7 times for 5 different hold times: t1=0.1s, t2=1s, t3=10s, t4=100s and t5=1000s). The indenter was advanced at a rate of 10mN/s to 500mN. There was used Oliver-Pharr method in calculation process. The increase of hold time is associated with the decrease of hardness parameters (HIT(t1)=418±34 MPa, HIT(t2)=390±50 MPa, HIT(t3)= 313±54 MPa, HIT(t4)=305±54 MPa, HIT(t5)=276±90 MPa) and elasticity (EIT(t1)=7.7±1.2 GPa, EIT(t2)=8.0±1.5 GPa, EIT(t3)=7.0±0.9 GPa, EIT(t4)=7.2±0.9 GPa, EIT(t5)=6.2±1.8 GPa) as well as with the increase of the elastic (Welastic(t1)=4.11∙10-7±4.2∙10-8Nm, Welastic(t2)= 4.12∙10-7±6.4∙10-8 Nm, Welastic(t3)=4.71∙10-7±6.0∙10-9 Nm, Welastic(t4)= 4.33∙10-7±5.5∙10-9Nm, Welastic(t5)=5.11∙10-7±7.4∙10-8Nm) and inelastic (Winelastic(t1)=1.05∙10-6±1.2∙10-7 Nm, Winelastic(t2) =1.07∙10-6±7.6∙10-8 Nm, Winelastic(t3)=1.26∙10-6±1.9∙10-7Nm, Winelastic(t4)=1.56∙10-6± 1.9∙10-7 Nm, Winelastic(t5)=1.67∙10-6±2.6∙10-7)) reaction of materials. The indentation creep increased logarithmically (R2=0.901) with increasing hold time: CIT(t1) = 0.08±0.01%, CIT(t2) = 0.7±0.1%, CIT(t3) = 3.7±0.3%, CIT(t4) = 12.2±1.5%, CIT(t5) = 13.5±3.8%. The pronounced impact of creep effect on the mechanical properties of human cancellous bone was observed in experimental studies. While the description elastic-inelastic, and thus the Oliver-Pharr method for data analysis, may apply in few limited cases, most biological tissues do not exhibit elastic-inelastic indentation responses. Viscoelastic properties of tissues may play a significant role in remodelling. The aspect is still under an analysis and numerical simulations. Acknowledgements: The presented results are part of the research project founded by National Science Centre (NCN), Poland, no.2014/15/B/ST7/03244.

Keywords: bone, creep, indentation, mechanical properties

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Authors: Shuai Yuan, Minxia Shi, Xu Zhang, Jianzhi Yang, Kangqi Tian, Yuzheng Ma

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The precise measurement of muscle activities is essential for understanding the function of various body movements. This work aims to develop a muscle magnetic field signal detection system based on mathematical analysis. Medical research has underscored that early detection of muscle atrophy, coupled with lifestyle adjustments such as dietary control and increased exercise, can significantly enhance muscle-related diseases. Currently, surface electromyography (sEMG) is widely employed in research as an early predictor of muscle atrophy. Nonetheless, the primary limitation of using sEMG to forecast muscle strength is its inability to directly measure the signals generated by muscles. Challenges arise from potential skin-electrode contact issues due to perspiration, leading to inaccurate signals or even signal loss. Additionally, resistance and phase are significantly impacted by adipose layers. The recent emergence of optically pumped magnetometers introduces a fresh avenue for bio-magnetic field measurement techniques. These magnetometers possess high sensitivity and obviate the need for a cryogenic environment unlike superconducting quantum interference devices (SQUIDs). They detect muscle magnetic field signals in the range of tens to thousands of femtoteslas (fT). The utilization of magnetometers for capturing muscle magnetic field signals remains unaffected by issues of perspiration and adipose layers. Since their introduction, optically pumped atomic magnetometers have found extensive application in exploring the magnetic fields of organs such as cardiac and brain magnetism. The optimal operation of these magnetometers necessitates an environment with an ultra-weak magnetic field. To achieve such an environment, researchers usually utilize a combination of active magnetic compensation technology with passive magnetic shielding technology. Passive magnetic shielding technology uses a magnetic shielding device built with high permeability materials to attenuate the external magnetic field to a few nT. Compared with more layers, the coils that can generate a reverse magnetic field to precisely compensate for the residual magnetic fields are cheaper and more flexible. To attain even lower magnetic fields, compensation coils designed by Biot-Savart law are involved to generate a counteractive magnetic field to eliminate residual magnetic fields. By solving the magnetic field expression of discrete points in the target region, the parameters that determine the current density distribution on the plane can be obtained through the conventional target field method. The current density is obtained from the partial derivative of the stream function, which can be represented by the combination of trigonometric functions. Optimization algorithms in mathematics are introduced into coil design to obtain the optimal current density distribution. A one-dimensional linear regression analysis was performed on the collected data, obtaining a coefficient of determination R2 of 0.9349 with a p-value of 0. This statistical result indicates a stable relationship between the peak-to-peak value (PPV) of the muscle magnetic field signal and the magnitude of grip strength. This system is expected to be a widely used tool for healthcare professionals to gain deeper insights into the muscle health of their patients.

Keywords: muscle magnetic signal, magnetic shielding, compensation coils, trigonometric functions.

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Authors: Nicola Milani, Carla Bellissimo, Davide Pogliana, Davide Panzin, Luca Garlaschelli, Giulia Facchinetti, Claudia Casson, Luca Marazzina, Andrea Sartori, Simone Rivaroli, Jeff Konin

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The patient is a 26 year-old male amateur football player from Milan, Italy; (81kg; 185cm; BMI 23.6 kg/m²). He sustained a non-contact anterior cruciate ligament tear to his right knee in June 2021. In September 2021, his right knee ligament was reconstructed using a semitendinosus graft. The injury occurred during a football match on natural grass with typical shoes on a warm day (32 degrees celsius). Playing as a defender he sustained the injury during a change of direction, where the foot was fixated on the grass. He felt pain and was unable to continue playing the match. The surgeon approved his rehabilitation to begin two weeks post-operative. The initial physiotherapist assessment determined performing two training sessions per day within the first three months. In the first three weeks, the pain was 4/10 on Numerical Rating Scale (NRS), no swelling, a range of motion was 0-110°, with difficulty fully extending his knee and minimal quadriceps activation. Crutches were discontinued at four weeks with improved walking. Active exercise, electrostimulator, physical therapy, massages, osteopathy, and passive motion were initiated. At week 6, he completed his first functional movement screen; the score was 16/21 with no pain and no swelling. At week 8, the isokinetic test showed a 23% differential deficit between the two legs in maximum strength (at 90°/s). At week 10, he improved to 15% of injury-induced deficit which suggested he was ready to start running. At week 12, the athlete sustained his first threshold test. At week 16, he performed his first return to sports movement assessment, which revealed a 10% stronger difference between the legs. At week 16, he had his second threshold test. At week 17, his first on-field test revealed a 5% differential deficit between the two legs in the hop test. At week 18, isokinetic test demonstrates that the uninjured leg was 7% stronger than the recovering leg in maximum strength (at 90°/s). At week 20, his second on-field test revealed a 2% difference in hop test; at week 21, his third isokinetic test demonstrated a difference of 5% in maximum strength (at 90°/s). At week 21, he performed his second return to sports movement assessment which revealed a 2% difference between the limbs. Since it was the end of the championship, the team asked him to partake in the playoffs; moreover the player was very motivated to participate in the playoffs also because he was the captain of the team. Together with the player and the team, we decided to let him play even though we were aware of a heightened risk of injury than what is reported in the literature because of two factors: biological recovery times and the results of the tests we performed. In the decision making process about the athlete’s recovery time, it is important to balance the information available from the literature with the desires of the patient to avoid frustration.

Keywords: ACL, football, rehabilitation, return to play

Procedia PDF Downloads 93

Authors: Dongping Fang, Lian Duan, Xiaojing Yuan, Mike Xu, Allyn Klunder, Kevin Tan, Suiting Cao, Yeqing Ji

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Accurate prediction of a medical condition with straight clinical evidence is a long-sought topic in the medical management and health insurance field. Although great progress has been made with machine learning algorithms, the medical community is still, to a certain degree, suspicious about the model's accuracy and interpretability. This paper presents an innovative hierarchical attention deep learning model to achieve good prediction and clear interpretability that can be easily understood by medical professionals. This deep learning model uses a hierarchical attention structure that matches naturally with the medical history data structure and reflects the member’s encounter (date of service) sequence. The model attention structure consists of 3 levels: (1) attention on the medical code types (diagnosis codes, procedure codes, lab test results, and prescription drugs), (2) attention on the sequential medical encounters within a type, (3) attention on the medical codes within an encounter and type. This model is applied to predict the occurrence of stage 3 chronic kidney disease (CKD3), using three years’ medical history of Medicare Advantage (MA) members from a top health insurance company. The model takes members’ medical events, both claims and electronic medical record (EMR) data, as input, makes a prediction of CKD3 and calculates the contribution from individual events to the predicted outcome. The model outcome can be easily explained with the clinical evidence identified by the model algorithm. Here are examples: Member A had 36 medical encounters in the past three years: multiple office visits, lab tests and medications. The model predicts member A has a high risk of CKD3 with the following well-contributed clinical events - multiple high ‘Creatinine in Serum or Plasma’ tests and multiple low kidneys functioning ‘Glomerular filtration rate’ tests. Among the abnormal lab tests, more recent results contributed more to the prediction. The model also indicates regular office visits, no abnormal findings of medical examinations, and taking proper medications decreased the CKD3 risk. Member B had 104 medical encounters in the past 3 years and was predicted to have a low risk of CKD3, because the model didn’t identify diagnoses, procedures, or medications related to kidney disease, and many lab test results, including ‘Glomerular filtration rate’ were within the normal range. The model accurately predicts members A and B and provides interpretable clinical evidence that is validated by clinicians. Without extra effort, the interpretation is generated directly from the model and presented together with the occurrence date. Our model uses the medical data in its most raw format without any further data aggregation, transformation, or mapping. This greatly simplifies the data preparation process, mitigates the chance for error and eliminates post-modeling work needed for traditional model explanation. To our knowledge, this is the first paper on an interpretable deep-learning model using a 3-level attention structure, sourcing both EMR and claim data, including all 4 types of medical data, on the entire Medicare population of a big insurance company, and more importantly, directly generating model interpretation to support user decision. In the future, we plan to enrich the model input by adding patients’ demographics and information from free-texted physician notes.

Keywords: deep learning, interpretability, attention, big data, medical conditions

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Authors: Anita Bajpai, Sarah Duan, Ashlee Erskine, Shehzein Khan, Raymond Kramer

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Introduction: This research focuses on exploring the beneficial effects if any, of Vitamin-D in reducing the need for supplemental oxygen among hospitalized COVID-19 patients. Two questions are investigated – Q1)Doeshaving a healthy level of baselineVitamin-D 25-OH (≥ 30ng/ml) help,andQ2) does administering Vitamin-D therapy after-the-factduring inpatient hospitalization help? Methods/Study Design: This is a comprehensive, retrospective, observational study of all inpatients at RUHS from March through December 2020 who tested positive for COVID-19 based on real-time reverse transcriptase–polymerase chain reaction assay of nasal and pharyngeal swabs and rapid assay antigen test. To address Q1, we looked atall N1=182 patients whose baseline plasma Vitamin-D 25-OH was known and who needed supplemental oxygen. Of this, a total of 121 patients had a healthy Vitamin-D level of ≥30 ng/mlwhile the remaining 61 patients had low or borderline (≤ 29.9ng/ml)level. Similarly, for Q2, we looked at a total of N2=893 patients who were given supplemental oxygen, of which713 were not given Vitamin-D and 180 were given Vitamin-D therapy. The numerical value of the maximum amount of oxygen flow rate(dependent variable) administered was recorded for each patient. The mean values and associated standard deviations for each group were calculated. Thesetwo sets of independent data served as the basis for independent, two-sample t-Test statistical analysis. To be accommodative of any reasonable benefitof Vitamin-D, ap-value of 0.10(α< 10%) was set as the cutoff point for statistical significance. Results: Given the large sample sizes, the calculated statistical power for both our studies exceeded the customary norm of 80% or better (β< 0.2). For Q1, the mean value for maximumoxygen flow rate for the group with healthybaseline level of Vitamin-D was 8.6 L/min vs.12.6L/min for those with low or borderline levels, yielding a p-value of 0.07 (p < 0.10) with the conclusion that those with a healthy level of baseline Vitamin-D needed statistically significant lower levels of supplemental oxygen. ForQ2, the mean value for a maximum oxygen flow rate for those not administered Vitamin-Dwas 12.5 L/min vs.12.8L/min for those given Vitamin-D, yielding a p-valueof 0.87 (p > 0.10). We thereforeconcludedthat there was no statistically significant difference in the use of oxygen therapy between those who were or were not administered Vitamin-D after-the-fact in the hospital. Discussion/Conclusion: We found that patients who had healthy levels of Vitamin-D at baseline needed statistically significant lower levels of supplemental oxygen. Vitamin-D is well documented, including in a recent article in the Lancet, for its anti-inflammatory role as an adjuvant in the regulation of cytokines and immune cells. Interestingly, we found no statistically significant advantage for giving Vitamin-D to hospitalized patients. It may be a case of “too little too late”. A randomized clinical trial reported in JAMA also did not find any reduction in hospital stay of patients given Vitamin-D. Such conclusions come with a caveat that any delayed marginal benefits may not have materialized promptly in the presence of a significant inflammatory condition. Since Vitamin-D is a low-cost, low-risk option, it may still be useful on an inpatient basis until more definitive findings are established.

Keywords: COVID-19, vitamin-D, supplemental oxygen, vitamin-D in primary care

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Authors: Jiujie Cai, Fengxia LI, Haibo Wang

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After years of research, offshore oil and gas development now are shifted to unconventional reservoirs, where multi-stage hydraulic fracturing technology has been widely used. However, the simulation of complex hydraulic fractures in tight reservoirs is faced with geological and engineering difficulties, such as large burial depths, sand-shale interbeds, and complex stress barriers. The objective of this work is to simulate the hydraulic fracture propagation in the tight sandstone matrix of the marine-continental transitional reservoirs, where the Shanxi Formation in Tianhuan syncline of the Dongsheng gas field was used as the research target. The characteristic parameters of the vertical rock samples with rich beddings were clarified through rock mechanics experiments. The influence of rock mechanical parameters, vertical stress difference of pay-zone and bedding layer, and fracturing parameters (such as injection rates, fracturing fluid viscosity, and number of perforation clusters within single stage) on fracture initiation and propagation were investigated. In this paper, a 3-D fracture propagation model was built to investigate the complex fracture propagation morphology by boundary element method, considering the strength of bonding surface between layers, vertical stress difference and fracturing parameters (such as injection rates, fluid volume and viscosity). The research results indicate that on the condition of vertical stress difference (3 MPa), the fracture height can break through and enter the upper interlayer when the thickness of the overlying bedding layer is 6-9 m, considering effect of the weak bonding surface between layers. The fracture propagates within the pay zone when overlying interlayer is greater than 13 m. Difference in fluid volume distribution between clusters could be more than 20% when the stress difference of each cluster in the segment exceeds 2MPa. Fracture cluster in high stress zones cannot initiate when the stress difference in the segment exceeds 5MPa. The simulation results of fracture height are much higher if the effect of weak bonding surface between layers is not involved. By increasing the injection rates, increasing fracturing fluid viscosity, and reducing the number of clusters within single stage can promote the fracture height propagation through layers. Optimizing the perforation position and reducing the number of perforations can promote the uniform expansion of fractures. Typical curves of fracture height estimation were established for the tight sandstone of the Lower Permian Shanxi Formation. The model results have good consistency with micro-seismic monitoring results of hydraulic fracturing in Well 1HF.

Keywords: fracture propagation, boundary element method, fracture height, offshore oil and gas, marine-continental transitional reservoirs, rock mechanics experiment

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Authors: Jelvin Tom Sebastian, Vinod Yeldho Baby

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In space, electronic devices are constantly attacked with radiation, which causes certain parts to fail or behave in unpredictable ways. To advance the thermal controllability for microsatellites, we need a new approach and thermal control system that is smaller than that on conventional satellites and that demand no electric power. Heat exchange inside the microsatellites is not that easy as conventional satellites due to the smaller size. With slight mass gain and no electric power, accommodating heat using phase change materials (PCMs) is a strong candidate for solving micro satellites' thermal difficulty. In other words, PCMs can absorb or produce heat in the form of latent heat, changing their phase and minimalizing the temperature fluctuation around the phase change point. The main restriction for these systems is thermal conductivity weakness of common PCMs. As PCM is having low thermal conductivity, it increases the melting and solidification time, which is not suitable for specific application like electronic cooling. In order to increase the thermal conductivity nanoparticles are introduced. Adding the nanoparticles in base PCM increases the thermal conductivity. Increase in weight concentration increases the thermal conductivity. This paper numerically investigates the thermal energy storage panel with nanoparticle enhanced phase change material. Silver nanostructure have increased the thermal properties of the base PCM, eicosane. Different weight concentration (1, 2, 3.5, 5, 6.5, 8, 10%) of silver enhanced phase change material was considered. Both steady state and transient analysis was performed to compare the characteristics of nanoparticle enhanced phase material at different heat loads. Results showed that in steady state, the temperature near the front panel reduced and temperature on NePCM panel increased as the weight concentration increased. With the increase in thermal conductivity more heat was absorbed into the NePCM panel. In transient analysis, it was found that the effect of nanoparticle concentration on maximum temperature of the system was reduced as the melting point of the material reduced with increase in weight concentration. But for the heat load of maximum 20W, the model with NePCM did not attain the melting point temperature. Therefore it showed that the model with NePCM is capable of holding more heat load. In order to study the heat load capacity double the load is given, maximum of 40W was given as first half of the cycle and the other is given constant OW. Higher temperature was obtained comparing the other heat load. The panel maintained a constant temperature for a long duration according to the NePCM melting point. In both the analysis, the uniformity of temperature of the TESP was shown. Using Ag-NePCM it allows maintaining a constant peak temperature near the melting point. Therefore, by altering the weight concentration of the Ag-NePCM it is possible to create an optimum operating temperature required for the effective working of the electronics components.

Keywords: carbon-fiber-reinforced polymer, micro/nano-satellite, nanoparticle phase change material, thermal energy storage

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Authors: Muhammad Bilal Shaikh

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Mineral processing plants play a pivotal role in extracting valuable minerals from raw ores, contributing significantly to various industries. However, the optimization of plant throughput remains a complex challenge, necessitating innovative approaches for increased efficiency and productivity. This research paper investigates the application of Multimodal Artificial Intelligence (MAI) techniques to address this challenge, aiming to improve overall plant throughput in mineral processing operations. The integration of multimodal AI leverages a combination of diverse data sources, including sensor data, images, and textual information, to provide a holistic understanding of the complex processes involved in mineral extraction. The paper explores the synergies between various AI modalities, such as machine learning, computer vision, and natural language processing, to create a comprehensive and adaptive system for optimizing mineral processing plants. The primary focus of the research is on developing advanced predictive models that can accurately forecast various parameters affecting plant throughput. Utilizing historical process data, machine learning algorithms are trained to identify patterns, correlations, and dependencies within the intricate network of mineral processing operations. This enables real-time decision-making and process optimization, ultimately leading to enhanced plant throughput. Incorporating computer vision into the multimodal AI framework allows for the analysis of visual data from sensors and cameras positioned throughout the plant. This visual input aids in monitoring equipment conditions, identifying anomalies, and optimizing the flow of raw materials. The combination of machine learning and computer vision enables the creation of predictive maintenance strategies, reducing downtime and improving the overall reliability of mineral processing plants. Furthermore, the integration of natural language processing facilitates the extraction of valuable insights from unstructured textual data, such as maintenance logs, research papers, and operator reports. By understanding and analyzing this textual information, the multimodal AI system can identify trends, potential bottlenecks, and areas for improvement in plant operations. This comprehensive approach enables a more nuanced understanding of the factors influencing throughput and allows for targeted interventions. The research also explores the challenges associated with implementing multimodal AI in mineral processing plants, including data integration, model interpretability, and scalability. Addressing these challenges is crucial for the successful deployment of AI solutions in real-world industrial settings. To validate the effectiveness of the proposed multimodal AI framework, the research conducts case studies in collaboration with mineral processing plants. The results demonstrate tangible improvements in plant throughput, efficiency, and cost-effectiveness. The paper concludes with insights into the broader implications of implementing multimodal AI in mineral processing and its potential to revolutionize the industry by providing a robust, adaptive, and data-driven approach to optimizing plant operations. In summary, this research contributes to the evolving field of mineral processing by showcasing the transformative potential of multimodal artificial intelligence in enhancing plant throughput. The proposed framework offers a holistic solution that integrates machine learning, computer vision, and natural language processing to address the intricacies of mineral extraction processes, paving the way for a more efficient and sustainable future in the mineral processing industry.

Keywords: multimodal AI, computer vision, NLP, mineral processing, mining

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

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Simulations are commonly used to predict the bistatic radar cross section (RCS) of military targets since characterization measurements can be expensive and time consuming. It is thus important to accurately predict the bistatic RCS of targets. Computational electromagnetic (CEM) methods can be used for bistatic RCS prediction. CEM methods are divided into full-wave and asymptotic methods. Full-wave methods are numerical approximations to the exact solution of Maxwell’s equations. These methods are very accurate but are computationally very intensive and time consuming. Asymptotic techniques make simplifying assumptions in solving Maxwell's equations and are thus less accurate but require less computational resources and time. Asymptotic techniques can thus be very valuable for the prediction of bistatic RCS of electrically large targets, due to the decreased computational requirements. This study extends previous work by validating the accuracy of asymptotic techniques to predict bistatic RCS through comparison with full-wave simulations as well as measurements. Validation is done with canonical structures as well as complex realistic aircraft models instead of only looking at a complex slicy structure. The slicy structure is a combination of canonical structures, including cylinders, corner reflectors and cubes. Validation is done over large bistatic angles and at different polarizations. Bistatic RCS measurements were conducted in a compact range, at the University of Pretoria, South Africa. The measurements were performed at different polarizations from 2 GHz to 6 GHz. Fixed bistatic angles of β = 30.8°, 45° and 90° were used. The measurements were calibrated with an active calibration target. The EM simulation tool FEKO was used to generate simulated results. The full-wave multi-level fast multipole method (MLFMM) simulated results together with the measured data were used as reference for validation. The accuracy of physical optics (PO) and geometrical optics (GO) was investigated. Differences relating to amplitude, lobing structure and null positions were observed between the asymptotic, full-wave and measured data. PO and GO were more accurate at angles close to the specular scattering directions and the accuracy seemed to decrease as the bistatic angle increased. At large bistatic angles PO did not perform well due to the shadow regions not being treated appropriately. PO also did not perform well for canonical structures where multi-bounce was the main scattering mechanism. PO and GO do not account for diffraction but these inaccuracies tended to decrease as the electrical size of objects increased. It was evident that both asymptotic techniques do not properly account for bistatic structural shadowing. Specular scattering was calculated accurately even if targets did not meet the electrically large criteria. It was evident that the bistatic RCS prediction performance of PO and GO depends on incident angle, frequency, target shape and observation angle. The improved computational efficiency of the asymptotic solvers yields a major advantage over full-wave solvers and measurements; however, there is still much room for improvement of the accuracy of these asymptotic techniques.

Keywords: asymptotic techniques, bistatic RCS, geometrical optics, physical optics

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Authors: Sara Rejeb, Catherine Duveau, Tabea Rebafka

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To monitor the production process of turbofan aircraft engines, multiple measurements of various geometrical parameters are systematically recorded on manufactured parts. Engine parts are subject to extremely high standards as they can impact the performance of the engine. Therefore, it is essential to analyze these databases to better understand the influence of the different parameters on the engine's performance. Self-organizing maps are unsupervised neural networks which achieve two tasks simultaneously: they visualize high-dimensional data by projection onto a 2-dimensional map and provide clustering of the data. This technique has become very popular for data exploration since it provides easily interpretable results and a meaningful global view of the data. As such, self-organizing maps are usually applied to aircraft engine condition monitoring. As databases in this field are huge and complex, they naturally contain multiple missing entries for various reasons. The classical Kohonen algorithm to compute self-organizing maps is conceived for complete data only. A naive approach to deal with partially observed data consists in deleting items or variables with missing entries. However, this requires a sufficient number of complete individuals to be fairly representative of the population; otherwise, deletion leads to a considerable loss of information. Moreover, deletion can also induce bias in the analysis results. Alternatively, one can first apply a common imputation method to create a complete dataset and then apply the Kohonen algorithm. However, the choice of the imputation method may have a strong impact on the resulting self-organizing map. Our approach is to address simultaneously the two problems of computing a self-organizing map and imputing missing values, as these tasks are not independent. In this work, we propose an extension of self-organizing maps for partially observed data, referred to as missSOM. First, we introduce a criterion to be optimized, that aims at defining simultaneously the best self-organizing map and the best imputations for the missing entries. As such, missSOM is also an imputation method for missing values. To minimize the criterion, we propose an iterative algorithm that alternates the learning of a self-organizing map and the imputation of missing values. Moreover, we develop an accelerated version of the algorithm by entwining the iterations of the Kohonen algorithm with the updates of the imputed values. This method is efficiently implemented in R and will soon be released on CRAN. Compared to the standard Kohonen algorithm, it does not come with any additional cost in terms of computing time. Numerical experiments illustrate that missSOM performs well in terms of both clustering and imputation compared to the state of the art. In particular, it turns out that missSOM is robust to the missingness mechanism, which is in contrast to many imputation methods that are appropriate for only a single mechanism. This is an important property of missSOM as, in practice, the missingness mechanism is often unknown. An application to measurements on one type of part is also provided and shows the practical interest of missSOM.

Keywords: imputation method of missing data, partially observed data, robustness to missingness mechanism, self-organizing maps

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Authors: De-Shin Liu, Po-Chun Wen, Zhen-Wei Zhuang, Hsueh-Chih Liu, Pei-Chen Huang

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Wafer probing tests play an important role in semiconductor manufacturing procedures in accordance with the yield and reliability requirement of the wafer after the backend-of-the-line process. Accordingly, the stable physical and electrical contact between the probe and the tested wafer during wafer probing is regarded as an essential issue in identifying the known good die. The probe card can be integrated with multiple probe needles, which are classified as vertical, cantilever and micro-electro-mechanical systems type probe selections. Among all potential probe types, the vertical probe has several advantages as compared with other probe types, including maintainability, high probe density and feasibility for high-speed wafer testing. In the present study, the mechanical response of the wafer probing test with the vertical cobra probe on 720 μm thick silicon (Si) substrate with a 1.4 μm thick aluminum (Al) pad is investigated by the experiment and transient dynamic simulation approach. Because the deformation mechanism of the vertical cobra probe is determined by both bending and buckling mechanisms, the stable correlation between contact forces and overdrive (OD) length must be carefully verified. Moreover, the decent OD length with corresponding contact force contributed to piercing the native oxide layer of the Al pad and preventing the probing test-induced damage on the interconnect system. Accordingly, the scratch depth of the Al pad under various OD lengths is estimated by the atomic force microscope (AFM) and simulation work. In the wafer probing test configuration, the contact phenomenon between the probe needle and the tested object introduced large deformation and twisting of mesh gridding, causing the subsequent numerical divergence issue. For this reason, the arbitrary Lagrangian-Eulerian method is utilized in the present simulation work to conquer the aforementioned issue. The analytic results revealed a slight difference when the OD is considered as 40 μm, and the simulated is almost identical to the measured scratch depths of the Al pad under higher OD lengths up to 70 μm. This phenomenon can be attributed to the unstable contact of the probe at low OD length with the scratch depth below 30% of Al pad thickness, and the contact status will be being stable when the scratch depth over 30% of pad thickness. The splash of the Al pad is observed by the AFM, and the splashed Al debris accumulates on a specific side; this phenomenon is successfully simulated in the transient dynamic simulation. Thus, the preferred testing OD lengths are found as 45 μm to 70 μm, and the corresponding scratch depths on the Al pad are represented as 31.4% and 47.1% of Al pad thickness, respectively. The investigation approach demonstrated in this study contributed to analyzing the mechanical response of wafer probing test configuration under large strain conditions and assessed the geometric designs and material selections of probe needles to meet the requirement of high resolution and high-speed wafer-level probing test for thinned wafer application.

Keywords: wafer probing test, vertical probe, probe mark, mechanical response, FEA simulation

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Authors: Asma Omri, Iheb Sifaoui, Sofiane Sayahi, Hichem Besbes

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Future vehicle systems demand advanced capabilities, notably in-cabin life detection and driver monitoring systems, with a particular emphasis on drowsiness detection. To meet these requirements, several techniques employ artificial intelligence methods based on real-time vital sign measurements. In parallel, Frequency-Modulated Continuous-Wave (FMCW) radar technology has garnered considerable attention in the domains of healthcare and biomedical engineering for non-invasive vital sign monitoring. FMCW radar offers a multitude of advantages, including its non-intrusive nature, continuous monitoring capacity, and its ability to penetrate through clothing. In this paper, we propose a system utilizing the AWR6843AOP radar from Texas Instruments (TI) to extract precise vital sign information. The radar allows us to estimate Ballistocardiogram (BCG) signals, which capture the mechanical movements of the body, particularly the ballistic forces generated by heartbeats and respiration. These signals are rich sources of information about the cardiac cycle, rendering them suitable for heart rate estimation. The process begins with real-time subject positioning, followed by clutter removal, computation of Doppler phase differences, and the use of various filtering methods to accurately capture subtle physiological movements. To address the challenges associated with FMCW radar-based vital sign monitoring, including motion artifacts due to subjects' movement or radar micro-vibrations, Long Short-Term Memory (LSTM) networks are implemented. LSTM's adaptability to different heart rate patterns and ability to handle real-time data make it suitable for continuous monitoring applications. Several crucial steps were taken, including feature extraction (involving amplitude, time intervals, and signal morphology), sequence modeling, heart rate estimation through the analysis of detected cardiac cycles and their temporal relationships, and performance evaluation using metrics such as Root Mean Square Error (RMSE) and correlation with reference heart rate measurements. For dataset construction and LSTM training, a comprehensive data collection system was established, integrating the AWR6843AOP radar, a Heart Rate Belt, and a smart watch for ground truth measurements. Rigorous synchronization of these devices ensured data accuracy. Twenty participants engaged in various scenarios, encompassing indoor and real-world conditions within a moving vehicle equipped with the radar system. Static and dynamic subject’s conditions were considered. The heart rate estimation through LSTM outperforms traditional signal processing techniques that rely on filtering, Fast Fourier Transform (FFT), and thresholding. It delivers an average accuracy of approximately 91% with an RMSE of 1.01 beat per minute (bpm). In conclusion, this paper underscores the promising potential of FMCW radar technology integrated with artificial intelligence algorithms in the context of automotive applications. This innovation not only enhances road safety but also paves the way for its integration into the automotive ecosystem to improve driver well-being and overall vehicular safety.

Keywords: ballistocardiogram, FMCW Radar, vital sign monitoring, LSTM

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Authors: Md Tanvir Rahman, Mahbube Subhani, Mahmud Ashraf, Paul Kremer

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Cross-laminated timber (CLT) is made from layers of timber boards orthogonally oriented in the thickness direction, and due to this, CLT can withstand bi-axial bending in contrast with most other engineered wood products such as laminated veneer lumber (LVL) and glued laminated timber (GLT). Wood is cylindrically anisotropic in nature and is characterized by significantly lower elastic modulus and shear modulus in the planes perpendicular to the fibre direction, and is therefore classified as orthotropic material and is thus characterized by 9 elastic constants which are three elastic modulus in longitudinal direction, tangential direction and radial direction, three shear modulus in longitudinal tangential plane, longitudinal radial plane and radial tangential plane and three Poisson’s ratio. For simplification, timber materials are generally assumed to be transversely isotropic, reducing the number of elastic properties characterizing it to 5, where the longitudinal plane and radial planes are assumed to be planes of symmetry. The validity of this assumption was investigated through numerical modelling of CLT with both orthotropic mechanical properties and transversely isotropic material properties for three softwood species, which are Norway spruce, Douglas fir, Radiata pine, and three hardwood species, namely Victorian ash, Beech wood, and Aspen subjected to uniformly distributed loading under simply supported boundary condition. It was concluded that assuming the timber to be transversely isotropic results in a negligible error in the order of 1 percent. It was also observed that along with longitudinal elastic modulus, ratio of longitudinal shear modulus (GL) and rolling shear modulus (GR) has a significant effect on a deflection for CLT panels of lower span to depth ratio. For softwoods such as Norway spruce and Radiata pine, the ratio of longitudinal shear modulus, GL to rolling shear modulus GR is reported to be in the order of 12 to 15 times in literature. This results in shear flexibility in transverse layers leading to increased deflection under out-of-plane loading. The rolling shear modulus of hardwoods has been found to be significantly higher than those of softwoods, where the ratio between longitudinal shear modulus to rolling shear modulus as low as 4. This has resulted in a significant rise in research into the manufacturing of CLT from entirely from hardwood, as well as from a combination of softwood and hardwoods. The commonly used beam theory to analyze the performance of CLT panels under out-of-plane loads are the Shear analogy method, Gamma method, and k-method. The shear analogy method has been found to be the most effective method where shear deformation is significant. The effect of the ratio of longitudinal shear modulus and rolling shear modulus of cross-layer on the deflection of CLT under uniformly distributed load with respect to its length to depth ratio was investigated using shear analogy method. It was observed that shear deflection is reduced significantly as the ratio of the shear modulus of the longitudinal layer and rolling shear modulus of cross-layer decreases. This indicates that there is significant room for improvement of the bending performance of CLT through developing hybrid CLT from a mix of softwood and hardwood.

Keywords: rolling shear modulus, shear deflection, ratio of shear modulus and rolling shear modulus, timber

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Authors: Rob Leslie, Taher Karimian

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The blockage of cross drainage structures is a risk that needs to be understood and managed or lessened through the design. A blockage is a random event, influenced by site-specific factors, which needs to be quantified for design. Under and overestimation of blockage can have major impacts on flood risk and cost associated with drainage structures. The importance of this matter is heightened for those projects located within sensitive lands. It is a particularly complex problem for large linear infrastructure projects (e.g., rail corridors) located within floodplains where blockage factors can influence flooding upstream and downstream of the infrastructure. The selection of the appropriate blockage factors for hydraulic modeling has been subject to extensive research by hydraulic engineers. This paper has been prepared to review the current Australian Rainfall and Runoff 2019 (ARR 2019) methodology for blockage assessment by applying this method to a transport corridor brownfield upgrade case study in New South Wales. The results of applying the method are also validated against asset data and maintenance records. ARR 2019 – Book 6, Chapter 6 includes advice and an approach for estimating the blockage of bridges and culverts. This paper concentrates specifically on the blockage of cross drainage structures. The method has been developed to estimate the blockage level for culverts affected by sediment or debris due to flooding. The objective of the approach is to evaluate a numerical blockage factor that can be utilized in a hydraulic assessment of cross drainage structures. The project included an assessment of over 200 cross drainage structures. In order to estimate a blockage factor for use in the hydraulic model, a process has been advanced that considers the qualitative factors (e.g., Debris type, debris availability) and site-specific hydraulic factors that influence blockage. A site rating associated with the debris potential (i.e., availability, transportability, mobility) at each crossing was completed using the method outlined in ARR 2019 guidelines. The hydraulic results inputs (i.e., flow velocity, flow depth) and qualitative factors at each crossing were developed into an advanced spreadsheet where the design blockage level for cross drainage structures were determined based on the condition relating Inlet Clear Width and L10 (average length of the longest 10% of the debris reaching the site) and the Adjusted Debris Potential. Asset data, including site photos and maintenance records, were then reviewed and compared with the blockage assessment to check the validity of the results. The results of this assessment demonstrate that the estimated blockage factors at each crossing location using ARR 2019 guidelines are well-validated with the asset data. The primary finding of the study is that the ARR 2019 methodology is a suitable approach for culvert blockage assessment that has been validated against a case study spanning a large geographical area and multiple sub-catchments. The study also found that the methodology can be effectively coded within a spreadsheet or similar analytical tool to automate its application.

Keywords: ARR 2019, blockage, culverts, methodology

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Authors: R. R. Galiev, I. I. Lykov, A. E. Shitikov, I. A. Bilenko

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Dual-comb interferometry is based on the mixing of two optical frequency combs with slightly different lines spacing which results in the mapping of the optical spectrum into the radio-frequency domain for future digitizing and numerical processing. The dual-comb approach enables diverse applications, including metrology, fast high-precision spectroscopy, and distance range. Ordinary frequency-modulated continuous-wave (FMCW) laser-based Light Identification Detection and Ranging systems (LIDARs) suffer from two main disadvantages: slow and unreliable mechanical, spatial scan and a rather wide linewidth of conventional lasers, which limits speed measurement resolution. Dual-comb distance measurements with Allan deviations down to 12 nanometers at averaging times of 13 microseconds, along with ultrafast ranging at acquisition rates of 100 megahertz, allowing for an in-flight sampling of gun projectiles moving at 150 meters per second, was previously demonstrated. Nevertheless, pump lasers with EDFA amplifiers made the device bulky and expensive. An alternative approach is a direct coupling of the laser to a reference microring cavity. Backscattering can tune the laser to the eigenfrequency of the cavity via the so-called self-injection locked (SIL) effect. Moreover, the nonlinearity of the cavity allows a solitonic frequency comb generation in the very same cavity. In this work, we developed a fully integrated, power-efficient, electrically driven dual-micro comb source based on the semiconductor lasers SIL to high-quality integrated Si3N4 microresonators. We managed to obtain robust 1400-1700 nm combs generation with a 150 GHz or 1 THz lines spacing and measure less than a 1 kHz Lorentzian withs of stable, MHz spaced beat notes in a GHz band using two separated chips, each pumped by its own, self-injection locked laser. A deep investigation of the SIL dynamic allows us to find out the turn-key operation regime even for affordable Fabry-Perot multifrequency lasers used as a pump. It is important that such lasers are usually more powerful than DFB ones, which were also tested in our experiments. In order to test the advantages of the proposed techniques, we experimentally measured a minimum detectable speed of a reflective object. It has been shown that the narrow line of the laser locked to the microresonator provides markedly better velocity accuracy, showing velocity resolution down to 16 nm/s, while the no-SIL diode laser only allowed 160 nm/s with good accuracy. The results obtained are in agreement with the estimations and open up ways to develop LIDARs based on compact and cheap lasers. Our implementation uses affordable components, including semiconductor laser diodes and commercially available silicon nitride photonic circuits with microresonators.

Keywords: dual-comb spectroscopy, LIDAR, optical microresonator, self-injection locking

Procedia PDF Downloads 49