Miracle-dz/UniFAR-FTUs huggingface dataset:
Lightweighting of stiffness critical advanced high strength steel structures using fibre reinforced plastics.

In the drive for lightweighting in many industries, optimum material selection is at the forefront of research. Many solutions are being investigated, including the fabrication of multi-material components. Following a state of the art review of the literature, it has been shown that there is an opportunity to improve basic knowledge and understanding of the characteristics of hybrid steel-FRP materials for lightweight applications. This dissertation explores the potential for designing lightweight automotive steel structures through novel use of lower gauges combined with local reinforcement by fibre-reinforced plastics to achieve desired stiffness performances. The main focus of the work is to provide underpinning research to enable the further understanding of the stiffness performance of hybrid steel-FRP materials, both experimentally and in simulation. This thesis focuses on the characterisation of high strength automotive grade steel (DP600) reinforced with a fibre reinforced polyamide (PA6 GF60) laminate, however, the results are readily applicable for other combinations. The project was achieved through two main phases; each phase consisting of an iteration loop between experimentation and simulation validations. Initial characterisation was achieved using coupon samples in quasi-static three-point bend, cross-validated in simulation providing a trusted material model. Correlating experimental and simulated results showed a potential lightweighting of up to 30 % of a hybrid DP600-GFRP over a DP600 counterpart with a matched stiffness performance. Further characterisation was performed using an idealised automotive component in flexure, confirming a potential lightweighting of up to 30 %. The simulation investigation demonstrated the effect of localised hybrid reinforcements, and identified difficulties in predicting the local geometrical effects of plastic hinging. For an overall application to an automotive body-in-white, these would require further investigating. This thesis has proven that downgauging steel whilst locally reinforcing (intelligent deployment) with FRP patches provides a significant lightweight solution with a matched stiffness performance. A hybrid material model has been validated and the application to an automotive component investigated. This work provides the basic understanding for a direct application in lightweight automotive designs using computer aided engineering (CAE).
Genomic and Physiological Characterization of Metabacillus flavus sp. nov., a Novel Carotenoid-Producing Bacilli Isolated from Korean Marine Mud

The newly isolated strain KIGAM252T was found to be facultatively anaerobic, Gram-stain-positive, spore-forming, and rod-shaped. They grew at 10–45 °C, pH 6.0–10.0, and were able to tolerate up to 6% NaCl in the growth medium. Phylogenetic analysis indicated that the KIGAM252T strain was related to the genus Metabacillus. The cell membrane fatty acid composition of strain KIGAM252T included C15:0 anteiso and C15:0 iso (25.6%) as the major fatty acids, and menaquinone 7 was the predominant isoprenoid quinone. The major polar lipids were diphosphatidylglycerol and phosphatidylglycerol. The size of the whole genome was 4.30 Mbp, and the G + C content of the DNA was 43.8%. Average nucleotide and amino acid identity and in silico DNA-DNA hybridization values were below the species delineation threshold. Pan-genomic analysis revealed that 15.8% of all genes present in strain KIGAM252T was unique to the strain. The analysis of the secondary biosynthetic pathway predicted the carotenoid synthetic gene cluster in the strain KIGAM252T. Based on these current polyphasic taxonomic data, strain KIGAM252T represents a novel species of the genus Metabacillus that produces carotenoids, for which we propose the name Metabacillus flavus sp. nov. The type of strain was KIGAM252T (=KCTC 43261T = JCM 34406T).
Shrinky‐Dink millifluidics‐based continuously reconfigurable dielectric liquid‐assisted microstrip patch antenna

Reconfigurable dielectric liquid‐assisted antennas provide a solution to the need for multifunctional antennas that can service the multiple frequency bands required for modern wireless communications. However, fabricating these reconfigurable antennas requires complex processes that lack the potential for more compact designs. In this study, a novel continuously reconfigurable dielectric liquid‐assisted microstrip patch antenna fabricated with a Shrinky‐Dink millifluidics‐based substrate is designed, fabricated, and characterized. Shrinky‐Dinks are prestretched polystyrene sheets that can have patterns cut into them before shrinking under heat to form a millifluidic channel. By filling the millifluidic channel with differing amounts of water, a microstrip patch antenna fabricated on top of the rigid polystyrene substrate can be continuously reconfigured from 4.70 to 5.16 GHz with a bandwidth of 660 MHz. The fabrication process demonstrated in this study provides a path forward to produce reconfigurable dielectric liquid‐assisted antennas with smaller footprints, as well as other reconfigurable radiofrequency and electronic devices and components.
A Study of Vehicle-to-Vehicle Power Transfer Operation in V2G-Equipped Microgrid

Bidirectional vehicle-to-grid (V2G) system utilizes the batteries of parked electric-drivevehicles to provide energy storage and backup services in a power system. Such services in a V2G-equipped microgrid system can be used as an enabler of enhancing the renewable energy source (RES) penetration by storing the energy during the surplus of RES supply and supplying the energy during the lack of RES supply. In this research, we aim at enhancing the storage capacity of V2G system by introducing a novel vehicle-to-vehicle power transfer operation that runs on the top of V2G services. The vehicle-to-vehicle (V2V) operation transfers the energy from the source vehicles (which are parked for relatively longer times) to the destination vehicles (which are parked for relatively shorter times). The depleted energy of the source vehicles is fulfilled by the surplus RES supply in the future. In this way, the destination vehicles are effectively charged by RES supply, thereby enhancing the storage capacity of the V2G system. We can also say that the V2V operation would become beneficial only when there is a sufficient amount of surplus RES supply in the future. We propose a decision rule to distinguish if a vehicle should be a source vehicle or a destination vehicle during the V2V operation. The decision rule is designed based on the two factors, namely the state-of-charge of vehicles battery, and the remaining time of vehicle to depart. In this research, we conduct a comprehensive study to analyze the impacts of state-of-charge and mobility pattern of vehicles on different performance metrics via simulation. The results shows that in order to achieve better performance of V2V operation, the state-of-charge of vehicles battery should be given more priority over the remaining time of vehicle to depart. The vehicle mobility pattern with unexpected departure greatly reduced the overall performance of the V2G system.
Design Exploration of Hardware Accelerators For The K-NN Algorithm

Design Exploration Of Hardware Accelerators For The K-NN Al gorithm Dunia Jamma University of Guelph, 2016 Advisor: Professor Shawki Areibi, Professor Gary Grewal Increasingly, machine-learning algorithms are playing an important role in the context of embedded and real-time systems. Applications such as wirel ess sensor networks, security, and commercial enterprises rely increasingly on machine-lear ning algorithms to efficiently make predictive decisions based on the large volumes of data these sy st ms collect. Most supervised machine-learning algorithms, however, require relativel y large amounts of runtime to perform training and/or classification due to the size and dimension ality of the data they must work with. Therefore, there is a need to accelerate the runtime of these algorithms, especially for real-time applications. In this thesis, several different hardware a ccelerators are proposed and compared for the K-Nearest Neighbor (K-NN) classification algorithm. Th ese accelerators are developed using Xilinx Vivado High-Level Synthesis (HLS) and Cadence Tensi lica tools, and represent different (tightly coupled versus semi-tightly coupled) architectu res. The experimental results, based on several benchmarks, show that hardware speedups for an HLS r ange from 48x-168x, while those obtained using Cadence Tensilica tools range from 86x-650x .
Focusing light through strongly scattering media by controlling binary amplitude optimization using genetic algorithm

Focusing light through strongly scattering media plays an important role in biomedical imaging and therapy. Here, we experimentally demonstrate light focusing through ZnO sample by controlling binary amplitude optimization using genetic algorithm. In the experiment, we use a Micro Electro-Mechanical System (MEMS)-based digital micromirror device (DMD) which is in amplitude-only modulation mode. The DMD consists of 1920×1080 square mirrors that can be independently controlled to reflect light to a desired position. We control only 160 thousand mirrors which are divided into 400 segments to modulate light focusing through the scattering media using advanced genetic algorithm. Light intensity at the target position is enhanced up to 50±5 times the average speckle intensity. The diameters of focusing spot can be changed ranging from 7 μm to 70 μm at arbitrary positions and multiple foci are obtained simultaneously. The spatial arrangement of multiple foci can be flexibly controlled. The advantage of DMDs lies in their switching speed up to 30 kHz, which has the potential to generate a focus in an ultra-short period of time. Our work provides a reference for the study of high speed wavefront shaping that is required in vivo tissues imaging.
Dependability of Alternative Computing Paradigms for Machine Learning: hype or hope?

Today we observe amazing performance achieved by Machine Learning (ML); for specific tasks it even surpasses human capabilities. Unfortunately, nothing comes for free: the hidden cost behind ML performance stems from its high complexity in terms of operations to be computed and the involved amount of data. For this reasons, custom Artificial Intelligence hardware accelerators based on alternative computing paradigms are attracting large interest. Such dedicated devices support the energy-hungry data movement, speed of computation, and memory resources that MLs require to realize their full potential. However, when ML is deployed on safety-/mission-critical applications, dependability becomes a concern. This paper presents the state of the art of custom Artificial Intelligence hardware architectures for ML, here Spiking and Convolutional Neural Networks, and shows the best practices to evaluate their dependability.
Development of a verification and validation framework for autonomous soft-docking of spacecraft with uncertain dynamic properties

Although soft-docking in space has been demonstrated in the past, these missions have required detailed information about the target vehicle for success, and often relied on manual control during the final stages. Autonomous docking, however, shows the potential to greatly reduce operation costs while accomplishing complex scenarios. Unfortunately, unknown dynamics and changing parameters stress current attitude control systems for docking applications such as spacecraft servicing, debris capture, and space robotics operations. Adaptation for example may assist with vehicle control under such conditions, however requires careful validation. Since autonomous soft-docking has limited heritage when there are system uncertainties, risk reduction prior to operation becomes very important for mission success. In this thesis a verification and validation framework was developed for autonomous soft-docking of spacecraft under such uncertainties. The approach combines riskmanagement techniques, simulation, Monte Carlo analysis, diagnostic tools and experimentation in the micro-gravity environment of the International Space Station (ISS) to create a comprehensive risk-reduction strategy. Development methods are described to provide general guidelines for design of future soft-docking missions. Additionally, this thesis explores how such verification and validation methods may be used to assess how an adaptive controller can maintain attitude control authority when a spacecraft joins with an object with limited physical parameter information. The goal is to chart a path for controller validation via future spaceflight experimentation. The risk reduction framework and controller analyses and tests are based on working with the Synchronized Position Hold Engage Reorient Experimental Satellite (SPHERES) facility at MIT and on the ISS. Thesis Supervisor: David W. Miller Title: Professor of Aeronautics and Astronautics
The development of telepresence robots for people with disabilities

A person’s quality of life is impacted when he or she is no longer able to participate in everyday activities with family and friends, which is often the case for people with special needs (e.g., seniors and people with disabilities) who are full time residents at medical and healthcare facilities. We posit that people with special needs may benefit from using telepresence robots to engage in social activities. Telepresence robots provide interactive two-way audio and video communication and can be controlled independently, allowing the person driving to use the robot to look around and explore a remote environment as he or she desires. However, to date, telepresence robots, their user interfaces, and their navigation behaviors have not been designed for use by people with special needs to be the robot operators. Over the course of three years, we have designed and architected a social telepresence robot research platform based on a VGo Communications’ VGo robot. Our work included designing a new processing and sensor system with three cameras to create a wide field of view, and laser range finder to support autonomous navigation. The images from each camera were combined into a vertical panoramic video stream, which was the foundation of our interface. Since the premise of a telepresence robot is that it is an embodiment for its user, we designed and implemented autonomous navigation behaviors that approximated a human’s as much as possible, given its inability to independently translate laterally. This research utilized an iterative, bottom-up, user-centered approach, drawing upon our assistive robotics experiences. We have conducted series of user studies to inform the design of an augmented reality style user interface. We conducted two formative evaluations (a focus group (n=5) and a follow-on “Wizard of Oz” experiment (n=12)) to investigate how members of our target population would want to direct a telepresence robot in a remote environment. Based on these studies, we developed an augmented reality user interface, which focuses primarily on the human-human interaction and communication through video, providing appropriate support for semiautonomous navigation behaviors. We present a case study (n=4), which demonstrates this research as a first critical step towards having our target population take the active role of the telepresence robot operator.
Multistage solid‐state power amplifier design by a new alternative synthesis technique

For the first time, the numerical real‐frequency technique has been applied to the design of a nonlinear power amplifier. This synthesis technique does not require any transistor model by directly using measured large‐signal S‐parameters of the power transistor device. The double power‐frequency dependence of these S‐parameters requires the use of a bidimensional interpolation (spline functions). With the Levenberg–Marquardt optimization and large‐signal S‐parameters, a specific CAD program optimizes the added‐power in order to synthesize the matching networks of the PA. The simulated and measured results of two S‐band power amplifiers validate the new synthesis approach. © 2004 Wiley Periodicals, Inc. Int J RF and Microwave CAE 14, 87–98, 2004.
Neuropsychological Performance, Substance Misuse, and Recidivism in Intimate Partner Violence Perpetrators

ABSTRACT Previous research has pointed out the importance of neuropsychological impairments in intimate partner violence (IPV) perpetrators for reoffending/recidivism once treatment ends. However, less is known about whether substance misuse is associated with impairments or deficits, which facilitate recidivism. In this study, we first aimed to assess whether IPV perpetrators with (n = 104) and without (n = 120) substance misuse showed differences in specific neuropsychological variables in comparison with non-violent men (n = 82). Second, we examined whether there were differences in IPV perpetrators’ recidivism and whether these differences were explained by neuropsychological performance. Our results revealed that IPV perpetrators with substance misuse showed worse cognitive performance than controls. Furthermore, we also found differences between IPV perpetrators without substance misuse and controls, but only in terms of executive functioning. There were no differences in neuropsychological performance between the two groups of IPV perpetrators, although those with substance misuse presented higher recidivism rates than those without substance misuse. Finally, cognitive flexibility, verbal fluency, and worse attention functioning were related to high recidivism in both groups of IPV perpetrators. This study underlies the importance of performing neuropsychological assessments during the initial stages of intervention programs for IPV perpetrators in order to design coadjutant neuropsychological/cognitive training to address not only the psychological needs (including substance misuse) of IPV perpetrators, but also their neuropsychological needs.
Evaluation of Nitrogen Removal Efficiency for Low Strength Wastewater with ANAMMOX Process

1. Introduction 1 2. Literature Review 3 2.1 Conventional nitrification denitrification 3 2.2 Anaerobic ammonium oxidation 5 2.3 Combined SHARON and ANAMMOX process 7 2.4 CANON process 9 3. Material and Methods 11 3.1 Reactor system and operational strategy 11 3.2 Inoculum and synthetic wastewater 14 3.3 Analytical Procedures 15 3.4 Microbial Analysis 16 3.5 Mass balance and rate equation 17 4. Results and Discussion 22 4.1 Reactor Performance 22 4.1. 1 HRT effect 22 4.1. 2 DO effect 25 4.2 Thermodynamic aspect 27 4.3 Oxygen supply control 29 4.4 Microbial community analysis 33 5. Discussion and Conclusions 38 References 41
Soya Yield Prediction on a Within-Field Scale Using Machine Learning Models Trained on Sentinel-2 and Soil Data

Agriculture is the backbone and the main sector of the industry for many countries in the world. Assessing crop yields is key to optimising on-field decisions and defining sustainable agricultural strategies. Remote sensing applications have greatly enhanced our ability to monitor and manage farming operation. The main objective of this research was to evaluate machine learning system for within-field soyayield prediction trained on Sentinel-2 multispectral images and soil parameters. Multispectral images used in the study came from ESA’s Sentinel-2 satellites. A total of 3 cloud-free Sentinel-2 multispectral images per year from specific periods of vegetation were used to obtain the time-series necessary for crop yield prediction. Yield monitor data were collected in three crop seasons (2018, 2019 and 2020) from a number of farms located in Upper Austria. The ground-truth database consisted of information about the location of the fields and crop yield monitor data on 411 ha of farmland. A novel method, namely the Polygon-Pixel Interpolation, for optimal fitting yield monitor data with satellite images is introduced. Several machine learning algorithms, such as Multiple Linear Regression, Support Vector Machine, eXtreme Gradient Boosting, Stochastic Gradient Descent and Random Forest, were compared for their performance in soya yield prediction. Among the tested machine learning algorithms, Stochastic Gradient Descent regression model performed better than the others, with a mean absolute error of 4.36 kg/pixel (0.436 t/ha) and a correlation coefficient of 0.83%.
Integration of virtual reality based assembly simulation into CAD/CAM environments

Due to growing, worldwide competition, manufacturing companies have to employ new technologies to cut costs, improve product quality and, more importantly, shorten the design-to-manufacturing and -assembly cycle for new products. During recent years, virtual reality (VR) has proved its potential for the visualization and interactive manipulation of complex data. This paper describes a process for integrating an interactive VR-based assembly simulation of a digital mockup into an existing CAD/CAM infrastructure. The necessary hard- and software preconditions for the process are explained so that it can easily be adopted by nonVR-specialists. The article outlines how assembly simulation can improve the CAD/CAM procedures and structures, which CAD model preparations have to be carried out and which virtual environment requirements have to be fulfilled. Finally, deficits of existing VR simulation software are discussed and possible solutions and necessary developments are presented.
Soil type and fertilizer rate affect wheat (Triticum aestivum L.) yield, quality and nutrient use efficiency in Ayiba, northern Ethiopia

The blanket NP fertilizer recommendation over the past five decades in Ethiopia did not result in a significant increment of crop productivity. The main lack of success was highly linked to the extrapolating approach of one site success to others without considering the climate, soil, and ecological setting and variations. As a result, a new fertilization approach was desperately needed, and with this premise, new blended fertilizers are now being introduced to replace the conventional approach. Thus, the objective of this study was to examine the effect of NPSZnB blended fertilizer on bread wheat yield attributes, quality traits and use efficiency in two different soil types under rain-fed conditions in Ayiba, northern Ethiopia. Relevant agronomic data were evaluated and recorded from plots of each soil types for analysis. The analysis of variance revealed a significant (p < 0.001) variation on all the agronomic and grain quality traits due to the main and interaction effects of soil type and fertilizer treatment factors. Most agronomic and quality characteristics recorded the highest result in the highest treatment applications (175 and 150 kg NPSZnB ha−1) in both soils. Yield and grain quality traits of bread wheat was also found better under fertilized plots than unfertilized plots. In both soil types increasing application of the new blended fertilizer rate from 50–175 kg NPSZnB ha−1 showed an increasing trend in grain yield from 1.6 to 4.3 and 2.5 to 5.4 t ha−1 in Vertisol and Cambisol soils, respectively. The varied yield as a response of fertilizer treatments across soils signifies soil-specific fertilization approach is critically important for production increment. On the other hand, based on the partial budget analysis the highest net benefit with the highest marginal rate of return in both Vertisol and Cambisol soils were obtained when treated with 100 and 125 kg NPSZnB ha−1, respectively. Therefore, to produce optimum bread wheat yield under rainfed conditions in Ayiba (northern Ethiopia) fertilizing Vertisols with 100 kg NPSZnB ha−1 and fertilizing Cambisols with 125 kg NPSZnB ha−1 is recommended.