Miracle-dz/UniFAR-FTUs huggingface dataset:
Particle filter design for tracking ground targets in airborne environment

To improve the robustness while tracking a ground target with strong mobility in the cabin environment, a particle filter was taken as tracking framework, and its dynamic model and observation model were investigated. According to the two-stage dynamic model proposed by Kristan et al, a two-stage acceleration (TSA) dynamic model was proposed for the characteristics of cabin environment and the strong mobility of the tracking target. According to the idea of asymmetric kernel function proposed by Yilmaz et al., a method was proposed by using Snake algorithm to extract the object contour and to construct an asymmetric kernel function based on contour information to solve the real-time moving target problem. Finally, the TSA-AK particle filter tracking algorithm was proposed based on above methods. The proposed algorithm was tested on the video tracking ground target in cabin environment. The results show that the proposed algorithm can stably track target moving in a wide range of velocity. The targeting accuracy is 98%, and the computing frame rate is 26 frame/s when the object scale is 25 pixel30 pixel.
A survey of multimodal deep generative models

Multimodal learning is a framework for building models that make predictions based on different types of modalities. Important challenges in multimodal learning are the inference of shared representations from arbitrary modalities and cross-modal generation via these representations; however, achieving this requires taking the heterogeneous nature of multimodal data into account. In recent years, deep generative models, i.e. generative models in which distributions are parameterized by deep neural networks, have attracted much attention, especially variational autoencoders, which are suitable for accomplishing the above challenges because they can consider heterogeneity and infer good representations of data. Therefore, various multimodal generative models based on variational autoencoders, called multimodal deep generative models, have been proposed in recent years. In this paper, we provide a categorized survey of studies on multimodal deep generative models. GRAPHICAL ABSTRACT
Optimal Vehicle Path Generator Using Optimization Methods

This research explores the idea of developing an optimal path generator that can be used in conjunction with a feedback steering controller to automate track testing experiment. This study specifically concentrates on applying optimization concepts to generate paths that meet two separate objective functions; minimum time and maximum tire forces. A three-degree-of freedom vehicle model is used to approximate the handling dynamics of the vehicle. Inputs into the vehicle model are steering angle and longitudinal force at the tire. These two variables approximate two requirements that are essential in operating a vehicle. The Third order Runge-Kutta integration routine is used to integrate vehicle dynamics equations of motion. The Optimization Toolbox of Matlab is used to evaluate the optimization algorithm. The vehicle is constrained with a series of conditions, includes, a travel within the boundaries of the track, traction force limitations at the tire, vehicle speed, and steering. The simulation results show that the optimization applied to vehicle dynamics can be useful in designing an automated track testing system. The optimal path generator can be used to develop meaningful test paths on existing test tracks. This study can be used to generate an accelerated tire wear test path, perform parametric study of suspension geometry design using vehicle dynamics handling test data, and to increase repeatability in generating track testing results. ii Acknowledgments First and foremost, I would like to say thank you to Dr. Mehdi Ahmadian for his help, support and guidance over the past two years. With his encouragement, I am able to successfully accomplish my goals in completing my Master's degree and also to get my foot in the door in the field of vehicle dynamics. He has taught me how to become successful in many aspects of life. I am very grateful to have had him as an advisor. I would also like to thank Dr. Pushkin Kachroo for his expertise in vehicle dynamic modeling (X-Y plane). His guidance helped me clarify the scope of this research and focus on the objective. I would like to thank Dr. Douglas J. Nelson for his time in serving on my committee. I also owe Dr. Eugene M. Cliff tremendous thanks for getting me over the hurdle in optimal controls. Special thanks are due to The Goodyear Tire and Rubber Company for their help in providing me with tire data. Specifically I would like to thank Steve Rohweder who not only …
Sex Difference and Benzene Exposure: Does It Matter?

Sex-related biological differences might lead to different effects in women and men when they are exposed to risk factors. A scoping review was carried out to understand if sex could be a discriminant in health outcomes due to benzene. Studies on both animals and humans were collected. In vivo surveys, focusing on genotoxicity, hematotoxicity and effects on metabolism suggested a higher involvement of male animals (mice or rats) in adverse health effects. Conversely, the studies on humans, focused on the alteration of blood parameters, myeloid leukemia incidence and biomarker rates, highlighted that, overall, women had significantly higher risk for blood system effects and a metabolization of benzene 23–26% higher than men, considering a similar exposure situation. This opposite trend highlights that the extrapolation of in vivo findings to human risk assessment should be taken with caution. However, it is clear that sex is a physiological parameter to consider in benzene exposure and its health effects. The topic of sex difference linked to benzene in human exposure needs further research, with more numerous samples, to obtain a higher strength of data and more indicative findings. Sex factor, and gender, could have significant impacts on occupational exposures and their health effects, even if there are still uncertainties and gaps that need to be filled.
Foot placement for running robots

Rubble-strewn corridors, stairs and steep natural terrain all present a challenge for wheels and tracks. Legs are a solution in these cases because foot placement allows the traversal of discontinuous terrain. Legged robots, however, currently lack the performance needed for practical applications. This work seeks to address an aspect of the problem, foot placement while running. A novel hopping height controller for a spring-loaded legged robot is presented. It is simple and performs well enough to allow control of the ballistic trajectory of hops and therefore foot placement. Additionally, it can adapt to different ground properties using the result from previous hops to update control gains. A control strategy of extending the leg at a fixed rate during the stance phase and modulating the rate of extension on each hop was used to control the hopping height. The extension rate was then determined by a feed-forward + proportional control loop. This performed sufficiently well allowing the ballistic trajectory of hops to be controlled. In simulation, the spring-loaded inverted pendulum (SLIP) model was extended to include actuation and losses due to friction. The control strategy was developed using this model then, in a planar simulation, the controller was run to perform foot placement while running over a series of platforms which vary in their horizontal and vertical spacing. To experimentally validate and further develop the control strategy, a one-legged hopping robot, constrained to move vertically, was used. The leg had 2 links, hydraulically actuated hip and knee joints and a spring-loaded foot. Results showed that the controller developed could be used to perform hops of randomly varying size on grounds with different properties and while running on a treadmill at different speeds. As an aside, the dynamics of hydraulic actuators presented a problem for foot repositioning during flight using a simple PID controller. This was solved through the novel implementation, in hydraulics, of a `zero-vibration' (ZV) filter in a closed-loop. Simulation and experimental results demonstrating this are presented.
Brain Tumor and Glioma Grade Classification Using Gaussian Convolutional Neural Network

Understanding brain diseases such as categorizing Brain-Tumor (BT) is critical to assess the tumors and facilitate the patient with proper cure as per their categorizations. Numerous imaging schemes exist for BT detection, such as Magnetic Resonance Imaging (MRI), generally utilized because of the better quality of images and the reality of depending on non-ionizing radiation. This paper proposes an approach to detect distinctive BT types using Gaussian Convolutional Neural Network (GCNN) on two datasets. One of the datasets is used to classify tumors into pituitary, glioma, and meningioma. The other one separates the three grades of glioma, i.e., Grade-two, Grade-three, and Grade-four. These datasets have ’233’ and ’73’ victims with a total of ’3064’ and ’516’ images on T1-weighted complexity improved pictures for the first and second datasets, separately. The proposed approach achieves an accuracy of 99.8% and 97.14% for the two datasets. The experimental results highlight the efficiency of the proposed approach for BT multi-class categorization.
The study of Tokopedia re-purchase intention during Covid-19 pandemic

This study aims to determine the impulsive behaviour using a brand image, brand ambassadors, service quality, and customer satisfaction on Tokopedia re-purchase intention. In this study, a quantitative approach was used to collect data from an online questionnaire (Google Form) for the Tokopedia users who had made a transaction more than once. The online questionnaire was distributed via social media and received 145 valid responses, with PLS-SEM being used to analyze the hypothesis testing of this research. The result shows that brand image indirectly influences the re-purchase intention of Tokopedia mediated by impulsive behaviour. The brand ambassador also has an indirect influence on the re-purchase intention of Tokopedia mediated by customer satisfaction and impulsive behaviour, and service quality has a direct and indirect influence on the re-purchase intention of Tokopedia mediated by customer satisfaction. Furthermore, customer satisfaction and impulsive behaviour have a significant influence on the re-purchase intention of Tokopedia. Tokopedia must pay attention to factors interested in re-purchase intention, which means that Tokopedia should maintain service quality and customer satisfaction and improve customers' experience when customers make impulsive behaviour.
Advanced control of renewable energy microgrids with hybrid energy storage system

Renewable energy will play an important role in the transition to a new energetic model which, along with other developments of the digital age, will probably bring about the Third Industrial Revolution. However, the change to this new energetic model is subject to overcoming technological barriers, namely the sporadic nature of renewable sources. Which in turn affects both, power quality and economic competitiveness. The imbalance of active and reactive power that renewable energies introduce in the grid causes variation in the voltage supply, grid frequency, harmonics, as well as producing other power quality issues. Energy storage systems appear to be a key factor in compensating generation and demand. The lack of controllability and the penalty for deviations in the regulation market hinder the economic competitiveness of renewable energy. Energy storage systems will be the technological solution enabling controllability in renewable energies, allowing their introduction in the spot energy market. Redesigning the grid into smaller, more manageable units based on microgrids appears as a solution to the outlined problems. In these microgrids, stored energy compensates both the intermittent nature of renewable generation and the randomness of the consumer's behaviour. Traditionally, energy storage has been developed by large hydropower-regulation plants, however, these kinds of plants are subject to natural emplacements and their implementation is subject to environmental impact grades. The high energy density of hydrogen as an energy carrier will play an important role in this new energetic paradigm. However, robust performance and the transient response are the main barriers for its technological implantation and, usually, hydrogen-based systems have a useful life that is sometimes too limited to buffer the associated cost. Batteries and supercapacitors have a better transient response, however, their low energy density does not provide enough autonomy to the system. The design of a hybrid energy storage system, having advanced control systems in charge of taking advantage of each storage system and avoiding the causes of degradation and/or limitations of them, emerges as a technological solution to the problems commented. The high number of constraints and variables to be optimized increases the complexity of the associated control problem, making it necessary to deploy advanced control algorithms. In this thesis, the development of optimal controllers for renewable energy microgrids with hybrid energy storage systems is explored using Model Predictive Control (MPC). The control system is introduction on different time scales resulting in an optimal control solution for the economic dispatch and the power quality of the microgrid. Meanwhile, degradation issues of energy storage systems are analyzed and minimized, improving the longevity of the whole energy storage system.
Natural Products as Novel Neuroprotective Agents; Computational Predictions of the Molecular Targets, ADME Properties, and Safety Profile

Neurodegenerative diseases (NDs) are one of the most challenging public health issues. Despite tremendous advances in our understanding of NDs, little progress has been made in establishing effective treatments. Natural products may have enormous potential in preventing and treating NDs by targeting microglia; yet, there have been several clinical concerns about their usage, primarily due to a lack of scientific evidence for their efficacy, molecular targets, physicochemical properties, and safety. To solve this problem, the secondary bioactive metabolites derived from neuroprotective medicinal plants were identified and selected for computational predictions for anti-inflammatory activity, possible molecular targets, physicochemical properties, and safety evaluation using PASS online, Molinspiration, SwissADME, and ProTox-II, respectively. Most of the phytochemicals were active as anti-inflammatory agents as predicted using the PASS online webserver. Moreover, the molecular target predictions for some phytochemicals were similar to the reported experimental targets. Moreover, the phytochemicals that did not violate important physicochemical properties, including blood-brain barrier penetration, GI absorption, molecular weight, and lipophilicity, were selected for further safety evaluation. After screening 54 neuroprotective phytochemicals, our findings suggest that Aromatic-turmerone, Apocynin, and Matrine are the most promising compounds that could be considered when designing novel neuroprotective agents to treat neurodegenerative diseases via modulating microglial polarization.
Diagnosing spatial variation patterns in manufacturing processes

Diagnosing Spatial Variation Patterns In Manufacturing Processes (May 2003) Ho Young Lee, B.S., Seoul National University M.S., Texas A&M University Chair of Advisory Committee: Dr. Daniel Apley This dissertation discusses a method that will aid in diagnosing the root causes of product and process variability in complex manufacturing processes when large quantities of multivariate in-process measurement data are available. As in any data mining application, this dissertation has as its objective the extraction of useful information from the data. A linear structured model, similar to the standard factor analysis model, is used to generically represent the variation patterns that result from the root causes. Blind source separation methods are investigated to identify spatial variation patterns in manufacturing data. Further, the existing blind source separation methods are extended, enhanced and improved to be a more effective, accurate and widely applicable method for manufacturing variation diagnosis. An overall strategy is offered to guide the use of the presented methods in conjunction with alternative methods.
The Mechanism of Influencing Green Technology Innovation Behavior: Evidence from Chinese Construction Enterprises

The Green Technology Innovation Behavior (GTIB) of construction enterprises is crucial for promoting green development in the construction industry. In order to clarify the mechanism of action affecting the GTIB of construction enterprises, this paper considers the context of green development in the construction industry based on the vector autoregressive model and constructs a theoretical model of GTIB in construction enterprises. Time series data collected by the Chinese government (2000–2018) were used to analyze the mechanism of action of the factors influencing the GTIB of construction enterprises by EViews 10.0. The results of the paper showed the following: (1) direct government investment has the greatest impact on the GTIB of construction enterprises and has made a positive contribution; (2) the added value of Gross Domestic Product (GDP) of the construction industry has a relatively small impact on the GTIB of construction enterprises; (3) the role of environmental regulation on the GTIB of construction enterprises is non-linear. This paper further broadens the research to the factors influencing the GTIB of construction enterprises. Meanwhile, this paper provides a reference basis for local governments to formulate policies related to the GTIB of construction enterprises.
Methodology to Design Multi-Octave Power Amplifier Using Extended Class B to Class J Continuous Mode

This paper presents the design of a multi-octave power amplifier (PA) using extended class B to class J continuous mode. In a multi-octave PA, an overlapping exists between the higher harmonic frequencies of the lower octave and the fundamental frequency of the higher octave. Therefore, a PA mode with overlapping between fundamental and second harmonic impedances is suitable to design a multioctave PA. An extended class B to class J continuous mode consists of overlapping between fundamental and second harmonic impedances. However, the design of a multi-octave PA is not straightforward. The impedance selected in each octave is important to design a highly-efficiency multi-octave PA. Therefore, this paper presents a methodology to choose the appropriate load in each octave to design extended class B to class J continuous mode PA. This paper also presents a valid range of design variables for the transistor’s reliable operation. The proof of concept PA has been designed using 10 W Wolfspeed GaN HEMT CGH40010F. The designed PA operates from 0.75 to 2.75 GHz. The corresponding fractional bandwidth is 114.2%. The measured drain efficiency, power added efficiency, and output power are 60-73.6%, 53-67.8%, and 39.3-42.3 dBm, respectively. The measured carrier to third-order intermodulation distortion is better than -15 dBc when tested with a 20 MHz spacing two-tone signal.
Risk factors for nasal polyps, a case-control study

Background : Nasal polyposis is considered one of the most common diseases that cause obstruction of the respiratory tract and impede the patient's quality of life.Aims : The aim is to determine if smoking and body mass index are risk factors for hyperplastic nasal polyps in patients with chronic obstructive rhinitis and allergic rhinitis, and thus conducting early diagnostic investigations to detect the disease early and limit its development. Methods : A cross-sectional case control study was conducted that includes patients who came to Damascus Hospital with complaints of chronic obstruction of the nose and chronic allergies that doesn’t respond to conventional treatments and who met the entry criteria and patients from ENT Division without symptoms of obstructive respiratory tracts . Results : 120 patients were included in the study and were distributed as follows: 60 patients with nasal polyps and 60 patients without nasal polyps. The arithmetic mean of age in the group of cases was 31 ± 16.7 years, while in the controls 32.2 ± 18.3, the BMI value was expressed as the arithmetic mean and it was 30.5 ± 4.16 years, while in the controls 23.6 ± 4.30, and the sex ratio was equal between cases and controls with A statistically non-significant value p = 1.000, while the proportion of smokers in cases was 70%, while in controls 37%, with a statistically significant value p = 0.0003. Conclusion : Smoking and body mass index had a statistically significant relation with the development of nasal polyposis .
High in Utero Exposure to Perfluoroalkyl Substances from Drinking Water and Birth Weight: A Cohort Study among Infants in Ronneby, Sweden

In 2013, the drinking water for one-third of the households in Ronneby, Sweden, was found to be contaminated by perfluorinated alkyl substances (PFAS, >10,000 ng/L) from Aqueous Film Forming Foam (AFFF). In utero PFAS exposure can influence birth weight, but little is known about the effects at very high levels. This study aimed to examine the association between in utero PFAS exposure and birth weight. Infants with mothers from Ronneby exposed to contaminated water at home (high exposure) and infants with mothers from Ronneby not exposed to contaminated water at home (low exposure) were compared to infants with mothers from Blekinge county excluding Ronneby (referents). All infants born in Blekinge county 1995–2013 were included (n = 30,360). Differences in birth weight were only seen among infants born after 2005. For boys, Ronneby high exposure had a lower mean birth weight than referents (−54 g, 95% CI −97; −11). For girls, Ronneby high exposure had a higher mean birth weight than referents (47 g, 95% CI 4; 90). There were no differences in birth weight between referents and Ronneby low exposure. In conclusion, high exposure to PFAS may influence birth weight in a sex-specific way, although the effect estimates were relatively small.
ANALYSIS OF DUST DELIQUESCENCE FOR FEP SCREENING

The purpose of this report is to evaluate the potential for penetration of the Alloy 22 (UNS N06022) waste package outer barrier by localized corrosion due to the deliquescence of soluble constituents in dust present on waste package surfaces. The results support a recommendation to exclude deliquescence-induced localized corrosion (pitting or crevice corrosion) of the outer barrier from the total system performance assessment for the license application (TSPA-LA). Preparation of this report, and supporting laboratory studies and calculations, were performed as part of the planned effort in Work Package AEBM21, as implemented in ''Technical Work Plan for: Screening Evaluation for Dust Deliquescence and Localized Corrosion'' (BSC 2004 [DIRS 172804]), by Bechtel SAIC Company, LLC, and staff from three national laboratories: Sandia National Laboratories, Lawrence Livermore National Laboratory (LLNL), and Lawrence Berkeley National Laboratory (LBNL). The analysis and conclusions presented in this report are quality affecting, as determined in the controlling technical work plan. A summary of background information, based on work that was not performed under a quality assurance program, is provided as Appendix E. In this instance, the use of unqualified information is provided for transparency and corroboration only, and is clearly separated from uses of qualified information. Thus,more » the qualification status of this information does not affect the conclusions of this report. The acceptance criteria addressed in Sections 4.2 and 7.2 were changed from the technical work plan in response to review comments received during preparation of this report. « less