Miracle-dz/UniFAR-FTUs huggingface dataset:
Turbulent Characteristics in Stirring Vessels: A Numerical Investigation

TURBULENCE CHARACTERISTICS IN STIRRING VESSELS: A NUMERICAL INVESTIGATION Vasileios N. Vlachakis Understanding the flow in stirred vessels can be useful for a wide number of industrial applications, like in mining, chemical and pharmaceutical processes. Remodeling and redesigning these processes may have a significant impact on the overall design characteristics, affecting directly product quality and maintenance costs. In most cases the flow around the rotating impeller blades interacting with stationary baffles can cause rapid changes of the flow characteristics, which lead to high levels of turbulence and higher shear rates. The flow is anisotropic and inhomogeneous over the entire volume. A better understanding and a detailed documentation of the turbulent flow field is needed in order to design stirred tanks that can meet the required operation conditions. This thesis describes efforts for accurate estimation of the velocity distribution and the turbulent characteristics (vorticity, turbulent kinetic energy, dissipation rate) in a cylindrical vessel agitated by a Rushton turbine (a disk with six flat blades) and in a tank typical of flotation cells. Results from simulations using FLUENT (a commercial CFD package) are compared with Time Resolved Digital Particle Image Velocimetry (DPIV) for baseline configurations in order to validate and verify the fidelity of the computations. Different turbulence models are used in this study in order to determine the most appropriate for the prediction of turbulent properties. Subsequently a parametric analysis of the flow characteristics as a function of the clearance height of the impeller from the vessel floor is performed for the Rushton tank as well as the flotation cell. Results are presented for both configurations along planes normal or parallel to the impeller axis, displaying velocity vector fields and contour plots of vorticity turbulent dissipation and others. Special attention is focused in the neighborhood of the impeller region and the radial jet generated there. This flow in this neighborhood involves even larger gradients and dissipation levels in tanks equipped with stators. The present results present useful information for the design of the stirring tanks and flotation cells, and provide some guidance on the use of the present tool in generating numerical solutions for such complex flow fields.
Reduced trolling on Russian holidays and daily US Presidential election odds

Russian trolls generally supported the Trump campaign and were particularly active on Twitter 2015-2017. We find that trolling fell 35% on Russian holidays and to a lesser extent, when temperatures were cold in St. Petersburg. Exogenous variation in trolling by day allows us to consider indirectly-affected political behaviors in the US—outcomes that are less traceable via tweet sharing but potentially more important to policymakers than the direct dissemination previously studied. As a case in point, we describe reduced form evidence that Russian holidays affected daily trading prices in 2016 election betting markets. This response is consistent with successful Russian interference in support of Trump.
Applications, Enrollment, Attendance, and Student Performance in Rebuilt School Facilities: A Case Study

ABSTRACT Public school construction represents a significant portion of all construction spending in the United States; yet, the average age of United States’ public schools is 42 years old. This article focuses on magnet schools in a large urban school district in the United States. The study examines whether construction, building age, and building condition have an impact on magnet applications, enrollment, attendance, and student achievement measures. Twenty-eight magnet elementary schools in the school district were chosen for analysis. The experimental group (n=14) included all magnet elementary schools (kindergarten to 5th grade [K-5]) rebuilt under three school bond programs. The control group (n=14) included randomly selected elementary schools (K-5) from the 32 remaining elementary schools in the district that were not rebuilt. Multiple regressions were conducted using building and student data gathered during the 2011-2012 school year. Results indicate that building composite score and building age had no observable predictive effects on magnet applications, student enrollment, or student attendance, in both groups. However, student achievement was positively affected by building composite score as measured by the ability to predict state percentile ranking. This study supports that building and maintaining high quality educational facilities has the potential to raise student achievement levels.
Opinion Extraction of Drug Reviews Using PAMM Model

By the drastic popularity of Internet, the study of online reviews through blogs, discussion forums etc., have become most popular way for the patients to have medicines for chronic diseases. This paper consists of various research parameters for opinion extraction of drug reviews and techniques used in it. A probabilistic theory is developed to extract useful information from those reviews, called as PAMM (Probabilistic Aspect Mining Model). PAMM has a particular characteristic that it concentrates on finding opinion aspect related to one class instead of finding aspect for all classes simultaneously on each execution. This reduces the chances of mixed concepts of other classes. The aspects found are also responsible to differentiate a class from other classes. The paper gives idea to propose an efficient EM algorithm to propose opinion aspects for various groups of ages. An EM algorithm is used for finding approximate parameters of an underlying distribution from data set when it has missing values.
Comparison of Aminoglycoside Antibiotics and Cobalt Chloride for Ablation of the Lateral Line System in Giant Danios

Synopsis The mechanoreceptive lateral line system in fish is composed of neuromasts containing hair cells, which can be temporarily ablated by aminoglycoside antibiotics and heavy metal ions. These chemicals have been used for some time in studies exploring the functional role of the lateral line system in many fish species. However, little information on the relative effectiveness and rate of action of these chemicals for ablation is available. In particular, aminoglycoside antibiotics are thought to affect canal neuromasts, which sit in bony or trunk canals, differently from superficial neuromasts, which sit directly on the skin. This assumed ablation pattern has not been fully quantified for commonly used lateral line ablation agents. This study provides a detailed characterization of the effects of two aminoglycoside antibiotics, streptomycin sulfate and neomycin sulfate, and a heavy metal salt, cobalt (II) chloride hexahydrate (CoCl2), on the ablation of hair cells in canal and superficial neuromasts in the giant danio (Devario aequipinnatus) lateral line system, as a model for adult teleost fishes. We also quantified the regeneration of hair cells after ablation using CoCl2 and gentamycin sulfate to verify the time course to full recovery, and whether the ablation method affects the recovery time. Using a fluorescence stain, 4-Di-2-ASP, we verified the effectiveness of each chemical by counting the number of fluorescing canal and superficial neuromasts present throughout the time course of ablation and regeneration of hair cells. We found that streptomycin and neomycin were comparably effective at ablating all neuromasts in less than 12 h using a 250 μM dosage and in less than 8 h using a 500 μM dosage. The 500 μM dosage of either streptomycin or neomycin can ablate hair cells in superficial neuromasts within 2–4 h, while leaving those in canal neuromasts mostly intact. CoCl2 (0.1 mM) worked the fastest, ablating all of the hair cells in less than 6 h. Complete regeneration of the neuromasts in the lateral line system took 7 days regardless of chemicals used to ablate the hair cells. This study adds to the growing knowledge in hearing research about how effective specific chemicals are at ablating hair cells in the acoustic system of vertebrates.
Loneliness during COVID-19: Development and influencing factors

In early pandemic waves, when vaccination against COVID-19 was not yet an option, distancing and reduced social contact were the most effective measures to slow down the pandemic. Changes in frequency and forms of social contact have reduced the spread of the COVID-19 virus and thus saved lives, yet there is increasing evidence for negative side effects such as mental health issues. In the present study, we investigate the development of loneliness and its predictors to examine the role of changes in social networks due to social distancing and other COVID-19-related life changes. A total of 737 participants (age range = 18–81 years) completed an online survey in three waves during the last quarter of 2020 at one-month intervals. Latent growth and multilevel modeling revealed that emotional loneliness increased over time, while social loneliness remained stable. Moreover, socially lonely individuals were likely to also develop emotional loneliness over time. Increased social distancing and sanitary measures were accompanied by decreased social interactions and loss of individuals considered SOS contacts and confidants. Changes in specific social network indicators were differentially associated with changes in emotional vs social loneliness: Loss of friends considered confidants was associated with increasing emotional loneliness, whereas loss of friends considered SOS contacts and reduced overall social interactions were related to increasing social loneliness. Lastly, individuals with more family-and-friend SOS contacts, more friends as confidants and an overall higher number of social interactions were more protected from feeling socially or emotionally lonely. Study findings enhance the understanding of underlying mechanisms differentially contributing to social and emotional loneliness and offer practical suggestions to reduce mental-health side effects of social distancing.
School Refusal Behavior: Role of Personality Styles, Social Functioning, and Psychiatric Symptoms in a Sample of Adolescent Help-Seekers

Objective School refusal (SR) in adolescence represents an important risk factor associated with adverse consequences. Although many clinical features of adolescents presenting with SR have been studied, the relationship between SR and personality styles—specifically in the help-seeking population—remains unclear. The present study aimed at investigating differences in personality style, adaptive functioning, and symptomology between Italian help-seeking adolescents who refused (SRa) and did not refuse (non-SRa) to attend school, to provide preliminary evidence of personality patterns in adolescent help-seekers presenting with SR. Method The study sample was comprised of 103 help-seeking adolescents (54 female, 49 male) aged 14–18 years. Participants were recruited during their first clinical visit and evaluated using the Shedler and Westen Assessment Procedure – Adolescent version (SWAP-A), the Hamilton Rating Scale for Depression (HAM-D), the Hamilton Rating Scale for Anxiety (HAM-A), the Maniac Rating Scale (MRS), the Global Assessment of Functioning (GAF), the Global Functioning Social Scale (GFSS), and the Global Functioning Role Scale (GFRS). Differences in the studied variables between SRa and non-SRa were measured and a multivariable logistic regression analysis was performed to identify possible predictive factors of SR. Results SRa presented with more anxious and depressive symptomatology and worse social functioning compared to non-SRa. With respect to personality, SRa displayed more schizoid and schizotypal characteristics and fewer adaptive and healthy personality features. Irrespective of any differences between groups, SRa were largely characterized by inhibited–self-constricted and emotionally dysregulated personality styles. Conclusions The results suggest that personality styles are clinical features that may contribute to broadening our knowledge of SR behavior and aid in the detection of SRa, also in the help-seeking population. The findings have clinical, social, and political implications for prevention, diagnosis, and treatment, in both clinical and non-clinical settings. However, more data are needed on personality features to clarify their contribution to the more complex phenomenon of school absenteeism.
Leaving the labor market: Exit routes, personality traits and well-being

In this paper we examine the buffering effects of personality traits when people leave their work in later life. Using large-scale panel data for the UK, we show that depending on the exit route and satisfaction related to overall life and the domains of income and leisure, different personality traits act as moderators. Besides augmenting leisure satisfaction for those who hit mandatory retirement, conscientiousness augments life satisfaction for those becoming unemployed. On the contrary, extraversion mitigates satisfaction with life, income, and leisure for those who retire early. This may be an indication that extraverted individuals who tend to be sociable and outgoing may suffer when losing social relationships from their work. At the same time, extraversion may be helpful in augmenting leisure satisfaction for those who stop working for reasons related to ill health or family care. Neuroticism augments income satisfaction for those who become unemployed, which may reflect that people high in neuroticism had a lower “baseline level” of income satisfaction relative to typical individuals so they were not affected as much. Finally, agreeableness mitigates life and leisure satisfaction for those hitting mandatory retirement, as is also the case with openness in terms of income satisfaction.
Enhancing Satellite Imagery using Deep Learning for the Sensor To Shooter Timeline

The sensor to shooter timeline is affected by two main variables: satellite positioning and asset positioning. Speeding up satellite positioning by adding more sensors or by decreasing processing time is important only if there is a prepared shooter, otherwise the main source of time is getting the shooter into position. However, the intelligence community should work towards the exploitation of sensors to the highest speed and effectiveness possible. Achieving a high effectiveness while keeping speed high is a tradeoff that must be considered in the sensor to shooter timeline. In this paper we investigate two main ideas, increasing the effectiveness of satellite imagery through image manipulation and how on-board image manipulation would affect the sensor to shooter timeline. We cover these ideas in four scenarios: Discrete Event Simulation of onboard processing versus ground station processing, quality of information with cloud cover removal, information improvement with super resolution, and data reduction with image to caption. This paper will show how image manipulation techniques such as Super Resolution, Cloud Removal, and Image to Caption will improve the quality of delivered information in addition to showing how those processes effect the sensor to shooter timeline.
DiffSkill: Skill Abstraction from Differentiable Physics for Deformable Object Manipulations with Tools

We consider the problem of sequential robotic manipulation of deformable objects using tools. Previous works have shown that differentiable physics simulators provide gradients to the environment state and help trajectory optimization to converge orders of magnitude faster than model-free reinforcement learning algorithms for deformable object manipulation. However, such gradient-based trajectory optimization typically requires access to the full simulator states and can only solve short-horizon, single-skill tasks due to local optima. In this work, we propose a novel framework, named DiffSkill, that uses a differentiable physics simulator for skill abstraction to solve long-horizon deformable object manipulation tasks from sensory observations. In particular, we first obtain short-horizon skills using individual tools from a gradient-based optimizer, using the full state information in a differentiable simulator; we then learn a neural skill abstractor from the demonstration trajectories which takes RGBD images as input. Finally, we plan over the skills by finding the intermediate goals and then solve long-horizon tasks. We show the advantages of our method in a new set of sequential deformable object manipulation tasks compared to previous reinforcement learning algorithms and compared to the trajectory optimizer.
Cyberattack Detection for Nonlinear Leader-Following Multi-Agent Systems Using Set-Membership Fuzzy Filtering

This paper is concerned with cyberattack detection in discrete-time, leader-following, nonlinear, multi-agent systems subject to unknown but bounded (UBB) system noises. The Takagi-Sugeno (T-S) fuzzy model is employed to approximate the nonlinear systems over the true value of the state. A distributed cyberattack detection method, based on a new fuzzy set-membership filtering method, which consists of two steps, namely a prediction step and a measurement update step, is developed for each agent to identify two types of cyberattacks at the time of their occurrence. The attacks are replay attacks and false data injection attacks affecting the leader-following consensus. We calculate an estimation ellipsoid set by updating the prediction ellipsoid set with the current sensor measurement data. Two criteria are provided to detect cyberattacks based on the intersection between the ellipsoid sets. If there is no intersection between the prediction set and the estimation set of an agent at the current time instant, a cyberattack on its sensors is declared. Control signal or communication signal data of an agent are under a cyberattack if its prediction set has no intersection with the estimation set updated at the previous time instant. Recursive algorithms for solving the consensus protocol and calculating the two ellipsoid sets for detecting attacks are proposed. Simulation results are provided to demonstrate the effectiveness of the proposed method.
A Rich Recipe Representation as Plan to Support Expressive Multi Modal Queries on Recipe Content and Preparation Process

Food is not only a basic human necessity but also a key factor driving a society's health and economic well-being. As a result, the cooking domain is a popular use-case to demonstrate decision-support (AI) capabilities in service of benefits like precision health with tools ranging from information retrieval interfaces to task-oriented chatbots. An AI here should understand concepts in the food domain (e.g., recipes, ingredients), be tolerant to failures encountered while cooking (e.g., browning of butter), handle allergy-based substitutions, and work with multiple data modalities (e.g. text and images). However, the recipes today are handled as textual documents which makes it difficult for machines to read, reason and handle ambiguity. This demands a need for better representation of the recipes, overcoming the ambiguity and sparseness that exists in the current textual documents. In this paper, we discuss the construction of a machine-understandable rich recipe representation (R3), in the form of plans, from the recipes available in natural language. R3 is infused with additional knowledge such as information about allergens and images of ingredients, possible failures and tips for each atomic cooking step. To show the benefits of R3, we also present TREAT, a tool for recipe retrieval which uses R3 to perform multi-modal reasoning on the recipe's content (plan objects - ingredients and cooking tools), food preparation process (plan actions and time), and media type (image, text). R3 leads to improved retrieval efficiency and new capabilities that were hither-to not possible in textual representation.
Learning the Effect of Registration Hyperparameters with HyperMorph

We introduce HyperMorph, a framework that facilitates efficient hyperparameter tuning in learning-based deformable image registration. Classical registration algorithms perform an iterative pair-wise optimization to compute a deformation field that aligns two images. Recent learning-based approaches leverage large image datasets to learn a function that rapidly estimates a deformation for a given image pair. In both strategies, the accuracy of the resulting spatial correspondences is strongly influenced by the choice of certain hyperparameter values. However, an effective hyperparameter search consumes substantial time and human effort as it often involves training multiple models for different fixed hyperparameter values and may lead to suboptimal registration. We propose an amortized hyperparameter learning strategy to alleviate this burden by learning the impact of hyperparameters on deformation fields. We design a meta network, or hypernetwork, that predicts the parameters of a registration network for input hyperparameters, thereby comprising a single model that generates the optimal deformation field corresponding to given hyperparameter values. This strategy enables fast, high-resolution hyperparameter search at test-time, reducing the inefficiency of traditional approaches while increasing flexibility. We also demonstrate additional benefits of HyperMorph, including enhanced robustness to model initialization and the ability to rapidly identify optimal hyperparameter values specific to a dataset, image contrast, task, or even anatomical region, all without the need to retrain models. We make our code publicly available at http://hypermorph.voxelmorph. net.
Model Predictive Optimized Path Integral Strategies

We generalize the derivation of model predictive path integral control (MPPI) to allow for a single joint distribution across controls in the control sequence. This reformation allows for the implementation of adaptive importance sampling (AIS) algorithms into the original importance sampling step while still maintaining the benefits of MPPI such as working with arbitrary system dynamics and cost functions. The benefit of optimizing the proposal distribution by integrating AIS at each control step is demonstrated in simulated environments including controlling multiple cars around a track. The new algorithm is more sample efficient than MPPI, achieving better performance with fewer samples. This performance disparity grows as the dimension of the action space increases. Results from simulations suggest the new algorithm can be used as an anytime algorithm, increasing the value of control at each iteration versus relying on a large set of samples. Repository—https://github.com/sisl/MPOPIS