Miracle-dz/UniFAR-FTUs huggingface dataset:
Stability analysis of swirling flow in a finite-length pipe

The three-dimensional, inviscid and viscous flow instability modes that appear on a solidbody rotation flow in a finite-length, straight, circular pipe are analysed. This study is a direct extension of the Wang & Rusak [66] analysis of axisymmetric instabilities on inviscid swirling flows in a pipe. The linear stability equations are the same as those derived by Kelvin (also known as Sir William Thomson) [24]. However, we study a general mode of perturbation that satisfies the inlet, outlet and wall conditions of a flow in a finite-length pipe with a fixed in time and in space vortex generator ahead of it. This mode is different from the classical normal mode of perturbations. The eigenvalue problem for the growth rate and the shape of the perturbations for any azimuthal wavenumber m consists of a linear system of partial differential equations in terms of the axial and radial coordinates (x;r). The stability problem is solved numerically for all azimuthal wavenumbers m. The computed growth rates and the related shapes of the various perturbation modes that appear in sequence as a function of the base flow swirl ratio (ω) and pipe length (L) are presented. In the inviscid flow case, the m = 1 modes are the first to become unstable as the swirl ratio is increased and dominate the perturbation’s growth rates in a certain range of swirl levels. The m = 1 instability modes compete with the axisymmetric (m = 0) instability modes as the swirl ratio is further increased. In the viscous flow case, the viscous damping effects reduce the modes’ growth rates. The neutral stability line is presented in a Reynolds number (Re) versus swirl ratio (ω) diagram, and it can be used to predict the first appearance of axisymmetric or spiral instabilities as a function of Re and L. We use the Reynolds-Orr equation to analyse the various production terms of the perturbation’s kinetic energy, and establish that the elimination of the flow axial homogeneity at high swirl levels as the underlying physical mechanism that leads to flow exchange of stability and to the appearance of both spiral and axisymmetric instabilities. The viscous effects in the bulk have only a passive influence on the modes’ shapes and growth rates. These effects decrease with the increase of Re. We show that the inviscid flow stability results are the inviscid-limit stability results of high-Re rotating flows. The stability of the Lamb-Oseen vortex with b = 4 in a finite-length pipe is studied. The study reveals that the dynamics of such flow is dominated by the axisymmetric perturbation mode.
The Influence of Nitrogen Absorption on Microstructure, Properties and Cytotoxicity Assessment of 316L Stainless Steel Alloy Reinforced with Boron and Niobium

In the past, 316L stainless steel (SS) has been the material of choice for implant manufacturing. However, the leaching of nickel ions from the SS matrix limits its usefulness as an implant material. In this study, an efficient approach for controlling the leaching of ions and improving its properties is presented. The composition of SS was modified with the addition of boron and niobium, which was followed by sintering in nitrogen atmosphere for 8 h. The X-ray diffraction (XRD) results showed the formation of strong nitrides, indicating the diffusion of nitrogen into the SS matrix. The X-ray photoelectron spectroscopy (XPS) analysis revealed that a nitride layer was deposited on the sample surface, thereby helping to control the leaching of metal ions. The corrosion resistance of the alloy systems in artificial saliva solution indicated minimal weight loss, indicating improved corrosion resistance. The cytotoxicity assessment of the alloy system showed that the developed modified stainless steel alloys are compatible with living cells and can be used as implant materials.
Hierarchical Sensitivity Analysis for Large Scale Process-based
Hydrological Modeling with Application in an Amazonian
Watershed

Abstract. Sensitivity analysis is an effective tool for identifying important uncertainty sources and improving model calibration and predictions, especially for integrated systems with heterogeneous parameter inputs and complex processes coevolution. In this work, an advanced hierarchical global sensitivity analysis framework, which integrates a hierarchical uncertainty framework and a variance-based global sensitivity analysis, was implemented to quantitatively analyze several uncertainties of a three-dimensional, process-based hydrologic model (PAWS). The uncertainty sources considered include model parameters, model structures (with/without overland flow module), and climate forcing. We apply the approach in a ~ 9000 km2 Amazon catchment modeled at 1 km resolution to provide a demonstration of multiple uncertainty source quantification using a large-scale process-based hydrologic model. The sensitivity indices are assessed based on three important hydrologic outputs: evapotranspiration (ET), ground evaporation (EG), and groundwater contribution to streamflow (QG). It is found that, in general, model parameters (especially those within the streamside model grid cells) are the most important uncertainty contributor for all sensitivity indices. In addition, the overland flow module significantly contributes to model predictive uncertainty. These results can assist model calibration and provide modelers a better understanding of the general sources of uncertainty in predictions of complex hydrological systems in Amazonia. We demonstrated a pilot example for comprehensive global sensitivity analysis of large-scale complex hydrological models in this research. The hierarchical sensitivity analysis methodology used is mathematically rigorous and can be applied to a wide range of large-scale hydrological models with various sources of uncertainty.
Performance Analysis of Today's Networks: Network of Social Entities | Network of Caches

Author(s): Azimdoost, Bita | Advisor(s): Sadjadpour, Hamid R. | Abstract: Extensive growth of Internet applications like communication, education, and leisure, along with the privacy concerns and significant amount of personalized contents, have clearly affected the connectivity of the networks and the users' behavior. The social nature of today's networks' users (the term 'social' is used as a general technical term for network users with specific mutual relationships, and can address a large domain of users, including but not limited to social media users) has in turn a major influence on the networks' shapes and characteristics. On the other hand, with the emergence of advanced hardware and software technologies which enable significant processing power and storage space in mobile devices, and with their ever increasing widespread use, today's Internet is moving from an infrastructure-based network towards a wireless ad-hoc network. Besides, caching data within the network is going to be inevitable to improve latency and reduce bandwidth consumption, and storage is being considered as one of the network primitives.This research, thus, investigates the impact of the above aspects of today's networks on the performance. It first discusses and evaluates the effect of users' social behavior on the maximum achievable data rate in the wireless ad-hoc networks, and proves that social connection among nodes may actually help in scaling wireless networks. It also reveals that due to their different social status, various users have different effects on the performance, and therefore, traditional transport capacity concept for wireless networks is not appropriate for these types of networks. Second part of this work investigates the improvements in the fundamental limits of the performance metrics in networks of caches, and evaluates the effect of different caching policies and content searching algorithms on the obtained improvements. Then a framework is presented to quantify the overhead traffic of locating contents, and is later used to define some optimal policies with respect to the contents that should be cached for an operator-driven content distribution system. This framework can generally be used in many other distributed systems contexts where a control plane has to stay aware of the state of the forwarding plane.
Counterfactual-based Incrementality Measurement in a Digital Ad-Buying Platform

The problem of measuring the true incremental effectiveness of a digital advertising campaign is of increasing importance to marketers. With a large and increasing percentage of digital advertising delivered via Demand-Side-Platforms (DSPs) executing campaigns via Real-Time-Bidding (RTB) auctions and programmatic approaches, a measurement solution that satisfies both advertiser concerns and the constraints of a DSP is of particular interest. MediaMath (a DSP) has developed the first practical, statistically sound randomization-based methodology for causal ad effectiveness (or Ad Lift) measurement by a DSP (or similar digital advertising execution system that may not have full control over the advertising transaction mechanisms). We describe our solution and establish its soundness within the causal framework of counterfactuals and potential outcomes, and present a Gibbs-sampling procedure for estimating confidence intervals around the estimated Ad Lift. We also address practical complications (unique to the digital advertising setting) that stem from the fact that digital advertising is targeted and measured via identifiers (e.g., cookies, mobile advertising IDs) that may not be stable over time. One such complication is the repeated occurrence of identifiers, leading to interference among observations. Another is due to the possibility of multiple identifiers being associated with the same consumer, leading to "contamination" with some of their identifiers being assigned to the Treatment group and others to the Control group. Complications such as these have severely impaired previous efforts to derive accurate measurements of lift in practice. In contrast to a few other papers on the subject, this paper has an expository aim as well, and provides a rigorous, self-contained, and readily-implementable treatment of all relevant concepts.
Hydride growth mechanism in zircaloy-4: Investigation of the partitioning of alloying elements

The long-term safety of water-based nuclear reactors relies in part on the reliability of zirconium-based nuclear fuel. Yet the progressive ingress of hydrogen during service makes zirconium alloys subject to delayed hydride cracking. Here, we use a combination of electron back-scattered diffraction and atom probe tomography to investigate specific microstructural features from the as-received sample and in the blocky-alpha microstructure, before and after electrochemical charging with hydrogen or deuterium followed by a low temperature heat treatment at 400C for 5 hours followed by furnace cooling at a rate of 0. 5C per min. Specimens for atom probe were prepared at cryogenic temperature to avoid the formation of spurious hydrides. We report on the compositional evolution of grains and grain boundaries over the course of the sample's thermal history, as well as the ways the growth of the hydrides modifies locally the composition and the structure of the alloy. We observe a significant amount of deuterium left in the matrix, even after the slow cooling and growth of the hydrides. Stacking faults form ahead of the growth front and Sn segregates at the hydride-matrix interface and on these faults. We propose that this segregation may facilitate further growth of the hydride. Our systematic investigation enables us discuss how the solute distribution affects the evolution of the alloy's properties during its service lifetime.
Python-by-contract dataset

Design-by-contract as a programming technique is becoming popular in Python community as various tools have been developed for automatically testing the code based on the contracts. However, there is no sufficiently large and representative Python code base with contracts to evaluate these different testing tools. We present Python-by-contract dataset containing 514 Python functions annotated with contracts using icontract library. We show that our Python-by-contract dataset can be easily used by existing testing tools that take advantage of contracts. The demo video can be found at https://youtu.be/08wZN-xh6mY.
Determinants of climate smart agriculture technology adoption in the Norther Province of Zambia

Our world, as we know it, is changing faster than what scientific evidence has thus far predicted. Globally, we see an increased occurrence of indeterminate and unpredictable climatic events changing the daily livelihood of people across the planet. Particularly, such impacts include the frequent occurrences of droughts, the increased incidences of pests and diseases in farmer fields (such as the fall army worm in Zambia), the reduced annual rainfall and shrinking freshwater supplies, the increased number of forest wild fires, and the reduction of farmers’ yields. This calls for the need to adapt and build resilience. To support the adaptation and resilience agenda, various global initiatives have been undertaken and include the Intergovernmental Panel on Climate Change (IPCC), the Kyoto Protocol, the Sustainable Development Goals (SDGs), and the Paris Agreement. Despite these global efforts, climate change impacts are still severe for developing countries like Zambia, experienced through erratic weather conditions leading to droughts and floods. This affects rural households more severely, where 70% of the Zambian population rely on agriculture (IAPRI, 2016). Between 1960 and 2003, Zambia’s average temperature rose by 1.3 degrees Celsius and rainfall decreased by 2.3 % each decade (Norimitsu, 2016). To counter these adverse effects, policies were formulated at national level to guide the national agenda on climate change, which includes the National Policy on Climate Change (NPCC) and the National Adaptation Program of Action (NAPA). These policy initiatives have explicitly identified environmentally friendly agricultural and natural resource management practices, which include: (1) improved agronomic practices, (2) tillage and residual management, (3) agroforestry, and (4) increased participation of women, youth and children in climate change programmes, among others, as the main tools for improving smallholder productivity and building resilience strategies. These measures have shown to suit the Climate Smart Agriculture (CSA) framework developed by the Food and Agricultural Organization
Assessment of video-based e-learning in a construction measurement course

ABSTRACT E-learning has been touted as a new phenomenon and formidable frontier of education, offering tremendous benefits to teachers, learners and educational administrators. However, despite the adoption of e-learning in many educational institutions worldwide, to enhancing teaching and learning experience, delivery of construction measurement courses has been limited to traditional lecture approach and the use of little technology such as PowerPoint. Therefore, this study investigates learners’ satisfaction, benefits, and the usefulness of e-learning approach, relative to instructional videos. A total of 76 undergraduate construction students were surveyed using a closed and open-ended questionnaire in one of the universities in Hong Kong. The results showed that students were satisfied with design and content of the instruction videos. Students also considered e-learning approach useful because it allows them to control their pace, time and location for learning. Learners prefer blended learning, i.e. a combination of e-learning (at 41%) and traditional face-to-face method (at 59%). The participants opined that blended learning has more significant benefits in delivering measurement course. The results of this study should assist educators in structuring blended learning approach.
Effect of three different disinfection materials on alginate impression by spray method

Introduction: The aim of this study was to investigate the effect of three different types of disinfectant agents on alginate impression material after 5 and 10 minutes. Method and Materials: In this in vitro experimental study, 66 circular samples of alginate impression material were contaminated with Staphylococcus aureus, Pseudomonas aeruginosa, and Candida albicans fungus. Except for control samples, all of them were disinfected with sodium hypochlorite 0.525, Deconex, and Epimax by way of spraying. Afterwards, they were kept in plastic bags with humid rolled cotton for 5 and 10 minutes. The number of colonies was counted after 24 and 48 hours for bacteria and after 72 hours for fungus. Result: Statistical Mann-Whitney test was used for data analysis (α = 0.05). Results. After 5 minutes, Epimax showed the highest disinfection action on Staphylococcus aureus as it completely eradicated the bacteria. The disinfection capacity of different agents can be increased as time elapses except for Pseudomonas aeruginosa which was eradicated completely in both 5 and 10 minutes. Conclusion: This study revealed that alginate can be effectively disinfected by three types of disinfecting agents by spraying method, although Epimax showed the highest disinfection action after 10 minutes compared to other agents.
Understanding why we cannot model how long a code review will take: an industrial case study

Code review is an effective practice for finding defects, but because it is manually intensive it can slow down the continuous integration of changes. Our goal was to understand the factors that influenced the time a change, ie a diff at Meta, would spend in review. A developer survey showed that diff reviews start to feel slow after they have been waiting for around 24 hour review. We built a review time predictor model to identify potential factors that may be causing reviews to take longer, which we could use to predict when would be the best time to nudge reviewers or to identify diff-related factors that we may need to address. The strongest feature of the time spent in review model we built was the day of the week because diffs submitted near the weekend may have to wait for Monday for review. After removing time on weekends, the remaining features, including size of diff and the number of meetings the reviewers have did not provide substantial predictive power, thereby not being able to predict how long a code review would take. We contributed to the effort to reduce stale diffs by suggesting that diffs be nudged near the start of the workday and that diffs published near the weekend be nudged sooner on Friday to avoid waiting the entire weekend. We use a nudging threshold rather than a model because we showed that TimeInReview cannot be accurately modelled. The NudgeBot has been rolled to over 30k developers at Meta.
A value-based goal model analysis tool

Goal-oriented analysis techniques analyzes goal models in order to help analysts making decisions about the system to be developed. These analyses can, for example, identify conflicts of interest among stakeholders or prioritize their goals. Although many techniques of this type have been proposed, the vast majority of them focus on the concept of satisfaction, resulting in a value-neutral approach. This means that all the elements in a goal model (e.g., stakeholders' goals) are considered to be equally important. Moreover, almost all these techniques use either a qualitative or a quantitative approach to assign weight or importance to the elements in a goal model. This paper presents a tool that provides technical support to a goal-oriented analysis technique that uses the concept of value (thereby considering that not all intentional elements are equally important). The technique uses fuzzy logic to combine a qualitative prioritization of individual intentional elements with a quantitative systematic propagation of the relationships among these intentional elements by means of Fuzzy Multi-Criteria Decision-Making (FM-CDM). This propagation calculates how valuable each intentional element of a goal model is with respect to the other elements of the model.
Conditioning effect of lysozyme combined with freeze-thaw on the municipal sludge dewatering performance

The effects of lysozyme (LZM) and freeze-thaw conditioning, alone or in combination, on sludge dewatering performance were comparatively investigated. After the optimization of the dewatering conditions using response surface methodology (RSM), the co-conditioning exhibited obvious superiority to the separate conditioning in improving the dewaterability of municipal sludge, with the capillary suction time (CST) and the water content (W) of the dewatered sludge reduced to 12 ± 0.5 s and 52.0% ± 0.4% from 61.8 s and 73.0%, respectively. The co-conditioning appeared not only to destroy the structure of extracellular polymeric substance (EPS) and microbial cell wall by virtue of enzymatic conditioning, but it formed larger particles and compact sludge ﬂoc structure with the help of freeze-thaw conditioning. Additionally, the bound water content of sludge decreased by 47.5% after co-conditioning, consistent with the enhancement in sludge dewaterability. All the results showed that LZM in combination with freeze-thaw conditioning had a great potential in sludge reduction, providing more opportunity of resource utilization for the dewatered sludge.
Towards A Relation Extractor Nested U-Architecture for Accurate Pulmonary Airway Segmentation in CT images

Pulmonary airway segmentation is essential to computer aided diagnosis and surgical planning systems for pulmonary diseases. Unfortunately, it usually gets trapped in voxel leakages at the peripheral (small) bronchi due to the partial-volume effect. To this end, we present a new deep learning relation extractor nested U-architecture that combines convolution and a self-attention mechanism in an encoder-decoder mode. Specifically, we employ convolution in the shallow layers to extract the feature of the single trachea and bronchi in a short range and introduce self-attention in the deep layers to capture the whole airway-tree structure in a long range. Both convolution and self-attention operations are embedded into an U-architecture. We evaluate our deep learning architecture on 30 computer tomography volumes, with the experimental results showing that our method can improve the average coefficient dice to 0.884 and reduce the false positive rate to 0.019.
Development of student worksheet based on guided inquiry with class activity and laboratory in thermochemistry material

The study of development student worksheet based on guided inquiry has been conducted for thermochemistry subject for high school. This study aims to determine the validity, practicality and effectiveness of development student worksheet using plomp model. The results show that the developed student worksheet in very valid categorized (82.1%). The application of guided inquiry in student worksheet has been practicality tested by teachers and students, and it show the result of 81.25% and 85.1%, respectively. This value indicates that student worksheet is very practical. Based on the students activity aspect, the effectiveness student worksheet based on guided inquiry reaches 84.4% (very effective), whereas based on learning outcomes have a n-gain value of 0.6 (medium). It can be concluded that the developed student worksheet is in very valid, very practical and very effective category so as to improve the students understanding.