Miracle-dz/UniFAR-FTUs huggingface dataset:
Method for High-Yield Hydrothermal Growth of Silica Shells on Nanoparticles

Coating processes are commonly used in materials science to protect a core or modify material properties. We describe a hydrothermal coating process using TEOS (tetraethyl orthosilicate), a widely used precursor for silica coatings, on three representative template materials (carbon nanotubes, silica, and polystyrene nanoparticles) with different properties and shapes. We compare the efficiency of previously published protocols for silica coatings at room temperature and atmospheric pressure with the hydrothermal process at 160 °C and 3 bar. The hydrothermal method achieves higher yields and thicker silica coatings with the same amount of precursor when compared to the conventional way, thus offering higher effectiveness. Furthermore, the hydrothermal coating process yields more homogeneous shells with a higher density, making hydrothermal coating the method of choice when mechanical integrity and low permeability of the coating are required.
Investigation of an aeroelastic model for a generic wing structure

Investigation of an Aeroelastic Model for a Generic Wing Structure M.E. Cilliers Department of Electrical and Electronic Engineering, University of Stellenbosch, Private Bag X1, Matieland 7602, South Africa. Thesis: MScEng (Electronic) October 2012 Computational Aeroelasticity is a complex research field which combines structural and aerodynamic analyses to describe a vehicle in flight. This thesis investigates the feasibility of including such an analysis in the development of control systems for unmanned aerial vehicles within the Electronic Systems Laboratory at the Department of Electrical and Electronic Engineering at Stellenbosch University. This is done through the development of a structural analysis algorithm using the Finite Element Method, an aerodynamic algorithm for Prandtl’s Lifting Line Theory and experimental work. The experimental work was conducted at the Low-Speed Wind Tunnel at the Department of Mechanical and Mechatronic Engineering. The structural algorithm was applied to 20-noded hexahedral elements in a winglike structure. The wing was modelled as a cantilever beam, with a fixed and a free end. Natural frequencies and deflections were verified with the experimental model and commercial software. The aerodynamic algorithm was applied to a Clark-Y airfoil with a chord of 0.1m and a half-span of 0.5m. This profile was also used on the experimental model. Experimental data was captured using single axis accelerometers. All postprocessing of data is also discussed in this thesis. Results show good correlation between the structural algorithm and experimental data. ii Stellenbosch University http://scholar.sun.ac.za
Riesz Basis Property and Exponential Stability for One-Dimensional Thermoelastic System with Variable Coefficients

In this paper, we study Riesz basis property and stability for a nonuniform thermoelastic system with Dirichlet-Dirichlet boundary condition, where the heat subsystem is considered as a control to the whole coupled system. By means of the matrix operator pencil method, we obtain the asymptotic expressions of the eigenpairs, which are exactly coincident to the constant coefficients case. We then show that there exists a sequence of generalized eigenfunctions of the system, which forms a Riesz basis for the state space and the spectrum determined growth condition is therefore proved. As a consequence, the exponential stability of the system is concluded.
Cognitive Uncertainty in Intertemporal Choice

This paper experimentally documents the relevance of cognitive uncertainty – subjective uncertainty over one’s utility-maximizing action – for understanding and predicting intertemporal choice. The main idea is that when people are cognitively noisy, such as when a decision problem is very complex, they implicitly treat different time delays to some degree alike. By experimentally measuring and exogenously manipulating cognitive uncertainty, we document that this logic sheds light on why observed choices often appear very impatient over short horizons but not over longer ones, and why intertemporal decisions frequently violate transitivity. Structural estimations provide evidence that cognitive noisiness is a quantitatively important driver of intertemporal decisions, so that accounting for measured cognitive uncertainty generates large improvements in model fit. Finally, cognitive uncertainty also matters for choice architecture: people who are nervous about making mistakes in intertemporal decisions are twice as likely to follow expert advice.
Diagnostics at the ELBE Bremsstrahlung Facility

The ELBE (Electron Linear accelerator with high Brilliance and low Emittance) machine is currently under transition from commissioning to regular user operation. The linac produces electron beams up to 40 MeV and 1 mA (CW), which are used to generate various kinds of secondary radiation. While the IR‐FEL and X‐ray facilities are still under construction, comprehensive nuclear physics experiments have been conducted at the Bremsstrahlung facility. Both, to meet the experimenter’s demands on beam quality, stability and reproducibility and to ensure a safe operation of the machine, several diagnostic elements were developed. These devices, including backward OTR viewers, radiator temperature diagnostics, stripline detectors to monitor the beam incidence angle on the radiator, and a four‐quadrant loss monitor, were developed, installed, and tested. These devices are described and results are presented in this paper.
Models for Bearing Damage Detection in Induction Motors Using Stator Current Monitoring

This paper describes a new analytical model for the influence of rolling-element bearing faults on induction motor stator current. Bearing problems are one major cause for drive failures. Their detection is possible by vibration monitoring of characteristic bearing frequencies. As it is possible to detect other machine faults by monitoring the stator current, a great interest exists in applying the same method for bearing fault detection. After a presentation of the existing fault model, a new detailed approach is proposed. It is based on the following two effects of a bearing fault: 1. the introduction of a particular radial rotor movement and 2. load torque variations caused by the bearing fault. The theoretical study results in new expressions for the stator current frequency content. Experimental tests with artificial and realistic bearing damage were conducted by measuring vibration, torque, and stator current. The obtained results by spectral analysis of the measured quantities validate the proposed theoretical approach.
Compact coplanar waveguide band‐pass filter at the S‐band

In this paper, it is demonstrated that split‐ring resonators (SRRs) magnetically coupled to a coplanar waveguide (CPW) structure can be applied to the design of miniaturized band‐pass filters. To this end, a two‐stage prototype device consisting of a CPW with two signal‐to‐ground shorts and a series gap, combined with SRRs, is designed and fabricated. By virtue of the subwavelength operation of SRRs at their quasi‐static resonance, the filter dimensions can be made to be very small in comparison to conventional distributed implementations. Other relevant features of the proposed structures are: (i) low insertion losses (<3.5 dB) in the allowed band, (ii) out‐of‐band rejection levels above 30 dB, and (iii) very sharp transition slopes. It is believed that these SRR‐based structures can be of interest for the design of planar filters with potential application in future communication transceivers. © 2005 Wiley Periodicals, Inc. Microwave Opt Technol Lett 46: 33–35, 2005; Published online in Wiley InterScience (www.interscience.wiley.com). DOI 10.1002/mop.20893
Giving the Underdog a Leg Up: A Counternarrative of Nonviolent Resistance Improves Sustained Third-Party Support of a Disempowered Group

In the current work, we experimentally examined the effect of exposure to a narrative of nonviolent resistance on third-party attitudes toward and support for a disempowered group involved in asymmetric conflict. Across three experiments, we found that Americans exposed to a brief video about Palestinian nonviolent resistance consistently registered more favorable attitudes toward Palestinians than people who watched a film trailer either unrelated to the Israeli–Palestinian conflict or a trailer to a Palestinianmade film about sympathetic Palestinians violently opposing Israelis. Americans’ attitudes toward Palestinians and behavior supporting Palestinian collective action persisted weeks after exposure to nonviolent resistance and were mediated by decreased perceptions that Palestinians are inherently violent. Importantly, positive attitudes toward Palestinians did not result in increased negativity toward Israelis. These data show that exposure to nonviolent resistance can have lasting effects on third-party attitudes and behavior toward an underdog/disempowered group, without driving partisanship.
Slope monitoring practices at open pit porphyry mines in British Columbia, Canada

A survey of pit wall monitoring practices was conducted at all large open pit copper–molybdenum mines in British Columbia. The survey found that these mines tend to rely on visual inspection and prism surveys as the primary techniques for pit wall management. Open standpipe piezometers and wire-line extensometers are also used. Newer monitoring technologies are slow to be adopted by the mines. One exception is slope stability radar, which is used at two mines. All mines have established movement thresholds that trigger different operational responses. These thresholds do not appear to be based on the pit depth or rock mass conditions.
Developing and Piloting a Quantitative Assessment Tool for Cybersecurity Courses

The rapid growth of the Internet over the past two decades has led to a proliferation of network-capable computing devices. This growth has occurred so rapidly that the academic pipeline is struggling to keep up with the demand for cybersecurity professionals capable of protecting the expanding infrastructure. Training a security-focused workforce has become a critical objective of government entities, businesses, and academic institutions. As educators respond to this growing demand, developing new curriculum and methodologies for training cybersecurity professionals, there has been little systematic effort to assess student outcomes from the variety of pedagogical approaches being used. This paper presents the second stage in our work to develop an assessment tool designed to measure student interest and self-efficacy in relation to cybersecurity. Such a tool will allow educators to detect changes in student outcomes and thus systematically improve pedagogical methods. Initial instrument development is based on a qualitative study of students enrolled in an introductory cybersecurity course. We piloted the survey during the Spring and Fall 2014 semesters, and present the results here along with discussions of our ongoing and future activities with this project.
ENHANCEMENT OF TOMATO ROOT-KNOT NEMATODES CONTROL USING A NOVEL BIOLOGICAL TREATMENT IN NORTH SINAI

This study focuses on using olive mill waste water (OMWW) as a novel biological treatment to decrease plant diseases while improving food quality. Root-knot nematode Meloidogyne is considered one of the main tomato diseases in North Sinai. The current study aimed to evaluate the efficiency of diffracting olive mill waste waters concentrations compared with the nematicide Oxamylon on Tomato Root-knot. OMWW was applied daily to tomato plants growing in sandy loam soil for three months. Represented five different concentration of OMWW (0%, 25%, 50%, 75%, and 100%) used for irrigation during the experiment. The positive control treatment was treated with Oxamyl at recommended dose. The negative control was watered only with tap water (0%). The results showed that mortality rates of M. incognita were significantly affected by concentrations. All treatments were superior to the untreated control in reducing nematode, except for OMWWs at 100 %, which was equivalent to the control treatment. The largest drop in root galling numbers Oxamyl was achieved (75.73%) was followed by 50% at (73.81%), 75% (68.01%), and 25% (39.67%). The highest reductions rated were recorded with the 50%and 75% concentrations used of OMWW. Overall, in the effort of efficient biological treatments was achieved for root knot control measures in tomato, the OMWW application may be used as one factor of integrated root-knot nematode management.
Haptics-based Curiosity for Sparse-reward Tasks

: Robots in many real-world settings have access to force/torque sensors in their gripper and tactile sensing is often necessary for tasks that involve contact-rich motion. In this work, we leverage surprise from mismatches in haptics feedback to guide exploration in hard sparse-reward reinforcement learning tasks. Our approach, Haptics-based Curiosity (HaC), learns what visible objects interactions are supposed to “feel” like. We encourage exploration by rewarding interactions where the expectation and the experience do not match. We test our approach on a range of haptics-intensive robot arm tasks (e.g. pushing objects, opening doors), which we also release as part of this work. Across multiple experiments in a simulated setting, we demonstrate that our method is able to learn these difﬁcult tasks through sparse reward and curiosity alone. We compare our cross-modal approach to single-modality (haptics-or vision-only) approaches as well as other curiosity-based methods and ﬁnd that our method performs better and is more sample-efﬁcient.
The Disease of 21 st Century: Digital Disease

- Constantly changing and developing technological life eases life on one hand, and creates new risks on the other. In the 21 st century which is termed as “the age of technology”, computer and internet usage have become indispensable elements of life. Internet addiction has recently emerged as a new type of addiction which came to the agenda with technological developments. Failing to impose restrictions on internet usage, continuing to use it despite its social and academic damages and suffering from excessive anxiety when access to internet is restricted are symptoms of this new type of addiction and is one of the first examples of digital disease types which entered psychiatry literature as of 1990s. These types of recently-defined digital diseases which can be seen at almost every age have turned into a risk factor especially for 12-18 age group. The purpose of this article is to examine the types of digital diseases which emerge out of social media usage and problematic usage as a result of digitalization.
Behavioural Intention Determinants of Augmented Reality Technology Adoption in Supermarkets/Hypermarkets

In this paper, the adoption of Augmented Reality, as one of the emerging and intriguing digital technologies, has been investigated. This research uses the extended Unified Theory on Acceptance and Use of Technology framework to analyze these factors. The student population respondents' data about Augmented Reality adoption was collected. The student population has been chosen due to the highest probability of accepting new technologies. The research results show a positive and significant performance expectancy and enjoyment, while effort expectancy showed a negative and significant impact on the behavioural intention dependent variable. These research results can be used for the potential development of Augmented Reality apps in the retail industry and the academic implications of the connections between variables in the UTAUT framework.
Optimization of magnetic multilayered shields

In this paper a procedure for the optimization of multilayered shields for low-frequency magnetic fields is presented. The shield is optimized with respect to the total volume of the composing materials, being the magnetic flux density magnitude at test points in the shielded region smaller than a given threshold value. For simplicity, shields composed of two different materials only are considered. The number and thickness of the shield layers belong to the set of the control variables. Only one layer thickness at a time is allowed to vary. The procedure is repeated for the different multilayered shield configurations, starting from the single-layer ones for each material, until the optimum shield volume is reached.
R ASL : Relational Algebra in Scikit-Learn Pipelines

Integrating data preparation with machine-learning (ML) pipelines has been a long-standing challenge. Prior work tried to solve it by building new data processing platforms such as MapReduce or Spark, and then implementing new libraries of ML algorithms for those. But despite the availability of these platforms, many ML practitioners continue to use scikit-learn instead, owing to its clean design and rich set of algorithms. Therefore, this paper proposes a different approach: instead of extending a data processing platform for ML, extend an ML library for data processing. Speciﬁcally, this paper proposes R ASL , an open-source library of relational algebra (RA) operators for scikit-learn (SL). We illustrate R ASL with a detailed case study involving joins and aggregation across multi-table input data. We hope our approach will lead to cleaner integration of data preparation with machine learning in practice.
NQF – IPSO – A node quality factor based CH selection & Improved PSO for efficient relay selection

The sensor node lifetime plays a key role in a wireless sensor network and it relies on the battery life. After deploying the batteries in an unattended or remote area, their replacement is very difficult. To overcome this issue, a lot of research had performed, but they are suffering from one or other way. Energy efficiency is improved by clustering technique. The efficient and stable CH selection is an important task in clustered network. The CHs failure can collapse the entire data aggregation of the network. Energy efficient relay selection for data aggregation plays an important role in controlling energy consumption during data aggregation. There are a plenty of energy efficient routing protocols exists in this cause. But most of them considers node residual energy as the primary parameter for relay selection and failed to consider other prominent parameters for relay selection which can affect the sensor node lifetime. To address the CH failure issue and to select the stable CHs, in this paper we introduce Node Quality factor algorithm to estimate and rank the nodes based on their selection quality. This method considers the channel quality of the relay-destination link, distance between the nodes and the residual energy as selection parameters. An improved Particle Swarm Optimization Algorithm is introduced for selecting the energy efficient relay nodes. The improved Particle Swarm Optimization Algorithm takes into account node residual energy, node distance to CH, average energy, node degree, and ECR for calculating the fitness function. The simulation results proves that the proposed NQA-IPSO method outperforms the previously proposed energy efficient techniques in network lifetime, data delivery rate, and energy consumption.
ESTIMATION OF PARAMETERS OF PERT DISTRIBUTION BY USING MAXIMUM PRODUCT OF SPACINGS METHOD

The PERT Distribution is one of the most important distributions for practical use in business, because it is widely used to generate random values within a range in the financial models and simulations in the area of processes and analysis in general. Present Maximum Product Spacings of the unknown parameters of PERT distribution using Newton-Raphson iterative procedure. We also computed Average Estimate (AE), Variance (VAR), Standard Deviation (SD), Mean Absolute Deviation (MAD), Mean Square Error (MSE), Relative Absolute Bias (RAB) and Relative Efficiency (RE) for both the parameters under sample based on 10,000 simulations to assess the performance of the estimators. A simulation study is conducted to evaluate the performance of the Maximum Product of Spacings estimates.
Predicting Your Next Trip: A Knowledge Graph-Based Multi-task Learning Approach for Travel Destination Recommendation

Recommending the next destination to a traveler is a task that has been at the forefront of the airline industry for a long time, and its relevance has never been more important than today to revive tourism after the Covid-19 crisis. Several factors influence a user's decision when faced with a variety of travel destination choices: geographic context, best time to go, personal experiences, places to visit, scheduled events, etc. The challenge of recommending the right travel destination lies in efficiently integrating and leveraging all of this information into the recommender system. Based on a real world application scenario, we propose a multi-task learning model based on a neural network architecture that leverages knowledge graph to recommend the next destination to a traveler. We experimentally evaluated our proposed approach by comparing it against the currently in-production system and state-of-the-art travel destination recommendation algorithms in an offline setting. The results confirm the significant contribution of using knowledge graphs as a means of representing the heterogeneous information used for the recommendation task, as well as the benefit of using a multi-task learning model in terms of recommendation performance and training time. Copyright © 2021 for this paper by its authors. Use permitted under Creative Commons License Attribution 4.0 International (CC BY 4.0).
Comparative analysis of coupling modularity metrics

Modular design has become a widely accepted developmental strategy to create products and systems that can be easily manufactured, upgraded and maintained. In order to achieve these benefits through improvement of a system's modularity, it must be measured. An ideal measure ought to capture modularity while being independent of other architectural factors such as size, system coupling density or the number of modules. In this work, we review past research on modularity measures. Eight modularity measures are selected for a detailed analysis. We use a design of experiments approach to analyse which metrics best measure the degree of modularity independent of other irrelevant factors. To do this, we conduct a factorial analysis of 24 canonical architectures with idealised modularity, including precisely integral, modular and bus architectures. We find that most measures produce inconsistent results, especially if the system architecture contains a bus or modules with loose internal coupling. We identify the metrics that are able to capture the degree of modularity in the most consistent manner.
Beliefs and Portfolios: Causal Evidence

We confront competing theories of expectation formation and asset pricing with causal evidence on beliefs and portfolios. Using a randomized information experiment we show that: i) Individuals do not revise their beliefs in line with Rational Expectations asset pricing models. Instead, they form pro-cyclical beliefs, both about capital gains and about earnings growth. ii) Individuals are heterogenous at the information acquisition and at the information processing stage. Their reaction to stock-market news depends on their preference for the type of news received. iii) Beliefs and portfolios are not only correlated, but causally linked. iv) The sensitivity of portfolio shares with respect to expected returns appears puzzlingly low, especially when accounting for individuals’ low perceived variance of stock returns. This puzzle can be resolved when considering non-linear constraints on individuals’ portfolio decisions. These results provide guidance on promising causal mechanisms for macro-finance models.