Miracle-dz/UniFAR-FTUs huggingface dataset:
Determination of Additional Willingness to Pay for Socially Responsible Technical Products Using Discrete Choice Analysis

Previous studies examined additional willingness to pay for socially responsible primary goods. However, technical products have not been considered. Therefore, the purpose of this study is to estimate additional willingness to pay for socially responsible technical products. Within an overview of given methods for measuring willingness to pay, the discrete choice analysis was applied to this study. As technical products, computer mice were chosen exemplary, since there is a partially fair mouse available. It was found that two of three fair labeled mice have a negative willingness to pay. Only consumers of the fair produced and labeled mouse has a positive willingness to pay. The consumers pay perhaps more attention to the aspect of social responsibility, if presented brands are comparatively unknown. In this connection, consumers allocate a higher value to social responsibility.
FISHEYE VIEW FOR SEARCHING PHOTOS ON A MOBILE PHONE

Abstract: Thumbnail preview is popularly used to preview photos on a small screen of a mobile phone. However, the more photos are displayed on a screen, the smaller size of a thumbnail is. When a number of photos are displayed on a screen at the same time, it is difficult to identify each photo. A solution to this problem is to apply the concept of fisheye view to searching for a photo on a small screen. An experiment was conducted to evaluate the effectiveness of the proposed method. The factors manipulated in the experiment were the number of thumbnails on a screen and the delay time of the original thumbnail preview into fisheye view. The results showed that displaying a 3×3 array and a 4×4 array were better than displaying a 5×5 array in terms of performance and preference. The effect of the delay time (0.8s and 1.2s) was not significant in performance comparing with the existing thumbnail preview, but it was preferred by the subjects. The results obtained in this study can be applied to other devices with small screens such as digital cameras and PDAs.
Learning, Transfer and Use of Affordances in Robotics Tasks

Utilization of robotics in various applications increase with technological improvements. Few recent examples are Roomba vacuum cleaner robot for homes, industrial robots for production lines and unmanned robots for dangerous environments, such as NASA Mars Rover. One important aspect of robotics is the intelligence mechanism. However it is not always easy to design controllers and decision makers since mechanical designs of robots are becoming more complex as well as robotic tasks. Furthermore robots are expected to be able to cope with unforeseen interaction in these environments. Therefore a different approach than hard-coding the robotic agents beforehand should be used, such as learning. The learning approach has its own difficulties, such as long learning sessions and not being able to transfer knowledge to new tasks efficiently to avoid performing another learning session. Usage of affordance concept is a solution for these difficulties since it maintains some kind of transition model of the environment and the robot. Affordance concept in combination with action planning enables to use the knowledge gained in previous tasks, environments or robots. This reduces or completely eliminates the learning time required for the new task, environment or robot. This methodology also enables agents to successfully perform tasks in environments which have known properties but are new to the agent without additional learning, which is not always the case with transfer learning for Reinforcement Learning (RL) [1]. By successfully it is meant that the agent completes the task in an optimal manner. For example in a navigation task this will be reaching the destination in shortest time or lowest amount of steps. Knowledge transfer between robotic agents will ideally decrease the required learning time and trials of new robotic agents in an environment. This can be done via passing some of the learned knowledge from the robotic agents which are already functioning in that very same environment. An agent which hasn’t learned anything about the environment will not even know the basic properties, such as a wall is not being pushable and a docking station affords charging. A common representation will be time saving for all agents that will benefit from such information. Indeed this is the case with the results presented in this thesis work.
THE INFLUENCE OF PLANTAGO OVATA ( DIETETIC FIBER ) ADMININSTRA TION ON THE COLONIC WALL PROTECTION IN THE ACETIC ACID INDUCED INFLAMMA TORY COLITIS : AN EXPERIMENT AL STEREOLOGIC STUDY IN RATS

Background: The objective of this paper was to study the role of the dietetic fiber ( Plantago ovata ) in the protection and recovery of the intestinal wall of rats submitted to acetic acid induced inflammator y c litis. Methods: 30 Wistar male rats wer e studied, weighing between 260 and 300g, divided in three groups of 10 individuals. The distribution of the groups was made as follows: Group I (n 10), named control group, received a standard diet during the whole experiment. Group II (n 10), named colitis group, received an equally standar d diet during the whole experiment and 8% acetic acid that induced inflammator y colitis. Group III (n 10), named colitis + fiber , received a standard diet with addition of dietetic fiber ( 10 g of seeds and peels of Plantago ovata for each 90 g of standard diet) and 8% acetic acid that induced inflammatory colitis. The experiment lasted 21 days and the inflammatory colitis induction was introduced on the four teenth day in gr oups II and III. Twenty-one days after the star t of the experiment the animals wer e sacrificed and total colectomies were performed. The specimens were submitted to stereologic and histological analysis, and they statistically evaluated by the Mann-Whitney test. The weight variation of the animals was also measured during the experiment. The studied parameters were: mucosa, muscularis mucosa, submucosa, lamina propria, epithelial and proper epithelial layer partial volumes. Conclu ion: The results, through stereologic, histological and weight analysis, lead us to conclude that the addition of Plantago ovata (dietetic fiber) prevents the alterations caused by the 8% acetic acid induced colitis in the experimental animals.
D* polarization as a probe to discriminate new physics in B --> D* tau nubar

The confirmation of excess in $R_{D^*}$ at the LHCb is an indication of lepton flavor non-universality. Various different new physics operators and their coupling strengths, which provide a good fit to $R_D$, $R_{D^*}$ and $q^2$ spectra, were identfied previously. In this work, we try to find angular observables in $\bar{B} \to D^* \tau \bar{\nu}$ which enable us to distinguish between these new physics operators. We find that the $D^*$ polarization fraction $f_L(q^2)$ is a good discriminant of scalar and tensor new physics operators. The change in $\langle f_L(q^2) \rangle$, induced by scalar and tensor operators, is about three times larger than the expected uncertainty in the upcoming Belle measurement.
Transparent Function Types: Clearing

Functional logic programming (FLP) is a paradigm that comes from the integration of lazy functional programming and logic programming. Although most FLP systems use static typing by means of a direct adaptation of Damas-Milner type system, it is wellknown that some FLP features like higher-order patterns or the equality operator lead to so-called opacity situations that are not properly handled by Damas-Milner type system, thus leading to the loss of type preservation. Previous works have addressed this problem either directly forbidding those HO patterns that are opaque or restricting its use. In this paper we propose a new approach that is based on eliminating the unintended opacity created by HO patterns and the equality operator by extending the expressiveness of the type language with decorations in the arrows of the functional types. We study diverse possibilities, which differ in the amount of information included in the decorations. The obtained type systems have different properties and expressiveness, but each of them recovers type preservation from simple extensions of Damas-Milner.
The Gauss-Bonnet Theorem for Noncommutative Two Tori With a General Conformal Structure

In this paper we give a proof of the Gauss-Bonnet theorem of Connes and Tretkoff for noncommutative two tori $\mathbb{T}_{\theta}^2$ equipped with an arbitrary translation invariant complex structure. More precisely, we show that for any complex number $\tau$ in the upper half plane, representing the conformal class of a metric on $\mathbb{T}_{\theta}^2$, and a Weyl factor given by a positive invertible element $k \in C^{\infty}(\mathbb{T}_{\theta}^2)$, the value at the origin, $\zeta (0)$, of the spectral zeta function of the Laplacian $\triangle'$ attached to $(\mathbb{T}_{\theta}^2, \tau, k)$ is independent of $\tau$ and $k$.
On Returns to Scale Assumption in Endogenous Growth

This study points out to a contradiction between increasing returns to scale assumption and steady-state or long run equilibrium or balanced growth analysis in models of endogenous growth. The paper reminds the necessity of the constant returns to scale assumption in models of economic growth, with special reference to models of endogenous growth, which are based on steady-state or long-run equilibrium or balanced growth. While analyzing balanced growth, some models of endogenous growth are based on increasing returns to scale but some others are based on constant returns to scale. The arguments in the present paper are simply explained by the some of the original texts on the debate. This study points out that assumption of constant returns to scale is compatible with the steady-state or long-run equilibrium or balanced growth, rather than increasing returns to scale. The paper emphasizes an important and neglected problem in endogenous growth.
Tensile measurement of single crystal gallium nitride nanowires on MEMS test stages

This paper reports direct tensile tests on n-type (Si-doped) gallium nitride single crystal nanowires1 that were grown by nitrogen plasma-assisted molecular beam epitaxy and which are essentially free of defects and residual strain. Nanowires were integrated with actuated, active microelectromechanical (MEMS) devices using dielectrophoresis-driven self-assembly and platinum-carbon clamps created using a gallium focused ion beam. For one nanowire, failure strain of 0.042 ± 0.011 was found. Most nanowire specimens appeared to demonstrate tensile strength in the range of 4.0 ± 1.7 GPa to 7.5 ± 3.4 GPa. Failure modes included clamp failure, transverse (nanowire c-plane) fractures, and insufficient force from the
Using cosmic voids to distinguish f(R) gravity in future galaxy surveys

We use properties of void populations identified in N-body simulations to forecast the ability of upcoming galaxy surveys to differentiate models of f (R) gravity from cold dark matter cosmology. We analyse multiple simulation realizations, which were designed to mimic the expected number densities, volumes, and redshifts of the upcoming Euclid satellite and a lower-redshift ground-based counterpart survey, using the public VIDE toolkit. We examine void abundances, ellipicities, radial density profiles, and radial velocity profiles at redshifts 1.0 and 0.43. We find that stronger f (R) coupling strengths eliminates small voids and produces voids up to ∼20 per cent larger in radius, leading to a significant tilt in the void number function. Additionally, under the influence of modified gravity, voids at all scales tend to be measurably emptier with correspondingly higher compensation walls. The velocity profiles reflect this, showing increased outflows inside voids and increased inflows outside voids. Using the void number function as an example, we forecast that future surveys can constrain the modified gravity coupling strength to ∼3 × 10−5 using voids.
DESIGN OF AN EXPERT SYSTEM FOR EFFICIENT SELECTION OF DATA MINING METHOD

The extraction of information and knowledge from large sets of data has been recognized as a key research topic in database systems and machine learning. Data mining tools predict future trends and behaviours, allowing business to take proactive decision. Also, these tools can answer to business problem that traditionally were too time consuming to resolve by scanning databases for hidden patterns, finding predictive information that experts may not see because it lies outside their expectation. We have realized a close analyze of the data mining methods, techniques and algorithms with their characteristics, with their advantages and weakness. As well we have taken into account the tasks to be resolved in order to discover the different types of knowledge, the kind of databases to work on (relational, object-relational, spatial, etc) and the type of data (categorical, numeric or boolean). All these aspects as well as the area for which on desire the implementation of the data mining system was been taken into account for the realization of the ExpertDM system which has the aim to finding the best data mining methods for solving a task and specifying the transformation which need to be made (others than the usual for data mining) for bringing the data at a proper form for applying these methods.
UvA-DARE ( Digital Academic Repository ) Desorption kinetics of Cornelissen

A technique using Tenax TAt beads as ‘‘sink’’ for desorbed solute was employed to measure the kinetics of desorption of chlorobenzenes, polychlorinated biphenyls, and polycyclic aromatic hydrocarbons from laboratory-contaminated sediment. Firstorder rate constants of rapid and slow desorption were in the order of 1021/h and 1023/h, respectively. The rate constants of slow desorption correlate well with the molecular volumes of the compounds used and decrease between 2 and 34 d of equilibration. Slowly desorbing fractions increase with both increasing solute hydrophobicity and increasing equilibration time. Keywords—Desorption kinetics Sediment Aging Organic compounds Sorption coefficients
Facebook as a means of promotion of tourist agencies specialized for youth travel in Croatia and Serbia

Social media are a trend and one of the most researched topics lately. The aim of the paper is to stress the need for all tourist companies to enter the world of social networks, the hottest online hobby of young people, especially those operating in the youth niche. Marketers are still searching for ways to commercially exploit social networks potentials and as a result they are still holding back and sticking to well known promotion models. The goal of the paper is to explore new models of promotion social networks offer as a young communication channel in tourism, based on the Facebook case.
Investigating the two-way interaction between landslides and vegetation under climate change (Ref IAP2 -18-122) Newcastle University, School of Engineering

Overview Landslides occur in humid mountain regions worldwide, and are both the dominant agent of erosion and a damaging hazard resulting in hundreds of deaths and billions of dollars of damage every year [Petley, 2012]. Vegetation plays an important role in maintaining the stability of mountain slopes but its characteristics are themselves influenced by the history of landsliding in a particular location. Climate and land management influence both the stability of the soil mantle and the structure and species composition of the vegetation. In the face of climate or land management changes the coupled behaviour of vegetation and soil has important implications for the future behaviour of both. By improving our understanding of this coupling the project will enable more informed decisions about how to manage steep landscapes whether in relation to the consequences of possible management strategies (such as ‘re-wilding’) or to the magnitude of change that should be expected and thus prepared for under climate change. Moreover, the project will prove the concept of using empirical niche models coupled with dynamic landscape evolution models for predicting the colonising pool of plant traits that are so significant to post-slip substrate stabilisation. For example, can niche models successfully filter the regional species pool predicting which trees, grasses or bryophytes will colonise given soil, management and climate in the aftermath of a landslide? If successful this will indicate that post-slip vegetation development can be usefully modelled without recourse to complex and costly local parameterisations of dynamic succession models.
Caregiving Revisited: Old and New Perspectives on Families Assisting Elders

It is a great honor to receive this award, particularly since it is named for M. Powell Lawton. Powell was a creative and eminent scholar. Those of us fortunate to have worked with him know that he was a kind, caring and positive man. Despite all his accomplishments, he was totally without pretensions. He always had time to talk to everyone, whether it was the President of the Gerontological Society seeking his advice or the greenest graduate student who just wanted to say hello. Powell is a model for all of us, not just for his outstanding scholarship, but for his decency and kindness.
Reverse and Boundary Attention Network for Road Segmentation

Road segmentation is an essential task to perceive the driving environment in autonomous driving and advanced driver assistance systems. With the development of deep learning, road segmentation has achieved great progress in recent years. However, there still remain some problems including the inaccurate road boundary and the illumination variations such as shadows and over-exposure regions. To solve these problems, we propose a residual learning-based network architecture with residual refinement module composed of the reverse attention and boundary attention units for road segmentation. The network first predicts a coarse road region from deeper-level feature maps and gradually refines the prediction by learning the residual in a top-down approach. The reverse and boundary attention units in residual refinement module guide the network to focus on the features in the previously missing region and the region near the road boundary. In addition, we introduce the boundary-aware weighted loss to reduce the false prediction. Experimental results demonstrate that the proposed approach outperforms the state-of-the-art methods in terms of the segmentation accuracy in various benchmark datasets for traffic scene understanding.
Dry matter accumulation and remobilization in grain sorghum genotypes(Sorghum bicolor L. Moench) under drought stress

A field experiment was conducted during growing season of 2007-2008 at Khorasan Agricultural and Natural Resource Research Center, Mashhad-Iran, to study dry matter accumulation and remobilization in grain sorghum genotypes under water stress and normal conditions. The main plots were allocated to two levels of moisture regimes, including water deficit after anthesis and normal condition (no water stress) and the subplots were disturbance photosynthesis with potassium iodide and non disturbance on current photosynthesis and three grain sorghum genotypes (Sepideh, M5 and M2 promising lines). The results showed that water stress significantly (p[less than or equal to]0.01) increased amounts of remobilized dry matter (ARDM), remobilization efficiency (REE), remobilization percentage (REP) by 11.21%, 32.37 % and 14.20%, respectively, compared with normal condition over all treatments. However it significantly (p[less than or equal to]0.05) decreased biological and grain yield. Disturbance in current photosynthesis caused 57.79 % and 21.20 % increase in remobilization percentage and remobilization efficiency compared to non disturbance status across all treatments. M5 genotype had the highest remobilization percentage and remobilization efficiency as compared with the two other genotypes in all experimental plots. The stepwise linear multiple regression indicated that grain yield would be properly predicted by biological yield and harvest index (R{2}=0.99, p[less than or equal to]0.01).
Examining the Effects of Behavior Variables (Skill, Attitude and Personality) on Employees’ Empowerment (Case Study: Karafarin Bank)

In this research, the role of behavioral variables (skill, attitude, and personality) was considered on employee empowerment in Karafarin bank. In order to research in this particular subject, some questionnaire provided and distributed among 120 employees and managers in Karafarin bank. Each of behavioral variables 'effectiveness on employee empowerment considered with utilizing stepwise regression after information gathered. Results of research indicated that behavioral variables (skill, attitude, and personality) have positive and significant effectiveness on employee empowerment, and personality among considered behavioral variables is most effective item on employee empowerment.
Synthesis and Electrochemical Performance of SnO 2 / Graphene Anode Material for Lithium Ion Batteries

SnO2/graphene nanocomposites were in-situ prepared by using graphite oxide and SnCl2·2H2O as raw material, in which external reducing agent is not required. Compared with mechanical mixing method, it can avoid the congregation of SnO2 nanoparticles and graphene. The analysis results (XRD, TEM, etc) indicate that SnO2 nanoparticles are homogeneously dispersed on the surface of graphene layers with a particle size of 3–6 nm, and the thickness of graphene layers is about 1.5–2 nm. The main electrochemical performance can be concluded as follows. 1) The discharge capacity of SnO2/graphene anode electrode after 100 cycles at 200 mA/g, remains at 552 mAh/g, and the capacity retention is 4.4 times of bare SnO2. 2) The discharge capacities of SnO2/graphene anode electrode at 40, 400 and 800 mA/g are 724.5, 426.0 and 241.3 mAh/g, respectively. The excellent rate capability should be attributed to the high electric conductivity and two-dimensional nanostructures of graphene.
Predicting solar power production from cloud data

Power production from solar cells is not only affected by the sun but also other weather phenomenons such as clouds and temperature. The purpose of this project is modelling the effect of clouds by predicting the values of future solar power production in part of Germany from data about cloud coverage. All phases of the analysis is explained from preprocessing of the data, experimenting with different models of varying complexity and comparing the results. Two main types of models are used, linear regression and neural networks. Experiments regarding the optimal amount of data have also been performed in order to limit computation costs. In the end the models are compared to existing forecasts which have also been analysed, although production of new forecasts is the main goal of the project. The new forecasts are generally inferior to the existing ones, but in some cases they are competitive. They also offer some advantages that could potentially lead to more accurate predictions, when they are combined with the existing forecasts. All experiments have been performed on data gathered from only a subset of a year, and more research should be made to find out how the results can be generalized to future data.