Miracle-dz/UniFAR-FTUs huggingface dataset:
Social Capital and Prosocial Behaviour as Sources of Well-Being

This chapter surveys evidence documenting positive linkages among social capital, prosocial behaviour, and subjective well-being. Whether in the workplace, at home, in the community, or among nations, better and deeper social connections, and especially higher levels of trust are linked to higher subjective wellbeing, even beyond the effects flowing through higher incomes and better health. Prosocial behaviour is also shown to be a robust predictor of well-being in both correlational and experimental contexts. These two lines of research are connected, as prosocial acts are most likely to increase well-being when they are delivered in ways that improve social capital, and reflect intentional generosity free of either compulsion or personal gain. We infer that these deep links between prosocial acts and well-being have an evolutionary benefit in maintaining the quality of social capital and thereby delivering cooperative human responses in times of crisis.
Towards a theory of degenerated solectrons in heavily doped lattices: properties and perspectives

The theory of solectrons is restricted so far to one, or two ch arges embedded into a chain or a layer of atoms. We discuss here the ope n problems arising by adding excess charges into a chain or a layer when generali zing the existing theory of individual solectrons to many-body thermal syste ms. Several methods to include degeneration effects and interactions of the solec tr ns are discussed in order to extend the theory to finite densities and to discuss densit yas well as temperature effects. In the present model the charges are embedded into o neor two-dimensional systems of atoms which are treated by classical embedded Lan gevin equations. By nonlinear interactions between atoms and charges moving qu asi-particles as solectrons or solectron pairs are formed which are treated in stoc hastic tight-binding approximation based on Pauli-type kinetic equations. We disc us results for degenerated solectron systems with zero spin including spatial d istributions, and energy distributions. We notice interesting structures of the ene rgy distribution including the Fermi edge and the possible existence of gaps in the spect rum. Finally we discuss the perspectives of many-body systems and give an estim ate of the phase plane temperature against density of doping charges discussing possibilities to create degenerate solectron systems in heavily doped lattices. Alexander P. Chetverikov Dept. of Physics, Saratov State University, Astrakhanskaya 83, S aratov-410012, Russia, e-mail: chetverikovap@info.sgu. ru Werner Ebeling Institut für Physik, Humboldt-Universit ät Berlin, Newtonstrasse 15, Berlin-12489, Germany, email: ebeling@physik.hu-berlin.de Manuel G. Velarde Instituto Pluridisciplinar, Universidad Complutense, Paseo Juan XXIII, 1, Madrid-28040, Spain, e-mail: mgvelarde@pluri.ucm. es
INFORMATION EXTRACTION FROM MESSAGES IN DISASTER MANAGEMENT

Managing natural and man made disasters effectively requires detailed information about the current situation. As a consequence an up to date map containing both the status and location of incidents and resources in the field is needed. Usually this information is represented by a large quantity of incoming messages written in natural language text. This paper presents a comprehensive approach for extracting relevant information from these messages automatically. An ontology supported knowledge base for the requirements of disaster management has been developed based on a standard from the military domain. Concerning semantic gaps, the importance of context knowledge and temporal logic has been investigated. For a complete representation the reliability and uncertainties, inherent in each message, were considered.
trnL-F Sequences Analysis and Molecular Phylogeny of 10 Species of Oxytropis

Total DNA from 54 individual plants collected in 27 sample sites of 10 Oxytropis species in Inner Mongolia was extracted in this research,and the trnL-F sequence was amplied and sequenced.Then the sequences were analyzed by ClustalX and MEGA 5.0. The phylogenetic tree was constructed by Maximum Likelihood methods in MEGA 5.0. The results showed as follows:(1)In the 10 Oxytropis species,there were 54 variable sites,46 informative sites and 1.9% of the inter-specific difference,and the GC contents of the trnL-F region were between 30.69%~31.50%. (2)The Oxytropis sp.were monophyletic,clearly separating from the Astragalus sp.The bootstrap value was 99%. (3)The samples of Oxytropis glabra itself clustered a group which was relatively evolved independently.The samples of O.verticillaris,O.racemosa and O.ochrantha mixed.Therefore they showed a close relationship.We are congruent with the points in Flora of Intromongolica,which consensus putting the three species into Subgen.Euoxytropis Sect.Baicalia Stell.ex Bunge. (4)Different samples of O.aciphylla were divided into two major branches,and the taxonomic position of O.aciphylla at subgenus level needs further investigation. (5)The samples of O.ciliata and O.inschanica clustered a group,which also supported the consensus which put them into Sect.Xerobia Bunge.The investigation results indicated that trnL-F sequences provided the molecular evidences for the study of the phylogenetic relationships in the Oxytropis at subgenus,sects and species levels.
The more legs the merrier: A new composition for symmetric (multi-)lenses

This paper develops a new composition of symmetric lenses that preserves information which is important for implementing system interoperation. It includes a cut-down but realistic example of a multi-system business supply chain and illustrates the new mathematical content with analysis of the systems, showing how the new composition facilitates the engineering required to implement the interoperations. All of the concepts presented here are based on either pure category theory or on experience in solving business problems using applied category theory.
Identifying environmental factors associated with the genetic structure of the New Zealand scallop: linking seascape genetics and ecophysiological tolerance

Understanding the processes responsible for shaping the spatial genetic patterns of species is critical for predicting evolutionary dynamics and defining significant evolutionary and/or management units. Here, we investigated the potential role of environmental factors in shaping the genetic structure of the endemic New Zealand scallop Pecten novaezelandiae using a seascape genetics approach. For this, we assayed genetic variation at 12 microsatellite markers in 952 individuals collected from 14 sites throughout New Zealand, and used data for 9 site-specific environmental variables (3 geospatial and 6 environmental variables). Our results indicate that a combination of environmental factors may be contributing to the observed patterns of genetic differentiation, but in particular, freshwater discharge and suspended particulate matter concentration were identified as being important. Environmental variation in these parameters may be acting as a barrier to gene flow. In terms of their ecophysiology, scallops are not particularly tolerant of high concentrations of either freshwater input or suspended sediment, making the identification of an association between these environmental variables and genetic variation particularly relevant across the full distributional range of this species. Although geographic distance between populations was also an important variable explaining the genetic variation among populations, it appears that levels of genetic differentiation are not a simple function of interpopulation distance. This study has identified previously unknown environmental factors that may be acting on the genetic structure of the New Zealand scallop and highlights the utility of seascape genetic studies to better understand the processes shaping the genetic structure of organisms.
A HIGH STEP-UP THREE-PORT DC-DC CONVERTER FOR STAND-ALONE PV/BATTERY POWER SYSTEM WITH FUZZY CONTROLLER

In this paper, we are using fuzzy logic controller for the control scheme of the proposed converter provides maximum utilization of PV power most of the time The coupled inductors are used to achieve high step-up voltage gain and to reduce the voltage stress of input side switches. Two sets of activeclamp circuits are used to recycle the energy stored in the leakage inductors and to improve the system efficiency. Fuzzy logic controller having more advantages than the other controllers. The operation mode does not need to be changed when a transition between charging and discharging occurs. Moreover, tracking maximum power point of the PV source and regulating the output voltage can be operated simultaneously during charging/discharging transitions. A three-port dc–dc converter integrating photovoltaic (PV) and battery power for high step-up applications with grid connected mode is proposed in this paper. The topology includes five power switches, two coupled inductors, and two active-clamp circuits. As long as the sun irradiation level is not too low, the maximum power point tracking (MPPT) algorithm will be disabled only when the battery charging voltage is too high. As a result, the proposed converter has merits of high boosting level, reduced number of devices, and simple control strategy. Experimental results of a 200-W laboratory prototype are presented to verify the performance of the proposed three-port converter.
Text Simplification: From Daedalian to Simple

The English word ‘daedalian’, which means ‘ingenious, intricate, and confusing’, is a word that originates from the Greek mythological character Daedalus, who is believed to have built a labyrinth so complex, that he himself couldn’t escape its confinesbut you would be forgiven for not knowing that. Daedalian is not a commonly used word, and its synonyms like ‘complex’ and ‘confusing’ are favored in day-to-day speech. But like the title of this thesis shows, rare words are still found in written text and this creates a need to simplify this text to make it accessible to the layperson. In this thesis, we study the problem of text simplification, the challenges it presents, and the different ways these challenges can be overcome. This thesis touches upon various topics along the vast expanse of text simplification research, starting from generic improvements to encoder-decoder models that are used for controlled Natural Language Generation (NLG) tasks (including text simplification), to domain-specific text simplification challenges and the specialized solutions that are required to solve them. Recent advances in deep learning and NLG research have enabled sophisticated text simplification models. The central idea behind most approaches to text simplification, and indeed most other controlled text generation tasks, is the encoder-decoder model. Thus, we believe that controlled text generation tasks like text simplification stand to gain by using improved encoder-decoder models. Encoder-decoder models have been vastly improved by the introduction of the attention mechanism, and architectures like the transformer, but there is scope to further improve these models by explicitly improving the encoder outputs. We do this by using a co-attention based loss in addition to the standard cross-entropy loss to train the model. We evaluate this proposal on multiple tasks and show that this not only benefits text simplification but also improves performance in adjacent tasks like style transfer and sentence compression. Another problem with current text simplification research is the lack of suitable datasets that are representative of real-world text simplification needs. We enumerate the problems with existing text simplification datasets and propose a new dataset that comes from diverse sources, has a richer vocabulary and is more representative of real-world text simplification tasks. We present various approaches to simplify text from our proposed dataset and recommend methods that can be used to make the generated text more readable and semantically similar to the source. We also study domain-specific text simplification, with a specific focus on the medical domain. We study the unique challenges that come with simplifying medical text and propose an unsupervised approach to solve the problem of medical text simplification, which goes a long way towards overcoming the challenge of the lack of parallel data in the medical domain.
Vulnerability Assessment of a Heritage Structure Subjected to Blast-Induced Ground Motions

R. Pradeep Kumar, D. Srinagesh, T. Seshunarayana, R. K. Chadha, Narender Bodige*, G. Suresh, D. Hima Chandan and C. V. R. Murty Earthquake Engineering Research Centre, International Institute of Information Technology, Hyderabad 500 032, India CSIR-National Geophysical Research Institute, Hyderabad 500 007, India Department of Civil Engineering, Anurag Group of Institutions, Hyderabad 501 301, India Department of Civil Engineering, Indian Institute of Technology Madras, Chennai 600 036, India
Predicting Reservoir Variation through Simulation of Turbidite Deposition over Restored Paleotopography: A Case Study from the Western Atwater Fold Belt, Gulf of Mexico, #40497 (2010)

Lower Miocene turbidite reservoirs drilled in the Western Atwater Fold Belt, Gulf of Mexico, exhibit significant variations in gross reservoir thickness, character and quality within individual fields on salt-cored structural highs. Three-dimensional structural restorations based on seismic and well data indicate the presence of significant paleotopography during deposition of these turbidite reservoirs and the observed reservoir variations appear to be systematically related to position on paleotopography. The primary driver of this paleotopography is autochthonous salt diapirism. Understanding the interaction between substrate topography and turbidite sedimentation is critical in attempting to quantitatively predict reservoir variation within these fields.
Road Topology Extraction from Satellite images by Knowledge Sharing

Motivated by human visual system machine or computer vision technology has been revolutionized in last few decades and make a strong impact in wide range of applications such as object recognition, face recognition and identification etc. However, despite much encouraging advancement, there are still many fields which lack to utilize the full potential of computer vision techniques. One such field is to analyze the satellite images for geo-spatial applications. In past, building and launching the satellites in to space was expensive, and was big hurdle in acquiring low cost images from satellites. However, with technological innovations, the inexpensive satellites are capable of sending terabytes of images of our planet on the daily basis that can provide insights on global-scale economic, social and industrial processes. The significant applications of satellite imagery are urban planning, crop yield forecasting, mapping and change detection. The most obvious application of satellite imagery is to extract topological road network from the satellite images, as it plays an important role in planning the mobility between multiple geographical locations of interest. The extraction of road topology from the satellite images is formulated as binary segmentation problem in vision community. Despite of huge satellite imagery, the fundamental hurdle in applying computer vision algorithms based on deep learning architectures is unavailability of labeled data and causes the poor results. Another challenge in extraction of the roads from satellite imagery is visual ambiguity in identifying the roads and occlusion by various objects. This challenge causes many standard algorithms in computer vision research to perform poorly and is the major concern. In this thesis we develop deep learning based models and techniques that allows us to address the above challenges. In the first part of our work, we make an attempt to perform road segmentation with the less labeled data and existing unsupervised feature learning techniques. In particular, we use self-supervised technique to learn visual representations with an artificial supervision, followed by fine tuning of model with labeled dataset. We use semantic image in-painting as an artificial/auxiliary task for supervision. The enhancement of road segmentation is in direct relation with the features captured by model through inpainting of the erased regions in the image. To further enhance the feature learning, we propose to inpaint the difficult regions of the image and develop a novel adversarial training scheme to learn mask used for erasing the image. The proposed scheme gradually learns to erase regions, which are difficult to inpaint. Thus, this increase in difficulty level of image in-painting leads to better road segmentation. Additionally, we study the proposed approach on scene parsing and land classification in satellite images.
STUDY OF THE EFFECT OF FLOW MODIFIERS ON THE OPERATION OF HEGLIG -PORT SUDAN PIPELINE

In this paper, the effect of a flow improver (i.e. Pour Point Depressant) on the operation of Heglig-Port Sudan Pipeline has been studied. Two types of PPD, namely PPD 25J1 and PPD 25J2, have been used with different doses and different operation scenarios were presumed. For every scenario, the pressure required to transport the flowing fluid through the pipeline has been calculated and pressure transverse between pump stations has been established. The optimum scenario has been selected based on critical analysis of the operation cost at different operation scenarios and PPD concentrations. It has been found that the optimum operation scenario is obtained by adding the PPD type 25J1 to the flowing fluid at 500 PPM.
Compressing Natural Graphs and a Practical Work-Efficient Parallel Connectivity Algorithm

Over the past two decades, the explosive growth of the internet has triggered an enormous increase in the size of natural graphs such as social networks and internet link-structures. Processing and representing large natural graphs efficiently in memory is thus crucial for a wide variety of applications. Our contributions are 1) A parallel graph processing framework for representing compressed graphs with significantly fewer bits per edge, and 2) A simple and practical expected linear-work, polylogarithmic depth parallel algorithm for graph connectivity. Real-world graphs tend to contain a large amount of locality vertices within a cluster mostly have edges to other vertices in their cluster. We discuss reordering techniques to make vertex labelings reflect locality inherent in the graph. We introduce a framework supporting compressed representations of graph – Cogra – based off of an earlier graph processing framework, Ligra, and show that algorithms operating on compressed graphs in our framework are as fast, or faster than their uncompressed counterparts. The Ligra and Cogra frameworks allow a user to easily implement simple parallel graph algorithms. In addition to designing simple algorithms, we would like them to be efficient and have good theoretical guarantees. While most algorithms implemented in Ligra and Cogra have good theoretical guarantees, the connected components algorithm does not, and requires O((V + E)d) where d is the diameter. Addressing this need and the lack of implementations of work-efficient connectivity algorithms, we present a simple and practical work-efficient parallel algorithm for graph connectivity that has a work of O(m) and depth O(log(n)). The algorithm is based on recently developed techniques for generating low-diameter graph decompositions in parallel. We discuss implementing both the decomposition algorithm and our connectivity algorithm in C++ using CILK+, and show that our connectivity algorithm on 40 cores achieves 18–35 times self-relative speedup, and 10–25 times speedup over the fastest sequential implementation.
Computational Aeroacoustic Investigation of Airfoil Cascades

At moderate Reynolds numbers and angles of attack, the Laminar Boundary Layer (LBL) becomes unstable on the surface of airfoils, and causes periodic vortex shedding, which means undesired tonal peaks in the spectrum of the emitted aeroacoustic noise along with increased vibration and decreased aerodynamic performance. In the past, numerous research campaigns focused on the LBL vortex shedding, including measurements and numerical simulations as well. The results of these investigation showed that, the formation of the LBL instability related to the presence of the laminar separation bubble. It was also shown that, the spectrum of the emitted noise has a multitonal behavior, and the scaling of the mean frequency with the free stream velocity has a ladder structure. Based on these results, the LBL instability is a complex phenomenon; however, in the preliminary design of axial flow turbomachines the prediction of the frequency of the vortex shedding is essential, therefore the use of semi-empirical formulas is usual to achieve this goal. The previous researches mostly focused on separated airfoils, however, in case of turbomachines, the blades form a cascade, which can significantly affect the aerodynamic of the airfoils, i.e. it can affect the behavior of the LBL instability as well. According to this, in the present paper the LBL instability of NACA 0012 cascades are investigated, using 2D computational fluid mechanics and aeroacoustics simulations. The investigation involves the variation of the angle of attack, the chord based Reynolds-number and the spacing. The results are compared to the semi-empirical Brooks-Pope-Marcolini model.
Low-profile folded dipole UHF RFID tag antenna with outer strip lines for metal mounting application

A metal mountable UHF RFID tag antenna with a low-profile folded dipole structure is proposed. It is fabricated on a single layer of polytetrafluoroethylene (PTFE) dielectric laminate. It is composed of two symmetrical C-shape resonators integrated with the outer strip lines. The IC chip’s terminals are connected directly to the center of the C-shaped resonators. The outer strip lines are integrated with the C-shaped resonators, which function to lower the reflection coefficient so as to match the IC chip impedance. In particular, the outer strip lines increase the inductive reactance of the antenna impedance in order to realize IC chip impedance. The design has a simple structure, due to the fact that it lacks any metallic vias. Moreover, the gain of the tag antenna can be enhanced when positioned on a large metallic sheet. Installed on a 40 × 40 cm metallic plate, the total gain of the proposed tag antenna is 3 dB, with a maximum reading range of 5.77 m, while it is 3.92 m in free space. The simulation results were in excellent agreement with the fabricated design results.