Miracle-dz/UniFAR-FTUs huggingface dataset:
Work and information: which workplace models still work in modern digital workplaces?

Introduction. In this paper, we outline some theoretical background for workplace studies in LIS to facilitate an understanding of what is new in the phenomena and how early frameworks may inform us about the modern digital workplaces. Method. We first characterise modern workplaces and discuss why new digital workplaces raise important questions for in relation to information-related activities in workplace. We explore significant, earlier frameworks on workplace information issues to question their utility in researching digital workplaces. Analysis. Our approach is to analytically examine the major trends and themes in early frameworks to present a discussion of which features may still be relevant to studying modern workplaces. Results. We see the continuance of this broad understanding of workplace information as a fruitful base for present and future studies of digital workplace information. This richness of views on workplace information leads to different understandings of information-related activities, which can create deep understandings but also conceptual confusion. Conclusion. Our workplaces have changed radically since the early frameworks; whilst some aspects may provide the underpinning for research in modern workplaces there is a need for further conceptual analyses and clarifications to facilitate future research and compile their findings.
The activation mechanisms of G protein-coupled receptors : the case of the adenosine A2B and HCA2/3 receptors

Identifying and elucidating the functions and activation of GPCRs will provide opportunities for novel drug discovery. We confirmed that a yeast system with an extended library of G proteins is very well suited for the study of GPCR activation, G protein coupling profiles, receptor-G protein binding and G protein selectivity. For example, we used a scanning mutagenesis approach of the NPxxY(x)5,6F motif and of helix 8 of the adenosine A2B receptor (A2BR), and learned among others that amino acid residues in these motifs are crucial for receptor function, since alanine mutants of these amino acid residues led to a complete loss of function. Hopefully, such findings can contribute to further drug development. We also focused on structure-kinetics relationship (SKR) studies next to the more traditional Structure-affinity relationship (SAR) studies. We found two compounds showing longer residence times than nicotinic acid, which may provide clues for further drug discovery efforts on this receptor. All in all, the variety of methods described in this thesis provided us a detailed understanding of receptor function, suggesting that novel avenues for further drug discovery on these established targets is entirely feasible.
DC-to-DC Buck Converter

The growth of sustainable energy requires many components to work in harmony creating one efficient and effective system. Sustainable energy generation that produces a DC current varying in voltage requires a subsystem to convert voltage to a practical and usable value. The DC to DC Buck converter converts high DC solar generated voltage to a 12V output. This Modular -Level power electronic (MLPE) device performs as a solar charge controller for small lithium ion batteries. Fluctuating voltage levels generated by Photovoltaic cells makes the solar controller a critical part of the solar system. Regulating the DC voltage allows safe and effective battery changing. The converter uses Pulse Width Modulation (PWM) to improve efficiency and longevity of the battery system [8]. The DC to DC converter receives 12V to 40V at a maximum of 1 amperes typical of portable solar applications. The step-down converter designed at the transistor and integrated circuit level uses PWM supplying constant voltage [3]. PWM controls switches (transistors) that enables or disables current flow via digital signals to control the step-down process. Inductors enable energy conservation in the circuit during on/off cycles. The size of the inductor determines the effectiveness and efficiency. The converter’s battery charging responsibility contains the most important performance criteria. Designed for charging lithium ion 18650 cells, this converter connects to compatible lithium ion charging stations. Development of the buck converter focuses on efficiency, magnetic design and voltage regulation. The project focuses less on charging cycles for the cells that charging docks handle.
Quantum storage and retrieval of light by sweeping the atomic frequency

We propose a quantum memory protocol based on dynamically changing the resonance frequency of an ensemble of two-level atoms. By sweeping the atomic frequency in an adiabatic fashion, photons are reversibly transferred into atomic coherences. We present a polaritonic description for this type of storage, which shares some similarities with electromagnetically induced transparency based quantum memories. On the other hand the proposed memory is also linked to the gradient echo memory due to the effective spatial gradient that pulses experience in the medium. We discuss a possible implementation of the protocol in hollow-core photonic crystal fibers.
Future EMU Membership and Wage Flexibility in Selected EU Candidate Countries

This paper attempts to evaluate wage rigidity related to risks of increased size and volatility of unemployment after the candidate countries enter the EMU. Such evaluation is done through the study of past labour market adjustment mechanisms and, in particular, the role played by the exchange rate movements and independent monetary policy. The paper examines some institutional and structural characteristics of candidate countries labour markets that could influence the wage elasticity. The analysis indicates that generally nominal wages are not flexible in candidate countries. Inflationary surprises and nominal exchange rate movements have an effect on the adjustment, especially during the Russian crisis. On the other hand fast productivity growth creates the environment in which unit labour can adjust to unfavourable labour market outcomes through moderation of real wage dynamics despite nominal stickiness. The paper indicates possible fields of further in-depth research in this area.
Virtual Vehicle Control Concept for Hydrostatic Dynamometer Control

An approach for controlling a hydrostatic dynamometer for the Hardware-In-the-Loop (HIL) testing of hybrid vehicles is proposed and experimentally evaluated. The hydrostatic dynamometer, which is capable of absorbing and regenerating power, was specifically designed and built in-house to evaluate the fuel economy and control strategy of a hydraulic hybrid vehicle being developed. Unlike a chassis dynamometer whose inertia is similar to the inertia of the vehicle being tested, the inertia of this hydro-static dynamometer is only 3% of the actual vehicle. While this makes the system low cost, compact and flexible for testing vehicles with different weights and drag characteristics, control challenges result. In particular, the dynamometer must apply, in addition to the torques to mimic the wind and road drag, also the torques to mimic the acceleration and deceleration of the missing inertia. To avoid estimating the acceleration and deceleration, which would be a non-causal operation, a virtual vehicle concept is introduced. The virtual vehicle model generates, in response to the applied vehicle torque, a reference speed profile which represents the behavior of the actual vehicle if driven on the road. This reformulates the dynamometer control problem into one of enabling the actual vehicle-dynamometer shaft to track the speed of the virtual vehicle, instead of directly applying a desired torque. To track the virtual vehicle speed, a controller with feedforward and feedback components is designed using an experimentally validated dynamic model of the dynamometer. The approach has been successfully tested on a power-split hydraulic hybrid vehicle with acceptable virtual vehicle speed and dynamometer torque tracking performance.
A Multi-Agent System for Facade Design

For contemporary design practices, there still remains a disconnect between design tools used for early stage design exploration and performance analysis, and those used for fabrication and construction of complex tectonic architectural systems. The research brings forward downstream fabrication constraints into the up-stream design exploration and design decision making. This paper addresses the issues of developing an integrated digital design work-flow and details a research framework for the incorporation of environmental performance into a robotic fabrication for early stage design exploration and generation of intricate and complex alternative façade designs. The method allows the user to import a design surface, define design parameters, set a number of environmental performance objectives, and then simulate and select a robotic construction strategy. Based on these inputs, design alternatives are generated and evaluated in terms of their performance criteria in consideration of their robotically simulated constructability. In order to validate the proposed framework, an experimental case study of office building façade designs that are generatively created from a multi-agent system for design methodology is design explored and evaluated. Initial results define a heuristic function for improving simulated robotic constructability and illustrate the functionality of our prototype. Project limitations and future research steps are then discussed.
Methodological reasons for the theory/practice divide in market segmentation

A theory/practice divide exists in market segmentation. The main reasons are the focus of academic studies on statistical techniques, and managers' lack of understanding of fundamental segmentation basics (Dibb 2005; Greenberg and McDonald 1989). To date, only one study has explored methodological reasons for the theory/practice divide: Dibb and Simkin 1994. We extend their work by: (1) detailing key methodological aspects likely to cause difficulties in translating segmentation findings into managerially useful information, and (2) empirically investigating specific research questions about methodology-induced reasons for the theory/practice divide derived from both theory and the propositions of previous studies. Results indicate a large proportion of managers lack a fundamental understanding of market segmentation methodology, thus over-interpreting the validity of solutions. More than 60% of managers indicated that to them, market segmentation is like a "black box", and they have difficulties interpreting presented segmentation solutions. Practical recommendations for improvement are provided.
Computational Modeling and Analysis of Transient Flow in an IC Engine with a Generic Inlet Tract

The geometry at the open end of the inlet tract and the effects of the transient phenomena due to the motion of piston and valves have a significant influence on tuning effects for optimum engine volumetric efficiency. Previous work at University of Leicester has been conducted into the effects of inlet tract radius on transient and steady flow properties that occur within the intake manifold using a general purpose CFD code, which are validated against the experimental data available. The optimum inlet tract radius along with the rest of the intake manifold geometry is consistent with the work reported here. In this project unsteady transient flow in a simplified computational model of a single cylinder and intake manifold of an internal combustion engine has been investigated, based on the successful application of dynamic mesh modeling techniques. The study mainly focuses on the effect of parameters such as bore of the cylinder (combustion chamber) and clearance length on the flow within both the intake manifold and the chamber itself. The work reported here will help build a set of optimum design parameters for achieving improved values of volumetric efficiency as well as highly efficient mixing of air and fuel in the combustion chamber.
L G ] 2 6 O ct 2 01 8 Online learning using multiple times weight updating

Online learning makes sequence of decisions with partial data arrival where next movement of data is unknown. In this paper, we have presented a new idea as multiple times weight updating that update the weight iteratively for same instance. The proposed technique analyzed with popular algorithms from literature and experimented using established tool. The results indicates that mistake rate reduces to zero or close to zero for various datasets and algorithms. The overhead running cost is not too expensive and achieving mistake rate close to zero further strengthen the proposed technique. The proposed technique could be helpful to meet real life challenges.
A role of catalase ( CAT ) in detoxification of reactive oxygen species ( ROS ) in tomato ( Lycopersicum esculentum ) contaminated with manganese ( Mn 2 + )

In the current study, tomato (Lycopersicum esculentum L.) plants were exposed to various concentrations of Mn (80, 160, 320, 640 and 1280 μM) cation. In the first part, in order to obtain evidence that plants were in stress, following the Mn treatments, lipid peroxidation were determined. In the second part, to gain an idea about regulation of the CAT gene product, enzyme activities were also recorded in Mn treated tomato plants. The maximum lipid peroxidation level was determined in plants which were exposed to 1280 μM concentration of Mn contamination. Changes in lipid peroxidation and CAT enzyme activities in tomato plants exposed to different concentration of Mn contamination revealed no positive correlation. The current study revealed that, ROS induced lipid peroxidation level has depended on concentration of Mn contamination. Also, antioxidant responses to Mn contamination could be reflected as changes in CAT enzyme activity in tomato plants.
THE SUPERSOFT X-RAY PHASE OF NOVA RS OPHIUCHI 2006

Swift X-ray observations of the ∼60 day supersoft phase of the recurrent nova RS Ophiuchi (RS Oph) 2006 show the progress of nuclear burning on the white dwarf (WD) in exquisite detail. First seen 26 days after the optical outburst, this phase started with extreme variability likely due to variable absorption, although intrinsic WD variations are not excluded. About 32 days later, a steady decline in count rate set in. NLTE model atmosphere spectral fits during the supersoft phase show that the effective temperature of the WD increases from ∼65 eV to ∼90 eV during the extreme variability phase, falling slowly after about day 60 and more rapidly after day 80. The bolometric luminosity is seen to be approximately constant and close to Eddington from day 45 up to day 60, the subsequent decline possibly signaling the end of extensive nuclear burning. Before the decline, a multiply-periodic ∼35 s modulation of the soft X-rays was present and may be the signature of a nuclear fusion driven instability. Our measurements are consistent with a WD mass near the Chandrasekhar limit; combined with a deduced accumulation of mass transferred from its binary companion, this leads us to suggest that RS Oph is a strong candidate for a future supernova explosion. The main uncertainty now is whether the WD is the CO type necessary for a Type Ia supernova. This may be confirmed by detailed abundance analyses of spectroscopic data from the outbursts.
Usage of Kd-Tree in DHT-Based Indexing Scheme

—Distributed Hash Table (DHT) is a promising approach for a distributed data management platform in a large-scale network environment. In order to provide an efficient query processing and resource sharing in information discovery services, any DHT-based P2P system essentially needs to build an efficient indexing scheme for supporting users’ desired queries (complex query, i.e., multi-dimensional and/or range query) . In this paper, multi-dimensional indexing scheme is built by generating multi-dimensional keys. For this purpose, k-dimensional tree (kd-tree) is proposed to be used in the indexing scheme. This paper focuses on the performance of kd-tree regarding to its splitting threshold value and evaluates it by using three metrics: (1) number of empty nodes on tree (2) number peers with data size zero, and (3) number of wrong labels. According to the simulated results, this paper defines that kd-tree is built at the splitting threshold value “200” and builds a multi-dimensional indexing scheme over Chord. This paper shows that how the proposed indexing scheme with the usage of kd-tree supports the complex query processing over Chord.
The influence of improvement in one mental function upon the efficiency of other functions. (I).

The functions trained in these experiments were those of observing accurately some detailed features of the words on printed pages. The subject would, for instance, practice marking every word containing the two letters e and s until he attained a considerable improvement in speed and possibly in accuracy as well. Before and after this training he would be tested in marking words containing other combinations of letters, misspelled words, a certain letter printed promiscuously amongst others, words of a certain length, different parts of speech, etc.
Producer Returns from Cotton Strength and Uniformity: An Hedonic Price Approach

Abstract Implicit (hedonic) producer prices for fiber strength uniformity were estimated for the southwest U.S. cotton market using seemingly unrelated regression and market sales data from 1983/84 and 1984/85. Fiber strength and length uniformity had significant effects on the price of cotton, but price was less responsive to both attributes than anticipated. Producer prices were most responsive to fiber length and micronaire and least responsive to color and strength. The market at the producer level appears to be making effective price adjustments with respect to factors such as fiber color, trash content, micronaire, fiber length, and location, but strength and length uniformity premiums and discounts are smaller than those paid by end users.
Asymmetric Error Correction Models for the Oil-Gasoline Price Relationship

The existing literature on price asymmetries does not systematically investigate the sensitivity of the empirical results to the choice of a particular econometric specification. This paper fills this gap by providing a detailed comparison of the three most popular models designed to describe asymmetric price behaviour, namely asymmetric ECM, autoregressive threshold ECM and ECM with threshold cointegration. Each model is estimated on a common monthly dataset for the gasoline markets of France, Germany, Italy, Spain and UK over the period 1985-2003. All models are able to capture the temporal delay in the reaction of retail prices to changes in spot gasoline and crude oil prices, as well as some evidence of asymmetric behaviour. However, the type of market and the number of countries which are characterized by asymmetric oil-gasoline price relations vary across models. The asymmetric ECM yields some evidence of asymmetry for all countries, mainly at the distribution stage. The threshold ECM strongly rejects the null hypothesis of symmetric price behaviour, particularly in the case of France and Germany. Finally, the ECM with threshold cointegration finds long-run asymmetry for each country in the reaction of retail prices to oil price changes.
UCLA On-Line Working Paper Series Title Heat or Eat ? Cold Weather Shocks and Nutrition in Poor American Families Permalink

We examine the effects of cold weather periods on family budgets and on nutritional outcomes in poor American families. Expenditures on food and home fuels are tracked by linking the Consumer Expenditure Survey to temperature data. Using the Third National Health and Nutrition Examination Survey, we track calorie consumption, dietary quality, vitamin deficiencies, and anemia in summer and winter months. We find that both rich and poor families increase fuel expenditures in response to unusually cold weather (a 10° F drop below normal). At same time, poor families reduce food expenditures by roughly the same amount as the increase in fuel expenditures, while rich families increase food expenditures. Poor adults and children reduce caloric intake by roughly 200 calories during winter months, unlike richer adults and children. In sensitivity analyses, we find that decreases in food expenditure are most pronounced outside the South. We conclude that poor parents and their children outside the South spend and eat less food during cold weather temperature shocks. We surmise that existing social programs fail to buffer against these shocks. Jayanta Bhattacharya Thomas DeLeire Stanford University School of Medicine Harris Graduate School of Public Policy Studies Center for Primary Care Outcomes Research University of Chicago Center for Health Policy 1155 E. 60th St. 218 Encina Commons Chicago, IL 60637 Stanford, CA 94305-6019 t-deleire@uchicago.edu jay@stanford.edu Steven Haider Janet Currie RAND UCLA 1700 Main Street 405 Hilgard Avenue Santa Monica, CA 90401 Los Angeles, CA 90095-1477 sjhaider@rand.org and NBER currie@simba.sscnet. ucla.edu
J un 2 01 8 State Representation Learning for Control : An Overview

Representation learning algorithms are designed to learn abstract features that characterize data. State representation learning (SRL) focuses on a particular kind of representation learning where learned features are in low dimension, evolve through time, and are influenced by actions of an agent. The representation is learned to capture the variation in the environment generated by the agent’s actions; this kind of representation is particularly suitable for robotics and control scenarios. In particular, the low dimension characteristic of the representation helps to overcome the curse of dimensionality, provides easier interpretation and utilization by humans and can help improve performance and speed in policy learning algorithms such as reinforcement learning. This survey aims at covering the state-of-the-art on state representation learning in the most recent years. It reviews different SRL methods that involve interaction with the environment, their implementations and their applications in robotics control tasks (simulated or real). In particular, it highlights how generic learning objectives are differently exploited in the reviewed algorithms. Finally, it discusses evaluation methods to assess the representation learned and summarizes current and future lines of research.
EGCG AND VOLUNTARY EXERCISE : AN EXAMINATION OF TREATMENT EFFECTS USING THE TGCRND 8 MOUSE MODEL OF ALZHEIMER ’ S DISEASE

Previous work with the TgCRND8 mouse model of Alzheimer’s disease has shown that voluntary exercise implemented for long time periods (5-months) and starting at very young ages (1-month), prior to the development of disease pathology, reduces cognitive impairments; however, approximately 1-month of exercise started at 80-days failed to provide cognitive improvements for male mice. Additionally, research has shown that EGCG, a botanical polyphenol, can reduce amyloid-beta levels, mitigate oxidative stress and reduce some of the cognitive impairments associated with Alzheimer’s disease in the Tg2576 murine model; however, oral administration of EGCG had not been yet evaluated in the TgCRND8 strain. The present study investigated the effects of 4-months of exercise treatment, implemented at the beginning of pathology development (2-months of age), in conjunction with the effects of EGCG treatment on: 1) behavioral measures: learning and memory performance in the Barnes maze, nest building, the open-field, anxiety in the light-dark box, and 2) soluble, amyloid-beta levels in the cortex and hippocampus. Untreated Tg mice showed deficits in nest building behaviors, as well as poor spatial learning in the Barnes maze. Four-months of EGCG and exercise treatment reversed nest building and spatial learning deficits, and lowered soluble, A1-42 levels in the cortex of Tg animals. This research was supported by NIH grant funding (2P01 AG18357).