Miracle-dz/UniFAR-FTUs huggingface dataset:
Issue Ownership and Issue Salience Effects: A Two-Stage Model of the Voting Decision Process

Parties are often associated with specific issues. They are said to ‘own’ an issue when they develop a reputation of competence and attention in that domain. They can also strategically emphasize specific issues in their campaign. This paper investigates the consequences of such associations for the voting decision process. I suggest that voters evaluate different parties on the basis of different issues, depending on these associations. I test these ideas by relying on a twostage model of the voting decision process, distinguishing between an evaluation stage and a choice stage. Results for the 1994 and 1998 Dutch elections clearly show that parties are not evaluated using the same set of criteria. The impact of issue preferences varies strongly across parties. The results also show that this variation is related to issue ownership. Issue salience, by contrast, does not seem to affect the criteria by which voters evaluate parties.
Drivers of female labour force participation in urban India during India's Economic Boom

In the past twenty years, India's economy has grown at increasingly faster rates and now belongs to the fastest-growing economies in the world. One would think that in such economic conditions, women are increasingly being pulled into the labour force by attractive pay and employment conditions. This paper examines trends and drivers of female labour force participation in urban India between 1987 and 2004; we do this using aggregate and unit level data and estimate econometric participation models. Our paper shows a much more nuanced picture than one might expect. While we find, as expected, that cultural and social factors strongly influence female labour force participation rates, among the somewhat unexpected findings are:- Only in the period between 1999 and 2004 did female labour force participation rates increase in urban areas; before, rates remained flat due to offsetting effects of increases among some groups and decreases among others;- At lower levels of education, increases in female labour force participation are driven more by distress than by increasing economic opportunities; this is linked to stagnant real wages at this level;- At mid-levels of education, the income effect of rising male incomes served to reduce female labour force participation rates considerably; while we find some evidence of a positive own wage effect, the income effect of husband's earning remains a very strong driver of female labour force participation;- Only at the highest education levels do we see some evidence from pull factors drawing women into the labour force at attractive employment and pay conditions; this affects, by 2004, only a tiny minority of India's women.As a result, the economic boom has offered remarkably few opportunities to women in India. In fact, for all but the very well-educated, it appears that the labour market conditions have not improved at all, or even deteriorated.
Classification Based on Weighted Extreme Learning Machine

The huge amount of documents in the internet led to the rapid need of text classification (TC). TC is used to organize these text documents. In this research paper, a new model is based on Extreme Machine learning (EML) is used. The proposed model consists of many phases including: preprocessing, feature extraction, Multiple Linear Regression (MLR) and ELM. The basic idea of the proposed model is built upon the calculation of feature weights by using MLR. These feature weights with the extracted features introduced as an input to the ELM that produced weighted Extreme Learning Machine (WELM). The results showed a great competence of the proposed WELM compared to the ELM.
An ECR-based charge breeder for the SPES project

SPES (Selective Production of Exotic Species) [1] is an INFN project supported by the two national laboratories LNL and LNS, and is currently under way at LNL. The main goal of the project is the development of an ISOL (Isotope Separation On Line) facility for neutron-rich exotic beams production to perform advanced research in nuclear structure, reaction dynamics and interdisciplinary fields. To boost the charge state of the radioactive ions produced, and allow post-acceleration at high energies by the ALPI [2] superconducting linac in operation at LNL, an ECR-based charge breeder [3] was chosen (SPES-CB): in particular, an updated version of the Phoenix booster [4], into operation at Laboratoire de Physique Subatomique & Cosmologie (LPSC) since about twenty years. Such devices are basically ECR sources where a radioactive 1+ beam is injected, captured by the plasma, further ionized and then extracted as a q+ beam. In June 2014 a Research Collaboration Agreement was signed between INFN and CNRS, for the delivery at LNL of an upgraded version of Phoenix ECR booster and ancillaries by LPSC, satisfying the SPES requirements. The acceptance tests were succesfully peformed between March and April 2015 on the LPSC test bench (details can be found in [5]), and the charge breeder has been delivered at LNL at the end of November 2015. The SPES-CB can be divided in three main subsystems: (i) the source body and the plasma chamber, (ii) the gas injection system and the ∆V -rack, and (iii) the extraction system. A brief description of each subsystem will be given in the following.
Trichloroethylene accelerates an autoimmune response by Th T cell activation in MRL qrq mice 1

Ž . Trichloroethylene 1,1,2-trichloroethene is a major environmental contaminant. There is increasing evidence relating exposure to trichloroethylene with autoimmunity. To investigate potential mechanisms, we treated the autoimmune-prone MRLqrq mice with trichloroethylene in the drinking water at 0, 2.5 or 5.0 mgrml and sacrificed them at 4, 8 and 22 weeks. As early as 4 weeks of treatment, Western blot analysis showed a dose-dependent increase in the level of trichloroethylene-modified proteins, indicating that a reactive metabolite of trichloroethylene was formed. Significant Ž . increases in antinuclear antibodies ANA and total serum immunoglobulins were found following 4–8 weeks of trichloroethylene treatment, indicating that trichloroethylene was accelerating an autoimmune response. Investigation into possible mechanisms of this autoimmune response revealed that trichloroethylene treatment dramatically increased the expression of the activation marker CD44 on splenic CD4 T cells at 4 weeks. In addition, splenic T cells from mice treated for 4 weeks with trichloroethylene secreted more IFN-g and less IL-4 than control T cells, consistent of a T-helper type 1 Ž . Th type immune or inflammatory response. A specific immune response directed against dichloroacetylated proteins was 1 found at 22 weeks of trichloroethylene treatment. Taken collectively, the results suggest that trichloroethylene treatment accelerated an autoimmune response characteristic of MRLqrq mice in association with nonspecific activation of Th1 cells. In addition, long-term treatment with trichloroethylene led to the initiation of a trichloroethylene-specific immune response. q 2000 Published by Elsevier Science B.V. All rights reserved.
2 . 3 Discrete Embeddings

Just as semantic hashing [Salakhutdinov and Hinton2009] can accelerate information retrieval, binary valued embeddings can significantly reduce latency in the retrieval of graphical data. We introduce a simple but effective model for learning such binary vectors for nodes in a graph. By imagining the embeddings as independent coin flips of varying bias, continuous optimization techniques can be applied to the approximate expected loss. Embeddings optimized in this fashion consistently outperform the quantization of both spectral graph embeddings and various learned real-valued embeddings, on both ranking and pre-ranking tasks for a variety of datasets.
Syddansk Universitet Differential alkylation-based redox proteomics-Lessons learnt

Cysteine is one of the most reactive amino acids. This is due to the electronegativity of sulphur atom in the side chain of thiolate group. It results in cysteine being present in several distinct redox forms inside the cell. Amongst these, reversible oxidations, S-nitrosylation and S-sulfenylation are crucial mediators of intracellular redox signalling, with known associations to health and disease. Study of their functionalities has intensified thanks to the development of various analytical strategies, with particular contribution from differential alkylation-based proteomics methods. Presented here is a critical evaluation of differential alkylation-based strategies for the analysis of S-nitrosylation and S-sulfenylation. The aim is to assess the current status and to provide insights for future directions in the dynamically evolving field of redox proteomics. To achieve that we collected 35 original research articles published since 2010 and analysed them considering the following parameters, (i) resolution of modification site, (ii) quantitative information, including correction of modification levels by protein abundance changes and determination of modification site occupancy, (iii) throughput, including the amount of starting material required for analysis. The results of this meta-analysis are the core of this review, complemented by issues related to biological models and sample preparation in redox proteomics, including conditions for free thiol blocking and labelling of target cysteine oxoforms. & 2015 The Authors. Published by Elsevier B.V. This is an open access article under the CC BY-NC-ND license (http://creativecommons.org/licenses/by-nc-nd/4.0/).
Traffic congestion detection in a city using clustering techniques in VANETs

Road traffic congestion, a serious illness in developing regions, is one of the biggest problems in our day-to-day life, resulting in delays, wastage of fuel and money. In this paper, a new model is developed using Simulation of Urban Mobility (SUMO) simulator for simulating a realistic traffic scenario for a large city like Bhubaneswar where, traffic congestion is a critical issue. In a city, traffic congestion is characterised by many parameters such as rapid growth of population, number of four wheelers, inadequate and poor road infrastructures and shortage of physical plan to govern the developments, which are focused on enhancing the volume of the roads by raising the number of lanes, over-passes, underpasses and over-bridges at many junctions. However, for the success of these master plans to fully overcome the congestion issues, it is necessary to transmit the congestion information to vehicles coming towards a congestion area by using a Vehicular Ad-hoc Network. This paper analyzes clustering techniques in Vehicular Ad-hoc Networks to detect congestion in roads with the minimal infrastructural support. The raw data from vehicles are classified using cluster analysis. Out of a number of algorithms that are used to solve the congestion detection problem, three important algorithms such as Centroid based -means, object based FCM and FKM algorithms are compared in this work on the basis of data points and number of clusters. The results of the algorithms are close to each other, but fuzzy techniques are preferable as the traffic situations are dynamic in nature.
A simple procedure to incorporate predictive models in a continuous time asset allocation

Stochastic optimisation has found a fertile ground for applications in finance. One of the greatest challenges remains to incorporate a set of scenarios that accurately model the behaviour of financial markets, and in particular their behaviour during crashes and crises, without sacrificing the tractability of the optimal investment policy. This paper shows how to incorporate return predictions and crash predictions as views into continuous time asset allocation models.
STUDY OF RELATIONSHIP BETWEEN UTUAT FACTORS AND INTENTION TOWARDS USAGE E-MANAGEMENT IN LIBYAN PETROLEUM INDUSTRY

Purpose of study:The main aim of this study is to investigate the relationship between Unified theory of acceptance and use of technology (UTAUT) factors and intention towards usage of e-management in Libyan petroleum industry.Methodology:Aseries of appropriate literature is explored and a conceptual model is suggested and deliberated. A review of existing literature recommendsnumerousaspectsaffects behavioral intention of the users.Main findings: This model is set to help the managers and decision makers to understand the importance of e-management.Implications of study:Exploring the theoretical and empirical studies, thefuture study would be valuable and necessary to examine the relationship between expectancy and the overall job acceptance level of e-management including the mediating effect of perceived information quality.Originality/Novelty of study:The proposed conceptual model is both novel and different that is positivelycontributed towards the improvement of the related literature.
The Relevance and Signicance of Preferential Trade in the World Trade Network

In this paper we assess the impact of preferential trade agreements (PTAs) on the structure of world trade looking for communities in the world trade network (WTN), and allowing the presence of preferential trade patterns to emerge endogenously. The network analysis of the world trade system, modeling the international transactions among countries as links between nodes in a network, is a useful tool for studying the pattern of trade ows, their evolution over time, and the
Blood-Derived Liquid Biopsies Using Foundation One® Liquid CDx for Children and Adolescents with High-Risk Malignancies: A Monocentric Experience

Simple Summary Precision oncology requires tumor molecular profiling to identify actionable targets. Blood-derived liquid biopsy (LB) is a potential alternative that is not yet documented in real-world settings, especially in pediatric oncology. Analyzing, retrospectively, the use of LB in children with refractory relapsing diseases, we were able to show that this is a feasible alternative to tissue biopsy, resulting in successful analysis in a subset of patients. Abstract Precision oncology requires tumor molecular profiling to identify actionable targets. Tumor biopsies are considered as the gold standard, but their indications are limited by the burden of procedures in children. Blood-derived liquid biopsy (LB) is a potential alternative that is not yet documented in real-world settings, especially in pediatric oncology. We performed a retrospective analysis of children and teenagers with a relapsing or refractory disease, upon whom LB was performed using the Foundation One® liquid CDx from 1 January 2020 to 31 December 2021 in a single center. Forty-five patients (27 boys) were included, with a median age of 9 years of age (range: 1.5–17 years old). Underlying malignancies were neuroblastoma (12 patients), bone sarcoma (12), soft tissue sarcoma (9), brain tumors (7), and miscellaneous tumors (5). Forty-three patients had metastatic disease. Six patients had more than one biopsy because of a failure in first LB. Median time to obtain results was 13 days. Overall, analysis was successful for 33/45 patients. Eight patients did not present any molecular abnormalities. Molecular alterations were identified in 25 samples with a mean of 2.1 alterations per sample. The most common alterations concerned TP53 (7 pts), EWS-FLI1 (5), ALK (3), MYC (3), and CREBBP (2). TMB was low in all cases. Six patients received treatment based on the results from LB analysis and all were treated off-trial. Three additional patients were included in early phase clinical trials. Mean duration of treatment was 85 days, with one patient with stable disease after eight months. Molecular profiling using Foundation One® Liquid CDx was feasible in pediatric patients with high-risk solid tumors and lead to identification of targetable mutations in a subset of patients.
Climate Change, Perceptions and the Heterogeneity of Adaptation and Rice Productivity: Evidence from Indonesian Villages

This paper examines how change in rainy season induces adaptation strategy among farmers and affects rice production using recently collected household data from seven provinces in Indonesia. The data demonstrate delays in the perceived onset of rainy season and increased uncertainty in rainfall pattern in the region. Empirical analysis shows that (i) delay in the onset significantly decreases land productivity growth in rice production; one month delay offsets the average growth observed in 1999-2007, (ii) irrigation share significantly explains the growth of land productivity, and (iii) farmers change planting timing in response to delays in the onset of rainy season (water availability). Our results show that delays in rainfall decrease returns to irrigation infrastructure. It is also found that individual-level variations in the perceived changes of the onset explain the extent to which such an adaptation strategy is taken as a measure to respond to climate change.
Appendix to the paper Identification of and correction for publication bias

This appendix contains proofs and supplementary results for the paper “Identification of and correction for publication bias. ” Section A collects proofs for the results stated in the main text. Section B discusses several details and extensions of the applications, including accounting for estimation error in p(·), identification with sign-normalized data, conditioning on covariates, and a range of robustness checks. Section C contains further empirical results, including moments and results for our GMM approaches which leave the distribution of true effects unrestricted, and empirical results for the Camerer et al. (2018) and Croke et al. (2016) applications. Finally, Section D gathers additional theoretical results on topics including the extension of our identification results to cases where publication probabilities depend on Σ and Ω, the interpretation of meta-regression coefficients in the presence of selectivity, the extension of our inference results to multivariate settings, the effect of selection on Bayesian inference, and optimal selection in a stylized model.
Flammability in tropical savannas: Variation among growth forms and seasons in Cerrado

The herbaceous layer is the dominant fuel for fire in tropical savannas, the world's most fire‐prone biome. However, little is known about variation in flammability among different growth forms, as well as how flammability varies seasonally. Here, we describe such variation in Brazilian Cerrado, the world's most phytodiverse tropical savanna. We measured three components of flammability (maximum burning temperature, burning rate, and burnt biomass) and morphophysiological traits (dead biomass percentage, moisture content, and specific leaf area) of five species of each of grasses, forbs, and shrubs throughout the wet and dry season. We found that grasses were the most flammable growth form and that their flammability was highest in the middle of the dry season when the percentage of dead biomass was highest (60%), and moisture content was low (25%). Flammability did not vary across seasons for either forbs or shrubs. Dead biomass, moisture content, and specific leaf area were all strongly correlated with flammability. These findings explain the dominant role played by grasses as drivers of flammability in tropical savannas, and they improve our understanding of how savanna flammability varies across space and time depending on the composition of the grass layer and its level of seasonal curing. This enables a better prediction of fire spread and behavior in different savanna types based on grass‐layer composition.
Traffic road obstacles detection based on analysis of relative motion vectors

Computer vision obstacle detection on either road lanes or sidewalks is very important for traffic participants. This paper presents an approach for obstacle detection from sequences of consecutive monocular color image frames. Keypoints are uniformly distributed in a grid structure on each input image. A Lucas-Kanade optical flow algorithm is performed between each pair of consecutive frames, on the considered key-points, in order to compute the relative motion vectors. Background movement estimation is computed across frames using a RANSAC procedure. Optical flow vectors that are belonging to the background are filtered out. The others are considered to be within the obstacles and are grouped by a hierarchical clustering algorithm in separate obstacles by analyzing their locations, angles and magnitudes. Spurious clusters with low number of motion vectors are filtered out. Finally, an imminent collision warning is issued both visually and acoustically when an obstacle is detected to be too close and it is about to crash with the ego-camera. Keywords-obstacle detection; motion vectors; hierarchical clustering; background movement estimation; collision alert.
A Phase and RSSI-Based Method for Indoor Localization Using Passive RFID System With Mobile Platform

In this paper, a phase and RSSI-based method for indoor localization of UHF RFID tags is proposed and demonstrated. The proposed method exploits the received phase and RSSI profile combined with the location of the moving antenna to locate target tags. The phase and RSSI measurements are collected by a mobile robot carrying a RFID reader and antennas. By analyzing the received RSSI, of which the strength can indicate whether the signal is stable or not, a valid phase profile can be obtained. After obtaining valid phase profile according to the received RSSI, the location of the target along the antenna trajectory is calculated from the stationary point of the phase curve. The location orthogonal to the antenna trajectory distance is estimated by finding the integer number of wavelengths at the point of closest approach which fits the phase profile. 2D localization requires only one straight-line trajectory while 3D localization requires an L-shape trajectory. After estimating the x and y coordinate using the stationary point on each trajectory, the height of the target can be calculated by considering the integer number of wavelengths at the point of the closest approach. A 12 cm 2D localization error and 14 cm 3D localization error are demonstrated.
A Compact Wideband Pattern Diversity Antenna for 5G-NR Applications

In this letter, a novel compact wideband antenna with broadside and conical diversity patterns is presented for fifth-generation (5G)-NR applications. The capacitive loading is implemented to enhance the port isolation and for size reduction (0.35 λ0 × 0.29 λ0 × 0.047 λ0). With an extra slot mode introduced by the coupled feeding scheme, a wide impedance bandwidth is obtained. A miniaturized broadband 180° hybrid coupler is designed to provide in-phase and out-of-phase excitations. It is composed of a quarter-wavelength parallel coupled-line and lumped LC components. As a result, conical and broadside diversity radiation patterns are achieved. The proposed antenna is analyzed, fabricated, and measured. Good agreement is achieved between the simulated and measured results. The measured overlapped −10 dB impedance bandwidth is 20.5% (3.16–3.81 GHz). The measured in-band isolation is better than 22.7 dB and the peak value achieves 34.7 dB. The proposed pattern diversity antenna exhibits good overall performance, including compact size, wide bandwidth, high isolation, and good radiation performance. Therefore, it is suitable for space-limited 5G-NR applications.