Miracle-dz/UniFAR-FTUs huggingface dataset:
Smiling AI agents: How anthropomorphism and broad smiles increase charitable giving

Abstract Anthropomorphism and construal level theories provide the bases for two studies showing that when nonprofit charity marketers design artificial intelligence (AI) agents to resemble humans and to smile like humans, potential donors feel greater psychological closeness to the agents and are motivated to increase charitable giving. Study 1 demonstrates that participants feel greater psychological closeness and willingness to donate in response to appeals from smiling AI agents that look like humans rather than like robots. Study 2 demonstrates that participants tend to donate more in reaction to appeals from humanlike (vs. machinelike) AI agents that smile broadly rather than slightly or not at all. The article concludes with a discussion of theoretical insights and practical implications for using AI representatives in nonprofit charity appeals.
Phenotyping Green and Roasted Beans of Nicaraguan Coffea Arabica Varieties Processed with Different Post-Harvest Practices

Metabolomics techniques have already been used to characterize two of the most common coffee species, C. arabica and C. canephora, but no studies have focused on the characterization of green and roasted coffee varieties of a certain species. We aim to provide, using NMR-based metabolomics, detailed and comprehensive information regarding the compositional differences of seven coffee varieties (C. arabica) of green and roasted coffee bean batches from Nicaragua. We also evaluate how different varieties react to the same post-harvest procedures such as fermentation time, type of drying and roasting. The study characterises the metabolomic profile of seven different Arabica varieties (Bourbon-typica) enabling us also to assess the possible use of an NMR spectra of bean aqueous extracts to recognize different farms, even from the same geographical area (Nueva Segovia). Here, we also evaluated the effect of post-harvest procedures such as fermentation time and type of drying on green and roasted coffee, suggesting that post-harvest procedures can be responsible for different flavours. This study provides proof of concept for the ability of NMR to phenotype coffee, helping to authenticate and optimise the best way of processing coffee.
Soft Computing Application for Gas Path Fault Isolation

A fuzzy system that automatically develops its rule base from a linearized performance model of the engine by selecting the membership functions and number of fuzzy sets is developed in this study to perform gas turbine fault isolation. The faults modeled are module faults in five modules: fan, low pressure compressor, high pressure compressor, high pressure turbine and low pressure turbine. The measurements used are deviations in exhaust gas temperature, low rotor speed, high rotor speed and fuel flow from a base line ‘good engine’. A genetic algorithm is used to tune the fuzzy sets to maximize fault isolation success rate. A novel scheme is developed which optimizes the fuzzy system using very few design variables and therefore is computationally efficient. Results with simulated data show that genetic fuzzy system isolates faults with accuracy greater than that of a manually developed fuzzy system developed by the authors. Furthermore, the genetic fuzzy system allows rapid development of the rule base if the fault signatures and measurement uncertainties change. In addition, the genetic fuzzy system reduces the human effort needed in the trial and error process used to design the fuzzy system and makes the development of such a system easier and faster. A radial basis neural network is also used to preprocess the measurements before fault isolation. The radial basis network shows significant noise reduction and when combined with the genetic fuzzy system leads to a diagnostic system that is highly robust to the presence of noise in data.Copyright © 2004 by ASME
Safety and perceptions of risk: a comparison between respondent perceptions and recorded accident data

This report is the third of a series examining ‘safety and perceptions of risk’ in the maritime sector. The report compares data obtained from two sources. The first are derived from a large scale questionnaire survey conducted in 2006 by The Lloyd’s Register Educational Trust Research Unit (The LRETRU) at the Seafarers International Research Centre (SIRC). This was designed to identify how workers in the maritime sector perceive risk. Completed responses were received from 2,372 individuals from 50 different countries, with a response rate of 36%. The second set of data consists of incident data obtained from 16 Maritime Administrations and two shipping companies. Only six of the Administration datasets were suitable for aggregation and thus comparison. The two sets of data are compared to determine the extent to which workers’ perceptions of risk correlate with reported incident frequencies. The analysis is presented in two parts. The first examines ship level incidents, i.e. collision, fire, grounding, etc. The second focuses on factors related to personal injury, and includes the following: cause of injury; task being undertaken; broader context; place of incident aboard ship; rank; work department; nationality; age; experience; and types of vessel.
Differential Expression of Glucose Transporter Proteins GLUT-1, GLUT-3, GLUT-8 and GLUT-12 in the Placenta of Macrosomic, Small-for-Gestational-Age and Growth-Restricted Foetuses

Placental transfer of glucose constitutes one of the major determinants of the intrauterine foetal growth. The objective of the present study was to evaluate the expression of glucose transporter proteins GLUT-1, GLUT-3, GLUT-8 and GLUT-12 in the placenta of macrosomic, small-for-gestational-age (SGA) and growth-restricted foetuses (FGR). A total of 70 placental tissue samples were collected from women who delivered macrosomic ≥4000 g (n = 26), SGA (n = 11), growth-restricted (n = 13) and healthy control neonates (n = 20). Computer-assisted quantitative morphometry of stained placental sections was performed to determine the expression of selected GLUT proteins. Immunohistochemical staining identified the presence of all glucose transporters in the placental tissue. Quantitative morphometric analysis performed for the vascular density-matched placental samples revealed a significant decrease in GLUT-1 and increase in GLUT-3 protein expression in pregnancies complicated by FGR as compared to other groups (p < 0.05). In addition, expression of GLUT-8 was significantly decreased among SGA foetuses (p < 0.05). No significant differences in GLUTs expression were observed in women delivering macrosomic neonates. In the SGA group foetal birth weight (FBW) was negatively correlated with GLUT-3 (rho = −0.59, p < 0.05) and positively with GLUT-12 (rho = 0.616, p < 0.05) placental expression. In addition, a positive correlation between FBW and GLUT-12 expression in the control group (rho = 0.536, p < 0.05) was noted. In placentas derived from FGR-complicated pregnancies the expression of two major glucose transporters GLUT-1 and GLUT-3 is altered. On the contrary, idiopathic foetal macrosomia is not associated with changes in the placental expression of GLUT-1, GLUT-3, GLUT-8 and GLUT-12 proteins.
A parameter prediction model of running-in based on surface topography

Although surface topography is a directive and useful method to evaluate the quality of worn components, the common belief that the surface roughness after running-in is independent of the nature of initial roughness makes it difficult to investigate the surface topography of worn components after running-in based on unworn surface before running-in process. Trying to build the connection of surface topography before and after running-in, this article assumed running-in as a black box and adopted support vector machine as machine learning method to simulate the complex process. Through the training and validation of the simulation, the predictive model of surface topography after running-in based on unworn surface topography was established, which indicated the existence of a correlation between the surface topographies before and after running-in. The model analysis revealed that the hybrid properties of surface topographies before and after running-in have strong correlation. Especially, the correlation between input parameter Sk and output parameter Sq is a particular one among all relationships of parameter pairs.
Multiple Attention-Guided Capsule Networks for Hyperspectral Image Classification

The profound impact of deep learning and particularly of convolutional neural networks (CNNs) in automatic image processing has been decisive for the progress and evolution of remote sensing (RS) hyperspectral imaging (HSI) processing. Indeed, CNNs have stated themselves as the current state of the art, reaching unparalleled results in HSI classification. However, most CNNs were designed for RGB images, and their direct application to HSI data analysis could lead to nonoptimal solutions. Moreover, CNNs perform classification based on the identification of specific features, neglecting the spatial relationships between different features (i.e., their arrangement) due to pooling techniques. The capsule network (CapsNet) architecture is an attempt to overcome this drawback by nesting several neural layers within a capsule, connected by dynamic routing, both to identify not only the presence of a feature but also its instantiation parameters and to learn the relationships between different features. Although this mechanism improves the data representations, enhancing the classification of HSI data, it still acts as a black box, without control of the most relevant features for classification purposes. Indeed, important features could be discriminated against. In this article, a new multiple attention-guided CapsNet is proposed to improve feature processing for RS-HSIs’ classification, both to improve computational efficiency (in terms of parameters) and increase accuracy. Hence, the most representative visual parts of the images are identified using a detailed feature extractor coupled with attention mechanisms. Extensive experimental results have been obtained on five real datasets, demonstrating the great potential of the proposed method compared to other state-of-the-art classifiers.
The Role of the Future Civil Engineers' Motivation in the Foreign Language Learning

The article is dedicated to the changes happening in the sphere of higher education and concerning the foreign language education of the future civil engineers. The research held studies the main motivational factors for English language learning among the students of the Institute of Civil Engineering. The conclusions made are meant to improve the system of technical students’ foreign language education .
A Comprehensive Framework to Evaluate the Effects of Anterior Cruciate Ligament Injury and Reconstruction on Graft and Cartilage Status through the Analysis of MRI T2 Relaxation Time and Knee Laxity: A Pilot Study

Background: Anterior cruciate ligament (ACL) tear represents a common orthopedic traumatic issue that often leads to an early development of osteoarthritis. To improve the diagnostic and prognostic techniques involved in the assessment of the joint after the trauma and during the healing process, the present work proposes a multi-parametric approach that aims to investigate the relationship between joint function and soft tissue status before and after ACL reconstruction. Methods: Thirteen consecutive patients who underwent ACL reconstruction were preliminarily enrolled in this study. Joint laxity assessment as well as magnetic resonance imaging with T2 mapping were performed in the pre-operative stage, at four and 18 months after surgery to acquire objective information to correlate knee function and soft tissue condition. Results: Correlations were found between graft and cartilage T2 signal, suggesting an interplay between these tissues within the knee joint. Moreover, graft maturation resulted in being connected to joint laxity, as underlined by the correlation between the graft T2 signal and the temporal evolution of knee function. Conclusions: This preliminary study represents a step forward in assessing the effects of ACL graft maturation on knee biomechanics, and vice versa. The presented integrated framework underlines the possibility to quantitatively assess the impact of ACL reconstruction on trauma recovery and cartilage homeostasis. Moreover, the reported findings—despite the preliminary nature of the clinical impacts—evidence the possibility of monitoring the surgery outcomes using a multi-parametric prognostic investigation tool.
Serum lubricin levels in patients with juvenile idiopathic arthritis

Objectives Lubricin, encoded by the proteoglycan 4 (PRG4) gene, is mainly responsible for lubricating joints. However, there is expanding evidence on its involvement in inflammatory pathways. Juvenile idiopathic arthritis (JIA) is a heterogeneous group of chronic arthritides with an unknown origin in children aged below 16 years. It is characterized by chronic joint inflammation, including synovial inflammation, and may result in cartilage destruction. We aimed to determine whether serum lubricin levels are affected in JIA patients. Material and methods This cross-sectional study included children diagnosed with JIA and 28 healthy controls. The patients were divided into two groups according to the presence of remission at the time of study. Lubricin protein analysis was performed by the enzyme-linked immunosorbent assay method. Serum samples were obtained at the study enrollment, and lubricin levels were measured once, and compared between JIA patients and healthy controls, and between JIA patients with active disease and remission. Results The study included 52 JIA patients (28 female, 24 male) and 28 healthy controls (18 female, 10 males). The mean age at study enrollment was 11.66 ±4.41 years and 12.72 ±4.52 years in the JIA patient and control groups, respectively. Although median serum lubricin level did not differ between JIA patients (median: 0.66 ng/μl, range: 0.02–3.85 ng/μl) and healthy controls (median: 0.52 ng/μl, range: 0.06–3.84 ng/μl), it was statistically significantly higher in patients with active disease (median: 1.58 ng/μ, range: 0.08–3.85 ng/μl) than both patients in remission (median: 0.57 ng/μl, range: 0.02–3.57 ng/μl) and healthy controls. A low degree positive correlation was also found between serum lubricin levels and erythroid sedimentation rate of the JIA patients (r = 0.383 and p = 0.011). Conclusions This is the first study investigating serum lubricin levels in JIA patients, and we found elevated serum lubricin levels in JIA patients with active disease. Further studies are needed to clarify our results.
Towards a unified approach to cooperative and non-cooperative RPAS detect-and-avoid

Cooperative and non-cooperative Detect-and-Avoid (DAA) functions are key enablers for Remotely Piloted Aircraft System (RPAS) to safely and routinely access all classes of airspace. In this paper state-of-the-art cooperative and non-cooperative DAA sensor/system technologies for small-to-medium size RPAS are reviewed and the associated multi-sensor data fusion techniques are discussed. A DAA system architecture is presented based on Boolean Decision Logics (BDL) for selecting non-cooperative and cooperative sensors/systems including both passive and active Forward Looking Sensors (FLS), Traffic Collision Avoidance System (TCAS) and Automatic Dependent Surveillance - Broadcast (ADS-B). After elaborating the DAA system processes, the key mathematical models associated with both non-cooperative and cooperative DAA functions are presented. The Interacting Multiple Model (IMM) algorithm is adopted to estimate the state vector of the intruders and this is propagated to predict the future trajectories using a probabilistic model. The analytical models adopted to compute the overall uncertainty volume in the airspace surrounding an intruder are outlined. Based on these mathematical models, the Sabatini Unified Method (SUM) for cooperative and non-cooperative DAA is presented. In this unified approach, navigation and tracking errors affecting the measurements are considered and translated to unified range and bearing uncertainty descriptors, which apply both to cooperative and non-cooperative scenarios. Simulation case studies are carried out to evaluate the performance of the proposed DAA approach on a representative host platform (AEROSONDE RPAS) and various intruder platforms. Results corroborate the validity of the proposed approach and demonstrate the impact of SUM towards providing a cohesive logical framework for the development of an airworthy DAA capability and a pathway for manned/unmanned aircraft coexistence in all classes of airspace.
Effect of Elevated Tropospheric Ozone on Vigna Mungo L. Varieties

ABSTRACT Tropospheric ozone (O3) is widely recognized as the most critical, regional atmospheric pollutant causing significant losses to agricultural productivity due to its phytotoxicity over agricultural areas and is expected to increase in future. In view of rising tropospheric ozone concentration over Indian regions, the present study aimed to evaluate the effect of elevated ozone stress on pulse crop blackgram (Vigna mungo L.), which contributes the major share of protein. The blackgram varieties namely CO 6, VBN 1, VBN 2, VBN 3, VBN 5, VBN 6, VBN 7, and VBN 8 were grown in open top chambers and factorial completely randomized block design was followed. The plants were exposed to elevated ozone concentration (50 and 100 ppb) from 10.00 h to 17.00 h over 10 days at flowering stage, with a weighted average ozone concentration of 50.1 and 101.2 ppb. Both the elevated ozone treatments significantly affected the plant physiological, biochemical, growth, and yield traits of all test varieties. On an average across eight blackgram varieties, decrease in chlorophyll content by 33.83 and 42.41%, stomatal conductance by 28.25 and 40.51% and photosynthetic rate by 29.43 and 42.30% exposed to 50 and 100 ppb ozone were observed, respectively. Correspondingly, the number of pods per plant decreased by 30.82 and 32.65%, 100 grain weight by 7.75 and 21.23% and plant weight by 16.03 and 21.23%, respectively, which were significant at 5% level. Furthermore in the observed traits, significantly higher reduction was observed in VBN3, while the least reduction was observed in VBN8. The path analysis displayed that all the observed physiological, biochemical, growth, and yield traits positively regulated the yield except leaf injury percentage, malondialdehyde, and proline content. The principal component analysis of two elevated ozone treatments confirmed VBN8 as ozone tolerant and VBN3 as ozone sensitive variety. Hence, cultivation of VBN8 variety at ozone hotspot regions would be the best option to overcome ozone induced yield loss.
Adaptive beamforming of ESPAR antenna based on stochastic approximation theory

A novel approach based on stochastic approximation theory is devoted to adaptive beamforming of electronically steerable passive array radiator (ESPAR) antennas. Because of the low cost, the ESPAR antenna has only a single-port output. The proposed algorithm, by unconventional method, performs adaptive control of loaded reactances on the passive radiators and thus forming both beam and nulls. Our study and simulation results show that ESPAR antenna can effectively suppress interference and noise background and is attractive in modem wireless communications systems.
Calculation of properties of carbon nanotube antennas

The effective method of carbon nanotubes antennas' parameters calculation has been developed. The frequency dependencies of input impedance of CNT in dielectric medium have been investigated. It is shown that an increase in the length of a nanotube length does not lead to the appearance of resonances in the centimeter wavelength range.
Gender Inequality in STEM Employment and Earnings at Career Entry: Evidence from Millennial Birth Cohorts

Although science, technology, engineering, and mathematics (STEM) majors remain male dominated, women’s greater enrollment in STEM is one of the greatest transformations to occur in U.S. higher education in the past half century. But to what extent have women’s gains in STEM enrollment translated to greater parity in labor market outcomes? Although the challenges women face in STEM have been well documented, questions about the influence of gender for STEM employment and earnings differences remain. In the present research, the authors use data from recent birth cohorts in the American Community Survey between 2009 and 2018 (starting with the first year college majors were available in the survey) and a reweighting technique from labor economics to track the evolution of gender inequalities in STEM employment and earnings inequality among STEM work at the onset of labor market entry. Even among a sample expected to produce highly conservative gender differences, sizable gender inequalities in STEM employment are observed. The authors show that despite women’s gains in STEM education among recent cohorts, women with STEM degrees face employment prospects in STEM work that more closely resemble those of men without STEM degrees than men with STEM degrees. Moreover, although modest gender earnings gaps eventually emerge for those without STEM degrees, large gaps occur at the outset of employment for STEM workers. Thus, although STEM education provides important opportunities for women’s earning potential, it may be less effective in itself to address significant gender inequalities among STEM employment.
Improved quality factor spiral inductors on GaAs substrates

We present test data for several spiral inductors with improved quality factors fabricated on GaAs substrates. It has been shown experimentally that the quality factor of spiral inductors can be increased by reducing magnetically induced currents and by narrowing the line width of the inner turns similar to the one reported for silicon micromachined inductor. Using this technique, we observed about 22% improvement in the quality factor of spiral inductors as compared to standard spiral inductors having constant line width.
Damping Power System Oscillations in Single-Machine Infinite-Bus Power System Using a PSO based STATCOM

Transmission networks of modern power systems are becoming increasingly stressed because of growing demand and restrictions on building new lines. One of the consequences of such a stressed system is the threat of losing stability following a disturbance. Flexible ac transmission system (FACTS) devices are found to be very effective in a transmission network for better utilization of its existing facilities without sacrificing the desired stability margin. Flexible AC Transmission System (FACTS) such as Static Synchronous Compensator (STATCOM) and Static VAR Compensator (SVC), employ the latest technology of power electronic switching devices in electric pow er transmission systems to control voltage and power flow. A static synchronous compensator (STATCOM) is a shunt device of the flexible AC transmission systems (FACTS) family. The STATCOM regulates voltage at its terminal by controlling the amount of reactive power injected into or absorbed from power system. When system voltage is low, STATCOM generates reactive power and when system voltage is high it absorbs reactive power. In this work STATCOM controller i.e. based on particle swarm optimization algorithm is proposed in single machine system. Proposed controller is implemented under MATLAB/SIMULNK environment.