Miracle-dz/UniFAR-FTUs huggingface dataset:
Different people, different incentives? Examining the public acceptance of smartphone-based persuasive strategies for sustainable travel using psychographic segmentation

Abstract The development and delivery of ICT-based persuasive strategies for the adoption of more environmental-friendly modes of transport is an emerging strategy to help tackle the problem of excessive use of individual Conventionally Fueled Vehicles (CFVs). The efficiency of this approach, like other information-based schemes, can benefit from the knowledge of which groups of individuals are more responsive to these types of interventions. The goal of this study is to examine the level of acceptability of persuasive schemes delivered using smartphone applications across different population segments. A cross-sectional survey was applied to 920 university students in Curitiba, a roughly 2-million inhabitant Brazilian city. The questionnaire was developed considering two groups of indicators: individual determinants of travel behavior, which were extracted from theories of social psychology (i.e. the Theory of Planned Behavior, the Norm-Activation Model and others), as well as stated intentions and attitudes toward a range of eleven different persuasive strategies (money or voucher rewards, points and rankings, social media sharing, etc.). Groups with different psychographic profiles were extracted from the sample using cluster and discriminant analyses. The acceptability of persuasive strategies were compared between segments. Results show a higher individual preference toward financial rewards and a relatively lower acceptability of social media tools. The acceptance level of groups that display higher control over the use of non-motorized forms of transport is higher than groups that are more psychologically predisposed to CFVs.
DOA Tracking of Two-Dimensional Coherent Distribution Source Based on Fast Approximated Power Iteration

Aiming at two-dimensional (2D) coherent distributed (CD) sources, this paper has proposed a direction of arrival (DOA) tracking algorithm based on signal subspace updating under the uniform rectangular array (URA). First, based on the hypothesis of small angular spreads of distributed sources, the rotating invariant relations of the signal subspace of the receive vector of URA are derived. An ESPRIT-like method is constructed for DOA estimation using two adjacent parallel linear arrays of URA. Through the synthesis of estimation by multiple groups of parallel linear arrays within URA arrays, the DOA estimation method for 2D CD sources based on URA is obtained. Then, fast approximated power iteration (FAPI) subspace tracking algorithm is used to update the signal subspace. In this way, DOA tracking of 2D CD sources can be realized by DOA estimation through signal subspace updating. This algorithm has a low computational complexity and good real-time tracking performance. In addition, the algorithm can track multiple CD sources without knowing the angular signal distribution functions, which is robust to model errors.
Knowledge, Experience, and Financial Decision-Making

The paper begins with codified and tacit knowledge, situates the issue in relation to financial decision-making, and focuses upon the relationship between knowledge, experience, and behaviour. Being about individual behaviour in financial markets, how people cope with risk and uncertainty is an important theme of the paper. Drawing upon cognitive science, I focus upon the relationship between codified knowledge as embodied in financial literacy, decision-making competence, and learning-bydoing. Throughout, issues of spatial scale are brought to the fore noting, for example, the presumption in much of the literature that tests of financial literacy transcend geography whereas learning-by-doing is always local.
Does the group matter? Effects of trust, cultural diversity, and group formation on engagement in group work in higher education

ABSTRACT Group work is a common active learning strategy in higher education when the goal is to enhance deep learning and develop teamwork skills. Culturally diverse learning groups are particularly valuable in preparing university students to participate in a globalized world. Student engagement in group work is critical in realizing these benefits. Therefore, more insight into what factors promote engagement is necessary. This study investigates the extent to which trust in the group, cultural diversity in the group, and group formation contribute to behavioral and cognitive engagement in group work. A questionnaire was filled out by 1025 bachelor’s students from six universities in the Netherlands and Canada. Structural equation modeling analyses identified students’ trust in the group as the strongest positive predictor of both behavioral and cognitive engagement. Greater perceived cultural diversity was found to promote behavioral and cognitive engagement, but compared with trust, the impacts were relatively small. Whether students could choose their group members did not affect behavioral or cognitive engagement significantly. Contrary to what was expected, trust did not act as a mediator. That is, cultural diversity and group formation did not indirectly affect engagement through trust. These findings prompt some suggestions for how to enhance student engagement in group work.
Polymeric Micelles for the Delivery of Poorly Soluble Drugs

Micelles are spherical, supramolecular, self-assembled, nano-sized, colloidal particles with a hydrophobic core and a hydrophilic shell formed by the aggregation of self-assembling amphiphilic molecules or surfactants in solutions. Amphiphilic macromolecules or surfactants, the structural unit of polymeric micelles have distinct hydrophobic (non-polar) and hydrophilic (polar, water-loving) block domains. Often, the hydrophilic part of the molecule is called the head and the hydrophobic part, usually a long alkyl substituent is called the tail. The simultaneous existence of chemical entities having an affinity and antipathy for water within the same molecule is the key phenomenon of amphipathicity [1–4]. The presence of a hydrophilic group makes the surfactants slightly soluble in water. Aqueous exposure induces the hydrophobic and hydrophilic segments to phase-separate and form nanoscopic, supramolecular core/shell structures, at a minimum particular polymeric concentration, termed micelles [5] The size and shape of micellar aggregates vary as spheres, rods, vesicles, tubules and lamellae, depending on the balance between the hydrophilicity and lipophilicity of a given amphiphilic molecule. Surfactants are classified on the basis of the charge carried by the polar head group as anionic, cationic, non-ionic, or amphoteric. Table 14.1 shows the typical structures of the few surfactants of these individual classes. The drastic changes in many physicochemical properties seen in an aqueous solution of amphiphilic molecules or surfactants with a hydrophobic domain take place when a specific concentration is exceeded because of the formation of oriented colloidal aggregates. The
Interpretations of mindfulness practices in organizations: A multi-level analysis of interpretations on an organizational, group, and individual level

Organizations increasingly experiment with spiritual and wellness practices in contemporary work contexts. Particularly, mindfulness practices play a dominant role in this movement. Rooted in spirituality and applied in clinical psychology, mindfulness is explained as a tool for dealing with workplace demands. Despite the increasing interest in mindfulness practices and the critical debate on their appropriation in the business context, knowledge on how organizational members interpret these practices, is underdeveloped. This inhibits a comprehensive understanding of what these practices are meant to achieve, and what they imply for organizational members. Viewing organizations as interpretive systems, we investigate how organizational members interpret mindfulness practices on the organizational, group, and individual level. Our qualitative study reveals the multiple interpretations of mindfulness practices existing in organizations. On an organizational level, mindfulness practices are interpreted as generalizable human resource development tools, aimed at boosting performance. Yet, mindfulness produces uncertainties in the organizational level interpretation. On a group level, mindfulness practices are interpreted as a means to enhance group efficiency via improved personal relationships; however, they are also associated with creating dysfunctional group dynamics. On an individual level, practicing mindfulness is interpreted as a tool for self-actualization. Even so, individuals associate the risk of negative social ramifications with the practice. We show the multiple meanings of mindfulness in organizations. This informs the critical debate among organizational scholars and explains the multitude of applications. Further, the paper offers a balanced view on benefits as well as unintended consequences, in relation to former applications and interpretations of the concept.
Nonreentrant and reentrant nematic liquid crystals under high pressure: A volumetric study

The results of volumetric measurements under high pressure near three different phase transitions in heptyloxyazoxybenzene (70AB), a nonreentrant nematic liquid crystal, and octyloxycyanobiphenol (80CB), a reentrant nematic liquid crystal, are reported. Volume changes at the isotropic–nematic transition in these two liquid crystals are similar at low pressure, but behave differently at high pressure. Volume changes at the nonreentrant nematic–smectic C transition are an order of magnitude larger than at the reentrant nematic–smectic A transition. The volume dependence along these two nematic–smectic coexistence curves also reveals the stronger role played by attractive forces in these transitions as compared to the isotropic–nematic transition. Finally, these data do not support a previously reported nematic–smectic C‐solid triple point in 70AB.
Anthropogenic Secondary Organic Aerosols Contribute Substantially to Air Pollution Mortality

Abstract. Anthropogenic secondary organic aerosol (ASOA), formed from anthropogenic emissions of organic compounds, constitutes a substantial fraction of the mass of submicron aerosol in populated areas around the world and contributes to poor air quality and premature mortality. However, the precursor sources of ASOA are poorly understood, and there are large uncertainties in the health benefits that might accrue from reducing anthropogenic organic emissions. We show that the production of ASOA in 11 urban areas on three continents is strongly correlated with the anthropogenic reactivity of specific volatile organic compounds. The differences in ASOA production across different cities can be explained by differences in the emissions of aromatics and intermediate- and semi-volatile organic compounds, indicating the importance of controlling these ASOA precursors. With an improved modeling representation of ASOA driven by the observations, we attribute 340,000 PM2.5 premature deaths per year to ASOA, which is over an order of magnitude higher than prior studies. A sensitivity case with a more recently proposed model for attributing mortality to PM2.5 (the Global Exposure Mortality Model) results up to 900,000 deaths. A limitation of this study is the extrapolation from regions with detailed data to others where data is not available. Comprehensive air quality campaigns in the countries in South and Central America, Africa, South Asia, and the Middle East are needed for further progress in this area.
Leader Humor and Employee Upward Voice: The Role of Employee Relationship Quality and Traditionality

Despite the continuous increase in empirical research on leader humor, the important issue of how and when leader humor affects employees’ interpersonal, proactive behaviors in the form of upward voice has largely been overlooked. Drawing on relational process model of humor and data from one multiwave, multisource field study and one experimental field study, we find that the positive effects of leader humor on upward voice behavior can be accounted for by both supervisor–subordinate nonwork ties (i.e., supervisor–subordinate guanxi) and supervisor–subordinate work ties (i.e., leader–member exchange). The indirect effects of both supervisor–subordinate guanxi and leader–member exchange on the relationship between leader humor and upward voice behavior are stronger when employees score low on traditionality. These results shed light on the role of leader humor in promoting the bottom–up flow of potentially critical information in organizations through high-quality relationships with followers and provide insights into who will benefit more from humor in leadership.
Deep learning support for intelligent transportation systems

Intelligent Transportation Systems (ITS) help improve the ever‐increasing vehicular flow and traffic efficiency in urban traffic to reduce the number of accidents. The generation of massive amounts of data generated by all the digital devices connected to the transportation network enables the creation of datasets to perform an in‐depth analysis of the data using deep learning methods. Such methods can help predict traffic performance, automated traffic light management, lane detection, and identifying objects near vehicles to increase the safety and efficiency of ITS. We discuss some of the challenges that need to be solved to achieve seamless integration between ITS and deep learning methods to address issues such as (1) improving traffic flow/transportation logistics, (2) predicting best routes for the transportation of goods, (3) optimal fuel consumption, (4) intelligent environmental conditions perception, (5) traffic speed management, and accident prevention.
Bus Dynamic Travel Time Prediction: Using a Deep Feature Extraction Framework Based on RNN and DNN

Travel time data is an important factor for evaluating the performance of a public transport system. In terms of time and space within the nature of uncertainty, bus travel time is dynamic and flexible. Since the change of traffic status is periodic, contagious or even sudden, the changing mechanism of that is a hidden mode. Therefore, bus travel time prediction is a challenging problem in intelligent transportation system (ITS). Allowing for a large amount of traffic data can be collected at present but lack of precisely-conducting, it is still worth exploring how to extract feature sets that can accurately predict bus travel time from these data. Hence, a feature extraction framework based on the deep learning models were developed to reflect the state of bus travel time. First, the study introduced different historical stages of bus signaling time, taxi speed, the stop identity (ID) of spatial characteristics, and real-time possible arrival time, signified by fourteen spatiotemporal characteristic values. Then, an embedding network is proposed to leverage a wide and deep structure to mate the spatial and temporal data. In order to meet the temporal dependence requirements, an attention mechanism for a Recurrent Neural Network (RNN) was designed in this research in order to capture the temporal information. Finally, a Deep Neural Networks (DNN) was implemented in this research in order to achieve the dynamic bus travel time prediction. Two case studies of Guangzhou and Shenzhen were tested. The results showed that the performance of the algorithm was more efficient than that of the traditional machine-learning model and promoted by 4.82% compared to the deep neural network applied to the initial feature space. Moreover, the study visualized the weighted cost of attention on the bus’s travel time features during a certain running state. Therefore, the study demonstrated the proposed model enabled to understand the characteristic data of transit travel time with visualization.
Effects of dietary xylo-oligosaccharide on growth performance, serum biochemical parameters, antioxidant function, and immunological function of nursery piglets

ABSTRACT This study investigated the effects of dietary xylo-oligosaccharide (XOS) on growth performance, serum biochemical parameters, antioxidant function, and immunological function of nursery piglets. In total, three groups including 72 nursery piglets were designed and fed one of three diets: a control basal diet, basal diet supplemented with 0.2% ZnO, or basal diet supplemented with 0.04% XOS, for 28 days. Compared with the control group, the XOS group significantly increased the final body weight and average daily weight gain. No [. ]
Extraction of lipids from microalgae using monophasic-biphasic separation via hexane–acetone–water system

Effective extraction of lipids from aqueous algae is challenging due to miscibility issues of lipophilic extraction solvents like hexane with the aqueous media. However, we observed that a polar aprotic solvent such as acetone can make the original biphasic system monophasic, thus permitting higher extraction yields. The attractiveness of such a system is the ability to modulate phase stability by changing solvent concentrations so that the lipid-rich phase can eventually be easily separated. In this paper, a solvent system containing hexane and acetone is presented as an alternative to the common Bligh and Dyer extraction system.
Polynomial-Based Robust Adaptive Impedance Control of Electrically Driven Robots

SUMMARY This paper presents a robust adaptive impedance controller for electrically driven robots using polynomials of degree N as a universal approximator. According to the universal approximation theorem, polynomials of degree N can approximate uncertainties including un-modeled dynamics and external disturbances. This fact is completely discussed and proved in this paper. The polynomial coefficients are estimated based on the adaptive law calculated in the stability analysis. A performance evaluation has been carried out to verify satisfactory performance of the controller. Simulation results on a two degree of freedom manipulator have been presented to guarantee its successful implementation.
The impact of corruption on the export intensity of SMEs in Tunisia: moderating effects of political instability and regulatory obstacles

Purpose This paper aims to investigate the moderating effect of political instability and regulatory obstacles on the relationship between corruption and export intensity in the context of Tunisian small- and medium-sized enterprises (SMEs). Design/methodology/approach This study uses data from the World Bank Enterprise Survey (WBES). The sample consists of 537 Tunisian SMEs. The partial least squares method was used to analyse the data. Findings The direct effect of corruption on export intensity was found to be non-significant. It was significantly negative when corruption was combined with regulatory obstacles, whereas it was positive when corruption coexisted with political instability. Additional analyses revealed that results were sensitive to firm size (small versus medium) and sector of activity (service versus manufacturing). Research limitations/implications This paper has some limitations related to the use of secondary data. Enhanced variable measurements and more detailed data collection are recommended for future studies. Practical implications This paper is useful to researchers and policymakers who are interested in understanding the effects of a poor institutional environment on SME exports in developing countries. Originality/value This paper considers the impact of corruption on the export intensity of SMEs in the presence of political instability and regulatory obstacles in Tunisia. To the best of the authors’ knowledge, the joint effect of these institutional variables on the exports of firms has not been examined in previous research.
Revolutionary superposition layout method for topology optimization of nonconcurrent multiload models: Connecting‐rod case study

This article proposes a new approach called revolutionary superposition layout (RSL) for obtaining optimum designs of nonconcurrent multiloads structures in general, and for connecting rod (CR) in particular. Since the compression and tension resulted from the combustion and exhaust strokes are nonconcurrent loads, the importance of this approach arises up. RSL depends on combining the optimum‐design layouts obtained from different individual nonconcurrent load cases into one resultant design. This final design efficiently sustains all different loads applied to it. RSL presents a simple, less computational effort in the dynamic environment, and less time‐consuming method. RSL has been compared with a multiload optimization method called bound formulation (BF) for obtaining the optimum design of some simple models before dealing with the CR model afterward. Two distinctive optimum topological designs of CR produced by RSL have proved their feasibility and achieved considerable improvements against both the conventional CR design being used nowadays in the automotive industry and the BF method designs, as well. A comparative study between them was accomplished based on both structural and modal analyses in ANSYS. As a result, the proposed RSL design shows a significant reduction of compliance, displacement/deflection and mass moment of inertia by 57.2%, 68.7%, and 5.9%, respectively, compared with conventional one.
Should value‐added school effects models include student‐ and school‐level covariates? Evidence from Australian population assessment data

There is an enduring issue on whether studentand school-level covariates should be included in value-added school effects models, in addition to prior achievement. Proponents argue that the addition of covariates allow fairer comparisons of schools, whereas opponents argue that it excuses poor performing schools and obscures policy-relevant school differences. School-level covariates are problematic statistically, but it has been argued that mean school prior achievement should be included in school effects analyses to reduce error. This paper reports on school effects analyses of Australia-wide data of approximately 1.5 million students in both primary and secondary schools that took national assessments in five achievement domains between 2013 and 2018. With appropriate controls for prior achievement, school effects are generally small and most often not statistically significant. The addition of student-level covariates: further reduces school effects since part of the school effects is absorbed by the effects of the covariates which are unlikely to reflect causal social processes; reduces the proportion of schools with significant school effects; does not improve predictive power; increases the amount of missing data; and further reduces the consistency of school effects between domains and their stability over-time. Mean school prior achievement did not improve consistency or stability. Incorporating covariates in school effects analyses opens a Pandora’s Box of specification and measurement issues, undermining the legitimacy of school comparisons. It is concluded that researchers and administrators of educational jurisdictions should focus mainly on simpler models based on prior achievement.