Miracle-dz/UniFAR-FTUs huggingface dataset:
Nonverbal category knowledge limits the amount of information encoded in object representations: EEG evidence from 12-month-old infants

To what extent does language shape how we think about the world? Studies suggest that linguistic symbols expressing conceptual categories (‘apple’, ‘squirrel’) make us focus on categorical information (e.g. that you saw a squirrel) and disregard individual information (e.g. whether that squirrel had a long or short tail). Across two experiments with preverbal infants, we demonstrated that it is not language but nonverbal category knowledge that determines what information is packed into object representations. Twelve-month-olds (N = 48) participated in an electroencephalography (EEG) change-detection task involving objects undergoing a brief occlusion. When viewing objects from unfamiliar categories, infants detected both across- and within-category changes, as evidenced by their negative central wave (Nc) event-related potential. Conversely, when viewing objects from familiar categories, they did not respond to within-category changes, which indicates that nonverbal category knowledge interfered with the representation of individual surface features necessary to detect such changes. Furthermore, distinct patterns of γ and α oscillations between familiar and unfamiliar categories were evident before and during occlusion, suggesting that categorization had an influence on the format of recruited object representations. Thus, we show that nonverbal category knowledge has rapid and enduring effects on object representation and discuss their functional significance for generic knowledge acquisition in the absence of language.
Finger motion and contact by a second finger influence the tactile perception of electrovibration

Electrovibration holds great potential for creating vivid and realistic haptic sensations on touchscreens. Ideally, a designer should be able to control what users feel independent of the number of fingers they use, the movements they make, and how hard they press. We sought to understand the perception and physics of such interactions by determining the smallest 125 Hz electrovibration voltage that 15 participants could reliably feel when performing four different touch interactions at two normal forces. The results proved for the first time that both finger motion and contact by a second finger significantly affect what the user feels. At a given voltage, a single moving finger experiences much larger fluctuating electrovibration forces than a single stationary finger, making electrovibration much easier to feel during interactions involving finger movement. Indeed, only about 30% of participants could detect the stimulus without motion. Part of this difference comes from the fact that relative motion greatly increases the electrical impedance between a finger and the screen, as shown via detailed measurements from one individual. By contrast, threshold-level electrovibration did not significantly affect the coefficient of kinetic friction in any conditions. These findings help lay the groundwork for delivering consistent haptic feedback via electrovibration.
Deep eutectic solvent-based emulsification liquid–liquid microextraction for the analysis of phenoxy acid herbicides in paddy field water samples

An emulsification liquid–liquid microextraction (ELLME) method was successfully developed using phenolic-based deep eutectic solvent (DES) as an extraction solvent for the determination of phenoxy acid herbicides, 3,6-dichloro-2-methoxybenzoic acid (dicamba) and 2-methyl-4-chlorophenoxyacetic acid (MCPA) in environmental water samples. Five different phenolics-based DESs were successfully synthesized by using phenol (DES 1), 2-chlorophenol (DES 2), 3-chlorophenol (DES 3), 4-chlorophenol (DES 4) and 3,4-dichlorophenol (DES 6) as the hydrogen-bond donor (HBD) and choline chloride as the hydrogen-bond acceptor (HBA). The DESs were mixed at 1 : 2 ratio. A homogeneous solution (clear solution) was observed upon the completion of successful synthesis. The synthesized DESs were characterized by using Fourier transform infrared and nuclear magnetic resonance (NMR). Under optimum ELLME conditions (50 µl of DES 2 as extraction solvent; 100 µl of THF as emulsifier solvent; pH 2; extraction time 5 min), enrichment factor obtained for dicamba and MCPA were 43.1 and 59.7, respectively. The limit of detection and limit of quantification obtained for dicamba were 1.66 and 5.03 µg l−1, respectively, meanwhile for MCPA were 1.69 and 5.12 µg l−1, respectively. The developed ELLME-DES method was applied on paddy field water samples, with extraction recoveries in the range of 79–91% for dicamba and 82–96% for MCPA.
Evaluation of the taxonomic position of the genus Carinina (Nemertea  :  Palaeonemertea), with descriptions of two new species

Abstract. The genus Carinina Hubrecht, 1885 has long been considered the most ‘archaic’ nemertean taxon because its members are distinguished by the basiepidermal position of the brain and lateral nerve cords, characters thought to be plesiomorphic for the phylum. Here we describe two new species, Carinina yushini sp. nov. from the Sea of Japan (Russia) and C. chocolata sp. nov. from the north-east Pacific (Oregon, USA), distinguished by brown body colour. A phylogenetic analysis based on partial sequences of five nuclear and mitochondrial gene regions, 18S rRNA, 28S rRNA, histone H3, 16S rRNA and COI, confirms the monophyly of Carinina (Family Carininidae), and points to a close relationship to Carinoma (Family Carinomidae). The two groups together form a sister clade to the rest of the palaeonemerteans (Family Tubulanidae + Family Cephalotrichidae s.l.). Carinina plecta most likely belongs to the Tubulanidae. A morphological synapomorphy of the clade Carininidae + Carinomidae is a larva with a single midventral eye (in contrast to eyeless larvae of the Tubulanidae and two-eyed larvae of the Cephalotrichidae). Our phylogenetic analysis suggests that the basiepidermal position of the central nervous system is an autapomorphy of Carininidae (and, independently, C. plecta), rather than a plesiomorphy of the phylum Nemertea or the class Palaeonemertea, emphasising that the genus Carinina is no more archaic than any other palaeonemertean genus.
Evaluating Capital Assets in Governing Protected Area Co-Management in the Rema-Kalenga Wildlife Sanctuary, Bangladesh

HIGHLIGHTS Communities in the study area were heterogeneous concerning forest dependency, income and occupation. The different forms of capital assets were closely linked to co-management, with social capital having the most significant influence while human and manufactured capital had no role in the governance of co-management. Within social capital, local leaders played a dominant role over the other variables. Poor dependent people were less likely to be selected as participants, which undermined the essence of co-management. The impact of co-management on forest vegetation cover was found to be positive during the project period. However, a slight decrease was observed at the end of the project indicating a challenge of sustaining this approach. SUMMARY Co-management in the Rema-Kalenga Wildlife Sanctuary was evaluated to assess how fairly capital assets were considered in the beneficiary selection, and to what extent it affected vegetation cover. A semi-structured questionnaire survey was employed to collect necessary information along with satellite images. The study revealed that many variables of social capital and a few variables of natural and financial capital played a significant role in the participant selection process. Analysis of dependency showed that the participants did not rely significantly on the forest both in terms of resource collection and their monetary value implying that the most dependent people were not adequately represented in the co-management team. The dominance of local leaders suppressed the voices of others in the management venture. An increase in forest vegetation cover was observed during the project period, although shortly after the end of co-management projects a slight deterioration of forest cover was noted. The findings of the study can serve as a guide in the future application of community forestry programmes in protected areas of Bangladesh and elsewhere in the world within similar socio-physiographic settings.
The self-fulfilling prophecy of insecurity: Mediation effects of conflict communication styles on the association between adult attachment and relationship adjustment

This study examines associations between the dimensions of insecure attachment (anxiety, avoidance) and dyadic adjustment, with a potential mediating effect of conflict communication style. Dyadic analyses and mediation tests were performed on data from 97 Czech and Slovak long-term heterosexual couples. Attachment dimensions were measured by Experiences in Close Relationships-Revised measure (ECR-R-16), six conflict communication styles by the Romantic Partner Conflict Scale (RPCS), and dyadic adjustment by the Dyadic Adjustment Scale (DAS). We confirmed previous findings according to which in both sexes, attachment anxiety and avoidance have a negative effect on dyadic adjustment. We also found that women’s anxiety is negatively associated with their partner’s dyadic adjustment. Our mediation tests showed that in both sexes, the association between attachment anxiety and low dyadic adjustment was partially mediated by a reactive conflict communication style. Moreover, the association between attachment avoidance and low dyadic adjustment in women was partially mediated by both the reactive and compromising communication styles. These results are discussed in the context of behavioral and emotional characteristics of both attachment insecurities and gender differences in interpersonal behavior.
Better relationships shut the wandering eye: Sociosexual orientation mediates the association between relationship quality and infidelity intentions

People who lack quality relationships are more likely to engage in infidelity but how relationship quality is associated with infidelity is less clear. One psychological mechanism that might help to explain these associations is sociosexuality. Here, we tested two models in which we assessed whether overall sociosexuality and sociosexuality subscales (i.e., attitude, behavior, desire) explain how relationship quality is related to infidelity intentions by sampling individuals in exclusive relationships (N = 219). We found that individuals with lower relationship quality had more unrestricted sexual orientation, which in turn predicted a greater intention toward infidelity. In addition, lower quality relationships were associated with greater intentions toward infidelity through behavior and desire, but not attitude dimensions of sociosexuality.
Nitrogen fertilisation and substrate pH effects on corky root disease (Pyrenochaeta lycopersici) in greenhouse tomatoes

Corky root disease is becoming increasingly important in organic tomato production. The effects of ammonium-dominated, nitrate-dominated or balanced nitrogen fertiliser on corky root were studied in greenhouse tomatoes. The lowest disease index occurred in tomatoes with the ammonium-dominated fertiliser and the highest with the balanced nitrogen fertiliser. Disease incidence increased with increasing nitrogen level and disease severity with increasing pH value of the soil-substrate. In complementary in vitro studies, the highest mycelial growth rate of Pyrenochaeta lycopersici was observed on balanced nitrogen medium (50% nitrate: 50% ammonium) and the lowest in the nitrate-dominated medium. Regarding pH, the highest mycelial growth occurred at the optimum pH for tomato growth (pH range 5.0–6.5). No practical advice concerning fertiliser composition was possible because no treatment achieved satisfactory control of corky root. However, N fertilisation of organic greenhouse tomatoes should be based on calculated nutrient uptake by the crop.
Leveraging Collaborative-Filtering for Personalized Behavior Modeling

The prevalence of mobile phones and wearable devices enables the passive capturing and modeling of human behavior at an unprecedented resolution and scale. Past research has demonstrated the capability of mobile sensing to model aspects of physical health, mental health, education, and work performance, etc. However, most of the algorithms and models proposed in previous work follow a one-size-fits-all (i.e., population modeling) approach that looks for common behaviors amongst all users, disregarding the fact that individuals can behave very differently, resulting in reduced model performance. Further, black-box models are often used that do not allow for interpretability and human behavior understanding. We present a new method to address the problems of personalized behavior classification and interpretability, and apply it to depression detection among college students. Inspired by the idea of collaborative-filtering, our method is a type of memory-based learning algorithm. It leverages the relevance of mobile-sensed behavior features among individuals to calculate personalized relevance weights, which are used to impute missing data and select features according to a specific modeling goal (e.g., whether the student has depressive symptoms) in different time epochs, i.e., times of the day and days of the week. It then compiles features from epochs using majority voting to obtain the final prediction. We apply our algorithm on a depression detection dataset collected from first-year college students with low data-missing rates and show that our method outperforms the state-of-the-art machine learning model by 5.1% in accuracy and 5.5% in F1 score. We further verify the pipeline-level generalizability of our approach by achieving similar results on a second dataset, with an average improvement of 3.4% across performance metrics. Beyond achieving better classification performance, our novel approach is further able to generate personalized interpretations of the models for each individual. These interpretations are supported by existing depression-related literature and can potentially inspire automated and personalized depression intervention design in the future.
Triggering Ideological Thinking: How Elections Foster Coherence of Welfare State Attitudes

Since Converse (1964) first proposed his “nonattitudes hypothesis,” numerous studies have confirmed that the public at large lacks ideologically consistent political attitudes. I put forth an event-based theory of attitude crystallization that explains how elections can strengthen attitude consistency and apply it to an issue domain integral to the left-right (liberal-conservative) cleavage: welfare politics. Specifically, I theorize that elections that give ideological opponents a majority will mobilize ideological predispositions, leading to more coherent welfare attitudes. I test the argument by relying on 11 Danish surveys linked to official records on local elections over four decades and using a regression discontinuity design. Evidence strongly supports the notion that elections increase attitude consistency if the majority produced goes against the individual’s ideological preferences. The findings stress the dynamic nature of attitude structure and the important role regular political events play in that regard.
Association between surgery with anesthesia and cognitive decline in older adults: Analysis using shared parameter models for informative dropout

Abstract Objectives/Goals: The association between surgery with general anesthesia (exposure) and cognition (outcome) among older adults has been studied with mixed conclusions. We revisited a recent analysis to provide missing data education and discuss implications of biostatistical methodology for informative dropout following dementia diagnosis. Methods/study population: We used data from the Mayo Clinic Study of Aging, a longitudinal study of prevalence, incidence, and risk factors for mild cognitive impairment (MCI) and dementia. We fit linear mixed effects models (LMMs) to assess the association between anesthesia exposure and subsequent trajectories of cognitive z-scores assuming data missing at random, hypothesizing that exposure is associated with greater decline in cognitive function. Additionally, we used shared parameter models for informative dropout assuming data missing not at random. Results: A total of 1948 non-demented participants were included. Median age was 79 years, 49% were female, and 16% had MCI at enrollment. Among median follow-up of 4 study visits over 6.6 years, 172 subjects developed dementia, 270 died, and 594 participants underwent anesthesia. In LMMs, exposure to anesthesia was associated with decline in cognitive function over time (change in annual cognitive z-score slope = −0.063, 95% CI: (−0.080, −0.046), p < 0.001). Accounting for informative dropout using shared parameter models, exposure was associated with greater cognitive decline (change in annual slope = −0.081, 95% CI: (−0.137, −0.026), p = 0.004). Discussion: We revisited prior work by our group with a focus on informative dropout. Although the conclusions are similar, we demonstrated the potential impact of novel biostatistics methodology in longitudinal clinical research.
Constitutional Origins and Liberal Democracy: A Global Analysis, 1900–2015

A strong tradition in democratic theory claims that only constitutions made with direct popular involvement can establish or deepen democracy. Against this view, we argue that new constitutions are likely to enhance liberal democracy when they emerge through a plural agreement among political elites with distinct bases of social support. Power dispersion during constitution writing induces the adoption of institutions that protect opposition forces from the arbitrary use of executive power without unduly impairing majority rule. However, since incumbents may renege on the bargain, the democratizing effect of politically plural constitutional agreements is likely to be larger in the short term, when the identity of negotiating political forces and the balance of power between them tend to remain stable. We find support for these arguments using an original global dataset on the origins of constitutions between 1900 and 2015 and a difference-in-differences design.
A CFD data-driven aerodynamic model for fast and precise prediction of flapping aerodynamics in various flight velocities

Abstract Precise prediction of unsteady flapping aerodynamics in insect flight is of potential importance in the analysis of maneuverability and flight control. While the quasi-steady model is a cheap while reasonable tool, accurate evaluation of unsteady dynamic effects in complex flight behaviours remains a challenge. Here we develop a computational fluid dynamics (CFD) data-driven aerodynamic model (CDAM), which is informed by high-fidelity CFD simulations using overset meshes to enable the precise and fast prediction of both cycle-averaged and transient aerodynamic force, torque and power with various flying motions and wing kinematics. The CDAM comprises a quasi-steady model for flapping wings and an aerodynamic model for a moving body. The least square method and a surrogate method are employed to achieve aerodynamic coefficient fitting through training using a CFD database. With comparison to CFD test data, the CDAM is validated to be capable of accurately evaluating the aerodynamic force, torque and power of a wing-body bumblebee model in various flight velocities. A genetic optimization algorithm embedded with CDAM is proposed to determine trimmed states for forward flight through adjusting wing kinematics, indicating that bumblebees likely fly in a minimized mass-specific aerodynamic power consumption. The CDAM is further applied to proportional-derivative-based longitudinal flight control of bumblebee hovering, with the control parameters optimized by Laplace transformation and the root locus method, which is implemented consistently in both CDAM and CFD environments. Our results demonstrate that CDAM provides a versatile tool to achieve fast and precise aerodynamical prediction for flying insects in various flight behaviours.
German Exports: Impact on the Selected EU Countries

Abstract This research focuses on the impact of German exports on exports of the other selected EU countries. We used the Global VAR approach to build a robust trade model between 23 EU countries, the USA, and China. By stressing this model with different shocks, we were able to observe how exports of the EU countries react to German loss of competitiveness and decline of demand from Germany. Based on our simulation, we could identify countries which i. are Germany’s competitors and would benefit from German loss of competitiveness, ii. are tied with German trade so tightly that loss of German competitiveness would negatively affect their exports.
Characteristics of Work-related COVID-19 in Croatian Healthcare Workers: A Preliminary Report

Abstract Healthcare workers (HCWs) are considered to run a higher occupational risk of becoming infected with severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2), and develop coronavirus disease (COVID-19) than the rest of the population. The aim of this study was to describe and analyse the characteristics of work-related COVID-19 in Croatian HCWs. Study participants were HCWs who contacted their occupational physician between 1 May 2020 and 12 November 2020 with a request for the registration of COVID-19 as an occupational disease. All participants filled out our online Occupational COVID-19 in Healthcare Workers Questionnaire. The study included 59 HCWs (median age 45.0, interquartile range 36.0–56.0 years). Most (78 %) were nurses or laboratory technicians, and almost all (94.9 %) worked in hospitals. Hierarchical cluster analysis revealed three clusters of COVID-19-related symptoms: 1) elevated body temperature with general weakness and fatigue, 2) diarrhoea, and 3) headache, muscle and joint pain, anosmia, ageusia, and respiratory symptoms (nasal symptoms, burning throat, cough, dyspnoea, tachypnoea). Almost half (44.6 %) reported comorbidities. Only those with chronic pulmonary conditions were more often hospitalised than those without respiratory disorders (57.1 % vs. 2.5 %, respectively; P=0.001). Our findings suggest that work-related COVID-19 among Croatian HCWs is most common in hospital nurses/laboratory technicians and takes a mild form, with symptoms clustering around three clinical phenotypes: general symptoms of acute infection, specific symptoms including neurological (anosmia, ageusia) and respiratory symptoms, and diarrhoea as a separate symptom. They also support evidence from other studies that persons with chronic pulmonary conditions are at higher risk for developing severe forms of COVID-19.