Miracle-dz/UniFAR-FTUs huggingface dataset:
Understanding Subtypes of Bullying Victims Using Target Congruence and Lifestyle-Routine Activities Theories: A Latent Class Analysis

Prior research on bullying victimization has often explored the behavior from a binary perspective in which victims and non-victims are examined. However, there has been an increasing amount of literature taking a more nuanced approach towards different subtypes of victims in recent years. This study sought to examine heterogeneous patterns of victimization and identify correlates associated with different victim groups through an integrated framework of both lifestyle-routine activities and target congruence theories. Using a national sample of 2217 Korean adolescents, the latent class analysis uncovered three groups of victims including physical/verbal, verbal-only, and low/non-victims. The results supported the relevance of integrated target congruence and lifestyle-routine activities theories to explain subtypes of bullying-victims. Among the dimensions of target congruence, vulnerability and antagonism factors were distinctively associated with the three subgroups of victims. Bullying perpetration was relevant in distinguishing physical/verbal and verbal-only victims from non-victims even when target congruence variables were controlled for.
Exposure to a Virtual Environment Induces Biological and Microbiota Changes in Onset-of-Lay Hens

Increasing demand for cage-free eggs arises from goals to provide hens with better welfare, particularly in terms of natural behavior. However, most laying hens are kept in conventional cages, and cage-free systems can present challenges, such as injuries, floor eggs, and bacterial infections. We proposed using virtual reality (VR) as a feasible means for combining the positive attributes of natural environments while mitigating health risks. To our knowledge, no animal study has provided evidence that VR can trigger biological changes to improve animal health and well-being nor whether VR can affect the gut microbiota. In this study, we used VR technology to simulate a natural environment in laying hen housing. Early-lay White Leghorn hens were placed in pens with (VR) or without (CON) video projections displaying free-range chickens interacting with indoor and outdoor environmental features over 5 days. Using in vitro blood bactericidal assays, VR hens exhibited higher resistance against avian pathogenic Escherichia coli versus CON (p < 0.05), which was positively associated with corticosterone levels (p < 0.01). Analyzing intestinal neurochemicals via ultra-high pressure liquid chromatography, salsolinol was the only neurochemical metabolite affected by VR, being greater in CON ileal content (p < 0.0001), in VR ileal mucus (p < 0.01), and in VR ceca tissue (p < 0.05). Using 16S rRNA sequencing and QIIME2 analyses, no differences in alpha nor beta diversity were determined between groups. Although several genera (Megamonas, Ruminococcus, Slackia) were reduced in VR hens versus CON, Mucispirillum schaedleri (member of Deferribacteres Phylum) was the only taxon increased in VR hens, being elevated in ileal mucus (p < 0.05). Lastly, using the QIIME2 plugin mmvec to map microbe-metabolite co-occurrences, we identified several positive relationships between bacterial phyla and neurochemical metabolites, notably finding dopamine and salsolinol levels were related to Deferribacteres and Tenericutes levels. In conclusion, we found that several biological parameters were influenced by VR treatment in hens, suggesting that VR can be used to improve host resistance to pathogens and gut health in poultry.
Experiences of childhood trauma and cyberbullying perpetration among adolescents in China

ABSTRACT While there is substantial empirical evidence on the adverse effects of childhood trauma experiences, less is known about whether and how those experiences are related to cyberbullying perpetration in adolescents. This study examined the potential effects of childhood trauma experiences on cyberbullying perpetration with consideration of the role of emotion dysregulation. Participants (N = 1305) were recruited from elementary and middle schools (ages 9–16) in Yiyang County, Jiangxi Province, China during 2019. Multi-phase logistic regression analysis was used to explore childhood trauma and emotion dysregulation as predictors of cyberbullying experiences. The results showed that both childhood trauma experiences and emotion dysregulation were positively related to cyberbullying perpetration, with the relationship between sexual abuse and cyber-perpetration showing the highest coefficient among forms of childhood trauma. These findings may provide valuable information for use in cyberbullying prevention/intervention research and practice by clarifying avenues for alleviating childhood trauma experiences among minors.
AN APPROACH TO UPDATING VECTOR FEATURES BY CLUSTERING ALGORITHM

Abstract. The availability of Geographic Information System (GIS) data has increased in recent years, as well as the need to update its data. One way of updating GIS vector data is by deleting and inserting data to the databases by manually defined unique value. Other one is by deleting and adding data manually. These methods all require manual predefined unique values or manual operations which is not suitable for updating data in a timely and fast way, especially large numbers of data are updated. Therefore, an automated vector data updating method has the potential to significantly increase productivity, particularly as existing GIS vector data application increase in size, the data become outdated more quickly. In this paper, we consider the difference between GIS vector data and other data that GIS vector data have spatial coordinates and topology relationships, propose an approach to updating of vector features based on clustering algorithm. First, the minimum bounding box and its center point of vector features are calculated, and then clusters the center points with DBSCAN to get some clusters. In the end, extract the smallest bounding box of every cluster and update vector features with the boxes. This approach uses smallest bounding box to update features is to reduce database query cost and improves the data update efficiency. Experiments show that the method is effective and feasible.
Social entrepreneurs’ use of spatial bricolage to create frugal innovation in a divided urban setting

This study examines how social entrepreneurs mobilize resources to innovate in divided urban settings. Over a period of two years, we followed four social start-up entrepreneurs in the city of Cape Town, South Africa, characterized by some of the highest inequality levels in the world. We analyzed how social entrepreneurs navigate this urban divided context to mobilize resources from both resource-affluent and resource-constrained spaces to achieve innovation. Our findings suggest that these entrepreneurs use bridging and building approaches that cross over between spatial and digital spaces. Specifically, we discovered that social entrepreneurs mobilize resources using four distinct types of spaces and observed that they make use of these very spaces in their bricolage, hence expanding their portfolio of resources at hand. Based in our analysis, we build on the recently introduced concept of spatial bricolage to develop the notion of spatial awareness. By identifying a link between spatial awareness and frugal innovation, we contribute to the literature on social entrepreneurship and innovation in the African context.
The mediating effect of self-criticism on college students' emotion dysregulation and intention to self-harm

College students are in a period of instability, which may be stressful and put them at risk of developing the intention to self-harm. This study examines the effect of self-criticism as a mediator in the association between emotion dysregulation and the intention to self-harm. Quantitative research was conducted on 354 students aged 18-23 years, using the disproportionate cluster random sampling technique. The instruments used were an intention to self-harm scale, self-criticism scale, and emotion dysregulation scale. The results from the mediation analysis conducted using the PROCESS program of Hayes shows that self-criticism significantly mediates the association between emotion dysregulation and students' intention to self-harm (ab = .39, BootSE = .05, 95% BootCI [.29 to .48]). Nevertheless, the results also suggest that even after accounting for the mediating effect of self-criticism, emotion dysregulation still positively impacts students' intention to self-harm (c' = .14, p = .017). The implications of the findings provide insights for college students and all the parties involved with them regarding the mechanisms behind the intention to self-harm.
Birth Mapping: A Visual Arts-Based Participatory Research Method Embedded in Feminist Epistemology

Reproductive and sexual health of women are sensitive areas of enquiry characterized by strong cultural oppression of women. Body mapping, an arts-based participatory research method, has proven useful in research with such sensitive topics. In this paper, I describe my experience of researching women’s experience of childbirth through birth maps, an adaptation of body mapping. Live size maps were co-created along with birthing story and body key with women in Bihar, India. Body mapping is a very cost-effective method that ensures better recall, richer narratives, reduced power-based inequalities that enables to explore reproductive, maternal & sexual health topics respectfully. The birth map and birthing story can generate awareness about how women give birth, as an attempt to improve the quality and respectfulness in care provision during labour and childbirth.
COMPARISON OF SELECTED AUGMENTED REALITY FRAMEWORKS FOR INTEGRATION IN GEOGRAPHIC CITIZEN SCIENCE PROJECTS

Abstract. Augmented reality (AR) offers functionalities that can be beneficial for citizen science (CS) projects. Especially location-based approaches have potential for geographically oriented CS projects, as objects can be placed based on geographic coordinates. Since choosing a suitable AR framework for integration into a CS project can be challenging, this paper gives an overview of the common AR frameworks and takes a closer look at three selected ones that are particularly suitable for CS projects (AR.js, AR Foundation, and ViroReact). Prototypes were implemented for the selected frameworks to investigate which framework is best suited for specific use cases. Marker-based tracking approaches, image recognition and location-based placement were considered. The results show that the framework AR.js is particularly suitable for marker-based tracking with very simple markers and therefore represents a good entry point for CS projects to integrate initial AR functionality into their project. AR Foundation and ViroReact, on the other hand, are faster and more reliable with the detection of more complex markers. The location-based approach can be implemented with all three frameworks, but the precision of the placement of the objects strongly depends on the accuracy of the sensors of the mobile device.
MCML gate design methodology ante the tradeoffs between MCML and CMOS applications

This work proposes a simulation-based methodology to design MOS Current-Mode Logic (MCML) gates and addresses the tradeoffs of the MCML versus static CMOS circuits. MCML is a design style developed focusing in a high-speed logic circuit. This logic style works with the principle of steering a constant bias current through a fully differential network of input transistors. The proposed methodology uses the quadratic transistor model to find the first design solution, through SPICE simulations, make decisions and resizes the gate to obtain the required solution. The method considers a uniform sizing of the pull-down network transistors. The target solution is the best propagation delay for a predefined gate noise margin. We design MCML gates for three different process technologies (XFAB XC06, IBM130 and PTM45), considering gates up to three inputs. We compare the solutions of the proposed methodology against commercial optimization software, WickedTM, that considers different sizing for PDN differential pairs. The solutions of the software results in a 20% of improvement, when compared to the proposed methodology, in the worst case input delay for the XFAB XC06 technology, and 3% in IBM130. We demonstrate through ring oscillators simulations that MCML gates are better for high speed and small logic path circuits when compared to the CMOS static gates. Moreover, by using MCML frequency dividers we obtained a maximum working frequency that almost doubles the frequency achieved by CMOS frequency dividers, dissipating less power than static CMOS circuits. We demonstrate through a reliability analysis that the analog behavior of MCML gates makes them susceptible to PVT variations. The global variations are compensated by the bias control circuits and with the increase of the PDN transistor width. This procedure compensates the gain loss of these transistors in a worst case variation. In other hand, this increasing degrades the propagation delay of the gates. The MCML gates reliability is heavily affected by the mismatching effects. The difference of the mirrored bias current and the mismatching of the differential pairs and the PUN degrade the design yield. The results of the layout extracted simulations demonstrate that MCML gates performs a better propagation delay performance over gates that depend on complexes pull-up networks in standard CMOS implementation, as well as multi-stages static CMOS gates. Considering the gate layout implementation we demonstrate that the standard structures of pull-up and bias current mirror present in the gate are prejudicial for the MCML gate area
Effects of Aerobic Training on Brain Plasticity in Patients with Mild Cognitive Impairment: A Systematic Review of Randomized Controlled Trials

The purpose of this study was to systematically review to find if aerobic exercise compared to no exercise or any other intervention affects brain plasticity among people with mild cognitive impairment (MCI). Searches were conducted in the Scopus, SciELO, PubMed, Web of Science, Science Direct, and Google Scholar databases. The included studies were randomized control trials (RCTs) written in English comprising individuals with MCI that evaluated the effects of aerobic training on brain-derived neurotrophic factor (BDNF), brain structures, or brain activity. The quality of trials was evaluated using the PEDro scale for RCTs. Twelve studies with medium to high quality were included, of which five studies focused on brain-derived neurotrophic factor (four articles reported elevation and one article reported no changes in BDNF levels following the aerobic exercise), two studies focused on brain structures (both reported increases in hippocampus volume following the aerobic exercise), and five studies focused on brain activity (four articles reported positive changes, and one article reported no changes in brain activity following the aerobic exercise). Research regarding the effects of aerobic training on brain plasticity in people with MCI is in its infancy. Still, aerobic exercise seems to be a promising therapy in people with MCI.
Lost in consolidation? Declining public investment, multiplier effects and alternatives to the path of fiscal consolidation in Portugal

Abstract This paper deals with one of the most pressing concerns that Eurozone periphery economies will face in the near future: how to achieve a sustained recovery from the COVID-19 crisis while dealing with growing public debt-to-GDP ratios. The paper assesses the macroeconomic relationship between public spending, economic growth and debt sustainability. We use a TSLS method to perform the econometric estimation of the public spending multiplier for a panel of 11 Eurozone economies over the 1995–2019 period. We find evidence that the multiplier is positive and close to 1,8, suggesting that the benefits of promoting public investment exceed its initial financing cost. As a result, we conclude that debt sustainability is not only compatible with, but in fact improved by a more expansive fiscal policy and present an alternative policy path for the Portuguese economy in the 2021–2025 period based on this conclusion.
Student - related factor for dropping out in the first year of studies at LLU engineering programmes

In the 17th SEFI seminar, “Mathematical Education of Engineers”, which was held in 2014, it was noted that many students have big problems starting their studies because of their lack of mathematical competencies. However, the low level of mathematical competence is not the only cause for dropping out in the first year of studies. There are many causes of student failure in universities and the main of them are: 1) studentrelated factors, 2) life and socioeconomic issues, 3) failures of the educational system. The aim of this research is to study the causes for dropping out in the first year of studies among engineering students at the Latvia University of Agriculture (LLU). The study is based on the analysis of mathematics test results of students from the Faculty of Engineering and the Faculty of Information Technologies as well as on the survey data analysis. The survey collected data and opinions regarding students’: 1) attitude towards learning; 2) efforts in the study process 3) motivation and interest; 4) personality issues such as lack of social connections, lack of the support system and network, and poor self-esteem and self-confidence 5) economic issues. The analysis of the survey results showed that the core problem at the LLU is not only insufficient students’ background knowledge in mathematics, but also their attitude towards learning, laziness to make efforts to do additional tasks or attend tutorials and lack of self-control. Students need to be motivated to want to learn and work hard to make faster gains and learn better than those who are bright but less motivated.
Hyperspectral Image Classification with a Multiscale Fusion-Evolution Graph Convolutional Network Based on a Feature-Spatial Attention Mechanism

Convolutional neural network (CNN) has achieved excellent performance in the classification of hyperspectral images (HSI) due to its ability to extract spectral and spatial feature information. However, the conventional CNN model does not perform well in regions with irregular geometric appearances. The recently proposed graph convolutional network (GCN) has been successfully applied to the analysis of non-Euclidean data and is suitable for irregular image regions. However, conventional GCN has problems such as very high computational cost on HSI data and cannot make full use of information in the image spatial domain. To this end, this paper proposes a multi-scale fusion-evolution graph convolutional network based on the feature-spatial attention mechanism (MFEGCN-FSAM). Our model enables the graph to be automatically evolved during the graph convolution process to produce more accurate embedding features. We have established multiple local and global input graphs to utilize the multiscale spectral and spatial information of the image. In addition, this paper designs a feature-spatial attention module to extract important features and structural information from the graph. The experimental results on four typical datasets show that the MFEGCN-FSAM proposed in this paper has better performance than most existing HSI classification methods.
The role of environmental and individual factors in pre-academic achievement for kindergarten children

ABSTRACT This study examined the effects of maternal and children’s resources on school readiness, literacy achievements, and reading motivation in children. The participants included children (N = 180) (124 girls; 56 boys) from 19 kindergartens, their mothers, and their kindergarten teachers. Mothers reported on their working hours, interaction quality, and learning stimulation range at home. Data on children’s literacy achievements and school readiness were gathered from the teachers. Data on reading motivation and task persistence were obtained directly from the children. Structural equation modelling analysis indicated that maternal working hours affect interaction quality, and the quality of maternal interaction affects children’s reading motivation. Children’s task persistence affects literacy achievement. Both maternal interaction quality and children’s task persistence affect their readiness for school. The analysis also revealed association between the pre-academic skills. Maternal availability and children’s reading motivation and persistence are critical factors in children’s development of academic skills.
Resilience in health workers: The role of social support and calling

Health workers are professionals with high exposure to various types of stress; therefore, they need resilience to survive in their profession. This research aims to test the effect of social support and calling on resilience in health workers. It is a quantitative-correlational study conducted cross-sectionally. The instruments used were the Connor-Davidson Resilience Scale, Multidimensional Support Scale, and Calling Scale. Snowball sampling was used to identify participants, consisting of 113 health workers in East Nusa Tenggara province, Indonesia. The data were analyzed with multiple linear regression techniques. Significant effects of social support and calling were found on resilience (p .01, F = 34.224), with calling shown to have more significant impact (p .01, F = 60.685) and 35.3% effective contribution. Amongst the types of social support, coworkers make the most significant contribution of 11.8% (p .01, F = 14.816), followed by family members at 9.6% (p .01, F = 11.752). On the other hand, social support from supervisors does not affect resilience (p .05, F = 0.904). Health workers should seek the social support they need proactively, and prospective health workers should consider their calling when choosing a career.