Miracle-dz/UniFAR-FTUs huggingface dataset:
Emotion Regulation Training in a First-Year Experience Course: A Qualitative Analysis of Students’ Experiences

ABSTRACT The transition to college is a stressful time that can present many emotional challenges and demands. One way to help ease this transition is through the implementation of emotion regulation skills training in first-year experience courses. However, little is known about how students who receive such instruction implement emotion regulation, and what challenges and difficulties they face in doing so. In the present study, first-semester college students (N= 24) who were enrolled in a first-year experience course and received emotion regulation traitning and reported on their experiences regulating their emotions in their everyday lives. Results of thematic analysis revealed six themes in the experience of emotion regulation: emotion regulation as an effortful process, the impact of regulatory goals on regulatory success, the role of timing, emotional awareness, affective forecasting, and regulatory clarity. Findings highlight the challenges that first-semester college students experience while attempting to regulate their emotions during emotional situations, and may aid counseling psychologists understanding of the transition to college with respect to emotion regulation skill development. Further, findings suggest that implementing emotion regulation training in first-year experience courses can provide a meaningful training ground for the development of emotion regulation during the first semester of college.
A Review on Five Recent and Near-Future Developments in Computational Processing of Emotion in the Human Voice

We provide a short review on the recent and near-future developments of computational processing of emotion in the voice, highlighting (a) self-learning of representations moving continuously away from traditional expert-crafted or brute-forced feature representations to end-to-end learning, (b) a movement towards the coupling of analysis and synthesis of emotional voices to foster better mutual understanding, (c) weakly supervised learning at a large scale, (d) transfer learning from related domains such as speech recognition or cross-modal transfer learning, and (e) reinforced learning through interactive applications at a large scale. For each of these trends, we shortly explain their implications and potential use such as for interpretation in psychological studies and usage in digital health and digital psychology applications. We also discuss further potential development.
A comparison study on growth, reproductive and food habit of Mesopotamichthys sharpeyi in the Al-Diwaniya River, middle of Iraq

Biological aspects of Mesopotamichthys sharpeyi was studied in terms of length and weight, age, growth, sex ratio, size at first sexual maturity, gonad development (GSI) and food habit in the Al-Diwaniya River, Iraq. These biological features were compared with other studies on the species. Fish were collected by different fishing gears. The length–weight relationship was calculated for fish sizes 11 to 39 cm as W = 0.032L 2.7017, with negative allometric growth. The monthly fluctuation in the relative condition factor of the species was influenced by the spawning cycle and feeding intensity of the fish. Seven ages were identified from scales and the mean lengths of these ages were 11.8, 21.4, 26.2, 30.5, 34.5, 37.8 and 41.1 cm, respectively. von Bertalanffy growth parameters were L∞= 56.0 cm, K= 0.229 and t0= - 0.103 years. The growth performance index (Φ) was found to be 1.99. The overall male to female ratio (1:1.48) was biased in favour of females. Length at maturity was 33 cm for males and 34 cm for females. The GSI was at the highest level in April then dropped dramatically for both sex, suggest that the species may spawn in late April to May. M. sharpeyi is a herbivore fed on macrophytes, algae, detritus, and diatoms. Some biological properties of the species were among those described for the species in other waters.
“They Eat What They Eat, I Eat What I Eat”: Examining the Perspectives and Experiences of African Americans Who Adopt Plant-Based Diets

Adopting a plant-rich or plant-based diet is one of the major recommendations for addressing obesity, overweight, and related health conditions in the United States. Currently, research on African Americans’ food choices in the context of plant-based diets is limited. The primary aim of this study was to understand food-related experiences and perceptions of African Americans who were participating in the Nutritious Eating with Soul (NEW Soul) study, a culturally tailored dietary intervention focused on increasing the consumption of plant-based foods. The roles of gender and ethnicity were also examined to identify how eating patterns were chosen or maintained. Twenty-one African American adults in South Carolina, who were randomly assigned to either a vegan diet (n = 11) or a low-fat omnivorous diet (n = 10) in the NEW Soul study, completed one-on-one, qualitative interviews. Emerging themes included awareness, being in control, and identity. The study revealed that access to social support and coping strategies for addressing negative comments about plant-based food choices may be important components to include in future nutrition interventions focused on African Americans.
The Dollar, Bank Leverage and Real Economic Activity: An Evolving Relationship

The interest in how financial conditions affect real economic activity has grown since the great financial crisis (GFC), not least because some of the mechanisms at play in the financial sector may have changed. We shed light on this issue by examining the empirical relationship between global Purchasing Managers' Indices, world trade, and indicators of global financial conditions, with a special focus on the broad dollar index. We show that the influence of the dollar on real economic activity and global trade seems to have increased since the GFC, while that of the VIX has decreased.
Compartmentalization and axon guidance in the Drosophila brain

The Drosophila brain is composed of many morphologically and functionally distinct processing centers and brain morphogenesis depends on the creation and maintenance of distinct boundaries between adjacent regions to prevent cells from mixing. In the Drosophila visual system, I have found that Slit and Roundabout (Robo) proteins function to prevent cells from adjacent compartments from mixing. I have defined a boundary between two distinct compartments, the lamina and lobula, and find that the secreted ligand Slit is present in the lamina, while the Robo receptors (Robo, Robo2 and Robo3) are expressed on lobula neurons. I examined the function of theses proteins by identifying a tissue-specific allele of slit and creating transgenic RNAi flies that inhibit the expression of the Robo proteins. Loss of Slit or all three Robo proteins in the visual system results in the invasion of lobula neurons into the lamina. Mixing of cells at the lamina/lobula boundary results in glial cell mispositioning and aberrant photoreceptor axon targeting. Thus, Slit and Robo proteins are required to restrict movement of cells across the lamina/lobula boundary. Additionally, I have characterized Ptpmeg, a highly conserved protein tyrosine phosphatase (PTP). In addition to the C-terminus PTP domain, Ptpmeg contains a central PDZ domain and an N-terminus FERM domain. The in vivo role of this family of proteins is unknown. To explore the function of Ptpmeg in flies, mutants were generated by targeted gene disruption. Examination of the adult nervous system of Ptpmeg mutants reveals a defect in the mushroom bodies (MB), brain structures required for olfactory learning and memory. In mutant animals, the MB lobes are disorganized and fail to elaborate their characteristic structure. I find that Ptpmeg is expressed on MB axons and targeted knockdown of Ptpmeg in the MB results in similar defects as seen in homozygous mutants. Thus, the MB neurons appear to require Ptpmeg for proper formation. Thesis Supervisor: Paul Garrity Title: Assistant Professor of Biology
Azadirachta Indica A. Juss: Wood Ash Affects Seedling Growth of Mung Bean Vigna Radiata L.

The burning of the wood ash is causing an environmental pollution related issue. The excess concentration of wood ash in the environment normally influenced on plant growth and development. This paper presents the effects of neem wood ash (Azadirachta inidca A. Juss.) on germination and seedling growth performance of mung bean (Vigna radiata L.) crop plants. Overall, the data reveals significant (p<0.05) effects of neem wood ash on the different growth variable of mung bean. The treatment of 4-20% neem wood ash significantly (p<0.05) affected shoot length of mung bean as compared to control. Neem wood ash treatment at all concentration produced fewer toxic effects on root and leaf growth of mung bean. The neem wood ash treatment at 20% also produced significantly (p<0.05) toxic effects on root and leaf dry weight of mung bean. Whereas, neem wood ash treatment at all level did produce any significant effects on shoot dry weight, total plant dry weight and leaf area ratio of mung bean.
A Statistical Investigation of Mesoscale Precursors of Significant Tornadoes: The Italian Case Study

In this study, mesoscale environments associated with 57 significant tornadoes occurring over Italy in the period 2000–2018 are analyzed. The role of the vertical Wind Shear in the lower and middle troposphere, in terms of low-level shear (LLS) and deep-level shear (DLS), and of the convective available potential energy (CAPE) as possible precursors of significant tornadoes is statistically investigated. Wind shear and CAPE data are extracted from the ERA-5 and ERA-Interim reanalyses. Overall, the study indicates that: (a) values of these variables in the two uppermost quartiles of their statistical distribution significantly increases the probability of tornado occurrences; (b) the probability increases for increasing values of LLS and DLS, and (c) is maximum when either wind shear or CAPE are large. These conclusions hold for both the reanalysis datasets and do not depend upon the season and/or the considered area. With the possible exception of weak tornadoes, which are not included in our study, our results show that large wind shear, in the presence of medium-to-high values of CAPE, are reliable precursors of tornadoes.
Parasitoids of the Bedeguar gall (Diplolepis rosae): effect of host scale on density and prevalence

Host plants have significant effects on parasitoid and herbivore distribution. There are just a few reports on tritrophic interactions. We aimed to study the relationships between rose host shrub and Bedeguar gall density with special attention to densities and prevalence of parasitoids at two scales: host shrub and gall. We found that gall density was inversely density dependent of shrub density. Parasitoid density was density dependent of both rose shrub and gall scales, while prevalence of parasitoids was density dependent at the shrub scale, but density independent at the gall scale. This pattern is likely to occur when parasitoids prefer those shrubs which are infected with galls and avoid uninfected shrubs and support optimal foraging theory.
Developing project-based learning tools for light vehicle engine maintenance subjects at vocational high school

This study aims to develop learning tools for project-based light vehicle engine maintenance and describe the teacher's response to the application of project-based learning tools in light vehicle engine maintenance subjects. This research is a research and development (R&D) model with ADDIE. The ADDIE development model consists of analysis, design, development, implementation, and evaluation. The results showed that the product developed was valid in terms of validity, very practical in terms of practicality, and very high in the aspect of effectiveness. Based on the aspects of validity, it can be specified that the syllabus gets a percentage score of 90.25%, 88.78% learning implementation plan, 86.56% module, 88.93% job sheet, 87.5% achievement test instrument, and project test instrument 87.08%. Based on aspects of practicality assessed from the teacher's response, it can be specified that the syllabus gets a percentage score of 88.67%, 86.88% learning implementation plan, 88.75% module, 88.77% job sheet, 85.42% test instrument for learning outcomes, and 83.33% project test instruments. Overall, teachers find it helpful to develop project-based learning tools. Based on the effectiveness, the results of students' knowledge tests with the percentage of mastery learning in the knowledge area amounted to 86.67%, and the skill area amounted to 86.67% with very high criteria. Researchers can conclude that project-based learning tools on light vehicle maintenance subjects are in accordance with valid, practical, and effective criteria.
Towards Decision Support in Dynamic Bi-Objective Vehicle Routing

We consider a dynamic bi-objective vehicle routing problem, where a subset of customers ask for service over time. Therein, the distance traveled by a single vehicle and the number of unserved dynamic requests is minimized by a dynamic evolutionary multi-objective algorithm (DEMOA), which operates on discrete time windows (eras). A decision is made at each era by a decision-maker, thus any decision depends on irreversible decisions made in foregoing eras. To understand effects of sequences of decision-making and interactions/dependencies between decisions made, we conduct a series of experiments. More precisely, we fix a set of decision-maker preferences D and the number of eras nt and analyze all $|D|^{n_{t}}$ combinations of decision-maker options. We find that for random uniform instances (a) the final selected solutions mainly depend on the final decision and not on the decision history, (b) solutions are quite robust with respect to the number of unvisited dynamic customers, and (c) solutions of the dynamic approach can even dominate solutions obtained by a clairvoyant EMOA. In contrast, for instances with clustered customers, we observe a strong dependency on decision-making history as well as more variance in solution diversity.
Molecular mechanisms of neutrophil and monocyte recruitment in acute lung inflammation

Neutrophils are implicated in many inflammatory lung disorders. However, the mechanisms regulating neutrophil migration in acute lung inflammation are incompletely understood. Although, integrin β2 mediates neutrophil migration in lungs in response to many stimuli such as E. coli, integrin involved in S. pneumoniae induced neutrophil migration is not known. Therefore, the role of integrin αvβ3 in neutrophil recruitment was tested. First, it was found that the number of neutrophils expressing the integrin subunits αv and β3 is reduced or remains in lung inflammation induced by E. coli or S. pneumoniae, respectively. Next, the role of integrin αvβ3 using β3 knockout mice (β3) and function blocking antibodies was addressed. Neutrophil recruitment did not vary between wild type and β3 mice. Although β3 antibodies reduced neutrophil recruitment, similar effect was observed with isotype antibodies. Therefore, one can conclude that integrin αvβ3 is not critical for neutrophil recruitment in S. pneumoniae induced pneumonia. Apart from integrins, TLR4 also regulate neutrophil migration. Because, the pattern of TLR4 expression at various times of lung inflammation is not known, TLR4 expression during different phases of lung inflammation in a rat model of LPS-induced inflammation was studied. TLR4 expression in the septum increased and decreased at 6h and 12-36h of inflammation, respectively. Since these correlate with the time of increase and decline of neutrophil recruitment, the findings support previously observed requirement for TLR4 in neutrophil recruitment. Neutrophils recruited into the lungs regulate the inflammatory process by controlling subsequent monocyte/macrophage recruitment. The mechanisms involved and the pattern of monocyte/macrophage recruitment in lungs are not completely understood. Therefore, the possible involvement of monocyte chemoattractant protein (MCP)-1, which is a premier chemokine in monocyte/macrophage migration and produced by neutrophils and other cells was tested. This was addressed by quantification of monocytes/macrophages at various times and using neutrophil depletion experiments in LPS-induced lung inflammation in rats. It was found that monocytes/macrophages migrate very early and before neutrophils in addition to their migration in the late phase of acute lung inflammation. Neutrophil depletion abrogated both early as well as the late monocyte/macrophage recruitment without altering the expression of MCP-1. Therefore, possibly other chemokines and not MCP-1 are involved in neutrophil dependent monocyte/macrophage recruitment. To conclude, the experiments further the understanding on acute lung inflammation by ruling-out the involvement of integrin αvβ3 and MCP-1 in β2-independent neutrophil migration and neutrophil dependent monocyte/macrophage recruitment, respectively. Further studies are essential to find the integrins and chemokines operating in the above situations. Equally important will be to understand the functional significance of early recruited monocytes/macrophages in the lung.
Self-Aware Neural Network Systems: A Survey and New Perspective

Neural network (NN) processors are specially designed to handle deep learning tasks by utilizing multilayer artificial NNs. They have been demonstrated to be useful in broad application fields such as image recognition, speech processing, machine translation, and scientific computing. Meanwhile, innovative self-aware techniques, whereby a system can dynamically react based on continuously sensed information from the execution environment, have attracted attention from both academia and industry. Actually, various self-aware techniques have been applied to NN systems to significantly improve the computational speed and energy efficiency. This article surveys state-of-the-art self-aware NN systems (SaNNSs), which can be achieved at different layers, that is, the architectural layer, the physical layer, and the circuit layer. At the architectural layer, SaNNS can be characterized from a data-centric perspective where different data properties (i.e., data value, data precision, dataflow, and data distribution) are exploited. At the physical layer, various parameters of physical implementation are considered. At the circuit layer, different logics and devices can be used for high efficiency. In fact, the self-awareness of existing SaNNS is still in a preliminary form. We propose a comprehensive SaNNS from a new perspective, that is, the model layer, to exploit more opportunities for high efficiency. The proposed system is called as MinMaxNN, which features model switching and elastic sparsity based on monitored information from the execution environment. The model switching mechanism implies that models (i.e., min and max model) dynamically switch given different inputs for both efficiency and accuracy. The elastic sparsity mechanism indicates that the sparsity of NNs can be dynamically adjusted in each layer for efficiency. The experimental results show that compared with traditional SaNNS, MinMaxNN can achieve $5.64\times $ and 19.66% performance improvement and energy reduction, respectively, without notable loss of accuracy and negative effects on developers’ productivity.
A METHOD FOR SEMIAUTOMATED ANALYSIS OF GENE EVOLUTION

Motivation: With increasing the amount of data on primary structures of genetic macromolecules it is very important to develop new methods for the large-scale evolutionary analysis. There exist a large number of programs aimed at solving of different types of evolutionary analysis, which are freely distributed. However, the computational tools often solve a very specific task. This makes difficult to implement the full cycle of analysis for the large scale datasets. Thus, the developing multitask and integrative approaches is very important. Results: The multitask system for solving a wide range of evolutionary analysis problems is developed by integration of a set of programs for evolutionary analysis. All the programs are united to an adaptable pipeline and allow solving different task of analysis from multiple alignment to analysis of adaptive evolution of the genes. Availability: The multitask system is written in Perl and is available from the authors upon request.
Impact of Refactoring on OO Metrics: A Study on the Extract Class, Extract Superclass, Encapsulate Field and Pull up Method

Refactoring is the key to improve software maintainability, reduce complexity and get clear code with the ability to understand and modify it in efficient way. In this paper we present four refactoring techniques which are: Extract Class, Extract Superclass, Encapsulate Field and Pull up Method to discover their effect on multi Object Oriented metrics and factors. The main objective of this study is to help the developers and maintenance engineers in choosing the best possible refactoring technique based on determined goals. The results demonstrate that the types of refactoring techniques have distinctive impact on the metrics values. The results show that all contemplated refactoring techniques increase the Cyclomatic number value (VG). However, the Encapsulate Field has no impact on: Coupling between Objects, Depth of Inheritance, Number of children and Outward Coupling metrics.
3D Object Detection in LiDAR Point Clouds using Graph Neural Networks

LiDAR (Light Detection and Ranging) is an advanced active remote sensing technique working on the principle of time of travel (ToT) for capturing highly accurate 3D information of the surroundings. LiDAR has gained wide attention in research and development with the LiDAR industry expected to reach 2.8 billion $ by 2025. Although the LiDAR dataset is of rich density and high spatial resolution, it is challenging to process LiDAR data due to its inherent 3D geometry and massive volume. But such a high-resolution dataset possesses immense potential in many applications and has great potential in 3D object detection and recognition. In this research we propose Graph Neural Network (GNN) based framework to learn and identify the objects in the 3D LiDAR point clouds. GNNs are class of deep learning which learns the patterns and objects based on the principle of graph learning which have shown success in various 3D computer vision tasks.
Essentiality of antioxidant vitamins for ruminants in relation to stress and reproduction

Summary The naturally occurring antioxidants: vitamin E, β-carotene and vitamin C are known to ameliorate the oxidative stress generated during various physiological and pathological conditions. The oxidative stress may occur at different sites, different times, and by different mechanisms in the animal system. Lower levels of the anti-oxidant vitamins are associated with poor fertility and production levels in ruminants. These antioxidants need to be replenished/regenerated or supplemented at appropriate timings in order to optimize the ruminant health and productive/reproductive performance.