Miracle-dz/UniFAR-FTUs huggingface dataset:
Attitudes of campus recreational sport employees toward LGBTQ+ participants: does targeting training make an impact?

ABSTRACT This study examined the impact of an LGBTQ+ diversity training on the attitudes of employees within a campus recreational sports setting. Diversity trainings are a common component of inclusive management, but little empirical research examining their efficacy exists. Specifically, a training program consisting of one in-person training session and four monthly inclusion guides detailing the promotion of LGBTQ+ participant inclusion was implemented and evaluated. A retrospective pretest-posttest design measured attitudes towards the LGBTQ+ population for both individuals who underwent the training and a control group of employees who did not participate in the training. Results indicate that there were significant differences in the pre- and post-training aggregated attitudinal sub-scale mean scores reported by campus recreational sports employees after participating in the training and no such differences were reported in the control group. Guided by previous research and pro-diversity theory, this study provides management implications for recreational sporting spaces.
A Robust Keyframe-Based Visual SLAM for RGB-D Cameras in Challenging Scenarios

The accuracy of RGB-D SLAM systems is sensitive to the image quality, and can be significantly compromised in adverse situations such as when input images are blurry, lacking in texture features, or overexposed. In this paper, based on Continuous Direct Sparse Visual Odometry (CVO), we present a novel Keyframe-based CVO (KF-CVO) with intrinsic keyframe selection mechanism that effectively reduces the tracking error. We then extend KF-CVO to a RGB-D SLAM system, CVO SLAM, equipped with place recognition via ORB features, and joint bundle adjustment & pose graph optimization. Comprehensive evaluations on publicly available benchmarks show that the proposed RGB-D SLAM system achieves a higher success rate than current state-of-the-art-methods. The proposed system is more robust to difficult benchmark sequences than current state-of-the-art methods, where adverse situations such as rapid camera motions, environments lacking in texture, and overexposed images when strong illumination exists.
Resilience and Sustainable Urban Tourism: Understanding Local Communities’ Perceptions after a Crisis

This study sought to examine the coronavirus COVID-19 pandemic’s impacts on local communities whose residents are directly or indirectly affected by city tourism. Qualitative research was conducted via in-depth interviews and Leximancer software analysis to explore locals’ perceptions in two highly tourism-dependent southern European cities. While the crisis has had predominantly negative impacts on tourism, the pandemic’s positive effects could contribute to cities’ greater resilience and more sustainable tourism models. The results highlight the variables that residents perceive as having the most influence on city tourism, as well as providing insights into locals’ expectations for the future.
A 2-Phase Strategy for Intelligent Cloud Operations

When operating large cloud computing infrastructures, ensuring healthiness of physical resources and software components is of paramount importance to meet the demanding service levels expected by customers. This is only possible using automations that can detect anomalies and alert the on-call personnel, or trigger healing procedures. In production-grade deployments, such automations are generally based on static thresholds or predefined pattern-matching rules, checked against relevant metrics and logs. Defining and maintaining them is cumbersome and, as the infrastructure grows, they need continuous adjustments. To tackle this problem, we propose an intelligent automation system for cloud operations that learns, from what operators have done in the past, what actions should be applied in response to the observed anomalies. Such system is designed to operate elastic groups of cloud instances realizing typical (replicated) cloud services. The mechanism is based on a 2-phase machine learning pipeline, composed of: a first, lighter, model that automatically detects anomalous patterns, based on past observations of the normal behavior, causing activation of the second, more involved, model; this is a model that recommends specific corrective actions, based on historical operational data reporting the actions applied to heal the faulty components. The approach was validated on an OpenStack deployment, where we deployed both a synthetic application and a multi-node Cassandra NoSQL data-store, and injected different types of anomalies while these systems were exercised using synthetic workloads. For both applications, we obtained a remarkable accuracy (mostly beyond 90%, and also going beyond 95% in some cases), for the anomaly detection and corrective action recommendation tasks, by applying the models on the respective test sets. This allows us to conclude that the presented mechanism constitutes an efficient and effective technique to help operating cloud services in presence of a number of faults, albeit the types and heterogeneity of faulty conditions might be expanded in future evolutions of the framework. The implementation and the material needed to reproduce our results are available under an open-source license.
Evading Antivirus Software Detection Using Python and PowerShell Obfuscation Framework

Avoiding antivirus detection in penetration testing activities is tricky. The simplest, most effective, and most efficient way is to disguise malicious code. However, the obfuscation process will also be very complex and time-consuming if done manually. To solve this problem, many tools or frameworks on the internet can automate the obfuscation process, but how effective are obfuscation tools to avoid antivirus detection are. This study aimed to provide an overview of the effectiveness of the obfus- cation framework in avoiding antivirus detection. This study used experimental design to test and determine the effectiveness of the payload obfuscation process. The first step was generating Python and PowerShell payloads, followed by the obfuscation process. The results showed that by using the right method of obfuscation, malware could become completely undetectable. The automatic obfus- cation process also did not deteriorate the malware’s function. It was proven that the malware could run and open a connection on the server. These findings required more Python obfuscator techniques to determine the effectiveness of the obfuscated payload on the target machines using both static and dynamic analysis
The Potentiating Effect of Graphene Oxide on the Arylhydrocarbon Receptor (AhR)–Cytochrome P4501A (Cyp1A) System Activated by Benzo(k)fluoranthene (BkF) in Rainbow Trout Cell Line

The increasing use of graphene oxide (GO) will result in its release into the environment; therefore, it is essential to determine its final fate and possible metabolism by organisms. The objective of this study was to assess the possible role of the aryl hydrocarbon receptor (AhR)-dependent cytochrome P4501A (Cyp1A) detoxification activities on the catabolism of GO. Our hypothesis is that GO cannot initially interact with the AhR, but that after an initial degradation caused by other mechanisms, small fractions of GO could activate the AhR, inducing Cyp1A. The environmental pollutant benzo(k)fluoranthene (BkF) was used for the initial activation of the AhR in the rainbow trout (Oncorhynchus mykiss) cell line RTL-W1. Pre-, co-, and post-exposure experiments with GO were performed and Cyp1A induction was monitored. The strong stimulation of Cyp1A observed in cells after exposure to GO, when BkF levels were not detected in the system, suggests a direct action of GO. The role of the AhR was confirmed by a blockage of the observed effects in co-treatment experiments with αNF (an AhR antagonist). These results suggest a possible role for the AhR and Cyp1A system in the cellular metabolism of GO and that GO could modulate the toxicity of environmental pollutants.
Modeling of wakes behind wind turbines

iii Acknowledgements . v Figures . ix Tables . xii Chapter 1-Description of the problem . 1 1.1 Basics of Wind Turbines . 1 1.2 In The Wake of a Wind Turbine. 3 1.3 Wake effects . 6 1.4 Experiments . 8 1.5 Review of numerical wake models. 12 Chapter 2The Developed Boundary Layer Wake Model . 19 2.1 Boundary layer theory . 21 2.2 The single wake model . 26 2.2.1 Basics . 27 2.3 The wake model parameters . 32 2.3.1 The turbulence modeling: Eddy viscosity closure . 32 2.3.2 Other input parameters . 48 2.3.3 The wake meandering correction . 54 2.4 Numerical solution . 57 2.5 Multiple wakes model . 69 Chapter 3 – Wake Analysis . 76 3.1 Basics . 76 3.2 Wake velocity deficit . 77 3.3 Estimation of turbulent properties . 79 3.3.1 Reynolds stresses . 79 3.3.2 Turbulent intensities . 80 3.4 Power. 83 Chapter 4 Results. Comparing measured and modeled data . 85 4.1 Convergence analysis . 86
Haplotype blocks for genomic prediction: a comparative evaluation in multiple crop datasets

In modern plant breeding, genomic selection is becoming the gold standard for selection of superior genotypes. The basis for genomic prediction models is a set of phenotyped lines along with their genotypic profile. With high marker density and linkage disequilibrium (LD) between markers, genotype data in breeding populations tends to exhibit considerable redundancy. Therefore, interest is growing in the use of haplotype blocks to overcome redundancy by summarizing co-inherited features. Moreover, haplotype blocks can help to capture local epistasis caused by interacting loci. Here, we compared genomic prediction methods that either used single SNPs or haplotype blocks with regards to their prediction accuracy for important traits in crop datasets. We used four published datasets from canola, maize, wheat and soybean. Different approaches to construct haplotype blocks were compared, including blocks based on LD, physical distance, number of adjacent markers and the algorithms implemented in the software “Haploview” and “HaploBlocker”. The tested prediction methods included Genomic Best Linear Unbiased Prediction (GBLUP), Extended GBLUP to account for additive by additive epistasis (EGBLUP), Bayesian LASSO and Reproducing Kernel Hilbert Space (RKHS) regression. We found improved prediction accuracy in some traits when using haplotype blocks compared to SNP-based predictions, however the magnitude of improvement was very trait- and model-specific. Especially in settings with low marker density, haplotype blocks can improve genomic prediction accuracy. In most cases, physically large haplotype blocks yielded a strong decrease in prediction accuracy. Especially when prediction accuracy varies greatly across different prediction models, prediction based on haplotype blocks can improve prediction accuracy of underperforming models. However, there is no “best” method to build haplotype blocks, since prediction accuracy varied considerably across methods and traits. Hence, criteria used to define haplotype blocks should not be viewed as fixed biological parameters, but rather as hyperparameters that need to be adjusted for every dataset.
Determining diffusion coefficients of oxygen in supercritical water with molecular dynamics

The supercritical water oxidation is a significant way for the waste disposal. The diffusion of the oxygen in the water at the infinite dilution is simulated at 300 K and 1 atm, and 650 K, 673 K, 773 K, 873 K, 973 K, and 250 atm with the molecular dynamics software. The mean squared displacement method is used to calculate the diffusion coefficient. At 300 K, 1 atm, our calculation gives 0.20 ? 10? 8 m2/s, which is very near to three empirical equations. When the condition is beyond the critical point, these empirical equations lost their accuracy, and only Kawasaki-Oppenheim equation can be compared to our calculation results. At supercritical conditions, we illustrate the diffusion coefficients with the Arrhenius equation and the activation energy is 22.54 kJ/mol.
Socio-spatial Learning Analytics in Co-located Collaborative Learning Spaces:: A Systematic Literature Review

Socio-spatial learning analytics (SSLA) is an emerging area within learning analytics research that seeks to un-cover valuable educational insights from individuals’ social and spatial data traces. These traces are capturedautomatically through sensing technologies in physical learning spaces, and the research is commonly based onthe theoretical foundations of social constructivism and cultural anthropology. With its growing empirical basis, asystematic literature review is timely in order to provide educational researchers and practitioners with a detailedsummary of the emerging works and the opportunities enabled by SSLA. This paper presents a systematic review of25 peer-reviewed articles on SSLA published between 2011 and 2023. Descriptive, network, and thematic analyseswere conducted to identify the citation networks, essential components, opportunities, and challenges enabled bySSLA. The findings illustrated that SSLA provides the opportunity to (1) contribute unobtrusive and unsupervisedresearch methodologies, (2) support educators’ classroom orchestration through visualizations, (3) support learnerreflection with continuous and reliable evidence, (4) develop novel theories about social and collaborative learning,and (5) empower educational stakeholders with the quantitative data to evaluate different learning spaces. Thesefindings could support learning analytics and educational technology scholars and practitioners to better understandand utilize SSLA to support future educational research and practice.
Generation of Antibodies Selectively Recognizing Epitopes in a Formaldehyde-Fixed Cell-Surface Antigen Using Virus-like Particle Display and Hybridoma Technology

Efficient induction of target-specific antibodies can be elicited upon immunization with highly immunogenic virus-like particles (VLPs) decorated with desired membrane-anchored target antigens (Ags). However, for example, for diagnostic purposes, monoclonal antibodies (mAbs) are required to enable the histological examination of formaldehyde-fixed paraffin-embedded (FFPE) biopsy tissue samples. Aiming at the generation of FFPE-antigen-specific mAbs and as a proof of concept (POC), we first established a simplified protocol using only formaldehyde and 90 °C heat fixation (FF90) of cells expressing the target Ag nerve growth factor receptor (NGFR). The FF90 procedure was validated using flow cytometric analysis and two mAbs recognizing either the native and FFPE-Ag or exclusively the native Ag. C-terminally truncated NGFR (trNGFR)-displaying native and FF90-treated VLPs derived from HIV-1 did not reveal distinctive changes in particle morphology using transmission electron microscopy (TEM) and dynamic light scattering (DLS) analysis. Mice were subsequently repetitively immunized with trNGFR-decorated FF90-VLPs and hybridoma technology was used to establish mAb-producing cell clones. In multiple screening rounds, nine cell clones were identified producing mAbs distinctively recognizing epitopes in FF90- and FFPE-NGFR. This POC of a new methodology should foster the future generation of mAbs selectively targeting FFPE-fixed cell-surface Ags.
Insights into crystallographic and topographic characteristics of local corrosion attack on zinc

ABSTRACT Through large area crystallographic analysis across the whole cross-section of a 0.5 mm zinc wire by backscattered electron diffraction (EBSD) excavated by plasma focused ion beam (pFIB) and topographic analysis by scanning electron microscopy (SEM), the crystallographic and topographic characteristics of corrosion on zinc are identified and correlated. Striated domains dominating the surface are the result of preferential dissolution of non-basal planes leaving (0001) plates intersecting the surface. Remnant fragments of zinc lie close to the original surface which is due to the combined effects of the initial protective film on the surface and the crystallographic corrosion. Three corrosion layers were found, namely from the outside inwards, a highly porous outer layer, a densified intermediate layer and an attacked layer.
Integrating Entity Attributes for Error-Aware Knowledge Graph Embedding

Knowledge graphs (KGs) can structurally organize large-scale information in the form of triples and significantly support many real-world applications. While most KG embedding algorithms hold the assumption that all triples are correct, considerable errors were inevitably injected during the construction process. It is urgent to develop effective error-aware KG embedding, since errors in KGs would lead to significant performance degradation in downstream applications. To this end, we propose a novel framework named Attributed Error-aware Knowledge Embedding (AEKE). It leverages the semantics contained in entity attributes to guide the KG embedding model learning against the impact of erroneous triples. We design two triple-level hypergraphs to model the topological structures of the KG and its attributes, respectively. The confidence score of each triple is jointly calculated based on self-contradictory within the triple, consistency between local and global structures, and homogeneity between structures and attributes. We leverage confidence scores to adaptively update the weighted aggregation in the multi-view graph learning framework and margin loss in KG embedding, such that potential errors will contribute little to KG learning. Experiments on three real-world KGs demonstrate that AEKE outperforms state-of-the-art KG embedding and error detection algorithms.
Animal Vocal Recognition-Based Breeding Tracking and Disease Warning

In this study, we investigate the application of a deep learning framework for the recognition of pig vocalizations. This innovative approach aims to actively monitor and evaluate the diverse states of pigs, with an overarching objective to improve the efficiency of pig farming through prompt identification and resolution of issues. In our comprehensive data collection effort, we carefully gathered a vast assortment of vocal samples from 50 pigs, representative of four distinct states: normal, frightened, coughing, and sneezing. We then meticulously analyzed this vocal data using Mel Frequency Cepstral Coefficients (MFCC). For accurate recognition of pig vocalizations, we devised a fusion model that combines the strengths of Residual Networks (ResNet) and Long Short-Term Memory Networks (LSTM). This model was subsequently tailored, trained, and optimized to meet our specific requirements. Upon rigorous evaluation, we found our model to exhibit exceptional performance in pig vocal recognition tasks, thereby reinforcing the potential of deep learning methodologies in revolutionizing the livestock industry. This research notably underscores the potential of deploying efficient real-time health monitoring systems, offering a promising avenue towards modernizing livestock management practices.
Unraveling the Interplay of Learning Agility, Burnout, and Executive Function in Fostering Agile Leadership among Millennial Leaders

In the rapidly evolving business landscape, agile leadership, especially among millennial leaders, has become pivotal. However, a comprehensive understanding of how Learning Agility, Burnout, and Executive Function interact to shape this leadership style is still limited, with burnout potentially impeding agile leadership development. This study aimed to elucidate the interplay between Learning Agility, Burnout, Executive Function, and their collective contribution to Agile Leadership among millennial leaders. We selected 121 millennial leaders from five different companies for this purposeful study. Utilizing Model 8 in Process Macro Hayes, we tested four hypotheses to evaluate the relationships between Learning Agility (LA) and Agile Leadership (AL), the mediating role of Executive Function (EF), the moderating effect of Burnout, and the conditional indirect impact of EF at various burnout levels. The results revealed a significant positive correlation between Learning Agility and Agile Leadership when Burnout was extremely low (p < 0.01). Still, this relationship was not significant at neutral and high burnout levels. While executive function emerged as a crucial mediator, it did not significantly mediate the Learning Agility-Agile Leadership relationship. Burnout was found to moderate this relationship, accounting for an additional 4.96% of the variability in Agile Leadership (p < 0.01), I would write p<. 01). The overall model explained 17.18 (17.2) % of Agile Leadership variability (F = (6.0150 =) 6.02, (p = 0.0002), p<. 001). However, the conditional indirect effects of Learning Agility on Agile Leadership were not statistically significant at different burnout levels. It can be concluded that Burnout is a significant moderator variable between Learning Agility and Agile Leadership among millennial leaders, emphasizing the need for Burnout reduction strategies to optimize the influence of Learning Agility on Agile Leadership. Future research is required to fully understand the mediating role of Executive Function in this relationship.
Posttraumatic Stress and Traumatic Brain Injury: Cognition, Behavior, and Neuroimaging Markers in Vietnam Veterans

Background: Posttraumatic stress disorder (PTSD) and traumatic brain injury (TBI) are common in Veterans and linked to behavioral disturbances, increased risk of cognitive decline, and Alzheimer’s disease. Objective: We studied the synergistic effects of PTSD and TBI on behavioral, cognitive, and neuroimaging measures in Vietnam war Veterans. Methods: Data were acquired at baseline and after about one-year from male Veterans categorized into: PTSD, TBI, PTSD+TBI, and Veteran controls without PTSD or TBI. We applied manual tractography to examine white matter microstructure of three fiber tracts: uncinate fasciculus (N = 91), cingulum (N = 87), and inferior longitudinal fasciculus (N = 95). ANCOVAs were used to compare Veterans’ baseline behavioral and cognitive functioning (N = 285), white matter microstructure, amyloid-β (N = 230), and tau PET (N = 120). Additional ANCOVAs examined scores’ differences from baseline to follow-up. Results: Veterans with PTSD and PTSD+TBI, but not Veterans with TBI only, exhibited poorer behavioral and cognitive functioning at baseline than controls. The groups did not differ in baseline white matter, amyloid-β, or tau, nor in behavioral and cognitive functioning, and tau accumulation change. Progression of white matter abnormalities of the uncinate fasciculus in Veterans with PTSD compared to controls was observed; analyses in TBI and PTSD+TBI were not run due to insufficient sample size. Conclusions: PTSD and PTSD+TBI negatively affect behavioral and cognitive functioning, while TBI does not contribute independently. Whether progressive decline in uncinate fasciculus microstructure in Veterans with PTSD might account for cognitive decline should be further studied. Findings did not support an association between PTSD, TBI, and Alzheimer’s disease pathology based on amyloid and tau PET.