Miracle-dz/UniFAR-FTUs huggingface dataset:
Pick the Freshmen Up for a “Healthy Study Start” Evaluation of a Health Promoting Onboarding Program for First Year Students at the Carinthia University of Applied Sciences, Austria

Introduction: Universities are an essential setting for creating health promoting environments. Evidence shows that university life can pose various threats to the students' health. Especially first year students are vulnerable to mental health issues. To support well-being and prevent psychological distress from the first day of studying, onboarding programs are needed to promote the students' health and their self- and social competencies. The study demonstrates a tailored multi-component onboarding intervention program named “Healthy Study Start. ” An evaluation of the effectiveness is presented focusing on outcomes regarding the students' sense of coherence (S-SoC), social support, sympathy, the work-related collective and the participative safety (a sub-scale of the team climate) among freshmen at the Carinthia University of Applied Sciences (CUAS, Austria). Methods: For the analyses, a quantitative controlled study design was used and results were measured three times. The intervention group (n = 72) was composed of freshmen selected from the bachelor study programs Occupational Therapy, Speech and Language Therapy, Biomedical Science and Radiologic Technology. Freshmen from the bachelor study program Healthcare and Nursing formed the control group (n = 81). As the requirements for analyses of variance were not fulfilled, the data had to be analyzed using e.g., Mann-Whitney U-tests. Results: Significant changes (all p < 0.016) between the two groups were found between T0/T1, and between T0/T2. Furthermore, changes within the intervention group (all p < 0.016) emerged in nearly all outcomes between T0/T1, while within the control group no changes were identified. However, the intervention group had statistically significantly higher values in the majority of outcomes at T1 and T2 compared to the control group. Conclusion: The onboarding program “Healthy Study Start” shows how an initiative at the beginning of their studies can support students in entering a new phase of their lives. The results indicate a positive effect on the students' self- and social competencies. However, students' health promotion is not only an investment for a health conscious university or an enhanced employability. Especially in health-related fields of study, students are future multipliers and play an essential role in implementing health promotion concepts for clients, patients and employees.
Modeling Input Factors in Second Language Acquisition of the English Article Construction

Based on the Competition Model, the current study investigated how cue availability and cue reliability as two important input factors influenced second language (L2) learners' cue learning of the English article construction. Written corpus data of university-level Chinese-L1 learners of English were sampled for a comparison of English majors and non-English majors who demonstrated two levels of L2 competence in English article usage. The path model analysis in structural equation modeling was utilized to investigate the relationship between the input factors and L2 usage (frequency and accuracy of article cue production). The findings contribute novel and scarce empirical evidence that confirms a central claim of the Competition Model, i.e., the changing importance of cue availability and cue reliability in the frequency and accuracy of production. Cue availability was found to determine L2 production frequency regardless of level of L2 competence. Cue reliability was the input factor that differentiated competence levels. When learners stayed at a relatively lower L2 proficiency, cue reliability played an important role in influencing L2 frequency of usage rather than accuracy of usage. When learners developed increased exposure to and stronger competence in the target language, cue reliability played a significant role in determining learners' success of cue learning. The study is methodologically innovative and expands the empirical applicability of the Competition Model to the domain of second language production and construction learning.
State Transitions During Discrimination Learning in the Gerbil Auditory Cortex Analyzed by Network Causality Metrics

This work studies the evolution of cortical networks during the transition from escape strategy to avoidance strategy in auditory discrimination learning in Mongolian gerbils trained by the well-established two-way active avoidance learning paradigm. The animals were implanted with electrode arrays centered on the surface of the primary auditory cortex and electrocorticogram (ECoG) recordings were made during performance of an auditory Go/NoGo discrimination task. Our experiments confirm previous results on a sudden behavioral change from the initial naïve state to an avoidance strategy as learning progresses. We employed two causality metrics using Granger Causality (GC) and New Causality (NC) to quantify changes in the causality flow between ECoG channels as the animals switched to avoidance strategy. We found that the number of channel pairs with inverse causal interaction significantly increased after the animal acquired successful discrimination, which indicates structural changes in the cortical networks as a result of learning. A suitable graph-theoretical model is developed to interpret the findings in terms of cortical networks evolving during cognitive state transitions. Structural changes lead to changes in the dynamics of neural populations, which are described as phase transitions in the network graph model with small-world connections. Overall, our findings underscore the importance of functional reorganization in sensory cortical areas as a possible neural contributor to behavioral changes.
Associations Between Sputum Torque Teno Virus Load and Lung Function and Disease Severity in Patients With Chronic Obstructive Pulmonary Disease

Purpose: Viral load of Torque Teno virus (TTV) is elevated in immunosuppressed patients. The weakened immune response is typical in chronic obstructive pulmonary disease (COPD) patients. However, the relationship between TTV and COPD is still unknown. Patients and Methods: We enrolled 91 patients admitted to hospitals with acute exacerbation of COPD (AECOPD) between January 2017 and August 2017 (ClinicalTrials.gov ID, NCT03236480). Sputum samples were gathered during hospitalization and the 120-day follow-up. TTV distribution and genogroups were assessed, and the associations between viral loads and clinical parameters were analyzed. Results: TTV DNA was detected in 95.6% of COPD patients, and the viral load was nearly invariable at the stable and exacerbation states. Most TTV DNA-positive patients carried four distinct genotypes. Sputum load of TTV was positively associated with RV/TLC (r = 0.378, p = 0.030), and negatively correlated with FEV1/pre and FEV1/FVC (r = −0.484, −0.432, p = 0.011, 0.024, respectively). Neutral correlation between the TTV DNA load and COPD assessment test (CAT) scores (r = 0.258, p = 0.018) was observed. Conclusion: Sputum loads of TTV DNA could be a novel indicator for lung function and disease severity assessment in COPD patients.
“Our Home Is a Muddy Structure”: Perceptions of Housing and Health Risks Among Older Adults in Contrasting Neighborhoods in Ghana

Aging occurs in a variety of social and physical environmental settings that affect health. However, despite their rapidly growing populations, public health research in sub-Saharan Africa has yet to address the role of residential environments in the health and well-being of older adults. In this study, we utilized an ethnographic research methodology to explore barriers and facilitators to health among older adults residing in two contrasting neighborhoods in Accra, Ghana. Our specific objective was to identify patterns of health risks among older adults in the two neighborhoods. Data were collected through qualitative interviews with a purposive sample of health workers (n = 5), community leaders (n = 2), and older adults residing in a slum and non-slum neighborhood (n = 30). Our thematic data analysis revealed that, despite different underlying drivers, health barriers across the slum and non-slum were largely similar. The harmful effects of these health barriers – poor built environments, housing precariousness, unsanitary living conditions, defective public services, and social incivilities – were mitigated by several facilitators to health, including affordable housing and social supports in the slum and better housing and appealing doors in the non-slum. Our study contributes to a more nuanced understanding of the ways in which aging and urban environments intersect to influence population health in resource poor settings. In particular, rather than the commonly referenced dichotomy of poor and non-poor settlements in discourses of neighborhood health, our findings point to convergence of health vulnerabilities that are broadly linked to urban poverty and governmental neglect of the elderly.
Diversity and Evolution of Mineralized Skeletal Tissues in Chondrichthyans

The diversity of skeletal tissues in extant vertebrates includes mineralized and unmineralized structures made of bone, cartilage, or tissues of intermediate nature. This variability, together with the diverse nature of skeletal tissues in fossil species question the origin of skeletonization in early vertebrates. In particular, the study of skeletal tissues in cartilaginous fishes is currently mostly restrained to tessellated cartilage, a derived form of mineralized cartilage that evolved at the origin of this group. In this work, we describe the architectural and histological diversity of neural arch mineralization in cartilaginous fishes. The observed variations in the architecture include tessellated cartilage, with or without more massive sites of mineralization, and continuously mineralized neural arches devoid of tesserae. The histology of these various architectures always includes globular mineralization that takes place in the cartilaginous matrix. In many instances, the mineralized structures also include a fibrous component that seems to emerge from the perichondrium and they may display intermediate features, ranging from partly cartilaginous to mostly fibrous matrix, similar to fibrocartilage. Among these perichondrial mineralized tissues is also found, in few species, a lamellar arrangement of the mineralized extracellular matrix. The evolution of the mineralized tissues in cartilaginous fishes is discussed in light of current knowledge of their phylogenetic relationships.
Chemical Composition, Phytotoxic and Antimicrobial Activities of the Essential Oil of Scutellaria strigillosa Hemsley

Abstract The chemical composition of the essential oil isolated by hydrodistillation from the aerial parts of Scutellaria strigillosa Hemsley was analyzed by GC-MS analysis. Forty one compounds were identified, representing 75.84% of total oil, which was found to be rich in sesquiterpene hydrocarbons (57.07%). The main constituents of the oil were: Germacrene D (37.78%), 1-octen-3-ol (8.96%), bicyclogermacrene (3.67%) and β-caryophyllene (3.02%). The phytotoxic effect of the essential oil was evaluated by conducting bioassays against a dicot plant, amaranth (Amaranthus retroflexus L.), and a monocot plant, bluegrass (Poa annua L.). Amaranth seemed to be more sensitive to the essential oil; 1 μl/ml essential oil application resulted in an 86.6% reduction on root elongation, and 3 μl/ml essential oil completely inhibited its seedling growth. In comparison, only 3 μl/ml essential oil showed significant inhibitory effect on bluegrass, which reduced shoot length to 20% of control and root length to 31% of control. The essential oil from S. strigillosa showed higher antimicrobial effects on gram positive bacteria than gram negative bacteria, and showed stronger effect on bacteria than fungi. This is the first report on the chemical composition, the phytotoxic and antimicrobial activity of the essential oil of S. strigillosa.
Youth entrepreneurship development: A review of literature and ten-year research results

Entrepreneurship plays an important role in economic development. Further, the concept of entrepreneurship has been argued as the important factor in overall economic growth. As youth unemployment rates are high both in developing and developed countries, improving the environment for entrepreneurial activities is a necessity as to increase youth entrepreneurship potential. In this paper, the results of ten-year research on youth entrepreneurship are reviewed. In this study 5670 participants – high school students, and university students from the Republic of Serbia, took part. Also, other literature sources were analyzed. The main goal of this paper was identify and determine the potential influence of students’ attitudes regarding entrepreneurship and their intention to start a business. The paper analyzes the existing body of literature in order to provide a thoroughly investigated and concisely presented “image” in the domain of youth entrepreneurship. Hence, effectively provide a solid base for future research.
Data in practice: A participatory approach to understanding pre-service teachers’ perspectives

Data about learning can support teachers in their decision-making processes as they design tasks aimed at improving student educational outcomes. However, to achieve systemic impact, a deeper understanding of teachers’ perspectives on, and expectations for, data as evidence is required. It is critical to understand how teachers’ actions align with emerging learning analytics technologies, including the practices of pre-service teachers who are developing their perspectives on data use in classroom in their initial teacher education programme. This may lead to an integration gap in which technology and data literacy align poorly with expectations of the role of data and enabling technologies. This paper describes two participatory workshops that provide examples of the value of human-centred approaches to understand teachers’ perspectives on, and expectations for, data as evidence. These workshops focus on the design of pre-service teachers enrolled in teacher education programmes (N = 21) at two Australian universities. The approach points to the significance of (a) pre-service teachers’ intentions to track their students’ dispositions to learning and their ability to learn effectively, (b) the materiality of learning analytics as an enabling technology and (c) the alignment of learning analytics with learning design, including the human-centred, ethical and inclusive use of educational data in the teaching practice. Implications for practice or policy: Pre-service teachers ought to be given opportunities to engage and understand more about learning design, learning analytics and the use of data in classrooms. Professional experience placements for pre-service teachers should include participatory data sessions or learning design workshops. Teacher education academics in universities must be provided with ongoing professional development to support their preparation work of pre-service teachers’ data literacy, learning analytics and the increasing presence of data.
Single-Event Response of 22-nm Fully Depleted Silicon-on-Insulator Static Random Access Memory

We are presenting single-event effect testing results on a 22-nm fully depleted silicon-on-insulator test chip from GlobalFoundries. The 128-Mb static random access memory (SRAMs) were irradiated with heavy ions, and the results are compared to previous partially depleted technology generations (32 and 45 nm). The per-bit cross section is approximately an order of magnitude lower than the previous generations with a higher onset linear energy transfer (LET). No dependence on roll angle or input pattern was found. Tilt angle data follow the cosine law. Increasing the SRAM array supply voltage from the minimum tested 0.73 V to the maximum 1.08 V decreases SEE sensitivity by as much as 8%. Decreasing the p-well voltage from the nominal 0 V to the maximum −2 V increases the SEE cross section by as much as $2\times $ . The n-well voltage has little effect on the SEE sensitivity due to the specifics of the transistor layout in the SRAM. Changing both the n- and p-well voltages simultaneously results in identical results as when only the p-well voltage was changed.
Comparison of Transportation Carriers in the Insulating Materials for HVDC Cable Joint

As an interface between the different insulating polymers exists in the joints of High Voltage Direct Current (HVDC) cables, the differences in the field-and-temperature dependence of the conductivities of the materials is regarded as a problem leading to interface charge accumulation and field distortion. In this report, using blocking materials to prevent charge injection from one or both sides of the electrodes, the contributions of charge injection and ionization in low-density polyethylene (LDPE), cross-linked polyethylene with nanoparticles (nano-XLPE) and ethylene propylene diene monomer (EPDM) are distinguished and evaluated based on the results from pulsed electro-acoustic (PEA) method. It is found that the contributions of charge injection and ionization behave differently in different materials. The mechanism behind the field-and-temperature dependences of the conductivities of the three materials is explained successfully. This study helps to clarify the charge transfer mechanism, and thus supports the quantitative calculation of the space charge and electric field distribution in cable joints.
Contour Extraction of Individual Cattle From an Image Using Enhanced Mask R-CNN Instance Segmentation Method

In animal husbandry, the traceability of individual cattle, their health information, and performance records greatly depend on computer vision and image processing-based approaches. However, some of these approaches perform below expectations in obtaining real-time information about individual cattle. No doubt, inaccurate segmentation and incomplete extraction of each cattle object from an image are notable contributory factors. As accurate segmentation is a prerequisite for obtaining real-time information about individual cattle, and since the algorithm of Mask R-CNN relies on the algorithm of simultaneous localization and mapping (SLAM), for the construction of the semantic map, which sometimes exchanges image background for the foreground, there is a need to enhance the available approaches towards achieving precision animal husbandry. To achieve this, an enhanced Mask R-CNN instance segmentation method is proposed to support indistinct boundaries and irregular shapes of cattle bodies. The methods employed in the research are in multiple folds: (1) Pre-enhancement of the image using generalized color Fourier descriptors (GCFD); (2) Provision of optimal filter size that was smaller than ResNet101 (the backbone of Mask R-CNN) for the extraction of smaller and composite features; (3) Utilization of multiscale semantic features using region proposals; (4) A fully connected layer of existing Mask R-CNN integrated with a sub-network for enhanced segmentation and (5) Post-enhancement of the image using Grabcut. Experiments on the datasets of cattle images produced better results when compared to other state-of-the-art methods with 0.93 mAP.
Control Plane Reflection Attacks and Defenses in Software-Defined Networks

Software-Defined Networking (SDN) continues to be deployed spanning from enterprise data centers to cloud computing with the proliferation of various SDN-enabled hardware switches and dynamic control plane applications. However, state-of-the-art SDN-enabled hardware switches have rather limited downlink message processing capability, especially for Flow-Mod and Statistic Query, which may not suffice the huge need of dynamic control plane applications. In this paper, we systematically study the interactions between the control plane applications and the data plane switches, and present two new attacks, namely Control Plane Reflection Attacks, to exploit the limited processing capability of SDN-enabled hardware switches. The reflection attacks adopt direct and indirect data plane events to force the control plane to issue massive expensive downlink messages towards SDN switches. Moreover, we propose a two-phase probing-triggering attack strategy, which makes the reflection attacks much more efficient and powerful. Experiments on a testbed with 3 different physical OpenFlow switches demonstrate that the attacks can lead to catastrophic results such as hurting the establishment of new flows and even disruption of connection between SDN controller and switches. To mitigate such attacks, we present several countermeasures from different perspectives. In particular, we propose a novel, systematical defense framework, SwitchGuard, to detect anomalies of downlink messages and prioritize these messages based on a novel monitoring granularity, i.e., host-application pair (HAP). Implementations and evaluations demonstrate that SwitchGuard can effectively reduce the latency for legitimate hosts and applications under the control plane reflection attacks with only minor overheads.
A Real-Time Architecture for Pruning the Effectual Computations in Deep Neural Networks

Integrating Deep Neural Networks (DNNs) into the Internet of Thing (IoT) devices could result in the emergence of complex sensing and recognition tasks that support a new era of human interactions with surrounding environments. However, DNNs are power-hungry, performing billions of computations in terms of one inference. Spatial DNN accelerators in principle can support computation-pruning techniques compared to other common architectures such as systolic arrays. Energy-efficient DNN accelerators skip bit-wise or word-wise sparsity in the input feature maps (ifmaps) and filter weights which means ineffectual computations are skipped. However, there is still room for pruning the effectual computations without reducing the accuracy of DNNs. In this paper, we propose a novel real-time architecture and dataflow by decomposing multiplications down to the bit level and pruning identical computations in spatial designs while running benchmark networks. The proposed architecture prunes identical computations by identifying identical bit values available in both ifmaps and filter weights without changing the accuracy of benchmark networks. When compared to the reference design, our proposed design achieves an average per layer speedup of $\times 1.4$ and an energy efficiency of $\times 1.21$ per inference while maintaining the accuracy of benchmark networks.
Event-Triggered Interval-Based Anomaly Detection and Attack Identification Methods for an In-Vehicle Network

Vehicle communication technology has been steadily progressing alongside the convergence of the in-vehicle network (IVN) and wireless communication technology. The communication with various external networks further reinforces the connectivity between the inside and outside of a vehicle. However, this bears risks of malicious packet attacks on computer-assisted mechanical mechanisms that are capable of hijacking the vehicle’s functions. The present study proposes a method to detect and identify abnormalities in vehicular networks based on the periodic event-triggered interval of the controller area network (CAN) messages. To this end, we first define four attack scenarios and then extract normal and abnormal driving data corresponding to these scenarios. Next, we analyze the CAN ID’s event-triggered interval and measure statistical moments depending on the defined time-window. Finally, we conduct extensive evaluations of the proposed methods’ performance by considering different attack scenarios and three types of machine learning models. The results demonstrate that the proposed method can effectively detect an abnormality in the IVN, with up to 99% accuracy. Our results suggest that when tree-based machine learning models are used as the classifier, the proposed method of attack identification can achieve more than 94% accuracy.