Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation-Assisted Learning about a Complex Economic System: Impact on Low- and High-Achieving Students

Sustainable learning requires students to develop knowledge and skills for survival in increasingly complex and dynamic environments. The development of systems thinking skills for exploring complex dynamic systems is regarded as crucial to sustainable learning. To facilitate student thinking and learning about complex systems, computer simulations have been widely promoted. However, learning using computer simulations involves complex cognitive processes, which may impose a high level of cognitive demand on learners, especially on low achievers. It remains unclear whether and how high- and low-achieving students may benefit differently from learning with computer simulations. To address the gap, we conducted this study with university students who participated in simulation-assisted learning about the economy as a complex system. The results show that the students developed subject knowledge and systems thinking skills by the end of the study; high-achievers outperformed low-achievers in a subject knowledge test, but there were no significant differences between the two groups in their systems thinking skills, cognitive load, and affective experience. The findings indicate that both low- and high-achieving students can benefit from simulation-assisted learning of a complex system. In addition to developing systems thinking skills, there is a need to help students to improve the construction of their subject knowledge when learning with computer simulations.
Heterologous Expression, Purification, and Immunomodulatory Effects of Recombinant Lipoprotein GUDIV-103 Isolated from Ureaplasma diversum

Ureaplasma diversum is a bacterial pathogen that infects cattle and can cause severe inflammation of the genital and reproductive systems. Lipid-associated membrane proteins (LAMPs), including GUDIV-103, are the main virulence factors in this bacterium. In this study, we heterologously expressed recombinant GUDIV-103 (rGUDIV-103) in Escherichia coli, purified it, and evaluated its immunological reactivity and immunomodulatory effects in bovine peripheral blood mononuclear cells (PBMCs). Samples from rabbits inoculated with purified rGUDIV-103 were analysed using indirect enzyme-linked immunosorbent assay and dot blotting to confirm polyclonal antibody production and assess kinetics, respectively. The expression of this lipoprotein in field isolates was confirmed via Western blotting with anti-rGUDIV-103 serum and hydrophobic or hydrophilic proteins from 42 U. diversum strains. Moreover, the antibodies produced against the U. diversum ATCC 49783 strain recognised rGUDIV-103. The mitogenic potential of rGUDIV-103 was evaluated using a lymphoproliferation assay in 5(6)-carboxyfluorescein diacetate succinimidyl ester–labelled bovine PBMCs, where it induced lymphocyte proliferation. Quantitative polymerase chain reaction analysis revealed that the expression of interleukin-1β, toll-like receptor (TLR)-α, TLR2, TLR4, inducible nitric oxide synthase, and caspase-3–encoding genes increased more in rGUDIV-103–treated PBMCs than in untreated cells (p < 0.05). Treating PBMCs with rGUDIV-103 increased nitric oxide and hydrogen peroxide levels. The antigenic and immunogenic properties of rGUDIV-103 suggested its suitability for immunobiological application.
A Resonant Inductor Integrated-Transformer Based Receiver for Wireless Power Transfer Systems

Wireless power transfer charging of modern electric vehicles is complicated by widely varying charging battery pack voltages between different models. This paper proposes a new resonant inductor integrated-transformer (RIIT) based receiver. It is compact, low cost, and can adapt to different battery pack voltages without any significant change of resonant parameters. The design approach, power losses, and power transfer capacity of the proposed system are presented. In addition, methods of autonomous current sharing and voltage sharing among different RIITs are proposed. An LCC-LCC WPT prototype compatible with both 400-V and 800-V battery pack voltages is built, and the system efficiency reaches 94.1% at 9.5 kW, which verifies the feasibility and performance of the proposed receiver.
Testing the efficacy of different Larson trap designs for trapping Egyptian geese (Alopochen aegyptiacus L.) in Flanders (northern Belgium)

In Western Europe, the Egyptian goose is considered one of the most rapidly spreading invasive bird species. Listed as a species of Union Concern by the EU, it is subject to restrictions and measures and European Member States are urged to develop management strategies. Since common techniques such as shooting, moult trapping and egg control have been inadequate at lowering population numbers, there is a high demand for alternative effective control strategies. Here, we report on field trials testing the use of walk-in traps with live decoy birds. Trials were spread out over several years to establish optimal trapping season and trap design and to explore different deployment options. We found that in Belgium the breeding period was the optimal season for deploying traps, which suggests the territorial response is the main driver of trapping efficiency. Land-based designs performed significantly better than other trap types at catching Egyptian geese and had far fewer by-catches. The strategy in which traps were deployed at short intervals over various locations had the highest efficiency. We conclude that the use of land-based versions of this trap type can be a useful addition to a wider management strategy when used on a large scale and aimed at trapping adult birds prior to breeding. Given the high effort needed for this type of trapping and the expected effectiveness of other management techniques, the method is best combined with post breeding shooting. KVM: research conceptualization, sample design and methodology, investigation and data collection, writing – review and editing.
Quantification of Malondialdehyde in Atmospheric Aerosols: Application of the Thiobarbituric Acid Method

Based on available toxicity data, malondialdehyde (MDA; O=CHCH 2 CH=O) has been designated as a potential human carcinogen. A handful of studies suggest that MDA forms in the gas and aerosol phase in the troposphere, potentially contributing to inhalation toxicity, yet it has never been quantified in ambient air. The thiobarbituric acid (TBA) acid assay for MDA has been used as a marker for reactive oxygen species (ROS), oxidative stress, and lipid peroxidation in biological samples for decades. Here we apply the TBA assay to estimate the amount of MDA in ambient fine particulate matter (PM 2.5 ) for the first time, in samples containing biomass burning/urban aerosol from Fresno, CA, and urban aerosol from Los Angeles. We found 0.31–0.75 ng m –3 MDA in the particle phase, similar to the low end, but up to three orders of magnitude lower than the upper end of reported concentrations of the common C 3 oxygenates methylglyoxal and malonic acid. Additionally, we investigated the response in the TBA assay to seven common small oxygenates, and found interference only from acrolein, but only when the acrolein was at millimolar concentrations, well above expected levels in aerosol extracts. In sum, this work suggests that MDA is present at moderate levels in biomass burning and urban aerosols; more may be in the gas phase.
A Novel Deep Learning Network and Its Application for Pulmonary Nodule Segmentation

Pulmonary nodules are the early manifestation of lung cancer, which appear as circular shadow of no more than 3 cm on the computed tomography (CT) image. Accurate segmentation of the contours of pulmonary nodules can help doctors improve the efficiency of diagnosis. Deep learning has achieved great success in computer vision. In this study, we propose a novel network for pulmonary nodule segmentation from CT images based on U-NET. The proposed network has two merits: one is that it introduces dense connection to transfer and utilize features. Additionally, the problem of gradient disappearance can be avoided. The second is that it introduces a new loss function which is tolerance on the pixels near the borders of the nodule. Experimental results show that the proposed network at least achieves 1% improvement compared with other state-of-art networks in terms of different criteria.
Large‐Eddy Simulations of a Convection Cloud Chamber: Sensitivity to Bin Microphysics and Advection

Bin microphysics schemes are useful tools for cloud simulations and are often considered to provide a benchmark for model intercomparison. However, they may experience issues with numerical diffusion, which are not well quantified, and the transport of hydrometeors depends on the choice of advection scheme, which can also change cloud simulation results. Here, an atmospheric large‐eddy simulation model is adapted to simulate a statistically steady‐state cloud in a convection cloud chamber under well‐constrained conditions. Two bin microphysics schemes, a spectral bin method and the method of moments, as well as several advection methods for the transport of the microphysical variables are employed for model intercomparison. Results show that different combinations of microphysics and advection schemes can lead to considerable differences in simulated cloud properties, such as cloud droplet number concentration. We find that simulations using the advection scheme that suffers more from numerical diffusion tends to have a smaller droplet number concentration and liquid water content, while simulation with the microphysics scheme that suffers more from numerical diffusion tends to have a broader size distribution and thus larger mean droplet sizes. Sensitivities of simulations to bin resolution, spatial resolution, and temporal resolution are also tested. We find that refining the microphysical bin resolution leads to a broader cloud droplet size distribution due to the advection of hydrometeors. Our results provide insight for using different advection and microphysics schemes in cloud chamber simulations, which might also help understand the uncertainties of the schemes used in atmospheric cloud simulations.
Nexus Between Intellectual Capital and Profitability: Evidence from Listed SMEs in Malaysia

This paper examines the impact of intellectual capital and its components on profitability using the Modified Value Added Intellectual Coefficient (MVAIC) methodology. Data was collected from 26 listed SMEs in Malaysia from 2017-2019 providing 78 observations. This study employed the fixed-effect and random-effect models. The results showed that the MVAIC was significant and positively associated with profitability. Of the four components of the MVAIC, human capital efficiency and capital employed efficiency correlated positively and significantly with profitability. Structural capital efficiency was negatively associated with profitability. While, relational capital efficiency revealed an insignificant association with profitability. Overall, listed SMEs profitability was driven by both intellectual capital and physical capital, however the magnitude of contribution varied. The effect of capital employed efficiency presented a superior relative magnitude in comparison to human capital, structural capital and relational capital suggesting that physical capital was the main drive behind profitability. This study presented a robust theoretical foundation and employed a validated methodology. Therefore, it extends knowledge of intellectual capital among academicians and stakeholders as well as highlights its contribution to value creation. Keywords: intellectual capital, profitability, SMEs, MVAIC models
Spasticity Management after Spinal Cord Injury: The Here and Now

Spasticity is a common comorbidity of spinal cord injury (SCI) that is characterized by velocity dependent tone and spasms manifested by uninhibited reflex activity of muscles below the level of injury. For some, spasticity can be beneficial and facilitate functional standing, transfers, and some activities of daily living. For others, it may be problematic, painful, and interfere with mobility and function. This manuscript will address the anatomy and physiology of neuromuscular reflexes as well as the pathophysiology that occurs after SCI. Spasticity assessment will be discussed in terms of clinical history and findings on physical examinations, including responses to passive and active movement, deep tendon reflexes, and other long tract signs of upper motor neuron injury, as well as gait and function. Management strategies will be discussed including stretch, modalities, pharmacotherapy, neurolysis, and surgical options.
Impact of the COVID-19 Pandemic on the Motor Development of Schoolchildren in Rural and Urban Environments

Social and school closures during the COVID-19 pandemic may have led to significant stagnation in children's motor development, but precise data on this are lacking. We aimed to examine the impact of the pandemic and society closure on motor development of school children and to find differences between rural and urban environments. From the SLOfit database, we obtained anonymous results from 756 6th grade children before the pandemic (11.3 ± 0.5 years, 52.5% boys) who performed physical fitness measurements in 2017 and 2019 in 8th grade and from 853 6th grade children (11.4 ± 0.5 years, 51% boys) who performed measurements in 2019 and 2021, after 3 pandemic waves. The results of eight physical activity tests and the overall physical fitness index were compared between the prepandemic and the pandemic generation. We divided the sample into four groups (rural and urban prepandemic group and rural and urban pandemic group) and compared the changes in test scores between 6th and 8th grade. We found a statistically significant decrease in the physical fitness index of the pandemic generation (from 51.6 ± 29.6 to 45.8 ± 30.3) compared to the prepandemic generation (from 50.4 ± 30.5 to 50.5 ± 29.7), p < 0.001. The greatest effects of pandemic closure were found in the 600-meter run, in polygon course backwards test, in the number of sit-ups in 60 seconds, and in the 60-meter sprint. Children from rural areas showed worse decrement in physical fitness index compared to urban areas, except for 600-meter run. We conclude that the pandemic closure has had a significant inhibitory effect on the motor development of schoolchildren and has reduced their overall physical fitness with worse decline in rural areas. The pandemic generation of children needs more physical education in schools and other systemic interventions to mitigate these consequences.
Consumer importance classification based on VOLL

The concept of VOLL(Value Of Load Loss) is applied in load shedding during power system incidents and it is expanded from economy to safety,politics and environment,based on which an index system of power consumer importance classification with four first - level indexes is constructed for power consumers. According to the features of constructed index system and the property of selected data,the weights of these indexes are determined by the improved fuzzy hierarchy analysis method and dynamically adjusted by the entropy method. An example is given to show its application.
Self-Stabilizing Capacitated Vertex Cover Algorithms for Internet-of-Things-Enabled Wireless Sensor Networks

Wireless sensor networks (WSNs) achieving environmental sensing are fundamental communication layer technologies in the Internet of Things. Battery-powered sensor nodes may face many problems, such as battery drain and software problems. Therefore, the utilization of self-stabilization, which is one of the fault-tolerance techniques, brings the network back to its legitimate state when the topology is changed due to node leaves. In this technique, a scheduler decides on which nodes could execute their rules regarding spatial and temporal properties. A useful graph theoretical structure is the vertex cover that can be utilized in various WSN applications such as routing, clustering, replica placement and link monitoring. A capacitated vertex cover is the generalized version of the problem which restricts the number of edges covered by a vertex by applying a capacity constraint to limit the covered edge count. In this paper, we propose two self-stabilizing capacitated vertex cover algorithms for WSNs. To the best of our knowledge, these algorithms are the first attempts in this manner. The first algorithm is stabilized under an unfair distributed scheduler (that is, the scheduler which does not grant all enabled nodes to make their moves but guarantees the global progress of the system) at most O(n2) step, where n is the count of nodes. The second algorithm assumes 2-hop (degree 2) knowledge about the network and runs under the unfair scheduler, which subsumes the synchronous and distributed fair scheduler and stabilizes itself after O(n) moves in O(n) step, which is acceptable for most WSN setups. We theoretically analyze the algorithms to provide proof of correctness and their step complexities. Moreover, we provide simulation setups by applying IRIS sensor node parameters and compare our algorithms with their counterparts. The gathered measurements from the simulations revealed that the proposed algorithms are faster than their competitors, use less energy and offer better vertex cover solutions.
Luenberger Observer-Based Microgrid Control Strategy for Mixed Load Conditions

In this paper, a Luenberger observer-based microgrid control strategy is proposed to enhance the power quality of microgrids, when the grid loads are mixed and strongly non-linear. To improve performance under this condition, a Luenberger observer is designed for three phase power grids. On the basis of the observer, the components of different frequencies and sequences of voltages and currents are obtained accurately. The virtual impedance of different frequencies and sequences is designed, which makes the equivalent line impedance meet the power-sharing condition, reducing the fundamental negative sequence voltages and harmonic voltages. The active power droop equation, meanwhile, is proposed, where the bus voltage is modified. The value range of virtual impedance is discussed in the complex frequency domain. The proposed control strategy does not require any communication lines, so the hardware structure is simplified. The simulations and experiments are provided to verify the effectiveness of the proposed method.
Remote workers’ well-being, perceived productivity, and engagement: which resources should HRM improve during COVID-19? A longitudinal diary study

Abstract In European countries, the COVID-19 pandemic has given rise to a demanding work situation for office employees who have been required to work from home (hereinafter referred to as remote workers). Due to curfews, school closures, and leisure restrictions in Austria, remote work circumstances have changed compared to before the pandemic. By combining event system theory with transactional stress theory, we aim to identify the most important resources for maintaining remote workers’ well-being, perceived productivity, and engagement during the COVID-19 pandemic. We collected daily data (five working days for each remote worker) once from March to May 2020 (2222 employees) and again from November 2020 to January 2021 (1268 employees) and explored the role of personal (self-goal setting, self-efficacy, home-office experience), external (equipment at home), and organizational (work-related and social) resources for changes in well-being, perceived productivity, and engagement. Well-being and engagement decreased less when remote workers indicated high resources of self-efficacy and social support at the beginning of the crises. Results further reveal that an improvement in resources from the first to the second measurement was associated with a reduced decline in well-being, productivity, and engagement, respectively. We discuss implications for HRM and provide suggestions for future research.
Three-Dimensional Imaging of Circular Array Synthetic Aperture Sonar for Unmanned Surface Vehicle

Synthetic aperture sonar (SAS) and interferometric synthetic aperture sonar (InSAS) have a range layover phenomenon during underwater observation, the AUV-mounted circular synthetic aperture sonar (CSAS) system, that insonifies targets using multiple circular scans that vary in height and can eliminate the layover phenomenon. However, this observation method is time-consuming and difficult to compensate. To solve this problem, the circular array synthetic aperture sonar (CASAS) based on the equivalent phase center was established for unmanned surface vehicles. Corresponding to the echo signal model of circular array synthetic aperture sonar, a novel three-dimensional imaging algorithm was derived. Firstly, the echo datacube was processed by signal calibration with near-field to far-field transformation and grid interpolation. Then, the sparse recover method was adopted to achieve the scattering coefficient in the height direction by sparse Bayesian learning. Thirdly, the Fourier slice theorem was adopted to obtain the 2D image of the ground plane. After the reconstruction of all height slice cells was accomplished, the final 3D image was obtained. Numerical simulations and experiments using the USV-mounted CASAS system were performed. The imaging results verify the effectiveness of the 3D imaging algorithm for the proposed model and validate the feasibility of CASAS applied in underwater target imaging and detection.
Noise Aware L₂-LP Decomposition-Based Enhancement in Extremely Low Light Conditions With Web Application

In this article, an algorithm is introduced which not only focuses on enhancing low light images but also denoises images to produce more visually pleasing outputs. It is observed that various methods ignore noise during enhancement processes. This causes, enhancement of noise which leads to significant information lost. The paper projected a novel framework for low light images that performs enhancement and denoising jointly. The proposed paper uses <inline-formula> <tex-math notation="LaTeX">$L_{2}$ </tex-math></inline-formula>-<inline-formula> <tex-math notation="LaTeX">$L_{P}$ </tex-math></inline-formula> image decomposition to obtain reflectance and illumination concurrently. The illumination layer is corrected using the proposed weighting distribution and generates enhanced output. Further to this, noise suppressed bilateral filter is employed here for denoising process and gamma function is applied in subsequent step for additional contrast improvement. Experimental trials illustrate that the proposed method yields result with good contrast and brightness when it is compared to various enhancement techniques.