Miracle-dz/UniFAR-FTUs huggingface dataset:
Deep Learning Modeling of a WBAN-MIMO Channel in Underground mine

In this paper, an efficient model of the channel matrix is developed for 2 × 2 Wireless Body Area Network Multiple Input Output (WBAN-MIMO) system, based on deep learning algorithms. The model is composed of three deep learning algorithms. Moreover, the model predicts simultaneously the channel matrix H in underground mine and identifies the position of the collected data in both Line of Sight (LoS) and Non-Line of the Sight (NLoS) scenarios. The model is trained and evaluated using the magnitude and phase of the collected data in an underground mine environment within the frequency range of 2.3 GHz – 2.5 GHz. These measurements, conducted with different antenna configurations in LoS and NLoS scenarios, constitute an input to the model. The latest predicts the channel matrix H with the position and identifies whether the channel is LoS or NLoS. Finally, the path loss and the channel impulse response models are compared with the measurements-based ones. The modeled channel prediction exhibited lower Root Mean Square Error (RMSE) for channel prediction and high classification accuracy for LoS-NLoS and position identification, respectively. The numerical results reveal that the deep learning MIMO WBAN modeling offers a powerful solution for future wireless systems in underground mine environments.
In vivo Mouse Intervertebral Disc Degeneration Models and Their Utility as Translational Models of Clinical Discogenic Back Pain: A Comparative Review

Low back pain is a leading cause of disability worldwide and studies have demonstrated intervertebral disc (IVD) degeneration as a major risk factor. While many in vitro models have been developed and used to study IVD pathophysiology and therapeutic strategies, the etiology of IVD degeneration is a complex multifactorial process involving crosstalk of nearby tissues and systemic effects. Thus, the use of appropriate in vivo models is necessary to fully understand the associated molecular, structural, and functional changes and how they relate to pain. Mouse models have been widely adopted due to accessibility and ease of genetic manipulation compared to other animal models. Despite their small size, mice lumbar discs demonstrate significant similarities to the human IVD in terms of geometry, structure, and mechanical properties. While several different mouse models of IVD degeneration exist, greater standardization of the methods for inducing degeneration and the development of a consistent set of output measurements could allow mouse models to become a stronger tool for clinical translation. This article reviews current mouse models of IVD degeneration in the context of clinical translation and highlights a critical set of output measurements for studying disease pathology or screening regenerative therapies with an emphasis on pain phenotyping. First, we summarized and categorized these models into genetic, age-related, and mechanically induced. Then, the outcome parameters assessed in these models are compared including, molecular, cellular, functional/structural, and pain assessments for both evoked and spontaneous pain. These comparisons highlight a set of potential key parameters that can be used to validate the model and inform its utility to screen potential therapies for IVD degeneration and their translation to the human condition. As treatment of symptomatic pain is important, this review provides an emphasis on critical pain-like behavior assessments in mice and explores current behavioral assessments relevant to discogenic back pain. Overall, the specific research question was determined to be essential to identify the relevant model with histological staining, imaging, extracellular matrix composition, mechanics, and pain as critical parameters for assessing degeneration and regenerative strategies.
Short-Term Traffic Flow Forecasting for Urban Roads Using Data-Driven Feature Selection Strategy and Bias-Corrected Random Forests

Urban traffic flow forecasting is essential to proactive traffic control and management. Most existing forecasting methods depend on proper and reliable input features, for example, weather conditions and spatiotemporal lagged variables of traffic flow. However, the feature selection process is often done manually without comprehensive evaluation and leads to inaccurate results. For that challenge, this paper presents an approach combining the bias-corrected random forests algorithm with a data-driven feature selection strategy for short-term urban traffic flow forecasting. First, several input features were extracted from traffic flow time series data. Then the importance of these features was quantified with the permutation importance measure. Next, a data-driven feature selection strategy was introduced to identify the most important features. Finally, the forecasting model was built on the bias-corrected random forests algorithm and the selected features. The proposed approach was validated with data collected from three types of urban roads (expressway, major arterial, and minor arterial) in Kunshan City, China. The proposed approach was also compared with 10 existing approaches to verify its effectiveness. The results of the validation and comparison show that even without further model tuning, the proposed approach achieves the lowest average mean absolute error and root mean square error on six stations while it achieves the second-best average performance in mean absolute percentage error. Meanwhile, the training efficiency is improved compared with the original random forests method owing to the use of the feature selection strategy.
COVID-19 Victimization Experience and College Students’ Mobile Phone Addiction: A Moderated Mediation Effect of Future Anxiety and Mindfulness

This study proposed a moderated mediation model to investigate the association between COVID-19 victimization experience and mobile phone addiction, the mediating role of future anxiety, and the moderating role of mindfulness. This study employed the COVID-19 victimization experience scale, the mobile phone addiction scale, a future anxiety scale, and a mindfulness scale in a survey study among Chinese college students; 840 valid questionnaires were received. The reliability and confirmatory factor analysis results showed that all four scales had good reliability and validity. Bootstrap results demonstrated that COVID-19 victimization experience significantly predicted mobile phone addiction in college students (B = 0.202, LLCI = 0.136, ULCI = 0.268). Future anxiety fully mediated the association between COVID-19 victimization experience and mobile phone addiction (B = 0.178, LLCI = 0.136, ULCI = 0.222). Mindfulness moderated the effect of COVID-19 victimization experience on the college students’ future anxiety (B = 0.159, LLCI = 0.007, ULCI = 0.054). A higher level of mindfulness was more likely than a lower level of mindfulness to attenuate the effect of COVID-19 victimization experience on the college students’ future anxiety. These findings broaden our understanding regarding the association between COVID-19 victimization experience and mobile phone addiction and the moderating role of mindfulness.
Accelerating Effects of Flow Behavior Index n on Breakup Dynamics for Droplet Evolution in Non-Newtonian Fluids

This paper studied the evolution of NaAlg solution micro-droplet in a coaxial microchannel. The Bird–Carreau model was used to characterize the flow properties of NaAlg solution. As the mass fraction decreased, the flow behavior index n also decreased, indicating that the NaAlg solution was increasingly shear-thinning. There were three stages during the micro-droplet evolution, which were the growth stage, the squeezing stage, and the pinch-off stage. This paper led the flow behavior index n to estimate the effects of rheological property on the breakup dynamics of micro-droplet. We proposed two new prediction models of the minimum neck width wm which were affected by |n| in the squeezing and pinch-off stages for the non-Newtonian fluids. In addition, this paper indicated the rate ratio Qd/Qc was another factor on the wm model in the squeezing stage and the H(λ) of Stokes mechanism was a function governed by |n|2 in the pinch-off stage.
Global maximal Sharpe ratios for active portfolios

ABSTRACT Active fund performance is assessed relative to specified benchmarks. Fund managers aim to generate portfolio returns higher than the benchmarks while adhering to constraints such as tracking errors (TE) and asset weight restrictions. For each TE, the maximum active portfolio return and associated absolute portfolio risk traces out a hyperbolic TE frontier in risk/return space. The locus of all possible active portfolio coordinates for a given level of TE traces out a closed, distorted ellipse in risk/return space. Attempts to optimize active portfolio performance at a given TE include the maximum active return portfolio, the maximum active return portfolio with the same absolute risk as the benchmark and the tangent portfolio (maximal risk adjusted return or Sharpe ratio (SR)). Using the maximum SR approach, the locus of maximal, active portfolio SRs for different TEs is roughly parabolic with a clearly defined maximum at a unique TE. We derive a solution for identifying these global maximum SR portfolios and find that these occur at feasible TEs (i.e. – within the range of most active portfolios) but only if inter-asset correlations are relatively low ( ). For asset correlations , maximal SRs occur at unrealistically high TEs ( ).
LPPN: A Lightweight Network for Fast Phase Picking

We here present one lightweight phase picking network (LPPN) to pick P/S phases from continuous seismic recordings. It first classifies the phase type for a segment of waveform, and then performs regression to get accurate phase arrival time. The network is optimized using deep separable convolution to reduce the number of trainable parameters and improve its computation efficiency. Experiments using the STanford EArthquake Dataset (STEAD) show that the precision of LPPN can reach 95.2% and 83.7% with the recalls 94.4% and 84.7% for P and S phases, respectively. The classification–regression approach shows comparable performance to traditional point-to-point methods with lower computation cost. LPPN can be configured to have different model size and run on a wide range of devices. It is open-source and can support phase picking for large-scale dataset or in other speed sensitive scenarios.
Insight Into Mouse Models of Hyperthyroidism

Hyperthyroidism is characterized by an increase in the synthesis and secretion of thyroid hormones in the thyroid gland, and the most common cause of overproduction of thyroid hormones is Graves’ disease (GD). Long-term disease models of hyperthyroidism have been established. In general, methods to induce GD include transfection of fibroblasts, injecting plasmids or adenovirus containing thyroid stimulating hormone receptor (TSHR) or TSHR subunit, and exogenous artificial thyroid hormone supplementation. Fortunately, in mouse studies, novel treatments for GD and Graves’ orbitopathy (GO) were discovered. It has been reported that prophylactic administration of TSHR A subunit protein in genetically susceptible individuals could induce immune tolerance and provide protection for the future development of GD. Biologically active monoclonal antibody against intracellular adhesion molecule-1 (ICAM-1 mAb) and siRNA targeting TSHR can also be used to treat GD. Moreover, new potential therapeutic targets have been identified in GO mouse models, and these targets could present novel therapeutic approaches. Besides, human placental mesenchymal stem cells (hPMSCs) into the orbit, fucoxanthin and icariin may be new alternative therapies that could be used in addition to the existing drugs, although further research is needed.
The effect of 40 Hz binaural beats on working memory

Binaural beats (BB), in which pure tones are delivered at fixed frequency, seek to influence neural activity during cognitive tasks and induce frequency-following response which can entrain brain rhythms to the frequency of external stimulation. An increasing number of studies have suggested that BB can be used to alter or enhance cognitive processes. This study aimed to explore the effect of 40 Hz binaural beats on working memory. In addition to further verifying whether there was a frequency-following response, changes in microstates were observed, providing new evidence for the effectiveness of binaural beats. 40 healthy volunteers completed visuospatial and verbal working memory tasks when listening to pink noise and 40 Hz binaural beats (R: 440 Hz, L: 400 Hz). The EEG was analyzed in terms of behavioral performance, relative power, Higuchi fractal dimension (HFD) and microstates after BB stimulation. We found that subjects performed better on the working memory task when stimulated by 40 Hz binaural beats, with significantly higher HFD in the temporal and parietal lobes and a significant positive correlation between HFD and relative power in the gamma band, in addition to a significant increase in the duration and coverage of microstate D and a significant decrease in those of microstate A. Our results demonstrate that 40 Hz binaural beats improve working memory, and induce frequency-following responses. Besides, changes in both HFD and microstates are correlated with attention, which may also account for the changes in working memory performance affected by 40 Hz binaural beats.
Simulation Research on Driving Behaviour of Autonomous Vehicles on Expressway Ramp Under the Background of Vehicle-Road Coordination

: Constructing a risk model with the subject of autonomous vehicles to screen out the vehicles of potential conflicts and analyze their choices under different strategies. Based on the co-simulation of Python and SUMO, establishing a model of on-ramp merge driving behaviour of autonomous vehicles based on non-cooperative static game. Under this model, the experiment results that the average speed in the merging area is increased by 12.7%, the standard deviation of the average speed is reduced by 35.46%, and the number of the vehicles successfully merged before the end of the merging area is 4.86 times that of traditional method, indicate that the model can effectively help the vehicles be merged and improve the traffic efficiency to a certain extent.
JACoW : Wall current monitor using PXI and LabVIEW at CERN

The new data acquisition system for the PS ring wall current monitors installed in the PS is able to perform high frequency measurements of a beam bunch up to a frequency of 2.7 GHz. This is an important improvement, since the oscillating signal within the bandwidth 500-700 MHz, is related to losses of a beam bunch. The losses could be eventually reduced by measuring the frequency and classifying the cause of the oscillations. The PXI-5661 is used to carry out spectral analysis of this signal. The acquisition is performed on a PXI running LabVIEW Real-Time and synchronized using a trigger from the accelerator timing system.
Antiproliferative and Antimicrobial Effects of Rosmarinus officinalis L. Loaded Liposomes

Rosmarinus officinalis L. is a species that is widely known for its culinary and medicinal uses. The purpose of the present study consisted of the evaluation of the antiproliferative and antimicrobial effects of R. officinalis-loaded liposomes (L-R). Characterization of the liposomes was performed by establishing specific parameters. The load of the obtained liposomes was analyzed using an LC-MS method, and antiproliferative assays evaluated the cell viability on a liver adenocarcinoma cell line and on a human hepatic stellate cell line. Antimicrobial assays were performed by agar–well diffusion and by broth microdilution assays. The obtained liposomes showed high encapsulation efficiency, suitable particle size, and good stability. High amounts of caffeic (81.07 ± 0.76), chlorogenic (14.10 ± 0.12), carnosic (20.03 ± 0.16), rosmarinic (39.81 ± 0.35), and ellagic (880.02 ± 0.14) acids were found in their composition, together with other polyphenols. Viability and apoptosis assays showed an intense effect on the cancerous cell line and a totally different pattern on the normal cells, indicating a selective toxicity towards the cancerous ones and an anti-proliferative mechanism. Antimicrobial potential was noticed against all tested bacteria, with a better efficacy towards Gram-positive species. These results further confirm the biological activities of R. officinalis leaf extract, and proposes and characterizes novel delivery systems for their encapsulation, enhancing the biological activities of polyphenols, and overcoming their limitations.
Izkušnje učiteljev s stresom med poučevanjem na daljavo: izkušnje, pridobljene v času pandemije COVID-19

Purpose: Lots of research has been published about the impact of COVID-19 pandemic on education, students in particular. However, little is known about the challenges and stress that Slovenian professors and assistant professors experienced during and after the lockdown, which is what this survey aims to address. Methodology: The survey was created in the 1KA survey platform and carried out between 27 May 2021 and 27 August 2021. We invited 120 higher education institutions to participate in the survey, out of which 30 responded. As a result, a total of 85 respondents were surveyed. Results: The results indicated that Slovenian professors and assistant professors were well versed with digital technology before the COVID-19 pandemic. Most of them were well equipped with digital skills and had experience with remote access, however, they had less experience with e-learning, video-conferencing and preparing video contents. During e-learning their work overload increased and they were putting in longer hours. Students demanded more instructions and guidance on distance learning, in terms of motivation, engagement and in developing confidence to give good multimedia presentation. However, despite all these, we have seen no significant increase in stress levels in professors and the majority of them felt able to cope with stress in a functional way.
Shear behavior of glass FRP bars‐reinforced ultra‐high performance concrete I‐shaped beams

In this paper, the shear behavior of glass fiber reinforced polymer (GFRP) bars reinforced ultra‐high‐performance concrete (UHPC) beams was investigated through experimental tests. Eight GFRP bars reinforced UHPC I‐beams were tested until shear failure with various stirrup ratio, reinforcement ratio, and shear span to depth ratio. The shear capacity, load–deflection relationship, cracking pattern, and failure mode were investigated in detail. The results show that the shear span to depth ratio has the greatest influence on the shear capacity of the beam among the three parameters, followed by the stirrup ratio and reinforcement ratio. The stirrup configuration can significantly improve the shear capacity and deformation resistance of the beam and can effectively reduce the stress concentration caused by the uneven distribution of steel fibers, which affects the failure mode of the beam. Increasing the stirrup ratio and reinforcement rate can improve the stiffness of the beam after cracking, and the larger the shear span to depth ratio is, the more significant the improvement effect is. Moreover, the stirrup enables the full development of the tensile capacity of GFRP bars. The existing equations of shear strength from five design codes and seven literatures are compared to the experimental results of 54 UHPC beams. It showed that the formula from the codes AFGC‐2013 and JSCE‐2006 codes is more accurate. The equations by Kwak, Jin, and Thiemicke's provide the best predictions.
Patient-reported outcome measures in MS: Do development processes and patient involvement support valid quantification of clinically important variables?

Background Patient-reported outcomes (PROs) are widely measured in multiple sclerosis (MS) studies. However, the quality of instrument development processes varies, raising concerns about the meaningfulness of associated data. Objectives To review the development of selected PROs commonly used in MS studies, including definitions of the concepts measured, use of conceptual frameworks, and degree of input from people living with MS (PlwMS). To gain insights and recommendations from PlwMS on their experience with these PROs. Methods We assessed 6 PROs (FSIQ-RMS, modified-FIS, MSQoL-54, Leeds 8-item MSQoL, MSIS-29 and EQ-5D) for alignment with regulatory and scientific requirements on PRO structure/development. PlwMS evaluated the degree to which the PROs reflect disease aspects they perceive important. Results Definitions, clarifications and conceptualisations of the measurement variables were often lacking. PlwMS were variably involved in PRO development. Ethnic diversity was rarely documented. PlwMS identified individualisation, ease of understanding, time burden, and mode of administration as factors affecting PRO usability. Conclusions To date, the PRO development process has consistently lacked clear definitions of concepts of interest, use of conceptual frameworks and patient involvement, thereby compromising the validity of data they generate. PRO instrument development must be conducted more robustly to maximise the value of pivotal clinical trials.
Land use and altitude: how do they influence the phosphorus fractions?

Soil use and management systems modify phosphorus (P) dynamics by changing the different P compartments. However, the interaction between land-use change and altitude is not clear. This study aimed to evaluate the effect of land use on P forms in different soil aggregate classes of Ultisols located at two altitudes. Soil samples were collected in four land-use systems (agroforestry system, secondary forest, pasture, and eucalyptus) located at two altitudes in the Atlantic Forest biome, Brazil. Phosphorus fractions were assessed using sequential fractionation. In general, recalcitrant forms represented about 70% in the agroforestry system, 66% in the eucalyptus, 65% in the pasture, and 57% in the secondary forest relative to the total P content of all land uses at both altitudes. The soil aggregate classes of smaller diameter presented higher P contents in all fractions. The smallest soil aggregate classes showed up to 65% more P than the classes with smaller diameters in the different land-use systems and altitudes.
Quality management vision of future early career operations managers

Purpose The purpose of this paper is to investigate the gap between the current vision and knowledge of future early career operations managers (OM) and a common strategic total quality management (TQM) framework. Design/methodology/approach A survey questionnaire and a non-parametric test for different groups of participants were adopted to identify the gap and analyse the significance of these groups on the factors in the TQM framework. Findings A new set of TQM factors with the necessity of more knowledge and understanding of future generation were identified, followed by the identification of clear differences amongst different groups of this generation. Practical implications A sustainable OM practice needs managers and leaders with a sustainable knowledge development of quality management (QM); and as the result of this study, the current vision of future young OM would not echo this. Originality/value This study has a systematic, non-parametric approach towards currently fragmented QM analysis, and is integrated with human resource and visionary elements of future young OM and universal QM models and theories.