Miracle-dz/UniFAR-FTUs huggingface dataset:
Multi-View Attributed Graph Clustering

Multi-view graph clustering has been intensively investigated during the past years. However, existing methods are still limited in two main aspects. On the one hand, most of them can not deal with data that have both attributes and graphs. Nowadays, multi-view attributed graph data are ubiquitous and the need for effective clustering methods is growing. On the other hand, many state-of-the-art algorithms are either shallow or deep models. Shallow methods may seriously restrict their capacity for modeling complex data, while deep approaches often involve large number of parameters and are expensive to train in terms of running time and space needed. In this paper, we propose a novel multi-view attributed graph clustering (MAGC) framework, which exploits both node attributes and graphs. Our novelty lies in three aspects. First, instead of deep neural networks, we apply a graph filtering technique to achieve a smooth node representation. Second, the original graph could be noisy or incomplete and is not directly applicable, thus we learn a consensus graph from data by considering the heterogeneous views. Third, high-order relations are explored in a flexible way by designing a new regularizer. Extensive experiments demonstrate the superiority of our method in terms of effectiveness and efficiency.
Impacts of Low Disturbance Liquid Dairy Manure Incorporation on Alfalfa Yield and Fluxes of Ammonia, Nitrous Oxide, and Methane

Surface applied liquid dairy manure application (i.e., broadcasting) after alfalfa (Medicago sativa L.) harvest is a common practice. Low disturbance manure incorporation (LDMI) may offer multiple benefits including lower ammonia (NH3), greenhouse gas (GHG) and hydrologic nutrient losses compared to broadcast. However, few studies have simultaneously quantified LDMI impacts on alfalfa yield, NH3 and greenhouse gas (GHG) fluxes. We measured NH3, nitrous oxide (N2O), and methane (CH4) fluxes for liquid dairy manure treatments applied to alfalfa plots for broadcast and LDMI over three seasons (2014 to 2016) in central Wisconsin, USA. There were minor differences in alfalfa yield and nitrogen (N) uptake across treatments and years. Shallow disk injection and aerator/band reduced NH3 loss by 95 and 52% of broadcast, respectively, however both substantially increased N2O fluxes (6 and 4.5 kg ha−1 year−1 versus 3.6 kg ha−1 year−1 for broadcast, respectively). The magnitude and timing of N2O fluxes were related to manure application and precipitation events. Average CH4 fluxes were similar among methods and increased with soil moisture after manure application. Results highlight the importance of quantitatively evaluating agri-environmental tradeoffs of LDMI versus broadcast manure application for dairy farms.
Research on Force Sensing and Zero Gravity Motion Simulation Technology of Industrial Robot

Towards the zero-gravity simulation test requirements of spacecraft for on-orbit service missions, the zero-gravity motion simulation technology based on industrial robot was studied. In order to realize the accurate force sensing of load on robot, the BP neural network was used to establish the force prediction model. The model uses the robot pose, acceleration, angular velocity, and angular acceleration as input layer parameters, and uses the data from force/torque sensor on robot wrist as the output layer parameter. In order to realize the accurate force perception in whole working space of the robot, the orthogonal experiment design method was adopted to determine the path points of the robot for sample data collection. Based on the established force prediction model, the accurate force sensing of the end load of the robot under moving conditions is realized. In experiment, for the load with 100kg mass on robot, the maximum error of force sensing is 39.8N, and the root mean square error of force sensing is 9.3N. The maximum error of torque sensing is 18.7Nm, and the root mean square error of torque sensing is 4.4Nm. Furthermore, according to the real-time force sensing results of the robot load, the motion speed of the load in zero-gravity state is calculated by dynamics theory. Then the robot is driven to execute the corresponding motion of the load, and the zero gravity motion simulation of the load is realized.
VAGINAL PLATELET-RICH PLASMA ADMINISTRATION TO IMPROVE FEMALE SEXUAL SATISFACTION

Objective:To investigate the effect of platelet-rich plasma (PRP) injection to the lower one-third of the anterior vaginal wall on sexual function, orgasm, in women with sexual dysfunction by scoring with FSFI. Materials and Methods:Three sessions of PRP (platelet rich human autologous plasma) were administered to the anterior vaginal wall of fiftyfemale patients with sexual dysfunctiondiagnosed by FSFI scoring card obtained in JAM clinic in Benha city Egypt and orgasmic disorder. Female Sexual Function Index (FSFI) total score ÃƒÂ¢Ã‚Â‰Ã‚Â¤26 orgasmic subdomain score ÃƒÂ¢Ã‚Â‰Ã‚Â¤3.75 were considered having sexual dysfunction and recruited in the study. Results:Following the application of the PRP, the total FSFI score was observed as 27. 7ÃƒÂ‚Ã‚Â±4.5 and the total score was 26 and above in patients having sexual dysfunction (p<0.001). Orgasm subdomain scores were found as 2. 2ÃƒÂ‚Ã‚Â±1.2 before the PRP treatment and 4. 4ÃƒÂ‚Ã‚Â±1.06 at the third dose after (p<0.001). Conclusion:PRP administration to the distal anterior vaginal wall improved female sexuality with high satisfaction by using multiple sessions (three month).
Cystatin C and Acute Kidney Injury in Acute Heart Failure; Impact on Hospital Course

Introduction: Acute heart failure (AHF) is a life-threatening disease with a high mortality rate. Cardio-renal syndrome (CRS) has received much attention because of its relation to the poor prognosis of hospitalized patients. In both acute and chronic cardiac disease, kidney impairment is a significant predictor of outcome. Finding a more sensitive renal damage marker is a top priority due to the disadvantage of the existing marker creatinine. Several new acute kidney injury (AKI) biomarkers are being studied. Cystatin Cis a kidney function biomarker that has also been investigated for its use in acute kidney injury early detection. Aim: was to assess the outcome of using cystatin C as a new AKI biomarker in patients diagnosed with AHF and its effect on hospital stay and in-hospital mortality. Methods: Cystatin C levels were measured on admission and 48 hours later in 200 hospitalized AHF patients. The amount of increase in cystatin C after 48 hours was measured, as well as the impact of a cystatin C increase on the hospital course. Results: The mean population age was 66.4 years old, while 49% of the population were females. AKI was detected in 16% of patients with >0.3 mg/L rise in cystatin C within two days of hospital admission, and this led to 3 days (P = 0.01) significant more extended hospital stay while in-hospital mortality was more but could not reach statistical significance (P = 0.239). Conclusion: In patients with acute heart failure, elevation in Cystatin C within 48 hours of hospital admission is a good detector of early acute kidney injury and has a detrimental impact on their hospital course.
Kualitas Antarmuka Terhadap Minat Beli: Dimediasi Kepercayaan Konsumen pada Aplikasi Belanja Online

Penelitian menguji model pengaruh kualitas antarmuka terhadap minat beli konsumen dengan variabel mediasi kepercayaan konsumen kepada penjual di aplikasi belanja online Shopee.com. Penelitian menggunakan pendekatan kuantitatif dengan sampel konsumen Shopee.com sebanyak 152 orang. Analisis menggunakan model struktural diolah software SMARTPLS 3.0. Hasil penelitian ini membuktikan bahwa kualitas antarmuka tidak berpengaruh terhadap minat beli, kualitas antarmuka berpengaruh terhadap kepercayaan, dan kepercayaan berpengaruh terhadap minat beli. Kepercayaan memediasi kualitas antarmuka terhadap minat beli konsumen pada aplikasi belanja online Shopee.com, sehingga tanpa kepercayaan maka tidak akan ada minat beli pada konsumen belanja online. The study aims to examine the model of the influence of interface quality on consumer buying interest with the mediating variable of consumer trust in sellers in the Shopee.com online shopping application. The study uses a quantitative approach with a sample of 152 Shopee.com consumers. Analysis using a structural model processed by SMARTPLS 3.0 software. The results of this study prove that the quality of the interface has no effect on buying interest, the quality of the interface has an effect on trust, and trust has an effect on buying interest. Trust mediates the effect of interface quality on consumer buying interest in the Shopee.com online shopping application, so without trust there will be no buying interest in online shopping consumers.
Evaluation of MODIS Aerosol Optical Depth and Surface Data Using an Ensemble Modeling Approach to Assess PM2.5 Temporal and Spatial Distributions

The use of statistical models and machine-learning techniques along satellite-derived aerosol optical depth (AOD) is a promising method to estimate ground-level particulate matter with an aerodynamic diameter of ≤2.5 μm (PM2.5), mainly in urban areas with low air quality monitor density. Nevertheless, the relationship between AOD and ground-level PM2.5 varies spatiotemporally and differences related to spatial domains, temporal schemes, and seasonal variations must be assessed. Here, an ensemble multiple linear regression (EMLR) model and an ensemble neural network (ENN) model were developed to estimate PM2.5 levels in the Monterrey Metropolitan Area (MMA), the second largest urban center in Mexico. Four AOD-SDSs (Scientific Datasets) from MODIS Collection 6 were tested using three spatial domains and two temporal schemes. The best model performance was obtained using AOD at 0.55 µm from MODIS-Aqua at a spatial resolution of 3 km, along meteorological parameters and daily scheme. EMLR yielded a correlation coefficient (R) of ~0.57 and a root mean square error (RMSE) of ~7.00 μg m−3. ENN performed better than EMLR, with an R of ~0.78 and RMSE of ~5.43 μg m−3. Satellite-derived AOD in combination with meteorology data allowed for the estimation of PM2.5 distributions in an urban area with low air quality monitor density.
Alpha and Theta Transcranial Alternating Current Stimulation Over the Right Dorsolateral Prefrontal Cortex Modulates Vigilance Performance, but Only When Arousal Levels Are Non-optimal

BackgroundCurrent theoretical accounts on the oscillatory nature of sustained attention predict that entrainment via transcranial alternating current stimulation (tACS) at alpha and theta frequencies on the frontoparietal network could prevent the drops in vigilance across time-on-task. Nonetheless, most previous studies have neglected both the fact that vigilance comprises two dissociable components (i.e. arousal and executive vigilance) and the potential role of differences in arousal baseline. MethodWe examined the effects of theta- and alpha-tACS over the right dorsolateral prefrontal cortex on both components of vigilance and on participants that differed in arousal baseline according to their chronotype and the time of testing. Intermediate-types performed the vigilance tasks when their arousal baseline was at the optimal level, whereas evening-types performed the vigilance tasks when their arousal baseline was at non-optimal levels. ResultsBoth theta- and alpha-tACS improved arousal vigilance, whereas alpha-tACS, but not theta-tACS, improved accuracy and attenuated the typical vigilance decrement in the executive vigilance task. Importantly, these stimulation effects were only found when arousal baseline was low (i.e., with evening-types performing the tasks at their non-optimal time of day). ConclusionThe results support the multicomponent view of vigilance, the relevance of heeding individual differences in arousal baseline, and the role of alpha oscillations as a long-range cortical scale synchronization mechanism that compensates the decrements in performance as a function of time-on-task by exerting and maintaining cognitive control attributed to activation of the frontoparietal network.
Comparison of acquisition and iterative reconstruction parameters in abdominal computed tomography-guided procedures: a phantom study.

Background Many computed tomography (CT) navigation systems have been developed to help radiologists improve the accuracy and safety of the procedure. We evaluated the accuracy of one CT computer-assisted guided procedure with different reduction dose protocols. Methods A total of 128 punctures were randomly made by two operators on two different anthropomorphic phantoms. The tube voltage was fixed to 100 kVp. Tube currents (mAs) were defined to obtain 4 dose levels: 180 mAs (D1.00), 90 mAs (D0.50), 45 mAs (D0.25) and 15 mAs (D0.10) with respective volume CT dose index (CTDIvol) of 7.02, 3.52, 1.75 and 0.59 mGy. The raw data were reconstructed using level 2 of advanced model-based iterative reconstruction (ADMIRE) (A2) for D1.00, A3 for D0.50, A4 for D0.25 and A5 for D0.10. Two 12-mm targets per phantom were selected. The mean Euclidean distance (EuD) between the tip of the needle and the isocenter of the target was measured for each puncture. The different measures were compared by paired Student's t-tests. Results The mean EuD was 7.0±3.1 mm for the 128 punctures performed. Regardless of which phantom was considered, no significant difference in accuracy occurred between the 4 dose levels, which were 7.1±3.5 mm for D1.00; 7.1±3.1 mm for D0.50; 7.2±3.0 mm for D0.25 and 6.6±2.6 mm for D0.10. Conclusions Abdominal CT-guided procedures, using computer-assisted navigation and iterative reconstruction algorithms, allow precise punctures on anthropomorphic phantoms with a dose reduction of -92% compared to a standard protocol.
Short-sale deregulation and corporate tax aggressiveness: evidence from the Chinese market

We study whether short selling affects corporate tax aggressiveness. Exploiting staggered short-sale deregulation in the Chinese stock market as a source of variation in market pressure and monitoring, our difference-in-differences estimates show that the introduction of a short-selling scheme significantly discourages pilot firms from engaging in aggressive tax avoidance, in contrast to the findings by Luo, Ni, and Tian (2020. “Short Selling and Corporate Tax Avoidance: Insights from a Financial Constraint View.” Pacific–Basin Finance Journal 61: 101323.). We also find that the negative effect of short selling on tax aggressiveness is more pronounced for firms that have high advertising costs, high institutional holdings, and CEO duality, and are located in regions with weak tax law enforcement. We further reveal that short selling has an indirect effect on tax aggressiveness through the additional external pressure exerted by auditors, media, and financial analysts, and lastly, challenge the main analysis by Luo, Ni, and Tian (2020). Our evidence highlights the monitoring and disciplinary roles that short sellers play in determining the level of corporate tax aggressiveness.
Two-Dimensional Shape Analysis of Complex Geometry Based on Photogrammetric Models of Iconostases

Three-dimensional digitization technologies have been proved as reliable methods for detailed and accurate spatial data collection from existing cultural heritage. In addition, the point segmentation techniques are particularly relevant for contour detection and classification of the unstructured point cloud. This paper describes an approach to obtain 2D CAD-like visualizations of complex geometry from photogrammetric models so that the detected contours of particular object elements can be used for 2D shape analysis. The work process uses the point clouds derived from photogrammetric models to create the plane visualization of the object’s geometry by segmenting points based on the verticality geometric feature. The research presented is on the case studies of iconostases as the specific art and architectural elements of the Christian Orthodox church that can be appreciated only in situ. To determine relations between the characteristics of the particular shapes and the iconostases’ style origins, the mathematical method of shape analysis was applied. This study aims to numerically describe the stylistic characteristics of the shapes of the main parts of the iconostasis concerning the artistic period to which it belongs to. The concept was based on the consideration of global shape descriptors and associated shape measurements which were used to analyze and classify the stylistic characteristics of the iconostases. The methodology was applied to the representative examples of three iconostases from the Baroque and Classicism art movements. The results illustrated that the proposed methods and techniques, with certain improvements, could be helpful for CAD visualization and shape analysis of complex geometry.
A Predictive Control Strategy for Aerial Payload Transportation with an Unmanned Aerial Vehicle

This paper presents the results of a model-based predictive control (MPC) design for a quadrotor aerial vehicle with a suspended load. Unlike previous works, the controller takes into account the hanging payload dynamics, the dynamics in three-dimensional space, and the vehicle rotation, achieving a good balance between fast stabilization times and small swing angles. The mathematical model is based on the Euler–Lagrange formulation and considers the dynamics of the vehicle, the cable, and the load. Then, the mathematical model is represented as an input-affine system to obtain the linear model for the control design. A constrained MPC strategy was designed and compared with an unconstrained MPC and an algorithm from the literature for the case of study. The constraints to be considered include the limits on the swing angles and the quadrotor position. The constrained control algorithm was constructed to stabilize the aerial vehicle. It aims to track a trajectory reference while attenuating the load swing, considering a maximum swing range of ±10∘. Numerical simulations were carried out to validate the control strategy.
QUALITY OF LAYING EGGS USING PREBIOTIC ADDITIVES CREATED IN CAGE

This study aims to evaluate the quality of eggs in laying hens reared at different densities in cage-free system, receiving diets with the addition of prebiotic additives. An observational study was carried out with 45 laying hens of the Novogen Brown scaling, which was in the phase of peak production (28 – 37 weeks), in prototypes of reduced scale without air conditioning and divided into group 1 (G1): density of two poultry /m² (11 poultry ) and diet with addition of a prebiotic pool; group 2 (G2): density of three poultry /m² (17 poultry ) and commercial diet without the addition of prebiotics and antibiotics; group 3 (G3): density of three poultry /m² (17 poultry ) and also diet with the addition of a prebiotic pool. In this study, the statistical evaluation was performed, the effects of densities 2 poultry /m² (G1) or 3 poultry /m² (G3) and the effects of diet with prebiotics (G3) and without prebiotics (G2) in laying poultry were performed. The data were submitted to variance analysis by the GLM procedure of the SAS Program V9 (2002), using the Student's T Test at a level of 5% significance. The results referring to eggeso (g), albumen height (mm), yolk color, haugh unit (UH), peel resistance (Kgf) between G1 and G3 showed no statistically difference, since in relation to the thickness of the shell (mm) differed (P>0.05). In the analysis between G2 and G3, the statistical difference (P>0.05) obtained among all the panels analyzed. In the face of the above, it can be reported that the results of quality variables undergo the action of prebiotics in laying hens created in the Cage-free system.
Graph-Convolved Factorization Machines for Personalized Recommendation

Factorization machines (FMs) and their neural network variants (neural FMs) for modeling second-order feature interactions are effective in building modern recommendation systems. However, feature interactions are based upon pairs of features, whereas multi-features correlations commonly arise in real-world financial product recommendation scenarios. We propose an effective neural recommender system, graph-convolved factorization machine (GCFM), with the spirit of the symbolic graph reasoning principle that provides lightweight and interpretable recommendation suggestions. Given a sample for the recommendation, GCFM constructs the corresponding dense feature embeddings and computes the sample-specific feature relationship graph. Then, a multi-filter graph-convolved feature crossing (GCFC) layer for feature embeddings establishes cross features with their neighboring embeddings. GCFM thus extends the feature interactions from pairs to neighbors to capture more comprehensive and explainable information while simultaneously reaping the advantages of representation learning. To exploit these capabilities, we apply a Graph Bayesian Optimization (GBO). During training, our GBO automatically optimizes our GCFM, including training hyperparameters and architecture hyperparameters. Besides, we conduct extensive experiments on two public financial applications benchmarks, USCFC and OTC, and two real-world datasets that we collect offline. Our GCFM significantly outperforms state-of-the-art algorithms and shows its interpretability in recommendation tasks. We further extend our model to online real-world applications, showing an appealing human-level decision intelligence in real scenarios.
Traditional Treatment for Rheumatoid Arthritis

The most prevalent musculoskeletal disorder is rheumatoid arthritis (RA). The main concern with RA is extreme fatigue, pain, and weakness. Patients having severe pain are compelled to take medications containing a variety of indigenous substances. These indigenous substances, on the other hand, exacerbated illnesses and delay in seeking appropriate healthcare. Treatment is delayed due to a number of reasons, including patients’ lack of access to trained healthcare professionals, delays in referral to a rheumatologist, and patients’ belief on traditional healing practices. The choice of inappropriate healthcare providers often causes a delay in referral to a rheumatologist. Self-medication and seeking treatment from traditional healers are often compelled the patient to engage in a variety of traditional practices. Cultural values have a significant influence on care-seeking behavior. Since healthcare promotion is dictated by community demands, the healthcare system should understand the contextual phenomena behind common practices for better health education. This chapter will address the beliefs and values that underpin traditional treatment, the sources of traditional learning pathways and ethical aspects of traditional practice.