Miracle-dz/UniFAR-FTUs huggingface dataset:
Sharing Economy with the Use of Car-Sharing Applications: An Empirical Study in Vietnam

The study aims to test the model of Yang et al. (2017) for the overall assessment of the transportation sector in the sharing economy and add some new elements on the benefits of customer loyalty from some previous studies that were not mentioned in Yang’s model. This study will take examples from the two big brands of car-sharing apps in the sharing economy in Vietnam Uber, and Grab. This research focuses on customer loyalty in the sharing economy with Uber and Grab as the transport sector in Ho Chi Minh City. Based on the survey data of 380 customers in Vietnam, the research results show that two independent factors positively impact commitment and satisfaction: social benefits and economic benefits. Simultaneously, economic benefit has a positive effect on satisfaction while trust benefit has a negative effect on commitment. Finally, commitment has a positive impact on customer loyalty in the sharing economy. As a result, companies in the sharing economy can identify which are the key factors that strongly influence customer satisfaction, commitment, trust, and loyalty to help managers to devise the appropriate solutions for promoting the strengths or overcoming limitations to contribute to perfecting and improving the quality of the company’s services.
Considering the benefits of research participation: insights from a study of adult EAL educators

Despite the professional learning benefits that may be realised through participation in research, many institutions and teachers are reluctant to get involved. They (correctly) anticipate that it will require some time, effort, and commitment. They may understand that research is important for improving education practices but more direct and immediate value for them and, importantly, how to gain it may not be obvious. To address this issue, we report a part of a six-month study that used institutional ethnography as the method of inquiry. We present and analyse three generative episodes that we observed and experienced in the context of our research collaboration with the participants at one adult community-based English as an Additional Language (EAL) institution in Melbourne (Australia). These episodes provide important insights into the ways in which our participants were proactive in realising the benefits of participating in the research. The participating teachers brought research and practice into regular dialogue and strategically utilised our partnership for their professional learning. We conclude by discussing some practical strategies for EAL institutions, teachers, and researchers who want to unlock and maximise the learning potential of research partnerships.
TURBOFAN BROADBAND NOISE PREDICTIONS BASED ON A ZDES CALCULATION OF A FAN-OGV STAGE

This paper presents undergoing acoustic analyses of an hybrid RANS/LES calculation performed at ONERA in the framework of an European project TurboNoiseBB. The numerical approach proposed here relies on a Zonal Detached Eddy Simulation strategy that is applied to a fan module equipped with 20 rotor blades and 44 outlet guide vanes and tested in AneCom facility (Wildau, Germany). The ZDES method with detailed numerical setup and aerodynamic flow features are discussed in a companion paper and the present one mainly focuses on acoustic analyses. Post-processed data are used to feed acoustic codes based on Amiet theory and Ffowcs-Williams and Hawkings analogy for which required inputs to assess rotor-stator interaction noise are the turbulence wake information and the unsteady pressure over the vane wall, respectively. The turbulent wake characteristics in terms of energy and spectral content are compared to hot-wire measurements. In particular radial profiles of turbulent velocity components and power spectrum density at selected duct height positions are analyzed, as well as integral and spanwise correlation length scales. Hence, sound power spectra in the bypass predicted by both acoustic codes are presented and compared to the experiment.
Vertical Structure-Based Classification of Oceanic Eddy Using 3-D Convolutional Neural Network

The eddy identification is an important part of human cognition of the ocean. Significant achievements have been made by using sea level anomaly (SLA) data observed by the altimeter. However, the abundant eddies, which do not cause sea surface characteristic anomalies, cannot be identified. In this study, the eddy subsurface vertical structure-oriented 3-D neural network is developed to classify the oceanic eddies. This study is among the first that explores the ability of deep learning in eddy identification with vertical structure. First, the purified eddy profiles dataset is constructed based on the fact that the structure derived from vertical profiles is highly correlated with the sea surface topography detected by altimetry. Then, the eddy vertical structure-oriented 3-D neural network based on the residual network (ResNet) is constructed, which can classify the eddies as anticyclonic eddies (AEs), cyclonic eddies (CEs), and noneddies (NEs) effectively. Furthermore, the spatial and temporal features can be combined in the proposed network as external factors. Meanwhile, through 3-D convolutions and 3-D pooling, the proposed network is capable of modeling 3-D eddy data and can be extended to the deeper network structure. Finally, the classification experiments are implemented to validate the performance of the proposed methodology. The most striking result emerging from experiments is that the proposed method can expand the capacity of eddy identification by using vertical profiles as calibrated by altimetry with competitive classification performance. Together these results provide important insights into the application of artificial intelligence in oceanic eddy research.
Detection of 1,5-anhydroglucitol as a Biomarker for Diabetes Using an Organic Field-Effect Transistor-Based Biosensor

Sensor devices that can be fabricated on a flexible plastic film produced at a low cost using inkjet-printing technology are suitable for point-of-care applications. An organic field-effect transistor (OFET)-based biosensor can function as a potentiometric electrochemical sensor. To investigate the usefulness of an OFET-based biosensor, we demonstrated the detection of 1,5-anhydroglucitol (1,5-AG) and glucose, which are monosaccharides used as biomarkers of diabetes. An OFET-based biosensor combined with a Prussian blue (PB) electrode, modified with glucose oxidase (GOx) or pyranose oxidase (POx), was utilized for the detection of the monosaccharides. When the GOx- or POx-PB electrode was immersed in glucose solution at the determined concentration, shifts in the low-voltage direction of transfer characteristic curves of the OFET were observed to be dependent on the glucose concentrations in the range of 0–10 mM. For 1,5-AG, the curve shifts were observed only with the POx-PB electrode. Detection of glucose and 1,5-AG was achieved in a substrate-specific manner of the enzymes on the printed OFET-biosensor. Although further improvements are required in the detection concentration range, the plastic-filmOFET-biosensors will enable the measurement of not only diabetes biomarkers but also various other biomarkers.
Establishing a pain management program in cardiology: what we can learn from social marketing and normalization process theory Implementation of pain management in cardiology

Background We analyzed how a pain management program (PMP) in a cardiology department of a high-volume center was implemented into daily practice. The results provide some very valuable insights into the dynamics of complex professional behavioral changes, including the barriers to and facilitators of such changes. Methodology The behavioral dynamics during the implementation of the PMP were analyzed using normalization process theory (NPT) because NPT reflects both individual and collective contributions. NPT elements were then studied in their relation to social marketing tools, especially the Cochrane Effective Practice and Organization of Care (EPOC) taxonomy. Where the EPOC tools did not wholly reflect the scope of the PMP, further marketing concepts were introduced. Results The frameworks used provided a useful structure to analyze the behavior change intervention, yielding insights into significant aspects of the change process (such as barriers and facilitators) at the level of the individual, within groups, and across the organization. By also integrating EPOC with NPT, we show how improvements in PMP tangibility can aid communication between PMP leaders and project participants. We also show how value can be co-created between the two groups. Conclusion Hospitals represent complex environments where effective behavior modification requires changes at many different levels. Nevertheless, we demonstrated, using NPT and the incorporation and adaptation of social marketing/EPOC tools, that an intervention designed to change behavior could succeed even in such extreme conditions.
Development of a Novel Intelligent Adjustable Vision Algorithm for LED Chip Repairing

The mass transferring (repairing) equipment of mini/micro LED chip has high requirements for the advanced machine vision detection algorithm to ensure that the chips can be identified in a high-efficiency and high-precision manner.The motivation of this article is to propose a novel chip repairing vision measure (NCR-VM) algorithm with the speed and accuracy intelligent adjustable property in XY-axes to handle this issue. An intelligent judgment mechanism is constructed by the NCR-VM algorithm. The information is analyzed and judged during the algorithm operation to ensure that each step of the algorithm is properly planned. Then, the reliability and robustness of the algorithm are analyzed and verified in the theory. Finally, the chip repairing visual detection experiments are carried out, including key parameters tests, comparative experiments with other five representative vision algorithms, and robustness tests. The experimental results uniformly prove that the NCR-VM algorithm has a great advantage in detection accuracy and speed (0.53 $\mu$m, 124.21 ms) and the developed algorithm has a better stability, which is validated under different illumination conditions. In addition, the NCR-VM algorithm is of great robustness under the disturbances of the pepper–salt noise with a density up to 0.6/pixel and the Gaussian noise with the largest variance of 0.5.
A Clinical Control Study on the Retroperitoneal Laparoscopy for Unilateral Simple Renal Cyst Decortication in the Day Ward

BackgroundUnder the constraints of the increasingly tight medical environment and market economy, day surgery has a good development trend and attracts more and more attention from clinicians. To investigate the feasibility and safety of unilateral simple renal cyst decortication by retroperitoneal laparoscopy in the day ward and to compare the advantages and disadvantages of conventional inpatient ward.MethodsA total of 41 patients with unilateral simple renal cysts meeting surgical indications were included in this study. The patients were divided into the day ward group and inpatient ward group by the patients' wishes. The indexes of operation time, time of hospitalization, intraoperative blood loss, time of postoperative extubation, postoperative complication rate, patients’ satisfaction rate, quality of life scores and total costs of hospitalization were compared between the two groups.Results41 cases of unilateral simple renal cyst decortication were completed successfully. Patients were followed up 10–12 months after surgery. CT or b-ultrasound examination indicated that the original renal cyst had been decorticated surgically and no recurrence of the cyst was observed after 10 months of postoperative follow-up. Time of postoperative extubation were (9.7 ± 1.8) hours and (20.8 ± 5.5) hours, total hospitalization costs were (5790 ± 99.8) RMB and (7496 ± 120.6) RMB, time of hospitalization were (22.7 ± 1.4) hours and (46.4 ± 11.3) hours in the day ward group and the inpatient ward group, respectively. In the above three aspects, the differences between the two groups were statistically significant (P < 0.05). ConclusionIn this study, we concluded that the retroperitoneal laparoscopy for unilateral simple renal cyst decortication in patients aged between 48 and 80 years, with no previous history of abdominal surgery, unilateral renal cysts (Bosniak grades I or II) on CT, ASA grades I or II, and no surgical or anesthetic contraindications, was safe and feasible in the day ward. Its obvious advantages of reducing time of hospitalization and total costs of hospitalization were worthy of clinical promotion.
Ankle instability – review and new trends

Ankle instability is an old topic with increasing importance in Orthopaedics’ world, as we start now realising how underdiagnosed this pathology has been. A lot of effort has been made in recent years to better understand the pathomechanics of this injury, with new surgical techniques being tried and even new anatomic structures being described. The objective of this paper is to review and compile the most important and recent literature on this matter, in an academic way, but oriented to clinical practice, so it can help surgeons keep updated in this so important subject.
Investigation of sarcopenia in hemodialysis patients in Adiyaman province

In this study, we aimed to evaluate the presence and frequency of sarcopenia in chronic kidney disease (CKD) patients under hemodialysis treatment, to analyze socio-demographic and clinical factors that may be associated with sarcopenia and to compare them with the control group. This study includes a sample of 61 patients over the age of 18, affected by stage 4 CKD under hemodialysis treatment at least six months, being followed in our CKD outpatient clinic and 55 healthy controls. In all patients, demographic characteristics such as gender and age, anthropometric characteristics such as height, weight, body mass index (BMI), waist circumference, upper arm circumference, hip circumference, calf circumference and skin thickness were measured. Body compositions was determined using a Bioelectrical Impedance Analyzer (BIA). In the evaluation of sarcopenia, hand grip strength with hand dynamometer, walking speed with 6 meter walking test and muscle mass with BIA were analyzed. European Working Group on Sarcopenia in Older People (EWGSOP) criteria were preferred for the diagnosis of sarcopenia. The prevalence of sarcopenia was 5.5% in the control group and 31.1% in the hemodialysis group. The incidence of sarcopenia was significantly higher in patients under hemodialysis treatment than controls. Sarcopenia is common in patients with CKD under hemodialysis treatment. In this patients, sarcopenia was associated with anthropometric measurements such as BMI, waist circumference, arm circumference, hip circumference, calf circumference, skin thickness.
First-principles study on the structures and electronic properties of graphene-supported Nin (n = 1–6) clusters

ABSTRACT The structural and electronic properties, such as adsorption energy, magnetic property, and charge-transferring process of Nin (n = 1–6) clusters interacting with pristine, strained and defective graphene were investigated by using the density functional theory calculations with the Perdew–Burke–Ernzerhof exchange-correlation energy functional. By introducing strain and defects, the stability of the cluster-graphene system was improved significantly. The magnetic moments increased monotonically for Nin clusters on pristine and strained graphene while exhibited an oscillating behaviour for defective graphene. On the other hand, more charges being transferred from Nin clusters to defective graphene were observed compared with pristine and strained graphene.
Business process model with “RandomForest” algorithm

Currently, the areas of application of machine learning are multifaceted: artificial intelligence, financial applications, bioinformatics, intellectual games, speech and text recognition, computer language processing, medical diagnostics, technical diagnostics, text search and rubrication. Machine learning is an area of scientific knowledge associated with learning-capable algorithms. The use of machine learning methods is explained by the fact that for most intelligent complex tasks (for example, speech recognition, etc.) it is almost impossible to develop an obvious algorithm for their solution. However, you can teach a computer to learn how to solve such problems. In our article, we propose a model based on the machine learning algorithm “RandomForest”, which allows one to recognize bots by HTTP sessions. The chosen algorithm provides many advantages: non-iterative learning; high quality of the resulting models (comparable to neural networks and ensembles of neural networks); a small number of adjustable parameters. It works well with missing data (retains good accuracy); internal assessment of the generalizability of the model; able to work with raw data without preprocessing. The algorithm was trained on a dataset of more than 5000 sessions. The prospects of this direction are obvious, since robotic traffic accounts for more than 40% of the total Internet traffic.
Landing mechanics of basketball and volleyball athletes: a kinematic approach

Purpose. Single-leg landing is frequent in basketball and volleyball, two sports with high incidence of anterior cruciate ligament injuries. the similarity in the number of landings between these sports could be the culprit of the high incidence of injuries. However, the comparison of knee joint motion during landings in both sports has yet to be investigated. this study aimed to contrast the knee kinematics of basketball and volleyball athletes during a single-leg landing task. Methods. Overall, 10 male athletes, 5 in basketball (181.4 ± 6.7 cm; 93.21 ± 33.06 kg) and 5 in volleyball (178.4 ± 6.6 cm, 79.11 ± 6.46 kg) performed single-leg drop landings. Differences between the groups were verified with the superposition of 95% confidence intervals. Additionally, the probability approach was applied with magnitude-based inferential statistics calculated to compare individual instants (40 ms after the initial ground contact, maximum flexion, and abduction). Results. Different knee movement patterns were detected between the groups. While volleyball athletes showed greater knee flexion, basketball players exhibited greater knee abduction. the magnitude-based inference also demonstrated that volleyball athletes presented higher values in the sagittal plane. Conclusions. Basketball athletes exhibited greater valgus in the instants before and after ground contact. In addition, volleyball athletes showed greater knee flexion during the single-leg drop landing.
CT Segmentation of Liver and Tumors Fused Multi-Scale Features

Liver cancer is one of frequent causes of death from malignancy in the world. Owing to the outstanding advantages of computer-aided diagnosis and deep learning, fully automatic segmentation of computed tomography (CT) images turned into a research hotspot over the years. The liver has quite low contrast with the surrounding tissues, together with its lesion areas are thoroughly complex. To deal with these problems, we proposed effective methods for enhancing features and processed public datasets from Liver Tumor Segmentation Challenge (LITS) for the verification. In this experiment, data pre-processing based on the image enhancement and noise reduction. This study redesigned the original UNet with two novel modules and named it DResUNet which was applied deformable convolution. The first module aimed to recalibrate information by the channel and spatial dimension. The other module enriched deep information of these liver CT images through fusing multi-scale features. Besides, we used cross-entropy loss function for adaptive weights to solve the troubles of class imbalance in the dataset samples. These can improve the performance of the network in-depth and breadth feature learning to deal with many complex segmentation scenes in abdominal CT images. More importantly, the effect of predicted images fully proved that our methods are highly competitive among the segmentation of liver and liver tumors.
Using Deep Learning Models Combined with Crowd Emotion Models to Identify Abnormal Behaviors in Crowds

Aiming at the problem that the definition of crowd abnormal behavior detection is ambiguous and difficult to combine with context semantics, an algorithm using OCC human emotion model combined with crowd entropy is proposed. First calculate the crowd entropy for the crowd, and determine whether the entropy value is abnormal, if it is abnormal, further extract the optical flow OF and HOG. Then project it into two-dimensional vector data, send it to CNN for local feature extraction and combine with OCC model to achieve the description of crowd emotions. Finally, predict whether the abnormality occurs according to the judgment factor. Verified on the data set, this method shows a high accuracy.
Stochastic Linearization of Feedback Systems With Multivariate Nonlinearities and Systems With State-Multiplicative Noise

Quasilinear control (QLC) theory provides a set of methods intended for the analysis and design of stochastic feedback systems with static nonlinearities. QLC leverages the method of stochastic linearization (SL), which linearizes the nonlinear functions by utilizing the statistical properties of the inputs to the nonlinearities. In the traditional QLC literature, SL has been thoroughly applied to systems having nonlinearities with only a single input. This article investigates the case of SL applied to feedback systems with nonlinear functions of multiple inputs. More specifically, the formulas for the SL gains and bias are derived for multivariate functions and then employed to explore SL of a trivariate saturation nonlinearity and study the SL of control systems with feedback loops. The developed theory is then applied to the analysis and optimal controller design of stochastic systems having randomly varying parameters or state-multiplicative noise. Finally, a recipe for investigating the robustness of SL is provided.