Miracle-dz/UniFAR-FTUs huggingface dataset:
Renal parenchyma segmentation based on a cascaded self-adaptive framework with local context-aware mix-up regularization in abdominal MR images

Segmentation of the renal parenchyma responsible for a renal function is necessary for surgical planning and decisionmaking of renal partial nephrectomy (RPN) by identifying the correlation between the renal parenchyma volume and renal function after RPN on abdominal magnetic resonance (MR) images without radiation exposure. This paper proposes a cascaded self-adaptive framework that uses local context-aware mix-up regularization on abdominal MR images acquired from multiple devices. The proposed renal parenchyma segmentation network consists of two stages: kidney bounding volume extraction and renal parenchyma segmentation. Before kidney bounding volume extraction, self-adaptive normalization is performed using nnU-Net as the backbone network to reduce differences in signal intensity and pixel spacing among MR images of different intensity ranges acquired from multiple MR devices. In the kidney bounding volume extraction stage, the renal parenchyma area is segmented using 3D U-Net with low-resolution data down-sampled twice from the original to efficiently localize the kidney in the abdomen. Bounding volume is generated to focus on the renal parenchyma area during the renal parenchyma segmentation stage by cropping to the volume-of-interest region using the segmentation results up-sampled to the original resolution. In the segmentation stage, the renal parenchyma is segmented using 3D U-Net with mix-up augmented bounding volume to improve the regularization performance of the model. The average F1-score of our method was 92.27%, which was 3.07%p and 0.32%p higher than the segmentation method using original 3D cascaded nnU-Net and 3D cascaded nnU-Net with kidney bounding volume extraction, respectively.
RPMG-FSS: Robust Prior Mask Guided Few-Shot Semantic Segmentation

Few-shot semantic segmentation (FSS) has been developed to perform pixel-level segmentation with only a few dense labeled examples for training, which relieves the expensive annotation problem in traditional segmentation models. Current researches on FSS generally act the labeled masks on the corresponding support images to obtain the class-specific embeddings, and predict the pixel-level masks for query images by matching their pixels to these class-specific embeddings. Their performance is difficult to further break through because of the limited supervision from single-view support images and the neglect of position information from similar pixels between query and support images. To solve these issues, we propose a novel robust prior mask guided model named RPMG-FSS for the challenging FSS task. The core of RPMG-FSS is to produce a robust prior mask with good generalization ability on novel classes to better assist the following query mask prediction. Note that each element in the prior mask corresponds to one pixel in query image. It not only considers the interaction within one view and between multiple views of the support image, but also fuses the top- $k$ similarity values to all support pixels and these pixels’ position information. The parameters in RPMG-FSS are optimized with the combination of segmentation loss and multi-view contrastive loss. Comprehensive experiments on two datasets show that our RPMG-FSS achieves outstanding performance comparing with the current popular baselines. The code is released on https://github.com/dxzxy12138/RPMG-FSS/tree/master
The Association between Smartphone Addiction, Depression and Anxiety among Medical Students in Jordan

Introduction: Awareness of psychological disorders such as smartphone addiction, anxiety, and depression is a trending research area in the scientific community that has further escalated with the impact of COVID-19. In this regard, young adults and medical students are already considered to be under a high level of stress academically and culturally. This study aimed to investigate the relationship between depression, anxiety, and smartphone addiction among medical students in Jordan. Methods: A cross-sectional study was conducted using an online structured questionnaire. The survey was completed by 164 male and female medical students stratified by years one to three in the basic medical sciences. The survey consisted of items from the Generalized Anxiety Disorder (GAD-7), Patient Health Questionnaire (PHQ-9), and Smartphone Addiction Scale-Short Version (SAS-SV), in addition to two questions concerning the impact of the COVID-19 pandemic on smartphone addiction. Results: Among the students who participated, the mean age was 18.9, with almost half (47.2%) being first-year students. The results showed no significant difference in stress, anxiety or cell phone addiction based on gender or academic year. The logistic regression model was not statistically significant regarding the covariates, except for GAD, which had an OR=1.15 (CI: 1.06 -1.25). Furthermore, SAS-SV was positively correlated with GAD-7 and PHQ-9 scores (r=0.42, r=0.2, p< 0.000, p=0.029, respectively). Conclusions: The results of the study showed a statistically significant positive correlation between smartphone addiction, depression, and anxiety. These variables were not statistically different among medical students in terms of gender and academic year.
Effect of tail dihedral angle on lateral directional stability due to sideslip angles

This paper will describe the aerodynamic characteristic of complete aircraft equipped with conventional tail and V-tail configurations. Based on lateral-directional stability, the introduction of tail dihedral angle can significantly affect the yaw stability derivative of an aircraft. The wind tunnel test was conducted for sideslip angle, from -25◦ to 25◦ and the results were used to verify the CFD works. Good agreements were achieved at a lower sideslip angle which is below 15◦. Then, CFD will be used to study the flow field around the tail region and figure out the effect on the directional stability. This study found that during sideslip condition, conventional tail stall at a lower sideslip angle and only effective at lower sideslip condition. This study shows that the V-tail configuration provide an advantage at higher sideslip condition. The interactions of V-tail vertical tailplane slightly increase rolling stability thus causes the reductions in yawing motion.
Positive solutions to advanced fractional differential equations with nonlocal boundary conditions

In this paper, we study the existence of positive solutions for a class of higher order fractional differential equations with advanced arguments and boundary value problems involving Stieltjes integral conditions. The fixed point theorem due to Avery-Peterson is used to obtain sufficient conditions for the existence of multiple positive solutions. Certain of our results improve on recent work in the literature.
Improving medical image segmentation and classification using a novel joint deep learning model

Although deep learning models have been widely used in medical imaging research field to perform lesion segmentation and classification tasks, several challenges remain to optimally apply deep learning models and improve model performance. The objective of this study is to investigate a new novel joint model and assess model performance improvement as increase of training dataset size. Specifically, we select and modify a novel J-Net as a joint model, which includes a two-way CNN architecture that combines a U-net model with an image classification model. A skin cancer dataset with 1200 images along with the annotated lesion masks and ground truth of “mild” and “severe” status is used. From this dataset, 11 subsets are randomly generated from 200 to 1200 images with an incremental rate of 100. Each subset is then divided into training, validation and testing groups using a ratio of 70:20:10, respectively. The performance of the new joint model is compared with two independent models to separately perform lesion segmentation and classification. The study results show when training the models using data subsets of 200 to 1200 images, accuracy levels increase from 0.80 to 0.92, or 0.86 to 0.95 in lesion segmentation. The lesion classification increases from 0.80 to 0.90, or 0.82 to 0.93 using two single models and one joint J-Net model, respectively. Thus, this study demonstrates that applying this new JNet joint model enables to achieve higher lesion segmentation and classification accuracy than two single models. Additionally, model performance also increases as increase of training dataset size.
Data-driven optimal tracking control for a class of affine non-linear continuous-time systems with completely unknown dynamics

In this study, the optimal tracking control problem (OTCP) for affine non-linear continuous-time systems with completely unknown dynamics is addressed based on data by introducing the reinforcement learning (RL) technique. Unlike existing methods to the OTCP, the proposed data-driven policy iteration (PI) method does not need to have or identify any knowledge of the system dynamics, including both drift dynamics and input dynamics. To carry out the proposed method, the original OTCP is pre-processed to construct an augmented system composed of the error system dynamics and the desired trajectory dynamics. Then, based on the augmented system, a data-driven PI, which introduces discount factor to solve the OTCP, is implemented on an actor–critic neural network (NN) structure by only using system data rather than the exact knowledge of system dynamics. Two NNs are used in the structure to generate the optimal cost and optimal control policy, respectively, and the weights are updated by a least-square approach which minimises the residual errors. The proposed method is an off-policy RL method, where the data can be arbitrarily sampled on the state and input domain. Finally, simulation results are provided to show the effectiveness of the proposed method.
Ranking Extreme and Non-Extreme Efficient Decision Making Units in Data Envelopment Analysis

In evaluating decision making units (DMU) by using Data Envelopment Analysis (DEA) technique, it happens that more than one unit got efficiency score one. In such a case there should be some criterion for ranking these DMUs. Up to now, all of DEA model could rank only extreme efficient units. In this paper the authors proposed a method for ranking extreme and non extreme efficient units.
Application of the complete eigenfunction expansion form of the Green's function to a scattering problem

The complete eigenfunction expansion form of the Green's function for elastic layered media developed by the author is applied to a scattering problem. This form of the Green's function is an extension of that presented by Lamb [1]. It clarifies the mathematical common frame between the residue terms and the branch line integrals of the Green's function in terms of the energy integrals and eigenfunctions. The advantage of the usage of the complete eigenfunction expansion form of the Green's function for the scattering problem is in that the possibility of analyzing the the scattering waves in terms of the eigenfunctions for the discrete and continuous spectra defined for the layered medium. Several numerical calculations are carried out to examine the properties of the scattering waves.
An extended technology acceptance model on immersive virtual reality use with primary school students

ABSTRACT Virtual Reality (VR) is an emerging technology with an increasing number of studies assessing its benefits in educational settings. However, there is a shortage of empirical studies aimed at evaluating the acceptance of this technology in primary education. The authors propose an extended version of the Technological Acceptance Model to examine students’ intention to use immersive VR. To this end, 111 fourth-graders completed a history teaching unit using immersive VR. Structural equation modelling was employed to assess causal relationships between six variables: prior knowledge, perceived attention, perceived utility, perceived ease of use, perceived enjoyment and attitude towards the use of immersive VR. The results indicate that students’ prior knowledge was not significant in explaining their intention to use immersive VR. However, perceived attention had a significant effect on this variable mediated through perceived utility. These results have important implications regarding the design and use of VR tools for educational purposes.
Antioxidant, anti‐inflammatory, and anticancer function of Engleromyces goetzei Henn aqueous extract on human intestinal Caco‐2 cells treated with t‐BHP

Abstract High body mass index (high BMI, obesity) is a serious public health problem, and “obesity‐induced oxidative stress, inflammation, and cancer” have become modern epidemic diseases. We carried out this study to explore a functional beverage that may protect against obesity‐induced diseases. The Engleromyces goetzei Henn herbal tea is such a candidate. For this study, we carried out LC–MS analysis of E. goetzei Henn aqueous extract (EgH‐AE); then used the Caco‐2 cell line for the model cells and treated the cells with t‐BHP to form an oxidative stress system. An MTT assay was used for testing the biocompatibility and cytoprotective effects; reactive oxygen species and malondialdehyde determination was used for evaluating the antioxidative stress effect; TNF‐α and IL‐1β were used for observing the anti‐inflammatory effect, and 8‐OHdG for monitoring anticancer activity. The results of this study demonstrate that the EgH‐AE has very good biocompatibility with the Caco‐2 cell line and has good cytoprotective, antioxidant, anti‐inflammatory, and anticancer properties. It is clear that EgH‐AE, a kind of ancient herbal tea, may be used to develop a functional beverage that can be given to people with a high BMI to protect against obesity‐induced diseases.
Near-Field Microwave Microscopy for 3D Surface Assessment of Manufactured Structures

Using near-field scanning microwave microscopy as a contact and non-contacting investigative tool for 3D surface metrology with three differing measurement modes, it has been possible to analyse structures that may be difficult for existing metrology systems. The system utilizes the small change in capacitance between a coaxial resonant probe (at around 2 GHz) ending in an open circuit tip, and the sample surface. This is measured in the frequency domain by the shift in the resonance frequency of the voltage transmission coefficient |S21|. It is also possible to investigate various materials (metallics, plastics etc.) owing to their differing dielectric properties. The probe has been tested on a computer-controlled 3D stage but is suitable for incorporation into a commercial co-ordinate measurement machine (CMM) to enhance its capability to inspect the inside surfaces of structures, e.g., threads in small bores.
Magnetic resonance image reconstruction based on graph convolutional Unet network

Magnetic Resonance Imaging (MRI) acquisition is a long time process that leads to patient discomfort and motion artifacts. Convolution network has been implemented in MRI image reconstruction to speed up MRI. Convolution network utilizes spacial local convolution kernel successfully, but non-local information with self-similarity is ignored. In order to effectively explore non-local information in MRI reconstruction, we propose graph convolutional Unet (GCN-Unet) for MRI image reconstruction. In the GCN-Unet, non-local information is represented by a patch graph extracted from MRI images, in which nodes are consist of image patches, and edges are self-similarities between nodes, reflecting non-local structure similarity intra an MRI image. We utilize graph convolution with a Unet framework to effectively represent features of non-local information with self-similarities and aggregate the extracted non-local features to reconstruct MRI image. Experiments demonstrate the effectiveness of the GCN-Unet, the Peak Signal-to-Noise Ratio (PSNR) and Structure Similarty Index Measure (SSIM) of reconstructions are improved.
Ameliorative Effects of Flavonoids from Platycodon grandiflorus Aerial Parts on Alloxan-Induced Pancreatic Islet Damage in Zebrafish

Platycodon grandiflorus (balloon flower), used as a food reserve as well as in traditional herbal medicine, is known for its multiple beneficial effects. In particular, this plant is widely used as a vegetable in Republic of Korea. We examined the ameliorative effects of P. grandiflorus on alloxan-induced pancreatic islet damage in zebrafish. The aerial part treatment led to a significant recovery in pancreatic islet size and glucose uptake. The efficacy of the aerial part was more potent than that of the root. Eight flavonoids (1–8) were isolated from the aerial part. Structures of two new flavone glycosides, designated dorajiside I (1) and II (2), were elucidated to be luteolin 7-O-α-L-rhamno-pyranosyl (1 → 2)-(6-O-acetyl)-β-D-glucopyranoside and apigenin 7-O-α-L-rhamnopyranosyl (1 → 2)-(6-O-acetyl)-β-D-glucopyranoside, respectively, by spectroscopic analysis. Compounds 1, 3, 4 and 6–8 yielded the recovery of injured pancreatic islets in zebrafish. Among them, compound 7 blocked KATP channels in pancreatic β-cells. Furthermore, compounds 3, 4, 6 and 7 showed significant changes with respect to the mRNA expression of GCK, GCKR, GLIS3 and CDKN2B compared to alloxan-induced zebrafish. In conclusion, the aerial part of P. grandiflorus and its constituents conferred a regenerative effect on injured pancreatic islets.
Daily, seasonal, and annual variation in area use of Lesser Black-backed Gulls Larus fuscus related to offshore renewable developments

ABSTRACT Capsule Individually tracked Lesser Black-backed Gulls Larus fuscus rarely visited offshore areas in this study but showed high variation across multiple time scales in overall area use. Aims To quantify the variation in space use by Lesser Black-backed Gulls, a species of conservation concern, from short- and long-term data on tracked individuals, and to assess the time spent offshore and interacting with offshore wind farms (OWFs) before and after their construction. Methods GPS tracking data were collected between 2016 and 2019 from 36 Lesser Black-backed Gulls breeding in a declining colony in the UK with several operational OWFs nearby. Variations in home ranges, foraging trip metrics and the proportion of time spent offshore or within OWFs were analysed at three temporal scales: (i) over a daily cycle, (ii) across the breeding season and (iii) between years. Results Individuals predominantly used terrestrial areas. Less than 1.25% of the overall breeding season time budgets were spent offshore across years, and offshore use and time within OWFs did not vary significantly between years. However, home range sizes and foraging trip metrics did vary significantly across all time scales and individual variation was significant. Conclusion Our results suggested that there was little change in the relative use of offshore areas over time and thus in the macro-scale response of Lesser Black-backed Gulls to the OWFs, one of which was under construction during the period of study. Colony productivity and changes in the terrestrial environment were likely to be more important for influencing foraging movements at this colony.
Analyse de champs browniens fractionnaires anisotropes

In this communication, we present the Directional Averages Method (DAM) to analyse Anisotropic Fractional Brownian Fields (AFBF) defined in the spectral domain by a Hurst parameter H( θ) depending on the direction θ. For a given direction ϕ on the image, the average is formed in that direction and the regularity R( ϕ) is estimated. We show that for θ = ϕ + π/2, the function H( θ) is equal to R( ϕ) -1/2 and can be thus recovered. We have tested DAM on isotropic fractal images as well as on AFBF generated by the inverse Fourier method. Results are fair even if difficulties arise linked to the synthesis of true AFBF, to the analysis of signals which Holder exponent is greater than one, and to the digitisation of images. Interesting perspectives can be developed concerning the analysis of fractal porous volumes only known from a 2D projection as it is the case for radiographs.
The Risk Contagion between Chinese and Mature Stock Markets: Evidence from a Markov-Switching Mixed-Clayton Copula Model

Exploring the risk spillover between Chinese and mature stock markets is a promising topic. In this study, we propose a Markov-switching mixed-Clayton (Ms-M-Clayton) copula model that combines a state transition mechanism with a weighted mixed-Clayton copula. It is applied to investigate the dynamic risk dependence between Chinese and mature stock markets in the Americas, Europe, and Asia–Oceania regions. Additionally, the conditional value at risk (CoVaR) is applied to analyze the risk spillovers between these markets. The empirical results demonstrate that there is mainly a time-varying but stable positive risk dependence structure between Chinese and mature stock markets, where the upside and downside risk correlations are asymmetric. Moreover, the risk contagion primarily spills over from mature stock markets to the Chinese stock market, and the downside effect is stronger. Finally, the risk contagion from Asia–Oceania to China is weaker than that from Europe and the Americas. The study provides insights into the risk association between emerging markets, represented by China, and mature stock markets in major regions. It is significant for investors and risk managers, enabling them to avoid investment risks and prevent risk contagion.
Asymptotic Solution Of Differential Equations In a Domain Containing a Regular Singular Point

1. Introduction. In this paper we study the asymptotic behavior in λ of the solutions about the origin in the z-plane of the differential equation . Both the variable z and the parameter λ are complex. The coefficient P(z, λ) is assumed to be analytic and single-valued in λ at infinity and in z throughout a bounded, closed, simply connected domain D containing z = 0.
Design of a Modular 3-D Force Sensor With Fiber Bragg Gratings for Continuum Surgical Robot

An instrument usually is installed at the distal end of a continuum surgical robot to perform specific tasks. The driving wire of the instrument needs to pass through the central channel of the continuum robot. And force perception of the continuum robot is often lacking. Based on this application background, a novel fiber Bragg grating (FBG)-based 3-D force sensor is proposed. This sensor consists of an axial force perceptive zone and a radial force perceptive zone. Four optical fibers with dual FBG nodes are arranged for the sensor. Thanks to the novel arrangement of FBGs, one central channel is retained to arrange the driving wire of the instrument. A novel force perceptive model is established through the temperature decoupling algorithms and Bernoulli–Euler beam theory. Calibration experiments show that the proposed sensor can achieve a resolution of 1.9, 2.4, and 9.8 mN for <inline-formula> <tex-math notation="LaTeX">${F_{x}},{F_{y}}$ </tex-math></inline-formula>, and <inline-formula> <tex-math notation="LaTeX">${F_{z}}$ </tex-math></inline-formula>, respectively. Dynamic performance and temperature decoupling algorithms show that the proposed FBG sensor can accurately realize 3-D force perception.