Miracle-dz/UniFAR-FTUs huggingface dataset:
Can magnetic resonance imaging differentiate muscle invasion (T2) and lamina propria invasion (T1) urothelial carcinoma of the bladder? A systematic review and meta-analysis of Vesical Imaging-Reporting and Data System accuracy

Abstract Background Vesical Imaging-Reporting and Data System (VI-RADS) was developed as a structured reporting tool to anticipate the possibility of muscle invasion. This study is aimed to investigate the diagnostic accuracy of VI-RADS for discriminating T2 from T1 bladder cancer. Materials and methods Scopus, Web of Science, PubMed, and Embase were searched on October 4, 2021, for studies with the following characteristics: (1) bladder cancer patient population, (2) VI-RADS as an index test, (3) retransurethral resection of bladder tumor/cystectomy as a reference, and (4) adequate VI-RADS score data for T1 and T2 lesions. The analyses were performed using the binary regression model of MIDAS in Stata. Results Six studies with 624 magnetic resonance imaging reports were included. The receiver operating characteristics curve for differentiation of T2 from T1 bladder cancer showed an area under the curve of 0.93 (95% confidence interval [CI], 0.91–0.95) for a VI-RADS ≥3 and 0.75 (95% CI, 0.71–0.79) for a VI-RADS ≥4. A VI-RADS ≥3 showed high sensitivity of 93% (95% CI, 85%–97%), specificity of 61% (95% CI, 30%–86%), positive likelihood ratio of 2.4 (95% CI, 1.1–5.3), and negative likelihood ratio of 0.11 (95% CI, 0.05–0.24). A total of 10.4% of T2 lesions were scored as VI-RADS 2, while 10% of T1 lesions were scored as VI-RADS 4 or 5. Conclusions The VI-RADS ≥3 has high accuracy and sensitivity for detecting muscle invasion in borderline populations of T1 or T2 bladder cancer. Thus, the VI-RADS could be a good non-invasive screening test for the detection of T2 urothelial lesions.
Sleep Duration, Sleep Quality, and Insomnia in Association With Self-Rated Health: A Systematic Review and Meta-Analysis

Background and Objective This study undertook a systematic review and meta-analysis of the relationship between sleep duration, sleep quality, and insomnia in association with self-rated health.Methods Studies that had examined the relationship between sleep duration, sleep quality, and insomnia with self-rated health were eligible. PubMed and Scopus were the two main databases for searching for studies related to this meta-analysis. The Google Scholar database as a source of gray literature was also searched by hand. This search was from the beginning of the formation of databases until the end of January 2022, and the search language was limited to articles published in English. The Effective Public Health Practice Project Quality Assessment Tool was used to assess the quality of studies. For this meta-analysis, odds ratio and 95% confidence interval were extracted or calculated. The pool of studies was processed by the random effects method.Results Twenty-six studies were included in this meta-analysis. Sleep duration of ≤ 8 hours per day (h/d) (odds ratio = 1.58 and 95% confidence interval = [1.41–1.77]) and sleep duration of > 8 h/d (odds ratio = 1.32 and 95% confidence interval = [1.17–1.50]) are associated with poor self-rated health. Poor sleep quality and insomnia are associated with poor self-rated health. Conclusions Sleep problems have a negative effect on self-rated health, and therefore, effective interventions can help improve sleep.
Application of the Altman Z-Score Model in Assessing the Financial Health of Garment Manufacturing Enterprises Listed on the Vietnam Stock Market

The article has provided the content and function of the Z-score model in predicting the possibility of bankruptcy through the actual financial situation of the enterprise. The study uses two research methods, namely the analytical method and the meta-analytic method. Through the methodology, the team obtained an overview of the study and identified gaps in which to conduct the research. Next, the research team aelected a sample of 9 garment manufacturing companies listed on the Vietnamese stock market. Combining the collection of secondary data from financial statements and the theory of the Z-score model, we have a basis to evaluate the financial situation of these 9 enterprises in 2022. 4 enterprises having in good financial condition, 4 enterprises with difficult financial situations and 1 enterprise with financial distress consistent with the actual situation of all 9 enterprises. The analysis results also show that the Z-score model is reliable and quite effective in assessing financial health and predicting the bankruptcy of enterprises.
Room temperature observation of high spin polarization in post annealed Co2FeSi/MgO/n+-Si on insulator devices

The post annealing temperature dependence of room temperature spin signals in Co2FeSi/MgO/n+-Si on insulator fabricated on Si(2×1) surface was investigated. For the devices fabricated on the Si(2×1) surface, the large and reliable three- and four-terminals spin signals were obtained even at room temperature. The magnitude of three-terminal narrow Hanle signals has a peak around 325 °C with respect to post annealing temperature. The trend of increasing spin accumulation signal with decreasing bias voltage was observed for both as deposited and sample annealed at 325 °C in the bias voltage range 600–800 mV. The enhancement of three- and four-terminals non-local magnetoresistance (MR) for post annealed sample at 325 °C, indicates that the spin polarization increases due to the structural ordering of Heusler alloy Co2FeSi. As a result, we observed large spin injection efficiency into Si (P ∼ 41.7%) even at room temperature. These results will pave a way to the future Si spintronics devices such a spin-MOSFET.
Visibility-enhanced dual-band infrared image fusion based on nonsubsampled contourlet transform

In this paper, a new dual-band infrared image fusion for enhancing visibility is proposed. Before proposing the new algorithm, we studied the human visual systems and found that human visual systems have directional characteristics, and the sensitivity of the human eye to the phase angle is higher than the change of the mode. Considering these characteristics of human visual systems, we put forward a dual-band infrared image fusion algorithm based on local window activity measure. First, the mid-wave and long-wave infrared source images are decomposed into the multi-scale and multidirectional subbands by using a nonsubsampled contourlet transform(NSCT) method. Then, the highpass subbands are fused by selecting the maximum absolute operator and the lowpass subbands are fused by using a method based on region energy and region variance. Finally, the image is reconstructed by inverse nonsubsampled contourlet transform and a fused image is obtained. We use a dual-band infrared camera with common optical path to acquire images for the fusion experiment. The method is compared with the gradient pyramid transform and the wavelet transform in fusion effectiveness. From the comparison of the evaluation parameters, we can find that the fusion results of the proposed algorithm are better than other algorithms, and this visibility-enhanced dual-band infrared image fusion algorithm based NSCT is more suitable for human eye observation and understanding.
Academic motivation in primary and secondary school students: The role of the perceived teachers' motivating styles

The aim of this research was to test whether students' school level (primary school or secondary school level) has a significantly moderating impact on relationships between students' perception of teachers' motivating styles (autonomy, structure and involvement support) and students' academic motivation (intrinsic, extrinsic, introjected motivation and amotivation) in the context of science-mathematics and socio-linguistics subjects. The sample was comprised of 494 students (females = 57.9%) divided into two school levels: 1) students in the final grades of primary school (53.8%); and 2) students in the first two grades of secondary school (46.2%). The results of the moderation analyses indicated that school level did not have a significant moderate influence on the tested relations in the context of socio-linguistic subjects, while it did in the context of science-mathematics subjects. The results of the simple slope tests indicated that the perceived structure support had a positive effect on the realization of intrinsic motivation, especially in the younger respondents, and that both perceived autonomy support and structure support displayed a negative association with amotivation in both age groups of students in the context of the science-mathematics subjects, but that these effects were stronger in the younger respondents. The results offer new insights into how teachers' motivational styles may influence different aspects of students' motivation to learn science-mathematics and socio-linguistics subjects in students at different school levels.
Automatic temporal clothing deformation prediction: applicable to variable shape and motion

Simulating temporal three-dimensional (3D) deformations of clothing worn by the human body is a core technology in computer graphics that plays a vital role in various fields, such as computer games, animation, and movies. Physics-based simulation and data-driven methods are two mainstream technologies used to generate clothing deformation. However, when it is necessary to quickly generate clothing animations of different body shapes and motions, the existing methods cannot balance efficiency and effectiveness. In this paper, we present a learning-based method, given human body shape and motion, which automatically synthesizes temporal 3D deformations of clothing in real-time. A temporal framework based on the transformer is designed to capture the correlation between clothing deformation and shape features of the moving human body, as well as the frame-level dependency. A feature fusion strategy is an innovation used to fuse the two features of shape and motion. We also perform post-processing on the penetration of clothing and the human body, generating collision-free cloth deformation sequences. To evaluate the method, we build a human motion dataset based on the large-scale public human body dataset AMASS, and further develop a clothing deformation dataset. We qualitatively and quantitatively demonstrate that our approach outperforms existing methods in terms of temporal clothing deformation with variable shape and motion, as well as producing realistic deformation at interactive rates.
A Comparison between the 3D and the Kirchhoff-Love Solutions for Cylinders under Creep-Damage Conditions

The 3D theory of creep deformation and creep damage growth in a cylindrical body of revolution is considered. Constitutive equations describing the creep deformation and unilateral creep damage in initially isotropic materials with characteristics dependent on the kind of the stress state are discussed. The numerical approach to obtain the 3D solution for a cylinder under creep-damage conditions is developed. The numerical results generated by the proposed 3D theory are compared with the analogous results based on the Kirchhoff-Love model. Thin and moderately thick cylindrical shells of revolution made from the material with the creep and creep damage characteristics dependent on the kind of the stress state are considered in this comparative analysis. The influence of tension–compression asymmetry on the stress–strain state and damage evolution with time in thin and moderately thick cylindrical shells is discussed. If it is assumed that the properties of the shell material do not depend on the kind of the stress state, this could lead to an overprediction of the creep damage growth and a significant underestimation of the failure initiation time.
An Evaluation of Experimental Designs for Constructing Vignette Sets in Factorial Surveys

Factorial surveys use a population of vignettes to elicit respondents’ attitudes or beliefs about different hypothetical scenarios. However, the vignette population is frequently too large to be assessed by each respondent. Experimental designs such as randomized block confounded factorial (RBCF) designs, D-optimal designs, or random sampling designs can be used to construct small subsets of vignettes. In a simulation study, we compare the three vignette designs with respect to their biases in effect estimates and show how the biases arise from the designs’ confounding structure, nonorthogonality, and unbalancedness. We particularly focus on the designs’ sensitivity to context effects and misspecifications of the analytic model. We argue that RBCF designs and D-optimal designs are preferable to random sampling designs because they offer a stronger protection against undesirable confounding, context effects, and model misspecifications. We also discuss strategies for dealing with context and order effects since none of the basic vignette designs can satisfactorily handle them.
Robust and Reliable SFC Placement in Resource-Constrained Multi-Tenant MEC-Enabled Networks

With the rapid development and incoming implementation of 5G networks, many use cases, such as Intelligent Transportation Systems (ITS), are being realized. Utilizing networking technologies, including Network Function Virtualization and Mobile Edge Computing, along with 5G network slicing, the Next-Generation Service Placement Problem (NGSPP) is gaining significant attention due to the criticality of its services and its resource-constrained network nodes. The placement of services on Next-Generation (NG) networks has inherent challenges, mainly ultra-low latency requirements and the complexity of NG network management and orchestration. A candidate solution to the NGSPP should provide a placement that adheres to the strict Quality of Service (QoS) requirements. This work presents the formulation of a robust optimization problem that optimizes the high-availability placement of applications in resource-constrained and multi-tenant NG networks, which complies with QoS requirements and is capable of protecting the performance of the solution under adverse conditions. Finally, a set of hierarchical clustering-based heuristic algorithms, which reduce the time-complexity of the solution are proposed. Results demonstrate that formulating the robust solution is a proactive method of injecting resilience into the system and can preserve performance across various levels of system uncertainty.
Statistical Analysis of COVID-19 Data in Iraq

The analysis of COVID-19 data in Iraq is carried out. Data includes daily cases and deaths since the outbreak of the pandemic in Iraq on February 2020 until the 28th of June 2022. This is done by fitting some distributions to the data in order to find out the most appropriate distribution fit to both daily cases and deaths due to the COVID-19 pandemic. The statistical analysis includes estimation of the parameters, the goodness of fit tests and illustrative probability plots. It was found that the generalized extreme value and the generalized Pareto distributions may provide a good fit for the data for both daily cases and deaths. However, they were rejected by the goodness of fit test statistics due to the high variability of the data.
Eyes on privacy: acceptance of video-based AAL impacted by activities being filmed

Introduction The use of video-based ambient assisted living (AAL) technologies represents an innovative approach to supporting older adults living as independently and autonomously as possible in their homes. These visual devices have the potential to increase security, perceived safety, and relief for families and caregivers by detecting, among others, emergencies or serious health situations. Despite these potentials and advantages, using video-based technologies for monitoring different activities in everyday life evokes concerns about privacy intrusion and data security. For a sustainable design and adoption of such technical innovations, a detailed analysis of future users' acceptance, including perceived benefits and barriers is required and possible effects and privacy needs of different activities being filmed should be taken into account. Methods Therefore, the present study investigated the acceptance and benefit-barrier-perception of using video-based AAL technologies for different activities of daily living based on a scenario-based online survey (N = 146). Results In the first step, the results identified distinct evaluation patterns for 25 activities of daily living with very high (e.g., changing clothes, showering) and very low privacy needs (e.g., gardening, eating, and drinking). In a second step, three exemplary activity types were compared regarding acceptance, perceived benefits, and barriers. The acceptance and the perceived benefits of using video-based AAL technologies revealed to be higher in household and social activities compared to intimate activities. The strongest barrier perception was found for intimate activities and mainly regarded privacy concerns. Discussion The results can be used to derive design and information recommendations for the conception, development, and communication of video-based AAL technologies in order to meet the requirements and needs of future users.
Identifying and ranking influential spreaders in complex networks by combining a local-degree sum and the clustering coefficient

Identifying influential spreaders is a crucial problem that can help authorities to control the spreading process in complex networks. Based on the classical degree centrality (DC), several improved measures have been presented. However, these measures cannot rank spreaders accurately. In this paper, we first calculate the sum of the degrees of the nearest neighbors of a given node, and based on the calculated sum, a novel centrality named clustered local-degree (CLD) is proposed, which combines the sum and the clustering coefficients of nodes to rank spreaders. By assuming that the spreading process in networks follows the susceptible–infectious–recovered (SIR) model, we perform extensive simulations on a series of real networks to compare the performances between the CLD centrality and other six measures. The results show that the CLD centrality has a competitive performance in distinguishing the spreading ability of nodes, and exposes the best performance to identify influential spreaders accurately.
Artificial magnetic conductor‐based circularly polarized crossed‐dipole antennas: 1. AMC structure with grounding pins

In this paper, we analyze low‐profile circularly polarized (CP) antennas comprising a crossed‐dipole radiator on finite artificial magnetic conductor (AMC) surfaces. The crossed dipole is fed by a pair of vacant‐quarter printed rings to produce CP radiation. The AMC structure consists of a lattice of square metal plates on a grounded dielectric substrate with connecting pins between the patches and the ground plane. In this paper, we focus on the excitation of surface waves propagating on the finite‐sized AMC surface, which generates extra resonances and CP radiations for the radiation structures. We predict the surface‐wave resonances using a cavity model. In this model, the finite‐sized AMC structure is considered as a waveguide resonator. We verify the predicted results computationally using the finite element method‐based full‐wave electromagnetic solver Ansoft high‐frequency structure simulator. The results show that these extra resonances and corresponding CP radiations can be used to broaden the impedance matching and axial ratio bandwidths of the antennas, respectively.
UWB Radar SLAM: An Anchorless Approach in Vision Denied Indoor Environments

LiDAR and cameras are frequently used as sensors for simultaneous localization and mapping (SLAM). However, these sensors are prone to failure under low visibility (e.g. smoke) or places with reflective surfaces (e.g. mirrors). On the other hand, electromagnetic waves exhibit better penetration properties when the wavelength increases, thus are not affected by low visibility. Hence, this letter presents ultra-wideband (UWB) radar as an alternative to the existing sensors. UWB is generally known to be used in anchor-tag SLAM systems. One or more anchors are installed in the environment and the tags are attached to the robots. Although this method performs well under low visibility, modifying the existing infrastructure is not always feasible. UWB has also been used in peer-to-peer ranging collaborative SLAM systems. However, this requires more than a single robot and does not include mapping in the mentioned environment like smoke. Therefore, the presented approach in this letter solely depends on the UWB transceivers mounted on-board. In addition, an extended Kalman filter (EKF) SLAM is used to solve the SLAM problem at the back-end. Experiments were conducted and demonstrated that the proposed UWB-based radar SLAM is able to map natural point landmarks inside an indoor environment while improving robot localization.
HyperQA: Hyperbolic Embeddings for Fast and Efficient Ranking of Question Answer Pairs

Deep learning has become a fundamental component in question answering (QA) research. It is a well established fact that the top QA systems are mostly comprised of neural ranking architectures. Model aer model, we see architectural innovations ranging from word interaction layers to creative new methods for learning aentions. It also seems like these complex interaction mechanisms are mandatory for good performance. Unfortunately, many of these mechanisms incur a prohibitive computation and memory cost making them unfavorable for practical applications. In lieu of that, this paper tackles the question of whether it is possible to achieve competitive performance with very simple neural networks. As such, we propose a simple but novel deep learning architecture for fast and ecient QA. Specically, we present HyperQA, a parameter ecient neural network model that outperforms several parameter heavy models such as Aentive Pooling BiLSTMs and Multi Perspective CNN on multiple standard benchmark datasets such as TrecQA, WikiQA and YahooQA. e key innovation to our model is a pairwise ranking objective that performs QA matching in Hyperbolic space instead of Euclidean space. is empowers our model with a self-organizing ability and enables automatic discovery of latent hierarchies while learning embeddings of questions and answers. Our model requires no feature engineering, no similarity matrix matching, no complicated aention mechanisms nor over-parameterized layers and yet outperforms many models that have these functionalities on multiple benchmarks.
Research on atmospheric transmission distortion of Gauss laser using multiple phase screen method

The laser beam is attenuated, broadened, defocused and may even be deflected from its initial propagation direction as it propagates through the atmosphere. It leads to the decrease of the laser intensity of the receiving surface. Gauss beam is the fundamental components of all possible laser waveforms. Therefore, research on the transmission of the Gauss laser has far-reaching consequences in optical communication, weaponry, target designation, ranging, remote sensing and other applications that require transmission of laser beams through the atmosphere. In this paper, we propose a laboratory simulation method using multi-phase screen to calculate the effects of atmospheric turbulence. Theoretical analysis of Gauss laser transmission in the atmosphere is given. By calculating the propagation of the Gauss beam TEM00, the far field intensity and phase distribution is shown. By the given method, the optical setup is presented and used for optimizing the adaptive optics algorithm.