Miracle-dz/UniFAR-FTUs huggingface dataset:
Simulation of Unsteady Interaction of Centrifugal Impeller with Its Diffuser Using Advanced Vortex Method

In this study, an advanced Lagrangian vortex-boundary element method is applied to simulate the unsteady impeller-diffuser interaction in a diffuser pump. Velocity calculations based on the Biot-Savart law reduce the need to grid large portions of the flow field and the calculation points are concentrated in the regions where vorticity is present. Lagrangian representation of the evolving vorticity field is well suited to moving boundaries. An integral pressure equation shows that pressure distribution can be estimated directly from the instantaneous velocity and vorticity field. The numerical results are compared with the experimental data and the comparisons show that the method used in this study can provide us insight into the complicated unsteady impeller-deffuser interaction phenomena in a diffuser pump.
Claw-free graphs, skeletal graphs, and a stronger conjecture on $\omega$, $\Delta$, and $\chi$

The second author's $\omega$, $\Delta$, $\chi$ conjecture proposes that every graph satisties $\chi \leq \lceil \frac 12 (\Delta+1+\omega)\rceil$. In this paper we prove that the conjecture holds for all claw-free graphs. Our approach uses the structure theorem of Chudnovsky and Seymour. Along the way we discuss a stronger local conjecture, and prove that it holds for claw-free graphs with a three-colourable complement. To prove our results we introduce a very useful $\chi$-preserving reduction on homogeneous pairs of cliques, and thus restrict our view to so-called "skeletal" graphs.
Subjective effects of white-balancing errors in digital photography

Automatic white-balancing algorithms play a key role in digital photography. Failure to estimate illumination chromaticity correctly will result in an invalid overall color cast in the final image. Such image-wide color casts are easily detected by human observers. The aim of this paper is to quantify the subjective effects of white-balancing errors in digital photography. It is achieved by means of subjective tests. Different natural images are utilized in order to study the effect of image contents on the acceptability of white point shifts. Multiple directions of white point error are covered in order to study the relation between image contents and the most acceptable direction of white point shift. Details of the camera system and sensor used are also discussed in order to separate the contribution of white balancing errors from the contribution of other color processing to the final color errors. Typically the performance of automatic white-balancing algorithms is presented in the literature by using different color or color difference measures. This paper provides a means to interpret those results from the point of view of digital photography, namely, to separate significant white-balancing errors from insignificant ones.
Follow the Flow: A Prospective on the On-Line Detection of Flow Mental State through Machine Learning

Flow is a precious mental status for achieving high sports performance. It is defined as an emotional state with high valence and high arousal levels. However, a viable detection system that could provide information about it in real-time is not yet recognized. The prospective work presented here aims to the creation of an online flow detection framework. A supervised machine learning model will be trained to predict valence and arousal levels, both on already existing databases and freshly collected physiological data. As final result, the definition of the minimally expensive (both in terms of sensors and time) amount of data needed to predict a flow status will enable the creation of a real-time detection interface of flow.
Battery Energy Storage System With Interleaving Structure of Dual-Active-Bridge Converter and Non-Isolated DC-to-DC Converter With Wide Input and Output Voltage

We propose a circuit topology suitable as a battery charge/discharge tester with a DAB converter and a non-isolated dc-dc converter as a module structure. The module structure can be configured to have a wide input and output voltage range because it is easy to expand. DAB converters are used for bidirectional power transfer and galvanic isolation. By controlling the phase difference of the voltages across the DAB to be almost identical, the generation of circulating current due to the phase difference is minimized, and the ZVS is guaranteed. In addition, by switching at a phase angle that flattens the DAB switch current, the peak current is reduced to diminish the switch’s conduction loss, lowering the switch current rating, and reducing the magnetic core size. The non-isolated dc-dc converter is used to control the battery output voltage. Through the interleaved structure, good dynamic characteristics and current ripple can be reduced, so the effect of improving the battery lifecycle can be expected. We present four series-parallel structures of the proposed converter and compare them with the case of generating an output voltage only with a DAB converter. Analyze the characteristics of automatic voltage balancing due to the difference between the input capacitors’ initial voltage and capacitance connected in series. We analyze the features of the proposed system through simulation and verify the technical feasibility and excellence of the proposed battery charge/discharge tester through charge/discharge experiments using supercapacitors instead of batteries.
Sharp band edge photoluminescence of high-purity CuInS2 single crystals

Temperature-dependent photoluminescence measurements were carried out between 8 and 300 K on CuInS2 single crystals grown by a traveling heater method. Ten distinct peaks were present in the near-band edge region. Four unknown peaks, observed at 8 K, were found to be due to bound exciton emission. Moreover, the luminescence remained stable up to room temperature.
Quantification of optical absorption coefficient from acoustic spectra in the optical diffusive regime using photoacoustic microscopy

Photoacoustic (PA) tomography (PAT) can image optical absorption contrast with ultrasonic spatial resolution in the optical diffusive regime. Multi-wavelength PAT can noninvasively monitor hemoglobin oxygen saturation (sO2) with high sensitivity and fine spatial resolution. However, accurate quantification in PAT requires knowledge of the optical fluence distribution, acoustic wave attenuation, and detection system bandwidth. We propose a method to circumvent this requirement using acoustic spectra of PA signals acquired at two optical wavelengths. With the acoustic spectral method, the absorption coefficients of an oxygenated bovine blood phantom at 560 and 575 nm were quantified with errors of ><5%.
Review of methodological issues in cost‐effectiveness analyses relating to injecting drug users, and case‐study illustrations

Guidelines for good practice in cost‐effectiveness analyses (CEAs) are available from the UK's National Institute for Health and Clinical Excellence, which address the scope of the CEA study, appropriateness of the data used and how to account for model as well as statistical uncertainty. Within these three broad headings, we identify 10 specific issues that may affect materially the CEA of a public health intervention for which injecting drug users are a major target group. The 10 issues relate to injecting drug users' under‐representation in randomized controlled trials; their risk of blood‐borne viruses—such as human immunodeficiency virus and hepatitis C virus—which have long‐term chronic sequelae, their markedly higher age‐specific mortality than in the general population and the relapsing–remitting nature of opiate dependence with its associated risk of overdose. We consider how adequately three key UK CEAs accounted for the relevant injecting drug user specific issues that we have highlighted. The three case‐studies are antiviral treatment of carriage of hepatitic C virus, opiate substitution therapy and needle and syringe programmes.
Dynamic Economic Dispatch of Thermal-Wind-Storage Systems Based on Reinforcement Learning

This paper studies a dynamic economic dispatch (DED) problem which includes thermal and wind-storage hybrid units, aiming at minimizing the total generation cost and penalty costs involving generation regulation, load shedding, and wind curtailment. Each unit is assigned with a fixed, discrete, constrained virtual action set, and its cost function is unknown. Based on the developed model, a reinforcement learning algorithm is applied to solve the DED problem under the wind uncertainty. Simulation results illustrate the effectiveness of the algorithm.
Drought-Induced Xylem Sulfate Activates the ABA-Mediated Regulation of Sulfate Assimilation and Glutathione Redox in Brassica napus Leaves

Drought intensity modifies the assimilatory pathway of glutathione (GSH) synthesis. Abscisic acid (ABA) is a representative signaling hormone involved in regulating plant stress responses. This study aimed to investigate an interactive regulation of sulfate and/or ABA in GSH metabolism and redox. The drought-responsive alterations in sulfate assimilation and GSH-based redox reactions were assessed relative to ABA responses on the time-course of drought intensity. Drought-responsive H2O2 concentrations were divided into two distinct phases—an initial 4 days of no change (Ψw ≥ −0.49 MPa) and a phase of higher accumulation during the late phase of the drought (days 10–14; Ψw ≤ −1.34 MPa). During the early phase of the drought, GSH/GSSG redox state turned to the slightly reduced state with a transient increase in GSH, resulting from a strong activation of H2O2 scavenging enzymes, ascorbate peroxidase (APOX) and glutathione reductase (GR). The late phase of the drought was characterized by a decrease in GSH due to cysteine accumulation, shifting GSH- and NADPH-based redox states to higher oxidization, increasing sulfate and ABA in xylem, and causing ABA accumulation in leaves. Regression analysis revealed that sulfate in xylem sap was positively correlated with H2O2 concentrations and ABA was closely related to decreases in the GSH pool and the oxidation of GSH catalyzed by glutathione peroxidase (GPOX). These results indicate that drought-induced oxidation proceeds through the suppression of GSH synthesis and further GSH oxidation in a sulfate-activated ABA-dependent manner.
Impact of Different Transportation Modes on the Transmission of COVID-19: Correlation and Strategies from a Case Study in Wuhan, China

Transportation is the main carrier of population movement, so it is significant to clarify how different transportation modes influence epidemic transmission. This paper verified the relationship between different levels of facilities and epidemic transmission by use of the K-means clustering method and the Mann–Whitney U test. Next, quantile regression and negative binomial regression were adopted to evaluate the relationship between transportation modes and transmission patterns. Finally, this paper proposed a control efficiency indicator to assess the differentiated strategies. The results indicated that the epidemic appeared 2–3 days earlier in cities with strong hubs, and the diagnoses were nearly fourfold than in other cities. In addition, air and road transportation were strongly associated with transmission speed, while railway and road transportation were more correlated with severity. A prevention strategy that considered transportation facility levels resulted in a reduction of the diagnoses of about 6%, for the same cost. The results of different strategies may provide valuable insights for cities to develop more efficient control measures and an orderly restoration of public transportation during the steady phase of the epidemic.
Ads Avoidance And Attitude Towards Online Advertising Among Net-Generation In Jakarta

Advertising is a form of non-personal presentation consisting of promoting ideas, goods, and services by certain sponsors and being paid. However, the trend of ad blocker users continues to increase and is expected to penetrate mobile devices. With this ad blocker, the advertisements carried out by the brand will not be able to reach the intended target market. This study examines factors influencing the Internet generation's avoidance of advertising on social media. This type of research is quantitative, taking all net-generation in Jakarta as the population and 490 net-generation as a sample. Sampling in this study is non-probability sampling. The results of this study indicate that the attitude towards online advertising is directly and significantly influenced by the variables of entertainment, informativeness, credibility, personalization, and incentives. In contrast, the irritation variable has no effect. The advertising avoidance variable is also directly and significantly influenced by the attitude towards online advertising and irritation. However, it is not influenced by entertainment, Informativeness, credibility, Personalization, and incentive variables. Meanwhile, advertising avoidance is influenced indirectly and significantly by the variables of entertainment, Informativeness, credibility, Personalization, and incentives through the attitude towards online advertising as a moderating variable. At the same time, the advertising avoidance variable is not affected indirectly and significantly by the irritation variable through the attitude towards online advertising as a moderating variable.
Effect of small‐scale surface heterogeneities and buildings on radiation fog: Large‐eddy simulation study at Paris–Charles de Gaulle airport

Large‐eddy simulations (LES) of radiation fog were performed over an airport area to study the effect of urban canopy on fog. These LES were performed with the Meso‐NH research model at very high resolution: 1.5 m in the horizontal and 1 m in the vertical and over a domain 4.5 km × 1.5 km.
A Time-Frequency Attention Module for Neural Speech Enhancement

Speech enhancement plays an essential role in a wide range of speech processing applications. Recent studies on speech enhancement tend to investigate how to effectively capture the long-term contextual dependencies of speech signals to boost performance. However, these studies generally neglect the time-frequency (T-F) distribution information of speech spectral components, which is equally important for speech enhancement. In this paper, we propose a simple yet very effective network module, which we term the T-F attention (TFA) module, that uses two parallel attention branches, i.e., time-frame attention and frequency-channel attention, to explicitly exploit position information to generate a 2-D attention map to characterise the salient T-F speech distribution. We validate our TFA module as part of two widely used backbone networks (residual temporal convolution network and Transformer) and conduct speech enhancement with four most popular training objectives. Our extensive experiments demonstrate that our proposed TFA module consistently leads to substantial enhancement performance improvements in terms of the five most widely used objective metrics, with negligible parameter overheads. In addition, we further evaluate the efficacy of speech enhancement as a front-end for a downstream speech recognition task. Our evaluation results show that the TFA module significantly improves the robustness of the system to noisy conditions.
LPASS-Net: Lightweight Progressive Attention Semantic Segmentation Network for Automatic Segmentation of Remote Sensing Images

Semantic segmentation of remote sensing images plays a crucial role in urban planning and development. How to perform automatic, fast, and effective semantic segmentation of considerable size and high-resolution remote sensing images has become the key to research. However, the existing segmentation methods based on deep learning are complex and often difficult to apply practically due to the high computational cost of the excessive parameters. In this paper, we propose an end-to-end lightweight progressive attention semantic segmentation network (LPASS-Net), which aims to solve the problem of reducing computational costs without losing accuracy. Firstly, its backbone features are based on a lightweight network, MobileNetv3, and a feature fusion network composed of a reverse progressive attentional feature fusion network work. Additionally, a lightweight non-local convolutional attention network (LNCA-Net) is proposed to effectively integrate global information of attention mechanisms in the spatial dimension. Secondly, an edge padding cut prediction (EPCP) method is proposed to solve the problem of splicing traces in the prediction results. Finally, evaluated on the public datasets BDCI 2017 and ISPRS Potsdam, the mIoU reaches 83.17% and 88.86%, respectively, with an inference time of 0.0271 s.
Interactions between Severe Allergy and Anxiety in Anti-SARS-CoV-2 Vaccinees

Severe drug allergy affects patient hesitancy to new treatments, posing unprecedented challenges to anti-SARS-CoV-2 vaccination campaigns. We aimed to analyze the psychological profile of vaccinees with a history of severe allergy in comparison to subjects with a milder allergy history. Patients attending a dedicated vaccination setting were administered an anonymized questionnaire including clinical data and the State-Trait Anxiety Inventory (STAI) scale (score range 20–80). Patients were also asked whether being in a protected setting affected their attitude toward vaccination. Data are expressed as median (interquartile range). We enrolled 116 patients (78% women), of whom 79% had a history of drug anaphylaxis. The median state anxiety score was 36.5 (30–47.2), while the trait anxiety score was 37 (32–48). State anxiety was higher in those with severe than mild allergy [39 (32–50) vs. 30 (25–37); p < 0.001], with the highest score found in a patient with previous drug anaphylaxis (42.5 [32–51.7]). More than 50% of patients reported that being in a protected setting had lowered their anxiety. Severe allergy is associated with a higher burden of situational anxiety in the setting of vaccination without affecting patient constitutional (trait) levels of anxiety. Vaccination in dedicated facilities might overcome issues related to hesitancy and improve patients’ quality of life.
Sub-bottom profiling using ocean ambient noise

Spectral factorization is shown to restore the phase of an incoherent layered sediment reflection coefficient so that its Fourier transform is the minimum phase impulse response at each angle. The method requires the reflection coefficient to be known over a range of frequencies and the grazing angles in question to be above critical. It is developed here in the context of another recently established technique for extracting the seabed’s plane wave reflection coefficient from ambient noise data measured on a moored or drifting vertical array (VLA). Thus it offers the possibility of sub-bottom profiling from a single platform with no sound source. Limitations of the phase restoration method are discussed and, using modeled data, comparisons are made between the “true” impulse response derived from the known complex reflection coefficient and the result of applying spectral factorization to the absolute value of the reflection coefficient. The method is also demonstrated on experimental reflection loss inf.
Pose Estimation Method for Non-Cooperative Target Based on Deep Learning

The scientific research strength in the aerospace field has become an essential criterion for measuring a country’s scientific and technological level and comprehensive national power, but in the grand scheme, many factors are beyond human control. As is known, the difficulty with non-cooperative target intersection docking is its failure to provide attitude information autonomously. The existing non-cooperative target poses estimation methods with low accuracy and high resource consumption. This paper proposes a deep-learning-based pose estimation method for solving these problems. The proposed pose estimation method consists of two distinctly innovative works. You Only Look Once v5 (YOLOv5) is an innovative and lightweight network that is used to pre-recognize non-cooperative targets. Another part introduces concurrent space and channel compressor theory modules in a lightweight High-Resolution Network (HRNet) to extend its advantages in real-time, and hence proposes a spatial and channel Squeeze and Excitation—Lightweight High-Resolution Network (scSE-LHRNet) network for pose estimation. To verify the superiority of the proposed network, experiments were conducted on a publicly available dataset with multiple evaluation metrics to compare and analyze existing methods. The experimental results show that the proposed pose estimation method dramatically reduces the complexity of the model, effectively decreases the amount of computation, and achieves significant pose estimation results.
3D Tumor Spheroid and Organoid to Model Tumor Microenvironment for Cancer Immunotherapy

The intricate microenvironment in which malignant cells reside is essential for the progression of tumor growth. Both the physical and biochemical features of the tumor microenvironment (TME) play a critical role in promoting the differentiation, proliferation, invasion, and metastasis of cancer cells. It is therefore essential to understand how malignant cells interact and communicate with an assortment of supportive tumor-associated cells including macrophages, fibroblasts, endothelial cells, and other immune cells. To study the complex mechanisms behind cancer progression, 3D spheroid and organoid models are widely in favor because they replicate the stromal environment and multicellular structure present within an in vivo tumor. It provides more precise data about the cell–cell interactions, tumor characteristics, drug discovery, and metabolic profile of cancer cells compared to oversimplified 2D systems and unrepresentative animal models. This review provides a description of the key elements of the tumor microenvironment as well as early research using cell-line derived, 3D spheroid tumor models that paved the way for the adoption of patient-derived spheroid and organoid models. In particular, 3D spheroid and organoid models provide a method for drug screening with a particular emphasis on influence of the TME in cancer immunotherapy.
The Role of Immediate Post-Procedural Cone-Beam Computed Tomography (CBCT) in Predicting the Early Radiologic Response of Hepatocellular Carcinoma (HCC) Nodules to Drug-Eluting Bead Transarterial Chemoembolization (DEB-TACE)

The purpose of this study was to evaluate the efficacy of unenhanced cone-beam computed tomography (CBCT) performed at the end of drug-eluting bead transarterial chemoembolization (DEB-TACE) in predicting HCC nodules’ early radiologic response to treatment, assessed using mRECIST criteria with a 30–60 day four-phase contrast-enhanced CT follow-up. Fifty-nine patients (81 lesions) subjected to DEB-TACE as exclusive treatment for HCC lesions (naive/relapse) between February 2020 and October 2021 were prospectively enrolled. In a post-interventional unenhanced CBCT procedure, two experienced radiologists evaluated for each lesion the overall intensity of the contrast media deposit, the homogeneity of the enhancement, and the presence of smooth and complete margins. The univariate analysis found that lesions with complete response (CR+) had a significantly higher incidence of clear and complete margins than CR− lesions (76.9% vs. 17.2%, p = 0.003) and a higher intensity score (67.3% vs. 27.6%, p = 0.0009). A Dmax <30 mm was significantly more common among CR+ than CR− lesions (92.3% vs. 69%, p = 0.01). These features were confirmed as significant predictors for CR+ by multivariate binary logistic regression. The homogeneity of the enhancement did not affect the DEB-TACE outcome. Post-interventional unenhanced CBCT is effective in predicting early radiological response to DEB-TACE, since the presence of an intense contrast media deposit with clear and complete margins in treated HCC lesions is associated with CR.