Miracle-dz/UniFAR-FTUs huggingface dataset:
3-D Vector Flow Imaging

Simulation and experimental results from 3-D vector flow estimations for a 62+62 2-D row-column (RC) array with integrated apodization are presented. A method for implementing a 3-D transverse oscillation (TO) velocity estimator on a 3.0 MHz RC array is developed and validated. First, a parametric simulation study is conducted where flow direction, ensemble length, number of pulse cycles, steering angles, transmit/receive apodization, and TO apodization profiles and spacing are varied, to find the optimal parameter configuration. The performance of the estimator is evaluated with respect to relative mean bias B̃ and mean standard deviation σ̃. Second, the optimal parameter configuration is implemented on the prototype RC probe connected to the experimental ultrasound scanner SARUS. Results from measurements conducted in a flow-rig system containing a constant laminar flow and a straight-vessel phantom with a pulsating flow are presented. Both an M-mode and a steered transmit sequence are applied. Three-dimensional vector flow is estimated in the flow-rig for four representative flow directions. In the setup with 90◦ beam-to-flow angle, the relative mean bias across the entire velocity profile is (-4.7, -0.9, 0.4)% with a relative standard deviation of (8.7, 5.1, 0.8)% for (vx, vy, vz). The estimated peak velocity is 48.5 cm/s ± 3.0 cm/s giving a -3% bias. The out-of-plane velocity component perpendicular to the cross section is used to estimate volumetric flow rates in the flow-rig at a 90◦ beam-to-flow angle. The estimated mean flow rate in this setup is 91.2 L/h ± 3.1 L/h corresponding to a bias of -11.1%. In a pulsating flow setup, flow rate measured during five cycles is 2.3 mL/stroke ± 0.1 mL/stroke giving a negative 9.7% bias. It is concluded that accurate 3-D vector flow estimation can be obtained using a 2-D RC addressed array.
Device- and circuit-level variability due to random discrete dopant in resist- and spacer-defined nanoscale FinFETs

Abstract. In current CMOS technology, parameter variations are playing a vital role in limiting the benefits of downscaling, especially in the nanodomain. This work presents the investigation of random dopant fluctuation (RDF) in several possible line-edge roughness (LER)-induced fin shapes of spacer- (i.e., correlated LER) and resist- (i.e., uncorrelated LER) defined patterning techniques for a 14-nm FinFET structure. The three-dimensional (3-D) technology computer-aided design simulation on a large statistical ensemble has been carried out to analyze the impact of the RDF on various device parameters such as threshold voltage, drive current, off current, and drain-induced barrier lowering. It is observed that the impact of RDF on electrical parameters mainly depends on the fin shape and is dominant in all resist-defined FinFET structures. It is also found that the impact of RDF can diminish significantly using the spacer-defined patterning technique. Further, the impact of RDF in spacer- and resist-defined FinFET structures has been investigated for noise margins of 6-T static random access memories (SRAM). It is concluded that random dopant variations in SRAM performance offer better immunity in the case of spacer-defined FinFETs compared with resist-defined FinFETs.
Expected Convergence Rate to Consensus in Asymmetric Networks: Analysis and Distributed Estimation

This paper investigates the expected rate of convergence to consensus in an asymmetric network represented by a weighted directed graph. The initial state of the network is represented by a random vector and the expectation is taken with respect to the random initial condition of the network. The proposed convergence rate is described in terms of the eigenvalues of the Laplacian matrix of the network graph. The generalized power iteration algorithm is then introduced based on the Krylov subspace method to compute the proposed expected convergence rate in a centralized fashion. To this end, the Laplacian matrix of the network is transformed to a new matrix such that existing techniques can be used to find the eigenvalue representing the expected convergence rate of the network. The convergence analysis of the centralized algorithm is performed with a prescribed upper bound on the approximation error of the algorithm. A distributed version of the centralized algorithm is then developed using the notion of consensus observer. The efficiency of the algorithms is subsequently demonstrated by simulations.
Progresses in implementation of STED microscopy

Optical fluorescence microscopy provides molecular specificity and high contrast, which are powerful aspects in biomedical researches. Moreover, super-resolution microscopy techniques have broken through the diffraction-limited resolution, which had hindered optical microscopy. Among various techniques, stimulated emission depletion (STED) microscopy quasi-instantaneously reduces the size of the effective focal spot by suppressing the peripheral fluorescence of the excited spot with an additional depletion laser, while also providing optical sectioning. With these advantages, the usage of STED microscopy is increasing in the various field of research. Nevertheless, STED microscopy has been continuously improved to answer more biological questions. This review summarises the recent advancements and new techniques implemented for STED microscopy, including microscopy architectures, multicolour ability, deep-tissue imaging, aberration correction, three-dimensional super-resolution, fast measurement, photostability, and multimodality. It is expected that STED microscopy will further evolve and become an more widely useful tool for life sciences.
Cyber-Physical Attacks Against State Estimators Based on a Finite Frequency Approach

This paper studies the design problem of the attack strategy for cyber-physical systems (CPSs). In this paper, a finite frequency attack strategy is designed for CPSs such that the performance of the state estimator can be reduced with a low attack detection probability. Different from the existing methods with a predefined finite frequency range, the attack strategy is designed for the system to satisfy ${H}{\_{}}/{H}_{\infty}$ performance index in different finite frequency ranges in the proposed method, and a suitable frequency interval can be selected as that of the attack signal. Since the property of the finite frequency of the attack signal could be made full use of such that a good attack effect can be obtained in the proposed method. Finally, an example is given to illustrate the effectiveness of the developed method.
What inferences do people actually make upon encountering informationally redundant utterances? An individual differences study

Utterances mentioning a highly predictable event are known to elicit atypicality inferences (Kravtchenko and Demberg, 2015; 2022). In those studies, pragmatic inferences are measured based on typicality ratings. It is assumed that comprehenders notice the redundancy and “repair” the utterance informativity by inferring that the mentioned event is atypical for the referent, resulting in a lower typicality rating. However, the actual inferences that people make have never been elicited. We ex-tend the original experimental design by asking participants to explain their ratings and administering several individual differences tests. This allows us to test (1) whether low ratings indeed correspond to the assumed inferences (they mostly do, but occasionally participants seem to make the inference but then reject it and give high ratings), and (2) whether the tendency to make atypicality inferences is modulated by cognitive factors. We find that people with higher reasoning abilities are more likely to draw inferences.
If graffiti changed anything, it would be illegal. The influence of political graffiti on the perception of neighborhoods and intergroup attitudes

In a series of three studies (total N = 956), we examined how political graffiti, which serves as a representation of prevailing social norms, influences the evaluation of social cohesion and neighborhood inhabitants depending on the individuals political orientation. In line with our hypothesis, results of Study 1 (N = 199) indicated that individuals tended to express more positive evaluations of the social cohesion within a neighborhood when the political graffiti aligns with their own political orientation. Conversely, when confronted with counter-attitudinal political graffiti, participants reported lower evaluations of social cohesion. In Study 2, a sensitive scale to assess social cohesion was developed. Study 3 (N = 550) investigated the dose–response relationship of right-wing political graffiti and replicated the results from Study 1. Consistent with our hypotheses, even a minimal presence of right-wing graffiti exerted a significant impact on participants’ evaluations of the neighborhood and interacted with the participants political orientation. Taken together, our studies shed light on the crucial role of the individuals’ own political orientation for the evaluation of neighborhoods and their inhabitants. Furthermore, we offer insights into how these perceptions may influence intergroup attitudes toward foreigners living in Germany. The implications of our findings are highly relevant to ongoing discussions surrounding social norms within neighborhoods. By highlighting the significance of political graffiti as a representation of social norms, our research contributes to a deeper understanding of the dynamics at play in evaluating neighborhoods and their social fabric.
Measurement of Flow Perturbation Spectra in Mach 4.5 Corner Separation Zone

Characterization of the M=4.5 flow over a flat-plate model with a 30 deg compression ramp was performed with low-enthalpy (T0=300 K) and high-enthalpy (T0=800–1250 K) flow conditions for a wide range of unit Reynolds numbers (Rel=4⋅105−1⋅107 m−1). Three measurement techniques were employed to measure the frequency spectrum of flow perturbations: a high-frequency Shack–Hartmann wavefront sensor (aerooptical method), high-frequency PCB™ pressure sensors, and a laser differential interferometer. The magnitude and frequency of flow oscillations measured by all three methods provide comprehensive and complementary results in determining spectra of gas disturbances within the flow. Of these measurement methods, the Shack–Hartmann wavefront sensor is shown to be the most suitable tool for analysis of the high-speed flow. Aerooptical measurements detect modification to the flow structure when plasma actuation is employed or when Rel is varied. In this work, flow characterization by the Shack–Hartmann sensor at.
Incorporating an Unsupervised Text Mining Approach into Studying Logistics Risk Management: Insights from Corporate Annual Reports and Topic Modeling

This study examined the Security and Exchange Commission (SEC) annual reports of selected logistics firms over the period from 2006 through 2021 for risk management terms. The purpose was to identify which risks are considered most important in supply chain logistics operations. Section 1A of the SEC reports includes risk factors. The COVID-19 pandemic has had a heavy impact on global supply chains. We also know that trucking firms have long had difficulties recruiting drivers. Fuel price has always been a major risk for airlines but also can impact shipping, trucking, and railroads. We were especially interested in pandemic, personnel, and fuel risks. We applied topic modeling, enabling us to identify some of the capabilities of unsupervised text mining as applied to SEC reports. We demonstrate the identification of terms, the time dimension, and correlation across topics by the topic model. Our analysis confirmed expectations about COVID-19’s impact, personnel shortages, and fuel. It also revealed common themes regarding the risks involved in international trade and perceived regulatory risks. We conclude with the supply chain management risks identified and discuss means of mitigation.
Reliability Analysis of Local Scour at Bridge Pier in Clay-Sand Mixed Sediments

Bridge pier scouring is an important issue for safety evaluation of bridges. The existing equations for bridge pier scour prediction are mostly deterministic in nature, which do not incorporate the uncertainties in various parameters of scouring. A methodology for reliability analysis of bridge pier against scour in cohesive sediments incorporating the uncertainties of the model and input parameters are presented herein using efficient spreadsheet algorithm for first order reliability method (FORM). The influence of uncertainties, the nature of probability distribution and correlation of basic input parameters on failure probability and reliability index has been studied and described briefly herein. A sensitivity analysis was also carried out to obtain the effect of the individual random variables on the reliability of pier scour. To achieve the desired safety level in the design of pier foundation, the reliability-based safety factor is proposed.
Hydrodynamics Analysis of an Underwater Foldable Arm

Improved designs for underwater manipulators are becoming increasingly important due to their utility in academic and industrial applications. In this work, an experiment was conducted in conjunction with a numerical simulation to investigate the hydrodynamic performance and structural reliability of the proposed foldable arm during the unfolding process at various movement velocities. A large-scale geometric model of the foldable arm with a single degree of freedom (DOF) was constructed. The distribution of the flow field, the movement stability and the equivalent stress of the foldable arm were quantitatively analyzed with a designed tank experiment and the Computational Fluid Dynamics (CFD) simulation. Simulation results show that the maximum deviation of the resistance and torque is 8.04% and 5.73%, respectively, compared with the experimental results of static postures. Comparison results prove the reliability of the numerical model. The results of transient simulation demonstrate that the optimal speed of the foldable arm is 3 Kn and the pressure distribution on the surface of foldable arm is relatively regular. Furthermore, a fluid–structure interaction (FSI) validation study of the foldable arm was presented. For the coupling between the fluid and structural mechanics domains, a nonmatching discretization approach was adopted. The results show that the directional deformation (Z axis) of the foldable arm is less than 0.50 mm. The proposed foldable arm has a large angle of rotation and high stability compared to the existing manipulators.
Survival and quality-of-life outcomes in early-stage NSCLC patients: a literature review of real-world evidence

Aim: Assess the long-term survival and quality-of-life outcomes in early-stage NSCLC (eNSCLC) patients. Methods: Review of long-term survival and quality-of-life after curative treatment in eNSCLC patients in observational studies. Results: Disease-free proportion decreased in stage III vs stage I patients. Recurrence-free proportion decreased with age and disease stage. Advanced stage and vascular invasion increased risk of late recurrence. Conditional 5-year relative survival rates did not exceed 87%, indicating higher mortality in eNSCLC survivors. Lower conditional survival rates and relative survival rates were associated with older age and advanced disease. Survivors of eNSCLC had poorer physical quality-of-life. Conclusion: Despite curative-intent therapy, survivors of eNSCLC still face significant risks of recurrence, excess mortality, and diminished quality-of-life.
Semiparametric Regression and Machine Learning Methods for Estimating Optimal Dynamic Treatment Regimes.

Semiparametric Regression and Machine Learning Methods for Estimating Optimal Dynamic Treatment Regimes by Yebin Tao Chair: Associate Professor Lu Wang Dynamic treatment regimes (DTRs) are sequential decision rules that focus simultaneously on treatment individualization and adaptation over time. They determine treatment prescriptions based on each individual’s specific characteristics (e.g., demographics, clinical outcomes and genetic makeup) and also adapt the prescriptions over time to evolving illness. We develop robust and flexible semiparametric regression and machine learning methods for estimating optimal DTRs. In the first project, we consider identifying the optimal personalized timing for treatment initiation. Instead of considering multiple fixed decision stages as in most DTR literature, we deal with continuous or multiple random decision points for treatment initiation given each patient’s individual disease and treatment history. For a set of predefined candidate DTRs, we propose to fit a flexible survival model with splines of time-varying covariates to estimate patient-specific probabilities of adherence to each DTR. Given the estimated probabilities, an inverse probability weighted estimator for the counterfactual mean utility (prespecified criteria) is employed to assess each DTR
A C OMPARATIVE A NALYSIS OF D ATA M INING M ETHODS AND H IERARCHICAL L INEAR M ODELING U SING PISA 2018 D ATA

Educational research often encounters clustered data sets, where observations are organized into multilevel units, consisting of lower-level units (individuals) nested within higher-level units (clusters). However, many studies in education utilize tree-based methods like Random Forest without considering the hierarchical structure of the data sets. Neglecting the clustered data structure can result in biased or inaccurate results. To address this issue, this study aimed to conduct a comprehensive survey of three tree-based data mining algorithms and hierarchical linear modeling (HLM). The study utilized the Programme for International Student Assessment (PISA) 2018 data to compare different methods, including non-mixed-effects tree models (e.g., Random Forest) and mixed-effects tree models (e.g., random effects expectation minimization recursive partitioning method, mixed-effects Random Forest), as well as the HLM approach. Based on the findings of this study, mixed-effects Random Forest demonstrated the highest prediction accuracy, while the random effects expectation minimization recursive partitioning method had the lowest prediction accuracy. However, it is important to note that tree-based methods limit deep interpretation of the results. Therefore, further analysis is needed to gain a more comprehensive understanding. In comparison, the HLM approach retains its value in terms of interpretability. Overall, this study offers valuable insights for selecting and utilizing suitable methods when analyzing clustered educational datasets.
Uncovering Candidate mRNAs, Signaling Pathways and Immune Cells in Atherosclerotic Plaque and Ischemic Stroke

Background The specific molecular mechanistic link between atherosclerotic plaques and ischemic stroke (IS) is not clear. The aim of this study is to explore the potential molecular relationship between atherosclerotic plaques and IS. Methods All data were downloaded from the Gene Expression Omnibus (GEO) database. Key hub differentially expressed mRNAs (DEmRNAs) related to atherosclerotic plaques and IS were identified by differential expression analysis and least absolute shrinkage and selection operator (LASSO) analysis. Subsequently, a diagnostic model was established based on the expression of key hub DEmRNAs and logistic regression. In order to understand the molecular mechanism of key hub DEmRNAs, the transcription factor (TF) regulatory network and mRNA-miRNA-lncRNA regulatory network were also constructed. In addition, functional enrichment analysis and single-sample Gene Set Enrichment Analysis (ssGSEA) analysis were also performed. Results Four key hub DEmRNAs (ADCY3, CLDN7, PPM1B and RRAS2) were identified by differential expression analysis and LASSO analysis. Moreover, the diagnostic model based on four key hub DEmRNAs has excellent diagnostic accuracy. We also found that Type 1 T helper cell may be associated with IS caused by atherosclerosis based on ssGSEA analysis. In the mRNA-miRNA-lncRNA regulatory network, we found that multiple signaling axes such as RRAS2-hsa-miR-3150b-3p-ILF3-AS1, PPM1B-hsa-miR-541-5p-LINC00294, CLDN7-hsa-miR-184-LINC00467 and ADCY3-hsa-miR-488-3p-URB1-AS1 may play an important role in the progression of IS. In addition, some signaling pathways, including chemokine signaling pathway, MAPK signaling pathway and cAMP signaling pathway, may be involved in regulating IS. Conclusion The identified key molecules, signaling pathways and immune cells may help to provide a theoretical basis for exploring the relationship between atherosclerotic plaque and the progression of IS.
Analysis of the Effect of Female Genital Tuberculosis on Ovarian Reserve Parameters

ABSTRACT Background: Female genital tuberculosis (FGTB) is a known cause of female infertility. Worldwide incidence is 5%–10% and annual burden in India is around 4%–7%. It is known to cause tubal and endometrial damage. However, the effect on ovarian damage is poorly known. The availability of ovarian markers has contributed to an improved understanding of ovarian reserve in FGTB. Aims: The aim of this study was to assess ovarian reserve by measuring anti-Mullerian hormone (AMH) and antral follicle count (AFC) amongst infertile women and analyse the effect of GTB on ovarian reserve parameters. Settings and Design: This was a prospective study at a tertiary referral centre for infertility for 18 months. Materials and Methods: A total of 133 infertile women who underwent diagnostic hysterolaparoscopy and cartridge-based nucleic acid amplification test testing of an endometrial biopsy were included in the study. AMH and AFC of all the infertile women were assessed and compared between cases with and without FGTB. Statistical Analysis Used: Independent t-test was used to find the outcome differences in the distribution of values. P < 0.05 was considered statistically significant. Results: Fifty-eight (43.6%) cases were diagnosed with FGTB (Group I), and 75 (56.3%) cases were without FGTB (Group II). The mean AMH level 1.88 ng/ml (±1.52) and mean AFC 9.0 (±5.50) were significantly lower (P < 0.001) in Group I than in Group II with AMH 3.57 ng/ml (±2.93) and AFC 12.50 (±6.0). Conclusion: In women with prolonged infertility and low ovarian reserve, FGTB should be ruled out. Early diagnosis and treatment of GTB may prevent further decline of ovarian reserve and improve the reproductive outcome.