Miracle-dz/UniFAR-FTUs huggingface dataset:
What does the public think about artificial intelligence?—A criticality map to understand bias in the public perception of AI

Introduction Artificial Intelligence (AI) has become ubiquitous in medicine, business, manufacturing and transportation, and is entering our personal lives. Public perceptions of AI are often shaped either by admiration for its benefits and possibilities, or by uncertainties, potential threats and fears about this opaque and perceived as mysterious technology. Understanding the public perception of AI, as well as its requirements and attributions, is essential for responsible research and innovation and enables aligning the development and governance of future AI systems with individual and societal needs. Methods To contribute to this understanding, we asked 122 participants in Germany how they perceived 38 statements about artificial intelligence in different contexts (personal, economic, industrial, social, cultural, health). We assessed their personal evaluation and the perceived likelihood of these aspects becoming reality. Results We visualized the responses in a criticality map that allows the identification of issues that require particular attention from research and policy-making. The results show that the perceived evaluation and the perceived expectations differ considerably between the domains. The aspect perceived as most critical is the fear of cybersecurity threats, which is seen as highly likely and least liked. Discussion The diversity of users influenced the evaluation: People with lower trust rated the impact of AI as more positive but less likely. Compared to people with higher trust, they consider certain features and consequences of AI to be more desirable, but they think the impact of AI will be smaller. We conclude that AI is still a “black box” for many. Neither the opportunities nor the risks can yet be adequately assessed, which can lead to biased and irrational control beliefs in the public perception of AI. The article concludes with guidelines for promoting AI literacy to facilitate informed decision-making.
Aerodynamic structural design and control for a new miniature coaxial dual-rotor unmanned aerial vehicle

This article mainly introduces a design and control method for a new type of portable coaxial dual-rotor unmanned aerial vehicle. The coaxial dual-rotor unmanned aerial vehicle prototype is constructed using three-dimensional computer-aided design software, and its aerodynamic characteristics are analyzed via computational fluid dynamics. A 6-degree-of-freedom nonlinear dynamic model of the coaxial dual-rotor unmanned aerial vehicle is derived by considering the characteristics of coaxial rotors and the downwash airflow. A proportional–integral–derivative tracking scheme including an inner-loop attitude controller and an outer-loop position controller is developed to track the target position and attitude, respectively. Finally, numerical simulations and actual experimental tests are employed to demonstrate the validity of the developed coaxial dual-rotor unmanned aerial vehicle system.
Comparing the effectiveness and safety of dual antiplatelet with ticagrelor or clopidogrel in elderly Asian patients with acute myocardial infraction

Background Current guidelines recommend potent P2Y12 inhibitors for patients after acute coronary syndrome. However, the data on the efficacy and safety of potent P2Y12 inhibitors in elderly Asian populations was limited. We aimed to investigate the major adverse cardiovascular events (MACE), bleeding events, and net adverse clinical events (NACE) with ticagrelor and clopidogrel in Taiwanese patients aged 65 and older after acute myocardial infarction (AMI). Methods This retrospective population-based cohort study was conducted using data from the National Health Insurance Research Database. The AMI patients aged ≥65 years who underwent percutaneous coronary intervention (PCI) and survived after 1 month were included. The patients were separated into 2 cohorts depending on the type of dual antiplatelet therapy (DAPT) they received: ticagrelor plus aspirin (T + A) or clopidogrel plus aspirin (C + A). We used inverse probability of treatment weighting to balance the difference between these 2 study groups. The outcome included all-cause mortality, MACE (cardiovascular death, nonfatal ischemic stroke, and nonfatal myocardial infarction), intracerebral hemorrhage, major bleeding, and NACE which is composed of cardiovascular death, ischemic and hemorrhagic events. The follow-up period was up to 12 months. Results From 2013 to 2017, a total of 14,715 patients who met the eligibility criteria were separated into 2 groups: 5,051 for T + A and 9,664 for C + A. Compared to patients with C + A, patients who received T + A had a lower risk of cardiovascular death and all-cause death, with an adjusted HR of 0.57 [95% confidence interval (CI), 0.38–0.85, p = 0.006] and 0.58 (95% CI 0.45–0.74, p < 0.001), respectively. No differences were found in MACE, intracranial and major bleeding between the 2 groups. In addition, the patients with T + A had a lower risk of NACE with an adjusted HR of 0.86 (95% CI 0.74–1.00, p = 0.045) Conclusion Among elderly AMI patients receiving DAPT after successful PCI, ticagrelor was a more favorable P2Y12 inhibitor than clopidogrel because of lowering the risk of death and NACE without increasing the risk of severe bleeding. Ticagrelor is an effective and safe P2Y12 inhibitor in Asian elderly survivors after PCI.
High frequency of autoimmune thyroiditis in euthyroid girls with premature adrenarche

Objective The purpose of this study was to investigate the frequency of autoimmune thyroiditis (AT) among euthyroid prepubertal girls presenting with premature adrenarche (PA). We also aimed to identify the clinical, metabolic, and endocrine profile of girls with AT and concurrent PA and compare them to girls with AT without PA, PA alone and healthy controls. Methods Ninety-one prepubertal girls aged 5–10 years, who attended our department for AT, PA and normal variants of growth and puberty were recruited for the study: 73 girls had PA, 6 AT without PA and 12 were referred for investigation of growth. All girls underwent clinical examination, detailed biochemical and hormonal screen. Standard dose Synachten stimulation test (SDSST) and oral glucose tolerance test (OGTT) were performed in all girls with PA. The whole study population was divided in 4 groups: Group PA−/AT+ included 6 girls with AT without PA; Group PA+/AT− PA subjects without AT; Group PA+/AT+ girls with PA and concomitant AT; Group PA-/AT- twelve healthy girls without PA nor AT (controls). Results Among 73 girls presenting with PA 19 had AT (26%). BMI, systolic blood pressure (SBP) and the presence of goiter significantly differed between the four groups (p = 0.016, p = 0.022 and p < 0.001, respectively). When comparing hormonal parameters among the four groups significant differences were found in leptin (p = 0.007), TSH (p = 0.044), anti-TPO (p = 0.002), anti-TG (p = 0.044), IGF-BP1 (p = 0.006), Δ4-Α (p = 0.01), DHEA-S (p = <0.001), IGF-1 (p = 0.012) and IGF-BP3 (p = 0.049) levels. TSH levels were significantly higher in Group PA+/AT+ compared to PA+/AT− and PA−/AT− (p = 0.043 and p = 0.016, respectively). Moreover, girls with AT (Groups PA−/AT+ and PA+/AT+) had higher TSH levels than those in Group PA+/AT- (p = 0.025). Girls in Group PA+/AT + showed higher cortisol response at 60 min post-SDSST than girls in Group PA+/AT− (p = 0.035). During the OGTT, insulin concentrations at 60 min were significantly higher in Group PA+/AT + compared to Group PA+/AT− (p = 0.042). Conclusion A high frequency of AT among euthyroid prepubertal girls with PA was observed. The combination of PA with AT even in euthyroid state may be associated with a greater degree of insulin resistance, than PA alone.
Passenger and pedestrian recognition based on neural networks and deep learning in stations

Pedestrian detection technology has high application value in various fields, and deep learning has become a key development direction in computer vision. Human object detection has also shifted from traditional detection algorithms to deep learning. Due to the influence of complex light and obstacles in the station, as well as the occlusions and size changes of passengers, the algorithm must be optimized for these complex scenes. This paper takes pedestrian detection technology as the goal, compares the methods based on human body parts recognition from the concepts and classification of artificial neural networks and deep learning, and profoundly discusses the convolutional neural network based on deep learning. Finally, pedestrian detection algorithms' problems and future trends are compared and discussed.
Regional Soil Moisture Biases and Their Influence on WRF Model Temperature Forecasts over the Intermountain West

AbstractOperational Weather Research and Forecasting (WRF) Model forecasts run over Dugway Proving Ground (DPG) in northwest Utah, produced by the U.S. Army Test and Evaluation Command Four-Dimensional Weather System (4DWX), underpredict the amplitude of the diurnal temperature cycle during September and October. Mean afternoon [2000 UTC (1300 LST)] and early morning [1100 UTC (0400 LST)] 2-m temperature bias errors evaluated against 195 surface stations using 6- and 12-h forecasts are –1.37° and 1.66°C, respectively. Bias errors relative to soundings and 4DWX-DPG analyses illustrate that the afternoon cold bias extends from the surface to above the top of the planetary boundary layer, whereas the early morning warm bias develops in the lowest model levels and is confined to valleys and basins. These biases are largest during mostly clear conditions and are caused primarily by a regional overestimation of near-surface soil moisture in operational land surface analyses, which do not currently assimilate in.
Targeting ER Stress with Saikosaponin A to Overcome Resistance under Radiation in Gastric Cancer Cells

Saikosaponin A is a triterpene saponin and a potentially bioactive compound derived from Bupleurum falcatum L. However, the molecular mechanisms and effects of saikosaponin A in gastric cancer remain unknown. In the present study, I evaluated the effects of saikosaponin A on cell death and endoplasmic reticulum stress via calcium and reactive oxygen species release. Targeting reactive oxygen species with diphenyleneiodonium and N-acetylcysteine inhibited cell death and protein kinase RNA-like ER kinase signaling pathway by down-regulating Nox4 and inducing glucose-regulated protein 78 exosomes. Furthermore, saikosaponin A caused a synergistic inhibitory effect of the epithelial mesenchymal transition phenomenon, indicating the reversible phenotype modulation by epithelial cells under radiation exposure in radiation-resistant gastric cancer cells. These results suggest that saikosaponin A-mediated calcium and reactive oxygen species-induced endoplasmic reticulum stress overcome radio-resistance and induce cell death under radiation in gastric cancer cells. Therefore, saikosaponin A in combination with radiation may be a potential strategy for gastric cancer therapy.
Noncompliance with Hypertension Treatment and Related Factors among Kumamoto Earthquake Victims Who Experienced the COVID-19 Pandemic during Postearthquake Recovery Period

Survivors of the Kumamoto earthquake of 2016 experienced the coronavirus disease (COVID-19) outbreak while carrying additional burdens that might bring inadequate coping. This cross-sectional survey aimed to identify untreated and interrupted consultations among those with hypertension and related factors and to identify the disaster’s long-term effects. Of the 19,212 earthquake survivors who had moved to permanent housing, 7367 (4196 women and 3171 men, mean age 61.8 ± 17.3 years) completed a self-administered questionnaire. The prevalence of hypertension was 41.4%. The results of the logistic regression analysis with the significant independent variables in the bivariate analysis were: reduced income due to COVID-19 (AOR = 3.23, 95%CI = 2.27–4.58) and poor self-rated health (AOR = 2.49, 95%CI = 1.72–3.61) were associated with a risk of untreated or discontinued treatment. Moreover, living in rental, public or restoration public housing was also significantly associated with a higher risk of hypertension noncompliance (AOR = 1.92, 95%CI = 1.20–3.07; AOR = 2.47, 95%CI = 1.38–4.42; AOR = 4.12, 95%CI = 1.14–14.90). These results suggest that changes due to COVID-19, the extent of self-rated health and the type of permanent housing influence the hypertension consulting behaviour of earthquake survivors during recovery. It is crucial to implement long-term public support for the mental health, income and housing concerns of the survivors.
Face Recognition Using Radon & Wavelet Transform

This paper presents new technique for pattern recognition case for face recognition which uses combination of Radon and wavelet transforms. It is invariant with the variations in facial expression, and brightness. It is able to withstand with zero mean white noise. This technique helps to compute radon projections in different directions and captures the features of face images based on directions. After that, the wavelet transform applied on radon space provides multi-resolution features of the facial images. Low frequency components used in face recognition can be improved by taking radon projections. For classification of these projections the nearest neighbour classifier has been used.
On the Meme Train to Sylt: Memetic Becoming and Ambivalent Identification Online

In June 2022, the German government implemented a transit policy offering travelers an unlimited ticket to ride the country’s regional train system all summer long for a mere 9€. Conservative pundits and commentators decried the policy, worrying that the rabble would use their 9€ tickets to overwhelm posh tourist destinations like the island of Sylt in the North of Germany. An overwhelming invasion never materialized, but a fair share of raucous punks did set up shop in Sylt’s public squares in the days after the ticket became available. Just as significant as the physical “invasion” of Sylt were the flurry of memes produced surrounding the Sylt controversy. Through memes, social media participants simultaneously mocked conservative worry about Sylt and punk presence on Sylt. All this memetic play inspired participants to become. Through a mix of identification and disidentification, memes hailed participants into political stances characterized by specific modes of expression. Sylt memery was lulzy, agonistic, and ephemeral. In other words, it was indicative of the broader ambivalence that characterizes public participation inflected by the internet culture. The Sylt story therefore holds lessons about the potentials and pitfalls of collective expression on social media. Memetic becoming, at best, can help people speak truth to power. At worst, it can inspire only a nihilistic smirk as the pixels speed across the screen.
Voxel modeling and association of ubiquitous spatiotemporal information in natural language texts

ABSTRACT The ubiquitous spatiotemporal information extracted from Internet texts limits its application in spatiotemporal association and analysis due to its unstructured nature and uncertainty. This study uses ST-Voxel modeling to solve the problem of structured modeling and the association of ubiquitous spatiotemporal information in natural language texts. It provides a new solution for associating ubiquitous spatiotemporal information on the Internet and discovering public opinion. The main contributions of this paper include: (1) It proposes a convolved method for ST-Voxel, which solves the voxel modeling problem of unstructured and uncertain spatiotemporal objects and spatiotemporal relation in natural language texts. Experiments show that this method can effectively model 5 types of spatiotemporal objects and 16 types of uncertain spatiotemporal relation founded in texts; (2) It realizes the unknown event discovery based on voxelized spatiotemporal information association. Experiments show that this method can effectively solve the aggregation of ubiquitous spatiotemporal information in multi-natural language texts, which is conducive to discovering spatiotemporal events. The selection of convolution parameters in voxel modeling is also discussed. A parameter selection method for balancing the discovery capability and discovery accuracy of spatiotemporal events is given.
Process parameter analysis of double pass friction stir composite welds

ABSTRACT The novelty of the research work stems from the investigation of double pass FSW-friction stir welding on 6061 T6 and 7075 T651 alloys of aluminum matrix reinforced/supplemented with nano h-BN. Preparation of weldments was accomplished by deploying a range of welding parameters specifically tool spindle speed, tool traverse feed, and tool profile. L20 orthogonal array based on a CCD-Central composite design was implemented for experimentation. Microstructural and grain analysis with the performance of parameters on strength and hardness property was detailed. Regression models were developed and optimized based on RSM-Response surface methodology and GRA-Grey relational analysis. The optimal conditions favoring improved tensile strength and hardness based on RSM were realized at 848 rpm, 36 mm min−1, and with a tool profile being cylindrical. A state of consistency prevails between the predicted and experimental responses. Analysis based on GRA yields optimal conditions that fairly corroborate with RSM.
Biomarker Detection in Early Diagnosis of Cancer: Recent Achievements in Point-of-Care Devices Based on Paper Microfluidics

Microfluidics is very crucial in lab-on-a-chip systems for carrying out operations in a large-scale laboratory environment on a single chip. Microfluidic systems are miniaturized devices in which the fluid behavior and control can be manipulated on a small platform, with surface forces on the platform being greater than volumetric forces depending on the test method used. In recent years, paper-based microfluidic analytical devices (μPADs) have been developed to be used in point-of-care (POC) technologies. μPADs have numerous advantages, including ease of use, low cost, capillary action liquid transfer without the need for power, the ability to store reagents in active form in the fiber network, and the capability to perform multiple tests using various measurement techniques. These benefits are critical in the advancement of paper-based microfluidics in the fields of disease diagnosis, drug application, and environment and food safety. Cancer is one of the most critical diseases for early detection all around the world. Detecting cancer-specific biomarkers provides significant data for both early diagnosis and controlling the disease progression. μPADs for cancer biomarker detection hold great promise for improving cure rates, quality of life, and minimizing treatment costs. Although various types of bioanalytical platforms are available for the detection of cancer biomarkers, there are limited studies and critical reviews on paper-based microfluidic platforms in the literature. Hence, this article aims to draw attention to these gaps in the literature as well as the features that future platforms should have.
Highly Productive 3D Printing Process to Transcend Intractability in Materials and Geometries via Interactive Machine‐Learning‐Based Technique

Herein, a highly productive and defect‐free 3D‐printing system enforced by deep‐learning (DL)‐based anomaly detection and reinforcement‐learning (RL)‐based optimization processes is developed. Unpredictable defect factors, such as machine setting errors or unexpected material flow, are analyzed by image‐based anomaly detection implemented using a variational autoencoder DL model. Real‐time detection and in situ correction of defects are implemented by an autocalibration algorithm in conjunction with the DL system. In view of productivity enhancement, the optimized set of diversified printing speeds can be generated from virtual simulation of RL, which is established using a physics‐based engineering model. The RL‐simulated parameter set maximizes printing speed and minimizes deflection‐related failures throughout the 3D‐printing process. With the synergistic assistance of DL and RL techniques, the developed system can overcome the inherent challenging intractability of 3D printing in terms of material property and geometry, achieving high process productivity.
Homeostatic normalization of alpha brain rhythms within the default-mode network and reduced symptoms in post-traumatic stress disorder following a randomized controlled trial of electroencephalogram neurofeedback

Abstract Collective research has identified a key electroencephalogram signature in patients with post-traumatic stress disorder, consisting of abnormally reduced alpha (8–12 Hz) rhythms. We conducted a 20-session, double-blind, randomized controlled trial of alpha desynchronizing neurofeedback in patients with post-traumatic stress disorder over 20 weeks. Our objective was to provide mechanistic evidence underlying potential clinical improvements by examining changes in aberrant post-traumatic stress disorder brain rhythms (namely, alpha oscillations) as a function of neurofeedback treatment. We randomly assigned participants with a primary diagnosis of post-traumatic stress disorder (n = 38) to either an experimental group (n = 20) or a sham-control group (n = 18). A multichannel electroencephalogram cap was used to record whole-scalp resting-state activity pre- and post-neurofeedback treatment, for both the experimental and sham-control post-traumatic stress disorder groups. We first observed significantly reduced relative alpha source power at baseline in patients with post-traumatic stress disorder as compared to an age/sex-matched group of neurotypical healthy controls (n = 32), primarily within regions of the anterior default mode network. Post-treatment, we found that only post-traumatic stress disorder patients in the experimental neurofeedback group demonstrated significant alpha resynchronization within areas that displayed abnormally low alpha power at baseline. In parallel, we observed significantly decreased post-traumatic stress disorder severity scores in the experimental neurofeedback group only, when comparing baseline to post-treatment (Cohen’s d = 0.77) and three-month follow-up scores (Cohen’s d = 0.75), with a remission rate of 60.0% at the three-month follow-up. Overall, our results indicate that neurofeedback training can rescue pathologically reduced alpha rhythmicity, a functional biomarker that has repeatedly been linked to symptoms of hyperarousal and cortical disinhibition in post-traumatic stress disorder. This randomized controlled trial provides long-term evidence suggesting that the ‘alpha rebound effect’ (i.e. homeostatic alpha resynchronization) occurs within key regions of the default mode network previously implicated in post-traumatic stress disorder.
Effect of microvoids on initial and light‐degraded efficiencies of hydrogenated amorphous silicon alloy solar cells

Using a combination of infrared absorption and small‐angle x‐ray scattering on hydrogenated amorphous silicon alloy films and efficiency measurements of solar cells with intrinsic layers prepared under nominally identical conditions to those for the deposition of the films, we observe a correlation between microstructure in the films and solar cell performance. With increasing microvoid density, both the initial and light‐degraded performance of solar cells are found to deteriorate.