Miracle-dz/UniFAR-FTUs huggingface dataset:
Improved Resonant Converter for Dynamic Wireless Power Transfer Employing a Floating-Frequency Switching Algorithm and an Optimized Coil Shape

This paper offers a new EF-class converter for dynamic wireless power transfer application. The proposed high-frequency converter employs a floating-frequency switching algorithm to control the converter in a continuous frequency range, eliminate the requirement to any additional operational data from the secondary (receiver) side, accelerate the load impedance match while moving, maximize the transferred power rate, reduce charging interval and compensate power transfer tolerances. Moreover, an optimized super elliptical shape coil is designed to cope with lateral misalignment, enhance coil coupling, and increase efficiency. In the proposed converter, (i) soft switching is implemented to increase switching frequency, decrease passive components size, and improve power density, (ii) undesired voltage harmonics are attenuated to reduce peak voltage stress of the power switch in a wide frequency range, (iii) the receiver side is enabled for higher mobility with stable power transfer, and (iv) the resonant frequency is updated to compensate non-accurate values of passive components in experimental prototyping. In this study, the operational analytics, compensation method, control algorithm, coil design and converter optimization are followed with some comparisons to present the converter capabilities. In addition, simulation and experimental results are provided under different degrees of misalignment to verify the accuracy of theoretical analytics.
Road Surface Anomaly Assessment Using Low-Cost Accelerometers: A Machine Learning Approach

Roads are a strategic asset of a country and are of great importance for the movement of passengers and goods. Increasing traffic volume and load, together with the aging of roads, creates various types of anomalies on the road surface. This work proposes a low-cost system for real-time screening of road pavement conditions. Acceleration signals provided by on-car sensors are processed in the time–frequency domain in order to extract information about the condition of the road surface. More specifically, a short-time Fourier transform is used, and significant features, such as the coefficient of variation and the entropy computed over the energy of segments of the signal, are exploited to distinguish between well-localized pavement distresses caused by potholes and manhole covers and spread distress due to fatigue cracking and rutting. The extracted features are fed to supervised machine learning classifiers in order to distinguish the pavement distresses. System performance is assessed using real data, collected by sensors located on the car’s dashboard and floorboard and manually labeled. The experimental results show that the proposed system is effective at detecting the presence and the type of distress with high classification rates.
Classifying the Mortality of People with Underlying Health Conditions Affected by COVID-19 Using Machine Learning Techniques

The COVID-19 pandemic has greatly affected populations worldwide and has posed a significant challenge to medical systems. With the constant increase in the number of severe COVID-19 infections, an essential area of research has been directed towards predicting the mortality rate of these patients, in order to make informed medical decisions about the necessary healthcare priorities. Although a large amount of research has attempted to predict the mortality rate of COVID-19 patients, the association between the mortality rate of COVID-19 patients and their underlying health conditions has been given significantly less attention. Meanwhile, patients with underlying conditions often face a worse COVID-19 prognosis. Therefore, the goal of this study was to classify the mortality rate of patients diagnosed with COVID-19, who also suffer from underlying health conditions or comorbidities. To achieve our goal, we applied machine learning (ML) models on a new publicly available dataset, not investigated by any existing literature. The dataset provides detailed information on 582 COVID-19 patients and facilitates a robust forecasting model of the mortality rate. The dataset was analysed using seven ML classifiers, namely, Bagging, J48, logistic regression (LR), random forest (RF), support vector machine (SVM), naïve Bayes (NB), and threshold selector. A comparative analysis was performed across the seven ML techniques, and their performance was assessed based on evaluation parameters including classification accuracy, true-positive rate, and false-positive rate. The best performance was demonstrated by the Bagging algorithm with an accuracy of 83.55% when using all the dataset features. The findings are intended to assist researchers and physicians in the early identification of at-risk COVID-19 patients and to make the appropriate intensive care decisions.
Two-Tailed Dogs, Social Unrest and COVID-19 Vaccination: Politics, Hesitancy and Vaccine Choice in Hungary and Thailand

Background: A long tradition of research has shown an association between political orientation and vaccine uptake. However, we know little about political preferences and the choice of specific vaccines. Methods: We conducted two national surveys, in Hungary (Study 1, online, n = 1130) and Thailand (Study 2, on the street survey: n = 1052), testing associations between political allegiance, trust in government, vaccine willingness, and vaccine choice. Results: In Hungary, those supporting the government or on the political right were more willing to be vaccinated, with this association strongest for government approved vaccines. These respondents were also more likely to accept Chinese and Russian vaccines and reject the Moderna vaccine. In Thailand, vaccinated respondents reported greater trust in the government, with preference for AstraZeneca associated with support for pro-government political parties and preference for Pfizer with anti-government attitudes. Conclusions: Vaccine campaigns need to recognise the role of political loyalties not only in vaccine willingness, but in vaccine choice, especially given the mixing of vaccines across doses.
Sensorless Robust Flatness-Based Control with Nonlinear Observer for Non-Ideal Parallel DC-AC Inverters

Parallel operation of multiple AC/DC inverters is favorable in hybrid AC/DC microgrids to avert consecutive conversion stages and increase the system’s efficacy. Yet, several emerging technical challenges impede the expansion of such layout such as the circulating currents, sensitiveness to the input voltage disturbances, load variations and complexity of the control structures caused by using several required measurements. This paper proposes a one-loop sensorless controller which is based on the flatness technique for a non-isolated power supply consisting of n-parallel inverters. The proposed control scheme primarily relies on employing a nonlinear online observer to estimate the line inductor currents and the dc link voltage via information from the input voltage, output voltage, and load conditions to avoid using excessive sensors. In this way, the system reliability is improved by reducing burdens of the communication delays and/or the failures, signal noise, thus the system is featured by simple control. Besides, the system entire losses are modeled by equivalent voltage sources and one current source which implicitly represent all types of the losses by using an online nonlinear estimator for the control purposes. The proposed controller not only has high dynamic performance, wide-bandwidth, low voltage THD but also robust to the abrupt variations in the load and the input voltage. To validate the applicability of the proposed control method and the observer, both simulations and experimental investigations are performed for two paralleled three-phase inverters setup. The obtained results assure the effectiveness of the proposed control method in regulating the output voltage of the parallel DC/AC inverters with fewer number of the sensors against the fluctuations of the input dc voltage and the load perturbations.
THE BLUE GREEN PATINA ON SCULPTURES CAST IN COPPER AND ITS ALLOYS - THEIR CHEMISTRY AND AESTHETICS

Copper and its alloys have a tendency to naturally corrode and form a blue green finish over them. This blue green finish has been observed in everyday copper and bronze utensils and even in famous sculptures like statue of liberty. Artists have long taken advantage of this patina to achieve aesthetic of desolateness, age, and damage. This patina tends to change the luster of the metal from reflective to matt and color from pink (gold in case of brass and bronze) to bright blue or green. Artists are known to use this change to add to visual language of their sculptures. The current paper takes in account the whole spectrum of blue – green patinas, from bright blue azurites to dull green verdigris and also dark green copper chloride salts. The paper discusses the chemistry behind the formation of this patina, tests age old recipes and discusses aesthetics with respect to famous Indian sculptors.
An Improved Through-Only De-Embedding Method for 110-GHz On-Wafer RF Device Characterization

In this letter, an improved through-only de-embedding method up to 110 GHz without additional dummy structure is developed and implemented. The highlight of this method is that the distributed effect of the metal interconnections between the pad and the intrinsic device is elaborately considered instead of simply modeled as a lumped constant circuit, and the optimal length of the transmission line is carefully derived. Based on this, the parasitics are removed by calculating the ABCD matrices. On-wafer RF passive devices, including T-coil and capacitor, are fabricated on 28-nm CMOS technology to verify the accuracy of the proposed method from 10 to 110 GHz. The measurement results after de-embedding by the proposed method are in good agreement with the electromagnetic simulation results of the intrinsic devices, which demonstrates that the proposed method has a better performance than conventional methods.
Chemical induction of leaf senescence and powdery mildew resistance involves ethylene-mediated chlorophyll degradation and ROS metabolism in cucumber

Abstract Timely initiation of leaf senescence is an integral part of plant development and, importantly, an adaptive strategy by which plants cope with various stresses, e.g. to limit the spread of pathogens. Powdery mildew is a major cucumber disease that promotes the initiation/progression of leaf senescence and reduces leaf photosynthesis, resulting in severe losses of yield and quality. However, how powdery mildew induces leaf senescence and how cucumber plants respond to enhance their resistance remain unclear. Here, with established agrochemical induction and pathogen inoculation systems, we demonstrate that both probenazole (PBZ) and powdery mildew activate ethylene (ET) biosynthesis and signal transduction, consequently promoting leaf senescence and enhancing plant resistance to powdery mildew through CsEIN3 to directly upregulate the expression of CsCCGs and CsRBOHs. Our analysis convincingly suggests that the regulation of leaf senescence and powdery mildew resistance is interconnected and mediated mainly by ET in cucumber.
Potential of Microneedle Systems for COVID-19 Vaccination: Current Trends and Challenges

To prevent the coronavirus disease 2019 (COVID-19) pandemic and aid restoration to prepandemic normality, global mass vaccination is urgently needed. Inducing herd immunity through mass vaccination has proven to be a highly effective strategy for preventing the spread of many infectious diseases, which protects the most vulnerable population groups that are unable to develop immunity, such as people with immunodeficiencies or weakened immune systems due to underlying medical or debilitating conditions. In achieving global outreach, the maintenance of the vaccine potency, transportation, and needle waste generation become major issues. Moreover, needle phobia and vaccine hesitancy act as hurdles to successful mass vaccination. The use of dissolvable microneedles for COVID-19 vaccination could act as a major paradigm shift in attaining the desired goal to vaccinate billions in the shortest time possible. In addressing these points, we discuss the potential of the use of dissolvable microneedles for COVID-19 vaccination based on the current literature.
Trauma-Directed Interaction (TDI): An Adaptation to Parent-Child Interaction Therapy for Families with a History of Trauma

Parent-Child Interaction Therapy (PCIT) is one of the strongest evidence-based treatments available for young children and their families. Research has supported the use of PCIT for children with a history of trauma; however, the treatment does not directly address trauma in the child. PCIT is a dyadic treatment; yet, the impact of the carer’s trauma on the carer-child relationship is not assessed or incorporated into treatment. For these reasons, therapists, families, agencies, and funders tend to view PCIT as a trauma treatment with skepticism. PCIT therapists who currently address trauma within the intervention do so without a standardized approach. Trauma-Directed Interaction (TDI) is an adaptation developed to directly address these concerns. TDI maintains the key elements and theoretical underpinnings of PCIT while adding sessions to cover psychoeducation about trauma, carer response to a child’s trauma reactions (SAFE skills), and coping skills to aid both the child and the carer to manage trauma activators (COPE skills). The TDI module creates a consistent strategy for PCIT therapists to address trauma, thus allowing research and replication which will advance the dual fields of PCIT and family trauma. The theoretical conceptualization of TDI is presented along with next steps in its evaluation.
Design and Implementation of Machine Vision-Based Quality Inspection System in Mask Manufacturing Process

With the advent of the 4th Industrial Revolution, research on anomaly detection in the manufacturing process using deep learning and machine vision is being actively conducted. There have been various attempts to innovate the manufacturing site by adopting advance information technologies such as machine vision, machine learning, and deep learning in many manufacturing processes. However, there have been no cases of designing and implementing these technologies at the mask manufacturing site, which is essential to tackle COVID-19 pandemic. The originality of this paper is to implement sustainability in the mask manufacturing environment and industrial eco-system by introducing the latest computer technology into the manufacturing process essential for pandemic-related disasters. In this study, the intention is to establish a machine vision-based quality inspection system in actual manufacturing process to improve sustainable productivity in the mask manufacturing process and try a new technical application that can contribute to the overall manufacturing process industry in Korea in the future. Therefore, the purpose of this paper is to specifically present hardware and software system construction and implementation procedures for inspection process automation, control automation, POP (Point Of Production) manufacturing monitoring system construction, smart factory implementation, and solutions. This paper is an application study applied to an actual mask manufacturing plant, and is a qualitative analysis study focused on improving mask productivity. “Company A” is a mask manufacturing company that produces tons of masks everyday located in Korea. This company planned to automate the identification of good and defective products in the mask manufacturing process by utilizing machine vision technology. To this end, a deep learning and machine vision-based anomaly detection manufacturing environment is implemented using the LAON PEOPLE NAVI AI Toolkit. As a result, the productivity of “Company A”’s mask defect detection process can be dramatically improved, and this technology is expected to be applied to similar mask manufacturing processes in the future to make similar manufacturing sites more sustainable.
High Optical Angle Test and Analysis for Automotive Camera

Mobile cameras which are one of the most popular formats along with automotive cameras, have a pixel count of over 10 million and is excellent for small sizes or image signal processing. Camera module test equipment have developed from the mobile camera market leading to dramatic technological advances. Automotive cameras have many differences compared to mobile cameras. Therefore, in this study, We tested and analyzed the automotive camera white balance, color reproduction.
Exchange Inquiry Letters and Stock Price Informativeness: Evidence from China

ABSTRACT Using the data of China A-share listed firms from 2015 to 2019, we examine the effect of exchange inquiry letter regulation on stock price informativeness. It is found that the stock prices of inquired firms contain more firm-level information after the receipt of inquiry letter. Further research demonstrates that attracting investors’ attention to more firm-specific information is the potential channel through which inquiry letters improve stock price informativeness. This paper provides empirical evidence for the effectiveness of exchange inquiry letter regulation, which has significant implications for listed firms improving information disclosure and for regulators playing a better role in emerging markets.
Association between burnout and insomnia in U.S. Air Force Pararescue personnel: A cross-sectional study

ABSTRACT Studies have examined burnout and its impact on health, to include its influence on sleep. While many studies report a significant relationship between burnout and insomnia in civilian populations, no studies have examined this relationship in a military population. The United States Air Force (USAF) Pararescue personnel are an elite combat force who are specially trained to conduct both first-line combat and full spectrum personnel recovery and may be at high risk of burnout and insomnia. The current study investigated the association between dimensions of burnout and insomnia, and also examined potential moderators of the associations. A cross-sectional survey was administered to 203 Pararescue personnel (Mean Age = 32.1 years; 100% Male; 90.1% Caucasian) recruited from six US bases. The survey included measures of three dimensions of burnout (emotional exhaustion, depersonalization, personal achievement), insomnia, psychological flexibility, and social support. Emotional exhaustion was significantly associated with insomnia with a moderate to large effect size, when controlling for covariates. Depersonalization, but not personal achievement, was also significantly associated with insomnia. There was no evidence that associations between burnout and insomnia were moderated by psychological flexibility or social support. These findings help to identify individuals at risk of insomnia and may ultimately be useful in developing interventions for insomnia in this population.
Dynamics of Connexin 43 Down Modulation in Human Articular Chondrocytes Stimulated by Tumor Necrosis Factor Alpha

Connexin 43 (Cx43) exerts pivotal functions in articular chondrocytes (CH). It is involved in the communication among cells and between cells and the extracellular environment, and it contributes to the maintenance of the correct cell phenotype. The pro-inflammatory cytokine TNFα induces a reduction in Cx43 expression in CH. Here, we studied the dynamics of this decrease in expression. We evaluated Cx43 protein and gene expression and the involvement of C-terminal domain (CTD) cleavage and proteasomal degradation. Treatments able to counteract TNFα action were also examined, together with Gap Junction (GJ) functionality and Cx43 localization. TNFα induced a significant reduction in Cx43 expression already at day 1, and the down modulation reached a peak at day 3 (−46%). The decrease was linked to neither gene expression modulation nor CTD cleavage. Differently, the proteasome inhibitor MG132 reverted TNFα effect, indicating the involvement of proteasomal degradation in Cx43 reduction. In addition, the co-treatment with the anabolic factor TGF-β1 restored Cx43 levels. Cx43 decrease occurred both at the membrane level, where it partially influenced GJ communication, and in the nucleus. In conclusion, TNFα induced a rapid and lasting reduction in Cx43 expression mostly via the proteasome. The down modulation could be reverted by cartilage-protective factors such as MG132 and TGF-β1. These findings suggest a possible involvement of Cx43 perturbation during joint inflammation.
Concomitant Statins and the Survival of Patients with Non-Small-Cell Lung Cancer Treated with Immune Checkpoint Inhibitors: A Meta-Analysis

Statins are suggested to improve cancer survival by possible anti-inflammatory effect. However, it remains unclear if concomitant use of statins could improve the efficacy of immune checkpoint inhibitors (ICIs) in patients with non-small-cell lung cancer (NSCLC). Accordingly, a meta-analysis was performed to systematically evaluate the effect of concomitant statins in NSCLC patients receiving ICIs. Relevant studies were obtained by literature search in PubMed, Embase, and Web of Science databases. A conservative random-effect model was used to combine the results. Eight cohorts including 2382 patients were included. The programmed death-1/ligand-1 inhibitors were used in seven studies; while the cytotoxic T-lymphocyte-associated protein 4 inhibitors were used in the other study. It was shown that concomitant use of statin did not significantly affect the progression-free survival (PFS, hazard ratio (HR): 0.86, 95% confidence interval (CI): 0.70 to 1.07, P=0.17; I2 = 62%) or overall survival (OS, HR: 0.86, 95% CI: 0.74 to 1.01, P=0.07; I2 = 29%) of NSCLC patients receiving ICIs. Subgroup analyses showed consistent results in studies with univariate or multivariate analytic models (P for subgroup analysis = 0.97 and 0.38 for the outcome of PFS and OS, respectively). In conclusion, concomitant use of statin seemed to have no significant influence on the survival of patients with NSCLC who were treated with ICIs.
An Indoor 3-D Quadrotor Localization Algorithm Based on WiFi RTT and MEMS Sensors

With the development of quadrotor-based location services, accurate indoor quadrotor localization plays an important role in various applications. Tight fusion refers to the process of integrating multisensor data into state estimation for optimization, and finally obtaining pose information. In this article, we propose a novel tight fusion method for quadrotor localization by fusing the WiFi round-trip time (RTT) and built-in smartphone microelectromechanical sensors. Unlike existing 3-D localization frameworks, the key contribution of the proposed method is to integrate 3-D outlier detection, state estimation, coordinate frame alignment, and data fusion into a nonlinear filtering framework. Specifically, this method is divided into four main steps: 1) the coordinates of the mobile phone and the quadrotor are converted to the same coordinate system through the coordinate alignment method we propose; 2) the proposed outlier detection method is used to obtain the 3-D coordinates of the quadrotor based on WiFi RTT; 3) the WiFi RTT localization results are integrated into an error-state Kalman filter (ESKF) to perform the integrated localization of the quadrotors; and 4) a Rauch–Tung–Striebel (RTS) smoothing method is used to optimize the localization results. The experimental results demonstrate that the proposed method outperforms the classic localization method in terms of both accuracy and robustness.