We report that implanting argon ions into a film of uniform atomic layer deposition (ALD)-grown SiOx enables electroforming and switching within films that previously failed to electroform at voltages <15 V. We note an implantation dose dependence of electroforming success rate: electroforming can be eliminated when the dosage is high enough. Our devices are capable of multi-level switching during both set and reset operations, and multiple resistance states can be retained for more than 30,000 s under ambient conditions. High endurance of more than 7 million (7.9 × 106) cycles is achieved alongside low switching voltages (±1 V). Comparing SiOx fabricated by this approach with sputtered SiOx we find similar conduction mechanisms between the two materials. Our results show that intrinsic SiOx switching can be achieved with defects created solely by argon bombardment; in contrast to defects generated during deposition, implantation generated defects are potentially more controllable. In the future, noble ion implantation into silicon oxide may allow optimization of already excellent resistance switching devices.

Since viral infectious diseases continue to be a global health threat, new antiviral drugs are urgently needed. A unique class of therapeutic compounds are antimicrobial peptides (AMPs). They can be found in humans, bacteria and plants. Plants express a wide variety of such defense peptides as part of their innate immune system to protect from invading pathogens. Cyclotides are non-classical AMPs that share a similar structure. Their unique topology consists of a circular peptide backbone and disulfide bonds. In previous studies they have been attributed to a wide range of biological activities. To identify novel cyclotides with antiviral activity, we established a library of plant extracts largely consisting of cyclotide-rich species and screened them as inhibitors of HIV-1 infection. Subsequent extraction and fractionation revealed four cyclotide-containing subfractions from Viola tricolor with antiviral activity. These subfractions inhibited HIV-1 infection with IC50 values between 0.6 and 11.2 μg/ml, and selectivity indices of up to 8.1. The identification and characterization of antiviral cyclotides and the determination of the antiviral mechanisms may allow to develop novel agents to combat viral infections. Therefore, cyclotides represent a natural source of bioactive molecules with prospects for development as therapeutics.

Rare flavour-changing neutral-current transitions b → sμ+μ− probe higher energy scales than what is directly accessible at the LHC. Therefore, the presence of new physics in such transitions, as suggested by the present-day LHCb anomalies, would have a major impact on the motivation and planning of future high-energy colliders. The two most prominent options currently debated are a proton-proton collider at 100 TeV (FCC-hh) and a multi-TeV muon collider (MuC). In this work, we compare the discovery prospects at these colliders on benchmark new physics models indirectly detectable in b → sμ+μ− decays but beyond the reach of the high-pT searches at the HL-LHC. We consider a comprehensive set of scenarios: semileptonic contact interactions, Z′ from a gauged U1B3−Lμ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \textrm{U}{(1)}_{B_3-{L}_{\mu }} $$\end{document} and U1Lμ−Lτ\documentclass[12pt]{minimal} \usepackage{amsmath} \usepackage{wasysym} \usepackage{amsfonts} \usepackage{amssymb} \usepackage{amsbsy} \usepackage{mathrsfs} \usepackage{upgreek} \setlength{\oddsidemargin}{-69pt} \begin{document}$$ \textrm{U}{(1)}_{L_{\mu }-{L}_{\tau }} $$\end{document}, the scalar leptoquark S3, and the vector leptoquark U1. We find that a 3 TeV MuC has a sensitivity reach comparable to the one of the FCC-hh. However, for a heavy enough mediator, the new physics effects at a 3 TeV MuC are only observed indirectly via deviations in the highest energy bin, while the FCC-hh has a greater potential for the discovery of a resonance. Finally, to completely cover the parameter space suggested by the bsμμ anomalies, among the proposed future colliders, only a MuC of 10 TeV (or higher) can meet the challenge.

Being used in key features, such as sensing and intelligent path planning, Artificial Intelligence (AI) has become an inevitable part of automated vehicles (AVs). However, their usage in the automotive industry always comes with a “label” that questions their impact on the overall AV safety. This paper focuses on the safe deployment of AI-based AVs. Among the various ways for ensuring the safety of AI-based AVs is to monitor the safe execution of the system responsible for automated driving (i.e., Automated Driving System (ADS)) at runtime (i.e., runtime monitoring). Most of the research done in the past years focused on verifying whether the path or trajectory generated by the ADS does not immediately collide with objects on the road. However, as we will show in this paper, there are other unsafe situations that do not immediately result in a collision but the monitor should check for them. To build our case, we have looked into the National Highway Traffic Safety Administration (NHTSA) database of 5.9 million police-reported light-vehicle accidents and categorized these accidents into five main categories of unsafe vehicle operations. Furthermore, we have performed a high-level evaluation of the runtime monitoring approach proposed in [1], by estimating what percentage of the total population of 5.9 million of unsafe operations the approach would be able to detect. Lastly, we have performed the same evaluation on other existing runtime monitoring approaches to make a basic comparison of their diagnostic capabilities.

Abstract Objectives To investigate the influence of maternal level of thyroid-stimulating hormone (TSH), free triiodothyronine (FT3) and free thyroxine (FT4) one by one or in combination on incidence of gestational hypertension and preeclampsia. Methods The study included pregnant women (n=107) hospitalized in the period from July 1, 2020 to October 10, 2021 at the Department of Pathology of Pregnancy of the University Clinic of Obstetrics and Gynecology, University Clinical Center Sarajevo (UCCS) (Bosnia and Herzegovina), due to hypertensive disorder in pregnancy without symptoms of impaired thyroid function. In all patients fulfilling inclusion criteria TSH, FT3, and FT4 using electrochemiluminescence immunoassay (ECLIA, Roche Diagnostics, Basel, Switzerland) were checked. There were two groups of patients: one with gestational hypertension (G1) and the other with preeclampsia (G2). The programs SPSS for Windows 25.0, SPSS Inc, Chicago, IL, USA and Microsoft Excel 11, Microsoft Corporation, Redmond, WA, USA were used for statistical analysis using nonparametric Mann-Whitney U test because the distribution of the data was not normal. The result was considered statistically significant if p<0.05. Results Gestational age at delivery (G2 36.86 ± 3.79 vs. G1 38.94 ± 2.15; p=0.002) and birth weight (G2 2,841.36 ± 1,006.39 vs. G2 3,290.73 ± 745.6; p=0,032) were significantly different between the investigated groups. The difference between the peak systolic (p=0.002), peak diastolic blood pressure (p=0.007), TSH (p=0.044), and FT3 (p=0.045) were statistically significant. Impaired thyroid function was observed more often in G2 than in G1. Conclusions Thyroid function was more often affected adversely in pregnancies complicated with preeclampsia than with gestational hypertension. Based on the results of our study it might be prudent to check thyroid hormones in all asymptomatic pregnancies with preeclampsia or gestational hypertension. These findings need confirmation in larger better designed prospective studies.

The expression pattern of Connexins (Cx) 37, 40, 43, 45 and Pannexin 1 (Pnx1) was analyzed immunohistochemically, as well as semi-quantitatively and quantitatively in histological sections of developing 8th- to 12th-week human eyes and postnatal healthy eye, in retinoblastoma and different uveal melanomas. Expressions of both Cx37 and Cx43 increased during development but diminished in the postnatal period, being higher in the retina than in the choroid. Cx37 was highly expressed in the choroid of retinoblastoma, and Cx43 in epitheloid melanoma, while they were both increasingly expressed in mixoid melanoma. In contrast, mild retinal Cx40 expression during development increased to strong in postnatal period, while it was significantly higher in the choroid of mixoid melanoma. Cx45 showed significantly higher expression in the developing retina compared to other samples, while it became low postnatally and in all types of melanoma. Pnx1 was increasingly expressed in developing choroid but became lower in the postnatal eye. It was strongly expressed in epithelial and spindle melanoma, and particularly in retinoblastoma. Our results indicate importance of Cx37 and Cx40 expression in normal and pathological vascularization, and Cx43 expression in inflammatory response. Whereas Cx45 is involved in early stages of eye development, Pnx1might influence cell metabolism. Additionally, Cx43 might be a potential biomarker of tumor prognosis.

Radio Frequency Fingerprint (RFF) identification on account of deep learning has the potential to enhance the security performance of wireless networks. Recently, several RFF datasets were proposed to satisfy requirements of large-scale datasets. However, most of these datasets are collected from 2.4G WiFi devices and through similar channel environments. Meanwhile, they only provided receiving data collected by the specific equipment. This paper utilizes software radio peripheral as a dataset generating platform. Therefore, the user can customize the parameters of the dataset, such as frequency band, modulation mode, antenna gain, and so on. In addition, the proposed dataset is generated through various and complex channel environments, which aims to better characterize the radio frequency signals in the real world. We collect the dataset at transmitters and receivers to simulate a real-world RFF dataset based on the long-term evolution (LTE). Furthermore, we verify the dataset and confirm its reliability. The dataset and reproducible code of this paper can be downloaded from GitHub link: https://github.com/njuptzsp/XSRPdataset.

The paper shows that the information of the first just noticeable difference (JND) point position can significantly improve the performance of the objective peak signal-to-noise ratio (PSNR) measure in assessing the quality of JPEG compressed images. The degree of improvement depends on the choice of the first JND point position prediction model. Also, the paper shows that simple features derived from the gradient magnitude (spatial information and spatial frequency) of the original uncompressed image can be used for reliable position prediction. The analysis was conducted on two publicly available JND subject-rated image datasets MCL-JCI and JND-Pano. Among others, the linear correlation coefficient is used as an objective measurement parameter in prediction and in image quality assessment analysis. The prediction based on spatial frequency provided the best results, with over 95% of agreement with ground truth JND points position. This simple picture-wise prediction model has significantly improved the performance of conventional PSNR measure, with over 90% of agreement with subjective scores in image quality assessment. The PSNR performance is most enhanced by using a deep learning approach, where the correlation with subjective test results is close to 92%.

this study aims to analyze the impact of data selection to train machine learning models and forecast Bitcoin prices. Specifically, we train elastic net regularization models using two datasets with almost identical total observations. One dataset emphasizes years of observations (depth) over total variables, while the second one emphasizes the number of variables (width) over years of data. Our results suggest that the dataset with more extended historical time series and fewer variables provides a lower forecasting error than the dataset with shorter time series and more variables. Our results may be helpful to practitioners looking to identify data selection strategies to train ML-based forecasting models.

Standardized monitoring of BCR::ABL1 mRNA levels is essential for the management of chronic myeloid leukemia (CML) patients. From 2016 to 2021 the European Treatment and Outcome Study for CML (EUTOS) explored the use of secondary, lyophilized cell-based BCR::ABL1 reference panels traceable to the World Health Organization primary reference material to standardize and validate local laboratory tests. Panels were used to assign and validate conversion factors (CFs) to the International Scale and assess the ability of laboratories to assess deep molecular response (DMR). The study also explored aspects of internal quality control. The percentage of EUTOS reference laboratories (n = 50) with CFs validated as optimal or satisfactory increased from 67.5% to 97.6% and 36.4% to 91.7% for ABL1 and GUSB, respectively, during the study period and 98% of laboratories were able to detect MR4.5 in most samples. Laboratories with unvalidated CFs had a higher coefficient of variation for BCR::ABL1IS and some laboratories had a limit of blank greater than zero which could affect the accurate reporting of DMR. Our study indicates that secondary reference panels can be used effectively to obtain and validate CFs in a manner equivalent to sample exchange and can also be used to monitor additional aspects of quality assurance.

Physics‐informed neural networks have gained growing interest. Specifically, they are used to solve partial differential equations governing several physical phenomena. However, physics‐informed neural network models suffer from several issues and can fail to provide accurate solutions in many scenarios. We discuss a few of these challenges and the techniques, such as the use of Fourier transform, that can be used to resolve these issues. This paper proposes and develops a physics‐informed neural network model that combines the residuals of the strong form and the potential energy, yielding many loss terms contributing to the definition of the loss function to be minimized. Hence, we propose using the coefficient of variation weighting scheme to dynamically and adaptively assign the weight for each loss term in the loss function. The developed PINN model is standalone and meshfree. In other words, it can accurately capture the mechanical response without requiring any labeled data. Although the framework can be used for many solid mechanics problems, we focus on three‐dimensional (3D) hyperelasticity, where we consider two hyperelastic models. Once the model is trained, the response can be obtained almost instantly at any point in the physical domain, given its spatial coordinates. We demonstrate the framework's performance by solving different problems with various boundary conditions.

COVID-19 is an illness caused by severe acute respiratory syndrome coronavirus 2. Due to its rapid spread, in March 2020 the World Health Organization (WHO) declared pandemic. Since the outbreak of pandemic many governments, scientists, and institutions started to work on new vaccines and finding of new and repurposing drugs. Drug repurposing is an excellent option for discovery of already used drugs, effective against COVID-19, lowering the cost of production, and shortening the period of delivery, especially when preclinical safety studies have already been performed. There are many approved drugs that showed significant results against COVID-19, like ivermectin and hydrochloroquine, including alternative treatment options against COVID-19, utilizing herbal medicine. This article summarized 11 repurposing drugs, their positive and negative health implications, along with traditional herbal alternatives, that harvest strong potential in efficient treatments options against COVID-19, with small or no significant side effects. Out of 11 repurposing drugs, four drugs are in status of emergency approval, most of them being in phase IV clinical trials. The first repurposing drug approved for clinical usage is remdesivir, whereas chloroquine and hydrochloroquine approval for emergency use was revoked by FDA for COVID-19 treatment in June 2020.

The aim of this study is to investigate the environmental risk of long-term metallurgical waste disposal. The investigated site was used for the open storage of lead and zinc waste materials originating from a lead smelter and refinery. Even after remediation was performed, the soil in the close vicinity of the metallurgical waste deposit was heavily loaded with heavy metals and arsenic. The pollutants were bound in various compounds in the form of sulfides, oxides, and chlorides, as well as complex minerals, impacting the pH values of the investigated soil, such that they varied between 2.8 for sample 6 and 7.34 for sample 8. In order to assess the environmental risk, some eight soil samples were analyzed by determining the total metal concentration by acid digestion and chemical fractionation of heavy metals using the BCR sequential extraction method. Inductively coupled plasma optical emission spectrometry (ICP-OES) was used to determine six elements (As, Cd, Cu, Pb, Zn, and Ni). Total concentrations of the elements in the tested soil samples were in the range of 3870.4–52,306.18 mg/kg for As, 2.19–49.84 mg/kg for Cd, 268.03–986.66 mg/kg for Cu, 7.34–114.67 mg/kg for Ni, 1223.13–30,339.74 mg/kg for Pb, and 58.21–8212.99 mg/kg for Zn. The ratio between the mean concentrations of the tested metals was determined in this order: As > Pb > Zn > Cu > Ni > Cd. The BCR results showed that Pb (50.7%), Zn (49.2%), and Cd (34.7%) had the highest concentrations in mobile fractions in the soil compared to the other metals. The contamination factor was very high for Pb (0.09–33.54), As (0.004–195.8), and Zn (0.14–16.06). According to the calculated index of potential environmental risk, it was confirmed that the mobility of Pb and As have a great impact on the environment.