Unknown Degradation Research Articles

On the Frequency Dependence of the Gate Switching Instability in Silicon Carbide MOSFETs

Silicon carbide power metal-oxide semiconductor field-effect transistors (MOSFETs) are suitable for more compact and energy efficient electric power conversion, pushing forward numerous key technologies in emission-free mobility and green power generation. These applications require fast gate switching up to hundreds of kilohertz. Typically, to assure a clear turn-off state of the electron channel, a negative turn-off gate bias is used in conjunction with a positive turn-on gate bias. Recently, several reports have revealed a new and so far unknown degradation mechanism that emerges during such operation conditions in apparently all commercial silicon carbide MOSFETs. As this mechanism arises upon gate switching, the terms gate switching instability (GSI) and gate switching stress (GSS) for the mechanism and the associated stress, respectively, have been introduced.Here, we show that this degradation mechanism does not depend on the used frequency up to at least 2 MHz, but that it is actually related to the cumulative number of switching cycles. For this purpose, we performed measurements at various frequencies comprising ultra-fast in-situ measurements of the threshold voltage and pre- and post-stress characterization by impedance analysis. The results are important both for understanding the underlying physics and for developing a correct methodology for industrial device qualification.

Maintaining a Secure Foundation of Cybersecurity Awareness while Reducing eWaste and Carbon Output through Ethical User Actions and Sustainable Green Computing

To better understand how we can help reduce the climate crisis, this research examined user computing activities in detail to analyze and identify eWaste actions causing unknown catastrophic climate degradation. Countless individuals are oblivious to the damage and devastation being caused to the climate by even a single user. As the world becomes more technologically based than ever before, the global impact on the planet has never been greater. This study examines in great detail end-users’ normal computer usage to identify where, how, and why they are generating excess eWaste. We argue that the resultant data collected will provide support for our theory, positing that increasing consumer awareness of better computational practices can lead to positive actions to reduce eWaste. This research study utilized a multiple case study approach to achieve our stated research objectives; recognizing computer actions identified as most detrimental to the climate by level of eWaste (CO2e output) and introducing alternative user actions that are ethical, green, and produce less eWaste. In addition to helping reduce the overall user-level carbon footprint and eWaste output, the sustainability of these alternative user actions can be maintained with zero reduction in privacy or security for end users. Results from this study contribute to the extant body of literature across multiple disciplines, including privacy, green computing, information system science and technology, cybersecurity, and sustainable computing.

Lightweight Implicit Blur Kernel Estimation Network for Blind Image Super-Resolution

Blind image super-resolution (Blind-SR) is the process of leveraging a low-resolution (LR) image, with unknown degradation, to generate its high-resolution (HR) version. Most of the existing blind SR techniques use a degradation estimator network to explicitly estimate the blur kernel to guide the SR network with the supervision of ground truth (GT) kernels. To solve this issue, it is necessary to design an implicit estimator network that can extract discriminative blur kernel representation without relying on the supervision of ground-truth blur kernels. We design a lightweight approach for blind super-resolution (Blind-SR) that estimates the blur kernel and restores the HR image based on a deep convolutional neural network (CNN) and a deep super-resolution residual convolutional generative adversarial network. Since the blur kernel for blind image SR is unknown, following the image formation model of blind super-resolution problem, we firstly introduce a neural network-based model to estimate the blur kernel. This is achieved by (i) a Super Resolver that, from a low-resolution input, generates the corresponding SR image; and (ii) an Estimator Network generating the blur kernel from the input datum. The output of both models is used in a novel loss formulation. The proposed network is end-to-end trainable. The methodology proposed is substantiated by both quantitative and qualitative experiments. Results on benchmarks demonstrate that our computationally efficient approach (12x fewer parameters than the state-of-the-art models) performs favorably with respect to existing approaches and can be used on devices with limited computational capabilities.

The identification of the new species Nitratireductor thuwali sp. nov. reveals the untapped diversity of hydrocarbon-degrading culturable bacteria from the arid mangrove sediments of the Red Sea.

The geological isolation, lack of freshwater inputs and specific internal water circulations make the Red Sea one of the most extreme-and unique-oceans on the planet. Its high temperature, salinity and oligotrophy, along with the consistent input of hydrocarbons due to its geology (e.g., deep-sea vents) and high oil tankers traffic, create the conditions that can drive and influence the assembly of unique marine (micro)biomes that evolved to cope with these multiple stressors. We hypothesize that mangrove sediments, as a model-specific marine environment of the Red Sea, act as microbial hotspots/reservoirs of such diversity not yet explored and described. To test our hypothesis, we combined oligotrophic media to mimic the Red Sea conditions and hydrocarbons as C-source (i.e., crude oil) with long incubation time to allow the cultivation of slow-growing environmentally (rare or uncommon) relevant bacteria. This approach reveals the vast diversity of taxonomically novel microbial hydrocarbon degraders within a collection of a few hundred isolates. Among these isolates, we characterized a novel species, Nitratireductor thuwali sp. nov., namely, Nit1536T. It is an aerobic, heterotrophic, Gram-stain-negative bacterium with optimum growth at 37°C, 8 pH and 4% NaCl, whose genome and physiological analysis confirmed the adaptation to extreme and oligotrophic conditions of the Red Sea mangrove sediments. For instance, Nit1536T metabolizes different carbon substrates, including straight-chain alkanes and organic acids, and synthesizes compatible solutes to survive in salty mangrove sediments. Our results showed that the Red Sea represent a source of yet unknown novel hydrocarbon degraders adapted to extreme marine conditions, and their discovery and characterization deserve further effort to unlock their biotechnological potential.

Triple‐Element Stable Isotope Analysis of Chloromethane Emitted by Royal Fern and Degraded by Club Moss

AbstractChloromethane (CH3Cl) is the most abundant natural chlorinated organic compound in the atmosphere playing an important role in catalyzing stratospheric ozone loss. Vegetation emits the largest amounts of CH3Cl to the atmosphere but its source strength is highly uncertain leading also to large uncertainties in the global budget of CH3Cl. Triple‐element stable isotope analysis may help to reduce uncertainties because it provides additional process‐level information compared to conventional quantification methods. In this study we performed experiments to obtain a first triple‐elemental isotopic fingerprint (2H, 13C, 37Cl) of CH3Cl emitted by a relevant plant species (royal fern, Osmunda regalis). Isotopic values of all three elements showed considerable differences compared to isotopic values of industrially manufactured CH3Cl which bodes well for future applications to distinguish individual sources. Isotopic analysis of potential precursors (rain, methoxy groups) of CH3Cl in plants revealed no measurable change of hydrogen and chlorine isotopic ratios during formation which may provide a simpler route to estimate the isotopic composition of CH3Cl emissions. Plant degradation experiments of CH3Cl were carried out with club moss (Selaginella kraussiana) revealing significant isotopic fractionation for all three elements. The fractionation pattern characterized by epsilon and lambda is inconsistent with known biotic dechlorination reactions indicating a yet unreported biotic degradation mechanism for CH3Cl. Overall, this study provides first insights into the triple‐elemental isotopic fingerprint of plant emissions and degradation. The results may represent important input data for future isotope‐based models to improve global budget estimates of CH3Cl and to explore the yet unknown degradation pathways.

An Antiviral Drug-Peramivir: Degradation and Identification of Impurities and the Endorsement of an HPLC-MS Method.

Peramivir is a neuraminidase inhibitor that serves as a transition state analogue for influenza neuraminidase, inhibiting the formation of new viruses in infected cells, and has been approved for intravenous administration. To validate an HPLC method used to identify the degraded products of the antiviral drug peramivir. Herein, we report the identification of compounds formed after the degradation of peramivir through acid, alkali, peroxide, thermal, and photolytic degradation. At the level of toxicology, a technique was devised for the isolation and measurement of peramivir. A sensitive and reliable LC-tandem mass spectrometry technique for the quantitative measurement of Peramivir and its impurities was developed and verified in order to comply with the recommendations made by the International Council for Harmonisation (ICH). The proposed protocol was in the 50-750 µg/mL range. Relative Standard Deviation values of less than 2.0% indicated good recovery in the range of 98.36-102.57%. Within the studied range, the calibration curves demonstrated good linearity and, in addition, the fitting of correlation coefficient was more than 0.999 for every impurity. Quantitative analysis of contaminants revealed the high efficiency at a low level. Given its ability to separate degradation products, quantitative analysis is used to detect and quantify known and unknown impurities and degradants in the peramivir drug substance during routine analysis and stability studies. No significant degradation was found in peroxide and photolytic degradation studies. An HPLC method was developed and put to the test in order to analyze the behavior of the impurities of peramivir as they degraded when subjected to the stress conditions suggested by the ICH. Peramivir was found to be stable under peroxide and photolysis conditions but not stable or degradable when exposed to the acid, base, and thermal stress conditions. The method developed was extremely precise, linear, accurate, robust, and rugged. As a result, this technology has the potential to be used in the medication production process for regular impurity analysis as well as for the stability analysis of peramivir.

From degrade to upgrade: Learning a self-supervised degradation guided adaptive network for blind remote sensing image super-resolution

Over the past few years, single image super-resolution (SR) has become a hotspot in the remote sensing area, and numerous methods have made remarkable progress in this fundamental task. However, they usually rely on the assumption that images suffer from a fixed known degradation process, e.g., bicubic downsampling. To save us from performance drop when real-world distribution deviates from the naive assumption, blind image super-resolution for multiple and unknown degradations has been explored. Nevertheless, the lack of a real-world dataset and the challenge of reasonable degradation estimation hinder us from moving forward. In this paper, a self-supervised degradation-guided adaptive network is proposed to mitigate the domain gap between simulation and reality. Firstly, the complicated degradations are characterized by robust representations in embedding space, which promote adaptability to the downstream SR network with degradation priors. Specifically, we incorporated contrastive learning to blind remote sensing image SR, which guides the reconstruction process by encouraging the positive representations (relevant information) while punishing the negatives. Besides, an effective dual-wise feature modulation network is proposed for feature adaptation. With the guide of degradation representations, we conduct modulation on feature and channel dimensions to transform the low-resolution features into the desired domain that is suitable for reconstructing high-resolution images. Extensive experiments on three mainstream datasets have demonstrated our superiority against state-of-the-art methods. Our source code can be found at https://github.com/XY-boy/DRSR

Development and optimization of a LC-MS/MS compatible method for quantification of losartan, hydrochlorothiazide and their impurities using AQbD approach and measurement uncertainty evaluation

The concept of Analytical Quality by Design (AQbD) comes as a more robust, economical, and scientifically based alternative for analytical development, to the detriment of OFAT (one factor at a time). This new understanding applicable to analytical development is recommended since regulatory flexibility can be achieved and ensure more reliable results throughout the life of the product. This new approach was applied to develop an analytical procedure indicative of stability for a pharmaceutical product of association of Losartan Potassium and Hydrochlorothiazide, considered a potential first line for the treatment of hypertension. The first stage of the analytical development consisted of defining analytical target profile (ATP), follow by a bibliographic survey of the physicochemical properties of the molecules in question to define an initial method. After defining the initial analytical conditions, statistical tools for design of experiments (DoE) were used for the screening and optimization steps. In the screening stage, the Plackett-Burman design was chosen, using 11 factors and 2 levels, through which it was possible to evaluate numerous variables and determine their significance in relation to the responses. Next, optimization was carried out with the experimental design of a central composite with 4 factors and 5 levels, which allowed modeling a complex response surface and evaluating the phenomena of interactions between the factors. Thus, the optimized analytical conditions were defined, considering a 0.3% formic acid gradient as eluent A and a mixture of acetonitrile and methanol (80:20) as eluent B, X-Bridge C18 chromatographic column (150 mm × 4 .6 mm × 3.5 μm), column temperature of 40°C, flow rate of 1.3 mL/min, injection volume of 10 μL. Through this methodology, it was possible to identify an unknown degradation product of Hydrochlorothiazide, formed by the reaction with lactose (excipient present in the drug formulation), proving that the method can be applicable both to DAD detectors and to spectrometry and mass detectors. It was also proven through the forced degradation study that the method is indicative of stability, in addition to being validated and robust for its purpose.

Identification and enrichment of a UV-induced degradant of Anagrelide drug substance

Anagrelide (ANG) is a widely used drug for the treatment of essential thrombocytosis and myeloproliferative neoplasms. Recently, a new oxidative degradant was identified when the drug product capsule underwent stress testing. Full structural characterization of this previously unidentified degradant was conducted. Preliminary LC-MS analysis indicated the targeted degradant as a mono-oxygenated product of ANG. For the purpose of facile isolation and purification, various forced degradation conditions were screened to enrich the desired degradant, among which, pyridinium chlorochromate (PCC)-treatment effectively afforded a yield of 55 % unknown degradant. Following isolation by prep-HPLC, 1D and 2D NMR studies and HRMS characterization assigned the products as a pair of 5-hydroxy-Anagrelide (5-OH-ANG) enantiomers. A plausible mechanism of formation is proposed.

Blind Super-Resolution of 3D MRI via Unsupervised Domain Transformation.

High-resolution medical images can be effectively used for clinical diagnosis. However, the acquisition of high-resolution images is difficult and often limited by medical instruments. Super-resolution (SR) methods provide a solution, where high-resolution (HR) images can be reconstructed from low-resolution (LR) ones. Most of existing deep neural networks for 3D SR medical images trained in a non-blind process, where LR images are directly degraded from HR data via a pre-determined downscale method. Such approaches rely heavily on the assumed degradation model, resulting in inevitable deviations in real clinical practice. Blind super-resolution, as a more attractive research line for this field, aims to generate HR images from LR inputs containing unknown degradation. Towards generalizing SR models for diverse types of degradation, we propose a robust blind SR of 3D medical images in an unsupervised manner with domain correction and upscaling treatment. First, a CycleGAN-based architecture is implemented to generate the LR data from the source domain to the target one for domain correction. Then, an upscaling network is learned via pre-determined HR-LR couples for reconstruction. The proposed framework is able to automatically learn noisy and blurry correction kernels for unpaired 3D SR magnetic resonance images (MRI). Our method achieves better and more robust performances in reconstruction of HR images from LR MRI with multiple unknown degradation processes, and show its superiority to other state-of-the-art supervised models and cycle-consistency based methods, especially in severe distortion cases.

Remaining useful life prediction for nonlinear two-phase degradation process with measurement errors and imperfect prior information

Remaining useful life (RUL) prediction is one of the most important issues of prognostic and health management, which can improve the reliability and security of the system. Due to the changeable internal mechanism and external environmental factors, the two-phase degradation process is frequently seen in practice. In addition, measurement errors in degradation signals and the case with imperfect prior degradation information are common, which could decrease the accuracy of RUL prediction. However, the current studies on two-phase degradation usually assume that each phase is linear. Furthermore, the effect of measurement errors and the possibility of incomplete prior degradation data are generally not taken into account simultaneously. Therefore, this paper proposes a novel linear–nonlinear two-phase Wiener process with a measurement errors degradation model, and obtains the probability density function expression of the RUL by fully considering the unknown degradation state at the change point. Meanwhile, in the absence of multiple sets of historical data, a parameter estimation method which only requires a set of prior information is proposed based on an expectation maximization (EM) algorithm and Kalman smoothing. Finally, a numerical example and two practical examples are used to illustrate the effectiveness and superiority of the proposed method.

A new concept for polarimetric Thomson scattering diagnostics

This paper proposes a new concept of polarimetric Thomson scattering diagnostics. Various studies show the polarization state of the scattered light from a laser pulse reflects electron temperature and the state can be measured with dedicated polarization tools. Since it does not require spectral measurements, it is believed that it could overcome an issue about unknown degradation in optical components, i.e., optical fibers and lenses, due to intense neutrons and γ-rays. Generally, a Thomson scattering diagnostic accompanies a laser beam linearly polarized in a direction perpendicular to the scattering plane. The new concept employs one additional laser polarization angle of which is parallel to the scattering plane. Taking the ratio of the two scattered intensities enables obtaining Te. The ratio is basically independent from the spectral transmission. This concept does not require any dedicated polarization tools. Calculations using designed parameters of a Thomson scattering system showed that the relative error in Te is less than 10% for Te= 5–40 keV. This paper also presents a way to calibrate different transmissions for polarization states using a gas scattering.

Unveiling facet-dependent degradation and facet engineering for stable perovskite solar cells.

A myriad of studies and strategies have already been devoted to improving the stability of perovskite films; however, the role of the different perovskite crystal facets in stability is still unknown. Here, we reveal the underlying mechanisms of facet-dependent degradation of formamidinium lead iodide (FAPbI3) films. We show that the (100) facet is substantially more vulnerable to moisture-induced degradation than the (111) facet. With combined experimental and theoretical studies, the degradation mechanisms are revealed; a strong water adhesion following an elongated lead-iodine (Pb-I) bond distance is observed, which leads to a δ-phase transition on the (100) facet. Through engineering, a higher surface fraction of the (111) facet can be achieved, and the (111)-dominated crystalline FAPbI3 films show exceptional stability against moisture. Our findings elucidate unknown facet-dependent degradation mechanisms and kinetics.

Meta-Learning based Degradation Representation for Blind Super-Resolution.

Blind image super-resolution (blind SR) aims to generate high-resolution (HR) images from low-resolution (LR) input images with unknown degradations. To enhance the performance of SR, the majority of blind SR methods introduce an explicit degradation estimator, which helps the SR model adjust to unknown degradation scenarios. Unfortunately, it is impractical to provide concrete labels for the multiple combinations of degradations (e.g., blurring, noise, or JPEG compression) to guide the training of the degradation estimator. Moreover, the special designs for certain degradations hinder the models from being generalized for dealing with other degradations. Thus, it is imperative to devise an implicit degradation estimator that can extract discriminative degradation representations for all types of degradations without requiring the supervision of degradation ground-truth. To this end, we propose a Meta-Learning based Region Degradation Aware SR Network (MRDA), including Meta-Learning Network (MLN), Degradation Extraction Network (DEN), and Region Degradation Aware SR Network (RDAN). To handle the lack of ground-truth degradation, we use the MLN to rapidly adapt to the specific complex degradation after several iterations and extract implicit degradation information. Subsequently, a teacher network MRDAT is designed to further utilize the degradation information extracted by MLN for SR. However, MLN requires iterating on paired LR and HR images, which is unavailable in the inference phase. Therefore, we adopt knowledge distillation (KD) to make the student network learn to directly extract the same implicit degradation representation (IDR) as the teacher from LR images. Furthermore, we introduce an RDAN module that is capable of discerning regional degradations, allowing IDR to adaptively influence various texture patterns. Extensive experiments under classic and real-world degradation settings show that MRDA achieves SOTA performance and can generalize to various degradation processes.

Learning Detail-Structure Alternative Optimization for Blind Super-Resolution

Existing convolutional neural networks (CNN) based image super-resolution (SR) methods have achieved impressive performance on bicubic kernel, which is not valid to handle unknown degradations in real-world applications. Recent blind SR methods suggest to reconstruct SR images relying on blur kernel estimation. However, their results still remain visible artifacts and detail distortion due to the estimation errors. To alleviate these problems, in this paper, we propose an effective and kernel-free network, namely DSSR, which enables recurrent detail-structure alternative optimization without blur kernel prior incorporation for blind SR. Specifically, in our DSSR, a detail-structure modulation module (DSMM) is built to exploit the interaction and collaboration of image details and structures. The DSMM consists of two components: a detail restoration unit (DRU) and a structure modulation unit (SMU). The former aims at regressing the intermediate HR detail reconstruction from LR structural contexts, and the latter performs structural contexts modulation conditioned on the learned detail maps at both HR and LR spaces. Besides, we use the output of DSMM as the hidden state and design our DSSR architecture from a recurrent convolutional neural network (RCNN) view. In this way, the network can alternatively optimize the image details and structural contexts, achieving co-optimization across time. Moreover, equipped with the recurrent connection, our DSSR allows low- and high-level feature representations complementary by observing previous HR details and contexts at every unrolling time. Extensive experiments on synthetic datasets and real-world images demonstrate that our method achieves the state-of-the-art against existing methods. The source code can be found at https://github.com/Arcananana/DSSR.

Enhanced Autoencoders With Attention-Embedded Degradation Learning for Unsupervised Hyperspectral Image Super-Resolution

Recently, unmixing-based networks have shown significant potential in unsupervised multispectral-aided hyperspectral image super-resolution task (MS-aided HS-SR). Nevertheless, the representation ability of unsupervised networks and the design of loss functions still have not been fully explored, leaving large room for further improvement. To this end, we propose an enhanced unmixing-inspired unsupervised network with attention-embedded degradation learning, EU2ADL for short, to realize MS-aided HS-SR. First, two coupled autoencoders serve as the backbone of EU2ADL network to simultaneously decompose input modalities into abundances and corresponding endmembers, whose encoder part is composed of a spatial-spectral two-stream subnetwork for modality-salient representation learning and a parameter-shared one-stream subnetwork for modality-interacted representation enhancement. More importantly, a hybrid model-constrained loss containing a perceptual abundance term and a degradation-guided term is introduced to further eliminate the latent distortions. Since the hybrid loss is built on the degradation model, we additionally present an attention-embedded degradation learning network to adaptively estimate the unknown degradation parameters. Extensive experimental results on four datasets demonstrate the effectiveness of our proposed methods when compared with state-of-the-arts.

Risk assessment and biodegradation potential of PAHs originating from Map Ta Phut Industrial Estate, Rayong, Thailand

ABSTRACT Petroleum hydrocarbon contamination is a serious concern across the globe. Here, the capability of native bacterial consortium enriched from sediment samples of Map Ta Phut Industrial Estate (MTPIE), Rayong, Thailand was described. The distribution of PAHs was assessed from the sediment samples collected from MTPIE by GC-FID and the toxic unit (TU) was calculated to assess the potential ecological risk to the surrounding biota. This study investigated the degradation potential and determined the PAH-degrading bacterial cultures by enriching collected sediments in PAHs mixtures (naphthalene, phenanthrene, and pyrene). The TPH degradation capacity of each bacterial consortium was validated in a soil microcosm using aged crude oil-contaminated soil. The MTPIE sediments were highly contaminated with PAHs (843.99-3904.39 ng g-1) and posed extremely high ecological risks to benthic biota (TU > 1). The consortium S5-P most significantly removed naphthalene (90.03%) and phenanthrene (88.14%) while the highest removal of pyrene was achieved by the S3-P consortium. Other consortia only partially degraded the PAHs. The dominant microbes in the consortia were determined using PCR-DGGE, it was found that the PAH degrading consortia were known PAH degraders such as Annwoodia, Bacillus, Brevibacillus, Lysinibacillus, Paracoccus, Rhodococcus, Sphingopyxis, Sulfurovum, and Sulfurimonas species and unknown PAH degraders such as Lithuaxuella species. The consortium S5-P showed the highest degradation capacity, removing 74.99% of TPHs in the soil microcosm. Furthermore, the inoculation of PAH-biodegrading bacterial consortia significantly promoted the catechol-2,3-dioxygenase (C23O) and dehydrogenase (DHA) activities which directly correlated with the degradation efficiency of petroleum hydrocarbons (p < 0.05).

Development and optimization of stability-indicating method of ethinylestradiol, levonorgestrel, and their main impurities using quality by design approach

The association of Ethinylestradiol 0.03 mg and Levonorgestrel 0.15 mg is a hormonal contraceptive that combines estrogen and progestogen. According to a bibliographic survey, these combined drugs present at least 18 known degradation products, which are required to control the potential impurities harmful to human health. The high number of impurities and the low concentrations of the active pharmaceutical ingredients (APIs) and their respective degradation products increase the complexity of the stability-indicating method development for this medicine. Thus, this work aimed to develop and optimize the stability-indicating method using the quality by design (QbD) approach and in-silico tools for application in samples of oral contraceptives sold in Brazil. The analysis samples were initially subjected to a forced degradation study through 7 days of exposure under acid and alkali hydrolysis, oxidative condition, and oxidation by metal ions. In addition to the chemical exposure, the sample was subjected to physical stress through 10 days of exposure under dry heat, moisture, and photolytic degradation. These exposure samples were analyzed in the development and optimization of chromatographic conditions. As a result, the developed method was able to separate 20 known substances, including the two APIs and their respective 18 degradation products, as well as unknown degradation products obtained by the forced degradation study. Finally, this stability-indicating method was successfully applied for comparative analysis of contraceptive drugs marketed in Brazil, newly purchased and subjected to accelerated stability condition at 40 °C and 75% RH over the 6-month period.

Toward extreme face super-resolution in the wild: A self-supervised learning approach

Extreme face super-resolution (FSR), that is, improving the resolution of face images by an extreme scaling factor (often greater than ×8) has remained underexplored in the literature of low-level vision. Extreme FSR in the wild must address the challenges of both unpaired training data and unknown degradation factors. Inspired by the latest advances in image super-resolution (SR) and self-supervised learning (SSL), we propose a novel two-step approach to FSR by introducing a mid-resolution (MR) image as the stepping stone. In the first step, we leverage ideas from SSL-based SR reconstruction of medical images (e.g., MRI and ultrasound) to modeling the realistic degradation process of face images in the real world; in the second step, we extract the latent codes from MR images and interpolate them in a self-supervised manner to facilitate artifact-suppressed image reconstruction. Our two-step extreme FSR can be interpreted as the combination of existing self-supervised CycleGAN (step 1) and StyleGAN (step 2) that overcomes the barrier of critical resolution in face recognition. Extensive experimental results have shown that our two-step approach can significantly outperform existing state-of-the-art FSR techniques, including FSRGAN, Bulat's method, and PULSE, especially for large scaling factors such as 64.

An ecotoxicological perspective of microplastics released by face masks.

The accelerated use, massive disposal, and contamination with face masks during the COVID-19 pandemic have raised new questions regarding their negative impact on the environment emerged. One major concern is whether microplastics (MPs) derived from face masks (FMPs) represent an important ecotoxicological hazard. Here, we discussed the shortcomings, loose ends, and considerations of the current literature investigating the ecotoxicological effects of FMPs on aquatic and terrestrial organisms. Overall, there are multiple uncertainties regarding the true impact of FMPs at a certain concentration due to the presence of uncontrolled or unknown degradation products, such as MPs of various size ranges even nano-sized (<1 µm) and chemical additives. It is apparent that FMPs may induce endocrine-disrupting and behavioral effects in different organisms. However, the results of FMPs should be carefully interpreted, as these cannot be extrapolated at a global scale, by taking into account a number of criteria such as face mask manufacturers, providers, consumer preferences, and type of face masks. Considering these uncertainties, it is still not possible to estimate the contribution of face masks to the already existing MP issue.