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Neural networks based on memristive devices 1 – 3 have the ability to improve throughput and energy efficiency for machine learning 4 , 5 and artificial intelligence 6 , especially in edge applications 7 – 21 . Because training a neural network model from scratch is costly in terms of hardware resources, time and energy, it is impractical to do it individually on billions of memristive neural networks distributed at the edge. A practical approach would be to download the synaptic weights obtained from the cloud training and program them directly into memristors for the commercialization of edge applications. Some post-tuning in memristor conductance could be done afterwards or during applications to adapt to specific situations. Therefore, in neural network applications, memristors require high-precision programmability to guarantee uniform and accurate performance across a large number of memristive networks 22 – 28 . This requires many distinguishable conductance levels on each memristive device, not only laboratory-made devices but also devices fabricated in factories. Analog memristors with many conductance states also benefit other applications, such as neural network training, scientific computing and even ‘mortal computing’ 25 , 29 , 30 . Here we report 2,048 conductance levels achieved with memristors in fully integrated chips with 256 × 256 memristor arrays monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry. We have identified the underlying physics that previously limited the number of conductance levels that could be achieved in memristors and developed electrical operation protocols to avoid such limitations. These results provide insights into the fundamental understanding of the microscopic picture of memristive switching as well as approaches to enable high-precision memristors for various applications. Chips with 256 × 256 memristor arrays that were monolithically integrated on complementary metal–oxide–semiconductor (CMOS) circuits in a commercial foundry achieved 2,048 conductance levels in individual memristors.

Adaptive radiotherapy (ART) was introduced in the late 1990s to improve the accuracy and efficiency of therapy and minimize radiation-induced toxicities. ART combines multiple tools for imaging, assessing the need for adaptation, treatment planning, quality assurance, and has been utilized to monitor inter- or intra-fraction anatomical variations of the target and organs-at-risk (OARs). Ethos™ (Varian Medical Systems, Palo Alto, CA), a cone beam computed tomography (CBCT) based radiotherapy treatment system that uses artificial intelligence (AI) and machine learning to perform ART, was introduced in 2020. Since then, numerous studies have been done to examine the potential benefits of Ethos™ CBCT-guided ART compared to non-adaptive radiotherapy. This review will explore the current trends of Ethos™, including improved CBCT image quality, a feasible clinical workflow, daily automated contouring and treatment planning, and motion management. Nevertheless, evidence of clinical improvements with the use of Ethos™ are limited and is currently under investigation via clinical trials.

Variable selection has always been an important issue in statistics. When a linear regression model is used to fit data, selecting appropriate explanatory variables that strongly impact the response variables has a significant effect on the model prediction accuracy and interpretation effect. redThis study introduces the Bayesian adaptive group Lasso method to solve the variable selection problem under a mixed linear regression model with a hidden state and explanatory variables with a grouping structure. First, the definition of the implicit state mixed linear regression model is presented. Thereafter, the Bayesian adaptive group Lasso method is used to determine the penalty function and parameters, after which each parameter’s specific form of the fully conditional posterior distribution is calculated. Moreover, the Gibbs algorithm design is outlined. Simulation experiments are conducted to compare the variable selection and parameter estimation effects in different states. Finally, a dataset of Alzheimer’s Disease is used for application analysis. The results demonstrate that the proposed method can identify the observation from different hidden states, but the results of the variable selection in different states are obviously different.

Energy has gained great attention and extensive demand as an important element of the development of human society over the globe. As the representative of the traditional energy use, coal and oil have made a significant contribution to the economic take-off. However, their negative effects are becoming increasingly obvious simultaneously. On the one hand, the world is facing severe energy depletion crisis as the energy consumption increases. Eventually, the development and use of energy at large lead to a deteriorating environment. Facing these dual effects, States have begun to attach importance to the exploitation of renewable energy. Marine renewable energy (hereinafter MRE) has become the gateway to energy revolution based on its particular advantages. This paper pays attention to the American institutional framework and legal system for the development of MRE, along with its advantages, prerequisites, opportunities, and barriers, followed by various pertinent recommendations, as the U.S. is the leading country in this field. It is concluded that the U.S. has a comprehensive legal system for the development of MRE. However, it has to timely intersect with the opportunities and barriers in the light of the administrative or legal framework and enhance its institutional structure for the successful implementation as well as to be competitive for the attainment of required goals. Graphic abstract

The state of the sintering end point can be indirectly reflected by the flame image characteristics of the material layer section at the end of the sintering machine. However, the image of tail section collected by industrial camera is easy to be interfered by smoke, dust and thermal radiation. As a result, the edge between the flame area and the material layer area becomes fuzzy, accompanied with halo and noise, which leads to the degradation of flame image. In order to solve the problem of image quality degradation, a new method based on weighted guided image filtering and fast adaptive fuzzy enhancement of flame image of sintering cross section is proposed in this paper; furthermore, the correlation analysis of the flame image characteristics of sintering section is carried out. The main contents of this paper include three parts: cross-sectional flame image enhancement, image brightness characteristics and geometric feature extraction, and image feature correlation analysis. The results show that the proposed method effectively eliminates the interference of noise and halo in the cross-sectional flame image. The brightness characteristics of the flame image are related to the length and height of the flame and the area of the red fire region, while there is no correlation between the brightness characteristics of the flame image and the centroid variance. Therefore, the brightness characteristics and the centroid variance can be used as the input feature for the discrimination of sintering state.

Placenta percreta is the most severe form of placenta accreta and is characterized by placental invasion through the entirety of the myometrium and possibly into extrauterine tissues. It is associated with prior cesarean deliveries and placenta previa. Herein, we present the case of a patient who developed placenta percreta and experienced massive blood loss of 27 liters. She developed many complications over the next 11 months, including deep vein thrombosis, pulmonary embolism, preeclampsia after pregnancy, hematoma, blood clots in the bladder, lactation failure, ileus, vesicovaginal fistula, excessive scar tissue requiring surgery, loss of an ovary, and recurrent bladder perforation. We analyze the mechanisms of these complications and the most common complications associated with placenta percreta.

* Context.–The coronavirus disease 2019 (COVID-19) is a highly contagious respiratory disease caused by severe acute respiratory syndrome coronavirus 2 (SARS-CoV-2). Coagulation dysfunction is a hallmark in patients with COVID-19. Fulminant thrombotic complications emerge as critical issues in patients with severe COVID-19.

Wi-Fi fingerprinting localization is one of the mainstream indoor localization technologies, which uses the Wi-Fi received signal strength indicator (RSSI) as the signal feature of the reference points and compares the online RSSI with that to predict the location. To promote localization accuracy, various methods are proposed to build the fingerprint database using the spatial information of the reference points in the offline stage. However, traditional techniques usually compare the RSSI of unknown locations with the fingerprints individually, whose structure is ignored in the online stage. Inspired by the label propagation algorithm, in this paper, we propose Graph-based Similarity Propagation Localization (GSPL), which refines the distance metric (DM) iteratively with the similarities between the RSSIs at the online stage to improve localization accuracy. Based on the offline data defined as the known data, the problem is formulated as a semi-supervised learning problem treating the online RSSI as the labeled one, which could be solved by the typical information propagation method. Compared with the traditional distance metric, the proposed one considers the structures of the feature spaces benefitting the correct selection. In the experiments, the proposed algorithm is accessed on UJIIndoorLoc and museum datasets, on which the classification accuracy and positioning accuracy are significantly improved.

Memristive devices can offer dynamic behaviour, analogue programmability, and scaling and integration capabilities. As a result, they are of potential use in the development of information processing and storage devices for both conventional and unconventional computing paradigms. Their memristive switching processes originate mainly from the modulation of the number and position of structural defects or compositional impurities—what are commonly referred to as imperfections. While the underlying mechanisms and potential applications of memristors based on traditional bulk materials have been extensively studied, memristors based on van der Waals materials have only been considered more recently. Here we examine imperfection-enabled memristive switching in van der Waals materials. We explore how imperfections—together with the inherent physicochemical properties of the van der Waals materials—create different switching mechanisms, and thus provide a range of opportunities to engineer switching behaviour in memristive devices. We also discuss the challenges involved in terms of material selection, mechanism investigation and switching uniformity control, and consider the potential of van der Waals memristors in system-level implementations of efficient computing technologies. This Review examines switching mechanisms in memristive devices based on van der Waals materials, and explores the advantages such devices offer and the challenges that must be faced for them to be of use in next-generation electronic and computing applications.

Pica is characterized as repeatedly eating or chewing a non-nutritious substance including, but not limited to ice, clay and dirt, starch, raw pasta, chalk, coal, paint, or paper. Pica symptoms can be intense and addiction-like and disrupt quality of life. It is strongly linked to iron deficiency. Since substantial iron loss occurs during blood donation, blood donors may be susceptible to development of iron deficiency and thereby pica behaviors. There are also frequent blood donors who contribute multiple times annually are important for maintaining an adequate blood supply. Understanding characteristics of these donors that are associated with increased risk for developing pica will help to identify them and prevent this adverse consequence of blood donation.We investigated demographic, clinical, and biochemical factors associated with pica using chi-square tests, t-tests, and regression analysis in a cohort of more than 13,000 racially diverse blood donors, which includes a subgroup of 1,693 high-intensity donors who gave nine or more units of whole blood in the preceding two years. In addition, a genome wide association study (GWAS) was performed on the 7,085 Caucasian blood donors to assess for genetic associations with pica. Pica was defined by questionnaire responses as consuming at least 8 oz of ice daily and/or consumption of non-ice substances regardless of the amount and frequency.Pica was present in 2.2% of the general, less frequent donors. Lower ferritin (p=0.001), non-Asian race (p<0.001), higher red cell distribution width (RDW) (p<0.001), younger age, and restless legs syndrome (RLS) (p=0.008) were independently associated with pica. Female sex is associated with iron deficiency but was not an independent predictor of pica suggesting that iron deficient males and females were equally susceptible to the development of pica behaviors. Donors with normal ferritin levels also reported pica, reinforcing the role of non-iron related factors in its presentation. In high-intensity blood donors, 1.5% had pica, and only occurred in those with ferritin <50 ng/mL. Of sixteen candidate variables, only hematocrit (OR=0.835, p=0.020) was independently associated with pica. Although severe iron deficiency was more prevalent in high-intensity donors, pica behaviors were less prevalent than in less frequent donors (2.2%). GWAS performed on the 7,085 Caucasian donors implicates gene NPVF, which was then replicated in an independent cohort from All of Us, comprised of roughly 100,000 participants with whole genome sequencing data.We have identified demographic, clinical, biochemical, and genetic predictors of pica that help identify those most at risk for developing pica behaviors, and thereby assist in its clinical diagnosis and treatment in order to maintain a robust blood supply.