Types Of Layers Research Articles

Layer 1 NDNF interneurons are specialized top-down master regulators of cortical circuits

Diverse types of inhibitory interneurons (INs) impart computational power and flexibility to neocortical circuits. Whereas markers for different IN types in cortical layers 2-6 (L2-L6) have been instrumental for generating a wealth of functional insights, only the recent identification of a selective marker (neuron-derived neurotrophic factor [NDNF]) has opened comparable opportunities for INs in L1 (L1INs). However, at present we know very little about the connectivity of NDNF L1INs with other IN types, their input-output conversion, and the existence of potential NDNF L1IN subtypes. Here, we report pervasive inhibition of L2/3 INs (including parvalbumin INs and vasoactive intestinal peptide INs) by NDNF L1INs. Intersectional genetics revealed similar physiology and connectivity in the NDNF L1IN subpopulation co-expressing neuropeptide Y. Finally, NDNF L1INs prominently and selectively engage in persistent firing, a physiological hallmark disconnecting their output from the current input. Collectively, our work therefore identifies NDNF L1INs as specialized master regulators of superficial neocortex according to their pervasive top-down afferents.

Exploring the daytime boundary layer evolution based on Doppler spectrum width from multiple coplanar wind lidars during CROSSINN

Abstract. Over heterogeneous, mountainous terrain, the determination of spatial heterogeneity of any type of a turbulent layer has been known to pose substantial challenges in mountain meteorology. In addition to the combined effect in which buoyancy and shear contribute to the turbulence intensity of such layers, it is well known that mountains add an additional degree of complexity via non-local transport mechanisms, compared to flatter topography. It is therefore the aim of this study to determine the vertical depths of both daytime convectively and shear-driven boundary layers within a fairly wide and deep Alpine valley during summertime. Specifically, three Doppler lidars deployed during the CROSSINN (Cross-valley flow in the Inn Valley investigated by dual-Doppler lidar measurements) campaign within a single week in August 2019 are used to this end, as they were deployed along a transect nearly perpendicular to the along-valley axis. To achieve this, a bottom-up exceedance threshold method based on turbulent Doppler spectrum width sampled by the three lidars has been developed and validated against a more traditional bulk Richardson number approach applied to radiosonde profiles obtained above the valley floor. The method was found to adequately capture the depths of convective turbulent boundary layers at a 1 min temporal and 50 m spatial resolution across the valley, with the degree of ambiguity increasing once surface convection decayed and upvalley flows gained in intensity over the course of the afternoon and evening hours. Analysis of four intensive observation period (IOP) events elucidated three regimes of the daytime mountain boundary layer in this section of the Inn Valley. Each of the three regimes has been analysed as a function of surface sensible heat flux H, upper-level valley stability Γ, and upper-level subsidence wL estimated with the coplanar retrieval method. Finally, the positioning of the three Doppler lidars in a cross-valley configuration enabled one of the most highly spatially and temporally resolved observational convective boundary layer depth data sets during daytime and over complex terrain to date.

Integrated machine learning and multimodal data fusion for patho-phenotypic feature recognition in iPSC models of dilated cardiomyopathy.

Integration of multiple data sources presents a challenge for accurate prediction of molecular patho-phenotypic features in automated analysis of data from human model systems. Here, we applied a machine learning-based data integration to distinguish patho-phenotypic features at the subcellular level for dilated cardiomyopathy (DCM). We employed a human induced pluripotent stem cell-derived cardiomyocyte (iPSC-CM) model of a DCM mutation in the sarcomere protein troponin T (TnT), TnT-R141W, compared to isogenic healthy (WT) control iPSC-CMs. We established a multimodal data fusion (MDF)-based analysis to integrate source datasets for Ca2+ transients, force measurements, and contractility recordings. Data were acquired for three additional layer types, single cells, cell monolayers, and 3D spheroid iPSC-CM models. For data analysis, numerical conversion as well as fusion of data from Ca2+ transients, force measurements, and contractility recordings, a non-negative blind deconvolution (NNBD)-based method was applied. Using an XGBoost algorithm, we found a high prediction accuracy for fused single cell, monolayer, and 3D spheroid iPSC-CM models (≥92±0.08 %), as well as for fused Ca2+ transient, beating force, and contractility models (>96±0.04 %). Integrating MDF and XGBoost provides a highly effective analysis tool for prediction of patho-phenotypic features in complex human disease models such as DCM iPSC-CMs.

The Removal of As(III) Using a Natural Laterite Fixed-Bed Column Intercalated with Activated Carbon: Solving the Clogging Problem to Achieve Better Performance

Natural laterite fixed-bed columns intercalated with two types of layers (inert materials, such as fine sand and gravel, and adsorbent materials, such as activated carbon prepared from Balanites aegyptiaca (BA-AC)) were used for As(III) removal from an aqueous solution. Investigations were carried out to solve the problem of column clogging, which appears during the percolation of water through a natural laterite fixed-bed column. Experimental tests were conducted to evaluate the hydraulic conductivities of several fixed-bed column configurations and the effects of various parameters, such as the grain size, bed height, and initial As(III) concentration. The permeability data show that, among the different types of fixed-bed columns investigated, the one filled with repeating layers of laterite and activated carbon is more suitable for As(III) adsorption, in terms of performance and cost, than the others (i.e., non-intercalated laterite; non-intercalated activated carbon, repeating layers of laterite and fine sand; and repeating layers of laterite and gravel). A study was carried out to determine the most efficient column using breakthrough curves. The breakthrough increased from 15 to 85 h with an increase in the bed height from 20 to 40 cm and decreased from 247 to 32 h with an increase in the initial As(III) concentration from 0.5 to 2 mg/L. The Bohart–Adams model results show that increasing the bed height induced a decrease in the kAB and N0 values. The critical bed depths determined using the bed depth service time (BDST) model for As(III) removal were 15.23 and 7.98 cm for 1 and 20% breakthroughs, respectively. The results show that the new low-cost adsorptive porous system based on laterite layers with alternating BA-AC layers can be used for the treatment of arsenic-contaminated water.

Soil-Microbial CNP Content and Ecological Stoichiometry Characteristics of Typical Broad-Leaved Tree Communities in Fanjing Mountain in Spring

This study aims to investigate the impact of diverse forest stand types and soil depths on soil ecological stoichiometry characteristics, shedding light on nutrient limitations and cycling patterns within the mid-subtropical forest ecosystem in southwest China during spring. The research focused on four representative forest stands situated in Fanjing Mountain: Castanopsis fargesii (C. fargesii), Cyclobalanopsis multiervis (C. multiervis), Cyclobalanopsis argyrotricha (C. argyrotricha), and Rhododendron argyrophyllum Franch (R. argyrophyllum). Sample plots were established in these forest types, and soil samples were collected from the 0–20 cm and 20–40 cm soil layers in March, spring of 2023. Various soil parameters, including pH, soil organic carbon (SOC), total nitrogen (TN), total phosphorus (TP), soil microbial biomass carbon (MBC), soil microbial nitrogen (MBN), and soil microbial phosphorus (MBP) were measured, and their stoichiometric ratios were calculated. The findings of the study were as follows: (1) In the 0–20 cm soil layer, C. argyrotricha exhibited the highest soil organic carbon, followed by C. fargesii, C. multiervis, and R. argyrophyllum with the lowest content. No significant differences in soil organic carbon were observed among the four forests in the 20–40 cm soil layer. Additionally, C. argyrotricha displayed a significantly higher soil C:N ratio compared to other forest types in different soil layers. In the typical broad-leaved forest area of Fanjing Mountain, the TP was classified as deficient. (2) In the 0–20 cm soil layer, the MBC of C. fargesii surpassed C. multiervis, C. argyrotricha, and R. argyrophyllum by 26.59%, 42.92%, and 24.67%, respectively. There were no significant differences in soil MBC:MBP ratio and MBN:MBP ratio, regardless of forest species and soil depths. The low availability of soil nitrogen in different forest stand types in Fanjing Mountain strongly limits soil microorganism biomass. (3) The correlation between SOC, TN, TP, and their stoichiometric ratios varied across different soil layers. Therefore, in managing the Fanjing Mountain forest area, attention should be paid to supplementing N and P in the soil.

A new uranyl silicate sheet derived from phosphuranylite topology in the structure of Cs4[(UO2)5(SiO3OH)2O2F4

Abstract A new uranyl silicate Cs4[(UO2)5(SiO3OH)2O2F4] (1), was obtained via a hydrothermal route. The new compound is monoclinic, P21/n, a = 8.3870(2), b = 13.4612(2), c = 10.9503(2) Å, β = 91.223(2)°, V = 1236.00(4) Å3; the structure has been solved and refined down to R 1 = 0.022. Therein, the phosphouranylite units ( PU s) associate into a new type of uranyl-silicate layers, [(UO2)5(SiO3OH)2O2F4]4−, which interleave with the Cs+ cations. Topological analysis of PU based structures indicates that these layers in 1 provide a unique example of complexes constructed only via association of the PU and not involving other building units.

Numerical optimization of interface engineering parameters for a highly efficient HTL-free perovskite solar cell

Decarbonizing the world’s energy system has set the stage in favor of renewable energy alternatives such as the more reliable, abundant, inexhaustible, and sustainable solar energy. However, the photovoltaic technology faces significant stability, longevity, and scalability challenges. These difficulties can be overcome by judicious optimization of interfacial engineering of the solar cell architecture that will decrease the cost of fabrication and enhance the solar cell performance and durability. Herein, a comprehensive analysis of a novel high-performance hole transport layer-free (HTL-free) perovskite solar cell (PSC) of the configuration, ITO/PC61BM/CH3NH3SnI3/Pt, was investigated by device simulation while optimizing electron transport materials, metal back contacts, front contact materials, and buffer layers. The focus is on enhancing device performance by introducing various types of buffer layers, such as cadmium sulphide (CdS) in the proposed solar cell model. By fine-tuning the ETL layers and manipulating perovskite acceptor and donor densities, better device performance was achieved. Furthermore, the Mott-Schottky capacitance analysis yielded critical insights into the interface properties and dynamics of charge carriers within the solar cell structure. Optimizing back contacts also play a critical role in charge extraction and reduced recombination losses. Leveraging a sophisticated simulation platform, SCAPS-1D, the study assesses how design strategies impact photovoltaic electrical outcomes such as open-circuit voltage (Voc), power conversion efficiency (PCE), short-circuit current density (Jsc), and Fill factor (FF). The proposed HTL-free device posted remarkable results – PCE of 38.11 %, Jsc of 35.32 mAcm−2, FF of 88.67 %, and Voc of 1.2168 V. Accordingly, this work inspires the promising prospects of HTL-free PSCs in achieving robust solar harvesting capabilities while streamlining device affordability. Therefore, the proposed device architecture stimulates the journey towards affordable and high-performance scalable solar cell modules.

MEAformer: An all-MLP transformer with temporal external attention for long-term time series forecasting

Transformer-based models have significantly improved performance in Long-term Time Series Forecasting (LTSF). These models employ various self-attention mechanisms to discover long-term dependencies. However, the computational efficiency is hampered by the inherent permutation invariance of self-attention, and they primarily focus on relationships within the sequence while neglecting potential relationships between different sample sequences. This limits the ability and flexibility of self-attention in LTSF. In addition, the Transformer's decoder outputs sequences in an autoregressive manner, leading to slow inference speed and error accumulation effects, especially for LTSF. Regarding the issues with Transformer-based models for LTSF, we propose a model better suited for LTSF, named MEAformer. MEAformer adopts a fully connected Multi-Layer Perceptron (MLP) architecture consisting of two types of layers: encoder layers and MLP layers. Unlike most encoder layers in Transformer-based models, the MEAformer replaces self-attention with temporal external attention. Temporal external attention explores potential relationships between different sample sequences in the training dataset. Compared to the quadratic complexity of self-attention mechanisms, temporal external attention has efficient linear complexity. Encoder layers can be stacked multiple times to capture time-dependent relationships at different scales. Furthermore, the MEAformer replaces the intricate decoder layers of the original model with more straightforward MLP layers. This modification aims to enhance inference speed and facilitate single-pass sequence generation, effectively mitigating the problem of error accumulation effects. Regarding long-term forecasting, MEAformer achieves state-of-the-art performance on six benchmark datasets, covering five real-world domains: energy, transportation, economy, weather, and disease. Code is available at: https://github.com/huangsiyuan924/MEAformer.

Effects of short-term aging on the mechanical and structural performance of hot mix asphalt mixtures: a case study complemented by statistical analysis

This study aimed to assess the effects of short-term aging on the mechanical behaviour of asphalt layers of flexible pavement structures. The effects of aging on their mechanical performance have not yet been detailed, in terms of structural analyses. Therefore, pavement structures with distinct types of asphalt layers (non-aged and aged) were evaluated. Stability, tensile strength, resilient modulus, fatigue behaviour and permanent deformation or static creep were determined. The mePADS software was used to carry out structural analyses of the pavements, based on variations in the bound layers’ mechanical properties, loading level and tire inflation pressure. Horizontal stresses and strains were determined at the bottom of the binder course. Fatigue lives were predicted and discussed based on statistical analyses. The short-term aging enhanced the mechanical parameters, but adversely affected the fatigue life for the examined strain states, which reinforces the importance of carrying out the structural analyses.

Effect of 2-Mercapto-1-methylimidazole on the Electrodeposition of Nickel on an Ordered Au(111) Electrode.

A nickel film electroplated onto a metal substrate can be used as a catalyst for water splitting and a magnetic material for spin valves. Although the nucleation and growth of Ni on Au(111) have already been examined with in situ scanning tunneling microscopy (STM), the current study provides new insights of the structure of the first layer of Ni on an ordered Au(111) electrode in 0.1 M KSO4 + 1 mM H2SO4 + 10 mM NiSO4 (pH 3). Prolonged STM scanning of the Ni monolayer on a Au(111) electrode revealed interfacial mixing to produce a surface alloy, initially assuming segregated Ni domains and later transforming them to a homogeneous Ni/Au phase. The formation of the Ni/Au(111) surface alloy affected the structure of the subsequent bulk Ni deposition. The inclusion of 2-mercapto-1-methylimidazole (MMI) in the deposition bath incurred Ni deposition at a less negative potential and a faster rate, resulting in an overall 5.3 times more Ni deposited on the Au electrode in potentiodynamic experiments. MMI molecules were adsorbed on the Ni deposit to prevent Ni dissolution in the Au(111) electrode. MMI could catalyze the presumed rate-determining step from Ni2+ to Ni+ en route to the metallic Ni. The resultant Ni film with MMI had a 3D texture without a preferred crystal orientation on the Au electrode, as opposed to a layer type growth of Ni on Au(111) without MMI.

A 3D multi-scale CycleGAN framework for generating synthetic PETs from MRIs for Alzheimer's disease diagnosis

This paper proposes a novel framework for generating synthesized PET images from MRIs to fill in missing PETs and help with Alzheimer's disease (AD) diagnosis. This framework employs a 3D multi-scale image-to-image CycleGAN architecture for the end-to-end translation of MRI and PET domains together. A hybrid loss function is also proposed to enforce structural similarity while preserving voxel-wise similarity and avoiding blurry images. As shown by the quantitative and visual assessment of the synthesized PETs, this framework is superior to the state-of-the-art. Moreover, using these synthesized PETs helps improve the ternary classification of AD subjects (AD vs. MCI vs. NC). Specifically, assuming an extreme case where none of the subjects has a PET, feeding the classifier with MRIs and their corresponding synthetic PETs results in a more accurate diagnosis than feeding it with just available MRIs. Accordingly, the proposed framework can help improve AD diagnosis, which is the final goal of the current study. Ablation investigation of the proposed multi-scale framework as well as the proposed loss function, is also conducted to study their contribution to the quality of synthesized PETs. Furthermore, other factors, such as stopping criteria, the type of normalization layer, the activation function, and dropouts, are examined, concluding that the appropriate use of these factors can significantly improve the quality of synthesized PETs.

European Atlantic deciduous forests are more resilient to fires than Pinus and Eucalyptus plantations

In this study, we assess how ecosystem type affects the short-term post-fire resilience of fire-prone forests, which are the predominant ecosystems in South West (SW) Europe. For this purpose, we focused on three forest systems, widespread across the European Atlantic coast, which are highly susceptible to fires at their SW distribution boundary, namely the European thermophilous Atlantic deciduous forests, dominated by Quercus robur L., and two types of forest plantations widespread globally, Eucalyptus globulus Labill. and Pinus pinaster Aiton plantations. At the community structure level, resilience to fire was measured based on changes in cover of each vertical stratum. At the functional level, resilience to fire was measured based on replacements of (both morphological and regenerative) plant functional types in the overstory and understory layers. The study variables were vertical structure and abundance of plant functional types in both overstory and understory layers. Vertical structure was assessed by defining nine vertical strata and estimating the cover in each vegetation stratum. All species identified in the overstory and understory layers were functionally classified according to their morphological attributes and regenerative traits pertaining to fire. One year after fire, we observed considerable differences in the structure between the three forest communities studied, particularly in the overstory layer, because most of the vertical strata were well recovered in the oak forest, partially recovered in the E. globulus plantation, and completely lost in the P. pinaster plantation. In contrast to the Atlantic deciduous forest and eucalyptus plantation, the pine plantation displayed distinct changes in its distribution of plant functional types. The pine plantation showed extremely low resilience to fire because the overstory layer decreased considerably, and the understory layer experienced substantial changes in the distribution of morphological and regenerative plant functional types. Fire favoured certain species which prevailed over others and, while grasses, herbaceous and spiny shrubs increased, the abundance of most morphologically functional plant types disappeared or decreased. These changes in the structure of forest communities and in the distribution of plant functional types have important implications for ecosystem functioning and services provision. Therefore, this study may serve as a foundation for devising forest management measures aimed at increasing ecosystem resilience to fire.

Growth of thin-film magnetic nanostructures promising for spintronics applications

Multilayered metallic nanostructures are promising for the fabrication of spin valves based on the giant magnetoresistive effect and for studies of the nature of topological magnetism, aimed at the development of new nanoscale data storage and transfer devices, e.g. those based on magnetic skyrmions. It is still an important task to develop methods of synthesis and configuration of thin-film nanostructures and control of spin textures in those nanostructures under electric and spin currents generated as a result of the spin Hall effect in external electric fields. Thin-film polycrystalline ferromagnetic / heavy metal Ru(10nm)/Co(0,8)/Ru(2), Ru(10)/Co(0,8)/Ru(2)/W(4), Pt(5)/Co(0,8)/MgO(2)/Pt(2) and Pt(15)/Co(0,8)/MgO(2)/Pt(2) nanostructures have been synthesized using magnetron sputtering. Electric contacts and Hall structures with different conductive bridge thicknesses have been synthesized on the specimens using electron beam photolithography. Experimental vibration magnetometric data have been utilized to calculate magnetic parameters of the specimens, i.e., saturation magnetization, magnetic anisotropy energy and field and coercive force as functions of ferromagnetic and heavy metal layer types. The domain structure of the specimens has been studied using Kerr microscopy. The electrical resistivity has been simulated and the critical current and current density of the nanostructures have been assessed. We show that all the film specimens exhibit perpendicular magnetic anisotropy and can be used in the studies of current-induced phenomena and spin moment transfer processes in nanostructures.

Advancing multimodal medical image fusion: an adaptive image decomposition approach based on multilevel Guided filtering.

With the rapid development of medical imaging methods, multimodal medical image fusion techniques have caught the interest of researchers. The aim is to preserve information from diverse sensors using various models to generate a single informative image. The main challenge is to derive a trade-off between the spatial and spectral qualities of the resulting fused image and the computing efficiency. This article proposes a fast and reliable method for medical image fusion depending on multilevel Guided edge-preserving filtering (MLGEPF) decomposition rule. First, each multimodal medical image was divided into three sublayer categories using an MLGEPF decomposition scheme: small-scale component, large-scale component and background component. Secondly, two fusion strategies-pulse-coupled neural network based on the structure tensor and maximum based-are applied to combine the three types of layers, based on the layers' various properties. The three different types of fused sublayers are combined to create the fused image at the end. A total of 40 pairs of brain images from four separate categories of medical conditions were tested in experiments. The pair of images includes various case studies including magnetic resonance imaging (MRI) , TITc, single-photon emission computed tomography (SPECT) and positron emission tomography (PET). We included qualitative analysis to demonstrate that the visual contrast between the structure and the surrounding tissue is increased in our proposed method. To further enhance the visual comparison, we asked a group of observers to compare our method's outputs with other methods and score them. Overall, our proposed fusion scheme increased the visual contrast and received positive subjective review. Moreover, objective assessment indicators for each category of medical conditions are also included. Our method achieves a high evaluation outcome on feature mutual information (FMI), the sum of correlation of differences (SCD), Qabf and Qy indexes. This implies that our fusion algorithm has better performance in information preservation and efficient structural and visual transferring.

Research on the Symbolic Meaning of White Cranes — A Case Study of Fly with the Crane

Fly with the Crane is a feature film directed by Chinese filmmaker Li Ruijun in 2012, adapted from the short story of the same name by Su Tong. Set against a family backdrop, the film explores the emotional conflicts and collisions of life philosophies between the elderly and their family members when facing life and death decisions. Cranes play a significant symbolic role in the film, reflecting the characters’ inner emotions and their different understandings of life, death, and destiny. From the crane pattern on the coffin to the real white cranes in the film, and to the crane in the heart of the protagonist, five types of white cranes present different layers of symbolic meanings, enriching the depth and connotation of the film. At the same time, the film also reflects the current reality of scarce land resources, conflicts in resource allocation, and changes in people’s consciousness, indicating that the “crane culture” may gradually disappear.

«Green» structures in the urbanized environment: studying the impact of «green» roofs on environmental parameters and rainwater quality. Bibliographic review

A quantitative analysis of the most cited articles published in the Scopus scientometric database by 2023 is carried out. It is shown that most of the publications, the object of study of which are "green" structures, focus on the study of "green" roofs. It is shown that since 1981, the number of studies on green roofs has been steadily increasing: approximately 45% of articles focus on the benefits in terms of thermal impact and thermal insulation, 18% on rainwater drainage, 15% on air quality, 5% on the noise-absorbing properties of green roofs, and 17% on the costs of building and operating green roofs, recreational facilities, fire protection, and other related issues. The requirements and commonly used materials for a standard green roof are described, as well as the percentage of scientific publications, depending on the functions and layers of green roof construction involved. An analysis of the most cited scientific studies on the impact of green roof systems on wastewater quality was conducted. It is shown that the results of the studies presented in these publications indicate the need for further study of the impact of a "green" roof on the amount of pollution in the water flowing from its surface. It is emphasized that further research should focus on the structural layers of the roof (thickness and materials used), namely the thickness and types of substrate layers, as well as on the conditions of plant care (e.g., fertilization), especially on intensive green roofs. In addition, it should be studied and analyzed how the retention of rainwater by plants and the soil layer of a green roof affects the relative humidity of the air near the building during periods without rain. The most cited scientific studies on the reduction of air pollutants by means of green roofs are analyzed. It is concluded that future research should focus on the study of plant species that are well adapted to growing on roofs (in temperate climates) and most effectively carry out the process of photosynthesis, absorbing CO2 and releasing O2 into the atmosphere around the building. The article analyzes the most cited scientific publications on the noise-absorbing properties of green roofs. It is substantiated that it is necessary to maximize the percentage of green roofs implementation on all buildings and structures in the city so that green roofs can significantly affect the quality of the environment in urbanized areas.

Development of Nb-based alloy membranes with improved resistance to corrosion by H2S

At present, the interest in non-Pd hydrogen permeation membranes mainly focuses on group 5 B metals (Nb, V, Ta) and their alloys. To solve the problem of easy oxidation and weak dissociation of hydrogen molecules, a "Pd catalytic layer" needs to be deposited on the membrane surface. Due to its limited stability towards sulfur, issues of irreversible poisoning by sulfur compounds must be addressed. To this end, Pd-, Pd65Ag35-, Pd90Cu10-, Pd80Au20-, and Pd55Ag35Au10-coated Nb30Hf35Co35 membranes were firstly synthesised by magnetron sputtering, and their hydrogen permeation behaviour in pure H2 and/or in different H2/H2S mixtures for up to 1000 ppm at 673 K were evaluated. At these conditions, the permeabilities of the Pd-coated membranes decrease rapidly with increasing the H2S concentration and cannot recover after removal of H2S from the feed mixtures. In comparison, the Pd–Ag-containing membrane greatly increases the pure H2 permeability, whereas the membrane doped with Au can effectively improve the response to H2S. Specifically, the Pd65Ag35-coated membrane shows the highest pure H2 permeability, but it loses maximum permeability value on exposure to H2/H2S feed mixtures. In contrast, Pd80Au20-containing membranes exhibit good tolerance of H2S poisoning and displayed a high fractional recovery (∼83%) of initial H2 permeability. The degradation of permeability for Pd- or Pd65Ag35-coated membranes after the H2S exposure can be ascribed to the deterioration of the structure caused by the possible formation of bulk Pd4S, which has been corroborated with EDS analysis, XRD, and XPS after testing. Finally, to improve both permeability and chemical tolerance, a Pd55Ag35Au10-coated membrane was successfully fabricated, and it possesses excellent comprehensive performance with respect to the permeability and H2S response. At 120 ppm H2S in the feed, the H2 flux equals ca. 43% of its original value at 673 K and can be stable for at least 166 h. Bulk sulfides did not form on the membrane surface after the testing. This paper not only developed a new type of group 5 B alloy membrane with improved resistance to corrosion by H2S, but also further confirmed the feasibility of improving H2S corrosion resistance by adjusting the catalytic layer type or composition of the outermost coating.

Directed Assembly of Ordered Mixed-Spacer Quasi-2D Halide Perovskites through Homomeric Chains of Intermolecular Bonds.

Two-dimensional (2D) halide perovskites (HPs) are of significant interest to researchers because of their natural structural frameworks and intriguing optoelectronic properties. However, the direct fabrication of ordered mixed-spacer quasi-2D HPs remains challenging. Herein, a synthetic strategy inspired by the principle of supramolecular synthons is employed for the self-assembly of a series of ordered mixed-spacer bilayered HPs. The key innovation involves the introduction of intermolecular hydrogen bonds using a bifunctional 3-aminopropionitrile cation. Three homogeneous n = 2 structures are obtained, with a subtly ordered perovskite connected by two distinct types of organic cation layers, resulting in a recurrent ABAB' stacking sequence. These three compounds exhibit attractive semiconducting properties. Moderate bandgaps in the range of 2.70 to 2.76eV with an absorption wavelength range of 448-459nm exhibit excellent photoelectric response. Moreover, the ordered structures facilitate excellent polarization-sensitive photodetection, with an impressive on/off ratio of 103. The response speed ranged from 298 to 381 µs, and the out-of-plane polarization-related dichroism ratio is determined to be 1.19. Such ordered mixed-spacer bilayered perovskites have not been reported. These results enrich the HPs system and play a significant role in the direct assembly of novel perovskites with ordered structures.

Self-supervised dual-domain balanced dropblock-network for low-dose CT denoising

Objective. Self-supervised learning methods have been successfully applied for low-dose computed tomography (LDCT) denoising, with the advantage of not requiring labeled data. Conventional self-supervised methods operate only in the image domain, ignoring valuable priors in the sinogram domain. Recently proposed dual-domain methods address this limitation but encounter issues with blurring artifacts in the reconstructed image due to the inhomogeneous distribution of noise levels in low-dose sinograms. Approach. To tackle this challenge, this paper proposes SDBDNet, an end-to-end dual-domain self-supervised method for LDCT denoising. With the network designed based on the properties of inhomogeneous noise in low-dose sinograms and the principle of moderate sinogram-domain denoising, SDBDNet achieves effective denoising in dual domains without introducing blurring artifacts. Specifically, we split the sinogram into two subsets based on the positions of detector cells to generate paired training data with high similarity and independent noise. These sub-sinograms are then restored to their original size using 1D interpolation and learning-based correction. To achieve adaptive and moderate smoothing in the sinogram domain, we integrate Dropblock, a type of convolution layer with regularization, into SDBDNet, and set a weighted average between the denoised sinograms and their noisy counterparts, leading to a well-balanced dual-domain approach. Main results. Numerical experiments show that our method outperforms popular non-learning and self-supervised learning methods, demonstrating its effectiveness and superior performance. Significance. While introducing a novel high-performance dual-domain self-supervised LDCT denoising method, this paper also emphasizes and verifies the importance of appropriate sinogram-domain denoising in dual-domain methods, which might inspire future work.

Integration mixer: An efficient mixed neural network for memory dynamic stability analysis in high dimensional variation space

In low-power designs, the SRAM performance suffers from the process variation. Statistical analysis for the yield of circuit block (e.g., static random-access memory) is extremely time-consuming due to the expensive simulations since the variation space is high-dimensional. In this paper, we construct a mixed neural network to substitute the simulation. We present Mixer. It mainly contains two types of layers: one with regularized sub-radial basis function networks (sub-RBFs) applied independently to extract the effects on circuit performance of the subsets of input process variables, and the other one with multi-layer perceptron (MLP) applied to learn the connection of these extracted effects. When trained with small datasets of high dimension generated from 28 nm memory circuits, our Mixer shows competitive accuracy and efficiency compared with other state-of-the-art models.*