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Efficient and durable catalysts for the oxygen evolution reaction are essential for realizing the large-scale application of water electrolysis technologies. Here, we report a novel Zn-doped NiOOH subnanowires (Zn–NiOOH SNWs) catalyst synthesized via the electrochemical reconstruction of Zn–NiMoO4SNWs. The inclusion of Zn triggers a transition in the oxygen evolution reaction mechanism of NiOOH from the adsorbate evolution mechanism to the lattice oxygen mechanism, resulted from Zn’s adaptive adjustment of coordination types, which also improves the reaction energetics, thereby enhancing the stability and activity. Furthermore, the subnanowire structure provides further stabilization of the lattice oxygen in Zn–NiOOH, preventing its destructive dissolution. Remarkably, Zn–NiOOH SNWs display a current density of 10 mA cm–2with an overpotential of only 179 mV and maintain stable operation at 200 mA cm–2for 800 h with minimal changes in overpotential, establishing them as one of the most effective catalysts involving lattice oxygen for the alkaline oxygen evolution reaction. When utilized as the anode in an alkaline water electrolyzer, our Zn–NiOOH SNWs catalyst demonstrates stability exceeding 500 h under a water-splitting current of 200 mA cm–2, indicating promising potential for practical applications.

Traumatic brain injury (TBI) activates the NF-κB pathway in microglia and astrocytes, which secrete pro-inflammatory cytokines that disrupt the blood–brain barrier (BBB). Curdlan derivatives are promising carriers for the delivery of siRNA drugs. Herein, we evaluated the glial cell specificity, siRNA delivery efficiency, and the subsequent phenotypic regulation of glial cells by the Curdlan derivatives in the TBI mouse model. Our in vitroand in vivostudies confirmed that the (1) pAVC4 or CuMAN polymer encapsulating siRNA were internalized by astrocytes and microglia in a receptor-dependent manner; (2) systemic administration of the pAVC4 or CuMAN polymer encapsulating siRNA resulted in significant gene silencing efficiency, altered the phenotypic polarization of glial cells, and regulated the secretion of inflammatory cytokines; (3) this lessened neuroinflammation, ameliorated BBB destruction, and improved vascular recovery. These data suggested that pAVC4 and CuMAN polymers are promising RNA delivery vehicles that can efficiently deliver siRNA to the target cells.

Pro-inflammatory polarization of microglia and astrocytes results in neuroinflammation and blood–brain barrier (BBB) disruption after a primary traumatic brain injury (TBI). Herein, we demonstrate that the dual-ligand functionalized lipid nanoparticles (AM31 LNPs) were actively and specifically internalized by microglia and astrocytes via mannose receptor (MR)- and adenosine receptor (AR)-mediated endocytosis, respectively, in a mouse model of TBI. Systemic administration of AM31 LNPs carrying siRNA against p65 resulted in internalization by the glial cells in the peri-infarct region and a robust knockdown of p65 at both mRNA and protein levels in these cells, leading to significant down-regulation of key pro-inflammatory cytokines and up-regulation of key anti-inflammatory cytokines. AM31 LNP-mediated silencing of p65 ameliorated TBI-induced BBB disruption. Our data proved that AM 31 LNP is a promising vehicle for RNA therapeutics for targeting microglia and astrocytes in neural disorder.

Diagram depicting reaction kinetics, cycle stability, and safety of sodium-ion batteries during the charge and discharge process of equipment at low temperatures.

A noncontact liquid-level sensor using a cylindrical coplanar waveguide (CCPW) is proposed. The concept is demonstrated using a prototype device made by directly spraying conductive paint on the outer surface of a glass cylinder. The position of the air–liquid interface is measured using the time-domain reflection (TDR) signal from the CCPW. The geometric parameters of the prototype sensor were optimized using a full-wave simulation tool. The experimental results show that the prototype CCPW-based liquid-level sensor has a resolution of $29.8 \mu \text{m}$ over a range of 210 mm at the sweeping frequency bandwidth of 500 MHz–4 GHz. The tradeoff between the resolution and the measurement range was discussed. The proposed sensor provides a low-cost, easy-to-fabricate, and noncontact method for liquid-level measurement, which has potential applications in fuel storage, chemical production, and wastewater treatment.

Remote sensing study of the carbon cycle in coastal marine systems using machine learning methods has received significant attention recently. The partial pressure of carbon dioxide (CO2) in seawater ( ${p}$ CO2w) is a crucial parameter for quantifying the air–sea carbon dioxide exchange. However, previous studies did not consider the effect of sea surface roughness (SSR) on ${p}$ CO2w caused by wind, waves, and other ocean dynamics. In this study, for the first time, we used SSR data derived from synthetic aperture radar (SAR), with sea surface temperature (SST), chlorophyll- ${a}$ (Chl- ${a}$ ) concentration, sea surface salinity (SSS) conventional remote sensing data to predict the ${p}$ CO2w data along the North American East Coast from 2015 to 2021 using the Cubist algorithm. Results show that the semi-analytic algorithm, Cubist, performs best among 20 statistical and machine learning models. Moreover, compared with the control experiment without the SSR data, after adding SSR as an independent variable, the final Cubist model’s coefficient of determination ( ${R} ^{2}$ ) increased from 0.88 to 0.95, and the root mean square error (RMSE) reduced from 21.75 to $14.79~\mu $ atm. Our results showed significant improvement over the previous study ( ${R} ^{2}$ = 0.8), proving the applicability of applying SSR data in retrieving high spatial resolution carbonate system parameters in the future, especially for coastal regions where wind and wave dynamics are more variable.

Subsurface density (SD) is a crucial dynamic environment parameter reflecting a 3-D ocean process and stratification, with significant implications for the physical, chemical, and biological processes of the ocean environment. Thus, accurate SD retrieval is essential for studying dynamic processes in the ocean interior. However, complete spatiotemporally accurate SD retrieval remains a challenge in terms of the equation of state and physical methods. This study proposes a novel multiscale mixed residual transformer (MMRT) neural network method to compensate for the inadequacy of the existing methods in dealing with spatiotemporal nonlinear processes and dependence. Considering the spatial correlation and temporal dependence of dynamic processes within the ocean, the MMRT addresses temporal dependence by fully using the transformer’s processing of time-series data and spatial correlation by compensating for deficiencies in spatial feature information through multiscale mixed residuals. The MMRT model was compared with the existing random forest (RF) and recurrent neural network (RNN) methods. The MMRT model achieves the best accuracy with an average determination coefficient ( ${R}^{2}$ ) of 0.988 and an average root mean square error (RMSE) of 0.050 kg/m3 for all layers. The MMRT model not only outperforms the RF and RNN methods regarding reliability and generalization ability when estimating global ocean SD from remote sensing data but also has a more interpretable encoding process. The MMRT model offers a new method for directly estimating SD using multisource satellite observations, providing significant technical support for future remote sensing super-resolution and prediction of subsurface parameters.

In this article, we introduce and demonstrate a smart acetabular trail with embedded photonic sensor arrays for force and contact location sensing. Finite element modeling is performed to study the strain distribution when an external force is loaded to the acetabular trail model, which is applied to guide strain sensor array routing. Then acetabular trail with sensor embedded channels is fabricated through polymer stereolithography (SLA) 3-D printing process. Intrinsic Fabry-Perot interferometer (IFPI) based optical fiber sensors are employed for in situ monitoring of local strain variation, thanks to their high spatial resolution. As a proof of concept, ten IFPI sensors are embedded for strain mapping. Experimental results demonstrate that localized strain change is captured by the IFPI spectra variation and loading force position can be estimated through the obtained strain distribution. With enhanced sensor robustness, strain distribution and contact location monitoring capability, this proposed smart acetabular trail could be attractive for intelligent medical applications, especially in total hip arthroplasty (THA).

ABSTRACTTropical cyclones (TC) are the most intense interactions between the ocean and atmosphere. Synthetic aperture radar (SAR) can capture high resolution marine atmospheric and oceanic features. Therefore, high-resolution sea surface wind information can be retrieved from SAR imagery through various methods. In this study, a new modified local gradient (ILG-Mod) method is proposed for TC wind direction retrieval. It combines SAR image smoothing and local gradient computation in the spectral domain, and changes the way to obtain the main local gradient direction in the spatial domain. Nineteen TC images acquired from Sentinel-1 SAR have been collected for validation. The proposed method is compared with the local gradients (LG) method, modified local gradients (LG-Mod) method, and the improved local gradients (ILG) method. In addition, ERA5 (the fifth generation European Centre for Medium-Range Weather Forecasts) reanalysis data and CCMP (the Cross-Calibrated Multi-Platform) data are used for validation. Statistical results show that wind directions retrieved by the ILG-Mod method are highly consistent with the validated data. Comparisons with validated data reveal bias, root mean square error (RMSE), and correlation coefficient (r) values of 17.09°, 19.63° and 0.96, respectively. The proposed ILG-mod method is therefore suitable to retrieve wind direction of TCs.

Both RF channelization and self-interference cancellation are urgently required for modern in-band full-duplex systems. A photonics-assisted RF receiver for simultaneous wideband channelization and self-interference cancellation is proposed and demonstrated. The key is the implementation of dual optical frequency combs (OFCs) followed by microwave photonic image-reject mixers (IRMs) using a dual-output Hartley structure. The received RF signal and the reference signal are modulated to a pair of signal OFCs with orthogonal polarization states, respectively. The microwave photonic IRM is applied on each polarization state, respectively. For each local OFC comb line, two components will be sliced and downconverted from each copy of the optically-carried received RF signal. Meanwhile, another two components will be sliced and downconverted from the optically-carried reference signal. After combining the outputs of the IRMs, the components from the image signal and the self-interference signal will be eliminated simultaneously. Two channels of the signal of interest will be obtained for each local OFC comb line. Experimental results show that an RF signal with an instantaneous bandwidth of 6 GHz (from 7 to 13 GHz) is channelized into 6 channels, each of which has a 1–GHz bandwidth. The isolation of larger than 28 dB between the channels is realized, and the simultaneous self-interference cancellation and image-rejection are achieved.

The development of fuel cells is conducive to achieving carbon peaking and carbon neutrality goals, but the oxygen reduction reaction (ORR) of its cathodes is still kinetically slow due to multi-step proton-coupled electron transfer. Currently, metal-organic frameworks-derived single-atom catalysts (SACs) are emerging as efficient ORR catalysts. However, the outer layer structure of the active site is difficult to be determined, resulting in the uncertainty of the reaction mechanism. Here, the dual effect of the outer γN atom on its neighboring βC atom and the distant metal center was investigated. The results show that the doping of γN with different locations and amounts changes the electronic configuration of the replaced C sites in Fe-N4/C-γNxstructures, which in turn affects the charge distribution of the βC atom closer to the metal center. And these changes indirectly affect the electron distribution and spin of the metal center through long-range effects, significantly enhancing the ORR performance of the 4-e−paths. More importantly, the electronegativity-enhanced βC atoms are thus more likely to adsorb the OH moiety in alkaline electrolyte typically used in experiments. Thus, a γN-modulated βC-assisted pathway was predicted and a higher thermodynamic limiting potential of ORR than that of the common 4-e−pathway was achieved. Further wavefunction analysis, based on the potential limiting steps, provided an understanding on the electronic level and revealed the significance of precise design and characteristics of γN site in analogous SACs.

Electrochemical CO2reduction reaction (eCO2RR) is of great significance to energy and environmental engineering, while fundamental questions remain regarding its mechanisms. Herein, we formulate a fundamental understanding of the interplay between the applied potential (U) and kinetics of CO2activation in eCO2RR on Cu surfaces. We find that the nature of the CO2activation mechanism in eCO2RR varies with U, and it is the sequential electron–proton transfer (SEPT) mechanism dominant at the working Ubut switched to the concerted proton–electron transfer (CPET) mechanism at highly negative U. We then identify that the barrier of the electron-transfer step in the SEPT mechanism exhibits an inverted region as Udecreases, which originates from the rapidly rising Pauli repulsion in the physisorption of CO2with decreasing U. We further demonstrate catalyst designs that effectively suppress the adverse effect of Pauli repulsion. This fundamental understanding may be general for the electrochemical reduction reactions of closed-shell molecules.

Pt-based electrocatalysts suffer from high water dissociation barriers that limit their overall hydrogen evolution reaction (HER) activities in alkaline media. Here we predict the bimetallic four-atom single-cluster catalysts (SCCs) M1A3(M as later transition metal and A as early transition metal) with pyramidal structure supported on graphdiyne (GDY) for alkaline HER. Theoretical calculations show that the stable Pt1Ti3/GDY SCC delivers high alkaline HER activity via the Volmer-Heyrovsky mechanism. The excellent catalytic performance of Pt1Ti3/GDY SCC is attributed to both the low-valent Pt site that renders an optimal hydrogen adsorption free energy (ΔG*H), and the synergic effect of adjacent Ti sites that leads to a low water dissociation barrier. By screening alternative M1in M1Ti3/GDY for optimal ΔG*Hand facile water dissociation, we further identify the Ir1Ti3/GDY SCC to be a potentially high-performing alkaline HER electrocatalyst at low *OH coverage. Our work provides new insights and guidelines for the rational design of alkaline HER electrocatalysts.

For oxygen reduction reaction (ORR), replacing the conventional electrocatalysts based on platinum-group metals (PGMs) with alternatives based on non-noble metals is vital for the large-scale applications of green energy conversion and chemical synthesis. Here, we report the discovery of ORR catalysts based on the p-block-metal bismuth (Bi); the selectivity in ORR pathways could be controlled by tailoring the size of Bi. Specifically, the metallic Bi nanoparticles gave a high selectivity (>96%) for 2e−ORR, an ultra-high kinetic current density (3.8 mA·cm−2at 0.65 V), and an excellent stability; in contrast, the single-atomic-site Bi catalysts gave a good selectivity for 4e−ORR and a corresponding half-wave potential of 0.875 V, approaching that of the best-known Fe/NC catalysts. This work presents new high-performance ORR catalysts based on the p-block-metal Bi, in contrast to the intensively studied d-block transition metals, and thus would provide new perspectives for developing PGM-free ORR catalysts.

It is challenging to achieve high selectivity over Pt-metal-oxide catalysts widely used in many selective oxidation reactions because Pt is prone to over-oxidize substrates. Herein, our sound strategy for enhancing the selectivity is to saturate the under-coordinated single Pt atoms with Cl–ligands. In this system, the weak electronic metal–support interactions between Pt atoms and reduced TiO2cause electron extraction from Pt to Cl–ligands, resulting in strong Pt–Cl bonds. Therefore, the two-coordinate single Pt atoms adopt a four-coordinate configuration and thus inactivated, thereby inhibiting the over-oxidation of toluene over Pt sites. The selectivity for the primary C–H bond oxidation products of toluene was increased from 50.1 to 100%. Meanwhile, the abundant active Ti3+sites were stabilized in reduced TiO2by Pt atoms, leading to a rising yield of the primary C–H oxidation products of 249.8 mmol gcat–1. The reported strategy holds great promise for selective oxidation with enhanced selectivity.

Copper (Cu), a promising catalyst for electrochemical CO2reduction (CO2R) to multi-electron reduction products, suffers from an unavoidable and uncontrollable reconstruction process during the reaction, which not only may lead to catalyst deactivation but also brings great challenges to the exploration of the structure–performance relationship. Herein, we present an efficient strategy for stabilizing Cu with silica and synthesize reconstruction-resistant CuSiOxamorphous nanotube catalysts with abundant atomic Cu–O–Si interfacial sites. The strong interfacial interaction between Cu and silica makes the Cu–O–Si interfacial sites ultrastable in the CO2R reaction without any apparent reconstruction, thus exhibiting high CO2-to-CH4selectivity (72.5%) and stability (FECH4remains above 60% after 12 h of test). A remarkable CO2-to-CH4conversion rate of 0.22 μmol cm–2s–1was also achieved in a flow cell device. This work provides a very promising route for the design of highly active and stable Cu-based CO2R catalysts.

Next generation displays based on quantum dot light-emitting diodes (QLEDs) require robust patterning methods for quantum dot layers. However, existing patterning methods mostly yield QLEDs with performance far inferior to the state-of-the-art individual devices. Here, we report a light-triggered, carbocation-enabled ligand stripping (CELS) approach to pattern QLEDs with high efficiency and stability. During CELS, photogenerated carbocations from triphenylmethyl chlorides remove native ligands of quantum dots, thereby producing patterns at microscale precision. Chloride anions passivate surface defects and endow patterned quantum dots with preserved photoluminescent quantum yields. It works for both cadmium-based and heavy-metal-free quantum dots. CELS-patterned QLEDs show remarkable external quantum efficiencies (19.1%, 17.5%, 12.0% for red, green, blue, respectively) and a long operation lifetime (T95at 1000 nits up to 8700 h). Both are among the highest for patterned QLEDs and approach the records for nonpatterned devices, which makes CELS promising for building high-performance QLED displays and related integrated devices.

Combining enzymatic and single-metal-atom catalysis is a promising approach for green chemical synthesis. We herein report a one-pot chemoenzymatic cascade reaction to asymmetric synthesize (R)-1-(4-biphenyl) ethanol by using the highly active and selective enzyme-metal-single-atom hybrid as the catalyst. We demonstrate that the Pd single atoms anchored lipase (Pd1/CALB-P) can efficiently drive one-pot cascade reactions in aqueous solution at 30 °C to achieve facile synthesis of chiral biaryl alcohols, which are important pharmaceutical intermediates that traditionally require complex synthesis procedures. The rate of (R)-1-(4-biphenyl) ethanol formation catalyzed by Pd1/CALB-P is more than 30-fold higher than that of the combination of commercial palladium on carbon (Pd/C) and lipase-pluronic conjugate (CALB-P). The enzyme-metal-single-atom hybrid catalyst provides a promising strategy for effectively merging the enzymatic and single-atom catalysis.

Transformer plays a vital role in grids and its internal hot-spot temperature is a crucial monitoring parameter. In this paper, three groups of temperature rise tests with the load rate of 70%, 100%, and 125% were carried out on a 110 kV transformer with built-in distributed sensing fiber. Based on the distributed temperature sensing technology, the temperature distribution of winding was acquired. Accuracy of IEC, Susa, and Swift thermal models was compared. To eliminate the non-negligible error in cooling process, a modified-Susa model based on driving factor and steady-state temperature rise was proposed. Using this, the top oil temperature prediction error during operation and cooling is reduced. In addition, the real-time tracking of hot spots and analysis of overall temperature gradient of windings was implemented. The accuracy of different hot spot calculation models was also compared. Results show that the prediction accuracy of each model is satisfactory during the operation, but the error during the cooling process cannot be ignored. The refined temperature distribution of the winding was also obtained by the high-resolution Brillouin optical time-domain analysis sensing method. Periodic temperature fluctuations and local hot spots inside windings are perceived, which is confirmed to be closely related to winding geometry.

Amide nucleating agents have been reported to be effective in promoting poly(L‐lactide) (PLLA) crystallization, while few research efforts have focused on the structure–activity relationship of the nucleating agents which is one basic issue to develop new agents. In this work, three amide nucleating agents with different symmetry were designed and synthesized to investigate the effect of the symmetry of benzamide nucleating agents on PLLA crystallization. It is found that the C3 symmetric amide structure of N, N′, N″‐tricyclohexyl‐1,3,5‐benzenetriamine (BTCA) agent can form a highly oriented structure by a triple hydrogen bond of π–π bond and benzene ring and can act as efficient nucleation sites. In addition, the strong intramolecular hydrogen bond with PLLA would further promote the crystallization of PLLA. In general, it is the self‐assembled ability and intramolecular hydrogen bonding with PLLA affects the rate and degree of PLLA crystallinity, and we find the C3 symmetric amide structure exhibits the best performance.

It is challenging to improve the long-term stability of perovskite solar cells (PSCs) without sacrificing efficiency. The perovskite absorbers degrade from the film surface/interfaces, which follows entangled mechanisms that have not been fully revealed yet. Herein, we decouple and elaborate two distinctive pathways regarding film degradation based on FACsPbI3perovskites. Moreover, a dual interfacial modification strategy has been developed for improving the material’s intrinsic stability, thus leading to the film degrading in a more retardant pathway. The corresponding PSCs achieve a stable power output efficiency of 23.75%. More importantly, the unencapsulated PSCs devices retain over 93% of their initial PCE after the maximum power point (MPP) tracking under the continuous 1-sun illumination and show significantly improved stability after aged under the thermal treatment or stored in ambient atmosphere for over 1500 hours without obvious PCE decay. This work shows the importance of modulating the degradation pathway on stability improvement, and at the same time, proposes a strategy for designing perovskite-based optoelectronics with excellent performance and stability.

Synthetic Aperture Radar (SAR) has emerged as a pivotal technology in geosciences, offering unparalleled insights into Earth's surface. Indeed, its ability to provide high‐resolution, all‐weather, and day‐night imaging has revolutionized our understanding of various geophysical processes. Recent advancements in SAR technology, that is, developing new satellite missions, enhancing signal processing techniques, and integrating machine learning algorithms, have significantly broadened the scope and depth of geosciences. Therefore, it is essential to summarize SAR's comprehensive applications for geosciences, especially emphasizing recent advancements in SAR technologies and applications. Moreover, current SAR‐related review papers have primarily focused on SAR technology or SAR imaging and data processing techniques. Hence, a review that integrates SAR technology with geophysical features is needed to highlight the significance of SAR in addressing challenges in geosciences, as well as to explore SAR's potential in solving complex geoscience problems. Spurred by these requirements, this review comprehensively and in‐depth reviews SAR applications for geosciences, broadly including various aspects in air‐sea dynamics, oceanography, geography, disaster and hazard monitoring, climate change, and geosciences data fusion. For each applied field, the scientific advancements produced because of SAR are demonstrated by combining the SAR techniques with characteristics of geophysical phenomena and processes. Further outlooks are also explored, such as integrating SAR data with other geophysical data and conducting interdisciplinary research to offer comprehensive insights into geosciences. With the support of deep learning, this synergy will enhance the capability to model, simulate, and forecast geophysical phenomena with greater accuracy and reliability. Synthetic aperture radar (SAR) uses microwaves to remotely see the Earth's surface under all weather conditions, day and night. SAR has been providing high‐resolution images for many decades and they have been applied to many fields in geosciences. Several SAR sensors have been launched in recent years, significantly increasing the SAR data volume and leading to great developments in SAR technology, thereby improving our understanding of geophysical phenomena and processes. This work comprehensively overviews the application of SAR in geosciences, including oceanography, geography, geodesy, climatology, seismology, meteorology, and environmental science. Moreover, this review paper highlights the significance of SAR in various aspects of geosciences, summarizes recent advancements in SAR technology, and demonstrates unique insights and important contributions of SAR in understanding and solving geophysical questions. Future directions and outlooks include integrating SAR with other geophysical data and interdisciplinary applications for complex questions. This review serves as an up‐to‐date guide to the cutting‐edge uses of SAR technology in comprehensive geophysical studies. It is aimed at researchers and practitioners in geosciences, as well as policymakers and stakeholders interested in leveraging SAR for geosciences. Synthetic Aperture Radar (SAR) for geosciences is comprehensively reviewed broadly including oceanography, geography, hazards, and climate changeScientific advances contributed by SAR techniques for each topic are overviewed in‐depth with recent developments and frontiers highlightedData, techniques, and scientific insights of SAR are summarized and prospected, highlighting the role of machine learning Synthetic Aperture Radar (SAR) for geosciences is comprehensively reviewed broadly including oceanography, geography, hazards, and climate change Scientific advances contributed by SAR techniques for each topic are overviewed in‐depth with recent developments and frontiers highlighted Data, techniques, and scientific insights of SAR are summarized and prospected, highlighting the role of machine learning

In this study, we annotated 49 odorant-binding proteins (OBPs) in Papilio xuthus, with four novel genes and seven improved sequences. Expression profiles identified numerous OBPs in antennae or reproductive tissues. Using two antenna-enriched general OBPs (PxutGOBP1 and PxutGOBP2) as targets, we screened three key compounds by a reverse chemical ecology strategy. Of these, an oviposition stimulant vicenin-2 could strongly interact with PxutGOBP1, representing a dissociation constant (Ki) value of 10.34 ± 0.07 μM. Molecular simulations and site-directed mutagenesis revealed the importance of His66, Thr73, and Phe118 between PxutGOBP1 and vicenin-2 interactions. Two other compounds, an ordinary floral scent β-ionone and a widely used insecticide chlorpyrifos, exhibited high affinities to PxutGOBPs (Ki< 13 μM). Furthermore, two mutations His66Ala and Thr73Ala of PxutGOBP1 significantly reduced the binding to chlorpyrifos. Our study provides insights into the putative roles of PxutGOBPs in odorant perception and identifies key binding sites of PxutGOBP1 to vicenin-2 and chlorpyrifos.

Roles of internal climate variabilities regulating global and ocean temperature changes is a hot but complex issue of scientific concern, influencing the comprehensive policy-making in response to global and regional warming. In this study, the time series of monthly global and ocean mean surface temperature (GST and OST, respectively) since 1866 is successfully reconstructed via natural and anthropogenic forcing factors and internal climate variability by using a Multi-Layer Perceptron (MLP) neural network technique. The MLP demonstrates prominent monthly GST and OST reconstruction skills on both interannual and annual time scales. Most of the warming in GST and OST since 1866 is found to be attributable to anthropogenic forcing, while the multidecadal and interannual GST and OST variations are considerably dominated by Atlantic Multidecadal Oscillation (AMO). Internal climate variabilities like Interdecadal Pacific Oscillation (IPO) can amplify the GST and OST changes and explain the global warming slowdown since 1998. Southern Oscillation Index (SOI) performs a similar role as IPO but to a lesser extent. Changes in OST caused by solar forcing are more considerable than those in GST. Moreover, the ‘biased warmth’ during the Second World War is successfully reconstructed in MLP. AMO and IPO can explain most annual and even sub-annual temperature variations during this period, offering an explanation for the existence of this abnormal warm period other than that it was entirely caused by instrumental errors. The generally high accuracy of reconstructions on interannual and annual time scales can enhance the ability to monitor the prompt feedback of specific external radiative forcings and internal variabilities to changes in climate.

In this paper, we obtain a new inequality between the inverse hyperbolic tangent and inverse sine functions, which is a conjecture of Chen-Males̆ević [(Chen in Rev Real Acad Cienc. Exactas Fis. Nat. Ser. A-Mat 114:105, 2020) conjecture 2.1]spsCM.

It is a challenge that extracting rubidium directly from the salt lakes by electrochemical method, because of the low content of rubidium ions and other complex co-existing ions in the salt lakes. Copper hexacyanoferrate (CuHCF) with good electrochemical performance in desalination and aqueous batteries, may be used as the electrode material of electrochemical extracting rubidium. In this paper, CuHCF is synthesized by one-step precipitation method by adding copper ions in K3Fe(CN)6solution. Cyclic voltammetry is studied with the CuHCF electrode sheets in alkali metal ions and simulated brine electrolytes. The alkali metal ions insertion and de-insertion in the electrodes are mainly controlled by diffusion process with cyclic stability. The diffusion rate of K+is the largest, Li+the smallest. The insertion and de-insertion process are reversible in Na+, K+and Rb+ionic solutions. The cyclic voltammetry current of the CuHCF electrode sheet in Na+ionic solution shows two pairs of redox peaks. The cyclic voltammetry currents of CuHCF electrode sheet in simulated brine mainly show is the peak current of that in Na+and K+ionic solutions.

Long non-coding RNAs (LncRNAs) have played very important roles in the malignancy behaviors of hepatocellular carcinoma (HCC). LncRNA LOC554202 (LOC554202) was a newly identified tumor-related lncRNA. However, its expression and function in HCC remained unknown. In this study, we firstly reported that LOC554202 expression was distinctly upregulated in HCC specimens and cell lines. Clinical assays indicated that increased LOC554202 expression had a diagnostic value for HCC patients and was positively associated with advanced stages and poor clinical prognosis. Additionally, forkhead box O3(FOXO3) could bind directly to the LOC554202 promoter region and activate its transcription. Functionally, we observed that knockdown of LOC554202 suppressed the proliferation, migration, invasion, and epithelial–mesenchymal transition (EMT) progress of HCC cells, and promoted apoptosis. Mechanistically, LOC554202 competitively bound to miR-485-5p and prevented the suppressive effects of miR-485-5p on its target gene basigin (BSG), which finally led to HCC metastasis, EMT, and docetaxel chemoresistance. Our data demonstrated that FOXO3-induced LOC554202 contributed to HCC progression by upregulating BSG via competitively binding to miR-485-5p, which suggested that the regulation of the FOXO3/LOC554202/miR-485-5p/BSG axis may have beneficial effects in the treatment of HCC.