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The Li–CO2 battery often suffers from high overpotential and limited capacity due to the challenges associated with the adsorption of Li+ and CO2 and the decomposition of Li2CO3. Herein, hexagonal rich-stepped NiSe crystals are in situ achieved on a three-dimensional (3D) free-standing porous Ni skeleton with double continuous channel architecture (namely p-Ni@NiSe-50) through an in situ selenization process. Structural characterization and theoretical calculation are applied to demonstrate the synergistic effects of marco/microstructural design and electronic band structure regulation. As a result, the adsorption/desorption of Li+ and CO2 and the formation/decomposition of Li2CO3 are effectively promoted, simultaneously, enabling an enhanced capacity and reversibility of p-Ni@NiSe-50 as the cathode of Li–CO2 battery. An ultra-low overpotential of 0.46 V and a remarkably high energy efficiency of 83.8% (20 μA cm−2) are achieved, along with a high full discharge specific capacity of 8844 μAh cm−2. Excellent long-term cycling stability (cycles up to 1000 h at a voltage gap of 1.14 V) of p-Ni@NiSe-50 is also obtained. The results of this work would provide a new insight and strategy to develop high-performance alkali metal-air batteries. Multi-scale synergistic effect of combining the cathode macroscopic structure design and the microscopic band structure design of NiSe was demonstrated to promote the adsorption of Li+ and CO2, as well as the decomposition of Li2CO3, so as to achieve ultra-low voltage gap and high capacity of Li-CO2 batteries. [ABSTRACT FROM AUTHOR]

Conversion‐type electrode materials have gained massive research attention in sodium‐ion batteries (SIBs), but their limited reversibility hampers practical use. Herein, we report a bifunctional nanoreactor to boost highly reversible sodium‐ion storage, wherein a record‐high reversible degree of 85.65 % is achieved for MoS2 anodes. Composed of nitrogen‐doped carbon‐supported single atom Mn (NC‐SAMn), this bifunctional nanoreactor concurrently confines active materials spatially and catalyzes reaction kinetics. In situ/ex situ characterizations including spectroscopy, microscopy, and electrochemistry, combined with theoretical simulations containing density functional theory and molecular dynamics, confirm that the NC‐SAMn nanoreactors facilitate the electron/ion transfer, promote the distribution and interconnection of discharging products (Na2S/Mo), and reduce the Na2S decomposition barrier. As a result, the nanoreactor‐promoted MoS2 anodes exhibit ultra‐stable cycling with a capacity retention of 99.86 % after 200 cycles in the full cell. This work demonstrates the superiority of bifunctional nanoreactors with two‐dimensional confined and catalytic effects, providing a feasible approach to improve the reversibility for a wide range of conversion‐type electrode materials, thereby enhancing the application potential for long‐cycled SIBs. [ABSTRACT FROM AUTHOR]

Motivation The quality scores data (QSD) account for 70% in compressed FastQ files obtained from the short and long reads sequencing technologies. Designing effective compressors for QSD that counterbalance compression ratio, time cost, and memory consumption is essential in scenarios such as large-scale genomics data sharing and long-term data backup. This study presents a novel parallel lossless QSD-dedicated compression algorithm named PQSDC, which fulfills the above requirements well. PQSDC is based on two core components: a parallel sequences-partition model designed to reduce peak memory consumption and time cost during compression and decompression processes, as well as a parallel four-level run-length prediction mapping model to enhance compression ratio. Besides, the PQSDC algorithm is also designed to be highly concurrent using multicore CPU clusters. Results We evaluate PQSDC and four state-of-the-art compression algorithms on 27 real-world datasets, including 61.857 billion QSD characters and 632.908 million QSD sequences. (1) For short reads, compared to baselines, the maximum improvement of PQSDC reaches 7.06% in average compression ratio, and 8.01% in weighted average compression ratio. During compression and decompression, the maximum total time savings of PQSDC are 79.96% and 84.56%, respectively; the maximum average memory savings are 68.34% and 77.63%, respectively. (2) For long reads, the maximum improvement of PQSDC reaches 12.51% and 13.42% in average and weighted average compression ratio, respectively. The maximum total time savings during compression and decompression are 53.51% and 72.53%, respectively; the maximum average memory savings are 19.44% and 17.42%, respectively. (3) Furthermore, PQSDC ranks second in compression robustness among the tested algorithms, indicating that it is less affected by the probability distribution of the QSD collections. Overall, our work provides a promising solution for QSD parallel compression, which balances storage cost, time consumption, and memory occupation primely. Availability and implementation The proposed PQSDC compressor can be downloaded from https://github.com/fahaihi/PQSDC. [ABSTRACT FROM AUTHOR]

Traffic flow prediction is essential for smart city management and planning, aiding in optimizing traffic scheduling and improving overall traffic conditions. However, due to the correlation and heterogeneity of traffic data, effectively integrating the captured temporal and spatial features remains a significant challenge. This paper proposes a model spatial–temporal fusion gated transformer network (STFGTN), which is based on an attention mechanism that integrates temporal and spatial features. This paper proposes an attention mechanism-based model to address these issues and model complex spatial–temporal dependencies in road networks. The self-attention mechanism enables the model to achieve long-term dependency modeling and global representation of time series data. Regarding temporal features, we incorporate a time embedding layer and a time transformer to learn temporal dependencies. This capability contributes to a more comprehensive and accurate understanding of spatial–temporal dynamic patterns throughout the entire time series. As for spatial features, we utilize DGCN and spatial transformers to capture both global and local spatial dependencies, respectively. Additionally, we propose two fusion gate mechanisms to effectively accommodate to the complex correlation and heterogeneity of spatial–temporal information, resulting in a more accurate reflection of the actual traffic flow. Our experiments on three real-world datasets illustrate the superior performance of our approach. [ABSTRACT FROM AUTHOR]

The considerable influence of crude oil prices on the international economy has motivated numerous scholars to develop various prediction models. Two difficulties are encountered in forecasting. One is that the time series of crude oil prices show massive jumps in high frequency. The other is that the time series of crude oil prices are characterised by nonlinearity, structural breaks and highly volatile states. Therefore, we propose a hybrid model that incorporates the principle of decomposition–integration. Its overall process can be divided into the steps of decomposition and integration. In decomposition step, modified ensemble empirical mode decomposition is used to decompose the data on crude oil prices. Next, K‐means and principal component analysis are utilised to extract the optimal intrinsic mode function (IMF) from diverse frequencies amongst decomposed IMFs. Following decomposition, different forecasting methods are used to match IMFs with various frequencies for prediction. In accordance with structural breaks and the highly volatile states of high‐frequency series, hidden Markov regression is applied to describe predicted values in a probabilistic manner, providing objective interpretation and forecasting. A neural network is taken to predict intermediate‐frequency IMFs by using a fully connected network. Owing to the low frequency and tendency of the residual sequence to be more stable than other frequency sequences, the autoregressive integrated moving average model is applied as the forecasting method for prediction. Finally, all individual predictions for the eventual results are ensembled as final predictions. Results confirm that the proposed model is better than several normal hybrid and single models in terms of prediction accuracy and Diebold–Mariano test results. All results confirm that the newly proposed hybrid model is a promising tool for the analysis and forecasting of crude oil prices. [ABSTRACT FROM AUTHOR]

Changing the composition is an important way to regulate the electrocatalytic performance of the oxygen evolution reaction (OER) for metallic compounds. Clarifying the synergistic mechanism among different compositions is a key scientific problem to be solved urgently. Here, based on first-principles calculations, a Ni–O–Fe multisite dynamic synergistic reaction mechanism (MDSM) for the OER of Fe-doped NiOOH (NiFeOOH) has been discovered. Based on the MDSM, Fe/O/Ni are triggered as the active sites in turn, resulting in an overpotential of 0.33 V. The factors affecting the deprotonation, O–O coupling, and O2 desorption during the OER process are analyzed. The electron channels related to the magnetic states among Fe–O–Ni is revealed, which results in the decoupling between OER sites and the oxidation reaction sites. O–O coupling and O2 desorption are affected by ferromagnetic coupling and the instability of the lattice O during the OER process, respectively. The results give a comprehensive understanding of the active sites in NiFeOOH and provide a new perspective on the synergistic effects among different compositions in metal compounds during the OER process. [ABSTRACT FROM AUTHOR]

Since the sluggish kinetics of the oxygen evolution process (OER) at the anode of water splitting remains to be a crucial bottleneck for hydrogen production, it is imperative to develop low-cost OER electrocatalysts with high efficiency. Here, (FeSe2 + CoSe2) nanoparticles loaded on nitrogen-doped three-dimensional porous carbon nanosheets (referred to as (FeSe2 + CoSe2)/N-3DCN) were prepared by freeze-drying, heat treatment and selenization. The favorable affinity and large surface area of N-3DCN enabled the synthesis of the ultrafine (FeSe2 + CoSe2) nanoparticles with a diameter of 10 nm, which were highly dispersed on carbon nanosheets and provided a significant number of active sites. The density functional theory (DFT) calculation demonstrated that the free energy of oxygen-containing intermediates during OER was optimized by coupling FeSe2 with CoSe2. The overpotential of (FeSe2 + CoSe2)/N-3DCN is 312 mV at 10 mA cm–2, which is superior to those of (FeSe2 + CoSe2) (390 mV), CoSe2/N-3DCN (377 mV), RuO2 (320 mV), and FeSe2/N-3DCN (479 mV). Additionally, after the 80 h stability test, the overpotential of (FeSe2 + CoSe2)/N-3DCN can still be maintained at 316 mV. [ABSTRACT FROM AUTHOR]