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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]

The compatibility between activity and stability of high-entropy electrocatalysts for the oxygen evolution reaction (OER) is crucial for their widespread application in alkaline water electrolysis. While activating lattice oxygen with high-valence metals may boost the catalytic activity, it also tends to increase oxygen vacancies and cation dissolution, which exacerbates the conflict phenomenon that has been seen. Low ionic electronegativity and inert Cu-incorporating nanoporous MnFeCoNiOOH (np-MnFeCoNiOOH) was designed in this study to serve as an ultra-long stable alkaline OER electrocatalyst without the need for noble metals. Np-MnFeCoNiCuOOH displayed a low overpotential (176 mV) and superb stability over the 5500 h durability test at 500 mA cm−2. The OER energy barrier was lowered by the enhanced adsorption of oxygen intermediates to active Ni sites. Moreover, low ionic electronegativity components limit the electrocatalytic metal active center's over-oxidation, which lowers the lattice oxygen activity and continuously reconstructs the electrode surface during the OER process, prolonging the catalyst's lifespan. [ABSTRACT FROM AUTHOR]

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]

Emerging high entropy compounds (HECs) have attracted huge attention in electrochemical energy‐related applications. The features of ultrafine size and carbon incorporation show great potential to boost the ion‐storage kinetics of HECs. However, they are rarely reported because high‐temperature calcination tends to result in larger crystallites, phase separation, and carbon reduction. Herein, using the NaCl self‐assembly template method, by introducing a high‐pressure field in the calcination process, the atom diffusion and phase separation are inhibited for the general formation of HECs, and the HEC aggregation is inhibited for obtaining ultrafine size. The general preparation of ultrafine‐sized (<10 nm) HECs (nitrides, oxides, sulfides, and phosphates) anchored on porous carbon composites is realized. They are demonstrated by combining advanced characterization technologies with theoretical computations. Ultrafine‐sized high entropy sulfides‐MnFeCoCuSnMo/porous carbon (HES‐MnFeCoCuSnMo/PC) as representative anodes exhibit excellent sodium‐ion storage kinetics and capacities (a high rating capacity of 278 mAh g−1 at 10 A g−1 for full cell and a high cycling capacity of 281 mAh g−1 at 20 A g−1 after 6000 cycles for half cell) due to the combining advantages of high entropy effect, ultrafine size, and PC incorporation. Our work provides a new opportunity for designing and fabricating ultrafine‐sized HECs. [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]

Powdery hexagonal boron nitride (h‐BN), as an important material for electrochemical energy storage, has been typically synthesized in bulk and one/two‐dimensional (1/2D) nanostructured morphologies. However, until now, no method has been developed to synthesize powdery three‐dimensional (3D) h‐BN. This work introduces a novel NaCl‐glucose‐assisted strategy to synthesize micron‐sized 3D h‐BN with a honeycomb‐like structure and its proposed formation mechanism. We propose that NaCl acts as the template of 3D structure and promotes the nitridation reaction by adsorbing NH3. Glucose facilitates the homogeneous coating of boric acid onto the NaCl surface via functionalizing the NaCl surface. During the nitridation reaction, boron oxides (BO4 and BO3) form from a dehydration reaction of boric acid, which is then reduced to O2‐B‐N and O‐B‐N2 intermediates before finally being reduced to BN3 by NH3. When incorporated into polyethylene oxide‐based electrolytes for Li metal batteries, 5 wt % of 3D h‐BN significantly enhances ionic conductivity and mechanical strength. Consequently, this composite electrolyte demonstrates superior electrochemical stability. It delivers 300 h of stable cycles in the Li//Li cell at 0.1 mA cm−2 and retains 89 % of discharge capacity (138.9 mAh g−1) after 100 cycles at 1 C in the LFP//Li full cell. [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]

To investigate the potential of the gut microbiome as a biomarker for predicting the early recurrence of HBV‐related hepatocellular carcinoma (HCC), we enrolled 124 patients diagnosed with HBV‐associated HCC and 82 HBV‐related hepatitis, and 86 healthy volunteers in our study, collecting 292 stool samples for 16S rRNA sequencing and 35 tumor tissue samples for targeted metabolomics. We performed an integrated bioinformatics analysis of gut microbiome and tissue metabolome data to explore the gut microbial–liver metabolite axis associated with the early recurrence of HCC. We constructed a predictive model based on the gut microbiota and validated its efficacy in the temporal validation cohort. Dialister, Veillonella, the Eubacterium coprostanoligenes group, and Lactobacillus genera, as well as the Streptococcus pneumoniae and Bifidobacterium faecale species, were associated with an early recurrence of HCC. We also found that 23 metabolites, including acetic acid, glutamate, and arachidonic acid, were associated with the early recurrence of HCC. A comprehensive analysis of the gut microbiome and tissue metabolome revealed that the entry of gut microbe‐derived acetic acid into the liver to supply energy for tumor growth and proliferation may be a potential mechanism for the recurrence of HCC mediated by gut microbe. We constructed a nomogram to predict early recurrence by combining differential microbial species and clinical indicators, achieving an AUC of 78.0%. Our study suggested that gut microbes may serve as effective biomarkers for predicting early recurrence of HCC, and the gut microbial–tumor metabolite axis may explain the potential mechanism by which gut microbes promote the early recurrence of HCC. [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]

By collecting and sorting the energy demand data of developing and developed countries, this paper makes a comprehensive analysis of their energy demand, including the change of energy demand and the change trend of energy load in various sectors. The survey scope of the article includes the overall change trend of energy supply, natural gas, oil, electricity, coal, renewable energy (such as wind energy, solar energy, geothermal energy, tidal energy, etc.), and the data change of global carbon dioxide emission. Besides, this paper selects a variety of energy sources for comprehensive analysis to analyze the existing change trend in chronological order. The analysis methods include data statistics of primary energy production and consumption, energy intensity analysis of gross domestic product (GDP), production, and demand balance of oil, natural gas, and coal, and study the trade balance between different types of energy in different countries and regions. The regions examined in this review include the organization for economic cooperation and development (OECD); the group of seven (G7); Brazil, Russia, India, China and South Africa (BRICs); the European Union; Europe; North America; the Commonwealth of Independent States (CIS); Asia; Latin America; the Pacific Ocean; the Middle East and Africa. By studying these data, we can make a better summary of the current energy use, so as to conveniently grasp the context of energy development and have a general understanding of the current energy structure. Therefore, individuals and policymakers in the fields of energy trade can think more deeply about the future situation and draw conclusions. [ABSTRACT FROM AUTHOR]

For conversion type Li/Na-ion batteries anode materials, constructing the nanocomposite with enough interface interaction is of great importance for enhancing the electrochemical reaction kinetics and keeping the integrity of the electrode structure even after a long-term cycle at a high current density. In addition, the highly reversible conversion of discharge product also plays a vital role in prolonging the cycling life of conversion type anode materials for Li/Na-ion batteries. In this work, a composite of isolated ultrafine Fe 3 N nanocrystals (<15 nm) strongly coupled with N doped 3D porous carbon networks (N-3DPCN) were successfully fabricated via salt template-assisted calcination strategy. Both experimental results and theoretical simulations have validated that the powerful interface interaction between ultrafine Fe 3 N nanocrystals (<15 nm) and N-3DPCN is bound up with the N-doping in 3DPCN, and the atomically dispersed metal Fe in the N-3DPCN (SAFe-N@3DPCN) formed during the discharge process of Fe 3 N can catalyze the reversible conversion of discharge product Na 3 N. Benefited from these advantages, the as-constructed Fe 3 N@N-3DPCN composite delivers excellent Li/Na ion storage performance. This work shed light on the composite design of conversion-type materials and carbon materials for applications in energy storage fields and beyond. [Display omitted] • An effective synthesis strategy to improve the complex preparation process of Fe x N/C is proposed. • The interface coupling effect could enhance the interface interaction between ultrafine Fe 3 N nanocrystals and N-3DPCN. • The repetitive charge-discharge of Fe 3 N@N-3DPCN electrode could induce the in-situ formation of atomically dispersed Fe. [ABSTRACT FROM AUTHOR]

Recent years have witnessed outstanding success in supervised domain adaptive person re-identification (ReID). However, the model often suffers serious performance drops when transferring to another domain in real-world applications. To address the domain gap situations, many unsupervised domain adaptive (UDA) methods have been proposed to adapt the model trained on the source domain to a target domain. Such methods are typically based on clustering algorithms to generate pseudo labels. Noisy labels, which often exist due to the instability of clustering algorithms, will substantially affect the performance of UDA methods. In this study, we focused on intermediate domains that can be regarded as a bridge that connects source and target domains. We added a domainness factor in the loss function of SPGAN that can decide the style of the image generated by the GAN model. We obtained a series of intermediate domains by changing the value of the domainness factor. Pseudo labels are more reliable because intermediate domains are closer to the source domain compared with the target domain. We then fine-tuned the model pre-trained with source data on these intermediate domains. The fine-tuning process was conducted repeatedly because intermediate domains are composed of more than one dataset. Finally, the model fine-tuned on intermediate domains was adapted to the target domain. The model can easily adapt to changes in image style as we gradually transfer the model to the target domain along the bridge consisting of several intermediate domains. To the best of our knowledge, we are the first to apply intermediate domains to UDA problems. We evaluated our method on Market1501, DukeMTMC-reID and MSMT17 datasets. Experimental results proved that our method brings a significant improvement and achieves a state-of-the-art performance. [ABSTRACT FROM AUTHOR]