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Electrospinning is a versatile technique to synthesize one-dimensional nanomaterials for energy-storage applications. This feature article summarizes the recent developments in carbon nanofiber-based electrodes for rechargeable potassium batteries.

Multiple lines of existing evidence suggest that increasing CO

In recent years, China has a large amount of infrastructure projects and increasingly serious environmental pollution. The impact of CO2 and other greenhouse gases on the climate is becoming more and more significant. With the proposal of the national "double carbon" goal and green development strategy, exploring a new model of carbon sequestration has become a research hotspot. This paper comprehensively expounds the technology of carbon dioxide curing concrete, and introduces the mechanism of the technology, the influencing factors in the curing process, and the influence of carbon dioxide curing on concrete. It is found that the water binder ratio of concrete, raw materials such as aggregates, and the concentration, pressure, pre-curing and other external conditions of carbon dioxide have different degrees of influence on the curing of concrete; In addition, it is also found that CO2 curing improves the mechanical properties, microstructure and durability of concrete. Finally, the research on CO2 curing concrete technology is summarized and the future development is prospected.

The explosive growth of road vehicles especially the private cars has brought unprecedented pressure to a series of problems in urban transportation systems, such as traffic congestion and environmental pollution. Private cars trajectory data and perceiving their information provide a promising solution to these problems. However, the collection of large-scale trajectory data for private cars with high accuracy and reliability is still delicate tasks in urban environments. In this paper, we propose a low-cost and user-friendly implementation method for achieving large-scale private cars trajectory data acquisition via designing lightweight GPS module and On Board Diagnostics (OBD) reader. To ensure reliable trajectory data acquisition via GPS/OBD integration, we propose an ensemble learning based Gauss Process Regression (GPR) method so as to cope with the non-linearity, non-stationarity and incremental training problems during trajectory collection. We design a classification-type loss (CTL) function and build a regression to classification (R2C) method with Learn++ for realizing ensemble learning. The proposed approach implements incremental learning when new trajectory data arrives and is able to resolve the concept drifting problem. Experiments in real-world urban environment have demonstrated the effectiveness and reliability of the proposed method, it achieves better trajectory prediction performance than the comparative methods under various road conditions in GPS-denied areas.

Determining the temperature dependence of wetland CH4 and CO2 emissions is critical for predicting the impacts of climate change on greenhouse gas (GHGs) emissions in wetland ecosystems. However, the spatial variation for temperature dependence of wetland CH4 and CO2 emissions is poorly understood, especially at the global scale. Here, we investigate the temperature dependencies of wetland CH4 and CO2 emissions across large-scale climatic gradients using 56,271 daily paired observations of ecosystem-level CH4 and CO2 emissions in 45 widely distributed wetlands from the FLUXNET-CH4 database. The temperature dependencies of CH4 and CO2 emissions show contrasting spatial patterns across globally geographic climate gradients. Specifically, the temperature dependence of CH4 emissions increased with increasing mean annual temperature (MAT), but the opposite was true for that of CO2 emissions. The ratio of CH4 to CO2 emissions was positively dependent on temperature when only MAT and mean annual precipitation were greater than 4.7 °C and 483 mm, respectively. Our results imply that the relative contribution of CH4 to total GHG emissions increases with ambient temperature increases in a warmer and wetter climate region and could act as a positive feedback mechanism in the future.

Purpose To evaluate the Metabolic syndrome(MetS) in patients with unilateral and bilateral staghorn calculus(SC) and compare the impact on the outcome of percutaneous nephrolithotomy(PCNL).Methods The patients who underwent PCNL for the treatment of SC were retrospectively reviewed between 2019–2022. SC were divided into unilateral and bilateral. The variables of the patients were calculated to evaluate the MetS difference between unilateral and bilateral SC and the impact on the outcome of PCNL.Results 1778 patients were received PCNL between 2019 and 2022, After screening computed tomography, A total of 379 patients with staghorn calculus, patients without follow-up and incomplete data were excluded, finaly 310 patients with SC were included. 84 with bilateral SC and 226 with unilateral. More higher body mass index, complete staghorn and metabolic syndrome were found in the patients with bilateral SC. Higher body mass index, hypertension, diabetes mellitus, hyperlipidemia, MetS were 62.58%, 44.84%, 21.94%, 60.65% and 27.42% in patients. 85 patients with MetS(+) resulted in higher rate of overall complication(Number(N)(%), 29(38.10) VS 54(23.89), p = 0.013) and comparable stone free rate with 225 MetS(-). Multivariable analysis confirmed that hyperlipidemia(p = 0.044, odds ratio[OR] = 1.991, 95% confidence interval[CI] 1.020–3.888) and MetS ( p = 0.005, OR = 2.427, 95%CI 1.316–4.477) were independent risk factors for overall complication.Conclusions MetS are strongly correlated with the formation of bilateral SC and is the core predictor in assessing overall complication for PCNL.

Purpose To compare the outcomes of standard-percutaneous nephrolithotomy combined ultrasonic lithotripsy system(s-PCNL+ULS) and mini-percutaneous nephrolithotomy combined high-power holmium laser(m-PCNL+hHL) for the treatment of staghorn calculus. Methods The data of patients who underwent PCNL for the treatment of staghorn calculus were retrospectively reviewed between 2019 and 2022 from the second hospital of Tianjin medical university. The nephrostomy tracts were dilatated to 24 F in standard PCNL and 16-18 F in mini PCNL, standard PCNL combined ultrasonic lithotripsy system and mini PCNL combined high-power holmium laser. The preoperative, intraoperative, and postoperative variables of the patients were calculated to evaluate the efficacy and reliability of the two groups. Results 1778 patients were received PCNL between 2019 and 2022, After screening computed tomography, finaly 310 patients with staghorn calculus were included. 98 underwent s-PCNL+ULS and 212 underwent m-PCNL+hHL. The total operative time (min) ,haemoglobin deficit (ΔHGB (g/L)) ,the postoperative hospitalization days for s-PCNL+ULS and m-PCNL+hHL groups were 76.07±26.68 vs 103.93±24.84(P＜0.001), 9.30±9.26 VS 7.73±7.50(P=0.017); 7.32±5.14 VS 4.71±2.42(P＜0.001). The incidence complications for s-PCNL+ULS and m-PCNL+hHL groups were 28.57% vs 16.04%(P = 0.064), s-PCNL+ULS group had more severity complications (9.18% vs 2.83%, P = 0.022) . The firist and second stone-free rates of PCNL were 56.12% VS 57.08%(P=0.902) and 92.86% VS 86.32%(P=0.126) for the m-PCNL+hHL and s-PCNL+ULS groups. Conclusion The efficacy of m-PCNL+hHL was comparable to s-PCNL+ULS in the treatment of staghorn calculus. The advantages of m-PCNL+hHL included the lower haemoglobin deficit, shorter hospital stay and lesser severity complications.

The detection of defects on the surface is of great importance for both the production and the application of strip steel. In order to detect the defects accurately, an improved YOLOv7-based model for detecting strip steel surface defects is developed. To enhances the ability of the model to extract features and identify small features, the ConvNeXt module is introduced to the backbone network structure, and the attention mechanism is embedded in the pooling module. To reduce the size and improves the inference speed of the model, an improved C3 module was used to replace the ELAN module in the head. The experimental results show that, compared with the original models, the mAP of the proposed model reached 82.9% and improved by 6.6%. The proposed model can satisfy the need for accurate detection and identification of strip steel surface defects.

Belowground litter decomposition represents an important source of the limiting nutrients nitrogen (N) and phosphorus (P) to forest soils. However, the rates and drivers of nutrient immobilization and release from root litter remain poorly understood. To address this gap, we conducted a seven-year field decomposition experiment using roots from three species, across five diameter classes (up to 20 mm) in a temperate forest. All roots released P to the surrounding soil within the first year of decomposition but immobilized N for much longer, particularly coarse roots. Long-term N release was mainly related to initial nutrient stoichiometry of the substrate, whereas P release was better predicted by substrate carbon (C) quality and decomposition rate. Initial root nutrients well predicted the difference between long-term N versus P release. Our results highlight the fact that N and P dynamics should be considered separately when modeling nutrient release during root decomposition.

With the development of the Internet of energy, more and more participants share data by different types of edge devices. However, such multi-source heterogenous data typically contain low-quality data, e.g., missing values, which may result in potential risks. Besides, resource-constrained devices incur large latency in edge computing networks. To alleviate such latency, distributed task offloading schemes are designed to share the computation burden between edge nodes and nearby servers. However, there are three main drawbacks of such schemes. First, low-quality data are not carefully evaluated by constraints under scenarios, which may result in slow convergence in distributed computation. Second, multi-source data including sensitive information are computed and shared among edge nodes without privacy protection. Third, distributed tasks on low-quality data may result in low-quality results even with an optimal offloading scheme. To address the problems above, a task offloading framework for edge computing based on consortium blockchain and distributed reinforcement learning is proposed in this paper, which can provide high-quality task offloading policies with data privacy protected. This framework consists of three key components: data quality evaluation (DQ) with multiple data quality dimensions, data repairing (DR) with a repairing algorithm based on a novel repairing consensus mechanism and distributed reinforcement learning for task arrangement (DELTA) with a distributed reinforcement learning algorithm based on a novel low-quality data distributing strategy. Numeric results are presented to illustrate the effectiveness and efficiency of the proposed task offloading framework for edge computing on low-quality data in the IoE.

Li metal anodes (LMAs) has attracted extensive research interest because of its extremely high theoretical capacity (3860 mAh/g) at low redox potential (−3.04 V vs. standard hydrogen electrode). However, the extremely high chemical reactivity and the intrinsic “hostless” nature of LMAs bring about serious dendritic growth and dramatic volume change during the plating/strapping process, thus resulting in poor Coulombic efficiency, short lifespan, and severe safety concerns. Of various strategies, the construction of three-dimensional carbonaceous scaffolds for LMAs can substantially reduce the local current density, inhibit Li dendrite growth, and accommodate volume variation. Electrospinning is a simple yet effective strategy to fabricate carbon nanofibers (CNFs), which have been regarded as promising skeletons for LMAs, owing to their large surface areas, good electrical conductivity, and high porosity. In this Mini Review, we briefly introduce the fabrication of CNFs using electrospinning and the modification of CNFs. We highlight the recent advances in electrospun CNF skeletons for LMAs, including pure CNF and CNF-based composite scaffolds. Finally, we discuss the remaining challenges of electrospun CNF scaffolds for LMAs and provide possible solutions to push forward the advancement in this field.

Highlights Clinoptilolite was used to adsorb ammonia from agricultural wastewater and as a nutrient carrier to cultivate crops. Nitrogen-loaded clinoptilolite (NZ) prolonged the N supply time, which met the N demand for plant growth. NZ under water-saving irrigation purified wastewater, saved irrigation water and increased grain yield.Abstract. The extensive application of nitrogen (N) fertilizer causes soil acidification and increases the risk of water eutrophication, especially in irrigated areas. To reduce the eutrophication level of the water body and improve the soil fertility, clinoptilolite (Z) was used to adsorb N in simulated eutrophic water bodies, and then as a nutrient carrier to supplement the soil N source and promote the reuse of N. The results show that the NH4+-N loading process caused ion bonds to break inside the Z, which increased the contact area for ion adsorption. Z successfully reduced the NH4+-N concentration by 36.25% in the simulated N-rich water. The soil NH4+-N and NO3--N contents of N-loaded clinoptilolite (NZ) under alternate wet-dry irrigation (AWD) were higher during the critical growth period, which enhanced the soil N pool and replenished N in time. The NZ as a slow-release fertilizer prolonged the N supply time under AWD, which increased rice tillers by 13.68% and yield by 11.24% compared with control (without NZ amendment). In conclusion, the approach of NZ combined with AWD is beneficial to the rice growth and improvement of the soil-water environment in irrigated areas. Keywords: Ammonium Adsorption, Clinoptilolite, Nitrogen Releaser, Paddy Soil, Water-saving Irrigation.

Hyperspectral images (HSIs) have gained high spectral resolution due to recent advances in spectral imaging technologies. This incurs problems, such as an increased data scale and an increased number of bands for HSIs, which results in a complex correlation between different bands. In the applications of remote sensing and earth observation, ground objects represented by each HSI pixel are composed of physical and chemical non-Euclidean structures, and HSI classification (HIC) is becoming a more challenging task. To solve the above problems, we propose a framework based on a deep attention graph convolutional network (DAGCN). Specifically, we first integrate an attention mechanism into the spectral similarity measurement to aggregate similar spectra. Therefore, we propose a new similarity measurement method, i.e., the mixed measurement of a kernel spectral angle mapper and spectral information divergence (KSAM-SID), to aggregate similar spectra. Considering the non-Euclidean structural characteristics of HSIs, we design deep graph convolutional networks (DeepGCNs) as a feature extraction method to extract deep abstract features and explore the internal relationship between HSI data. Finally, we dynamically update the attention graph adjacency matrix to adapt to the changes in each feature graph. Experiments on three standard HSI data sets, namely, the Indian Pines, Pavia University, and Salinas data sets, demonstrate that the DAGCN outperforms the baselines in terms of various evaluation criteria. For example, on the Indian Pines data set, the overall accuracy of the proposed method achieves 98.61% when the training sample is 10%.

Sensor networks are an indispensable part of the Internet of Things (IoT), where sensors perform data acquisition and information processing tasks to obtain the parameters of interest so that IoT-based monitoring, diagnosis and other systems respond quickly to the changing conditions, instantaneous faults, etc. Distributed estimation algorithms are usually employed to estimate the parameters of interest in these IoT-based applications. However, when sensor networks have highly correlated input signals and nonstationary behavior in which the parameters of interest are time-varying, conventional distributed estimation algorithms suffer from severely degraded learning performance due to the large eigenvalue spread in the covariance matrix of the input signals and the random perturbation of the parameters of interest. To address these problems, this paper proposes two diffusion Bayesian subband adaptive filter (DBSAF) algorithms from a Bayesian learning perspective. As the highly-correlated input signal is whitened in a multiband structure and an estimate of the uncertainty in the parameters of interest is obtained by performing Bayesian inference, the proposed DBSAF algorithms are able to achieve better learning performance in comparison with the competing diffusion algorithms. The transient and steady-state mean square error performance of the proposed DBSAF algorithms are analyzed, and are verified by numerical simulations. A lower bound on the time-varying step-size is derived to maintain the optimal steady-state performance in nonstationary scenarios. A new method for the estimation of the noise variance is also proposed. Numerical simulations demonstrate the excellent learning performance of the proposed algorithms in comparison with benchmark algorithms.

Elevated CO2 concentrations (eCO2) have been widely observed to stimulate microbial growth. However, the effect of eCO2 on soil microbial biomass may depend on several factors and their interactions, such as the increase in atmospheric CO2 levels, experimental duration and mean annual precipitation (MAP). We conducted a global meta-analysis from 62 studies that included the responses of soil microbial biomass to eCO2. We found a significant positive eCO2 effect on the bacterial biomass (+9.1 %), fungi (+11 %), arbuscular mycorrhizal fungi (AMF) (+10.2 %) and actinomycetes (ACT) (+16.4 %). The positive effects were mainly observed in studies with low eCO2 levels (≤200 ppm) rather than high levels of eCO2 (>200 ppm), which could be attributable to soil N limitation. It was also found that eCO2 had a significant positive effect on soil microbial biomass in the short term (≤3 y) and under a high MAP (>800 mm). Importantly, we revealed interactive effects between the eCO2 levels, experimental duration on soil microbial biomass. With an increase in eCO2, the total microbial biomass (TMB), bacterial biomass and fungal biomass decreased over the long term (>3 y). These findings indicate the need to incorporate interactions between eCO2 and environmental factors into ecosystem models, to predict future global climate change effects more accurately and their impact on ecosystem functions.

Deciphering 3D genome conformation is important for understanding gene regulation and cellular function at a spatial level. The recent advances of single cell Hi-C technologies have enabled the profiling of the 3D architecture of DNA within individual cell, which allows us to study the cell-to-cell variability of 3D chromatin organization. Computational approaches are in urgent need to comprehensively analyze the sparse and heterogeneous single cell Hi-C data. Here, we proposed scDEC-Hi-C, a new framework for single cell Hi-C analysis with deep generative neural networks. scDEC-Hi-C outperforms existing methods in terms of single cell Hi-C data clustering and imputation. Moreover, the generative power of scDEC-Hi-C could help unveil the heterogeneity of chromatin architecture across different cell types. We expect that scDEC-Hi-C could shed light on deepening our understanding of the complex mechanism underlying the formation of chromatin contacts. scDEC-Hi-C is freely available at https://github.com/kimmo1019/scDEC-Hi-C.Key pointsscDEC-Hi-C provides an end-to-end framework based on autoencoder and deep generative model to comprehensively analyze single cell Hi-C data, including low-dimensional embedding and clustering.Through a series of experiments including single cell Hi-C data clustering and structural difference identification, scDEC-Hi-C demonstrates suprioir performance over existing methods.In the downstream analysis of chromatin loops from single cell Hi-C data, scDEC-Hi-C is capable of significantly enhancing the ability for identifying single cell chromatin loops by data imputation.

Clustering analysis aims to group unlabeled data in an unsupervised learning manner. However, most existing methods are tailored for single-task data and do not work for a sequence of task...

Hybrid precoding architecture, as a cost-effective approach for millimeter-wave (mmWave) communications, can achieve an excellent tradeoff between spectrum efficiency and hardware implementation complexity. However, the design of a robust hybrid precoding for improving the physical layer security (PLS), which is insensitive to the uncertainty of eavesdropper’s channel state information (CSI), has not been well studied. This article for the first time designs a probabilistically robust hybrid precoding scheme for securing broadcast communications in Internet of Things (IoT) with eavesdropper’s imperfect CSI. Specifically, considering the Gaussian CSI error model, we maximize the minimum secrecy rate of multiple IoT devices (IoDs) by jointly designing analog and digital precoders under the constraints in terms of secrecy outage probability and per IoD’s information rate. The optimization problem is challenging due to the coupling of the analog and digital precoders, and the secrecy outage constraint. To handle these challenges, we first employ a conservative probability inequality to transform the secrecy outage probability constraint into a deterministic one. Then, by employing the penalty dual decomposition (PDD) method, we develop a novel iterative algorithm to convert the resultant nonconvex problem into a sequence of convex problems, which can guarantee the convergence to its Karush–Kuhn–Tucker (KKT) solution. Simulation results show that the proposed algorithm can achieve significant secrecy performance gains compared with the benchmark algorithm.

Wetland CH4 emissions have been demonstrated to be more sensitive than wetland CO2 emissions to increasing temperatures, which may result in a greater relative contribution of CH4 to total GHG emissions under climate warming. However, it is not clear whether this greater sensitivity occurs globally across diverse hydrologic regimes. Here, we evaluate the temperature dependence of CO2 and CH4 emissions on water table depth using a global database and show similarities in the temperature dependence of CO2 and CH4 emissions. A lower water table is associated with a decrease in the temperature dependence of CH4 emissions and a higher water table has the opposite effect. Water table depth does not affect the temperature dependence of CO2 emissions. Our findings suggest the stimulatory effect of increasing temperature on wetland CH4 emissions may not always be stronger than that on CO2 emissions and depends on the wetland water table. Climate change may result in larger releases of CH4 than CO2 from wetlands as CH4 emissions seem to be more sensitive to temperature. Globally, CO2 and CH4 emissions show a similar temperature dependence but this is modulated by wetland water table depth, which affects CH4 (but not CO2) emissions.

To efficiently analyze the electromagnetic scattering from composite perfect electric conductor (PEC)-dielectric objects with coexisting closed-open PEC junctions, a modified hybrid integral equation (HIE) is established as the surface integral equation (SIE) part of the volume surface integral equation (VSIE), which employs the combined field integral equation (CFIE) and the electric field integral equation (EFIE) on the closed and open PEC surfaces, respectively. Different from the traditional HIE modeled for the objects whose closed and open PEC surfaces are strictly separate, the modified HIE can be applied to the objects containing closed-open junctions. A matrix equation is obtained by using the Galerkin’s method of moments (MoM), which is augmented with the spherical harmonics expansion-based multilevel fast multipole algorithm (SE-MLFMA), improved by the mixed-potential representation and the triangle/tetrahedron-based grouping scheme. Because in the improved SE-MLFMA, the memory usage for storing the radiation patterns of basis functions is independent of the SIE type in the VSIE, it is highly appropriate for the fast solution of the VSIE that contains the HIE. Various numerical experiments demonstrate that during the calculation of composite objects containing closed-open PEC junctions, the application of the modified HIE in the VSIE can give reliable results with fast convergence speed.

Phase Modulation on the Hypersphere (PMH) is a power efficient modulation scheme for the load-modulated multiple-input multiple-output (MIMO) transmitters with central power amplifiers (CPA). However, it is difficult to obtain the precise channel state information (CSI), and the traditional optimal maximum likelihood (ML) detection scheme incurs high complexity which increases exponentially with the number of transmitting antennas and the number of bits carried per antenna in the PMH modulation. To detect the PMH signals without knowing the prior CSI, we first propose a signal detection scheme, termed as the hypersphere clustering scheme based on the expectation maximization (EM) algorithm with maximum likelihood detection (HEM-ML). By leveraging machine learning, the proposed detection scheme can accurately obtain information of the channel from a few of the received symbols with little resource cost and achieve comparable detection results as that of the optimal ML detector. To further reduce the computational complexity in the ML detection in HEM-ML, we also propose the second signal detection scheme, termed as the hypersphere clustering scheme based on the EM algorithm with KD-tree detection (HEM-KD). The CSI obtained from the EM algorithm is used to build a spatial KD-tree receiver codebook and the signal detection problem can be transformed into a nearest neighbor search (NNS) problem. The detection complexity of HEM-KD is significantly reduced without any detection performance loss as compared to HEM-ML. Extensive simulation results verify the effectiveness of our proposed detection schemes.