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As a fundamental energy infrastructure, the power system has a direct impact on people’s daily lives and production. If the security of the electrical power technology is compromised by malicious network attacks, it can result in widespread blackouts and have significant consequences on every aspect of society. Therefore, to ensure power control system security, this study introduces a network security risk prediction algorithm for power control systems. The algorithm is based on categorization-constrained Boltzmann machine and Markov time-varying model. It classifies network security risk states using classification-constrained Boltzmann machine and predicts security risk using Markov time-varying model. The experimental results demonstrate that the classification-constrained Boltzmann machine achieved higher average accuracy (78.4%), average precision (76%), and average recall (73.4%) for the network risk state compared to the Hidden Markov Model, Multi-kernel Support Vector Machines, and Bayesian Networks. Therefore, it is evident that the classification-constrained Boltzmann machine is better suited for the power control system. Additionally, when considering the prediction of security risks, the categorization-constrained Boltzmann machine and Markov time-varying model produced impressive results with a combined accuracy, precision, F-score, and consistency index of 0.964, 0.947, 0.96, and 0.974, respectively. These results demonstrate that this particular security risk prediction method has high accuracy and is adaptable to complex equipment and network environments found in power control systems.

Metallene materials with atomic thicknesses are receiving increasing attention in electrocatalysis due to ultrahigh surface areas and distinctive surface strain. However, the continuous strain regulation of metallene remains a grand challenge. Herein, taking advantage of autocatalytic reduction of Cu2+on biaxially strained, carbon-intercalated Ir metallene, we achieve control over the carbon extraction kinetics, enabling fine regulation of carbon intercalation concentration and continuous tuning of (111) in-plane (−2.0%–2.6%) and interplanar (3.5%–8.8%) strains over unprecedentedly wide ranges. Electrocatalysis measurements reveal the strain-dependent activity toward hydrogen evolution reaction (HER), where weakly strained Ir metallene (w-Ir metallene) with the smallest lattice constant presents the highest mass activity of 2.89 A mg–1Irat −0.02 V vs reversible hydrogen electrode (RHE). Theoretical calculations validated the pivotal role of lattice compression in optimizing H binding on carbon-intercalated Ir metallene surfaces by downshifting the d-band center, further highlighting the significance of strain engineering for boosted electrocatalysis.

Ferroelectric materials hold great promise in photocatalytic water splitting because their built-in electric field induced by the depolarization field can fulfill the separation of photogenerated carriers. However, a number of intrinsic charged vacancy defects are simultaneously generated to screen the depolarized field for stabilizing the crystal structure, always resulting in severe recombination of photogenerated carriers and thus poor overall water splitting activity. Herein, we proposed a strategy to promote the separation and transport of photogenerated carriers of ferroelectric photocatalysts by adjusting the ferroelectric polarization and altering the coordination environment of elements to reduce the defect concentration. Specifically, we prepared a series of Ta-doped PbTiO3with low Pb (VPb) and O (VO) vacancy concentrations by reducing the polarization intensity and strengthening the Pb–O interaction. The Ta-doped PbTiO3shows efficient charge separation and greatly enhanced photocatalytic overall water splitting activity with the assistance of cocatalyst. This work highlights the importance of regulating ferroelectric polarization and vacancy defect concentration by the doping strategy in charge separation for photocatalytic water splitting.

The level of serum uric acid (SUA) in treatment follow-up is associated with mortality in peritoneal dialysis (PD) patients, but it remains unknown if the longitudinal change in SUA affects mortality. 309 PD patients who were not using UA-lowering agents were enrolled. The longitudinal change in SUA was estimated by comparing the values between the run-in and follow-up periods. Based the calculated values, the patients were divided into SUA decliner and SUA non-decliner. A propensity score (PS) was calculated using the parameters measured in run-in period. After PS matching, the time-to-event analysis was performed for all-cause death. After PS matching, 86 patients of each group were left. A higher mortality of 19/86 existed in SUA decliner compared with SUA non-decliner which is 3/86 (p< 0.001). Kaplan–Meier analysis in sub-cohort showed worse survival in the SUA decliner. Standard and stratified Cox regression analysis both showed SUA decline to be an independent risk factor for all-cause death in PD patients. The decline in SUA in the follow-up may predict the all-cause mortality of PD patients, the reason of which may result from reducing scavenging effects of SUA or may reflect general condition. More studies need to be done.

In order to overcome the problems of low accuracy and poor correlation in the early warning method of investment risk of listed companies, this paper designs an early warning method of investment risk of listed companies based on EMD-RF-LSTM. First of all, determine the internal and external early warning factors of investment risk, and realise the screening of risk early warning factors. Then, the maximum likelihood estimate of the influencing factors is calculated by clustering algorithm to realise the factor quantification. Finally, the EMD method is used to extract the characteristics of early warning factors, build the early warning function through RF, and solve the critical value of risk early warning through LSTM to achieve risk early warning. The results show that the accuracy of the proposed method can reach 99%, and the correlation coefficient is 0.97, which plays an important role in the early warning of investment risk.

Spliceosome is often assembled across an exon and undergoes rearrangement to span a neighboring intron. Most states of the intron-defined spliceosome have been structurally characterized. However, the structure of a fully assembled exon-defined spliceosome remains at large. During spliceosome assembly, the pre-catalytic state (B complex) is converted from its precursor (pre-B complex). Here we report atomic structures of the exon-defined human spliceosome in four sequential states: mature pre-B, late pre-B, early B, and mature B. In the previously unknown late pre-B state, U1 snRNP is already released but the remaining proteins are still in the pre-B state; unexpectedly, the RNAs are in the B state, with U6 snRNA forming a duplex with 5′-splice site and U5 snRNA recognizing the 3′-end of the exon. In the early and mature B complexes, the B-specific factors are stepwise recruited and specifically recognize the exon 3′-region. Our study reveals key insights into the assembly of the exon-defined spliceosomes and identifies mechanistic steps of the pre-B-to-B transition.

In nature, when encountering unexpected uncertainty, animals tend to react quickly to ensure safety as the top priority, and gradually adapt to it based on recent valuable experience. We present a framework, namely EVOLutionary model-based uncertainty obserVER (EVOLVER), to mimic the bio-behavior for robotics to achieve rapid transient reaction ability and high-precision steady-state performance simultaneously. In particular, the Koopman operator is leveraged to explore the latent structure of internal and external disturbances, which is subsequently utilized in an evolutionary model-based disturbance observer to estimate the eventual disturbance. The resulting observer can guarantee a provable convergence in optimal conditions. Several practical considerations, including construction of a training dataset, data noise handling, and lifting functions selection, are elaborated in pursuit of the theoretical optimality in real applications. The lightweight feature of our framework enables online computation, even on a microprocessor (STM32F7 with 100 Hz control frequency). The framework is thoroughly evaluated by one simulation and three experiments. The experimental scenarios include: 1) Trajectory prediction of an irregular free-flying object subject to aerodynamic drag, 2) indoor and outdoor agile flights of a quadrotor subject to wind gust, and 3) high-precision end-effector control of a manipulator subject to base moving disturbance. Comparison results show that the performance of our proposed EVOLVER is superior to several state-of-the-art model-based and learning-based schemes.

Passive monitoring of acoustic or radio sources has important applications in modern convenience, public safety, and surveillance. A key task in passive monitoring is multiobject tracking (MOT). This paper presents a Bayesian method for multisensor MOT for challenging tracking problems where the object states are high-dimensional, and the measurements follow a nonlinear model. Our method is developed in the framework of factor graphs and the sum-product algorithm (SPA) and implemented using random samples or “particles”. The multimodal probability density functions provided by the SPA are effectively represented by a Gaussian mixture model (GMM). To perform the operations of the SPA with improved sample efficiency, we make use of particle flow (PFL). Here, particles are migrated towards regions of high likelihood based on the solution of a partial differential equation. This makes it possible to obtain good object detection and tracking performance even in challenging multisensor MOT scenarios with single sensor measurements that have a lower dimension than the object positions. We perform a numerical evaluation in a passive acoustic monitoring scenario where multiple sources are tracked in 3-D from 1-D time-difference-of-arrival (TDOA) measurements provided by pairs of hydrophones. Our numerical results, obtained by processing synthetic and real data, demonstrate favorable detection and estimation accuracy compared to state-of-the-art reference techniques.

The self-assembly behavior of a tadpole-like giant molecule (TGM) constructed from a hydrophobic nanoparticle (NP) monotethered by a single amphiphilic AB diblock copolymer chain was investigated by combining self-consistent field theory and density functional theory in solution. The effects of the hydrophobicities of the B blocks and NPs (i.e., solvent properties) on the self-assembly behavior of the TGMs were investigated in the cases of weak and strong intramolecular interactions (i.e., incompatibilities) between the components of giant molecules, respectively. Besides conventional ordered aggregates (such as spheres, rings, and vesicles) with hydrophobic B-cores covered by NP shells, several aggregates with novel hierarchical structures, including vesicles with NP-inserted hydrophobic walls, bead-string-like micelles, and long cylindrical micelles with NP bumps, were obtained by tuning the solvent properties under different intramolecular interactions. Noteworthy that the simulation results show that the arrangement of the NP bumps on the long cylindrical micelles may have a certain degree of helicity, which means that these micelles may have some unique electromagnetic features such as circular dichroism. Phase diagrams as a function of the hydrophobicities of the B blocks and NPs were constructed to show the formation conditions of these novel structures. These findings can not only offer new insights into understanding of the self-assembly behavior of the TGM in solution but also provide useful guidance for simple and efficient regulation of the morphology, as well as the NP distribution and arrangement of the ordered aggregates in experiments.

Semantic communication is a newly emerged communication paradigm that exploits deep learning (DL) models to realize communication processes like source coding and channel coding. Recent advances have demonstrated that DL-based joint source-channel coding (DeepJSCC) can achieve exciting data compression and noise-resiliency performances for wireless image transmission tasks, especially in environments with low channel signal-to-noises (SNRs). However, existing DeepJSCC-based semantic communication frameworks still cannot achieve adaptive code rates for different channel SNRs and image contents, which reduces its flexibility and bandwidth efficiency. In this paper, we propose a predictive and adaptive deep coding (PADC) framework for realizing flexible code rate optimization with a given target transmission quality requirement. PADC is realized by a variable code length enabled DeepJSCC (DeepJSCC-V) model for realizing flexible code length adjustment, an Oracle Network (OraNet) model for predicting peak-signal-to-noise (PSNR) value for an image transmission task according to its contents, channel signal to noise ratio (SNR) and the compression ratio (CR) value, and a CR optimizer aims at finding the minimal data-level or instance-level CR with a PSNR quality constraint. By using the above three modules, PADC can transmit the image data with minimal CR, which greatly increases bandwidth efficiency. Simulation results demonstrate that the proposed DeepJSCC-V model can achieve similar PSNR performances compared with the state-of-the-art Attention-based DeepJSCC (ADJSCC) model, and the proposed OraNet model is able to predict high-quality PSNR values with an average error lower than 0.5dB. Results also demonstrate that the proposed PADC can use nearly minimal bandwidth consumption for wireless image transmission tasks with different channel SNR and image contents, at the same time guaranteeing the PSNR constraint for each image data.

Since the concept of aggregation‐induced emission (AIE) was proposed by Benzhong Tang's research group in 2001, the exploration of the mechanism of AIE and the development of new high‐performance AIE materials have been the focus and goal of this field. On the basis of a large number of experiment results, AIE mechanism has been well explained by lots of works, such as restricted intramolecular motion (RIM), J‐aggregate et al. As tetraphenylethlene (TPE) molecules are stacked, the rotation of the benzene ring rotor is blocked, and the energy attenuation is released in the form of radiation, showing the AIE effect. In order to further explore the AIE effect of TPE, we performed electronic structure, spectrum simulation, and AIE mechanism calculations of the anthryl‐tetraphenylethene (TPE‐an) monomer and dimer in the gas phase, tetrahydrofuran (THF), and aqueous solutions at the B3LYP/6‐31G** level. The calculation results show that TPE‐an molecule is in a propeller‐like configuration, and its fluorescence intensity is weak; compared with the monomer, the fluorescence intensity of the dimer increases by 87% in aqueous solution; the fluorescence intensity in the gas phase, THF solution, and aqueous solution gradually enhances with the increase of the degree of aggregation, which are consistent with the experimental results. The enhancement of fluorescence intensity is caused by the change of molecular structure caused by aggregation. This detailed AIE luminescence mechanism will provide theoretical guidance for AIE material design. Franck‐Condon simulation fluorescence of anthryl‐tetraphenylethene gradually enhanced from the gas phase, to tetrahydrofuran (THF) solution and to the non‐solvent aqueous solution owing to the enhanced aggregation with the decrease of solubility, which resulted in the increasing molecular rigidity, hindering rotation of the benzene ring, and weakening non‐radiation energy dissipation through structural change.

Permanent magnet synchronous motor (PMSM) is widely used in new energy vehicles. At present, to make electric vehicles have a wider speed range, the motor can reach the rated speed above through the field-weakening control. However, when the traditional field-weakening control strategy is above the rated speed, the dynamic response ability of the vehicle declines. Problems such as torque oscillation and current jump occur. To solve these problems, based on a pigeon-inspired optimization (PIO) algorithm and optimized light gradient boosting machine (LightGBM), the dynamic response capability of the permanent magnet synchronous motor is improved. The robust adaptability of the control system to disturbances and parameter changes is also further improved. By collecting experimental data, the importance of relevant variables is analyzed, and the variable with the largest weight is selected as the input of the model. PIO is used to optimize LightGBM, and the loss function is optimized. Finally, the regression model is established. Simulation and experimental results show that the method is effective.

Edge computing is a commonly used paradigm for providing low-latency computation services by locally deploying computation and storage resources close to the user equipments (UEs). Since the computation resource demand of the offloaded tasks of a UE is naturally a random variable, it is possible that the real-time computation capacity demand of a resource-limited hosting virtual machine (VM) or edge computing server (ECS) is larger than its computation capacity, causing unexpected delay or delay-jitter to the services, which should be avoided if possible, for delay-sensitive applications. We consider an edge computing scenario wherein the transmission links are unmanageable and computation resource demands of VM servers are stochastic. We propose a novel Logistic function-based service reliability probability (SRP) estimation model without specifying the distributions of the resource demands. We study the average SRP maximization problem (ASRPMP) in a VM-based edge computing server (ECS) by jointly optimizing the service quality ratios (SQRs) and the computation resource allocations, and we propose an alternative optimization algorithm (AOA) by decomposing the problem into a resource allocation problem (RAP) and a service quality control problem (SQCP). Based on the derived analytical solutions of the two subproblems, we propose an effective and low-complexity heuristic AOA (HAOA) to solve the ASRPMP. The simulation results obtained from both synthetic Gaussian workload data and PlanetLab trace data demonstrate that, given the same target SQR or computation resource, the proposed method can achieve similar performance compared with the convex AOA (CAOA) method with much higher complexity, and can improve the reliability of the services compared with the baseline weighted allocation method (WAM) in both high and low SRP regimes.

Reconfigurable intelligent surface (RIS) has emerged as a promising technology for the 6G networks. In this article, we study a RIS with power splitting aided broadcasting network where one access point (AP) transmits identical messages to multiple users. The RIS is capable of reflecting a part of the incident signals to assist the AP and harvesting energy from the other part to achieve power self-sustainability by controlling reflect amplitude coefficients. We aim at minimizing the AP transmit power while ensuring the quality of service at users and the self-sustainability of the RIS by optimizing the AP transmit beamforming vector and the RIS reflect beamforming matrix which involves the reflect amplitude and phase shift coefficients. To tackle the beamforming optimization problem which is non-convex, an algorithm is proposed by employing the block coordinate descent, convex approximation and alternating direction method of multipliers techniques. Simulation results demonstrate the effectiveness of the proposed algorithm.

Online portfolio selection has been actively studied to maximise overall returns by selecting the optimal portfolio weights using online algorithms. However, most work has focused on long-only portfolios, and developing efficient algorithms with loose portfolio constraints remains a challenge. In this letter, the classical online portfolio selection problem is reformulated to allow long/short and margin. For this problem, conventional gradient-based online algorithms face the challenges of high regret and computational complexity due to non-optimal gradients and high-dimensional projections. To tackle this, we propose a novel online algorithm that introduces mirror descent to achieve dimension-free regret in a non-Euclidean space. Specifically, a Bregman divergence is introduced to replace the $\ell _{2}$ norm as a valid proximal setup for the problem to achieve uniform gradients and reduce projection computations. Furthermore, a smoothing technique is developed to reduce the variance of the gradients. The evaluation shows that our algorithm achieves low regret bound and computational complexity, which guarantees a 30% advantage over other strategies in Chinese futures market.

Global navigation satellite system has been widely used, but it is vulnerable to jamming. In military satellite communications, frequency hopping (FH) signal is usually used for anti-jamming communications. If the FH signal can be used in satellite navigation, the anti-jamming ability of satellite navigation can be improved. Although a recently proposed time-frequency matrix ranging method (TFMR) can use FH signals to realize pseudorange measurement, it cannot transmit navigation messages using the ranging signal which is crucial for satellite navigation. In this article, we propose dual-tone binary frequency shift keying-based TFMR (DBFSK-TFMR). DBFSK-TFMR designs an extended time-frequency matrix (ETFM) and its generation algorithm, which can use the frequency differences in different dual-tone signals in ETFM to modulate data and eliminate the negative impact of data modulation on pseudorange measurement. Using ETFM, DBFSK-TFMR not only realizes the navigation message transmission but also ensures the precision and unambiguous measurement range of pseudorange measurement. DBFSK-TFMR can be used as an integrated solution for anti-jamming communication and navigation based on FH signals. Simulation results show that DBFSK-TFMR has almost the same ranging performance as TFMR.

As a promising method for organizing remanufacturing production activities, remanufacturing scheduling has attracted increasing attention in recent years. However, extant studies have primarily focused on solving remanufacturing scheduling problems in a deterministic environment, while neglecting the impact of uncertainties on remanufacturing. Therefore, a new scenario-based robust remanufacturing scheduling problem was investigated in this study, and a robust optimization model for this problem was established. In the proposed model, a discrete scenario set is used to describe the uncertain arrival time and uncertain processing time of end-of-life products, and the variable start-up batch size constraint is considered to improve the practicality and flexibility of the model. To solve this model, an improved biogeography-based optimization algorithm with a new three-dimensional unequal-length representation scheme is proposed, in which, new migration and mutation operators, a local search strategy, and a new batch promotion mechanism are designed to improve the algorithmic performance. The results of the experiments demonstrate the feasibility of the proposed model and the superiority of the presented algorithm in solving the proposed model.

Accurately capturing the spatial correlations of traffic network significantly benefits short-term traffic forecasting. Some existing works represent spatial correlations in a simple one-dimensional space, but they cannot represent the real spatial correlations among sensors comprehensively. The other existing works represent the spatial correlations through grid-based method, but the local correlation of constructed spatial map is too superficial to extract deep spatial features effectively. Therefore, a novel deep learning model is proposed, which aims to represent the spatial correlations more effectively through a new correlation matrix structure. In the proposed model, the correlations among sensors are calculated from multiple perspectives to construct the speed, volume, and occupancy correlation matrices respectively. Then, considering that highly correlated sensors are close in the spatial dimension, an enhanced heuristic optimization algorithm is proposed to evolve these three correlation matrices into optimal ones by reorganizing the highly correlated sensors into each other's neighborhood. Finally, the three optimal correlation matrices are combined to form a three-dimensional multivariate correlation matrix characterized by locally high correlation, which is beneficial to exploit the deep spatial features of traffic network. The experiments show that the proposed model has better accuracy and stability than other commonly used baseline models.

Seeking new chemo-catalytic routes to produce glyceric acid using renewable biomass-based sugar as feedstock is of significance but remains a big challenge due to a quite low yield (<5%). Herein, we prepare an MgO catalyst with an oxygen vacancy and explore a simple but robust MgO–NaBF4system, achieving the selective production of glyceric acid from fructose with a high yield of 78.8% in an O2atmosphere via chemo-catalysis at mild conditions (373 K), eliminating the use of strong inorganic alkalis and severe reaction conditions. As far as we know, this has the highest value among the state-of-the-art chemo-catalytic strategies. Evidenced by the isotope-probe experiment and theoretical calculation, it was revealed that MgO and NaBF4exhibited pronounced cooperative effects. The hydroxyl group exposed on the MgO surface first facilitated the adsorption of fructose. With the assistance of MgO, B(OH)4–from NaBF4hydrolysis preferentially combined with β-d-fructofuranose, as evidenced by 11B and 13C NMR spectroscopy, due to its strong electron-withdrawing ability, producing a complex which could easily undergo ring-opening reaction with an attack of OH–derived from the alkaline MgO. More importantly, the oxygen vacancies on the MgO surface subsequently assisted the C3–C4 cleavage in fructose by MgO species to produce a glyceraldehyde intermediate and activated O2to oxidize glyceraldehyde, yielding glyceric acid. The conjunction of B(OH)4–with a glyceraldehyde intermediate or glyceric acid could also hinder its over-oxidation. The insights in this work might provide new chemo-catalytic approaches for glyceric acid production from renewable biomass.

The sulfur evolution behavior during the chemical looping combustion (CLC) process of petroleum coke was investigated in a fluidized bed reactor with a low-cost solid waste of red mud as the oxygen carrier. Effects of temperature, oxygen carrier, and potassium catalyst addition on sulfur release were investigated, and H2S oxidization kinetics with the red mud and the effect of elements in the red mud on sulfur conversion were evaluated in detail. The red mud oxygen carrier had a promoting effect on H2S generation and thus enhanced sulfur conversion. The addition of potassium remarkably accelerated carbon conversion due to its catalytic effect. Meanwhile, it promoted H2generation and then enhanced the generation of H2S. The addition of potassium into the process could weaken the release of gaseous sulfur but had no obvious negative influence on the reactivity of the red mud oxygen carrier. The experiment H2S interaction with the red mud suggested that the conversion of H2S to SO2decreased with increasing temperature. The red mud has a low ability to oxidize H2S to SO2but a high reactivity to capture sulfur-containing gases. Solid sulfur-containing products such as CaSO4and Fe3S4as well as K2S2in the case of K addition into the process were detected.

Although UV light-switchable luminescent films are of importance for application in soft optical devices and anticounterfeiting labels, there are still challenges in developing such films integrated with outstanding luminescent property, high self-healing efficiency, and simultaneously excellent mechanical strength. Herein, double-network (DN) luminescent films are designed and constructed via an intermolecular hydrogen bond crosslinking strategy of poly(ethylene glycol) (PEG) in sulfur quantum dots (S-QDs) and polyurethane (PU), where S-QDs (“stone” one) play dual roles of acting both as a soft segment to crosslink another segment PU (“bird” one) and also as the origin of a luminescence center (“bird” two) in films. In addition, lanthanide(III) complexes (LnCs, Ln═Eu3+, Tb3+) are employed as another emission source to embed in the films and switch the emission colors of DN films from the multicolor (red–yellow–green) of LnCs to the blue color of S-QDs by changing the ultraviolet excitation wavelength from 254 to 365 nm. It is worth noting that the crosslinking network strategy can effectively prevent S-QDs and LnCs from aggregating or leaking and enable both luminescence centers to homogeneously distribute, resulting in luminescent DN films possessing extraordinary UV light-switchable luminescence, improved mechanical property, and excellent self-healing ability. This work presents a viable method for the design and fabrication of luminescent films with multifunctional applications in flexible robotics, wearable devices, and dual-luminescent anticounterfeiting materials.

Monocytic acute myeloid leukemia (AML) responds poorly to current treatments, including venetoclax-based therapy. We conducted in vivoand in vitroCRISPR-Cas9 library screenings using a mouse monocytic AML model and identified SETDB1 and its binding partners (ATF7IP and TRIM33) as crucial tumor promoters in vivo. The growth-inhibitory effect of Setdb1depletion in vivois dependent mainly on natural killer (NK) cell-mediated cytotoxicity. Mechanistically, SETDB1depletion upregulates interferon-stimulated genes and NKG2D ligands through the demethylation of histone H3 Lys9 at the enhancer regions, thereby enhancing their immunogenicity to NK cells and intrinsic apoptosis. Importantly, these effects are not observed in non-monocytic leukemia cells. We also identified the expression of myeloid cell nuclear differentiation antigen (MNDA) and its murine counterpart Ifi203 as biomarkers to predict the sensitivity of AML to SETDB1depletion. Our study highlights the critical and selective role of SETDB1 in AML with granulo-monocytic differentiation and underscores its potential as a therapeutic target for current unmet needs.

In traditional battery energy storage systems (BESS), batteries are usually connected in a simple series or parallel form, and separate converters and balancing modules are typically used for energy exchange between the battery and external sources, as well as for balancing energy between batteries. This paper proposes an integrated battery energy storage system (IBESS) with reconfigurable batteries and DC/DC converters, resulting in a more compact structure. The IBESS can reconfigure the connection of energy storage batteries based on the battery's status and external demand. The DC/DC converters can operate in different modes such as boost, buck, or buck-boost converters. The IBESS has five working modes: discharging mode, balancing mode, battery discharging and balancing mode, constant current and constant voltage (CCCV) charging mode and backup mode. To verify the feasibility of the proposed energy storage system, a model with four battery modules was built using MATLAB/SIMULINK, and the operation of all working modes was demonstrated. The simulation results verified the system's performance and reliability, demonstrating its flexibility and efficiency in different operating modes.

Accurately predicting the evolution of water vapor content has significant practical engineering implications for accurately determining the timing of artificial rain enhancement operations. However, the nonlinear, unstable, and chaotic nature of water vapor content presents considerable challenges to its precise prediction. To address this, our research proposes a combined prediction model, based on a neural network model selection mechanism and small sample water vapor content data observed by microwave radiometers in the Zhengzhou area. This combined model utilizes seven neural network models as candidate models and dynamically selects the optimal three models as the central prediction components via evaluation indicators. Additionally, we employ the maximum information coefficient method to preserve the optimal input features and use a time-varying rate wave mode decomposition algorithm as the front-end processing module. This module processes the original water vapor content sequence and extracts relevant latent information. Finally, we incorporate the African vulture intelligent optimization algorithm as the backend processing component to optimize the weighted combination coefficients, achieving the integration of prediction results. Experimental results reveal that the Mean Absolute Percentage Errors (MAPE) of the proposed model on three types of water vapor content datasets are 0.208%, 0.876%, and 0.369%, respectively. This suggests a predictive performance improvement of at least 10% compared to existing models. The prediction model proposed in this study not only establishes an accurate and reliable mechanism for predicting water vapor content but also offers more comprehensive decision support information for weather modification operations. • Accurate prediction of the evolution of water vapor is crucial for the implementation of weather modification engineering. • An optimal model based on multiple neural networks selection and models fussion is proposed to improve the prediction ability. • The data preprocessing scheme filters noise and measures data relevance, providing more supporting information for the system's prediction. • The proposed model's validity is confirmed with real-world data, offering an alternative solution for the prediction of water vapor. [ABSTRACT FROM AUTHOR]

Direct formation of light olefins (C2=–C4=) from CO2hydrogenation is attractive because it decreases the greenhouse effect and gives value-added chemicals concurrently. Although it has been extensively studied and significant progress has been made, controllable regulation of light olefin distribution remains a challenge. Herein, the bifunctional catalysts composed of various zinc-based binary metal oxides and H-RUB-13 zeolite were prepared, including ZnZrOx/H-RUB-13, ZnAlOx/H-RUB-13, ZnGaOx/H-RUB-13, and ZnCrOx/H-RUB-13, and the C2–4olefin selectivity in hydrocarbons reaches 64.7–83.2% at a CO2conversion of 10.2–15.5%. ZnZrOx/H-RUB-13 shows the highest CO2conversion as a result of the presence of larger amounts of oxygen vacancies that promote CO2adsorption and activation. More interestingly, propene and butene are the dominant hydrocarbons, and C3=+ C4=accounts for >90% of light olefins. This confirms the potential of H-RUB-13 zeolite to suppress ethene formation. However, the distribution of light olefins also depends on the global acidic property of the bifunctional catalyst. ZnCrOx/H-RUB-13 with strong acidity shows higher ethene selectivity because of the promotion of the aromatic-based cycle.

To study the influence of expansion temperature on the properties of expanded graphite (EBG), EBG300, EBG600, and EBG900were prepared by heating expandable graphite (EG) at 300, 600, and 900 °C, respectively. Furthermore, the influence of these EBGs on the combustion performance and physical-mechanical properties of linear low density polyethylene (LLDPE) were investigated. The expansion volumes of EBG300, EBG600, and EBG900increase with the rise of temperature, and a four-stage ordered structure of “graphite worm” gradually forms. The thermal stability increases gradually for EBG300, EBG600, and EBG900. On the contrary, the thermal conductivity decreases in sequence. However, the incorporation of EBG900promotes the formation of a continuous network structure and makes the modified LLDPE to present the best heat transmission. The addition of 30 wt% of these EBGs significantly improves LLDPE’s flame retardancy and high-temperature thermal stability. The total heat release, the peak value of heat release rate, and the fire growth index of 70LLDPE/30EBG300reduce by 69, 91, and 87% respectively, while the effective fire performance index improves seven times. The addition of these additives reduces the tensile strength and elongation at break, the larger the EBG size, the more obvious the effect.

Bismuth vanadate (BiVO4) is one of the semiconductors that are often used for photoelectrochemical water splitting because of its low band gap and various crystalline phases. Using density functional theory (DFT) based calculations, the surface properties, electronic structures, and photocatalytic properties of different facets are obtained. These include the (001), (011), and (101) facets that are truncated from ms-BiVO4and the comparable {001}, {011}, and {101} facets that are produced by means of cleavage from ts-BiVO4. Our findings show a surface stability order of (001)/{001} > (101)/{101} > (011)/{011}. The (011) and {011} facets present distinct surface properties owing to the asymmetric a–bplane of ms-BiVO4, in contrast to ts-BiVO4. The work function of the {011} facet is dramatically decreased by 1 eV in comparison to the other facets, resulting from a positive surface dipole with an open lattice. Surface (001) shares geometric and electronic structure characteristics with {001}, and surface (101) possesses identical features with {101}. The electronic structures of surfaces (001)/{001} and (011) show indirect band gaps, while surfaces (101)/{101} possess direct band gaps. The mid-band gap states appear at surface {011} caused by the isolated O 2p states. For the photocatalytic properties, surfaces (001)/{001} have excellent visible-light absorption capacity and support H2O molecules to be adsorbed. Meanwhile, the flat-band potentials of (101)/{101} exhibit more negative behaviors than other surfaces. Our work indicates that surfaces (001)/{001} display outstanding photocatalytic performance, and surfaces (101)/{101} offer a promising and controllable potential for visible-light-driven photocatalytic activity. Surface (011) is perfectly suitable for the adsorption of the H2O molecule with constrained visible-light response. Moreover, the obtained surface–properties relationships provide comprehensive comparisons between the facets stemming from the two bulk phases. We confirm that the discrepancies between the (011) and {011} surfaces in facet morphologies and electronic structures are one of the reasons accounting for the distinct photoelectrochemical activities of ms- and ts-BiVO4from experiments. Facet {011} can be exploited as a powerful photocatalyst if the mid-gap states are eliminated. We propose that regulating or decorating the exposed facets of ts-BiVO4can be generalized to mitigate the differences between ms- and ts-BiVO4in photoelectrochemical activities.

Long-chain alkanes are a type of intermediate volatility organic compound (IVOC) in the atmosphere and a potential source of secondary organic aerosols (SOAs). C12–C14n-alkylcyclohexanes are important compositions of IVOCs, with considerable concentrations and emission rates. The reaction rate constants and SOA formation of the reactions of C12–C14n-alkylcyclohexanes with Cl atoms were investigated in the present study. The reaction rate constants of the long-chain alkanes obtained via the relative-rate method at 298 ± 0.2 K (in units of ×10–10cm3molecule–1s–1) were as follows: khexylcyclohexane= 5.11 ± 0.28, kheptylcyclohexane= 5.56 ± 0.30, and koctylcyclohexane= 5.74 ± 0.31. The gas-phase products of the reactions were identified as mainly small molecules of aldehydes, ketones, and acids. The particle-phase products were mostly monomers and oligomers, but there were still trimers even under high-NOxconditions. Moreover, under high-NOxconditions (urban atmosphere), the SOA yields of hexylcyclohexane are higher than that under low-NOxconditions (remote atmosphere), indicating that more attention should be given to the SOA formation of Cl-initiated n-alkylcyclohexane oxidations in polluted regions. This research can further clarify the oxidation processes and SOA formation of n-alkylcyclohexanes in the atmosphere.