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This letter investigates the three-dimensional (3D) downlink sparse channel estimation for tethered aerial platform-enabled multi-user communication systems operating in a frequency division duplexing mode with large-scale antenna arrays. To this end, we design a non-identical Bernoulli-Gaussian distribution-based channel model that reflects the potential common sparsity caused by distant scatterers in a low-altitude environment. A low-complexity message passing-based channel estimation algorithm without the need for prior knowledge of channel sparsity is proposed. Specifically, this algorithm leverages a sparsity rate-aided adaptive majority voting scheme to automatically identify diverse kinds of channel supports. It also utilizes an improved sparse channel estimator to recover sparse channels accurately with the achieved channel supports. Compared with its existing counterparts, the proposed algorithm achieves better estimation performance under various parameter settings and approaches the performance limit with perfect knowledge of the channel common supports.

Multimodal neuroimaging provides complementary information critical for accurate early diagnosis of Alzheimer’s disease (AD). However, the inherent variability between multimodal neuroimages hinders the effective fusion of multimodal features. Moreover, achieving reliable and interpretable diagnoses in the field of multimodal fusion remains challenging. To address them, we propose a novel multimodal diagnosis network based on multi-fusion and disease-induced learning (MDL-Net) to enhance early AD diagnosis by efficiently fusing multimodal data. Specifically, MDL-Net proposes a multi-fusion joint learning (MJL) module, which effectively fuses multimodal features and enhances the feature representation from global, local, and latent learning perspectives. MJL consists of three modules, global-aware learning (GAL), local-aware learning (LAL), and outer latent-space learning (LSL) modules. GAL via a self-adaptive Transformer (SAT) learns the global relationships among the modalities. LAL constructs local-aware convolution to learn the local associations. LSL module introduces latent information through outer product operation to further enhance feature representation. MDL-Net integrates the disease-induced region-aware learning (DRL) module via gradient weight to enhance interpretability, which iteratively learns weight matrices to identify AD-related brain regions. We conduct the extensive experiments on public datasets and the results confirm the superiority of our proposed method. Our code will be available at: https://github.com/qzf0320/MDL-Net.

Agglomeration is a significant challenge in antisolvent crystallization, especially when it comes to crystallizing insoluble drugs. This study aimed to address this issue by taking a unique, dynamic molecular perspective and proposing a solution based on microscopic molecular exchange. Atorvastatin calcium, a widely prescribed medication for lowering blood lipids, was used as a case for antisolvent crystallization, which is also prone to agglomeration. Kinetic experiments revealed the rapid exchange between solvent and antisolvent, leading to precipitation occurring within a mere 0.0333 s. Thermodynamic investigations further showed that each droplet had the potential to generate an astonishing equivalent nucleation rate of up to 5 billion nuclei per second. The rapid precipitation and small nucleation region, coupled with a large number of nuclei, worsened the crystal agglomeration problem. However, it was found that this molecular exchange process can be slowed by the solvent composition. Specifically, when the methanol mole fraction in the crystallization system reached approximately 50%, the equivalent nucleation rate decreased significantly to about 100 million per second. Armed with this crucial finding, a strategy was devised to overcome agglomeration by simultaneously adding the solution and antisolvent to maintain a lower exchange rate, which effectively avoided agglomeration and promoted the purification of atorvastatin calcium.

The integration of a near-space information network (NSIN) with the reconfigurable intelligent surface (RIS) is envisioned to significantly enhance the communication performance of future wireless communication systems by proactively altering wireless channels. This paper investigates the problem of deploying a RIS-integrated NSIN to provide energy-efficient, ultra-reliable and low-latency communications (URLLC) services. We mathematically formulate this problem as a resource optimization problem, aiming to maximize the effective throughput and minimize the system power consumption, subject to URLLC and physical resource constraints. The formulated problem is challenging in terms of accurate channel estimation, RIS phase alignment, and effective solution design. We propose a joint resource allocation algorithm to handle these challenges. In this algorithm, we develop an accurate channel estimation approach by exploring message passing and optimize phase shifts of RIS reflecting elements to further increase the channel gain. Besides, we derive an analysis-friendly expression of decoding error probability and decompose the problem into two-layered optimization problems by analyzing the monotonicity, which makes the formulated problem analytically tractable. Extensive simulations have been conducted to verify the performance of the proposed algorithm. Simulation results show that the proposed algorithm can achieve outstanding channel estimation performance and is more energy-efficient than diverse benchmark algorithms.

Fire and explosion accidents frequently occur during hydrogen production, transportation, storage, and utilization processes, leading to significant casualties and economic losses. In this work, the characteristics of confined hydrogen explosions, hydrogen cloud explosions, and hydrogen blending explosions are summarized. Moreover, strategies for mitigating hydrogen deflagration, including explosion inhibition, explosion venting, and flame quenching, are analyzed. The results show that the deflagration behaviors of hydrogen under extreme initial conditions, such as low temperature and high pressure, are not yet well-understood. Research studies have demonstrate the marked effects of mitigating hydrogen deflagration, but this process has certain disadvantages. Porous materials and alkyl halides show dual promotion and inhibition effects. The suppression efficiency of inert gas and water mist must be improved. Powder suppressants with low pyrolysis temperatures and high decomposition efficiencies can be designed and employed to suppress hydrogen explosions.

Targeting c-Met is a clinical trend for the precise treatment of HCC, but the potential issue of acquired drug resistance cannot be ignored. Targeted protein degradation technology has demonstrated promising prospects in disease treatment and overcoming drug resistance due to its special mechanism of action. In this study, we designed and synthesized two series of novel c-Met degraders and conducted a systematic biological evaluation of the optimal compound H11. H11exhibited good c-Met degradation activity and anti-HCC activity. Importantly, H11also demonstrated more potent inhibitory activity against Ba/F3-TPR-MET-D1228N and Ba/F3-TPR-MET-Y1230H cell lines than did tepotinib. In summary, H11displayed potent anti-HCC activity as a degrader and may overcome resistance to type Ib inhibitors, making it a new therapeutic strategy for HCC with METalterations.

Novel and covert fluorescence is quite desirable for fluorescent anticounterfeiting application. Here, Cs2InCl5·H2O/Sb and Cs2NaInCl6/Sb with high photoluminescence quantum yields (PLQYs) of 99.61 and 99.9%, respectively, were achieved. Considering the excellent optical performances together with the high similarity of the two crystal structures, we tried to realize the crystal structure transition from Cs2InCl5·H2O/Sb to Cs2NaInCl6/Sb by an ion-exchange method. It was well done by just adding the NaCl precursor with different concentrations in the Cs2InCl5·H2O/Sb product. Interestingly, a gradual color change from yellow to orange, warm white, white, cool white, and blue was achieved in the process of crystal structure transition. The energy-transfer dynamic models of Cs2InCl5·H2O/Sb, the white product, and Cs2NaInCl6/Sb were identified. The chemical reaction and UV fluorescence properties made it possible for application in chemical and fluorescent double-modal anticounterfeiting and highly decreased the possibility of being cracked and copied. Especially, when salt for daily cooking was used to replace NaCl, a similar phenomenon happened as that of the 99.9% NaCl precursor, which made it easy to be applicated. The combination of chemical and optical verifications provides two levels of security and unbreakable encryption. The results demonstrate that the transition from Cs2InCl5·H2O/Sb to Cs2NaInCl6/Sb is highly promising in fluorescent anticounterfeiting application.

Neural network pruning is a popular approach to reducing the computational complexity of deep neural networks. In recent years, as growing evidence shows that conventional network pruning methods employ inappropriate proxy metrics, and as new types of hardware become increasingly available, hardware-aware network pruning that incorporates hardware characteristics in the loop of network pruning has gained growing attention. Both network accuracy and hardware efficiency (latency, memory consumption, etc.) are critical objectives to the success of network pruning, but the conflict between the multiple objectives makes it impossible to find a single optimal solution. Previous studies mostly convert the hardware-aware network pruning to optimization problems with a single objective. In this paper, we propose to solve the hardware-aware network pruning problem with Multi-Objective Evolutionary Algorithms (MOEAs). Specifically, we formulate the problem as a multi-objective optimization problem, and propose a novel memetic MOEA, namely HAMP, that combines an efficient portfolio-based selection and a surrogate-assisted local search, to solve it. Empirical studies demonstrate the potential of MOEAs in providing simultaneously a set of alternative solutions and the superiority of HAMP compared to the state-of-the-art hardware-aware network pruning method.

Two-dimensional (2D) tellurium (Te) is emerging as a promising p-type candidate for constructing complementary metal-oxide-semiconductor (CMOS) architectures. However, its small bandgap leads to a high leakage current and a low on/off current ratio. Although alloying Te with selenium (Se) can tune its bandgap, thermally evaporated SexTe1–xthin films often suffer from grain boundaries and high-density defects. Herein, we introduce a precursor-confined chemical vapor deposition (CVD) method for synthesizing single-crystalline SexTe1–xalloy nanosheets. These nanosheets, with tunable compositions, are ideal for high-performance field-effect transistors (FETs) and 2D inverters. The preformation of Se–Te frameworks in our developed CVD method plays a critical role in the growth of SexTe1–xnanosheets with high crystallinity. Optimizing the Se composition resulted in a Se0.30Te0.70nanosheet-based p-type FET with a large on/off current ratio of 4 × 105and a room-temperature hole mobility of 120 cm2·V–1·s–1, being eight times higher than thermally evaporated SexTe1–xwith similar composition and thickness. Moreover, we successfully fabricated an inverter based on p-type Se0.30Te0.70and n-type MoS2nanosheets, demonstrating a typical voltage transfer curve with a gain of 30 at an operation voltage of Vdd= 3 V.

Phase heterogeneity of bromine–iodine (Br–I) mixed wide-bandgap (WBG) perovskites has detrimental effects on solar cell performance and stability. Here, we report a heterointerface anchoring strategy to homogenize the Br–I distribution and mitigate the segregation of Br-rich WBG-perovskite phases. We find that methoxy-substituted phenyl ethylammonium (x-MeOPEA+) ligands not only contribute to the crystal growth with vertical orientation but also promote halide homogenization and defect passivation near the buried perovskite/hole transport layer (HTL) interface as well as reduce trap-mediated recombination. Based on improvements in WBG-perovskite homogeneity and heterointerface contacts, NiOx-based opaque WBG-perovskite solar cells (WBG-PSCs) achieved impressive open-circuit voltage (Voc) and fill factor (FF) values of 1.22 V and 83%, respectively. Moreover, semitransparent WBG-PSCs exhibit a PCE of 18.5% (15.4% for the IZO front side) and a high FF of 80.7% (79.4% for the IZO front side) for a designated illumination area (da) of 0.12 cm2. Such a strategy further enables 24.3%-efficient two-terminal perovskite/silicon (double-polished) tandem solar cells (da of 1.159 cm2) with a high Vocof over 1.90 V. The tandem devices also show high operational stability over 1000 h during T90lifetime measurements.

In plant secondary metabolite biosynthesis, acylation is a diverse physiological process, with BAHD acyltransferases playing an essential role. Borneol acetyltransferase (BAT) is an alcohol acetyltransferase, which catalyzes borneol and acetyl-CoA to synthesize bornyl acetate (BA). However, the enzymes involved in the biosynthesis of BA have so far only been characterized in Wurfbainia villosa, the studies on the WvBATs have only been conducted in vitro, and the catalytic activity was relatively low. In this research, three genes (WlBAT1, WlBAT2, and WlBAT3) have been identified to encode BATs that are capable of acetylating borneol to synthesize BA in vitro. We also determined that WlBAT1 has the highest catalytic efficiency for borneol-type substrates, including (+)-borneol, (−)-borneol, and isoborneol. Furthermore, we found that BATs could catalyze a wide range of substrate types in vitro, but in vivo, they exclusively catalyzed borneol-type substrates. Through molecular simulations and site-directed mutagenesis, it was revealed that residues D32, N36, H168, N297, N355, and H384 are crucial for the catalytic activity of WlBAT1, while the R382I-D385R double mutant of WlBAT1 exhibited an increasing acylation efficiency for borneol-type substrates in vitroand in vivo. These findings offer key genetic elements for the metabolic engineering of plants and synthetic biology to produce BA.

Dimethyl carbonate (DMC) is an important synthetic intermediate with widespread applications in the chemical industry. In recent years, the one-step conversion of CO2into DMC has gained significant attention, but the development of an efficient catalytic system remains a considerable challenge to realize this process. Herein, a series of organotin-oxometalate coordination polymers were developed for DMC synthesis from CO2and methanol without any solvent/dehydrating agent, and it was found that the metal complexes displayed excellent catalytic performances, especially (nBu3Sn)2MoO4with the n-butyl group (Bu) was very active, and a high DMC yield (15.7 mmol gcat–1) and high TON value (3.90) were achieved under mild conditions, which was remarkably higher than the recently reported catalytic systems. Moreover, the polymer was highly stable and easy to be recycled with no significant activity loss. Experimental and in situ FT-IR studies revealed that the synergetic catalysis of [Bu3Sn]+and [MoO4]2–to active CO2and methanol was the key reason for improved catalytic activity. This study offers a promising strategy for the efficient synthesis of organic carbonates catalyzed by metal complexes to realize better CO2utilization.

Halide perovskites, particularly zero-dimensional (0D) A2BX6-type structures, have garnered significant attention in diverse fields due to their exceptional photoelectric properties. This study focuses on the optical property regulation of 0D A2ZrCl6through nonprotonated cation substitution (A = [(CH3)3SO]+and [(CH3)4N]+) combined with different guest ions (i.e., Sb3+, Bi3+, and Mn2+). The varied host–guest interaction is regarded as the reason for the different luminescent behaviors. The Sb3+dopant has a weaker interaction with the host [(CH3)3SO]2ZrCl6than Mn2+does. As a result, Sb3+@[(CH3)3SO]2ZrCl6shows an excitation-dependent photoluminescence ranging from blue to orange, while Mn2+@[(CH3)3SO]2ZrCl6exhibits only Mn2+dopant emission under different excitation wavelengths. Besides the dopant effect on the host–guest interaction, the organic cation can impact the host–guest behavior as well. [(CH3)3SO]2ZrCl6and [(CH3)4N]2ZrCl6have different preferences for dopant ions. Sb3+and Mn2+are favorable in the host [(CH3)3SO]2ZrCl6, while Sb3+and Bi3+are adopted in [(CH3)4N]2ZrCl6. Due to the varied emission colors from Sb3+and Bi3+dopants, [(CH3)4N]2ZrCl6possesses diverse photoluminescence behaviors through dopant engineering. Inside [(CH3)4N]2ZrCl6, Sb3+and Bi3+have a weak host–guest interaction, showing a varied PL from the host and guest emission centers under different excitations.

The epidermal growth factor receptor (EGFR) tertiary C797S mutation is an important cause of resistance to Osimertinib, which seriously hinders the clinical application of Osimertinib. Developing proteolysis-targeting chimeras (PROTACs) targeting EGFR mutants can offer a promising strategy to overcome drug resistance. In this study, some novel PROTACs targeting C797S mutation were designed and synthesized based on a new EGFR inhibitor and displayed a potent degradation effect in H1975-TM cells harboring EGFRL858R/T790M/C797S. The representative compound C6exhibited a DC50of 10.2 nM against EGFRL858R/T790M/C797Sand an IC50of 10.3 nM against H1975-TM. Furthermore, C6also showed potent degradation activity against various main EGFR mutants, including EGFRDel19/T790M/C797S. Mechanistic studies revealed that the protein degradation was achieved through the ubiquitin–proteasome system. Finally, C6inhibited tumor growth in the H1975-TM xenograft tumor model effectively and safely. This study identifies a novel and potent EGFR PROTAC to overcome Osimertinib resistance mediated by C797S mutation.

To obtain enhanced physical and biological properties, various nanoparticles are typically assembled into hybrid nanoparticles through the binding of multiple homologous DNA strands to their complementary counterparts, commonly referred to as homomultivalent assembly. However, the poor binding affinity and limited controllability of homomultivalent disassembly restrict the assembly yield and dynamic functionality of the hybrid nanoparticles. To achieve a higher binding affinity and flexible assembly choice, we utilized the paired heteromultivalency DNA to construct hybrid nanoparticles and demonstrate their excellent assembly characteristics and dynamic applications. Specifically, through heteromultivalency, DNA-functionalized magnetic beads (MBs) and gold nanoparticles (AuNPs) were efficiently assembled. By utilizing ICP–MS, the assembly efficiency of AuNPs on MBs was directly monitored, enabling quantitative analysis and optimization of heteromultivalent binding events. As a result, the enhanced assembly yield is primarily attributed to the fact that heteromultivalency allows for the maximization of effective DNA probes on the surface of nanoparticles, eliminating steric hindrance interference. Subsequently, with external oligonucleotides as triggers, it was revealed that the disassembly mechanism of hybrid nanoparticles was initiated, which was based on an increased local concentration rather than toehold-mediated displacement of paired heteromultivalency DNA probes. Capitalizing on these features, an output platform was then established based on ICP–MS signals that several Boolean operations and analytical applications can be achieved by simply modifying the design sequences. The findings provide new insights into DNA biointerface interaction, with potential applications to complex logic operations and the construction of large DNA nanostructures.

Improving the efficiency of platinum group metals (Pt, Pd, Rh, etc.) in catalytic oxidation reactions remains an urgent topic. The conflict between the low-temperature activity and high-temperature stability of noble metals can hardly reach a consensus. For instance, Pt cluster catalysts supported on CeO2with high low-temperature activity will suffer from deactivation due to the redispersion under high-temperature lean-burn reaction conditions. Herein, two Pt1/CeO2prepared by the incipient wetness impregnation method using different Pt precursors possessed varied Pt–O and Pt–O–Ce coordination numbers (CNs). They showed various priorities in CO oxidation versus NH3selective catalytic oxidation, materials with higher CNPt–O–Ceselectively catalyzing NH3oxidation to N2more superior, conversely materials with lower CNPt–O–Ceperforming better in CO oxidation. After activation by H2reduction, both formed massive Pt clusters on the CeO2surface but showed drastically distinct stability in lean-burn CO oxidation reactions. By summarizing the experimental results of high-angle annular dark-field scanning transmission electron microscopy, X-ray absorption spectroscopy, Raman spectroscopy, in situdiffuse reflectance infrared Fourier transform spectroscopy, etc., it is beyond doubt that the difference in the initial states of Pt1due to distinct precursors indeed determine the redispersion behavior of the reduced Pt clusters on CeO2. Materials with lower CNPt–O–Ceand higher CNPt–Oare more likely to form robust Pt clusters, as they are not conducive to Pt anchoring, thus restricting the reversible structural evolution occurring under lean-burn CO oxidation and reductive conditions. This approach serves as a guide for the convenient and efficient construction and exploration of robust Pt cluster catalysts.

The eye, as a specialized visual organ, is directly exposed to the external environment, and, therefore, it faces constant challenges from external pathogenic organisms and toxins. In the ocular mucosa (OM) of mammals, mucosal-associated lymphoid tissues (MALTs) constitute the primary line of defense. However, the immune defense role of the OM remains unknown in aquatic vertebrates. To gain insights into the immune processes within the OM of teleost fish, we developed an infection model of rainbow trout (Oncorhynchus mykiss) OM using a parasite, Ichthyophthirius multifiliis(Ich). Immunofluorescence, qPCR, and H&E staining revealed that Ich successfully infiltrates the OM of rainbow trout, leading to pathological structural changes, as evidenced by A&B staining. Importantly, the qPCR results indicate an up-regulation of immune-related genes following Ich infection in the OM. Moreover, transcriptome analyses were conducted to detect immune responses and impairments in eye function within the OM of rainbow trout with Ich infection. The results of the transcriptome analysis that Ich infection can cause an extensive immune response in the OM, ultimately affecting ocular function. To the best of our knowledge, our findings represent for the first time that the teleost OM could act as an invasion site for parasites and trigger a strong mucosal immune response to parasitic infection.

Pipeline transport at high Reynolds number can result in significant turbulent losses. One of the most effective methods for turbulent drag reduction is adding a very small amount of polymer drag-reducing agent to the pipeline. However, due to the complex interaction between polymers and turbulence, turbulence models incorporating polymer additives remain to be studied and developed. In the present work, we investigated the turbulence model using Reynolds averaged numerical simulation (RANS) to describe polyacrylamide drag reduction flow. A low-Reynolds-number k–εmodel in turbulent flow has been developed by considering the concentration and type of polymers, which can be applied for polymer drag reduction prediction in the pipe. Mean velocity profile Uf, turbulent intensity, turbulent kinetic energy k, and turbulent dissipation rate εin the regions of viscous sublayer, buffer layer and logarithmic layer have been predicted with various concentration θ, Reynolds number Re, degradation degrees, and changing laws of these factors have been revealed with wall distance. The developed turbulence model showed a good capability to qualitatively forecast mean velocity profile, turbulent intensity, turbulent kinetic energy, and turbulent dissipation rate, and the prediction error between the experimental and simulated values falls along the y= xcurve, which can be used for the investigation and prediction of varies water-soluble, oil-soluble polymers in turbulent drag reduction flow in pipes with other parameters such as pipe diameter, pipe length, and the Reynolds number.

The Yellow River Basin (YRB) is a vital ecological zone in China owing to its sensitive and fragile environment. Under the long-term influence of climate changes and artificial factors, the relationship between precipitation, vegetation, and surface water in the YRB has changed drastically, ultimately affecting the water resources and environmental management. Therefore, we applied multivariate statistical analysis to investigate the precipitation, normalized difference vegetation index (NDVI), and surface water changes in the YRB from 2000 to 2021. Furthermore, we attempted to clarify the ecological effects of precipitation by explaining the relationship between precipitation and vegetation in terms of the time-lag relationship using the Integrated Multi-satellite Retrievals for Global Precipitation Measurement algorithm, Moderate Resolution Imaging Spectroradiometer, and hydrological databases. Precipitation, vegetation, and area of surface water in the YRB showed increasing trends from 2000–2021 (e.g., 7.215 mm/yr, 0.004 NDVI/yr, and 0.932 km2/yr, respectively). The water level in the upper reaches of the YRB showed a downward trend, whereas that in the middle and lower reaches exhibited an upward trend. Changes in precipitation had a positive effect on vegetation and surface water in the YRB, with correlation coefficients of 0.63 and 0.51, respectively. The responses of NDVI and surface water elevation to precipitation were heterogeneous and delayed, with the majority showing a lag time of approximately ⩽ 16 days. Moreover, the lag times of Longyangxia Lake and Ngoring-Co Lake were 0 and 8 days, respectively. We showed that precipitation variability can effectively explain vegetation improvement and increases in surface water elevation, while providing a proven scenario for predicting the surface water and vegetation productivity under the influence of climate change.

VISTA (V-domain Ig suppressor of T cell activation) is a novel immune checkpoint protein and represents a promising target for cancer immunotherapy. Here, we report the design, synthesis, and evaluation of a series of methoxy-pyrimidine-based VISTA small molecule inhibitors with potent antitumor activity. By employing molecular docking and microscale thermophoresis (MST) assay, we identified a lead compound A1that binds to VISTA protein with high affinity and optimized its structure. A4was then obtained, which exhibited the strongest binding ability to VISTA protein, with a KDvalue of 0.49 ± 0.20 μM. In vitro, A4significantly activated peripheral blood mononuclear cells (PBMCs) induced the release of cytokines such as IFN-γ and enhanced the cytotoxicity of PBMCs against tumor cells. In vivo, A4displayed potent antitumor activity and synergized with PD-L1 antibody to enhance the therapeutic effect against cancer. These results suggest that compound A4is an effective VISTA small molecule inhibitor, providing a basis for the future development of VISTA-targeted drugs.

With the growing popularity of movies as a source of entertainment and relaxation in modern society, movie recommendation systems have become increasingly important for helping viewers navigate a vast selection of movie products. However, existing methods may not accurately capture the preferences and opinions of viewers. To address this gap, we propose a novel approach that utilizes the Pythagorean hesitant fuzzy distance measure in combination with the VIKOR method to deal with the extracted review information and evaluate the similarity between different movies in a given genre. We then transform this information into Pythagorean hesitant fuzzy attribute evaluation terms using the Probabilistic Linguistic Term Set (PLTS) and apply conflict degree theory to calculate similarity scores. The algorithm proposed in this paper employs the VIKOR method to select recommended movie products that match the preferences of users and satisfy their needs. Comparative analysis demonstrates its reliability and stability. In addition, according to the comparative data, its performance is better than the comparable method in the selection of alternative schemes, which highlights its contribution in the field of movie recommendation systems.

The gas–liquid two-phase flow is of great significance to industrial production. The nonlinear turbulence characteristics of pressure fluctuation in a Quatro static mixer (QSM) are experimentally investigated under different superficial gas/liquid velocities to further study the flow characteristics of the gas–liquid two-phase. It is shown that there is a generalized self-similarity scaling law in the QSM, which is obtained by fitting the structure function of pressure fluctuation signals at different inlet and outlet flow velocities. Through self-similarity scaling law analysis, the scaling exponent parameters of the structure function of two-phase flow pressure fluctuation under apparent liquid velocity (UL) = 0.028 m/s and different apparent gas velocities (UG) in QSM are calculated. The calculation results show that the singular scaling exponent (γ) and intermittency parameter (β) are in the ranges of −0.142–0.311 and 0.286–0.966, respectively. This indicates that there exists an extended self-similarity scaling law in QSM under a low Reynolds number (Re). The R/Sand autocorrelation analysis reveal that the detailed signals at the inlet and outlet have three similar flow behaviors, which consist of the liquid turbulence at the inlet, the bubble movement, breakup and coalescence, and the liquid swirling flow. The nonlinear analysis shows that flow in QSM presents a pseudohomogeneous flow pattern when the Hurst index (H) = 0.4–0.6, while it presents a heterogeneous flow pattern when H= 0.6–0.72. Support vector machine (SVM) and Random Forest models (RF) are utilized to better predict the flow pattern, with the recognition precision reaching 99.08 and 99.39%, respectively.

The energy conversion efficiency of a triboelectric nanogenerator (TENG) is severely limited by the charge density of triboelectric materials, while drastic and unavoidable charge decay happens during contact due to the insufficient charge retention capacity of positive triboelectric materials. Here, elaborately synthesized acid-ion-doped pyridine-based polybenzimidazole processing with strong charge retention capability is demonstrated to couple with negatively corona-polarized electrets. As illustrated by thermal stimulation and an ion mass spectrometer, the formation of acid-ion chimerism processes high activation energy for stored charges, and the selective anion migration can compensate the escape of polarized charge. Accordingly, the charge density can reach up to 596 μC m–2and the charge retention rate reaches 49.7%, which is so far the highest intrinsic charge density obtained in the open air. Thus, the ionic chimerism strategy provides an effective way to suppress the charge escaping in the open air and gives a great expandable avenue for the material challenges of TENG’s practical deployment.

Purpose: Amidst the rapid development of the global digital economy, digital transformation has become a strategic choice that firms must use to respond to the changing times. This study analyzes the impact of digital transformation on corporate environmental, social and governance (ESG) performance. Design/methodology/approach: This study analyzes the impact of digital transformation on corporate ESG performance. Findings: Using panel data from Chinese A-share-listed companies from 2010 to 2019, the authors found that digital transformation has a positive impact on corporate ESG performance, especially for high-tech firms and state-owned firms. In particular, the authors find that the digital production and digital marketing exert a positive effect on corporate ESG performance. Mechanism tests showed that digital transformation helps promote corporate green innovation, improve information transparency and improve corporate governance, thus enhancing ESG performance. A moderating effect analysis revealed that the positive impact of digital transformation on ESG performance is more significant in firms with government subsidies and chief executive officers (CEOs) with rich career experience. Originality/value: Most existing research has confirmed the positive effect of digital transformation on firms' financial performance, whereas fewer studies have focused on the impact of digital transformation on the non-financial performance of firms. [ABSTRACT FROM AUTHOR]

Flying Ad Hoc Networks (FANETs) are essential for a wide range of military and civil applications as they significantly reduce mission duration and improve coverage compared to using a single Unmanned Aerial Vehicle (UAV). Nevertheless, the dynamic and mobile nature of FANETs, coupled with fluctuating data traffic patterns, pose significant challenges for adaptive routing and efficient packet delivery. At present, most of the existing routing protocols for FANETs are designed based on mobility or topology information without taking into account data traffic and the associated joint decision-making approach. This narrow focus can result in poor network performance, characterized by longer delivery delay and lower delivery ratio. Thus, this paper proposes the Joint Prediction and Entropy weight-based (JPE) routing protocol by considering both current and future network conditions. The protocol uses a Long Short-Term Memory (LSTM) model to predict and obtain the mobility, buffer available size, and Link Expiration Time (LET) of each Neighbouring UAV (NU) in order to avoid high-mobility, high-traffic, and weak-link UAVs and establish an appropriate path. The routing decision problem is then formulated as an optimization problem and solved using the proposed Entropy Weight-based Multi-Metric (EWMM) approach. The integrated prediction and decision process considers both current and future multi-metric factors. Simulation results demonstrate the effectiveness of the LSTM-based Joint Prediction (JP) model and show that the JPE protocol outperforms the PAP and SPA protocols, improving Packet Delivery Ratio (PDR) and delay performance by 30.13% and 31.24% respectively.

Two-dimensional (2D) protein films can be used to modify the properties of surfaces, and find applications predominantly in the fields of biomaterials, lithography, optics and electronics. However, it is difficult to produce scalable homogeneous and robust protein films with an easy, low-cost, green and efficient method. Further challenges include encapsulating and releasing functional building blocks in the film without inactivating them, and maintaining or improving the bioactivities of proteins used for the formation of the films. Here we detail the process to prepare large 2D protein films with user-defined features and structures via the amyloid-like aggregation of commonly synthesized proteins. These films can be synthesized at meter scales, have high interface adhesion, high functional expansibility and tunable functional properties, obtained by controlling the position of the disulfide bond breakage. For example, we can retain or even enhance the natural antibacterial, biomineralization and antifouling activity of proteins involved in film formation, and the properties can also be expanded through the physical blending or chemical grafting of additional functional blocks on the surface of the film. A 2D protein film can be prepared in ~3 h using four alternative coating techniques: immersion, transfer, hydrogel stamping and spraying. The characterization process of the film requires ~5 d. The procedure can be carried out by users with basic expertise in materials science.

Cellular Internet card (IC) as a new business model emerges, which penetrates rapidly and holds the potential to foster a great business market. However, with the explosive growth of IC users, the user churn problem becomes severe, affecting the IC business significantly, while there is lacking appropriate techniques in the literature to deal with the issue. In this article, we take the lead to study one large-scale data set from a provincial network operator of China, which contains about 4 million IC users and 22 million traditional card (TC) users. We first justify the IC user churn issue with data, and categorize the user churning reasons. Then, we shed light on understanding user portraits, which is the building block to enable efficient model design. Particularly, we conduct a systematical analytics on usage data by studying the difference of two types of users, examining the impact of user properties, and characterizing the user Internet using behaviors. Finally, by using the IC user portraits and usage patterns, we propose an IC user Churn Prediction model, named ICCP, which consists of a feature extraction component and a learning-based churn prediction architecture design. For feature extraction, both the static portrait features and temporal sequential features are captured. In the learning architecture, we devise the principal component analysis (PCA) block and the embedding/transformer layers to learn the respective information of two types of features, which are collectively fed into the classification multilayer perceptron layer (MPL) for churn prediction. A reference implementation of ICCP is conducted within the telecom system and extensive experiments corroborate the efficiency of ICCP.

A recurrent neural network (RNN) has shown powerful performance in tackling various natural language processing (NLP) tasks, resulting in numerous powerful models containing both RNN neurons and feedforward neurons. On the other hand, the deep structure of RNN has heavily restricted its implementation on mobile devices, where quite a few applications involve NLP tasks. Magnitude-based pruning (MP) is a promising way to address such a challenge. However, the existing MP methods are mostly designed for feedforward neural networks that do not involve a recurrent structure, and, thus, have performed less satisfactorily on pruning models containing RNN layers. In this article, a novel stage-wise MP method is proposed by explicitly taking the featured recurrent structure of RNN into account, which can effectively prune feedforward layers and RNN layers, simultaneously. The connections of neural networks are first grouped into three types according to how they are intersected with recurrent neurons. Then, an optimization-based pruning method is applied to compress each group of connections, respectively. Empirical studies show that the proposed method performs significantly better than the commonly used RNN pruning methods; i.e., up to 96.84% connections are pruned with little or even no degradation of precision indicators on the testing datasets.

A [2 + 4]/[1 + 2] annulation approach was successfully established to construct pyrroloquinoline-fused cyclopropane in a highly diastereoselective fashion (>20:1 dr). The tetracyclic 1,7-fused indoles were efficiently obtained from readily available starting materials under mild conditions. This methodology displays impressive substrate generality with two reaction components. The products resulting from this doubly annulative strategy are useful synthetic intermediates.

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Emission of volatile organic compounds has important influence on complex air pollution and human health. In this paper, a series of tungsten–iron composite oxides with different proportions and preparation methods were synthesized and first used for catalytic combustion of chlorobenzene and toluene, as typical polluting gas sources. These WO3-based solid catalytic materials were systematically characterized by modern analytical methods, and the results showed that there was strong electron interaction between W and Fe elements in the composite oxides, and the presence of a certain amount of tungsten oxide inhibited the crystallization of iron oxide, and vice versa, which were beneficial to the uniform dispersion of tungsten–iron components into each other and the improvement of redox properties. Compared with single-component oxide, the formation of tungsten–iron composite oxide affected the micro-structure, improved the specific surface area and optimized the pore structure of materials. The performance test results showed that the tungsten–iron composite oxide (FeWO4–0.5Fe2O3, molar ratio of tungsten and iron was 1/2) prepared using citric acid-based sol–gel method was the optimal, and its catalytic degradation efficiency could reach 90% for chlorobenzene and 83% for toluene at 320 °C, and maintain at least 60 h without obvious deactivation, with high selectivity to the formation of HCl and CO2.

In the interaction between rubber tire and granular terrain, the dynamic behavior of granular terrain is significantly affected not only by the shape of soil grains, but also by the deformation contact of tread rubber. Therefore, the effect of these two factors should be considered in tire–sand interactions. In this study, an improved contact model including the effect of sand grain shape and tread rubber deformation is developed for dealing with the interaction between rubber tire and sand terrain. In sand–sand contact model, the interaction between sand grains takes the form of surface contact instead of conventional point contact, where the contact calculation contains four interactions, i.e., normal force, tangential force, rolling resistance and twisting resistance. And in tread–sand contact model, the contact between tread rubber and sand grains is surface contact, which includes the rolling resistance and twisting resistance on the grains caused by rubber deformation during contact. As a result, the complete and realistic evaluation of contact forces is accomplished. Next, a comparison of sandpile simulation of coarse particles and experiment is carried out to verify the effectiveness of the sand–sand contact model. Finally, the novel contact model is applied to tire–sand interaction simulations and compared with the single-wheel experiments. The results indicate that the proposed contact model can be a powerful tool to simulate the interactions between rubber tire and sand terrain.

The change processes and trends of shoreline and tidal flat forced by human activities are essential issues for the sustainability of coastal area, which is also of great significance for understanding coastal ecological environment changes and even global changes. Based on field measurements, combined with Linear Regression (LR) model and Inverse Distance Weighing (IDW) method, this paper presents detailed analysis on the change history and trend of the shoreline and tidal flat in Bohai Bay. The shoreline faces a high erosion chance under the action of natural factors, while the tidal flat faces a different erosion and deposition patterns in Bohai Bay due to the impact of human activities. The implication of change rule for ecological protection and recovery is also discussed. Measures should be taken to protect the coastal ecological environment. The models used in this paper show a high correlation coefficient between observed and modeling data, which means that this method can be used to predict the changing trend of shoreline and tidal flat. The research results of present study can provide scientific supports for future coastal protection and management.

This research investigates the ecological importance, changes, and status of mangrove wetlands along China’s coastline. Visual interpretation, geological surveys, and ISO clustering unsupervised classification methods are employed to interpret mangrove distribution from remote sensing images from 2021, utilizing ArcGIS software platform. Furthermore, the carbon storage capacity of mangrove wetlands is quantified using the carbon storage module of InVEST model. Results show that the mangrove wetlands in China covered an area of 278.85 km2in 2021, predominantly distributed in Hainan, Guangxi, Guangdong, Fujian, Zhejiang, Taiwan, Hong Kong, and Macao. The total carbon storage is assessed at 2.11×106t, with specific regional data provided. Trends since the 1950s reveal periods of increase, decrease, sharp decrease, and slight-steady increases in mangrove areas in China. An important finding is the predominant replacement of natural coastlines adjacent to mangrove wetlands by artificial ones, highlighting the need for creating suitable spaces for mangrove restoration. This study is poised to guide future mangrove-related investigations and conservation strategies.

Various attention mechanisms have been extensively adopted in visual object tracking networks. However, the research of Siamese networks with convolutional attention mechanisms as the main architecture remains limited. In this letter, we propose a novel Siamese tracker that leverages convolutional attention and a hierarchical architecture. By utilizing the deformable convolution, we expand the receptive field to capture more contextual information. In addition, our hierarchical architecture optimizes the attention by hierarchically adjusting the focus of attention based on the significance of features at different levels. On the other hand, we decompose the step of feature compression during the feature-to-result transformation process and integrate it in segments with the feature fusion process to address the information loss and underfitting issues. Extensive experimental results demonstrate that our tracker achieves state-of-the-art performance on multiple benchmarks including LaSOT, TrackingNet, UAV123, and NFS while maintaining a high processing speed of 40 FPS.

Federated learning (FL) is an emerging technique that enables privacy-preserving distributed learning. Most related works focus on centralized FL, which leverages the coordination of a parameter server to implement local model aggregation. However, this scheme heavily relies on the parameter server, which could cause scalability, communication, and reliability issues. To tackle these problems, decentralized FL, where information is shared through gossip, starts to attract attention. Nevertheless, current research mainly relies on first-order optimization methods that have a relatively slow convergence rate, which leads to excessive communication rounds in wireless networks. To design communication-efficient decentralized FL, we propose a novel over-the-air decentralized second-order federated algorithm. Benefiting from the fast convergence rate of the second-order method, total communication rounds are significantly reduced. Meanwhile, owing to the low-latency model aggregation enabled by over-the-air computation, the communication overheads in each round can also be greatly decreased. The convergence behavior of our approach is then analyzed. The result reveals an error term, which involves a cumulative noise effect, in each iteration. To mitigate the impact of this error term, we conduct system optimization from the perspective of the accumulative term and the individual term, respectively. Numerical experiments demonstrate the superiority of our proposed approach and the effectiveness of system optimization.

In this article, a highly sensitive salinity sensor based on the virtual Vernier effect of a micro–nano tapered two-mode fiber (TTMF) is proposed, theoretically analyzed, and experimentally verified. Theoretical calculations show that the dispersion turning point (DTP) occurs when the group effective refractive index (RI) difference between the fundamental and higher order modes of the micro–nano TTMF is equal to zero, and thus, ultrahigh RI sensitivity can be achieved to measure seawater salinity. After considering the robustness of the sensor, the cone size near the DTP was chosen and the sensor was designed in a semi-package. Meanwhile, applying the sensing signal demodulation method of the virtual Vernier effect to the TTMF salinity sensor not only exponentially amplifies the sensitivity of salinity measurement but also conveniently obtains the tunable sensitivity amplification by changing the modulation function, which has the incomparable flexibility of the conventional optical Vernier effect. The experimental results show that the proposed salinity sensor has a salinity sensitivity of 6.138 nm/ ‰ in the range of 0 ‰–39.22 ‰ and a temperature sensitivity of -3.672 nm/°C in the range of 30 °C–50 °C, which provides an improvement of 13.64 and 14.81 times in salinity and temperature sensitivities, respectively, when compared with a single TTMF. The TTMF packaged sensor demonstrated in this article has the advantages of low cost, robustness, and high reproducibility, which provides key technical support for optical sensing applied in the ocean and also provides some useful references for other sensing applications.

Keyphrase generation (KG) is an essential problem in many natural language processing (NLP) tasks. Deep learning keyphrase generation methods often combine the copy and generating action-aware probabilities to model keyphrase accuracy, ignoring the copy and generating specificities for each keyword. In this work, we design a novel KG method that utilizes reinforcement learning (RL), namely KGAgent, where a relative reward based RL agent makes effective controllable manipulations on the decoupled action-aware policies to further improve the keyphrase generability. While RL is unstable and hard to train, several strategies, including momentum mechanism, distribution alignment and proximal policy optimization, are employed to stabilize the actor-critic network. Additionally, the proposed method reflects algorithm scalability that it can be plugged into any existing deep learning methods under different training paradigms, and consistently achieves higher accuracy than the state-of-the-art methods on the scientific article and social media datasets.

Power system transient stability has been translated into a Lyapunov stability problem of the postdisturbance equilibrium for decades. Despite substantial results, conventional theories suffer from the stringent requirement of knowing the postdisturbance equilibrium a priori. In contrast, the wisdom from practice, which certificates stability by only the observation of converging frequencies and voltages, seems to provide an equilibrium-independent approach. Here, we formulate empirical wisdom by the concept of augmented synchronization and aim to bridge such a theory–practice gap. First, we derive conditions under which the convergence to augmented synchronization implies the convergence to the equilibrium set, laying the first theoretical foundation for empirical wisdom. Then, we reveal from what initial values the power system can achieve augmented synchronization. Our results open the possibility of an equilibrium-independent power system stability analytic that redefines the nominal motion as augmented synchronization rather than a certain equilibrium. Single-machine examples and the IEEE 9-bus system verify our results and illustrate promising implications.

The fabrication of polyoxometalates (POMs)-modified TiO2catalysts with connected pore structure has attracted great interests due to its prominent effect on mass transfer and catalytic oxidation activity. Here, we report a series of hierarchically macro/mesoporous (M/m) phosphotungstic acid (HPW)/TiO2composites, which are fabricated by colloidal crystal template method and applied as deep desulfurization catalyst in fuel oil. As-synthesized hierarchical HPW/TiO2composite is interconnected by ordered macroporous channel with disordered mesoporous embedded on pore walls. Moreover, Keggin-type HPW is homogeneously dispersed on the TiO2matrix. Hierarchical macro/mesoporous HPW/TiO2shows an excellent catalytic performance, the removal rate for dibenzothiophene in model fuel reaches 99% under the optimum conditions. This high performance of the three-dimensional ordered macropores (3DOM) HPW/TiO2can be attributed to its hierarchically porous which is highly beneficial for the mass transfer during the catalytic process. Moreover, the used catalyst could be regenerated by centrifugation and only slight decreasing of the catalytic activity after five cycles.

Internet of unmanned aerial vehicle (I-UAV) networks promise to accomplish sensing and communication tasks quickly, robustly, safely, and cost-efficiently via effective situation awareness and cooperation among UAVs. To achieve the promising benefits, the crucial I-UAV networking issue should be tackled. This article argues that I-UAV networking can be classified into two categories: quality-of-service (QoS) driven networking and quality-of-experience (QoE) driven networking. Each category of networking poses emerging challenges that have severe effects on the safe and efficient accomplishment of I-UAV missions. This article elaborately analyzes these challenges and expounds on the corresponding intelligent approaches to tackle the I-UAV networking issue. Besides, considering the uplifting effect of extending the scalability of I-UAV networks through cooperating with high altitude platforms (HAPs), this article gives an overview of the integrated I-UAV and HAP network and presents the corresponding networking challenges and intelligent approaches.

In this communication, the implementation and experimental validation of harmonic beamforming in a planar 2-bit time-modulated array (TMA) is proposed. The incorporation of a 2-bit phase modulators effectively enhances the complex architecture, substantial dimensions, and limited integration level observed in existing TMA. By utilizing real/imaginary part modulation and time shift in Fourier transform, the optimization process is streamlined, enabling the desired weighting of both amplitude and phase without relying on T/R modules. Both simulation and experimental results confirm the array’s capability for low-sidelobe beam scanning and flat-top beam forming at the +1st-harmonic frequency.

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In recent years, the Vernier effect has received much attention from researchers in the field of fiber sensing because of its ability to improve measurement sensitivity significantly. However, two prominent problems occur when it is used for strain sensing: low extinction ratio (ER) and high-temperature cross-sensitivity. A compact fiber strain sensor based on the Vernier effect with the advantages of high strain sensitivity, high ER, and low-temperature cross-sensitivity is proposed in this study. The sensing Fabry–Perot interferometer (SFPI) utilizes a hollow-core fiber (HCF) as the FP cavity. HCF can effectively confine the light beam and increase the ER of the interference fringes. Moreover, the second reflecting surface of SFPI is implemented using an indirect fusion method, which increases the active length to improve strain sensitivity. A reference Fabry–Perot interferometer (RFPI) is inserted into a hollow silica capillary (HSC) to isolate it from external environmental effects while serving as one of the reflecting surfaces for SFPI. The factors influencing the strain sensitivity of the sensor are analyzed through theoretical and experimental analyses. The maximum strain sensitivity is $- 940.02\,\,{{\text {pm}}/\mu \varepsilon }$ , and its temperature sensitivity is only $0.50 {{\text {pm}}/}{ }{{^{\circ }}}{\text {C}}$ . This sensor also exhibits excellent stability and reversibility. It also has potential applications in the field of strain sensing.

The absorption of laser energy by pure copper powders has a huge impact on the formation of high quality pure copper parts by selective laser melting. In this paper, surface modification technology was used to treat pure copper powder to improve the laser absorption effect of pure copper powder. It was found that modifying the surface of pure copper powder to generate a layer of CuO film effectively improved the absorption of laser energy by pure copper powder. Uniform and dense CuO films improved the quality of selective laser melting pure copper parts, which solved the problem of preparing pure copper powders for selective laser melting of high-density parts. The microstructure of selective laser melting high-density pure copper parts consisted of fine columnar and cellular crystals. The presence of subcrystals and dislocations was found in localized areas. The results of the physical phase analysis showed that the CuO phase existed in the parts prepared from the surface-modified pure copper powder, and the presence of the nano-CuO phase also enhanced, which effectively improved the microstructure and properties of the constituent selective laser melting pure copper parts.

A fiber-optic strain sensor with antiresonance suppression was proposed, and sensor sensitization was achieved by constructing a simulated reference interferometer (SRI) using the Vernier effect. In the capillary-based Fabry–Perot interferometer (FPI), antiresonance resulted in uneven contrast of the reflection spectrum, which could not meet the conditions for the use of the Vernier effect. In addition to controlling the length of the capillary to below the critical length to overcome this problem, antiresonance could be suppressed by increasing the thickness. A strain sensitivity of 14.58 pm/ $\mu \varepsilon $ , which is 7.15 times higher than the strain sensitivity of a single sensing interferometer, was achieved using the antiresonance-suppressed FPI as a sensing interferometer and modulating it with the constructed SRI. In addition, tunable sensitivities of 22.36 and 32.83 pm/ $\mu \varepsilon $ were obtained by only changing the parameters of SRI instead of remaking the reference interferometer. The sensor realized the Vernier effect with only one interferometer. It has the advantages of online control sensitivity, simple structure, and low cost and has potentially important applications in the field of strain measurement.

With the growth of model/dataset/system size for distributed model training in datacenters, the widely used Parameter Server (PS) architecture suffers from communication bottleneck of gradient transmission. Recent works attempt to utilize programmable switches to implement in-network gradient aggregation and alleviate communication bottlenecks on PSs. Due to the limited on-chip memory of programmable switches, gradient transmission requires strict synchronization to achieve ideal aggregation performance. However, the distributed training system is usually heterogeneous in datacenters (e.g., computation and bandwidth heterogeneity), and the gradient will reach the aggregation nodes asynchronously, thereby seriously affecting the aggregation performance. To solve the above issue, we propose XAgg, which accelerates heterogeneous gradient aggregation by deploying the eXpress Data Path (XDP) based aggregator on servers. Specifically, the abundant idle memory on servers can cache the entire gradient, so as to effectively deal with asynchronous gradient transmission in heterogeneous scenarios. Moreover, XDP can provide high-performance and low-latency gradient aggregation. We conduct microbenchmark and testbed with real-world DNN models and datasets. Experimental results show that XAgg improves the gradient aggregation throughput by 3.3 $\times $ compared with TCP-based aggregation, reaching 100 Gbps with 10 CPU cores. In addition, XAgg reduces communication time by 49%-82% compared with state-of-the-art solutions.

In this paper, a family of Ladder multi-resonant switched-capacitor converters (MRSCC) is proposed to deal with the issues of a high transient current spike, inherent capacitor charge sharing loss, hard-switched operation, and inability for lossless voltage regulation for traditional Ladder switched-capacitor converters (SCC). By incorporating a multi-resonant core (MRC) and adopting the on-time fixed frequency modulation, a “charge balance” state with overlapped resonant currents is introduced. Consequently, the conversion ratio of the Ladder MRSCC can be regulated continuously, widely, and efficiently, even at light load conditions. In addition, all transistors achieve zero-current-switching (ZCS) turn-on, and all diodes obtain ZCS turn-off. The complete soft-charging operation between capacitors is obtained, eliminating the high transient current spike and charge-sharing loss effectively. In addition, the synchronous Ladder MRSCC with a clamp circuit is proposed to further eliminate the high-frequency ringing and reduce the conduction loss, which makes it potentially applicable to compact applications, including power supplies for portable devices and panel-level photovoltaic systems. A comprehensive analysis of different operation modes, boundary conditions, component stress, topology implementation, and topology comparison is presented to assist converter operation and hardware design. A 55W prototype with the 3X Ladder MRSCC was developed to verify the effectiveness of the proposed topology, modulation strategy, and circuits.