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In mobile edge computing (MEC) systems, multiple unmanned aerial vehicles (UAVs) can be utilized as aerial servers to provide computing, communication, and storage services for edge users, called UAV-assisted MEC, which has emerged as a promising technology to improve both the computing and communication performances. Unlike existing works without considering jamming attacks, we investigate a multi-UAV-assisted-MEC scenario under multiple malicious jammers and then propose a resource management approach with the objective of minimizing both the system energy consumption and latency. Due to the time-varying nature of communication environments, we design a multi-agent deep reinforcement learning (MADRL)-based resource management approach to dynamically adjust the CPU frequency, communication bandwidth, and channel access selection of UAVs to enhance the system performance against jamming attacks. On this basis, in order to enhance the algorithm learning efficiency, we propose a multi-agent twin-delayed deep deterministic policy algorithm in combination with the prioritized experience replay mechanism (PER-MATD3) to effectively search for the joint resource management strategy under high-dimensional state and action spaces, where the time-varying channel state information and imperfect attack behavior information are also effectively trained to improve the learning capacity and convergence speed. Simulation and experimental results verify that the proposed approach can significantly decrease the overall system latency (i.e., computing and communication latency) and energy consumption compared to other benchmark algorithms under different real-world settings.

Fringe projection profilometry (FPP) is widely used in the field of 3-D shape measurement with the advantages of noncontact, high speed, and nondestructive. However, measuring the complex reflective surface is a severe challenge for the FPP system. In this article, an active optimization method based on the reflection characteristics of the surface is proposed. We first design an ultrasonic atomization optimization measurement (UAOM) device. It is capable of uniformly and stably covering the measured surface with a microscopic water mist layer. The water mist layer is used to change the reflection characteristics, thereby reducing the impact of overexposure and underexposure on the measurement results. Apart from that, a bidirectional reflectance distribution function (BRDF) model and projector-object–camera radiation transfer model are established, which could be used to quantitatively describe the changes in reflection characteristics caused by the microscopic water mist layer. Comparative experimental results demonstrate that the proposed method yields improved measurements of complex reflective surfaces without the need for intricate algorithms. The final 3-D result of the points cloud based on our method is more complete and accurate, which improves the efficiency and versatility of 3-D measurement.

In this paper, we introduce a simplified digital resolution enhancer (DRE) achieved by reducing the survived state of the Viterbi algorithm for pre-equalization systems with low-resolution digital-to-analog converters (DACs). The clipping technique is employed based on the simplified DRE to mitigate the impact of quantization noise and improve the signal power. Both clipping and quantization noise are considered in the branch metric calculation of the simplified DRE aiming to improve the overall system performance. Simulation and experiment have been conducted to verify its effectiveness by transmitting 50 Gbaud pulse amplitude modulation (PAM)-4 and 40 Gbaud PAM-8 signals. The results show that for 50 Gbaud PAM-4 signal, 3-tap channel impulse response (CIR)-supported DRE with 3 soft-quantization possibilities designed for both clipping and quantization noise suppression can achieve 1 dB receiver optical power (ROP) improvement compared to the channel response dependent noise shaping (CRD-NS) technique at the 7% hard-decision forward error correction (HD-FEC) threshold. Increasing both CIR length and soft-quantization possibilities from 3 to 5 can yield an additional 0.4 dB ROP improvement. For 40 Gbaud PAM-8 signal transmission, only 5-tap CIR-supported DRE with 5 soft-quantization possibilities can guarantee the bit error rate (BER) of the signal below the HD-FEC threshold. DRE shaped for both clipping and quantization noise can obtain an additional 1 dB ROP improvement. Considering computational complexity and BER performance, simplified 3-tap CIR-supported DRE with 3 soft-quantization possibilities and 5-tap CIR-supported DRE with 5 soft-quantization possibilities have been experimentally studied for 50 Gbaud PAM-4 and 40 Gbaud PAM-8 signals, respectively. The results indicate that a computational complexity compression ratio of 55% and 95.2% can be realized for 50 Gbaud PAM-4 and 40 Gbaud PAM-8 signals in comparison with the traditional DRE, respectively, while only 0.2 dB ROP penalty is observed.

Near-infrared (NIR) phosphor-converted light-emitting diodes (pc-LEDs) are considered promising light sources for night vision, food analysis, biomedicine, and plant growth. Yet, the application potential of this technology is vulnerable to the function degradation of the phosphors used, such as thermal quenching, which needs to be addressed urgently. Herein, the NIR phosphors K2LiMF6:Cr3+(M = Al, Ga, In) with a cubic double-perovskite structure synthesized by a green hydrofluoric acid-free hydrothermal method exhibit outstanding thermal stability. Under 450 nm excitation, the as-synthesized K2LiMF6:Cr3+phosphors all exhibited broadband NIR emission covering 650–1000 nm peaking at 755–780 nm. The prepared K2LiAlF6:Cr3+phosphor shows a unique zero-thermal quenching performance (I423 K/I298 K= 102%). The comprehensive effects of a wide band gap, large thermal energy barrier, weak electron–phonon coupling effect, and high structural rigidity are responsible for the suppression of thermal quenching in this material. The output power of the NIR pc-LED device reached 285 mW at 100 mA. This series of phosphors has promise in night vision and bioimaging applications.

Mixed lineage kinase domain–like pseudokinase (MLKL) initiates necroptosis and could serve as a therapeutic target related to a series of human diseases. Proteolysis-targeting chimeras (PROTACs) are useful tools for degrading pathological proteins and blocking disease processes. Using computer-aided modeling and molecular dynamics simulations, we developed a series of covalent MLKL PROTACs by linking and optimizing a theophylline derivative that covalently targets MLKL. Via structure–activity relationship studies, MP-11was identified as a potent MLKL PROTAC degrader. Furthermore, MP-11showed lower toxicity than the original MLKL ligand, exhibiting nanomolar-scale antinecroptotic activity on human cell lines. Xenograft model studies showed that MP-11effectively degraded MLKL in vivo. Importantly, our study demonstrates that the covalent binding strategy is an effective approach for designing MLKL-targeting PROTACs, serving as a model for developing PROTACs to treat future necroptosis-related human diseases.

Device-to-device (D2D)-assisted decentralized learning has been proposed for collaborative model training without the parameter server while protecting the data privacy. However, in such task-oriented network, a large latency would be aroused due to the limited communication resource and straggling D2D links. To tackle these challenges, in this letter, we take into consideration the D2D relay in the decentralized learning system. The device can transmit the local model to others over relay links, thereby alleviating the effect of straggling D2D links. Then, a joint relay selection and spectrum allocation algorithm is proposed to minimize the learning latency while guaranteeing the leaning performance. Finally, thorough tests are carried out to show the effectiveness of the proposed algorithm. The results show that the learning latency can be reduced while maintaining the convergence rate and learning accuracy, as compared against the traditional methods.

Restricted by the deficient training samples, deep learning-based automatic target recognition (ATR) methods for synthetic aperture radar (SAR) are prone to performance degradation of target recognition with extremely few samples, making them difficult to apply in practical scenarios. To address this issue, we propose a scattering attribute embedded network (SANet) for few-shot SAR ATR. SANet embeds inherent physical attributes provided by attribute scattering centers of SAR targets to enable effective generalization to novel-class targets unseen in the training set with only an extremely small number of samples. The method employs a two-stream architecture, comprising attribute induced global semantic extraction and graph-based structure representation learning, which are optimized to enhance intraclass compactness and interclass discriminability to achieve physics embedded few-shot SAR target recognition. Several few-shot recognition experiments are conducted using the moving and stationary target acquisition and recognition dataset of different depression angles. The experimental results demonstrate that the proposed SANet significantly outperforms state-of-the-art few-shot learning methods in terms of SAR target recognition accuracy. Additionally, SANet exhibits better generalization and robustness across different depression angles and occlusions in SAR images, which shows a great potential for practical applications.

A peroxymonosulfate oxidation system was developed via modification of β-cyclodextrin (β-CD) on the surface of Fe2+-doped ZIF-67 (CD/Fe@ZIF-67) as an activator. The 99.7% carbamazepine, 91.3% bisphenol A (BPA), and 95.4% diclofenac (DCF) degradation efficiency were achieved within 10 min, 60, and 1 min, respectively. The hydrophobicity of these three pollutants is positively correlated with their adsorption kinetic constants by CD/Fe@ZIF-67 due to the introduction of β-CD. Scavenger experiments and electron spin resonance spectra confirmed that carbamazepine was preferentially oxidized by SO4•–[λ(SO4•–)(70.5%) > λ(•OH)(28.2%) > λ(O2•–)(1.3%)], where SO4•–and O2•–played dominant roles in the degradation of BPA [λ(SO4•–)(71.7%) > λ(O2•–)(22.8%) > λ(•OH)(5.5%)], and O2•–was responsible for DCF removal [λ(O2•–) = 93.2%]. Additionally, the particulate catalyst was immobilized in the shell side of a ceramic membrane in a membrane reactor for catalyst recovery. This reactor achieved nearly 100% removal efficiency under optimal conditions: 0.036 wt % catalyst loading, 0.5 mM peroxymonosulfate concentration, 1 L inflow, 10 mg/L initial carbamazepine concentration, and 0.012 L/min hydraulic retention time. In summary, this study elucidates the active role of β-CD in a polymetallic/peroxymonosulfate system and provides valuable insights into the development of effective oxidation methods for pharmaceutical and personal care products in wastewater.

In this work, we used cellulose nanofibers (CNF) as the skeleton, Fe3O4@ZnO composite particles as magnetic synergist particles, 3‐(2‐aminoethylamino) propyltrimethoxysilane (AS) and trimethoxy(octyl)silane (OTMS) as water‐based hydrophobic modifiers to prepare magnetic and superhydrophobic cellulose nanofibers based‐aerogel with low density and intricate three‐dimensional structure. Fe3O4@ZnO confers magnetic properties (3.82 emu/g) and exceptional thermal stability (water contact angle of 150.1° at 200 °C) to the system, while the combination with OTMS/AS endows the system superhydrophobic (157.5°) and excellent mechanical properties (stress of 96.95 kPa at 80% strain). It is worth noting that in the process of modifying the system with OTMS/AS, no organic solvents and acidic substances are used in the solution. Benefiting from their synergies, the system demonstrates a notable oil absorption capacity (12.31–41.91 g/g) and outstanding oil selectivity (exceeding 90%), driven by gravity alone. Interestingly, this system, marked by its cost‐effectiveness, simplicity, eco‐friendliness, and heightened efficiency, holds promising prospects for diverse applications in different oil–water separation behavior and purifying industrial oil wastewater, as well as oil flooding incidents.

The accurate simulation of regional-scale winter wheat yield is important for national food security and the balance of grain supply and demand in China. Presently, most remote sensing process models use the “biomass×harvest index (HI)” method to simulate regional-scale winter wheat yield. However, spatiotemporal differences in HI contribute to inaccuracies in yield simulation at the regional scale. Time-series dry matter partition coefficients (Fr) can dynamically reflect the dry matter partition of winter wheat. In this study, Fr equations were fitted for each organ of winter wheat using site-scale data. These equations were then coupled into a process-based and remote sensing-driven crop yield model for wheat (PRYM-Wheat) to improve the regional simulation of winter wheat yield over the North China Plain (NCP). The improved PRYM-Wheat model integrated with the fitted Fr equations (PRYM-Wheat-Fr) was validated using data obtained from provincial yearbooks. A 3-year (2000–2002) averaged validation showed that PRYM-Wheat-Fr had a higher coefficient of determination (R²=0.55) and lower root mean square error (RMSE=0.94 t ha–1) than PRYM-Wheat with a stable HI (abbreviated as PRYM-Wheat-HI), which had R² and RMSE values of 0.30 and 1.62 t ha–1, respectively. The PRYM-Wheat-Fr model also performed better than PRYM-Wheat-HI for simulating yield in verification years (2013–2015). In conclusion, the PRYM-Wheat-Fr model exhibited a better accuracy than the original PRYM-Wheat model, making it a useful tool for the simulation of regional winter wheat yield.

Byline: Cheng. Li, Zhuoran. Gu, Peiqian. Ni, Wentao. Zhang, Fuhan. Yang, Wei. Li, Xudong. Yao, Yifan. Chen Objective: Imaging examination, tumor marker detection, bladder biopsy, and other methods are the [...]

The aim of this study was to evaluate the factors influencing the inactivation effect of intense pulsed light (IPL) on Aeromonas salmonicidagrown on chicken meat and skin, and to further develop prediction models of inactivation. In this work, chicken meat and skin inoculated with meat-borne A. salmonicidaisolates were subjected to IPL treatments under different conditions. The results showed that IPL had obvious bactericidal effect in the chicken skin and thickness groups when the treatment voltage and time were 7 V combined with 5 s. In addition, the lethality curves of A. salmonicidawere fitted under IPL conditions of 3.5–7.5 V. The comparison of statistical parameters revealed that the Weibull model could best fit the mortality curves and could accurately predict the mortality dynamic of A. salmonicidagrown on chicken skin. And further a secondary model between the scale factor band the treatment voltage in Weibull model was established using linear equations, which determined that the secondary model could accurately predict the inactivation of A. salmonicida. This study provides a theoretical basis for future prediction models of Aeromonas, and also provides new ideas for sterilization approaches of meat-borne Aeromonas.

Arene-tethered diols constitute a valuable class of structural motifs of drug and bioactive natural product molecules. In this study, a regioselective protocol for olefination and arylation of arene-tethered 1,2-diols and 1,3-diols has been developed using easily foldable acetal structures for attaching pyridine and nitrile directing groups. The method overcomes the steric hindrance effect of the short-chain diols and affords products in high yield and regioselectivity. This efficient cascaded catalysis has been successfully utilized in the syntheses of natural products such as peucedanol, decursinol, and marmesin.

Silicon (Si) stands out as a highly promising anode material for next-generation lithium-ion batteries. However, its low intrinsic conductivity and the severe volume changes during the lithiation/delithiation process adversely affect cycling stability and hinder commercial viability. Rational design of electrode architecture to enhance charge transfer and optimize stress distribution of Si is a transformative way to enhance cycling stability, which still remains a great challenge. In this work, we fabricated a stable integrated Si electrode by combining two-dimensional graphene sheets (G), one-dimensional Si nanowires (SiNW), and carbon nanotubes (CNT) through the cyclization process of polyacrylonitrile (PAN). The integrated electrode features a G/SiNW framework enveloped by a conformal coating consisting of cyclized PAN (cPAN) and CNT. This configuration establishes interconnected electron and lithium-ion transport channels, coupled with a rigid-flexible encapsulated coating, ensuring both high conductivity and resistance against the substantial volume changes in the electrode. The unique multidimensional structural design enhances the rate performance, cyclability, and structural stability of the integrated electrode, yielding a gravimetric capacity (based on the total mass of the electrode) of 650 mAh g–1after 1000 cycles at 3.0 A g–1. When paired with a commercial LiNi0.5Co0.2Mn0.3O2cathode, the resulting full cell retains 84.8% of its capacity after 160 cycles at 2.0 C and achieves an impressive energy density of 435 Wh kg–1at 0.5 C, indicating significant potential for practical applications. This study offers valuable insights into comprehensive electrode structure design at the electrode level for Si-based materials.

To provide a new perspective on the formation of the Ge-rich coals, the depositional environment of the Wulantuga coals was studied with the incorporation of coal maceral and geochemistry-based indicators. The results show that the No.6 coal seam in the Wulantuga mine was formed in a mire with a succession of swamps, fens, and marsh. The average contents of Ge in coals formed in different mires, from high to low, are swamp (220 μg/g), marsh (205 μg/g), and fen (185 μg/g). The accumulation of the No.6 seam has been divided into four stages from bottom to top based on the identified coal facies types. The reducing condition and gelification of the ecosystem environment ranged from strong to weak, to strong, and back to weak. The variation of Ge concentrations also occurs in the same way. Strong reduction and gelification of the ecosystem environments can favor Ge enrichments in the Wulantuga coals. Sufficient sources and favorable conditions are essential for the unusual Ge enrichments in coals. This study provides a new perspective for the depositional environment of Ge-rich coals and is useful for the exploration of Ge-rich coal resources.

Morphological rearrangement of the endoplasmic reticulum (ER) is critical for metazoan mitosis. Yet, how the ER is remodeled by the mitotic signaling remains unclear. Here, we report that mitotic Aurora kinase A (AURKA) employs a small GTPase, Rab1A, to direct ER remodeling. During mitosis, AURKA phosphorylates Rab1A at Thr75. Structural analysis demonstrates that Thr75 phosphorylation renders Rab1A in a constantly active state by preventing interaction with GDP-dissociation inhibitor (GDI). Activated Rab1A is retained on the ER and induces the oligomerization of ER-shaping protein RTNs and REEPs, eventually triggering an increase of ER complexity. In various models, from Caenorhabditiselegansand Drosophilato mammals, inhibition of Rab1AThr75phosphorylation by genetic modifications disrupts ER remodeling. Thus, our study reveals an evolutionarily conserved mechanism explaining how mitotic kinase controls ER remodeling and uncovers a critical function of Rab GTPases in metaphase.

Under extreme conditions such as rapid acceleration, the engine speed of hybrid electric vehicle (HEV) is forced to decrease, making it difficult for the vehicle to meet power demands. However, the inability to obtain the degree of engine speed reduction in different environments in advance makes the energy management strategy (EMS) of HEV less adaptive. In this paper, a simple outcome-oriented engine characteristic recognition model is presented. Firstly, the autoencoder is combined with the traditional clustering algorithm, and the characteristics of the engine are defined artificially. Secondly, the long short-term memory (LSTM) neural network is introduced to recognize engine characteristics online, and different thresholds of EMS under different characteristics are given. In the face of unknown operating conditions and unknown engine states, the recognition model can provide recognition results online and assist EMS in power distribution. Simulation results demonstrate that this strategy effectively controls the SOC trajectory. Compared with the benchmark methods, the fuel economy of the proposed strategy is improved by 6.95%, which is close to the global optimal strategy. The effectiveness of this strategy is validated.

This study proposes an innovative deep learning algorithm for pose estimation based on point clouds, aimed at addressing the challenges of pose estimation for objects affected by the environment. Previous research on using deep learning for pose estimation has primarily been conducted using RGB-D data. This paper introduces an algorithm that utilizes point cloud data for deep learning-based pose computation. The algorithm builds upon previous work by integrating PointNet +  + technology and the classical Point Pair Features algorithm, achieving accurate pose estimation for objects across different scene scales. Additionally, an adaptive parameter-density clustering method suitable for point clouds is introduced, effectively segmenting clusters in varying point cloud density environments. This resolves the complex issue of parameter determination for density clustering in different point cloud environments and enhances the robustness of clustering. Furthermore, the LineMod dataset is transformed into a point cloud dataset, and experiments are conducted on the transformed dataset to achieve promising results with our algorithm. Finally, experiments under both strong and weak lighting conditions demonstrate the algorithm's robustness.

Traditional transgenic detection methods require high test conditions and struggle to be both sensitive and efficient. In this study, a one-tube dual recombinase polymerase amplification (RPA) reaction system for CP4-EPSPSand Cry1Ab/Acwas proposed and combined with a lateral flow immunochromatographic assay, named "Dual-RPA-LFD", to visualize the dual detection of genetically modified (GM) crops. In which, the herbicide tolerance gene CP4-EPSPSand the insect resistance gene Cry1Ab/Acwere selected as targets taking into account the current status of the most widespread application of insect resistance and herbicide tolerance traits and their stacked traits. Gradient diluted plasmids, transgenic standards, and actual samples were used as templates to conduct sensitivity, specificity, and practicality assays, respectively. The constructed method achieved the visual detection of plasmid at levels as low as 100 copies, demonstrating its high sensitivity. In addition, good applicability to transgenic samples was observed, with no cross-interference between two test lines and no influence from other genes. In conclusion, this strategy achieved the expected purpose of simultaneous detection of the two popular targets in GM crops within 20 min at 37 ℃ in a rapid, equipment-free field manner, providing a new alternative for rapid screening for transgenic assays in the field.

An imaging radar is expected to provide high-quality images for interesting targets. To this end, an information-theoretic approach is used in this article to jointly optimize waveform and receive filter with desired cross-correlation properties. First, the problem formulation is achieved by maximizing the mutual information (MI), subject to constant modulus, high resolution, and low peak sidelobe ratio (PSLR) constraints. Second, to solve the resultant problem with a fractional quadratic objective function and various nonconvex constraints, four customized iterative loops are performed to transform the problem into a series of solvable subproblems, via minorization-maximization (MM), alternate direction penalty method (ADPM), and feasible point pursuit successive convex (FPP-SCA) approximation. Convergence of every iterative loop is proved, resulting in guaranteed convergence of the whole procedure with polynomial-time complexity. Finally, numerical examples are presented to demonstrate that the proposed method can construct a unimodular waveform and filter with better information acquisition ability and more desirable cross-correlation function.

We propose a new theoretical framework for quantification and sensitization of a disease state (DS). This is in contrast to the traditional discrete description of the disease, where the inherent characteristic of its progression is ignored, and a finite number of DSs are resulted, effectively leading the sensing process to the act of classifying. Central to the framework is a canonical fuzzy model of DS that allows for the conversion of its linguistic description into a normalized numerical variable. This generates the mapping between one set with the domain of the universe of DSs and another set with the domain of the universe of disease labels (DLs). Subsequently, the framework is analyzed from the fuzzy-set-theoretic perspective by utilizing the medical expert knowledge system of disease severity, consisting of the acute physiology and chronic health evaluation, which provides useful insight that enables the disease diagnosis process to be designed and optimized as a soft-computing problem. Built on this fuzzy analysis, we present two multimodal strategies, namely 1) reversibility combining and 2) reliability combining, to enhance the sensing performance measured through the degree of nonreversibility of the surjective mapping from DLs to sensing outputs. Finally, we utilize some numerical examples based on highly realistic synthetic medical data to elaborate on the proposed framework, which offers a new perspective for disease diagnosis by monitoring continuously the individual's disease progression.

The sand-casting process is predominantly used worldwide, especially in small-scale industries. It is very important for small component manufacturing and is one of the simple metal casting methods. However, the sand-casting process has some flaws, which also sometimes lead to a higher cost in some cases, especially the disadvantages like the surface finishing and the porosity add up to higher cost in the cases of complicated castings and their machining. This review investigates various research works done in and around the sand-casting process and tries to implement the parameters to obtain an appropriate final product from the sand-casting process, focusing on the intermediaries involved during the sand process casting and using them efficiently. These intermediaries may include the components used during the sand-casting process, predominantly sand, the metal used, the mold used for making the cavity, the chill used for promoting homogeneous nucleation, etc. Another focus is on decreasing the wastage of resources during the process. The discussed parameters will attempt to provide future directions to maximally control the disadvantages to some extent in the current process.

Causalityholds profound potentials to dissipate confusion and improve accuracy in cuffless continuous blood pressure (BP) estimation, an area often neglected in current research. In this study, we propose a two-stage framework, CiGNN, that seamlessly integrates causality and graph neural network (GNN) for cuffless continuous BP estimation. The first stage concentrates on the generation of a causal graph between BP and wearable features from the the perspective of causal inference, so as to identify features that are causally related to BP variations. This stage is pivotal for the identification of novel causal features from the causal graph beyond pulse transit time (PTT). We found these causal features empower better tracking in BP changes compared to PTT. For the second stage, a spatio-temporal GNN (STGNN) is utilized to learn from the causal graph obtained from the first stage. The STGNN can exploit both the spatial information within the causal graph and temporal information from beat-by-beat cardiac signals for refined cuffless continuous BP estimation. We evaluated the proposed method with three datasets that include 305 subjects (102 hypertensive patients) with age ranging from 20–90 and BP at different levels, with the continuous Finapres BP as references. The mean absolute difference (MAD) for estimated systolic blood pressure (SBP) and diastolic blood pressure (DBP) were 3.77 mmHg and 2.52 mmHg, respectively, which outperformed comparison methods. In all cases including subjects with different age groups, while doing various maneuvers that induces BP changes at different levels and with or without hypertension, the proposed CiGNN method demonstrates superior performance for cuffless continuous BP estimation. These findings suggest that the proposed CiGNN is a promising approach in elucidating the causal mechanisms of cuffless BP estimation and can substantially enhance the precision of BP measurement.

Microwave medical imaging (MMI) operating over the frequency range covering hundreds of megahertz to tens of gigahertz has the potential to provide proactive healthcare solutions to patients with acute (for early diagnosis) or chronic (for daily monitoring) medical conditions. This technology exploits the tissue dielectric properties for disease diagnosis by using quantitative or qualitative algorithms. The advantages of MMI include low health risk, low operational cost, lightweight implementation, and ease of use, given its perspective of miniaturization and integration into portable and handheld devices with networking capability. MMI has been proposed for cancer detection, stroke detection, heart imaging, bone imaging, tracking of in-body drug-loaded nanorobots, etc. It is, however, challenging to develop accurate and robust MMI algorithms for both sensitive and selective diagnosis, due to the inherently ill-conditioned inverse scattering problems and the low dielectric contrast between healthy and diseased tissues. As such, using the a priori knowledge (APK) about the scattering profile to improve the performance of MMI is crucial for practical implementation and clinical deployment of MMI systems. This perspective article presents a new viewpoint of categorizing and utilizing various types of APK, which is acquired from the space, time, or frequency (STF) domain. The article starts with a general categorization framework of APK, followed by formulations of MMI algorithms utilizing APK. Subsequently, the existing APK-oriented MMI algorithms are reviewed in the respective STF domain. Finally, the influence of accuracy of APK on MMI performance is discussed using numerical examples. Through the analysis of the distorted Born iterative method (DBIM) and the pulse radar method, we have discussed the accurate usage of time-domain APK for both quantitative and qualitative evaluations, and the performance improvements of the quantitative and qualitative algorithms are 92% and 80%, respectively. The results demonstrate that the proper implementation of APK can significantly improve imaging accuracy, further validating the effectiveness and generalizability of the proposed model. This perspective would offer some useful insights into the future directions of MMI algorithmic development.

Accurately estimating the 6-D pose is crucial in various modern production fields. This article introduces a novel approach for efficiently and precisely estimating the 6-D pose of known objects within point cloud scenes of various resolutions, aiming to tackle the challenge of rapid and accurate 6-D pose estimation. This framework utilizes point pair features (PPFs) to conduct pose voting through a combination of offline training and online matching. Scene coefficients and model coefficients are introduced, and the required neighborhood point number for filtering is calculated based on different target objects, scenes, and point cloud densities. The appropriate downsampling rate is also calculated, and a novel hypothesis verification method is utilized to eliminate false positives and predict the accurate 6-D pose of the target object. The framework demonstrates good performance on industrial part datasets, showing good robustness under different point cloud densities.

Rock bridges are important structures for maintaining rock mass stability, but their shapes are not well known. The researchers propose a method for determining the shape of rock bridges based on experiments, discrete element methods and machine learning, which is applicable to complex joints with arbitrary spatial distribution. Numerical models are constructed using the discrete element method, and parameter matching is performed based on experimental results. The particles were clustered using the k-means algorithm with the maximum principal stress (σ1) as an indicator and the selection of initial values was optimized. The density-based spatial clustering of applications with noise (DBSCAN) algorithm was used to delete the noise from the particles. Finally, the boundary lines of the particles were extracted by self-programming, and the shape of the rock bridges was determined. Twenty-four sets of simulations were used to analyze the effect of rock bridges on the specimens. The results show that the failure mode of the specimen changes from shear to tensile damage as the cohesive force of the rock bridges increases. The peak strength and peak strain of the specimens increased with the increase of cohesion in the rock bridge. Rock bridges are the fastest growing areas of stress in the specimen.

In this work, a Play model based on recurrent neural network (RNN) is proposed to predict hysteresis characteristics of electrical steel sheets under complex excitation conditions. The proposed model combines the neural network with the Play hysteresis operator, replacing the distribution function in the hysteresis model with the trained neural network parameter structure. An automatic selection method for neural network hyperparameters based on genetic algorithm (GA) is suggested, which improves the prediction accuracy. Comparing the experimental with the model prediction results, the proposed model can accurately predict the hysteresis characteristics of materials under sinusoidal and harmonic conditions.

Polylactic acid (PLA) is often used in the preparation of environmentally friendly biodegradable polymer plastics, and how to improve the flame retardant performance of polylactic acid has been concerned by experts and scholars. Here, we provide a new idea, using bamboo activated carbon as the main material, and phytic acid, urea and Zn(NO3)2·6(H2O) as modifiers to produce a new type of carbon flame retardant. It has bamboo activated carbon as carbon source; second, it has P, N elements and metal oxides. The two synergistically play a flame retardant role on polylactic acid. The polylactic acid composite showed good thermal stability, from no grade optimization to V-0 in the UL-94 test, and the limiting oxygen index was also increased from 20.1 to 31.2%. The above tests show that bamboo activated carbon loaded with ZnO has a good flame retardant effect on polylactic acid.

Metasurface, consisting of subwavelength cells arranged in a periodic pattern, has emerged as a promising structure for controlling the scattering. However, studies in this field have primarily focused on the electromagnetic characteristic of metasurface itself. There are few studies on developing computational models for the scattering characteristic of electrically large target coated with metasurfaces. In this article, a scattering characteristic calculation model is proposed for such target, in which the metasurfaces are treated as rectangular electromagnetic surface radiators. Based on a rectangular plate scattering model and high-frequency approximation theory, the field of metasurface cell and homogeneous material cell is derived. Then, the total scattered field can be obtained by summing up the field of all the cells. Our simulation results demonstrate that the proposed model offers accurate scattering characteristic of electrically large target coated with phase-modulated metasurfaces. In addition, the echo and synthetic aperture radar (SAR) images of targets coated with metasurfaces are simulated, providing the intuitive variation of scattering characteristic. This article offers a theoretical foundation and practical guidance for the design and optimization of metasurfaces based on demand, which have potential applications in diverse areas, such as radar, communication, and sensing.

In clustering-based speaker diarization systems, the embedding clusters for distinctive speakers exhibit wide variability in size and density, posing difficulty for clustering accuracy. In spite of this, with the assistance of the overall distance relationships among speaker embeddings, most of the embeddings can be grouped to the correct cluster by sophisticated offline clustering algorithms. However, in online scenarios, such a complete distance relationships of the embeddings can not be obtained due to the incremental arrival of embeddings. Consequently, determining the number of clusters and then correctly grouping the embeddings become challenging in an online fashion. Furthermore, errors would accumulate quickly over time if the online clustering algorithm assigns the embeddings into clusters erroneously in the beginning. To address these problems, we designed a novel framework for online clustering. To reduce the high variability of speaker embeddings, we proposed the clustering guided embedding extractor training (CGEET) algorithm to encourage similarity between the size of the embedding space for different speakers in attempt to simplify the distance relationships of embeddings. The CGEET algorithm can grasp the distance information of the entire speaker embedding space and provide it to the online clustering algorithm. With this preliminary information, the distance thresholds guided online clustering (DTGOC) algorithm then processes incoming embeddings using a divide-and-conquer approach. It first handles the embeddings with explicit distance relationships and then searches for possible path combination they have with remaining embeddings in an online fashion. Moreover, in order to utilize the distance relationships of embeddings that are far apart in time, an online re-clustering strategy is incorporated in our DTGOC algorithm, which can alleviate error accumulation during online clustering. By implementing the above innovations, our proposed online clustering system achieves 14.00% DER with collar 0.25 at 2.5 s latency on the AISHELL-4, while the DER of the offline agglomerative hierarchical clustering system is 14.54%.

AimsTo investigate longitudinal choroid and ganglion cell–inner plexiform layer (GCIPL) changes in type 2 diabetes mellitus (T2DM) patients and healthy populations across 2 years.MethodsThis prospective cohort study included T2DM patients and healthy controls. T2DM patients were divided into mild non-proliferative diabetic retinopathy (NPDR) or non-DR (NDR) groups. Macular choroidal and GCIPL thickness was measured using swept-source optical coherence tomography at baseline and follow-up after 2 years. A linear-mixed effect model compared rates of change in choroidal and GCIPL thicknesses between the three groups.Results895 T2DM patients (770 in the NDR group and 125 in the NPDR group) and 847 healthy controls were included. Following 2 years, choroidal thinning occurred at a rate of −7.7±9.2 µm/year, −8.1±8.7 µm/year and −5.2±8.1 µm/year in NDR, NPDR and control groups, respectively (p<0.001). GCIPL loss occurred quickest in NPDR patients (−0.97±0.97 µm/year), followed by NDR (−0.91±0.89 µm/year) and the control group (−0.04±0.55 µm/year) (p<0.001). Following multivariate adjustment, choroidal thinning was −2.04 µm/year (95% CI: −4.05 to –0.03; p=0.047) and −1.95 µm/year (95% CI: −3.14 to –0.75; p=0.001) faster in NPDR and NDR groups than in the control group, respectively, and GCIPL thinning was −1.02 µm/year (95% CI: −1.19 to –0.84; p<0.001) and −0.88 µm/year (95% CI: −0.98 to –0.78; p<0.001) faster in the NPDR and NDR groups than in the control group, respectively.ConclusionProgressive choroidal and GCIPL thinning occurs in healthy individuals and T2DM patients; however, T2DM undergoes accelerated choroidal and GCIPL loss in NPDR patients.

DNA-based molecular communication (DNA-MC) is a biological communication mechanism that uses DNA strands as information carriers. The longevity, stability, high information density, massive parallelism, and biological compatibility of DNA offer a dramatic potential for DNA-based storage, computing, and communication. This letter extends our previous work, which used the directional and controllable molecular hopper along the track to replace the slow and random diffusion mechanisms. This letter proposes a multiple-track-hopper parallel communication mechanism to achieve high throughput by parallel transmission and sequencing. We recommend utilizing interleaved coding to mitigate the bit error rate (BER) caused by the back-stepping motion, resulting in successive symmetric errors. Additionally, we have explored the proper interleaving depth necessary to preserve the diminished DNA information density that results from the redundancy for error correction. Simulations show that interleaved coding efficiently reduces BER in parallel DNA-MC while requiring less redundancy. This letter demonstrates the feasibility and potential of high-throughput and low-error DNA-MC, which could enable novel interdisciplinary advances between DNA communication, nanotechnology, and synthetic biology.

Taking lessons from epidemic contact tracing, this communication proposes a method for boosting the efficiency of a full-wave electromagnetic solver by tracking its simulated energy distribution. When the energy within a subdomain of the problem is near zero, such areas can be safely ignored by the solver, reducing computational load with negligible impact on accuracy. We show that time-domain problems can be adaptively partitioned into energy-active (infections), energy-adjacent (exposed), and energy-null (unexposed) domains. To demonstrate the high efficiency and accuracy of this method, it is successfully applied to several computational electromagnetic problems. Due to its reliance on the causality principle to formulate the energy tracking procedure, this transformative method may be generalized beyond the scope of electromagnetics and optics to include other domains of computational physics.

Microwave medical imaging systems have shown a competitive advantage in stroke detection due to their cost-effectiveness, non-ionization, and portability. However, these systems often rely on time-consuming image reconstruction techniques, which are disadvantageous for timely stroke treatment. In this article, a novel microwave medical sensing (MMS) method is proposed for fast stroke classification and localization, which utilizes space division of the region under examination (RUE) (i.e., the head). The space division is enabled by the generalized scattering matrix (GSM) theory and incorporates the brain anatomy. Then a novel decision-tree learning method is proposed, which facilitates efficient stroke feature identification for classification. The spatial information acquired from the decision-tree also results in rapid stroke localization. To verify the proposed method, we investigate the feasibility of classifying brain strokes between an intracranial hemorrhage (ICH) stroke and an ischemic stroke (IS) with a wearable MMS system. Both numerical and experimental results are obtained. Compared to the traditional method, the classification rates for simulation and experimental results are improved by 14.1% and 19.2%, respectively. Furthermore, by utilizing the a priori information, the localization time is reduced by 21.1%. Finally, the localization accuracy is higher than 0.90 in both simulation and experimental studies. The classification accuracy and localization efficiency are shown to be greatly improved compared to the traditional method, which has great significance for wearable devices. This study proposes an efficient space-division-based detection method to localize the brain stroke without imaging.

In network representation learning we learn how to represent heterogeneous information networks in a low-dimensional space so as to facilitate effective search, classification, and prediction solutions. Previous network representation learning methods typically require sufficient task-specific labeled data to address domain-specific problems. The trained model usually cannot be transferred to out-of-domain datasets. We propose a self-supervised pre-training and fine-tuning framework, $\mathsf{PF\text{-}HIN}$PF-HIN, to capture the features of a heterogeneous information network. Unlike traditional network representation learning models that have to train the entire model all over again for every downstream task and dataset, $\mathsf{PF\text{-}HIN}$PF-HIN only needs to fine-tune the model and a small number of extra task-specific parameters, thus improving model efficiency and effectiveness. During pre-training, we first transform the neighborhood of a given node into a sequence. $\mathsf{PF\text{-}HIN}$PF-HIN is pre-trained based on two self-supervised tasks, masked node modeling and adjacent node prediction. We adopt deep bi-directional transformer encoders to train the model, and leverage factorized embedding parameterization and cross-layer parameter sharing to reduce the parameters. In the fine-tuning stage, we choose four benchmark downstream tasks, i.e., link prediction, similarity search, node classification, and node clustering. $\mathsf{PF\text{-}HIN}$PF-HIN outperforms state-of-the-art alternatives on each of these tasks, on four datasets.

Modeling sequential behaviors is the core of sequential recommendation. As users visit items in chronological order, existing methods typically capture a user’s present interests from his/her past-to-present behaviors, i.e., making recommendations with only the unidirectional past information. This article argues that future information is another critical factor for the sequential recommendation. However, directly learning from future-to-present behaviors inevitably causes data leakage. Here, it is pointed out that future information can be learned from users’ collaborative behaviors. Toward this end, this article introduces sequential graphs to depict item transition relationships: where and how each item transits from and will transit to. This temporal evolution information is called the light cone in special and general relativity. Then, a bidirectional sequential graph convolutional network (BiSGCN) is proposed to learn item representations by encoding past and future light cones. Finally, a manifold translating embedding (MTE) method is proposed to model item transition patterns in Riemannian manifolds, which helps to better capture the geometric structures of light cones and item transition patterns. Experimental comparisons and ablation studies verify the outstanding performance of BiSGCN, the benefits of learning from the future, and the improvements of learning in Riemannian manifolds.

We have recently proposed a framework of in vivo computation (IVC) which transforms the early tumor sensing problem into a computational problem. In the framework, a tumor-triggered biological gradient field (BGF) guides the swarm-intelligence-assisted targeting process, where externally manipulable and trackable magnetic nanorobots act as computational agents for the optimization procedure. As BGF can be viewed as an objective function which is utilized to define the fitness landscape for the agents, the inherent attributes of BGF are critical to the IVC process. All our previous investigations are based on the hypothesis that the BGF landscape remains time invariant during the tumor-targeting process, which results in a static function optimization problem. However, the properties of internal environment, such as the flow state of body fluid, will naturally lead to time-dependent variation of BGF, which means that the targeting process should be modeled as a dynamic function optimization problem. Based on this consideration, we focus on dynamic IVC by considering different variation patterns of BGF in this article. Two computational intelligence strategies named “swarm-based learning” and “individual-based learning” are proposed for dealing with the turbulence of the fitness estimation caused by the BGF variation. The in silico experiments and statistical results demonstrate the effectiveness of the proposed strategies. In addition, the above process is conducted in a 3-D search space, where the tumor vascular network is generated by an invasion percolation algorithm, which is more realistic compared to the 2-D search space in our previous works.

ABSTRACTThe blue light hazard (BLH) and luminous efficacy of a source () are associated with the photobiological safety and energy saving of light-emitting diodes (LEDs), respectively. In this study, we used genetic algorithm (GA) to optimize the BLH, , and color rendering parameters of five-chip hybrid white LEDs. Based on the optimal results, we used five-chip LEDs to obtain the hybrid white light. Their peak wavelengths were 461.5 (blue), 523.9 (green), 588.2 (orange), 643.3 (red), and 694.2 nm (far-red), respectively. In practice, the blue light hazard efficiency of radiation (BLHER) of the hybrid white light was less than 0.10, which was lower than half of the most common light sources. The ranged from 51.6 to 115.6 lm/W at correlated color temperature (CCT) from 2700 to 6500 K. On average, the CIE general color rendering index (Ra), CIE special color rendering index for the ninth deep red test sample (R9), ANSI/IES TM-30 color fidelity index (Rf), and color gamut index (Rg) of hybrid white light were 91.3, 87.8, 83.3, and 96.7, respectively. The melanopic efficacy of luminous radiation () was tunable in the range of 0.60 to 1.35 mW/lm. These results demonstrated that we used far-red LEDs to fabricate white light with low BLH and relatively high efficiency.

In this paper, a joint design of both transmit waveform and imaging filters for synthetic aperture radar (SAR) is proposed to improve its information acquisition capability. First, the mutual information between SAR image and target scattering characteristics is considered as the performance metric, and its equivalent analytical version is derived in the 2-D frequency domain. The design is formulated as an optimization problem with an energy condition and a similarity constraint. Then, to tackle the resultant non-convex problem, by referring to the Dinkelbach's method, an algorithm is derived to find the desired solution via a cyclic maximization procedure alternating between three subproblems. Based on minorization-maximization, a unified optimization method with an increasing penalty on constraint violation is proposed to solve all subproblems. Convergence of the developed algorithm is analytically proved. Finally, numerical examples are presented to demonstrate the effectiveness of the proposed design.

AimsTo investigate the relationship between choriocapillaris (CC) blood perfusion and the severity of diabetic retinopathy (DR) using swept-source optical coherence tomography angiography (SS-OCTA).MethodsType 2 diabetes mellitus in the Guangzhou was recruited. DR was graded according to the standardised seven-field colour retinal images adhering to the ETDRS scale. CC perfusion was quantified by evaluating the flow deficit (FD) density, FD number and FD size using SS-OCTA. Multivariable linear regressions were used to evaluate the correlation between CC FD metrics and DR severity.ResultsA total of 1692 patients (eyes) were included in the final analysis. Participants with DR had a significantly higher FD density than the NDR group, with the differences of 1.61% (95% CI 1.04 to 2.18; p<0.001) among mild non-proliferative DR (NPDR) patients, 2.23% (95% CI 1.76 to 2.70; p<0.001) among moderate NPDR patients and 3.31% (95% CI 2.27 to 4.36; p<0.001) among severe DR than NDR patients after adjusting for confounding factors. Similarly, the higher FD number and size were also correlated with severer degree of DR (all p<0.05). Furthermore, the addition of FD density to conventional risk factors significantly improved the performance for discriminating DR from NDR patients (AUC 0. 829, 95% CI 0. 804 to 0. 855; p<0.001).ConclusionQuantitative FD density, number and size assessed by using SS-OCTA were independently correlated with the severity of DR. The assessment of the CC perfusion metrics in the macula may be a sensitive biomarker of DR changes.

Emotion Cause Extraction Field (ECEF) focuses on the cause that triggers an emotion in a document. Traditional ECEF aims to extract the cause based on a given emotion while recent ECEF focuses more on extracting both the emotion and its corresponding cause. ECEF has attracted a lot of attention due to the significant developments in deep learning techniques, especially machine reading comprehension and neural network-based information retrieval. However, a comprehensive review of existing approaches and recent trends in ECEF is lacking. In this paper, we present a thorough survey to summarise existing methods for ECEF, including those for Emotion Cause Extraction (ECE), Emotion Cause Pair Extraction (ECPE), and Conversational Emotion Cause Extraction Field (CECEF). We also detail the widely used public datasets and discuss the limitations and prospects of existing methods in ECEF.

Exploring the relationship between circular RNA (circRNA) and disease is beneficial for revealing the mechanisms of disease pathogenesis. However, a blind search for all possible associations between circRNAs and diseases through biological experiments is time-consuming. Although some prediction methods have been proposed, they still have limitations. In this study, a novel computational framework, called GATCL2CD, is proposed to forecast unknown circRNA-disease associations (CDAs). First, we calculate Gaussian interactive profile kernel (GIP) similarity and semantic similarity for diseases, circRNA sequence similarity and function similarity, and GIPs for circRNAs. Then, we combine them to construct a heterogeneous graph. Thereafter, GATCL2CD proposes a feature convolution learning framework, that uses a multi-head dynamic attention mechanism to obtain different aggregated representations of features that correspond to the nodes in the heterogeneous graph. Then, it extracts rich higher-order features from the stacked feature representations of each node by using of a single-layer convolutional neural network with filter kernels of different sizes. Finally, a pairwise element-wise product operation is implemented to capture the interactions of higher-order feature representations, and a multilayer perceptron neural network is introduced as an efficient classifier for inferring potential CDAs. Major experimental results under 5-fold cross-validation (5-fold CV) on three different datasets show that GATCL2CD is superior to five other state-of-the-art methods. Furthermore, case studies demonstrate the suitability of GATCL2CD as a useful tool for identifying potential disease-related circRNAs.

We provide a concise review of the exponentially convergent multiscale finite element method (ExpMsFEM) for efficient model reduction of PDEs in heterogeneous media without scale separation and in high-frequency wave propagation. The ExpMsFEM is built on the non-overlapped domain decomposition in the classical MsFEM while enriching the approximation space systematically to achieve a nearly exponential convergence rate regarding the number of basis functions. Unlike most generalizations of the MsFEM in the literature, the ExpMsFEM does not rely on any partition of unity functions. In general, it is necessary to use function representations dependent on the right-hand side to break the algebraic Kolmogorov n-width barrier to achieve exponential convergence. Indeed, there are online and offline parts in the function representation provided by the ExpMsFEM. The online part depends on the right-hand side locally and can be computed in parallel efficiently. The offline part contains basis functions that are used in the Galerkin method to assemble the stiffness matrix; they are all independent of the right-hand side, so the stiffness matrix can be used repeatedly in multi-query scenarios.

PurposeTo investigate the association of self-reported cataract surgery with all-cause and cause-specific mortality using a large-scale population-based sample.MethodsData from the 1999–2008 cycles of the National Health and Nutrition Examination Survey were used. A self-reported history of cataract surgery was considered a surrogate for the presence of clinically significant cataract surgery. Mortality data were ascertained from National Death Index records. Hazard ratios (HRs) and 95% confidence intervals (CIs) for survival were estimated using Cox proportional hazards regression models.ResultsA total of 14 918 participants were included in the analysis. During a median follow-up of 10.8 (Interquartile range, IQR, 8.25–13.7) years, 3966 (19.1%) participants died. Participants with self-reported cataract surgery were more likely to die from all causes and specific causes (vascular disease, cancer, accident, Alzheimer’s disease, respiratory disease, renal disease and others) compared with those without (all Ps <0.05). The association between self-reported cataract surgery and all-cause mortality remained significant after multiple adjustments (HR=1.13; 95% CI 1.01 to 1.26). For cause-specific mortality, multivariable Cox models showed that self-reported cataract surgery predicted a 36% higher risk of vascular-related mortality (HR=1.36; 95% CI 1.01 to 1.82). The association with other specific causes of mortality did not reach statistical significance after multiple adjustments.ConclusionsThis study found significant associations of self-reported cataract surgery with all-cause and vascular mortalities. Our findings provide potential insights into the pathogenic pathways underlying cataract.

AimsTo investigate the associations of ocular biometric parameters with incident diabetic retinopathy (DR), incident vision-threatening DR (VTDR) and DR progression.MethodsThis community-based prospective cohort study recruited participants with type 2 diabetes aged 35–80 years from 2017 to 2019 in Guangzhou, China. Refractive error and ocular biometric parameters were measured at baseline, including axial length (AL), axial length-to-corneal radius (AL/CR) ratio, corneal curvature (CC), lens thickness (LT), anterior chamber depth (ACD), lens power and corneal diameter (CD).ResultsA total of 1370 participants with a mean age of 64.3±8.1 years were followed up for two consecutive years. During the follow-up period, 342 out of 1195 (28.6%) participants without DR at baseline had incident DR, 15 out of 175 (8.57%) participants with baseline DR had DR progression and 11 of them progressed to VTDR. After multiple adjustments, a longer AL (OR=0.76; 95% CI, 0.66 to 0.86; p<0.001) and a larger AL/CR ratio (OR=0.20; 95% CI, 0.07 to 0.55; p=0.002) were associated with significantly reduced risks of incident DR but were not associated with incident VTDR or DR progression. Refractive status and other ocular biometric parameters investigated, including ACD, CC, CD, lens power and LT were not associated with any of the DR outcomes (all p>0.05).ConclusionsA longer AL and a larger AL/CR ratio are protective against incident DR. These parameters may be incorporated into future DR risk prediction models to individualise the frequency of DR screening and prevention measures.

In this paper, a channel response dependent noise shaping (CRD-NS) technique is proposed and experimentally demonstrated for the pre-equalized discrete multi-tone (DMT) signal transmission in intensity modulation and direct detection (IM/DD) system with a low-resolution digital-to-analog converter (DAC). Compared with traditional NS technique, the proposed CRD-NS technique can bring signal-to-quantization noise ratio (SQNR) improvement and solve the unevenly distributed SQNR problem in pre-equalized DMT signal induced by residual quantization noise without introducing additional computational complexity. Transmission and reception of 100 Gb/s 16 quadrature amplitude modulation (QAM)-DMT signal generated by a 3-bit DAC are experimentally demonstrated to verify the effectiveness of the proposed CRD-NS technique. The experimental results show that the bit-error ratio (BER) performance of 100 Gb/s 16 QAM-DMT signal with CRD-NS technique outperforms the corresponding signal with traditional NS technique. Compared with digital resolution enhancer (DRE) technique, in which channel response is also considered, the CRD-NS technique shows a similar noise shaping capability with DRE in pre-equalized DMT signal transmission, while the computational complexity can be significantly reduced, the number of real multiplications per sample is reduced from 81 to 7.

This paper aims to investigate the torque production mechanism and its improvement design in switched reluctance machines (SRMs) based on field modulation principle. Firstly, the analytical expressions of the air-gap magnetic field are derived from the perspective of DC- and AC-components, respectively. Meanwhile, different slot/pole combinations and winding arrangements are considered. Secondly, the torque productions are analyzed and evaluated with emphasis on the interaction between the DC- and AC-components of air-gap fields. Thirdly, the 12-slot/8-pole and 12-slot/10-pole SRMs are established and studied by using the finite-element method. The effects of slot/pole combination and winding arrangement on the average torque production are clarified. Then, two new designs to improve the average torque are proposed. Finally, the prototype of the 12-slot/10-pole SRM is manufactured, and the experiments are carried out for validation.

ABSTRACTThe transport of microplastics (MPs) with two different sizes in the absence and presence of perfluorooctanoic acid (PFOA) in sediment were systematically investigated in this study. Smaller size of MPs exhibited great transport mobility, while larger size of MPs significantly retained in sediment via electrostatic repulsion and straining mechanisms. MPs transport was insignificantly affected by influent pH values (4.7 and 6.0), likely due to the great buffer capacity of sediment. Effect of PFOA on MPs transport in sediment varied with MPs size and influent pH conditions. PFOA induced more negatively charged MPs surface and greater MPs transport at influent pH of 4.7, and this accelerating effect was more significant for MPs with greater size. In contrast, PFOA have little effect on the transport of both sized MPs at influent pH of 6.0. This work improved our understanding of MPs transport with the co-presence of PFOA in sediment environment.

Perovskite solar cells, promising a bright future in the energy market, are now being prioritized for high-throughput production to match their remarkable success at the laboratory scale. However, the use of toxic solvents proved to be one of the major constraints on scaling up production. Herein, a green solvent system consist of dimethyl sulfoxide (DMSO) and 1-dodecyl-3-methylimidazolium chloride ([C12MIM]Cl) ionic liquids (ILs) was developed to modulate the crystallization of wide-bandgap (WBG) perovskite films combined with a antisolvent-free process, i.e., nitrogen (N2) quenching method. The [C12MIM]Cl IL promoted the crystallization of WBG perovskite films with large grain sizes, reduced photo-active PbI2, modulated residual strain, prolonged carrier lifetimes as well as improved energy alignment. Consequently, the [C12MIM]Cl-modified single-junction 1.77 eV perovskite solar cells (PSCs) achieved a champion efficiency of 18.75 % with an excellent operational stability, retaining an initial PCE of 93 % after 2000 h of maximum-power-point tacking test. Meanwhile, the positive effect of the [C12MIM]Cl ILs was universal in perovskite with different bandgaps at 1.53, 1.68 and 1.72 eV, respectively. Furthermore, stacking semi-transparent [C12MIM]Cl-modified 1.77 eV WBG PSCs as top cells coupled with 1.27 eV OPV or 1.24 eV Sn-Pb PSC as bottom cells for the 4 T tandem configuration showed impressive PCE of 26.01 % and 27.44 %, respectively. This study opens a new avenue toward the sustainable fabrication of highly efficient and stable perovskite-based semitransparent and tandem solar cells.