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Abstract With the emergence of the new generation vision architecture Vmamba and the further demand for agricultural yield and efficiency, we propose an efficient and high-accuracy target detection network for automated pear picking tasks based on Vmamba, aiming to address the issue of low efficiency in current Transformer architectures. The proposed network, named SRSMamba, employs a Reward and Punishment Mechanism (RPM) to focus on important information while minimizing redundancy interference. It utilizes 3D Selective Scan (SS3D) to extend scanning dimensions and integrates global information across channel dimensions, thereby enhancing the model's robustness in complex agricultural environments and effectively adapting to the extraction of complex features in pear orchards and farmlands. Additionally, a Stacked Feature Pyramid Network (SFPN) is introduced to enhance semantic information during the feature fusion stage, particularly improving the detection capability for small targets. Experimental results show that SRSMamba has a low parameter count of 21.1 M, GFLOPs of 50.4, mAP of 72.0%, mAP50 reaching 94.8%, mAP75 at 68.1%, and FPS at 26.9. Compared with other state-of-the-art (SOTA) object detection methods, it achieves a good trade-off between model efficiency and detection accuracy.

Developing highly active oxygen evolution reaction (OER) electrocatalysts with robust durability is essential in producing high-purity hydrogen through water electrolysis. Layered double hydroxide (LDH) based catalysts have demonstrated efficient catalytic performance toward the relatively sluggish OER. By considering the promotion effect of phosphate (Pi) on proton transfer, herein, a facile phosphate acid (PA) surface-neutralization strategy is developed to in-situ construct NiCo-LDH/NiCoPi hetero-sheets toward OER catalysis. OER activity of NiCo-LDH is significantly boosted due to the proton promotion effect and the electronic modulation effect of NiCoPi. As a result, the facilely prepared NiCo-LDH/NiCoPi catalyst displays superior OER catalytic activity with a low overpotential of 300 mV to deliver 100 mA cm−2 OER and a Tafel slope of 73 mV dec−1. Furthermore, no visible activity decay is detected after a 200-h continuous OER operation. The present work, therefore, provides a promising strategy to exploit robust OER electrocatalysts for commercial water electrolysers.

Abstract The electrochemical alcohol oxidation reaction (AOR) is pivotal for the development of sustainable energy. The complete oxidation of alcohols has attracted extensive attention as a vital process in fuel cells. Moreover, as an alternative reaction to the oxygen evolution reaction, the selective oxidation of alcohols emerges as an effective means to lower the energy expenditure associated with electrolytic hydrogen production while yielding high‐value products. Nonprecious metal materials have been widely applied in the selective oxidation catalysis of alcohols due to their cost‐effectiveness and excellent durability. In recent years, leveraging the advantages of nonprecious metal materials in electrocatalytic AOR, researchers have delved into catalytic mechanisms and various efficient catalysts have been fabricated and evaluated. This review provides an overview of the current advancements in the electrocatalytic selective oxidation of diverse alcohols and the catalytic systems centered around nonprecious metal materials. It systematically summarizes the shared traits and distinctions in catalytic reaction characteristics across various systems, thereby laying the theoretical foundation for developing novel catalyst systems that are efficient, stable, and highly selective. This review will facilitate the utilization of nonprecious metal catalysts further toward the electrocatalytic oxidation of alcohols.

High-entropy materials (HEMs) have attracted extensive attention in the field of electrochemical catalysis due to their unique properties. However, the preparation of high-entropy catalysts typically relies on high-temperature, energy-intensive, and time-consuming synthesis methods due to their compositional complexity. In this study, a facile low-temperature electrochemical reconstruction approach is adopted to synthesize Ag-decorated septenary Co-Cu-Fe-Mo-Zn-Ag-Ru high-entropy (oxy)hydroxide electrocatalysts for oxygen evolution reaction (OER). By introducing Ag and Ru elements and implanting Ag nanoparticles to co-regulate the electronic structure of the catalysts, the as-prepared catalyst achieves remarkable OER performance with a low overpotential of 298 mV at 100 mA/cm2 and a small Tafel slope of 30.1 mV/dec in 1 mol/L KOH. This work offers a valuable strategy for developing high-performance high-entropy OER electrocatalysts.

The E3 enzyme in the UPS pathway is a crucial factor for inhibiting substrate specificity. In Solanaceae, the U-box E3 ubiquitin ligase has a complex relationship with plant growth and development, and plays a pivotal role in responding to various biotic and abiotic stresses. The analysis of the U-box gene family in Solanaceae and its expression profile under different stresses holds significant implications. A total of 116 tobacco NtU-boxs and 56 eggplant SmU-boxs were identified based on their respective genome sequences. Phylogenetic analysis of U-box genes in tobacco, eggplant, tomato, Arabidopsis, pepper, and potato revealed five distinct subgroups (I-V). Gene structure and protein motifs analysis found a high degree of conservation in both exon/intron organization and protein motifs among tobacco and eggplant U-box genes especially the members within the same subfamily. A total of 15 pairs of segmental duplication and 1 gene pair of tandem duplication were identified in tobacco based on the analysis of gene duplication events, while 10 pairs of segmental duplication in eggplant. It is speculated that segmental duplication events are the primary driver for the expansion of the U-box gene family in both tobacco and eggplant. The promoters of NtU-box and SmU-box genes contained cis-regulatory elements associated with cellular development, phytohormones, environment stress, and photoresponsive elements. Transcriptomic data analysis shows that the expression levels of the tobacco and eggplant U-box genes in different tissues and various abiotic stress conditions. Using cultivar Hongda of tobacco and cultivar Yanzhi of eggplant as materials, qRT-PCR analysis has revealed that 15 selected NtU-box genes and 8 SmU-box may play important roles in response to pathogen Ras invasion both in tobacco and eggplant.

BackgroundDiffuse uterine leiomyomatosis (DUL) is a seldom-seen condition, with only a handful of cases of magnetic resonance imaging (MRI) findings documented. In clinical settings, it is often mistaken for multiple uterine leiomyomas due to a lack of adequate recognition of DUL.ObjectiveThis study shows two instances of DUL, underscoring their MRI findings to improve preoperative diagnostic precision.ConclusionFor patients exhibiting multiple uterine leiomyomas with masses present in the parametrial and abdominal cavities, consideration should be given to diagnosing DUL with DPL. The discoveries outlined in this paper furnish insights that can assist in directing treatment choices.

Abstract Miniaturized spectrometers are of immense interest for various on-chip and implantable photonic and optoelectronic applications. State-of-the-art conventional spectrometer designs rely heavily on bulky dispersive components (such as gratings, photodetector arrays, and interferometric optics) to capture different input spectral components that increase their integration complexity. Here, we report a high-performance broadband spectrometer based on a simple and compact van der Waals heterostructure diode, leveraging a careful selection of active van der Waals materials- molybdenum disulfide and black phosphorus, their electrically tunable photoresponse, and advanced computational algorithms for spectral reconstruction. We achieve remarkably high peak wavelength accuracy of ~2 nanometers, and broad operation bandwidth spanning from ~500 to 1600 nanometers in a device with a ~ 30×20 μm2 footprint. This diode-based spectrometer scheme with broadband operation offers an attractive pathway for various applications, such as sensing, surveillance and spectral imaging.

Abstract Background To compare the effects of laryngeal mask mechanical ventilation and preserved spontaneous breathing on postoperative atelectasis in children undergoing day surgery. Methods Children aged 3–7 who underwent elective day surgery were randomly divided into a spontaneous breathing group (n = 23) and a mechanical ventilation group (n = 23). All children enrolled in this trial used the same anesthesia induction protocol, the incidence and severity of atelectasis before induction and after operation were collected. In addition, the baseline data, intraoperative vital signs, ventilator parameters and whether there were complications such as reflux and aspiration were also collected. SPSS was used to calculate whether there was a statistical difference between these indicators. Results The incidence of atelectasis in the spontaneous breathing group was 91.30%, and 39.13% in the mechanical ventilation group, and the difference was statistically significant (P = 0.001). There was a statistically significant difference in carbon dioxide (P 0.05). Conclusion Laryngeal mask mechanical ventilation can reduce the incidence and severity of postoperative atelectasis in children undergoing day surgery, and we didn’t encounter any complications such as reflux and aspiration in children during the perioperative period, so mechanical ventilation was recommended to be used for airway management. Trial registration The clinical trial was registered retrospectively at the Chinese Clinical Trial Registry. ( https://www.chictr.org.cn . Registration number ChiCTR2300071396, Weiwei Cai, 15 May 2023).

Abstract High temperature and high humidity (HTHH) conditions increase plant susceptibility to a variety of diseases, including bacterial wilt in solanaceous plants. Some solanaceous plant cultivars have evolved mechanisms to activate HTHH-specific immunity to cope with bacterial wilt disease. However, the underlying mechanisms remain poorly understood. Here we find that CaKAN3 and CaHSF8 upregulate and physically interact with each other in nuclei under HTHH conditions without inoculation or early after inoculation with R. solanacearum in pepper. Consequently, CaKAN3 and CaHSF8 synergistically confer immunity against R. solanacearum via activating a subset of NLRs which initiates immune signaling upon perception of unidentified pathogen effectors. Intriguingly, when HTHH conditions are prolonged without pathogen attack or the temperature goes higher, CaHSF8 no longer interacts with CaKAN3. Instead, it directly upregulates a subset of HSP genes thus activating thermotolerance. Our findings highlight mechanisms controlling context-specific activation of high-temperature-specific pepper immunity and thermotolerance mediated by differential CaKAN3-CaHSF8 associations.

Introduction Laparoscopic sleeve gastrectomy (LSG) is one of the most effective interventions to promote weight-loss and induce reversal of brain functional and structural abnormalities. However, it is unclear whether these brain alterations occur simultaneously as well as the relationship with weight-loss. Methods We employed magnetic resonance imaging to investigate brain changes in 35 patients with obesity (OB), 36 normal-weight (NW) individuals, and 22 OB who received LSG and were studied at baseline and 1 month after LSG. Results Comparisons between OB and NW and between pre- and post-LSG showed (1) Resting-state activity differences in amplitude of low frequency fluctuation (ALFF) in the orbitofrontal cortex (OFC), caudate, hippocampus (HIPP), amygdala, thalamus, anterior cingulate cortex (ACC), and dorsolateral prefrontal cortex. (2) Structural differences in fractional anisotropy (FA) in the middle cerebellar peduncle (MCP), genu corpus collosum (GCC), cingulate (Cin), and superior longitudinal fasciculus. Correlation analysis showed that (1) Reduced BMI/waist-circumference was positively correlated with decreased ALFF in the OFC and negatively associated with increased FA in the GCC following LSG. (2) Increased ALFF in the ACC was positively correlated with increased FA in the GCC and MCP. ALFF in the HIPP was negatively correlated with FA in the Cin at post-LSG. Conclusions These findings provide evidence that LSG induces simultaneous changes in brain function and structure which are associated with weight-loss.

Abstract Deep convolutional neural network (CNN) has made great progress in medical image classification. However, it is difficult to establish effective spatial associations, and always extracts similar low-level features, resulting in redundancy of information. To solve these limitations, we propose a stereo spatial discoupling network (TSDNets), which can leverage the multi-dimensional spatial details of medical images. Then, we use an attention mechanism to progressively extract the most discriminative features from three directions: horizontal, vertical, and depth. Moreover, a cross feature screening strategy is used to divide the original feature maps into three levels: important, secondary and redundant. Specifically, we design a cross feature screening module (CFSM) and a semantic guided decoupling module (SGDM) to model multi-dimension spatial relationships, thereby enhancing the feature representation capabilities. The extensive experiments conducted on multiple open source baseline datasets demonstrate that our TSDNets outperforms previous state-of-the-art models.

Sluggish kinetics of methanol oxidation reaction (MOR) and alkaline hydrogen evolution reaction (HER) even on precious Pt catalyst impede the large-scale commercialization of direct methanol fuel cell (DMFC) and water electrolysis technologies. Since both of MOR and alkaline HER are related to water dissociation reaction (WDR), it is reasonable to invite secondary active sites toward WDR to pair with Pt for boosted MOR and alkaline HER activity on Pt. Mo2C and Ni species are therefore employed to engineer NiPt–Mo2C active site pairs, which can be encapsulated in carbon cages, via an in-situ self-confinement strategy. Mass activity of Pt in NiPt–Mo2C@C toward HER is boosted to 11.3 A mgpt−1, 33 times higher than that of Pt/C. Similarly, MOR catalytic activity of Pt in NiPt–Mo2C@C is also improved by 10.5 times and the DMFC maximum power density is hence improved by 9-fold. By considering the great stability, NiPt–Mo2C@C exhibits great practical application potential in DMFCs and water electrolysers.

Abstract Background Approximately 60% of patients with autoimmune encephalitis (AE) exhibit secondary acute symptomatic seizures and showed highly sensitive to immunotherapy. However, it is difficult for many patients to receive early immunotherapy since the early identification of the cause in AE is more complex. This study aimed to investigate the early predictors of initial immune-related seizures and to guide the evaluation of treatment and prognosis. Methods One hundred and fifty-four patients with new-onset “unknown etiology” seizures with a course of disease less than 6 months were included. Serum and/or cerebrospinal fluid neuron-specific autoantibodies (NSAbs), including N-methyl-D-aspartate receptor (NMDAR), α-amino-3-hydroxy-5- Methyl-4-isoxazole propionic acid receptor 1 (AMPAR1), AMPAR2, anti-leucine rich glioma inactivated 1 antibody (LGI1), anti-gamma-aminobutyric acid type B receptor (GABABR), anti-contact protein-related protein-2 (CASPR2) were used to screen for immune etiology of the seizures. In addition, patients with epilepsy and encephalopathy were also examined via brain MRI, long-term video EEG, antibody prevalence in epilepsy and encephalopathy (APE2) score, and modified Rankin Scale (mRS). A logistic regression model was used to analyze the early predictors of immune etiology. Results Thirty-four cases (22.1%) were positive for NSAbs. Among all 154 patients, 23 cases of autoimmune encephalitis (AE) (21 cases of NSAbs positive), 1 case of ganglionic glioma (NSAbs positive), 130 cases of epilepsy or seizures (12 cases of NSAbs positive) were recorded. Also, there were 17 patients (11.0%) with APE2 ≥ 4 points, and all of them met the clinical diagnosis of AE. The sensitivity and specificity of APE2 ≥ 4 points for predicting AE were 73.9% and 100%. The results of multivariate analysis showed that the NSAbs and APE2 scores independently influenced the early prediction of initial immune-related seizures (P

The optimization and advancement of effective catalysts in the oxygen evolution reaction (OER) are integral to the evolution of diverse green power technologies. In this study, cobalt–nitrogen–graphene (Co-N-g) catalysts are analyzed for their OER contribution via density functional theory (DFT). The influence of vacancies and nitrogen doping on catalyst performance was probed via electronic features and related Frontier Molecular Orbitals. The research reveals that the double-vacancy nitrogen-doped catalyst (DV-N4) exhibits remarkable OER effectiveness, characterized by a notably low overpotential of 0.61 V. This is primarily attributed to enhanced metal–ligand bonding interactions, a diminished energy gap indicating augmented reactivity, and advantageous charge redistribution upon water adsorption. Additionally, nitrogen doping is found to facilitate electron loss from Co, thus promoting water oxidation and improving OER performance. This research provides crucial insights into high-performance OER catalyst design, informing future developments in efficient renewable energy devices.

Van der Waals (vdW) interfaces can be formed via layer stacking regardless of the lattice constant or symmetry of the individual building blocks. Herein, we constructed a vdW interface of layered Ta2NiS5 and CrOCl, which exhibited remarkably enhanced in-plane anisotropy via polarized Raman spectroscopy and electrical transport measurements. Compared with pristine Ta2NiS5, the anisotropy ratio of the Raman intensities for the B2g, 2Ag, and 3Ag modes increased in the heterostructure. More importantly, the anisotropy ratios of conductivity and mobility in the heterostructure increased by one order of magnitude. Specifically speaking, the conductivity ratio changed from ~2.1 (Ta2NiS5) to ~15 (Ta2NiS5/CrOCl), while the mobility ratio changed from ~2.7 (Ta2NiS5) to ~32 (Ta2NiS5/CrOCl). Such prominent enhancement may be attributed to the symmetry reduction caused by lattice mismatch at the heterostructure interface and the introduction of strain into the Ta2NiS5. Our research provides a new perspective for enhancing artificial anisotropy physics and offers feasible guidance for future functionalized electronic devices.

Although the synthesis of molybdenum disulfide (MoS2) on sapphire has made a lot of progress, how the substrate surface affects the growth still needs to be further studied. Herein, the impact of the sapphire step height on the growth of monolayer MoS2 through chemical vapor deposition (CVD) is studied. The results show that MoS2 exhibits a highly oriented triangular grain on a low-step (0.44–1.54 nm) substrate but nanoribbons with a consistent orientation on a high-step (1.98–3.30 nm) substrate. Triangular grains exhibit cross-step growth, with one edge parallel to the step edge, while nanoribbons do not cross steps and possess the same orientation as the step. Scanning electron microscopy (SEM) reveals that nanoribbons are formed by splicing multiple grains, and the consistency of the orientation of these grains is demonstrated with a transmission electron microscope (TEM) and second-harmonic generation (SHG). Furthermore, our CP2K calculations, conducted using the generalized gradient approximation and the Perdew–Burke–Ernzerhof (PBE) functional with D3 (BJ) correction, show that MoS2 domains prefer to nucleate at higher steps, while climbing across a higher step is more difficult. This work not only sheds light on the growth mechanism of monolayer MoS2 but also promotes its applications in electrical, optical, and energy-related devices.

Hydrogen peroxide (H2O2) is an important chemical with a diverse array of applications. However, the existing scenario of centralized high-concentration production is in contrast with the demand for low-concentration decentralized production. In this context, the on-site green and efficient two-electron oxygen reduction reaction (ORR) for H2O2 production has developed into a promising synthetic approach. The development of low-cost, highly active, and durable advanced catalysts is the core requirement for realizing this approach. In recent years, single-atom catalysts (SACs) have become a research hotspot owing to their maximum atom utilization efficiency, tunable electronic structure, and exceptional catalytic performance. The coordination engineering of SACs is one of the key strategies to unlock their full potential for electrocatalytic H2O2 synthesis and holds significant research value. Despite considerable efforts, precisely controlling the electronic structure of active sites in SACs remains challenging. Therefore, this review summarizes the latest progress in coordination engineering strategies for SACs, aiming to elucidate the relevance between structure and performance. Our goal is to provide valuable guidance and insights to aid in the design and development of high-performance SACs for electrocatalytic H2O2 synthesis.

Suspended sediment transport and deposition are crucial physical processes controlling the geomorphological evolution of estuaries and bays. Specially, under the context of worldwide coastal erosion, knowledge of the spatio-temporal distribution of suspended sediment concentration (SSC) and its associated sediment load have become increasingly important for bay management. However, our understanding of the mechanisms of suspended sediment dynamics continues to be hampered by the lack of high-resolution observations. Here, we present a study of the transport mechanisms and controlling factors of suspended sediment over Laizhou Bay. For this, we conducted continuous measurements of SSC, salinity, temperature, and flow velocity at nine stations throughout Laizhou Bay for one 25-h period during each of the spring and moderate tides. Based on these data, residual current, gradient Richardson numbers, and suspended sediment flux were calculated. Our results indicate that a strong current field occurs near the Yellow River mouth, corresponding to the zone with high SSC. The overall diffusion characteristics of suspended sediment are controlled by the tidal current field. Furthermore, our findings suggest that different degrees of stratification occur in the water column, which inhibit the effective vertical diffusion of suspended sediment; Higher water temperature was the main cause of stronger water column stratification of spring tide than moderate tide. Finally, our results reveal that the type of seabed sediment is an important factor controlling SSC by influencing resuspension flux. We conclude that the primary mechanisms controlling suspended sediment transport in Laizhou Bay are advection and tidal pumping, especially advection. Our research provides both a foundational reference for the sediment source-to-sink process from the Yellow River to the sea, as well as guideline implications for coastal engineering construction and channel dredging.

Vaccarin is a flavonoid glycoside, which has a variety of pharmacological properties and plays a protective role in diabetes and its complications, but its mechanism is unclear. In this study, we aim to investigate whether histone deacetylase 1(HDAC1), a gene that plays a pivotal role in regulating eukaryotic gene expression, is the target of miR-570-3p in diabetic vascular endothelium, and the potential molecular mechanism of vaccarin regulating endothelial inflammatory injury through miR-570-3p/HDAC1 pathway. The HFD and streptozotocin (STZ) induced diabetes mice model, a classical type 2 diabetic model, was established. The aorta of diabetic mice displayed a decrease of miR-570-3p, the elevation of HDAC1, and inflammatory injury, which were alleviated by vaccarin. Next, we employed the role of vaccarin in regulating endothelial cells miR-570-3p and HDAC1 under hyperglycemia conditions in vitro. We discovered that overexpression of HDAC1 counteracted the inhibitory effect of vaccarin on inflammatory injury in human umbilical vein endothelial cells (HUVECs). Manipulation of miRNA levels in HUVECs was achieved by transfecting cells with miR-570-3p mimic and inhibitor. Overexpression of miR-570-3p could decrease the expression of downstream components of HDAC1 including TNF-α, IL-1β, and malondialdehyde, while increasing GSH-Px activity in HUVECs under hyperglycemic conditions. Nevertheless, such phenomenon was completely reversed by miR-570-3p inhibitor, and administration of miR-570-3p inhibitor could block the inhibition of vaccarin on HDAC1 and inflammatory injury. Luciferase reporter assay confirmed the 3′- UTR of the HDAC1 gene was a direct target of miR-570-3p. In summary, our findings suggest that vaccarin alleviates endothelial inflammatory injury in diabetes by mediating miR-570-3p/HDAC1 pathway. Our study provides a new pathogenic link between deregulation of miRNA expression in the vascular endothelium of diabetes and inflammatory injury and provides new ideas, insights, and choices for the scope of application and medicinal value of vaccarin and some potential biomarkers or targets in diabetic endothelial dysfunction and vascular complications.

WTe2, a low-symmetry transition metal dichalcogenide, has broad prospects in functional device applications due to its excellent physical properties. When WTe2 flake is integrated into practical device structures, its anisotropic thermal transport could be affected greatly by the substrate, which matters a lot to the energy efficiency and functional performance of the device. To investigate the effect of SiO2/Si substrate, we carried out a comparative Raman thermometry study on a 50 nm-thick supported WTe2 flake (with κzigzag = 62.17 W·m−1·K−1 and κarmchair = 32.93 W·m−1·K−1), and a suspended WTe2 flake of similar thickness (with κzigzag = 4.45 W·m−1·K−1, κarmchair = 4.10 W·m−1·K−1). The results show that the thermal anisotropy ratio of supported WTe2 flake (κzigzag/κarmchair ≈ 1.89) is about 1.7 times that of suspended WTe2 flake (κzigzag/κarmchair ≈ 1.09). Based on the low symmetry nature of the WTe2 structure, it is speculated that the factors contributing to thermal conductivity (mechanical properties and anisotropic low-frequency phonons) may have affected the thermal conductivity of WTe2 flake in an uneven manner when supported on a substrate. Our findings could contribute to the 2D anisotropy physics and thermal transport study of functional devices based on WTe2 and other low-symmetry materials, which helps solve the heat dissipation problem and optimize thermal/thermoelectric performance for practical electronic devices.

Pepper is an important condiment, and its aroma affects its commercial value. In this study, transcriptome sequencing and combined headspace solid-phase microextraction and gas chromatography–mass spectrometry (HS-SPME-GC-MS) were used to analyze the differentially expressed genes and volatile organic compounds in spicy and non-spicy pepper fruits. Compared with non-spicy fruits, there were 27 up-regulated volatile organic compounds (VOCs) and 3353 up-regulated genes (Up-DEGs) in spicy fruits. The results of KEGG enrichment analysis of the Up-DEGs combined with differential VOCs analysis showed that fatty acid biosynthesis and terpenoid biosynthesis may be the main metabolic pathways for aroma differences between non-spicy and spicy pepper fruits. The expression levels of the fatty acid biosynthesis-related genes FAD, LOX1, LOX5, HPL, and ADH and the key terpene synthesis gene TPS in spicy pepper fruits were significantly higher than those in non-spicy pepper fruits. The differential expression of these genes may be the reason for the different aroma. The results can provide reference for the development and utilization of high-aroma pepper germplasm resources and the breeding of new varieties.

WRKY transcription factors have been implicated in plant response to pathogens but how WRKY-mediated networks are organized and operate to produce appropriate transcriptional outputs remains largely unclear. Here, we identify a member of the WRKY family from pepper (Capsicum annuum), CaWRKY28, that physically interacts with CaWRKY40, a positive regulator of pepper immunity and thermotolerance. We confirmed CaWRKY28–CaWRKY40 interaction by coimmunoprecipitation, bimolecular fluorescence complementation, and microscale thermophoresis. Our findings supported the idea that CaWRKY28 is a nuclear protein that acts as positive regulator in pepper responses to infection by the pathogenic bacterium Ralstonia solanacearum. It performs its function not by directly modulating the W-box containing immunity-related genes but by promoting CaWRKY40 via physical interaction to bind and activate its immunity-related target genes, including CaPR1, CaNPR1, CaDEF1, and CaABR1, but not its thermotolerance-related target gene, CaHSP24. All of these data indicate that CaWRKY28 interacts with and potentiates CaWRKY40 in regulating immunity against R. solanacearum infection but not thermotolerance. Importantly, we discovered that CaWRKY28 Cys249, shared by CaWRKY28 and its orthologs probably only in the family Solanaceae, is crucial for the CaWRKY28–CaWRKY40 interaction. These results highlight how CaWRKY28 associates with CaWRKY40 during the establishment of WRKY networks, and how CaWRKY40 achieves its functional specificity during pepper responses to R. solanacearum infection.[Graphic: see text] Copyright © 2021 The Author(s). This is an open access article distributed under the CC BY-NC-ND 4.0 International license.

Time series data has been considered to be a massive information provider for comprehending more about microbial dynamics and interaction, leading to a causality inference in a complex microbial community. Granger causality and correlation analysis have been investigated and applied for the construction of a microbial causal correlation network (MCCN) and efficient prediction of the ecological interaction within activated sludge, which thereby exhibited ecological interactions at the OTU-level. Application of MCCN to a time series of activated sludge data revealed that the hub species OTU56, classified as the one belonging to the genus Nitrospira, was responsible for nitrification in activated sludge and interaction with Proteobacteria and Bacteroidetes in the form of amensal and commensal relationships, respectively. The phylogenetic tree suggested a mutualistic relationship between Nitrospira and denitrifiers. Zoogloea displayed the highest ncf value within the classified OTUs of the MCCN, indicating that it could be a foundation for activated sludge through the formation of characteristic cell aggregate matrices where other organisms embed during floc formation. Inclusively, the research outcomes of this study have provided a deep insight into the ecological interactions within the communities of activated sludge.

Affected by various environmental factors, citrus will frequently suffer from diseases during the growth process, which has brought huge obstacles to the development of agriculture. This paper proposes a new method for identifying and classifying citrus diseases. Firstly, this paper designs an image enhancement method based on the MSRCR algorithm and homomorphic filtering algorithm optimized by Laplacian (HFLF-MS) to highlight the disease characteristics of citrus. Secondly, we designed a new neural network DS-MENet based on the DenseNet-121 backbone structure. In DS-MENet, the regular convolution in Dense Block is replaced with depthwise separable convolution, which reduces the network parameters. The ReMish activation function is used to alleviate the neuron death problem caused by the ReLU function and improve the robustness of the model. To further enhance the attention to citrus disease information and the ability to extract feature information, a multi-channel fusion backbone enhancement method (MCF) was designed in this work to process Dense Block. We use the 10-fold cross-validation method to conduct experiments. The average classification accuracy of DS-MENet on the dataset after adding noise can reach 95.02%. This shows that the method has good performance and has certain feasibility for the classification of citrus diseases in real life.

Alzheimer’s disease (AD) is a progressive neurodegenerative disease with insidious and irreversible onset. The recognition of the disease stage of AD and the administration of effective interventional treatment are important to slow down and control the progression of the disease. However, due to the unbalanced distribution of the acquired data volume, the problem that the features change inconspicuously in different disease stages of AD, and the scattered and narrow areas of the feature areas (hippocampal region, medial temporal lobe, etc.), the effective recognition of AD remains a critical unmet need. Therefore, we first employ class-balancing operation using data expansion and Synthetic Minority Oversampling Technique (SMOTE) to avoid the AD MRI dataset being affected by classification imbalance in the training. Subsequently, a recognition network based on Multi-Phantom Convolution (MPC) and Space Conversion Attention Mechanism (MPC-STANet) with ResNet50 as the backbone network is proposed for the recognition of the disease stages of AD. In this study, we propose a Multi-Phantom Convolution in the way of convolution according to the channel direction and integrate it with the average pooling layer into two basic blocks of ResNet50: Conv Block and Identity Block to propose the Multi-Phantom Residual Block (MPRB) including Multi-Conv Block and Multi-Identity Block to better recognize the scattered and tiny disease features of Alzheimer’s disease. Meanwhile, the weight coefficients are extracted from both vertical and horizontal directions using the Space Conversion Attention Mechanism (SCAM) to better recognize subtle structural changes in the AD MRI images. The experimental results show that our proposed method achieves an average recognition accuracy of 96.25%, F1 score of 95%, and mAP of 93%, and the number of parameters is only 1.69 M more than ResNet50.

Abstract Industrial application of hydrogen generation from water electrolysis is hindered by the strong dependence of cathodic hydrogen evolution reaction (HER) on Pt catalyst. Herein, this study designs a facile strategy to engine heterointerfaces of Pt and Mo2C to boost the HER catalytic activity and enhance the Pt utilization by considering the catalytic ability of Mo2C toward water dissociation reaction. Co is employed to assist the stable Pt/Mo2C heterointerface engineering as self‐confinement agent. By considering the catalytic ability of metallic Co, carbon cages are in‐situ generated around the CoPt/Mo2C nanoparticles to obtain the CoPt‐Mo2C@C catalyst. Overpotential to deliver 100 mA cm‐2 alkaline HER for CoPt‐Mo2C@C is therefore low to 153 mV while Pt content in CoPt‐Mo2C@C is low to 2.8 wt.%. Pt utilization of CoPt‐Mo2C@C toward alkaline HER catalysis is therefore enhanced by 12 times compared with the commercial Pt/C catalyst, demonstrating the potential for practical applications.

In a natural environment, factors such as weathering and sun exposure will degrade the characteristics of dog feces; disturbances such as decaying wood and dirt are likely to make false detections; the recognition distinctions between different kinds of feces are slight. To address these issues, this paper proposes a fine-grained image classification approach for dog feces using MC-SCMNet under complex backgrounds. First, a multi-scale attention down-sampling module (MADM) is proposed. It carefully retrieves tiny feces feature information. Second, a coordinate location attention mechanism (CLAM) is proposed. It inhibits the entry of disturbance information into the network’s feature layer. Then, an SCM-Block containing MADM and CLAM is proposed. We utilized the block to construct a new backbone network to increase the efficiency of fecal feature fusion in dogs. Throughout the network, we decrease the number of parameters using depthwise separable convolution (DSC). In conclusion, MC-SCMNet outperforms all other models in terms of accuracy. On our self-built DFML dataset, it achieves an average identification accuracy of 88.27% and an F1 value of 88.91%. The results of the experiments demonstrate that it is more appropriate for dog fecal identification and maintains stable results even in complex backgrounds, which may be applied to dog gastrointestinal health checks.

Apples are susceptible to infection by various pathogens during growth, which induces various leaf diseases and thus affects apple quality and yield. The timely and accurate identification of apple leaf diseases is essential to ensure the high-quality development of the apple industry. In practical applications in orchards, the complex background in which apple leaves are located poses certain difficulties for the identification of leaf diseases. Therefore, this paper suggests a novel approach to identifying and classifying apple leaf diseases in complex backgrounds. First, we used a bilateral filter-based MSRCR algorithm (BF-MSRCR) to pre-process the images, aiming to highlight the color and texture features of leaves and to reduce the difficulty of extracting leaf disease features with subsequent networks. Then, BAM-Net, with ConvNext-T as the backbone network, was designed to achieve an accurate classification of apple leaf diseases. In this network, we used the aggregate coordinate attention mechanism (ACAM) to strengthen the network’s attention to disease feature regions and to suppress the interference of redundant background information. Then, the multi-scale feature refinement module (MFRM) was used to further identify deeper disease features and to improve the network’s ability to discriminate between similar disease features. In our self-made complex background apple leaf disease dataset, the proposed method achieved 95.64% accuracy, 95.62% precision, 95.89% recall, and a 95.25% F1-score. Compared with existing methods, BAM-Net has higher disease recognition accuracy and classification results. It is worth mentioning that BAM-Net still performs well when applied to the task of the leaf disease identification of other crops in the PlantVillage public dataset. This indicates that BAM-Net has good generalization ability. Therefore, the method proposed in this paper can be helpful for apple disease control in modern agriculture, and it also provides a new reference for the disease identification of other crops.

High temperature stress (HTS), with growth and development impairment, is one of the most important abiotic stresses frequently encountered by plants, in particular solanacaes such as pepper, that mainly distribute in tropical and subtropical regions. Plants activate thermotolerance to cope with this stress; however, the underlying mechanism is currently not fully understood. SWC4, a shared component of SWR1- and NuA4 complexes implicated in chromatin remodeling, was previously found to be involved in the regulation of pepper thermotolerance, but the underlying mechanism remains poorly understood. Herein, PMT6, a putative methyltranferase was originally found to interact with SWC4 by co-immunoprecipitation (Co-IP)-combined LC/MS assay. This interaction was further confirmed by bimolecular fluorescent complimentary (BiFC) and Co-IP assay, and PMT6 was further found to confer SWC4 methylation. By virus-induced gene silencing, it was found that PMT6 silencing significantly reduced pepper basal thermotolerance and transcription of CaHSP24 and significantly reduced the enrichment of chromatin-activation-related H3K9ac, H4K5ac, and H3K4me3 in TSS of CaHSP24, which was previously found to be positively regulated by CaSWC4. By contrast, the overexpression of PMT6 significantly enhanced basal thermotolerance of pepper plants. All these data indicate that PMT6 acts as a positive regulator in pepper thermotolerance, likely by methylating SWC4.

Road cracks are one of the external manifestations of safety hazards in transportation. At present, the detection and segmentation of road cracks is still an intensively researched issue. With the development of image segmentation technology of the convolutional neural network, the identification of road cracks has also ushered in new opportunities. However, the traditional road crack segmentation method has these three problems: 1. It is susceptible to the influence of complex background noise information. 2. Road cracks usually appear in irregular shapes, which increases the difficulty of model segmentation. 3. The cracks appear discontinuous in the segmentation results. Aiming at these problems, a network segmentation model of HC-Unet++ road crack detection is proposed in this paper. In this network model, a deep parallel feature fusion module is first proposed, one which can effectively detect various irregular shape cracks. Secondly, the SEnet attention mechanism is used to eliminate complex backgrounds to correctly extract crack information. Finally, the Blurpool pooling operation is used to replace the original maximum pooling in order to solve the crack discontinuity of the segmentation results. Through the comparison with some advanced network models, it is found that the HC-Unet++ network model is more precise for the segmentation of road cracks. The experimental results show that the method proposed in this paper has achieved 76.32% mIOU, 82.39% mPA, 85.51% mPrecision, 70.26% dice and Hd95 of 5.05 on the self-made 1040 road crack dataset. Compared with the advanced network model, the HC-Unet++ network model has stronger generalization ability and higher segmentation accuracy, which is more suitable for the segmentation detection of road cracks. Therefore, the HC-Unet++ network model proposed in this paper plays an important role in road maintenance and traffic safety.

The appearance quality of apples directly affects their price. To realize apple grading automatically, it is necessary to find an effective method for detecting apple surface defects. Aiming at the problem of a low recognition rate in apple surface defect detection under small sample conditions, we designed an apple surface defect detection network (ASDINet) suitable for small sample learning. The self-developed apple sorting system collected RGB images of 50 apple samples for model verification, including non-defective and defective apples (rot, disease, lacerations, and mechanical damage). First, a segmentation network (AU-Net) with a stronger ability to capture small details was designed, and a Dep-conv module that could expand the feature capacity of the receptive field was inserted in its down-sampling path. Among them, the number of convolutional layers in the single-layer convolutional module was positively correlated with the network depth. Next, to achieve real-time segmentation, we replaced the flooding of feature maps with mask output in the 13th layer of the network. Finally, we designed a global decision module (GDM) with global properties, which inserted the global spatial domain attention mechanism (GSAM) and performed fast prediction on abnormal images through the input of masks. In the comparison experiment with state-of-the-art models, our network achieved an AP of 98.8%, and a 97.75% F1-score, which were higher than those of most of the state-of-the-art networks; the detection speed reached 39ms per frame, achieving accuracy-easy deployment and substantial trade-offs that are in line with actual production needs. In the data sensitivity experiment, the ASDINet achieved results that met the production needs under the training of 42 defective pictures. In addition, we also discussed the effect of the ASDINet in actual production, and the test results showed that our proposed network demonstrated excellent performance consistent with the theory in actual production.

The digital divide between urban and rural communities has substantially narrowed as information and communication technology has evolved, enabling increasingly more interactions between urban and rural areas. Rural areas now have the foundation and conditions to take advantage of e-commerce opportunities, which is no longer exclusively a city-centric economic mode. Taking advantage of the Internet’s vast resources, many villagers jumped at the opportunity to launch rural e-commerce businesses. Rural inhabitants, however, face several challenges when it comes to starting their own e-commerce enterprises. Meanwhile, local governments and rural e-commerce platform providers, find it difficult to provide accurate help to practitioners and entrepreneurs. To this end, a system of indicators based on a model of entrepreneurial events was developed to identify e-commerce entrepreneurs. And the main objective of this paper is to explore the factors influencing the abilities and expectations of rural residents to set up their own e-commerce businesses in order to provide them with tailored support. Survey data from 162 rural e-commerce practitioners were analyzed using a deep neural network in R. The results reveal that the index system developed in this paper has a good level of reliability and validity, and the prediction approach has a high degree of precision (over 90%), indicating that it can successfully identify rural e-commerce entrepreneurs. Based on the findings of this study, we recommend that local governments and e-commerce businesses work together to address the practical issues of perceived feasibility and desirability for rural e-commerce practitioners. Residents in rural areas who want to start their own businesses can take advantage of the development opportunities provided by the information and communication technology, while local governments should keep up with the speed of digitization and informatization to better manage rural economic growth.

Grape disease is a significant contributory factor to the decline in grape yield, typically affecting the leaves first. Efficient identification of grape leaf diseases remains a critical unmet need. To mitigate background interference in grape leaf feature extraction and improve the ability to extract small disease spots, by combining the characteristic features of grape leaf diseases, we developed a novel method for disease recognition and classification in this study. First, Gaussian filters Sobel smooth de-noising Laplace operator (GSSL) was employed to reduce image noise and enhance the texture of grape leaves. A novel network designated coordinated attention shuffle mechanism-asymmetric multi-scale fusion module net (CASM-AMFMNet) was subsequently applied for grape leaf disease identification. CoAtNet was employed as the network backbone to improve model learning and generalization capabilities, which alleviated the problem of gradient explosion to a certain extent. The CASM-AMFMNet was further utilized to capture and target grape leaf disease areas, therefore reducing background interference. Finally, Asymmetric multi-scale fusion module (AMFM) was employed to extract multi-scale features from small disease spots on grape leaves for accurate identification of small target diseases. The experimental results based on our self-made grape leaf image dataset showed that, compared to existing methods, CASM-AMFMNet achieved an accuracy of 95.95%, F1 score of 95.78%, and mAP of 90.27%. Overall, the model and methods proposed in this report could successfully identify different diseases of grape leaves and provide a feasible scheme for deep learning to correctly recognize grape diseases during agricultural production that may be used as a reference for other crops diseases.

Alzheimer’s disease (AD) is a progressive dementia in which the brain shrinks as the disease progresses. The use of machine learning and brain magnetic resonance imaging (MRI) for the early diagnosis of AD has a high probability of clinical value and social significance. Sparse representation classifier (SRC) is widely used in MRI image classification. However, the traditional SRC only considers the reconstruction error and classification error of the dictionary, and does not consider the global and local structural information between images, which results in unsatisfactory classification performance. Therefore, a large margin and local structure preservation sparse representation classifier (LMLS-SRC) is developed in this manuscript. The LMLS-SRC algorithm uses the classification large margin term based on the representation coefficient, which results in compactness between representation coefficients of the same class and a large margin between representation coefficients of different classes. The LMLS-SRC algorithm uses local structure preservation term to inherit the manifold structure of the original data. In addition, the LMLS-SRC algorithm imposes the ℓ2,1-norm on the representation coefficients to enhance the sparsity and robustness of the model. Experiments on the KAGGLE Alzheimer’s dataset show that the LMLS-SRC algorithm can effectively diagnose non AD, moderate AD, mild AD, and very mild AD.

The latest methods based on deep learning have achieved amazing results regarding the complex work of inpainting large missing areas in an image. But this type of method generally attempts to generate one single “optimal” result, ignoring many other plausible results. Considering the uncertainty of the inpainting task, one sole result can hardly be regarded as a desired regeneration of the missing area. In view of this weakness, which is related to the design of the previous algorithms, we propose a novel deep generative model equipped with a brand new style extractor which can extract the style feature (latent vector) from the ground truth. Once obtained, the extracted style feature and the ground truth are both input into the generator. We also craft a consistency loss that guides the generated image to approximate the ground truth. After iterations, our generator is able to learn the mapping of styles corresponding to multiple sets of vectors. The proposed model can generate a large number of results consistent with the context semantics of the image. Moreover, we evaluated the effectiveness of our model on three datasets, i.e., CelebA, PlantVillage, and MauFlex. Compared to state-of-the-art inpainting methods, this model is able to offer desirable inpainting results with both better quality and higher diversity. The code and model will be made available on https://github.com/vivitsai/PiiGAN.

Although hyperspectral remote sensing images have rich spectral features, for small samples of remote sensing images, feature selection, feature mining, and feature integration are very important. A single model is difficult to apply to multiple tasks such as feature selection, feature mining, and feature integration during training, resulting in poor classification results for small sample classification of hyperspectral images. To improve the classification of small samples, a sequential joint deep learning algorithm is proposed in this paper. (In this algorithm, the deep features of multiscale convolution under an attention mechanism are integrated by using Bidirectional Long Short-Term Memory(Bi-LSTM) and AML.) First, we used principal component analysis (PCA) to reduce the dimensionality of the hyperspectral data and retain their key features. Second, the model uses an integrated attention mechanism to distribute the probability weight of the key input feature. Third, the model uses multiscale convolution to mine features after the distribution weight to obtain deep features. Fourth, the model uses bidirectional long short-term memory (Bi-LSTM) to integrate the convolution results at different scales. Finally, the softmax classifier is used to complete the classification of multiclass hyperspectral remote sensing images. Experiments were carried out on three public hyperspectral data sets, and the results proved that our proposed AML algorithm is effective, thus demonstrating powerful performance in the prediction of hyperspectral images (HSIs) of small samples.

Pepper (Capsicum annuum) responds differently to high temperature stress (HTS) and Ralstonia solanacearum infection (RSI) but employs some shared transcription factors (TFs), such as CabZIP63 and CaWRKY40, in both cases. How the plant activates and balances these distinct responses, however, was unclear. Here, we show that the protein CaSWC4 interacts with CaRUVBL2 and CaTAF14b and they all act positively in pepper response to RSI and thermotolerance. CaSWC4 activates chromatin of immunity or thermotolerance related target genes of CaWRKY40 or CabZIP63 by promoting deposition of H2A.Z, H3K9ac and H4K5ac, simultaneously recruits CabZIP63 and CaWRKY40 through physical interaction and brings them to their targets (immunity- or thermotolerance-related genes) via binding AT-rich DNA element. The above process relies on the recruitment of CaRUVBL2 and TAF14 by CaSWC4 via physical interaction, which occurs at loci of immunity related target genes only when the plants are challenged with RSI, and at loci of thermotolerance related target genes only upon HTS. Collectively, our data suggest that CaSWC4 regulates rapid, accurate responses to both RSI and HTS by modulating chromatin of specific target genes opening and recruiting the TFs, CaRUVBL2 and CaTAF14b to the specific target genes, thereby helping achieve the balance between immunity and thermotolerance. Author summary Transcription mediated by different transcription factors (TFs) and chromatin remodeling during plant stress response is currently one of the most intensive studied topics, but how transcription factors associate with chromatin remodeling modulators in coordinating responses of plant to multiple stresses remain poorly understood. Herein, using pepper response to RSI and to HTS as an example, we studied how CaWRKY40 and CabZIP63, the two TFs that positively regulate pepper immunity against RSI and termotolerance, associate with SWR1-c and other chromatin remodeling modulators in coordinating responses of plant immunity and thermotolerance. Our results indicate that CaSWC4, a component shared by SWR1- and NuA4 complex, activates the chromatin loci of the tested immunity and thermotolerance related genes targeted by CaWRKY40 and CabZIP63 through promoting the deposition of chromatin active markers including H2A.Z, H3H9ac and H4K5ac, and bring CaWRKY40 and CabZIP63 to the promoters of their target genes by physical interactions. In addition, through interaction between CaSWC4 and CaRUVBL2/CaTAF14b and their co-deposition to the target genes of CaWRKY40 and CabZIP63 in a stress dependent manner, immunity against RSI or thermotolerance is specifically and accurately activated by CaWRKY40/CabZIP63-CaSWC4 nodule. Our results provide new insight into the mechanism underlying plant immunity, thermotolerance and their balance.

Crack is the external expression form of potential safety risks in bridge construction. Currently, automatic detection and segmentation of bridge cracks remains the top priority of civil engineers. With the development of image segmentation techniques based on convolutional neural networks, new opportunities emerge in bridge crack detection. Traditional bridge crack detection methods are vulnerable to complex background and small cracks, which is difficult to achieve effective segmentation. This study presents a bridge crack segmentation method based on a densely connected U-Net network (BC-DUnet) with a background elimination module and cross-attention mechanism. First, a dense connected feature extraction model (DCFEM) integrating the advantages of DenseNet is proposed, which can effectively enhance the main feature information of small cracks. Second, the background elimination module (BEM) is proposed, which can filter the excess information by assigning different weights to retain the main feature information of the crack. Finally, a cross-attention mechanism (CAM) is proposed to enhance the capture of long-term dependent information and further improve the pixel-level representation of the model. Finally, 98.18% of the Pixel Accuracy was obtained by comparing experiments with traditional networks such as FCN and Unet, and the IOU value was increased by 14.12% and 4.04% over FCN and Unet, respectively. In our non-traditional networks such as HU-ResNet and F U N-4s, SAM-DUnet has better and higher accuracy and generalization is not prone to overfitting. The BC-DUnet network proposed here can eliminate the influence of complex background on the segmentation accuracy of bridge cracks, improve the detection efficiency of bridge cracks, reduce the detection cost, and have practical application value.

A proton exchange membrane fuel cell (PEMFC), as an efficient energy conversion device, has many advantages, such as high energy conversion efficiency and environmentally friendly zero emissions, and is expected to have great potential for addressing the uneven distribution of global green energy. As a core component, the performance of the proton exchange membrane (PEM) directly affects the overall output of the fuel cell system. At present, Nafion membranes with good, comprehensive properties are the most widely used commercial proton exchange membrane materials. However, Nafion membranes demonstrate a great inadaptability with an increase in operating temperatures, such as a rapid decay in proton conductivity. Therefore, enhancing the overall performance of Nafion membranes under high temperatures and low relative humidity (RH) has become an urgent problem. Although many efforts have been made to solve this problem, it is difficult to find the balance point between high-temperature conductivity and overall stability for researchers. In this paper, we summarize the recent approaches to improving the operating temperature of Nafion membranes from the following two perspectives: (1) using different materials for the modification of Nafion membranes, and (2) applying different modification methods to the Nafion membranes. Based on the structural and functional characteristics of Nafion, the non-destructive targeted filling of fillers and the efficient synergy of the two-phase region are two vital research directions for the preparation of high-performance composite membranes.

Silver (Ag) has difficult forming strong bonding with oxides due to its deep d band beneath the Fermi level and completely filled 4d orbital. Thus, it is difficult to fabricate silver-based composites with superior plasticity and processability because of the easy debonding at their interface. Herein, La2Sn2O7 pyrochlore was used as a reinforcing phase for a silver matrix. The enhanced interfacial bonding strength of Ag-La2Sn2O7 was confirmed both theoretically and experimentally, indicating that Ag could form more localized ionic bonding with La2Sn2O7 than with SnO2. The superior plasticity was further confirmed for the Ag-La2Sn2O7 composite, as the uniform elongation (UE) of the Ag-La2Sn2O7 composite was ~19%, i.e., ~14% higher than and 2.8 times that of the conventional Ag-SnO2 composite. The plasticity enhancement mechanism was also unraveled by calculating the interfacial mobility. This work verified the usefulness of pyrochlore to fabricate silver-based composites with superior plasticity and also provides a new strategy for the construction of advanced silver-based composites for application in the electrical contact field.

With the development of electronic technologies, electronic devices become smaller, while their power density increases dramatically. The resulting excessive heat requires excellent heat dissipation to ensure great performance of the devices. A good thermal interface material (TIM), with excellent bulk thermal conductivity and proper elastic modulus, which can fill the gap between contact surfaces, is of great importance to improve overall performance of thermal management in the electronic devices. Carbon-based materials, such as carbon nanotubes (CNTs) and graphene (Gr), have attracted great attentions, due to their intrinsic high thermal conductivity. In this paper, carbon-based TIMs are reviewed, as well as the thermal conducting mechanisms and techniques to measure thermal properties for materials. The unique three-dimensional network of 3D-Gr provides not only high thermal conductivity, but also excellent mechanical properties, which makes it more competitive as TIM than CNTs and Gr. Furthermore, there is currently no universal characterization techniques, which are suitable to measure thermal properties of all TIMs. Hence, special attention must be paid to select a proper technique based on the measuring principle, in order to obtain accurate results. An outlook of the future challenges of the thermal interface materials is proposed at the end of the paper.

Understanding human emotions and psychology is a critical step toward realizing artificial intelligence, and correct recognition of facial expressions is essential for judging emotions. However, the differences caused by changes in facial expression are very subtle, and different expression features are less distinguishable, making it difficult for computers to recognize human facial emotions accurately. Therefore, this paper proposes a novel multi-layer interactive feature fusion network model with angular distance loss. To begin, a multi-layer and multi-scale module is designed to extract global and local features of facial emotions in order to capture part of the feature relationships between different scales, thereby improving the model's ability to discriminate subtle features of facial emotions. Second, a hierarchical interactive feature fusion module is designed to address the issue of loss of useful feature information caused by layer-by-layer convolution and pooling of convolutional neural networks. In addition, the attention mechanism is also used between convolutional layers at different levels. Improve the neural network's discriminative ability by increasing the saliency of information about different features on the layers and suppressing irrelevant information. Finally, we use the angular distance loss function to improve the proposed model's inter-class feature separation and intra-class feature clustering capabilities, addressing the issues of large intra-class differences and high inter-class similarity in facial emotion recognition. We conducted comparison and ablation experiments on the FER2013 dataset. The results illustrate that the performance of the proposed MIFAD-Net is 1.02–4.53% better than the compared methods, and it has strong competitiveness.

Summary: We investigated the short-term dynamics of microbial composition and function in bioreactors with inocula collected from full-scale and laboratory-based anaerobic digestion (AD) systems. The Bray-Curtis dissimilarity of both inocula was approximately 10% of the predicted Kyoto Encyclopedia of Genes and Genomes pathway and 40% of the taxonomic composition and yet resulted in a similar performance in methane production, implying that the variation of community composition may be decoupled from performance. However, the significant correlation of volatile fatty acids with taxonomic variation suggested that the pathways of AD could be different because of the varying genus. The predicted function of the significantly varying genus was mostly related to fermentation, which strengthened the conclusion that most microbial variation occurred within the fermentative species and led to alternative routes to result in similar methane production in methanogenic bioreactors. This finding sheds some light on the understanding of AD community regulation, which depends on the aims to recover intermediates or methane.

The commonly used working condition recognition method in the mineral flotation process is based on shallow features of flotation froth images. However, the shallow features of flotation froth images frequently have an excessive amount of redundant and noisy information, which has an impact on the recognition effect and prevents the flotation process from being effectively optimized. Therefore, a working condition recognition method for the mineral flotation process based on a deep and shallow feature fusion densely connected network decision tree (DSFF-DenseNet-DT) is proposed in this paper. Firstly, the color texture distribution (CTD) and size distribution (SD) of a flotation froth image obtained in advance are approximated by the nonparametric kernel density estimation method, and a set of kernel function weights is obtained to represent the color texture and size features, while the deep features of the flotation froth image are extracted through the densely connected network (DenseNet). Secondly, a two-stage feature fusion method based on a stacked autoencoder after Concat (Cat-SAE) is proposed to fuse and reduce the dimensionality of the extracted shallow features and deep features so as to maximize the comprehensive description of the features and eliminate redundant and noisy information. Finally, the feature vectors after fusion dimensionality reduction are fed into the densely connected network decision tree (DenseNet-DT) for working condition recognition. Multiple experiments employing self-built industrial datasets reveal that the suggested method’s average recognition accuracy, precision, recall and F1 score reach 92.67%, 93.9%, 94.2% and 0.94, respectively. These results demonstrate the proposed method’s usefulness.

Ultrafiltration is widely used to treat various environmental waters, and on-line membrane cleaning with various chemical reagents is frequently employed to sustain the filtration flux. However, the residue of cleaning agents in the ultrafiltration system is unavoidable, which may affect microbiological properties and biofilm formation during the next-round filtration. By investigating the changes in microbial characteristics, and their biofouling behaviors after exposure to HCl, NaOH, NaClO, citric acid (CA), and sodium dodecyl sulfonate (SDS), this study fills a knowledge gap in microbial responses to various types of chemical cleaning agents in an ultrafiltration system. The result shows that HCl, NaOH, and NaClO affect the bacterial properties and subsequent attachment on the membrane surface, while CA and SDS have no obvious influence on microorganisms. Specifically, HCl, NaOH, and NaClO reduce the hydrophobicity and mean size of suspended microorganisms, increase the extracellular polymeric substances (EPS) release, and trigger intracellular reactive oxygen species (ROS) generation, resulting in the death of a large quantity of microorganisms. Due to the self-protecting strategy, plenty of living cells aggregate on the membrane surface and form a cake layer with a stratified structure, causing more severe membrane biofouling.

Tablet-mediated interventions have shown promise in improving the mathematical skills of individuals with autism spectrum disorder (ASD) and/or intellectual disability (ID). This meta-analysis aims to provide a quantitative synthesis of single-case experimental studies of using tablet-mediated interventions to teach mathematics to individuals with ASD and/or ID. Twenty-seven published studies between 2012 and 2022 were included. The Tau-U effect size index was used to gauge the overall effect size of tablet-mediated interventions. The obtained effect size (Tau-U = 0.98, 95% CI 0.92-1.00) indicated large improvements in mathematics performance after using tablet-mediated interventions. The analysis of potential moderating variables, including participant characteristics, intervention components, and target mathematical skills found no statistically significant moderators. Implications for researchers and practitioners who use tablet-mediated interventions to teach mathematics to individuals with ASD and/or ID are also discussed.

Two-dimensional molybdenum disulfide (MoS2) has attracted significant attention for next-generation electronics, flexible devices, and optical applications. Chemical vapor deposition is the most promising route for the production of large-scale, high-quality MoS2 films. Recently, the chemical vapor deposition of MoS2 films on soda-lime glass has attracted great attention due to its low cost, fast growth, and large domain size. Typically, a piece of Mo foil or graphite needs to be used as a buffer layer between the glass substrates and the CVD system to prevent the glass substrates from being fragmented. In this study, a novel method was developed for synthesizing MoS2 on glass substrates. Inert Al2O3 was used as the buffer layer and high-quality, uniform, triangular monolayer MoS2 crystals with domain sizes larger than 400 μm were obtained. To demonstrate the advantages of glass/Al2O3 substrates, a direct comparison of CVD MoS2 on glass/Mo and glass/Al2O3 substrates was performed. When Mo foil was used as the buffer layer, serried small bilayer islands and bright core centers could be observed on the MoS2 domains at the center and edges of glass substrates. As a control, uniform MoS2 crystals were obtained when Al2O3 was used as the buffer layer, both at the center and the edge of glass substrates. Raman and PL spectra were further characterized to show the merit of glass/Al2O3 substrates. In addition, the thickness of MoS2 domains was confirmed by an atomic force microscope and the uniformity of MoS2 domains was verified by Raman mapping. This work provides a novel method for CVD MoS2 growth on soda-lime glass and is helpful in realizing commercial applications of MoS2.

Clusters of dead trees are forest fires-prone. To maintain ecological balance and realize its protection, timely detection of dead trees in forest remote sensing images using existing computer vision methods is of great significance. Remote sensing images captured by Unmanned aerial vehicles (UAVs) typically have several issues, e.g., mixed distribution of adjacent but different tree classes, interference of redundant information, and high differences in scales of dead tree clusters, making the detection of dead tree clusters much more challenging. Therefore, based on the Multipath dense composite network (MDCN), an object detection method called LLAM-MDCNet is proposed in this paper. First, a feature extraction network called Multipath dense composite network is designed. The network’s multipath structure can substantially increase the extraction of underlying and semantic features to enhance its extraction capability for rich-information regions. Following that, in the row, column, and diagonal directions, the Longitude Latitude Attention Mechanism (LLAM) is presented and incorporated into the feature extraction network. The multi-directional LLAM facilitates the suppression of irrelevant and redundant information and improves the representation of high-level semantic feature information. Lastly, an AugFPN is employed for down-sampling, yielding a more comprehensive representation of image features with the combination of low-level texture features and high-level semantic information. Consequently, the network’s detection effect for dead tree cluster targets with high-scale differences is improved. Furthermore, we make the collected high-quality aerial dead tree cluster dataset containing 19,517 images shot by drones publicly available for other researchers to improve the work in this paper. Our proposed method achieved 87.25% mAP with an FPS of 66 on our dataset, demonstrating the effectiveness of the LLAM-MDCNet for detecting dead tree cluster targets in forest remote sensing images.