Search

Multimodal Sentiment Analysis (MSA) is gaining attention, but faces two main challenges: efficient extraction of cross-modal features without redundancy and removing spurious correlations between sentiment labels and multimodal features. In this paper, we propose a novel multimodal learning debiasing model, named Bilateral Cross-modal Debias Multimodal sentiment analysis Model (BCD-MM), to address these issues. Specifically, BCD-MM ultimately enhances the generalisation of the model to out-of-distribution (OOD) situations by improving the ability of cross-modal low-redundancy feature extraction and reducing the reliance on non-causal correlations. First, BCD-MM utilizes an attention score-based method to preserve critical information and eliminate redundancy within modalities. It also employs a gated crossmodal attention mechanism to filter inconsistencies through modal interaction, thereby enhancing the extraction of cross-modal specific features. Second, BCD-MM incorporates a debiasing approach with double bias extraction, using a Tanh-based Mean Absolute Error (TMAE) loss function and inverse probability weighting to mitigate spurious correlations. Finally, extensive testing on three public datasets (MOSI, MOSEI, and SIMS) and two OOD datasets (OOD MOSI and OOD MOSEI) demonstrates our model’s effectiveness in both MSA and debiasing tasks.

Abstract Sulfide minerals hold significant importance in both fundamental science and industrial advancement. However, certain natural sulfide minerals, such as NaFe3S5·2H2O (NFS), pose great challenges for exploitation and synthesis due to their high susceptibility to oxidation. To date, no successful precedent exists for synthesizing NFS. Here, a novel approach to synthesizing low‐cost and pollution‐free NFS with high stability using the high‐pressure hydrothermal method based solely on knowledge of its chemical formula is presented. Moreover, an innovative strategy inspired by the cicada's molting process to develop unstable natural materials is proposed. The mechanical, thermal, optical, electrochemical, and magnetic properties of the NFS are thoroughly investigated. The storage of lithium, sodium, and potassium ions is primarily concentrated in the gap between (0 0 1) crystal planes. Additionally, as a catalyst for hydrogen evolution reaction (HER) at 10 mA cm−2, micron‐sized NFS exhibits an excellent overpotential of 6.5 mV at 90 °C, surpassing those of reported HER catalysts of similar size. This research bridges the gap in the sulfide mineral family, overcomes limitations of the high‐pressure hydrothermal method, and paves the way for future synthesis of natural minerals, lunar minerals, and Martian minerals.

Electric vehicles (EVs) have considerable potential in promoting energy efficiency and carbon neutrality. State of health (SOH) estimations for battery systems can be effective for avoiding accidents involving EVs. However, existing methods have rarely been developed using real driving data. The complex working environments of EVs and their limited data acquisition capability increase the challenges for estimating SOH. In this study, a novel battery SOH definition for EVs was established by analyzing and extracting six potential SOH indicators from driving data. The definition proposed using the entropy weight method (EWM) described the degradation trend for different EV batteries. Combined with a denoising autoencoder, a novel long short-term memory neural network model was established for SOH prediction. It can learn robust features using noisy input data without being affected by different environments or driver behaviors. The network achieved a maximum mean absolute percentage error (MAPE) of 0.8827% and root mean square error (RMSE) of 0.9802%. The results have shown that the proposed method has a higher level of accuracy and is more robust than existing methods in the field.

This work developed a simple and mild strategy for controllable synthesis of highly and homogeneously distributed TiO2 nanoparticles on a cellulose template via tuning the cellulose crystallinity. As the crystallinity of cellulose increases, the restriction from crystallinity on nanoparticles increases, leading to homogeneous distribution, a decrease in size, and structural changes of TiO2 from crystalline to amorphous. The carboxyl groups grafted on the periodically arranged hydroxyl groups of crystalline cellulose, which act as anchor sites for metal oxide precursor, restrict the distribution, growth and crystallization of TiO2 nanoparticles. The amorphous TiO2 nanoparticles on the cellulose with the highest degree of crystallinity (72.40 %) give the highest adsorption capacity toward tetracycline hydrochloride (152.89 mg/g), which is the state of art among the supported TiO2. The high adsorption capacity is proposed to be the oxygen defects caused by the Ti3+ species, originating from the amorphous TiO2 nanoparticles.

This work introduces a new information extraction task called Topic-Oriented Fragment Recognition (TOFR), whose goal is to recognize information related to a specific topic in long documents from professional fields. In this paper, we introduce two TOFR datasets to study the problems of processing long documents. We propose a novel neural framework named Zooming Network (ZoomNet), which overcomes the challenge of combining information over long distances with limited computing resources by flexibly switching between skimming and intensive reading in processing long documents. In general, ZoomNet first establishes a hierarchical representation aligned to the text structure, which relieves the conflict between local information and extensive contextual information. Then, it synthesizes different levels of information to assign tags via multi-scale actions. We combine supervised and reinforcement learning methods to train our model. Experiments show that the proposed model outperforms several state-of-the-art sequence labeling models, including BiLSTM-CRF, BERT, XLNET, RoBERTa, and ELECTRA, on both TOFR datasets with big margins.

Abstract Early racial disparities in COVID-19 vaccination rates have been attributed primarily to personal vaccine attitudes and behavior. Little attention has been paid to the possibility that inequitable vaccine distribution may have contributed to racial disparities in vaccine uptake when supplies were most scarce. We test the hypothesis that scarce vaccines were distributed inequitably using the shipping addresses of 385,930 COVID-19 vaccine doses distributed in the first 17 weeks of Pennsylvania’s Phase 1 rollout (December 14, 2020 through April 12, 2021). All shipments we analyze were allocated via the Federal Retail Pharmacy Program, a public-private partnership coordinated by the Centers for Disease Control and Prevention. Overall, White people had an average of 81.4% more retail pharmacy program doses shipped to their neighborhoods than did Black people. Regression models reveal that weekly vaccine allocations determined by pharmacy chains—rather than initial shipment and administration site decisions requiring state and federal approval—drove these effects. All findings remained consistent after controlling for neighborhood differences in income, population density, insurance coverage, number of pharmacies, and other social determinants of health. Our findings suggest that the private distribution of scarce public resources should be assessed for racial impact, regulated as public resources, and monitored continuously.

Abstract Planning and problem solving are cornerstones of higher brain function. But we do not know how the brain does that. We show that learning of a suitable cognitive map of the problem space suffices. Furthermore, this can be reduced to learning to predict the next observation through local synaptic plasticity. Importantly, the resulting cognitive map encodes relations between actions and observations, and its emergent high-dimensional geometry provides a sense of direction for reaching distant goals. This quasi-Euclidean sense of direction provides a simple heuristic for online planning that works almost as well as the best offline planning algorithms from AI. If the problem space is a physical space, this method automatically extracts structural regularities from the sequence of observations that it receives so that it can generalize to unseen parts. This speeds up learning of navigation in 2D mazes and the locomotion with complex actuator systems, such as legged bodies. The cognitive map learner that we propose does not require a teacher, similar to self-attention networks (Transformers). But in contrast to Transformers, it does not require backpropagation of errors or very large datasets for learning. Hence it provides a blue-print for future energy-efficient neuromorphic hardware that acquires advanced cognitive capabilities through autonomous on-chip learning.

Efficiently handling huge data amounts and enabling processing-intensive applications to run in faraway areas simultaneously is the ultimate objective of 5G networks. Currently, in order to distribute computing tasks, ongoing studies are exploring the incorporation of fog-cloud servers onto satellites, presenting a promising solution to enhance connectivity in remote areas. Nevertheless, analyzing the copious amounts of data produced by scattered sensors remains a challenging endeavor. The conventional strategy of transmitting this data to a central server for analysis can be costly. In contrast to centralized learning methods, distributed machine learning (ML) provides an alternative approach, albeit with notable drawbacks. This paper addresses the comparative learning expenses of centralized and distributed learning systems to tackle these challenges directly. It proposes the creation of an integrated system that harmoniously merges cloud servers with satellite network structures, leveraging the strengths of each system. This integration could represent a major breakthrough in satellite-based networking technology by streamlining data processing from remote nodes and cutting down on expenses. The core of this approach lies in the adaptive tailoring of learning techniques for individual entities based on their specific contextual nuances. The experimental findings underscore the prowess of the innovative lightweight strategy, LMAED2L (Enhanced Deep Learning for Earth Data Analysis), across a spectrum of machine learning assignments, showcasing remarkable and consistent performance under diverse operational conditions. Through a strategic fusion of centralized and distributed learning frameworks, the LMAED2L method emerges as a dynamic and effective remedy for the intricate data analysis challenges encountered within satellite networks interfaced with cloud servers. The empirical findings reveal a significant performance boost of our novel approach over traditional methods, with an average increase in reward (4.1 %), task completion rate (3.9 %), and delivered packets (3.4 %). This report suggests that these advancements will catalyze the integration of cutting-edge machine learning algorithms within future networks, elevating responsiveness, efficiency, and resource utilization to new heights.

Abstract Background Although previous studies have found a close relationship between sense of place and creativity, few studies have been conducted considering the micro-environment of the classroom. The mediating role of classmate relationships in the association between students’ sense of place and creativity remains unclear. Methods This study explores classmate relationships as a mediating factor in the relationship between sense of place and creativity. Therefore, we considered a sample of 1555 Chinese high-school students and used a paper-based questionnaire survey. Data analysis was performed using SPSS 24.0, PROCESS 3.2 plugin, and AMOS. Results Sense of place in the micro-environment of the classroom has a significant positive predictive effect on creativity. Sense of place also has a significant positive predictive effect on peer relationships. The mediation analysis reveals that peer relationships play a mediating role in the relationship between the sense of place and creativity. Conclusions This study revealed the associations between sense of place, classmate relationships, and creativity. Creativity is better expressed in students with a strong sense of place in the classroom. Moreover, a student’s sense of place can enhance their creativity by influencing their peer relationships. These findings enrich the research in educational psychology within the classroom, providing new insights for fostering creativity.

The exploration of microbial genetic resources for the production of fFen-flavor Baijiu has not only enriched the microbial library for baijiu production but has also laid the foundation for process improvement and strain optimization in baijiu brewing. In this study, a total of 177 fungal isolates were screened, including Saccharomyces cerevisiae, non-Saccharomyces cerevisiae, molds, and some pathogenic bacteria. Among them, Saccharomyces cerevisiae was the most abundant with 119 isolates, playing a major role in the fermentation of baijiu production. A total of 148 bacterial isolates were obtained from the fermentation mash samples, showing greater diversity compared to fungi. Bacillus species were the most abundant, with 94 isolates. Bacillus licheniformis, in particular, can produce a rich enzymatic system and flavor precursors, making it an important contributor to the sensory quality of baijiu. Lactic acid bacteria were the second most abundant, with 16 isolates. Additionally, five pathogenic fungal species were identified, including Candida pelliculosa, Candida lusitaniae, Fusarium oxysporum, Fusarium solani and Talaromyces marneffei. Six pathogenic bacterial species were also isolated, namely Staphylococcus aureus, Staphylococcus epidermidis, Staphylococcus xylosus, Moraxella osloensis, Actinomyces meyeri and Stenotrophomonas maltophilia. Finally, two strains of high acetate ethyl ester-producing yeast and lactate-degrading bacteria with good tolerance to temperature, pH, and ethanol concentration were identified as Saccharomyces cerevisiae and Bacillus licheniformis, respectively.

Abstract Purpose To evaluate the safety and the effectiveness of our novel penetrating keratoplasty for infectious keratitis. Methods Retrospective, noncomparative, interventional case series of patients with infectious keratitis who received the novel penetrating keratoplasty technique were analyzed. A prepared plastic sheet was located between the diseased cornea and iris-lens diaphragm. After the diseased lesions were removed, the graft was positioned on the plastic sheet and sutured to the recipient bed. The plastic sheet was pulled out from the anterior chamber before the all interrupted sutures were placed. The intra- and post-operative complications, the outcome of the graft and the number of corneal endothelial cells were analyzed. Results A total of 82 eyes of 82 patients was included. The mean follow-up period was 29 ± 16 months (range from 13 to 45 months). No intraocular content extrusion, simultaneous cataract extraction and suprachoroidal hemorrhage occurred. Direct contact between the infectious cornea and the graft was successfully avoided. Greater than expected endothelial cell reduction or complications were not found. Conclusions This modified technique effectively prevents the extrusion of intraocular contents while avoiding the direct contact with donor endothelium during the procedure. The occurrence rate of complications such as endothelial cell loss is not higher than the conventional methods.

Cutting-edge detection is a critical step in mechanized rice harvesting. Through visual cutting-edge detection, an algorithm can sense in real-time whether the rice harvesting process is along the cutting-edge, reducing loss and improving the efficiency of mechanized harvest. Although convolutional neural network-based models, which have strong local feature acquisition ability, have been widely used in rice production, these models involve large receptive fields only in the deep network. Besides, a self-attention-based Transformer can effectively provide global features to complement the disadvantages of CNNs. Hence, to quickly and accurately complete the task of cutting-edge detection in a complex rice harvesting environment, this article develops a Transformer Convolution Network (TCNet). This cutting-edge detection algorithm combines the Transformer with a CNN. Specifically, the Transformer realizes a patch embedding through a 3 × 3 convolution, and the output is employed as the input of the Transformer module. Additionally, the multi-head attention in the Transformer module undergoes dimensionality reduction to reduce overall network computation. In the Feed-forward network, a 7 × 7 convolution operation is used to realize the position-coding of different patches. Moreover, CNN uses depth-separable convolutions to extract local features from the images. The global features extracted by the Transformer and the local features extracted by the CNN are integrated into the fusion module. The test results demonstrated that TCNet could segment 97.88% of the Intersection over Union and 98.95% of the Accuracy in the unharvested region, and the number of parameters is only 10.796M. Cutting-edge detection is better than common lightweight backbone networks, achieving the detection effect of deep convolutional networks (ResNet-50) with fewer parameters. The proposed TCNet shows the advantages of a Transformer combined with a CNN and provides real-time and reliable reference information for the subsequent operation of rice harvesting.

The fermentation process of Chinese Baijiu’s fermented grains involves the intricate succession and metabolism of microbial communities, collectively shaping the Baijiu’s quality. Understanding the composition and succession of these living microbial communities within fermented grains is crucial for comprehending fermentation and flavor formation mechanisms. However, conducting high-throughput analysis of living microbial communities within the complex microbial system of fermented grains poses significant challenges. Thus, this study addressed this challenge by devising a high-throughput analysis framework using light-flavor Baijiu as a model. This framework combined propidium monoazide (PMA) pretreatment technology with amplicon sequencing techniques. Optimal PMA treatment parameters, including a concentration of 50 μM and incubation in darkness for 5 min followed by an exposure incubation period of 5 min, were identified. Utilizing this protocol, viable microorganism biomass ranging from 8.71 × 106 to 1.47 × 108 copies/μL was successfully detected in fermented grain samples. Subsequent amplicon sequencing analysis revealed distinct microbial community structures between untreated and PMA-treated groups, with notable differences in relative abundance compositions, particularly in dominant species such as Lactobacillus, Bacillus, Pediococcus, Saccharomycopsis, Issatchenkia and Pichia, as identified by LEfSe analysis. The results of this study confirmed the efficacy of PMA-amplicon sequencing technology for analyzing living microbial communities in fermented grains and furnished a methodological framework for investigating living microbial communities in diverse traditional fermented foods. This technical framework holds considerable significance for advancing our understanding of the fermentation mechanisms intrinsic to traditional fermented foods.

This research presents a new, eco-friendly, and swift method combining solid-phase extraction and hydrophobic deep eutectic solvents (DES) with high-performance liquid chromatography (SPE-DES-HPLC) for extracting and quantifying catechin and epicatechin in Shanxi aged vinegar (SAV). The parameters, such as the elution solvent type, the XAD-2 macroporous resin dosage, the DES ratio, the DES volume, the adsorption time, and the desorption time, were optimized via a one-way experiment. A central composite design using the Box–Behnken methodology was employed to investigate the effects of various factors, including 17 experimental runs and the construction of three-dimensional response surface plots to identify the optimal conditions. The results show that the optimal conditions were an HDES (tetraethylammonium chloride and octanoic acid) ratio of 1:3, an XAD-2 macroporous resin dosage of 188 mg, and an adsorption time of 11 min. Under these optimal conditions, the coefficients of determination of the method were greater than or equal to 0.9917, the precision was less than 5%, and the recoveries ranged from 98.8% to 118.8%. The environmentally friendly nature of the analytical process and sample preparation was assessed via the Analytical Eco-Scale and AGREE, demonstrating that this method is a practical and eco-friendly alternative to conventional determination techniques. In summary, this innovative approach offers a solid foundation for the assessment of flavanol compounds present in SAV samples.

Abstract Objective Purpose To observe the safety and effect of the C-pupilloplasty for the treatment of iris coloboma and traumatic iris defects. Methods A total of 21 cases (21 eyes) with iris coloboma or traumatic iris defects who underwent C-pupilloplasty (a single-pass three-throw technique) from Feb. 2016 to Mar. 2020 were analyzed retrospectively. Uncorrected visual acuity, refraction, corneal topographic keratometry and endothelial cell density were examined. Results All the patients were successfully treated, and a central and round pupil was restored. The mean follow-up duration was 8.76 ± 3.58 months (ranging from 2 to 14 months). All patients had round or round-like pupils with a diameter less than or equal to 3 mm after the C-pupilloplasty. Very slightly endothelial loss, negligible symptoms such as glare, distortion, dizziness and photophobia were observed. Conclusion We introduced a new technique of pupilloplasty (C-pupilloplasty) which could be a more straight forward and more effective treatment for iris coloboma and traumatic iris defect.

Abstract We investigated the prevalence and clinical metabolic characteristics of lean nonalcoholic fatty liver disease (NAFLD) in an elderly Chinese population and assessed the relevance of lipid markers and genetic variation. All 5,338 community subjects underwent detailed clinical and laboratory examinations and were divided into three groups: lean (Body mass index (BMI)

This study explored the tensile and shear characteristics of fully prefabricated partially composite floor slab joints through the design and testing of two tensile specimens, three steel–concrete specimens, and three concrete–concrete shear specimens. These tests aimed to evaluate how various connection designs influence the joints’ load-bearing capabilities and failure patterns. The findings revealed that the tensile specimens predominantly showed bond failures at the interface of the precast and cast-in-place layers, accompanied by rebar pull-out. Incorporating reinforcing bars or sleeves was found to potentially increase their ultimate load-bearing capacity by about 20%. The shear failures in the steel–concrete specimens were primarily due to interactions between the steel beam and adjacent composite slab, whereas the concrete–concrete specimens mostly underwent local crushing at the load application point and failure at the bonding interface. These observations affirmed the accuracy of the existing methods for calculating tensile and shear strengths, offering vital insights for the architectural design and construction of such floor joints.

Abstract Microvascular endothelial cells (MiVECs) impair angiogenic potential, leading to microvascular rarefaction, which is a characteristic feature of chronic pressure overload‐induced cardiac dysfunction. Semaphorin3A (Sema3A) is a secreted protein upregulated in MiVECs following angiotensin II (Ang II) activation and pressure overload stimuli. However, its role and mechanism in microvascular rarefaction remain elusive. The function and mechanism of action of Sema3A in pressure overload‐induced microvascular rarefaction, is explored, through an Ang II‐induced animal model of pressure overload. RNA sequencing, immunoblotting analysis, enzyme‐linked immunosorbent assay, quantitative reverse transcription polymerase chain reaction (qRT‐PCR), and immunofluorescence staining results indicate that Sema3A is predominantly expressed and significantly upregulated in MiVECs under pressure overload. Immunoelectron microscopy and nano‐flow cytometry analyses indicate small extracellular vesicles (sEVs), with surface‐attached Sema3A, to be a novel tool for efficient release and delivery of Sema3A from the MiVECs to extracellular microenvironment. To investigate pressure overload‐mediated cardiac microvascular rarefaction and cardiac fibrosis in vivo, endothelial‐specific Sema3A knockdown mice are established. Mechanistically, serum response factor (transcription factor) promotes the production of Sema3A; Sema3A‐positive sEVs compete with vascular endothelial growth factor A to bind to neuropilin‐1. Therefore, MiVECs lose their ability to respond to angiogenesis. In conclusion, Sema3A is a key pathogenic mediator that impairs the angiogenic potential of MiVECs, which leads to cardiac microvascular rarefaction in pressure overload‐induced heart disease.

Rice lodging is a crucial problem in rice production. Lodging during growing and harvesting periods can decrease rice yields. Practical lodging judgment for rice can provide effective reference information for yield prediction and harvesting. This article proposes a binocular camera-based lodging judgment method for rice in real-time. As a first step, the binocular camera and Inertial Measurement Unit (IMU) were calibrated. Secondly, Census and Grayscale Level cost features are constructed for stereo matching of left and right images. The Cross-Matching Cost Aggregation method is improved to compute the aggregation space in the LAB color space. Then, the Winner-Takes-All algorithm is applied to determine the optimal disparity for each pixel. A disparity map is constructed, and Multi-Step Disparity Refinement is applied to the disparity map to generate the final one. Finally, coordinate transformation obtains 3D world coordinates corresponding to pixels. IMU calculates the real-time pose of the binocular camera. A pose transformation is applied to the 3D world coordinates of the rice to obtain its 3D world coordinates in the horizontal state of the camera (pitch and roll angles are equal to 0). Based on the distance between the rice and the camera level, thresholding was used to determine whether the region to be detected belonged to lodging rice. The disparity map effect of the proposed matching algorithm was tested on the Middlebury Benchmark v3 dataset. The results show that the proposed algorithm is superior to the widely used Semi-Global Block Matching (SGBM) stereo-matching algorithm. Field images of rice were analyzed for lodging judgments. After the threshold judgment, the lodging region results were accurate and could be used to judge rice lodging. By combining the algorithms with binocular cameras, the research results can provide practical technical support for yield estimation and intelligent control of rice harvesters.

In this paper, we prepared a self-crosslinking amino-functional graphene oxide/styrene acrylic hybrid emulsion (AGO/SA). The styrene acrylic emulsion (SA) was synthesized with acetoacetoxyethyl methacrylate (AAEM) by semicontinuous seeded emulsion polymerization. Then, amino-functionalized graphene oxide (AGO) was prepared via the modification of graphene oxide (GO) with 3-aminopropyltriethoxysilane (APTES) and reacted with the SA emulsion as a crosslinking agent by in situ polymerization. Fourier transform infrared spectroscopy (FT-IR), scanning electron microscopy (SEM), X-ray diffraction (XRD), thermal gravity analysis (TGA), and transmission electron microscopy (TEM) were applied to explore the chemical structures and morphologies of the samples. The anti-corrosion performance of the AGO/SA coatings was evaluated by Tafel polarization and electrochemical impedance spectroscopy (EIS). Along with the crosslink between AGO and SA, the micropores in the matrix were reduced, the coating expressed improved corrosion resistance due to the graphene barrier, and the hybrid emulsion with 0.7 wt.% AGO exhibited the best anti-corrosion properties. These results indicate that AGO can act as both a crosslinking agent and an anti-corrosion agent in self-crosslinking hybrid emulsions, which have great potential in the field of environmentally friendly emulsions.

Abstract Background At present, the residual film pollution in cotton fields is crucial. The commonly used recycling method is the manual-driven recycling machine, which is heavy and time-consuming. The development of a visual navigation system for the recovery of residual film is conducive, in order to improve the work efficiency. The key technology in the visual navigation system is the cotton stubble detection. A successful cotton stubble detection can ensure the stability and reliability of the visual navigation system. Methods Firstly, it extracts the three types of texture features of GLCM, GLRLM and LBP, from the three types of images of stubbles, residual films and broken leaves between rows. It then builds three classifiers: Random Forest, Back Propagation Neural Network and Support Vector Machine in order to classify the sample images. Finally, the possibility of improving the classification accuracy using the texture features extracted from the wavelet decomposition coefficients, is discussed. Results The experiment proves that the GLCM texture feature of the original image has the best performance under the Back Propagation Neural Network classifier. As for the different wavelet bases, the vertical coefficient texture feature of coif3 wavelet decomposition, combined with the texture feature of the original image, is the feature having the best classification effect. Compared with the original image texture features, the classification accuracy is increased by 3.8%, the sensitivity is increased by 4.8%, and the specificity is increased by 1.2%. Conclusions The algorithm can complete the task of stubble detection in different locations, different periods and abnormal driving conditions, which shows that the wavelet coefficient texture feature combined with the original image texture feature is a useful fusion feature for detecting stubble and can provide a reference for different crop stubble detection.

The monitoring of heavy metal ions content has positive significance for water resources quality monitoring and environmental protection. Herein, a tapered microfiber sensor functionalized polydopamine/4-mercaptopyridine (PDA/4-MPY) film for detection of trace mercury ion (Hg2+) concentration in water is proposed and demonstrated. The microfiber interferometer has excellent sensing performance including a refractive index (RI) sensitivity response of 1146 nm/RIU and a low-cross temperature response of −0.03 nm/℃. Hg2+ heavy metal ion is captured by the surface functional film to affect the RI of the microfiber surface, which lead to the wavelength shift of the interference spectrum. Experiments demonstrate that the sensor has an obvious regular response to Hg2+ in the range of 0–100 nM, in which the sensitivity reaches 1.015 nm/nM in the range of 0–10 nM with a high reliability (R2 = 0.99). This sensor has high sensitivity response and strong specific recognition to trace Hg2+ concentration under the advantages of simple fabrication and low cost. Furthermore, the success of the proposed sensor also indicates that the combination of microfiber and nanomaterial has a bright prospect in the field of optical detection.

The introduction of negative pressure wound therapy has revolutionized the management of complicated wounds. However, the maintenance of an effective negative pressure environment is difficult in some instances, such as wounds in close proximity to an intestinal stoma or wounds surrounded by external fixations. We found that adhesive tape adheres more easily to itself than to wet skin or uneven surfaces. Therefore, we placed adhesive tape around surgical wounds prior to covering them with foam and sealing them with more tape. As a result of the strong adhesive force between pieces of tape, this method could provide a better environmental seal, even in situations where space is limited. Pre-placed adhesive tape around the wound site prior to foam placement could provide sufficient adhesion to maintain a continuous negative pressure environment during treatment.

This study aimed to explore key physicochemical characteristics and evolutionary patterns of microbial community structure during the fermentation of aged vinegar. The correlation between microorganisms and physicochemical characteristics during fermentation was examined. The results revealed significant differences in genera at different stages of fermentation. The dominant bacteria in R1 were Bacillus, Lactobacillus, Aspergillus, and Issatchenkia. During the R2 fermentation stage, Lactobacillus, Acetobacter, and Saccharomyces exhibited an upward trend and finally became the dominant bacteria. Aspergillus was the main bacterial genus at the end of overall fermentation. The correlation analysis showed that the bacterial genera significantly positively and negatively correlated with reducing sugars and amino acid nitrogen were the same in Cuqu. Similarly, the bacterial genera significantly positively and negatively correlated with pH and saccharification power were the same. pH, reducing sugar, and saccharification ability were mainly positively correlated with bacterial genera during fermentation. Further, studies found that the overall correlation between fungal communities and physicochemical characteristics was weaker than the correlation with bacteria during fermentation.

In order to investigate the antioxidant activity of Elaeagnus umbellata polysaccharides, the physicochemical characteristics of purified Elaeagnus umbellata polysaccharides (EUP, consisting of two fractions, EUP1 and EUP2) were investigated using UV spectrophotometry, high-performance liquid chromatography (HPLC), high-performance gel permeation chromatography (HPGPC), and Fourier transform infrared spectroscopy (FT-IR). This revealed that EUP1 and EUP2 were acidic polysaccharides with an average molecular weight (MW) of 63 and 38 kDa, respectively. EUP1 mainly consisted of L-rhamnose and D-galactose in a molar ratio of 2.05:1, and EUP2 consisted of D-mannose, L-rhamnose, D-galactose, and D-arabinose in a molar ratio of 2.06:1:2.78:1. Furthermore, EUP exhibited considerable antioxidant potential for scavenging hydroxyl, superoxide anion, DPPH, and ABTS radicals. Therefore, EUP can be developed as a potential antioxidant for the functional food or pharmaceutical field.

Abstract Background Female sex hormone secretion and reproductive ability decrease with ageing. Bone marrow mesenchymal stem cells (BMMSCs) have been postulated to play a key role in treating ovarian ageing. Methods We used macaque ovarian ageing models to observe the structural and functional changes after juvenile BMMSC treatment. Moreover, RNA-seq was used to analyse the ovarian transcriptional expression profile and key pathways through which BMMSCs reverse ovarian ageing. Results In the elderly macaque models, the ovaries were atrophied, the regulation ability of sex hormones was reduced, the ovarian structure was destroyed, and only local atretic follicles were observed, in contrast with young rhesus monkeys. Intravenous infusion of BMMSCs in elderly macaques increased ovarian volume, strengthened the regulation ability of sex hormones, reduced the degree of pulmonary fibrosis, inhibited apoptosis, increased density of blood vessels, and promoted follicular regeneration. In addition, the ovarian expression characteristics of ageing-related genes of the elderly treatment group reverted to that of the young control group, 1258 genes that were differentially expressed, among which 415 genes upregulated with age were downregulated, 843 genes downregulated with age were upregulated after BMMSC treatment, and the top 20 differentially expressed genes (DEGs) in the protein-protein interaction (PPI) network were significantly enriched in oocyte meiosis and progesterone-mediated oocyte maturation pathways. Conclusion The BMMSCs derived from juvenile macaques can reverse ovarian ageing in elderly macaques.

This research presents a novel, eco-friendly, vortex-assisted liquid–liquid microextraction (VALLME) approach, integrating hydrophobic deep eutectic solvents (DESs) with HPLC for the identification and quantification of nine specific flavonoids in Shanxi aged vinegar (SAV). The parameters of DES-VALLME, including the ratio of trioctylmethylammonium chloride to 1,4-butanediol (1:6), DES volume (150 μL), vortex duration (5 min), the concentration of NaCl (0.40 g), and centrifugation time (10 min), were optimized to achieve the maximum extraction efficiency of target substances. Under these optimal conditions, quantitative analyses performed via HPLC demonstrated a broad linear range of 0.20–50.00 μg/mL and correlation coefficients (r2) greater than 0.9944 for all nine calibration curves. The limits of detection (LOD) and limits of quantitation (LOQ) were 0.09–0.18 μg/mL and 0.30–0.60 μg/mL, respectively, ensuring high sensitivity. The relative standard deviations for intra-day and inter-day variability were within the acceptable range, 2.34–3.77% and 3.04–4.96%, respectively, demonstrating the method’s reliability. The recovery rates ranged from 85.97% to 108.11%, underscoring the method’s precision. This technique exhibited a significant enrichment effect (enrichment factor: 43 to 296) on SAV flavonoids. Notably, the eco-friendliness of this procedure was evaluated using the Analytical Eco-Scale, Green Analytical Procedure Index, and Analytical Greenness Metric. The results suggested that this technique is a viable green alternative to traditional flavonoid determination methods in SAV. In summary, this novel method provides a theoretical basis for assessing flavonoid content in SAV samples and tracing SAV products. This contribution has significant implications for enhancing analytical techniques in food chemistry and environmental science and the sustainable development of the food industry.

Abstract Background To determine plasma ephrin-A1 and VEGF165 levels in a cohort of diabetic retinopathy patients. Methods Plasma ephrin-A1 and VEGF165 levels in fifty-five subjects including 19 individuals without diabetes (non-DM), 16 patients with diabetes (DM) but without diabetic retinopathy, and 20 patients with diabetic retinopathy (DR), were determined by ELISA. Serum creatinine, total cholesterol, fasting blood glucose and HbA1c were also measured. One-way ANOVA, Kruskal-Wallis Test, Mann-Whitney U Test corrected by Bonferroni, Pearson Correlation Analysis and Spearman Correlation Coefficient Analysis were used for data analysis. Results Ephrin-A1 expression could be detected in human plasma with an average of 1.52 ± 0.43 (mean ± SEM) ng/ml. In DR subjects, the plasma ephrin-A1concentration was 3.63 ± 4.63 ng/ml, which was significantly higher than that of the other two groups (non-DM: 0.27 ± 0.13 ng/ml, DM: 0.35 ± 0.34 ng/ml). The expression of VEGF165 in human plasma was 34.00 ± 42.55 pg/ml, with no statistical difference among the three groups. There was no correlation between ephrin-A1 and VEGF165 in human plasma, but there was a correlation between plasma ephrin-A1 and duration of diabetes. Conclusions Plasma ephrin-A1 was highly expressed in patients with diabetic retinopathy, and there was no difference of plasma VEGF165 expression in patients with diabetic retinopathy compared to the other two groups, suggesting that changes of plasma ephrin-A1 may be a more sensitive biomarker than plasma VEGF165 in detecting diabetic retinopathy.

The stubble after cotton harvesting was used as the detection object to achieve the visual navigation operation for residual film recovery after autumn. An improved (You Only Look Once v3) YOLOv3-based target detection algorithm was proposed to detect cotton stubble. First, field images of residual film recycling were collected. Considering the inconsistency between stubble size and shape, a segmented labeling data set of stubble is proposed. Secondly, the Darknet-53 backbone of the original YOLOv3 network is improved to accommodate tiny targets. Next, the prediction anchor box of the improved detection backbone is clustered using K-means++, and the size of the prediction anchor box suitable for improved YOLOv3 is determined. Finally, for the false detection points after detection, a mean value denoising method is used to remove the false detection points. Feature points are extracted from the denoised stubble, and the candidate points are fitted by the least square method to obtain the navigation line. The optimal model with a mean average precision (mAP) of 0.925 is selected for testing at the test stage. The test results show that the algorithm in this article can detect the stubble of residual film recovery images at different locations, different time periods, and different camera depression angles without misdetection. The detection speed of a single image is 98.6 ms. Based on an improvement over YOLOv3, the improved model has a significantly higher detection rate in different scenarios than YOLOv3. This can provide practical technical support for the visual navigation of residual film recovery.

Elaeagnus mollis oil (EMO) meal, a by-product of oil production with plentiful protein, is considered a cheap and good quality source of plant protein for use in the food industry. In this study, the influence of limited enzymatic hydrolysis of EMO meal protein on the structure, solubility, foaming and emulsifying capacities was investigated in detail. The hydrolysates with different DH values (5, 10, 15, and 17) were obtained by controlling the time of enzymatic hydrolysis with alcalase. The results showed that enzymatic hydrolysis decreased molecular weight and increased flexibility and surface hydrophobicity. At the given range of pH and concentration of NaCl, the solubility, foaming and emulsifying capacities of hydrolysates were significantly improved, especially in the area of isoelectric point, and increased with the increase of DH. It was also found that the hydrolysate with DH10 had better foaming and emulsifying stability. In general, appropriate enzymatic hydrolysis could improve the functional properties in favor of their potential use as food ingredients.

As one kind of tetracycline antibiotics, oxytetracycline (OTC) residues in animal-derived foods can do serious harm to human health. Though aptasensors for the detection of OTC are widely reported, label-free electrochemical aptasensor with low cost, simple preparation and good applicability should be further explored to meet the current urgent needs. In this study, a label-free electrochemical aptasensor for OTC determination was designed. The detection was based on specific recognition by the aptamer covalently-bound on the sensing interface via diazonium coupling reaction, which was verified by computer simulation. Experiment results showed that the differential pulse voltammetry (DPV) current response of the constructed electrochemical aptasensor was linear with the logarithm of OTC concentration in the range of 1.0 × 10−9 g mL−1–1.0 × 10−4 g mL−1 (R2 = 0.9897) with the limit of detection of 2.29 × 10−10 g mL−1. The proposed aptasensor indicated good selectivity, reproducibility and stability, which could be attributed to the stable modification of aptamer on the sensing interface. The OTC in the milk sample was detected by the spiked recovery method, and good results were obtained, with the recovery rate being 87.0%-99.3%. The proposed label-free electrochemical aptasensor can provide methods for low-cost, accurate and rapid detection of antibiotic residues.

This paper proposes a hybrid coupled partially encased composite (PEC) wall system, obtained through the connection of two PEC walls by means of the shear critical steel coupling beams with an innovative welded connection. This structural solution is designed to take advantage of both the stiffness of the PEC walls (required to limit building damage under frequent earthquakes) and the ductility of the steel coupling beams (necessary to dissipate energy under medium-intensity and high-intensity earthquakes). The connection performance of an innovative rigid joint with different configurations in this system is studied through pseudostatic analysis, and the seismic performance of the proposed hybrid coupled PEC wall system is evaluated through multirecord nonlinear dynamic analysis of a set of case studies. Adopted finite element models are developed and validated against the available experimental results. A summary of the results is presented and discussed to highlight the potential of the proposed hybrid coupled PEC wall systems. The key feature of this system is development of a reasonable two-level yielding mechanism (the first level is the yielding of the coupling beams, and the second level is the yielding of the PEC wall) without damage to the welded joints.

The Visual Place Recognition problem aims to use an image to recognize the location that has been visited before. In most of the scenes revisited, the appearance and view are drastically different. Most previous works focus on the 2-D image-based deep learning method. However, the convolutional features are not robust enough to the challenging scenes mentioned above. In this paper, in order to take advantage of the information that helps the Visual Place Recognition task in these challenging scenes, we propose a new graph construction approach to extract the useful information from an RGB image and a depth image and fuse them in graph data. Then, we deal with the Visual Place Recognition problem as a graph classification problem. We propose a new Global Pooling method—Global Structure Attention Pooling (GSAP), which improves the classification accuracy by improving the expression ability of the Global Pooling component. The experiments show that our GSAP method improves the accuracy of graph classification by approximately 2–5%, the graph construction method improves the accuracy of graph classification by approximately 4–6%, and that the whole Visual Place Recognition model is robust to appearance change and view change.

Sports analysis has recently attracted increasing research efforts in computer vision. Among them, basketball video analysis is very challenging due to severe occlusions and fast motions. As a typical tracking-by-detection method, k-shortest paths (KSP) tracking framework has been well used for multiple-person tracking. While effective and fast, the neglect of the appearance model would easily lead to identity switches, especially when two or more players are intertwined with each other. This paper addresses this problem by taking the appearance features into account based on the KSP framework. Furthermore, we also introduce a similarity measurement method that can fuse multiple appearance features together. In this paper, we select jersey color and jersey number as two example features. Experiments indicate that about 70% of jersey color and 50% of jersey number over a whole sequence would ensure our proposed method preserve the player identity better than the existing KSP tracking method.

Herein, the problem of target tracking in wireless sensor networks (WSNs) is investigated in the presence of Byzantine attacks. More specifically, we analyze the impact of Byzantine attacks on the performance of a tracking system. First, under the condition of jointly estimating the target state and the attack parameters, the posterior Cramer−Rao lower bound (PCRLB) is calculated. Then, from the perspective of attackers, we define the optimal Byzantine attack and theoretically find a way to achieve such an attack with minimal cost. When the attacked nodes are correctly identified by the fusion center (FC), we further define the suboptimal Byzantine attack and also find a way to realize such an attack. Finally, in order to alleviate the negative impact of attackers on the system performance, a modified sampling importance resampling (SIR) filter is proposed. Simulation results show that the tracking results of the modified SIR filter can be close to the true trajectory of the moving target. In addition, when the quantization level increases, both the security performance and the estimation performance of the tracking system are improved.

In order to maintain and enhance the operational reliability of a robotic manipulator deployed in space, an operational reliability system control method is presented in this paper. First, a method to divide factors affecting the operational reliability is proposed, which divides the operational reliability factors into task-related factors and cost-related factors. Then the models describing the relationships between the two kinds of factors and control variables are established. Based on this, a multivariable and multiconstraint optimization model is constructed. Second, a hierarchical system control model which incorporates the operational reliability factors is constructed. The control process of the space manipulator is divided into three layers: task planning, path planning, and motion control. Operational reliability related performance parameters are measured and used as the system’s feedback. Taking the factors affecting the operational reliability into consideration, the system can autonomously decide which control layer of the system should be optimized and how to optimize it using a control level adjustment decision module. The operational reliability factors affect these three control levels in the form of control variable constraints. Simulation results demonstrate that the proposed method can achieve a greater probability of meeting the task accuracy requirements, while extending the expected lifetime of the space manipulator.

The design and synthesis of drug molecules is a pivotal stage in drug development that traditionally requires significant investment in time and finances. However, the integration of artificial intelligence (AI) in drug design accelerates the identification of potential drug candidates, optimizes the drug development process, and contributes to more informed decision-making. The application of AI in molecular generation is changing the way researchers explore the chemical space and design novel compounds. It accelerates the process of drug discovery and materials science, enabling rapid exploration of the vast chemical landscapes for the identification of promising candidates for further experimental validation. The application of AI in predicting reaction products accelerates the synthesis planning process, contributes to the automation of synthetic chemistry tasks, and supports chemists in making informed decisions during drug discovery. This paper reviewed the recent advances in two interrelated areas: the application of AI in molecular generation and synthesis routes. It will provide insights into the innovative ways in which AI is transforming traditional approaches in drug development and predict its future progress in these key fields.