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With the adoption of cutting-edge communication technologies such as 5G/6G systems and the extensive development of devices, crowdsensing systems in the Internet of Things (IoT) are now conducting complicated video analysis tasks such as behaviour recognition. These applications have dramatically increased the diversity of IoT systems. Specifically, behaviour recognition in videos usually requires a combinatorial analysis of the spatial information about objects and information about their dynamic actions in the temporal dimension. Behaviour recognition may even rely more on the modeling of temporal information containing short-range and long-range motions, in contrast to computer vision tasks involving images that focus on understanding spatial information. However, current solutions fail to jointly and comprehensively analyse short-range motions between adjacent frames and long-range temporal aggregations at large scales in videos. In this paper, we propose a novel behaviour recognition method based on the integration of multigranular (IMG) motion features, which can provide support for deploying video analysis in multimedia IoT crowdsensing systems. In particular, we achieve reliable motion information modeling by integrating a channel attention-based short-term motion feature enhancement module (CSEM) and a cascaded long-term motion feature integration module (CLIM). We evaluate our model on several action recognition benchmarks, such as HMDB51, Something-Something and UCF101. The experimental results demonstrate that our approach outperforms the previous state-of-the-art methods, which confirms its effectiveness and efficiency.

Abstract Biological computing is a promising field with potential applications in biosafety, environmental monitoring, and personalized medicine. Here we present work on the design of bacterial computers using spatial patterning to process information in the form of diffusible morphogen-like signals. We demonstrate, mathematically and experimentally, that single, modular, colonies can perform simple digital logic, and that complex functions can be built by combining multiple colonies, removing the need for further genetic engineering. We extend our experimental system to incorporate sender colonies as morphogen sources, demonstrating how one might integrate different biochemical inputs. Our approach will open up ways to perform biological computation, with applications in bioengineering, biomaterials and biosensing. Ultimately, these computational bacterial communities will help us explore information processing in natural biological systems.

Abstract As a key factor in determining testis size and sperm number, sertoli cells (SCs) play a crucial role in male infertility. Heat stress (HS) reduces SCs counts, negatively impacting nutrient transport and supply to germ cells, and leading to spermatogenesis failure in humans and animals. However, how HS affects the number of SCs remains unclear. We hypothesized that changes in SC metabolism contribute to the adverse effects of HS. In this study, we first observed an upregulation of arachidonic acid (AA), an unsaturated fatty acid after HS exposure by LC-MS/MS metabolome detection. By increasing ROS levels, expression of KEAP1 and NRF2 proteins as well as LC3 and LAMP2, 100 µM AA induced autophagy in SCs by activating oxidative stress (OS). We observed adverse effects of AA on mitochondria under HS with a decrease of mitochondrial number and an increase of mitochondrial membrane potential (MMP). We also found that AA alternated the oxygen transport and absorption function of mitochondria by increasing glycolysis flux and decreasing oxygen consumption rate as well as the expression of mitochondrial electron transport chain (ETC) proteins Complex I, II, V. However, pretreatment with 5 mM NAC (ROS inhibitor) and 2 µM Rotenone (mitochondrial ETC inhibitor) reversed the autophagy induced by AA. In summary, AA modulates autophagy in SCs during HS by disrupting mitochondrial ETC function, inferring that the release of AA is a switch-like response, and providing insight into the underlying mechanism of high temperatures causing male infertility.

Digital twinning enables manufacturers to create digital representations of physical entities, thus implementing virtual simulations for product development. Previous efforts of digital twinning neglect the decisive consumer feedback in product development stages, failing to cover the gap between physical and digital spaces. This work mines real-world consumer feedbacks through social media topics, which is significant to product development. We specifically analyze the prevalent time of a product topic, giving an insight into both consumer attention and the widely-discussed time of a product. The primary body of current studies regards the prevalent time prediction as an accompanying task or assumes the existence of a preset distribution. Therefore, these proposed solutions are either biased in focused objectives and underlying patterns or weak in the capability of generalization towards diverse topics. To this end, this work combines deep learning and survival analysis to predict the prevalent time of topics. We propose a specialized deep survival model which consists of two modules. The first module enriches input covariates by incorporating latent features of the time-varying text, and the second module fully captures the temporal pattern of a rumor by a recurrent network structure. Moreover, a specific loss function different from regular survival models is proposed to achieve a more reasonable prediction. Extensive experiments on real-world datasets demonstrate that our model significantly outperforms the state-of-the-art methods.

Based on the solid waste-based modification, green shear-thickening functionalized ballistic composites were prepared. Silica aerosols were obtained from solid waste diatomaceous earth through sol–gel preparation, gel aging, and supercritical drying. The modified silica aerosols acted as the dispersed phase, regulating the green shear-thickening liquid. A comparison was made between the performance of the modified shear-thickening liquid (SG@STF) and traditional shear-thickening liquids. By modifying solid waste based shear thickener (SG@STF) Combined with aramid fabric (AF) to test Ballistic performance. Characterization tests revealed that the rheological properties of SG@STF were significantly enhanced by increasing the volume fraction of the dispersed phase in STF from 50 % to 60 %. This enhancement was attributed to the addition of reactive functional groups on the surface of the silica sol. Yarn pull-out tests demonstrated that SG@STF/AF improved the tensile strength by 120 % compared to AF fabric, thanks to its superior friction properties. Ballistic tests showed that SG@STF increased the ballistic ultimate velocity of AF fabric by 105 %, elevating AF’s ballistic limiting velocity from 51 m/s to 105 m/s. Consequently, SG@STF/AF exhibited superior ballistic properties compared to AF fabric. The doping of SG enhanced the friction between yarns and increased the load-bearing area of the fabrics, thereby improving the ballistic properties of SG@STF/AF and achieving the desired modulation of their ballistic properties.

Purposes The situation of post-disaster site of coal mine underground is complex. Aiming at the fact that traditional A* algorithm-based coal mine robot rescue path planning bears path insecurity, low efficiency, and other problems, and by combining with the raster map and the characteristics that the rescue robot can move along any direction, multi-faceted improvement of the traditional A* algorithm is realized. Methods First, the concept of robot size superimposed to obstacles is introduced to ensure the safety of the path. Second, the computational complexity is reduced by optimising the heuristic function to reduce the number of extensions that deviate from the optimal path nodes, and to reduce the redundant nodes and path transitions. Finally, a dynamic adjustment algorithm is used to round the corners of path turns, and the resulting path is smoothed to improve the operability of path. The optimised path with smaller path cost, shorter time consumption, and fewer turns is finally obtained. Based on the improved A* algorithm, the simulation test of rescue robot path planning under different sizes and different obstacle coverages is carried out by using MATLAB. Findings Compared with the traditional A* algorithm, the improved A* algorithm in rugged environment maps reduces the searching nodes by 26.51%, the computation time by 80.42%, the path length by 31.85%, and the number of turns by 44.44%, which improves the search efficiency and smoothness of the path. Conclusions The improved A* algorithm provides a theoretical basis for the rescue work of coal mine robots under complex working conditions.

Over the past decades, there has been ongoing effort to develop complex biomimetic tissue engineering strategies for in vitro cultivation and maintenance of organoids. The defined hydrogels can create organoid models for various organs by changing their properties and various active molecules. An increasing number of researches has been done on the application of hydrogels in organoids, and a large number of articles have been published on the topic. Although there have been existing reviews describing the application of hydrogels in the field of organoids, there is still a lack of comprehensive studies summarizing and analyzing the overall research trends in this field. The citation can be used as an indicator of the scientific influence of an article in its field. This study aims to evaluate the application of hydrogels in organoids through bibliometric analysis, and to predict the hotspots and developing trends in this field.

Abstract Single image super-resolution (SISR) refers to the reconstruction from the corresponding low-resolution (LR) image input to a high-resolution (HR) image. However, since a single low-resolution image corresponds to multiple high-resolution images, this is an ill-posed problem. In recent years, generative model-based SISR methods have outperformed conventional SISR methods in performance. However, the SISR methods based on GAN, VAE, and Flow have the problems of unstable training, low sampling quality, and expensive computational cost. These models also struggle to achieve the trifecta of diverse, high-quality, and fast sampling. In particular, denoising diffusion probabilistic models have shown impressive variety and high quality of samples, but their expensive sampling cost prevents them from being well applied in the real world. In this paper, we investigate the fundamental reason for the slow sampling speed of the SISR method based on the diffusion model lies in the Gaussian assumption used in the previous diffusion model, which is only applicable for small step sizes. We propose a new Single Image Super-Resolution with Denoising Diffusion GANS (SRDDGAN) to achieve large-step denoising, sample diversity, and training stability. Our approach combines denoising diffusion models with GANs to generate images conditionally, using a multimodal conditional GAN to model each denoising step. SRDDGAN outperforms existing diffusion model-based methods regarding PSNR and perceptual quality metrics, while the added latent variable Z solution explores the diversity of likely HR spatial domain. Notably, the SRDDGAN model infers nearly 11 times faster than diffusion-based SR3, making it a more practical solution for real-world applications.

AIM: To explore the brain mechanism of acupuncture for children with anisometropic amblyopia using the voxel-mirror homotopic connectivity (VMHC) analysis method of resting functional magnetic resonance imaging (rs-fMRI) technology based on clinical effectiveness. METHODS: Eighty children with anisometropic monocular amblyopia were randomly divided into two groups: control (40 cases, 1 case of shedding) and acupuncture (40 cases, 1 case of shedding) groups. The control group was treated with glasses, red flash, grating, and visual stimulations, with each procedure conducted for 5min per time. Based on routine treatment, the acupuncture group underwent acupuncture of “regulating qi and unblocking meridians to bright eyes”, Jingming (BL1), Cuanzhu (BL2), Guangming (GB37), Fengchi (GB20) acupoints were taken on both sides, with the needle kept for 30min each time. Both groups were treated once every other day, three times per week, for a total of 4wk. After the treatment, the overall curative effect of the two groups and the latency and amplitude changes of P100 wave of pattern visual-evoked potential were counted. At the same time, nine children with left eye amblyopia were randomly selected from the two groups and were scanned with rs-fMRI before and after treatment. The differences in the brain regions between the two groups were compared and analyzed with VMHC. RESULTS: Chi-square test showed a notable difference in the total efficiency rate between the acupuncture (94.87%) and control groups (79.49%). Regarding the P100 wave latency and amplitude, the acupuncture group had significantly shorter latency and higher amplitude of P100 wave than the control group. Moreover, the VMHC values of the bilateral temporal lobe, superior temporal gyrus, and middle temporal gyrus were notably increased in the acupuncture group after treatment. CONCLUSION: Acupuncture combined with conventional treatment can significantly improve the corrected visual acuity and optic nerve conduction in children with anisometropic amblyopia. Compared with the conventional treatment, the regulation of acupuncture on the functional activities of the relevant brain areas in the anterior cerebellum may be an effective acupuncture mechanism for anisometropic amblyopia.

ABSTRACTObjectives Microbial contamination of various accessory parts of the dental chair units (DCUs) is an essential source of cross infection, while the accessories of the crucial suction function are usually overlooked. In this study, we aim to find an effective disinfectant and a cost-effective method to remove bacterioplankton and bacterial biofilm deposited in the negative pressure suction pipelines to control cross infection during dental treatment.Methods Double-chain quaternary ammonium salt disinfectant (Orotol Plus®), 3% hydrogen peroxide solution plus multi-enzyme cleaning agent and chlorine disinfectant are used to clean and disinfect the negative pressure pipelines of DCUs. Microbiological examinations, air condition detection, corrosion tests and gene sequencing are performed.Results Little bacteria grow in the pipelines disinfected with double-chain quaternary ammonium salt disinfectants, destruction of biofilms in these pipelines appears, and multi-resistant bacteria cannot be detected. Minimal damage to metal sheets and fittings is caused by double-chain quaternary ammonium salt disinfectants.Conclusion Double-chain quaternary ammonium salt disinfectant has excellent bactericidal ability and anti-biofilm effect, and it is less corrosive to the fittings of the pipelines. Thus, the double-chain quaternary ammonium salt disinfectant is a potential novel disinfectant for negative pressure suction pipelines of DCUs to control cross infection during dental treatment.Clinical significance It is essential to add all these data to our dental practice to control cross infection with a broader landscape.

Current research in air-conditioning and mechanical ventilation (ACMV) operation focuses on isolated sub-processes and analytical models. Digital twins, as digital replicas of assets, processes, or systems in the built environment, enable facilities manager (FM) to gain insights into the physical features of space, equipment performance, and energy efficiency. This study presents the 3D reconstruction of semantic-rich digital twins, which encompasses conditional and machine learning-enabled monitoring with 3D geometric models, for ACMV modeling and operation. The proposed framework involves a hybrid rule-based and data-driven approach to forecast the performance of indoor environment and identify potential anomalies throughout ACMV operation. Following this, a scan-to-BIM process is undertaken, with the aid of Simultaneous Localization and Mapping algorithms, to semi-automatically generate the as-built geometric models. Lastly, semantic enrichment of BIM is performed by incorporating time-series data from the rule-based and data-driven approach with 3D geometric models. The proposed approach supports the reconstruction of content-aware and semantic-rich digital twins, which utilize sensor-derived time-series data and 3D geometric models, to conduct advanced analysis for intelligent ACMV operation towards energy efficiency and occupant comfort.

Recent studies show that artificial intelligence (AI), such as machine learning and deep learning, models can be adopted and have advantages in fault detection and diagnosis for building energy systems. This paper aims to conduct a comprehensive and systematic literature review on fault detection and diagnosis (FDD) methods for heating, ventilation, and air conditioning (HVAC) systems. This review covers the period from 2013 to 2023 to identify and analyze the existing research in this field. Our work concentrates explicitly on synthesizing AI-based FDD techniques, particularly summarizing these methods and offering a comprehensive classification. First, we discuss the challenges while developing FDD methods for HVAC systems. Next, we classify AI-based FDD methods into three categories: those based on traditional machine learning, deep learning, and hybrid AI models. Additionally, we also examine physical model-based methods to compare them with AI-based methods. The analysis concludes that AI-based HVAC FDD, despite its higher accuracy and reduced reliance on expert knowledge, has garnered considerable research interest compared to physics-based methods. However, it still encounters difficulties in dynamic and time-varying environments and achieving FDD resolution. Addressing these challenges is essential to facilitate the widespread adoption of AI-based FDD in HVAC.

Hydrogel presents a three-dimensional polymer network with high water content. Over the past decade, hydrogel has developed from static material to intelligent material with controllable response. Various stimuli are involved in the formation of hydrogel network, among which photo-stimulation has attracted wide attention due to the advantages of controllable conditions, which has a good application prospect in the treatment of ophthalmic diseases. This paper reviews the application of photo-crosslink hydrogels in ophthalmology, focusing on the types of photo-crosslink hydrogels and their applications in ophthalmology, including drug delivery, tissue engineering and 3D printing. In addition, the limitations and future prospects of photo-crosslink hydrogels are also provided.

Traditional fibrous membranes employed in guided tissue regeneration (GTR) in the treatment of periodontitis have limitations of bioactive and immunomodulatory properties. We fabricated a novel nTPG/PLGA/PCL fibrous membrane by electrospinning which exhibit excellent hydrophilicity, mechanical properties and biocompatibility. In addition, we investigated its regulatory effect on polarization of macrophages and facilitating the regeneration of periodontal tissue both in vivo and in vitro. These findings showed the 0.5%TPG/PLGA/PCL may inhibit the polarization of RAW 264.7 into M1 phenotype by suppressing the PI3K/AKT and NF-κB signaling pathways. Furthermore, it directly up-regulated the expression of cementoblastic differentiation markers (CEMP-1 and CAP) in periodontal ligament stem cells (hPDLSCs), and indirectly up-regulated the expression of cementoblastic (CEMP-1 and CAP) and osteoblastic (ALP, RUNX2, COL-1, and OCN) differentiation markers by inhibiting the polarization of M1 macrophage. Upon implantation into a periodontal bone defect rats model, histological assessment revealed that the 0.5%TPG/PLGA/PCL membrane could regenerate oriented collagen fibers and structurally intact epithelium. Micro-CT (BV/TV) and the expression of immunohistochemical markers (OCN, RUNX-2, COL-1, and BMP-2) ultimately exhibited satisfactory regeneration of alveolar bone, periodontal ligament. Overall, 0.5%TPG/PLGA/PCL did not only directly promote osteogenic effects on hPDLSCs, but also indirectly facilitated cementoblastic and osteogenic differentiation through its immunomodulatory effects on macrophages. These findings provide a novel perspective for the development of materials for periodontal tissue regeneration.

Recurrent exposures to a pathogenic antigen remodel the CD8+ T cell compartment and generate a functional memory repertoire that is polyclonal and complex. At the clonotype level, the response to the conserved influenza antigen, M158–66 has been well characterized in healthy individuals, but not in patients receiving immunosuppressive therapy or with aberrant immunity, such as those with juvenile idiopathic arthritis (JIA). Here we show that patients with JIA have a reduced number of M158–66 specific RS/RA clonotypes, indicating decreased clonal richness and, as a result, have lower repertoire diversity. By using a rank-frequency approach to analyze the distribution of the repertoire, we found several characteristics of the JIA T cell repertoire to be akin to repertoires seen in healthy adults, including an amplified RS/RA-specific antigen response, representing greater clonal unevenness. Unlike mature repertoires, however, there is more fluctuation in clonotype distribution, less clonotype stability, and more variable IFNy response of the M158–66 specific RS/RA clonotypes in JIA. This indicates that functional clonal expansion is altered in patients with JIA on immunosuppressive therapies. We propose that the response to the influenza M158–66 epitope described here is a general phenomenon for JIA patients receiving immunosuppressive therapy, and that the changes in clonal richness and unevenness indicate a retarded and uneven generation of a mature immune response.

In the quest for sustainable urban development, precise quantification of urban green space is paramount. This research delineates the implementation of a Cosine Adaptive Particle Swarm Optimization Long Short-Term Memory (CAPSO-LSTM) model, utilizing a comprehensive dataset from Beijing (1998–2021) to train and test the model. The CAPSO-LSTM model, which integrates a cosine adaptive mechanism into particle swarm optimization, advances the optimization of long short-term memory (LSTM) network hyperparameters. Comparative analyses are conducted against conventional LSTM and Partical Swarm Optimization (PSO)-LSTM frameworks, employing mean absolute error (MAE), root mean square error (RMSE), and mean absolute percentage error (MAPE) as evaluative benchmarks. The findings indicate that the CAPSO-LSTM model exhibits a substantial improvement in prediction accuracy over the LSTM model, manifesting as a 66.33% decrease in MAE, a 73.78% decrease in RMSE, and a 57.14% decrease in MAPE. Similarly, when compared to the PSO-LSTM model, the CAPSO-LSTM model demonstrates a 58.36% decrease in MAE, a 65.39% decrease in RMSE, and a 50% decrease in MAPE. These results underscore the efficacy of the CAPSO-LSTM model in enhancing urban green space area prediction, suggesting its significant potential for aiding urban planning and environmental policy formulation.

To comprehend the genuine reading habits and preferences of diverse user cohorts and furnish tailored reading recommendations, this study introduces an English text reading recommendation model designed specifically for long-tail users. This model integrates collaborative filtering algorithms with the FastText classification method. Initially, the integrated collaborative filtering algorithm is explicated, followed by the calculation of the user's interest distribution across various types of English texts, achieved through an enhanced Ebbinghaus forgetting curve and analysis of user reading behaviors. Subsequently, an intelligent English text reading recommendation is generated by amalgamating collaborative filtering algorithms with association rule-based recommendation algorithms. Through optimization of the recommendation generation process, the model's recommendation accuracy is enhanced, thereby augmenting the performance and user satisfaction of the recommendation system. Finally, a comparative analysis is conducted with respect to the Top-N algorithm model, matrix factorization-based algorithm model, and FastText classification model, illustrating the superior recommendation accuracy and F-Measure value of the proposed model. The study findings indicate that when the recommendation list contains 10, 30, 50, and 70 texts, the recommendation accuracy of the proposed algorithm model is 0.75, 0.79, 0.8, and 0.74, respectively, outperforming other algorithms. Furthermore, as the number of texts increases, the F-Measure of all four models gradually improves, with the final F-Measure of the proposed model reaching 0.81. Notably, the F-Measure of the English text reading recommendation model proposed in this study significantly surpasses that of the other three recommendation methods. Demonstrating commendable performance in recall rate, root mean square error, normalized cumulative gain, precision, and accuracy, the model adeptly reflects user reading interests, thereby enhancing the accuracy of text recommendations and the overall system performance. The study findings offer crucial insights and guidance for enhancing the accuracy and overall efficacy of English text recommendation systems.

Abstract Background Therapeutic peptides play an essential role in human physiology, treatment paradigms and bio-pharmacy. Several computational methods have been developed to identify the functions of therapeutic peptides based on binary classification and multi-label classification. However, these methods fail to explicitly exploit the relationship information among different functions, preventing the further improvement of the prediction performance. Besides, with the development of peptide detection technology, peptide functions will be more comprehensively discovered. Therefore, it is necessary to explore computational methods for detecting therapeutic peptide functions with limited labeled data. Results In this study, a novel method called TPpred-LE based on Transformer framework was proposed for predicting therapeutic peptide multiple functions, which can explicitly extract the function correlation information by using label embedding methodology and exploit the specificity information based on function-specific classifiers. Besides, we incorporated the multi-label classifier retraining approach (MCRT) into TPpred-LE to detect the new therapeutic functions with limited labeled data. Experimental results demonstrate that TPpred-LE outperforms the other state-of-the-art methods, and TPpred-LE with MCRT is robust for the limited labeled data. Conclusions In summary, TPpred-LE is a function-specific classifier for accurate therapeutic peptide function prediction, demonstrating the importance of the relationship information for therapeutic peptide function prediction. MCRT is a simple but effective strategy to detect functions with limited labeled data.

Abstract High-dimensional optimization presents a novel challenge within the realm of intelligent computing, necessitating innovative approaches. When tackling high-dimensional spaces, traditional evolutionary tools often encounter pitfalls, including dimensional catastrophes and a propensity to become trapped in local optima, ultimately compromising result accuracy. To address this issue, we introduce the Pair Barracuda Swarm Optimization (PBSO) algorithm in this paper. PBSO employs a unique strategy for constructing barracuda pairs, effectively mitigating the challenges posed by high dimensionality. Furthermore, we enhance global search capabilities by incorporating a support barracuda alongside the leading barracuda pair. To assess the algorithm’s performance, we conduct experiments utilizing the CEC2017 standard function and compare PBSO against five state-of-the-art natural-inspired optimizers in the control group. Across 29 test functions, PBSO consistently secures top rankings with 9 first-place, 13 second-place, 5 third-place, 1 fourth-place, and 1 fifth-place finishes, yielding an average rank of 2.0345. These empirical findings affirm that PBSO stands as the superior choice among all test algorithms, offering a dependable solution for high-dimensional optimization challenges.

Abstract Objective Otitis media (OM) is among the most frequently diagnosed pediatric diseases in the US. Despite the significant public health burden of OM and the contribution research in culture models has made to understanding its pathobiology, a singular immortalized human middle ear epithelial (MEE) cell line exists (HMEEC‐1, adult‐derived). We previously developed MEE cultures from pediatric patients with non‐inflamed MEE (PCI), recurrent OM (ROM), or OM with effusion (OME) and demonstrated differences in their baseline inflammatory cytokine expression and response to stimulation with an OM‐relevant pathogen lysate and cytokines. Herein, we sought to immortalize these cultures and assess retention of their phenotypes. Methods MEE cultures were immortalized via lentivirus encoding temperature‐sensitive SV40 T antigen. Immortalized MEE lines and HMEEC‐1 grown in monolayer were stimulated with non‐typeable Haemophilus influenzae (NTHi) lysate. Gene expression (TNFA, IL1B, IL6, IL8, MUC5AC, and MUC5B) was assessed by qPCR. Results Similar to parental cultures, baseline cytokine expressions were higher in pediatric OM lines than in HMEEC‐1 and PCI, and HMEEC‐1 cells were less responsive to stimulation than pediatric lines. Conclusion Immortalized MEE lines retained the inflammatory expression and responsiveness of their tissues of origin and differences between non‐OM versus OM and pediatric versus adult cultures, supporting their value as novel in vitro culture models for OM.

Electrokinetic remediation technology can effectively remove heavy metal ions from contaminated soil in coal mine area. In order to clarify the effect of electric field on the adsorption characteristics of heavy metal ions on the surface of kaolinite, three common heavy metal elements Pb(II), Cd(II) and Cu(II) in mine soil were studied, a three-dimensional visualization model was established, and a stable kaolinite-solution-kaolinite simulation system was optimized. Molecular dynamics simulation method was used. The adsorption mode, atomic density distribution, diffusion coefficient and radial distribution function of heavy metal ions on kaolinite (001) crystal plane under electric field were investigated. The results show that when the electric field direction is +Z, the electric field promotes the complexation between heavy metal ions and siloxane surface, and the coordination number of the first hydration shell of heavy metal ions decreases with the increase of electric field intensity. The adsorption percentage of outer complexes increases with the increase of electric field intensity. When the electric field direction is −Z, the adsorption percentage of the outer complex decreases with the increase of the electric field intensity, the interaction between the kaolinite surface and heavy metal ions weakens, and more heavy metal ions dissociate in the solution and pair with chloride ions; under the action of electric field polarization, there is no obvious boundary between the first and second hydration shells of heavy metal ions, and there is water molecular exchange between the hydration shells; under the action of electric field, the MSD curve of heavy metal ions on kaolinite shows anisotropy. The diffusion coefficient in X and Y direction is larger than that in Z direction, the total diffusion coefficient increases with the increase of electric field intensity, and the diffusion coefficient is not obviously affected by the positive and negative direction of electric field. Pb(II) ion and Cd(II) ion only have outer complexes on the surface of kaolinite, while Cu(II) ions have the adsorption mode of coexistence of inner and outer complexes. The adsorption priority of kaolinite to three heavy metal ions is Pb(II) > Cu(II) > Cd(II).

Abstract Background Postoperative urine retention (POUR) after lumbar interbody fusion surgery may lead to recatheterization and prolonged hospitalization. In this study, a predictive model was constructed and validated. The objective was to provide a nomogram for estimating the risk of POUR and then reducing the incidence. Methods A total of 423 cases of lumbar fusion surgery were included; 65 of these cases developed POUR, an incidence of 15.4%. The dataset is divided into a training set and a validation set according to time. 18 candidate variables were selected. The candidate variables were screened through LASSO regression. The stepwise regression and random forest analysis were then conducted to construct the predictive model and draw a nomogram. The area under the curve (AUC) of the receiver operating characteristic (ROC) curve and the calibration curve were used to evaluate the predictive effect of the model. Results The best lambda value in LASSO was 0.025082; according to this, five significant variables were screened, including age, smoking history, surgical method, operative time, and visual analog scale (VAS) score of postoperative low back pain. A predictive model containing four variables was constructed by stepwise regression. The variables included age (β = 0.047, OR = 1.048), smoking history (β = 1.950, OR = 7.031), operative time (β = 0.022, OR = 1.022), and postoperative VAS score of low back pain (β = 2.554, OR = 12.858). A nomogram was drawn based on the results. The AUC of the ROC curve of the training set was 0.891, the validation set was 0.854 in the stepwise regression model. The calibration curves of the training set and validation set are in good agreement with the actual curves, showing that the stepwise regression model has good prediction ability. The AUC of the training set was 0.996, and that of the verification set was 0.856 in the random forest model. Conclusion This study developed and internally validated a new nomogram and a random forest model for predicting the risk of POUR after lumbar interbody fusion surgery. Both of the nomogram and the random forest model have high accuracy in this study.