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Peripheral nerve injuries may result in severe long-gap interruptions that are challenging to repair. Autografting is the gold standard surgical approach for repairing long-gap nerve injuries but can result in prominent donor-site complications. Instead, imitating the native neural microarchitecture using synthetic conduits is expected to offer an alternative strategy for improving nerve regeneration. Here, we designed nerve conduits composed of high-resolution anisotropic microfiber grid-cordes with randomly organized nanofiber sheaths to interrogate the positive effects of these biomimetic structures on peripheral nerve regeneration. Anisotropic microfiber-grids demonstrated the capacity to directionally guide Schwann cells and neurites. Nanofiber sheaths conveyed adequate elasticity and permeability, whilst exhibiting a barrier function against the infiltration of fibroblasts. We then used the composite nerve conduits bridge 30-mm long sciatic nerve defects in canine models. At 12 months post-implant, the morphometric and histological recovery, gait recovery, electrophysiological function, and degree of muscle atrophy were assessed. The newly regenerated nerve tissue that formed within the composite nerve conduits showed restored neurological functions that were superior compared to sheaths-only scaffolds and Neurolac nerve conduit controls. Our findings demonstrate the feasibility of using synthetic biophysical cues to effectively bridge long-gap peripheral nerve injuries and indicates the promising clinical application prospects of biomimetic composite nerve conduits.

Estimating and monitoring chlorophyll content is a critical step in crop spectral image analysis. The quick, non-destructive assessment of chlorophyll content in rice leaves can optimize nitrogen fertilization, benefit the environment and economy, and improve rice production management and quality. In this research, spectral analysis of rice leaves is performed using hyperspectral and fluorescence spectroscopy for the detection of chlorophyll content in rice leaves. This study generated ninety experimental spectral datasets by collecting rice leaf samples from a farm in Sichuan Province, China. By implementing a feature extraction algorithm, this study compresses redundant spectral bands and subsequently constructs machine learning models to reveal latent correlations among the extracted features. The prediction capabilities of six feature extraction methods and four machine learning algorithms in two types of spectral data are examined, and an accurate method of predicting chlorophyll concentration in rice leaves was devised. The IVSO-IVISSA (Iteratively Variable Subset Optimization–Interval Variable Iterative Space Shrinkage Approach) quadratic feature combination approach, based on fluorescence spectrum data, has the best prediction performance among the CNN+LSTM (Convolutional Neural Network Long Short-Term Memory) algorithms, with corresponding RMSE-Train (Root Mean Squared Error), RMSE-Test, and RPD (Ratio of standard deviation of the validation set to standard error of prediction) indexes of 0.26, 0.29, and 2.64, respectively. We demonstrated in this study that hyperspectral and fluorescence spectroscopy, when analyzed with feature extraction and machine learning methods, provide a new avenue for rapid and non-destructive crop health monitoring, which is critical to the advancement of smart and precision agriculture.

Mathematical modelling is an indispensable tool to support water resource recovery facility (WRRF) operators and engineers with the ambition of creating a truly circular economy and assuring a sustainable future. Despite the successful application of mechanistic models in the water sector, they show some important limitations and do not fully profit from the increasing digitalisation of systems and processes. Recent advances in data-driven methods have provided options for harnessing the power of Industry 4.0, but they are often limited by the lack of interpretability and extrapolation capabilities. Hybrid modelling (HM) combines these two modelling paradigms and aims to leverage both the rapidly increasing volumes of data collected, as well as the continued pursuit of greater process understanding. Despite the potential of HM in a sector that is undergoing a significant digital and cultural transformation, the application of hybrid models remains vague. This article presents an overview of HM methodologies applied to WRRFs and aims to stimulate the wider adoption and development of HM. We also highlight challenges and research needs for HM design and architecture, good modelling practice, data assurance, and software compatibility. HM is a paradigm for WRRF modelling to transition towards a more resource-efficient, resilient, and sustainable future. HIGHLIGHTS HM combines mechanistic and data-driven models.; WRRFs are too sensitive to rely only on data-driven techniques; HM is an alternative.; Hybrid models have a high predictive power at low computational cost, proving them useful for online optimisation and control.; Major challenges for more widespread implementation of HM are discussed.; HM supports the transition towards a digital and resource-efficient water sector.;

Modelling, automation, and control are widely used for water resource recovery facility (WRRF) optimization. An influent generator (IG) is a model, aiming to provide the flowrate and pollutant concentration dynamics at the inlet of a WRRF for a range of modelling applications. In this study, a new data-driven IG model is proposed, only using routine data and weather information, and without need for any additional data collection. The model is constructed by an artificial neural network (ANN) and completed with a multivariate regression to generate time series for certain pollutants. The model is able to generate flowrate and quality data (TSS, COD, and nutrients) at different time scales and resolutions (daily or hourly), depending on various user objectives. The model performance is analyzed by a series of statistical criteria. It is shown that the model can generate a very reliable dataset for different model applications. HIGHLIGHTS A data-driven influent generator (IG) to create a long-term time series for flow and pollutant concentration at hourly and daily frequencies.; The IG application only needs WRRF routine data and weather information, without need for any additional data collection.; The IG also generates nutrient concentration time series.; A random walk stochastic process is added to better approximate observed temporal variability.;

Nowadays, modelling, automation and control are widely used for Water Resource Recovery Facilities (WRRF) upgrading and optimization. Influent generator (IG) models are used to provide relevant input time series for dynamic WRRF simulations used in these applications. Current IG models found in literature are calibrated on the basis of a single performance criterion, such as the mean percentage error or the root mean square error. This results in the IG being adequate on average but with a lack of representativeness of, for instance, the observed temporal variability of the dataset. However, adequately capturing influent variability may be important for certain types of WRRF optimization, e.g., reaction to peak loads, control system performance evaluation, etc. Therefore, in this study, a data-driven IG model is developed based on the long short-term memory (LSTM) recurrent neural network and is optimized by a multi-objective genetic algorithm for both mean percentage error and variability. Hence, the influent generator model is able to generate a time series with a probability distribution that better represents reality, thus giving a better influent description for WRRF design and operation. To further increase the variability of the generated time series and in this way approximate the true variability better, the model is extended with a random walk process. HIGHLIGHTS A data-driven influent generator based on the long short-term memory (LSTM) is proposed.; To represent both the average behaviour and variability of the influent, the IG is trained by a multi-objective genetic algorithm.; The model is extended with a random walk stochastic process with a probability distribution that better represents reality, giving a better influent description for model-based WRRF design and operation.;

The sex ratio is an important factor affecting the economic benefits of duck groups in the process of hemp duck breeding. However, the current manual counting method is inefficient, and the results are not always accurate. On the one hand, ducks are in constant motion, and on the other hand, the manual counting method relies on manpower; thus, it is difficult to avoid repeated and missed counts. In response to these problems, there is an urgent need for an efficient and accurate way of calculating the sex ratio of ducks to promote the farming industry. Detecting the sex ratio of ducks requires accurate counting of male ducks and female ducks. We established the world’s first manually marked sex classification dataset for hemp ducks, including 1663 images of duck groups; 17,090 images of whole, individual duck bodies; and 15,797 images of individual duck heads, which were manually captured and had sex information markers. Additionally, we used multiple deep neural network models for the target detection and sex classification of ducks. The average accuracy reached 98.68%, and with the combination of Yolov5 and VovNet_27slim, we achieved 99.29% accuracy, 98.60% F1 score, and 269.68 fps. The evaluation of the algorithm’s performance indicates that the automation method proposed in this paper is feasible for the sex classification of ducks in the farm environment, and is thus a feasible tool for sex ratio estimation.

A video-based method to quantify animal posture movement is a powerful way to analyze animal behavior. Both humans and fish can judge the physiological state through the skeleton framework. However, it is challenging for farmers to judge the breeding state in the complex underwater environment. Therefore, images can be transmitted by the underwater camera and monitored by a computer vision model. However, it lacks datasets in artificial intelligence and is unable to train deep neural networks. The main contributions of this paper include: (1) the world’s first fish posture database is established. 10 key points of each fish are manually marked. The fish flock images were taken in the experimental tank and 1000 single fish images were separated from the fish flock. (2) A two-stage attitude estimation model is used to detect fish key points. The evaluation of the algorithm performance indicates the precision of detection reaches 90.61%, F1-score reaches 90%, and Fps also reaches 23.26. We made a preliminary exploration on the pose estimation of fish and provided a feasible idea for fish pose estimation.

Abstract In this paper, we discuss quantum friction in a system formed by two metallic surfaces separated by a ferromagnetic intermedium of a certain thickness. The internal degrees of freedom in the two metallic surfaces are assumed to be plasmons, while the excitations in the intermediate material are magnons, modeling plasmons coupled to magnons. During relative sliding, one surface moves uniformly parallel to the other, causing friction in the system. By calculating the effective action of the magnons, we can determine the particle production probability, which shows a positive correlation between the probability and the sliding speed. Finally, we derive the frictional force of the system, with both theoretical and numerical results indicating that the friction, like the particle production probability, also has a positive correlation with the speed.

Summary: Background: Genome-wide association studies (GWAS) have revealed many brain disorder-associated SNPs residing in the noncoding genome, rendering it a challenge to decipher the underlying pathogenic mechanisms. Methods: Here, we present an unsupervised Bayesian framework to identify disease-associated genes by integrating risk SNPs with long-range chromatin interactions (iGOAT), including SNP-SNP interactions extracted from ∼500,000 patients and controls from the UK Biobank, and enhancer–promoter interactions derived from multiple brain cell types at different developmental stages. Findings: The application of iGOAT to three psychiatric disorders and three neurodegenerative/neurological diseases predicted sets of high-risk (HRGs) and low-risk (LRGs) genes for each disorder. The HRGs were enriched in drug targets, and exhibited higher expression during prenatal brain developmental stages than postnatal stages, indicating their potential to affect brain development at an early stage. The HRGs associated with Alzheimer's disease were found to share genetic architecture with schizophrenia, bipolar disorder and major depressive disorder according to gene co-expression module analysis and rare variants analysis. Comparisons of this method to the eQTL-based method, the TWAS-based method, and the gene-level GWAS method indicated that the genes identified by our method are more enriched in known brain disorder-related genes, and exhibited higher precision. Finally, the method predicted 205 risk genes not previously reported to be associated with any brain disorder, of which one top-risk gene, MLH1, was experimentally validated as being schizophrenia-associated. Interpretation: iGOAT can successfully leverage epigenomic data, phenotype–genotype associations, and protein–protein interactions to advance our understanding of brain disorders, thereby facilitating the development of new therapeutic approaches. Funding: The work was funded by the National Key Research and Development Program of China (2024YFF1204902), the Natural Science Foundation of China (82371482), Guangzhou Science and Technology Research Plan (2023A03J0659) and Natural Science Foundation of Guangdong (2024A1515011363).

Abstract This paper describes an image processing-based technique used to measure the volume of residual water in the drinking water bottle for the laboratory mouse. This technique uses a camera to capture the bottle's image and then processes the image to calculate the volume of water in the bottle. Firstly, the Grabcut method separates the foreground and background to avoid the influence of background on image feature extraction. Then Canny operator was used to detect the edge of the water bottle and the edge of the liquid surface. The cumulative probability Hough detection identified the water bottle edge line segment and the liquid surface line segment from the edge image. Finally, the spatial coordinate system is constructed, and the length of each line segment on the water bottle is calculated by using plane analytical geometry. Then the volume of water is calculated. By comparing image processing time, the pixel number of liquid level, and other indexes, the optimal illuminance and water bottle color were obtained. The experimental results show that the average deviation rate of this method is less than 5%, which significantly improves the accuracy and efficiency of measurement compared with traditional manual measurement.

Age-related hearing loss (ARHL) is a prevalent condition affecting millions of individuals globally. This study investigated the role of the cell survival regulator Bcl2 in ARHL through in vitro and in vivo experiments and metabolomics analysis. The results showed that the lack of Bcl2 in the auditory cortex affects lipid metabolism, resulting in reduced synaptic function and neurodegeneration. Immunohistochemical analysis demonstrated enrichment of Bcl2 in specific areas of the auditory cortex, including the secondary auditory cortex, dorsal and ventral areas, and primary somatosensory cortex. In ARHL rats, a significant decrease in Bcl2 expression was observed in these areas. RNAseq analysis showed that the downregulation of Bcl2 altered lipid metabolism pathways within the auditory pathway, which was further confirmed by metabolomics analysis. These results suggest that Bcl2 plays a crucial role in regulating lipid metabolism, synaptic function, and neurodegeneration in ARHL; thereby, it could be a potential therapeutic target. We also revealed that Bcl2 probably has a close connection with lipid peroxidation and reactive oxygen species (ROS) production occurring in cochlear hair cells and cortical neurons in ARHL. The study also identified changes in hair cells, spiral ganglion cells, and nerve fiber density as consequences of Bcl2 deficiency, which could potentially contribute to the inner ear nerve blockage and subsequent hearing loss. Therefore, targeting Bcl2 may be a promising potential therapeutic intervention for ARHL. These findings provide valuable insights into the molecular mechanisms underlying ARHL and may pave the way for novel treatment approaches for this prevalent age-related disorder.

Abstract The charge transport in quasi‐2D perovskites limits their applications despite the superior stability and optoelectronic properties. Herein, a novel strategy is proposed to enhance the charge transport by regulating 3D perovskite phase in quasi‐2D perovskite films. The carbohydrazide (CBH) as an additive is introduced into (PEA)2MA3Pb4I13 precursors, which slows down the crystallization process and improves the phase ratio and crystal quality of the 3D phase. This structure change results in a significant improvement in charge transport and extraction, leading to the device demonstrating an almost 100% internal quantum efficiency, a peak responsivity of 0.41 A W−1, and a detectivity of 1.31 × 1012 Jones at 570 nm under 0 V bias. Furthermore, the air and moisture stability of (PEA)2MA3Pb4I13 films is not deteriorated but gets significantly improved due to the better crystal quality and the passivation of defects by the residual CBH molecule. This work demonstrates a strategy for improving the charge transport properties of quasi‐2D perovskites and also sheds light on solving the stability issue of 3D perovskite films via the proper passivation or additives, which will inspire the fast development of the perovskite community.

Abstract The pair-contact process with diffusion (PCPD), a generalized model of the ordinary pair-contact process (PCP) without diffusion, exhibits a continuous absorbing phase transition. Unlike the PCP, whose nature of phase transition is clearly classified into the directed percolation (DP) universality class, the model of PCPD has been controversially discussed since its infancy. To our best knowledge, there is so far no consensus on whether the phase transition of the PCPD falls into the unknown university classes or else conveys a new kind of non-equilibrium phase transition. In this paper, both unsupervised and supervised learning are employed to study the PCPD with scrutiny. Firstly, two unsupervised learning methods, principal component analysis (PCA) and autoencoder, are taken. Our results show that both methods can cluster the original configurations of the model and provide reasonable estimates of thresholds. Therefore, no matter whether the non-equilibrium lattice model is a random process of unitary (for instance the DP) or binary (for instance the PCP), or whether it contains the diffusion motion of particles, unsupervised learning can capture the essential, hidden information. Beyond that, supervised learning is also applied to learning the PCPD at different diffusion rates. We proposed a more accurate numerical method to determine the spatial correlation exponent $$\nu _{\perp }$$ ν ⊥ , which, to a large degree, avoids the uncertainty of data collapses through naked eyes.

Abstract The volume detection of medical mice feed is crucial to understand the food intake requirements of mice at different growth stages and to grasp their growth, development, and health status. Aiming at the problem of volume calculation in the way of feed bulk in mice, a method for detecting the bulk volume of feed in mice based on binocular stereo vision was proposed. Firstly, the three-dimensional point coordinates of the feed's surface were calculated using the binocular stereo vision three-dimensional reconstruction technology. The coordinates of these dense points formed a point cloud, and then the projection method was used to calculate the volume of the point cloud; and finally, the volume of the mice feed was obtained. We use the stereo matching data set provided by the Middlebury evaluation platform to conduct experimental verification. The results show that our method effectively improves the matching degree of stereo matching and makes the three-dimensional point coordinates of the obtained feed's surface more accurate. The point cloud is then denoised and Delaunay triangulated, and the volume of the tetrahedron obtained after the triangulation is calculated and summed to obtain the total volume. We used different sizes of wood instead of feed for multiple volume calculations, and the average error between the calculated volume and the real volume was 7.12%. The experimental results show that the volume of the remaining feed of mice can be calculated by binocular stereo vision.

Machine learning (ML) of phase transitions (PTs) has gradually become an effective approach that enables us to explore the nature of various PTs more promptly in equilibrium and nonequilibrium systems. Unlike equilibrium systems, non-equilibrium systems display more complicated and diverse features because of the extra dimension of time, which is not readily tractable, both theoretically and numerically. The combination of ML and most renowned nonequilibrium model, directed percolation (DP), led to some significant findings. In this study, ML is applied to $(1+1)$ -d, even offspring branching annihilating random walks (BAW), whose universality class is not DP-like. The supervised learning of $(1+1)$ -d BAW via convolutional neural networks (CNN) results in a more accurate prediction of the critical point than the Monte Carlo (MC) simulation for the same system sizes. The dynamic exponent z and spatial correlation length correlation exponent $\nu_{\perp}$ were also measured and found to be consistent with their respective theoretical values. Furthermore, the unsupervised learning of $(1+1)$ -d BAW via an autoencoder (AE) gives rise to a transition point, which is the same as the critical point. The latent layer of AE, through a single neuron, can be regarded as the order parameter of the system being properly re-scaled. Therefore, we believe that ML has exciting application prospects in reaction-diffusion systems such as BAW and DP.

Soil respiration (Rs) is a key ecosystem function that controls the terrestrial energy balance and element cycling. Quantifying how plant diversity affects Rs is critical for predicting the impact of global plant diversity loss on ecosystem function. However, it is unclear how soil factors and plant diversity are spatially interrelated under natural environmental gradients, and how plant diversity affects the spatial variation of Rs on the basis of soil factors. Our objectives were to quantify the direction and magnitude of direct and indirect effects of plant diversity on Rs. We assessed spatial variability in the relationships between soil factors (soil chemistry and microclimate), multiple dimensions of plant diversity (taxonomic, functional, and phylogenetic diversity), and Rs using a new combination of geographically weighted regression and structural equation modeling based on survey data from three plots (river bank, transitional zone, and desert margin plots) along an environmental gradient in northwest China. Reduced plant diversity had a negative impact on Rs. However, when the effects of soil factors on Rs and spatial non-stationarity were considered simultaneously, the relationship between plant diversity and Rs changed in magnitude and direction. From the river bank to the desert margin, the positive effect of taxonomic and functional diversity on Rs gradually weakened, while the effect of phylogenetic diversity on Rs changed from a negative to a positive effect. Plant diversity and soil factors together explained 55%–75% of the spatial variation in Rs. Among them, the contribution of plant diversity to the spatial variation of Rs gradually decreased from the river bank to the desert margin, while the contribution of soil factors gradually increased. Functional diversity (17.39%–18.93%) and phylogenetic diversity (10.75%–19.53%) better explained the spatial variability of Rs compared with taxonomic diversity (2.67%–6.01%). The results provide strong evidence that plant diversity, especially functional and phylogenetic diversity, is a key driver of Rs, expanding our understanding of the relationship between plant diversity and Rs. These findings more accurately reveal the changing characteristics of carbon dynamics in desert ecosystems, and provide a methodological framework for studying the spatial variability of the relationship between plant diversity and ecosystem function.

Many studies suggest that species diversity and abiotic factors promote ecosystem multifunctionality. However, whether ecosystem multifunctionality is impacted by phylogenetic diversity remains controversial. The present study tested this in an arid desert ecosystem in Ebinur Lake Basin using soil C:N ratio, soil pH, and soil salinity as abiotic factors, and species diversity and phylogenetic diversity as indicators of plant diversity. The effects of plant diversity and abiotic factors on single ecosystem functions (nutrient cycling, carbon stocks, water regulation, and wood production) and ecosystem multifunctionality were studied. We used structural equation modeling to assess the relationships among different functional groups and factors. The results showed that: (1) abiotic factors, particularly pH and C:N ratio in soil, had the strongest positive impact on multifunctionality (P < 0.001). The phylogenetic diversity and species diversity showed inconsistent changes, and their contribution to multifunctionality were not outstanding. (2) Abiotic factors were closely related to different ecosystem functions. Soil C:N had a significant positive effect on carbon stocks (P < 0.001), with an effect index of 0.89. Soil pH significantly enhanced nutrient cycling and water regulation. The role of plant diversity varied with the combination of different ecosystem functions. Phylogenetic diversity and species diversity influenced wood production, but showed opposite functions. (3) The importance of four single-ecosystem functions in an arid region was ranked as follows: carbon stocks > water regulation > nutrient cycling > wood production, emphasizing the importance of carbon elements in these ecosystems. These results improve our understanding of the drivers of multifunctionality in arid ecosystems, facilitating the elucidation of the influence of abiotic factors and phylogenetic diversity.

The issue of employee turnover is always critical for companies, and accurate predictions can help them prepare in time. Most past studies on employee turnover have focused on analyzing impact factors or using simple network centrality measures. In this paper, we study the problem from a completely new perspective by modeling users' historical job records as a dynamic bipartite graph. Specifically, we propose a bipartite graph embedding method with temporal information called dynamic bipartite graph embedding (DBGE) to learn the vector representation of employees and companies. Our approach not only considers the relations between employees and companies but also incorporates temporal information embedded in consecutive work records. We first define the Horary Random Walk on a bipartite graph to generate a sequence for each vertex in chronological order. Then, we employ the skip-gram model to obtain a temporal low-dimensional vector representation for each vertex and apply machine learning methods to predict employee turnover behavior by combining embedded features with employees' basic information. Experiments on a real-world dataset collected from one of China's largest online professional social networks show that the features learned through DBGE can significantly improve turnover prediction performance. Moreover, experiments on public Amazon and Taobao datasets show that our approach achieves better performance in the link prediction and visualization task than other graph embedding methods that do not consider temporal information.